From a3043989a4d2fb082e19173e430809c8025afba2 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 13 Dec 2016 16:57:09 +0800 Subject: [PATCH 001/370] Extract RowBuffer class for SparseRowMatrix. * The original SparseRowMatrix use two fields to store each rows, which let code very confusing. Try to extract a RowBuffer class, for SparseRowMatrix data storage, and manage auto-growth logic. --- paddle/gserver/tests/test_PyDataProvider2.cpp | 2 +- paddle/math/SparseRowMatrix.h | 107 ++++++++++++++---- 2 files changed, 83 insertions(+), 26 deletions(-) diff --git a/paddle/gserver/tests/test_PyDataProvider2.cpp b/paddle/gserver/tests/test_PyDataProvider2.cpp index 436318d35634f4..7a3b51da8b27f7 100644 --- a/paddle/gserver/tests/test_PyDataProvider2.cpp +++ b/paddle/gserver/tests/test_PyDataProvider2.cpp @@ -293,7 +293,7 @@ TEST(PyDataProvider2, can_over_batch_size) { while (true) { int64_t realBatchSize = provider->getNextBatchInternal(batchSize, &batch); if (realBatchSize) { - CHECK_LE(realBatchSize, batchSize); + CHECK_LE((size_t)realBatchSize, batchSize); } else { break; } diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index badb4b9c1cce4d..db1530f7cfd529 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -24,6 +24,73 @@ P_DECLARE_bool(allow_inefficient_sparse_update); namespace paddle { +/** + * @brief The RowBuffer class + * Represent the SparseRow Matrix Data. + * + * If not set memory handler, then the data could be auto growth. + */ +class RowBuffer { +public: + explicit RowBuffer(size_t width) : width_(width) {} + RowBuffer(const CpuMemHandlePtr& mem, size_t width) + : preallocatedBuf_(mem), width_(width) {} + + inline void reserve(int rowCnt) { + if (preallocatedBuf_) { + CHECK(preallocatedBuf_->getSize() < rowCnt * width_ * sizeof(real)); + } else { + rowStore_.reserve(rowCnt * width_); + } + } + + inline const real* get(int row) const { + if (preallocatedBuf_) { + CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize()); + return reinterpret_cast(preallocatedBuf_->getBuf()) + row * width_; + } else { + CHECK_LE((row + 1) * width_, rowStore_.size()); + return rowStore_.data() + row * width_; + } + } + + inline const real* getWithAutoGrowth(int row) { + if (preallocatedBuf_) { + return get(row); + } else { + if ((rowStore_.size() <= row * width_)) { + rowStore_.resize((row + 1) * width_); + } + return rowStore_.data() + row * width_; + } + } + + inline real* data() { + if (preallocatedBuf_) { + return reinterpret_cast(preallocatedBuf_->getBuf()); + } else { + return rowStore_.data(); + } + } + + inline void clear() { rowStore_.clear(); } + + inline size_t getRowCount() const { + if (preallocatedBuf_) { + return preallocatedBuf_->getSize() / sizeof(float) / width_; + } else { + return rowStore_.size() / width_; + } + } + + inline bool canAutoGrowth() const { return preallocatedBuf_ == nullptr; } + +private: + CpuMemHandlePtr preallocatedBuf_; + std::vector> rowStore_; + size_t width_; +}; + /** * Sparse Row */ @@ -45,12 +112,9 @@ class SparseRowCpuMatrix : public CpuMatrix { IndexDictPtr indexDictHandle = nullptr, bool trans = false) : CpuMatrix(nullptr, height, width, trans), - storeMat_(dataHandle, - dataHandle ? dataHandle->getSize() / sizeof(real) / width : 0, - width, - trans), indexDictHandle_(indexDictHandle) { init(height, width); + buf_.reset(new RowBuffer(dataHandle, width)); } virtual ~SparseRowCpuMatrix() {} @@ -72,24 +136,17 @@ class SparseRowCpuMatrix : public CpuMatrix { * @param row row id in local storage */ real* getLocalRow(size_t row) { - if (storeMat_.getData()) return storeMat_.rowBuf(row); - if (rowStore_.size() <= row * width_) { - rowStore_.resize((row + 1) * width_); - } - return rowStore_.data() + row * width_; + return const_cast(buf_->getWithAutoGrowth(row)); } /** - * reserve the storage for rows according to current size of indexDictHandle. + * reserve the storage for rows according to current size of + * indexDictHandle. * * This is only used when SparseRowCpuMatrix is constructed with * indexDictHandle. */ - void reserveStore() { - if (!storeMat_.getData() && !localIndices_->empty()) { - rowStore_.resize(localIndices_->size() * width_); - } - } + void reserveStore() { buf_->reserve(localIndices_->size()); } // row is the row id in the original matrix virtual real* getRowBuf(size_t row) { return getRow(row); } @@ -117,7 +174,8 @@ class SparseRowCpuMatrix : public CpuMatrix { * * If L1 decay set use L1, else if L2 set use L2, otherwise no decay atall. * - * t0 is a int vector used by L1/L2 decay, size = height of parameter matrix, + * t0 is a int vector used by L1/L2 decay, size = height of parameter + * matrix, * store the time that each weight row last updated. * * Time is batchId, currentTime is current batchId. @@ -176,8 +234,7 @@ class SparseRowCpuMatrix : public CpuMatrix { protected: template void apply(Func f) { - real* data = storeMat_.getData() ? storeMat_.getData() : rowStore_.data(); - f(data, localIndices_->size() * width_); + f(buf_->data(), localIndices_->size() * width_); } void init(size_t height, size_t width); @@ -188,25 +245,25 @@ class SparseRowCpuMatrix : public CpuMatrix { globalIndices_[id] = kUnusedId_; } localIndices_->clear(); - rowStore_.clear(); + buf_->clear(); } inline void checkStoreSize() { - if (storeMat_.getData()) { - CHECK_LE(localIndices_->size(), storeMat_.getHeight()); - } else if (!FLAGS_allow_inefficient_sparse_update) { - if (localIndices_->size() > 0.5 * height_) { + if (buf_->canAutoGrowth()) { + if (buf_->getRowCount() > 0.5 * height_) { LOG(WARNING) << "There are more than 0.5*height (" << localIndices_->size() << ") rows are used for sparse " << "update, which is not efficient. Considering not use " << "sparse_update or set --allow_inefficient_sparse_update=true"; + + } else { + CHECK_LE(localIndices_->size(), buf_->getRowCount()); } } } - CpuMatrix storeMat_; - std::vector> rowStore_; + std::unique_ptr buf_; IndexDictPtr indexDictHandle_; std::vector* localIndices_; // =&indexDictHandle_->localIndices unsigned int* globalIndices_; // =indexDictHandle_->globalIndices.data(); From fa2c06fb053769f1c82e4d5c98b2bcdee376d6d0 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 13 Dec 2016 17:25:38 +0800 Subject: [PATCH 002/370] Add comments --- paddle/math/RowBuffer.h | 127 ++++++++++++++++++++++++++++++++++ paddle/math/SparseRowMatrix.h | 72 +------------------ 2 files changed, 130 insertions(+), 69 deletions(-) create mode 100644 paddle/math/RowBuffer.h diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h new file mode 100644 index 00000000000000..e35820461214aa --- /dev/null +++ b/paddle/math/RowBuffer.h @@ -0,0 +1,127 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include +#include "MemoryHandle.h" +#include "paddle/utils/Util.h" + +namespace paddle { + +/** + * @brief The RowBuffer class + * Represent the SparseRow Matrix Data. + * + * If not set memory handler, then the data could be auto growth. + */ +class RowBuffer { +public: + /** + * @brief RowBuffer create a auto-growth row buffer. The row length is width. + * @param width the length of each row, a.k.a matrix width. + */ + explicit RowBuffer(size_t width) : width_(width) {} + + /** + * @brief RowBuffer create a row buffer, which cannot be auto-growth. + * @param mem the pre-allocated memory. + * @param width the length of each row, a.k.a matrix width. + */ + RowBuffer(const CpuMemHandlePtr& mem, size_t width) + : preallocatedBuf_(mem), width_(width) {} + + /** + * @brief resize resize the buffer with rowCount + * @param rowCnt number of row. matrix height. + */ + inline void resize(int rowCnt) { + if (preallocatedBuf_) { + CHECK(preallocatedBuf_->getSize() < rowCnt * width_ * sizeof(real)); + } else { + rowStore_.resize(rowCnt * width_); + } + } + + /** + * @brief get a row buffer with row index. + * @param row the index of row. + * @return row buffer. + */ + inline const real* get(int row) const { + if (preallocatedBuf_) { + CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize()); + return reinterpret_cast(preallocatedBuf_->getBuf()) + row * width_; + } else { + CHECK_LE((row + 1) * width_, rowStore_.size()); + return rowStore_.data() + row * width_; + } + } + + /** + * @brief get a row buffer with row index. If row index is larger than local + * buffer, the size of local buffer will grow. + * @param row the index of row. + * @return row buffer. + */ + inline const real* getWithAutoGrowth(int row) { + if (preallocatedBuf_) { + return get(row); + } else { + if ((rowStore_.size() <= row * width_)) { + rowStore_.resize((row + 1) * width_); + } + return rowStore_.data() + row * width_; + } + } + + /** + * @return raw data buffer. + */ + inline real* data() { + if (preallocatedBuf_) { + return reinterpret_cast(preallocatedBuf_->getBuf()); + } else { + return rowStore_.data(); + } + } + + /** + * @brief clear local buffer. It only affect auto-growth buffer. + */ + inline void clear() { rowStore_.clear(); } + + /** + * @brief get current number of rows. + * @return number of rows. + */ + inline size_t getRowCount() const { + if (preallocatedBuf_) { + return preallocatedBuf_->getSize() / sizeof(float) / width_; + } else { + return rowStore_.size() / width_; + } + } + + /** + * @brief get is this buffer can automatically grow or not. + * @return ture if can automacitally grow. + */ + inline bool isAutoGrowth() const { return preallocatedBuf_ == nullptr; } + +private: + CpuMemHandlePtr preallocatedBuf_; + std::vector> rowStore_; + size_t width_; +}; +} // namespace paddle diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index db1530f7cfd529..d77d8c3ed13b14 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include "Matrix.h" +#include "RowBuffer.h" #include "paddle/utils/CommandLineParser.h" #include "paddle/utils/Util.h" @@ -24,73 +25,6 @@ P_DECLARE_bool(allow_inefficient_sparse_update); namespace paddle { -/** - * @brief The RowBuffer class - * Represent the SparseRow Matrix Data. - * - * If not set memory handler, then the data could be auto growth. - */ -class RowBuffer { -public: - explicit RowBuffer(size_t width) : width_(width) {} - RowBuffer(const CpuMemHandlePtr& mem, size_t width) - : preallocatedBuf_(mem), width_(width) {} - - inline void reserve(int rowCnt) { - if (preallocatedBuf_) { - CHECK(preallocatedBuf_->getSize() < rowCnt * width_ * sizeof(real)); - } else { - rowStore_.reserve(rowCnt * width_); - } - } - - inline const real* get(int row) const { - if (preallocatedBuf_) { - CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize()); - return reinterpret_cast(preallocatedBuf_->getBuf()) + row * width_; - } else { - CHECK_LE((row + 1) * width_, rowStore_.size()); - return rowStore_.data() + row * width_; - } - } - - inline const real* getWithAutoGrowth(int row) { - if (preallocatedBuf_) { - return get(row); - } else { - if ((rowStore_.size() <= row * width_)) { - rowStore_.resize((row + 1) * width_); - } - return rowStore_.data() + row * width_; - } - } - - inline real* data() { - if (preallocatedBuf_) { - return reinterpret_cast(preallocatedBuf_->getBuf()); - } else { - return rowStore_.data(); - } - } - - inline void clear() { rowStore_.clear(); } - - inline size_t getRowCount() const { - if (preallocatedBuf_) { - return preallocatedBuf_->getSize() / sizeof(float) / width_; - } else { - return rowStore_.size() / width_; - } - } - - inline bool canAutoGrowth() const { return preallocatedBuf_ == nullptr; } - -private: - CpuMemHandlePtr preallocatedBuf_; - std::vector> rowStore_; - size_t width_; -}; - /** * Sparse Row */ @@ -146,7 +80,7 @@ class SparseRowCpuMatrix : public CpuMatrix { * This is only used when SparseRowCpuMatrix is constructed with * indexDictHandle. */ - void reserveStore() { buf_->reserve(localIndices_->size()); } + void reserveStore() { buf_->resize(localIndices_->size()); } // row is the row id in the original matrix virtual real* getRowBuf(size_t row) { return getRow(row); } @@ -249,7 +183,7 @@ class SparseRowCpuMatrix : public CpuMatrix { } inline void checkStoreSize() { - if (buf_->canAutoGrowth()) { + if (buf_->isAutoGrowth()) { if (buf_->getRowCount() > 0.5 * height_) { LOG(WARNING) << "There are more than 0.5*height (" << localIndices_->size() From f63fbcdd2981b2bd9f478e784097b8ec5c6e463c Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 13 Dec 2016 21:34:39 +0800 Subject: [PATCH 003/370] Add unit test for row buffer. --- paddle/math/RowBuffer.h | 12 +++-- paddle/math/SparseRowMatrix.h | 4 +- paddle/math/tests/CMakeLists.txt | 1 + paddle/math/tests/test_RowBuffer.cpp | 65 ++++++++++++++++++++++++++++ 4 files changed, 76 insertions(+), 6 deletions(-) create mode 100644 paddle/math/tests/test_RowBuffer.cpp diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h index e35820461214aa..bb55ca5f9f0d24 100644 --- a/paddle/math/RowBuffer.h +++ b/paddle/math/RowBuffer.h @@ -58,13 +58,13 @@ class RowBuffer { * @param row the index of row. * @return row buffer. */ - inline const real* get(int row) const { + inline real* get(int row) const { if (preallocatedBuf_) { CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize()); return reinterpret_cast(preallocatedBuf_->getBuf()) + row * width_; } else { CHECK_LE((row + 1) * width_, rowStore_.size()); - return rowStore_.data() + row * width_; + return const_cast(rowStore_.data() + row * width_); } } @@ -74,7 +74,7 @@ class RowBuffer { * @param row the index of row. * @return row buffer. */ - inline const real* getWithAutoGrowth(int row) { + inline real* getWithAutoGrowth(int row) { if (preallocatedBuf_) { return get(row); } else { @@ -119,6 +119,12 @@ class RowBuffer { */ inline bool isAutoGrowth() const { return preallocatedBuf_ == nullptr; } + /** + * @brief return the width of matrix. a.k.a length of row. + * @return width of matrix + */ + inline size_t getWidth() const { return width_; } + private: CpuMemHandlePtr preallocatedBuf_; std::vector> rowStore_; diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index d77d8c3ed13b14..8532bca879dfda 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -69,9 +69,7 @@ class SparseRowCpuMatrix : public CpuMatrix { * * @param row row id in local storage */ - real* getLocalRow(size_t row) { - return const_cast(buf_->getWithAutoGrowth(row)); - } + real* getLocalRow(size_t row) { return buf_->getWithAutoGrowth(row); } /** * reserve the storage for rows according to current size of diff --git a/paddle/math/tests/CMakeLists.txt b/paddle/math/tests/CMakeLists.txt index fe5177291c21c3..9403bb073a273a 100644 --- a/paddle/math/tests/CMakeLists.txt +++ b/paddle/math/tests/CMakeLists.txt @@ -4,6 +4,7 @@ add_simple_unittest(test_ExecViaCpu) add_simple_unittest(test_SIMDFunctions) add_simple_unittest(test_TrainingAlgorithm) add_simple_unittest(test_SparseMatrix) +add_simple_unittest(test_RowBuffer) # TODO(yuyang18): Refactor TestUtil.cpp. Remove this cross module reference. add_unittest(test_matrixCompare diff --git a/paddle/math/tests/test_RowBuffer.cpp b/paddle/math/tests/test_RowBuffer.cpp new file mode 100644 index 00000000000000..5f66f22ef73dcf --- /dev/null +++ b/paddle/math/tests/test_RowBuffer.cpp @@ -0,0 +1,65 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "paddle/math/RowBuffer.h" + +TEST(RowBuffer, testAutoGrow) { + paddle::RowBuffer buf(128); + ASSERT_EQ(128, buf.getWidth()); + ASSERT_TRUE(buf.isAutoGrowth()); + buf.resize(2); + ASSERT_EQ(2, buf.getRowCount()); + for (size_t i = 0; i < buf.getWidth() * 2; ++i) { + buf.data()[i] = i; + } + for (size_t i = 0; i < buf.getRowCount(); ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5); + } + } + + auto data = buf.getWithAutoGrowth(2); + for (size_t i = 0; i < buf.getWidth(); ++i) { + data[i] = i; + } + + ASSERT_EQ(3, buf.getRowCount()); + for (size_t i = 0; i < buf.getRowCount() - 1; ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5); + } + } + for (size_t i = 0; i < buf.getWidth(); ++i) { + ASSERT_NEAR(i, buf.get(2)[i], 1e-5); + } +} + +TEST(RowBuffer, testWithMemBuf) { + paddle::CpuMemHandlePtr mem = + std::make_shared(128 * 2 * sizeof(real)); + paddle::RowBuffer buf(mem, 128); + ASSERT_TRUE(!buf.isAutoGrowth()); + ASSERT_EQ(2, buf.getRowCount()); + for (size_t i = 0; i < buf.getWidth() * 2; ++i) { + buf.data()[i] = i; + } + for (size_t i = 0; i < buf.getRowCount(); ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.getWithAutoGrowth(i)[j], 1e-5); + } + } + + ASSERT_DEATH_IF_SUPPORTED(buf.getWithAutoGrowth(3), ".*"); +} From ea7dd7c92590116d69e3244b8718012fd0d6b68d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 13 Dec 2016 22:48:32 +0800 Subject: [PATCH 004/370] Fix logic error before --- paddle/math/SparseRowMatrix.h | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index 8532bca879dfda..dd4d85611d6fca 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -188,10 +188,9 @@ class SparseRowCpuMatrix : public CpuMatrix { << ") rows are used for sparse " << "update, which is not efficient. Considering not use " << "sparse_update or set --allow_inefficient_sparse_update=true"; - - } else { - CHECK_LE(localIndices_->size(), buf_->getRowCount()); } + } else { + CHECK_LE(localIndices_->size(), buf_->getRowCount()); } } From 8b5431d5e1e4f54339594ba6a76ecbccf256080f Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 6 Jan 2017 19:19:56 +0800 Subject: [PATCH 005/370] padding operation --- paddle/function/CMakeLists.txt | 1 + paddle/function/PadOp.cpp | 185 + paddle/function/PadOp.h | 96 + paddle/function/PadOpGpu.cu | 102 + paddle/function/PadOpTest.cpp | 70 + paddle/gserver/layers/PadLayer.cpp | 115 + paddle/gserver/layers/PadLayer.h | 45 + paddle/gserver/tests/test_LayerGrad.cpp | 3090 +++++++++-------- proto/ModelConfig.proto | 8 + python/paddle/trainer/config_parser.py | 26 + .../paddle/trainer_config_helpers/layers.py | 84 +- .../tests/configs/test_pad.py | 21 + 12 files changed, 2317 insertions(+), 1526 deletions(-) create mode 100644 paddle/function/PadOp.cpp create mode 100644 paddle/function/PadOp.h create mode 100644 paddle/function/PadOpGpu.cu create mode 100644 paddle/function/PadOpTest.cpp create mode 100644 paddle/gserver/layers/PadLayer.cpp create mode 100644 paddle/gserver/layers/PadLayer.h create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_pad.py diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 0b3126155d0c08..70b25406a4afda 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -17,6 +17,7 @@ if(WITH_TESTING) # file(GLOB test_files . *OpTest.cpp) # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files}) add_simple_unittest(CrossMapNormalOpTest) + add_simple_unittest(PadOpTest) add_unittest(ContextProjectionOpTest ContextProjectionOpTest.cpp ../gserver/tests/TestUtil.cpp) diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp new file mode 100644 index 00000000000000..e10011da2a1750 --- /dev/null +++ b/paddle/function/PadOp.cpp @@ -0,0 +1,185 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "PadOp.h" +#include "paddle/math/Vector.h" + +namespace paddle { + +template <> +void Pad(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1) { + int outC = inC + padc0 + padc1; + int outH = inH + padh0 + padh1; + int outW = inW + padw0 + padw1; + for (int i = 0; i < num; i++) { + for (int c = 0; c < inC; c++) { + for (int h = 0; h < inH; h++) { + int inoff = ((i * inC + c) * inH + h) * inW; + int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0; + memcpy(outputs + outoff, inputs + inoff, inW * sizeof(real)); + } + } + } +} + +template <> +void PadGrad(real* inGrad, + const real* outGrad, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1) { + int outC = inC + padc0 + padc1; + int outH = inH + padh0 + padh1; + int outW = inW + padw0 + padw1; + for (int i = 0; i < num; i++) { + for (int c = 0; c < inC; c++) { + for (int h = 0; h < inH; h++) { + int inoff = ((i * inC + c) * inH + h) * inW; + int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0; + CpuVector inG = CpuVector(inW, inGrad + inoff); + CpuVector outG = CpuVector(inW, const_cast(outGrad + outoff)); + inG += outG; + } + } + } +} + +/** + * \param inputs[0] input value. + * \param outputs[0] output value. + */ +template +class PadFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override { + padc0_ = config.get("padc0"); + padc1_ = config.get("padc1"); + padh0_ = config.get("padh0"); + padh1_ = config.get("padh1"); + padw0_ = config.get("padw0"); + padw1_ = config.get("padw1"); + } + + void calc(const Arguments& inputs, + const Arguments& outputs, + const Arguments& inouts) override { + CHECK_EQ(1, inputs.size()); + CHECK_EQ(1, outputs.size()); + CHECK_EQ(0, inouts.size()); + + size_t num = inputs[0].dims_[0]; + size_t inC = inputs[0].dims_[1]; + size_t inH = inputs[0].dims_[2]; + size_t inW = inputs[0].dims_[3]; + + Pad(outputs[0].getData(), + inputs[0].getData(), + num, + inC, + inH, + inW, + padc0_, + padc1_, + padh0_, + padh1_, + padw0_, + padw1_); + } + +private: + int padc0_; + int padc1_; + int padh0_; + int padh1_; + int padw0_; + int padw1_; +}; + +/** + * \param inputs[0] input grad. + * \param outputs[0] output grad. + */ +template +class PadGradFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override { + padc0_ = config.get("padc0"); + padc1_ = config.get("padc1"); + padh0_ = config.get("padh0"); + padh1_ = config.get("padh1"); + padw0_ = config.get("padw0"); + padw1_ = config.get("padw1"); + } + + void calc(const Arguments& inputs, + const Arguments& outputs, + const Arguments& inouts) override { + CHECK_EQ(1, inputs.size()); + CHECK_EQ(0, outputs.size()); + CHECK_EQ(1, inouts.size()); + + size_t n = inouts[0].dims_[0]; + size_t inC = inouts[0].dims_[1]; + size_t inH = inouts[0].dims_[2]; + size_t inW = inouts[0].dims_[3]; + + PadGrad(inouts[0].getData(), + inputs[0].getData(), + n, + inC, + inH, + inW, + padc0_, + padc1_, + padh0_, + padh1_, + padw0_, + padw1_); + } + +private: + int padc0_; + int padc1_; + int padh0_; + int padh1_; + int padw0_; + int padw1_; +}; + +REGISTER_TYPED_FUNC(Pad, CPU, PadFunc); +REGISTER_TYPED_FUNC(PadGrad, CPU, PadGradFunc); +#ifndef PADDLE_ONLY_CPU +REGISTER_TYPED_FUNC(Pad, GPU, PadFunc); +REGISTER_TYPED_FUNC(PadGrad, GPU, PadGradFunc); +#endif + +} // namespace paddle diff --git a/paddle/function/PadOp.h b/paddle/function/PadOp.h new file mode 100644 index 00000000000000..4a5e8fe33857aa --- /dev/null +++ b/paddle/function/PadOp.h @@ -0,0 +1,96 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "Function.h" + +namespace paddle { + +/** + * \brief This funtion pads zeros to inputs according to the specify dimension. + * The data structure of image data is NCHW. + * + * \param[out] outputs save results. + * \param[in] inputs input data. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + * \param[in] padc0 how many values to add before the data in dimension of + * channel. + * \param[in] padc1 how many values to add after the data in dimension of + * channel. + * \param[in] padh0 how many values to add before the data in dimension of + * height. + * \param[in] padh1 how many values to add after the data in dimension of + * height. + * \param[in] padw0 how many values to add before the data in dimension of + * width. + * \param[in] padw1 how many values to add after the data in dimension of + * width. + * + */ +template +void Pad(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1); + +/** + * \brief Padding operation backward. + * The data structure of image data is NCHW. + * + * \param[out] inGrad gradients of previous layer. + * \param[in] outGrad output gradients. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + * \param[in] padc0 how many values to add before the data in dimension of + * channel. + * \param[in] padc1 how many values to add after the data in dimension of + * channel. + * \param[in] padh0 how many values to add before the data in dimension of + * height. + * \param[in] padh1 how many values to add after the data in dimension of + * height. + * \param[in] padw0 how many values to add before the data in dimension of + * width. + * \param[in] padw1 how many values to add after the data in dimension of + * width. + * + */ +template +void PadGrad(real* inGrad, + const real* outGrad, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1); +} // namespace paddle diff --git a/paddle/function/PadOpGpu.cu b/paddle/function/PadOpGpu.cu new file mode 100644 index 00000000000000..578d6e86d7534e --- /dev/null +++ b/paddle/function/PadOpGpu.cu @@ -0,0 +1,102 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "hl_base.h" +#include "PadOp.h" + +namespace paddle { + +__global__ void KePad(real* outputs, const real* inputs, + int inC, int inH, int inW, + int padc, int padh, int padw, + int outC, int outH, int outW, int nthreads) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int w = idx % inW; + const int h = (idx / inW) % inH; + const int c = (idx / inW / inH) % inC; + const int n = idx / inW / inH / inC; + + const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w; + outputs[off] = inputs[idx]; + } +} + +template <> +void Pad(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1) { + size_t nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + int outC = inC + padc0 + padc1; + int outH = inH + padh0 + padh1; + int outW = inW + padw0 + padw1; + KePad<<>> + (outputs, inputs, inC, inH, inW, padc0, padh0, padw0, + outC, outH, outW, nth); + CHECK_SYNC("Pad"); +} + +__global__ void KePadDiff(real* inGrad, const real* outGrad, + int inC, int inH, int inW, + int padc, int padh, int padw, + int outC, int outH, int outW, int nthreads) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int w = idx % inW; + const int h = (idx / inW) % inH; + const int c = (idx / inW / inH) % inC; + const int n = idx / inW / inH / inC; + + const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w; + inGrad[idx] += outGrad[off]; + } +} + +template <> +void PadGrad(real* inGrad, + const real* outGrad, + const int num, + const int inC, + const int inH, + const int inW, + const int padc0, + const int padc1, + const int padh0, + const int padh1, + const int padw0, + const int padw1) { + int nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + int outC = inC + padc0 + padc1; + int outH = inH + padh0 + padh1; + int outW = inW + padw0 + padw1; + KePadDiff <<>> + (inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0, + outC, outH, outW, nth); + CHECK_SYNC("PadGrad"); +} + +} // namespace paddle diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp new file mode 100644 index 00000000000000..ee2834d793d348 --- /dev/null +++ b/paddle/function/PadOpTest.cpp @@ -0,0 +1,70 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "FunctionTest.h" + +namespace paddle { + +TEST(Pad, real) { + for (size_t numSamples : {5, 32}) { + for (size_t channels : {1, 5, 32}) { + for (size_t imgSizeH : {5, 33, 100}) { + for (size_t imgSizeW : {5, 32, 96}) { + VLOG(3) << " numSamples=" << numSamples << " channels=" << channels + << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; + + FunctionCompare compare("Pad", + FuncConfig() + .set("padc0", 2) + .set("padc1", 3) + .set("padh0", 1) + .set("padh1", 2) + .set("padw0", 3) + .set("padw1", 2)); + Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; + Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; + compare.cmpWithArg( + {Tensor(nullptr, inDims)}, {Tensor(nullptr, outDims)}, {}); + } + } + } + } +} + +// TEST(PadGrad, real) { +// for (size_t numSamples : {5, 32}) { +// for (size_t channels : {1, 5, 32}) { +// for (size_t imgSizeH : {5, 33, 100}) { +// for (size_t imgSizeW : {5, 32, 96}) { +// VLOG(3) << " numSamples=" << numSamples << " channels=" << channels +// << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; +// +// FunctionCompare compare("PadGrad", +// FuncConfig() +// .set("padc0", 2).set("padc1", 3) +// .set("padh0", 1).set("padh1", 2) +// .set("padw0", 3).set("padw1", 2)); +// Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; +// Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; +// compare.cmpWithArg({Tensor(nullptr, inDims)}, +// {Tensor(nullptr, outDims)}, +// {}); +// } +// } +// } +// } +//} + +} // namespace paddle diff --git a/paddle/gserver/layers/PadLayer.cpp b/paddle/gserver/layers/PadLayer.cpp new file mode 100644 index 00000000000000..62c50af32dd055 --- /dev/null +++ b/paddle/gserver/layers/PadLayer.cpp @@ -0,0 +1,115 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "PadLayer.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +REGISTER_LAYER(pad, PadLayer); + +bool PadLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + + auto& pad_conf = config_.inputs(0).pad_conf(); + auto& img_conf = pad_conf.image_conf(); + CHECK_EQ(config_.inputs_size(), 1); + inDims_.push_back(0); + inDims_.push_back(img_conf.channels()); + inDims_.push_back(img_conf.has_img_size_y() ? img_conf.img_size_y() + : img_conf.img_size()); + inDims_.push_back(img_conf.img_size()); + + CHECK_EQ(2UL, pad_conf.pad_c_size()); + CHECK_EQ(2UL, pad_conf.pad_h_size()); + CHECK_EQ(2UL, pad_conf.pad_w_size()); + padc_.push_back(pad_conf.pad_c(0)); + padc_.push_back(pad_conf.pad_c(1)); + padh_.push_back(pad_conf.pad_h(0)); + padh_.push_back(pad_conf.pad_h(1)); + padw_.push_back(pad_conf.pad_w(0)); + padw_.push_back(pad_conf.pad_w(1)); + + outDims_.resize(4); + setOutDims(0); + + createFunction(forward_, + "Pad", + FuncConfig() + .set("padc0", padc_[0]) + .set("padc1", padc_[1]) + .set("padh0", padh_[0]) + .set("padh1", padh_[1]) + .set("padw0", padw_[0]) + .set("padw1", padw_[1])); + createFunction(backward_, + "PadGrad", + FuncConfig() + .set("padc0", padc_[0]) + .set("padc1", padc_[1]) + .set("padh0", padh_[0]) + .set("padh1", padh_[1]) + .set("padw0", padw_[0]) + .set("padw1", padw_[1])); + + return true; +} + +void PadLayer::setOutDims(int batchSize) { + outDims_[0] = batchSize; + outDims_[1] = inDims_[1] + padc_[0] + padc_[1]; + outDims_[2] = inDims_[2] + padh_[0] + padh_[1]; + outDims_[3] = inDims_[3] + padw_[0] + padw_[1]; +} + +void PadLayer::setTensorDim(int batchSize) { + CHECK_EQ(inputLayers_.size(), 1UL); + inDims_[0] = batchSize; + int h = inputLayers_[0]->getOutput().getFrameHeight(); + if (h != 0) inDims_[2]; + int w = inputLayers_[0]->getOutput().getFrameWidth(); + if (w != 0) inDims_[3]; + setOutDims(batchSize); +} + +void PadLayer::forward(PassType passType) { + Layer::forward(passType); + MatrixPtr input = inputLayers_[0]->getOutputValue(); + size_t batchSize = input->getHeight(); + setTensorDim(batchSize); + int size = outDims_[1] * outDims_[2] * outDims_[3]; + resetOutput(batchSize, size); + MatrixPtr outV = getOutputValue(); + REGISTER_TIMER_INFO("PadForward", getName().c_str()); + forward_[0]->calc({Tensor(input->getData(), inDims_)}, + {Tensor(outV->getData(), outDims_)}, + {}); +} + +void PadLayer::backward(const UpdateCallback& callback) { + (void)callback; + + MatrixPtr preGrad = inputLayers_[0]->getOutputGrad(); + if (NULL == preGrad) { + return; + } + MatrixPtr outGrad = getOutputGrad(); + REGISTER_TIMER_INFO("PadBackward", getName().c_str()); + backward_[0]->calc({Tensor(outGrad->getData(), outDims_)}, + {}, + {Tensor(preGrad->getData(), inDims_)}); +} +} // namespace paddle diff --git a/paddle/gserver/layers/PadLayer.h b/paddle/gserver/layers/PadLayer.h new file mode 100644 index 00000000000000..834622a7af396e --- /dev/null +++ b/paddle/gserver/layers/PadLayer.h @@ -0,0 +1,45 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "Layer.h" + +namespace paddle { + +/** + * @brief response normalization across feature maps + * namely normalize in number of size_ channels + */ +class PadLayer : public Layer { +public: + explicit PadLayer(const LayerConfig& config) : Layer(config) {} + + ~PadLayer() {} + + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + void forward(PassType passType); + void backward(const UpdateCallback& callback = nullptr); + +protected: + void setOutDims(int batchSize); + void setTensorDim(int batchSize); + + std::vector padc_; + std::vector padh_; + std::vector padw_; + Dims inDims_; + Dims outDims_; +}; +} // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 2cc25f6b211e36..3094b3a4a02e3e 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -32,1534 +32,1580 @@ DECLARE_double(checkgrad_eps); DECLARE_bool(thread_local_rand_use_global_seed); DECLARE_bool(prev_batch_state); -TEST(Operator, dot_mul) { +// TEST(Operator, dot_mul) { +// TestConfig config; +// config.layerConfig.set_size(10); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); +// operatorConf.set_type("dot_mul"); +// operatorConf.set_dotmul_scale(-1); +// +// testOperatorGrad(config, operatorConf, 100, false, false); +// } +// +// TEST(Projection, context) { +// for (auto contextStart : {-5, -3, -1, 0, 3}) { +// for (auto contextLength : {1, 2, 5, 7}) { +// for (auto batchSize : {1, 2, 5, 20, 50}) { +// for (auto trainablePadding : {false, true}) { +// LOG(INFO) << " contextStart=" << contextStart +// << " contextLength=" << contextLength +// << " batchSize=" << batchSize +// << " trainablePadding=" << trainablePadding; +// ProjectionConfig conf; +// conf.set_type("context"); +// conf.set_input_size(10); +// conf.set_context_start(contextStart); +// conf.set_context_length(contextLength); +// conf.set_trainable_padding(trainablePadding); +// conf.set_output_size(conf.context_length() * conf.input_size()); +// int pad = +// std::max(0, -conf.context_start()) + +// std::max(0, conf.context_start() + conf.context_length() - 1); +// for (auto useGpu : {false, true}) { +// testProjectionGrad( +// conf, +// INPUT_SEQUENCE_DATA, +// trainablePadding ? conf.input_size() * pad : 0, +// batchSize, +// useGpu, +// contextStart + contextLength <= 1); // = testState +// } +// } +// } +// } +// } +// } +// +// TEST(Projection, trans_fc) { +// ProjectionConfig conf; +// conf.set_type("trans_fc"); +// conf.set_input_size(50); +// conf.set_output_size(20); +// for (auto useGpu : {false, true}) { +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ 1000, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// TEST(Projection, fc) { +// ProjectionConfig conf; +// conf.set_type("fc"); +// conf.set_input_size(10); +// conf.set_output_size(20); +// for (auto useGpu : {false, true}) { +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ 200, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// TEST(Projection, dot_mul) { +// ProjectionConfig conf; +// conf.set_type("dot_mul"); +// conf.set_input_size(20); +// conf.set_output_size(20); +// for (auto useGpu : {false, true}) { +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ 20, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// TEST(Projection, table) { +// ProjectionConfig conf; +// conf.set_type("table"); +// conf.set_input_size(10); +// conf.set_output_size(20); +// for (auto useGpu : {false, true}) { +// testProjectionGrad(conf, +// INPUT_LABEL, +// /* parameterSize */ 200, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// TEST(Projection, identity) { +// ProjectionConfig conf; +// conf.set_type("identity"); +// conf.set_input_size(10); +// conf.set_output_size(10); +// for (auto useGpu : {false, true}) { +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ 0, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// TEST(Projection, scaling) { +// ProjectionConfig conf; +// conf.set_type("scaling"); +// conf.set_input_size(10); +// conf.set_output_size(10); +// for (auto useGpu : {false}) { +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ 1, +// /* batchSize */ 100, +// useGpu); +// } +// } +// +// void testProjectionConv(size_t groups) { +// const int NUM_FILTERS = 18; +// const int FILTER_SIZE = 2; +// const int FILTER_SIZE_Y = 3; +// const int CHANNELS = 3; +// const int IMAGE_SIZE = 16; +// +// ProjectionConfig conf; +// conf.set_type("conv"); +// conf.set_num_filters(NUM_FILTERS); +// +// ConvConfig* conv = conf.mutable_conv_conf(); +// conv->set_filter_size(FILTER_SIZE); +// conv->set_filter_size_y(FILTER_SIZE_Y); +// conv->set_channels(CHANNELS); +// conv->set_padding(0); +// conv->set_padding_y(1); +// conv->set_stride(2); +// conv->set_stride_y(2); +// conv->set_groups(groups); +// conv->set_filter_channels(conv->channels() / conv->groups()); +// conv->set_img_size(IMAGE_SIZE); +// int output_x = outputSize(conv->img_size(), +// conv->filter_size(), +// conv->padding(), +// conv->stride(), +// /* caffeMode */ true); +// int output_y = outputSize(conv->img_size(), +// conv->filter_size_y(), +// conv->padding_y(), +// conv->stride_y(), +// /* caffeMode */ true); +// conv->set_output_x(output_x); +// conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS); +// conf.set_output_size(output_x * output_y * NUM_FILTERS); +// +// testProjectionGrad(conf, +// INPUT_DATA, +// /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE +// * +// FILTER_SIZE_Y / groups, +// /* batchSize */ 100, +// true, +// false, +// NUM_FILTERS, +// true); +// } +// +// #ifndef PADDLE_ONLY_CPU +// TEST(Projection, conv) { +// testProjectionConv(1); +// testProjectionConv(3); +// } +// #endif +// +// TEST(Layer, BilinearInterpLayer) { +// TestConfig config; +// config.layerConfig.set_type("bilinear_interp"); +// config.biasSize = 0; +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); +// +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); +// ImageConfig* image = bilinear->mutable_image_conf(); +// image->set_img_size(32); +// image->set_img_size_y(32); +// image->set_channels(4); +// +// for (auto useGpu : {false, true}) { +// for (auto outSize : {32, 64}) { +// bilinear->set_out_size_x(outSize); +// bilinear->set_out_size_y(outSize); +// testLayerGrad(config, "bilinear_interp", 10, false, useGpu); +// } +// } +// } +// +// TEST(Layer, concat) { +// TestConfig config; +// config.biasSize = 0; +// config.layerConfig.set_type("concat"); +// config.layerConfig.set_size(15); +// config.layerConfig.set_active_type("sigmoid"); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "concat", 100, false, useGpu); +// } +// } +// +// TEST(Layer, AddtoLayer) { +// TestConfig config; +// config.biasSize = 0; +// config.layerConfig.set_type("addto"); +// config.layerConfig.set_size(10); +// config.layerConfig.set_active_type("sigmoid"); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "addto", 100, false, useGpu); +// } +// } +// +// TEST(Layer, CRFLayer) { +// TestConfig config; +// config.layerConfig.set_type("crf"); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120}); +// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // Not support GPU now +// testLayerGrad(config, +// "crf", +// 100, +// /* trans */ false, +// /* useGpu */ false, +// false /*useWeight*/, +// 0.03 /*epsilon*/); +// } +// +// TEST(Layer, CTCLayer) { +// TestConfig config; +// config.layerConfig.set_type("ctc"); +// config.layerConfig.set_norm_by_times(false); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ +// useGpu); +// } +// } +// +// TEST(Layer, cosSimLayer) { +// TestConfig config; +// config.layerConfig.set_type("cos"); +// config.layerConfig.set_size(1); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "cos", 100, false, useGpu); +// } +// } +// +// TEST(Layer, CosSimVecMatLayer) { +// TestConfig config; +// config.layerConfig.set_type("cos_vm"); +// config.layerConfig.set_size(5); // output size +// config.layerConfig.set_cos_scale(2.0); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "cos_vm", 100, false, useGpu); +// } +// } +// +// void testConvLayer(const string& type, bool trans, bool useGpu) { +// TestConfig config; +// config.biasSize = 16; +// config.layerConfig.set_type(type); +// config.layerConfig.set_num_filters(16); +// config.layerConfig.set_partial_sum(1); +// config.layerConfig.set_shared_biases(true); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// ConvConfig* conv = input->mutable_conv_conf(); +// conv->set_filter_size(2); +// conv->set_filter_size_y(3); +// conv->set_channels(3); +// conv->set_padding(0); +// conv->set_padding_y(1); +// conv->set_stride(2); +// conv->set_stride_y(2); +// conv->set_groups(1); +// conv->set_filter_channels(conv->channels() / conv->groups()); +// conv->set_img_size(16); +// conv->set_img_size_y(8); +// conv->set_output_x(outputSize(conv->img_size(), +// conv->filter_size(), +// conv->padding(), +// conv->stride(), +// /* caffeMode */ true)); +// conv->set_output_y(outputSize(conv->img_size_y(), +// conv->filter_size_y(), +// conv->padding_y(), +// conv->stride_y(), +// /* caffeMode */ true)); +// config.layerConfig.set_size(conv->output_x() * conv->output_y() * +// config.layerConfig.num_filters()); +// +// testLayerGrad(config, "conv", 100, trans, useGpu); +// // Use small batch_size and useWeight=true to test biasGrad +// testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02); +// } +// +// TEST(Layer, convLayer) { +// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); +// #ifndef PADDLE_ONLY_CPU +// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); +// testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); +// #endif +// } +// +// void testConvTransLayer(const string& type, bool trans, bool useGpu) { +// TestConfig config; +// config.biasSize = 3; +// config.layerConfig.set_type(type); +// config.layerConfig.set_num_filters(3); +// config.layerConfig.set_partial_sum(1); +// config.layerConfig.set_shared_biases(true); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// ConvConfig* conv = input->mutable_conv_conf(); +// conv->set_filter_size(2); +// conv->set_filter_size_y(3); +// conv->set_channels(16); +// conv->set_padding(0); +// conv->set_padding_y(1); +// conv->set_stride(2); +// conv->set_stride_y(2); +// conv->set_groups(1); +// conv->set_filter_channels(3 / conv->groups()); +// conv->set_img_size(16); +// conv->set_output_x(outputSize(conv->img_size(), +// conv->filter_size(), +// conv->padding(), +// conv->stride(), +// /* caffeMode */ true)); +// +// config.layerConfig.set_size(conv->img_size() * conv->img_size() * +// config.layerConfig.num_filters()); +// +// testLayerGrad(config, "convTrans", 100, trans, useGpu); +// // Use small batch_size and useWeight=true to test biasGrad +// testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02); +// } +// +// TEST(Layer, convTransLayer) { +// for (auto useGpu : {false, true}) { +// testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); +// } +// } +// +// TEST(Layer, blockExpandLayer) { +// TestConfig config; +// config.biasSize = 0; +// config.layerConfig.set_type("blockexpand"); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// BlockExpandConfig* blockExpand = input->mutable_block_expand_conf(); +// blockExpand->set_img_size_x(64); +// blockExpand->set_img_size_y(32); +// blockExpand->set_channels(3); +// blockExpand->set_padding_x(0); +// blockExpand->set_padding_y(0); +// blockExpand->set_block_x(4); +// blockExpand->set_block_y(32); +// blockExpand->set_stride_x(2); +// blockExpand->set_stride_y(2); +// blockExpand->set_output_x(outputSize(blockExpand->img_size_x(), +// blockExpand->block_x(), +// blockExpand->padding_x(), +// blockExpand->stride_x(), +// /* caffeMode */ false)); +// blockExpand->set_output_y(outputSize(blockExpand->img_size_y(), +// blockExpand->block_y(), +// blockExpand->padding_y(), +// blockExpand->stride_y(), +// /* caffeMode */ false)); +// config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() +// * +// blockExpand->channels()); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "blockexpand", 100, false, useGpu); +// } +// } +// +// TEST(Layer, maxoutLayer) { +// TestConfig config; +// config.biasSize = 0; +// config.layerConfig.set_type("maxout"); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// MaxOutConfig* maxout = input->mutable_maxout_conf(); +// ImageConfig* image = maxout->mutable_image_conf(); +// +// image->set_img_size(32); +// image->set_img_size_y(32); +// image->set_channels(4); +// maxout->set_groups(2); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "maxout", 10, false, useGpu); +// } +// } +// void testFcLayer(string format, size_t nnz) { +// TestConfig config; +// config.biasSize = 4096; +// config.layerConfig.set_type("fc"); +// config.layerConfig.set_size(4096); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_drop_rate(0.1); +// +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)}); +// config.layerConfig.add_inputs(); +// +// LOG(INFO) << config.inputDefs[0].sparse.sparse << " " +// << config.inputDefs[0].sparse.format; +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, +// "fc", +// 100, +// /* trans */ false, +// useGpu, +// /* weight */ true); +// } +// } +// +// TEST(Layer, fcLayer) { +// testFcLayer("", 4096 * 4096 * 2); +// testFcLayer("csc", 4096 * 40); +// testFcLayer("csr", 4096 * 40); +// } +// +// TEST(Layer, SelectiveFullyConnectedLayer) { +// TestConfig config; +// size_t nin = 16; +// size_t nout = 256; +// config.layerConfig.set_type("selective_fc"); +// config.layerConfig.set_size(nout); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_has_selected_colums(true); +// config.layerConfig.set_selective_fc_pass_generation(false); +// config.biasSize = nout; +// +// config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back( +// {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", +// true)}); +// config.layerConfig.add_inputs(); +// +// testLayerGrad(config, +// "selective_fc", +// 100, +// /* trans= */ false, +// /* useGup= */ false, +// false); +// #ifndef PADDLE_ONLY_CPU +// testLayerGrad(config, +// "selective_fc", +// 100, +// /* trans= */ false, +// /* useGup= */ true, +// false); +// #endif +// } +// +// TEST(Layer, DataNormLayer) { +// TestConfig config; +// config.layerConfig.set_type("data_norm"); +// config.layerConfig.set_size(20); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100}); +// config.inputDefs.back().isStatic = true; +// config.layerConfig.add_inputs(); +// +// for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) { +// config.layerConfig.set_data_norm_strategy(strategy); +// // The parameters are static, so not support GPU now +// testLayerGrad(config, +// "data_norm", +// 200, +// /* trans */ false, +// /* useGpu */ false); +// } +// } +// +// TEST(Layer, hsigmoidLayer) { +// TestConfig config; +// config.layerConfig.set_type("hsigmoid"); +// config.layerConfig.set_num_classes(5); +// config.layerConfig.set_size(1); +// config.biasSize = config.layerConfig.num_classes() - 1; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200}); +// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // Not support GPU now +// testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ +// false); +// } +// +// TEST(Layer, multi_cross) { +// TestConfig config; +// config.layerConfig.set_type("multi-class-cross-entropy"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad( +// config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu); +// } +// } +// +// TEST(Layer, multi_binary_label_sparse_mat) { +// TestConfig config; +// config.layerConfig.set_type("multi_binary_label_cross_entropy"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, +// "multi_binary_label_cross_entropy", +// 100, +// /* trans */ false, +// useGpu); +// } +// } +// +// TEST(layer, multi_binary_label_id) { +// TestConfig config; +// config.layerConfig.set_type("multi_binary_label_cross_entropy"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, +// "multi_binary_label_cross_entropy", +// 100, +// /* trans */ false, +// useGpu); +// } +// } +// +// TEST(Layer, multi_cross_with_selfnorm) { +// TestConfig config; +// config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm"); +// config.layerConfig.set_softmax_selfnorm_alpha(0.1); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // Not support GPU now +// testLayerGrad(config, +// "multi_class_cross_entropy_with_selfnorm", +// 100, +// /* trans */ false, +// /* useGpu */ false); +// } +// +// TEST(Layer, multi_cross_soft) { +// TestConfig config; +// config.layerConfig.set_type("soft_binary_class_cross_entropy"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, +// "soft_binary_class_cross_entropy", +// 100, +// /* trans */ false, +// useGpu); +// } +// } +// +// TEST(Layer, square_error) { +// TestConfig config; +// config.layerConfig.set_type("square_error"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); +// } +// } +// +// TEST(Layer, sparse_square_error) { +// TestConfig config; +// config.layerConfig.set_type("square_error"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // "GpuSparseMatrix" as label is not supported +// testLayerGrad(config, +// "square_error", +// 100, +// /* trans */ false, +// /* useGpu */ false); +// } +// +// TEST(Layer, sparse_float_square_error) { +// TestConfig config; +// config.layerConfig.set_type("square_error"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); +// config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // "GpuSparseMatrix" as label is not supported +// testLayerGrad(config, +// "square_error", +// 100, +// /* trans */ false, +// /* useGpu */ false); +// } +// +// TEST(Layer, square_error_weighted) { +// TestConfig config; +// config.layerConfig.set_type("square_error"); +// config.biasSize = 0; +// config.testAccumulate = false; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); +// } +// } +// +// TEST(Layer, huber_two_class) { +// TestConfig config; +// config.layerConfig.set_type("huber"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); +// } +// } +// +// void testExpandLayer(string trans_type, bool hasSubseq) { +// TestConfig config; +// config.layerConfig.set_type("expand"); +// +// config.inputDefs.push_back( +// {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA, +// "layer_0", +// 10, +// 0}); +// config.inputDefs.push_back( +// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, +// "layer_1", +// 10, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.set_trans_type(trans_type); +// LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq; +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "expand", 30, false, useGpu); +// } +// } +// +// TEST(Layer, ExpandLayer) { +// testExpandLayer("non-seq", false); // non-seq expand to seq +// testExpandLayer("non-seq", true); // non-seq expand to hasSubseq +// testExpandLayer("seq", true); // seq expand to hasSubseq +// } +// +// void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) { +// TestConfig config; +// config.layerConfig.set_type(layer_type); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back( +// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, +// "layer_0", +// 10, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.set_trans_type(trans_type); +// +// auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) { +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, layer_type, 100, false, useGpu); +// } +// }; +// +// if (layer_type == "average") { +// for (auto strategy : {"average", "sum", "squarerootn"}) { +// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type +// << " average_strategy=" << strategy; +// config.layerConfig.set_average_strategy(strategy); +// testDegradeLayerGrad(config, layer_type); +// } +// } else { +// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type; +// testDegradeLayerGrad(config, layer_type); +// } +// } +// +// TEST(Layer, MaxLayer) { +// testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq +// testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq +// testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq +// } +// +// TEST(Layer, SequenceLastInstanceLayer) { +// testDegradeLayer(false, +// "seqlastins", +// "non-seq"); // seq seqlastins to non-seq +// testDegradeLayer(true, +// "seqlastins", +// "non-seq"); // hasSubseq seqlastins to non-seq +// testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to +// seq +// } +// +// TEST(Layer, AverageLayer) { +// testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq +// testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to +// non-seq +// testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq +// } +// +// TEST(Layer, SequenceConcatLayer) { +// TestConfig config; +// config.layerConfig.set_type("seqconcat"); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "seqconcat", 100, false, useGpu); +// } +// } +// +// TEST(Layer, SequenceReshapeLayer) { +// TestConfig config; +// config.layerConfig.set_type("seqreshape"); +// config.layerConfig.set_size(10); +// +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "seqreshape", 100, false, useGpu); +// } +// } +// +// TEST(Layer, ConvShiftLayer) { +// TestConfig config; +// config.layerConfig.set_type("conv_shift"); +// config.layerConfig.set_size(10); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// // Not support GPU now +// testLayerGrad(config, "conv_shift", 100, false, false); +// } +// +// TEST(Layer, PowerLayer) { +// TestConfig config; +// config.layerConfig.set_type("power"); +// config.layerConfig.set_size(10); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "power", 100, false, useGpu); +// } +// } +// +// TEST(Layer, ConvexCombinationLayer) { +// TestConfig config; +// config.layerConfig.set_type("convex_comb"); +// config.layerConfig.set_size(20); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "convex_comb", 100, false, useGpu); +// } +// } +// +// TEST(Layer, InterpolationLayer) { +// TestConfig config; +// config.layerConfig.set_type("interpolation"); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "interpolation", 100, false, useGpu); +// } +// } +// +// TEST(Layer, OuterProdLayer) { +// TestConfig config; +// config.layerConfig.set_type("out_prod"); +// config.layerConfig.set_size(100); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "out_prod", 100, false, useGpu); +// } +// } +// +// TEST(Layer, SlopeInterceptLayer) { +// TestConfig config; +// config.layerConfig.set_type("slope_intercept"); +// config.layerConfig.set_size(10); +// config.layerConfig.set_slope(1.0); +// config.layerConfig.set_intercept(0.1); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "slope_intercept", 100, false, useGpu); +// } +// } +// +// TEST(Layer, ScalingLayer) { +// TestConfig config; +// config.layerConfig.set_type("scaling"); +// config.layerConfig.set_size(10); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.layerConfig.add_inputs(); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "scaling", 100, false, useGpu); +// } +// } +// +// void testNormLayer(const string& normType, bool trans, bool useGpu) { +// TestConfig config; +// config.layerConfig.set_type("norm"); +// config.layerConfig.set_active_type("relu"); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// NormConfig* norm = input->mutable_norm_conf(); +// norm->set_norm_type(normType); +// norm->set_channels(16); +// norm->set_size(5); +// norm->set_scale(0.001); +// norm->set_pow(0.75); +// norm->set_blocked(0); +// norm->set_img_size(14); +// norm->set_img_size_y(7); +// norm->set_output_x(norm->img_size()); +// norm->set_output_y(norm->img_size_y()); +// if (norm->norm_type() == "cmrnorm" || +// norm->norm_type() == "cmrnorm-projection") { +// norm->set_scale(norm->scale() / norm->size()); +// } else { +// norm->set_scale(norm->scale() / (norm->size() * norm->size())); +// } +// +// config.layerConfig.set_size(norm->output_x() * norm->output_y() * +// norm->channels()); +// config.biasSize = 0; +// +// testLayerGrad(config, "norm", 100, trans, useGpu); +// } +// +// TEST(Layer, NormLayer) { +// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ +// true); +// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ +// false); +// } +// +// void setPoolConfig(TestConfig* config, +// PoolConfig* pool, +// const string& poolType) { +// (*config).biasSize = 0; +// (*config).layerConfig.set_type("pool"); +// (*config).layerConfig.set_num_filters(16); +// +// int kw = 3, kh = 3; +// int pw = 0, ph = 0; +// int sw = 2, sh = 2; +// pool->set_pool_type(poolType); +// pool->set_channels(16); +// pool->set_size_x(kw); +// pool->set_size_y(kh); +// pool->set_start(0); +// pool->set_padding(pw); +// pool->set_padding_y(ph); +// pool->set_stride(sw); +// pool->set_stride_y(sh); +// +// int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); +// int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); +// pool->set_output_x(ow); +// pool->set_output_y(oh); +// } +// +// void testPoolLayer(const string& poolType, bool trans, bool useGpu) { +// TestConfig config; +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// PoolConfig* pool = input->mutable_pool_conf(); +// +// pool->set_img_size(14); +// pool->set_img_size_y(14); +// setPoolConfig(&config, pool, poolType); +// config.layerConfig.set_size(pool->output_x() * pool->output_y() * +// pool->channels()); +// +// testLayerGrad(config, "pool", 100, trans, useGpu); +// } +// +// #ifndef PADDLE_ONLY_CPU +// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { +// TestConfig config; +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// PoolConfig* pool = input->mutable_pool_conf(); +// +// pool->set_size_y(4); +// pool->set_stride_y(3); +// pool->set_img_size(10); +// pool->set_img_size_y(20); +// setPoolConfig(&config, pool, poolType); +// pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) / +// ((float)pool->stride_y()) + +// 1.5); +// config.layerConfig.set_size(pool->output_x() * pool->output_y() * +// pool->channels()); +// +// testLayerGrad(config, "pool", 100, trans, useGpu); +// } +// #endif +// +// TEST(Layer, PoolLayer) { +// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); +// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); +// +// #ifndef PADDLE_ONLY_CPU +// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); +// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); +// testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); +// testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); +// testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); +// testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); +// #endif +// } +// +// void testSppLayer(const string& poolType, +// const int pyramidHeight, +// bool trans, +// bool useGpu) { +// TestConfig config; +// config.layerConfig.set_type("spp"); +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// SppConfig* sppConfig = input->mutable_spp_conf(); +// sppConfig->set_pool_type(poolType); +// sppConfig->set_pyramid_height(pyramidHeight); +// ImageConfig* imageConfig = sppConfig->mutable_image_conf(); +// imageConfig->set_channels(16); +// imageConfig->set_img_size(10); +// imageConfig->set_img_size_y(20); +// int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); +// config.layerConfig.set_size(outputSize * imageConfig->channels()); +// testLayerGrad(config, "spp", 100, trans, useGpu); +// } +// +// TEST(Layer, SpatialPyramidPoolLayer) { +// for (auto useGpu : {false, true}) { +// for (auto pyramidHeight : {1, 2, 3}) { +// testSppLayer("avg-projection", pyramidHeight, false, useGpu); +// testSppLayer("max-projection", pyramidHeight, false, useGpu); +// } +// } +// } +// +// TEST(Layer, rankCostLayer) { +// TestConfig config; +// config.layerConfig.set_type("rank-cost"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "rank-cost", 100, false, useGpu); +// } +// } +// +// TEST(Layer, sumCostLayer) { +// TestConfig config; +// config.layerConfig.set_type("sum_cost"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "sum_cost", 100, false, useGpu); +// } +// } +// +// TEST(Layer, weightedRankCostLayer) { +// TestConfig config; +// config.layerConfig.set_type("rank-cost"); +// config.biasSize = 0; +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); +// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu); +// } +// } +// +// TEST(Layer, TensorLayer) { +// TestConfig config; +// config.layerConfig.set_type("tensor"); +// config.layerConfig.set_size(10); +// config.layerConfig.set_active_type("sigmoid"); +// config.biasSize = config.layerConfig.size(); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250}); +// config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "tensor", 100, false, useGpu); +// } +// } +// +// TEST(Layer, RecurrentLayer) { +// TestConfig config; +// config.layerConfig.set_type("recurrent"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("tanh"); +// config.biasSize = 4; +// +// config.inputDefs.push_back( +// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// for (auto reversed : {false, true}) { +// config.layerConfig.set_reversed(reversed); +// config.testState = !reversed; +// testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu); +// } +// } +// } +// +// TEST(Layer, LstmLayer) { +// TestConfig config; +// config.layerConfig.set_type("lstmemory"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("tanh"); +// config.layerConfig.set_active_state_type("sigmoid"); +// config.layerConfig.set_active_gate_type("sigmoid"); +// config.biasSize = 28; +// +// config.inputDefs.push_back( +// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// for (auto reversed : {false, true}) { +// config.layerConfig.set_reversed(reversed); +// config.testState = !reversed; +// testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu); +// } +// } +// for (auto useGpu : {true}) { +// config.testBatchState = true; +// config.layerConfig.set_reversed(false); +// testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu); +// } +// } +// +// TEST(Layer, MDLstmLayer) { +// TestConfig config; +// config.layerConfig.set_type("mdlstmemory"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_active_state_type("sigmoid"); +// config.layerConfig.set_active_gate_type("sigmoid"); +// config.biasSize = 4 * 9; +// +// config.inputDefs.push_back( +// {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_directions(true); +// config.layerConfig.add_directions(true); +// +// for (auto useGpu : {false, true}) { +// for (int i = 0; i < 2; i++) { +// for (int j = 0; j < 2; j++) { +// config.layerConfig.set_directions(0, bool(i)); +// config.layerConfig.set_directions(1, bool(j)); +// testLayerGrad(config, "mdlstmemory", 100, false, useGpu); +// } +// } +// } +// } +// +// TEST(Layer, ParameterReluLayer) { +// auto testParameterReluLayer = [&](size_t inputSize, size_t channels) { +// TestConfig config; +// config.layerConfig.set_type("prelu"); +// config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels}); +// config.layerConfig.add_inputs(); +// config.layerConfig.set_size(inputSize); +// config.layerConfig.set_partial_sum(inputSize / +// channels); // size of feature map +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "prelu", 100, false, useGpu); +// } +// }; +// +// testParameterReluLayer(192, 1); +// testParameterReluLayer(192, 3); +// testParameterReluLayer(192, 192); +// } +// +// TEST(Layer, ResizeLayer) { +// TestConfig config; +// config.biasSize = 0; +// config.layerConfig.set_type("resize"); +// config.layerConfig.set_size(64); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "resize", 100, false, useGpu); +// } +// } +// +// TEST(Layer, NCELayer) { +// TestConfig config; +// size_t numClasses = 4; +// config.layerConfig.set_type("nce"); +// config.layerConfig.set_size(1); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_num_classes(numClasses); +// config.biasSize = numClasses; +// +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * +// numClasses}); +// config.inputDefs.push_back( +// {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto withWeight : {false, true}) { +// if (withWeight) { +// config.inputDefs.push_back( +// {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// } +// +// for (auto isIdLabel : {false, true}) { +// config.inputDefs[1] = { +// isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA, +// "label", +// /* dim= */ numClasses, +// /* paraSize= */ 0}; +// +// for (auto withDist : {false, true}) { +// config.layerConfig.clear_neg_sampling_dist(); +// if (withDist) { +// double sum = 0; +// for (size_t i = 0; i < numClasses; ++i) { +// real p = rand(); // NOLINT use rand_r +// config.layerConfig.add_neg_sampling_dist(p); +// sum += p; +// } +// for (size_t i = 0; i < numClasses; ++i) { +// real p = config.layerConfig.neg_sampling_dist(i) / sum; +// config.layerConfig.set_neg_sampling_dist(i, p); +// } +// } +// LOG(INFO) << "NCELayer " +// << " isIdLabel=" << isIdLabel << " withWeight=" << +// withWeight +// << " withDist=" << withDist; +// // Not support GPU now +// testLayerGrad(config, +// "nce", +// 100, +// /* trans= */ false, +// /* useGpu */ false); +// } +// } +// } +// } +// +// TEST(Layer, GatedRecurrentLayer) { +// TestConfig config; +// config.layerConfig.set_type("gated_recurrent"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_active_gate_type("sigmoid"); +// config.biasSize = 12; +// +// config.inputDefs.push_back( +// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// for (auto reversed : {false, true}) { +// config.layerConfig.set_reversed(reversed); +// config.testState = !reversed; +// testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, +// useGpu); +// } +// } +// } +// +// TEST(Layer, GruStepLayer) { +// TestConfig config; +// config.layerConfig.set_type("gru_step"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_active_gate_type("sigmoid"); +// config.biasSize = 12; +// +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu); +// } +// } +// +// TEST(Layer, LstmStepLayer) { +// TestConfig config; +// config.layerConfig.set_type("lstm_step"); +// config.layerConfig.set_size(4); +// config.layerConfig.set_active_type("sigmoid"); +// config.layerConfig.set_active_state_type("sigmoid"); +// config.layerConfig.set_active_gate_type("sigmoid"); +// config.biasSize = 12; +// config.testAccumulate = false; +// +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0}); +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu); +// } +// } +// +// void testBatchNormLayer(const string& type, bool trans, bool useGpu) { +// TestConfig config; +// const int CHANNELS = 10; +// const int IMG_SIZE = 16; +// const int IMG_SIZE_Y = 8; +// size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; +// config.layerConfig.set_type(type); +// config.layerConfig.set_size(size); +// config.layerConfig.set_active_type("sigmoid"); +// config.biasSize = CHANNELS; +// config.inputDefs.push_back({INPUT_DATA, +// "layer_0", +// /* dim= */ size, +// /* paraSize= */ CHANNELS}); +// +// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, +// CHANNELS}); +// config.inputDefs.back().isStatic = true; +// config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, +// CHANNELS}); +// config.inputDefs.back().isStatic = true; +// +// LayerInputConfig* input = config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// ImageConfig* img_conf = input->mutable_image_conf(); +// img_conf->set_channels(CHANNELS); +// img_conf->set_img_size(IMG_SIZE); +// img_conf->set_img_size_y(IMG_SIZE_Y); +// +// testLayerGrad(config, +// "batch_norm", +// 64, +// /* trans= */ trans, +// useGpu, +// /* useWeight */ true); +// } +// +// TEST(Layer, BatchNormalizationLayer) { +// testBatchNormLayer("batch_norm", false, false); +// #ifndef PADDLE_ONLY_CPU +// testBatchNormLayer("batch_norm", false, true); +// if (hl_get_cudnn_lib_version() >= int(4000)) { +// testBatchNormLayer("cudnn_batch_norm", false, true); +// } +// #endif +// } +// +// TEST(Operator, conv) { +// TestConfig config; +// const int NUM_FILTERS = 16; +// const int FILTER_SIZE = 2; +// const int FILTER_SIZE_Y = 3; +// const int CHANNELS = 3; +// const int IMAGE_SIZE = 16; +// const int IMAGE_SIZE_Y = 8; +// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); +// operatorConf.set_type("conv"); +// ConvConfig* conv = operatorConf.mutable_conv_conf(); +// operatorConf.set_num_filters(NUM_FILTERS); +// conv->set_filter_size(FILTER_SIZE); +// conv->set_filter_size_y(FILTER_SIZE_Y); +// conv->set_channels(CHANNELS); +// conv->set_padding(0); +// conv->set_padding_y(1); +// conv->set_stride(2); +// conv->set_stride_y(2); +// conv->set_groups(1); +// conv->set_filter_channels(conv->channels() / conv->groups()); +// conv->set_img_size(IMAGE_SIZE); +// conv->set_img_size_y(IMAGE_SIZE_Y); +// conv->set_output_x(outputSize(conv->img_size(), +// conv->filter_size(), +// conv->padding(), +// conv->stride(), +// /* caffeMode */ true)); +// conv->set_output_y(outputSize(conv->img_size_y(), +// conv->filter_size_y(), +// conv->padding_y(), +// conv->stride_y(), +// /* caffeMode */ true)); +// config.layerConfig.set_size(conv->output_x() * conv->output_y() * +// NUM_FILTERS); +// +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); +// config.inputDefs.push_back( +// {INPUT_DATA, +// "layer_1", +// FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, +// 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false); +// } +// +// TEST(Layer, FeatureMapExpandLayer) { +// TestConfig config; +// config.layerConfig.set_type("featmap_expand"); +// const int CHANNELS = 10; +// const int INPUT_SIZE = 100; +// config.layerConfig.set_size(INPUT_SIZE * CHANNELS); +// config.layerConfig.set_num_filters(CHANNELS); +// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, +// "layer_0", +// /* dim= */ INPUT_SIZE, +// /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, +// "featmap_expand", +// /*batch_size*/ 100, +// /* trans= */ false, +// useGpu, +// /* useWeight */ true); +// } +// } +// +// TEST(Layer, MultiplexLayer) { +// TestConfig config; +// const int LAYER_SIZE = 100; +// config.layerConfig.set_type("multiplex"); +// config.layerConfig.set_size(LAYER_SIZE); +// +// config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0}); +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); +// config.inputDefs.push_back( +// {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// config.layerConfig.add_inputs(); +// +// for (auto useGpu : {false, true}) { +// testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu); +// } +// } +// +TEST(Layer, PadLayer) { TestConfig config; - config.layerConfig.set_size(10); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); - operatorConf.set_type("dot_mul"); - operatorConf.set_dotmul_scale(-1); - - testOperatorGrad(config, operatorConf, 100, false, false); -} - -TEST(Projection, context) { - for (auto contextStart : {-5, -3, -1, 0, 3}) { - for (auto contextLength : {1, 2, 5, 7}) { - for (auto batchSize : {1, 2, 5, 20, 50}) { - for (auto trainablePadding : {false, true}) { - LOG(INFO) << " contextStart=" << contextStart - << " contextLength=" << contextLength - << " batchSize=" << batchSize - << " trainablePadding=" << trainablePadding; - ProjectionConfig conf; - conf.set_type("context"); - conf.set_input_size(10); - conf.set_context_start(contextStart); - conf.set_context_length(contextLength); - conf.set_trainable_padding(trainablePadding); - conf.set_output_size(conf.context_length() * conf.input_size()); - int pad = - std::max(0, -conf.context_start()) + - std::max(0, conf.context_start() + conf.context_length() - 1); - for (auto useGpu : {false, true}) { - testProjectionGrad( - conf, - INPUT_SEQUENCE_DATA, - trainablePadding ? conf.input_size() * pad : 0, - batchSize, - useGpu, - contextStart + contextLength <= 1); // = testState - } - } - } - } - } -} - -TEST(Projection, trans_fc) { - ProjectionConfig conf; - conf.set_type("trans_fc"); - conf.set_input_size(50); - conf.set_output_size(20); - for (auto useGpu : {false, true}) { - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ 1000, - /* batchSize */ 100, - useGpu); - } -} - -TEST(Projection, fc) { - ProjectionConfig conf; - conf.set_type("fc"); - conf.set_input_size(10); - conf.set_output_size(20); - for (auto useGpu : {false, true}) { - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ 200, - /* batchSize */ 100, - useGpu); - } -} - -TEST(Projection, dot_mul) { - ProjectionConfig conf; - conf.set_type("dot_mul"); - conf.set_input_size(20); - conf.set_output_size(20); - for (auto useGpu : {false, true}) { - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ 20, - /* batchSize */ 100, - useGpu); - } -} - -TEST(Projection, table) { - ProjectionConfig conf; - conf.set_type("table"); - conf.set_input_size(10); - conf.set_output_size(20); - for (auto useGpu : {false, true}) { - testProjectionGrad(conf, - INPUT_LABEL, - /* parameterSize */ 200, - /* batchSize */ 100, - useGpu); - } -} - -TEST(Projection, identity) { - ProjectionConfig conf; - conf.set_type("identity"); - conf.set_input_size(10); - conf.set_output_size(10); - for (auto useGpu : {false, true}) { - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ 0, - /* batchSize */ 100, - useGpu); - } -} - -TEST(Projection, scaling) { - ProjectionConfig conf; - conf.set_type("scaling"); - conf.set_input_size(10); - conf.set_output_size(10); - for (auto useGpu : {false}) { - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ 1, - /* batchSize */ 100, - useGpu); - } -} - -void testProjectionConv(size_t groups) { - const int NUM_FILTERS = 18; - const int FILTER_SIZE = 2; - const int FILTER_SIZE_Y = 3; - const int CHANNELS = 3; - const int IMAGE_SIZE = 16; - - ProjectionConfig conf; - conf.set_type("conv"); - conf.set_num_filters(NUM_FILTERS); - - ConvConfig* conv = conf.mutable_conv_conf(); - conv->set_filter_size(FILTER_SIZE); - conv->set_filter_size_y(FILTER_SIZE_Y); - conv->set_channels(CHANNELS); - conv->set_padding(0); - conv->set_padding_y(1); - conv->set_stride(2); - conv->set_stride_y(2); - conv->set_groups(groups); - conv->set_filter_channels(conv->channels() / conv->groups()); - conv->set_img_size(IMAGE_SIZE); - int output_x = outputSize(conv->img_size(), - conv->filter_size(), - conv->padding(), - conv->stride(), - /* caffeMode */ true); - int output_y = outputSize(conv->img_size(), - conv->filter_size_y(), - conv->padding_y(), - conv->stride_y(), - /* caffeMode */ true); - conv->set_output_x(output_x); - conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS); - conf.set_output_size(output_x * output_y * NUM_FILTERS); - - testProjectionGrad(conf, - INPUT_DATA, - /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * - FILTER_SIZE_Y / groups, - /* batchSize */ 100, - true, - false, - NUM_FILTERS, - true); -} - -#ifndef PADDLE_ONLY_CPU -TEST(Projection, conv) { - testProjectionConv(1); - testProjectionConv(3); -} -#endif - -TEST(Layer, BilinearInterpLayer) { - TestConfig config; - config.layerConfig.set_type("bilinear_interp"); - config.biasSize = 0; - config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); - - LayerInputConfig* input = config.layerConfig.add_inputs(); - BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); - ImageConfig* image = bilinear->mutable_image_conf(); - image->set_img_size(32); - image->set_img_size_y(32); - image->set_channels(4); - - for (auto useGpu : {false, true}) { - for (auto outSize : {32, 64}) { - bilinear->set_out_size_x(outSize); - bilinear->set_out_size_y(outSize); - testLayerGrad(config, "bilinear_interp", 10, false, useGpu); - } - } -} - -TEST(Layer, concat) { - TestConfig config; - config.biasSize = 0; - config.layerConfig.set_type("concat"); - config.layerConfig.set_size(15); - config.layerConfig.set_active_type("sigmoid"); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "concat", 100, false, useGpu); - } -} - -TEST(Layer, AddtoLayer) { - TestConfig config; - config.biasSize = 0; - config.layerConfig.set_type("addto"); - config.layerConfig.set_size(10); - config.layerConfig.set_active_type("sigmoid"); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "addto", 100, false, useGpu); - } -} - -TEST(Layer, CRFLayer) { - TestConfig config; - config.layerConfig.set_type("crf"); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120}); - config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // Not support GPU now - testLayerGrad(config, - "crf", - 100, - /* trans */ false, - /* useGpu */ false, - false /*useWeight*/, - 0.03 /*epsilon*/); -} - -TEST(Layer, CTCLayer) { - TestConfig config; - config.layerConfig.set_type("ctc"); - config.layerConfig.set_norm_by_times(false); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ useGpu); - } -} - -TEST(Layer, cosSimLayer) { - TestConfig config; - config.layerConfig.set_type("cos"); - config.layerConfig.set_size(1); config.biasSize = 0; + config.layerConfig.set_type("pad"); - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "cos", 100, false, useGpu); - } -} - -TEST(Layer, CosSimVecMatLayer) { - TestConfig config; - config.layerConfig.set_type("cos_vm"); - config.layerConfig.set_size(5); // output size - config.layerConfig.set_cos_scale(2.0); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "cos_vm", 100, false, useGpu); - } -} - -void testConvLayer(const string& type, bool trans, bool useGpu) { - TestConfig config; - config.biasSize = 16; - config.layerConfig.set_type(type); - config.layerConfig.set_num_filters(16); - config.layerConfig.set_partial_sum(1); - config.layerConfig.set_shared_biases(true); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); + int c = 4; + int h = 31; + int w = 36; + size_t size = c * h * w; + config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0}); LayerInputConfig* input = config.layerConfig.add_inputs(); - ConvConfig* conv = input->mutable_conv_conf(); - conv->set_filter_size(2); - conv->set_filter_size_y(3); - conv->set_channels(3); - conv->set_padding(0); - conv->set_padding_y(1); - conv->set_stride(2); - conv->set_stride_y(2); - conv->set_groups(1); - conv->set_filter_channels(conv->channels() / conv->groups()); - conv->set_img_size(16); - conv->set_img_size_y(8); - conv->set_output_x(outputSize(conv->img_size(), - conv->filter_size(), - conv->padding(), - conv->stride(), - /* caffeMode */ true)); - conv->set_output_y(outputSize(conv->img_size_y(), - conv->filter_size_y(), - conv->padding_y(), - conv->stride_y(), - /* caffeMode */ true)); - config.layerConfig.set_size(conv->output_x() * conv->output_y() * - config.layerConfig.num_filters()); - - testLayerGrad(config, "conv", 100, trans, useGpu); - // Use small batch_size and useWeight=true to test biasGrad - testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02); -} - -TEST(Layer, convLayer) { - testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU - testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); - testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); -#endif -} - -void testConvTransLayer(const string& type, bool trans, bool useGpu) { - TestConfig config; - config.biasSize = 3; - config.layerConfig.set_type(type); - config.layerConfig.set_num_filters(3); - config.layerConfig.set_partial_sum(1); - config.layerConfig.set_shared_biases(true); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - ConvConfig* conv = input->mutable_conv_conf(); - conv->set_filter_size(2); - conv->set_filter_size_y(3); - conv->set_channels(16); - conv->set_padding(0); - conv->set_padding_y(1); - conv->set_stride(2); - conv->set_stride_y(2); - conv->set_groups(1); - conv->set_filter_channels(3 / conv->groups()); - conv->set_img_size(16); - conv->set_output_x(outputSize(conv->img_size(), - conv->filter_size(), - conv->padding(), - conv->stride(), - /* caffeMode */ true)); - - config.layerConfig.set_size(conv->img_size() * conv->img_size() * - config.layerConfig.num_filters()); - - testLayerGrad(config, "convTrans", 100, trans, useGpu); - // Use small batch_size and useWeight=true to test biasGrad - testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02); -} - -TEST(Layer, convTransLayer) { - for (auto useGpu : {false, true}) { - testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); - } -} - -TEST(Layer, blockExpandLayer) { - TestConfig config; - config.biasSize = 0; - config.layerConfig.set_type("blockexpand"); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - BlockExpandConfig* blockExpand = input->mutable_block_expand_conf(); - blockExpand->set_img_size_x(64); - blockExpand->set_img_size_y(32); - blockExpand->set_channels(3); - blockExpand->set_padding_x(0); - blockExpand->set_padding_y(0); - blockExpand->set_block_x(4); - blockExpand->set_block_y(32); - blockExpand->set_stride_x(2); - blockExpand->set_stride_y(2); - blockExpand->set_output_x(outputSize(blockExpand->img_size_x(), - blockExpand->block_x(), - blockExpand->padding_x(), - blockExpand->stride_x(), - /* caffeMode */ false)); - blockExpand->set_output_y(outputSize(blockExpand->img_size_y(), - blockExpand->block_y(), - blockExpand->padding_y(), - blockExpand->stride_y(), - /* caffeMode */ false)); - config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() * - blockExpand->channels()); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "blockexpand", 100, false, useGpu); - } -} - -TEST(Layer, maxoutLayer) { - TestConfig config; - config.biasSize = 0; - config.layerConfig.set_type("maxout"); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - MaxOutConfig* maxout = input->mutable_maxout_conf(); - ImageConfig* image = maxout->mutable_image_conf(); - - image->set_img_size(32); - image->set_img_size_y(32); - image->set_channels(4); - maxout->set_groups(2); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "maxout", 10, false, useGpu); - } -} -void testFcLayer(string format, size_t nnz) { - TestConfig config; - config.biasSize = 4096; - config.layerConfig.set_type("fc"); - config.layerConfig.set_size(4096); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_drop_rate(0.1); - - config.inputDefs.push_back( - {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)}); - config.layerConfig.add_inputs(); - - LOG(INFO) << config.inputDefs[0].sparse.sparse << " " - << config.inputDefs[0].sparse.format; - - for (auto useGpu : {false, true}) { - testLayerGrad(config, - "fc", - 100, - /* trans */ false, - useGpu, - /* weight */ true); - } -} - -TEST(Layer, fcLayer) { - testFcLayer("", 4096 * 4096 * 2); - testFcLayer("csc", 4096 * 40); - testFcLayer("csr", 4096 * 40); -} - -TEST(Layer, SelectiveFullyConnectedLayer) { - TestConfig config; - size_t nin = 16; - size_t nout = 256; - config.layerConfig.set_type("selective_fc"); - config.layerConfig.set_size(nout); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_has_selected_colums(true); - config.layerConfig.set_selective_fc_pass_generation(false); - config.biasSize = nout; - - config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back( - {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)}); - config.layerConfig.add_inputs(); - - testLayerGrad(config, - "selective_fc", - 100, - /* trans= */ false, - /* useGup= */ false, - false); -#ifndef PADDLE_ONLY_CPU - testLayerGrad(config, - "selective_fc", - 100, - /* trans= */ false, - /* useGup= */ true, - false); -#endif -} - -TEST(Layer, DataNormLayer) { - TestConfig config; - config.layerConfig.set_type("data_norm"); - config.layerConfig.set_size(20); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100}); - config.inputDefs.back().isStatic = true; - config.layerConfig.add_inputs(); - - for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) { - config.layerConfig.set_data_norm_strategy(strategy); - // The parameters are static, so not support GPU now - testLayerGrad(config, - "data_norm", - 200, - /* trans */ false, - /* useGpu */ false); - } -} - -TEST(Layer, hsigmoidLayer) { - TestConfig config; - config.layerConfig.set_type("hsigmoid"); - config.layerConfig.set_num_classes(5); - config.layerConfig.set_size(1); - config.biasSize = config.layerConfig.num_classes() - 1; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200}); - config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // Not support GPU now - testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ false); -} - -TEST(Layer, multi_cross) { - TestConfig config; - config.layerConfig.set_type("multi-class-cross-entropy"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad( - config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu); - } -} - -TEST(Layer, multi_binary_label_sparse_mat) { - TestConfig config; - config.layerConfig.set_type("multi_binary_label_cross_entropy"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, - "multi_binary_label_cross_entropy", - 100, - /* trans */ false, - useGpu); - } -} - -TEST(layer, multi_binary_label_id) { - TestConfig config; - config.layerConfig.set_type("multi_binary_label_cross_entropy"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, - "multi_binary_label_cross_entropy", - 100, - /* trans */ false, - useGpu); - } -} - -TEST(Layer, multi_cross_with_selfnorm) { - TestConfig config; - config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm"); - config.layerConfig.set_softmax_selfnorm_alpha(0.1); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // Not support GPU now - testLayerGrad(config, - "multi_class_cross_entropy_with_selfnorm", - 100, - /* trans */ false, - /* useGpu */ false); -} - -TEST(Layer, multi_cross_soft) { - TestConfig config; - config.layerConfig.set_type("soft_binary_class_cross_entropy"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, - "soft_binary_class_cross_entropy", - 100, - /* trans */ false, - useGpu); - } -} - -TEST(Layer, square_error) { - TestConfig config; - config.layerConfig.set_type("square_error"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); - } -} - -TEST(Layer, sparse_square_error) { - TestConfig config; - config.layerConfig.set_type("square_error"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // "GpuSparseMatrix" as label is not supported - testLayerGrad(config, - "square_error", - 100, - /* trans */ false, - /* useGpu */ false); -} - -TEST(Layer, sparse_float_square_error) { - TestConfig config; - config.layerConfig.set_type("square_error"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); - config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // "GpuSparseMatrix" as label is not supported - testLayerGrad(config, - "square_error", - 100, - /* trans */ false, - /* useGpu */ false); -} - -TEST(Layer, square_error_weighted) { - TestConfig config; - config.layerConfig.set_type("square_error"); - config.biasSize = 0; - config.testAccumulate = false; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); - } -} - -TEST(Layer, huber_two_class) { - TestConfig config; - config.layerConfig.set_type("huber"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); - } -} - -void testExpandLayer(string trans_type, bool hasSubseq) { - TestConfig config; - config.layerConfig.set_type("expand"); - - config.inputDefs.push_back( - {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA, - "layer_0", - 10, - 0}); - config.inputDefs.push_back( - {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, - "layer_1", - 10, - 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.set_trans_type(trans_type); - LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq; - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "expand", 30, false, useGpu); - } -} - -TEST(Layer, ExpandLayer) { - testExpandLayer("non-seq", false); // non-seq expand to seq - testExpandLayer("non-seq", true); // non-seq expand to hasSubseq - testExpandLayer("seq", true); // seq expand to hasSubseq -} - -void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) { - TestConfig config; - config.layerConfig.set_type(layer_type); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back( - {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, - "layer_0", - 10, - 0}); - config.layerConfig.add_inputs(); - config.layerConfig.set_trans_type(trans_type); - - auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) { - for (auto useGpu : {false, true}) { - testLayerGrad(config, layer_type, 100, false, useGpu); - } - }; - - if (layer_type == "average") { - for (auto strategy : {"average", "sum", "squarerootn"}) { - LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type - << " average_strategy=" << strategy; - config.layerConfig.set_average_strategy(strategy); - testDegradeLayerGrad(config, layer_type); - } - } else { - LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type; - testDegradeLayerGrad(config, layer_type); - } -} - -TEST(Layer, MaxLayer) { - testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq - testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq - testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq -} - -TEST(Layer, SequenceLastInstanceLayer) { - testDegradeLayer(false, - "seqlastins", - "non-seq"); // seq seqlastins to non-seq - testDegradeLayer(true, - "seqlastins", - "non-seq"); // hasSubseq seqlastins to non-seq - testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to seq -} - -TEST(Layer, AverageLayer) { - testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq - testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to non-seq - testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq -} - -TEST(Layer, SequenceConcatLayer) { - TestConfig config; - config.layerConfig.set_type("seqconcat"); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "seqconcat", 100, false, useGpu); - } -} - -TEST(Layer, SequenceReshapeLayer) { - TestConfig config; - config.layerConfig.set_type("seqreshape"); - config.layerConfig.set_size(10); - - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "seqreshape", 100, false, useGpu); - } -} - -TEST(Layer, ConvShiftLayer) { - TestConfig config; - config.layerConfig.set_type("conv_shift"); - config.layerConfig.set_size(10); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - // Not support GPU now - testLayerGrad(config, "conv_shift", 100, false, false); -} - -TEST(Layer, PowerLayer) { - TestConfig config; - config.layerConfig.set_type("power"); - config.layerConfig.set_size(10); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "power", 100, false, useGpu); - } -} - -TEST(Layer, ConvexCombinationLayer) { - TestConfig config; - config.layerConfig.set_type("convex_comb"); - config.layerConfig.set_size(20); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "convex_comb", 100, false, useGpu); - } -} - -TEST(Layer, InterpolationLayer) { - TestConfig config; - config.layerConfig.set_type("interpolation"); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "interpolation", 100, false, useGpu); - } -} - -TEST(Layer, OuterProdLayer) { - TestConfig config; - config.layerConfig.set_type("out_prod"); - config.layerConfig.set_size(100); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "out_prod", 100, false, useGpu); - } -} - -TEST(Layer, SlopeInterceptLayer) { - TestConfig config; - config.layerConfig.set_type("slope_intercept"); - config.layerConfig.set_size(10); - config.layerConfig.set_slope(1.0); - config.layerConfig.set_intercept(0.1); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "slope_intercept", 100, false, useGpu); - } -} - -TEST(Layer, ScalingLayer) { - TestConfig config; - config.layerConfig.set_type("scaling"); - config.layerConfig.set_size(10); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.layerConfig.add_inputs(); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "scaling", 100, false, useGpu); - } -} - -void testNormLayer(const string& normType, bool trans, bool useGpu) { - TestConfig config; - config.layerConfig.set_type("norm"); - config.layerConfig.set_active_type("relu"); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - NormConfig* norm = input->mutable_norm_conf(); - norm->set_norm_type(normType); - norm->set_channels(16); - norm->set_size(5); - norm->set_scale(0.001); - norm->set_pow(0.75); - norm->set_blocked(0); - norm->set_img_size(14); - norm->set_img_size_y(7); - norm->set_output_x(norm->img_size()); - norm->set_output_y(norm->img_size_y()); - if (norm->norm_type() == "cmrnorm" || - norm->norm_type() == "cmrnorm-projection") { - norm->set_scale(norm->scale() / norm->size()); - } else { - norm->set_scale(norm->scale() / (norm->size() * norm->size())); - } - - config.layerConfig.set_size(norm->output_x() * norm->output_y() * - norm->channels()); - config.biasSize = 0; - - testLayerGrad(config, "norm", 100, trans, useGpu); -} - -TEST(Layer, NormLayer) { - testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ true); - testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ false); -} - -void setPoolConfig(TestConfig* config, - PoolConfig* pool, - const string& poolType) { - (*config).biasSize = 0; - (*config).layerConfig.set_type("pool"); - (*config).layerConfig.set_num_filters(16); - - int kw = 3, kh = 3; - int pw = 0, ph = 0; - int sw = 2, sh = 2; - pool->set_pool_type(poolType); - pool->set_channels(16); - pool->set_size_x(kw); - pool->set_size_y(kh); - pool->set_start(0); - pool->set_padding(pw); - pool->set_padding_y(ph); - pool->set_stride(sw); - pool->set_stride_y(sh); - - int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); - int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); - pool->set_output_x(ow); - pool->set_output_y(oh); -} - -void testPoolLayer(const string& poolType, bool trans, bool useGpu) { - TestConfig config; - config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - PoolConfig* pool = input->mutable_pool_conf(); - - pool->set_img_size(14); - pool->set_img_size_y(14); - setPoolConfig(&config, pool, poolType); - config.layerConfig.set_size(pool->output_x() * pool->output_y() * - pool->channels()); - - testLayerGrad(config, "pool", 100, trans, useGpu); -} - -#ifndef PADDLE_ONLY_CPU -void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { - TestConfig config; - config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - PoolConfig* pool = input->mutable_pool_conf(); - - pool->set_size_y(4); - pool->set_stride_y(3); - pool->set_img_size(10); - pool->set_img_size_y(20); - setPoolConfig(&config, pool, poolType); - pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) / - ((float)pool->stride_y()) + - 1.5); - config.layerConfig.set_size(pool->output_x() * pool->output_y() * - pool->channels()); - - testLayerGrad(config, "pool", 100, trans, useGpu); -} -#endif - -TEST(Layer, PoolLayer) { - testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); - testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); - -#ifndef PADDLE_ONLY_CPU - testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); - testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); - testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); - testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); - testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); - testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); -#endif -} - -void testSppLayer(const string& poolType, - const int pyramidHeight, - bool trans, - bool useGpu) { - TestConfig config; - config.layerConfig.set_type("spp"); - config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); - LayerInputConfig* input = config.layerConfig.add_inputs(); - SppConfig* sppConfig = input->mutable_spp_conf(); - sppConfig->set_pool_type(poolType); - sppConfig->set_pyramid_height(pyramidHeight); - ImageConfig* imageConfig = sppConfig->mutable_image_conf(); - imageConfig->set_channels(16); - imageConfig->set_img_size(10); - imageConfig->set_img_size_y(20); - int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); - config.layerConfig.set_size(outputSize * imageConfig->channels()); - testLayerGrad(config, "spp", 100, trans, useGpu); -} - -TEST(Layer, SpatialPyramidPoolLayer) { - for (auto useGpu : {false, true}) { - for (auto pyramidHeight : {1, 2, 3}) { - testSppLayer("avg-projection", pyramidHeight, false, useGpu); - testSppLayer("max-projection", pyramidHeight, false, useGpu); - } - } -} - -TEST(Layer, rankCostLayer) { - TestConfig config; - config.layerConfig.set_type("rank-cost"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "rank-cost", 100, false, useGpu); - } -} - -TEST(Layer, sumCostLayer) { - TestConfig config; - config.layerConfig.set_type("sum_cost"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "sum_cost", 100, false, useGpu); - } -} - -TEST(Layer, weightedRankCostLayer) { - TestConfig config; - config.layerConfig.set_type("rank-cost"); - config.biasSize = 0; - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); - config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu); - } -} - -TEST(Layer, TensorLayer) { - TestConfig config; - config.layerConfig.set_type("tensor"); - config.layerConfig.set_size(10); - config.layerConfig.set_active_type("sigmoid"); - config.biasSize = config.layerConfig.size(); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250}); - config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "tensor", 100, false, useGpu); - } -} - -TEST(Layer, RecurrentLayer) { - TestConfig config; - config.layerConfig.set_type("recurrent"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("tanh"); - config.biasSize = 4; - - config.inputDefs.push_back( - {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - for (auto reversed : {false, true}) { - config.layerConfig.set_reversed(reversed); - config.testState = !reversed; - testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu); - } - } -} - -TEST(Layer, LstmLayer) { - TestConfig config; - config.layerConfig.set_type("lstmemory"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("tanh"); - config.layerConfig.set_active_state_type("sigmoid"); - config.layerConfig.set_active_gate_type("sigmoid"); - config.biasSize = 28; - - config.inputDefs.push_back( - {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - for (auto reversed : {false, true}) { - config.layerConfig.set_reversed(reversed); - config.testState = !reversed; - testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu); - } - } - for (auto useGpu : {true}) { - config.testBatchState = true; - config.layerConfig.set_reversed(false); - testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu); - } -} - -TEST(Layer, MDLstmLayer) { - TestConfig config; - config.layerConfig.set_type("mdlstmemory"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_active_state_type("sigmoid"); - config.layerConfig.set_active_gate_type("sigmoid"); - config.biasSize = 4 * 9; - - config.inputDefs.push_back( - {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5}); - config.layerConfig.add_inputs(); - config.layerConfig.add_directions(true); - config.layerConfig.add_directions(true); - - for (auto useGpu : {false, true}) { - for (int i = 0; i < 2; i++) { - for (int j = 0; j < 2; j++) { - config.layerConfig.set_directions(0, bool(i)); - config.layerConfig.set_directions(1, bool(j)); - testLayerGrad(config, "mdlstmemory", 100, false, useGpu); - } - } - } -} - -TEST(Layer, ParameterReluLayer) { - auto testParameterReluLayer = [&](size_t inputSize, size_t channels) { - TestConfig config; - config.layerConfig.set_type("prelu"); - config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels}); - config.layerConfig.add_inputs(); - config.layerConfig.set_size(inputSize); - config.layerConfig.set_partial_sum(inputSize / - channels); // size of feature map - for (auto useGpu : {false, true}) { - testLayerGrad(config, "prelu", 100, false, useGpu); - } - }; - - testParameterReluLayer(192, 1); - testParameterReluLayer(192, 3); - testParameterReluLayer(192, 192); -} - -TEST(Layer, ResizeLayer) { - TestConfig config; - config.biasSize = 0; - config.layerConfig.set_type("resize"); - config.layerConfig.set_size(64); - - config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "resize", 100, false, useGpu); - } -} - -TEST(Layer, NCELayer) { - TestConfig config; - size_t numClasses = 4; - config.layerConfig.set_type("nce"); - config.layerConfig.set_size(1); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_num_classes(numClasses); - config.biasSize = numClasses; - - config.inputDefs.push_back( - {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses}); - config.inputDefs.push_back( - {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto withWeight : {false, true}) { - if (withWeight) { - config.inputDefs.push_back( - {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - } - - for (auto isIdLabel : {false, true}) { - config.inputDefs[1] = { - isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA, - "label", - /* dim= */ numClasses, - /* paraSize= */ 0}; - - for (auto withDist : {false, true}) { - config.layerConfig.clear_neg_sampling_dist(); - if (withDist) { - double sum = 0; - for (size_t i = 0; i < numClasses; ++i) { - real p = rand(); // NOLINT use rand_r - config.layerConfig.add_neg_sampling_dist(p); - sum += p; - } - for (size_t i = 0; i < numClasses; ++i) { - real p = config.layerConfig.neg_sampling_dist(i) / sum; - config.layerConfig.set_neg_sampling_dist(i, p); - } - } - LOG(INFO) << "NCELayer " - << " isIdLabel=" << isIdLabel << " withWeight=" << withWeight - << " withDist=" << withDist; - // Not support GPU now - testLayerGrad(config, - "nce", - 100, - /* trans= */ false, - /* useGpu */ false); - } - } - } -} - -TEST(Layer, GatedRecurrentLayer) { - TestConfig config; - config.layerConfig.set_type("gated_recurrent"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_active_gate_type("sigmoid"); - config.biasSize = 12; - - config.inputDefs.push_back( - {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - for (auto reversed : {false, true}) { - config.layerConfig.set_reversed(reversed); - config.testState = !reversed; - testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu); - } - } -} - -TEST(Layer, GruStepLayer) { - TestConfig config; - config.layerConfig.set_type("gru_step"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_active_gate_type("sigmoid"); - config.biasSize = 12; - - config.inputDefs.push_back( - {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); - config.inputDefs.push_back( - {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu); - } -} - -TEST(Layer, LstmStepLayer) { - TestConfig config; - config.layerConfig.set_type("lstm_step"); - config.layerConfig.set_size(4); - config.layerConfig.set_active_type("sigmoid"); - config.layerConfig.set_active_state_type("sigmoid"); - config.layerConfig.set_active_gate_type("sigmoid"); - config.biasSize = 12; - config.testAccumulate = false; - - config.inputDefs.push_back( - {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0}); - config.inputDefs.push_back( - {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - for (auto useGpu : {false, true}) { - testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu); - } -} - -void testBatchNormLayer(const string& type, bool trans, bool useGpu) { - TestConfig config; - const int CHANNELS = 10; - const int IMG_SIZE = 16; - const int IMG_SIZE_Y = 8; - size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; - config.layerConfig.set_type(type); - config.layerConfig.set_size(size); - config.layerConfig.set_active_type("sigmoid"); - config.biasSize = CHANNELS; - config.inputDefs.push_back({INPUT_DATA, - "layer_0", - /* dim= */ size, - /* paraSize= */ CHANNELS}); - - config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS}); - config.inputDefs.back().isStatic = true; - config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS}); - config.inputDefs.back().isStatic = true; - - LayerInputConfig* input = config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - ImageConfig* img_conf = input->mutable_image_conf(); - img_conf->set_channels(CHANNELS); - img_conf->set_img_size(IMG_SIZE); - img_conf->set_img_size_y(IMG_SIZE_Y); - - testLayerGrad(config, - "batch_norm", - 64, - /* trans= */ trans, - useGpu, - /* useWeight */ true); -} - -TEST(Layer, BatchNormalizationLayer) { - testBatchNormLayer("batch_norm", false, false); -#ifndef PADDLE_ONLY_CPU - testBatchNormLayer("batch_norm", false, true); - if (hl_get_cudnn_lib_version() >= int(4000)) { - testBatchNormLayer("cudnn_batch_norm", false, true); - } -#endif -} - -TEST(Operator, conv) { - TestConfig config; - const int NUM_FILTERS = 16; - const int FILTER_SIZE = 2; - const int FILTER_SIZE_Y = 3; - const int CHANNELS = 3; - const int IMAGE_SIZE = 16; - const int IMAGE_SIZE_Y = 8; - OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); - operatorConf.set_type("conv"); - ConvConfig* conv = operatorConf.mutable_conv_conf(); - operatorConf.set_num_filters(NUM_FILTERS); - conv->set_filter_size(FILTER_SIZE); - conv->set_filter_size_y(FILTER_SIZE_Y); - conv->set_channels(CHANNELS); - conv->set_padding(0); - conv->set_padding_y(1); - conv->set_stride(2); - conv->set_stride_y(2); - conv->set_groups(1); - conv->set_filter_channels(conv->channels() / conv->groups()); - conv->set_img_size(IMAGE_SIZE); - conv->set_img_size_y(IMAGE_SIZE_Y); - conv->set_output_x(outputSize(conv->img_size(), - conv->filter_size(), - conv->padding(), - conv->stride(), - /* caffeMode */ true)); - conv->set_output_y(outputSize(conv->img_size_y(), - conv->filter_size_y(), - conv->padding_y(), - conv->stride_y(), - /* caffeMode */ true)); - config.layerConfig.set_size(conv->output_x() * conv->output_y() * - NUM_FILTERS); - - config.inputDefs.push_back( - {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); - config.inputDefs.push_back( - {INPUT_DATA, - "layer_1", - FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, - 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - - testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false); -} - -TEST(Layer, FeatureMapExpandLayer) { - TestConfig config; - config.layerConfig.set_type("featmap_expand"); - const int CHANNELS = 10; - const int INPUT_SIZE = 100; - config.layerConfig.set_size(INPUT_SIZE * CHANNELS); - config.layerConfig.set_num_filters(CHANNELS); - config.inputDefs.push_back({INPUT_SEQUENCE_DATA, - "layer_0", - /* dim= */ INPUT_SIZE, - /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - for (auto useGpu : {false, true}) { - testLayerGrad(config, - "featmap_expand", - /*batch_size*/ 100, - /* trans= */ false, - useGpu, - /* useWeight */ true); - } -} - -TEST(Layer, MultiplexLayer) { - TestConfig config; - const int LAYER_SIZE = 100; - config.layerConfig.set_type("multiplex"); - config.layerConfig.set_size(LAYER_SIZE); - - config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0}); - config.inputDefs.push_back( - {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); - config.inputDefs.push_back( - {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); - config.layerConfig.add_inputs(); + PadConfig* pad = input->mutable_pad_conf(); + ImageConfig* image = pad->mutable_image_conf(); + + image->set_channels(c); + image->set_img_size(h); + image->set_img_size_y(w); + pad->add_pad_c(1); + pad->add_pad_c(2); + pad->add_pad_h(2); + pad->add_pad_h(3); + pad->add_pad_w(3); + pad->add_pad_w(5); for (auto useGpu : {false, true}) { - testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu); + testLayerGrad(config, "pad", 10, false, useGpu); } } diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 3a9d339976fff9..0456404832c301 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -255,6 +255,13 @@ message PriorBoxConfig { repeated float variance = 4; } +message PadConfig { + required ImageConfig image_conf = 1; + repeated uint32 pad_c = 2; + repeated uint32 pad_h = 3; + repeated uint32 pad_w = 4; +} + message LayerInputConfig { required string input_layer_name = 1; optional string input_parameter_name = 2; @@ -271,6 +278,7 @@ message LayerInputConfig { optional MaxOutConfig maxout_conf = 11; optional SppConfig spp_conf = 12; optional PriorBoxConfig priorbox_conf = 13; + optional PadConfig pad_conf = 14; } message LayerConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 674b5ac58b6feb..6e5922166da1b1 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -493,6 +493,7 @@ def __init__( block_expand=None, maxout=None, spp=None, + pad=None, format=None, nnz=None, is_static=None, @@ -844,6 +845,12 @@ def __init__(self, pool_type, pyramid_height, channels): self.add_keys(locals()) +@config_class +class Pad(Cfg): + def __init__(self, channels, pad_c, pad_h, pad_w): + self.add_keys(locals()) + + @config_class class Norm(Cfg): def __init__(self, @@ -1842,6 +1849,25 @@ def __init__(self, name, inputs, device=None): self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels) +@config_layer('pad') +class PadLayer(LayerBase): + def __init__(self, name, inputs, **xargs): + super(PadLayer, self).__init__(name, 'pad', 0, inputs=inputs, **xargs) + pad = self.inputs[0].pad + self.config.inputs[0].pad_conf.pad_c.extend(pad.pad_c) + self.config.inputs[0].pad_conf.pad_h.extend(pad.pad_h) + self.config.inputs[0].pad_conf.pad_w.extend(pad.pad_w) + + input_layer = self.get_input_layer(0) + image_conf = self.config.inputs[0].pad_conf.image_conf + parse_image(pad, input_layer.name, image_conf) + out_ch = pad.channels + pad.pad_c[0] + pad.pad_c[1] + out_h = image_conf.img_size_y + pad.pad_h[0] + pad.pad_h[1] + out_w = image_conf.img_size + pad.pad_w[0] + pad.pad_w[1] + self.set_cnn_layer(name, out_h, out_w, out_ch) + self.config.size = out_ch * out_h * out_w + + @config_layer('batch_norm') class BatchNormLayer(LayerBase): layer_type = 'batch_norm' diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 9b6e5774bc82dc..56c335a0504d04 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -170,6 +170,7 @@ class LayerType(object): BLOCK_EXPAND = "blockexpand" MAXOUT = "maxout" SPP_LAYER = "spp" + PAD_LAYER = "pad" PRINT_LAYER = "print" PRIORBOX_LAYER = "priorbox" @@ -3488,9 +3489,6 @@ def conv_projection(input, groups=1, param_attr=None): """ - ConvProjection with a layer as input. - It performs element-wise multiplication with weight. - Different from img_conv_layer and conv_op, conv_projection is an Projection, which can be used in mixed_layer and conat_layer. It use cudnn to implement conv and only support GPU mode. @@ -3499,7 +3497,7 @@ def conv_projection(input, .. code-block:: python - proj = conv_projection(img=input1, + proj = conv_projection(input=input1, filter_size=3, num_filters=64, num_channels=64) @@ -3582,6 +3580,84 @@ def conv_projection(input, return proj +@wrap_name_default("pad") +@layer_support() +def pad_layer(input, + pad_c=None, + pad_h=None, + pad_w=None, + name=None, + layer_attr=None): + """ + This operation pads zeros to the input data according to pad_c,pad_h + and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size + of padding. And the input data shape is NCHW. + + For example, pad_c=[2,3] means padding 2 zeros before the + input data and 3 zeros after the input data in channel dimension. + pad_h means padding zeros in height dimension. pad_w means padding zeros + in width dimension. + + .. code-block:: python + + pad = pad_layer(input=ipt, + pad_c=[4,4], + pad_h=[0,0], + pad_w=[2,2]) + + :param input: layer's input. + :type input: LayerOutput + :param pad_c: padding size in channel dimension. + :type pad_c: list|None + :param pad_h: padding size in height dimension. + :type pad_h: list|None + :param pad_w: padding size in width dimension. + :type pad_w: list|None + :param layer_attr: Extra Layer Attribute. + :type layer_attr: ExtraLayerAttribute + :param name: layer name. + :type name: basestring + :return: LayerOutput object. + :rtype: LayerOutput + """ + if pad_c is not None: + assert isinstance(pad_c, collections.Sequence) and len(pad_c) == 2 + else: + pad_c = [0, 0] + + if pad_h is not None: + assert isinstance(pad_h, collections.Sequence) and len(pad_h) == 2 + else: + pad_h = [0, 0] + + if pad_w is not None: + assert isinstance(pad_w, collections.Sequence) and len(pad_w) == 2 + else: + pad_w = [0, 0] + + assert input.num_filters is not None + in_ch = input.num_filters + out_ch = in_ch + pad_c[0] + pad_c[1] + + l = Layer( + name=name, + type=LayerType.PAD_LAYER, + inputs=Input( + input.name, + pad=Pad( + channels=in_ch, + pad_c=pad_c, + pad_h=pad_h, + pad_w=pad_w, )), + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, + layer_type=LayerType.PAD_LAYER, + parents=[input], + num_filters=out_ch, + size=l.config.size) + + @wrap_name_default() @layer_support() def conv_shift_layer(a, b, name=None, layer_attr=None): diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_pad.py b/python/paddle/trainer_config_helpers/tests/configs/test_pad.py new file mode 100644 index 00000000000000..bb5f13410dbbba --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_pad.py @@ -0,0 +1,21 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +data = data_layer(name='data', size=2304, height=48, width=42) + +conv = img_conv_layer( + input=data, + filter_size=3, + num_channels=1, + num_filters=16, + padding=1, + act=LinearActivation(), + bias_attr=True) + +pool = img_pool_layer( + input=conv, num_channels=8, pool_size=2, stride=2, pool_type=MaxPooling()) + +pad = pad_layer(input=pool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1]) + +outputs(pad) From 6b61a096e13d04e1927c0760e96f2474df0085c7 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sat, 7 Jan 2017 16:01:44 +0800 Subject: [PATCH 006/370] Optional padding mode, namely ceil or floor, ceil by default. --- paddle/function/PadOp.cpp | 16 +- paddle/function/PadOpTest.cpp | 48 +- paddle/gserver/tests/test_LayerGrad.cpp | 3093 ++++++++--------- python/paddle/trainer/config_parser.py | 36 +- .../paddle/trainer_config_helpers/layers.py | 21 +- 5 files changed, 1614 insertions(+), 1600 deletions(-) diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index e10011da2a1750..2dfe03dcf61ce3 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -73,10 +73,6 @@ void PadGrad(real* inGrad, } } -/** - * \param inputs[0] input value. - * \param outputs[0] output value. - */ template class PadFunc : public FunctionBase { public: @@ -89,6 +85,10 @@ class PadFunc : public FunctionBase { padw1_ = config.get("padw1"); } + /** + * \param inputs[0] input value. + * \param outputs[0] output value. + */ void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { @@ -124,10 +124,6 @@ class PadFunc : public FunctionBase { int padw1_; }; -/** - * \param inputs[0] input grad. - * \param outputs[0] output grad. - */ template class PadGradFunc : public FunctionBase { public: @@ -140,6 +136,10 @@ class PadGradFunc : public FunctionBase { padw1_ = config.get("padw1"); } + /** + * \param inputs[0] output grad. + * \param inouts[0] input grad. + */ void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp index ee2834d793d348..c6d573c1d94537 100644 --- a/paddle/function/PadOpTest.cpp +++ b/paddle/function/PadOpTest.cpp @@ -43,28 +43,30 @@ TEST(Pad, real) { } } -// TEST(PadGrad, real) { -// for (size_t numSamples : {5, 32}) { -// for (size_t channels : {1, 5, 32}) { -// for (size_t imgSizeH : {5, 33, 100}) { -// for (size_t imgSizeW : {5, 32, 96}) { -// VLOG(3) << " numSamples=" << numSamples << " channels=" << channels -// << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; -// -// FunctionCompare compare("PadGrad", -// FuncConfig() -// .set("padc0", 2).set("padc1", 3) -// .set("padh0", 1).set("padh1", 2) -// .set("padw0", 3).set("padw1", 2)); -// Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; -// Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; -// compare.cmpWithArg({Tensor(nullptr, inDims)}, -// {Tensor(nullptr, outDims)}, -// {}); -// } -// } -// } -// } -//} +TEST(PadGrad, real) { + for (size_t numSamples : {5, 32}) { + for (size_t channels : {1, 5, 32}) { + for (size_t imgSizeH : {5, 33, 100}) { + for (size_t imgSizeW : {5, 32, 96}) { + VLOG(3) << " numSamples=" << numSamples << " channels=" << channels + << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; + + FunctionCompare compare("PadGrad", + FuncConfig() + .set("padc0", 2) + .set("padc1", 3) + .set("padh0", 1) + .set("padh1", 2) + .set("padw0", 3) + .set("padw1", 2)); + Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; + Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; + compare.cmpWithArg( + {Tensor(nullptr, inDims)}, {}, {Tensor(nullptr, outDims)}); + } + } + } + } +} } // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 2be52c17addc41..0560bb3ed91d36 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -32,1554 +32,1551 @@ DECLARE_double(checkgrad_eps); DECLARE_bool(thread_local_rand_use_global_seed); DECLARE_bool(prev_batch_state); -// TEST(Operator, dot_mul) { -// TestConfig config; -// config.layerConfig.set_size(10); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); -// operatorConf.set_type("dot_mul"); -// operatorConf.set_dotmul_scale(-1); -// -// testOperatorGrad(config, operatorConf, 100, false, false); -// } -// -// TEST(Projection, context) { -// for (auto contextStart : {-5, -3, -1, 0, 3}) { -// for (auto contextLength : {1, 2, 5, 7}) { -// for (auto batchSize : {1, 2, 5, 20, 50}) { -// for (auto trainablePadding : {false, true}) { -// LOG(INFO) << " contextStart=" << contextStart -// << " contextLength=" << contextLength -// << " batchSize=" << batchSize -// << " trainablePadding=" << trainablePadding; -// ProjectionConfig conf; -// conf.set_type("context"); -// conf.set_input_size(10); -// conf.set_context_start(contextStart); -// conf.set_context_length(contextLength); -// conf.set_trainable_padding(trainablePadding); -// conf.set_output_size(conf.context_length() * conf.input_size()); -// int pad = -// std::max(0, -conf.context_start()) + -// std::max(0, conf.context_start() + conf.context_length() - 1); -// for (auto useGpu : {false, true}) { -// testProjectionGrad( -// conf, -// INPUT_SEQUENCE_DATA, -// trainablePadding ? conf.input_size() * pad : 0, -// batchSize, -// useGpu, -// contextStart + contextLength <= 1); // = testState -// } -// } -// } -// } -// } -// } -// -// TEST(Projection, trans_fc) { -// ProjectionConfig conf; -// conf.set_type("trans_fc"); -// conf.set_input_size(50); -// conf.set_output_size(20); -// for (auto useGpu : {false, true}) { -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ 1000, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// TEST(Projection, fc) { -// ProjectionConfig conf; -// conf.set_type("fc"); -// conf.set_input_size(10); -// conf.set_output_size(20); -// for (auto useGpu : {false, true}) { -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ 200, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// TEST(Projection, dot_mul) { -// ProjectionConfig conf; -// conf.set_type("dot_mul"); -// conf.set_input_size(20); -// conf.set_output_size(20); -// for (auto useGpu : {false, true}) { -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ 20, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// TEST(Projection, table) { -// ProjectionConfig conf; -// conf.set_type("table"); -// conf.set_input_size(10); -// conf.set_output_size(20); -// for (auto useGpu : {false, true}) { -// testProjectionGrad(conf, -// INPUT_LABEL, -// /* parameterSize */ 200, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// TEST(Projection, identity) { -// ProjectionConfig conf; -// conf.set_type("identity"); -// conf.set_input_size(10); -// conf.set_output_size(10); -// for (auto useGpu : {false, true}) { -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ 0, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// TEST(Projection, scaling) { -// ProjectionConfig conf; -// conf.set_type("scaling"); -// conf.set_input_size(10); -// conf.set_output_size(10); -// for (auto useGpu : {false}) { -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ 1, -// /* batchSize */ 100, -// useGpu); -// } -// } -// -// void testProjectionConv(size_t groups) { -// const int NUM_FILTERS = 18; -// const int FILTER_SIZE = 2; -// const int FILTER_SIZE_Y = 3; -// const int CHANNELS = 3; -// const int IMAGE_SIZE = 16; -// -// ProjectionConfig conf; -// conf.set_type("conv"); -// conf.set_num_filters(NUM_FILTERS); -// -// ConvConfig* conv = conf.mutable_conv_conf(); -// conv->set_filter_size(FILTER_SIZE); -// conv->set_filter_size_y(FILTER_SIZE_Y); -// conv->set_channels(CHANNELS); -// conv->set_padding(0); -// conv->set_padding_y(1); -// conv->set_stride(2); -// conv->set_stride_y(2); -// conv->set_groups(groups); -// conv->set_filter_channels(conv->channels() / conv->groups()); -// conv->set_img_size(IMAGE_SIZE); -// int output_x = outputSize(conv->img_size(), -// conv->filter_size(), -// conv->padding(), -// conv->stride(), -// /* caffeMode */ true); -// int output_y = outputSize(conv->img_size(), -// conv->filter_size_y(), -// conv->padding_y(), -// conv->stride_y(), -// /* caffeMode */ true); -// conv->set_output_x(output_x); -// conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS); -// conf.set_output_size(output_x * output_y * NUM_FILTERS); -// -// testProjectionGrad(conf, -// INPUT_DATA, -// /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE -// * -// FILTER_SIZE_Y / groups, -// /* batchSize */ 100, -// true, -// false, -// NUM_FILTERS, -// true); -// } -// -// #ifndef PADDLE_ONLY_CPU -// TEST(Projection, conv) { -// testProjectionConv(1); -// testProjectionConv(3); -// } -// #endif -// -// TEST(Layer, BilinearInterpLayer) { -// TestConfig config; -// config.layerConfig.set_type("bilinear_interp"); -// config.biasSize = 0; -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); -// -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); -// ImageConfig* image = bilinear->mutable_image_conf(); -// image->set_img_size(32); -// image->set_img_size_y(32); -// image->set_channels(4); -// -// for (auto useGpu : {false, true}) { -// for (auto outSize : {32, 64}) { -// bilinear->set_out_size_x(outSize); -// bilinear->set_out_size_y(outSize); -// testLayerGrad(config, "bilinear_interp", 10, false, useGpu); -// } -// } -// } -// -// TEST(Layer, concat) { -// TestConfig config; -// config.biasSize = 0; -// config.layerConfig.set_type("concat"); -// config.layerConfig.set_size(15); -// config.layerConfig.set_active_type("sigmoid"); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "concat", 100, false, useGpu); -// } -// } -// -// TEST(Layer, AddtoLayer) { -// TestConfig config; -// config.biasSize = 0; -// config.layerConfig.set_type("addto"); -// config.layerConfig.set_size(10); -// config.layerConfig.set_active_type("sigmoid"); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "addto", 100, false, useGpu); -// } -// } -// -// TEST(Layer, CRFLayer) { -// TestConfig config; -// config.layerConfig.set_type("crf"); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120}); -// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // Not support GPU now -// testLayerGrad(config, -// "crf", -// 100, -// /* trans */ false, -// /* useGpu */ false, -// false /*useWeight*/, -// 0.03 /*epsilon*/); -// } -// -// TEST(Layer, CTCLayer) { -// TestConfig config; -// config.layerConfig.set_type("ctc"); -// config.layerConfig.set_norm_by_times(false); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ -// useGpu); -// } -// } -// -// TEST(Layer, cosSimLayer) { -// TestConfig config; -// config.layerConfig.set_type("cos"); -// config.layerConfig.set_size(1); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "cos", 100, false, useGpu); -// } -// } -// -// TEST(Layer, CosSimVecMatLayer) { -// TestConfig config; -// config.layerConfig.set_type("cos_vm"); -// config.layerConfig.set_size(5); // output size -// config.layerConfig.set_cos_scale(2.0); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "cos_vm", 100, false, useGpu); -// } -// } -// -// void testConvLayer(const string& type, bool trans, bool useGpu) { -// TestConfig config; -// config.biasSize = 16; -// config.layerConfig.set_type(type); -// config.layerConfig.set_num_filters(16); -// config.layerConfig.set_partial_sum(1); -// config.layerConfig.set_shared_biases(true); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// ConvConfig* conv = input->mutable_conv_conf(); -// conv->set_filter_size(2); -// conv->set_filter_size_y(3); -// conv->set_channels(3); -// conv->set_padding(0); -// conv->set_padding_y(1); -// conv->set_stride(2); -// conv->set_stride_y(2); -// conv->set_groups(1); -// conv->set_filter_channels(conv->channels() / conv->groups()); -// conv->set_img_size(16); -// conv->set_img_size_y(8); -// conv->set_output_x(outputSize(conv->img_size(), -// conv->filter_size(), -// conv->padding(), -// conv->stride(), -// /* caffeMode */ true)); -// conv->set_output_y(outputSize(conv->img_size_y(), -// conv->filter_size_y(), -// conv->padding_y(), -// conv->stride_y(), -// /* caffeMode */ true)); -// config.layerConfig.set_size(conv->output_x() * conv->output_y() * -// config.layerConfig.num_filters()); -// -// testLayerGrad(config, "conv", 100, trans, useGpu); -// // Use small batch_size and useWeight=true to test biasGrad -// testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02); -// } -// -// TEST(Layer, convLayer) { -// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); -// #ifndef PADDLE_ONLY_CPU -// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); -// testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); -// #endif -// } -// -// void testConvTransLayer(const string& type, bool trans, bool useGpu) { -// TestConfig config; -// config.biasSize = 3; -// config.layerConfig.set_type(type); -// config.layerConfig.set_num_filters(3); -// config.layerConfig.set_partial_sum(1); -// config.layerConfig.set_shared_biases(true); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// ConvConfig* conv = input->mutable_conv_conf(); -// conv->set_filter_size(2); -// conv->set_filter_size_y(3); -// conv->set_channels(16); -// conv->set_padding(0); -// conv->set_padding_y(1); -// conv->set_stride(2); -// conv->set_stride_y(2); -// conv->set_groups(1); -// conv->set_filter_channels(3 / conv->groups()); -// conv->set_img_size(16); -// conv->set_output_x(outputSize(conv->img_size(), -// conv->filter_size(), -// conv->padding(), -// conv->stride(), -// /* caffeMode */ true)); -// -// config.layerConfig.set_size(conv->img_size() * conv->img_size() * -// config.layerConfig.num_filters()); -// -// testLayerGrad(config, "convTrans", 100, trans, useGpu); -// // Use small batch_size and useWeight=true to test biasGrad -// testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02); -// } -// -// TEST(Layer, convTransLayer) { -// for (auto useGpu : {false, true}) { -// testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); -// } -// } -// -// TEST(Layer, blockExpandLayer) { -// TestConfig config; -// config.biasSize = 0; -// config.layerConfig.set_type("blockexpand"); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// BlockExpandConfig* blockExpand = input->mutable_block_expand_conf(); -// blockExpand->set_img_size_x(64); -// blockExpand->set_img_size_y(32); -// blockExpand->set_channels(3); -// blockExpand->set_padding_x(0); -// blockExpand->set_padding_y(0); -// blockExpand->set_block_x(4); -// blockExpand->set_block_y(32); -// blockExpand->set_stride_x(2); -// blockExpand->set_stride_y(2); -// blockExpand->set_output_x(outputSize(blockExpand->img_size_x(), -// blockExpand->block_x(), -// blockExpand->padding_x(), -// blockExpand->stride_x(), -// /* caffeMode */ false)); -// blockExpand->set_output_y(outputSize(blockExpand->img_size_y(), -// blockExpand->block_y(), -// blockExpand->padding_y(), -// blockExpand->stride_y(), -// /* caffeMode */ false)); -// config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() -// * -// blockExpand->channels()); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "blockexpand", 100, false, useGpu); -// } -// } -// -// TEST(Layer, maxoutLayer) { -// TestConfig config; -// config.biasSize = 0; -// config.layerConfig.set_type("maxout"); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// MaxOutConfig* maxout = input->mutable_maxout_conf(); -// ImageConfig* image = maxout->mutable_image_conf(); -// -// image->set_img_size(32); -// image->set_img_size_y(32); -// image->set_channels(4); -// maxout->set_groups(2); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "maxout", 10, false, useGpu); -// } -// } -// void testFcLayer(string format, size_t nnz) { -// TestConfig config; -// config.biasSize = 4096; -// config.layerConfig.set_type("fc"); -// config.layerConfig.set_size(4096); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_drop_rate(0.1); -// -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)}); -// config.layerConfig.add_inputs(); -// -// LOG(INFO) << config.inputDefs[0].sparse.sparse << " " -// << config.inputDefs[0].sparse.format; -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, -// "fc", -// 100, -// /* trans */ false, -// useGpu, -// /* weight */ true); -// } -// } -// -// TEST(Layer, fcLayer) { -// testFcLayer("", 4096 * 4096 * 2); -// testFcLayer("csc", 4096 * 40); -// testFcLayer("csr", 4096 * 40); -// } -// -// TEST(Layer, SelectiveFullyConnectedLayer) { -// TestConfig config; -// size_t nin = 16; -// size_t nout = 256; -// config.layerConfig.set_type("selective_fc"); -// config.layerConfig.set_size(nout); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_has_selected_colums(true); -// config.layerConfig.set_selective_fc_pass_generation(false); -// config.biasSize = nout; -// -// config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back( -// {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", -// true)}); -// config.layerConfig.add_inputs(); -// -// testLayerGrad(config, -// "selective_fc", -// 100, -// /* trans= */ false, -// /* useGup= */ false, -// false); -// #ifndef PADDLE_ONLY_CPU -// testLayerGrad(config, -// "selective_fc", -// 100, -// /* trans= */ false, -// /* useGup= */ true, -// false); -// #endif -// } -// -// TEST(Layer, DataNormLayer) { -// TestConfig config; -// config.layerConfig.set_type("data_norm"); -// config.layerConfig.set_size(20); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100}); -// config.inputDefs.back().isStatic = true; -// config.layerConfig.add_inputs(); -// -// for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) { -// config.layerConfig.set_data_norm_strategy(strategy); -// // The parameters are static, so not support GPU now -// testLayerGrad(config, -// "data_norm", -// 200, -// /* trans */ false, -// /* useGpu */ false); -// } -// } -// -// TEST(Layer, hsigmoidLayer) { -// TestConfig config; -// config.layerConfig.set_type("hsigmoid"); -// config.layerConfig.set_num_classes(5); -// config.layerConfig.set_size(1); -// config.biasSize = config.layerConfig.num_classes() - 1; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200}); -// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // Not support GPU now -// testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ -// false); -// } -// -// TEST(Layer, multi_cross) { -// TestConfig config; -// config.layerConfig.set_type("multi-class-cross-entropy"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad( -// config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu); -// } -// } -// -// TEST(Layer, multi_binary_label_sparse_mat) { -// TestConfig config; -// config.layerConfig.set_type("multi_binary_label_cross_entropy"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, -// "multi_binary_label_cross_entropy", -// 100, -// /* trans */ false, -// useGpu); -// } -// } -// -// TEST(layer, multi_binary_label_id) { -// TestConfig config; -// config.layerConfig.set_type("multi_binary_label_cross_entropy"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, -// "multi_binary_label_cross_entropy", -// 100, -// /* trans */ false, -// useGpu); -// } -// } -// -// TEST(Layer, multi_cross_with_selfnorm) { -// TestConfig config; -// config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm"); -// config.layerConfig.set_softmax_selfnorm_alpha(0.1); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // Not support GPU now -// testLayerGrad(config, -// "multi_class_cross_entropy_with_selfnorm", -// 100, -// /* trans */ false, -// /* useGpu */ false); -// } -// -// TEST(Layer, multi_cross_soft) { -// TestConfig config; -// config.layerConfig.set_type("soft_binary_class_cross_entropy"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, -// "soft_binary_class_cross_entropy", -// 100, -// /* trans */ false, -// useGpu); -// } -// } -// -// TEST(Layer, square_error) { -// TestConfig config; -// config.layerConfig.set_type("square_error"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); -// } -// } -// -// TEST(Layer, sparse_square_error) { -// TestConfig config; -// config.layerConfig.set_type("square_error"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // "GpuSparseMatrix" as label is not supported -// testLayerGrad(config, -// "square_error", -// 100, -// /* trans */ false, -// /* useGpu */ false); -// } -// -// TEST(Layer, sparse_float_square_error) { -// TestConfig config; -// config.layerConfig.set_type("square_error"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); -// config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // "GpuSparseMatrix" as label is not supported -// testLayerGrad(config, -// "square_error", -// 100, -// /* trans */ false, -// /* useGpu */ false); -// } -// -// TEST(Layer, square_error_weighted) { -// TestConfig config; -// config.layerConfig.set_type("square_error"); -// config.biasSize = 0; -// config.testAccumulate = false; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); -// } -// } -// -// TEST(Layer, huber_two_class) { -// TestConfig config; -// config.layerConfig.set_type("huber"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); -// } -// } -// -// void testExpandLayer(string trans_type, bool hasSubseq) { -// TestConfig config; -// config.layerConfig.set_type("expand"); -// -// config.inputDefs.push_back( -// {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA, -// "layer_0", -// 10, -// 0}); -// config.inputDefs.push_back( -// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, -// "layer_1", -// 10, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.set_trans_type(trans_type); -// LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq; -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "expand", 30, false, useGpu); -// } -// } -// -// TEST(Layer, ExpandLayer) { -// testExpandLayer("non-seq", false); // non-seq expand to seq -// testExpandLayer("non-seq", true); // non-seq expand to hasSubseq -// testExpandLayer("seq", true); // seq expand to hasSubseq -// } -// -// void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) { -// TestConfig config; -// config.layerConfig.set_type(layer_type); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back( -// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, -// "layer_0", -// 10, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.set_trans_type(trans_type); -// -// auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) { -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, layer_type, 100, false, useGpu); -// } -// }; -// -// if (layer_type == "average") { -// for (auto strategy : {"average", "sum", "squarerootn"}) { -// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type -// << " average_strategy=" << strategy; -// config.layerConfig.set_average_strategy(strategy); -// testDegradeLayerGrad(config, layer_type); -// } -// } else { -// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type; -// testDegradeLayerGrad(config, layer_type); -// } -// } -// -// TEST(Layer, MaxLayer) { -// testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq -// testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq -// testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq -// } -// -// TEST(Layer, SequenceLastInstanceLayer) { -// testDegradeLayer(false, -// "seqlastins", -// "non-seq"); // seq seqlastins to non-seq -// testDegradeLayer(true, -// "seqlastins", -// "non-seq"); // hasSubseq seqlastins to non-seq -// testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to -// seq -// } -// -// TEST(Layer, AverageLayer) { -// testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq -// testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to -// non-seq -// testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq -// } -// -// TEST(Layer, SequenceConcatLayer) { -// TestConfig config; -// config.layerConfig.set_type("seqconcat"); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "seqconcat", 100, false, useGpu); -// } -// } -// -// TEST(Layer, SequenceReshapeLayer) { -// TestConfig config; -// config.layerConfig.set_type("seqreshape"); -// config.layerConfig.set_size(10); -// -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "seqreshape", 100, false, useGpu); -// } -// } -// -// TEST(Layer, ConvShiftLayer) { -// TestConfig config; -// config.layerConfig.set_type("conv_shift"); -// config.layerConfig.set_size(10); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// // Not support GPU now -// testLayerGrad(config, "conv_shift", 100, false, false); -// } -// -// TEST(Layer, PowerLayer) { -// TestConfig config; -// config.layerConfig.set_type("power"); -// config.layerConfig.set_size(10); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "power", 100, false, useGpu); -// } -// } -// -// TEST(Layer, ConvexCombinationLayer) { -// TestConfig config; -// config.layerConfig.set_type("convex_comb"); -// config.layerConfig.set_size(20); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "convex_comb", 100, false, useGpu); -// } -// } -// -// TEST(Layer, InterpolationLayer) { -// TestConfig config; -// config.layerConfig.set_type("interpolation"); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "interpolation", 100, false, useGpu); -// } -// } -// -// TEST(Layer, OuterProdLayer) { -// TestConfig config; -// config.layerConfig.set_type("out_prod"); -// config.layerConfig.set_size(100); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "out_prod", 100, false, useGpu); -// } -// } -// -// TEST(Layer, SlopeInterceptLayer) { -// TestConfig config; -// config.layerConfig.set_type("slope_intercept"); -// config.layerConfig.set_size(10); -// config.layerConfig.set_slope(1.0); -// config.layerConfig.set_intercept(0.1); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "slope_intercept", 100, false, useGpu); -// } -// } -// -// TEST(Layer, ScalingLayer) { -// TestConfig config; -// config.layerConfig.set_type("scaling"); -// config.layerConfig.set_size(10); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.layerConfig.add_inputs(); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "scaling", 100, false, useGpu); -// } -// } -// -// void testNormLayer(const string& normType, bool trans, bool useGpu) { -// TestConfig config; -// config.layerConfig.set_type("norm"); -// config.layerConfig.set_active_type("relu"); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// NormConfig* norm = input->mutable_norm_conf(); -// norm->set_norm_type(normType); -// norm->set_channels(16); -// norm->set_size(5); -// norm->set_scale(0.001); -// norm->set_pow(0.75); -// norm->set_blocked(0); -// norm->set_img_size(14); -// norm->set_img_size_y(7); -// norm->set_output_x(norm->img_size()); -// norm->set_output_y(norm->img_size_y()); -// if (norm->norm_type() == "cmrnorm" || -// norm->norm_type() == "cmrnorm-projection") { -// norm->set_scale(norm->scale() / norm->size()); -// } else { -// norm->set_scale(norm->scale() / (norm->size() * norm->size())); -// } -// -// config.layerConfig.set_size(norm->output_x() * norm->output_y() * -// norm->channels()); -// config.biasSize = 0; -// -// testLayerGrad(config, "norm", 100, trans, useGpu); -// } -// -// TEST(Layer, NormLayer) { -// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ -// true); -// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ -// false); -// } -// -// void setPoolConfig(TestConfig* config, -// PoolConfig* pool, -// const string& poolType) { -// (*config).biasSize = 0; -// (*config).layerConfig.set_type("pool"); -// (*config).layerConfig.set_num_filters(16); -// -// int kw = 3, kh = 3; -// int pw = 0, ph = 0; -// int sw = 2, sh = 2; -// pool->set_pool_type(poolType); -// pool->set_channels(16); -// pool->set_size_x(kw); -// pool->set_size_y(kh); -// pool->set_start(0); -// pool->set_padding(pw); -// pool->set_padding_y(ph); -// pool->set_stride(sw); -// pool->set_stride_y(sh); -// -// int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); -// int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); -// pool->set_output_x(ow); -// pool->set_output_y(oh); -// } -// -// void testPoolLayer(const string& poolType, bool trans, bool useGpu) { -// TestConfig config; -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// PoolConfig* pool = input->mutable_pool_conf(); -// -// pool->set_img_size(14); -// pool->set_img_size_y(14); -// setPoolConfig(&config, pool, poolType); -// config.layerConfig.set_size(pool->output_x() * pool->output_y() * -// pool->channels()); -// -// testLayerGrad(config, "pool", 100, trans, useGpu); -// } -// -// #ifndef PADDLE_ONLY_CPU -// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { -// TestConfig config; -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// PoolConfig* pool = input->mutable_pool_conf(); -// -// pool->set_size_y(4); -// pool->set_stride_y(3); -// pool->set_img_size(10); -// pool->set_img_size_y(20); -// setPoolConfig(&config, pool, poolType); -// pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) / -// ((float)pool->stride_y()) + -// 1.5); -// config.layerConfig.set_size(pool->output_x() * pool->output_y() * -// pool->channels()); -// -// testLayerGrad(config, "pool", 100, trans, useGpu); -// } -// #endif -// -// TEST(Layer, PoolLayer) { -// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); -// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); -// -// #ifndef PADDLE_ONLY_CPU -// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); -// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); -// testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); -// testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); -// testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); -// testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); -// #endif -// } -// -// void testSppLayer(const string& poolType, -// const int pyramidHeight, -// bool trans, -// bool useGpu) { -// TestConfig config; -// config.layerConfig.set_type("spp"); -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// SppConfig* sppConfig = input->mutable_spp_conf(); -// sppConfig->set_pool_type(poolType); -// sppConfig->set_pyramid_height(pyramidHeight); -// ImageConfig* imageConfig = sppConfig->mutable_image_conf(); -// imageConfig->set_channels(16); -// imageConfig->set_img_size(10); -// imageConfig->set_img_size_y(20); -// int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); -// config.layerConfig.set_size(outputSize * imageConfig->channels()); -// testLayerGrad(config, "spp", 100, trans, useGpu); -// } -// -// TEST(Layer, SpatialPyramidPoolLayer) { -// for (auto useGpu : {false, true}) { -// for (auto pyramidHeight : {1, 2, 3}) { -// testSppLayer("avg-projection", pyramidHeight, false, useGpu); -// testSppLayer("max-projection", pyramidHeight, false, useGpu); -// } -// } -// } -// -// TEST(Layer, rankCostLayer) { -// TestConfig config; -// config.layerConfig.set_type("rank-cost"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "rank-cost", 100, false, useGpu); -// } -// } -// -// TEST(Layer, sumCostLayer) { -// TestConfig config; -// config.layerConfig.set_type("sum_cost"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "sum_cost", 100, false, useGpu); -// } -// } -// -// TEST(Layer, weightedRankCostLayer) { -// TestConfig config; -// config.layerConfig.set_type("rank-cost"); -// config.biasSize = 0; -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); -// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu); -// } -// } -// -// TEST(Layer, TensorLayer) { -// TestConfig config; -// config.layerConfig.set_type("tensor"); -// config.layerConfig.set_size(10); -// config.layerConfig.set_active_type("sigmoid"); -// config.biasSize = config.layerConfig.size(); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250}); -// config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "tensor", 100, false, useGpu); -// } -// } -// -// TEST(Layer, RecurrentLayer) { -// TestConfig config; -// config.layerConfig.set_type("recurrent"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("tanh"); -// config.biasSize = 4; -// -// config.inputDefs.push_back( -// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// for (auto reversed : {false, true}) { -// config.layerConfig.set_reversed(reversed); -// config.testState = !reversed; -// testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu); -// } -// } -// } -// -// TEST(Layer, LstmLayer) { -// TestConfig config; -// config.layerConfig.set_type("lstmemory"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("tanh"); -// config.layerConfig.set_active_state_type("sigmoid"); -// config.layerConfig.set_active_gate_type("sigmoid"); -// config.biasSize = 28; -// -// config.inputDefs.push_back( -// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// for (auto reversed : {false, true}) { -// config.layerConfig.set_reversed(reversed); -// config.testState = !reversed; -// testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu); -// } -// } -// for (auto useGpu : {true}) { -// config.testBatchState = true; -// config.layerConfig.set_reversed(false); -// testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu); -// } -// } -// -// TEST(Layer, MDLstmLayer) { -// TestConfig config; -// config.layerConfig.set_type("mdlstmemory"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_active_state_type("sigmoid"); -// config.layerConfig.set_active_gate_type("sigmoid"); -// config.biasSize = 4 * 9; -// -// config.inputDefs.push_back( -// {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_directions(true); -// config.layerConfig.add_directions(true); -// -// for (auto useGpu : {false, true}) { -// for (int i = 0; i < 2; i++) { -// for (int j = 0; j < 2; j++) { -// config.layerConfig.set_directions(0, bool(i)); -// config.layerConfig.set_directions(1, bool(j)); -// testLayerGrad(config, "mdlstmemory", 100, false, useGpu); -// } -// } -// } -// } -// -// TEST(Layer, ParameterReluLayer) { -// auto testParameterReluLayer = [&](size_t inputSize, size_t channels) { -// TestConfig config; -// config.layerConfig.set_type("prelu"); -// config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels}); -// config.layerConfig.add_inputs(); -// config.layerConfig.set_size(inputSize); -// config.layerConfig.set_partial_sum(inputSize / -// channels); // size of feature map -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "prelu", 100, false, useGpu); -// } -// }; -// -// testParameterReluLayer(192, 1); -// testParameterReluLayer(192, 3); -// testParameterReluLayer(192, 192); -// } -// -// TEST(Layer, ResizeLayer) { -// TestConfig config; -// config.biasSize = 0; -// config.layerConfig.set_type("resize"); -// config.layerConfig.set_size(64); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "resize", 100, false, useGpu); -// } -// } -// -// TEST(Layer, NCELayer) { -// TestConfig config; -// size_t numClasses = 4; -// config.layerConfig.set_type("nce"); -// config.layerConfig.set_size(1); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_num_classes(numClasses); -// config.biasSize = numClasses; -// -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * -// numClasses}); -// config.inputDefs.push_back( -// {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto withWeight : {false, true}) { -// if (withWeight) { -// config.inputDefs.push_back( -// {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// } -// -// for (auto isIdLabel : {false, true}) { -// config.inputDefs[1] = { -// isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA, -// "label", -// /* dim= */ numClasses, -// /* paraSize= */ 0}; -// -// for (auto withDist : {false, true}) { -// config.layerConfig.clear_neg_sampling_dist(); -// if (withDist) { -// double sum = 0; -// for (size_t i = 0; i < numClasses; ++i) { -// real p = rand(); // NOLINT use rand_r -// config.layerConfig.add_neg_sampling_dist(p); -// sum += p; -// } -// for (size_t i = 0; i < numClasses; ++i) { -// real p = config.layerConfig.neg_sampling_dist(i) / sum; -// config.layerConfig.set_neg_sampling_dist(i, p); -// } -// } -// LOG(INFO) << "NCELayer " -// << " isIdLabel=" << isIdLabel << " withWeight=" << -// withWeight -// << " withDist=" << withDist; -// // Not support GPU now -// testLayerGrad(config, -// "nce", -// 100, -// /* trans= */ false, -// /* useGpu */ false); -// } -// } -// } -// } -// -// TEST(Layer, GatedRecurrentLayer) { -// TestConfig config; -// config.layerConfig.set_type("gated_recurrent"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_active_gate_type("sigmoid"); -// config.biasSize = 12; -// -// config.inputDefs.push_back( -// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// for (auto reversed : {false, true}) { -// config.layerConfig.set_reversed(reversed); -// config.testState = !reversed; -// testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, -// useGpu); -// } -// } -// } -// -// TEST(Layer, GruStepLayer) { -// TestConfig config; -// config.layerConfig.set_type("gru_step"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_active_gate_type("sigmoid"); -// config.biasSize = 12; -// -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu); -// } -// } -// -// TEST(Layer, LstmStepLayer) { -// TestConfig config; -// config.layerConfig.set_type("lstm_step"); -// config.layerConfig.set_size(4); -// config.layerConfig.set_active_type("sigmoid"); -// config.layerConfig.set_active_state_type("sigmoid"); -// config.layerConfig.set_active_gate_type("sigmoid"); -// config.biasSize = 12; -// config.testAccumulate = false; -// -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0}); -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu); -// } -// } -// -// void testBatchNormLayer(const string& type, bool trans, bool useGpu) { -// TestConfig config; -// const int CHANNELS = 10; -// const int IMG_SIZE = 16; -// const int IMG_SIZE_Y = 8; -// size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; -// config.layerConfig.set_type(type); -// config.layerConfig.set_size(size); -// config.layerConfig.set_active_type("sigmoid"); -// config.biasSize = CHANNELS; -// config.inputDefs.push_back({INPUT_DATA, -// "layer_0", -// /* dim= */ size, -// /* paraSize= */ CHANNELS}); -// -// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, -// CHANNELS}); -// config.inputDefs.back().isStatic = true; -// config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, -// CHANNELS}); -// config.inputDefs.back().isStatic = true; -// -// LayerInputConfig* input = config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// ImageConfig* img_conf = input->mutable_image_conf(); -// img_conf->set_channels(CHANNELS); -// img_conf->set_img_size(IMG_SIZE); -// img_conf->set_img_size_y(IMG_SIZE_Y); -// -// testLayerGrad(config, -// "batch_norm", -// 64, -// /* trans= */ trans, -// useGpu, -// /* useWeight */ true); -// } -// -// TEST(Layer, BatchNormalizationLayer) { -// testBatchNormLayer("batch_norm", false, false); -// #ifndef PADDLE_ONLY_CPU -// testBatchNormLayer("batch_norm", false, true); -// if (hl_get_cudnn_lib_version() >= int(4000)) { -// testBatchNormLayer("cudnn_batch_norm", false, true); -// } -// #endif -// } -// -// TEST(Operator, conv) { -// TestConfig config; -// const int NUM_FILTERS = 16; -// const int FILTER_SIZE = 2; -// const int FILTER_SIZE_Y = 3; -// const int CHANNELS = 3; -// const int IMAGE_SIZE = 16; -// const int IMAGE_SIZE_Y = 8; -// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); -// operatorConf.set_type("conv"); -// ConvConfig* conv = operatorConf.mutable_conv_conf(); -// operatorConf.set_num_filters(NUM_FILTERS); -// conv->set_filter_size(FILTER_SIZE); -// conv->set_filter_size_y(FILTER_SIZE_Y); -// conv->set_channels(CHANNELS); -// conv->set_padding(0); -// conv->set_padding_y(1); -// conv->set_stride(2); -// conv->set_stride_y(2); -// conv->set_groups(1); -// conv->set_filter_channels(conv->channels() / conv->groups()); -// conv->set_img_size(IMAGE_SIZE); -// conv->set_img_size_y(IMAGE_SIZE_Y); -// conv->set_output_x(outputSize(conv->img_size(), -// conv->filter_size(), -// conv->padding(), -// conv->stride(), -// /* caffeMode */ true)); -// conv->set_output_y(outputSize(conv->img_size_y(), -// conv->filter_size_y(), -// conv->padding_y(), -// conv->stride_y(), -// /* caffeMode */ true)); -// config.layerConfig.set_size(conv->output_x() * conv->output_y() * -// NUM_FILTERS); -// -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); -// config.inputDefs.push_back( -// {INPUT_DATA, -// "layer_1", -// FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, -// 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false); -// } -// -// TEST(Layer, FeatureMapExpandLayer) { -// TestConfig config; -// config.layerConfig.set_type("featmap_expand"); -// const int CHANNELS = 10; -// const int INPUT_SIZE = 100; -// config.layerConfig.set_size(INPUT_SIZE * CHANNELS); -// config.layerConfig.set_num_filters(CHANNELS); -// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, -// "layer_0", -// /* dim= */ INPUT_SIZE, -// /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, -// "featmap_expand", -// /*batch_size*/ 100, -// /* trans= */ false, -// useGpu, -// /* useWeight */ true); -// } -// } -// -// TEST(Layer, MultiplexLayer) { -// TestConfig config; -// const int LAYER_SIZE = 100; -// config.layerConfig.set_type("multiplex"); -// config.layerConfig.set_size(LAYER_SIZE); -// -// config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0}); -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); -// config.inputDefs.push_back( -// {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// config.layerConfig.add_inputs(); -// -// for (auto useGpu : {false, true}) { -// testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu); -// } -// } -// +TEST(Operator, dot_mul) { + TestConfig config; + config.layerConfig.set_size(10); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); + operatorConf.set_type("dot_mul"); + operatorConf.set_dotmul_scale(-1); + + testOperatorGrad(config, operatorConf, 100, false, false); +} + +TEST(Projection, context) { + for (auto contextStart : {-5, -3, -1, 0, 3}) { + for (auto contextLength : {1, 2, 5, 7}) { + for (auto batchSize : {1, 2, 5, 20, 50}) { + for (auto trainablePadding : {false, true}) { + LOG(INFO) << " contextStart=" << contextStart + << " contextLength=" << contextLength + << " batchSize=" << batchSize + << " trainablePadding=" << trainablePadding; + ProjectionConfig conf; + conf.set_type("context"); + conf.set_input_size(10); + conf.set_context_start(contextStart); + conf.set_context_length(contextLength); + conf.set_trainable_padding(trainablePadding); + conf.set_output_size(conf.context_length() * conf.input_size()); + int pad = + std::max(0, -conf.context_start()) + + std::max(0, conf.context_start() + conf.context_length() - 1); + for (auto useGpu : {false, true}) { + testProjectionGrad( + conf, + INPUT_SEQUENCE_DATA, + trainablePadding ? conf.input_size() * pad : 0, + batchSize, + useGpu, + contextStart + contextLength <= 1); // = testState + } + } + } + } + } +} + +TEST(Projection, trans_fc) { + ProjectionConfig conf; + conf.set_type("trans_fc"); + conf.set_input_size(50); + conf.set_output_size(20); + for (auto useGpu : {false, true}) { + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ 1000, + /* batchSize */ 100, + useGpu); + } +} + +TEST(Projection, fc) { + ProjectionConfig conf; + conf.set_type("fc"); + conf.set_input_size(10); + conf.set_output_size(20); + for (auto useGpu : {false, true}) { + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ 200, + /* batchSize */ 100, + useGpu); + } +} + +TEST(Projection, dot_mul) { + ProjectionConfig conf; + conf.set_type("dot_mul"); + conf.set_input_size(20); + conf.set_output_size(20); + for (auto useGpu : {false, true}) { + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ 20, + /* batchSize */ 100, + useGpu); + } +} + +TEST(Projection, table) { + ProjectionConfig conf; + conf.set_type("table"); + conf.set_input_size(10); + conf.set_output_size(20); + for (auto useGpu : {false, true}) { + testProjectionGrad(conf, + INPUT_LABEL, + /* parameterSize */ 200, + /* batchSize */ 100, + useGpu); + } +} + +TEST(Projection, identity) { + ProjectionConfig conf; + conf.set_type("identity"); + conf.set_input_size(10); + conf.set_output_size(10); + for (auto useGpu : {false, true}) { + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ 0, + /* batchSize */ 100, + useGpu); + } +} + +TEST(Projection, scaling) { + ProjectionConfig conf; + conf.set_type("scaling"); + conf.set_input_size(10); + conf.set_output_size(10); + for (auto useGpu : {false}) { + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ 1, + /* batchSize */ 100, + useGpu); + } +} + +void testProjectionConv(size_t groups) { + const int NUM_FILTERS = 18; + const int FILTER_SIZE = 2; + const int FILTER_SIZE_Y = 3; + const int CHANNELS = 3; + const int IMAGE_SIZE = 16; + + ProjectionConfig conf; + conf.set_type("conv"); + conf.set_num_filters(NUM_FILTERS); + + ConvConfig* conv = conf.mutable_conv_conf(); + conv->set_filter_size(FILTER_SIZE); + conv->set_filter_size_y(FILTER_SIZE_Y); + conv->set_channels(CHANNELS); + conv->set_padding(0); + conv->set_padding_y(1); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_groups(groups); + conv->set_filter_channels(conv->channels() / conv->groups()); + conv->set_img_size(IMAGE_SIZE); + int output_x = outputSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + /* caffeMode */ true); + int output_y = outputSize(conv->img_size(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + /* caffeMode */ true); + conv->set_output_x(output_x); + conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS); + conf.set_output_size(output_x * output_y * NUM_FILTERS); + + testProjectionGrad(conf, + INPUT_DATA, + /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * + FILTER_SIZE_Y / groups, + /* batchSize */ 100, + true, + false, + NUM_FILTERS, + true); +} + +#ifndef PADDLE_ONLY_CPU +TEST(Projection, conv) { + testProjectionConv(1); + testProjectionConv(3); +} +#endif + +TEST(Layer, BilinearInterpLayer) { + TestConfig config; + config.layerConfig.set_type("bilinear_interp"); + config.biasSize = 0; + config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); + + LayerInputConfig* input = config.layerConfig.add_inputs(); + BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); + ImageConfig* image = bilinear->mutable_image_conf(); + image->set_img_size(32); + image->set_img_size_y(32); + image->set_channels(4); + + for (auto useGpu : {false, true}) { + for (auto outSize : {32, 64}) { + bilinear->set_out_size_x(outSize); + bilinear->set_out_size_y(outSize); + testLayerGrad(config, "bilinear_interp", 10, false, useGpu); + } + } +} + +TEST(Layer, concat) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("concat"); + config.layerConfig.set_size(15); + config.layerConfig.set_active_type("sigmoid"); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "concat", 100, false, useGpu); + } +} + +TEST(Layer, AddtoLayer) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("addto"); + config.layerConfig.set_size(10); + config.layerConfig.set_active_type("sigmoid"); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "addto", 100, false, useGpu); + } +} + +TEST(Layer, CRFLayer) { + TestConfig config; + config.layerConfig.set_type("crf"); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120}); + config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // Not support GPU now + testLayerGrad(config, + "crf", + 100, + /* trans */ false, + /* useGpu */ false, + false /*useWeight*/, + 0.03 /*epsilon*/); +} + +TEST(Layer, CTCLayer) { + TestConfig config; + config.layerConfig.set_type("ctc"); + config.layerConfig.set_norm_by_times(false); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "ctc", + 100, + /* trans */ false, /* useGpu */ + useGpu); + } +} + +TEST(Layer, cosSimLayer) { + TestConfig config; + config.layerConfig.set_type("cos"); + config.layerConfig.set_size(1); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "cos", 100, false, useGpu); + } +} + +TEST(Layer, CosSimVecMatLayer) { + TestConfig config; + config.layerConfig.set_type("cos_vm"); + config.layerConfig.set_size(5); // output size + config.layerConfig.set_cos_scale(2.0); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "cos_vm", 100, false, useGpu); + } +} + +void testConvLayer(const string& type, bool trans, bool useGpu) { + TestConfig config; + config.biasSize = 16; + config.layerConfig.set_type(type); + config.layerConfig.set_num_filters(16); + config.layerConfig.set_partial_sum(1); + config.layerConfig.set_shared_biases(true); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + ConvConfig* conv = input->mutable_conv_conf(); + conv->set_filter_size(2); + conv->set_filter_size_y(3); + conv->set_channels(3); + conv->set_padding(0); + conv->set_padding_y(1); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_groups(1); + conv->set_filter_channels(conv->channels() / conv->groups()); + conv->set_img_size(16); + conv->set_img_size_y(8); + conv->set_output_x(outputSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + /* caffeMode */ true)); + conv->set_output_y(outputSize(conv->img_size_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + /* caffeMode */ true)); + config.layerConfig.set_size(conv->output_x() * conv->output_y() * + config.layerConfig.num_filters()); + + testLayerGrad(config, "conv", 100, trans, useGpu); + // Use small batch_size and useWeight=true to test biasGrad + testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02); +} + +TEST(Layer, convLayer) { + testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); +#ifndef PADDLE_ONLY_CPU + testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); + testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); +#endif +} + +void testConvTransLayer(const string& type, bool trans, bool useGpu) { + TestConfig config; + config.biasSize = 3; + config.layerConfig.set_type(type); + config.layerConfig.set_num_filters(3); + config.layerConfig.set_partial_sum(1); + config.layerConfig.set_shared_biases(true); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + ConvConfig* conv = input->mutable_conv_conf(); + conv->set_filter_size(2); + conv->set_filter_size_y(3); + conv->set_channels(16); + conv->set_padding(0); + conv->set_padding_y(1); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_groups(1); + conv->set_filter_channels(3 / conv->groups()); + conv->set_img_size(16); + conv->set_output_x(outputSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + /* caffeMode */ true)); + + config.layerConfig.set_size(conv->img_size() * conv->img_size() * + config.layerConfig.num_filters()); + + testLayerGrad(config, "convTrans", 100, trans, useGpu); + // Use small batch_size and useWeight=true to test biasGrad + testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02); +} + +TEST(Layer, convTransLayer) { + for (auto useGpu : {false, true}) { + testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); + } +} + +TEST(Layer, blockExpandLayer) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("blockexpand"); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + BlockExpandConfig* blockExpand = input->mutable_block_expand_conf(); + blockExpand->set_img_size_x(64); + blockExpand->set_img_size_y(32); + blockExpand->set_channels(3); + blockExpand->set_padding_x(0); + blockExpand->set_padding_y(0); + blockExpand->set_block_x(4); + blockExpand->set_block_y(32); + blockExpand->set_stride_x(2); + blockExpand->set_stride_y(2); + blockExpand->set_output_x(outputSize(blockExpand->img_size_x(), + blockExpand->block_x(), + blockExpand->padding_x(), + blockExpand->stride_x(), + /* caffeMode */ false)); + blockExpand->set_output_y(outputSize(blockExpand->img_size_y(), + blockExpand->block_y(), + blockExpand->padding_y(), + blockExpand->stride_y(), + /* caffeMode */ false)); + config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() * + blockExpand->channels()); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "blockexpand", 100, false, useGpu); + } +} + +TEST(Layer, maxoutLayer) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("maxout"); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + MaxOutConfig* maxout = input->mutable_maxout_conf(); + ImageConfig* image = maxout->mutable_image_conf(); + + image->set_img_size(32); + image->set_img_size_y(32); + image->set_channels(4); + maxout->set_groups(2); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "maxout", 10, false, useGpu); + } +} +void testFcLayer(string format, size_t nnz) { + TestConfig config; + config.biasSize = 4096; + config.layerConfig.set_type("fc"); + config.layerConfig.set_size(4096); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_drop_rate(0.1); + + config.inputDefs.push_back( + {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)}); + config.layerConfig.add_inputs(); + + LOG(INFO) << config.inputDefs[0].sparse.sparse << " " + << config.inputDefs[0].sparse.format; + + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "fc", + 100, + /* trans */ false, + useGpu, + /* weight */ true); + } +} + +TEST(Layer, fcLayer) { + testFcLayer("", 4096 * 4096 * 2); + testFcLayer("csc", 4096 * 40); + testFcLayer("csr", 4096 * 40); +} + +TEST(Layer, SelectiveFullyConnectedLayer) { + TestConfig config; + size_t nin = 16; + size_t nout = 256; + config.layerConfig.set_type("selective_fc"); + config.layerConfig.set_size(nout); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_has_selected_colums(true); + config.layerConfig.set_selective_fc_pass_generation(false); + config.biasSize = nout; + + config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back( + {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)}); + config.layerConfig.add_inputs(); + + testLayerGrad(config, + "selective_fc", + 100, + /* trans= */ false, + /* useGup= */ false, + false); +#ifndef PADDLE_ONLY_CPU + testLayerGrad(config, + "selective_fc", + 100, + /* trans= */ false, + /* useGup= */ true, + false); +#endif +} + +TEST(Layer, DataNormLayer) { + TestConfig config; + config.layerConfig.set_type("data_norm"); + config.layerConfig.set_size(20); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100}); + config.inputDefs.back().isStatic = true; + config.layerConfig.add_inputs(); + + for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) { + config.layerConfig.set_data_norm_strategy(strategy); + // The parameters are static, so not support GPU now + testLayerGrad(config, + "data_norm", + 200, + /* trans */ false, + /* useGpu */ false); + } +} + +TEST(Layer, hsigmoidLayer) { + TestConfig config; + config.layerConfig.set_type("hsigmoid"); + config.layerConfig.set_num_classes(5); + config.layerConfig.set_size(1); + config.biasSize = config.layerConfig.num_classes() - 1; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200}); + config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // Not support GPU now + testLayerGrad(config, + "hsigmoid", + 100, + /* trans */ false, /* useGpu */ + false); +} + +TEST(Layer, multi_cross) { + TestConfig config; + config.layerConfig.set_type("multi-class-cross-entropy"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad( + config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu); + } +} + +TEST(Layer, multi_binary_label_sparse_mat) { + TestConfig config; + config.layerConfig.set_type("multi_binary_label_cross_entropy"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "multi_binary_label_cross_entropy", + 100, + /* trans */ false, + useGpu); + } +} + +TEST(layer, multi_binary_label_id) { + TestConfig config; + config.layerConfig.set_type("multi_binary_label_cross_entropy"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "multi_binary_label_cross_entropy", + 100, + /* trans */ false, + useGpu); + } +} + +TEST(Layer, multi_cross_with_selfnorm) { + TestConfig config; + config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm"); + config.layerConfig.set_softmax_selfnorm_alpha(0.1); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // Not support GPU now + testLayerGrad(config, + "multi_class_cross_entropy_with_selfnorm", + 100, + /* trans */ false, + /* useGpu */ false); +} + +TEST(Layer, multi_cross_soft) { + TestConfig config; + config.layerConfig.set_type("soft_binary_class_cross_entropy"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "soft_binary_class_cross_entropy", + 100, + /* trans */ false, + useGpu); + } +} + +TEST(Layer, square_error) { + TestConfig config; + config.layerConfig.set_type("square_error"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); + } +} + +TEST(Layer, sparse_square_error) { + TestConfig config; + config.layerConfig.set_type("square_error"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // "GpuSparseMatrix" as label is not supported + testLayerGrad(config, + "square_error", + 100, + /* trans */ false, + /* useGpu */ false); +} + +TEST(Layer, sparse_float_square_error) { + TestConfig config; + config.layerConfig.set_type("square_error"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); + config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // "GpuSparseMatrix" as label is not supported + testLayerGrad(config, + "square_error", + 100, + /* trans */ false, + /* useGpu */ false); +} + +TEST(Layer, square_error_weighted) { + TestConfig config; + config.layerConfig.set_type("square_error"); + config.biasSize = 0; + config.testAccumulate = false; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); + } +} + +TEST(Layer, huber_two_class) { + TestConfig config; + config.layerConfig.set_type("huber"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); + } +} + +void testExpandLayer(string trans_type, bool hasSubseq) { + TestConfig config; + config.layerConfig.set_type("expand"); + + config.inputDefs.push_back( + {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA, + "layer_0", + 10, + 0}); + config.inputDefs.push_back( + {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, + "layer_1", + 10, + 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.set_trans_type(trans_type); + LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq; + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "expand", 30, false, useGpu); + } +} + +TEST(Layer, ExpandLayer) { + testExpandLayer("non-seq", false); // non-seq expand to seq + testExpandLayer("non-seq", true); // non-seq expand to hasSubseq + testExpandLayer("seq", true); // seq expand to hasSubseq +} + +void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) { + TestConfig config; + config.layerConfig.set_type(layer_type); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back( + {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, + "layer_0", + 10, + 0}); + config.layerConfig.add_inputs(); + config.layerConfig.set_trans_type(trans_type); + + auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) { + for (auto useGpu : {false, true}) { + testLayerGrad(config, layer_type, 100, false, useGpu); + } + }; + + if (layer_type == "average") { + for (auto strategy : {"average", "sum", "squarerootn"}) { + LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type + << " average_strategy=" << strategy; + config.layerConfig.set_average_strategy(strategy); + testDegradeLayerGrad(config, layer_type); + } + } else { + LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type; + testDegradeLayerGrad(config, layer_type); + } +} + +TEST(Layer, MaxLayer) { + testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq + testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq + testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq +} + +TEST(Layer, SequenceLastInstanceLayer) { + testDegradeLayer(false, + "seqlastins", + "non-seq"); // seq seqlastins to non-seq + testDegradeLayer(true, + "seqlastins", + "non-seq"); // hasSubseq seqlastins to non-seq + testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to + seq +} + +TEST(Layer, AverageLayer) { + testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq + testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to + non - + seq testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq +} + +TEST(Layer, SequenceConcatLayer) { + TestConfig config; + config.layerConfig.set_type("seqconcat"); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "seqconcat", 100, false, useGpu); + } +} + +TEST(Layer, SequenceReshapeLayer) { + TestConfig config; + config.layerConfig.set_type("seqreshape"); + config.layerConfig.set_size(10); + + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "seqreshape", 100, false, useGpu); + } +} + +TEST(Layer, ConvShiftLayer) { + TestConfig config; + config.layerConfig.set_type("conv_shift"); + config.layerConfig.set_size(10); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + // Not support GPU now + testLayerGrad(config, "conv_shift", 100, false, false); +} + +TEST(Layer, PowerLayer) { + TestConfig config; + config.layerConfig.set_type("power"); + config.layerConfig.set_size(10); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "power", 100, false, useGpu); + } +} + +TEST(Layer, ConvexCombinationLayer) { + TestConfig config; + config.layerConfig.set_type("convex_comb"); + config.layerConfig.set_size(20); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "convex_comb", 100, false, useGpu); + } +} + +TEST(Layer, InterpolationLayer) { + TestConfig config; + config.layerConfig.set_type("interpolation"); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "interpolation", 100, false, useGpu); + } +} + +TEST(Layer, OuterProdLayer) { + TestConfig config; + config.layerConfig.set_type("out_prod"); + config.layerConfig.set_size(100); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "out_prod", 100, false, useGpu); + } +} + +TEST(Layer, SlopeInterceptLayer) { + TestConfig config; + config.layerConfig.set_type("slope_intercept"); + config.layerConfig.set_size(10); + config.layerConfig.set_slope(1.0); + config.layerConfig.set_intercept(0.1); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "slope_intercept", 100, false, useGpu); + } +} + +TEST(Layer, ScalingLayer) { + TestConfig config; + config.layerConfig.set_type("scaling"); + config.layerConfig.set_size(10); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.layerConfig.add_inputs(); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "scaling", 100, false, useGpu); + } +} + +void testNormLayer(const string& normType, bool trans, bool useGpu) { + TestConfig config; + config.layerConfig.set_type("norm"); + config.layerConfig.set_active_type("relu"); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + NormConfig* norm = input->mutable_norm_conf(); + norm->set_norm_type(normType); + norm->set_channels(16); + norm->set_size(5); + norm->set_scale(0.001); + norm->set_pow(0.75); + norm->set_blocked(0); + norm->set_img_size(14); + norm->set_img_size_y(7); + norm->set_output_x(norm->img_size()); + norm->set_output_y(norm->img_size_y()); + if (norm->norm_type() == "cmrnorm" || + norm->norm_type() == "cmrnorm-projection") { + norm->set_scale(norm->scale() / norm->size()); + } else { + norm->set_scale(norm->scale() / (norm->size() * norm->size())); + } + + config.layerConfig.set_size(norm->output_x() * norm->output_y() * + norm->channels()); + config.biasSize = 0; + + testLayerGrad(config, "norm", 100, trans, useGpu); +} + +TEST(Layer, NormLayer) { + testNormLayer("cmrnorm-projection", + /* trans= */ false, /* useGpu= */ + true); + testNormLayer("cmrnorm-projection", + /* trans= */ false, /* useGpu= */ + false); +} + +void setPoolConfig(TestConfig* config, + PoolConfig* pool, + const string& poolType) { + (*config).biasSize = 0; + (*config).layerConfig.set_type("pool"); + (*config).layerConfig.set_num_filters(16); + + int kw = 3, kh = 3; + int pw = 0, ph = 0; + int sw = 2, sh = 2; + pool->set_pool_type(poolType); + pool->set_channels(16); + pool->set_size_x(kw); + pool->set_size_y(kh); + pool->set_start(0); + pool->set_padding(pw); + pool->set_padding_y(ph); + pool->set_stride(sw); + pool->set_stride_y(sh); + + int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); + int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); + pool->set_output_x(ow); + pool->set_output_y(oh); +} + +void testPoolLayer(const string& poolType, bool trans, bool useGpu) { + TestConfig config; + config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + PoolConfig* pool = input->mutable_pool_conf(); + + pool->set_img_size(14); + pool->set_img_size_y(14); + setPoolConfig(&config, pool, poolType); + config.layerConfig.set_size(pool->output_x() * pool->output_y() * + pool->channels()); + + testLayerGrad(config, "pool", 100, trans, useGpu); +} + +#ifndef PADDLE_ONLY_CPU +void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { + TestConfig config; + config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + PoolConfig* pool = input->mutable_pool_conf(); + + pool->set_size_y(4); + pool->set_stride_y(3); + pool->set_img_size(10); + pool->set_img_size_y(20); + setPoolConfig(&config, pool, poolType); + pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) / + ((float)pool->stride_y()) + + 1.5); + config.layerConfig.set_size(pool->output_x() * pool->output_y() * + pool->channels()); + + testLayerGrad(config, "pool", 100, trans, useGpu); +} +#endif + +TEST(Layer, PoolLayer) { + testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); + testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); + +#ifndef PADDLE_ONLY_CPU + testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); + testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); + testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); + testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); + testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); + testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); +#endif +} + +void testSppLayer(const string& poolType, + const int pyramidHeight, + bool trans, + bool useGpu) { + TestConfig config; + config.layerConfig.set_type("spp"); + config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + SppConfig* sppConfig = input->mutable_spp_conf(); + sppConfig->set_pool_type(poolType); + sppConfig->set_pyramid_height(pyramidHeight); + ImageConfig* imageConfig = sppConfig->mutable_image_conf(); + imageConfig->set_channels(16); + imageConfig->set_img_size(10); + imageConfig->set_img_size_y(20); + int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); + config.layerConfig.set_size(outputSize * imageConfig->channels()); + testLayerGrad(config, "spp", 100, trans, useGpu); +} + +TEST(Layer, SpatialPyramidPoolLayer) { + for (auto useGpu : {false, true}) { + for (auto pyramidHeight : {1, 2, 3}) { + testSppLayer("avg-projection", pyramidHeight, false, useGpu); + testSppLayer("max-projection", pyramidHeight, false, useGpu); + } + } +} + +TEST(Layer, rankCostLayer) { + TestConfig config; + config.layerConfig.set_type("rank-cost"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "rank-cost", 100, false, useGpu); + } +} + +TEST(Layer, sumCostLayer) { + TestConfig config; + config.layerConfig.set_type("sum_cost"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "sum_cost", 100, false, useGpu); + } +} + +TEST(Layer, weightedRankCostLayer) { + TestConfig config; + config.layerConfig.set_type("rank-cost"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu); + } +} + +TEST(Layer, TensorLayer) { + TestConfig config; + config.layerConfig.set_type("tensor"); + config.layerConfig.set_size(10); + config.layerConfig.set_active_type("sigmoid"); + config.biasSize = config.layerConfig.size(); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250}); + config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "tensor", 100, false, useGpu); + } +} + +TEST(Layer, RecurrentLayer) { + TestConfig config; + config.layerConfig.set_type("recurrent"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("tanh"); + config.biasSize = 4; + + config.inputDefs.push_back( + {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + for (auto reversed : {false, true}) { + config.layerConfig.set_reversed(reversed); + config.testState = !reversed; + testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu); + } + } +} + +TEST(Layer, LstmLayer) { + TestConfig config; + config.layerConfig.set_type("lstmemory"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("tanh"); + config.layerConfig.set_active_state_type("sigmoid"); + config.layerConfig.set_active_gate_type("sigmoid"); + config.biasSize = 28; + + config.inputDefs.push_back( + {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + for (auto reversed : {false, true}) { + config.layerConfig.set_reversed(reversed); + config.testState = !reversed; + testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu); + } + } + for (auto useGpu : {true}) { + config.testBatchState = true; + config.layerConfig.set_reversed(false); + testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu); + } +} + +TEST(Layer, MDLstmLayer) { + TestConfig config; + config.layerConfig.set_type("mdlstmemory"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_active_state_type("sigmoid"); + config.layerConfig.set_active_gate_type("sigmoid"); + config.biasSize = 4 * 9; + + config.inputDefs.push_back( + {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5}); + config.layerConfig.add_inputs(); + config.layerConfig.add_directions(true); + config.layerConfig.add_directions(true); + + for (auto useGpu : {false, true}) { + for (int i = 0; i < 2; i++) { + for (int j = 0; j < 2; j++) { + config.layerConfig.set_directions(0, bool(i)); + config.layerConfig.set_directions(1, bool(j)); + testLayerGrad(config, "mdlstmemory", 100, false, useGpu); + } + } + } +} + +TEST(Layer, ParameterReluLayer) { + auto testParameterReluLayer = [&](size_t inputSize, size_t channels) { + TestConfig config; + config.layerConfig.set_type("prelu"); + config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels}); + config.layerConfig.add_inputs(); + config.layerConfig.set_size(inputSize); + config.layerConfig.set_partial_sum(inputSize / + channels); // size of feature map + for (auto useGpu : {false, true}) { + testLayerGrad(config, "prelu", 100, false, useGpu); + } + }; + + testParameterReluLayer(192, 1); + testParameterReluLayer(192, 3); + testParameterReluLayer(192, 192); +} + +TEST(Layer, ResizeLayer) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("resize"); + config.layerConfig.set_size(64); + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "resize", 100, false, useGpu); + } +} + +TEST(Layer, NCELayer) { + TestConfig config; + size_t numClasses = 4; + config.layerConfig.set_type("nce"); + config.layerConfig.set_size(1); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_num_classes(numClasses); + config.biasSize = numClasses; + + config.inputDefs.push_back( + {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses}); + config.inputDefs.push_back( + {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto withWeight : {false, true}) { + if (withWeight) { + config.inputDefs.push_back( + {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + } + + for (auto isIdLabel : {false, true}) { + config.inputDefs[1] = { + isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA, + "label", + /* dim= */ numClasses, + /* paraSize= */ 0}; + + for (auto withDist : {false, true}) { + config.layerConfig.clear_neg_sampling_dist(); + if (withDist) { + double sum = 0; + for (size_t i = 0; i < numClasses; ++i) { + real p = rand(); // NOLINT use rand_r + config.layerConfig.add_neg_sampling_dist(p); + sum += p; + } + for (size_t i = 0; i < numClasses; ++i) { + real p = config.layerConfig.neg_sampling_dist(i) / sum; + config.layerConfig.set_neg_sampling_dist(i, p); + } + } + LOG(INFO) << "NCELayer " + << " isIdLabel=" << isIdLabel << " withWeight=" << withWeight + << " withDist=" << withDist; + // Not support GPU now + testLayerGrad(config, + "nce", + 100, + /* trans= */ false, + /* useGpu */ false); + } + } + } +} + +TEST(Layer, GatedRecurrentLayer) { + TestConfig config; + config.layerConfig.set_type("gated_recurrent"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_active_gate_type("sigmoid"); + config.biasSize = 12; + + config.inputDefs.push_back( + {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + for (auto reversed : {false, true}) { + config.layerConfig.set_reversed(reversed); + config.testState = !reversed; + testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu); + } + } +} + +TEST(Layer, GruStepLayer) { + TestConfig config; + config.layerConfig.set_type("gru_step"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_active_gate_type("sigmoid"); + config.biasSize = 12; + + config.inputDefs.push_back( + {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); + config.inputDefs.push_back( + {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu); + } +} + +TEST(Layer, LstmStepLayer) { + TestConfig config; + config.layerConfig.set_type("lstm_step"); + config.layerConfig.set_size(4); + config.layerConfig.set_active_type("sigmoid"); + config.layerConfig.set_active_state_type("sigmoid"); + config.layerConfig.set_active_gate_type("sigmoid"); + config.biasSize = 12; + config.testAccumulate = false; + + config.inputDefs.push_back( + {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0}); + config.inputDefs.push_back( + {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu); + } +} + +void testBatchNormLayer(const string& type, bool trans, bool useGpu) { + TestConfig config; + const int CHANNELS = 10; + const int IMG_SIZE = 16; + const int IMG_SIZE_Y = 8; + size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; + config.layerConfig.set_type(type); + config.layerConfig.set_size(size); + config.layerConfig.set_active_type("sigmoid"); + config.biasSize = CHANNELS; + config.inputDefs.push_back({INPUT_DATA, + "layer_0", + /* dim= */ size, + /* paraSize= */ CHANNELS}); + + config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + + LayerInputConfig* input = config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + ImageConfig* img_conf = input->mutable_image_conf(); + img_conf->set_channels(CHANNELS); + img_conf->set_img_size(IMG_SIZE); + img_conf->set_img_size_y(IMG_SIZE_Y); + + testLayerGrad(config, + "batch_norm", + 64, + /* trans= */ trans, + useGpu, + /* useWeight */ true); +} + +TEST(Layer, BatchNormalizationLayer) { + testBatchNormLayer("batch_norm", false, false); +#ifndef PADDLE_ONLY_CPU + testBatchNormLayer("batch_norm", false, true); + if (hl_get_cudnn_lib_version() >= int(4000)) { + testBatchNormLayer("cudnn_batch_norm", false, true); + } +#endif +} + +TEST(Operator, conv) { + TestConfig config; + const int NUM_FILTERS = 16; + const int FILTER_SIZE = 2; + const int FILTER_SIZE_Y = 3; + const int CHANNELS = 3; + const int IMAGE_SIZE = 16; + const int IMAGE_SIZE_Y = 8; + OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); + operatorConf.set_type("conv"); + ConvConfig* conv = operatorConf.mutable_conv_conf(); + operatorConf.set_num_filters(NUM_FILTERS); + conv->set_filter_size(FILTER_SIZE); + conv->set_filter_size_y(FILTER_SIZE_Y); + conv->set_channels(CHANNELS); + conv->set_padding(0); + conv->set_padding_y(1); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_groups(1); + conv->set_filter_channels(conv->channels() / conv->groups()); + conv->set_img_size(IMAGE_SIZE); + conv->set_img_size_y(IMAGE_SIZE_Y); + conv->set_output_x(outputSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + /* caffeMode */ true)); + conv->set_output_y(outputSize(conv->img_size_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + /* caffeMode */ true)); + config.layerConfig.set_size(conv->output_x() * conv->output_y() * + NUM_FILTERS); + + config.inputDefs.push_back( + {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); + config.inputDefs.push_back( + {INPUT_DATA, + "layer_1", + FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, + 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false); +} + +TEST(Layer, FeatureMapExpandLayer) { + TestConfig config; + config.layerConfig.set_type("featmap_expand"); + const int CHANNELS = 10; + const int INPUT_SIZE = 100; + config.layerConfig.set_size(INPUT_SIZE * CHANNELS); + config.layerConfig.set_num_filters(CHANNELS); + config.inputDefs.push_back({INPUT_SEQUENCE_DATA, + "layer_0", + /* dim= */ INPUT_SIZE, + /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + for (auto useGpu : {false, true}) { + testLayerGrad(config, + "featmap_expand", + /*batch_size*/ 100, + /* trans= */ false, + useGpu, + /* useWeight */ true); + } +} + +TEST(Layer, MultiplexLayer) { + TestConfig config; + const int LAYER_SIZE = 100; + config.layerConfig.set_type("multiplex"); + config.layerConfig.set_size(LAYER_SIZE); + + config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0}); + config.inputDefs.push_back( + {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); + config.inputDefs.push_back( + {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu); + } +} + TEST(Layer, PadLayer) { TestConfig config; config.biasSize = 0; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 6e5922166da1b1..c80ed0274461da 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1109,7 +1109,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf): bilinear_conf.out_size_y = bilinear.out_size_y -def parse_pool(pool, input_layer_name, pool_conf): +def parse_pool(pool, input_layer_name, pool_conf, ceil_mode): pool_conf.pool_type = pool.pool_type config_assert(pool.pool_type in [ 'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool' @@ -1134,10 +1134,10 @@ def parse_pool(pool, input_layer_name, pool_conf): pool_conf.padding_y = default(pool.padding_y, pool_conf.padding) pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x, pool_conf.padding, pool_conf.stride, - False) + not ceil_mode) pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y, pool_conf.padding_y, - pool_conf.stride_y, False) + pool_conf.stride_y, not ceil_mode) def parse_spp(spp, input_layer_name, spp_conf): @@ -1810,9 +1810,8 @@ class ConvTransLayer(ConvTransLayerBase): @config_layer('norm') class NormLayer(LayerBase): - def __init__(self, name, inputs, device=None): - super(NormLayer, self).__init__( - name, 'norm', 0, inputs=inputs, device=device) + def __init__(self, name, inputs, **xargs): + super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **xargs) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) norm_conf = self.config.inputs[input_index].norm_conf @@ -1824,23 +1823,22 @@ def __init__(self, name, inputs, device=None): @config_layer('pool') class PoolLayer(LayerBase): - def __init__(self, name, inputs, device=None): - super(PoolLayer, self).__init__( - name, 'pool', 0, inputs=inputs, device=device) + def __init__(self, name, inputs, ceil_mode=True, **xargs): + super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) pool_conf = self.config.inputs[input_index].pool_conf parse_pool(self.inputs[input_index].pool, input_layer.name, - pool_conf) + pool_conf, ceil_mode) self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x, pool_conf.channels) @config_layer('spp') class SpatialPyramidPoolLayer(LayerBase): - def __init__(self, name, inputs, device=None): + def __init__(self, name, inputs, **xargs): super(SpatialPyramidPoolLayer, self).__init__( - name, 'spp', 0, inputs=inputs, device=device) + name, 'spp', 0, inputs=inputs, **xargs) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) spp_conf = self.config.inputs[input_index].spp_conf @@ -1877,7 +1875,6 @@ def __init__(self, inputs, active_type="linear", bias=True, - device=None, use_global_stats=True, moving_average_fraction=0.9, batch_norm_type=None, @@ -1919,7 +1916,6 @@ def __init__(self, 0, active_type=active_type, inputs=inputs, - device=device, **xargs) if use_global_stats is not None: @@ -1953,9 +1949,9 @@ def calc_parameter_size(self, image_conf): @config_layer('trans') class TransLayer(LayerBase): - def __init__(self, name, inputs, device=None): + def __init__(self, name, inputs, **xargs): super(TransLayer, self).__init__( - name, 'trans', 0, inputs=inputs, device=device) + name, 'trans', 0, inputs=inputs, **xargs) config_assert( len(self.inputs) == 1, 'TransLayer must have one and only one input') @@ -1964,9 +1960,9 @@ def __init__(self, name, inputs, device=None): @config_layer('resize') class ResizeLayer(LayerBase): - def __init__(self, name, size, inputs, device=None): + def __init__(self, name, size, inputs, **xargs): super(ResizeLayer, self).__init__( - name, 'resize', size=size, inputs=inputs, device=device) + name, 'resize', size=size, inputs=inputs, **xargs) config_assert( len(self.inputs) == 1, 'ResizeLayer must have one and only one input') @@ -1974,9 +1970,9 @@ def __init__(self, name, size, inputs, device=None): @config_layer('blockexpand') class BlockExpandLayer(LayerBase): - def __init__(self, name, inputs, device=None): + def __init__(self, name, inputs, **xargs): super(BlockExpandLayer, self).__init__( - name, 'blockexpand', 0, inputs=inputs, device=device) + name, 'blockexpand', 0, inputs=inputs, **xargs) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) parse_block_expand( diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 56c335a0504d04..3f88727b63066f 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1980,7 +1980,8 @@ def img_pool_layer(input, layer_attr=None, pool_size_y=None, stride_y=None, - padding_y=None): + padding_y=None, + ceil_mode=True): """ Image pooling Layer. @@ -2011,6 +2012,23 @@ def img_pool_layer(input, :type stride_y: int|None :param layer_attr: Extra Layer attribute. :type layer_attr: ExtraLayerAttribute + :param ceil_mode: Wether to use ceil mode to calculate output height and with. + Defalut is True. If set false, Otherwise use floor. + + - ceil_mode=True: + + .. math:: + + w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride)) + h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y)) + + - ceil_mode=False: + + .. math:: + + w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride)) + h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y)) + :type ceil_mode: bool :return: LayerOutput object. :rtype: LayerOutput """ @@ -2048,6 +2066,7 @@ def img_pool_layer(input, stride_y=stride_y, padding_y=padding_y)) ], + ceil_mode=ceil_mode, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( name, From 74f76e61d885dcc0478086c54ad330875b92bbd4 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 9 Jan 2017 10:30:39 +0800 Subject: [PATCH 007/370] update code and fix bug --- paddle/function/PadOp.cpp | 12 ++++++------ paddle/function/PadOpTest.cpp | 2 +- paddle/gserver/tests/test_LayerGrad.cpp | 8 +++----- 3 files changed, 10 insertions(+), 12 deletions(-) diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index 2dfe03dcf61ce3..ff13f27e94217b 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -92,9 +92,9 @@ class PadFunc : public FunctionBase { void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { - CHECK_EQ(1, inputs.size()); - CHECK_EQ(1, outputs.size()); - CHECK_EQ(0, inouts.size()); + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); + CHECK_EQ(0UL, inouts.size()); size_t num = inputs[0].dims_[0]; size_t inC = inputs[0].dims_[1]; @@ -143,9 +143,9 @@ class PadGradFunc : public FunctionBase { void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { - CHECK_EQ(1, inputs.size()); - CHECK_EQ(0, outputs.size()); - CHECK_EQ(1, inouts.size()); + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(0UL, outputs.size()); + CHECK_EQ(1UL, inouts.size()); size_t n = inouts[0].dims_[0]; size_t inC = inouts[0].dims_[1]; diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp index c6d573c1d94537..be989807d13688 100644 --- a/paddle/function/PadOpTest.cpp +++ b/paddle/function/PadOpTest.cpp @@ -62,7 +62,7 @@ TEST(PadGrad, real) { Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; compare.cmpWithArg( - {Tensor(nullptr, inDims)}, {}, {Tensor(nullptr, outDims)}); + {Tensor(nullptr, outDims)}, {}, {Tensor(nullptr, inDims)}); } } } diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0560bb3ed91d36..8c8e876bd64fb9 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -850,15 +850,13 @@ TEST(Layer, SequenceLastInstanceLayer) { testDegradeLayer(true, "seqlastins", "non-seq"); // hasSubseq seqlastins to non-seq - testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to - seq + testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to seq } TEST(Layer, AverageLayer) { testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq - testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to - non - - seq testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq + testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to non-seq + testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq } TEST(Layer, SequenceConcatLayer) { From 93fef79e1f5936fbe45a4474cf20cf2298c5c49d Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 9 Jan 2017 12:40:49 +0800 Subject: [PATCH 008/370] fix build --- paddle/gserver/layers/PadLayer.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/gserver/layers/PadLayer.cpp b/paddle/gserver/layers/PadLayer.cpp index 62c50af32dd055..9069d382ef9273 100644 --- a/paddle/gserver/layers/PadLayer.cpp +++ b/paddle/gserver/layers/PadLayer.cpp @@ -33,9 +33,9 @@ bool PadLayer::init(const LayerMap& layerMap, : img_conf.img_size()); inDims_.push_back(img_conf.img_size()); - CHECK_EQ(2UL, pad_conf.pad_c_size()); - CHECK_EQ(2UL, pad_conf.pad_h_size()); - CHECK_EQ(2UL, pad_conf.pad_w_size()); + CHECK_EQ(2, pad_conf.pad_c_size()); + CHECK_EQ(2, pad_conf.pad_h_size()); + CHECK_EQ(2, pad_conf.pad_w_size()); padc_.push_back(pad_conf.pad_c(0)); padc_.push_back(pad_conf.pad_c(1)); padh_.push_back(pad_conf.pad_h(0)); @@ -76,7 +76,7 @@ void PadLayer::setOutDims(int batchSize) { } void PadLayer::setTensorDim(int batchSize) { - CHECK_EQ(inputLayers_.size(), 1UL); + CHECK_EQ(static_cast(inputLayers_.size()), 1); inDims_[0] = batchSize; int h = inputLayers_[0]->getOutput().getFrameHeight(); if (h != 0) inDims_[2]; From 2e47c9d828ea48b775572384260cc806674663aa Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 11 Jan 2017 17:44:18 +0800 Subject: [PATCH 009/370] Fix bug in DenseScanner of DataProviderConverter. --- paddle/py_paddle/dataprovider_converter.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 981d10afda2671..21d1cb75f4d40e 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -34,6 +34,10 @@ def finish_scan(self, argument): class DenseScanner(IScanner): + """ + :type __mat__: numpy.ndarray + """ + def __init__(self, input_type, pos): IScanner.__init__(self, input_type, pos) self.__mat__ = None @@ -47,6 +51,8 @@ def scan(self, dat): def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) assert isinstance(self.input_type, dp2.InputType) + if self.__mat__.dtype != numpy.float32: + self.__mat__ = self.__mat__.astype(numpy.float32) m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False) argument.setSlotValue(self.pos, m) From 2629d43ff7c798f1b3cd3be3883449a4b2877c35 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 12 Jan 2017 14:49:19 +0800 Subject: [PATCH 010/370] New FunctionTest --- paddle/function/FunctionTest.h | 138 ++++++++++++++++++++++++++++++--- 1 file changed, 126 insertions(+), 12 deletions(-) diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 32131037f6de4a..2847188fd67989 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -15,9 +15,33 @@ limitations under the License. */ #include "Function.h" #include "paddle/math/Vector.h" #include "paddle/math/tests/TensorCheck.h" +#include "paddle/testing/TestUtil.h" namespace paddle { +/** + * \brief A class for comparing CPU and GPU implementations of Function. + * + * + * Use case: + * // Initializes a test object, the corresponding cpu and gpu Function + * // are constructed according to FunctionName and FuncConfig. + * FunctionCompare test(FunctionName, FuncConfig); + * // Prepare inputs and outputs arguments. + * // Here the input and output can not contain real data, + * // only contains the argument type and shape. + * test.addInputs(input1); + * test.addInputs(input2); + * test.addOutputs(output1); + * test.addOutputs(output2); + * // Run. + * // Will according to the type and shape of arguments(inputs_/outputs_), + * // automatic initialization cpu and gpu function required arguments + * // (cpuInputs_/cpuOutputs_/gpuInputs_/gpuOutputs_). + * // Call the CPU and GPU Function calculation results. + * // Compares CPU and GPU calculation results for consistency. + * test.run(); + */ class FunctionCompare { public: FunctionCompare(const std::string& name, const FuncConfig& config) @@ -27,6 +51,32 @@ class FunctionCompare { gpu->init(config); } + void addInputs(const BufferArg& input) { inputs.push_back(input); } + + void addOutputs(const BufferArg& output) { outputs.push_back(output); } + + void run() { + // prepare cpu/gpu arguments + prepareArgs(); + + // function calculate + cpu->calc(cpuInputs, cpuOutputs); + gpu->calc(gpuInputs, gpuOutputs); + + // check outputs and inouts + auto checkArgs = [=](const BufferArgs& cpuArgs, const BufferArgs& gpuArgs) { + for (size_t i = 0; i < cpuArgs.size(); i++) { + auto cpu = cpuArgs[i]; + auto gpu = gpuArgs[i]; + CpuVector cpuVector(cpu.shape().getElements(), (real*)cpu.getData()); + GpuVector gpuVector(cpu.shape().getElements(), (real*)gpu.getData()); + + autotest::TensorCheckErr(cpuVector, gpuVector); + } + }; + checkArgs(cpuOutputs, gpuOutputs); + } +#if 0 void cmpWithArg(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) { @@ -64,11 +114,10 @@ class FunctionCompare { }; initArgs(cpuInputs, gpuInputs, inputs); initArgs(cpuOutputs, gpuOutputs, outputs); - initArgs(cpuInouts, gpuInouts, inouts); // function calculate - cpu->calc(cpuInputs, cpuOutputs, cpuInouts); - gpu->calc(gpuInputs, gpuOutputs, gpuInouts); + cpu->calc(cpuInputs, cpuOutputs); + gpu->calc(gpuInputs, gpuOutputs); // check outputs and inouts auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) { @@ -86,24 +135,89 @@ class FunctionCompare { } }; checkArgs(cpuOutputs, gpuOutputs); - checkArgs(cpuInouts, gpuInouts); } +#endif std::shared_ptr getCpuFunction() const { return cpu; } std::shared_ptr getGpuFunction() const { return gpu; } +protected: + void prepareArgs() { + // TODO, if inputs has data + } + + void createArg(BufferArgs& cpuArgs, BufferArgs& gpuArgs, BufferArg& arg) { + size_t size = arg.shape().getElements() * sizeOfValuType(arg.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuArgs.emplace_back( + BufferArg(cpuMemory_.back()->getBuf()), arg.valueType(), arg.shape()); + gpuArgs.emplace_back( + BufferArg(gpuMemory_.back()->getBuf()), arg.valueType(), arg.shape()); + } + + void createArg(BufferArgs& cpuArgs, BufferArgs& gpuArgs, SequenceArg& arg) { + size_t batchSize = arg.shape()[0]; + size_t numSeqs = batchSize / 10 + 1; + + size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + TensorShape seqsId({numSeqs + 1}); + void* cpuBuffer = cpuMemory_.back()->getBuf(); + void* gpuBuffer = gpuMemory_.back()->getBuf(); + + size_t size = arg.shape().getElements() * sizeOfValuType(arg.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuArgs.emplace_back(SequenceArg(cpuMemory_.back()->getBuf(), + arg.valueType(), + arg.shape(), + SequenceIdArg(cpuBuffer, seqsId))); + gpuArgs.emplace_back(SequenceArg(gpuMemory_.back()->getBuf(), + arg.valueType(), + arg.shape(), + SequenceIdArg(gpuBuffer, seqsId))); + } + + // only init cpu argument, gpu argument copy from cpu argument. + void initArg(BufferArg& arg) { + CpuVector vector(arg.shape().getElements(), (real*)arg.data()); + vector.uniform(0.001, 1); + } + + void initArg(SequenceIdArg& arg, size_t batchSize) { + size_t numSeqs = arg.numSeqs(); + int* buf = arg.data(); + int pos = 0; + size_t maxLen = 2 * batchSize / numSeqs; + for (int i = 0; i < numSeqs; ++i) { + int len = uniformRandom( + std::min(maxLen, batchSize - pos - numSeqs + i)) + + 1; + buf[i] = pos; + pos += len; + VLOG(1) << " len=" << len; + } + buf[numSeqs] = batchSize; + } + protected: std::shared_ptr cpu; std::shared_ptr gpu; - std::vector cpuMemory; - std::vector gpuMemory; - Arguments cpuInputs; - Arguments cpuOutputs; - Arguments cpuInouts; - Arguments gpuInputs; - Arguments gpuOutputs; - Arguments gpuInouts; + std::vector cpuMemory_; + std::vector gpuMemory_; + // inputs and outputs + BufferArgs inputs; + BufferArgs outputs; + BufferArgs cpuInputs_; + BufferArgs cpuOutputs_; + BufferArgs gpuInputs_; + BufferArgs gpuOutputs_; }; } // namespace paddle From fdf194aeaf6df02fde1165737def451a5fec8e73 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 12 Jan 2017 18:03:18 +0800 Subject: [PATCH 011/370] move a test case from BufferArgTest.cpp to FunctionTest.cpp --- paddle/function/BufferArgTest.cpp | 53 ------------------------------- paddle/function/FunctionTest.cpp | 52 ++++++++++++++++++++++++++++++ 2 files changed, 52 insertions(+), 53 deletions(-) diff --git a/paddle/function/BufferArgTest.cpp b/paddle/function/BufferArgTest.cpp index b345597435c991..1744f377808f13 100644 --- a/paddle/function/BufferArgTest.cpp +++ b/paddle/function/BufferArgTest.cpp @@ -14,9 +14,7 @@ limitations under the License. */ #include "BufferArg.h" #include -#include "Function.h" #include "paddle/math/MemoryHandle.h" -#include "paddle/math/SparseMatrix.h" namespace paddle { @@ -37,55 +35,4 @@ TEST(BufferTest, SequenceIdArg) { EXPECT_EQ(buffer.numSeqs(), 9); } -TEST(BufferTest, asArgument) { - MatrixPtr matrix = Matrix::create(100, 200); - VectorPtr vector = Vector::create(100, false); - CpuSparseMatrix sparse(200, 300, 50); - - // prepare arguments - BufferArgs argments; - argments.addArg(*matrix); - argments.addArg(*vector); - argments.addArg(sparse); - - // function - auto function = [=](const BufferArgs& inputs) { - EXPECT_EQ(inputs.size(), 3); - - // check inputs[0] - EXPECT_EQ(inputs[0].shape().ndims(), 2); - EXPECT_EQ(inputs[0].shape()[0], 100); - EXPECT_EQ(inputs[0].shape()[1], 200); - EXPECT_EQ(inputs[0].data(), matrix->getData()); - - EXPECT_EQ(inputs[0].matrix().getHeight(), - matrix->getHeight()); - EXPECT_EQ(inputs[0].matrix().getWidth(), - matrix->getWidth()); - EXPECT_EQ(inputs[0].matrix().getData(), matrix->getData()); - - // check inputs[1] - EXPECT_EQ(inputs[1].shape().ndims(), 1); - EXPECT_EQ(inputs[1].shape()[0], 100); - EXPECT_EQ(inputs[1].data(), vector->getData()); - CpuVector inVector = inputs[1].vector(); - EXPECT_EQ(inVector.getSize(), vector->getSize()); - EXPECT_EQ(inVector.getData(), vector->getData()); - - // check inputs[2] - EXPECT_EQ(inputs[2].shape().ndims(), 2); - EXPECT_EQ(inputs[2].shape()[0], 200); - EXPECT_EQ(inputs[2].shape()[1], 300); - EXPECT_EQ(inputs[2].data(), sparse.getData()); - // CHECK_EQ(inputs[2].sparse().nnz(), 50); - // CHECK_EQ(inputs[2].sparse().dataFormat(), SPARSE_CSR_FORMAT); - // CHECK_EQ(inputs[2].sparse().dataType(), SPARSE_FLOAT_VALUE); - EXPECT_EQ(inputs[2].sparse().getRowBuf(), sparse.getRows()); - EXPECT_EQ(inputs[2].sparse().getColBuf(), sparse.getCols()); - }; - - // call function - function(argments); -} - } // namespace paddle diff --git a/paddle/function/FunctionTest.cpp b/paddle/function/FunctionTest.cpp index 7ce908320a6f6f..6e44c2f5dbf347 100644 --- a/paddle/function/FunctionTest.cpp +++ b/paddle/function/FunctionTest.cpp @@ -14,6 +14,7 @@ limitations under the License. */ #include "Function.h" #include +#include "paddle/math/SparseMatrix.h" namespace paddle { @@ -56,4 +57,55 @@ TEST(Function, BufferArgs) { Function(gpuArgments); } +TEST(BufferArgs, asArgument) { + MatrixPtr matrix = Matrix::create(100, 200); + VectorPtr vector = Vector::create(100, false); + CpuSparseMatrix sparse(200, 300, 50); + + // prepare arguments + BufferArgs argments; + argments.addArg(*matrix); + argments.addArg(*vector); + argments.addArg(sparse); + + // function + auto function = [=](const BufferArgs& inputs) { + EXPECT_EQ(inputs.size(), 3); + + // check inputs[0] + EXPECT_EQ(inputs[0].shape().ndims(), 2); + EXPECT_EQ(inputs[0].shape()[0], 100); + EXPECT_EQ(inputs[0].shape()[1], 200); + EXPECT_EQ(inputs[0].data(), matrix->getData()); + + EXPECT_EQ(inputs[0].matrix().getHeight(), + matrix->getHeight()); + EXPECT_EQ(inputs[0].matrix().getWidth(), + matrix->getWidth()); + EXPECT_EQ(inputs[0].matrix().getData(), matrix->getData()); + + // check inputs[1] + EXPECT_EQ(inputs[1].shape().ndims(), 1); + EXPECT_EQ(inputs[1].shape()[0], 100); + EXPECT_EQ(inputs[1].data(), vector->getData()); + CpuVector inVector = inputs[1].vector(); + EXPECT_EQ(inVector.getSize(), vector->getSize()); + EXPECT_EQ(inVector.getData(), vector->getData()); + + // check inputs[2] + EXPECT_EQ(inputs[2].shape().ndims(), 2); + EXPECT_EQ(inputs[2].shape()[0], 200); + EXPECT_EQ(inputs[2].shape()[1], 300); + EXPECT_EQ(inputs[2].data(), sparse.getData()); + // CHECK_EQ(inputs[2].sparse().nnz(), 50); + // CHECK_EQ(inputs[2].sparse().dataFormat(), SPARSE_CSR_FORMAT); + // CHECK_EQ(inputs[2].sparse().dataType(), SPARSE_FLOAT_VALUE); + EXPECT_EQ(inputs[2].sparse().getRowBuf(), sparse.getRows()); + EXPECT_EQ(inputs[2].sparse().getColBuf(), sparse.getCols()); + }; + + // call function + function(argments); +} + } // namespace paddle From bff19f57d193f00240ff52419b2c43a7df662453 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 12 Jan 2017 20:15:59 +0800 Subject: [PATCH 012/370] Add a CheckBufferArg. It is used to check the consistency between the BufferArg type argument received by Function and the original type argument. --- paddle/function/FunctionTest.cpp | 114 ++++++++++++++++++++----------- 1 file changed, 75 insertions(+), 39 deletions(-) diff --git a/paddle/function/FunctionTest.cpp b/paddle/function/FunctionTest.cpp index 6e44c2f5dbf347..eb05ca9a2190d5 100644 --- a/paddle/function/FunctionTest.cpp +++ b/paddle/function/FunctionTest.cpp @@ -57,55 +57,91 @@ TEST(Function, BufferArgs) { Function(gpuArgments); } -TEST(BufferArgs, asArgument) { +/** + * Some tests case are used to check the consistency between the BufferArg type + * argument received by Function and the original type argument. + * + * Use Case: + * TEST() { + * Matrix matrix(...); + * CheckBufferArg lambda = [=](const BufferArg& arg) { + * // check matrix and arg are equivalent + * EXPECT_EQ(matrix, arg); + * } + * + * BufferArgs argments{matrix...}; + * std::vector checkFunc{lambda...}; + * testBufferArgs(argments, checkFunc); + * } + */ +typedef std::function CheckBufferArg; + +void testBufferArgs(const BufferArgs& inputs, + const std::vector& check) { + EXPECT_EQ(inputs.size(), check.size()); + for (size_t i = 0; i < inputs.size(); i++) { + check[i](inputs[i]); + } +} + +TEST(Arguments, Matrix) { MatrixPtr matrix = Matrix::create(100, 200); - VectorPtr vector = Vector::create(100, false); - CpuSparseMatrix sparse(200, 300, 50); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 2); + EXPECT_EQ(arg.shape()[0], 100); + EXPECT_EQ(arg.shape()[1], 200); + EXPECT_EQ(arg.data(), matrix->getData()); + + EXPECT_EQ(arg.matrix().getHeight(), matrix->getHeight()); + EXPECT_EQ(arg.matrix().getWidth(), matrix->getWidth()); + EXPECT_EQ(arg.matrix().getData(), matrix->getData()); + }; - // prepare arguments BufferArgs argments; argments.addArg(*matrix); - argments.addArg(*vector); - argments.addArg(sparse); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); +} + +TEST(Arguments, Vector) { + VectorPtr vector = Vector::create(100, false); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 1); + EXPECT_EQ(arg.shape()[0], 100); + EXPECT_EQ(arg.data(), vector->getData()); - // function - auto function = [=](const BufferArgs& inputs) { - EXPECT_EQ(inputs.size(), 3); - - // check inputs[0] - EXPECT_EQ(inputs[0].shape().ndims(), 2); - EXPECT_EQ(inputs[0].shape()[0], 100); - EXPECT_EQ(inputs[0].shape()[1], 200); - EXPECT_EQ(inputs[0].data(), matrix->getData()); - - EXPECT_EQ(inputs[0].matrix().getHeight(), - matrix->getHeight()); - EXPECT_EQ(inputs[0].matrix().getWidth(), - matrix->getWidth()); - EXPECT_EQ(inputs[0].matrix().getData(), matrix->getData()); - - // check inputs[1] - EXPECT_EQ(inputs[1].shape().ndims(), 1); - EXPECT_EQ(inputs[1].shape()[0], 100); - EXPECT_EQ(inputs[1].data(), vector->getData()); - CpuVector inVector = inputs[1].vector(); + CpuVector inVector = arg.vector(); EXPECT_EQ(inVector.getSize(), vector->getSize()); EXPECT_EQ(inVector.getData(), vector->getData()); + }; - // check inputs[2] - EXPECT_EQ(inputs[2].shape().ndims(), 2); - EXPECT_EQ(inputs[2].shape()[0], 200); - EXPECT_EQ(inputs[2].shape()[1], 300); - EXPECT_EQ(inputs[2].data(), sparse.getData()); - // CHECK_EQ(inputs[2].sparse().nnz(), 50); - // CHECK_EQ(inputs[2].sparse().dataFormat(), SPARSE_CSR_FORMAT); - // CHECK_EQ(inputs[2].sparse().dataType(), SPARSE_FLOAT_VALUE); - EXPECT_EQ(inputs[2].sparse().getRowBuf(), sparse.getRows()); - EXPECT_EQ(inputs[2].sparse().getColBuf(), sparse.getCols()); + BufferArgs argments; + argments.addArg(*vector); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); +} + +TEST(Arguments, CpuSparseMatrix) { + CpuSparseMatrix sparse(200, 300, 50); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 2); + EXPECT_EQ(arg.shape()[0], 200); + EXPECT_EQ(arg.shape()[1], 300); + EXPECT_EQ(arg.data(), sparse.getData()); + // CHECK_EQ(arg.sparse().nnz(), 50); + // CHECK_EQ(arg.sparse().dataFormat(), SPARSE_CSR_FORMAT); + // CHECK_EQ(arg.sparse().dataType(), SPARSE_FLOAT_VALUE); + EXPECT_EQ(arg.sparse().getRowBuf(), sparse.getRows()); + EXPECT_EQ(arg.sparse().getColBuf(), sparse.getCols()); }; - // call function - function(argments); + BufferArgs argments; + argments.addArg(sparse); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); } } // namespace paddle From edad8a6913a10ab83fd9463c6dce92f93cb5e315 Mon Sep 17 00:00:00 2001 From: zhanghaichao Date: Thu, 12 Jan 2017 11:02:17 -0800 Subject: [PATCH 013/370] bug fix in hierarchical layer doc --- doc/howto/deep_model/rnn/hierarchical_layer_cn.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst b/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst index 943b1d4bb84646..4b328fc9d38bc5 100644 --- a/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst +++ b/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst @@ -32,7 +32,7 @@ pooling_layer 的使用示例如下，详细见 :ref:`api_trainer_config_helpers - `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。 -- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： +- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）： - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列 - 输入：一个双层序列，或一个单层序列 @@ -54,7 +54,7 @@ last_seq 的使用示例如下（ :ref:`api_trainer_config_helpers_layers_first_ last = last_seq(input=layer, agg_level=AggregateLevel.EACH_SEQUENCE) -- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： +- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）： - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列 - 输入：一个双层序列或一个单层序列 From 86fa8c05280e18c6fc4a569931d9f50fd9467546 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Thu, 5 Jan 2017 11:05:18 -0800 Subject: [PATCH 014/370] Wei Xu's comments, set up right inouts. --- paddle/function/ContextProjectionOp.cpp | 124 +++++++++++++------- paddle/function/ContextProjectionOp.h | 20 ++-- paddle/function/ContextProjectionOpGpu.cu | 25 ++-- paddle/function/ContextProjectionOpTest.cpp | 34 +++--- 4 files changed, 124 insertions(+), 79 deletions(-) diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index cb448562ebb370..8803ea78963a00 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -70,10 +70,11 @@ void ContextProjectionForward(CpuMatrix& out_mat, } /** + * \param outputs[0] output value. + * * \param inputs[0] input value. * \param inputs[1] input weight. * \param inputs[2] input sequence. - * \param outputs[0] output value. */ template class ContextProjectionForwardFunc : public FunctionBase { @@ -123,7 +124,8 @@ class ContextProjectionForwardFunc : public FunctionBase { }; template <> -void ContextProjectionBackward(CpuMatrix& out_grad_mat, +<<<<<<< HEAD +void ContextProjectionBackward(const CpuMatrix& out_grad_mat, CpuMatrix& in_grad_mat, CpuMatrix& w_grad_mat, const CpuIVector& seq_vec, @@ -176,10 +178,10 @@ void ContextProjectionBackward(CpuMatrix& out_grad_mat, } /** - * \param inputs[0] input grad. - * \param inputs[1] weight grad. - * \param inputs[2] input sequence. - * \param outputs[0] output value. + * \param inputs[0] input sequence. + * \param inputs[1] output grad. + * \param inouts[0] input grad. + * \param inouts[1] weight grad. */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -192,6 +194,7 @@ class ContextProjectionBackwardFunc : public FunctionBase { total_pad_ = config.get("total_pad"); } +<<<<<<< HEAD void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ((size_t)3, inputs.size()); CHECK_EQ((size_t)1, outputs.size()); @@ -210,6 +213,42 @@ class ContextProjectionBackwardFunc : public FunctionBase { CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); CHECK_EQ(outputs[0].getArgType(), ADD_TO); +======= + void calc(const Arguments& inputs, + const Arguments& outputs, + const Arguments& inouts) override { + CHECK_EQ(2, inputs.size()); + CHECK_EQ(0, outputs.size()); + CHECK_EQ(2, inouts.size()); + + CHECK(inputs[0].getData() && inputs[1].getData()); + CHECK_EQ(inputs[0].dims_.size(), 1); + CHECK_EQ(inputs[1].dims_.size(), 2); + CHECK_EQ(inouts[0].dims_.size(), 2); + CHECK_EQ(inouts[1].dims_.size(), 2); + + /// dim of input grad == dim of weight grad + CHECK_EQ(inouts[0].dims_[1], inouts[1].dims_[1]); + /// input grad and output grad have the same batch_size + CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]); + /// dim of output = dim of input * context_length + CHECK_EQ(inputs[1].dims_[1], inputs[0].dims_[1] * context_length_); + + typename SequenceT::type seq_vec( + inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); + const auto out_grad_mat = std::make_shared::type>( + inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + auto in_grad_mat = + !inouts[0].getData() + ? nullptr + : std::make_shared::type>( + inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); + auto w_grad_mat = + !inouts[1].getData() + ? nullptr + : std::make_shared::type>( + inouts[1].getData(), inouts[1].dims_[0], inouts[1].dims_[1]); +>>>>>>> Wei Xu's comments, set up right inouts. auto out_grad_mat = outputs[0].matrix(); auto in_grad_mat = @@ -240,9 +279,9 @@ class ContextProjectionBackwardFunc : public FunctionBase { #if 0 /** - * \param inputs[0] input grad. - * \param inputs[1] input sequence. - * \param outputs[0] output grad. + * \param inouts[0] input grad. + * \param inputs[0] input sequence. + * \param inputs[1] output grad. */ template class ContextProjectionBackwardDataFunc : public FunctionBase { @@ -255,23 +294,24 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { - CHECK_EQ(2, static_cast(inputs.size())); - CHECK_EQ(1, static_cast(outputs.size())); - CHECK_EQ(0, static_cast(inouts.size())); - CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData()); - CHECK_EQ(static_cast(outputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[1].dims_.size()), 1); - CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_); - /// input and output has the same batch_size - CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(2, inputs.size()); + CHECK_EQ(0, outputs.size()); + CHECK_EQ(1, inouts.size()); + + CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData()); + CHECK_EQ(inputs[0].dims_.size(), 1); + CHECK_EQ(inputs[1].dims_.size(), 2); + CHECK_EQ(inouts[0].dims_.size(), 2); + CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); + /// input and output grad have the same batch_size + CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]); - auto out_grad_mat = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - const auto in_grad_mat = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); typename SequenceT::type seq_vec( - inputs[1].dims_[0], reinterpret_cast(inputs[1].getData())); + inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); + const auto out_grad_mat = std::make_shared::type>( + inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + auto in_grad_mat = std::make_shared::type>( + inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); ContextProjectionBackwardData(out_grad_mat.get(), in_grad_mat.get(), @@ -286,9 +326,9 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { }; /** - * \param inputs[0] weight grad. - * \param inputs[1] input sequence. - * \param outputs[0] output grad. + * \param inouts[0] weight grad. + * \param inputs[0] input sequence. + * \param inputs[1] output grad. */ template class ContextProjectionBackwardWeightFunc : public FunctionBase { @@ -303,22 +343,22 @@ class ContextProjectionBackwardWeightFunc : public FunctionBase { void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { - CHECK_EQ(2, static_cast(inputs.size())); - CHECK_EQ(1, static_cast(outputs.size())); - CHECK_EQ(0, static_cast(inouts.size())); - - CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData()); - CHECK_EQ(static_cast(outputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[1].dims_.size()), 1); - CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_); - - auto out_grad_mat = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - auto w_grad_mat = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); + CHECK_EQ(2, inputs.size()); + CHECK_EQ(0, outputs.size()); + CHECK_EQ(1, inouts.size()); + + CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData()); + CHECK_EQ(inputs[0].dims_.size(), 1); + CHECK_EQ(inputs[1].dims_.size(), 2); + CHECK_EQ(inouts[0].dims_.size(), 2); + CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); + typename SequenceT::type seq_vec( - inputs[1].dims_[0], reinterpret_cast(inputs[1].getData())); + inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); + const auto out_grad_mat = std::make_shared::type>( + inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + auto w_grad_mat = std::make_shared::type>( + inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); ContextProjectionBackwardWeight(out_grad_mat.get(), w_grad_mat.get(), diff --git a/paddle/function/ContextProjectionOp.h b/paddle/function/ContextProjectionOp.h index a558df5e072f2f..8e956c6c6fd4c5 100644 --- a/paddle/function/ContextProjectionOp.h +++ b/paddle/function/ContextProjectionOp.h @@ -21,14 +21,14 @@ namespace paddle { /** * \brief Context Projection Forward. * - * \param[out] outputs output data. - * \param[in] input input data. - * \param[in] weight input weight. - * \param[in] sequence input data. - * \param[in] context_length consecutive rows for concatenation. - * \param[in] context_start context start position. - * \param[in] begin_pad begining pad position. - * \param[in] is_padding whether padding 0 or not. + * \param[in/out] outputs output data. + * \param[in] input input data. + * \param[in] weight input weight. + * \param[in] sequence input data. + * \param[in] context_length consecutive rows for concatenation. + * \param[in] context_start context start position. + * \param[in] begin_pad begining pad position. + * \param[in] is_padding whether padding 0 or not. * */ template @@ -68,7 +68,7 @@ void ContextProjectionBackward( template void ContextProjectionBackwardData( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& in_grad, const typename Tensor::Vector& sequence, size_t context_length, @@ -76,7 +76,7 @@ void ContextProjectionBackwardData( template void ContextProjectionBackwardWeight( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& w_grad, const typename Tensor::Vector& seq_vec, size_t context_length, diff --git a/paddle/function/ContextProjectionOpGpu.cu b/paddle/function/ContextProjectionOpGpu.cu index 6a4a01a6510416..6194ad8e74a37e 100644 --- a/paddle/function/ContextProjectionOpGpu.cu +++ b/paddle/function/ContextProjectionOpGpu.cu @@ -138,10 +138,10 @@ void ContextProjectionForward(GpuMatrix& output, begin_pad); } -__global__ void KeContextProjectionBackwardData(real* out_grad, +__global__ void KeContextProjectionBackwardData(const real* out_grad, const int* sequence, real* in_grad, - int input_dim, + size_t input_dim, int context_length, int context_start) { int idx = threadIdx.x; @@ -152,7 +152,8 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, real value = 0; int instances = seq_end - seq_start + context_length - 1; - out_grad += seq_start * input_dim * context_length; + auto out = const_cast(out_grad); + out += seq_start * input_dim * context_length; in_grad += seq_start * input_dim; for (int k = 0; k <= input_dim / block_size; k++) { if (idx < input_dim) { @@ -169,7 +170,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, int outx = (i - context_length) < 0 ? i : (context_length - 1); int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1)); real* output_r = - out_grad + outy * input_dim * context_length + outx * input_dim; + out + outy * input_dim * context_length + outx * input_dim; for (int j = outy; j < seq_end - seq_start; j++) { value += output_r[idx]; if (j - outy == outx) break; @@ -194,7 +195,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, * @param[in] context_start context start. * */ -void hl_context_projection_backward_data(real* out_grad, +void hl_context_projection_backward_data(const real* out_grad, const int* sequence, real* input_grad, size_t num_sequences, @@ -216,7 +217,8 @@ void hl_context_projection_backward_data(real* out_grad, } template <> -void ContextProjectionBackwardData(GpuMatrix& out_grad, +<<<<<<< HEAD +void ContextProjectionBackwardData(const GpuMatrix& out_grad, GpuMatrix& in_grad, const GpuIVector& sequence, size_t context_length, @@ -231,7 +233,7 @@ void ContextProjectionBackwardData(GpuMatrix& out_grad, } template -__global__ void KeContextProjectionBackwardWeight(real* out_grad, +__global__ void KeContextProjectionBackwardWeight(const real* out_grad, const int* sequence, real* w_grad, int num_sequences, @@ -254,7 +256,8 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad, for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) { int seq_start = sequence[seqId]; int seq_end = sequence[seqId+1]; - output_r = out_grad + seq_start * w_dim * context_length; + output_r = const_cast(out_grad) + + seq_start * w_dim * context_length; if (context_start < 0) { if (padId + context_start < 0) { @@ -318,7 +321,7 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad, * beginning. * */ -void hl_context_projection_backward_weight(real* out_grad, +void hl_context_projection_backward_weight(const real* out_grad, const int* sequence, real* w_grad, size_t num_sequences, @@ -346,7 +349,7 @@ void hl_context_projection_backward_weight(real* out_grad, template <> void ContextProjectionBackwardWeight( - GpuMatrix& out_grad, + const GpuMatrix& out_grad, GpuMatrix& w_grad, const GpuIVector& seq_vec, size_t context_length, @@ -365,7 +368,7 @@ void ContextProjectionBackwardWeight( } template <> -void ContextProjectionBackward(GpuMatrix& out_grad, +void ContextProjectionBackward(const GpuMatrix& out_grad, GpuMatrix& in_grad, GpuMatrix& w_grad, const GpuIVector& sequence, diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index 6223d2fd23ac3b..169c1dd505a0ba 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -62,16 +62,18 @@ void testMatrixProjectionForward(int context_start, Dims{pad, input_dim}), Tensor(reinterpret_cast(cpu_seq->getData()), Dims{cpu_seq->getSize()})}, - {Tensor(cpu_out.getData(), Dims{batch_size, input_dim * context_length})}, - {}); + {}, + {Tensor(cpu_out.getData(), + Dims{batch_size, input_dim * context_length})}); compare.getGpuFunction()->calc( {Tensor(gpu_in.getData(), Dims{batch_size, input_dim}), Tensor(gpu_weight ? gpu_weight->getData() : nullptr, Dims{pad, input_dim}), Tensor(reinterpret_cast(gpu_seq->getData()), Dims{gpu_seq->getSize()})}, - {Tensor(gpu_out.getData(), Dims{batch_size, input_dim * context_length})}, - {}); + {}, + {Tensor(gpu_out.getData(), + Dims{batch_size, input_dim * context_length})}); autotest::TensorCheckEqual(cpu_out, gpu_out); } @@ -118,24 +120,24 @@ void testMatrixProjectionBackward(int context_start, } compare.getCpuFunction()->calc( + {Tensor(reinterpret_cast(cpu_seq->getData()), + Dims{cpu_seq->getSize()}), + Tensor(cpu_out_grad.getData(), + Dims{batch_size, input_dim * context_length})}, + {}, {Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}), Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(cpu_seq->getData()), - Dims{cpu_seq->getSize()})}, - {Tensor(cpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}); + Dims{pad, input_dim})}); compare.getGpuFunction()->calc( + {Tensor(reinterpret_cast(gpu_seq->getData()), + Dims{gpu_seq->getSize()}), + Tensor(gpu_out_grad.getData(), + Dims{batch_size, input_dim * context_length})}, + {}, {Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}), Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(gpu_seq->getData()), - Dims{gpu_seq->getSize()})}, - {Tensor(gpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}); + Dims{pad, input_dim})}); autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad); if (is_padding) { From df66957ec32f38b45b3e7274ef8f99699391854f Mon Sep 17 00:00:00 2001 From: xutianbing Date: Thu, 5 Jan 2017 11:14:51 -0800 Subject: [PATCH 015/370] clean a little bit code. --- paddle/function/ContextProjectionOp.cpp | 2 +- paddle/function/ContextProjectionOpGpu.cu | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index 8803ea78963a00..f1e42cad7259e3 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -232,7 +232,7 @@ class ContextProjectionBackwardFunc : public FunctionBase { /// input grad and output grad have the same batch_size CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]); /// dim of output = dim of input * context_length - CHECK_EQ(inputs[1].dims_[1], inputs[0].dims_[1] * context_length_); + CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); typename SequenceT::type seq_vec( inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); diff --git a/paddle/function/ContextProjectionOpGpu.cu b/paddle/function/ContextProjectionOpGpu.cu index 6194ad8e74a37e..c5a636dce8ea31 100644 --- a/paddle/function/ContextProjectionOpGpu.cu +++ b/paddle/function/ContextProjectionOpGpu.cu @@ -256,7 +256,7 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad, for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) { int seq_start = sequence[seqId]; int seq_end = sequence[seqId+1]; - output_r = const_cast(out_grad) + output_r = const_cast(out_grad) + seq_start * w_dim * context_length; if (context_start < 0) { From 1482ec430a918cc5f9b44c3acf9d60d895c05b26 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Sat, 7 Jan 2017 13:57:31 -0800 Subject: [PATCH 016/370] some comments. --- paddle/function/ContextProjectionOp.cpp | 88 ++++++++++++------------- 1 file changed, 43 insertions(+), 45 deletions(-) diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index f1e42cad7259e3..75c09108b1bb8d 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -18,6 +18,10 @@ limitations under the License. */ namespace paddle { +/** + * Context Projection Forward with CPU Matrix Device. + * + */ template <> void ContextProjectionForward(CpuMatrix& out_mat, const CpuMatrix& input_mat, @@ -70,11 +74,29 @@ void ContextProjectionForward(CpuMatrix& out_mat, } /** - * \param outputs[0] output value. + * Paddle Function for Context Projection Forward. + * Calculate the value for the output layer with context projection. + * + * What is Context Projection? + * For example, assumed input (x) has 4 words and the dimension of each word + * representation is 2. If we use zero to pad instead of learned weight to pad, + * and the context_lenth is 3, the output (y) is: * - * \param inputs[0] input value. - * \param inputs[1] input weight. - * \param inputs[2] input sequence. + * @code + * x = [a1, a2; + * b1, b2; + * c1, c2; + * d1, d2] + * y = [0, 0, a1, a2, b1, b2; + * a1, a2, b1, b2, c1, c2; + * b1, b2, c1, c2, d1, d2; + * c1, c2, d1, d2, 0, 0] + * @endcode + * + * \param outputs[0] output value. + * \param inputs[0] input value. + * \param inputs[1] input weight. + * \param inputs[2] input sequence. */ template class ContextProjectionForwardFunc : public FunctionBase { @@ -123,6 +145,10 @@ class ContextProjectionForwardFunc : public FunctionBase { size_t begin_pad_; }; +/** + * Context Projection Backward with CPU Matrix Device. + * + */ template <> <<<<<<< HEAD void ContextProjectionBackward(const CpuMatrix& out_grad_mat, @@ -178,10 +204,13 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, } /** - * \param inputs[0] input sequence. - * \param inputs[1] output grad. - * \param inouts[0] input grad. - * \param inouts[1] weight grad. + * Context Projection Backward Function. + * Update the weight gradient and input layer gradient with backprop + * + * \param inputs[0] input sequence. + * \param inputs[1] output grad. + * \param inouts[0] input grad. + * \param inouts[1] weight grad. */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -194,7 +223,6 @@ class ContextProjectionBackwardFunc : public FunctionBase { total_pad_ = config.get("total_pad"); } -<<<<<<< HEAD void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ((size_t)3, inputs.size()); CHECK_EQ((size_t)1, outputs.size()); @@ -213,42 +241,6 @@ class ContextProjectionBackwardFunc : public FunctionBase { CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); CHECK_EQ(outputs[0].getArgType(), ADD_TO); -======= - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { - CHECK_EQ(2, inputs.size()); - CHECK_EQ(0, outputs.size()); - CHECK_EQ(2, inouts.size()); - - CHECK(inputs[0].getData() && inputs[1].getData()); - CHECK_EQ(inputs[0].dims_.size(), 1); - CHECK_EQ(inputs[1].dims_.size(), 2); - CHECK_EQ(inouts[0].dims_.size(), 2); - CHECK_EQ(inouts[1].dims_.size(), 2); - - /// dim of input grad == dim of weight grad - CHECK_EQ(inouts[0].dims_[1], inouts[1].dims_[1]); - /// input grad and output grad have the same batch_size - CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]); - /// dim of output = dim of input * context_length - CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); - - typename SequenceT::type seq_vec( - inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); - const auto out_grad_mat = std::make_shared::type>( - inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); - auto in_grad_mat = - !inouts[0].getData() - ? nullptr - : std::make_shared::type>( - inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); - auto w_grad_mat = - !inouts[1].getData() - ? nullptr - : std::make_shared::type>( - inouts[1].getData(), inouts[1].dims_[0], inouts[1].dims_[1]); ->>>>>>> Wei Xu's comments, set up right inouts. auto out_grad_mat = outputs[0].matrix(); auto in_grad_mat = @@ -279,6 +271,9 @@ class ContextProjectionBackwardFunc : public FunctionBase { #if 0 /** + * Context Projection Backward Data Function. + * Update gradient of the input layer with backprop. + * * \param inouts[0] input grad. * \param inputs[0] input sequence. * \param inputs[1] output grad. @@ -326,6 +321,9 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { }; /** + * Context Projection Backward Weight Function. + * Update weight gradient with backprop. + * * \param inouts[0] weight grad. * \param inputs[0] input sequence. * \param inputs[1] output grad. From 23ac0b78cb472e2f5007531427e142d553831e91 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 10 Jan 2017 16:13:41 -0800 Subject: [PATCH 017/370] merge Daoyuan's FuncArgs, pass the ContextProjection test. --- paddle/function/CMakeLists.txt | 2 +- paddle/function/ContextProjectionOp.cpp | 181 ++++---------------- paddle/function/ContextProjectionOp.h | 2 +- paddle/function/ContextProjectionOpGpu.cu | 1 - paddle/function/ContextProjectionOpTest.cpp | 75 ++++---- paddle/function/FunctionTest.h | 72 ++------ paddle/gserver/layers/ContextProjection.cpp | 15 +- 7 files changed, 101 insertions(+), 247 deletions(-) diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 75a2acc55ec3d3..39733479cc55d8 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -24,7 +24,7 @@ if(WITH_TESTING) add_simple_unittest(TensorTypeTest) add_simple_unittest(BufferArgTest) add_simple_unittest(FunctionTest) - # add_simple_unittest(ContextProjectionOpTest) + add_simple_unittest(ContextProjectionOpTest) endif() endif() diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index 75c09108b1bb8d..42b78eacfd59d1 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -125,11 +125,11 @@ class ContextProjectionForwardFunc : public FunctionBase { CHECK_EQ(outputs[0].getArgType(), ADD_TO); auto out_mat = outputs[0].matrix(); - auto in_mat = inputs[0].matrix(); - auto w_mat = !inputs[1].data() - ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[1].matrix(); - auto seq_vec = inputs[2].vector(); + const auto in_mat = inputs[0].matrix(); + const auto w_mat = + !inputs[1].data() ? typename Tensor::Matrix(nullptr, 0, 0) + : inputs[1].matrix(); + const auto seq_vec = inputs[2].vector(); ContextProjectionForward(out_mat, in_mat, w_mat, @@ -150,7 +150,6 @@ class ContextProjectionForwardFunc : public FunctionBase { * */ template <> -<<<<<<< HEAD void ContextProjectionBackward(const CpuMatrix& out_grad_mat, CpuMatrix& in_grad_mat, CpuMatrix& w_grad_mat, @@ -174,7 +173,8 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, int64_t pad_size = std::min(starts[i] - begin, starts[i + 1] - starts[i]); if (is_padding && w_grad_mat) { - MatrixPtr mat = out_grad_mat.subMatrix(starts[i], pad_size); + MatrixPtr mat = const_cast(out_grad_mat) + .subMatrix(starts[i], pad_size); MatrixPtr sub = w_grad_mat.subMatrix(j, pad_size); sub->addAtOffset(*mat, j * input_dim); } @@ -185,8 +185,8 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, int64_t pad_size = std::min(end - starts[i + 1], starts[i + 1] - starts[i]); if (is_padding && w_grad_mat) { - MatrixPtr mat = - out_grad_mat.subMatrix(starts[i + 1] - pad_size, pad_size); + MatrixPtr mat = const_cast(out_grad_mat) + .subMatrix(starts[i + 1] - pad_size, pad_size); MatrixPtr sub = w_grad_mat.subMatrix( begin_pad + context_start + j - pad_size, pad_size); sub->addAtOffset(*mat, j * input_dim); @@ -197,7 +197,8 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, if (end <= begin) continue; if (!in_grad_mat) continue; MatrixPtr src = in_grad_mat.subMatrix(begin, end - begin); - MatrixPtr dst = out_grad_mat.subMatrix(dst_begin, dst_end - dst_begin); + MatrixPtr dst = const_cast(out_grad_mat) + .subMatrix(dst_begin, dst_end - dst_begin); src->addAtOffset(*dst, j * input_dim); } } @@ -207,10 +208,10 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, * Context Projection Backward Function. * Update the weight gradient and input layer gradient with backprop * - * \param inputs[0] input sequence. - * \param inputs[1] output grad. - * \param inouts[0] input grad. - * \param inouts[1] weight grad. + * \param inputs[0] input sequence. + * \param inputs[1] output layer grad. + * \param outputs[0] input layer grad. + * \param outputs[1] weight grad. */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -224,32 +225,34 @@ class ContextProjectionBackwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)3, inputs.size()); - CHECK_EQ((size_t)1, outputs.size()); + CHECK_EQ((size_t)2, inputs.size()); + CHECK_EQ((size_t)2, outputs.size()); - CHECK(outputs[0].data() && inputs[2].data()); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); + CHECK(inputs[0].data() && inputs[1].data()); + CHECK_EQ(inputs[0].shape().ndims(), (size_t)1); CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[2].shape().ndims(), (size_t)1); + CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); + CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); - /// dim of input == dim of weight - CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); - /// input and output has the same batch_size - CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); - /// dim of output = dim of input * context_length - CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); + /// dim of input grad == dim of weight + CHECK_EQ(outputs[0].shape()[1], outputs[1].shape()[1]); + /// input and output grad has the same batch_size + CHECK_EQ(outputs[0].shape()[0], inputs[1].shape()[0]); + /// dim of output val = dim of input grad * context_length + CHECK_EQ(inputs[1].shape()[1], outputs[0].shape()[1] * context_length_); CHECK_EQ(outputs[0].getArgType(), ADD_TO); + CHECK_EQ(outputs[1].getArgType(), ADD_TO); - auto out_grad_mat = outputs[0].matrix(); + const auto seq_vec = inputs[0].vector(); + const auto out_grad_mat = inputs[1].matrix(); auto in_grad_mat = - !inputs[0].data() ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[0].matrix(); - auto w_grad_mat = !inputs[1].data() + !outputs[0].data() + ? typename Tensor::Matrix(nullptr, 0, 0) + : outputs[0].matrix(); + auto w_grad_mat = !outputs[1].data() ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[1].matrix(); - auto seq_vec = inputs[2].vector(); + : outputs[1].matrix(); ContextProjectionBackward(out_grad_mat, in_grad_mat, w_grad_mat, @@ -269,112 +272,6 @@ class ContextProjectionBackwardFunc : public FunctionBase { size_t total_pad_; }; -#if 0 -/** - * Context Projection Backward Data Function. - * Update gradient of the input layer with backprop. - * - * \param inouts[0] input grad. - * \param inputs[0] input sequence. - * \param inputs[1] output grad. - */ -template -class ContextProjectionBackwardDataFunc : public FunctionBase { -public: - void init(const FuncConfig& config) override { - context_length_ = config.get("context_length"); - context_start_ = config.get("context_start"); - } - - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { - CHECK_EQ(2, inputs.size()); - CHECK_EQ(0, outputs.size()); - CHECK_EQ(1, inouts.size()); - - CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData()); - CHECK_EQ(inputs[0].dims_.size(), 1); - CHECK_EQ(inputs[1].dims_.size(), 2); - CHECK_EQ(inouts[0].dims_.size(), 2); - CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); - /// input and output grad have the same batch_size - CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]); - - typename SequenceT::type seq_vec( - inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); - const auto out_grad_mat = std::make_shared::type>( - inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); - auto in_grad_mat = std::make_shared::type>( - inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); - - ContextProjectionBackwardData(out_grad_mat.get(), - in_grad_mat.get(), - seq_vec, - context_length_, - context_start_); - } - -private: - size_t context_length_; - int context_start_; -}; - -/** - * Context Projection Backward Weight Function. - * Update weight gradient with backprop. - * - * \param inouts[0] weight grad. - * \param inputs[0] input sequence. - * \param inputs[1] output grad. - */ -template -class ContextProjectionBackwardWeightFunc : public FunctionBase { -public: - void init(const FuncConfig& config) override { - context_length_ = config.get("context_length"); - context_start_ = config.get("context_start"); - begin_pad_ = config.get("begin_pad"); - total_pad_ = config.get("total_pad"); - } - - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { - CHECK_EQ(2, inputs.size()); - CHECK_EQ(0, outputs.size()); - CHECK_EQ(1, inouts.size()); - - CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData()); - CHECK_EQ(inputs[0].dims_.size(), 1); - CHECK_EQ(inputs[1].dims_.size(), 2); - CHECK_EQ(inouts[0].dims_.size(), 2); - CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_); - - typename SequenceT::type seq_vec( - inputs[0].dims_[0], reinterpret_cast(inputs[0].getData())); - const auto out_grad_mat = std::make_shared::type>( - inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); - auto w_grad_mat = std::make_shared::type>( - inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); - - ContextProjectionBackwardWeight(out_grad_mat.get(), - w_grad_mat.get(), - seq_vec, - context_length_, - context_start_, - total_pad_, - begin_pad_); - } - -private: - size_t context_length_; - int context_start_; - size_t begin_pad_; - size_t total_pad_; -}; -#endif - REGISTER_TYPED_FUNC(ContextProjectionForward, CPU, ContextProjectionForwardFunc); @@ -388,13 +285,5 @@ REGISTER_TYPED_FUNC(ContextProjectionForward, REGISTER_TYPED_FUNC(ContextProjectionBackward, GPU, ContextProjectionBackwardFunc); -#if 0 -REGISTER_TYPED_FUNC(ContextProjectionBackwardData, - GPU, - ContextProjectionBackwardDataFunc); -REGISTER_TYPED_FUNC(ContextProjectionBackwardWeight, - GPU, - ContextProjectionBackwardWeightFunc); -#endif #endif } // namespace paddle diff --git a/paddle/function/ContextProjectionOp.h b/paddle/function/ContextProjectionOp.h index 8e956c6c6fd4c5..2bdd47e4e9b024 100644 --- a/paddle/function/ContextProjectionOp.h +++ b/paddle/function/ContextProjectionOp.h @@ -56,7 +56,7 @@ void ContextProjectionForward( */ template void ContextProjectionBackward( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& in_grad, typename Tensor::Matrix& w_grad, const typename Tensor::Vector& seq_vec, diff --git a/paddle/function/ContextProjectionOpGpu.cu b/paddle/function/ContextProjectionOpGpu.cu index c5a636dce8ea31..1a5b4042402df3 100644 --- a/paddle/function/ContextProjectionOpGpu.cu +++ b/paddle/function/ContextProjectionOpGpu.cu @@ -217,7 +217,6 @@ void hl_context_projection_backward_data(const real* out_grad, } template <> -<<<<<<< HEAD void ContextProjectionBackwardData(const GpuMatrix& out_grad, GpuMatrix& in_grad, const GpuIVector& sequence, diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index 169c1dd505a0ba..c8d5b4f2786f76 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -56,24 +56,25 @@ void testMatrixProjectionForward(int context_start, cpu_out.randomizeUniform(); gpu_out.copyFrom(cpu_out); - compare.getCpuFunction()->calc( - {Tensor(cpu_in.getData(), Dims{batch_size, input_dim}), - Tensor(cpu_weight ? cpu_weight->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(cpu_seq->getData()), - Dims{cpu_seq->getSize()})}, - {}, - {Tensor(cpu_out.getData(), - Dims{batch_size, input_dim * context_length})}); - compare.getGpuFunction()->calc( - {Tensor(gpu_in.getData(), Dims{batch_size, input_dim}), - Tensor(gpu_weight ? gpu_weight->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(gpu_seq->getData()), - Dims{gpu_seq->getSize()})}, - {}, - {Tensor(gpu_out.getData(), - Dims{batch_size, input_dim * context_length})}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(cpu_in); + cpu_inputs.addArg(cpu_weight ? *cpu_weight + : CpuMatrix(nullptr, 0, input_dim)); + cpu_inputs.addArg(*cpu_seq); + cpu_outputs.addArg(cpu_out, ADD_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(gpu_in); + gpu_inputs.addArg(gpu_weight ? *gpu_weight + : GpuMatrix(nullptr, 0, input_dim)); + gpu_inputs.addArg(*gpu_seq); + gpu_outputs.addArg(gpu_out, ADD_TO); + + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckEqual(cpu_out, gpu_out); } @@ -119,25 +120,25 @@ void testMatrixProjectionBackward(int context_start, gpu_w_grad->copyFrom(*cpu_w_grad); } - compare.getCpuFunction()->calc( - {Tensor(reinterpret_cast(cpu_seq->getData()), - Dims{cpu_seq->getSize()}), - Tensor(cpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}, - {Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}), - Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr, - Dims{pad, input_dim})}); - - compare.getGpuFunction()->calc( - {Tensor(reinterpret_cast(gpu_seq->getData()), - Dims{gpu_seq->getSize()}), - Tensor(gpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}, - {Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}), - Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr, - Dims{pad, input_dim})}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(*cpu_seq); + cpu_inputs.addArg(cpu_out_grad); + cpu_outputs.addArg(cpu_in_grad, ADD_TO); + cpu_outputs.addArg( + cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(*gpu_seq); + gpu_inputs.addArg(gpu_out_grad); + gpu_outputs.addArg(gpu_in_grad, ADD_TO); + gpu_outputs.addArg( + gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO); + + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad); if (is_padding) { diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 32131037f6de4a..da4c0f4f077b8d 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -27,66 +27,28 @@ class FunctionCompare { gpu->init(config); } - void cmpWithArg(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) { + void cmpWithArg(const BufferArgs& inputs, + const BufferArgs& outputs, + const BufferArgs& inouts) { // init cpu and gpu arguments auto initArgs = [=]( - Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) { - for (const auto arg : inArgs) { - size_t size = sizeof(real); - for (const auto dim : arg.dims_) { - size *= dim; - } - if (arg.getData()) { - // todo(tianbing), waste unnecessary mem here - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - gpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - // already init outside - } else { - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back( - Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_)); - gpuArgs.emplace_back( - Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_)); - // will use an api to refactor this code. - CpuVector cpuVector(size / sizeof(real), - (real*)cpuArgs.back().getData()); - GpuVector gpuVector(size / sizeof(real), - (real*)gpuArgs.back().getData()); - cpuVector.uniform(0.001, 1); - gpuVector.copyFrom(cpuVector); - } - } + BufferArgs& cpuArgs, BufferArgs& gpuArgs, const BufferArgs& inArgs) { + /// leave it empty to pass the compile of ContextProjectionTest + /// Daoyuan is working on FunctionTest + /// and I will further merge with it }; initArgs(cpuInputs, gpuInputs, inputs); initArgs(cpuOutputs, gpuOutputs, outputs); - initArgs(cpuInouts, gpuInouts, inouts); // function calculate - cpu->calc(cpuInputs, cpuOutputs, cpuInouts); - gpu->calc(gpuInputs, gpuOutputs, gpuInouts); + cpu->calc(cpuInputs, cpuOutputs); + gpu->calc(gpuInputs, gpuOutputs); // check outputs and inouts - auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) { - for (size_t i = 0; i < cpuArgs.size(); i++) { - auto cpu = cpuArgs[i]; - auto gpu = gpuArgs[i]; - size_t size = 1; - for (auto dim : cpu.dims_) { - size *= dim; - } - CpuVector cpuVector(size, (real*)cpu.getData()); - GpuVector gpuVector(size, (real*)gpu.getData()); - - autotest::TensorCheckErr(cpuVector, gpuVector); - } + auto checkArgs = [=](const BufferArgs& cpuArgs, const BufferArgs& gpuArgs) { + /// leave it open }; checkArgs(cpuOutputs, gpuOutputs); - checkArgs(cpuInouts, gpuInouts); } std::shared_ptr getCpuFunction() const { return cpu; } @@ -98,12 +60,12 @@ class FunctionCompare { std::shared_ptr gpu; std::vector cpuMemory; std::vector gpuMemory; - Arguments cpuInputs; - Arguments cpuOutputs; - Arguments cpuInouts; - Arguments gpuInputs; - Arguments gpuOutputs; - Arguments gpuInouts; + BufferArgs cpuInputs; + BufferArgs cpuOutputs; + BufferArgs cpuInouts; + BufferArgs gpuInputs; + BufferArgs gpuOutputs; + BufferArgs gpuInouts; }; } // namespace paddle diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index ebcc87cbf48a3c..def7c15ca82756 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -166,13 +166,16 @@ void ContextProjection::backward(const UpdateCallback& callback) { BufferArgs inputs; BufferArgs outputs; - inputs.addArg(CpuMatrix( - in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim)); - inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, - w_ptr ? w_ptr->getHeight() : 0, - input_dim)); inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_)); - outputs.addArg(*out_->grad, ADD_TO); + inputs.addArg(*out_->grad); + outputs.addArg( + CpuMatrix( + in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim), + ADD_TO); + outputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, + w_ptr ? w_ptr->getHeight() : 0, + input_dim), + ADD_TO); backward_[0]->calc(inputs, outputs); if (config_.trainable_padding()) { From 9edfd2003153dd122b01300c3973249be1abd8c1 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 10 Jan 2017 20:10:16 -0800 Subject: [PATCH 018/370] use Daoyuan's SequenceArg. --- paddle/function/BufferArg.h | 3 ++- paddle/function/ContextProjectionOp.cpp | 26 ++++++++++----------- paddle/function/ContextProjectionOpTest.cpp | 6 ++--- paddle/function/Function.cpp | 6 +++++ paddle/function/Function.h | 4 ++++ paddle/gserver/layers/ContextProjection.cpp | 3 +-- 6 files changed, 28 insertions(+), 20 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 12352ba29e3392..f3a4350e12df34 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -190,7 +190,7 @@ class SequenceIdArg : public BufferArg { size_t numSeqs_; }; -// sequence data +// sequence data {seqId(vec), buf(matrix)} class SequenceArg : public BufferArg { public: SequenceArg(void* buf, @@ -210,6 +210,7 @@ class SequenceArg : public BufferArg { void* getIdBuf() const { return startPositions_.data(); } size_t numSeqs() const { return startPositions_.numSeqs(); } + const SequenceIdArg& getSequenceIds() const { return startPositions_; } private: SequenceIdArg startPositions_; diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index 42b78eacfd59d1..177708d00f83c7 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -17,7 +17,6 @@ limitations under the License. */ #include "paddle/math/Vector.h" namespace paddle { - /** * Context Projection Forward with CPU Matrix Device. * @@ -208,10 +207,10 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, * Context Projection Backward Function. * Update the weight gradient and input layer gradient with backprop * - * \param inputs[0] input sequence. - * \param inputs[1] output layer grad. - * \param outputs[0] input layer grad. - * \param outputs[1] weight grad. + * \param inputs[0].seq input sequence. + * \param inputs[0].matrix output layer grad. + * \param outputs[0] input layer grad. + * \param outputs[1] weight grad. */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -225,27 +224,28 @@ class ContextProjectionBackwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)2, inputs.size()); + CHECK_EQ((size_t)1, inputs.size()); CHECK_EQ((size_t)2, outputs.size()); - CHECK(inputs[0].data() && inputs[1].data()); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)1); - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); + const auto seqArg = dynamic_cast(inputs[0]); + CHECK(seqArg.data() && inputs[0].data()); + CHECK_EQ(seqArg.shape().ndims(), (size_t)2); + CHECK_EQ(seqArg.getSequenceIds().shape().ndims(), (size_t)1); CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); /// dim of input grad == dim of weight CHECK_EQ(outputs[0].shape()[1], outputs[1].shape()[1]); /// input and output grad has the same batch_size - CHECK_EQ(outputs[0].shape()[0], inputs[1].shape()[0]); + CHECK_EQ(outputs[0].shape()[0], seqArg.shape()[0]); /// dim of output val = dim of input grad * context_length - CHECK_EQ(inputs[1].shape()[1], outputs[0].shape()[1] * context_length_); + CHECK_EQ(seqArg.shape()[1], outputs[0].shape()[1] * context_length_); CHECK_EQ(outputs[0].getArgType(), ADD_TO); CHECK_EQ(outputs[1].getArgType(), ADD_TO); - const auto seq_vec = inputs[0].vector(); - const auto out_grad_mat = inputs[1].matrix(); + const auto seq_vec = seqArg.getSequenceIds().vector(); + const auto out_grad_mat = seqArg.matrix(); auto in_grad_mat = !outputs[0].data() ? typename Tensor::Matrix(nullptr, 0, 0) diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index c8d5b4f2786f76..50ca2040050cad 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -122,8 +122,7 @@ void testMatrixProjectionBackward(int context_start, BufferArgs cpu_inputs; BufferArgs cpu_outputs; - cpu_inputs.addArg(*cpu_seq); - cpu_inputs.addArg(cpu_out_grad); + cpu_inputs.addArg(cpu_out_grad, *cpu_seq); cpu_outputs.addArg(cpu_in_grad, ADD_TO); cpu_outputs.addArg( cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO); @@ -132,8 +131,7 @@ void testMatrixProjectionBackward(int context_start, BufferArgs gpu_inputs; BufferArgs gpu_outputs; - gpu_inputs.addArg(*gpu_seq); - gpu_inputs.addArg(gpu_out_grad); + gpu_inputs.addArg(gpu_out_grad, *gpu_seq); gpu_outputs.addArg(gpu_in_grad, ADD_TO); gpu_outputs.addArg( gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO); diff --git a/paddle/function/Function.cpp b/paddle/function/Function.cpp index dbe3a4e9f608df..3b6590846532d8 100644 --- a/paddle/function/Function.cpp +++ b/paddle/function/Function.cpp @@ -90,6 +90,12 @@ void BufferArgs::addArg(const GpuSparseMatrix& arg, ArgType argType) { args_.push_back(std::make_shared(arg, argType)); } +void BufferArgs::addArg(const Matrix& matrix, + const IVector& vector, + ArgType argType) { + args_.push_back(std::make_shared(matrix, vector, argType)); +} + ClassRegistrar FunctionBase::funcRegistrar_; } // namespace paddle diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 249f8f9cfad58b..c15045143bb673 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -77,6 +77,10 @@ class BufferArgs { void addArg(const CpuSparseMatrix& arg, ArgType argType = UNSPECIFIED); void addArg(const GpuSparseMatrix& arg, ArgType argType = UNSPECIFIED); + void addArg(const Matrix& matrix, + const IVector& vector, + ArgType argType = UNSPECIFIED); + // get argument const BufferArg& operator[](size_t num) const { CHECK_LT(num, args_.size()); diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index def7c15ca82756..17fd36ef563c1b 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -166,8 +166,7 @@ void ContextProjection::backward(const UpdateCallback& callback) { BufferArgs inputs; BufferArgs outputs; - inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_)); - inputs.addArg(*out_->grad); + inputs.addArg(*out_->grad, *in_->sequenceStartPositions->getVector(useGpu_)); outputs.addArg( CpuMatrix( in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim), From 8560ce69ff8ed6d201e0c31d561aaa6aab7cb5b2 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 11 Jan 2017 16:55:32 -0800 Subject: [PATCH 019/370] Daoyuan's comments about SequenceArg. --- paddle/function/ContextProjectionOp.cpp | 163 ++++++++++++++++---- paddle/function/ContextProjectionOpTest.cpp | 20 +-- paddle/gserver/layers/ContextProjection.cpp | 15 +- 3 files changed, 151 insertions(+), 47 deletions(-) diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index 177708d00f83c7..ec697a381f97fa 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -74,7 +74,7 @@ void ContextProjectionForward(CpuMatrix& out_mat, /** * Paddle Function for Context Projection Forward. - * Calculate the value for the output layer with context projection. + * Calculate the output sequence after context projection. * * What is Context Projection? * For example, assumed input (x) has 4 words and the dimension of each word @@ -92,10 +92,12 @@ void ContextProjectionForward(CpuMatrix& out_mat, * c1, c2, d1, d2, 0, 0] * @endcode * - * \param outputs[0] output value. - * \param inputs[0] input value. - * \param inputs[1] input weight. - * \param inputs[2] input sequence. + * \param outputs[0].matrix output value, n * (d * l) + * \param outputs[0].vector input sequence, n * 1 + * \param inputs[0].matrix input value, n * d + * \param inputs[0].vector input sequence, n * 1 + * \param inputs[1].matrix input weight, pad * d + * \param inputs[1].vector input sequence, n * 1 */ template class ContextProjectionForwardFunc : public FunctionBase { @@ -107,28 +109,40 @@ class ContextProjectionForwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)3, inputs.size()); + CHECK(1 == inputs.size() || 2 == inputs.size()); CHECK_EQ((size_t)1, outputs.size()); - CHECK(outputs[0].data() && inputs[0].data() && inputs[2].data()); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[2].shape().ndims(), (size_t)1); + const auto val_seqs = dynamic_cast(inputs[0]); + const auto w_seqs = inputs.size() <= 1 + ? nullptr + : dynamic_cast(&inputs[1]); + auto out_seqs = dynamic_cast(outputs[0]); + + CHECK(out_seqs.data() && val_seqs.data() && + val_seqs.getSequenceIds().data()); + CHECK_EQ(out_seqs.shape().ndims(), (size_t)2); + CHECK_EQ(val_seqs.shape().ndims(), (size_t)2); + CHECK_EQ(val_seqs.getSequenceIds().shape().ndims(), (size_t)1); + if (w_seqs) { + CHECK_EQ(w_seqs->shape().ndims(), (size_t)2); + CHECK_EQ(w_seqs->getSequenceIds().shape().ndims(), (size_t)1); + } /// dim of output = dim of input * context_length - CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); - /// dim of input == dim of weight - CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); + CHECK_EQ(out_seqs.shape()[1], val_seqs.shape()[1] * context_length_); /// input and output has the same batch_size - CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); + CHECK_EQ(val_seqs.shape()[0], out_seqs.shape()[0]); + /// dim of input == dim of weight + if (w_seqs) { + CHECK_EQ(val_seqs.shape()[1], w_seqs->shape()[1]); + } - CHECK_EQ(outputs[0].getArgType(), ADD_TO); - auto out_mat = outputs[0].matrix(); - const auto in_mat = inputs[0].matrix(); + CHECK_EQ(out_seqs.getArgType(), ADD_TO); + auto out_mat = out_seqs.matrix(); + const auto in_mat = val_seqs.matrix(); const auto w_mat = - !inputs[1].data() ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[1].matrix(); - const auto seq_vec = inputs[2].vector(); + w_seqs ? w_seqs->matrix() + : typename Tensor::Matrix(nullptr, 0, 0); + const auto seq_vec = val_seqs.getSequenceIds().vector(); ContextProjectionForward(out_mat, in_mat, w_mat, @@ -227,25 +241,25 @@ class ContextProjectionBackwardFunc : public FunctionBase { CHECK_EQ((size_t)1, inputs.size()); CHECK_EQ((size_t)2, outputs.size()); - const auto seqArg = dynamic_cast(inputs[0]); - CHECK(seqArg.data() && inputs[0].data()); - CHECK_EQ(seqArg.shape().ndims(), (size_t)2); - CHECK_EQ(seqArg.getSequenceIds().shape().ndims(), (size_t)1); + const auto seq_arg = dynamic_cast(inputs[0]); + CHECK(seq_arg.data() && inputs[0].data()); + CHECK_EQ(seq_arg.shape().ndims(), (size_t)2); + CHECK_EQ(seq_arg.getSequenceIds().shape().ndims(), (size_t)1); CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); /// dim of input grad == dim of weight CHECK_EQ(outputs[0].shape()[1], outputs[1].shape()[1]); /// input and output grad has the same batch_size - CHECK_EQ(outputs[0].shape()[0], seqArg.shape()[0]); + CHECK_EQ(outputs[0].shape()[0], seq_arg.shape()[0]); /// dim of output val = dim of input grad * context_length - CHECK_EQ(seqArg.shape()[1], outputs[0].shape()[1] * context_length_); + CHECK_EQ(seq_arg.shape()[1], outputs[0].shape()[1] * context_length_); CHECK_EQ(outputs[0].getArgType(), ADD_TO); CHECK_EQ(outputs[1].getArgType(), ADD_TO); - const auto seq_vec = seqArg.getSequenceIds().vector(); - const auto out_grad_mat = seqArg.matrix(); + const auto seq_vec = seq_arg.getSequenceIds().vector(); + const auto out_grad_mat = seq_arg.matrix(); auto in_grad_mat = !outputs[0].data() ? typename Tensor::Matrix(nullptr, 0, 0) @@ -272,6 +286,91 @@ class ContextProjectionBackwardFunc : public FunctionBase { size_t total_pad_; }; +/** + * \param inputs[0].matrix input grad, n*d + * \param inputs[0].vector input sequence, n*1 + * \param outputs[0] output grad, n*(d*l) + */ +template +class ContextProjectionBackwardDataFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override { + context_length_ = config.get("context_length"); + context_start_ = config.get("context_start"); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1, static_cast(inputs.size())); + CHECK_EQ(1, static_cast(outputs.size())); + const auto in_seqs = dynamic_cast(inputs[0]); + CHECK(in_seqs.data() && outputs[0].data() && + in_seqs.getSequenceIds().data()); + CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); + CHECK_EQ(static_cast(in_seqs.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seqs.getSequenceIds().shape().ndims()), 1); + CHECK_EQ(outputs[0].shape().ndims(), + in_seqs.shape().ndims() * context_length_); + /// input and output has the same batch_size + CHECK_EQ(in_seqs.shape()[0], outputs[0].shape()[0]); + const auto out_grad_mat = outputs[0].matrix(); + auto in_grad_mat = in_seqs.matrix(); + const auto seq_vec = in_seqs.getSequenceIds().vector(); + + ContextProjectionBackwardData( + out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_); + } + +private: + size_t context_length_; + int context_start_; +}; + +/** + * \param inputs[0].matrix weight grad, pad * d + * \param inputs[0].vecotr input sequence, n * 1 + * \param outputs[0] output grad, n * (d * l) + */ +template +class ContextProjectionBackwardWeightFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override { + context_length_ = config.get("context_length"); + context_start_ = config.get("context_start"); + begin_pad_ = config.get("begin_pad"); + total_pad_ = config.get("total_pad"); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1, static_cast(inputs.size())); + CHECK_EQ(1, static_cast(outputs.size())); + + const auto in_seqs = dynamic_cast(inputs[0]); + CHECK(in_seqs.data() && in_seqs.getSequenceIds().data() && + outputs[0].data()); + CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); + CHECK_EQ(static_cast(in_seqs.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seqs.getSequenceIds().shape().ndims()), 1); + CHECK_EQ(in_seqs.shape()[0], outputs[0].shape()[0]); + CHECK_EQ(outputs[0].shape()[1], in_seqs.shape()[1] * context_length_); + const auto out_grad_mat = outputs[0].matrix(); + auto w_grad_mat = inputs[0].matrix(); + const auto seq_vec = in_seqs.getSequenceIds().vector(); + ContextProjectionBackwardWeight(out_grad_mat, + w_grad_mat, + seq_vec, + context_length_, + context_start_, + total_pad_, + begin_pad_); + } + +private: + size_t context_length_; + int context_start_; + size_t begin_pad_; + size_t total_pad_; +}; + REGISTER_TYPED_FUNC(ContextProjectionForward, CPU, ContextProjectionForwardFunc); @@ -285,5 +384,11 @@ REGISTER_TYPED_FUNC(ContextProjectionForward, REGISTER_TYPED_FUNC(ContextProjectionBackward, GPU, ContextProjectionBackwardFunc); +REGISTER_TYPED_FUNC(ContextProjectionBackwardData, + GPU, + ContextProjectionBackwardDataFunc); +REGISTER_TYPED_FUNC(ContextProjectionBackwardWeight, + GPU, + ContextProjectionBackwardWeightFunc); #endif } // namespace paddle diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index 50ca2040050cad..bd0c06c5f64a88 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -58,21 +58,21 @@ void testMatrixProjectionForward(int context_start, BufferArgs cpu_inputs; BufferArgs cpu_outputs; - cpu_inputs.addArg(cpu_in); - cpu_inputs.addArg(cpu_weight ? *cpu_weight - : CpuMatrix(nullptr, 0, input_dim)); - cpu_inputs.addArg(*cpu_seq); - cpu_outputs.addArg(cpu_out, ADD_TO); + cpu_inputs.addArg(cpu_in, *cpu_seq); + if (cpu_weight) { + cpu_inputs.addArg(*cpu_weight, *cpu_seq); + } + cpu_outputs.addArg(cpu_out, *cpu_seq, ADD_TO); compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); BufferArgs gpu_inputs; BufferArgs gpu_outputs; - gpu_inputs.addArg(gpu_in); - gpu_inputs.addArg(gpu_weight ? *gpu_weight - : GpuMatrix(nullptr, 0, input_dim)); - gpu_inputs.addArg(*gpu_seq); - gpu_outputs.addArg(gpu_out, ADD_TO); + gpu_inputs.addArg(gpu_in, *gpu_seq); + if (gpu_weight) { + gpu_inputs.addArg(*gpu_weight, *gpu_seq); + } + gpu_outputs.addArg(gpu_out, *gpu_seq, ADD_TO); compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index 17fd36ef563c1b..edcef17ad4705f 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -118,16 +118,15 @@ void ContextProjection::forward() { /// first use state_, otherwise use weight_(padding false === w nullptr) auto w_ptr = state_ ? state_.get() : is_padding ? weight_->getW().get() : nullptr; - auto start_pos = in_->sequenceStartPositions; - + const auto start_pos = in_->sequenceStartPositions->getVector(useGpu_); BufferArgs inputs; BufferArgs outputs; - inputs.addArg(*in_->value); - inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, - w_ptr ? w_ptr->getHeight() : 0, - input_dim)); - inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_)); - outputs.addArg(*out_->value, ADD_TO); + inputs.addArg(*in_->value, *start_pos); + if (w_ptr) { + inputs.addArg(CpuMatrix(w_ptr->getData(), w_ptr->getHeight(), input_dim), + *start_pos); + } + outputs.addArg(*out_->value, *start_pos, ADD_TO); forward_[0]->calc(inputs, outputs); if (state_ && config_.context_start() < 0) { From e9794214cbca438b1b467d614c6398ec09ab1d0b Mon Sep 17 00:00:00 2001 From: xutianbing Date: Thu, 12 Jan 2017 13:26:10 -0800 Subject: [PATCH 020/370] Address further comments. --- paddle/function/BufferArg.cpp | 12 +- paddle/function/BufferArg.h | 30 +++- paddle/function/ContextProjectionOp.cpp | 169 +++++++++++--------- paddle/function/ContextProjectionOpTest.cpp | 4 +- paddle/gserver/layers/ContextProjection.cpp | 1 + 5 files changed, 126 insertions(+), 90 deletions(-) diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index fde48a73b61c31..5d595deb12c6c8 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -20,23 +20,27 @@ limitations under the License. */ namespace paddle { const SequenceArg& BufferArg::sequence() const { - // CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA); + CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA); return dynamic_cast(*this); } const SparseMatrixArg& BufferArg::sparse() const { - // CHECK_EQ(bufferType_, TENSOR_SPARSE); + CHECK_EQ(bufferType_, TENSOR_SPARSE); return dynamic_cast(*this); } SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) {} + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + bufferType_ = TENSOR_SPARSE; +} SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) {} + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + bufferType_ = TENSOR_SPARSE; +} } // namespace paddle diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index f3a4350e12df34..440a924a7a63d8 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -23,10 +23,11 @@ limitations under the License. */ namespace paddle { enum BufferType { - TENSOR_NORMAL = 0, - TENSOR_SEQUENCE_ID = 1, - TENSOR_SEQUENCE_DATA = 2, - TENSOR_SPARSE = 3 + TENSOR_UNKNOWN = 0, + TENSOR_NORMAL = 1, + TENSOR_SEQUENCE_ID = 2, + TENSOR_SEQUENCE_DATA = 3, + TENSOR_SPARSE = 4 }; enum SparseDataType { @@ -86,6 +87,7 @@ class BufferArg { valueType_(DataType::value), shape_(2), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, matrix.getHeight()); shape_.setDim(1, matrix.getWidth()); } @@ -98,6 +100,7 @@ class BufferArg { valueType_(DataType::value), shape_(shape), argType_(argType) { + bufferType_ = TENSOR_NORMAL; CHECK_EQ(matrix.getElementCnt(), shape.getElements()); } @@ -107,6 +110,7 @@ class BufferArg { valueType_(DataType::value), shape_(1), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, vector.getSize()); } @@ -116,6 +120,7 @@ class BufferArg { valueType_(VALUE_TYPE_INT32), shape_(1), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, vector.getSize()); } @@ -150,6 +155,8 @@ class BufferArg { ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } + bool isSparse() const { return (TENSOR_SPARSE == bufferType_); } + bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } const SequenceArg& sequence() const; const SparseMatrixArg& sparse() const; @@ -158,8 +165,8 @@ class BufferArg { void* buf_; ValueType valueType_; TensorShape shape_; - BufferType bufferType_; - ArgType argType_ = UNSPECIFIED; + BufferType bufferType_{TENSOR_UNKNOWN}; + ArgType argType_{UNSPECIFIED}; // leading dimensions. The size is dims_.size() // Dims lds_; }; @@ -174,11 +181,13 @@ class SequenceIdArg : public BufferArg { const TensorShape& shape, ArgType argType = UNSPECIFIED) : BufferArg(buf, VALUE_TYPE_INT32, shape, argType) { + bufferType_ = TENSOR_SEQUENCE_ID; CHECK_EQ(shape_.ndims(), (size_t)1); numSeqs_ = shape_[0] - 1; } SequenceIdArg(const IVector& vector) : BufferArg(vector) { + bufferType_ = TENSOR_SEQUENCE_ID; numSeqs_ = shape_[0] - 1; } @@ -199,12 +208,16 @@ class SequenceArg : public BufferArg { const SequenceIdArg& startPositions, ArgType argType = UNSPECIFIED) : BufferArg(buf, valueType, shape, argType), - startPositions_(startPositions) {} + startPositions_(startPositions) { + bufferType_ = TENSOR_SEQUENCE_DATA; + } SequenceArg(const Matrix& matrix, const IVector& vector, ArgType argType = UNSPECIFIED) - : BufferArg(matrix, argType), startPositions_(vector) {} + : BufferArg(matrix, argType), startPositions_(vector) { + bufferType_ = TENSOR_SEQUENCE_DATA; + } ~SequenceArg() {} @@ -236,6 +249,7 @@ class SparseMatrixArg : public BufferArg { nnz_(nnz), format_(format), type_(type) { + bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); CHECK_EQ(row_.shape().ndims(), (size_t)1); diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index ec697a381f97fa..2ef53cd6d9deda 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -74,9 +74,9 @@ void ContextProjectionForward(CpuMatrix& out_mat, /** * Paddle Function for Context Projection Forward. - * Calculate the output sequence after context projection. + * Calculate the output layer value sequence after context projection. * - * What is Context Projection? + * What is Context Projection for a sequence? * For example, assumed input (x) has 4 words and the dimension of each word * representation is 2. If we use zero to pad instead of learned weight to pad, * and the context_lenth is 3, the output (y) is: @@ -92,12 +92,11 @@ void ContextProjectionForward(CpuMatrix& out_mat, * c1, c2, d1, d2, 0, 0] * @endcode * - * \param outputs[0].matrix output value, n * (d * l) - * \param outputs[0].vector input sequence, n * 1 - * \param inputs[0].matrix input value, n * d - * \param inputs[0].vector input sequence, n * 1 - * \param inputs[1].matrix input weight, pad * d - * \param inputs[1].vector input sequence, n * 1 + * \param outputs[0].matrix output layer value, n * (d * l) + * \param outputs[0].vector start position sequence, n * 1 + * \param inputs[0].matrix input layer value, n * d + * \param inputs[0].vector start position sequence, n * 1 + * \param inputs[1].matrix input layer weight, pad * d */ template class ContextProjectionForwardFunc : public FunctionBase { @@ -111,37 +110,35 @@ class ContextProjectionForwardFunc : public FunctionBase { void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK(1 == inputs.size() || 2 == inputs.size()); CHECK_EQ((size_t)1, outputs.size()); - + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; const auto val_seqs = dynamic_cast(inputs[0]); - const auto w_seqs = inputs.size() <= 1 - ? nullptr - : dynamic_cast(&inputs[1]); - auto out_seqs = dynamic_cast(outputs[0]); + auto out_seq = dynamic_cast(outputs[0]); - CHECK(out_seqs.data() && val_seqs.data() && + CHECK(out_seq.data() && val_seqs.data() && val_seqs.getSequenceIds().data()); - CHECK_EQ(out_seqs.shape().ndims(), (size_t)2); + CHECK_EQ(out_seq.shape().ndims(), (size_t)2); CHECK_EQ(val_seqs.shape().ndims(), (size_t)2); CHECK_EQ(val_seqs.getSequenceIds().shape().ndims(), (size_t)1); - if (w_seqs) { - CHECK_EQ(w_seqs->shape().ndims(), (size_t)2); - CHECK_EQ(w_seqs->getSequenceIds().shape().ndims(), (size_t)1); + if (2 == inputs.size()) { + CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); } /// dim of output = dim of input * context_length - CHECK_EQ(out_seqs.shape()[1], val_seqs.shape()[1] * context_length_); + CHECK_EQ(out_seq.shape()[1], val_seqs.shape()[1] * context_length_); /// input and output has the same batch_size - CHECK_EQ(val_seqs.shape()[0], out_seqs.shape()[0]); + CHECK_EQ(val_seqs.shape()[0], out_seq.shape()[0]); /// dim of input == dim of weight - if (w_seqs) { - CHECK_EQ(val_seqs.shape()[1], w_seqs->shape()[1]); + if (2 == inputs.size()) { + CHECK_EQ(val_seqs.shape()[1], inputs[1].shape()[1]); } - CHECK_EQ(out_seqs.getArgType(), ADD_TO); - auto out_mat = out_seqs.matrix(); + CHECK_EQ(out_seq.getArgType(), ADD_TO); + auto out_mat = out_seq.matrix(); const auto in_mat = val_seqs.matrix(); const auto w_mat = - w_seqs ? w_seqs->matrix() - : typename Tensor::Matrix(nullptr, 0, 0); + (2 == inputs.size()) + ? inputs[1].matrix() + : typename Tensor::Matrix(nullptr, 0, 0); const auto seq_vec = val_seqs.getSequenceIds().vector(); ContextProjectionForward(out_mat, in_mat, @@ -221,10 +218,11 @@ void ContextProjectionBackward(const CpuMatrix& out_grad_mat, * Context Projection Backward Function. * Update the weight gradient and input layer gradient with backprop * - * \param inputs[0].seq input sequence. - * \param inputs[0].matrix output layer grad. - * \param outputs[0] input layer grad. - * \param outputs[1] weight grad. + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vector start position sequence, n * 1 + * \param outputs[0].matrix input layer grad, n * d + * \param outputs[0].vector start position sequence, n * 1 + * \param outputs[1] weight grad, pad * d */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -240,30 +238,31 @@ class ContextProjectionBackwardFunc : public FunctionBase { void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ((size_t)1, inputs.size()); CHECK_EQ((size_t)2, outputs.size()); - - const auto seq_arg = dynamic_cast(inputs[0]); - CHECK(seq_arg.data() && inputs[0].data()); - CHECK_EQ(seq_arg.shape().ndims(), (size_t)2); - CHECK_EQ(seq_arg.getSequenceIds().shape().ndims(), (size_t)1); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + auto out_seq = dynamic_cast(outputs[0]); + CHECK(in_seq.data() && in_seq.getSequenceIds().data()); + CHECK_EQ(in_seq.shape().ndims(), (size_t)2); + CHECK_EQ(in_seq.getSequenceIds().shape().ndims(), (size_t)1); + CHECK_EQ(out_seq.shape().ndims(), (size_t)2); + CHECK_EQ(out_seq.getSequenceIds().shape().ndims(), (size_t)1); CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); /// dim of input grad == dim of weight - CHECK_EQ(outputs[0].shape()[1], outputs[1].shape()[1]); + CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]); /// input and output grad has the same batch_size - CHECK_EQ(outputs[0].shape()[0], seq_arg.shape()[0]); - /// dim of output val = dim of input grad * context_length - CHECK_EQ(seq_arg.shape()[1], outputs[0].shape()[1] * context_length_); - - CHECK_EQ(outputs[0].getArgType(), ADD_TO); + CHECK_EQ(out_seq.shape()[0], in_seq.shape()[0]); + /// dim of output grad = dim of input grad * context_length + CHECK_EQ(in_seq.shape()[1], out_seq.shape()[1] * context_length_); + CHECK_EQ(out_seq.getArgType(), ADD_TO); CHECK_EQ(outputs[1].getArgType(), ADD_TO); - const auto seq_vec = seq_arg.getSequenceIds().vector(); - const auto out_grad_mat = seq_arg.matrix(); + const auto seq_vec = in_seq.getSequenceIds().vector(); + const auto out_grad_mat = in_seq.matrix(); auto in_grad_mat = - !outputs[0].data() - ? typename Tensor::Matrix(nullptr, 0, 0) - : outputs[0].matrix(); + !out_seq.data() ? typename Tensor::Matrix(nullptr, 0, 0) + : out_seq.matrix(); auto w_grad_mat = !outputs[1].data() ? typename Tensor::Matrix(nullptr, 0, 0) : outputs[1].matrix(); @@ -287,9 +286,15 @@ class ContextProjectionBackwardFunc : public FunctionBase { }; /** - * \param inputs[0].matrix input grad, n*d - * \param inputs[0].vector input sequence, n*1 - * \param outputs[0] output grad, n*(d*l) + * Context Projection Backward Data Function + * Update input layer grad + * input: sequence of output layer grad + * output: sequence of input layer grad + * + * \param outputs[0].matrix input layer grad, n * d + * \param outputs[0].vector start position sequence, n * 1 + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vector start positon sequence, n * 1 */ template class ContextProjectionBackwardDataFunc : public FunctionBase { @@ -302,19 +307,24 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(1, static_cast(inputs.size())); CHECK_EQ(1, static_cast(outputs.size())); - const auto in_seqs = dynamic_cast(inputs[0]); - CHECK(in_seqs.data() && outputs[0].data() && - in_seqs.getSequenceIds().data()); - CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); - CHECK_EQ(static_cast(in_seqs.shape().ndims()), 2); - CHECK_EQ(static_cast(in_seqs.getSequenceIds().shape().ndims()), 1); - CHECK_EQ(outputs[0].shape().ndims(), - in_seqs.shape().ndims() * context_length_); + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + const auto out_seq = dynamic_cast(outputs[0]); + + CHECK(in_seq.data() && out_seq.data() && in_seq.getSequenceIds().data()); + CHECK_EQ(static_cast(out_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.getSequenceIds().shape().ndims()), 1); + /// output layer grad dim == input layer grad dim * context_length_ + CHECK_EQ(in_seq.shape().ndims(), out_seq.shape().ndims() * context_length_); /// input and output has the same batch_size - CHECK_EQ(in_seqs.shape()[0], outputs[0].shape()[0]); - const auto out_grad_mat = outputs[0].matrix(); - auto in_grad_mat = in_seqs.matrix(); - const auto seq_vec = in_seqs.getSequenceIds().vector(); + CHECK_EQ(in_seq.shape()[0], out_seq.shape()[0]); + CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); + + const auto out_grad_mat = in_seq.matrix(); + const auto seq_vec = in_seq.getSequenceIds().vector(); + auto in_grad_mat = out_seq.matrix(); ContextProjectionBackwardData( out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_); @@ -326,9 +336,14 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { }; /** - * \param inputs[0].matrix weight grad, pad * d - * \param inputs[0].vecotr input sequence, n * 1 - * \param outputs[0] output grad, n * (d * l) + * Context Projection Backward Weight Function + * Update weight grad by backprop + * input: sequence of output layer grad + * output: weight grad + * + * \param outputs[0] weight grad, pad * d + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vecotr start positon sequence, n * 1 */ template class ContextProjectionBackwardWeightFunc : public FunctionBase { @@ -343,18 +358,20 @@ class ContextProjectionBackwardWeightFunc : public FunctionBase { void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(1, static_cast(inputs.size())); CHECK_EQ(1, static_cast(outputs.size())); - - const auto in_seqs = dynamic_cast(inputs[0]); - CHECK(in_seqs.data() && in_seqs.getSequenceIds().data() && - outputs[0].data()); + CHECK(inputs[0].isSequenceArg()) << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + CHECK(in_seq.data() && in_seq.getSequenceIds().data() && outputs[0].data()); CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); - CHECK_EQ(static_cast(in_seqs.shape().ndims()), 2); - CHECK_EQ(static_cast(in_seqs.getSequenceIds().shape().ndims()), 1); - CHECK_EQ(in_seqs.shape()[0], outputs[0].shape()[0]); - CHECK_EQ(outputs[0].shape()[1], in_seqs.shape()[1] * context_length_); - const auto out_grad_mat = outputs[0].matrix(); - auto w_grad_mat = inputs[0].matrix(); - const auto seq_vec = in_seqs.getSequenceIds().vector(); + CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.getSequenceIds().shape().ndims()), 1); + CHECK_EQ(in_seq.shape()[0], outputs[0].shape()[0]); + /// output layer grad dim == weight dim * context_length_ + CHECK_EQ(in_seq.shape()[1], outputs[0].shape()[1] * context_length_); + CHECK_EQ(outputs[0].getArgType(), ADD_TO); + + const auto seq_vec = in_seq.getSequenceIds().vector(); + const auto out_grad_mat = in_seq.matrix(); + auto w_grad_mat = outputs[0].matrix(); ContextProjectionBackwardWeight(out_grad_mat, w_grad_mat, seq_vec, diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index bd0c06c5f64a88..c9db2ff8008e0b 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -123,7 +123,7 @@ void testMatrixProjectionBackward(int context_start, BufferArgs cpu_inputs; BufferArgs cpu_outputs; cpu_inputs.addArg(cpu_out_grad, *cpu_seq); - cpu_outputs.addArg(cpu_in_grad, ADD_TO); + cpu_outputs.addArg(cpu_in_grad, *cpu_seq, ADD_TO); cpu_outputs.addArg( cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO); @@ -132,7 +132,7 @@ void testMatrixProjectionBackward(int context_start, BufferArgs gpu_inputs; BufferArgs gpu_outputs; gpu_inputs.addArg(gpu_out_grad, *gpu_seq); - gpu_outputs.addArg(gpu_in_grad, ADD_TO); + gpu_outputs.addArg(gpu_in_grad, *gpu_seq, ADD_TO); gpu_outputs.addArg( gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO); diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index edcef17ad4705f..d7042af1c25e74 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -169,6 +169,7 @@ void ContextProjection::backward(const UpdateCallback& callback) { outputs.addArg( CpuMatrix( in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim), + *in_->sequenceStartPositions->getVector(useGpu_), ADD_TO); outputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, w_ptr ? w_ptr->getHeight() : 0, From 143ff015c66fb70db0c89de77bbd8cb60bb082ce Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 5 Jan 2017 14:14:51 -0800 Subject: [PATCH 021/370] add permission AWSKeyManagementServicePowerUser for aws kms key creation Without the permission command `aws kms --region=us-west-1 create-key --description="kube-aws assets"` will fail with: An error occurred (AccessDeniedException) when calling the CreateKey operation: --- doc/howto/usage/k8s/k8s_aws_en.md | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index b04bfba590de42..a82243995a3b42 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -31,6 +31,7 @@ the user group: - IAMUserSSHKeys - IAMFullAccess - NetworkAdministrator +- AWSKeyManagementServicePowerUser By the time we write this tutorial, we noticed that Chinese AWS users From 2778a65b0ff9aa825d1ccaf6907c7364427058f1 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 12 Jan 2017 17:19:05 -0800 Subject: [PATCH 022/370] first pass change for k8s aws en tutorial --- doc/howto/usage/k8s/k8s_aws_en.md | 108 +++++++++++++++--------------- 1 file changed, 55 insertions(+), 53 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index a82243995a3b42..c776ba9eb97a8c 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -2,15 +2,9 @@ ## Create AWS Account and IAM Account -To use AWS, we need to sign up an AWS account on Amazon's Web site. -An AWS account allows us to login to the AWS Console Web interface to -create IAM users and user groups. Usually, we create a user group with -privileges required to run PaddlePaddle, and we create users for -those who are going to run PaddlePaddle and add these users into the -group. IAM users can identify themselves using password and tokens, -where passwords allows users to log in to the AWS Console, and tokens -make it easy for users to submit and inspect jobs from the command -line. +AWS account allow us to manage AWS from Web Console. Amazon AMI enable us to manage AWS from command line interface. + +We need to create an AMI user with sufficient privilege to create kubernetes cluster on AWS. To sign up an AWS account, please follow @@ -19,8 +13,7 @@ To create users and user groups under an AWS account, please follow [this guide](http://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html). -Please be aware that this tutorial needs the following privileges in -the user group: +Please be aware that this tutorial needs the following privileges for the user in AMI: - AmazonEC2FullAccess - AmazonS3FullAccess @@ -47,9 +40,11 @@ it. Here we will show you step by step on how to run PaddlePaddle training on AWS cluster. -###Download kube-aws and kubectl +### Download kube-aws and kubectl + +#### kube-aws -####kube-aws +[kube-aws](https://github.com/coreos/kube-aws) is a CLI tool to automate cluster deployment to AWS. Import the CoreOS Application Signing Public Key: @@ -89,24 +84,22 @@ mv ${PLATFORM}/kube-aws /usr/local/bin ``` -####kubectl +#### kubectl + +[kubectl](https://kubernetes.io/docs/user-guide/kubectl-overview/) is a command line interface for running commands against Kubernetes clusters. Go to the [releases](https://github.com/kubernetes/kubernetes/releases) and download the latest release tarball. Extract the tarball and then concate the kubernetes binaries directory into PATH: ``` -export PATH=/platforms/linux/amd64:$PATH - +export PATH=/platforms/linux/amd64:$PATH # The exact path depend on your platform ``` -User credentials and security tokens will be generated later in user directory, not in `~/.kube/config`, they will be necessary to use the CLI or the HTTP Basic Auth. - - -###Configure AWS Credentials -First check out [this](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) for installing the AWS command line interface, if you use ec2 instance with default amazon AMI, the cli tool has already been installed on your machine. +### Configure AWS Credentials +First check out [this](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) for installing the AWS command line interface. And then configure your AWS account information: @@ -127,33 +120,35 @@ Default output format: json ``` -Test that your credentials work by describing any instances you may already have running on your account: +Verify that your credentials work by describing any instances you may already have running on your account: ``` aws ec2 describe-instances ``` -###Define Cluster Parameters +### Define Cluster Parameters -####EC2 key pair +#### EC2 key pair The keypair that will authenticate SSH access to your EC2 instances. The public half of this key pair will be configured on each CoreOS node. -After creating a key pair, you will use the name you gave the keys to configure the cluster. Key pairs are only available to EC2 instances in the same region. More info in the [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html). +Follow [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) to create a EC2 key pair -####KMS key +After creating a key pair, you will use the name you gave the keys to configure the cluster. Key pairs are only available to EC2 instances in the same region. + +#### KMS key Amazon KMS keys are used to encrypt and decrypt cluster TLS assets. If you already have a KMS Key that you would like to use, you can skip creating a new key and provide the Arn string for your existing key. You can create a KMS key in the AWS console, or with the aws command line tool: ``` -$ aws kms --region=us-west-2 create-key --description="kube-aws assets" +$ aws kms --region=us-west-1 create-key --description="kube-aws assets" { "KeyMetadata": { "CreationDate": 1458235139.724, "KeyState": "Enabled", - "Arn": "arn:aws:kms:us-west-2:xxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx", + "Arn": "arn:aws:kms:us-west-1:xxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx", "AWSAccountId": "xxxxxxxxxxxxx", "Enabled": true, "KeyUsage": "ENCRYPT_DECRYPT", @@ -167,7 +162,9 @@ You will use the `KeyMetadata.Arn` string to identify your KMS key in the init s And then you need to add several inline policies in your user permission. -kms inline policy: +Go to AMI user page, click on `Add inline policy` button, and then select `Custom Policy` + +paste into following inline policies: ``` { @@ -183,16 +180,8 @@ kms inline policy: "Resource": [ "arn:aws:kms:*:xxxxxxxxx:key/*" ] - } - ] -} -``` -cloudformation inline policy: - -``` -"Version": "2012-10-17", - "Statement": [ - { + }, + { "Sid": "Stmt1482205746000", "Effect": "Allow", "Action": [ @@ -201,10 +190,11 @@ cloudformation inline policy: "cloudformation:DeleteStack", "cloudformation:DescribeStacks", "cloudformation:DescribeStackResource", - "cloudformation:GetTemplate" + "cloudformation:GetTemplate", + "cloudformation:DescribeStackEvents" ], "Resource": [ - "arn:aws:cloudformation:us-west-2:xxxxxxxxx:stack/YOUR_CLUSTER_NAME/*" + "arn:aws:cloudformation:us-west-1:xxxxxxxxx:stack/YOUR_CLUSTER_NAME/*" ] } ] @@ -212,15 +202,23 @@ cloudformation inline policy: ``` -####External DNS name +#### External DNS name When the cluster is created, the controller will expose the TLS-secured API on a public IP address. You will need to create an A record for the external DNS hostname you want to point to this IP address. You can find the API external IP address after the cluster is created by invoking kube-aws status. -####S3 bucket +#### S3 bucket You need to create an S3 bucket before startup the Kubernetes cluster. -####Initialize an asset directory +command (need to have a global unique name): + +``` +paddle aws s3api --region=us-west-1 create-bucket --bucket bucket-name +``` + +If you get an error message, try a different bucket name. The bucket name needs to be globally unique. + +#### Initialize an asset directory Create a directory on your local machine to hold the generated assets: @@ -238,12 +236,16 @@ $ kube-aws init \ --region=us-west-1 \ --availability-zone=us-west-1c \ --key-name=key-pair-name \ ---kms-key-arn="arn:aws:kms:us-west-2:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" +--kms-key-arn="arn:aws:kms:us-west-1:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" ``` +Here `us-west-1c` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. + +Please check if `us-west-1c` is supported by `aws ec2 --region us-west-1 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-1a`, or `us-west-1b`) + There will now be a cluster.yaml file in the asset directory. This is the main configuration file for your cluster. -####Render contents of the asset directory +#### Render contents of the asset directory In the simplest case, you can have kube-aws generate both your TLS identities and certificate authority for you. @@ -286,21 +288,21 @@ $ tree These assets (templates and credentials) are used to create, update and interact with your Kubernetes cluster. -###Kubernetes Cluster Start Up +### Kubernetes Cluster Start Up -####Create the instances defined in the CloudFormation template +#### Create the instances defined in the CloudFormation template -Now for the exciting part, creating your cluster: +Now for the exciting part, creating your cluster (choose any ``): ``` $ kube-aws up --s3-uri s3:/// ``` -####Configure DNS +#### Configure DNS -You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation, if necessary. This command can take a while. And then dig the load balancer hostname to get the ip address, use this ip to setup an A record for your external dns name. +You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation, if necessary. This command can take a while. And use command `dig` to check the load balancer hostname to get the ip address, use this ip to setup an A record for your external dns name. -####Access the cluster +#### Access the cluster Once the API server is running, you should see: @@ -313,7 +315,7 @@ ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m ``` -###Setup PaddlePaddle Environment on AWS +### Setup PaddlePaddle Environment on AWS Now, we've created a cluster with following network capability: From 2a20fdc14bce87ce3d092ab9bff8349be5194b05 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 13 Jan 2017 14:35:56 +0800 Subject: [PATCH 023/370] Change BufferArgPtr to BufferArg* --- paddle/function/Function.cpp | 9 ++++++--- paddle/function/Function.h | 33 +++++++++++++++++++++++++++++++-- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/paddle/function/Function.cpp b/paddle/function/Function.cpp index dbe3a4e9f608df..3fdc37b9689559 100644 --- a/paddle/function/Function.cpp +++ b/paddle/function/Function.cpp @@ -79,15 +79,18 @@ FuncConfig& FuncConfig::set(const std::string& key, bool v) { void BufferArgs::addArg(const Matrix& arg, const TensorShape& shape, ArgType argType) { - args_.push_back(std::make_shared(arg, shape, argType)); + _args_.push_back(new BufferArg(arg, shape, argType)); + addArg(*_args_.back()); } void BufferArgs::addArg(const CpuSparseMatrix& arg, ArgType argType) { - args_.push_back(std::make_shared(arg, argType)); + _args_.push_back(new SparseMatrixArg(arg, argType)); + addArg(*_args_.back()); } void BufferArgs::addArg(const GpuSparseMatrix& arg, ArgType argType) { - args_.push_back(std::make_shared(arg, argType)); + _args_.push_back(new SparseMatrixArg(arg, argType)); + addArg(*_args_.back()); } ClassRegistrar FunctionBase::funcRegistrar_; diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 249f8f9cfad58b..afbd4911b0a2b9 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -50,10 +50,25 @@ class FuncConfig { * Argument type for Function::calc(). * A BufferArgs contains a set of BufferArg, * because Function can have multiple inputs and outputs. + * + * addArg() with Matix object used to adapt Layer Argument. + * Will create a BufferArg object in addArg(), + * and free in destructor of BufferArgs. + * + * addArg() with BufferArg object, just save BufferArg object address, + * and the caller needs to guarantee the validity of the BufferArg object + * in the BufferArgs life time. */ class BufferArgs { public: BufferArgs() {} + + ~BufferArgs() { + for (auto arg : _args_) { + delete arg; + } + } + size_t size() const { return args_.size(); } // add argument into BufferArgs @@ -62,7 +77,8 @@ class BufferArgs { // For outputs, the argType needs to be specified as ASSIGN_TO or ADD_TO. template void addArg(const Tensor& arg, ArgType argType = UNSPECIFIED) { - args_.push_back(std::make_shared(arg, argType)); + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); } // Add arg into BufferArgs and reshape the arg. @@ -83,14 +99,27 @@ class BufferArgs { return *args_[num]; } + void addArg(BufferArg& arg) { args_.push_back(&arg); } + + void addArg(SequenceIdArg& arg) { args_.push_back(&arg); } + + void addArg(SequenceArg& arg) { args_.push_back(&arg); } + + void addArg(SparseMatrixArg& arg) { args_.push_back(&arg); } + private: - std::vector args_; + std::vector args_; + // The BufferArg object is constructed and freed by BufferArgs. + std::vector _args_; }; /** * \brief Base class for Function. * The basic Function implementation requires override init and calc interfaces. * + * The caller needs to ensure the validity of the arguments + * during Function execution. + * * Function inputs are readonly, Function outputs have two modes: ASSIGN_TO * and ADD_TO. * If output.getArgType() == ASSIGN_TO, this is assign mode, and the calculation From 039c0bf22384607446fcd38a4ba6c349ce073213 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 13 Jan 2017 15:05:22 +0800 Subject: [PATCH 024/370] Add some constructors for generating object that only contains shape (do not contains data). --- paddle/function/BufferArg.h | 33 ++++++++++++++++++++++++++++++-- paddle/function/FunctionTest.cpp | 18 +++++++++++++++++ 2 files changed, 49 insertions(+), 2 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 12352ba29e3392..28542a86574812 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -39,7 +39,6 @@ enum SparseDataFormat { SPARSE_CSR_FORMAT = 0, SPARSE_CSC_FORMAT = 1 }; class BufferArg; class SequenceArg; class SparseMatrixArg; -typedef std::shared_ptr BufferArgPtr; /** * \brief BufferArg used as the argument type of Function. @@ -50,6 +49,11 @@ typedef std::shared_ptr BufferArgPtr; * 3. SequenceArg for a Buffer of sequence data. * 4. SparseMatrixArg for a Buffer of sparse matrix. * + * Buffer shape + * For most buffers, the first dimension `shape()[0]` represents + * the size of the mini-batch. + * + * Buffer argType * There is an ArgType property for the BufferArg used as Function Output. * Whether the result of the Function calculation is assigned to the * output Buffer or added to the output Buffer is determined by the @@ -71,6 +75,14 @@ class BufferArg { ArgType getArgType() const { return argType_; } public: + BufferArg(ValueType valueType, + const TensorShape& shape, + ArgType argType = UNSPECIFIED) + : buf_(nullptr), + valueType_(valueType), + shape_(shape), + argType_(argType) {} + BufferArg(void* buf, ValueType valueType, const TensorShape& shape, @@ -170,6 +182,12 @@ class BufferArg { // if a < b then value_.buf_[a] < value_.buf_[b] class SequenceIdArg : public BufferArg { public: + SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED) + : BufferArg(VALUE_TYPE_INT32, shape, argType) { + CHECK_EQ(shape_.ndims(), (size_t)1); + numSeqs_ = shape_[0] - 1; + } + SequenceIdArg(void* buf, const TensorShape& shape, ArgType argType = UNSPECIFIED) @@ -190,9 +208,18 @@ class SequenceIdArg : public BufferArg { size_t numSeqs_; }; -// sequence data +// sequences data +// For mini-batch calculate, +// one batch can contain more than one sequence of data. +// SequenceArg can be used to represent sequences that contain multiple +// unequal lengths. class SequenceArg : public BufferArg { public: + SequenceArg(ValueType valueType, + const TensorShape& shape, + ArgType argType = UNSPECIFIED) + : BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {} + SequenceArg(void* buf, ValueType valueType, const TensorShape& shape, @@ -210,6 +237,8 @@ class SequenceArg : public BufferArg { void* getIdBuf() const { return startPositions_.data(); } size_t numSeqs() const { return startPositions_.numSeqs(); } + SequenceIdArg& getSequenceId() { return startPositions_; } + const SequenceIdArg& getSequenceId() const { return startPositions_; } private: SequenceIdArg startPositions_; diff --git a/paddle/function/FunctionTest.cpp b/paddle/function/FunctionTest.cpp index eb05ca9a2190d5..03c609b5242777 100644 --- a/paddle/function/FunctionTest.cpp +++ b/paddle/function/FunctionTest.cpp @@ -84,6 +84,10 @@ void testBufferArgs(const BufferArgs& inputs, } } +void testBufferArgs(const BufferArgs& inputs, const CheckBufferArg& check) { + check(inputs[0]); +} + TEST(Arguments, Matrix) { MatrixPtr matrix = Matrix::create(100, 200); CheckBufferArg check = [=](const BufferArg& arg) { @@ -144,4 +148,18 @@ TEST(Arguments, CpuSparseMatrix) { testBufferArgs(argments, checkFunc); } +TEST(Arguments, BufferArg) { + BufferArg arg(nullptr, VALUE_TYPE_FLOAT, {1, 2, 3}); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 3); + EXPECT_EQ(arg.shape()[0], 1); + EXPECT_EQ(arg.shape()[1], 2); + EXPECT_EQ(arg.shape()[2], 3); + }; + + BufferArgs argments; + argments.addArg(arg); + testBufferArgs(argments, check); +} + } // namespace paddle From 678174339fcc1aeb953684f795e1bf8cf1d631a5 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 13 Jan 2017 17:46:05 +0800 Subject: [PATCH 025/370] Implement the FunctionTest --- paddle/function/Function.h | 13 +- paddle/function/FunctionTest.h | 225 +++++++++++++++------------------ 2 files changed, 112 insertions(+), 126 deletions(-) diff --git a/paddle/function/Function.h b/paddle/function/Function.h index afbd4911b0a2b9..b0c6ba0facc7f8 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -75,8 +75,17 @@ class BufferArgs { // Tensor can be Matrix, Vector, IVector. // For inputs, do not need argType. // For outputs, the argType needs to be specified as ASSIGN_TO or ADD_TO. - template - void addArg(const Tensor& arg, ArgType argType = UNSPECIFIED) { + void addArg(const Matrix& arg, ArgType argType = UNSPECIFIED) { + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); + } + + void addArg(const Vector& arg, ArgType argType = UNSPECIFIED) { + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); + } + + void addArg(const IVector& arg, ArgType argType = UNSPECIFIED) { _args_.push_back(new BufferArg(arg, argType)); addArg(*_args_.back()); } diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 2847188fd67989..412e3a7d1b30a7 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -19,6 +19,8 @@ limitations under the License. */ namespace paddle { +typedef std::shared_ptr BufferArgPtr; + /** * \brief A class for comparing CPU and GPU implementations of Function. * @@ -45,143 +47,121 @@ namespace paddle { class FunctionCompare { public: FunctionCompare(const std::string& name, const FuncConfig& config) - : cpu(FunctionBase::funcRegistrar_.createByType(name + "-CPU")), - gpu(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) { - cpu->init(config); - gpu->init(config); + : cpuFunc_(FunctionBase::funcRegistrar_.createByType(name + "-CPU")), + gpuFunc_(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) { + cpuFunc_->init(config); + gpuFunc_->init(config); + } + + ~FunctionCompare() {} + + // input need only contains shape, do not contains data. + void addInputs(const BufferArg& input) { + size_t size = + input.shape().getElements() * sizeOfValuType(input.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuInputs_.emplace_back(std::make_shared( + cpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + gpuInputs_.emplace_back(std::make_shared( + gpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + } + + // output need only contains shape, do not contains data. + void addOutputs(const BufferArg& output) { + size_t size = + output.shape().getElements() * sizeOfValuType(output.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuOutputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + ASSIGN_TO)); + gpuOutputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + ASSIGN_TO)); } - void addInputs(const BufferArg& input) { inputs.push_back(input); } + void addInputs(const SequenceArg& input) { + size_t batchSize = input.shape()[0]; + size_t numSeqs = batchSize / 10 + 1; + + size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); + cpuMemory_.emplace_back(std::make_shared(sizeId)); + gpuMemory_.emplace_back(std::make_shared(sizeId)); - void addOutputs(const BufferArg& output) { outputs.push_back(output); } + TensorShape seqsId({numSeqs + 1}); + // void* cpuBuffer = cpuMemory_.back()->getBuf(); + // void* gpuBuffer = gpuMemory_.back()->getBuf(); + + size_t size = + input.shape().getElements() * sizeOfValuType(input.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + // TODO: need be implemented. + } void run() { // prepare cpu/gpu arguments - prepareArgs(); + initInputs(); // function calculate - cpu->calc(cpuInputs, cpuOutputs); - gpu->calc(gpuInputs, gpuOutputs); - - // check outputs and inouts - auto checkArgs = [=](const BufferArgs& cpuArgs, const BufferArgs& gpuArgs) { - for (size_t i = 0; i < cpuArgs.size(); i++) { - auto cpu = cpuArgs[i]; - auto gpu = gpuArgs[i]; - CpuVector cpuVector(cpu.shape().getElements(), (real*)cpu.getData()); - GpuVector gpuVector(cpu.shape().getElements(), (real*)gpu.getData()); - - autotest::TensorCheckErr(cpuVector, gpuVector); + auto callFunction = [](FunctionBase* function, + std::vector& inputs, + std::vector& outputs) { + BufferArgs inArgs; + BufferArgs outArgs; + for (auto arg : inputs) { + inArgs.addArg(*arg); } - }; - checkArgs(cpuOutputs, gpuOutputs); - } -#if 0 - void cmpWithArg(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) { - // init cpu and gpu arguments - auto initArgs = [=]( - Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) { - for (const auto arg : inArgs) { - size_t size = sizeof(real); - for (const auto dim : arg.dims_) { - size *= dim; - } - if (arg.getData()) { - // todo(tianbing), waste unnecessary mem here - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - gpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - // already init outside - } else { - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back( - Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_)); - gpuArgs.emplace_back( - Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_)); - // will use an api to refactor this code. - CpuVector cpuVector(size / sizeof(real), - (real*)cpuArgs.back().getData()); - GpuVector gpuVector(size / sizeof(real), - (real*)gpuArgs.back().getData()); - cpuVector.uniform(0.001, 1); - gpuVector.copyFrom(cpuVector); - } + for (auto arg : outputs) { + outArgs.addArg(*arg); } + function->calc(inArgs, outArgs); }; - initArgs(cpuInputs, gpuInputs, inputs); - initArgs(cpuOutputs, gpuOutputs, outputs); - // function calculate - cpu->calc(cpuInputs, cpuOutputs); - gpu->calc(gpuInputs, gpuOutputs); + callFunction(cpuFunc_.get(), cpuInputs_, cpuOutputs_); + callFunction(gpuFunc_.get(), gpuInputs_, gpuOutputs_); // check outputs and inouts - auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) { - for (size_t i = 0; i < cpuArgs.size(); i++) { - auto cpu = cpuArgs[i]; - auto gpu = gpuArgs[i]; - size_t size = 1; - for (auto dim : cpu.dims_) { - size *= dim; - } - CpuVector cpuVector(size, (real*)cpu.getData()); - GpuVector gpuVector(size, (real*)gpu.getData()); - - autotest::TensorCheckErr(cpuVector, gpuVector); - } - }; - checkArgs(cpuOutputs, gpuOutputs); + compareOutputs(); } -#endif - std::shared_ptr getCpuFunction() const { return cpu; } + std::shared_ptr getCpuFunction() const { return cpuFunc_; } - std::shared_ptr getGpuFunction() const { return gpu; } + std::shared_ptr getGpuFunction() const { return gpuFunc_; } protected: - void prepareArgs() { - // TODO, if inputs has data - } + void initInputs() { + for (size_t i = 0; i < cpuInputs_.size(); i++) { + initArg(*cpuInputs_[i]); - void createArg(BufferArgs& cpuArgs, BufferArgs& gpuArgs, BufferArg& arg) { - size_t size = arg.shape().getElements() * sizeOfValuType(arg.valueType()); - cpuMemory_.emplace_back(std::make_shared(size)); - gpuMemory_.emplace_back(std::make_shared(size)); + // TODO: Need a BufferCopy used to copy from one BufferArg to another. + CpuVector cpuVector(cpuInputs_[i]->shape().getElements(), + (real*)cpuInputs_[i]->data()); + GpuVector gpuVector(gpuInputs_[i]->shape().getElements(), + (real*)gpuInputs_[i]->data()); - cpuArgs.emplace_back( - BufferArg(cpuMemory_.back()->getBuf()), arg.valueType(), arg.shape()); - gpuArgs.emplace_back( - BufferArg(gpuMemory_.back()->getBuf()), arg.valueType(), arg.shape()); + gpuVector.copyFrom(cpuVector); + } } - void createArg(BufferArgs& cpuArgs, BufferArgs& gpuArgs, SequenceArg& arg) { - size_t batchSize = arg.shape()[0]; - size_t numSeqs = batchSize / 10 + 1; + void compareOutputs() { + for (size_t i = 0; i < cpuOutputs_.size(); i++) { + // TODO, Need a BufferCheck used to compare the two buffers. + auto cpu = cpuOutputs_[i]; + auto gpu = gpuOutputs_[i]; + CpuVector cpuVector(cpu->shape().getElements(), (real*)cpu->data()); + GpuVector gpuVector(cpu->shape().getElements(), (real*)gpu->data()); - size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); - cpuMemory_.emplace_back(std::make_shared(size)); - gpuMemory_.emplace_back(std::make_shared(size)); - - TensorShape seqsId({numSeqs + 1}); - void* cpuBuffer = cpuMemory_.back()->getBuf(); - void* gpuBuffer = gpuMemory_.back()->getBuf(); - - size_t size = arg.shape().getElements() * sizeOfValuType(arg.valueType()); - cpuMemory_.emplace_back(std::make_shared(size)); - gpuMemory_.emplace_back(std::make_shared(size)); - - cpuArgs.emplace_back(SequenceArg(cpuMemory_.back()->getBuf(), - arg.valueType(), - arg.shape(), - SequenceIdArg(cpuBuffer, seqsId))); - gpuArgs.emplace_back(SequenceArg(gpuMemory_.back()->getBuf(), - arg.valueType(), - arg.shape(), - SequenceIdArg(gpuBuffer, seqsId))); + autotest::TensorCheckErr(cpuVector, gpuVector); + } } // only init cpu argument, gpu argument copy from cpu argument. @@ -192,10 +172,10 @@ class FunctionCompare { void initArg(SequenceIdArg& arg, size_t batchSize) { size_t numSeqs = arg.numSeqs(); - int* buf = arg.data(); + int* buf = (int*)arg.data(); int pos = 0; size_t maxLen = 2 * batchSize / numSeqs; - for (int i = 0; i < numSeqs; ++i) { + for (int i = 0; i < (int)numSeqs; ++i) { int len = uniformRandom( std::min(maxLen, batchSize - pos - numSeqs + i)) + 1; @@ -207,17 +187,14 @@ class FunctionCompare { } protected: - std::shared_ptr cpu; - std::shared_ptr gpu; + std::shared_ptr cpuFunc_; + std::shared_ptr gpuFunc_; std::vector cpuMemory_; std::vector gpuMemory_; - // inputs and outputs - BufferArgs inputs; - BufferArgs outputs; - BufferArgs cpuInputs_; - BufferArgs cpuOutputs_; - BufferArgs gpuInputs_; - BufferArgs gpuOutputs_; + std::vector cpuInputs_; + std::vector cpuOutputs_; + std::vector gpuInputs_; + std::vector gpuOutputs_; }; } // namespace paddle From b791dcddcc0d83c7397cbd075ccce08b60dfcb27 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 13 Jan 2017 17:56:44 +0800 Subject: [PATCH 026/370] Fix CrossMapNormal Test --- paddle/function/CMakeLists.txt | 2 +- paddle/function/CrossMapNormalOpTest.cpp | 51 ++++++++++++++---------- 2 files changed, 31 insertions(+), 22 deletions(-) diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 75a2acc55ec3d3..566fe53b14d85d 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -19,7 +19,7 @@ if(WITH_TESTING) # TODO: # file(GLOB test_files . *OpTest.cpp) # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files}) - # add_simple_unittest(CrossMapNormalOpTest) + add_simple_unittest(CrossMapNormalOpTest) add_simple_unittest(TensorShapeTest) add_simple_unittest(TensorTypeTest) add_simple_unittest(BufferArgTest) diff --git a/paddle/function/CrossMapNormalOpTest.cpp b/paddle/function/CrossMapNormalOpTest.cpp index d65d9310affd7c..da196a699cc32b 100644 --- a/paddle/function/CrossMapNormalOpTest.cpp +++ b/paddle/function/CrossMapNormalOpTest.cpp @@ -27,15 +27,19 @@ TEST(CrossMapNormal, real) { << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW << " size=" << size; - FunctionCompare compare("CrossMapNormal", - FuncConfig() - .set("size", size) - .set("scale", (real)1.5) - .set("pow", (real)0.5)); - Dims dims{numSamples, channels, imgSizeH, imgSizeW}; - compare.cmpWithArg({Tensor(nullptr, dims)}, - {Tensor(nullptr, dims), Tensor(nullptr, dims)}, - {}); + // init Test object + FunctionCompare test("CrossMapNormal", + FuncConfig() + .set("size", size) + .set("scale", (real)1.5) + .set("pow", (real)0.5)); + // prepare input arguments + TensorShape shape{numSamples, channels, imgSizeH, imgSizeW}; + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + // run Function + test.run(); } } } @@ -43,6 +47,9 @@ TEST(CrossMapNormal, real) { } } +#if 0 +// TODO(hedaoyuan): Now CrossMapNormalGrad not support ASSIGN_TO mode. +// Maybe all Function need support ASSIGN_TO mode. TEST(CrossMapNormalGrad, real) { for (size_t numSamples : {5, 32}) { for (size_t channels : {1, 5, 32}) { @@ -53,23 +60,25 @@ TEST(CrossMapNormalGrad, real) { << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW << " size=" << size; - FunctionCompare compare("CrossMapNormalGrad", - FuncConfig() - .set("size", size) - .set("scale", (real)1.5) - .set("pow", (real)0.5)); - Dims dims{numSamples, channels, imgSizeH, imgSizeW}; - compare.cmpWithArg({Tensor(nullptr, dims), - Tensor(nullptr, dims), - Tensor(nullptr, dims), - Tensor(nullptr, dims)}, - {Tensor(nullptr, dims)}, - {}); + FunctionCompare test("CrossMapNormalGrad", + FuncConfig() + .set("size", size) + .set("scale", (real)1.5) + .set("pow", (real)0.5)); + TensorShape shape{numSamples, channels, imgSizeH, imgSizeW}; + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + // run Function + test.run(); } } } } } } +#endif } // namespace paddle From 2efdd460dac2ff0ed15a9b8ba079bc83e10e6d0d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Fri, 13 Jan 2017 13:48:47 -0800 Subject: [PATCH 027/370] Add Dockerfile.dev for building a standard develop environment --- paddle/scripts/docker/Dockerfile.dev | 47 ++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 paddle/scripts/docker/Dockerfile.dev diff --git a/paddle/scripts/docker/Dockerfile.dev b/paddle/scripts/docker/Dockerfile.dev new file mode 100644 index 00000000000000..e60b9e30732583 --- /dev/null +++ b/paddle/scripts/docker/Dockerfile.dev @@ -0,0 +1,47 @@ +FROM ubuntu:16.04 +MAINTAINER PaddlePaddle Authors + +ARG DEBIAN_FRONTEND=noninteractive +ARG UBUNTU_MIRROR +RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' + +RUN apt-get update \ + && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ + libgoogle-glog-dev libgflags-dev libgtest-dev \ + libatlas-dev libatlas3-base g++ m4 python-pip \ + python-protobuf python-numpy python-dev swig openssh-server \ + wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ + sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ + clang-3.8 llvm-3.8 libclang-3.8-dev clang-format-3.8 clang-tidy-3.8 \ + && apt-get clean -y +RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib +RUN pip install -U BeautifulSoup docopt PyYAML pillow \ + sphinx sphinx_rtd_theme recommonmark jupyter + +ARG WITH_AVX +ARG WITH_DOC +ARG WITH_SWIG_PY +ARG WITH_STYLE_CHECK + +ENV WITH_GPU=OFF +ENV WITH_AVX=${WITH_AVX:-ON} +ENV WITH_DOC=${WITH_DOC:-ON} +ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} +ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} + +RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile + +# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service +RUN mkdir /var/run/sshd +RUN echo 'root:root' | chpasswd +RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config +RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config +EXPOSE 22 + +# Jupyter Notebook directory. +RUN mkdir /notes/ +WORKDIR "/notes" +EXPOSE 8888 + +RUN mkdir -p /opt/bin +COPY ./paddle/scripts/docker/entrypoint /opt/bin/ From 02480316016bd04c10676d8cb859c473f07a819f Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 12 Jan 2017 17:18:39 +0800 Subject: [PATCH 028/370] Add Status --- paddle/utils/Status.h | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 paddle/utils/Status.h diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h new file mode 100644 index 00000000000000..398ae182ab053e --- /dev/null +++ b/paddle/utils/Status.h @@ -0,0 +1,39 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ +#include +#include + +namespace paddle { + +class Status final : public std::exception { +public: + Status() noexcept {} + + Status(const std::string& msg) : errMsg_(new std::string(msg)) {} + + virtual const char* what() const noexcept override { + if (errMsg_) { + return errMsg_->data(); + } else { + return nullptr; + } + } + + inline bool isOK() const noexcept { return errMsg_ == nullptr; } + +private: + std::unique_ptr errMsg_; +}; + +} // namespace paddle From 6c20e08b042e1351a4ea8c97a74129d211b1d636 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 12 Jan 2017 17:55:36 +0800 Subject: [PATCH 029/370] Try using status to handle Paddle Error --- .../activations/ActivationFunction.cpp | 126 ++++++++++++++---- .../gserver/activations/ActivationFunction.h | 5 +- paddle/gserver/layers/Layer.cpp | 7 +- paddle/utils/Status.h | 36 ++++- paddle/utils/tests/CMakeLists.txt | 1 + paddle/utils/tests/test_Status.cpp | 29 ++++ 6 files changed, 169 insertions(+), 35 deletions(-) create mode 100644 paddle/utils/tests/test_Status.cpp diff --git a/paddle/gserver/activations/ActivationFunction.cpp b/paddle/gserver/activations/ActivationFunction.cpp index f8c4bcac2f8eb4..8a938cf7e9d730 100644 --- a/paddle/gserver/activations/ActivationFunction.cpp +++ b/paddle/gserver/activations/ActivationFunction.cpp @@ -69,8 +69,14 @@ static ClassRegistrar gActivationRegistrar; class IdentityActivation : public ActivationFunction { public: static const std::string name; - void forward(Argument& act) { (void)act; } - void backward(Argument& act) { (void)act; } + Status forward(Argument& act) { + (void)act; + return Status(); + } + Status backward(Argument& act) { + (void)act; + return Status(); + } const std::string& getName() const { return name; } }; const std::string IdentityActivation::name = ""; @@ -86,8 +92,14 @@ static InitFunction __reg_activation__identity([] { * \f] */ BEGIN_DEFINE_ACTIVATION(sigmoid) -void forward(Argument& act) { act.value->sigmoid(*act.value); } -void backward(Argument& act) { act.grad->sigmoidDerivative(*act.value); } +Status forward(Argument& act) { + act.value->sigmoid(*act.value); + return Status(); +} +Status backward(Argument& act) { + act.grad->sigmoidDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(sigmoid) /** @@ -103,9 +115,12 @@ MatrixPtr sftMaxDot_; MatrixPtr one_; public: -void forward(Argument& act) { act.value->softmax(*act.value); } +Status forward(Argument& act) { + act.value->softmax(*act.value); + return Status(); +} -void backward(Argument& act) { +Status backward(Argument& act) { MatrixPtr outputV = act.value; MatrixPtr outputG = act.grad; @@ -137,6 +152,7 @@ void backward(Argument& act) { act.grad->softmaxDerivative(*act.value, *sftMaxSum_); } + return Status(); } END_DEFINE_ACTIVATION(softmax) @@ -151,8 +167,11 @@ ACTIVATION_CLASS_NAME(softmax) softmax_; Argument argument_; public: -void forward(Argument& act) { - CHECK_EQ(act.value->getWidth(), 1UL); +Status forward(Argument& act) { + if (act.value->getWidth() != 1UL) { + return Status( + "Input width for each timestep of sequence softmax should be 1"); + } if (!argument_.value) { argument_.value = Matrix::create(nullptr, @@ -169,10 +188,14 @@ void forward(Argument& act) { auto starts = act.sequenceStartPositions->getVector(useGpu(act.deviceId)); act.value->sequenceSoftmax(*act.value, *starts); + return Status(); } -void backward(Argument& act) { - CHECK_EQ(act.grad->getWidth(), 1UL); +Status backward(Argument& act) { + if (act.value->getWidth() != 1UL) { + return Status( + "Input width for each timestep of sequence softmax should be 1"); + } size_t numSequences = act.getNumSequences(); const int* starts = act.sequenceStartPositions->getData(false); @@ -186,6 +209,7 @@ void backward(Argument& act) { softmax_.backward(argument_); } + return Status(); } END_DEFINE_ACTIVATION(sequence_softmax) @@ -200,9 +224,15 @@ END_DEFINE_ACTIVATION(sequence_softmax) * 0 otherwise. */ BEGIN_DEFINE_ACTIVATION(relu) -void forward(Argument& act) { act.value->relu(*act.value); } +Status forward(Argument& act) { + act.value->relu(*act.value); + return Status(); +} -void backward(Argument& act) { act.grad->reluDerivative(*act.value); } +Status backward(Argument& act) { + act.grad->reluDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(relu) /** @@ -219,9 +249,15 @@ END_DEFINE_ACTIVATION(relu) * TODO(yuyang18): Remove magic number 24 or make it configuable. */ BEGIN_DEFINE_ACTIVATION(brelu) -void forward(Argument& act) { act.value->brelu(*act.value); } +Status forward(Argument& act) { + act.value->brelu(*act.value); + return Status(); +} -void backward(Argument& act) { act.grad->breluDerivative(*act.value); } +Status backward(Argument& act) { + act.grad->breluDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(brelu) /** @@ -231,9 +267,15 @@ END_DEFINE_ACTIVATION(brelu) * \f] */ BEGIN_DEFINE_ACTIVATION(tanh) -void forward(Argument& act) { act.value->tanh(*act.value); } +Status forward(Argument& act) { + act.value->tanh(*act.value); + return Status(); +} -void backward(Argument& act) { act.grad->tanhDerivative(*act.value); } +Status backward(Argument& act) { + act.grad->tanhDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(tanh) /** @@ -248,10 +290,14 @@ real a, b; public: ACTIVATION_CLASS_NAME(stanh)() : a(1.7159), b(2. / 3.) {} -void forward(Argument& act) { act.value->scaledTanh(*act.value, a, b); } +Status forward(Argument& act) { + act.value->scaledTanh(*act.value, a, b); + return Status(); +} -void backward(Argument& act) { +Status backward(Argument& act) { act.grad->scaledTanhDerivative(*act.value, a, b); + return Status(); } END_DEFINE_ACTIVATION(stanh) @@ -262,9 +308,15 @@ END_DEFINE_ACTIVATION(stanh) * \f] */ BEGIN_DEFINE_ACTIVATION(softrelu) -void forward(Argument& act) { act.value->softrelu(*act.value); } +Status forward(Argument& act) { + act.value->softrelu(*act.value); + return Status(); +} -void backward(Argument& act) { act.grad->softreluDerivative(*act.value); } +Status backward(Argument& act) { + act.grad->softreluDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(softrelu) /** @@ -280,7 +332,7 @@ END_DEFINE_ACTIVATION(softrelu) * 0 if z=0 */ BEGIN_DEFINE_ACTIVATION(abs) -void forward(Argument& act) { +Status forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -290,9 +342,13 @@ void forward(Argument& act) { act.in->copyFrom(*act.value); act.value->abs2(*act.value); + return Status(); } -void backward(Argument& act) { act.grad->absDerivative(*act.in); } +Status backward(Argument& act) { + act.grad->absDerivative(*act.in); + return Status(); +} END_DEFINE_ACTIVATION(abs) /** @@ -302,7 +358,7 @@ END_DEFINE_ACTIVATION(abs) * \f] */ BEGIN_DEFINE_ACTIVATION(square) -void forward(Argument& act) { +Status forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -312,9 +368,13 @@ void forward(Argument& act) { act.in->copyFrom(*act.value); act.value->square2(*act.value); + return Status(); } -void backward(Argument& act) { act.grad->squareDerivative(*act.in); } +Status backward(Argument& act) { + act.grad->squareDerivative(*act.in); + return Status(); +} END_DEFINE_ACTIVATION(square) /** @@ -324,9 +384,15 @@ END_DEFINE_ACTIVATION(square) * \f] */ BEGIN_DEFINE_ACTIVATION(exponential) -void forward(Argument& act) { act.value->exp2(*act.value); } +Status forward(Argument& act) { + act.value->exp2(*act.value); + return Status(); +} -void backward(Argument& act) { act.grad->expDerivative(*act.value); } +Status backward(Argument& act) { + act.grad->expDerivative(*act.value); + return Status(); +} END_DEFINE_ACTIVATION(exponential) /** @@ -336,7 +402,7 @@ END_DEFINE_ACTIVATION(exponential) * \f] */ BEGIN_DEFINE_ACTIVATION(log) -void forward(Argument& act) { +Status forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -346,9 +412,13 @@ void forward(Argument& act) { act.in->copyFrom(*act.value); act.value->log2(*act.value); + return Status(); } -void backward(Argument& act) { act.grad->dotDiv(*act.grad, *act.in); } +Status backward(Argument& act) { + act.grad->dotDiv(*act.grad, *act.in); + return Status(); +} END_DEFINE_ACTIVATION(log) ActivationFunction* ActivationFunction::create(const std::string& type) { diff --git a/paddle/gserver/activations/ActivationFunction.h b/paddle/gserver/activations/ActivationFunction.h index 601e3b6c0cd401..ad395ac28da7d9 100644 --- a/paddle/gserver/activations/ActivationFunction.h +++ b/paddle/gserver/activations/ActivationFunction.h @@ -15,6 +15,7 @@ limitations under the License. */ #pragma once #include #include +#include "paddle/utils/Status.h" namespace paddle { @@ -48,7 +49,7 @@ class ActivationFunction { * * Usually, act is Layer::output_ */ - virtual void forward(Argument& act) = 0; + virtual Status forward(Argument& act) = 0; /** * @brief Backward propagaion @@ -57,7 +58,7 @@ class ActivationFunction { * - Before calling backward(), act.grad = dE / dy, where E is the error/cost * - After backward() returns, act.grad = dE / dx = (dE/dy) * (dy/dx) */ - virtual void backward(Argument& act) = 0; + virtual Status backward(Argument& act) = 0; virtual const std::string& getName() const = 0; }; diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index c47943f81c0158..06c936c3aec670 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/math/SparseMatrix.h" #include "paddle/utils/Logging.h" +#include "paddle/utils/Status.h" #include "AddtoLayer.h" #include "CRFLayer.h" @@ -334,7 +335,8 @@ void Layer::showOutputStats() { void Layer::forwardActivation() { /* activation */ - activation_->forward(output_); + auto status = activation_->forward(output_); + CHECK(status.isOK()) << status.what(); /* dropout */ if (config_.drop_rate() > 0) { @@ -372,7 +374,8 @@ void Layer::backwardActivation() { oGrad->dotMul(*oGrad, *dropOutMask_); } - activation_->backward(output_); + auto status = activation_->backward(output_); + CHECK(status.isOK()) << status.what(); } void Layer::forwardDropOut() { diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h index 398ae182ab053e..3456d7b686f0d9 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Status.h @@ -11,18 +11,44 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ + +#pragma once + #include #include namespace paddle { +/** + * Status is Paddle error code. It only contain a std::string as error message. + * Although Status inherits the std::exception, but do not throw it except you + * know what you are doing. + */ class Status final : public std::exception { public: + /** + * Default Status. OK + */ Status() noexcept {} - Status(const std::string& msg) : errMsg_(new std::string(msg)) {} + /** + * @brief Create Status with error message + * @param msg + */ + explicit Status(const std::string& msg) : errMsg_(new std::string(msg)) {} + + /** + * @brief set a error message for status. + * @param msg + */ + inline void set(const std::string& msg) noexcept { + errMsg_.reset(new std::string(msg)); + } - virtual const char* what() const noexcept override { + /** + * @brief what will return the error message. If status is OK, return nullptr. + */ + const char* what() const noexcept override { if (errMsg_) { return errMsg_->data(); } else { @@ -30,10 +56,14 @@ class Status final : public std::exception { } } + /** + * @brief isOK + * @return true if OK. + */ inline bool isOK() const noexcept { return errMsg_ == nullptr; } private: - std::unique_ptr errMsg_; + std::shared_ptr errMsg_; }; } // namespace paddle diff --git a/paddle/utils/tests/CMakeLists.txt b/paddle/utils/tests/CMakeLists.txt index 26fafbd1ab3f29..a1cc32668d5100 100644 --- a/paddle/utils/tests/CMakeLists.txt +++ b/paddle/utils/tests/CMakeLists.txt @@ -4,6 +4,7 @@ add_simple_unittest(test_CustomStackTrace) add_simple_unittest(test_ThreadBarrier) add_simple_unittest(test_SpinLock) add_simple_unittest(test_SIMDFlags) +add_simple_unittest(test_Status) add_executable( test_CustomStackTracePrint diff --git a/paddle/utils/tests/test_Status.cpp b/paddle/utils/tests/test_Status.cpp new file mode 100644 index 00000000000000..e2c2ae537d8b62 --- /dev/null +++ b/paddle/utils/tests/test_Status.cpp @@ -0,0 +1,29 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/utils/Status.h" + +#include + +TEST(Status, testAll) { + paddle::Status status; + ASSERT_TRUE(status.isOK()); + status.set("I'm the error"); + ASSERT_FALSE(status.isOK()); + ASSERT_STREQ("I'm the error", status.what()); + + paddle::Status status2("error2"); + ASSERT_FALSE(status2.isOK()); + ASSERT_STREQ("error2", status2.what()); +} From df62df7e6726b502d34ed5805ba26f44b7cfdd92 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sat, 14 Jan 2017 16:30:24 +0800 Subject: [PATCH 030/370] Partially follow comments --- paddle/math/RowBuffer.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h index bb55ca5f9f0d24..0edcefd7562460 100644 --- a/paddle/math/RowBuffer.h +++ b/paddle/math/RowBuffer.h @@ -47,7 +47,7 @@ class RowBuffer { */ inline void resize(int rowCnt) { if (preallocatedBuf_) { - CHECK(preallocatedBuf_->getSize() < rowCnt * width_ * sizeof(real)); + CHECK(preallocatedBuf_->getSize() >= rowCnt * width_ * sizeof(real)); } else { rowStore_.resize(rowCnt * width_); } @@ -107,7 +107,7 @@ class RowBuffer { */ inline size_t getRowCount() const { if (preallocatedBuf_) { - return preallocatedBuf_->getSize() / sizeof(float) / width_; + return preallocatedBuf_->getSize() / sizeof(real) / width_; } else { return rowStore_.size() / width_; } @@ -117,7 +117,7 @@ class RowBuffer { * @brief get is this buffer can automatically grow or not. * @return ture if can automacitally grow. */ - inline bool isAutoGrowth() const { return preallocatedBuf_ == nullptr; } + inline bool isAutoGrowth() const { return !preallocatedBuf_; } /** * @brief return the width of matrix. a.k.a length of row. From cdf6af64a87c46da070da139925c32cc4064e6d3 Mon Sep 17 00:00:00 2001 From: liaogang Date: Sat, 14 Jan 2017 21:42:04 +0800 Subject: [PATCH 031/370] Add external openblas --- .travis.yml | 2 +- cmake/external/openblas.cmake | 32 +++++++++++++++---- cmake/system.cmake | 4 +-- .../build_and_install/build_from_source_en.md | 5 +-- paddle/scripts/travis/before_install.osx.sh | 4 +-- paddle/scripts/travis/build_and_test.sh | 2 +- paddle/scripts/travis/docs.sh | 2 +- 7 files changed, 34 insertions(+), 17 deletions(-) diff --git a/.travis.yml b/.travis.yml index 0705baa1aca8b4..162bebba091d84 100644 --- a/.travis.yml +++ b/.travis.yml @@ -25,9 +25,9 @@ addons: packages: - gcc-4.8 - g++-4.8 + - gfortran-4.8 - git - build-essential - - libatlas-base-dev - python - python-pip - python2.7-dev diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 0e8c29c831c823..43ebb39cd6f3aa 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -15,7 +15,6 @@ INCLUDE(cblas) IF(NOT ${CBLAS_FOUND}) - MESSAGE(FATAL_ERROR "Please install OpenBlas, MKL or ATLAS.") INCLUDE(ExternalProject) SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas) @@ -28,20 +27,39 @@ IF(NOT ${CBLAS_FOUND}) SET(CBLAS_LIBRARIES "${CBLAS_INSTALL_DIR}/lib/libopenblas.a" CACHE FILEPATH "openblas library" FORCE) ENDIF(WIN32) + IF(CMAKE_COMPILER_IS_GNUCC) + ENABLE_LANGUAGE(Fortran) + LIST(APPEND CBLAS_LIBRARIES gfortran pthread) + ENDIF(CMAKE_COMPILER_IS_GNUCC) + + IF(NOT CMAKE_Fortran_COMPILER) + MESSAGE(FATAL_ERROR "To build lapack in libopenblas, " + "you need to set gfortran compiler: cmake .. -DCMAKE_Fortran_COMPILER=...") + ENDIF(NOT CMAKE_Fortran_COMPILER) + ExternalProject_Add( openblas - ${EXTERNAL_PROJECT_LOG_ARGS} - URL "https://github.com/xianyi/OpenBLAS/archive/v0.2.19.tar.gz" + GIT_REPOSITORY https://github.com/xianyi/OpenBLAS.git + GIT_TAG v0.2.19 PREFIX ${CBLAS_SOURCES_DIR} INSTALL_DIR ${CBLAS_INSTALL_DIR} BUILD_IN_SOURCE 1 - CONFIGURE_COMMAND "" - BUILD_COMMAND make CC=${CMAKE_C_COMPILER} FC=${CMAKE_Fortran_COMPILER} - INSTALL_COMMAND make install PREFIX= + BUILD_COMMAND ${CMAKE_MAKE_PROGRAM} FC=${CMAKE_Fortran_COMPILER} CC=${CMAKE_C_COMPILER} HOSTCC=${CMAKE_C_COMPILER} NO_SHARED=1 libs netlib + INSTALL_COMMAND ${CMAKE_MAKE_PROGRAM} install NO_SHARED=1 PREFIX= UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + ) + + ExternalProject_Add_Step( + openblas lapacke_install + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_mangling_with_flags.h" "${CBLAS_INSTALL_DIR}/include/lapacke_mangling.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke.h" "${CBLAS_INSTALL_DIR}/include/lapacke.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_config.h" "${CBLAS_INSTALL_DIR}/include/lapacke_config.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_utils.h" "${CBLAS_INSTALL_DIR}/include/lapacke_utils.h" + DEPENDEES install ) LIST(APPEND external_project_dependencies openblas) -ENDIF() +ENDIF(NOT ${CBLAS_FOUND}) INCLUDE_DIRECTORIES(${CBLAS_INC_DIR}) diff --git a/cmake/system.cmake b/cmake/system.cmake index 788db404ebfb6f..1e9f79496441c8 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -47,7 +47,7 @@ SET(EXTERNAL_PROJECT_LOG_ARGS LOG_DOWNLOAD 0 # Wrap download in script to log output LOG_UPDATE 1 # Wrap update in script to log output LOG_CONFIGURE 1 # Wrap configure in script to log output - LOG_BUILD 1 # Wrap build in script to log output + LOG_BUILD 0 # Wrap build in script to log output LOG_TEST 1 # Wrap test in script to log output - LOG_INSTALL 1 # Wrap install in script to log output + LOG_INSTALL 0 # Wrap install in script to log output ) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 6954be3b2bb956..1abd7b698bc09c 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -64,7 +64,8 @@ As a simple example, consider the following: 1. **BLAS Dependencies(optional)** - Paddle will find BLAS from system's default path. But you can specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. + CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. + To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. ```bash # specify MKL @@ -99,7 +100,7 @@ As a simple example, consider the following: ```bash # necessary sudo apt-get update - sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev git + sudo apt-get install -y g++ make cmake build-essential python python-pip libpython-dev git sudo pip install wheel numpy sudo pip install 'protobuf>=3.0.0' ``` diff --git a/paddle/scripts/travis/before_install.osx.sh b/paddle/scripts/travis/before_install.osx.sh index 7036f971fdd7ba..80f031a74e7052 100755 --- a/paddle/scripts/travis/before_install.osx.sh +++ b/paddle/scripts/travis/before_install.osx.sh @@ -1,6 +1,4 @@ #!/bin/bash brew update brew tap homebrew/science -brew install python -sudo pip install --upgrade protobuf -brew install swig openblas md5sha1sum protobuf +brew install openblas swig md5sha1sum diff --git a/paddle/scripts/travis/build_and_test.sh b/paddle/scripts/travis/build_and_test.sh index fd3aeb02b21d65..5e6350b5745859 100755 --- a/paddle/scripts/travis/build_and_test.sh +++ b/paddle/scripts/travis/build_and_test.sh @@ -6,7 +6,7 @@ if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then export PYTHONPATH=/opt/python/2.7.12/lib/python2.7/site-packages export PYTHONHOME=/opt/python/2.7.12 export PATH=/opt/python/2.7.12/bin:${PATH} - cmake .. -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS} + cmake .. -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS} NRPOC=`nproc` make -j $NPROC make coveralls diff --git a/paddle/scripts/travis/docs.sh b/paddle/scripts/travis/docs.sh index bdafb145bcd4e5..6b43cad20b76e9 100755 --- a/paddle/scripts/travis/docs.sh +++ b/paddle/scripts/travis/docs.sh @@ -4,7 +4,7 @@ source ./common.sh # Compile Documentation only. -cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON ${EXTRA_CMAKE_OPTS} +cmake .. -DCMAKE_BUILD_TYPE=Debug -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DWITH_GPU=OFF -DWITH_DOC=ON ${EXTRA_CMAKE_OPTS} make paddle_docs paddle_docs_cn # check websites for broken links From 589bb84241baca1f53cee290b55ced88c331fc02 Mon Sep 17 00:00:00 2001 From: liaogang Date: Sat, 14 Jan 2017 21:53:52 +0800 Subject: [PATCH 032/370] Add openblas log configuration --- cmake/external/openblas.cmake | 1 + 1 file changed, 1 insertion(+) diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 43ebb39cd6f3aa..29d17691db9f45 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -39,6 +39,7 @@ IF(NOT ${CBLAS_FOUND}) ExternalProject_Add( openblas + ${EXTERNAL_PROJECT_LOG_ARGS} GIT_REPOSITORY https://github.com/xianyi/OpenBLAS.git GIT_TAG v0.2.19 PREFIX ${CBLAS_SOURCES_DIR} From 35d7b17f79342431c7e392e4644140326d37feca Mon Sep 17 00:00:00 2001 From: liaogang Date: Sun, 15 Jan 2017 00:02:57 +0800 Subject: [PATCH 033/370] Disable externel python --- cmake/external/python.cmake | 1 + 1 file changed, 1 insertion(+) diff --git a/cmake/external/python.cmake b/cmake/external/python.cmake index 29247d5c3d474a..209e679f2cb217 100644 --- a/cmake/external/python.cmake +++ b/cmake/external/python.cmake @@ -31,6 +31,7 @@ IF(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) "please use pip to upgrade protobuf.") ENDIF(${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") ELSE(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) + MESSAGE(FATAL_ERROR "Please install python 2.7 before building PaddlePaddle.") ##################################### PYTHON ######################################## SET(PYTHON_SOURCES_DIR ${THIRD_PARTY_PATH}/python) SET(PYTHON_INSTALL_DIR ${THIRD_PARTY_PATH}/install/python) From 94618d8776229254d52075f9aabd998d7ed6be8d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 16 Jan 2017 10:20:26 +0800 Subject: [PATCH 034/370] Fix merge error. --- paddle/math/SparseRowMatrix.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index d7dfb2fe579950..c05fc98ff9fe73 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -19,8 +19,8 @@ limitations under the License. */ #include #include "Matrix.h" #include "RowBuffer.h" -#include "paddle/utils/CommandLineParser.h" #include "paddle/utils/Util.h" + DECLARE_bool(allow_inefficient_sparse_update); namespace paddle { From 66851af107c9eb3e6cea8485db467daf9b6cc078 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 16 Jan 2017 10:44:51 +0800 Subject: [PATCH 035/370] Detect CentOS 7 in CMake --- cmake/system.cmake | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/cmake/system.cmake b/cmake/system.cmake index 788db404ebfb6f..d40499c39da736 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -21,6 +21,7 @@ ELSE(WIN32) SET(MACOS_VERSION ${VERSION}) SET(HOST_SYSTEM "macosx") ELSE(APPLE) + IF(EXISTS "/etc/issue") FILE(READ "/etc/issue" LINUX_ISSUE) IF(LINUX_ISSUE MATCHES "CentOS") @@ -31,6 +32,14 @@ ELSE(WIN32) SET(HOST_SYSTEM "ubuntu") ENDIF() ENDIF(EXISTS "/etc/issue") + + IF(EXISTS "/etc/redhat-release") + FILE(READ "/etc/redhat-release" LINUX_ISSUE) + IF(LINUX_ISSUE MATCHES "CentOS") + SET(HOST_SYSTEM "centos") + ENDIF() + ENDIF(EXISTS "/etc/redhat-release") + ENDIF(APPLE) ENDIF(WIN32) From 014a72713009b52a6e6642adf2c360b2e1097ff7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 16 Jan 2017 12:04:15 +0800 Subject: [PATCH 036/370] Add todos --- paddle/math/RowBuffer.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h index 0edcefd7562460..dbb829c4e24a65 100644 --- a/paddle/math/RowBuffer.h +++ b/paddle/math/RowBuffer.h @@ -126,6 +126,8 @@ class RowBuffer { inline size_t getWidth() const { return width_; } private: + //! TODO(yuyang18): Add resize method to CpuMemHandlePtr, then we can get rid + //! of std::vector here. CpuMemHandlePtr preallocatedBuf_; std::vector> rowStore_; size_t width_; From 741637eba41f66b51bd1764900e75cc7d5bd9ce6 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 16 Jan 2017 16:14:29 +0800 Subject: [PATCH 037/370] Add printf method to Status. --- paddle/utils/Status.h | 23 +++++++++++++++++++++++ paddle/utils/tests/test_Status.cpp | 5 +++++ 2 files changed, 28 insertions(+) diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h index 3456d7b686f0d9..db1edfb7c735fc 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Status.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include #include @@ -45,6 +46,28 @@ class Status final : public std::exception { errMsg_.reset(new std::string(msg)); } + /** + * @brief set a error message for status. Use C style printf + * @param fmt + */ + template + inline void setByPrintf(const char* fmt, ARGS... args) noexcept { + constexpr size_t bufferSize = 4096; + char buffer[bufferSize]; + snprintf(buffer, bufferSize, fmt, args...); + errMsg_.reset(new std::string(buffer)); + } + + /** + * create a error status by C style printf. + */ + template + inline static Status printf(const char* fmt, ARGS... args) noexcept { + Status s; + s.setByPrintf(fmt, args...); + return s; + } + /** * @brief what will return the error message. If status is OK, return nullptr. */ diff --git a/paddle/utils/tests/test_Status.cpp b/paddle/utils/tests/test_Status.cpp index e2c2ae537d8b62..04cef095792c73 100644 --- a/paddle/utils/tests/test_Status.cpp +++ b/paddle/utils/tests/test_Status.cpp @@ -26,4 +26,9 @@ TEST(Status, testAll) { paddle::Status status2("error2"); ASSERT_FALSE(status2.isOK()); ASSERT_STREQ("error2", status2.what()); + + int i = 3; + auto status3 = paddle::Status::printf("error%d", i); + ASSERT_FALSE(status3.isOK()); + ASSERT_STREQ("error3", status3.what()); } From 1e233171ff59decc3d179e30ab29e65735583747 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 16 Jan 2017 17:18:24 +0800 Subject: [PATCH 038/370] Fix merge error --- paddle/function/Function.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/function/Function.cpp b/paddle/function/Function.cpp index 2632c17e3ac61a..f47d55a4ade97d 100644 --- a/paddle/function/Function.cpp +++ b/paddle/function/Function.cpp @@ -96,7 +96,8 @@ void BufferArgs::addArg(const GpuSparseMatrix& arg, ArgType argType) { void BufferArgs::addArg(const Matrix& matrix, const IVector& vector, ArgType argType) { - args_.push_back(std::make_shared(matrix, vector, argType)); + _args_.push_back(new SequenceArg(matrix, vector, argType)); + addArg(*_args_.back()); } ClassRegistrar FunctionBase::funcRegistrar_; From 19cb2a7fb3e750eb2a81337bed063782977d7195 Mon Sep 17 00:00:00 2001 From: zhangruiqing01 Date: Mon, 16 Jan 2017 11:26:29 +0800 Subject: [PATCH 039/370] fix default value of cos_sim scale --- python/paddle/trainer/config_parser.py | 2 +- python/paddle/trainer_config_helpers/layers.py | 2 +- .../tests/configs/protostr/test_ntm_layers.protostr | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 674b5ac58b6feb..8e9c40877bac59 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2628,7 +2628,7 @@ def __init__(self, @config_layer('cos') class CosSimLayer(LayerBase): - def __init__(self, name, inputs, cos_scale=5, device=None): + def __init__(self, name, inputs, cos_scale=1, device=None): super(CosSimLayer, self).__init__( name, 'cos', 1, inputs=inputs, device=device) config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 9b6e5774bc82dc..23c0e9174ff958 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1673,7 +1673,7 @@ def trans_layer(input, name=None, layer_attr=None): @wrap_name_default() @layer_support() -def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None): +def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): """ Cosine Similarity Layer. The cosine similarity equation is here. diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr index b30bbb2a4e24d7..c1bfdf1b19c61d 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr @@ -79,7 +79,7 @@ layers { inputs { input_layer_name: "b" } - cos_scale: 5 + cos_scale: 1 } layers { name: "__cos_sim_1__" @@ -92,7 +92,7 @@ layers { inputs { input_layer_name: "c" } - cos_scale: 5 + cos_scale: 1 } layers { name: "__sum_to_one_norm_layer_0__" From f8c9c889c34dd3530b899fc12523579802d4f582 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 16 Jan 2017 21:30:44 +0800 Subject: [PATCH 040/370] Fix CrossMapNormalTest --- paddle/function/CrossMapNormalOp.cpp | 9 +++++++-- paddle/function/CrossMapNormalOpTest.cpp | 4 ---- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 92980c503fdaaa..8e7dc72524a768 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -188,8 +188,13 @@ class CrossMapNormalGradFunc : public FunctionBase { CHECK(inputs[0].shape() == inputs[3].shape()); CHECK(inputs[0].shape() == outputs[0].shape()); - // TODO(hedaoyuan): need support ASSIGN_TO mode. - CHECK_EQ(outputs[0].getArgType(), ADD_TO); + if (outputs[0].getArgType() != ADD_TO) { + // Currently, some algorithm implementations are ASSIGN_TO mode, + // if need to support the ADD_TO calculation, need to clear the output. + typename Tensor::Vector tmp( + outputs[0].shape().getElements(), outputs[0].data()); + tmp.zero(); + } size_t samples = inputs[0].shape()[0]; size_t channels = inputs[0].shape()[1]; diff --git a/paddle/function/CrossMapNormalOpTest.cpp b/paddle/function/CrossMapNormalOpTest.cpp index da196a699cc32b..51f5da81bfc9ae 100644 --- a/paddle/function/CrossMapNormalOpTest.cpp +++ b/paddle/function/CrossMapNormalOpTest.cpp @@ -47,9 +47,6 @@ TEST(CrossMapNormal, real) { } } -#if 0 -// TODO(hedaoyuan): Now CrossMapNormalGrad not support ASSIGN_TO mode. -// Maybe all Function need support ASSIGN_TO mode. TEST(CrossMapNormalGrad, real) { for (size_t numSamples : {5, 32}) { for (size_t channels : {1, 5, 32}) { @@ -79,6 +76,5 @@ TEST(CrossMapNormalGrad, real) { } } } -#endif } // namespace paddle From 07787f72ba69dd0bbca4ee01f84c59fb34dc02c9 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Mon, 16 Jan 2017 09:19:05 -0800 Subject: [PATCH 041/370] clarify and fix problems in paddle on aws k8s (create cluster part) --- doc/howto/usage/k8s/k8s_aws_en.md | 138 ++++++++++++++++++++---------- 1 file changed, 93 insertions(+), 45 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index c776ba9eb97a8c..bd9eee72963932 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -2,18 +2,18 @@ ## Create AWS Account and IAM Account -AWS account allow us to manage AWS from Web Console. Amazon AMI enable us to manage AWS from command line interface. +AWS account allow us to manage AWS from Web Console. Amazon IAM enable us to manage AWS from command line interface. -We need to create an AMI user with sufficient privilege to create kubernetes cluster on AWS. +We need to create an IAM user with sufficient privilege to create kubernetes cluster on AWS. To sign up an AWS account, please follow [this guide](http://docs.aws.amazon.com/lambda/latest/dg/setting-up.html). -To create users and user groups under an AWS account, please +To create IAM users and user groups under an AWS account, please follow [this guide](http://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html). -Please be aware that this tutorial needs the following privileges for the user in AMI: +Please be aware that this tutorial needs the following privileges for the user in IAM: - AmazonEC2FullAccess - AmazonS3FullAccess @@ -27,14 +27,6 @@ Please be aware that this tutorial needs the following privileges for the user i - AWSKeyManagementServicePowerUser -By the time we write this tutorial, we noticed that Chinese AWS users -might suffer from authentication problems when running this tutorial. -Our solution is that we create a VM instance with the default Amazon -AMI and in the same zone as our cluster runs, so we can SSH to this VM -instance as a tunneling server and control our cluster and jobs from -it. - - ## PaddlePaddle on AWS Here we will show you step by step on how to run PaddlePaddle training on AWS cluster. @@ -59,7 +51,7 @@ gpg2 --fingerprint FC8A365E ``` The correct key fingerprint is `18AD 5014 C99E F7E3 BA5F 6CE9 50BD D3E0 FC8A 365E` -Go to the [releases](https://github.com/coreos/kube-aws/releases) and download the latest release tarball and detached signature (.sig) for your architecture. +Go to the [releases](https://github.com/coreos/kube-aws/releases) and download release tarball (this tutorial is using v0.9.1) and detached signature (.sig) for your architecture. Validate the tarball's GPG signature: @@ -88,14 +80,22 @@ mv ${PLATFORM}/kube-aws /usr/local/bin [kubectl](https://kubernetes.io/docs/user-guide/kubectl-overview/) is a command line interface for running commands against Kubernetes clusters. -Go to the [releases](https://github.com/kubernetes/kubernetes/releases) and download the latest release tarball. - -Extract the tarball and then concate the kubernetes binaries directory into PATH: +Download `kubectl` from the Kubernetes release artifact site with the `curl` tool. ``` -export PATH=/platforms/linux/amd64:$PATH # The exact path depend on your platform +# OS X +curl -O https://storage.googleapis.com/kubernetes-release/release/"$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)"/bin/darwin/amd64/kubectl + +# Linux +curl -O https://storage.googleapis.com/kubernetes-release/release/"$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)"/bin/linux/amd64/kubectl ``` +Make the kubectl binary executable and move it to your PATH (e.g. `/usr/local/bin`): + +``` +chmod +x ./kubectl +sudo mv ./kubectl /usr/local/bin/kubectl +``` ### Configure AWS Credentials @@ -109,17 +109,18 @@ aws configure ``` -Fill in the required fields (You can get your AWS aceess key id and AWS secrete access key by following [this](http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html) instruction): +Fill in the required fields: ``` AWS Access Key ID: YOUR_ACCESS_KEY_ID AWS Secrete Access Key: YOUR_SECRETE_ACCESS_KEY -Default region name: us-west-2 +Default region name: us-west-1 Default output format: json - ``` +`YOUR_ACCESS_KEY_ID`, and `YOUR_SECRETE_ACCESS_KEY` is the IAM key and secret from [Create AWS Account and IAM Account](#create-aws-account-and-iam-account) + Verify that your credentials work by describing any instances you may already have running on your account: ``` @@ -134,7 +135,9 @@ The keypair that will authenticate SSH access to your EC2 instances. The public Follow [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) to create a EC2 key pair -After creating a key pair, you will use the name you gave the keys to configure the cluster. Key pairs are only available to EC2 instances in the same region. +After creating a key pair, you will use the key pair name to configure the cluster. + +Key pairs are only available to EC2 instances in the same region. We are using us-west-1 in our tutorial, so make sure to creat key pairs in that region (N. California). #### KMS key @@ -143,12 +146,12 @@ Amazon KMS keys are used to encrypt and decrypt cluster TLS assets. If you alrea You can create a KMS key in the AWS console, or with the aws command line tool: ``` -$ aws kms --region=us-west-1 create-key --description="kube-aws assets" +aws kms --region=us-west-1 create-key --description="kube-aws assets" { "KeyMetadata": { "CreationDate": 1458235139.724, "KeyState": "Enabled", - "Arn": "arn:aws:kms:us-west-1:xxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx", + "Arn": "arn:aws:kms:us-west-1:aaaaaaaaaaaaa:key/xxxxxxxxxxxxxxxxxxx", "AWSAccountId": "xxxxxxxxxxxxx", "Enabled": true, "KeyUsage": "ENCRYPT_DECRYPT", @@ -158,11 +161,11 @@ $ aws kms --region=us-west-1 create-key --description="kube-aws assets" } ``` -You will use the `KeyMetadata.Arn` string to identify your KMS key in the init step. +We will need to use the value of `Arn` later. And then you need to add several inline policies in your user permission. -Go to AMI user page, click on `Add inline policy` button, and then select `Custom Policy` +Go to IAM user page, click on `Add inline policy` button, and then select `Custom Policy` paste into following inline policies: @@ -178,7 +181,7 @@ paste into following inline policies: "kms:Encrypt" ], "Resource": [ - "arn:aws:kms:*:xxxxxxxxx:key/*" + "arn:aws:kms:*:AWS_ACCOUNT_ID:key/*" ] }, { @@ -194,29 +197,37 @@ paste into following inline policies: "cloudformation:DescribeStackEvents" ], "Resource": [ - "arn:aws:cloudformation:us-west-1:xxxxxxxxx:stack/YOUR_CLUSTER_NAME/*" + "arn:aws:cloudformation:us-west-1:AWS_ACCOUNT_ID:stack/MY_CLUSTER_NAME/*" ] } ] } ``` +`AWS_ACCOUNT_ID`: You can get it from following command line: + +``` +aws sts get-caller-identity --output text --query Account +``` + +`MY_CLUSTER_NAME`: Pick a MY_CLUSTER_NAME that you like, you will use it later as well. #### External DNS name -When the cluster is created, the controller will expose the TLS-secured API on a public IP address. You will need to create an A record for the external DNS hostname you want to point to this IP address. You can find the API external IP address after the cluster is created by invoking kube-aws status. +When the cluster is created, the controller will expose the TLS-secured API on a DNS name. + +The A record of that DNS name needs to be point to the cluster ip address. + +We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. #### S3 bucket You need to create an S3 bucket before startup the Kubernetes cluster. -command (need to have a global unique name): +There are some bug in aws cli in creating S3 bucket, so let's use [web console](https://console.aws.amazon.com/s3/home?region=us-west-1). -``` -paddle aws s3api --region=us-west-1 create-bucket --bucket bucket-name -``` +Click on `Create Bucket`, fill in a unique BUCKET_NAME, and make sure region is us-west-1 (Northern California). -If you get an error message, try a different bucket name. The bucket name needs to be globally unique. #### Initialize an asset directory @@ -230,33 +241,44 @@ $ cd my-cluster Initialize the cluster CloudFormation stack with the KMS Arn, key pair name, and DNS name from the previous step: ``` -$ kube-aws init \ ---cluster-name=my-cluster-name \ ---external-dns-name=my-cluster-endpoint \ +kube-aws init \ +--cluster-name=MY_CLUSTER_NAME \ +--external-dns-name=MY_EXTERNAL_DNS_NAME \ --region=us-west-1 \ ---availability-zone=us-west-1c \ ---key-name=key-pair-name \ +--availability-zone=us-west-1a \ +--key-name=KEY_PAIR_NAME \ --kms-key-arn="arn:aws:kms:us-west-1:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" ``` -Here `us-west-1c` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. +`MY_CLUSTER_NAME`: the one you picked in [KMS key](#kms-key) + +`MY_EXTERNAL_DNS_NAME`: see [External DNS name](#external-dns-name) -Please check if `us-west-1c` is supported by `aws ec2 --region us-west-1 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-1a`, or `us-west-1b`) +`KEY_PAIR_NAME`: see [EC2 key pair](#ec2-key-pair) + +`--kms-key-arn`: the "Arn" in [KMS key](#kms-key) + +Here `us-west-1a` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. + +Please check if `us-west-1a` is supported by `aws ec2 --region us-west-1 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-1a`, or `us-west-1b`) + +Note: please don't use `us-west-1c`. Subnets can currently only be created in the following availability zones: us-west-1b, us-west-1a. There will now be a cluster.yaml file in the asset directory. This is the main configuration file for your cluster. + #### Render contents of the asset directory In the simplest case, you can have kube-aws generate both your TLS identities and certificate authority for you. ``` -$ kube-aws render credentials --generate-ca +kube-aws render credentials --generate-ca ``` The next command generates the default set of cluster assets in your asset directory. ``` -sh $ kube-aws render stack +kube-aws render stack ``` Here's what the directory structure looks like: @@ -292,15 +314,41 @@ These assets (templates and credentials) are used to create, update and interact #### Create the instances defined in the CloudFormation template -Now for the exciting part, creating your cluster (choose any ``): +Now let's create your cluster (choose any PREFIX for the command below): ``` -$ kube-aws up --s3-uri s3:/// +kube-aws up --s3-uri s3://BUCKET_NAME/PREFIX ``` +`BUCKET_NAME`: the bucket name that you used in [S3 bucket](#s3-bucket) + + #### Configure DNS -You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation, if necessary. This command can take a while. And use command `dig` to check the load balancer hostname to get the ip address, use this ip to setup an A record for your external dns name. +You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation. + +``` +$ kube-aws status +Cluster Name: paddle-cluster +Controller DNS Name: paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com +``` + +Use command `dig` to check the load balancer hostname to get the ip address. + +``` +$ dig paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com + +;; QUESTION SECTION: +;paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. IN A + +;; ANSWER SECTION: +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.241.164.52 +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.67.102.112 +``` + +In the above output, both ip `54.241.164.52`, `54.67.102.112` will work. + +If you own a DNS name, set the A record to any of the above ip. Otherwise you can edit `/etc/hosts` to associate ip with the DNS name. #### Access the cluster From 50afa35a59de9e9c2a31e873ab650eb181de801d Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Mon, 16 Jan 2017 14:29:52 -0800 Subject: [PATCH 042/370] fixes according to comment --- doc/howto/usage/k8s/k8s_aws_en.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index bd9eee72963932..00bc41e5c3e2e9 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -2,9 +2,7 @@ ## Create AWS Account and IAM Account -AWS account allow us to manage AWS from Web Console. Amazon IAM enable us to manage AWS from command line interface. - -We need to create an IAM user with sufficient privilege to create kubernetes cluster on AWS. +Under each AWS account, we can create multiple [IAM](http://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html) users. This allows us to grant some privileges to each IAM user and to create/operate AWS clusters as an IAM user. To sign up an AWS account, please follow @@ -51,7 +49,7 @@ gpg2 --fingerprint FC8A365E ``` The correct key fingerprint is `18AD 5014 C99E F7E3 BA5F 6CE9 50BD D3E0 FC8A 365E` -Go to the [releases](https://github.com/coreos/kube-aws/releases) and download release tarball (this tutorial is using v0.9.1) and detached signature (.sig) for your architecture. +We can download `kube-aws` from its [release page](https://github.com/coreos/kube-aws/releases). In this tutorial, we use version 0.9.1 Validate the tarball's GPG signature: @@ -224,7 +222,7 @@ We will need to use DNS name later in tutorial. If you don't already own one, yo You need to create an S3 bucket before startup the Kubernetes cluster. -There are some bug in aws cli in creating S3 bucket, so let's use [web console](https://console.aws.amazon.com/s3/home?region=us-west-1). +There are some bugs in aws cli in creating S3 bucket, so let's use the [Web console](https://console.aws.amazon.com/s3/home?region=us-west-1). Click on `Create Bucket`, fill in a unique BUCKET_NAME, and make sure region is us-west-1 (Northern California). From 8aefc30499e09729b5755fe8edfd32ba72a9baed Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 17 Jan 2017 10:59:27 +0800 Subject: [PATCH 043/370] Fix compile error. --- paddle/utils/Status.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h index db1edfb7c735fc..52f312378eedaa 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Status.h @@ -52,9 +52,9 @@ class Status final : public std::exception { */ template inline void setByPrintf(const char* fmt, ARGS... args) noexcept { - constexpr size_t bufferSize = 4096; - char buffer[bufferSize]; - snprintf(buffer, bufferSize, fmt, args...); + constexpr size_t kBufferSize = 4096; + char buffer[kBufferSize]; + snprintf(buffer, kBufferSize, fmt, args...); errMsg_.reset(new std::string(buffer)); } From ae0f953eb03f021e5892bdea2009d7088a346e46 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 17 Jan 2017 03:10:53 +0000 Subject: [PATCH 044/370] add centos build doc --- .../build_and_install/build_from_source_en.md | 63 +++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 1abd7b698bc09c..924ccf01169234 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -4,6 +4,8 @@ Installing from Sources * [1. Download and Setup](#download) * [2. Requirements](#requirements) * [3. Build on Ubuntu](#ubuntu) +* [4. Build on Centos](#centos) + ## Download and Setup You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle). @@ -151,3 +153,64 @@ export PATH=/bin:$PATH # install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl ``` +## Build on Centos 7 + +### Install Dependencies + +- **CPU Dependencies** + + ```bash + # necessary + sudo yum update + sudo yum install -y epel-release + sudo yum install -y make cmake3 python-devel python-pip gcc-gfortran swig git + sudo pip install wheel numpy + sudo pip install 'protobuf>=3.0.0' + ``` + +- **GPU Dependencies (optional)** + + To build GPU version, you will need the following installed: + + 1. a CUDA-capable GPU + 2. A supported version of Linux with a gcc compiler and toolchain + 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) + 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + + The CUDA development environment relies on tight integration with the host development environment, + including the host compiler and C runtime libraries, and is therefore only supported on + distribution versions that have been qualified for this CUDA Toolkit release. + + After downloading cuDNN library, issue the following commands: + + ```bash + sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local + sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* + ``` + Then you need to set LD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc. + + ```bash + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH + export PATH=/usr/local/cuda/bin:$PATH + ``` + +### Build and Install + +As usual, the best option is to create build folder under paddle project directory. + +```bash +mkdir build && cd build +``` + +Finally, you can build and install PaddlePaddle: + +```bash +# you can add build option here, such as: +cmake3 .. -DCMAKE_INSTALL_PREFIX= +# please use sudo make install, if you want to install PaddlePaddle into the system +make -j `nproc` && make install +# set PaddlePaddle installation path in ~/.bashrc +export PATH=/bin:$PATH +# install PaddlePaddle Python modules. +sudo pip install /opt/paddle/share/wheels/*.whl +``` From ceb2d39799fa600ff77fdbe019191f846829e916 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 17 Jan 2017 12:49:05 +0800 Subject: [PATCH 045/370] Fix bugs in config_helpers unittest. * It gets wrong command line arguments before. --- .../tests/configs/run_tests.sh | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh b/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh index a37eb6439e6d28..c8a3b190b19148 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh @@ -2,16 +2,18 @@ cd `dirname $0` set -e +PYTHON_EXEC=$1 +COMPARE_PROTO_UTIL=$2 protostr=`dirname $0`/protostr files=`ls $protostr | grep -v "unittest"` -./generate_protostr.sh $1 +./generate_protostr.sh ${PYTHON_EXEC} . ./file_list.sh -if [ -z $1 ]; then +if [ -z ${COMPARE_PROTO_UTIL} ]; then for file in $files do base_protostr=$protostr/$file @@ -22,20 +24,20 @@ if [ -z $1 ]; then else for file in ${configs[*]} do - if ! $1 $protostr/$file.protostr $protostr/$file.protostr.unittest; then + if ! ${COMPARE_PROTO_UTIL} $protostr/$file.protostr $protostr/$file.protostr.unittest; then diff $protostr/$file.protostr $protostr/$file.protostr.unittest -u fi - if ! $1 $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest; then + if ! ${COMPARE_PROTO_UTIL} $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest; then diff $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest -u fi done for file in ${whole_configs[*]} do - if ! $1 $protostr/$file.protostr $protostr/$file.protostr.unittest --whole; then + if ! ${COMPARE_PROTO_UTIL} $protostr/$file.protostr $protostr/$file.protostr.unittest --whole; then diff $protostr/$file.protostr $protostr/$file.protostr.unittest -u fi - if ! $1 $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest --whole; then + if ! ${COMPARE_PROTO_UTIL} $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest --whole; then diff $protostr/$file.protostr $protostr/$file.protostr.non_file_config.unittest -u fi done From 4837665d227f0357e3aac51712e6bac7a595cb72 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 17 Jan 2017 17:01:44 +0800 Subject: [PATCH 046/370] Update dockerfiles for develop branch --- .../build_and_install/build_from_source_en.md | 3 +- paddle/scripts/docker/Dockerfile | 31 ++++++++++--------- paddle/scripts/docker/Dockerfile.gpu | 31 ++++++++++--------- 3 files changed, 36 insertions(+), 29 deletions(-) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 1abd7b698bc09c..7d963a5a6df3cf 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -17,8 +17,9 @@ cd paddle To compile the source code, your computer must be equipped with the following dependencies. - **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) -- **CMake**: version >= 3.0 (at least CMake 3.4 on Mac OS X) +- **CMake**: CMake >= 3.0 (at least CMake 3.4 on Mac OS X) - **BLAS**: MKL, OpenBlas or ATLAS +- **Python**: only support Python 2.7 **Note:** For CUDA 7.0 and CUDA 7.5, GCC 5.0 and up are not supported! For CUDA 8.0, GCC versions later than 5.3 are not supported! diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 1522be023f6de3..13a5758f7b66a1 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -4,28 +4,31 @@ MAINTAINER PaddlePaddle Authors ARG DEBIAN_FRONTEND=noninteractive ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' -RUN apt-get update \ - && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ - libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base g++ m4 python-pip \ - python-protobuf python-numpy python-dev swig openssh-server \ - wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ - sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ - clang-3.8 llvm-3.8 libclang-3.8-dev \ - && apt-get clean -y -RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib -RUN pip install -U BeautifulSoup docopt PyYAML pillow \ - sphinx sphinx_rtd_theme recommonmark jupyter + +RUN apt-get update && \ + apt-get install -y git python-pip python-dev openssh-server && \ + apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ + apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ + apt-get install -y g++ clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get clean -y + +RUN pip install --upgrade pip && \ + pip install 'protobuf==3.1.0.post1' && \ + pip install -U numpy wheel matplotlib pillow && \ + pip install -U BeautifulSoup docopt PyYAML sphinx && \ + pip install -U sphinx_rtd_theme recommonmark jupyter + +RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd .. && rm -rf cmake-3.4.1 ARG WITH_AVX ARG WITH_DOC -ARG WITH_SWIG_PY ARG WITH_STYLE_CHECK ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} -ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 09f07043e21723..bab86114feaf99 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -4,28 +4,31 @@ MAINTAINER PaddlePaddle Authors ARG DEBIAN_FRONTEND=noninteractive ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' -RUN apt-get update \ - && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ - libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base g++ m4 python-pip \ - python-protobuf python-numpy python-dev swig openssh-server \ - wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ - sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ - clang-3.8 llvm-3.8 libclang-3.8-dev \ - && apt-get clean -y -RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib -RUN pip install -U BeautifulSoup docopt PyYAML pillow \ - sphinx sphinx_rtd_theme recommonmark jupyter + +RUN apt-get update && \ + apt-get install -y git python-pip python-dev openssh-server && \ + apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ + apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ + apt-get install -y g++ clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get clean -y + +RUN pip install --upgrade pip && \ + pip install 'protobuf==3.1.0.post1' && \ + pip install -U numpy wheel matplotlib pillow && \ + pip install -U BeautifulSoup docopt PyYAML sphinx && \ + pip install -U sphinx_rtd_theme recommonmark jupyter + +RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd .. && rm -rf cmake-3.4.1 ARG WITH_AVX ARG WITH_DOC -ARG WITH_SWIG_PY ARG WITH_STYLE_CHECK ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} -ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle From a5c1658d455008bbe4e4a5a3075bbd6eced30f28 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 17 Jan 2017 17:20:13 +0800 Subject: [PATCH 047/370] Always create protobuf_equal * Because currently Paddle only use protobuf 3. --- .../tests/CMakeLists.txt | 21 +++++++------------ 1 file changed, 7 insertions(+), 14 deletions(-) diff --git a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt index 403aafabe91434..93dd7796c246ae 100644 --- a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt +++ b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt @@ -9,17 +9,10 @@ add_test(NAME test_reset_hook ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/test_reset_hook.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) -if (PROTOBUF_3) - add_paddle_exe(protobuf_equal - ProtobufEqualMain.cpp) - add_test(NAME test_layerHelpers - COMMAND - ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal - ) -else() - add_test(NAME test_layerHelpers - COMMAND - ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh ${PYTHON_EXECUTABLE} - ) -endif() +add_paddle_exe(protobuf_equal + ProtobufEqualMain.cpp) +add_test(NAME test_layerHelpers + COMMAND + ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh ${PYTHON_EXECUTABLE} + ${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal +) From 1c5a7c431690ba6a981ac9f72aacf556da1bc1db Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Tue, 17 Jan 2017 17:33:15 +0800 Subject: [PATCH 048/370] follow comments --- paddle/function/BufferArg.h | 1 + paddle/function/FunctionTest.cpp | 1 + paddle/function/FunctionTest.h | 2 +- 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 9c792c6bb72aa2..84209265ce7634 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -192,6 +192,7 @@ class SequenceIdArg : public BufferArg { SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED) : BufferArg(VALUE_TYPE_INT32, shape, argType) { CHECK_EQ(shape_.ndims(), (size_t)1); + CHECK_GT(shape_[0], 1); numSeqs_ = shape_[0] - 1; } diff --git a/paddle/function/FunctionTest.cpp b/paddle/function/FunctionTest.cpp index 03c609b5242777..fdf7e631e5ab8c 100644 --- a/paddle/function/FunctionTest.cpp +++ b/paddle/function/FunctionTest.cpp @@ -85,6 +85,7 @@ void testBufferArgs(const BufferArgs& inputs, } void testBufferArgs(const BufferArgs& inputs, const CheckBufferArg& check) { + EXPECT_EQ(inputs.size(), 1); check(inputs[0]); } diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 412e3a7d1b30a7..24e7a36a43cfa8 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -172,7 +172,7 @@ class FunctionCompare { void initArg(SequenceIdArg& arg, size_t batchSize) { size_t numSeqs = arg.numSeqs(); - int* buf = (int*)arg.data(); + int* buf = reinterpret_cast(arg.data()); int pos = 0; size_t maxLen = 2 * batchSize / numSeqs; for (int i = 0; i < (int)numSeqs; ++i) { From c21595d6d9ac964763638531687080477e6c75ca Mon Sep 17 00:00:00 2001 From: cxysteven Date: Tue, 17 Jan 2017 17:33:24 +0800 Subject: [PATCH 049/370] bug fixed --- demo/traffic_prediction/predict.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/traffic_prediction/predict.sh b/demo/traffic_prediction/predict.sh index cec35dce11d1c1..2dbd5e8805dd97 100755 --- a/demo/traffic_prediction/predict.sh +++ b/demo/traffic_prediction/predict.sh @@ -25,6 +25,6 @@ paddle train \ --config_args=is_predict=1 \ --predict_output_dir=. -python gen_result.py > result.txt +python gen_result.py > result.csv rm -rf rank-00000 From d79cac158d5436b987c07e398b029e26210f2c96 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 17 Jan 2017 17:45:50 +0800 Subject: [PATCH 050/370] Update python-matplotlib in Dockerfile --- paddle/scripts/docker/Dockerfile | 4 ++-- paddle/scripts/docker/Dockerfile.gpu | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 13a5758f7b66a1..01261d7a2df468 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -9,12 +9,12 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y g++ clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y python-matplotlib g++ && \ apt-get clean -y RUN pip install --upgrade pip && \ pip install 'protobuf==3.1.0.post1' && \ - pip install -U numpy wheel matplotlib pillow && \ + pip install -U numpy wheel pillow && \ pip install -U BeautifulSoup docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index bab86114feaf99..108cfd9c9eb9ff 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -9,12 +9,12 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y g++ clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y python-matplotlib g++ && \ apt-get clean -y RUN pip install --upgrade pip && \ pip install 'protobuf==3.1.0.post1' && \ - pip install -U numpy wheel matplotlib pillow && \ + pip install -U numpy wheel pillow && \ pip install -U BeautifulSoup docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter From 832bb6a745975c8327da396a48303f407649337c Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 17 Jan 2017 17:57:38 +0800 Subject: [PATCH 051/370] Update docs --- doc/getstarted/build_and_install/build_from_source_en.md | 4 ++-- paddle/scripts/docker/Dockerfile | 6 +++--- paddle/scripts/docker/Dockerfile.gpu | 6 +++--- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 7d963a5a6df3cf..6cd2183f489881 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -101,8 +101,8 @@ As a simple example, consider the following: ```bash # necessary sudo apt-get update - sudo apt-get install -y g++ make cmake build-essential python python-pip libpython-dev git - sudo pip install wheel numpy + sudo apt-get install -y g++ make cmake build-essential python python-pip python-numpy libpython-dev git + sudo pip install wheel sudo pip install 'protobuf>=3.0.0' ``` diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 01261d7a2df468..28d7696c8cfcc2 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -9,13 +9,13 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y python-matplotlib g++ && \ + apt-get install -y python-numpy python-matplotlib g++ && \ apt-get clean -y RUN pip install --upgrade pip && \ pip install 'protobuf==3.1.0.post1' && \ - pip install -U numpy wheel pillow && \ - pip install -U BeautifulSoup docopt PyYAML sphinx && \ + pip install -U wheel pillow BeautifulSoup && \ + pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 108cfd9c9eb9ff..b1344f275b61c8 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -9,13 +9,13 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y python-matplotlib g++ && \ + apt-get install -y python-numpy python-matplotlib g++ && \ apt-get clean -y RUN pip install --upgrade pip && \ pip install 'protobuf==3.1.0.post1' && \ - pip install -U numpy wheel pillow && \ - pip install -U BeautifulSoup docopt PyYAML sphinx && \ + pip install -U wheel pillow BeautifulSoup && \ + pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ From 3d01c60e25c1b5874e6854ac565646d6ad9432d7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 17 Jan 2017 15:44:09 +0800 Subject: [PATCH 052/370] Stash --- paddle/utils/Compiler.h | 0 paddle/utils/Status.h | 9 ++++++++- 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 paddle/utils/Compiler.h diff --git a/paddle/utils/Compiler.h b/paddle/utils/Compiler.h new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h index 52f312378eedaa..cb66e4b225e92c 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Status.h @@ -24,6 +24,13 @@ namespace paddle { * Status is Paddle error code. It only contain a std::string as error message. * Although Status inherits the std::exception, but do not throw it except you * know what you are doing. + * + * + * There are two styles to return status in Paddle. + * + * 1. Return Status + * + * */ class Status final : public std::exception { public: @@ -52,7 +59,7 @@ class Status final : public std::exception { */ template inline void setByPrintf(const char* fmt, ARGS... args) noexcept { - constexpr size_t kBufferSize = 4096; + constexpr size_t kBufferSize = 1024; // 1KB buffer char buffer[kBufferSize]; snprintf(buffer, kBufferSize, fmt, args...); errMsg_.reset(new std::string(buffer)); From ee59d109373e1417460e10227692548698bbcac7 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 17 Jan 2017 21:08:02 +0800 Subject: [PATCH 053/370] Add gfortran in docker --- .../build_and_install/build_from_source_en.md | 17 +++++++++++------ paddle/scripts/docker/Dockerfile | 2 +- paddle/scripts/docker/Dockerfile.gpu | 2 +- 3 files changed, 13 insertions(+), 8 deletions(-) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 6cd2183f489881..fbca407030e682 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -16,7 +16,7 @@ cd paddle To compile the source code, your computer must be equipped with the following dependencies. -- **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) +- **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) and gfortran compiler - **CMake**: CMake >= 3.0 (at least CMake 3.4 on Mac OS X) - **BLAS**: MKL, OpenBlas or ATLAS - **Python**: only support Python 2.7 @@ -96,16 +96,21 @@ As a simple example, consider the following: ### Install Dependencies -- **CPU Dependencies** +- **Paddle Dependencies** ```bash # necessary sudo apt-get update - sudo apt-get install -y g++ make cmake build-essential python python-pip python-numpy libpython-dev git - sudo pip install wheel - sudo pip install 'protobuf>=3.0.0' + sudo apt-get install -y git curl gcc g++ gfortran make build-essential + sudo apt-get install -y python python-pip python-numpy libpython-dev + sudo pip install 'protobuf==3.1.0.post1' + + # install cmake 3.4 + curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && sudo make install && \ + cd .. && rm -rf cmake-3.4.1 ``` - + - **GPU Dependencies (optional)** To build GPU version, you will need the following installed: diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 28d7696c8cfcc2..127ebf26c23730 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -9,7 +9,7 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y python-numpy python-matplotlib g++ && \ + apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ apt-get clean -y RUN pip install --upgrade pip && \ diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index b1344f275b61c8..a729e33d468cd3 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -9,7 +9,7 @@ RUN apt-get update && \ apt-get install -y git python-pip python-dev openssh-server && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ - apt-get install -y python-numpy python-matplotlib g++ && \ + apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ apt-get clean -y RUN pip install --upgrade pip && \ From 70dfd7f13732ab10452809ce68d394b0b300382f Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 17 Jan 2017 21:37:30 +0800 Subject: [PATCH 054/370] Add automake in dockerfile --- doc/getstarted/build_and_install/build_from_source_en.md | 4 ++-- paddle/scripts/docker/Dockerfile | 1 + paddle/scripts/docker/Dockerfile.gpu | 1 + 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index fbca407030e682..27b478a0fdd9ab 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -101,8 +101,8 @@ As a simple example, consider the following: ```bash # necessary sudo apt-get update - sudo apt-get install -y git curl gcc g++ gfortran make build-essential - sudo apt-get install -y python python-pip python-numpy libpython-dev + sudo apt-get install -y git curl gcc g++ gfortran make build-essential autotools-dev + sudo apt-get install -y python python-pip python-numpy libpython-dev automake sudo pip install 'protobuf==3.1.0.post1' # install cmake 3.4 diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 127ebf26c23730..d46dd48f744b33 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -10,6 +10,7 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ + apt-get install -y autotools-dev automake && \ apt-get clean -y RUN pip install --upgrade pip && \ diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index a729e33d468cd3..58070b2ad9c862 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -10,6 +10,7 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ + apt-get install -y autotools-dev automake && \ apt-get clean -y RUN pip install --upgrade pip && \ From 878b321a128bd405e0cb66efc00e800ea03d0fad Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 17 Jan 2017 11:59:26 -0800 Subject: [PATCH 055/370] changes wording for paddle on k8s tutorial --- doc/howto/usage/k8s/k8s_aws_en.md | 92 ++++++++---------- .../usage/k8s/src/pserver_and_trainer.png | Bin 0 -> 71688 bytes 2 files changed, 41 insertions(+), 51 deletions(-) create mode 100644 doc/howto/usage/k8s/src/pserver_and_trainer.png diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 00bc41e5c3e2e9..10f5a2ef2fcaf9 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -361,20 +361,9 @@ ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m ``` -### Setup PaddlePaddle Environment on AWS +### Setup Elastic File System for Cluster -Now, we've created a cluster with following network capability: - -1. All Kubernetes nodes can communicate with each other. - -1. All Docker containers on Kubernetes nodes can communicate with each other. - -1. All Kubernetes nodes can communicate with all Docker containers on Kubernetes nodes. - -1. All other traffic loads from outside of Kubernetes nodes cannot reach to the Docker containers on Kubernetes nodes except for creating the services for containers. - - -For sharing the training data across all the Kubernetes nodes, we use EFS (Elastic File System) in AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps: +Training data is usually served on a distributed filesystem, we use Elastic File System (EFS) on AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps: 1. Make sure you added AmazonElasticFileSystemFullAccess policy in your group. @@ -391,57 +380,71 @@ For sharing the training data across all the Kubernetes nodes, we use EFS (Elast
![](src/efs_mount.png)
-Before starting the training, you should place your user config and divided training data onto EFS. When the training start, each task will copy related files from EFS into container, and it will also write the training results back onto EFS, we will show you how to place the data later in this article. +We will place user config and divided training data onto EFS. Training task will cache related files by copying them from EFS into container. It will also write the training results back onto EFS. We will show you how to place the data later in this article. + + + +### Core Concepts of PaddlePaddle Training on AWS +Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume. In this training demo, we will create three Kubernetes pods and schedule them on three nodes. Each pod contains a PaddlePaddle container. When container gets created, it will start parameter server (pserver) and trainer process, load the training data from EFS volume and start the distributed training task. +#### Distributed Training Job -###Core Concept of PaddlePaddle Training on AWS +Distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). -Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume, in this training demo, we will create three Kubernetes pod and scheduling them on 3 node. Each pod contains a PaddlePaddle container. When container gets created, it will start pserver and trainer process, load the training data from EFS volume and start the distributed training task. +Kubernetes job is described by a job config file. The file contains lots of configuration information. For example, PaddlePaddle's node number, `paddle pserver` open port number, the network card info etc. These information are passed into container for `pserver` and `trainer` to use as environment variables. -####Use Kubernetes Job +In one distributed training job, we will: -We use Kubernetes job to represent one time of distributed training. After the job get finished, Kubernetes will destroy job container and release all related resources. +1. Upload the pre-divided training data and configuration file onto EFS volume. +1. Create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. -We can write a yaml file to describe the Kubernetes job. The file contains lots of configuration information, for example PaddlePaddle's node number, `paddle pserver` open port number, the network card info etc., these information are passed into container for processes to use as environment variables. +#### Parameter Server and Trainer -In one time of distributed training, user will confirm the PaddlePaddle node number first. And then upload the pre-divided training data and configuration file onth EFS volume. And then create the Kubernetes job yaml file; submit to the Kubernetes cluster to start the training job. +There are two roles in a PaddlePaddle cluster: `parameter server` and `trainer`. Each parameter server process maintains a shard of the global model. Each trainer has its local copy of the model, and uses its local data to update the model. During the training process, trainers send model updates to parameter servers, parameter servers are responsible for aggregating these updates, so that trainers can synchronize their local copy with the global model. -####Create PaddlePaddle Node +
![Model is partitioned into two shards. Managed by two parameter servers respectively.](src/pserver_and_trainer.png)
-After Kubernetes master gets the request, it will parse the yaml file and create several pods (defined by PaddlePaddle's node number), Kubernetes will allocate these pods onto cluster's node. A pod represents a PaddlePaddle node, when pod is successfully allocated onto one physical/virtual machine, Kubernetes will startup the container in the pod, and this container will use the environment variables in yaml file and start up `paddle pserver` and `paddle trainer` processes. +In order to communicate with pserver, trainer needs to know the ip address of each pserver. In kubernetes it's better to use a service discovery mechanism (e.g., DNS hostname) rather than static ip address, since any pserver's pod may be killed and a new pod could be schduled onto another node of different ip address. We will improve paddlepaddle's service discovery ability. For now we will use static ip. +Parameter server and trainer are packaged into a same docker image. They will run once pod is scheduled by kubernetes job. -####Start up Training +#### Trainer ID -After container gets started, it starts up the distributed training by using scripts. We know `paddle train` process need to know other node's ip address and it's own trainer_id, since PaddlePaddle currently don't have the ability to do the service discovery, so in the start up script, each node will use job pod's name to query all to pod info from Kubernetes apiserver (apiserver's endpoint is an environment variable in container by default). +Trainer id is the index of trainer within all trainers of a job. Trainer needs this information to do things like reading the correct shared of data. -With pod information, we can assign each pod a unique trainer_id. Here we sort all the pods by pod's ip, and assign the index to each PaddlePaddle node as it's trainer_id. The workflow of starting up the script is as follows: +#### Training -1. Query the api server to get pod information, and assign the trainer_id by sorting the ip. +After container gets started, it starts up the distributed training by using scripts. Each node will use job pod's name to query Kubernetes apiserver for information of all pods in current job. + +From pods information, script knows static ip addresses of pservers. And assign trainer it's own `trainer_id`. The workflow of the script is as follows: + +1. Query the api server to get pod information, and assign the `trainer_id` by sorting the ip. 1. Copy the training data from EFS sharing volume into container. -1. Parse the `paddle pserver` and 'paddle trainer' startup parameters from environment variables, and then start up the processes. -1. PaddlePaddle will automatically write the result onto the PaddlePaddle node with trainer_id:0, we set the output path to be the EFS volume to save the result data. +1. Parse the `paddle pserver` and `paddle trainer` startup parameters from environment variables, and then start up the processes. +1. Trainer with `train_id` 0 will automatically write results onto EFS volume. -###Start PaddlePaddle Training Demo on AWS +### Start PaddlePaddle Training Demo on AWS Now we'll start a PaddlePaddle training demo on AWS, steps are as follows: 1. Build PaddlePaddle Docker image. 1. Divide the training data file and upload it onto the EFS sharing volume. -1. Create the training job yaml file, and start up the job. +1. Create the training job config file, and start up the job. 1. Check the result after training. -####Build PaddlePaddle Docker Image +#### Build PaddlePaddle Docker Image -PaddlePaddle docker image need to provide the runtime environment for `paddle pserver` and `paddle train`, so the container use this image should have two main function: +PaddlePaddle docker image need to provide the runtime environment for `pserver` and `trainer`, so the container use this image should have two main function: 1. Copy the training data into container. -1. Generate the startup parameter for `paddle pserver` and `paddle train` process, and startup the training. +1. Generate the startup parameter for `pserver` and `trainer` process, and startup the training. + +We need to create a new image since official `paddledev/paddle:cpu-latest` only have PaddlePaddle binary, but lack of the above functionalities. -Since official `paddledev/paddle:cpu-latest` have already included the PaddlePaddle binary, but lack of the above functionalities, so we will create the startup script based on this image, to achieve the work above. the detailed Dockerfile is as follows: +Dockerfile for creating the new image is as follows: ``` FROM paddledev/paddle:cpu-latest @@ -530,7 +533,7 @@ And then push the built image onto docker registry. docker push your_repo/paddle:mypaddle ``` -####Upload Training Data File +#### Upload Training Data File Here we will use PaddlePaddle's official recommendation demo as the content for this training, we put the training data file into a directory named by job name, which located in EFS sharing volume, the tree structure for the directory looks like: @@ -550,7 +553,7 @@ efs The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the pre-divided data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. -####Create Kubernetes Job +#### Create Kubernetes Job Kubernetes use yaml file to describe job details, and then use command line tool to create the job in Kubernetes cluster. @@ -632,7 +635,7 @@ After we execute the above command, Kubernetes will create 3 pods and then pull -####Check Training Results +#### Check Training Results During the training, we can see the logs and models on EFS sharing volume, the output directory contains the training results. (Caution: node_0, node_1, node_2 directories represents PaddlePaddle node and train_id, not the Kubernetes node) @@ -689,7 +692,7 @@ I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7 It'll take around 8 hours to finish this PaddlePaddle recommendation training demo on three 2 core 8 GB EC2 machine (m3.large). -###Kubernetes Cluster Tear Down +### Kubernetes Cluster Tear Down If you want to tear down the whole Kubernetes cluster, make sure to *delete* the EFS volume first (otherwise, you will get stucked on following steps), and then use the following command: @@ -700,16 +703,3 @@ kube-aws destroy It's an async call, it might take 5 min to tear down the whole cluster. If you created any Kubernetes Services of type LoadBalancer, you must delete these first, as the CloudFormation cannot be fully destroyed if any externally-managed resources still exist. - - - -## For Experts with Kubernetes and AWS - -Sometimes we might need to create or manage the cluster on AWS manually with limited privileges, so here we will explain more on what’s going on with the Kubernetes setup script. - -### Some Presumptions - -* Instances run on CoreOS, the official IAM. -* Kubernetes node use instance storage, no EBS get mounted. 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z;Ov|$Tl`%D+J!KcuBM{vf$Z`@Wm*{ncc`#r1di+!9b-Sp*J>Kvg=!SQ7)b6SC<#6Q z_3%}dSv}7`Py0U(|9^h`(_Z~yga7j@|5=j%oTERH!+(s&e=Ng)EW>{+!~dI>fi;Qf aRF+60qV`#@@0$zYkJ>{mrE&%9=l=tnkqu@5 literal 0 HcmV?d00001 From 20b851c10f91e884b5174d7c6b2870e615a6cbbf Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 17 Jan 2017 13:40:58 -0800 Subject: [PATCH 056/370] fix according to comments --- doc/howto/usage/k8s/k8s_aws_en.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 10f5a2ef2fcaf9..c144bab649f31b 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -392,14 +392,14 @@ Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each no Distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). -Kubernetes job is described by a job config file. The file contains lots of configuration information. For example, PaddlePaddle's node number, `paddle pserver` open port number, the network card info etc. These information are passed into container for `pserver` and `trainer` to use as environment variables. +Each Kuberentes job is described by a job config file, which specifies the information like the number of pods in the job and environment variables. -In one distributed training job, we will: +In a distributed training job, we would: -1. Upload the pre-divided training data and configuration file onto EFS volume. -1. Create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. +1. upload the partitioned training data and configuration file onto EFS volume, and +1. create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. -#### Parameter Server and Trainer +#### Parameter Servers and Trainers There are two roles in a PaddlePaddle cluster: `parameter server` and `trainer`. Each parameter server process maintains a shard of the global model. Each trainer has its local copy of the model, and uses its local data to update the model. During the training process, trainers send model updates to parameter servers, parameter servers are responsible for aggregating these updates, so that trainers can synchronize their local copy with the global model. @@ -411,13 +411,13 @@ Parameter server and trainer are packaged into a same docker image. They will ru #### Trainer ID -Trainer id is the index of trainer within all trainers of a job. Trainer needs this information to do things like reading the correct shared of data. +Each trainer process requires a trainer ID, a zero-based index value, passed in as a command-line parameter. The trainer process thus reads the data partition indexed by this ID. #### Training -After container gets started, it starts up the distributed training by using scripts. Each node will use job pod's name to query Kubernetes apiserver for information of all pods in current job. +The entry-point of a container is a Python script. As it runs in a pod, it can see some environment variables pre-defined by Kubernetes. This includes one that gives the job's identity, which can be used in a remote call to the Kubernetes apiserver that lists all pods in the job. -From pods information, script knows static ip addresses of pservers. And assign trainer it's own `trainer_id`. The workflow of the script is as follows: +We rank each pod by sorting them by their ips. The rank of each pod could be the "pod ID". Because we run one trainer and one parameter server in each pod, we can use this "pod ID" as the trainer ID. A detailed workflow of the entry-point script is as follows: 1. Query the api server to get pod information, and assign the `trainer_id` by sorting the ip. 1. Copy the training data from EFS sharing volume into container. @@ -550,7 +550,7 @@ efs └── recommendation ``` -The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the pre-divided data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. +The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the partitioned data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. #### Create Kubernetes Job From f4ff8d26d6cb50b6749264f8db460732fda1e37b Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 17 Jan 2017 13:41:45 -0800 Subject: [PATCH 057/370] fix according to comments --- doc/howto/usage/k8s/k8s_aws_en.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index c144bab649f31b..a6422b9be00e21 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -390,7 +390,7 @@ Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each no #### Distributed Training Job -Distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). +A distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). Each Kuberentes job is described by a job config file, which specifies the information like the number of pods in the job and environment variables. From b89f2f2f0c97e3f5328bb17061c4c551b8af91cd Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 18 Jan 2017 09:35:01 +0800 Subject: [PATCH 058/370] Update build doc and dockerfile --- cmake/external/swig.cmake | 23 ++++--------------- .../build_and_install/build_from_source_en.md | 4 ++-- paddle/scripts/docker/Dockerfile | 4 ++-- paddle/scripts/docker/Dockerfile.gpu | 4 ++-- 4 files changed, 11 insertions(+), 24 deletions(-) diff --git a/cmake/external/swig.cmake b/cmake/external/swig.cmake index 63e8bd25462e50..744c766ee7b067 100644 --- a/cmake/external/swig.cmake +++ b/cmake/external/swig.cmake @@ -38,14 +38,6 @@ IF(NOT SWIG_FOUND) SET(SWIG_DIR ${SWIG_SOURCES_DIR} CACHE FILEPATH "SWIG Directory" FORCE) SET(SWIG_EXECUTABLE ${SWIG_SOURCES_DIR}/swig.exe CACHE FILEPATH "SWIG Executable" FORCE) ELSE(WIN32) - # From PCRE configure - ExternalProject_Add(pcre - ${EXTERNAL_PROJECT_LOG_ARGS} - GIT_REPOSITORY https://github.com/svn2github/pcre.git - PREFIX ${SWIG_SOURCES_DIR}/pcre - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${SWIG_INSTALL_DIR}/pcre - ) - # swig uses bison find it by cmake and pass it down FIND_PACKAGE(BISON) @@ -54,16 +46,11 @@ IF(NOT SWIG_FOUND) GIT_REPOSITORY https://github.com/swig/swig.git GIT_TAG rel-3.0.10 PREFIX ${SWIG_SOURCES_DIR} - CONFIGURE_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && ./autogen.sh - CONFIGURE_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && - env "PCRE_LIBS=${SWIG_INSTALL_DIR}/pcre/lib/libpcre.a ${SWIG_INSTALL_DIR}/pcre/lib/libpcrecpp.a ${SWIG_INSTALL_DIR}/pcre/lib/libpcreposix.a" - ./configure - --prefix=${SWIG_INSTALL_DIR} - --with-pcre-prefix=${SWIG_INSTALL_DIR}/pcre - BUILD_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && make - INSTALL_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && make install - UPDATE_COMMAND "" - DEPENDS pcre + CONFIGURE_COMMAND cd && ./autogen.sh && ./configure + --prefix=${SWIG_INSTALL_DIR} --without-pcre + BUILD_COMMAND cd && make + INSTALL_COMMAND cd && make install + UPDATE_COMMAND "" ) SET(SWIG_DIR ${SWIG_INSTALL_DIR}/share/swig/${SWIG_TARGET_VERSION}) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index 27b478a0fdd9ab..7e0942b211bb1d 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -101,8 +101,8 @@ As a simple example, consider the following: ```bash # necessary sudo apt-get update - sudo apt-get install -y git curl gcc g++ gfortran make build-essential autotools-dev - sudo apt-get install -y python python-pip python-numpy libpython-dev automake + sudo apt-get install -y git curl gcc g++ gfortran make build-essential automake + sudo apt-get install -y python python-pip python-numpy libpython-dev bison sudo pip install 'protobuf==3.1.0.post1' # install cmake 3.4 diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index d46dd48f744b33..79c4efbed0b856 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -6,11 +6,11 @@ ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' RUN apt-get update && \ - apt-get install -y git python-pip python-dev openssh-server && \ + apt-get install -y git python-pip python-dev openssh-server bison && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y autotools-dev automake && \ + apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y RUN pip install --upgrade pip && \ diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 58070b2ad9c862..6c1c2225d1a304 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -6,11 +6,11 @@ ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' RUN apt-get update && \ - apt-get install -y git python-pip python-dev openssh-server && \ + apt-get install -y git python-pip python-dev openssh-server bison && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y autotools-dev automake && \ + apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y RUN pip install --upgrade pip && \ From 9bc12034002d0c0ca5f30bd1f11b30188978e327 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 18 Jan 2017 12:45:00 +0800 Subject: [PATCH 059/370] Add more comments, also add __must_check. --- .../activations/ActivationFunction.cpp | 55 +++++++++--------- .../gserver/activations/ActivationFunction.h | 4 +- paddle/gserver/layers/Layer.cpp | 4 +- paddle/gserver/layers/MDLstmLayer.cpp | 25 +++++---- paddle/gserver/layers/NCELayer.cpp | 6 +- paddle/gserver/layers/RecurrentLayer.cpp | 21 +++---- .../layers/SelectiveFullyConnectedLayer.cpp | 3 +- paddle/gserver/tests/test_WarpCTCLayer.cpp | 4 +- paddle/utils/Compiler.h | 25 +++++++++ paddle/utils/Status.h | 56 +++++++++++++++++++ 10 files changed, 147 insertions(+), 56 deletions(-) diff --git a/paddle/gserver/activations/ActivationFunction.cpp b/paddle/gserver/activations/ActivationFunction.cpp index 8a938cf7e9d730..666c2e01c8bdaf 100644 --- a/paddle/gserver/activations/ActivationFunction.cpp +++ b/paddle/gserver/activations/ActivationFunction.cpp @@ -69,11 +69,11 @@ static ClassRegistrar gActivationRegistrar; class IdentityActivation : public ActivationFunction { public: static const std::string name; - Status forward(Argument& act) { + Status __must_check forward(Argument& act) { (void)act; return Status(); } - Status backward(Argument& act) { + Status __must_check backward(Argument& act) { (void)act; return Status(); } @@ -92,11 +92,11 @@ static InitFunction __reg_activation__identity([] { * \f] */ BEGIN_DEFINE_ACTIVATION(sigmoid) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->sigmoid(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->sigmoidDerivative(*act.value); return Status(); } @@ -115,12 +115,12 @@ MatrixPtr sftMaxDot_; MatrixPtr one_; public: -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->softmax(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { MatrixPtr outputV = act.value; MatrixPtr outputG = act.grad; @@ -167,7 +167,7 @@ ACTIVATION_CLASS_NAME(softmax) softmax_; Argument argument_; public: -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { if (act.value->getWidth() != 1UL) { return Status( "Input width for each timestep of sequence softmax should be 1"); @@ -191,7 +191,7 @@ Status forward(Argument& act) { return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { if (act.value->getWidth() != 1UL) { return Status( "Input width for each timestep of sequence softmax should be 1"); @@ -207,7 +207,8 @@ Status backward(Argument& act) { argument_.value->setData(act.value->getData() + offset, 1UL, size); argument_.grad->setData(act.grad->getData() + offset, 1UL, size); - softmax_.backward(argument_); + Status status = softmax_.backward(argument_); + if (!status.isOK()) return status; } return Status(); } @@ -224,12 +225,12 @@ END_DEFINE_ACTIVATION(sequence_softmax) * 0 otherwise. */ BEGIN_DEFINE_ACTIVATION(relu) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->relu(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->reluDerivative(*act.value); return Status(); } @@ -249,12 +250,12 @@ END_DEFINE_ACTIVATION(relu) * TODO(yuyang18): Remove magic number 24 or make it configuable. */ BEGIN_DEFINE_ACTIVATION(brelu) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->brelu(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->breluDerivative(*act.value); return Status(); } @@ -267,12 +268,12 @@ END_DEFINE_ACTIVATION(brelu) * \f] */ BEGIN_DEFINE_ACTIVATION(tanh) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->tanh(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->tanhDerivative(*act.value); return Status(); } @@ -290,12 +291,12 @@ real a, b; public: ACTIVATION_CLASS_NAME(stanh)() : a(1.7159), b(2. / 3.) {} -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->scaledTanh(*act.value, a, b); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->scaledTanhDerivative(*act.value, a, b); return Status(); } @@ -308,12 +309,12 @@ END_DEFINE_ACTIVATION(stanh) * \f] */ BEGIN_DEFINE_ACTIVATION(softrelu) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->softrelu(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->softreluDerivative(*act.value); return Status(); } @@ -332,7 +333,7 @@ END_DEFINE_ACTIVATION(softrelu) * 0 if z=0 */ BEGIN_DEFINE_ACTIVATION(abs) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -345,7 +346,7 @@ Status forward(Argument& act) { return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->absDerivative(*act.in); return Status(); } @@ -358,7 +359,7 @@ END_DEFINE_ACTIVATION(abs) * \f] */ BEGIN_DEFINE_ACTIVATION(square) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -371,7 +372,7 @@ Status forward(Argument& act) { return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->squareDerivative(*act.in); return Status(); } @@ -384,12 +385,12 @@ END_DEFINE_ACTIVATION(square) * \f] */ BEGIN_DEFINE_ACTIVATION(exponential) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { act.value->exp2(*act.value); return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->expDerivative(*act.value); return Status(); } @@ -402,7 +403,7 @@ END_DEFINE_ACTIVATION(exponential) * \f] */ BEGIN_DEFINE_ACTIVATION(log) -Status forward(Argument& act) { +Status __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -415,7 +416,7 @@ Status forward(Argument& act) { return Status(); } -Status backward(Argument& act) { +Status __must_check backward(Argument& act) { act.grad->dotDiv(*act.grad, *act.in); return Status(); } diff --git a/paddle/gserver/activations/ActivationFunction.h b/paddle/gserver/activations/ActivationFunction.h index ad395ac28da7d9..737df2219dd36f 100644 --- a/paddle/gserver/activations/ActivationFunction.h +++ b/paddle/gserver/activations/ActivationFunction.h @@ -49,7 +49,7 @@ class ActivationFunction { * * Usually, act is Layer::output_ */ - virtual Status forward(Argument& act) = 0; + virtual Status __must_check forward(Argument& act) = 0; /** * @brief Backward propagaion @@ -58,7 +58,7 @@ class ActivationFunction { * - Before calling backward(), act.grad = dE / dy, where E is the error/cost * - After backward() returns, act.grad = dE / dx = (dE/dy) * (dy/dx) */ - virtual Status backward(Argument& act) = 0; + virtual Status __must_check backward(Argument& act) = 0; virtual const std::string& getName() const = 0; }; diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index 06c936c3aec670..f96070fe6e269b 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -336,7 +336,7 @@ void Layer::showOutputStats() { void Layer::forwardActivation() { /* activation */ auto status = activation_->forward(output_); - CHECK(status.isOK()) << status.what(); + status.check(); /* dropout */ if (config_.drop_rate() > 0) { @@ -375,7 +375,7 @@ void Layer::backwardActivation() { } auto status = activation_->backward(output_); - CHECK(status.isOK()) << status.what(); + status.check(); } void Layer::forwardDropOut() { diff --git a/paddle/gserver/layers/MDLstmLayer.cpp b/paddle/gserver/layers/MDLstmLayer.cpp index fb41af56319549..88d934d782b549 100644 --- a/paddle/gserver/layers/MDLstmLayer.cpp +++ b/paddle/gserver/layers/MDLstmLayer.cpp @@ -506,9 +506,12 @@ void MDLstmLayer::forwardGate2OutputSequence(int start, *frameState_[start + preOffsetV[i]].value, *checkFgOneDim, 1.0, 1.0); } } - activationGate_->forward(frameInputGate_[idxCurr]); - activationGate_->forward(frameForgetGate_[idxCurr]); - activation_->forward(frameInputNode_[idxCurr]); + auto status = activationGate_->forward(frameInputGate_[idxCurr]); + status.check(); + status = activationGate_->forward(frameForgetGate_[idxCurr]); + status.check(); + status = activation_->forward(frameInputNode_[idxCurr]); + status.check(); frameState_[idxCurr].value->zeroMem(); for (int i = 0; i < numDims_; i++) { @@ -530,10 +533,12 @@ void MDLstmLayer::forwardGate2OutputSequence(int start, frameOutputGate_[idxCurr].value->addDotMul( *frameState_[idxCurr].value, *checkOg_, 1.0, 1.0); - activationGate_->forward(frameOutputGate_[idxCurr]); + status = activationGate_->forward(frameOutputGate_[idxCurr]); + status.check(); framePreOutput_[idxCurr].value->copyFrom(*(frameState_[idxCurr].value)); - activationState_->forward(framePreOutput_[idxCurr]); + status = activationState_->forward(framePreOutput_[idxCurr]); + status.check(); frameOutput_[idxCurr].value->dotMul(*framePreOutput_[idxCurr].value, *frameOutputGate_[idxCurr].value); @@ -640,12 +645,12 @@ void MDLstmLayer::backwardGate2OutputSequence(int start, framePreOutput_[idxCurr].grad->dotMul(*frameOutput_[idxCurr].grad, *frameOutputGate_[idxCurr].value); - activationState_->backward(framePreOutput_[idxCurr]); + activationState_->backward(framePreOutput_[idxCurr]).check(); frameState_[idxCurr].grad->copyFrom(*(framePreOutput_[idxCurr].grad)); frameOutputGate_[idxCurr].grad->dotMul(*frameOutput_[idxCurr].grad, *framePreOutput_[idxCurr].value); - activationGate_->backward(frameOutputGate_[idxCurr]); + activationGate_->backward(frameOutputGate_[idxCurr]).check(); frameState_[idxCurr].grad->addDotMul( *frameOutputGate_[idxCurr].grad, *checkOg_, 1.0, 1.0); @@ -702,9 +707,9 @@ void MDLstmLayer::backwardGate2OutputSequence(int start, } } - activationGate_->backward(frameInputGate_[idxCurr]); - activationGate_->backward(frameForgetGate_[idxCurr]); - activation_->backward(frameInputNode_[idxCurr]); + activationGate_->backward(frameInputGate_[idxCurr]).check(); + activationGate_->backward(frameForgetGate_[idxCurr]).check(); + activation_->backward(frameInputNode_[idxCurr]).check(); if (bias_->getWGrad()) { for (int i = 0; i < numDims_; i++) { diff --git a/paddle/gserver/layers/NCELayer.cpp b/paddle/gserver/layers/NCELayer.cpp index 5ab765247f63df..3542e739df8d03 100644 --- a/paddle/gserver/layers/NCELayer.cpp +++ b/paddle/gserver/layers/NCELayer.cpp @@ -193,7 +193,8 @@ class NCELayer : public Layer { forwardOneInput(l); } - activation_->forward(sampleOut_); + auto status = activation_->forward(sampleOut_); + status.check(); forwardCost(); } @@ -207,7 +208,8 @@ class NCELayer : public Layer { backwardCost(); - activation_->backward(sampleOut_); + auto status = activation_->backward(sampleOut_); + status.check(); if (biases_->getWGrad()) { backwardBias(callback); diff --git a/paddle/gserver/layers/RecurrentLayer.cpp b/paddle/gserver/layers/RecurrentLayer.cpp index 55e0fdfb9048c0..b843fa1265cf3c 100644 --- a/paddle/gserver/layers/RecurrentLayer.cpp +++ b/paddle/gserver/layers/RecurrentLayer.cpp @@ -217,21 +217,22 @@ void RecurrentLayer::forwardOneSequence(int start, int length) { if (prevOutput_) { frameOutput_[start].value->mul(*prevOutput_, *weight_->getW(), 1, 1); } - activation_->forward(frameOutput_[start]); + activation_->forward(frameOutput_[start]).check(); + for (int i = 1; i < length; ++i) { frameOutput_[start + i].value->mul( *frameOutput_[start + i - 1].value, *weight_->getW(), 1, 1); - activation_->forward(frameOutput_[start + i]); + activation_->forward(frameOutput_[start + i]).check(); } if (prevOutput_) { prevOutput_->assign(*frameOutput_[start + length - 1].value); } } else { - activation_->forward(frameOutput_[start + length - 1]); + activation_->forward(frameOutput_[start + length - 1]).check(); for (int i = length - 2; i >= 0; --i) { frameOutput_[start + i].value->mul( *frameOutput_[start + i + 1].value, *weight_->getW(), 1, 1); - activation_->forward(frameOutput_[start + i]); + activation_->forward(frameOutput_[start + i]).check(); } } } @@ -280,11 +281,11 @@ void RecurrentLayer::backwardOneSequence(int start, int length) { MatrixPtr weightT = weight_->getW()->getTranspose(); if (!reversed_) { for (int i = length - 1; i > 0; --i) { - activation_->backward(frameOutput_[start + i]); + activation_->backward(frameOutput_[start + i]).check(); frameOutput_[start + i - 1].grad->mul( *frameOutput_[start + i].grad, *weightT, 1, 1); } - activation_->backward(frameOutput_[start]); + activation_->backward(frameOutput_[start]).check(); if (weight_->getWGrad()) { weight_->getWGrad()->mul( *output_.value->subMatrix(start, length - 1)->getTranspose(), @@ -294,11 +295,11 @@ void RecurrentLayer::backwardOneSequence(int start, int length) { } } else { for (int i = 0; i < length - 1; ++i) { - activation_->backward(frameOutput_[start + i]); + activation_->backward(frameOutput_[start + i]).check(); frameOutput_[start + i + 1].grad->mul( *frameOutput_[start + i].grad, *weightT, 1, 1); } - activation_->backward(frameOutput_[start + length - 1]); + activation_->backward(frameOutput_[start + length - 1]).check(); if (weight_->getWGrad()) { weight_->getWGrad()->mul( *output_.value->subMatrix(start + 1, length - 1)->getTranspose(), @@ -333,7 +334,7 @@ void RecurrentLayer::forwardBatch(int batchSize, } Argument arg; arg.value = batch2; - activation_->forward(arg); + activation_->forward(arg).check(); } } batchValue_->copyBackSeq(*output_.value); @@ -363,7 +364,7 @@ void RecurrentLayer::backwardBatch(int batchSize, Argument arg; arg.value = batch1; arg.grad = batch2; - activation_->backward(arg); + activation_->backward(arg).check(); if (n != 0) { batch1 = batchGrad_->getBatchValue(n - 1, batch2->getHeight()); diff --git a/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp b/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp index 5eacff6b714399..d9a91de8a6f4da 100644 --- a/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp +++ b/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp @@ -192,7 +192,8 @@ void SelectiveFullyConnectedLayer::forward(PassType passType) { nnz, /*trans=*/false, /*useGpu=*/useGpu_); - activation_->forward(arg); + //! TODO(yuyang18): Why we cannot invoke forwardActivation here? + activation_->forward(arg).check(); } else /* train and test in train, not generating */ { // during training, this layer output value is *Matrix*, which is input of // eg. multi-class-cross-entropy diff --git a/paddle/gserver/tests/test_WarpCTCLayer.cpp b/paddle/gserver/tests/test_WarpCTCLayer.cpp index 23ae95852e8421..55427e2f12fd7b 100644 --- a/paddle/gserver/tests/test_WarpCTCLayer.cpp +++ b/paddle/gserver/tests/test_WarpCTCLayer.cpp @@ -148,11 +148,11 @@ LayerPtr createCTCLayer(string name, ActivationFunction* softmaxActivation = ActivationFunction::create("softmax"); - softmaxActivation->forward(dataLayer->getOutput()); + softmaxActivation->forward(dataLayer->getOutput()).check(); layer->forward(PASS_GC); layer->backward(); - softmaxActivation->backward(dataLayer->getOutput()); + softmaxActivation->backward(dataLayer->getOutput()).check(); return layer; } diff --git a/paddle/utils/Compiler.h b/paddle/utils/Compiler.h index e69de29bb2d1d6..22812e83987d8d 100644 --- a/paddle/utils/Compiler.h +++ b/paddle/utils/Compiler.h @@ -0,0 +1,25 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + http://www.apache.org/licenses/LICENSE-2.0 +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#ifdef __GNUC__ +#define GCC_VERSION \ + (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) +#else +#define GCC_VERSION +#endif + +#if GCC_VERSION >= 30400 +#define __must_check __attribute__((warn_unused_result)) +#else +#define __must_check +#endif diff --git a/paddle/utils/Status.h b/paddle/utils/Status.h index cb66e4b225e92c..26329f8d19bae6 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Status.h @@ -14,9 +14,11 @@ limitations under the License. */ #pragma once +#include #include #include #include +#include "Compiler.h" namespace paddle { @@ -29,8 +31,55 @@ namespace paddle { * There are two styles to return status in Paddle. * * 1. Return Status + * When method return a status, the return must use `__must_check` attribute. + * Example as below. + * @code{cpp} + * Status __must_check foo(); * + * Status __must_check bar() { + * // do something. + * Status s = foo(); // invoke other method return status. + * if (!s.isOK()) return s; + * // do something else. + * return Status(); + * } + * @endcode{cpp} * + * 2. Return by parameter. + * It is another way to return a status, by using a pointer parameter. + * Example as below. + * + * @code{cpp} + * Status bar(); + * + * int foo(Status* status) { + * // Do something. + * Status s = bar(); + * if (!s.isOK()) { + * *status = s; + * return 0; + * } + * // Do something else. + * if (someInternalErrorHappend) { + * status->setByPrintf("Some dimension is too large, %d", dimension); + * return 0; + * } + * // End of method. + * return someValue; + * } + * + * Status foobar() { + * Status s; + * // do something. + * foo(&s); + * if (!s.isOK()) return s; + * } + * @endcode{cpp} + * + * + * Currently there is a helper method 'check' in status, because Paddle always + * use log(FATAL) or CHECK to make program exit before. When we clean all + * log(FATAL) and CHECK in Paddle, 'check' method will be removed. */ class Status final : public std::exception { public: @@ -92,6 +141,13 @@ class Status final : public std::exception { */ inline bool isOK() const noexcept { return errMsg_ == nullptr; } + /** + * @brief check this status by glog. + * @note It is a temp method used during cleaning Paddle code. It will be + * removed later. + */ + inline void check() const { CHECK(isOK()) << what(); } + private: std::shared_ptr errMsg_; }; From 8605544c0b0cbc5ad43d86a71402f3f4075b48e3 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 18 Jan 2017 12:49:00 +0800 Subject: [PATCH 060/370] Add some comments to compiler.h --- paddle/utils/Compiler.h | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/paddle/utils/Compiler.h b/paddle/utils/Compiler.h index 22812e83987d8d..cebca5a2a37661 100644 --- a/paddle/utils/Compiler.h +++ b/paddle/utils/Compiler.h @@ -10,7 +10,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #pragma once - +/** + * This header defines some useful attribute by each compiler. It is the + * abstract layer of compilers. + */ #ifdef __GNUC__ #define GCC_VERSION \ (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) @@ -18,6 +21,11 @@ limitations under the License. */ #define GCC_VERSION #endif +/** + * __must_check macro. It make the function's return value must be used, + * otherwise it will raise a compile warning. And also Paddle treat all compile + * warnings as errors. + */ #if GCC_VERSION >= 30400 #define __must_check __attribute__((warn_unused_result)) #else From ec790e1050b52b09a182eed95fc030c7879e5012 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 18 Jan 2017 13:24:02 +0800 Subject: [PATCH 061/370] Rename Status => Error. * Also make ErrorF as a global method. --- .../activations/ActivationFunction.cpp | 110 +++++++++--------- .../gserver/activations/ActivationFunction.h | 6 +- paddle/gserver/layers/Layer.cpp | 2 +- paddle/utils/{Status.h => Error.h} | 92 ++++++++------- paddle/utils/tests/CMakeLists.txt | 2 +- .../tests/{test_Status.cpp => test_Error.cpp} | 14 +-- 6 files changed, 114 insertions(+), 112 deletions(-) rename paddle/utils/{Status.h => Error.h} (70%) rename paddle/utils/tests/{test_Status.cpp => test_Error.cpp} (76%) diff --git a/paddle/gserver/activations/ActivationFunction.cpp b/paddle/gserver/activations/ActivationFunction.cpp index 666c2e01c8bdaf..f1f96fc67d04f4 100644 --- a/paddle/gserver/activations/ActivationFunction.cpp +++ b/paddle/gserver/activations/ActivationFunction.cpp @@ -69,13 +69,13 @@ static ClassRegistrar gActivationRegistrar; class IdentityActivation : public ActivationFunction { public: static const std::string name; - Status __must_check forward(Argument& act) { + Error __must_check forward(Argument& act) { (void)act; - return Status(); + return Error(); } - Status __must_check backward(Argument& act) { + Error __must_check backward(Argument& act) { (void)act; - return Status(); + return Error(); } const std::string& getName() const { return name; } }; @@ -92,13 +92,13 @@ static InitFunction __reg_activation__identity([] { * \f] */ BEGIN_DEFINE_ACTIVATION(sigmoid) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->sigmoid(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->sigmoidDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(sigmoid) @@ -115,12 +115,12 @@ MatrixPtr sftMaxDot_; MatrixPtr one_; public: -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->softmax(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { MatrixPtr outputV = act.value; MatrixPtr outputG = act.grad; @@ -152,7 +152,7 @@ Status __must_check backward(Argument& act) { act.grad->softmaxDerivative(*act.value, *sftMaxSum_); } - return Status(); + return Error(); } END_DEFINE_ACTIVATION(softmax) @@ -167,9 +167,9 @@ ACTIVATION_CLASS_NAME(softmax) softmax_; Argument argument_; public: -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { if (act.value->getWidth() != 1UL) { - return Status( + return ErrorF( "Input width for each timestep of sequence softmax should be 1"); } @@ -188,12 +188,12 @@ Status __must_check forward(Argument& act) { auto starts = act.sequenceStartPositions->getVector(useGpu(act.deviceId)); act.value->sequenceSoftmax(*act.value, *starts); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { if (act.value->getWidth() != 1UL) { - return Status( + return ErrorF( "Input width for each timestep of sequence softmax should be 1"); } @@ -207,10 +207,10 @@ Status __must_check backward(Argument& act) { argument_.value->setData(act.value->getData() + offset, 1UL, size); argument_.grad->setData(act.grad->getData() + offset, 1UL, size); - Status status = softmax_.backward(argument_); + Error status = softmax_.backward(argument_); if (!status.isOK()) return status; } - return Status(); + return Error(); } END_DEFINE_ACTIVATION(sequence_softmax) @@ -225,14 +225,14 @@ END_DEFINE_ACTIVATION(sequence_softmax) * 0 otherwise. */ BEGIN_DEFINE_ACTIVATION(relu) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->relu(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->reluDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(relu) @@ -250,14 +250,14 @@ END_DEFINE_ACTIVATION(relu) * TODO(yuyang18): Remove magic number 24 or make it configuable. */ BEGIN_DEFINE_ACTIVATION(brelu) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->brelu(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->breluDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(brelu) @@ -268,14 +268,14 @@ END_DEFINE_ACTIVATION(brelu) * \f] */ BEGIN_DEFINE_ACTIVATION(tanh) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->tanh(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->tanhDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(tanh) @@ -291,14 +291,14 @@ real a, b; public: ACTIVATION_CLASS_NAME(stanh)() : a(1.7159), b(2. / 3.) {} -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->scaledTanh(*act.value, a, b); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->scaledTanhDerivative(*act.value, a, b); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(stanh) @@ -309,14 +309,14 @@ END_DEFINE_ACTIVATION(stanh) * \f] */ BEGIN_DEFINE_ACTIVATION(softrelu) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->softrelu(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->softreluDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(softrelu) @@ -333,7 +333,7 @@ END_DEFINE_ACTIVATION(softrelu) * 0 if z=0 */ BEGIN_DEFINE_ACTIVATION(abs) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -343,12 +343,12 @@ Status __must_check forward(Argument& act) { act.in->copyFrom(*act.value); act.value->abs2(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->absDerivative(*act.in); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(abs) @@ -359,7 +359,7 @@ END_DEFINE_ACTIVATION(abs) * \f] */ BEGIN_DEFINE_ACTIVATION(square) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -369,12 +369,12 @@ Status __must_check forward(Argument& act) { act.in->copyFrom(*act.value); act.value->square2(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->squareDerivative(*act.in); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(square) @@ -385,14 +385,14 @@ END_DEFINE_ACTIVATION(square) * \f] */ BEGIN_DEFINE_ACTIVATION(exponential) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { act.value->exp2(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->expDerivative(*act.value); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(exponential) @@ -403,7 +403,7 @@ END_DEFINE_ACTIVATION(exponential) * \f] */ BEGIN_DEFINE_ACTIVATION(log) -Status __must_check forward(Argument& act) { +Error __must_check forward(Argument& act) { SetDevice device(act.deviceId); Matrix::resizeOrCreate(act.in, act.value->getHeight(), @@ -413,12 +413,12 @@ Status __must_check forward(Argument& act) { act.in->copyFrom(*act.value); act.value->log2(*act.value); - return Status(); + return Error(); } -Status __must_check backward(Argument& act) { +Error __must_check backward(Argument& act) { act.grad->dotDiv(*act.grad, *act.in); - return Status(); + return Error(); } END_DEFINE_ACTIVATION(log) diff --git a/paddle/gserver/activations/ActivationFunction.h b/paddle/gserver/activations/ActivationFunction.h index 737df2219dd36f..f208224e304a79 100644 --- a/paddle/gserver/activations/ActivationFunction.h +++ b/paddle/gserver/activations/ActivationFunction.h @@ -15,7 +15,7 @@ limitations under the License. */ #pragma once #include #include -#include "paddle/utils/Status.h" +#include "paddle/utils/Error.h" namespace paddle { @@ -49,7 +49,7 @@ class ActivationFunction { * * Usually, act is Layer::output_ */ - virtual Status __must_check forward(Argument& act) = 0; + virtual Error __must_check forward(Argument& act) = 0; /** * @brief Backward propagaion @@ -58,7 +58,7 @@ class ActivationFunction { * - Before calling backward(), act.grad = dE / dy, where E is the error/cost * - After backward() returns, act.grad = dE / dx = (dE/dy) * (dy/dx) */ - virtual Status __must_check backward(Argument& act) = 0; + virtual Error __must_check backward(Argument& act) = 0; virtual const std::string& getName() const = 0; }; diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index f96070fe6e269b..f76d41ad3e8a3b 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -15,8 +15,8 @@ limitations under the License. */ #include "paddle/utils/Util.h" #include "paddle/math/SparseMatrix.h" +#include "paddle/utils/Error.h" #include "paddle/utils/Logging.h" -#include "paddle/utils/Status.h" #include "AddtoLayer.h" #include "CRFLayer.h" diff --git a/paddle/utils/Status.h b/paddle/utils/Error.h similarity index 70% rename from paddle/utils/Status.h rename to paddle/utils/Error.h index 26329f8d19bae6..f1597f93d2579a 100644 --- a/paddle/utils/Status.h +++ b/paddle/utils/Error.h @@ -34,9 +34,9 @@ namespace paddle { * When method return a status, the return must use `__must_check` attribute. * Example as below. * @code{cpp} - * Status __must_check foo(); + * Error __must_check foo(); * - * Status __must_check bar() { + * Error __must_check bar() { * // do something. * Status s = foo(); // invoke other method return status. * if (!s.isOK()) return s; @@ -50,9 +50,9 @@ namespace paddle { * Example as below. * * @code{cpp} - * Status bar(); + * Error bar(); * - * int foo(Status* status) { + * int foo(Error* status) { * // Do something. * Status s = bar(); * if (!s.isOK()) { @@ -61,15 +61,15 @@ namespace paddle { * } * // Do something else. * if (someInternalErrorHappend) { - * status->setByPrintf("Some dimension is too large, %d", dimension); + * *status = ErrorF("Some dimension is too large, %d", dimension); * return 0; * } * // End of method. * return someValue; * } * - * Status foobar() { - * Status s; + * Error foobar() { + * Error s; * // do something. * foo(&s); * if (!s.isOK()) return s; @@ -81,48 +81,12 @@ namespace paddle { * use log(FATAL) or CHECK to make program exit before. When we clean all * log(FATAL) and CHECK in Paddle, 'check' method will be removed. */ -class Status final : public std::exception { +class Error final : public std::exception { public: /** * Default Status. OK */ - Status() noexcept {} - - /** - * @brief Create Status with error message - * @param msg - */ - explicit Status(const std::string& msg) : errMsg_(new std::string(msg)) {} - - /** - * @brief set a error message for status. - * @param msg - */ - inline void set(const std::string& msg) noexcept { - errMsg_.reset(new std::string(msg)); - } - - /** - * @brief set a error message for status. Use C style printf - * @param fmt - */ - template - inline void setByPrintf(const char* fmt, ARGS... args) noexcept { - constexpr size_t kBufferSize = 1024; // 1KB buffer - char buffer[kBufferSize]; - snprintf(buffer, kBufferSize, fmt, args...); - errMsg_.reset(new std::string(buffer)); - } - - /** - * create a error status by C style printf. - */ - template - inline static Status printf(const char* fmt, ARGS... args) noexcept { - Status s; - s.setByPrintf(fmt, args...); - return s; - } + Error() noexcept {} /** * @brief what will return the error message. If status is OK, return nullptr. @@ -148,8 +112,46 @@ class Status final : public std::exception { */ inline void check() const { CHECK(isOK()) << what(); } + /** + * friend method to create Error. + */ + template + friend Error __must_check ErrorF(const char* fmt, ARGS... args); + private: std::shared_ptr errMsg_; }; +/** + * ErrorF will create an Error by printf syntax. + * + * Specialize this method because clang will give a warning when use printf(fmt) + * without arguments. + */ +template <> +inline Error __must_check ErrorF(const char* msg) { + Error e; + e.errMsg_.reset(new std::string(msg)); + return e; +} + +/** + * ErrorF will create an Error by printf syntax. + * + * Examples: + * @code{cpp} + * auto err = ErrorF("SomeError"); + * auto err2 = ErrorF("SomeErrorWithParameter %f %d", real_val, int_val); + * @endcode{cpp} + */ +template +inline Error __must_check ErrorF(const char* fmt, ARGS... args) { + constexpr size_t kBufferSize = 1024; + char buffer[kBufferSize]; + snprintf(buffer, kBufferSize, fmt, args...); + Error e; + e.errMsg_.reset(new std::string(buffer)); + return e; +} + } // namespace paddle diff --git a/paddle/utils/tests/CMakeLists.txt b/paddle/utils/tests/CMakeLists.txt index a1cc32668d5100..aa923b35537775 100644 --- a/paddle/utils/tests/CMakeLists.txt +++ b/paddle/utils/tests/CMakeLists.txt @@ -4,7 +4,7 @@ add_simple_unittest(test_CustomStackTrace) add_simple_unittest(test_ThreadBarrier) add_simple_unittest(test_SpinLock) add_simple_unittest(test_SIMDFlags) -add_simple_unittest(test_Status) +add_simple_unittest(test_Error) add_executable( test_CustomStackTracePrint diff --git a/paddle/utils/tests/test_Status.cpp b/paddle/utils/tests/test_Error.cpp similarity index 76% rename from paddle/utils/tests/test_Status.cpp rename to paddle/utils/tests/test_Error.cpp index 04cef095792c73..96115f7053aec5 100644 --- a/paddle/utils/tests/test_Status.cpp +++ b/paddle/utils/tests/test_Error.cpp @@ -12,23 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "paddle/utils/Status.h" +#include "paddle/utils/Error.h" #include TEST(Status, testAll) { - paddle::Status status; + paddle::Error status; ASSERT_TRUE(status.isOK()); - status.set("I'm the error"); + status = paddle::ErrorF("I'm the error"); ASSERT_FALSE(status.isOK()); ASSERT_STREQ("I'm the error", status.what()); - paddle::Status status2("error2"); - ASSERT_FALSE(status2.isOK()); - ASSERT_STREQ("error2", status2.what()); + status = paddle::ErrorF("error2"); + ASSERT_FALSE(status.isOK()); + ASSERT_STREQ("error2", status.what()); int i = 3; - auto status3 = paddle::Status::printf("error%d", i); + auto status3 = paddle::ErrorF("error%d", i); ASSERT_FALSE(status3.isOK()); ASSERT_STREQ("error3", status3.what()); } From 699d18f11701aae1efe72c8bf6edc50723445050 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 18 Jan 2017 13:34:20 +0800 Subject: [PATCH 062/370] Change unittest variable name --- paddle/utils/tests/test_Error.cpp | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/paddle/utils/tests/test_Error.cpp b/paddle/utils/tests/test_Error.cpp index 96115f7053aec5..e8643de9d2dccf 100644 --- a/paddle/utils/tests/test_Error.cpp +++ b/paddle/utils/tests/test_Error.cpp @@ -16,19 +16,19 @@ limitations under the License. */ #include -TEST(Status, testAll) { - paddle::Error status; - ASSERT_TRUE(status.isOK()); - status = paddle::ErrorF("I'm the error"); - ASSERT_FALSE(status.isOK()); - ASSERT_STREQ("I'm the error", status.what()); +TEST(Error, testAll) { + paddle::Error error; + ASSERT_TRUE(error.isOK()); + error = paddle::ErrorF("I'm the error"); + ASSERT_FALSE(error.isOK()); + ASSERT_STREQ("I'm the error", error.what()); - status = paddle::ErrorF("error2"); - ASSERT_FALSE(status.isOK()); - ASSERT_STREQ("error2", status.what()); + error = paddle::ErrorF("error2"); + ASSERT_FALSE(error.isOK()); + ASSERT_STREQ("error2", error.what()); int i = 3; - auto status3 = paddle::ErrorF("error%d", i); - ASSERT_FALSE(status3.isOK()); - ASSERT_STREQ("error3", status3.what()); + auto error3 = paddle::ErrorF("error%d", i); + ASSERT_FALSE(error3.isOK()); + ASSERT_STREQ("error3", error3.what()); } From b6dfd2891bd658e1212f5e38bf764cf0f901574f Mon Sep 17 00:00:00 2001 From: backyes Date: Wed, 18 Jan 2017 18:14:41 +0800 Subject: [PATCH 063/370] fix bug: * update function can not be inherited since updateImpl exsits * remove update implementation in one inherited updater --- paddle/parameter/ParameterUpdaterBase.h | 2 +- paddle/trainer/ParameterUpdater.h | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/paddle/parameter/ParameterUpdaterBase.h b/paddle/parameter/ParameterUpdaterBase.h index b230e170c15f1b..6265c828a1a254 100644 --- a/paddle/parameter/ParameterUpdaterBase.h +++ b/paddle/parameter/ParameterUpdaterBase.h @@ -55,7 +55,7 @@ class ParameterUpdater { // between startBatch() and finishBatch(), update() will be called // by the trainer multiple times, each time for updating one Parameter // with its gradient in PARAMETER_GRADIENT - virtual void update(Parameter* para) { + void update(Parameter* para) { SetDevice setDevice(para->getDeviceId()); para->updateHook(); this->updateImpl(para); diff --git a/paddle/trainer/ParameterUpdater.h b/paddle/trainer/ParameterUpdater.h index c3207e63ce72b7..9e9e948b8856d2 100644 --- a/paddle/trainer/ParameterUpdater.h +++ b/paddle/trainer/ParameterUpdater.h @@ -184,7 +184,6 @@ class SgdCpuUpdater : public SgdLocalUpdater, public Deprecated { * @param para */ virtual void updateImpl(Parameter* para) {} - virtual void update(Parameter* para) {} }; /** From 5a15c70e167a83e6b06686e3152ca9b30ed7800e Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 18 Jan 2017 20:27:52 +0800 Subject: [PATCH 064/370] Make Error interface cleaner --- .../activations/ActivationFunction.cpp | 6 +- paddle/utils/Error.h | 92 +++++++------------ paddle/utils/tests/test_Error.cpp | 20 ++-- 3 files changed, 45 insertions(+), 73 deletions(-) diff --git a/paddle/gserver/activations/ActivationFunction.cpp b/paddle/gserver/activations/ActivationFunction.cpp index f1f96fc67d04f4..c541b72e104bf2 100644 --- a/paddle/gserver/activations/ActivationFunction.cpp +++ b/paddle/gserver/activations/ActivationFunction.cpp @@ -169,7 +169,7 @@ Argument argument_; public: Error __must_check forward(Argument& act) { if (act.value->getWidth() != 1UL) { - return ErrorF( + return Error( "Input width for each timestep of sequence softmax should be 1"); } @@ -193,7 +193,7 @@ Error __must_check forward(Argument& act) { Error __must_check backward(Argument& act) { if (act.value->getWidth() != 1UL) { - return ErrorF( + return Error( "Input width for each timestep of sequence softmax should be 1"); } @@ -208,7 +208,7 @@ Error __must_check backward(Argument& act) { argument_.grad->setData(act.grad->getData() + offset, 1UL, size); Error status = softmax_.backward(argument_); - if (!status.isOK()) return status; + if (!status) return status; } return Error(); } diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index f1597f93d2579a..a8de56b9808b81 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -23,14 +23,12 @@ limitations under the License. */ namespace paddle { /** - * Status is Paddle error code. It only contain a std::string as error message. - * Although Status inherits the std::exception, but do not throw it except you - * know what you are doing. + * Error is Paddle error code. It only contain a std::string as error message. * * - * There are two styles to return status in Paddle. + * There are two styles to return error in Paddle. * - * 1. Return Status + * 1. Return Error * When method return a status, the return must use `__must_check` attribute. * Example as below. * @code{cpp} @@ -39,29 +37,29 @@ namespace paddle { * Error __must_check bar() { * // do something. * Status s = foo(); // invoke other method return status. - * if (!s.isOK()) return s; + * if (!s) return s; * // do something else. * return Status(); * } * @endcode{cpp} * * 2. Return by parameter. - * It is another way to return a status, by using a pointer parameter. + * It is another way to return an error, by using a pointer parameter. * Example as below. * * @code{cpp} * Error bar(); * - * int foo(Error* status) { + * int foo(Error* error) { * // Do something. - * Status s = bar(); - * if (!s.isOK()) { - * *status = s; + * Error s = bar(); + * if (!s) { + * *error = s; * return 0; * } * // Do something else. * if (someInternalErrorHappend) { - * *status = ErrorF("Some dimension is too large, %d", dimension); + * *error = Error("Some dimension is too large, %d", dimension); * return 0; * } * // End of method. @@ -72,7 +70,7 @@ namespace paddle { * Error s; * // do something. * foo(&s); - * if (!s.isOK()) return s; + * if (!s) return s; * } * @endcode{cpp} * @@ -81,17 +79,31 @@ namespace paddle { * use log(FATAL) or CHECK to make program exit before. When we clean all * log(FATAL) and CHECK in Paddle, 'check' method will be removed. */ -class Error final : public std::exception { +class Error final { public: /** * Default Status. OK */ - Error() noexcept {} + inline Error() {} /** - * @brief what will return the error message. If status is OK, return nullptr. + * @brief Create an Error use printf syntax. */ - const char* what() const noexcept override { + inline explicit Error(const char* fmt, ...) { + va_list ap; + va_start(ap, fmt); + constexpr size_t kBufferSize = 1024; + this->errMsg_.reset(new std::string(kBufferSize, 0)); + auto sz = vsnprintf(&(*errMsg_)[0], kBufferSize, fmt, ap); + this->errMsg_->resize(sz); + this->errMsg_->shrink_to_fit(); + va_end(ap); + } + + /** + * @brief what will return the error message. If no error, return nullptr. + */ + inline const char* msg() const { if (errMsg_) { return errMsg_->data(); } else { @@ -100,58 +112,18 @@ class Error final : public std::exception { } /** - * @brief isOK - * @return true if OK. + * @brief operator bool, return True if there is no error. */ - inline bool isOK() const noexcept { return errMsg_ == nullptr; } - + inline operator bool() const { return !errMsg_; } /** * @brief check this status by glog. * @note It is a temp method used during cleaning Paddle code. It will be * removed later. */ - inline void check() const { CHECK(isOK()) << what(); } - - /** - * friend method to create Error. - */ - template - friend Error __must_check ErrorF(const char* fmt, ARGS... args); + inline void check() const { CHECK(*this) << msg(); } private: std::shared_ptr errMsg_; }; -/** - * ErrorF will create an Error by printf syntax. - * - * Specialize this method because clang will give a warning when use printf(fmt) - * without arguments. - */ -template <> -inline Error __must_check ErrorF(const char* msg) { - Error e; - e.errMsg_.reset(new std::string(msg)); - return e; -} - -/** - * ErrorF will create an Error by printf syntax. - * - * Examples: - * @code{cpp} - * auto err = ErrorF("SomeError"); - * auto err2 = ErrorF("SomeErrorWithParameter %f %d", real_val, int_val); - * @endcode{cpp} - */ -template -inline Error __must_check ErrorF(const char* fmt, ARGS... args) { - constexpr size_t kBufferSize = 1024; - char buffer[kBufferSize]; - snprintf(buffer, kBufferSize, fmt, args...); - Error e; - e.errMsg_.reset(new std::string(buffer)); - return e; -} - } // namespace paddle diff --git a/paddle/utils/tests/test_Error.cpp b/paddle/utils/tests/test_Error.cpp index e8643de9d2dccf..85156466e2cafd 100644 --- a/paddle/utils/tests/test_Error.cpp +++ b/paddle/utils/tests/test_Error.cpp @@ -18,17 +18,17 @@ limitations under the License. */ TEST(Error, testAll) { paddle::Error error; - ASSERT_TRUE(error.isOK()); - error = paddle::ErrorF("I'm the error"); - ASSERT_FALSE(error.isOK()); - ASSERT_STREQ("I'm the error", error.what()); + ASSERT_TRUE(error); + error = paddle::Error("I'm the error"); + ASSERT_FALSE(error); + ASSERT_STREQ("I'm the error", error.msg()); - error = paddle::ErrorF("error2"); - ASSERT_FALSE(error.isOK()); - ASSERT_STREQ("error2", error.what()); + error = paddle::Error("error2"); + ASSERT_FALSE(error); + ASSERT_STREQ("error2", error.msg()); int i = 3; - auto error3 = paddle::ErrorF("error%d", i); - ASSERT_FALSE(error3.isOK()); - ASSERT_STREQ("error3", error3.what()); + auto error3 = paddle::Error("error%d", i); + ASSERT_FALSE(error3); + ASSERT_STREQ("error3", error3.msg()); } From 312326899e779d96811b7dfab3cfe5817b1b6fdb Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 18 Jan 2017 21:18:33 +0800 Subject: [PATCH 065/370] add api to doc --- doc/api/trainer_config_helpers/layers.rst | 9 +++++++++ python/paddle/trainer_config_helpers/layers.py | 1 + 2 files changed, 10 insertions(+) diff --git a/doc/api/trainer_config_helpers/layers.rst b/doc/api/trainer_config_helpers/layers.rst index 4e429650e54517..8b0e553eacc932 100644 --- a/doc/api/trainer_config_helpers/layers.rst +++ b/doc/api/trainer_config_helpers/layers.rst @@ -382,6 +382,15 @@ sampling_id_layer :members: sampling_id_layer :noindex: +Slicing and Joining Layers +========================== + +pad_layer +----------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: pad_layer + :noindex: + .. _api_trainer_config_helpers_layers_cost_layers: Cost Layers diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 4e4744b518d859..66817fc93be43b 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -108,6 +108,7 @@ 'print_layer', 'priorbox_layer', 'spp_layer', + 'pad_layer', ] From 9200b955c3312ffa3cbb103ca65dbc214f9d3f16 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Wed, 18 Jan 2017 16:13:06 -0800 Subject: [PATCH 066/370] Update Dockerfile.dev --- paddle/scripts/docker/Dockerfile.dev | 45 ++++++++++++++++++---------- 1 file changed, 30 insertions(+), 15 deletions(-) diff --git a/paddle/scripts/docker/Dockerfile.dev b/paddle/scripts/docker/Dockerfile.dev index e60b9e30732583..2f245e2580f486 100644 --- a/paddle/scripts/docker/Dockerfile.dev +++ b/paddle/scripts/docker/Dockerfile.dev @@ -5,16 +5,37 @@ ARG DEBIAN_FRONTEND=noninteractive ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' -RUN apt-get update \ - && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ +RUN apt-get update + +# Paddle development tools. +RUN apt-get install -y coreutils git cmake g++ m4 python-pip swig + +# Paddle data tools. +RUN apt-get install -y wget unzip tar xz-utils bzip2 gzip sed grep graphviz + +# Paddle documentation tools. +RUN apt-get install -y doxygen + +# Paddle code auto-reformat tools. +RUN apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev clang-format-3.8 clang-tidy-3.8 + +# Paddle prerequisites. +RUN apt-get install -y \ + libprotobuf-dev protobuf-compiler \ libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base g++ m4 python-pip \ - python-protobuf python-numpy python-dev swig openssh-server \ - wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ - sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ - clang-3.8 llvm-3.8 libclang-3.8-dev clang-format-3.8 clang-tidy-3.8 \ - && apt-get clean -y + libatlas-dev libatlas3-base \ + python-protobuf python-numpy python-dev python-matplotlib \ + libjpeg-dev zlib1g-dev + +# Development environment: +RUN apt-get install -y openssh-server + +RUN apt-get clean -y + +# Build Google test RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib + +# Python dependencies. RUN pip install -U BeautifulSoup docopt PyYAML pillow \ sphinx sphinx_rtd_theme recommonmark jupyter @@ -38,10 +59,4 @@ RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config EXPOSE 22 -# Jupyter Notebook directory. -RUN mkdir /notes/ -WORKDIR "/notes" -EXPOSE 8888 - -RUN mkdir -p /opt/bin -COPY ./paddle/scripts/docker/entrypoint /opt/bin/ +CMD ["/usr/sbin/sshd", "-D"] From a6d4a31deae38f78e24ecaf198e9250927416041 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 19 Jan 2017 10:01:47 +0800 Subject: [PATCH 067/370] Follow comments --- paddle/utils/Error.h | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index a8de56b9808b81..ff11541bbd781f 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -79,33 +79,32 @@ namespace paddle { * use log(FATAL) or CHECK to make program exit before. When we clean all * log(FATAL) and CHECK in Paddle, 'check' method will be removed. */ -class Error final { +class Error { public: /** - * Default Status. OK + * Construct an no-error value. */ - inline Error() {} + Error() {} /** * @brief Create an Error use printf syntax. */ - inline explicit Error(const char* fmt, ...) { + explicit Error(const char* fmt, ...) { va_list ap; va_start(ap, fmt); constexpr size_t kBufferSize = 1024; - this->errMsg_.reset(new std::string(kBufferSize, 0)); - auto sz = vsnprintf(&(*errMsg_)[0], kBufferSize, fmt, ap); - this->errMsg_->resize(sz); - this->errMsg_->shrink_to_fit(); + char buffer[kBufferSize]; + vsnprintf(buffer, kBufferSize, fmt, ap); + this->msg_.reset(new std::string(buffer)); va_end(ap); } /** * @brief what will return the error message. If no error, return nullptr. */ - inline const char* msg() const { - if (errMsg_) { - return errMsg_->data(); + const char* msg() const { + if (msg_) { + return msg_->c_str(); } else { return nullptr; } @@ -114,16 +113,16 @@ class Error final { /** * @brief operator bool, return True if there is no error. */ - inline operator bool() const { return !errMsg_; } + operator bool() const { return !msg_; } /** * @brief check this status by glog. * @note It is a temp method used during cleaning Paddle code. It will be * removed later. */ - inline void check() const { CHECK(*this) << msg(); } + void check() const { CHECK(*this) << msg(); } private: - std::shared_ptr errMsg_; + std::shared_ptr msg_; }; } // namespace paddle From c88dec209f367cb3ac1bd3fe6964e63f7274d975 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 19 Jan 2017 10:08:33 +0800 Subject: [PATCH 068/370] Fix typo --- paddle/utils/Error.h | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index ff11541bbd781f..6fe7b6ea8884ec 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -82,7 +82,7 @@ namespace paddle { class Error { public: /** - * Construct an no-error value. + * Construct a no-error value. */ Error() {} @@ -100,7 +100,7 @@ class Error { } /** - * @brief what will return the error message. If no error, return nullptr. + * @brief msg will return the error message. If no error, return nullptr. */ const char* msg() const { if (msg_) { @@ -114,6 +114,7 @@ class Error { * @brief operator bool, return True if there is no error. */ operator bool() const { return !msg_; } + /** * @brief check this status by glog. * @note It is a temp method used during cleaning Paddle code. It will be From 42ea1376e25f7ed02ea55e11cb6114983c0d1e4c Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 19 Jan 2017 10:11:06 +0800 Subject: [PATCH 069/370] bug fix in GatedRecurrentLayer which only occurs in predicting or job=test mode. --- paddle/gserver/layers/GatedRecurrentLayer.cpp | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/paddle/gserver/layers/GatedRecurrentLayer.cpp b/paddle/gserver/layers/GatedRecurrentLayer.cpp index 930d9a056164e7..d3aeea921801da 100644 --- a/paddle/gserver/layers/GatedRecurrentLayer.cpp +++ b/paddle/gserver/layers/GatedRecurrentLayer.cpp @@ -314,13 +314,13 @@ void GatedRecurrentLayer::forwardBatch(int batchSize, batchValue_->resizeOrCreate(*output_.value); batchValue_->copy(*inputValue, *gate_.value, /* seq2batch */ true); - if (bias_ && bias_->getWGrad()) { + if (bias_) { gate_.value->addBias(*(bias_->getW()), 1); } { int numBatch = batchValue_->getNumBatch(); - int batchSize = 0; + int curBatchSize = 0; AsyncGpuBlock asyncGpuBlock; for (int n = 0; n < numBatch; n++) { MatrixPtr outputValueTmp = batchValue_->getBatchValue(n); @@ -330,16 +330,17 @@ void GatedRecurrentLayer::forwardBatch(int batchSize, gruValue.resetOutputValue = (batchValue_->getBatchValue(*resetOutput_.value, n))->getData(); - batchSize = outputValueTmp->getHeight(); + curBatchSize = outputValueTmp->getHeight(); gruValue.prevOutValue = - (n == 0 ? nullptr - : (batchValue_->getBatchValue(n - 1, batchSize))->getData()); + (n == 0 + ? nullptr + : (batchValue_->getBatchValue(n - 1, curBatchSize))->getData()); { if (useGpu_) { - GruCompute::forward<1>(gruValue, getSize(), batchSize); + GruCompute::forward<1>(gruValue, getSize(), curBatchSize); } else { - GruCompute::forward<0>(gruValue, getSize(), batchSize); + GruCompute::forward<0>(gruValue, getSize(), curBatchSize); } } } From 843fb2ea32d4f0b2d1f3667545487c8084229819 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 19 Jan 2017 15:12:42 +0800 Subject: [PATCH 070/370] Make code more readable --- paddle/utils/Error.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index 6fe7b6ea8884ec..2b4fbef4e015e7 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -15,6 +15,7 @@ limitations under the License. */ #pragma once #include +#include #include #include #include @@ -113,7 +114,7 @@ class Error { /** * @brief operator bool, return True if there is no error. */ - operator bool() const { return !msg_; } + operator bool() const { return msg_ == nullptr; } /** * @brief check this status by glog. From 7ff8c8eec3f44497816ae4fe66516f3ca3ba7cb7 Mon Sep 17 00:00:00 2001 From: xuwei06 Date: Thu, 19 Jan 2017 01:33:28 -0800 Subject: [PATCH 071/370] Compile glog with WITH_GFLAGS=ON Also initialize glog after gflags in Util.cpp initMain() Change-Id: I09ff062b462aa76d9f7b5f97e883b92939e6dcb2 --- cmake/external/glog.cmake | 9 +++++---- paddle/utils/Util.cpp | 4 ++-- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake index 71e20c85276b01..e1eade601ea007 100644 --- a/cmake/external/glog.cmake +++ b/cmake/external/glog.cmake @@ -1,11 +1,11 @@ # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. -# +# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at -# +# # http://www.apache.org/licenses/LICENSE-2.0 -# +# # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. @@ -29,12 +29,13 @@ INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR}) ExternalProject_Add( glog ${EXTERNAL_PROJECT_LOG_ARGS} + DEPENDS gflags GIT_REPOSITORY "https://github.com/google/glog.git" PREFIX ${GLOG_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR} CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DWITH_GFLAGS=OFF + CMAKE_ARGS -DWITH_GFLAGS=ON CMAKE_ARGS -DBUILD_TESTING=OFF ) diff --git a/paddle/utils/Util.cpp b/paddle/utils/Util.cpp index 411a64aa8d0737..220aac1ff11e0f 100644 --- a/paddle/utils/Util.cpp +++ b/paddle/utils/Util.cpp @@ -144,20 +144,20 @@ void runInitFunctions() { } void initMain(int argc, char** argv) { - initializeLogging(argc, argv); installLayerStackTracer(); std::string line; for (int i = 0; i < argc; ++i) { line += argv[i]; line += ' '; } - LOG(INFO) << "commandline: " << line; #ifndef GFLAGS_GFLAGS_H_ namespace gflags = google; #endif gflags::ParseCommandLineFlags(&argc, &argv, true); + initializeLogging(argc, argv); + LOG(INFO) << "commandline: " << line; CHECK_EQ(argc, 1) << "Unknown commandline argument: " << argv[1]; installProfilerSwitch(); From 63118767bf0ede148a7404604ea966d6f0bf35c7 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 19 Jan 2017 17:36:00 +0800 Subject: [PATCH 072/370] Add detect redhat and unknown in CMake --- cmake/system.cmake | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/cmake/system.cmake b/cmake/system.cmake index ab124a89dcdc1c..485cf0eea1c2e7 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -30,6 +30,10 @@ ELSE(WIN32) SET(HOST_SYSTEM "debian") ELSEIF(LINUX_ISSUE MATCHES "Ubuntu") SET(HOST_SYSTEM "ubuntu") + ELSEIF(LINUX_ISSUE MATCHES "Red Hat") + SET(HOST_SYSTEM "redhat") + ELSEIF(LINUX_ISSUE MATCHES "Fedora") + SET(HOST_SYSTEM "fedora") ENDIF() ENDIF(EXISTS "/etc/issue") @@ -40,6 +44,10 @@ ELSE(WIN32) ENDIF() ENDIF(EXISTS "/etc/redhat-release") + IF(NOT HOST_SYSTEM) + SET(HOST_SYSTEM "unknown") + ENDIF() + ENDIF(APPLE) ENDIF(WIN32) From febf9a0b070a7c5036f2b32f12167952f1597727 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 20 Jan 2017 00:11:08 +0800 Subject: [PATCH 073/370] Add comments and CMAKE_SYSTEM_NAME --- cmake/system.cmake | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/cmake/system.cmake b/cmake/system.cmake index 485cf0eea1c2e7..3e472da7e0bd9c 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -12,6 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +# Detects the OS and sets appropriate variables. +# CMAKE_SYSTEM_NAME only give us a coarse-grained name, +# but the name like centos is necessary in some scenes +# to distinguish system for customization. +# +# for instance, protobuf libs path is /lib64 +# on CentOS, but /lib on other systems. + IF(WIN32) SET(HOST_SYSTEM "win32") ELSE(WIN32) @@ -45,7 +53,7 @@ ELSE(WIN32) ENDIF(EXISTS "/etc/redhat-release") IF(NOT HOST_SYSTEM) - SET(HOST_SYSTEM "unknown") + SET(HOST_SYSTEM ${CMAKE_SYSTEM_NAME}) ENDIF() ENDIF(APPLE) From 0cc3d829024a140a5d388437b021ded4d20d1661 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 20 Jan 2017 18:41:04 +0800 Subject: [PATCH 074/370] Add some comment of CrossMapNormalFunc --- paddle/function/CrossMapNormalOp.cpp | 54 ++++++++++++++++++++++------ 1 file changed, 44 insertions(+), 10 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 8e7dc72524a768..568b8faef4566a 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -112,11 +112,31 @@ void CrossMapNormalGrad(real* inputsGrad, } /** - * \brief {o_0, o_1} = calc(i_0) + * \brief Normalization with across maps. * - * \param inputs[0] input value. - * \param outputs[0] output value. - * \param outputs[1] denoms. + * This Function comes from the paper + * "ImageNet Classification with Deep Convolutional Neural Networks". + * + * The original formula is: + * + * Input(x, y) + * Output(x, y) = ------------------------------------------------ + * alpha /min(F, f-[N/2] + N) + * (1 + ----- * | (Input(x, y))^2 ) ^ (beta) + * N /max(0, f-[N/2]) + * + * Argument in the Function: + * \param size_ represent N + * \param scale_ represent alpha / N + * \param pow_ represent beta + * \param inputs[0] represent Input + * \param outputs[0] represent Output + * \param outputs[1] represent The denominator in the formula(except beta) + * + * note: + * Save output[1] is to simplify the backward calculation. + * So, if only consider the forward calculation, we can optimize to + * remove the output[1]. */ template class CrossMapNormalFunc : public FunctionBase { @@ -161,13 +181,27 @@ class CrossMapNormalFunc : public FunctionBase { }; /** - * \brief {o_0} = calc(i_0, i_1, i_2, i_3) + * \brief Backward calculation for normalization with across maps. + * + * The implementation of this Function is derived from the + * CrossMapNormalFunc implementation. + * + * InputGrad = OutputGrad * denoms ^ (-beta) + * / + * + | (OutputGrad * OutputValue * (-2 * alpha * beta) / denoms) * InputValue + * / * - * \param inputs[0] input value. - * \param inputs[1] output value. - * \param inputs[2] output grad. - * \param inputs[3] denoms. - * \param outputs[0] input grad. + * Argument in the Function: + * \param size_ represent N + * \param scale_ represent alpha / N + * \param pow_ represent beta + * \param inputs[0] represent InputValue, inputs[0] of CrossMapNormalFunc + * \param inputs[1] represent OutputValue, outputs[0] of CrossMapNormalFunc + * \param inputs[2] represent OutputGrad + * \param inputs[3] represent denoms, outputs[1] of CrossMapNormalFunc + * This is the intermediate result that is + * preserved in the forward calculation. + * \param outputs[0] represent InputGrad */ template class CrossMapNormalGradFunc : public FunctionBase { From 95dec805aab0ddaef551b367f0c2f4e42a393819 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 20 Jan 2017 18:48:42 +0800 Subject: [PATCH 075/370] Make external/glog use local gflags. --- cmake/external/glog.cmake | 1 + 1 file changed, 1 insertion(+) diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake index e1eade601ea007..ab105611c812a4 100644 --- a/cmake/external/glog.cmake +++ b/cmake/external/glog.cmake @@ -36,6 +36,7 @@ ExternalProject_Add( CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR} CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON CMAKE_ARGS -DWITH_GFLAGS=ON + CMAKE_ARGS -Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags CMAKE_ARGS -DBUILD_TESTING=OFF ) From a8583f0ccb230fb0564103a28a034a87156bac78 Mon Sep 17 00:00:00 2001 From: reyoung Date: Fri, 20 Jan 2017 11:39:52 +0000 Subject: [PATCH 076/370] Correct Handle Python Proto2 --- cmake/external/python.cmake | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cmake/external/python.cmake b/cmake/external/python.cmake index 209e679f2cb217..6372a9a768e580 100644 --- a/cmake/external/python.cmake +++ b/cmake/external/python.cmake @@ -26,10 +26,10 @@ IF(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) find_python_module(wheel REQUIRED) find_python_module(google.protobuf REQUIRED) FIND_PACKAGE(NumPy REQUIRED) - IF(${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") + IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.0.0, " - "please use pip to upgrade protobuf.") - ENDIF(${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") + "please use pip to upgrade protobuf. pip install -U protobuf") + ENDIF() ELSE(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) MESSAGE(FATAL_ERROR "Please install python 2.7 before building PaddlePaddle.") ##################################### PYTHON ######################################## From 51fa6baebec5c29f4ff0f1c2e72f6feab02bbe5e Mon Sep 17 00:00:00 2001 From: FoREacH Date: Sat, 21 Jan 2017 00:24:38 +0200 Subject: [PATCH 077/370] Fix issue #1186 --- cmake/external/warpctc.cmake | 1 + 1 file changed, 1 insertion(+) diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index f5e4b3e1eb39ac..f23a3969e95259 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -54,6 +54,7 @@ ExternalProject_Add( CMAKE_ARGS -DWITH_GPU=${WITH_GPU} CMAKE_ARGS -DWITH_OMP=${USE_OMP} CMAKE_ARGS -DWITH_TORCH=OFF + CMAKE_ARGS -DCMAKE_DISABLE_FIND_PACKAGE_TORCH=TRUE CMAKE_ARGS -DBUILD_SHARED=ON ) From 7709b697ba85a99c9f784e5f2f8267f7a52c61fc Mon Sep 17 00:00:00 2001 From: F0REacH Date: Sat, 21 Jan 2017 00:52:39 +0200 Subject: [PATCH 078/370] Update warpctc.cmake Fix case-sensitiveness --- cmake/external/warpctc.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index f23a3969e95259..172c318b35d611 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -54,7 +54,7 @@ ExternalProject_Add( CMAKE_ARGS -DWITH_GPU=${WITH_GPU} CMAKE_ARGS -DWITH_OMP=${USE_OMP} CMAKE_ARGS -DWITH_TORCH=OFF - CMAKE_ARGS -DCMAKE_DISABLE_FIND_PACKAGE_TORCH=TRUE + CMAKE_ARGS -DCMAKE_DISABLE_FIND_PACKAGE_Torch=TRUE CMAKE_ARGS -DBUILD_SHARED=ON ) From ed5624023549374259c0fc9c4849d5ae46347b1b Mon Sep 17 00:00:00 2001 From: FoREacH Date: Sat, 21 Jan 2017 05:46:07 +0200 Subject: [PATCH 079/370] Set protobuf CMAKE_INSTALL_LIBDIR to fixed value lib --- cmake/external/protobuf.cmake | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index 613614c0e3d42f..84f459033f06f8 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -29,17 +29,12 @@ IF(WIN32) "${PROTOBUF_INSTALL_DIR}/lib/libprotoc.lib" CACHE FILEPATH "protoc library." FORCE) SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc.exe" CACHE FILEPATH "protobuf executable." FORCE) ELSE(WIN32) - IF(${HOST_SYSTEM} STREQUAL "centos") - SET(LIB "lib64") - ELSE() - SET(LIB "lib") - ENDIF() SET(PROTOBUF_LITE_LIBRARY - "${PROTOBUF_INSTALL_DIR}/${LIB}/libprotobuf-lite.a" CACHE FILEPATH "protobuf lite library." FORCE) + "${PROTOBUF_INSTALL_DIR}/lib/libprotobuf-lite.a" CACHE FILEPATH "protobuf lite library." FORCE) SET(PROTOBUF_LIBRARY - "${PROTOBUF_INSTALL_DIR}/${LIB}/libprotobuf.a" CACHE FILEPATH "protobuf library." FORCE) + "${PROTOBUF_INSTALL_DIR}/lib/libprotobuf.a" CACHE FILEPATH "protobuf library." FORCE) SET(PROTOBUF_PROTOC_LIBRARY - "${PROTOBUF_INSTALL_DIR}/${LIB}/libprotoc.a" CACHE FILEPATH "protoc library." FORCE) + "${PROTOBUF_INSTALL_DIR}/lib/libprotoc.a" CACHE FILEPATH "protoc library." FORCE) SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc" CACHE FILEPATH "protobuf executable." FORCE) ENDIF(WIN32) @@ -58,6 +53,7 @@ ExternalProject_Add( -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR} + -DCMAKE_INSTALL_LIBDIR=lib ) LIST(APPEND external_project_dependencies protobuf) From fae5d82c367482e7236489bff41c875a230598fd Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 22 Jan 2017 10:29:55 +0800 Subject: [PATCH 080/370] Fix destroy error in test_ProtoServer. --- paddle/pserver/test/test_ProtoServer.cpp | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/paddle/pserver/test/test_ProtoServer.cpp b/paddle/pserver/test/test_ProtoServer.cpp index 9f86ee80f4e5cc..e70e1670d31993 100644 --- a/paddle/pserver/test/test_ProtoServer.cpp +++ b/paddle/pserver/test/test_ProtoServer.cpp @@ -12,14 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "paddle/utils/Util.h" - #include - +#include #include "ParameterService.pb.h" #include "paddle/math/Vector.h" #include "paddle/pserver/ProtoServer.h" #include "paddle/utils/Stat.h" +#include "paddle/utils/Util.h" DEFINE_string(server_addr, "127.0.0.1", "Server address"); DEFINE_int64(dim, 50000000, "Data size"); @@ -163,17 +162,15 @@ int main(int argc, char** argv) { paddle::initMain(argc, argv); testing::InitGoogleTest(&argc, argv); - MyServer* server; + std::unique_ptr server; if (FLAGS_rdma_tcp == "rdma") { - server = new MyServer(FLAGS_port, 0); + server.reset(new MyServer(FLAGS_port, 0)); } else { - server = new MyServer(FLAGS_port); + server.reset(new MyServer(FLAGS_port)); } server->start(); usleep(10000); - int ret = RUN_ALL_TESTS(); - - exit(ret); + return RUN_ALL_TESTS(); } From 7acd669ee291d42492dd9f6d5a9716ab2f5ac314 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Sun, 22 Jan 2017 16:25:19 +0800 Subject: [PATCH 081/370] Supplement the data format specifications for the parameters. --- paddle/function/CrossMapNormalOp.cpp | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 568b8faef4566a..2d2ff0ca868204 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -126,6 +126,11 @@ void CrossMapNormalGrad(real* inputsGrad, * N /max(0, f-[N/2]) * * Argument in the Function: + * Input is NCHW format, while input.shape.ndims() is equal 4. + * And the meaning of each dimension(0-3) is respectively batch size, + * feature maps, rows and columns. + * The above formula is for each image. + * * \param size_ represent N * \param scale_ represent alpha / N * \param pow_ represent beta @@ -135,7 +140,7 @@ void CrossMapNormalGrad(real* inputsGrad, * * note: * Save output[1] is to simplify the backward calculation. - * So, if only consider the forward calculation, we can optimize to + * TODO, if only consider the forward calculation, we can optimize to * remove the output[1]. */ template @@ -192,6 +197,9 @@ class CrossMapNormalFunc : public FunctionBase { * / * * Argument in the Function: + * The data of inputs/outputs format is the same as the forward interface + * and is NCHW. + * * \param size_ represent N * \param scale_ represent alpha / N * \param pow_ represent beta From 44e3c724f90f6944478de11cb0e0acbd71c6ce74 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sun, 22 Jan 2017 16:38:35 +0800 Subject: [PATCH 082/370] Refine and comments --- paddle/function/PadOp.cpp | 161 +++++++++++------- paddle/function/PadOp.h | 87 ++++------ paddle/function/PadOpGpu.cu | 36 ++-- paddle/function/PadOpTest.cpp | 24 +-- paddle/gserver/layers/PadLayer.cpp | 24 +-- .../paddle/trainer_config_helpers/layers.py | 25 +++ 6 files changed, 197 insertions(+), 160 deletions(-) diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index 75e64a8ee41513..df44fd0fa628d8 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -24,20 +24,19 @@ void Pad(real* outputs, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1) { - int outC = inC + padc0 + padc1; - int outH = inH + padh0 + padh1; - int outW = inW + padw0 + padw1; + const PadConf& pad) { + int cstart = pad.channelStart, cend = pad.channelEnd; + int hstart = pad.heightStart, hend = pad.heightEnd; + int wstart = pad.widthStart, wend = pad.widthEnd; + int outC = inC + cstart + cend; + int outH = inH + hstart + hend; + int outW = inW + wstart + wend; for (int i = 0; i < num; i++) { for (int c = 0; c < inC; c++) { for (int h = 0; h < inH; h++) { int inoff = ((i * inC + c) * inH + h) * inW; - int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0; + int outoff = + ((i * outC + c + cstart) * outH + h + hstart) * outW + wstart; memcpy(outputs + outoff, inputs + inoff, inW * sizeof(real)); } } @@ -51,20 +50,19 @@ void PadGrad(real* inGrad, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1) { - int outC = inC + padc0 + padc1; - int outH = inH + padh0 + padh1; - int outW = inW + padw0 + padw1; + const PadConf& pad) { + int cstart = pad.channelStart, cend = pad.channelEnd; + int hstart = pad.heightStart, hend = pad.heightEnd; + int wstart = pad.widthStart, wend = pad.widthEnd; + int outC = inC + cstart + cend; + int outH = inH + hstart + hend; + int outW = inW + wstart + wend; for (int i = 0; i < num; i++) { for (int c = 0; c < inC; c++) { for (int h = 0; h < inH; h++) { int inoff = ((i * inC + c) * inH + h) * inW; - int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0; + int outoff = + ((i * outC + c + cstart) * outH + h + hstart) * outW + wstart; CpuVector inG = CpuVector(inW, inGrad + inoff); CpuVector outG = CpuVector(inW, const_cast(outGrad + outoff)); inG += outG; @@ -73,22 +71,71 @@ void PadGrad(real* inGrad, } } +/** + * \brief Padding zeros to input according to the specify dimension. + * The struct pad_ contains the padding size in each dimension. + * The input and output is a 4D tensor. In PadFunc, we only + * pad zeros to the 2nd to 4th dimension. + * + * Argument in this Function: + * \param pad_ A struct object contains the padding size in each dimension. + * It has six integers. The channelStart and channelEnd indicates + * how many zeros to add before and after the input in channel + * dimension. And the heightStart and heightEnd indicates padding + * in height dimension. The widthStart and widthEnd indicates the + * padding in width dimension. + * \param inputs A 4D tensor, only one input. + * \param outputs A 4D tensor, the output value after padding. + * + * For example, + * Input(2,2,2,3) = [ + * [ [[1,2,3], [3,4,5]], + * [[2,3,5], [1,6,7]] ], + * [ [[4,3,1], [1,8,7]], + * [[3,8,9], [2,3,5]] ] + * ] # the shape is (1,2,2,3) + * + * pad_: if channelStart = channelEnd = 1, others are 0. + * Output(2,4,2,3) = [ + * [ [[0,0,0], [0,0,0]], + * [[1,2,3], [3,4,5]], + * [[2,3,5], [1,6,7]], + * [[0,0,0], [0,0,0]] ], + * [ [[0,0,0], [0,0,0]], + * [[4,3,1], [1,8,7]], + * [[3,8,9], [2,3,5]], + * [[0,0,0], [0,0,0]] ] + * ] # the shape is (2,4,2,3) + * + * pad_: if widthStart = 1, widthEnd = 2, others are 0. + * Output(2,2,2,6) = [ + * [ [[0,1,2,3,0,0], [0,3,4,5,0,0]], + * [[0,2,3,5,0,0], [0,1,6,7,0,0]] ], + * [ [[0,4,3,1,0,0], [0,1,8,7,0,0]], + * [[0,3,8,9,0,0], [0,2,3,5,0,0]] ], + * ] # the shape is (2,2,2,6) + * + * pad_: if heightStart = 1, heightEnd = 1, others are 0. + * Output(2,2,4,3) = [ + * [ [[0,0,0], [1,2,3], [3,4,5], [0,0,0]], + * [[0,0,0], [2,3,5], [1,6,7], [0,0,0]] ], + * [ [[0,0,0], [4,3,1], [1,8,7], [0,0,0]], + * [[0,0,0], [3,8,9], [2,3,5], [0,0,0]] ], + * ] # the shape is (2,2,4,3) + */ + template class PadFunc : public FunctionBase { public: void init(const FuncConfig& config) override { - padc0_ = config.get("padc0"); - padc1_ = config.get("padc1"); - padh0_ = config.get("padh0"); - padh1_ = config.get("padh1"); - padw0_ = config.get("padw0"); - padw1_ = config.get("padw1"); + pad_.channelStart = config.get("cstart"); + pad_.channelEnd = config.get("cend"); + pad_.heightStart = config.get("hstart"); + pad_.heightEnd = config.get("hend"); + pad_.widthStart = config.get("wstart"); + pad_.widthEnd = config.get("wend"); } - /** - * \param inputs[0] input value. - * \param outputs[0] output value. - */ void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(1UL, inputs.size()); CHECK_EQ(1UL, outputs.size()); @@ -108,39 +155,35 @@ class PadFunc : public FunctionBase { inC, inH, inW, - padc0_, - padc1_, - padh0_, - padh1_, - padw0_, - padw1_); + pad_); } private: - int padc0_; - int padc1_; - int padh0_; - int padh1_; - int padw0_; - int padw1_; + PadConf pad_; }; +/** + * \brief The backward propagation of padding Function. Remove the elements + * in the padding positions of forward. + * + * Argument in this Function: + * \param pad_ The same meaning as it in PadFunc. + * \param inputs The gradient with respect to the output value of PadFunc. + * \param outputs The gradient with respect to the input value of PadFunc. + */ + template class PadGradFunc : public FunctionBase { public: void init(const FuncConfig& config) override { - padc0_ = config.get("padc0"); - padc1_ = config.get("padc1"); - padh0_ = config.get("padh0"); - padh1_ = config.get("padh1"); - padw0_ = config.get("padw0"); - padw1_ = config.get("padw1"); + pad_.channelStart = config.get("cstart"); + pad_.channelEnd = config.get("cend"); + pad_.heightStart = config.get("hstart"); + pad_.heightEnd = config.get("hend"); + pad_.widthStart = config.get("wstart"); + pad_.widthEnd = config.get("wend"); } - /** - * \param inputs[0] output grad. - * \param inouts[0] input grad. - */ void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(1UL, inputs.size()); CHECK_EQ(1UL, outputs.size()); @@ -163,21 +206,11 @@ class PadGradFunc : public FunctionBase { inC, inH, inW, - padc0_, - padc1_, - padh0_, - padh1_, - padw0_, - padw1_); + pad_); } private: - int padc0_; - int padc1_; - int padh0_; - int padh1_; - int padw0_; - int padw1_; + PadConf pad_; }; REGISTER_TYPED_FUNC(Pad, CPU, PadFunc); diff --git a/paddle/function/PadOp.h b/paddle/function/PadOp.h index 4a5e8fe33857aa..7b5c730a6a0fa5 100644 --- a/paddle/function/PadOp.h +++ b/paddle/function/PadOp.h @@ -18,29 +18,34 @@ limitations under the License. */ namespace paddle { +struct PadConf { + /// how many values to add before the data along channel dimension. + int channelStart; + /// how many values to add after the data along channel dimension. + int channelEnd; + /// how many values to add before the data along height dimension. + int heightStart; + /// how many values to add after the data along height dimension. + int heightEnd; + /// how many values to add before the data along width dimension. + int widthStart; + /// how many values to add after the data along width dimension. + int widthEnd; +}; + /** * \brief This funtion pads zeros to inputs according to the specify dimension. - * The data structure of image data is NCHW. - * - * \param[out] outputs save results. - * \param[in] inputs input data. - * \param[in] num batch size of input data. - * \param[in] inC channel number of input data. - * \param[in] inH height of input data. - * \param[in] inH with of input data. - * \param[in] padc0 how many values to add before the data in dimension of - * channel. - * \param[in] padc1 how many values to add after the data in dimension of - * channel. - * \param[in] padh0 how many values to add before the data in dimension of - * height. - * \param[in] padh1 how many values to add after the data in dimension of - * height. - * \param[in] padw0 how many values to add before the data in dimension of - * width. - * \param[in] padw1 how many values to add after the data in dimension of - * width. + * The input and output is a 4D tensor. Padding zeros from the 2nd to + * the 4th dimenstion according argument of pad. * + * \param[out] outputs save results. + * \param[in] inputs input data. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + * \param[in] pad the padding config, contains the size along the + * specify dimension. */ template void Pad(real* outputs, @@ -49,36 +54,19 @@ void Pad(real* outputs, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1); + const PadConf& pad); /** * \brief Padding operation backward. - * The data structure of image data is NCHW. - * - * \param[out] inGrad gradients of previous layer. - * \param[in] outGrad output gradients. - * \param[in] num batch size of input data. - * \param[in] inC channel number of input data. - * \param[in] inH height of input data. - * \param[in] inH with of input data. - * \param[in] padc0 how many values to add before the data in dimension of - * channel. - * \param[in] padc1 how many values to add after the data in dimension of - * channel. - * \param[in] padh0 how many values to add before the data in dimension of - * height. - * \param[in] padh1 how many values to add after the data in dimension of - * height. - * \param[in] padw0 how many values to add before the data in dimension of - * width. - * \param[in] padw1 how many values to add after the data in dimension of - * width. * + * \param[out] inGrad gradients of previous layer. + * \param[in] outGrad output gradients. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + * \param[in] pad the padding config, contains the size along the + * specify dimension. */ template void PadGrad(real* inGrad, @@ -87,10 +75,5 @@ void PadGrad(real* inGrad, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1); + const PadConf& pad); } // namespace paddle diff --git a/paddle/function/PadOpGpu.cu b/paddle/function/PadOpGpu.cu index 578d6e86d7534e..9104b1aca507c5 100644 --- a/paddle/function/PadOpGpu.cu +++ b/paddle/function/PadOpGpu.cu @@ -40,20 +40,18 @@ void Pad(real* outputs, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1) { + const PadConf& pad) { size_t nth = num * inC * inH * inW; int blockSize = 1024; int gridSize = (nth + 1024 - 1) / 1024; - int outC = inC + padc0 + padc1; - int outH = inH + padh0 + padh1; - int outW = inW + padw0 + padw1; + int cstart = pad.channelStart, cend = pad.channelEnd; + int hstart = pad.heightStart, hend = pad.heightEnd; + int wstart = pad.widthStart, wend = pad.widthEnd; + int outC = inC + cstart + cend; + int outH = inH + hstart + hend; + int outW = inW + wstart + wend; KePad<<>> - (outputs, inputs, inC, inH, inW, padc0, padh0, padw0, + (outputs, inputs, inC, inH, inW, cstart, hstart, wstart, outC, outH, outW, nth); CHECK_SYNC("Pad"); } @@ -81,20 +79,18 @@ void PadGrad(real* inGrad, const int inC, const int inH, const int inW, - const int padc0, - const int padc1, - const int padh0, - const int padh1, - const int padw0, - const int padw1) { + const PadConf& pad) { int nth = num * inC * inH * inW; int blockSize = 1024; int gridSize = (nth + 1024 - 1) / 1024; - int outC = inC + padc0 + padc1; - int outH = inH + padh0 + padh1; - int outW = inW + padw0 + padw1; + int cstart = pad.channelStart, cend = pad.channelEnd; + int hstart = pad.heightStart, hend = pad.heightEnd; + int wstart = pad.widthStart, wend = pad.widthEnd; + int outC = inC + cstart + cend; + int outH = inH + hstart + hend; + int outW = inW + wstart + wend; KePadDiff <<>> - (inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0, + (inGrad, outGrad, inC, inH, inW, cstart, hstart, wstart, outC, outH, outW, nth); CHECK_SYNC("PadGrad"); } diff --git a/paddle/function/PadOpTest.cpp b/paddle/function/PadOpTest.cpp index dce2bac3e9a6ec..cd22d911356791 100644 --- a/paddle/function/PadOpTest.cpp +++ b/paddle/function/PadOpTest.cpp @@ -27,12 +27,12 @@ TEST(Pad, real) { FunctionCompare compare("Pad", FuncConfig() - .set("padc0", 2) - .set("padc1", 3) - .set("padh0", 1) - .set("padh1", 2) - .set("padw0", 3) - .set("padw1", 2)); + .set("cstart", 2) + .set("cend", 3) + .set("hstart", 1) + .set("hend", 2) + .set("wstart", 3) + .set("wend", 2)); TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW}; TensorShape outDims{ numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; @@ -54,12 +54,12 @@ TEST(PadGrad, real) { << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; FunctionCompare compare("PadGrad", FuncConfig() - .set("padc0", 2) - .set("padc1", 3) - .set("padh0", 1) - .set("padh1", 2) - .set("padw0", 3) - .set("padw1", 2)); + .set("cstart", 2) + .set("cend", 3) + .set("hstart", 1) + .set("hend", 2) + .set("wstart", 3) + .set("wend", 2)); TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW}; TensorShape outDims{ numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; diff --git a/paddle/gserver/layers/PadLayer.cpp b/paddle/gserver/layers/PadLayer.cpp index a2a469ff92fb2a..bb618c09f97777 100644 --- a/paddle/gserver/layers/PadLayer.cpp +++ b/paddle/gserver/layers/PadLayer.cpp @@ -49,21 +49,21 @@ bool PadLayer::init(const LayerMap& layerMap, createFunction(forward_, "Pad", FuncConfig() - .set("padc0", padc_[0]) - .set("padc1", padc_[1]) - .set("padh0", padh_[0]) - .set("padh1", padh_[1]) - .set("padw0", padw_[0]) - .set("padw1", padw_[1])); + .set("cstart", padc_[0]) + .set("cend", padc_[1]) + .set("hstart", padh_[0]) + .set("hend", padh_[1]) + .set("wstart", padw_[0]) + .set("wend", padw_[1])); createFunction(backward_, "PadGrad", FuncConfig() - .set("padc0", padc_[0]) - .set("padc1", padc_[1]) - .set("padh0", padh_[0]) - .set("padh1", padh_[1]) - .set("padw0", padw_[0]) - .set("padw1", padw_[1])); + .set("cstart", padc_[0]) + .set("cend", padc_[1]) + .set("hstart", padh_[0]) + .set("hend", padh_[1]) + .set("wstart", padw_[0]) + .set("wend", padw_[1])); return true; } diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 66817fc93be43b..85a28e14aeb8eb 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -3617,6 +3617,31 @@ def pad_layer(input, input data and 3 zeros after the input data in channel dimension. pad_h means padding zeros in height dimension. pad_w means padding zeros in width dimension. + + For example, + + .. code-block:: + + input(2,2,2,3) = [ + [ [[1,2,3], [3,4,5]], + [[2,3,5], [1,6,7]] ], + [ [[4,3,1], [1,8,7]], + [[3,8,9], [2,3,5]] ] + ] + + pad_c=[1,1], pad_h=[0,0], pad_w=[0,0] + output(2,4,2,3) = [ + [ [[0,0,0], [0,0,0]], + [[1,2,3], [3,4,5]], + [[2,3,5], [1,6,7]], + [[0,0,0], [0,0,0]] ], + [ [[0,0,0], [0,0,0]], + [[4,3,1], [1,8,7]], + [[3,8,9], [2,3,5]], + [[0,0,0], [0,0,0]] ] + ] + + The simply usage is: .. code-block:: python From 4426573a6ef42dee2877b5b9bfb162c2485f9345 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Sun, 22 Jan 2017 16:51:54 +0800 Subject: [PATCH 083/370] follow comments --- paddle/function/CrossMapNormalOp.cpp | 29 +++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 2d2ff0ca868204..e795567cd12fae 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -120,25 +120,36 @@ void CrossMapNormalGrad(real* inputsGrad, * The original formula is: * * Input(x, y) - * Output(x, y) = ------------------------------------------------ - * alpha /min(F, f-[N/2] + N) - * (1 + ----- * | (Input(x, y))^2 ) ^ (beta) - * N /max(0, f-[N/2]) + * Output(x, y) = --------------------------------------------- + * -- upper + * (k + alpha * > (Input(x, y))^2) ^ (beta) + * -- lower * - * Argument in the Function: - * Input is NCHW format, while input.shape.ndims() is equal 4. + * upper is `min(F, f-[N/2] + N)` + * lower if `max(0, f-[N/2])` + * + * Function implementation: + * + * inputs and outpus is NCHW format, while input.shape.ndims() is equal 4. * And the meaning of each dimension(0-3) is respectively batch size, * feature maps, rows and columns. - * The above formula is for each image. + * + * Input and Output in the above formula is for each map of one image, and + * Input(x, y), Output(x, y) represents an element in an image. + * + * In the implementation of Function, k is equal to 1, + * so Function has no argument for k. + * + * Function Arguments: * * \param size_ represent N - * \param scale_ represent alpha / N + * \param scale_ represent alpha * \param pow_ represent beta * \param inputs[0] represent Input * \param outputs[0] represent Output * \param outputs[1] represent The denominator in the formula(except beta) * - * note: + * Note: * Save output[1] is to simplify the backward calculation. * TODO, if only consider the forward calculation, we can optimize to * remove the output[1]. From abeb3aae84027a296cf310034e398f8a9271c1e1 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sun, 22 Jan 2017 16:55:26 +0800 Subject: [PATCH 084/370] Minor changes --- paddle/function/PadOp.cpp | 6 +++--- paddle/gserver/layers/PadLayer.h | 5 +++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index df44fd0fa628d8..f1a0d2a1a96f24 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -79,10 +79,10 @@ void PadGrad(real* inGrad, * * Argument in this Function: * \param pad_ A struct object contains the padding size in each dimension. - * It has six integers. The channelStart and channelEnd indicates + * It has six integers. The channelStart and channelEnd indicate * how many zeros to add before and after the input in channel - * dimension. And the heightStart and heightEnd indicates padding - * in height dimension. The widthStart and widthEnd indicates the + * dimension. And the heightStart and heightEnd indicate padding + * in height dimension. The widthStart and widthEnd indicate the * padding in width dimension. * \param inputs A 4D tensor, only one input. * \param outputs A 4D tensor, the output value after padding. diff --git a/paddle/gserver/layers/PadLayer.h b/paddle/gserver/layers/PadLayer.h index 1f5a4a54a0a07e..3e3a21a9970370 100644 --- a/paddle/gserver/layers/PadLayer.h +++ b/paddle/gserver/layers/PadLayer.h @@ -19,8 +19,9 @@ limitations under the License. */ namespace paddle { /** - * @brief response normalization across feature maps - * namely normalize in number of size_ channels + * \brief This layer pads zeros to inputs according to the specify dimension. + * The input and output is a 4D tensor. Padding zeros from the 2nd to + * the 4th dimenstion according padc_, padh_ and padw_. */ class PadLayer : public Layer { public: From 5b9450ae0869673ae11000c89adeec4d237c4ccd Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Sun, 22 Jan 2017 17:46:28 +0800 Subject: [PATCH 085/370] follow comments --- paddle/function/CrossMapNormalOp.cpp | 38 ++++++++++++++++++---------- 1 file changed, 24 insertions(+), 14 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index e795567cd12fae..5c0bdd933b1e4a 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -119,14 +119,14 @@ void CrossMapNormalGrad(real* inputsGrad, * * The original formula is: * - * Input(x, y) - * Output(x, y) = --------------------------------------------- - * -- upper - * (k + alpha * > (Input(x, y))^2) ^ (beta) - * -- lower + * Input(i, x, y) + * Output(i, x, y) = ---------------------------------------------- + * -- upper + * (k + alpha * > (Input(j, x, y))^2) ^ (beta) + * -- j = lower * - * upper is `min(F, f-[N/2] + N)` - * lower if `max(0, f-[N/2])` + * upper is `min(C, c + N/2)` + * lower if `max(0, c - N/2)` * * Function implementation: * @@ -134,8 +134,12 @@ void CrossMapNormalGrad(real* inputsGrad, * And the meaning of each dimension(0-3) is respectively batch size, * feature maps, rows and columns. * - * Input and Output in the above formula is for each map of one image, and - * Input(x, y), Output(x, y) represents an element in an image. + * Input and Output in the above formula is for each map(i) of one image, and + * Input(i, x, y), Output(i, x, y) represents an element in an image. + * + * C is the number of feature maps of one image, and N is a hyper-parameters + * is configured when Function is initialized. The sum in the denominator + * is the sum of the same position in the neighboring maps. * * In the implementation of Function, k is equal to 1, * so Function has no argument for k. @@ -199,20 +203,26 @@ class CrossMapNormalFunc : public FunctionBase { /** * \brief Backward calculation for normalization with across maps. * + * Function implementation: + * * The implementation of this Function is derived from the * CrossMapNormalFunc implementation. * * InputGrad = OutputGrad * denoms ^ (-beta) - * / - * + | (OutputGrad * OutputValue * (-2 * alpha * beta) / denoms) * InputValue - * / + * -- upper + * + > (OutputGrad * OutputValue * (-2 * alpha * beta) / denoms) * InputValue + * -- lower * - * Argument in the Function: * The data of inputs/outputs format is the same as the forward interface * and is NCHW. * + * The upper and lower is the same as forward. The logic of the sum + * is also the same as forward. + * + * Function Arguments: + * * \param size_ represent N - * \param scale_ represent alpha / N + * \param scale_ represent alpha * \param pow_ represent beta * \param inputs[0] represent InputValue, inputs[0] of CrossMapNormalFunc * \param inputs[1] represent OutputValue, outputs[0] of CrossMapNormalFunc From 225a8fa14b8fa04c814da02ff9f240f1819373f3 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 23 Jan 2017 11:26:20 +0800 Subject: [PATCH 086/370] Add numInputs_ and numOutputs_ --- paddle/function/CrossMapNormalOp.cpp | 18 ++++++++++++++---- paddle/function/Function.h | 13 +++++++++++++ 2 files changed, 27 insertions(+), 4 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 5c0bdd933b1e4a..3fab2127a15116 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -162,14 +162,19 @@ template class CrossMapNormalFunc : public FunctionBase { public: void init(const FuncConfig& config) override { + // function arguments size_ = config.get("size"); scale_ = config.get("scale"); pow_ = config.get("pow"); + + // number of inputs and outputs + numInputs_ = 1; + numOutputs_ = 2; } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)1, inputs.size()); - CHECK_EQ((size_t)2, outputs.size()); + CHECK_EQ((size_t)numInputs_, inputs.size()); + CHECK_EQ((size_t)numOutputs_, outputs.size()); CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); CHECK(inputs[0].shape() == outputs[0].shape()); @@ -236,14 +241,19 @@ template class CrossMapNormalGradFunc : public FunctionBase { public: void init(const FuncConfig& config) override { + // function arguments size_ = config.get("size"); scale_ = config.get("scale"); pow_ = config.get("pow"); + + // number of inputs and outputs + numInputs_ = 4; + numOutputs_ = 1; } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)4, inputs.size()); - CHECK_EQ((size_t)1, outputs.size()); + CHECK_EQ((size_t)numInputs_, inputs.size()); + CHECK_EQ((size_t)numOutputs_, outputs.size()); CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); CHECK(inputs[0].shape() == inputs[1].shape()); diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 9215c137eb8e85..4a6c79b6ebdf82 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -153,7 +153,20 @@ class FunctionBase { virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {} + int getNumInputs() const { return numInputs_; } + + int getNumOutputs() const { return numOutputs_; } + static ClassRegistrar funcRegistrar_; + +protected: + // numInputs_ and numOutputs_ represents the maximum + // input and output supported by Function. + // Some functions are optimized for input and output, + // so when comparing the number of arguments, for these functions + // inputs.size() <= numInputs_ or outputs.size() <= numOutputs_ + size_t numInputs_; + size_t numOutputs_; }; #define FUNC_NAME(typeName, deviceName) #typeName "-" #deviceName From 9896f15e7cabd5d68ec03157439a44bbb709c221 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 23 Jan 2017 12:44:03 +0800 Subject: [PATCH 087/370] Add FunctionBase::ops() --- paddle/function/CrossMapNormalOp.cpp | 30 ++++++++++++++++++++-------- paddle/function/Function.h | 7 +++++++ 2 files changed, 29 insertions(+), 8 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 3fab2127a15116..8749a48327604b 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -182,23 +182,37 @@ class CrossMapNormalFunc : public FunctionBase { CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); CHECK_EQ(outputs[1].getArgType(), ASSIGN_TO); - size_t samples = inputs[0].shape()[0]; - size_t channels = inputs[0].shape()[1]; - size_t height = inputs[0].shape()[2]; - size_t width = inputs[0].shape()[3]; + size_t batchSize = inputs[0].shape()[0]; + size_t maps = inputs[0].shape()[1]; + size_t rows = inputs[0].shape()[2]; + size_t columns = inputs[0].shape()[3]; CrossMapNormal(outputs[0].data(), outputs[1].data(), inputs[0].data(), - samples, - channels, - height, - width, + batchSize, + maps, + rows, + columns, size_, scale_, pow_); } + // Only need the shape of the input, can calculate the + // floating-point operation. + size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ((size_t)numInputs_, inputs.size()); + size_t batchSize = inputs[0].shape()[0]; + size_t maps = inputs[0].shape()[1]; + size_t rows = inputs[0].shape()[2]; + size_t columns = inputs[0].shape()[3]; + + // number of floating-point operations + // an approximate value + size_t ops = batchSize * maps * ((rows * columns) * size_); + } + private: size_t size_; real scale_; diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 4a6c79b6ebdf82..65688eebee975c 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -153,6 +153,13 @@ class FunctionBase { virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {} + // Calculate the number of floating-point operations of this Function. + // The inputs and outputs arguments do not need to contain the actual data, + // only the shape. + virtual size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) { + return 0; + } + int getNumInputs() const { return numInputs_; } int getNumOutputs() const { return numOutputs_; } From c4437fa2312b7550fef89ddac00d057361804385 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 23 Jan 2017 13:07:16 +0800 Subject: [PATCH 088/370] Add FunctionBase::check() --- paddle/function/CrossMapNormalOp.cpp | 21 ++++++++++++++------- paddle/function/Function.h | 6 ++++++ 2 files changed, 20 insertions(+), 7 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 8749a48327604b..99af02ac744146 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -173,13 +173,9 @@ class CrossMapNormalFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)numInputs_, inputs.size()); - CHECK_EQ((size_t)numOutputs_, outputs.size()); - - CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); - CHECK(inputs[0].shape() == outputs[0].shape()); - CHECK(inputs[0].shape() == outputs[1].shape()); - + check(inputs, outputs); + // ArgType check still on here, + // not sure whether it is better to put inside the check. CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); CHECK_EQ(outputs[1].getArgType(), ASSIGN_TO); size_t batchSize = inputs[0].shape()[0]; @@ -199,6 +195,15 @@ class CrossMapNormalFunc : public FunctionBase { pow_); } + void check(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ((size_t)numInputs_, inputs.size()); + CHECK_EQ((size_t)numOutputs_, outputs.size()); + + CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); + CHECK(inputs[0].shape() == outputs[0].shape()); + CHECK(inputs[0].shape() == outputs[1].shape()); + } + // Only need the shape of the input, can calculate the // floating-point operation. size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) override { @@ -211,6 +216,8 @@ class CrossMapNormalFunc : public FunctionBase { // number of floating-point operations // an approximate value size_t ops = batchSize * maps * ((rows * columns) * size_); + + return ops; } private: diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 65688eebee975c..4802c2e846cfa2 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -153,6 +153,12 @@ class FunctionBase { virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {} + // This member function is used to check whether the BufferType and shape of + // the inputs and outputs arguments of the Function are correct. + // General calc function which will call this check to do arguments check. + // Also before the call calc, the caller can also check their own arguments. + virtual void check(const BufferArgs& inputs, const BufferArgs& outputs) {} + // Calculate the number of floating-point operations of this Function. // The inputs and outputs arguments do not need to contain the actual data, // only the shape. From a9228e2a406ecb3588ea0c2d112971260d87e1a3 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 23 Jan 2017 13:49:19 +0800 Subject: [PATCH 089/370] Fix CrossMapNormalGradFunc --- paddle/function/CrossMapNormalOp.cpp | 59 ++++++++++++++++++---------- paddle/function/Function.h | 5 ++- 2 files changed, 43 insertions(+), 21 deletions(-) diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 99af02ac744146..ef878bfbba961b 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -196,8 +196,8 @@ class CrossMapNormalFunc : public FunctionBase { } void check(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)numInputs_, inputs.size()); - CHECK_EQ((size_t)numOutputs_, outputs.size()); + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); CHECK(inputs[0].shape() == outputs[0].shape()); @@ -215,7 +215,7 @@ class CrossMapNormalFunc : public FunctionBase { // number of floating-point operations // an approximate value - size_t ops = batchSize * maps * ((rows * columns) * size_); + size_t ops = batchSize * maps * rows * columns * (size_ * 2 + 3); return ops; } @@ -273,15 +273,7 @@ class CrossMapNormalGradFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)numInputs_, inputs.size()); - CHECK_EQ((size_t)numOutputs_, outputs.size()); - - CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); - CHECK(inputs[0].shape() == inputs[1].shape()); - CHECK(inputs[0].shape() == inputs[2].shape()); - CHECK(inputs[0].shape() == inputs[3].shape()); - CHECK(inputs[0].shape() == outputs[0].shape()); - + check(inputs, outputs); if (outputs[0].getArgType() != ADD_TO) { // Currently, some algorithm implementations are ASSIGN_TO mode, // if need to support the ADD_TO calculation, need to clear the output. @@ -290,25 +282,52 @@ class CrossMapNormalGradFunc : public FunctionBase { tmp.zero(); } - size_t samples = inputs[0].shape()[0]; - size_t channels = inputs[0].shape()[1]; - size_t height = inputs[0].shape()[2]; - size_t width = inputs[0].shape()[3]; + size_t batchSize = inputs[0].shape()[0]; + size_t maps = inputs[0].shape()[1]; + size_t rows = inputs[0].shape()[2]; + size_t columns = inputs[0].shape()[3]; CrossMapNormalGrad(outputs[0].data(), inputs[0].data(), inputs[1].data(), inputs[2].data(), inputs[3].data(), - samples, - channels, - height, - width, + batchSize, + maps, + rows, + columns, size_, scale_, pow_); } + void check(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); + + CHECK_EQ(inputs[0].shape().ndims(), (size_t)4); + CHECK(inputs[0].shape() == inputs[1].shape()); + CHECK(inputs[0].shape() == inputs[2].shape()); + CHECK(inputs[0].shape() == inputs[3].shape()); + CHECK(inputs[0].shape() == outputs[0].shape()); + } + + // Only need the shape of one input, can calculate the + // floating-point operation. + size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_LT((size_t)1, inputs.size()); + size_t batchSize = inputs[0].shape()[0]; + size_t maps = inputs[0].shape()[1]; + size_t rows = inputs[0].shape()[2]; + size_t columns = inputs[0].shape()[3]; + + // number of floating-point operations + // an approximate value + size_t ops = batchSize * maps * rows * columns * (size_ * 4 + 2); + + return ops; + } + private: size_t size_; real scale_; diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 4802c2e846cfa2..3bbeb6e525f85b 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -156,12 +156,15 @@ class FunctionBase { // This member function is used to check whether the BufferType and shape of // the inputs and outputs arguments of the Function are correct. // General calc function which will call this check to do arguments check. - // Also before the call calc, the caller can also check their own arguments. + // And before the calc called, the caller can also check their own arguments. virtual void check(const BufferArgs& inputs, const BufferArgs& outputs) {} // Calculate the number of floating-point operations of this Function. // The inputs and outputs arguments do not need to contain the actual data, // only the shape. + // And some Functions have the same input and output shapes, + // so you may not need to enter the complete number of arguments. + // But entering the full arguments is always correct for this interface. virtual size_t ops(const BufferArgs& inputs, const BufferArgs& outputs) { return 0; } From a3f0aed00d304d3ac43735dcc83460b7b724f0a9 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 23 Jan 2017 18:44:51 +0800 Subject: [PATCH 090/370] add python v2 package --- python/CMakeLists.txt | 4 +++- python/paddle/v2/__init__.py | 13 +++++++++++++ python/setup.py.in | 3 ++- 3 files changed, 18 insertions(+), 2 deletions(-) create mode 100644 python/paddle/v2/__init__.py diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 1cda4762eb2a55..ee7a5bff84ca96 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -4,11 +4,13 @@ set(OUTPUT_DIR file(GLOB TRAINER_PY_FILES . ./paddle/trainer/*.py) file(GLOB HELPERS_PY_FILES . ./paddle/trainer_config_helpers/*.py) file(GLOB UTILS_PY_FILES . ./paddle/utils/*.py) +file(GLOB V2_PY_FILES . ./paddle/v2/*.py) set(PY_FILES paddle/__init__.py ${TRAINER_PY_FILES} ${HELPERS_PY_FILES} - ${UTILS_PY_FILES}) + ${UTILS_PY_FILES} + ${V2_PY_FILES}) configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup.py) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py new file mode 100644 index 00000000000000..f662d6826321eb --- /dev/null +++ b/python/paddle/v2/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/python/setup.py.in b/python/setup.py.in index b66a42e87c7870..1e1324eea825ab 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -4,7 +4,8 @@ packages=['paddle', 'paddle.proto', 'paddle.trainer', 'paddle.trainer_config_helpers', - 'paddle.utils'] + 'paddle.utils', + 'paddle.v2'] setup(name='paddle', version='${PADDLE_VERSION}', From 12e9c003b6654e6cb621858285f7d3b09b1b85bc Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 23 Jan 2017 19:38:42 +0800 Subject: [PATCH 091/370] add optimizer --- demo/mnist/api_train.py | 40 +++++++++--------------- python/paddle/v2/__init__.py | 4 +++ python/paddle/v2/optimizer.py | 58 +++++++++++++++++++++++++++++++++++ 3 files changed, 77 insertions(+), 25 deletions(-) create mode 100644 python/paddle/v2/optimizer.py diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index f301da382ff8a5..8573d8143a085b 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -13,15 +13,7 @@ import random from mnist_util import read_from_mnist from paddle.trainer_config_helpers import * - - -def optimizer_config(): - settings( - learning_rate=1e-4, - learning_method=AdamOptimizer(), - batch_size=1000, - model_average=ModelAverage(average_window=0.5), - regularization=L2Regularization(rate=0.5)) +import paddle.v2 def network_config(): @@ -75,19 +67,23 @@ def input_order_converter(generator): def main(): api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores - # get enable_types for each optimizer. - # enable_types = [value, gradient, momentum, etc] - # For each optimizer(SGD, Adam), GradientMachine should enable different - # buffers. - opt_config_proto = parse_optimizer_config(optimizer_config) - opt_config = api.OptimizationConfig.createFromProto(opt_config_proto) - _temp_optimizer_ = api.ParameterOptimizer.create(opt_config) - enable_types = _temp_optimizer_.getParameterTypes() + optimizer = paddle.v2.optimizer.Adam( + learning_rate=1e-4, + batch_size=1000, + model_average=ModelAverage(average_window=0.5), + regularization=L2Regularization(rate=0.5)) + + # Create Local Updater. Local means not run in cluster. + # For a cluster training, here we can change to createRemoteUpdater + # in future. + updater = optimizer.create_local_updater() + assert isinstance(updater, api.ParameterUpdater) # Create Simple Gradient Machine. model_config = parse_network_config(network_config) - m = api.GradientMachine.createFromConfigProto( - model_config, api.CREATE_MODE_NORMAL, enable_types) + m = api.GradientMachine.createFromConfigProto(model_config, + api.CREATE_MODE_NORMAL, + optimizer.enable_types()) # This type check is not useful. Only enable type hint in IDE. # Such as PyCharm @@ -96,12 +92,6 @@ def main(): # Initialize Parameter by numpy. init_parameter(network=m) - # Create Local Updater. Local means not run in cluster. - # For a cluster training, here we can change to createRemoteUpdater - # in future. - updater = api.ParameterUpdater.createLocalUpdater(opt_config) - assert isinstance(updater, api.ParameterUpdater) - # Initialize ParameterUpdater. updater.init(m) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index f662d6826321eb..b2ea87b086101d 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -11,3 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +import optimizer + +__all__ = ['optimizer'] diff --git a/python/paddle/v2/optimizer.py b/python/paddle/v2/optimizer.py new file mode 100644 index 00000000000000..aa2942bc9faeb2 --- /dev/null +++ b/python/paddle/v2/optimizer.py @@ -0,0 +1,58 @@ +import py_paddle.swig_paddle as swig_api +import paddle.trainer_config_helpers.optimizers as v1_optimizers +import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils +import paddle.v2 + +__all__ = ['Adam', 'Adamax'] + + +class Optimizer(object): + def __init__(self, **kwargs): + if 'batch_size' in kwargs: + del kwargs['batch_size'] # not important for python library. + + def __impl__(): + v1_optimizers.settings(batch_size=1, **kwargs) + + self.__opt_conf_proto__ = config_parser_utils.parse_optimizer_config( + __impl__) + self.__opt_conf__ = swig_api.OptimizationConfig.createFromProto( + self.__opt_conf_proto__) + + def enable_types(self): + """ + get enable_types for each optimizer. + enable_types = [value, gradient, momentum, etc] + For each optimizer(SGD, Adam), GradientMachine should enable different + buffers. + """ + tmp = swig_api.ParameterOptimizer.create(self.__opt_conf__) + assert isinstance(tmp, swig_api.ParameterOptimizer) + return tmp.getParameterTypes() + + def create_local_updater(self): + return swig_api.ParameterUpdater.createLocalUpdater(self.__opt_conf__) + + def create_remote_updater(self, pass_num): + return swig_api.ParameterUpdater.createRemoteUpdater(self.__opt_conf__, + pass_num) + + +class Adam(Optimizer): + def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8, **kwargs): + learning_method = v1_optimizers.AdamOptimizer( + beta1=beta1, beta2=beta2, epsilon=epsilon) + super(Adam, self).__init__(learning_method=learning_method, **kwargs) + + +class Adamax(Optimizer): + def __init__(self, beta1=0.9, beta2=0.999, **kwargs): + learning_method = v1_optimizers.AdamaxOptimizer( + beta1=beta1, beta2=beta2) + super(Adamax, self).__init__(learning_method=learning_method, **kwargs) + + +if __name__ == '__main__': + swig_api.initPaddle('--use_gpu=false') + opt = paddle.v2.optimizer.Adam() + print opt.enable_types() From 23c8ad859c4acbf133fd00ec65643fffa27754df Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 24 Jan 2017 13:51:32 +0800 Subject: [PATCH 092/370] Make MyServer as a stack variable --- paddle/pserver/test/test_ProtoServer.cpp | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/paddle/pserver/test/test_ProtoServer.cpp b/paddle/pserver/test/test_ProtoServer.cpp index e70e1670d31993..04236fda2fb62b 100644 --- a/paddle/pserver/test/test_ProtoServer.cpp +++ b/paddle/pserver/test/test_ProtoServer.cpp @@ -161,15 +161,8 @@ TEST(ProtoServer, extended) { int main(int argc, char** argv) { paddle::initMain(argc, argv); testing::InitGoogleTest(&argc, argv); - - std::unique_ptr server; - if (FLAGS_rdma_tcp == "rdma") { - server.reset(new MyServer(FLAGS_port, 0)); - } else { - server.reset(new MyServer(FLAGS_port)); - } - - server->start(); + MyServer server(FLAGS_port, FLAGS_rdma_tcp == "rdma" ? 0 : -1); + server.start(); usleep(10000); return RUN_ALL_TESTS(); From 1f972361774a60c011715280880600249a8d6203 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 24 Jan 2017 14:03:35 +0800 Subject: [PATCH 093/370] Remove unused init method in paddle::Trainer --- paddle/trainer/Trainer.cpp | 10 ---------- paddle/trainer/Trainer.h | 5 ----- 2 files changed, 15 deletions(-) diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 8465addaf9e038..bd84545375117b 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -90,16 +90,6 @@ DEFINE_string(model_list, "", "File that saves the model list when evaluation"); namespace paddle { -void Trainer::init(int argc, char** argv) { - initMain(argc, argv); - initPython(argc, argv); - - auto config = TrainerConfigHelper::createFromFlagConfig(); - feenableexcept(FE_INVALID | FE_DIVBYZERO | FE_OVERFLOW); - - init(config); -} - void Trainer::init(const std::shared_ptr& config, bool testing, const std::shared_ptr& gradientMachine, diff --git a/paddle/trainer/Trainer.h b/paddle/trainer/Trainer.h index 7cbf18ace7a5fe..c8ee4726c24c33 100644 --- a/paddle/trainer/Trainer.h +++ b/paddle/trainer/Trainer.h @@ -71,11 +71,6 @@ class Trainer { const std::shared_ptr& dataProvider = nullptr, const std::shared_ptr& testDataProvider = nullptr); - /** - * Initialize Trainer from command line flags. - */ - void init(int argc, char** argv); - /** * Train until num_passes reached. * One pass means neural network train through all training data. From 5b1f66fe9788a16110d6a3932d381d8299c481b1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 24 Jan 2017 15:05:06 +0800 Subject: [PATCH 094/370] Removed unused GradientMachine::create --- .../gradientmachines/GradientMachine.cpp | 49 ----- .../gradientmachines/GradientMachine.h | 33 ---- paddle/trainer/tests/CMakeLists.txt | 8 - paddle/trainer/tests/test_Prediction.cpp | 174 ------------------ 4 files changed, 264 deletions(-) delete mode 100644 paddle/trainer/tests/test_Prediction.cpp diff --git a/paddle/gserver/gradientmachines/GradientMachine.cpp b/paddle/gserver/gradientmachines/GradientMachine.cpp index 36ca05b919b136..3eb87d9b85c820 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.cpp +++ b/paddle/gserver/gradientmachines/GradientMachine.cpp @@ -60,55 +60,6 @@ GradientMachine* GradientMachine::create( return nullptr; } -GradientMachine* GradientMachine::create(const std::string& modelFile, - DataConfig* dataConfig) { - std::ifstream is(modelFile); - CHECK(is) << "Fail to open " << modelFile; - return create(is, dataConfig); -} - -GradientMachine* GradientMachine::create(std::istream& is, - DataConfig* dataConfig) { - TrainerConfig trainerConfig; - GradientMachine* ret = create(is, &trainerConfig); - if (dataConfig && trainerConfig.has_data_config()) { - *dataConfig = trainerConfig.data_config(); - } - return ret; -} - -GradientMachine* GradientMachine::create(const std::string& modelFile, - TrainerConfig* trainerConfig) { - std::ifstream is(modelFile); - CHECK(is) << "Fail to open " << modelFile; - return create(is, trainerConfig); -} - -GradientMachine* GradientMachine::create(std::istream& is, - TrainerConfig* trainerConfig) { - TrainerConfig trainerConfigTemp; - int64_t size; - CHECK(is.read((char*)&size, sizeof(size))) << "Fail to read "; - std::string buf; - buf.resize(size); - CHECK(is.read(&buf[0], size)) << "Fail to read "; - CHECK(trainerConfigTemp.ParseFromString(buf)) << "Fail to parse config"; - std::unique_ptr machine( - create(trainerConfigTemp.model_config())); - std::vector& parameters = machine->getParameters(); - for (auto& para : parameters) { - para->load(is); - } - - machine->onLoadParameter(); - - if (trainerConfig) { - *trainerConfig = trainerConfigTemp; - } - - return machine.release(); -} - void GradientMachine::saveParameters(const std::string& dir) const { LOG(INFO) << "Saving parameters to " << dir; diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index 1e35c7e2b8d185..0829968d87c5dc 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -89,39 +89,6 @@ class GradientMachine { std::vector{ PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM}); - /** - * Create a gradient machine from the merged model file. - * The merged model file can be generated using tools/merge_model - * If dataConfig is not null, it will be filled with the DataConfig - * from the TrainerConfig - */ - static GradientMachine* create(const std::string& modelFile, - DataConfig* dataConfig); - - /** - * Create a gradient machine from a stream which contains the merged - * model file. The merged model file can be generated using tools/merge_model - * If dataConfig is not null, it will be filled with the DataConfig - * from the TrainerConfig - */ - static GradientMachine* create(std::istream& is, DataConfig* dataConfig); - - /** - * Create a gradient machine from the merged model file. - * The merged model file can be generated using tools/merge_model - * If trainerConfig is not null, it will be filled with the TrainerConfig - */ - static GradientMachine* create(const std::string& modelFile, - TrainerConfig* trainerConfig); - - /** - * Create a gradient machine from a stream which contains the merged - * model file. The merged model file can be generated using tools/merge_model - * If trainerConfig is not null, it will be filled with the TrainerConfig - */ - static GradientMachine* create(std::istream& is, - TrainerConfig* trainerConfig); - virtual ~GradientMachine() {} /** diff --git a/paddle/trainer/tests/CMakeLists.txt b/paddle/trainer/tests/CMakeLists.txt index 22e07bd0e98a4c..c5c76a030d9e5f 100644 --- a/paddle/trainer/tests/CMakeLists.txt +++ b/paddle/trainer/tests/CMakeLists.txt @@ -1,11 +1,3 @@ -################# test_Prediction ###################### -add_unittest_without_exec(test_Prediction - test_Prediction.cpp) -add_test(NAME test_Prediction - COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python - ${CMAKE_CURRENT_BINARY_DIR}/test_Prediction --merger=${CMAKE_CURRENT_BINARY_DIR}/../paddle_merge_model - WORKING_DIRECTORY ${PROJ_ROOT}/paddle/) - ################# test_Compare ############################ add_unittest_without_exec(test_Compare test_Compare.cpp) diff --git a/paddle/trainer/tests/test_Prediction.cpp b/paddle/trainer/tests/test_Prediction.cpp deleted file mode 100644 index 0c79404eee1c09..00000000000000 --- a/paddle/trainer/tests/test_Prediction.cpp +++ /dev/null @@ -1,174 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#include - -#include "paddle/trainer/Trainer.h" - -#include - -DECLARE_string(config); -DECLARE_string(config_args); -DEFINE_string(merger, - "./paddle_merge_model", - "path to paddle_merge_model binary"); - -using namespace paddle; // NOLINT -using namespace std; // NOLINT - -static const string& configFile = "trainer/tests/sample_trainer_config.conf"; -static const string& mergedModelFile = "./test_model_file"; -static const string& modelDir = "./test_model_dir"; - -void checkBuffer(real* vec1, real* vec2, size_t len) { - for (size_t i = 0; i < len; i++) { - EXPECT_EQ(vec1[i], vec2[i]) << "vec1:" << vec1[i] << " vec2:" << vec2[i]; - } -} - -void checkParameters(vector A, vector B) { - CHECK_EQ(B.size(), A.size()) << "parameter size not equal"; - for (size_t i = 0; i < A.size(); i++) { - auto vec1 = A[i]->getBuf(PARAMETER_VALUE); - auto vec2 = B[i]->getBuf(PARAMETER_VALUE); - CHECK_EQ(vec1->useGpu_, vec2->useGpu_) << "use gpu not equal"; - CHECK_EQ(vec1->getSize(), vec2->getSize()) << "size not equal"; - - if (vec1->useGpu_ == false) { - checkBuffer(vec1->getData(), vec2->getData(), vec1->getSize()); - } else { - VectorPtr cpuVec1 = Vector::create(vec1->getSize(), false); - VectorPtr cpuVec2 = Vector::create(vec2->getSize(), false); - cpuVec1->copyFrom(*vec1, HPPL_STREAM_DEFAULT); - cpuVec2->copyFrom(*vec2, HPPL_STREAM_DEFAULT); - hl_stream_synchronize(HPPL_STREAM_DEFAULT); - checkBuffer(cpuVec1->getData(), cpuVec2->getData(), cpuVec1->getSize()); - } - } -} - -TEST(GradientMachine, create) { -#ifdef PADDLE_ONLY_CPU - FLAGS_use_gpu = false; -#endif - mkDir(modelDir.c_str()); - FLAGS_config = configFile; - FLAGS_config_args = "with_cost=False"; - auto config = TrainerConfigHelper::createFromFlagConfig(); - - // save model to directory - unique_ptr gradientMachine1( - GradientMachine::create(*config)); - gradientMachine1->saveParameters(modelDir); - Trainer trainer; - trainer.init(config); - ParameterUtil* paramUtil = trainer.getParameterUtilPtr(); - if (paramUtil != NULL) { - paramUtil->saveConfigWithPath(modelDir); - } - - // create a different GradientMachine - unique_ptr gradientMachine2( - GradientMachine::create(*config)); - gradientMachine2->randParameters(); - - // merge config and model to one file - string cmd = FLAGS_merger + " --model_dir=" + modelDir + - " --config_args=with_cost=False" + " --model_file=" + - mergedModelFile; - LOG(INFO) << cmd; - int ret = system(cmd.c_str()); - EXPECT_EQ(0, ret); - if (ret) { - return; - } - - // create GradientMachine from the merged model - DataConfig dataConfig; - unique_ptr gradientMachine3( - GradientMachine::create(mergedModelFile, &dataConfig)); - CHECK(gradientMachine3); - EXPECT_EQ(dataConfig.type(), "simple"); - EXPECT_EQ(dataConfig.feat_dim(), 3); - - // compare the parameters of GradientMachine and GradientMachine3 - std::vector paraMachine1 = gradientMachine1->getParameters(); - std::vector paraMachine3 = gradientMachine3->getParameters(); - checkParameters(paraMachine1, paraMachine3); - - // Test that the GradientMachine created from the merged model - // is same as the orginnal one. - vector inArgs(1); - vector outArgs; - - int inputDim = 3; - int numSamples = 2; - CpuMatrix cpuInput(numSamples, inputDim); - for (int i = 0; i < numSamples; ++i) { - for (int j = 0; j < inputDim; ++j) { - cpuInput.getData()[i * inputDim + j] = - rand() / (real)RAND_MAX; // NOLINT TODO(yuyang): use rand_r - } - } - MatrixPtr input = Matrix::create(numSamples, - inputDim, - /* trans */ false, - FLAGS_use_gpu); - input->copyFrom(cpuInput); - inArgs[0].value = input; - gradientMachine1->forward(inArgs, &outArgs, PASS_TEST); - EXPECT_EQ((size_t)1, outArgs.size()); - - vector outArgs2; - gradientMachine2->forward(inArgs, &outArgs2, PASS_TEST); - CpuMatrix out1(outArgs[0].value->getHeight(), outArgs[0].value->getWidth()); - CpuMatrix out2(outArgs2[0].value->getHeight(), outArgs2[0].value->getWidth()); - out1.copyFrom(*outArgs[0].value); - out2.copyFrom(*outArgs2[0].value); - for (size_t i = 0; i < out1.getHeight() * out1.getWidth(); i++) { - EXPECT_NE(out1.getData()[i], out2.getData()[i]); - } - - gradientMachine3->forward(inArgs, &outArgs2, PASS_TEST); - out2.copyFrom(*outArgs2[0].value); - checkBuffer( - out1.getData(), out2.getData(), out2.getHeight() * out2.getWidth()); - - cmd = " rm -rf " + modelDir + "/*"; - LOG(INFO) << "cmd " << cmd; - ret = system(cmd.c_str()); - EXPECT_EQ(0, ret); - if (ret) { - return; - } - - cmd = " rm -rf " + mergedModelFile; - LOG(INFO) << "cmd " << cmd; - ret = system(cmd.c_str()); - EXPECT_EQ(0, ret); - if (ret) { - return; - } - - // clean up - rmDir(modelDir.c_str()); - remove(mergedModelFile.c_str()); -} - -int main(int argc, char** argv) { - initMain(argc, argv); - initPython(argc, argv); - testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} From 936301f157f8c557eea80eda4c0b9d2525dbab52 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Fri, 13 Jan 2017 14:55:43 -0800 Subject: [PATCH 095/370] Use and test Daoyuan's SparseMatrixArg. --- paddle/function/BufferArg.cpp | 6 ++-- paddle/function/BufferArg.h | 28 ++++++++++++++-- paddle/function/CMakeLists.txt | 1 + paddle/function/MulOp.cpp | 60 ++++++++++++++++++++++++++++++++++ paddle/function/MulOp.h | 30 +++++++++++++++++ paddle/function/MulOpGpu.cu | 57 ++++++++++++++++++++++++++++++++ paddle/function/MulOpTest.cpp | 56 +++++++++++++++++++++++++++++++ paddle/function/TensorType.h | 26 ++++++++++++++- 8 files changed, 259 insertions(+), 5 deletions(-) create mode 100644 paddle/function/MulOp.cpp create mode 100644 paddle/function/MulOp.h create mode 100644 paddle/function/MulOpGpu.cu create mode 100644 paddle/function/MulOpTest.cpp diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index 5d595deb12c6c8..4064daf4159e22 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -32,14 +32,16 @@ const SparseMatrixArg& BufferArg::sparse() const { SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), + trans_(const_cast(sparse).getTranspose()) { bufferType_ = TENSOR_SPARSE; } SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), + trans_(const_cast(sparse).getTranspose()) { bufferType_ = TENSOR_SPARSE; } diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 84209265ce7634..3489510b25f098 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -167,7 +167,7 @@ class BufferArg { ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } - bool isSparse() const { return (TENSOR_SPARSE == bufferType_); } + bool isSparse() const { return TENSOR_SPARSE == bufferType_; } bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } const SequenceArg& sequence() const; @@ -271,13 +271,15 @@ class SparseMatrixArg : public BufferArg { size_t nnz, SparseDataFormat format, SparseDataType type, + bool trans = false, ArgType argType = UNSPECIFIED) : BufferArg(buf, valueType, shape, argType), row_(row), col_(col), nnz_(nnz), format_(format), - type_(type) { + type_(type), + trans_(trans) { bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); @@ -294,6 +296,24 @@ class SparseMatrixArg : public BufferArg { SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); + template + typename Tensor::SparseMatrix SparseMatrix() const { + CHECK(buf_); + CHECK(valueType_ == DataType::value); + // CHECK(deviceType_ == DType); + CHECK_EQ(2, shape_.ndims()); + return typename Tensor::SparseMatrix( + reinterpret_cast(buf_), + reinterpret_cast(row_.data()), + reinterpret_cast(col_.data()), + shape_[0], + shape_[1], + nnz_, + static_cast(type_), + static_cast(format_), + trans_); + } + ~SparseMatrixArg() {} void* getRowBuf() const { return row_.data(); } @@ -302,6 +322,8 @@ class SparseMatrixArg : public BufferArg { size_t nnz() const { return nnz_; } + bool isTranspose() const { return trans_; } + SparseDataFormat dataFormat() const { return format_; } SparseDataType dataType() const { return type_; } @@ -312,6 +334,8 @@ class SparseMatrixArg : public BufferArg { size_t nnz_; SparseDataFormat format_; SparseDataType type_; + /// todo(tianbing), move trans_ up to BufferArg + bool trans_; }; } // namespace paddle diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 6d20868072c3ac..fae3b7b20a70b5 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -26,6 +26,7 @@ if(WITH_TESTING) add_simple_unittest(FunctionTest) add_simple_unittest(ContextProjectionOpTest) add_simple_unittest(PadOpTest) + add_simple_unittest(MulOpTest) endif() endif() diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp new file mode 100644 index 00000000000000..1fa29fae8d4e1e --- /dev/null +++ b/paddle/function/MulOp.cpp @@ -0,0 +1,60 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "MulOp.h" + +namespace paddle { + +/** + * mul operator + * out = scaleT * out + scaleAB*(in1 * in2) + * + * \param outputs[0] output matrix, N * M + * \param inputs[0] first input (sparse) matrix, N * K + * \param inputs[1] second input matrix, K * M (non-transpose) + */ +template +class MulFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override { + scaleAB_ = config.get("scaleAB"); + scaleT_ = config.get("scaleT"); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + /// todo(tianbing), add more checks + CHECK_EQ((size_t)1, inputs.size()); + CHECK_EQ((size_t)2, outputs.size()); + CHECK(inputs[0].data() && inputs[1].data() && outputs[0].data()); + CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); + CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); + CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); + CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); + + CHECK(inputs[0].isSparse()) << "SparseMatrix requried here"; + const auto in1_mat = inputs[0].sparse().SparseMatrix(); + auto out_mat = outputs[0].matrix(); + const auto in2_mat = inputs[1].matrix(); + MulOp(out_mat, in1_mat, in2_mat, scaleAB_, scaleT_); + } + +private: + real scaleAB_; + real scaleT_; +}; + +#ifndef PADDLE_ONLY_CPU +REGISTER_TYPED_FUNC(MulOp, GPU, MulFunc); +#endif +} // namespace paddle diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h new file mode 100644 index 00000000000000..bcea1864026b27 --- /dev/null +++ b/paddle/function/MulOp.h @@ -0,0 +1,30 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "Function.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" + +namespace paddle { + +template +void MulOp(GpuMatrix& out, + const GpuSparseMatrix& a, + const GpuMatrix& b, + real scaleAB, + real scaleT); + +} // namespace paddle diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu new file mode 100644 index 00000000000000..db716c1e46b4e1 --- /dev/null +++ b/paddle/function/MulOpGpu.cu @@ -0,0 +1,57 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "hl_base.h" +#include "MulOp.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" + +namespace paddle { +/** + * out = scale_t * out + scale_ab * (a * b) + */ +template <> +void MulOp(GpuMatrix& out, + const GpuSparseMatrix& a, + const GpuMatrix& b, + real scale_ab, + real scale_t) { + CHECK(out.isContiguous()); + CHECK(b.isContiguous()); + CHECK(b.useGpu_ == true) << "Matrix type are not equal"; + CHECK(!out.trans_ && !b.trans_) << "not supported"; + if (!a.trans_) { + CHECK(out.width_ == b.width_ && out.height_ == a.height_ + && a.width_ == b.height_) << "Matrix dimensions are not equal"; + } else { + CHECK(out.width_ == b.width_ && out.height_ == a.width_ + && a.height_ == b.height_) << "Matrix dimensions are not equal"; + } + hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N; + hl_sparse_matrix_s a_data = a.sMatrix_.get(); + real* b_data = b.data_; + real* out_data = out.data_; + hl_matrix_csr_mul_dense(a_data, + a_trans, + b_data, + HPPL_OP_N, + out_data, + out.height_, + out.width_, + b.height_, + scale_ab, + scale_t); +} + +} // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp new file mode 100644 index 00000000000000..bc1fa9f607a575 --- /dev/null +++ b/paddle/function/MulOpTest.cpp @@ -0,0 +1,56 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "FunctionTest.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" +#include "paddle/testing/TestUtil.h" + +using namespace paddle; // NOLINT + +void testSpMatrixMul(int M, int N, int K, real rate, real scale1, real scale2) { + /// todo(tianbing) check CPU/GPU + const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOP-GPU"); + gpuFunc->init(FuncConfig().set("scaleAB", scale1).set("scaleT", scale2)); + + int nnz = M * K * rate; + auto gpuA = std::make_shared(M, K, nnz); + const auto gpuB = std::make_shared(K, N); + const auto gpuOut = std::make_shared(M, N); + + gpuA->randomizeUniform(); + gpuB->randomizeUniform(); + gpuOut->randomizeUniform(); + + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*gpuA); + inputs.addArg(*gpuB); + outputs.addArg(*gpuOut); + + gpuFunc->calc(inputs, outputs); +} + +TEST(SMatrix, sMatrixMul) { + for (auto M : {1, 40, 128, 200}) { + for (auto N : {100, 2000, 20480}) { + for (auto K : {100, 512, 1024}) { + /// todo(tianbing), add scaleAB and scaleT + VLOG(3) << " M=" << M << " N=" << N << " K=" << K; + testSpMatrixMul(M, N, K, 0.05, 1, 1); + } + } + } +} diff --git a/paddle/function/TensorType.h b/paddle/function/TensorType.h index 98942cff9e2ea4..73ac1cc70d2cd8 100644 --- a/paddle/function/TensorType.h +++ b/paddle/function/TensorType.h @@ -87,6 +87,29 @@ struct MatrixT { using type = void; // Not implemented }; +template +struct SparseMatrixT; + +template <> +struct SparseMatrixT { + using type = CpuSparseMatrix; +}; + +template <> +struct SparseMatrixT { + using type = GpuSparseMatrix; +}; + +template <> +struct SparseMatrixT { + using type = void; // Not implemented +}; + +template <> +struct SparseMatrixT { + using type = void; // Not implemented +}; + template struct VectorT; @@ -114,8 +137,9 @@ struct VectorT { template struct Tensor { - typedef typename detail::MatrixT::type Matrix; typedef typename detail::VectorT::type Vector; + typedef typename detail::MatrixT::type Matrix; + typedef typename detail::SparseMatrixT::type SparseMatrix; }; } // namespace paddle From 1f0cbcf3508f061548636f0a703dac1908a98104 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 16 Jan 2017 16:10:45 -0800 Subject: [PATCH 096/370] add GpuMatrix::mul, CpuMatrix::mul operators --- paddle/function/BufferArg.h | 2 +- paddle/function/MulOp.cpp | 478 +++++++++++++++++++++++++++++++++- paddle/function/MulOp.h | 41 +++ paddle/function/MulOpGpu.cu | 120 ++++++++- paddle/function/MulOpTest.cpp | 36 ++- 5 files changed, 652 insertions(+), 25 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 3489510b25f098..1f86f49911c484 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -167,7 +167,7 @@ class BufferArg { ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } - bool isSparse() const { return TENSOR_SPARSE == bufferType_; } + bool isSparseArg() const { return TENSOR_SPARSE == bufferType_; } bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } const SequenceArg& sequence() const; diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 1fa29fae8d4e1e..7d341182523cbb 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -13,16 +13,471 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "MulOp.h" +#include "paddle/math/MathFunctions.h" +#include "paddle/math/SIMDFunctions.h" +#include "paddle/utils/ThreadLocal.h" + +#ifndef PADDLE_TYPE_DOUBLE +#define GEMM paddle::gemm +#else +#define GEMM paddle::gemm +#endif + +namespace { +inline void vecAddTo(real* a, const real* b, size_t len) { + for (unsigned int i = 0; i < len; ++i) { + a[i] += b[i]; + } +} + +inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { + for (unsigned int i = 0; i < len; ++i) { + a[i] += scaleB * b[i]; + } +} + +inline void colVecAddTo( + real* a, const real* b, size_t len, size_t aWidth, size_t bWidth) { + for (unsigned int i = 0; i < len; ++i) { + a[i * aWidth] += b[i * bWidth]; + } +} + +inline void colVecAddTo( + real* a, real* b, real c, size_t len, size_t aWidth, size_t bWidth) { + for (unsigned int i = 0; i < len; ++i) { + a[i * aWidth] += b[i * bWidth] * c; + } +} +} // namespace namespace paddle { +template <> +void MulOp(CpuSparseMatrix& out, + const CpuMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT) { + /// todo(tianbing), clean the code + CHECK(!out.isTransposed()) << "Not supported"; + CHECK_EQ(out.getValueType(), FLOAT_VALUE); + + const real* A = a.getData(); + const real* B = b.getData(); + real* C = out.getValue(); + int* rows = out.getRows(); + int* cols = out.getCols(); + size_t height = out.getHeight(); + size_t width = out.getWidth(); + if (scaleT == 0) { + out.zeroMem(); + } + + if (!a.isTransposed() && !b.isTransposed()) { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), m); + CHECK_EQ(a.getHeight(), height); + CHECK_EQ(b.getWidth(), width); + if (out.getFormat() == SPARSE_CSC) { + for (size_t i = 0; i < width; i++) { + size_t start = out.getColStartIdx(i); + size_t end = out.getColStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t rowIdx = rows[j]; + for (size_t k = 0; k < m; k++) { + sum += A[rowIdx * m + k] * B[k * width + i]; + } + C[j] = scaleAB * sum + scaleT * C[j]; + } + } + } else { + for (size_t i = 0; i < height; i++) { + size_t start = out.getRowStartIdx(i); + size_t end = out.getRowStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t colIdx = cols[j]; + for (size_t k = 0; k < m; k++) { + sum += A[i * m + k] * B[k * width + colIdx]; + } + C[j] = scaleAB * sum + scaleT * C[j]; + } + } + } + } else if (a.isTransposed() && !b.isTransposed()) { + size_t m = a.getHeight(); + CHECK_EQ(m, b.getHeight()); + CHECK_EQ(b.getWidth(), width); + CHECK_EQ(a.getWidth(), height); + + if (out.getFormat() == SPARSE_CSC) { + for (size_t i = 0; i < width; i++) { + size_t start = out.getColStartIdx(i); + size_t end = out.getColStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t rowIdx = rows[j]; + for (size_t k = 0; k < m; k++) { + sum += A[k * height + rowIdx] * B[k * width + i]; + } + C[j] = scaleAB * sum + scaleT * C[j]; + } + } + } else { + for (size_t i = 0; i < height; i++) { + int start = out.getRowStartIdx(i); + int end = out.getRowStartIdx(i + 1); + for (int j = start; j < end; j++) { + real sum = 0; + size_t colIdx = cols[j]; + for (size_t k = 0; k < m; k++) { + sum += A[k * height + i] * B[k * width + colIdx]; + } + C[j] = scaleAB * sum + scaleT * C[j]; + } + } + } + } else if (!a.isTransposed() && b.isTransposed()) { + size_t m = a.getWidth(); + CHECK_EQ(b.getWidth(), m); + CHECK_EQ(a.getHeight(), height); + CHECK_EQ(b.getHeight(), width); + if (out.getFormat() == SPARSE_CSR) { + for (size_t i = 0; i < height; i++) { + size_t start = out.getRowStartIdx(i); + size_t end = out.getRowStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t colIdx = cols[j]; + for (size_t k = 0; k < m; k++) { + sum += A[i * m + k] * B[colIdx * m + k]; + } + C[j] = scaleAB * sum + scaleT * C[j]; + } + } + } else { + LOG(FATAL) << "Not supported csc format " + "when a is not trans and b is trans"; + } + } else { + LOG(FATAL) << "Not supported"; + } +} + +template <> +void MulOp(CpuMatrix& out, + const CpuMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT) { + /// todo(tianbing), clean the code + CHECK(!out.isTransposed()) << "Not supported"; + CBLAS_TRANSPOSE aTrans = CblasNoTrans; + size_t aRow = a.getHeight(); + size_t aCol = a.getWidth(); + CBLAS_TRANSPOSE bTrans = CblasNoTrans; + size_t bRow = b.getHeight(); + size_t bCol = b.getWidth(); + if (a.isTransposed()) { + aTrans = CblasTrans; + aRow = a.getWidth(); + aCol = a.getHeight(); + } + if (b.isTransposed()) { + bTrans = CblasTrans; + bRow = b.getWidth(); + bCol = b.getHeight(); + } + + /// C = A * B, for matrix format + CHECK_EQ(aCol, bRow); + CHECK_EQ(aRow, out.getHeight()); + CHECK_EQ(bCol, out.getWidth()); + + const real* A = a.getData(); + const real* B = b.getData(); + real* C = out.getData(); + + int M = out.getHeight(); + int N = out.getWidth(); + int K = aCol; + int lda = a.getStride(); + int ldb = b.getStride(); + int ldc = out.getStride(); + + GEMM(aTrans, bTrans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc); + + VLOG(2) << " A[0]=" << A[0] << " A[1]=" << A[1] << " B[0]=" << B[0] + << " B[1]=" << B[1] << " C[0]=" << C[0] << " C[1]=" << C[1]; +} + +static ThreadLocal> threadLocalColArray; + +template <> +void MulOp(CpuMatrix& out, + const CpuSparseMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT) { + /// todo(tianbing), clean the code + CHECK(!out.isTransposed()) << "Not supported"; + CHECK(!b.isTransposed()) << "Not supported"; + CHECK(scaleT == 0 || scaleT == 1) << "Not support"; + CHECK_EQ(scaleAB, static_cast(1.0)) << "Not supported"; + CHECK_EQ(a.getFormat(), SPARSE_CSR) << "Not supported"; + + const real* B = b.getData(); + real* C = out.getData(); + size_t height = out.getHeight(); + size_t width = out.getWidth(); + int* cols = a.getCols(); + real* values = a.getValue(); + + if (scaleT == 0) { + out.zeroMem(); + } + + if (!a.isTransposed()) { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), m); + CHECK_EQ(a.getHeight(), height); + CHECK_EQ(b.getWidth(), width); + + if (a.getValueType() == NO_VALUE) { + if (width % 32 == 0) { // use libaddto + CHECK_EQ((size_t)B % 32, 0UL); + CHECK_EQ((size_t)C % 32, 0UL); + auto& colArray = *threadLocalColArray; + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + size_t colNum = end - start; + colArray.resize(colNum); + for (int j = 0; j < end - start; ++j) { + colArray[j] = const_cast(b).getRow(cols[j + start]); + } + simd::batchAddTo(out.getRow(i), &colArray[0], colNum, width); + } + + } else { + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + vecAddTo(out.getRow(i), + const_cast(b).getRow(cols[j]), + width); + } + } + } + } else if (a.getValueType() == FLOAT_VALUE) { + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + vecAddTo(out.getRow(i), + const_cast(b).getRow(cols[j]), + values[j], + width); + } + } + } + } else /*if (a->isTransposed())*/ { + size_t m = a.getHeight(); + CHECK_EQ(b.getHeight(), m); + CHECK_EQ(a.getWidth(), height); + CHECK_EQ(b.getWidth(), width); + if (a.getValueType() == NO_VALUE) { + if (width % 32 == 0) { // use libaddto + CHECK_EQ((size_t)B % 32, 0UL); + CHECK_EQ((size_t)C % 32, 0UL); + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + simd::addTo(out.getRow(cols[j]), + const_cast(b).getRow(i), + width); + } + } + + } else { + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + vecAddTo(out.getRow(cols[j]), + const_cast(b).getRow(i), + width); + } + } + } + } else if (a.getValueType() == FLOAT_VALUE) { + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + vecAddTo(out.getRow(cols[j]), + const_cast(b).getRow(i), + values[j], + width); + } + } + } + } +} + +template <> +void MulOp(CpuMatrix& out, + const CpuMatrix& a, + const CpuSparseMatrix& b, + real scaleAB, + real scaleT) { + /// todo(tianbing), clean the code + CHECK(!out.trans_) << "Not supported"; + CHECK(!a.isTransposed()) << "Not supported"; + CHECK(scaleT == 0 || scaleT == 1); + CHECK_EQ(scaleAB, static_cast(1.0)); + + real* A = const_cast(a.getData()); + real* B = const_cast(b.getValue()); + real* C = out.getData(); + int* rows = b.getRows(); + int* cols = b.getCols(); + + if (scaleT == 0) { + out.zeroMem(); + } + /// todo(tianbing), clean the code + if (b.getFormat() == SPARSE_CSC) { + if (!b.isTransposed()) { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), m); + CHECK_EQ(a.getHeight(), out.height_); + CHECK_EQ(b.getWidth(), out.width_); + + if (b.getValueType() == NO_VALUE) { + for (size_t j = 0; j < b.getWidth(); ++j) { + int start = b.getColStartIdx(j); + int end = b.getColStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo( + C + j, A + rows[i], out.height_, out.width_, a.getWidth()); + } + } + } else if (b.getValueType() == FLOAT_VALUE) { + for (size_t j = 0; j < b.getWidth(); ++j) { + int start = b.getColStartIdx(j); + int end = b.getColStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo(C + j, + A + rows[i], + B[i], + out.height_, + out.width_, + a.getWidth()); + } + } + } + } else /*if (b.isTransposed())*/ { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), out.width_); + CHECK_EQ(a.getHeight(), out.height_); + CHECK_EQ(b.getWidth(), m); + if (b.getValueType() == NO_VALUE) { + for (size_t i = 0; i < b.getWidth(); ++i) { + int start = b.getColStartIdx(i); + int end = b.getColStartIdx(i + 1); + for (int j = start; j < end; ++j) { + colVecAddTo( + C + rows[j], A + i, out.height_, out.width_, a.getWidth()); + } + } + } else if (b.getValueType() == FLOAT_VALUE) { + for (size_t i = 0; i < b.getWidth(); ++i) { + int start = b.getColStartIdx(i); + int end = b.getColStartIdx(i + 1); + for (int j = start; j < end; ++j) { + colVecAddTo(C + rows[j], + A + i, + B[j], + out.height_, + out.width_, + a.getWidth()); + } + } + } + } + } else { + if (!b.isTransposed()) { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), m); + CHECK_EQ(a.getHeight(), out.height_); + CHECK_EQ(b.getWidth(), out.width_); + + if (b.getValueType() == NO_VALUE) { + for (size_t j = 0; j < b.getHeight(); ++j) { + int start = b.getRowStartIdx(j); + int end = b.getRowStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo( + C + cols[i], A + j, out.height_, out.width_, a.getWidth()); + } + } + } else if (b.getValueType() == FLOAT_VALUE) { + for (size_t j = 0; j < b.getHeight(); ++j) { + int start = b.getRowStartIdx(j); + int end = b.getRowStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo(C + cols[i], + A + j, + B[i], + out.height_, + out.width_, + a.getWidth()); + } + } + } + } else /*if (b.isTransposed())*/ { + size_t m = a.getWidth(); + CHECK_EQ(b.getHeight(), out.width_); + CHECK_EQ(a.getHeight(), out.height_); + CHECK_EQ(b.getWidth(), m); + if (b.getValueType() == NO_VALUE) { + for (size_t i = 0; i < b.getHeight(); ++i) { + int start = b.getRowStartIdx(i); + int end = b.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + colVecAddTo( + C + i, A + cols[j], out.height_, out.width_, a.getWidth()); + } + } + } else if (b.getValueType() == FLOAT_VALUE) { + for (size_t i = 0; i < b.getHeight(); ++i) { + int start = b.getRowStartIdx(i); + int end = b.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + colVecAddTo(C + i, + A + cols[j], + B[j], + out.height_, + out.width_, + a.getWidth()); + } + } + } + } + } +} /** * mul operator * out = scaleT * out + scaleAB*(in1 * in2) * - * \param outputs[0] output matrix, N * M - * \param inputs[0] first input (sparse) matrix, N * K - * \param inputs[1] second input matrix, K * M (non-transpose) + * \param outputs[0] output matrix, M * N + * \param inputs[0] first input (sparse) matrix, M * K (if non-trans) + * \param inputs[1] second input matrix, K * N (if non-trans) */ template class MulFunc : public FunctionBase { @@ -33,19 +488,23 @@ class MulFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - /// todo(tianbing), add more checks - CHECK_EQ((size_t)1, inputs.size()); - CHECK_EQ((size_t)2, outputs.size()); + CHECK_EQ((size_t)2, inputs.size()); + CHECK_EQ((size_t)1, outputs.size()); CHECK(inputs[0].data() && inputs[1].data() && outputs[0].data()); CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); - CHECK(inputs[0].isSparse()) << "SparseMatrix requried here"; - const auto in1_mat = inputs[0].sparse().SparseMatrix(); + auto in1_mat = inputs[0].matrix(); + if (inputs[0].isSparseArg()) { + in1_mat = inputs[0].sparse().SparseMatrix(); + } + auto in2_mat = inputs[1].matrix(); + if (inputs[1].isSparseArg()) { + in2_mat = inputs[1].sparse().SparseMatrix(); + } auto out_mat = outputs[0].matrix(); - const auto in2_mat = inputs[1].matrix(); MulOp(out_mat, in1_mat, in2_mat, scaleAB_, scaleT_); } @@ -54,6 +513,7 @@ class MulFunc : public FunctionBase { real scaleT_; }; +REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); #ifndef PADDLE_ONLY_CPU REGISTER_TYPED_FUNC(MulOp, GPU, MulFunc); #endif diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index bcea1864026b27..f3699f8c78cda7 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -19,6 +19,40 @@ limitations under the License. */ #include "paddle/math/SparseMatrix.h" namespace paddle { +template +void MulOp(CpuMatrix& out, + const CpuMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT); + +template +void MulOp(CpuMatrix& out, + const CpuSparseMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT); + +template +void MulOp(CpuMatrix& out, + const CpuMatrix& a, + const CpuSparseMatrix& b, + real scaleAB, + real scaleT); + +template +void MulOp(CpuSparseMatrix& out, + const CpuMatrix& a, + const CpuMatrix& b, + real scaleAB, + real scaleT); + +template +void MulOp(GpuMatrix& out, + const GpuMatrix& a, + const GpuMatrix& b, + real scaleAB, + real scaleT); template void MulOp(GpuMatrix& out, @@ -27,4 +61,11 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT); +template +void MulOp(GpuMatrix& out, + const GpuMatrix& a, + const GpuSparseMatrix& b, + real scaleAB, + real scaleT); + } // namespace paddle diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index db716c1e46b4e1..73d788a4743326 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -20,6 +20,65 @@ limitations under the License. */ namespace paddle { /** * out = scale_t * out + scale_ab * (a * b) + * out : output matrix, M * N + */ +template <> +void MulOp(GpuMatrix& out, + const GpuMatrix& a, + const GpuMatrix& b, + real scale_ab, + real scale_t) { + CHECK(!out.isTransposed()) << "Not supported"; + + if (!a.isTransposed() && !b.isTransposed()) { + /// a : M * K, b: K * N + CHECK_EQ(out.width_, b.width_); + CHECK_EQ(out.height_, a.height_); + CHECK_EQ(a.width_, b.height_); + } else if (a.isTransposed() && !b.isTransposed()) { + /// a : K * M, b : K * N + CHECK_EQ(out.width_, b.width_); + CHECK_EQ(out.height_, a.width_); + CHECK_EQ(a.height_, b.height_); + } else if (!a.isTransposed() && b.isTransposed()) { + /// a: M * K, b : N * K + CHECK_EQ(out.width_, b.height_); + CHECK_EQ(out.height_, a.height_); + CHECK_EQ(a.width_, b.width_); + } else { + LOG(FATAL) << "Is not supported"; + } + + real* a_data = a.data_; + real* b_data = b.data_; + real* out_data = out.data_; + int dim_m = out.getHeight(); + int dim_n = out.getWidth(); + int dim_k = !a.isTransposed() ? a.width_ : a.height_; + int lda = a.getStride(); + int ldb = b.getStride(); + int ldc = out.getStride(); + hl_trans_op_t trans_a = !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T; + hl_trans_op_t trans_b = !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T; + + hl_matrix_mul(a_data, + trans_a, + b_data, + trans_b, + out_data, + dim_m, + dim_n, + dim_k, + scale_ab, + scale_t, + lda, + ldb, + ldc); +} + +/** + * out = scale_t * out + scale_ab * (a * b) + * out : M * N */ template <> void MulOp(GpuMatrix& out, @@ -32,12 +91,15 @@ void MulOp(GpuMatrix& out, CHECK(b.useGpu_ == true) << "Matrix type are not equal"; CHECK(!out.trans_ && !b.trans_) << "not supported"; if (!a.trans_) { + /// a: M * K, b: K * N CHECK(out.width_ == b.width_ && out.height_ == a.height_ - && a.width_ == b.height_) << "Matrix dimensions are not equal"; + && a.width_ == b.height_) << "Matrix dimensions are not equal"; } else { + /// a: K * M, transpose, b: K * N CHECK(out.width_ == b.width_ && out.height_ == a.width_ - && a.height_ == b.height_) << "Matrix dimensions are not equal"; + && a.height_ == b.height_) << "Matrix dimensions are not equal"; } + hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N; hl_sparse_matrix_s a_data = a.sMatrix_.get(); real* b_data = b.data_; @@ -54,4 +116,58 @@ void MulOp(GpuMatrix& out, scale_t); } +/** + * out = scale_t * out + scale_ab * (a * b) + * out : M * N + */ +template <> +void MulOp(GpuMatrix& out, + const GpuMatrix& a, + const GpuSparseMatrix& b, + real scale_ab, + real scale_t) { + CHECK(out.isContiguous()); + CHECK(a.isContiguous()); + CHECK(a.useGpu_ == true) << "Matrix type are not equal"; + + hl_sparse_matrix_s b_data = b.sMatrix_.get(); + real* a_data = a.data_; + real* out_data = out.data_; + hl_trans_op_t trans_b = b.trans_ ? HPPL_OP_T : HPPL_OP_N; + if (!b.trans_) { + /// a : M * K, b : K * N + CHECK(out.width_ == b.width_ && + out.height_ == a.height_ && a.width_ == b.height_) + << "Matrix dimensions are not equal"; + } else { + /// a : M * K, b : N * K, transpose + CHECK(out.width_ == b.height_ && + out.height_ == a.height_ && a.width_ == b.width_) + << "Matrix dimensions are not equal"; + } + if (b.format_ == SPARSE_CSC) { + hl_matrix_dense_mul_csc(a_data, + HPPL_OP_N, + b_data, + trans_b, + out_data, + out.height_, + out.width_, + a.width_, + scale_ab, + scale_t); + } else { + hl_matrix_dense_mul_csr(a_data, + HPPL_OP_N, + b_data, + trans_b, + out_data, + out.height_, + out.width_, + a.width_, + scale_ab, + scale_t); + } +} + } // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index bc1fa9f607a575..ce9d37d664710c 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -22,31 +22,41 @@ using namespace paddle; // NOLINT void testSpMatrixMul(int M, int N, int K, real rate, real scale1, real scale2) { /// todo(tianbing) check CPU/GPU - const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOP-GPU"); + const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); gpuFunc->init(FuncConfig().set("scaleAB", scale1).set("scaleT", scale2)); - int nnz = M * K * rate; - auto gpuA = std::make_shared(M, K, nnz); - const auto gpuB = std::make_shared(K, N); - const auto gpuOut = std::make_shared(M, N); + int nnz = M * N * rate; + MatrixPtr cpuA = std::make_shared(M, K); + MatrixPtr cpuB = std::make_shared(N, K); + MatrixPtr cpuC(new CpuSparseMatrix(M, N, nnz)); - gpuA->randomizeUniform(); - gpuB->randomizeUniform(); - gpuOut->randomizeUniform(); + MatrixPtr gpuA = std::make_shared(M, K); + MatrixPtr gpuB = std::make_shared(N, K); + MatrixPtr gpuC(new GpuSparseMatrix(M, N, nnz)); + + cpuA->randomizeUniform(); + cpuB->randomizeUniform(); + cpuC->randomizeUniform(); + + hl_stream_t stream(HPPL_STREAM_3); + gpuA->copyFrom(*cpuA, stream); + gpuB->copyFrom(*cpuB, stream); + gpuC->copyFrom(*cpuC, stream); + hl_stream_synchronize(stream); BufferArgs inputs; BufferArgs outputs; - inputs.addArg(*gpuA); - inputs.addArg(*gpuB); - outputs.addArg(*gpuOut); + inputs.addArg(*gpuA->getTranspose()); + inputs.addArg(*gpuB->getTranspose()); + outputs.addArg(*gpuC, ASSIGN_TO); gpuFunc->calc(inputs, outputs); } TEST(SMatrix, sMatrixMul) { for (auto M : {1, 40, 128, 200}) { - for (auto N : {100, 2000, 20480}) { - for (auto K : {100, 512, 1024}) { + for (auto N : {100}) { + for (auto K : {100}) { /// todo(tianbing), add scaleAB and scaleT VLOG(3) << " M=" << M << " N=" << N << " K=" << K; testSpMatrixMul(M, N, K, 0.05, 1, 1); From 2df8eec5204506ddfb7f55e8c88e0271fbf9bf3b Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 16 Jan 2017 20:30:27 -0800 Subject: [PATCH 097/370] Pass Unit test for GpuMatrix::mul(GpuMatrix, GpuMatrix) and CpuMatrix::mul(CpuMatrix, CpuMatrix) --- paddle/function/BufferArg.cpp | 6 +-- paddle/function/BufferArg.h | 17 +++---- paddle/function/MulOp.cpp | 12 ++--- paddle/function/MulOp.h | 7 +++ paddle/function/MulOpGpu.cu | 9 ++++ paddle/function/MulOpTest.cpp | 85 +++++++++++++++++++++++------------ 6 files changed, 88 insertions(+), 48 deletions(-) diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index 4064daf4159e22..5d595deb12c6c8 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -32,16 +32,14 @@ const SparseMatrixArg& BufferArg::sparse() const { SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), - trans_(const_cast(sparse).getTranspose()) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { bufferType_ = TENSOR_SPARSE; } SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), - trans_(const_cast(sparse).getTranspose()) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { bufferType_ = TENSOR_SPARSE; } diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 1f86f49911c484..2da1115ec968cc 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -98,7 +98,8 @@ class BufferArg { const_cast(reinterpret_cast(matrix.getData()))), valueType_(DataType::value), shape_(2), - argType_(argType) { + argType_(argType), + trans_(matrix.isTransposed()) { bufferType_ = TENSOR_NORMAL; shape_.setDim(0, matrix.getHeight()); shape_.setDim(1, matrix.getWidth()); @@ -111,7 +112,8 @@ class BufferArg { const_cast(reinterpret_cast(matrix.getData()))), valueType_(DataType::value), shape_(shape), - argType_(argType) { + argType_(argType), + trans_(matrix.isTransposed()) { bufferType_ = TENSOR_NORMAL; CHECK_EQ(matrix.getElementCnt(), shape.getElements()); } @@ -143,7 +145,7 @@ class BufferArg { // CHECK(deviceType_ == DType); CHECK_EQ((size_t)2, shape_.ndims()); return typename Tensor::Matrix( - reinterpret_cast(buf_), shape_[0], shape_[1]); + reinterpret_cast(buf_), shape_[0], shape_[1], trans_); } template @@ -179,6 +181,7 @@ class BufferArg { TensorShape shape_; BufferType bufferType_{TENSOR_UNKNOWN}; ArgType argType_{UNSPECIFIED}; + bool trans_{false}; // leading dimensions. The size is dims_.size() // Dims lds_; }; @@ -271,15 +274,13 @@ class SparseMatrixArg : public BufferArg { size_t nnz, SparseDataFormat format, SparseDataType type, - bool trans = false, ArgType argType = UNSPECIFIED) : BufferArg(buf, valueType, shape, argType), row_(row), col_(col), nnz_(nnz), format_(format), - type_(type), - trans_(trans) { + type_(type) { bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); @@ -322,8 +323,6 @@ class SparseMatrixArg : public BufferArg { size_t nnz() const { return nnz_; } - bool isTranspose() const { return trans_; } - SparseDataFormat dataFormat() const { return format_; } SparseDataType dataType() const { return type_; } @@ -334,8 +333,6 @@ class SparseMatrixArg : public BufferArg { size_t nnz_; SparseDataFormat format_; SparseDataType type_; - /// todo(tianbing), move trans_ up to BufferArg - bool trans_; }; } // namespace paddle diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 7d341182523cbb..1c593bb083e009 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -483,8 +483,8 @@ template class MulFunc : public FunctionBase { public: void init(const FuncConfig& config) override { - scaleAB_ = config.get("scaleAB"); - scaleT_ = config.get("scaleT"); + alpha_ = config.get("scaleAB"); + beta_ = config.get("scaleT"); } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { @@ -494,7 +494,7 @@ class MulFunc : public FunctionBase { CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); + CHECK_EQ(outputs[0].getArgType(), ADD_TO); auto in1_mat = inputs[0].matrix(); if (inputs[0].isSparseArg()) { @@ -505,12 +505,12 @@ class MulFunc : public FunctionBase { in2_mat = inputs[1].sparse().SparseMatrix(); } auto out_mat = outputs[0].matrix(); - MulOp(out_mat, in1_mat, in2_mat, scaleAB_, scaleT_); + MulOp(out_mat, in1_mat, in2_mat, alpha_, beta_); } private: - real scaleAB_; - real scaleT_; + real alpha_; + real beta_; }; REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index f3699f8c78cda7..b7b1f56af10375 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -68,4 +68,11 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT); +template +void MulOp(GpuSparseMatrix& out, + const GpuMatrix& a, + const GpuMatrix& b, + real scaleAB, + real scaleT); + } // namespace paddle diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index 73d788a4743326..3691c7f3206126 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -170,4 +170,13 @@ void MulOp(GpuMatrix& out, } } +template <> +void MulOp(GpuSparseMatrix& out, + const GpuMatrix& a, + const GpuMatrix& b, + real scale_ab, + real scale_t) { +/// todo(tianbing), implement it +} + } // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index ce9d37d664710c..3229193660ea03 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -16,50 +16,79 @@ limitations under the License. */ #include "FunctionTest.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" +#include "paddle/math/tests/test_matrixUtil.h" #include "paddle/testing/TestUtil.h" using namespace paddle; // NOLINT -void testSpMatrixMul(int M, int N, int K, real rate, real scale1, real scale2) { - /// todo(tianbing) check CPU/GPU +/** + * C = alpha * C + beta * (A * B) + */ +void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) { + real alpha = 1.5; + real beta = 2.0; + + const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); + cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); - gpuFunc->init(FuncConfig().set("scaleAB", scale1).set("scaleT", scale2)); + gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - int nnz = M * N * rate; - MatrixPtr cpuA = std::make_shared(M, K); - MatrixPtr cpuB = std::make_shared(N, K); - MatrixPtr cpuC(new CpuSparseMatrix(M, N, nnz)); + int heightA = (transa == false) ? dimM : dimK; + int widthA = (transa == false) ? dimK : dimM; + int heightB = (transb == false) ? dimK : dimN; + int widthB = (transb == false) ? dimN : dimK; + int heightC = dimM; + int widthC = dimN; - MatrixPtr gpuA = std::make_shared(M, K); - MatrixPtr gpuB = std::make_shared(N, K); - MatrixPtr gpuC(new GpuSparseMatrix(M, N, nnz)); + auto cpuA = std::make_shared(heightA, widthA, transa); + auto cpuB = std::make_shared(heightB, widthB, transb); + auto cpuC = std::make_shared(heightC, widthC); + auto gpuA = std::make_shared(heightA, widthA, transa); + auto gpuB = std::make_shared(heightB, widthB, transb); + auto gpuC = std::make_shared(heightC, widthC); cpuA->randomizeUniform(); cpuB->randomizeUniform(); cpuC->randomizeUniform(); + gpuA->copyFrom(*cpuA); + gpuB->copyFrom(*cpuB); + gpuC->copyFrom(*cpuC); - hl_stream_t stream(HPPL_STREAM_3); - gpuA->copyFrom(*cpuA, stream); - gpuB->copyFrom(*cpuB, stream); - gpuC->copyFrom(*cpuC, stream); - hl_stream_synchronize(stream); + BufferArgs cpuInputs; + BufferArgs cpuOutputs; + cpuInputs.addArg(*cpuA); + cpuInputs.addArg(*cpuB); + cpuOutputs.addArg(*cpuC, ADD_TO); + cpuFunc->calc(cpuInputs, cpuOutputs); - BufferArgs inputs; - BufferArgs outputs; - inputs.addArg(*gpuA->getTranspose()); - inputs.addArg(*gpuB->getTranspose()); - outputs.addArg(*gpuC, ASSIGN_TO); + BufferArgs gpuInputs; + BufferArgs gpuOutputs; + gpuInputs.addArg(*gpuA); + gpuInputs.addArg(*gpuB); + gpuOutputs.addArg(*gpuC, ADD_TO); + gpuFunc->calc(gpuInputs, gpuOutputs); - gpuFunc->calc(inputs, outputs); + autotest::TensorCheckErr(*cpuC, *gpuC); } -TEST(SMatrix, sMatrixMul) { - for (auto M : {1, 40, 128, 200}) { - for (auto N : {100}) { - for (auto K : {100}) { - /// todo(tianbing), add scaleAB and scaleT - VLOG(3) << " M=" << M << " N=" << N << " K=" << K; - testSpMatrixMul(M, N, K, 0.05, 1, 1); +TEST(Matrix, mul) { + for (auto transa : {false, true}) { + for (auto transb : {false, true}) { + for (auto dimM : {1, 10, 100}) { + for (auto dimN : {1, 10}) { + for (auto dimK : {8}) { + if (true == transa && true == transb) { + continue; + } + VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + << " transa=" << transa << " transb=" << transb + << " dimM=" << std::setw(5) << dimM + << " dimN=" << std::setw(5) << dimN + << " dimK=" << std::setw(5) << dimK; + + testMatrixMul(transa, transb, dimM, dimN, dimK); + } + } } } } From 1ca2846ef688bfc532918e9e977eb4b519f3c4e8 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 17 Jan 2017 21:50:57 -0800 Subject: [PATCH 098/370] Pass unit test for CpuMatrix::mul(CpuMatrix, CpuSparseMatrix) and GpuMatrix::mul(CpuMatrix, GpuSparseMatrix) --- paddle/function/BufferArg.cpp | 12 ++- paddle/function/BufferArg.h | 27 +++---- paddle/function/MulOp.cpp | 50 +++++++++++-- paddle/function/MulOp.h | 2 + paddle/function/MulOpTest.cpp | 134 ++++++++++++++++++++++++++++++++++ 5 files changed, 198 insertions(+), 27 deletions(-) diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index 5d595deb12c6c8..39773c76280ba3 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -32,14 +32,22 @@ const SparseMatrixArg& BufferArg::sparse() const { SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), + /// todo(tianbing), make sure how to get NNZ + nnz_(sparse.getElementCnt()), + format_(sparse.getFormat()), + type_(sparse.getValueType()) { bufferType_ = TENSOR_SPARSE; } SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), + /// todo(tianbing), make sure how to get NNZ + nnz_(sparse.getElementCnt()), + format_(sparse.getFormat()), + type_(sparse.getValueType()) { bufferType_ = TENSOR_SPARSE; } diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 2da1115ec968cc..eac3fe4420369b 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -30,13 +30,6 @@ enum BufferType { TENSOR_SPARSE = 4 }; -enum SparseDataType { - SPARSE_NO_VALUE = 0, // do not need value pointer, all values are 1 - SPARSE_FLOAT_VALUE = 1 -}; - -enum SparseDataFormat { SPARSE_CSR_FORMAT = 0, SPARSE_CSC_FORMAT = 1 }; - class BufferArg; class SequenceArg; class SparseMatrixArg; @@ -272,8 +265,8 @@ class SparseMatrixArg : public BufferArg { const BufferArg& row, const BufferArg& col, size_t nnz, - SparseDataFormat format, - SparseDataType type, + SparseFormat format, + SparseValueType type, ArgType argType = UNSPECIFIED) : BufferArg(buf, valueType, shape, argType), row_(row), @@ -286,9 +279,9 @@ class SparseMatrixArg : public BufferArg { CHECK_EQ(shape_.ndims(), (size_t)2); CHECK_EQ(row_.shape().ndims(), (size_t)1); CHECK_EQ(col_.shape().ndims(), (size_t)1); - if (format == SPARSE_CSR_FORMAT) { + if (format == SPARSE_CSR) { CHECK_EQ(nnz, col.shape()[0]); - } else if (format == SPARSE_CSC_FORMAT) { + } else if (format == SPARSE_CSC) { CHECK_EQ(nnz, row.shape()[0]); } } @@ -310,8 +303,8 @@ class SparseMatrixArg : public BufferArg { shape_[0], shape_[1], nnz_, - static_cast(type_), - static_cast(format_), + type_, + format_, trans_); } @@ -323,16 +316,16 @@ class SparseMatrixArg : public BufferArg { size_t nnz() const { return nnz_; } - SparseDataFormat dataFormat() const { return format_; } + SparseFormat dataFormat() const { return format_; } - SparseDataType dataType() const { return type_; } + SparseValueType dataType() const { return type_; } private: BufferArg row_; BufferArg col_; size_t nnz_; - SparseDataFormat format_; - SparseDataType type_; + SparseFormat format_; + SparseValueType type_; }; } // namespace paddle diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 1c593bb083e009..85f7f535dcfa42 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "MulOp.h" +/// todo(tianbing), delete it +#include #include "paddle/math/MathFunctions.h" #include "paddle/math/SIMDFunctions.h" #include "paddle/utils/ThreadLocal.h" @@ -496,16 +498,48 @@ class MulFunc : public FunctionBase { CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].getArgType(), ADD_TO); - auto in1_mat = inputs[0].matrix(); - if (inputs[0].isSparseArg()) { - in1_mat = inputs[0].sparse().SparseMatrix(); + /// todo(tianbing), support SparseMatrixArg for out_mat + auto out_mat = outputs[0].matrix(); + LOG(INFO) << "out_mat:"; + out_mat.print(std::cout); + if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg()) { + LOG(INFO) << "in1_mat:"; + inputs[0].matrix().print(std::cout); + LOG(INFO) << "in2_mat:"; + inputs[1].matrix().print(std::cout); + MulOp(out_mat, + inputs[0].matrix(), + inputs[1].matrix(), + alpha_, + beta_); + return; } - auto in2_mat = inputs[1].matrix(); - if (inputs[1].isSparseArg()) { - in2_mat = inputs[1].sparse().SparseMatrix(); + + if (!inputs[0].isSparseArg() && inputs[1].isSparseArg()) { + LOG(INFO) << "in1_mat:"; + inputs[0].matrix().print(std::cout); + LOG(INFO) << "in2_mat:"; + inputs[1].sparse().SparseMatrix().print(std::cout); + MulOp(out_mat, + inputs[0].matrix(), + inputs[1].sparse().SparseMatrix(), + alpha_, + beta_); + return; + } + + if (inputs[0].isSparseArg() && !inputs[1].isSparseArg()) { + LOG(INFO) << "in1_mat:"; + inputs[0].sparse().SparseMatrix().print(std::cout); + LOG(INFO) << "in2_mat:"; + inputs[1].matrix().print(std::cout); + MulOp(out_mat, + inputs[0].sparse().SparseMatrix(), + inputs[1].matrix(), + alpha_, + beta_); + return; } - auto out_mat = outputs[0].matrix(); - MulOp(out_mat, in1_mat, in2_mat, alpha_, beta_); } private: diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index b7b1f56af10375..23bfd0fa932178 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -15,6 +15,8 @@ limitations under the License. */ #pragma once #include "Function.h" +/// todo(tianbing), delete +#include #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 3229193660ea03..fd02504678efd6 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +/// todo(tianbing), delete +#include #include "FunctionTest.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" @@ -72,6 +74,7 @@ void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) { } TEST(Matrix, mul) { + LOG(INFO) << "test for dense = dense * dense matrix"; for (auto transa : {false, true}) { for (auto transb : {false, true}) { for (auto dimM : {1, 10, 100}) { @@ -93,3 +96,134 @@ TEST(Matrix, mul) { } } } + +struct MatrixPara { + size_t height; + size_t width; + bool trans; + bool sparse; + size_t nnz; + SparseFormat format; +}; + +/** + * C += A * B, A, C dense, B sparse + */ +void testDSparseDMatrix() { + real alpha = 1.0; + real beta = 1.0; + const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); + cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); + gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + + constexpr size_t dimM = 2; + constexpr size_t dimN = 2; + constexpr size_t dimK = 3; + constexpr size_t NNZ = 3; + constexpr SparseFormat FORMAT = SPARSE_CSC; + + MatrixPara paraA{dimM, dimK, /*trans*/ false, /*sparse*/ false, NNZ, FORMAT}; + MatrixPara paraB{dimK, dimN, /*trans*/ false, /*sparse*/ true, NNZ, FORMAT}; + MatrixPara paraC{dimM, dimN, /*trans*/ false, /*sparse*/ false, NNZ, FORMAT}; + + auto cpuMatrixA = + Matrix::create(paraA.height, paraA.width, paraA.trans, false); + auto gpuMatrixA = + Matrix::create(paraA.height, paraA.width, paraA.trans, true); + auto cpuDenseA = + Matrix::create(paraA.height, paraA.width, paraA.trans, false); + CpuSparseMatrix cpuMatrixB(paraB.height, + paraB.width, + paraB.nnz, + FLOAT_VALUE, + paraB.format, + paraB.trans); + + GpuSparseMatrix gpuMatrixB(paraB.height, + paraB.width, + paraB.nnz, + FLOAT_VALUE, + paraB.format, + paraB.trans); + + auto cpuDenseB = + Matrix::create(paraB.height, paraB.width, paraB.trans, false); + auto cpuMatrixC = + Matrix::create(paraC.height, paraC.width, paraC.trans, false); + auto gpuMatrixC = + Matrix::create(paraC.height, paraC.width, paraC.trans, true); + auto cpuDenseC = + Matrix::create(paraC.height, paraC.width, paraC.trans, false); + auto gpuMatrixC_d2h = + Matrix::create(paraC.height, paraC.width, paraC.trans, false); + /*matrix init*/ + hl_stream_t stream(HPPL_STREAM_1); + cpuMatrixA->randomizeUniform(); + cpuMatrixB.randomizeUniform(); + cpuMatrixC->randomizeUniform(); + + gpuMatrixA->copyFrom(*cpuMatrixA, stream); + gpuMatrixB.copyFrom(cpuMatrixB, stream); + gpuMatrixC->copyFrom(*cpuMatrixC, stream); + + cpuDenseA->copyFrom(*cpuMatrixA); + cpuDenseB->copyFrom(cpuMatrixB); + cpuDenseC->copyFrom(*cpuMatrixC); + hl_stream_synchronize(stream); + + LOG(INFO) << "cpuMatrixA: "; + cpuMatrixA->print(std::cout); + LOG(INFO) << "cpuMatrixB: "; + (&cpuMatrixB)->print(std::cout); + LOG(INFO) << "cpuMatrixC: "; + cpuMatrixC->print(std::cout); + + LOG(INFO) << "cpuDenseA: "; + cpuDenseA->print(std::cout); + LOG(INFO) << "cpuDenseB: "; + cpuDenseB->print(std::cout); + LOG(INFO) << "cpuDenseC: "; + cpuDenseC->print(std::cout); + + LOG(INFO) << "gpuMatrixA: "; + gpuMatrixA->print(std::cout); + LOG(INFO) << "gpuMatrixB: "; + (&gpuMatrixB)->print(std::cout); + LOG(INFO) << "gpuMatrixC: "; + gpuMatrixC->print(std::cout); + + /*matrix mul*/ + BufferArgs cpuInputs; + BufferArgs cpuOutputs; + cpuInputs.addArg(*cpuMatrixA); + cpuInputs.addArg(cpuMatrixB); + cpuOutputs.addArg(*cpuMatrixC, ADD_TO); + cpuFunc->calc(cpuInputs, cpuOutputs); + + BufferArgs gpuInputs; + BufferArgs gpuOutputs; + gpuInputs.addArg(*gpuMatrixA); + gpuInputs.addArg(gpuMatrixB); + gpuOutputs.addArg(*gpuMatrixC, ADD_TO); + gpuFunc->calc(gpuInputs, gpuOutputs); + + BufferArgs denseInputs; + BufferArgs denseOutputs; + denseInputs.addArg(*cpuDenseA); + denseInputs.addArg(*cpuDenseB); + denseOutputs.addArg(*cpuDenseC, ADD_TO); + cpuFunc->calc(denseInputs, denseOutputs); + + gpuMatrixC_d2h->copyFrom(*gpuMatrixC, stream); + hl_stream_synchronize(stream); + /*check result*/ + // autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); + checkMatrixEqual(cpuMatrixC, cpuDenseC); + checkMatrixEqual(cpuMatrixC, gpuMatrixC_d2h); +} + +TEST(Matrix, SparseMatrixMul) { + LOG(INFO) << "test for dense = dense * sparse matrix"; + testDSparseDMatrix(); +} From 4751cc8f7e7f8fd00dc95aa117a767a3247f0796 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 18 Jan 2017 15:50:47 -0800 Subject: [PATCH 099/370] Pass unit test for SparseCpuMatrix::mul(CpuMatrix, CpuMatrix), SparseGpuMatrix::mul(GpuMatrix, GpuMatrix), CpuMatrix::mul(CpuSparseMatrix, CpuMatrix), and GpuMatrix::mul(GpuSparseMatrix, GpuMatrix) --- paddle/function/MulOp.cpp | 39 ++-- paddle/function/MulOpGpu.cu | 31 ++- paddle/function/MulOpTest.cpp | 301 ++++++++++++++++++++-------- paddle/math/SparseMatrix.cpp | 1 - paddle/math/tests/test_matrixUtil.h | 41 ++++ 5 files changed, 312 insertions(+), 101 deletions(-) diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 85f7f535dcfa42..37f8808605e10c 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -498,15 +498,10 @@ class MulFunc : public FunctionBase { CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].getArgType(), ADD_TO); - /// todo(tianbing), support SparseMatrixArg for out_mat auto out_mat = outputs[0].matrix(); - LOG(INFO) << "out_mat:"; - out_mat.print(std::cout); - if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg()) { - LOG(INFO) << "in1_mat:"; - inputs[0].matrix().print(std::cout); - LOG(INFO) << "in2_mat:"; - inputs[1].matrix().print(std::cout); + /// matrix = matrix * matrix + if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && + !outputs[0].isSparseArg()) { MulOp(out_mat, inputs[0].matrix(), inputs[1].matrix(), @@ -515,11 +510,9 @@ class MulFunc : public FunctionBase { return; } - if (!inputs[0].isSparseArg() && inputs[1].isSparseArg()) { - LOG(INFO) << "in1_mat:"; - inputs[0].matrix().print(std::cout); - LOG(INFO) << "in2_mat:"; - inputs[1].sparse().SparseMatrix().print(std::cout); + /// matrix = matrix * sparse matrix + if (!inputs[0].isSparseArg() && inputs[1].isSparseArg() && + !outputs[0].isSparseArg()) { MulOp(out_mat, inputs[0].matrix(), inputs[1].sparse().SparseMatrix(), @@ -528,11 +521,9 @@ class MulFunc : public FunctionBase { return; } - if (inputs[0].isSparseArg() && !inputs[1].isSparseArg()) { - LOG(INFO) << "in1_mat:"; - inputs[0].sparse().SparseMatrix().print(std::cout); - LOG(INFO) << "in2_mat:"; - inputs[1].matrix().print(std::cout); + /// matrix = sparse matrix * matrix + if (inputs[0].isSparseArg() && !inputs[1].isSparseArg() && + !outputs[0].isSparseArg()) { MulOp(out_mat, inputs[0].sparse().SparseMatrix(), inputs[1].matrix(), @@ -540,6 +531,18 @@ class MulFunc : public FunctionBase { beta_); return; } + + /// sparse matrix = matrix * matrix + auto out_sparse_mat = outputs[0].sparse().SparseMatrix(); + if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && + outputs[0].isSparseArg()) { + MulOp(out_sparse_mat, + inputs[0].matrix(), + inputs[1].matrix(), + alpha_, + beta_); + return; + } } private: diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index 3691c7f3206126..3c4654b9b27574 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -176,7 +176,36 @@ void MulOp(GpuSparseMatrix& out, const GpuMatrix& b, real scale_ab, real scale_t) { -/// todo(tianbing), implement it + /// todo(tianbing), clean the code + CHECK(a.useGpu_ && b.useGpu_) << "type not match"; + CHECK(!out.trans_) << "trans not supported"; + real* a_data = const_cast(a.getData()); + real* b_data = const_cast(b.getData()); + hl_sparse_matrix_s out_data = out.sMatrix_.get(); + hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N; + hl_trans_op_t b_trans = b.trans_ ? HPPL_OP_T : HPPL_OP_N; + + if (!a.trans_ && !b.trans_) { + CHECK(out.height_ == a.getHeight()); + CHECK(out.width_ == b.getWidth()); + CHECK(a.getWidth() == b.getHeight()); + } else if (a.trans_ && !b.trans_) { + CHECK(out.height_ == a.getWidth()); + CHECK(out.width_ == b.getWidth()); + CHECK(a.getHeight() == b.getHeight()); + } else if (!a.trans_ && b.trans_) { + CHECK(out.height_ == a.getHeight()); + CHECK(out.width_ == b.getHeight()); + CHECK(a.getWidth() == b.getWidth()); + } else { + LOG(INFO) << "Not support"; + } + int dim_m = out.height_; + int dim_n = out.width_; + int dim_k = !b.trans_ ? b.getHeight() : b.getWidth(); + hl_sparse_matrix_mul( + a_data, a_trans, b_data, b_trans, out_data, + dim_m, dim_n, dim_k, scale_ab, scale_t); } } // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index fd02504678efd6..630070b845a9af 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -24,9 +24,10 @@ limitations under the License. */ using namespace paddle; // NOLINT /** - * C = alpha * C + beta * (A * B) + * C = alpha * C + beta * (A * B), A, B, C dense matrix + * dense = dense * dense */ -void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) { +void testDDDMatrix(bool transa, bool transb, int dimM, int dimN, int dimK) { real alpha = 1.5; real beta = 2.0; @@ -73,7 +74,7 @@ void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) { autotest::TensorCheckErr(*cpuC, *gpuC); } -TEST(Matrix, mul) { +TEST(Matrix, DDDMul) { LOG(INFO) << "test for dense = dense * dense matrix"; for (auto transa : {false, true}) { for (auto transb : {false, true}) { @@ -89,7 +90,7 @@ TEST(Matrix, mul) { << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK; - testMatrixMul(transa, transb, dimM, dimN, dimK); + testDDDMatrix(transa, transb, dimM, dimN, dimK); } } } @@ -97,19 +98,100 @@ TEST(Matrix, mul) { } } -struct MatrixPara { - size_t height; - size_t width; - bool trans; - bool sparse; - size_t nnz; - SparseFormat format; -}; +/** + * C += A * B, B, C dense, A sparse + * dense = sparse * dense + */ +void testDSparseDMatrix( + size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { + real alpha = 1.0; + real beta = 1.0; + const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); + cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); + gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + + CpuSparseMatrix cpuMatrixA(dimM, dimK, nnz, FLOAT_VALUE, FORMAT, false); + GpuSparseMatrix gpuMatrixA(dimM, dimK, nnz, FLOAT_VALUE, FORMAT, false); + CpuMatrix cpuDenseA(dimM, dimK, false); + + auto cpuMatrixB = Matrix::create(dimK, dimN, false, false); + auto gpuMatrixB = Matrix::create(dimK, dimN, false, true); + auto cpuDenseB = Matrix::create(dimK, dimN, false, false); + + auto cpuMatrixC = Matrix::create(dimM, dimN, false, false); + auto gpuMatrixC = Matrix::create(dimM, dimN, false, true); + auto cpuDenseC = Matrix::create(dimM, dimN, false, false); + + /*matrix init*/ + hl_stream_t stream(HPPL_STREAM_1); + cpuMatrixA.randomizeUniform(); + cpuMatrixB->randomizeUniform(); + cpuMatrixC->randomizeUniform(); + + gpuMatrixA.copyFrom(cpuMatrixA, stream); + gpuMatrixB->copyFrom(*cpuMatrixB, stream); + gpuMatrixC->copyFrom(*cpuMatrixC, stream); + + cpuDenseA.copyFrom(cpuMatrixA); + cpuDenseB->copyFrom(*cpuMatrixB); + cpuDenseC->copyFrom(*cpuMatrixC); + hl_stream_synchronize(stream); + + /*matrix mul*/ + BufferArgs cpuInputs; + BufferArgs cpuOutputs; + cpuInputs.addArg(cpuMatrixA); + cpuInputs.addArg(*cpuMatrixB); + cpuOutputs.addArg(*cpuMatrixC, ADD_TO); + cpuFunc->calc(cpuInputs, cpuOutputs); + + BufferArgs gpuInputs; + BufferArgs gpuOutputs; + gpuInputs.addArg(gpuMatrixA); + gpuInputs.addArg(*gpuMatrixB); + gpuOutputs.addArg(*gpuMatrixC, ADD_TO); + gpuFunc->calc(gpuInputs, gpuOutputs); + + BufferArgs denseInputs; + BufferArgs denseOutputs; + denseInputs.addArg(cpuDenseA); + denseInputs.addArg(*cpuDenseB); + denseOutputs.addArg(*cpuDenseC, ADD_TO); + cpuFunc->calc(denseInputs, denseOutputs); + + /*check result*/ + autotest::TensorCheckErr(*cpuMatrixC, *cpuDenseC); + autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); +} + +TEST(Matrix, DSparseDMul) { + LOG(INFO) << "test for dense = sparse * dense matrix"; + for (const auto dimM : {10, 100, 1000}) { + for (const auto dimN : {10, 100}) { + for (const auto dimK : {3, 10}) { + for (const auto nnz : {3, 10}) { + for (const auto FORMAT : {SPARSE_CSR}) { + VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + << " dimM=" << std::setw(5) << dimM + << " dimN=" << std::setw(5) << dimN + << " dimK=" << std::setw(5) << dimK + << " nnz=" << std::setw(5) << nnz + << " format=" << std::setw(5) << FORMAT; + testDSparseDMatrix(dimM, dimN, dimK, nnz, FORMAT); + } + } + } + } + } +} /** * C += A * B, A, C dense, B sparse + * dense = dense * sparse */ -void testDSparseDMatrix() { +void testDDSparseMatrix( + size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { real alpha = 1.0; real beta = 1.0; const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); @@ -117,46 +199,19 @@ void testDSparseDMatrix() { const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - constexpr size_t dimM = 2; - constexpr size_t dimN = 2; - constexpr size_t dimK = 3; - constexpr size_t NNZ = 3; - constexpr SparseFormat FORMAT = SPARSE_CSC; - - MatrixPara paraA{dimM, dimK, /*trans*/ false, /*sparse*/ false, NNZ, FORMAT}; - MatrixPara paraB{dimK, dimN, /*trans*/ false, /*sparse*/ true, NNZ, FORMAT}; - MatrixPara paraC{dimM, dimN, /*trans*/ false, /*sparse*/ false, NNZ, FORMAT}; - - auto cpuMatrixA = - Matrix::create(paraA.height, paraA.width, paraA.trans, false); - auto gpuMatrixA = - Matrix::create(paraA.height, paraA.width, paraA.trans, true); - auto cpuDenseA = - Matrix::create(paraA.height, paraA.width, paraA.trans, false); - CpuSparseMatrix cpuMatrixB(paraB.height, - paraB.width, - paraB.nnz, - FLOAT_VALUE, - paraB.format, - paraB.trans); - - GpuSparseMatrix gpuMatrixB(paraB.height, - paraB.width, - paraB.nnz, - FLOAT_VALUE, - paraB.format, - paraB.trans); - - auto cpuDenseB = - Matrix::create(paraB.height, paraB.width, paraB.trans, false); - auto cpuMatrixC = - Matrix::create(paraC.height, paraC.width, paraC.trans, false); - auto gpuMatrixC = - Matrix::create(paraC.height, paraC.width, paraC.trans, true); - auto cpuDenseC = - Matrix::create(paraC.height, paraC.width, paraC.trans, false); - auto gpuMatrixC_d2h = - Matrix::create(paraC.height, paraC.width, paraC.trans, false); + auto cpuMatrixA = Matrix::create(dimM, dimK, false, false); + auto gpuMatrixA = Matrix::create(dimM, dimK, false, true); + auto cpuDenseA = Matrix::create(dimM, dimK, false, false); + + CpuSparseMatrix cpuMatrixB(dimK, dimN, nnz, FLOAT_VALUE, FORMAT, false); + + GpuSparseMatrix gpuMatrixB(dimK, dimN, nnz, FLOAT_VALUE, FORMAT, false); + + auto cpuDenseB = Matrix::create(dimK, dimN, false, false); + auto cpuMatrixC = Matrix::create(dimM, dimN, false, false); + auto gpuMatrixC = Matrix::create(dimM, dimN, false, true); + auto cpuDenseC = Matrix::create(dimM, dimN, false, false); + /*matrix init*/ hl_stream_t stream(HPPL_STREAM_1); cpuMatrixA->randomizeUniform(); @@ -172,27 +227,6 @@ void testDSparseDMatrix() { cpuDenseC->copyFrom(*cpuMatrixC); hl_stream_synchronize(stream); - LOG(INFO) << "cpuMatrixA: "; - cpuMatrixA->print(std::cout); - LOG(INFO) << "cpuMatrixB: "; - (&cpuMatrixB)->print(std::cout); - LOG(INFO) << "cpuMatrixC: "; - cpuMatrixC->print(std::cout); - - LOG(INFO) << "cpuDenseA: "; - cpuDenseA->print(std::cout); - LOG(INFO) << "cpuDenseB: "; - cpuDenseB->print(std::cout); - LOG(INFO) << "cpuDenseC: "; - cpuDenseC->print(std::cout); - - LOG(INFO) << "gpuMatrixA: "; - gpuMatrixA->print(std::cout); - LOG(INFO) << "gpuMatrixB: "; - (&gpuMatrixB)->print(std::cout); - LOG(INFO) << "gpuMatrixC: "; - gpuMatrixC->print(std::cout); - /*matrix mul*/ BufferArgs cpuInputs; BufferArgs cpuOutputs; @@ -215,15 +249,120 @@ void testDSparseDMatrix() { denseOutputs.addArg(*cpuDenseC, ADD_TO); cpuFunc->calc(denseInputs, denseOutputs); - gpuMatrixC_d2h->copyFrom(*gpuMatrixC, stream); - hl_stream_synchronize(stream); /*check result*/ - // autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); - checkMatrixEqual(cpuMatrixC, cpuDenseC); - checkMatrixEqual(cpuMatrixC, gpuMatrixC_d2h); + autotest::TensorCheckErr(*cpuMatrixC, *cpuDenseC); + autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); } -TEST(Matrix, SparseMatrixMul) { +TEST(Matrix, DDSparseMul) { LOG(INFO) << "test for dense = dense * sparse matrix"; - testDSparseDMatrix(); + for (const auto dimM : {10, 100, 1000}) { + for (const auto dimN : {10, 100}) { + for (const auto dimK : {3, 10}) { + for (const auto nnz : {3, 10}) { + for (const auto FORMAT : {SPARSE_CSR, SPARSE_CSC}) { + VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + << " dimM=" << std::setw(5) << dimM + << " dimN=" << std::setw(5) << dimN + << " dimK=" << std::setw(5) << dimK + << " nnz=" << std::setw(5) << nnz + << " format=" << std::setw(5) << FORMAT; + testDDSparseMatrix(dimM, dimN, dimK, nnz, FORMAT); + } + } + } + } + } +} + +/** + * C += A * B, A sparse, B, C dense + * sparse = dense * dense + */ +void testSparseDDMatrix( + size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { + real alpha = 1.0; + real beta = 1.0; + const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); + cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); + gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + + auto cpuMatrixA = Matrix::create(dimM, dimK, false, false); + auto gpuMatrixA = Matrix::create(dimM, dimK, false, true); + auto cpuDenseA = Matrix::create(dimM, dimK, false, false); + + auto cpuMatrixB = Matrix::create(dimK, dimN, false, false); + auto gpuMatrixB = Matrix::create(dimK, dimN, false, true); + auto cpuDenseB = Matrix::create(dimK, dimN, false, false); + + CpuSparseMatrix cpuMatrixC(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); + CpuSparseMatrix gpuMatrixC_d2h(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); + GpuSparseMatrix gpuMatrixC(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); + CpuMatrix cpuDenseC(dimM, dimN, false); + + /*matrix init*/ + hl_stream_t stream(HPPL_STREAM_1); + cpuMatrixA->randomizeUniform(); + cpuMatrixB->randomizeUniform(); + cpuMatrixC.randomizeUniform(); + + gpuMatrixA->copyFrom(*cpuMatrixA, stream); + gpuMatrixB->copyFrom(*cpuMatrixB, stream); + gpuMatrixC.copyFrom(cpuMatrixC, stream); + + cpuDenseA->copyFrom(*cpuMatrixA); + cpuDenseB->copyFrom(*cpuMatrixB); + cpuDenseC.copyFrom(cpuMatrixC); + hl_stream_synchronize(stream); + + /*matrix mul*/ + BufferArgs cpuInputs; + BufferArgs cpuOutputs; + cpuInputs.addArg(*cpuMatrixA); + cpuInputs.addArg(*cpuMatrixB); + cpuOutputs.addArg(cpuMatrixC, ADD_TO); + cpuFunc->calc(cpuInputs, cpuOutputs); + + BufferArgs gpuInputs; + BufferArgs gpuOutputs; + gpuInputs.addArg(*gpuMatrixA); + gpuInputs.addArg(*gpuMatrixB); + gpuOutputs.addArg(gpuMatrixC, ADD_TO); + gpuFunc->calc(gpuInputs, gpuOutputs); + + BufferArgs denseInputs; + BufferArgs denseOutputs; + denseInputs.addArg(*cpuDenseA); + denseInputs.addArg(*cpuDenseB); + denseOutputs.addArg(cpuDenseC, ADD_TO); + cpuFunc->calc(denseInputs, denseOutputs); + + gpuMatrixC_d2h.copyFrom(gpuMatrixC, stream); + hl_stream_synchronize(stream); + + /*check result*/ + checkSMatrixEqual(cpuMatrixC, gpuMatrixC_d2h); + checkSMatrixEqual2Dense(cpuMatrixC, cpuDenseC); +} + +TEST(Matrix, SparseDDMul) { + LOG(INFO) << "test for sparse = dense * dense matrix"; + for (const auto dimM : {10, 100, 1000}) { + for (const auto dimN : {10, 100}) { + for (const auto dimK : {3, 10}) { + for (const auto nnz : {3, 10}) { + for (const auto FORMAT : {SPARSE_CSC, SPARSE_CSR}) { + VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + << " dimM=" << std::setw(5) << dimM + << " dimN=" << std::setw(5) << dimN + << " dimK=" << std::setw(5) << dimK + << " nnz=" << std::setw(5) << nnz + << " format=" << std::setw(5) << FORMAT; + testSparseDDMatrix(dimM, dimN, dimK, nnz, FORMAT); + } + } + } + } + } } diff --git a/paddle/math/SparseMatrix.cpp b/paddle/math/SparseMatrix.cpp index 720a035ecbd26d..3bae6d373f240f 100644 --- a/paddle/math/SparseMatrix.cpp +++ b/paddle/math/SparseMatrix.cpp @@ -177,7 +177,6 @@ GpuSparseMatrix::GpuSparseMatrix(real* value, hl_sparse_matrix_s_ptr tmp2(tmp, hl_destruct_sparse_matrix); sMatrix_ = tmp2; } - LOG(INFO) << "weight to matrix "; } } diff --git a/paddle/math/tests/test_matrixUtil.h b/paddle/math/tests/test_matrixUtil.h index 9aa74b15193723..47f461474622d1 100644 --- a/paddle/math/tests/test_matrixUtil.h +++ b/paddle/math/tests/test_matrixUtil.h @@ -30,6 +30,17 @@ void checkMatrixEqual(const MatrixPtr& a, const MatrixPtr& b) { } } +void checkSMatrixEqual(const CpuSparseMatrix& a, const CpuSparseMatrix& b) { + ASSERT_EQ(a.getWidth(), b.getWidth()); + ASSERT_EQ(a.getHeight(), b.getHeight()); + ASSERT_EQ(a.isTransposed(), b.isTransposed()); + ASSERT_EQ(a.getFormat(), b.getFormat()); + ASSERT_EQ(a.getElementCnt(), b.getElementCnt()); + for (size_t r = 0; r < a.getElementCnt(); ++r) { + ASSERT_FLOAT_EQ(a.getValue()[r], b.getValue()[r]); + } +} + void checkSMatrixEqual(const CpuSparseMatrixPtr& a, const CpuSparseMatrixPtr& b) { ASSERT_EQ(a->getWidth(), b->getWidth()); @@ -73,6 +84,36 @@ void checkSMatrixEqual2(const CpuSparseMatrixPtr& a, } } +void checkSMatrixEqual2Dense(const CpuSparseMatrix& a, const CpuMatrix& b) { + ASSERT_EQ(a.getWidth(), b.getWidth()); + ASSERT_EQ(a.getHeight(), b.getHeight()); + ASSERT_EQ(a.isTransposed(), b.isTransposed()); + + if (a.getFormat() == SPARSE_CSC) { + int* rows = a.getRows(); + for (size_t i = 0; i < a.getWidth(); i++) { + for (size_t j = a.getColStartIdx(i); j < a.getColStartIdx(i + 1); j++) { + if (a.getValueType() == FLOAT_VALUE) { + ASSERT_FLOAT_EQ(a.getValue()[j], b.getElement(rows[j], i)); + } else { + ASSERT_FLOAT_EQ(1.0, b.getElement(rows[j], i)); + } + } + } + } else { + int* cols = a.getCols(); + for (size_t i = 0; i < a.getHeight(); i++) { + for (size_t j = a.getRowStartIdx(i); j < a.getRowStartIdx(i + 1); j++) { + if (a.getValueType() == FLOAT_VALUE) { + ASSERT_FLOAT_EQ(a.getValue()[j], b.getElement(i, cols[j])); + } else { + ASSERT_FLOAT_EQ(1.0, b.getElement(i, cols[j])); + } + } + } + } +} + void checkSMatrixEqual2Dense(const CpuSparseMatrixPtr& a, const CpuMatrixPtr& b) { ASSERT_EQ(a->getWidth(), b->getWidth()); From 171eaff21689de58fd8ecc29926151d9ba304b60 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 18 Jan 2017 18:16:37 -0800 Subject: [PATCH 100/370] clean the code a little bit. --- paddle/function/MulOp.cpp | 162 +++++---------------- paddle/function/MulOpGpu.cu | 255 ++++++++++++++++------------------ paddle/function/MulOpTest.cpp | 13 +- 3 files changed, 166 insertions(+), 264 deletions(-) diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 37f8808605e10c..1117944a4ed994 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -38,13 +38,6 @@ inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { } } -inline void colVecAddTo( - real* a, const real* b, size_t len, size_t aWidth, size_t bWidth) { - for (unsigned int i = 0; i < len; ++i) { - a[i * aWidth] += b[i * bWidth]; - } -} - inline void colVecAddTo( real* a, real* b, real c, size_t len, size_t aWidth, size_t bWidth) { for (unsigned int i = 0; i < len; ++i) { @@ -336,140 +329,59 @@ void MulOp(CpuMatrix& out, const CpuSparseMatrix& b, real scaleAB, real scaleT) { - /// todo(tianbing), clean the code CHECK(!out.trans_) << "Not supported"; CHECK(!a.isTransposed()) << "Not supported"; CHECK(scaleT == 0 || scaleT == 1); CHECK_EQ(scaleAB, static_cast(1.0)); + if (!b.isTransposed()) { /// b is not Transpose + CHECK(b.getHeight() == a.getWidth() && a.getHeight() == out.getHeight() && + b.getWidth() == out.getWidth()); + } else { + CHECK(b.getHeight() == out.getWidth() && a.getHeight() == out.getHeight() && + b.getWidth() == a.getWidth()); + } + if (scaleT == 0) { + out.zeroMem(); + } real* A = const_cast(a.getData()); real* B = const_cast(b.getValue()); real* C = out.getData(); int* rows = b.getRows(); int* cols = b.getCols(); - if (scaleT == 0) { - out.zeroMem(); - } - /// todo(tianbing), clean the code + /// b.getFormat() == SPARSE_CSC if (b.getFormat() == SPARSE_CSC) { - if (!b.isTransposed()) { - size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), m); - CHECK_EQ(a.getHeight(), out.height_); - CHECK_EQ(b.getWidth(), out.width_); - - if (b.getValueType() == NO_VALUE) { - for (size_t j = 0; j < b.getWidth(); ++j) { - int start = b.getColStartIdx(j); - int end = b.getColStartIdx(j + 1); - for (int i = start; i < end; ++i) { - colVecAddTo( - C + j, A + rows[i], out.height_, out.width_, a.getWidth()); - } - } - } else if (b.getValueType() == FLOAT_VALUE) { - for (size_t j = 0; j < b.getWidth(); ++j) { - int start = b.getColStartIdx(j); - int end = b.getColStartIdx(j + 1); - for (int i = start; i < end; ++i) { - colVecAddTo(C + j, - A + rows[i], - B[i], - out.height_, - out.width_, - a.getWidth()); - } - } - } - } else /*if (b.isTransposed())*/ { - size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), out.width_); - CHECK_EQ(a.getHeight(), out.height_); - CHECK_EQ(b.getWidth(), m); - if (b.getValueType() == NO_VALUE) { - for (size_t i = 0; i < b.getWidth(); ++i) { - int start = b.getColStartIdx(i); - int end = b.getColStartIdx(i + 1); - for (int j = start; j < end; ++j) { - colVecAddTo( - C + rows[j], A + i, out.height_, out.width_, a.getWidth()); - } - } - } else if (b.getValueType() == FLOAT_VALUE) { - for (size_t i = 0; i < b.getWidth(); ++i) { - int start = b.getColStartIdx(i); - int end = b.getColStartIdx(i + 1); - for (int j = start; j < end; ++j) { - colVecAddTo(C + rows[j], - A + i, - B[j], - out.height_, - out.width_, - a.getWidth()); - } - } + for (size_t j = 0; j < b.getWidth(); ++j) { + int start = b.getColStartIdx(j); + int end = b.getColStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo(!b.isTransposed() ? C + j : C + rows[i], + !b.isTransposed() ? A + rows[i] : A + j, + (b.getValueType() == NO_VALUE) ? (real)1.0 : B[i], + out.getHeight(), + out.getWidth(), + a.getWidth()); } } - } else { - if (!b.isTransposed()) { - size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), m); - CHECK_EQ(a.getHeight(), out.height_); - CHECK_EQ(b.getWidth(), out.width_); - - if (b.getValueType() == NO_VALUE) { - for (size_t j = 0; j < b.getHeight(); ++j) { - int start = b.getRowStartIdx(j); - int end = b.getRowStartIdx(j + 1); - for (int i = start; i < end; ++i) { - colVecAddTo( - C + cols[i], A + j, out.height_, out.width_, a.getWidth()); - } - } - } else if (b.getValueType() == FLOAT_VALUE) { - for (size_t j = 0; j < b.getHeight(); ++j) { - int start = b.getRowStartIdx(j); - int end = b.getRowStartIdx(j + 1); - for (int i = start; i < end; ++i) { - colVecAddTo(C + cols[i], - A + j, - B[i], - out.height_, - out.width_, - a.getWidth()); - } - } - } - } else /*if (b.isTransposed())*/ { - size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), out.width_); - CHECK_EQ(a.getHeight(), out.height_); - CHECK_EQ(b.getWidth(), m); - if (b.getValueType() == NO_VALUE) { - for (size_t i = 0; i < b.getHeight(); ++i) { - int start = b.getRowStartIdx(i); - int end = b.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - colVecAddTo( - C + i, A + cols[j], out.height_, out.width_, a.getWidth()); - } - } - } else if (b.getValueType() == FLOAT_VALUE) { - for (size_t i = 0; i < b.getHeight(); ++i) { - int start = b.getRowStartIdx(i); - int end = b.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - colVecAddTo(C + i, - A + cols[j], - B[j], - out.height_, - out.width_, - a.getWidth()); - } - } + return; + } + + /// b.getFormat() == SPARSE_CSR + if (b.getFormat() == SPARSE_CSR) { + for (size_t j = 0; j < b.getHeight(); ++j) { + int start = b.getRowStartIdx(j); + int end = b.getRowStartIdx(j + 1); + for (int i = start; i < end; ++i) { + colVecAddTo(!b.isTransposed() ? C + cols[i] : C + j, + !b.isTransposed() ? A + j : A + cols[i], + (b.getValueType() == NO_VALUE) ? (real)1.0 : B[i], + out.getHeight(), + out.getWidth(), + a.getWidth()); } } + return; } } diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index 3c4654b9b27574..09d2a764911f18 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -19,154 +19,147 @@ limitations under the License. */ namespace paddle { /** - * out = scale_t * out + scale_ab * (a * b) + * out = scaleT * out + scaleAB * (a * b) * out : output matrix, M * N */ template <> void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuMatrix& b, - real scale_ab, - real scale_t) { - CHECK(!out.isTransposed()) << "Not supported"; - + real scaleAB, + real scaleT) { + CHECK(!out.isTransposed()) << "Transpose not supported for out matrix"; if (!a.isTransposed() && !b.isTransposed()) { - /// a : M * K, b: K * N - CHECK_EQ(out.width_, b.width_); - CHECK_EQ(out.height_, a.height_); - CHECK_EQ(a.width_, b.height_); + /// a : M * K, b: K * N + CHECK(out.getWidth() == b.getWidth() && + out.getHeight() == a.getHeight() && + a.getWidth() == b.getHeight()); } else if (a.isTransposed() && !b.isTransposed()) { - /// a : K * M, b : K * N - CHECK_EQ(out.width_, b.width_); - CHECK_EQ(out.height_, a.width_); - CHECK_EQ(a.height_, b.height_); + /// a : K * M, b : K * N + CHECK(out.getWidth() == b.getWidth() && + out.getHeight() == a.getWidth() && + a.getHeight() == b.getHeight()); } else if (!a.isTransposed() && b.isTransposed()) { - /// a: M * K, b : N * K - CHECK_EQ(out.width_, b.height_); - CHECK_EQ(out.height_, a.height_); - CHECK_EQ(a.width_, b.width_); + /// a: M * K, b : N * K + CHECK(out.getWidth() == b.getHeight() && + out.getHeight() == a.getHeight() && + a.getWidth() == b.getWidth()); } else { - LOG(FATAL) << "Is not supported"; + LOG(FATAL) << "Not support for both a and b are Transposed Matrices"; } - real* a_data = a.data_; - real* b_data = b.data_; - real* out_data = out.data_; - int dim_m = out.getHeight(); - int dim_n = out.getWidth(); - int dim_k = !a.isTransposed() ? a.width_ : a.height_; - int lda = a.getStride(); - int ldb = b.getStride(); - int ldc = out.getStride(); - hl_trans_op_t trans_a = !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T; - hl_trans_op_t trans_b = !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T; - - hl_matrix_mul(a_data, - trans_a, - b_data, - trans_b, - out_data, - dim_m, - dim_n, - dim_k, - scale_ab, - scale_t, - lda, - ldb, - ldc); + real* aData = const_cast(a.getData()); + real* bData = const_cast(b.getData()); + real* outData = const_cast(out.getData()); + hl_matrix_mul(aData, + !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T, + bData, + !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T, + outData, + out.getHeight(), + out.getWidth(), + !a.isTransposed() ? a.getWidth() : a.getHeight(), + scaleAB, + scaleT, + a.getStride(), + b.getStride(), + out.getStride()); } /** - * out = scale_t * out + scale_ab * (a * b) + * out = scaleT * out + scaleAB * (a * b) * out : M * N */ template <> void MulOp(GpuMatrix& out, const GpuSparseMatrix& a, const GpuMatrix& b, - real scale_ab, - real scale_t) { + real scaleAB, + real scaleT) { CHECK(out.isContiguous()); CHECK(b.isContiguous()); - CHECK(b.useGpu_ == true) << "Matrix type are not equal"; - CHECK(!out.trans_ && !b.trans_) << "not supported"; - if (!a.trans_) { + CHECK(b.useGpu_) << "Matrix type are not equal"; + CHECK(!out.isTransposed() && !b.isTransposed()) << "not supported"; + if (!a.isTransposed()) { /// a: M * K, b: K * N - CHECK(out.width_ == b.width_ && out.height_ == a.height_ - && a.width_ == b.height_) << "Matrix dimensions are not equal"; + CHECK(out.getWidth() == b.getWidth() && out.getHeight() == a.getHeight() + && a.getWidth() == b.getHeight()) << "Matrix dimensions are not equal"; } else { /// a: K * M, transpose, b: K * N - CHECK(out.width_ == b.width_ && out.height_ == a.width_ - && a.height_ == b.height_) << "Matrix dimensions are not equal"; + CHECK(out.getWidth() == b.getWidth() && out.getHeight() == a.getWidth() + && a.getHeight() == b.getHeight()) << "Matrix dimensions are not equal"; } - hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N; - hl_sparse_matrix_s a_data = a.sMatrix_.get(); - real* b_data = b.data_; - real* out_data = out.data_; - hl_matrix_csr_mul_dense(a_data, - a_trans, - b_data, + hl_trans_op_t aTrans = a.isTransposed() ? HPPL_OP_T : HPPL_OP_N; + hl_sparse_matrix_s aData = a.sMatrix_.get(); + real* bData = const_cast(b.getData()); + real* outData = const_cast(out.getData()); + hl_matrix_csr_mul_dense(aData, + aTrans, + bData, HPPL_OP_N, - out_data, - out.height_, - out.width_, - b.height_, - scale_ab, - scale_t); + outData, + out.getHeight(), + out.getWidth(), + b.getHeight(), + scaleAB, + scaleT); } /** - * out = scale_t * out + scale_ab * (a * b) + * out = scaleT * out + scaleAB * (a * b) * out : M * N */ template <> void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuSparseMatrix& b, - real scale_ab, - real scale_t) { + real scaleAB, + real scaleT) { CHECK(out.isContiguous()); CHECK(a.isContiguous()); - CHECK(a.useGpu_ == true) << "Matrix type are not equal"; - - hl_sparse_matrix_s b_data = b.sMatrix_.get(); - real* a_data = a.data_; - real* out_data = out.data_; - hl_trans_op_t trans_b = b.trans_ ? HPPL_OP_T : HPPL_OP_N; - if (!b.trans_) { - /// a : M * K, b : K * N - CHECK(out.width_ == b.width_ && - out.height_ == a.height_ && a.width_ == b.height_) - << "Matrix dimensions are not equal"; + CHECK(a.useGpu_) << "Matrix type are not equal"; + if (!b.isTransposed()) { + /// a : M * K, b : K * N + CHECK(out.getWidth() == b.getWidth() && + out.getHeight() == a.getHeight() && + a.getWidth() == b.getHeight()) + << "Matrix dimensions are not equal"; } else { - /// a : M * K, b : N * K, transpose - CHECK(out.width_ == b.height_ && - out.height_ == a.height_ && a.width_ == b.width_) - << "Matrix dimensions are not equal"; + /// a : M * K, b : N * K, transpose + CHECK(out.getWidth() == b.getHeight() && + out.getHeight() == a.getHeight() && + a.getWidth() == b.getWidth()) + << "Matrix dimensions are not equal"; } + + hl_trans_op_t bTrans = b.isTransposed() ? HPPL_OP_T : HPPL_OP_N; + hl_sparse_matrix_s bData = b.sMatrix_.get(); + real* aData = const_cast(a.getData()); + real* outData = const_cast(out.getData()); + if (b.format_ == SPARSE_CSC) { - hl_matrix_dense_mul_csc(a_data, + hl_matrix_dense_mul_csc(aData, HPPL_OP_N, - b_data, - trans_b, - out_data, - out.height_, - out.width_, - a.width_, - scale_ab, - scale_t); + bData, + bTrans, + outData, + out.getHeight(), + out.getWidth(), + a.getWidth(), + scaleAB, + scaleT); } else { - hl_matrix_dense_mul_csr(a_data, + hl_matrix_dense_mul_csr(aData, HPPL_OP_N, - b_data, - trans_b, - out_data, - out.height_, - out.width_, - a.width_, - scale_ab, - scale_t); + bData, + bTrans, + outData, + out.getHeight(), + out.getWidth(), + a.getWidth(), + scaleAB, + scaleT); } } @@ -174,38 +167,36 @@ template <> void MulOp(GpuSparseMatrix& out, const GpuMatrix& a, const GpuMatrix& b, - real scale_ab, - real scale_t) { - /// todo(tianbing), clean the code - CHECK(a.useGpu_ && b.useGpu_) << "type not match"; - CHECK(!out.trans_) << "trans not supported"; - real* a_data = const_cast(a.getData()); - real* b_data = const_cast(b.getData()); - hl_sparse_matrix_s out_data = out.sMatrix_.get(); - hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N; - hl_trans_op_t b_trans = b.trans_ ? HPPL_OP_T : HPPL_OP_N; - - if (!a.trans_ && !b.trans_) { - CHECK(out.height_ == a.getHeight()); - CHECK(out.width_ == b.getWidth()); - CHECK(a.getWidth() == b.getHeight()); - } else if (a.trans_ && !b.trans_) { - CHECK(out.height_ == a.getWidth()); - CHECK(out.width_ == b.getWidth()); - CHECK(a.getHeight() == b.getHeight()); - } else if (!a.trans_ && b.trans_) { - CHECK(out.height_ == a.getHeight()); - CHECK(out.width_ == b.getHeight()); - CHECK(a.getWidth() == b.getWidth()); + real scaleAB, + real scaleT) { + CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; + CHECK(!out.isTransposed()) << "Transpose is not supported for out matrix"; + + if (!a.isTransposed() && !b.isTransposed()) { + CHECK(out.getHeight() == a.getHeight() && + out.getWidth() == b.getWidth() && + a.getWidth() == b.getHeight()); + } else if (a.isTransposed() && !b.isTransposed()) { + CHECK(out.getHeight() == a.getWidth() && + out.getWidth() == b.getWidth() && + a.getHeight() == b.getHeight()); + } else if (!a.isTransposed() && b.isTransposed()) { + CHECK(out.getHeight() == a.getHeight() && + out.getWidth() == b.getHeight() && + a.getWidth() == b.getWidth()); } else { - LOG(INFO) << "Not support"; + LOG(FATAL) << "Not support for both a and b are Transposed Matrices"; } - int dim_m = out.height_; - int dim_n = out.width_; - int dim_k = !b.trans_ ? b.getHeight() : b.getWidth(); - hl_sparse_matrix_mul( - a_data, a_trans, b_data, b_trans, out_data, - dim_m, dim_n, dim_k, scale_ab, scale_t); + + hl_trans_op_t aTrans = a.isTransposed() ? HPPL_OP_T : HPPL_OP_N; + hl_trans_op_t bTrans = b.isTransposed() ? HPPL_OP_T : HPPL_OP_N; + int dimK = !b.isTransposed() ? b.getHeight() : b.getWidth(); + real* aData = const_cast(a.getData()); + real* bData = const_cast(b.getData()); + hl_sparse_matrix_s outData = out.sMatrix_.get(); + + hl_sparse_matrix_mul(aData, aTrans, bData, bTrans, outData, + out.getHeight(), out.getWidth(), dimK, scaleAB, scaleT); } } // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 630070b845a9af..965ffea20ce7fb 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -76,12 +76,12 @@ void testDDDMatrix(bool transa, bool transb, int dimM, int dimN, int dimK) { TEST(Matrix, DDDMul) { LOG(INFO) << "test for dense = dense * dense matrix"; - for (auto transa : {false, true}) { - for (auto transb : {false, true}) { - for (auto dimM : {1, 10, 100}) { - for (auto dimN : {1, 10}) { - for (auto dimK : {8}) { - if (true == transa && true == transb) { + for (const auto transa : {false, true}) { + for (const auto transb : {false, true}) { + for (const auto dimM : {1, 10, 100}) { + for (const auto dimN : {1, 10}) { + for (const auto dimK : {8}) { + if (transa && transb) { continue; } VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') @@ -89,7 +89,6 @@ TEST(Matrix, DDDMul) { << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK; - testDDDMatrix(transa, transb, dimM, dimN, dimK); } } From 9ade63e61cfb50753714118864d0f6680c025f96 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 18 Jan 2017 22:19:44 -0800 Subject: [PATCH 101/370] clean code a little bit. --- paddle/function/MulOp.cpp | 224 ++++++++++++-------------------------- 1 file changed, 72 insertions(+), 152 deletions(-) diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 1117944a4ed994..b911ccd13b607a 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -56,7 +56,16 @@ void MulOp(CpuSparseMatrix& out, /// todo(tianbing), clean the code CHECK(!out.isTransposed()) << "Not supported"; CHECK_EQ(out.getValueType(), FLOAT_VALUE); + CHECK(!a.isTransposed() || !b.isTransposed()) + << "Not support both a and b are transpose matrices"; + if (!a.isTransposed() && b.isTransposed()) { + CHECK(out.getFormat() != SPARSE_CSC) + << "Not supported CSC format when a is not trans and b is trans"; + } + if (scaleT == 0) { + out.zeroMem(); + } const real* A = a.getData(); const real* B = b.getData(); real* C = out.getValue(); @@ -64,15 +73,11 @@ void MulOp(CpuSparseMatrix& out, int* cols = out.getCols(); size_t height = out.getHeight(); size_t width = out.getWidth(); - if (scaleT == 0) { - out.zeroMem(); - } if (!a.isTransposed() && !b.isTransposed()) { + CHECK(b.getHeight() == a.getWidth() && a.getHeight() == height && + b.getWidth() == width); size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), m); - CHECK_EQ(a.getHeight(), height); - CHECK_EQ(b.getWidth(), width); if (out.getFormat() == SPARSE_CSC) { for (size_t i = 0; i < width; i++) { size_t start = out.getColStartIdx(i); @@ -86,26 +91,27 @@ void MulOp(CpuSparseMatrix& out, C[j] = scaleAB * sum + scaleT * C[j]; } } - } else { + } else { /// out.getFormat() == SPARSE_CSR for (size_t i = 0; i < height; i++) { size_t start = out.getRowStartIdx(i); size_t end = out.getRowStartIdx(i + 1); for (size_t j = start; j < end; j++) { real sum = 0; size_t colIdx = cols[j]; - for (size_t k = 0; k < m; k++) { + for (size_t k = 0; k < a.getWidth(); k++) { sum += A[i * m + k] * B[k * width + colIdx]; } C[j] = scaleAB * sum + scaleT * C[j]; } } } - } else if (a.isTransposed() && !b.isTransposed()) { - size_t m = a.getHeight(); - CHECK_EQ(m, b.getHeight()); - CHECK_EQ(b.getWidth(), width); - CHECK_EQ(a.getWidth(), height); + return; + } + if (a.isTransposed() && !b.isTransposed()) { + CHECK(a.getHeight() == b.getHeight() && b.getWidth() == width && + a.getWidth() == height); + size_t m = a.getHeight(); if (out.getFormat() == SPARSE_CSC) { for (size_t i = 0; i < width; i++) { size_t start = out.getColStartIdx(i); @@ -119,25 +125,27 @@ void MulOp(CpuSparseMatrix& out, C[j] = scaleAB * sum + scaleT * C[j]; } } - } else { + } else { /// out.getFormat() == SPARSE_CSR for (size_t i = 0; i < height; i++) { int start = out.getRowStartIdx(i); int end = out.getRowStartIdx(i + 1); for (int j = start; j < end; j++) { real sum = 0; size_t colIdx = cols[j]; - for (size_t k = 0; k < m; k++) { + for (size_t k = 0; k < a.getHeight(); k++) { sum += A[k * height + i] * B[k * width + colIdx]; } C[j] = scaleAB * sum + scaleT * C[j]; } } } - } else if (!a.isTransposed() && b.isTransposed()) { + return; + } + + if (!a.isTransposed() && b.isTransposed()) { + CHECK(b.getWidth() == a.getWidth() && a.getHeight() == height && + b.getHeight() == width); size_t m = a.getWidth(); - CHECK_EQ(b.getWidth(), m); - CHECK_EQ(a.getHeight(), height); - CHECK_EQ(b.getHeight(), width); if (out.getFormat() == SPARSE_CSR) { for (size_t i = 0; i < height; i++) { size_t start = out.getRowStartIdx(i); @@ -151,12 +159,8 @@ void MulOp(CpuSparseMatrix& out, C[j] = scaleAB * sum + scaleT * C[j]; } } - } else { - LOG(FATAL) << "Not supported csc format " - "when a is not trans and b is trans"; } - } else { - LOG(FATAL) << "Not supported"; + return; } } @@ -166,159 +170,75 @@ void MulOp(CpuMatrix& out, const CpuMatrix& b, real scaleAB, real scaleT) { - /// todo(tianbing), clean the code - CHECK(!out.isTransposed()) << "Not supported"; - CBLAS_TRANSPOSE aTrans = CblasNoTrans; - size_t aRow = a.getHeight(); - size_t aCol = a.getWidth(); - CBLAS_TRANSPOSE bTrans = CblasNoTrans; - size_t bRow = b.getHeight(); - size_t bCol = b.getWidth(); - if (a.isTransposed()) { - aTrans = CblasTrans; - aRow = a.getWidth(); - aCol = a.getHeight(); - } - if (b.isTransposed()) { - bTrans = CblasTrans; - bRow = b.getWidth(); - bCol = b.getHeight(); - } + CHECK(!out.isTransposed()) << "out matrix transpose not supported"; + CBLAS_TRANSPOSE aTrans = a.isTransposed() ? CblasTrans : CblasNoTrans; + size_t aRow = a.isTransposed() ? a.getWidth() : a.getHeight(); + size_t aCol = a.isTransposed() ? a.getHeight() : a.getWidth(); + CBLAS_TRANSPOSE bTrans = b.isTransposed() ? CblasTrans : CblasNoTrans; + size_t bRow = b.isTransposed() ? b.getWidth() : b.getHeight(); + size_t bCol = b.isTransposed() ? b.getHeight() : b.getWidth(); /// C = A * B, for matrix format CHECK_EQ(aCol, bRow); CHECK_EQ(aRow, out.getHeight()); CHECK_EQ(bCol, out.getWidth()); - const real* A = a.getData(); - const real* B = b.getData(); - real* C = out.getData(); - - int M = out.getHeight(); - int N = out.getWidth(); - int K = aCol; - int lda = a.getStride(); - int ldb = b.getStride(); - int ldc = out.getStride(); - - GEMM(aTrans, bTrans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc); - - VLOG(2) << " A[0]=" << A[0] << " A[1]=" << A[1] << " B[0]=" << B[0] - << " B[1]=" << B[1] << " C[0]=" << C[0] << " C[1]=" << C[1]; + GEMM(aTrans, + bTrans, + out.getHeight(), + out.getWidth(), + aCol, + scaleAB, + a.getData(), + a.getStride(), + b.getData(), + b.getStride(), + scaleT, + out.getData(), + out.getStride()); } -static ThreadLocal> threadLocalColArray; - template <> void MulOp(CpuMatrix& out, const CpuSparseMatrix& a, const CpuMatrix& b, real scaleAB, real scaleT) { - /// todo(tianbing), clean the code CHECK(!out.isTransposed()) << "Not supported"; CHECK(!b.isTransposed()) << "Not supported"; CHECK(scaleT == 0 || scaleT == 1) << "Not support"; CHECK_EQ(scaleAB, static_cast(1.0)) << "Not supported"; CHECK_EQ(a.getFormat(), SPARSE_CSR) << "Not supported"; - const real* B = b.getData(); - real* C = out.getData(); - size_t height = out.getHeight(); - size_t width = out.getWidth(); - int* cols = a.getCols(); - real* values = a.getValue(); + if (!a.isTransposed()) { + CHECK(b.getHeight() == a.getWidth() && a.getHeight() == out.getHeight() && + b.getWidth() == out.getWidth()); + } else { + CHECK(b.getHeight() == a.getHeight() && a.getWidth() == out.getHeight() && + b.getWidth() == out.getWidth()); + } if (scaleT == 0) { out.zeroMem(); } + const real* B = b.getData(); + real* C = out.getData(); + if (out.getWidth() % 32 == 0) { + CHECK_EQ((size_t)B % 32, 0UL); + CHECK_EQ((size_t)C % 32, 0UL); + } - if (!a.isTransposed()) { - size_t m = a.getWidth(); - CHECK_EQ(b.getHeight(), m); - CHECK_EQ(a.getHeight(), height); - CHECK_EQ(b.getWidth(), width); - - if (a.getValueType() == NO_VALUE) { - if (width % 32 == 0) { // use libaddto - CHECK_EQ((size_t)B % 32, 0UL); - CHECK_EQ((size_t)C % 32, 0UL); - auto& colArray = *threadLocalColArray; - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - size_t colNum = end - start; - colArray.resize(colNum); - for (int j = 0; j < end - start; ++j) { - colArray[j] = const_cast(b).getRow(cols[j + start]); - } - simd::batchAddTo(out.getRow(i), &colArray[0], colNum, width); - } - - } else { - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - vecAddTo(out.getRow(i), - const_cast(b).getRow(cols[j]), - width); - } - } - } - } else if (a.getValueType() == FLOAT_VALUE) { - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - vecAddTo(out.getRow(i), - const_cast(b).getRow(cols[j]), - values[j], - width); - } - } - } - } else /*if (a->isTransposed())*/ { - size_t m = a.getHeight(); - CHECK_EQ(b.getHeight(), m); - CHECK_EQ(a.getWidth(), height); - CHECK_EQ(b.getWidth(), width); - if (a.getValueType() == NO_VALUE) { - if (width % 32 == 0) { // use libaddto - CHECK_EQ((size_t)B % 32, 0UL); - CHECK_EQ((size_t)C % 32, 0UL); - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - simd::addTo(out.getRow(cols[j]), - const_cast(b).getRow(i), - width); - } - } - - } else { - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - vecAddTo(out.getRow(cols[j]), - const_cast(b).getRow(i), - width); - } - } - } - } else if (a.getValueType() == FLOAT_VALUE) { - for (size_t i = 0; i < a.getHeight(); ++i) { - const int start = a.getRowStartIdx(i); - const int end = a.getRowStartIdx(i + 1); - for (int j = start; j < end; ++j) { - vecAddTo(out.getRow(cols[j]), - const_cast(b).getRow(i), - values[j], - width); - } - } + int* cols = a.getCols(); + real* values = a.getValue(); + for (size_t i = 0; i < a.getHeight(); ++i) { + const int start = a.getRowStartIdx(i); + const int end = a.getRowStartIdx(i + 1); + for (int j = start; j < end; ++j) { + vecAddTo(!a.isTransposed() ? out.getRow(i) : out.getRow(cols[j]), + !a.isTransposed() ? const_cast(b).getRow(cols[j]) + : const_cast(b).getRow(i), + (a.getValueType() == FLOAT_VALUE) ? values[j] : (real)1.0, + out.getWidth()); } } } From 316bf75afd7676b3bb4f5d53af44a6b1d88203bf Mon Sep 17 00:00:00 2001 From: xutianbing Date: Thu, 19 Jan 2017 13:21:07 -0800 Subject: [PATCH 102/370] clean code in function/MulOp.cpp --- paddle/function/MulOp.cpp | 145 +++++++++++++------------------------- paddle/function/MulOp.h | 2 - 2 files changed, 48 insertions(+), 99 deletions(-) diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index b911ccd13b607a..bd3bc5c087d669 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -26,22 +26,16 @@ limitations under the License. */ #endif namespace { -inline void vecAddTo(real* a, const real* b, size_t len) { - for (unsigned int i = 0; i < len; ++i) { - a[i] += b[i]; - } -} - inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { for (unsigned int i = 0; i < len; ++i) { - a[i] += scaleB * b[i]; + a[i] += (1.0 == scaleB) ? b[i] : scaleB * b[i]; } } inline void colVecAddTo( real* a, real* b, real c, size_t len, size_t aWidth, size_t bWidth) { for (unsigned int i = 0; i < len; ++i) { - a[i * aWidth] += b[i * bWidth] * c; + a[i * aWidth] += (1.0 == c) ? b[i * bWidth] : b[i * bWidth] * c; } } } // namespace @@ -53,15 +47,19 @@ void MulOp(CpuSparseMatrix& out, const CpuMatrix& b, real scaleAB, real scaleT) { - /// todo(tianbing), clean the code CHECK(!out.isTransposed()) << "Not supported"; CHECK_EQ(out.getValueType(), FLOAT_VALUE); CHECK(!a.isTransposed() || !b.isTransposed()) << "Not support both a and b are transpose matrices"; - if (!a.isTransposed() && b.isTransposed()) { - CHECK(out.getFormat() != SPARSE_CSC) - << "Not supported CSC format when a is not trans and b is trans"; - } + + size_t height = out.getHeight(); + size_t width = out.getWidth(); + size_t aRow = !a.isTransposed() ? a.getHeight() : a.getWidth(); + size_t aCol = !a.isTransposed() ? a.getWidth() : a.getHeight(); + size_t bRow = !b.isTransposed() ? b.getHeight() : b.getWidth(); + size_t bCol = !b.isTransposed() ? b.getWidth() : b.getHeight(); + /// C = A * B, for matrix format + CHECK(aCol == bRow && aRow == height && bCol == width); if (scaleT == 0) { out.zeroMem(); @@ -71,93 +69,46 @@ void MulOp(CpuSparseMatrix& out, real* C = out.getValue(); int* rows = out.getRows(); int* cols = out.getCols(); - size_t height = out.getHeight(); - size_t width = out.getWidth(); - if (!a.isTransposed() && !b.isTransposed()) { - CHECK(b.getHeight() == a.getWidth() && a.getHeight() == height && - b.getWidth() == width); - size_t m = a.getWidth(); - if (out.getFormat() == SPARSE_CSC) { - for (size_t i = 0; i < width; i++) { - size_t start = out.getColStartIdx(i); - size_t end = out.getColStartIdx(i + 1); - for (size_t j = start; j < end; j++) { - real sum = 0; - size_t rowIdx = rows[j]; - for (size_t k = 0; k < m; k++) { - sum += A[rowIdx * m + k] * B[k * width + i]; - } - C[j] = scaleAB * sum + scaleT * C[j]; - } - } - } else { /// out.getFormat() == SPARSE_CSR - for (size_t i = 0; i < height; i++) { - size_t start = out.getRowStartIdx(i); - size_t end = out.getRowStartIdx(i + 1); - for (size_t j = start; j < end; j++) { - real sum = 0; - size_t colIdx = cols[j]; - for (size_t k = 0; k < a.getWidth(); k++) { - sum += A[i * m + k] * B[k * width + colIdx]; - } - C[j] = scaleAB * sum + scaleT * C[j]; - } - } - } - return; - } - - if (a.isTransposed() && !b.isTransposed()) { - CHECK(a.getHeight() == b.getHeight() && b.getWidth() == width && - a.getWidth() == height); - size_t m = a.getHeight(); - if (out.getFormat() == SPARSE_CSC) { - for (size_t i = 0; i < width; i++) { - size_t start = out.getColStartIdx(i); - size_t end = out.getColStartIdx(i + 1); - for (size_t j = start; j < end; j++) { - real sum = 0; - size_t rowIdx = rows[j]; - for (size_t k = 0; k < m; k++) { - sum += A[k * height + rowIdx] * B[k * width + i]; - } - C[j] = scaleAB * sum + scaleT * C[j]; - } - } - } else { /// out.getFormat() == SPARSE_CSR - for (size_t i = 0; i < height; i++) { - int start = out.getRowStartIdx(i); - int end = out.getRowStartIdx(i + 1); - for (int j = start; j < end; j++) { - real sum = 0; - size_t colIdx = cols[j]; - for (size_t k = 0; k < a.getHeight(); k++) { - sum += A[k * height + i] * B[k * width + colIdx]; - } - C[j] = scaleAB * sum + scaleT * C[j]; + /// SPARSE_CSC, {a any, b not trans} + if (out.getFormat() == SPARSE_CSC) { + /// b not trans and a any + CHECK(!b.isTransposed()); + size_t m = !a.isTransposed() ? a.getWidth() : a.getHeight(); + for (size_t i = 0; i < width; i++) { + size_t start = out.getColStartIdx(i); + size_t end = out.getColStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t rowIdx = rows[j]; + for (size_t k = 0; k < m; k++) { + sum += + (!a.isTransposed() ? A[rowIdx * m + k] : A[k * height + rowIdx]) * + B[k * width + i]; } + C[j] = scaleAB * sum + scaleT * C[j]; } } return; } - if (!a.isTransposed() && b.isTransposed()) { - CHECK(b.getWidth() == a.getWidth() && a.getHeight() == height && - b.getHeight() == width); + /// SPARSE_CSR, {a any, b not trans} or {a not trans, b trans} + if (out.getFormat() == SPARSE_CSR) { + /// a and b can not both transpose + CHECK(!(a.isTransposed() && b.isTransposed())); size_t m = a.getWidth(); - if (out.getFormat() == SPARSE_CSR) { - for (size_t i = 0; i < height; i++) { - size_t start = out.getRowStartIdx(i); - size_t end = out.getRowStartIdx(i + 1); - for (size_t j = start; j < end; j++) { - real sum = 0; - size_t colIdx = cols[j]; - for (size_t k = 0; k < m; k++) { - sum += A[i * m + k] * B[colIdx * m + k]; - } - C[j] = scaleAB * sum + scaleT * C[j]; + for (size_t i = 0; i < height; i++) { + size_t start = out.getRowStartIdx(i); + size_t end = out.getRowStartIdx(i + 1); + for (size_t j = start; j < end; j++) { + real sum = 0; + size_t colIdx = cols[j]; + for (size_t k = 0; k < m; k++) { + sum += + (!a.isTransposed() ? A[i * m + k] : A[k * height + i]) * + (!b.isTransposed() ? B[k * width + colIdx] : B[colIdx * m + k]); } + C[j] = scaleAB * sum + scaleT * C[j]; } } return; @@ -330,11 +281,11 @@ class MulFunc : public FunctionBase { CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].getArgType(), ADD_TO); - auto out_mat = outputs[0].matrix(); + auto outMat = outputs[0].matrix(); /// matrix = matrix * matrix if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { - MulOp(out_mat, + MulOp(outMat, inputs[0].matrix(), inputs[1].matrix(), alpha_, @@ -345,7 +296,7 @@ class MulFunc : public FunctionBase { /// matrix = matrix * sparse matrix if (!inputs[0].isSparseArg() && inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { - MulOp(out_mat, + MulOp(outMat, inputs[0].matrix(), inputs[1].sparse().SparseMatrix(), alpha_, @@ -356,7 +307,7 @@ class MulFunc : public FunctionBase { /// matrix = sparse matrix * matrix if (inputs[0].isSparseArg() && !inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { - MulOp(out_mat, + MulOp(outMat, inputs[0].sparse().SparseMatrix(), inputs[1].matrix(), alpha_, @@ -365,10 +316,10 @@ class MulFunc : public FunctionBase { } /// sparse matrix = matrix * matrix - auto out_sparse_mat = outputs[0].sparse().SparseMatrix(); + auto outSparseMat = outputs[0].sparse().SparseMatrix(); if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && outputs[0].isSparseArg()) { - MulOp(out_sparse_mat, + MulOp(outSparseMat, inputs[0].matrix(), inputs[1].matrix(), alpha_, diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index 23bfd0fa932178..b7b1f56af10375 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -15,8 +15,6 @@ limitations under the License. */ #pragma once #include "Function.h" -/// todo(tianbing), delete -#include #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" From 077f936aa5d3ead02880256774189ee8c47999f6 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Sat, 21 Jan 2017 21:24:23 -0800 Subject: [PATCH 103/370] Support SparseMatrixArg unit test using Daoyuan's new Function Test. --- paddle/function/BufferArg.cpp | 2 - paddle/function/BufferArg.h | 49 ++++++- paddle/function/FunctionTest.h | 98 ++++++++++--- paddle/function/MulOp.h | 2 + paddle/function/MulOpTest.cpp | 249 ++++++++++----------------------- 5 files changed, 203 insertions(+), 197 deletions(-) diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index 39773c76280ba3..a4f1c2d96417b3 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -33,7 +33,6 @@ SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), - /// todo(tianbing), make sure how to get NNZ nnz_(sparse.getElementCnt()), format_(sparse.getFormat()), type_(sparse.getValueType()) { @@ -44,7 +43,6 @@ SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) : BufferArg(sparse, argType), row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), - /// todo(tianbing), make sure how to get NNZ nnz_(sparse.getElementCnt()), format_(sparse.getFormat()), type_(sparse.getValueType()) { diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index eac3fe4420369b..17a4e4a6b0eec9 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -71,17 +71,24 @@ class BufferArg { public: BufferArg(ValueType valueType, const TensorShape& shape, - ArgType argType = UNSPECIFIED) + ArgType argType = UNSPECIFIED, + bool trans = false) : buf_(nullptr), valueType_(valueType), shape_(shape), - argType_(argType) {} + argType_(argType), + trans_(trans) {} BufferArg(void* buf, ValueType valueType, const TensorShape& shape, - ArgType argType = UNSPECIFIED) - : buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) {} + ArgType argType = UNSPECIFIED, + bool trans = false) + : buf_(buf), + valueType_(valueType), + shape_(shape), + argType_(argType), + trans_(trans) {} BufferArg(void* buf, ValueType valueType) : buf_(buf), valueType_(valueType) {} @@ -162,6 +169,7 @@ class BufferArg { ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } + bool isTransposed() const { return trans_; } bool isSparseArg() const { return TENSOR_SPARSE == bufferType_; } bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } @@ -175,6 +183,7 @@ class BufferArg { BufferType bufferType_{TENSOR_UNKNOWN}; ArgType argType_{UNSPECIFIED}; bool trans_{false}; + // todo(tianbing), add deviceType_ // leading dimensions. The size is dims_.size() // Dims lds_; }; @@ -267,8 +276,9 @@ class SparseMatrixArg : public BufferArg { size_t nnz, SparseFormat format, SparseValueType type, - ArgType argType = UNSPECIFIED) - : BufferArg(buf, valueType, shape, argType), + ArgType argType = UNSPECIFIED, + bool trans = false) + : BufferArg(buf, valueType, shape, argType, trans), row_(row), col_(col), nnz_(nnz), @@ -286,6 +296,33 @@ class SparseMatrixArg : public BufferArg { } } + SparseMatrixArg(ValueType valueType, + const TensorShape& shape, + size_t nnz, + SparseFormat format, + SparseValueType type, + ArgType argType = UNSPECIFIED, + bool trans = false) + : BufferArg(valueType, shape, argType, trans), + /// len of row_ : height + 1 (CSR), buf_ == nullptr + row_(format == SPARSE_CSR + ? BufferArg(VALUE_TYPE_INT32, TensorShape{shape[0] + 1}) + : BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})), + /// len of col_ : width + 1 (CSC), buf_ == nullptr + col_(format == SPARSE_CSR + ? BufferArg(VALUE_TYPE_INT32, TensorShape{nnz}) + : BufferArg(VALUE_TYPE_INT32, TensorShape{shape[1] + 1})), + nnz_(nnz), + format_(format), + type_(type) { + bufferType_ = TENSOR_SPARSE; + /// todo(tianbing) + /// valueType and shape_.ndims() == 2 need to check before + /// this constructor to make sure row_ and col_ are right + CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); + CHECK_EQ(shape_.ndims(), (size_t)2); + } + SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 24e7a36a43cfa8..9d38671bc3c5c9 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "Function.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" #include "paddle/math/Vector.h" #include "paddle/math/tests/TensorCheck.h" #include "paddle/testing/TestUtil.h" @@ -62,29 +64,41 @@ class FunctionCompare { cpuMemory_.emplace_back(std::make_shared(size)); gpuMemory_.emplace_back(std::make_shared(size)); - cpuInputs_.emplace_back(std::make_shared( - cpuMemory_.back()->getBuf(), input.valueType(), input.shape())); - gpuInputs_.emplace_back(std::make_shared( - gpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + cpuInputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + input.valueType(), + input.shape(), + UNSPECIFIED, + input.isTransposed())); + gpuInputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + input.valueType(), + input.shape(), + UNSPECIFIED, + input.isTransposed())); } // output need only contains shape, do not contains data. - void addOutputs(const BufferArg& output) { + void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) { size_t size = output.shape().getElements() * sizeOfValuType(output.valueType()); cpuMemory_.emplace_back(std::make_shared(size)); gpuMemory_.emplace_back(std::make_shared(size)); - cpuOutputs_.emplace_back( - std::make_shared(cpuMemory_.back()->getBuf(), - output.valueType(), - output.shape(), - ASSIGN_TO)); - gpuOutputs_.emplace_back( - std::make_shared(gpuMemory_.back()->getBuf(), - output.valueType(), - output.shape(), - ASSIGN_TO)); + cpuOutputs_.emplace_back(std::make_shared( + cpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + // todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO + argType, + output.isTransposed())); + gpuOutputs_.emplace_back(std::make_shared( + gpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + // todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO + argType, + output.isTransposed())); } void addInputs(const SequenceArg& input) { @@ -107,10 +121,36 @@ class FunctionCompare { // TODO: need be implemented. } + void addInputs(const SparseMatrixArg& input) { + cpuSparse_ = std::make_shared(input.shape()[0], + input.shape()[1], + input.nnz(), + input.dataType(), + input.dataFormat(), + input.isTransposed()); + + gpuSparse_ = std::make_shared(input.shape()[0], + input.shape()[1], + input.nnz(), + input.dataType(), + input.dataFormat(), + input.isTransposed()); + + /// init sparse matrix + hl_stream_t stream(HPPL_STREAM_1); + cpuSparse_->randomizeUniform(); + gpuSparse_->copyFrom(*cpuSparse_, stream); + hl_stream_synchronize(stream); + + cpuInputs_.emplace_back(std::make_shared(*cpuSparse_)); + gpuInputs_.emplace_back(std::make_shared(*gpuSparse_)); + } + void run() { // prepare cpu/gpu arguments initInputs(); + initOutputs(); // function calculate auto callFunction = [](FunctionBase* function, std::vector& inputs, @@ -129,7 +169,7 @@ class FunctionCompare { callFunction(cpuFunc_.get(), cpuInputs_, cpuOutputs_); callFunction(gpuFunc_.get(), gpuInputs_, gpuOutputs_); - // check outputs and inouts + // check outputs compareOutputs(); } @@ -140,6 +180,10 @@ class FunctionCompare { protected: void initInputs() { for (size_t i = 0; i < cpuInputs_.size(); i++) { + if (cpuInputs_[i]->isSparseArg()) { + continue; /// sparse matrix already init + } + initArg(*cpuInputs_[i]); // TODO: Need a BufferCopy used to copy from one BufferArg to another. @@ -152,6 +196,25 @@ class FunctionCompare { } } + void initOutputs() { + for (size_t i = 0; i < cpuOutputs_.size(); i++) { + if (cpuOutputs_[i]->isSparseArg()) { + LOG(INFO) << "output sparse matrix already init"; + continue; + } + + initArg(*cpuOutputs_[i]); + + // TODO: Need a BufferCopy used to copy from one BufferArg to another. + CpuVector cpuVector(cpuOutputs_[i]->shape().getElements(), + (real*)cpuOutputs_[i]->data()); + GpuVector gpuVector(gpuOutputs_[i]->shape().getElements(), + (real*)gpuOutputs_[i]->data()); + + gpuVector.copyFrom(cpuVector); + } + } + void compareOutputs() { for (size_t i = 0; i < cpuOutputs_.size(); i++) { // TODO, Need a BufferCheck used to compare the two buffers. @@ -159,7 +222,6 @@ class FunctionCompare { auto gpu = gpuOutputs_[i]; CpuVector cpuVector(cpu->shape().getElements(), (real*)cpu->data()); GpuVector gpuVector(cpu->shape().getElements(), (real*)gpu->data()); - autotest::TensorCheckErr(cpuVector, gpuVector); } } @@ -195,6 +257,8 @@ class FunctionCompare { std::vector cpuOutputs_; std::vector gpuInputs_; std::vector gpuOutputs_; + std::shared_ptr cpuSparse_; + std::shared_ptr gpuSparse_; }; } // namespace paddle diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index b7b1f56af10375..fda5b0924982e8 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -15,6 +15,8 @@ limitations under the License. */ #pragma once #include "Function.h" +/// todo(tianbing), delete it +#include #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 965ffea20ce7fb..7300a2014bd8ed 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -24,58 +24,39 @@ limitations under the License. */ using namespace paddle; // NOLINT /** - * C = alpha * C + beta * (A * B), A, B, C dense matrix + * C += A * B, A, B, C dense matrix * dense = dense * dense */ -void testDDDMatrix(bool transa, bool transb, int dimM, int dimN, int dimK) { - real alpha = 1.5; - real beta = 2.0; - - const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); - cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); - gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - - int heightA = (transa == false) ? dimM : dimK; - int widthA = (transa == false) ? dimK : dimM; - int heightB = (transb == false) ? dimK : dimN; - int widthB = (transb == false) ? dimN : dimK; - int heightC = dimM; - int widthC = dimN; - - auto cpuA = std::make_shared(heightA, widthA, transa); - auto cpuB = std::make_shared(heightB, widthB, transb); - auto cpuC = std::make_shared(heightC, widthC); - auto gpuA = std::make_shared(heightA, widthA, transa); - auto gpuB = std::make_shared(heightB, widthB, transb); - auto gpuC = std::make_shared(heightC, widthC); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - - BufferArgs cpuInputs; - BufferArgs cpuOutputs; - cpuInputs.addArg(*cpuA); - cpuInputs.addArg(*cpuB); - cpuOutputs.addArg(*cpuC, ADD_TO); - cpuFunc->calc(cpuInputs, cpuOutputs); - - BufferArgs gpuInputs; - BufferArgs gpuOutputs; - gpuInputs.addArg(*gpuA); - gpuInputs.addArg(*gpuB); - gpuOutputs.addArg(*gpuC, ADD_TO); - gpuFunc->calc(gpuInputs, gpuOutputs); - - autotest::TensorCheckErr(*cpuC, *gpuC); +void testFuncDDDMatrix( + bool transa, bool transb, size_t dimM, size_t dimN, size_t dimK) { + real alpha = 1.0; + real beta = 1.0; + size_t heightA = (transa == false) ? dimM : dimK; + size_t widthA = (transa == false) ? dimK : dimM; + size_t heightB = (transb == false) ? dimK : dimN; + size_t widthB = (transb == false) ? dimN : dimK; + size_t heightC = dimM; + size_t widthC = dimN; + // init Test object + FunctionCompare test("MulOp", + FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + // prepare input arguments + /// matrix A : HA * WA + test.addInputs(BufferArg( + VALUE_TYPE_FLOAT, TensorShape{heightA, widthA}, UNSPECIFIED, transa)); + /// matrix B: HB * WB + test.addInputs(BufferArg( + VALUE_TYPE_FLOAT, TensorShape{heightB, widthB}, UNSPECIFIED, transb)); + + /// output matrix C: HC * WC + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightC, widthC}), + ADD_TO); + // run Function + test.run(); } -TEST(Matrix, DDDMul) { - LOG(INFO) << "test for dense = dense * dense matrix"; +TEST(MulOp, DDDMatrixMul) { + LOG(INFO) << "function test for dense = dense * dense matrix"; for (const auto transa : {false, true}) { for (const auto transb : {false, true}) { for (const auto dimM : {1, 10, 100}) { @@ -89,7 +70,7 @@ TEST(Matrix, DDDMul) { << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK; - testDDDMatrix(transa, transb, dimM, dimN, dimK); + testFuncDDDMatrix(transa, transb, dimM, dimN, dimK); } } } @@ -101,71 +82,33 @@ TEST(Matrix, DDDMul) { * C += A * B, B, C dense, A sparse * dense = sparse * dense */ -void testDSparseDMatrix( +void testFuncDSparseDMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { real alpha = 1.0; real beta = 1.0; - const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); - cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); - gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - - CpuSparseMatrix cpuMatrixA(dimM, dimK, nnz, FLOAT_VALUE, FORMAT, false); - GpuSparseMatrix gpuMatrixA(dimM, dimK, nnz, FLOAT_VALUE, FORMAT, false); - CpuMatrix cpuDenseA(dimM, dimK, false); - - auto cpuMatrixB = Matrix::create(dimK, dimN, false, false); - auto gpuMatrixB = Matrix::create(dimK, dimN, false, true); - auto cpuDenseB = Matrix::create(dimK, dimN, false, false); - - auto cpuMatrixC = Matrix::create(dimM, dimN, false, false); - auto gpuMatrixC = Matrix::create(dimM, dimN, false, true); - auto cpuDenseC = Matrix::create(dimM, dimN, false, false); - - /*matrix init*/ - hl_stream_t stream(HPPL_STREAM_1); - cpuMatrixA.randomizeUniform(); - cpuMatrixB->randomizeUniform(); - cpuMatrixC->randomizeUniform(); - - gpuMatrixA.copyFrom(cpuMatrixA, stream); - gpuMatrixB->copyFrom(*cpuMatrixB, stream); - gpuMatrixC->copyFrom(*cpuMatrixC, stream); - - cpuDenseA.copyFrom(cpuMatrixA); - cpuDenseB->copyFrom(*cpuMatrixB); - cpuDenseC->copyFrom(*cpuMatrixC); - hl_stream_synchronize(stream); - - /*matrix mul*/ - BufferArgs cpuInputs; - BufferArgs cpuOutputs; - cpuInputs.addArg(cpuMatrixA); - cpuInputs.addArg(*cpuMatrixB); - cpuOutputs.addArg(*cpuMatrixC, ADD_TO); - cpuFunc->calc(cpuInputs, cpuOutputs); - - BufferArgs gpuInputs; - BufferArgs gpuOutputs; - gpuInputs.addArg(gpuMatrixA); - gpuInputs.addArg(*gpuMatrixB); - gpuOutputs.addArg(*gpuMatrixC, ADD_TO); - gpuFunc->calc(gpuInputs, gpuOutputs); - - BufferArgs denseInputs; - BufferArgs denseOutputs; - denseInputs.addArg(cpuDenseA); - denseInputs.addArg(*cpuDenseB); - denseOutputs.addArg(*cpuDenseC, ADD_TO); - cpuFunc->calc(denseInputs, denseOutputs); - - /*check result*/ - autotest::TensorCheckErr(*cpuMatrixC, *cpuDenseC); - autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); + // init Test object + FunctionCompare test("MulOp", + FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + // prepare input arguments + /// sparse matrix A : M * K + test.addInputs(SparseMatrixArg(VALUE_TYPE_FLOAT, + TensorShape{dimM, dimK}, + nnz, + FORMAT, + FLOAT_VALUE, + UNSPECIFIED, + false)); + /// matrix B: K * N + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN})); + + /// output matrix C: M * N + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), ADD_TO); + // run Function + test.run(); } -TEST(Matrix, DSparseDMul) { - LOG(INFO) << "test for dense = sparse * dense matrix"; +TEST(MuLOp, DSparseDMul) { + LOG(INFO) << "function test for dense = sparse * dense matrix"; for (const auto dimM : {10, 100, 1000}) { for (const auto dimN : {10, 100}) { for (const auto dimK : {3, 10}) { @@ -177,7 +120,7 @@ TEST(Matrix, DSparseDMul) { << " dimK=" << std::setw(5) << dimK << " nnz=" << std::setw(5) << nnz << " format=" << std::setw(5) << FORMAT; - testDSparseDMatrix(dimM, dimN, dimK, nnz, FORMAT); + testFuncDSparseDMatrix(dimM, dimN, dimK, nnz, FORMAT); } } } @@ -189,72 +132,34 @@ TEST(Matrix, DSparseDMul) { * C += A * B, A, C dense, B sparse * dense = dense * sparse */ -void testDDSparseMatrix( +void testFuncDDSparseMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { real alpha = 1.0; real beta = 1.0; - const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); - cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); - gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - - auto cpuMatrixA = Matrix::create(dimM, dimK, false, false); - auto gpuMatrixA = Matrix::create(dimM, dimK, false, true); - auto cpuDenseA = Matrix::create(dimM, dimK, false, false); - - CpuSparseMatrix cpuMatrixB(dimK, dimN, nnz, FLOAT_VALUE, FORMAT, false); - - GpuSparseMatrix gpuMatrixB(dimK, dimN, nnz, FLOAT_VALUE, FORMAT, false); - - auto cpuDenseB = Matrix::create(dimK, dimN, false, false); - auto cpuMatrixC = Matrix::create(dimM, dimN, false, false); - auto gpuMatrixC = Matrix::create(dimM, dimN, false, true); - auto cpuDenseC = Matrix::create(dimM, dimN, false, false); - - /*matrix init*/ - hl_stream_t stream(HPPL_STREAM_1); - cpuMatrixA->randomizeUniform(); - cpuMatrixB.randomizeUniform(); - cpuMatrixC->randomizeUniform(); - - gpuMatrixA->copyFrom(*cpuMatrixA, stream); - gpuMatrixB.copyFrom(cpuMatrixB, stream); - gpuMatrixC->copyFrom(*cpuMatrixC, stream); - - cpuDenseA->copyFrom(*cpuMatrixA); - cpuDenseB->copyFrom(cpuMatrixB); - cpuDenseC->copyFrom(*cpuMatrixC); - hl_stream_synchronize(stream); - - /*matrix mul*/ - BufferArgs cpuInputs; - BufferArgs cpuOutputs; - cpuInputs.addArg(*cpuMatrixA); - cpuInputs.addArg(cpuMatrixB); - cpuOutputs.addArg(*cpuMatrixC, ADD_TO); - cpuFunc->calc(cpuInputs, cpuOutputs); - - BufferArgs gpuInputs; - BufferArgs gpuOutputs; - gpuInputs.addArg(*gpuMatrixA); - gpuInputs.addArg(gpuMatrixB); - gpuOutputs.addArg(*gpuMatrixC, ADD_TO); - gpuFunc->calc(gpuInputs, gpuOutputs); - - BufferArgs denseInputs; - BufferArgs denseOutputs; - denseInputs.addArg(*cpuDenseA); - denseInputs.addArg(*cpuDenseB); - denseOutputs.addArg(*cpuDenseC, ADD_TO); - cpuFunc->calc(denseInputs, denseOutputs); - - /*check result*/ - autotest::TensorCheckErr(*cpuMatrixC, *cpuDenseC); - autotest::TensorCheckErr(*cpuMatrixC, *gpuMatrixC); + // init Test object + FunctionCompare test("MulOp", + FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + // prepare input arguments + /// matrix A : M * K + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); + + /// matrix B: K * N + test.addInputs(SparseMatrixArg(VALUE_TYPE_FLOAT, + TensorShape{dimK, dimN}, + nnz, + FORMAT, + FLOAT_VALUE, + UNSPECIFIED, + false)); + + /// output matrix C: M * N + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), ADD_TO); + // run Function + test.run(); } -TEST(Matrix, DDSparseMul) { - LOG(INFO) << "test for dense = dense * sparse matrix"; +TEST(MulOp, DDSparseMul) { + LOG(INFO) << "function test for dense = dense * sparse matrix"; for (const auto dimM : {10, 100, 1000}) { for (const auto dimN : {10, 100}) { for (const auto dimK : {3, 10}) { @@ -266,7 +171,7 @@ TEST(Matrix, DDSparseMul) { << " dimK=" << std::setw(5) << dimK << " nnz=" << std::setw(5) << nnz << " format=" << std::setw(5) << FORMAT; - testDDSparseMatrix(dimM, dimN, dimK, nnz, FORMAT); + testFuncDDSparseMatrix(dimM, dimN, dimK, nnz, FORMAT); } } } From bc5d7bb6d221d7f921db70a8d1c7757bbff8ac15 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Sun, 22 Jan 2017 14:46:40 -0800 Subject: [PATCH 104/370] Add Sparse = dense * dense unit test with Daoyuan's Function test --- paddle/function/BufferArg.h | 3 ++ paddle/function/FunctionTest.h | 40 +++++++++++++--- paddle/function/MulOp.cpp | 7 +++ paddle/function/MulOpTest.cpp | 86 +++++++++------------------------- 4 files changed, 67 insertions(+), 69 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 17a4e4a6b0eec9..7565047a570778 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -172,6 +172,7 @@ class BufferArg { bool isTransposed() const { return trans_; } bool isSparseArg() const { return TENSOR_SPARSE == bufferType_; } bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } + virtual size_t numElements() const { return shape_.getElements(); } const SequenceArg& sequence() const; const SparseMatrixArg& sparse() const; @@ -353,6 +354,8 @@ class SparseMatrixArg : public BufferArg { size_t nnz() const { return nnz_; } + size_t numElements() const override { return nnz_; } + SparseFormat dataFormat() const { return format_; } SparseValueType dataType() const { return type_; } diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 9d38671bc3c5c9..6515cba1629c07 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -101,6 +101,34 @@ class FunctionCompare { output.isTransposed())); } + /// add and init output sparse matrix + void addOutputs(const SparseMatrixArg& output, ArgType argType = ASSIGN_TO) { + cpuSparse_ = std::make_shared(output.shape()[0], + output.shape()[1], + output.nnz(), + output.dataType(), + output.dataFormat(), + output.isTransposed()); + + gpuSparse_ = std::make_shared(output.shape()[0], + output.shape()[1], + output.nnz(), + output.dataType(), + output.dataFormat(), + output.isTransposed()); + + /// init sparse matrix + hl_stream_t stream(HPPL_STREAM_1); + cpuSparse_->randomizeUniform(); + gpuSparse_->copyFrom(*cpuSparse_, stream); + hl_stream_synchronize(stream); + + cpuOutputs_.emplace_back( + std::make_shared(*cpuSparse_, argType)); + gpuOutputs_.emplace_back( + std::make_shared(*gpuSparse_, argType)); + } + void addInputs(const SequenceArg& input) { size_t batchSize = input.shape()[0]; size_t numSeqs = batchSize / 10 + 1; @@ -199,8 +227,7 @@ class FunctionCompare { void initOutputs() { for (size_t i = 0; i < cpuOutputs_.size(); i++) { if (cpuOutputs_[i]->isSparseArg()) { - LOG(INFO) << "output sparse matrix already init"; - continue; + continue; /// sparse matrix already init } initArg(*cpuOutputs_[i]); @@ -218,10 +245,11 @@ class FunctionCompare { void compareOutputs() { for (size_t i = 0; i < cpuOutputs_.size(); i++) { // TODO, Need a BufferCheck used to compare the two buffers. - auto cpu = cpuOutputs_[i]; - auto gpu = gpuOutputs_[i]; - CpuVector cpuVector(cpu->shape().getElements(), (real*)cpu->data()); - GpuVector gpuVector(cpu->shape().getElements(), (real*)gpu->data()); + const auto cpu = cpuOutputs_[i]; + const auto gpu = gpuOutputs_[i]; + CHECK_EQ(cpu->numElements(), gpu->numElements()); + CpuVector cpuVector(cpu->numElements(), (real*)cpu->data()); + GpuVector gpuVector(gpu->numElements(), (real*)gpu->data()); autotest::TensorCheckErr(cpuVector, gpuVector); } } diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index bd3bc5c087d669..4d7f1a7fa92a56 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -319,6 +319,13 @@ class MulFunc : public FunctionBase { auto outSparseMat = outputs[0].sparse().SparseMatrix(); if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && outputs[0].isSparseArg()) { + /* + LOG(INFO) << "input0"; + inputs[0].matrix().print(std::cout); + LOG(INFO) << "input1"; + inputs[1].matrix().print(std::cout); + LOG(INFO) << "output sparse matrix"; + outSparseMat.print(std::cout); */ MulOp(outSparseMat, inputs[0].matrix(), inputs[1].matrix(), diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 7300a2014bd8ed..05460c80970d93 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -183,75 +183,35 @@ TEST(MulOp, DDSparseMul) { * C += A * B, A sparse, B, C dense * sparse = dense * dense */ -void testSparseDDMatrix( +void testFuncSparseDDMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { real alpha = 1.0; real beta = 1.0; - const auto cpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-CPU"); - cpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - const auto gpuFunc = FunctionBase::funcRegistrar_.createByType("MulOp-GPU"); - gpuFunc->init(FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); - - auto cpuMatrixA = Matrix::create(dimM, dimK, false, false); - auto gpuMatrixA = Matrix::create(dimM, dimK, false, true); - auto cpuDenseA = Matrix::create(dimM, dimK, false, false); - - auto cpuMatrixB = Matrix::create(dimK, dimN, false, false); - auto gpuMatrixB = Matrix::create(dimK, dimN, false, true); - auto cpuDenseB = Matrix::create(dimK, dimN, false, false); - - CpuSparseMatrix cpuMatrixC(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); - CpuSparseMatrix gpuMatrixC_d2h(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); - GpuSparseMatrix gpuMatrixC(dimM, dimN, nnz, FLOAT_VALUE, FORMAT, false); - CpuMatrix cpuDenseC(dimM, dimN, false); - - /*matrix init*/ - hl_stream_t stream(HPPL_STREAM_1); - cpuMatrixA->randomizeUniform(); - cpuMatrixB->randomizeUniform(); - cpuMatrixC.randomizeUniform(); - - gpuMatrixA->copyFrom(*cpuMatrixA, stream); - gpuMatrixB->copyFrom(*cpuMatrixB, stream); - gpuMatrixC.copyFrom(cpuMatrixC, stream); - - cpuDenseA->copyFrom(*cpuMatrixA); - cpuDenseB->copyFrom(*cpuMatrixB); - cpuDenseC.copyFrom(cpuMatrixC); - hl_stream_synchronize(stream); - - /*matrix mul*/ - BufferArgs cpuInputs; - BufferArgs cpuOutputs; - cpuInputs.addArg(*cpuMatrixA); - cpuInputs.addArg(*cpuMatrixB); - cpuOutputs.addArg(cpuMatrixC, ADD_TO); - cpuFunc->calc(cpuInputs, cpuOutputs); - - BufferArgs gpuInputs; - BufferArgs gpuOutputs; - gpuInputs.addArg(*gpuMatrixA); - gpuInputs.addArg(*gpuMatrixB); - gpuOutputs.addArg(gpuMatrixC, ADD_TO); - gpuFunc->calc(gpuInputs, gpuOutputs); - - BufferArgs denseInputs; - BufferArgs denseOutputs; - denseInputs.addArg(*cpuDenseA); - denseInputs.addArg(*cpuDenseB); - denseOutputs.addArg(cpuDenseC, ADD_TO); - cpuFunc->calc(denseInputs, denseOutputs); + // init Test object + FunctionCompare test("MulOp", + FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + // prepare input arguments + /// matrix A : M * K + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); - gpuMatrixC_d2h.copyFrom(gpuMatrixC, stream); - hl_stream_synchronize(stream); + /// matrix B: K * N + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN})); - /*check result*/ - checkSMatrixEqual(cpuMatrixC, gpuMatrixC_d2h); - checkSMatrixEqual2Dense(cpuMatrixC, cpuDenseC); + /// output sparse matrix C: M * N + test.addOutputs(SparseMatrixArg(VALUE_TYPE_FLOAT, + TensorShape{dimM, dimN}, + nnz, + FORMAT, + FLOAT_VALUE, + UNSPECIFIED, + false), + ADD_TO); + // run Function + test.run(); } -TEST(Matrix, SparseDDMul) { - LOG(INFO) << "test for sparse = dense * dense matrix"; +TEST(MulOp, SparseDDMul) { + LOG(INFO) << "function test for sparse = dense * dense matrix"; for (const auto dimM : {10, 100, 1000}) { for (const auto dimN : {10, 100}) { for (const auto dimK : {3, 10}) { @@ -263,7 +223,7 @@ TEST(Matrix, SparseDDMul) { << " dimK=" << std::setw(5) << dimK << " nnz=" << std::setw(5) << nnz << " format=" << std::setw(5) << FORMAT; - testSparseDDMatrix(dimM, dimN, dimK, nnz, FORMAT); + testFuncSparseDDMatrix(dimM, dimN, dimK, nnz, FORMAT); } } } From b3be73580717f571d37bb655887d6449024d0ab7 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 23 Jan 2017 15:00:36 -0800 Subject: [PATCH 105/370] Daoyuan's comments. --- paddle/function/BufferArg.h | 37 +++---- paddle/function/FunctionTest.h | 40 +++----- paddle/function/MulOp.cpp | 173 ++++++++++++++++----------------- paddle/function/MulOp.h | 40 ++++++-- paddle/function/MulOpGpu.cu | 114 +++++++--------------- paddle/function/MulOpTest.cpp | 72 +++++++------- 6 files changed, 217 insertions(+), 259 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 7565047a570778..f3634364ab29f7 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -71,24 +71,17 @@ class BufferArg { public: BufferArg(ValueType valueType, const TensorShape& shape, - ArgType argType = UNSPECIFIED, - bool trans = false) + ArgType argType = UNSPECIFIED) : buf_(nullptr), valueType_(valueType), shape_(shape), - argType_(argType), - trans_(trans) {} + argType_(argType) {} BufferArg(void* buf, ValueType valueType, const TensorShape& shape, - ArgType argType = UNSPECIFIED, - bool trans = false) - : buf_(buf), - valueType_(valueType), - shape_(shape), - argType_(argType), - trans_(trans) {} + ArgType argType = UNSPECIFIED) + : buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) {} BufferArg(void* buf, ValueType valueType) : buf_(buf), valueType_(valueType) {} @@ -98,8 +91,7 @@ class BufferArg { const_cast(reinterpret_cast(matrix.getData()))), valueType_(DataType::value), shape_(2), - argType_(argType), - trans_(matrix.isTransposed()) { + argType_(argType) { bufferType_ = TENSOR_NORMAL; shape_.setDim(0, matrix.getHeight()); shape_.setDim(1, matrix.getWidth()); @@ -112,8 +104,7 @@ class BufferArg { const_cast(reinterpret_cast(matrix.getData()))), valueType_(DataType::value), shape_(shape), - argType_(argType), - trans_(matrix.isTransposed()) { + argType_(argType) { bufferType_ = TENSOR_NORMAL; CHECK_EQ(matrix.getElementCnt(), shape.getElements()); } @@ -145,7 +136,7 @@ class BufferArg { // CHECK(deviceType_ == DType); CHECK_EQ((size_t)2, shape_.ndims()); return typename Tensor::Matrix( - reinterpret_cast(buf_), shape_[0], shape_[1], trans_); + reinterpret_cast(buf_), shape_[0], shape_[1]); } template @@ -169,7 +160,6 @@ class BufferArg { ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } - bool isTransposed() const { return trans_; } bool isSparseArg() const { return TENSOR_SPARSE == bufferType_; } bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } virtual size_t numElements() const { return shape_.getElements(); } @@ -183,7 +173,6 @@ class BufferArg { TensorShape shape_; BufferType bufferType_{TENSOR_UNKNOWN}; ArgType argType_{UNSPECIFIED}; - bool trans_{false}; // todo(tianbing), add deviceType_ // leading dimensions. The size is dims_.size() // Dims lds_; @@ -277,9 +266,8 @@ class SparseMatrixArg : public BufferArg { size_t nnz, SparseFormat format, SparseValueType type, - ArgType argType = UNSPECIFIED, - bool trans = false) - : BufferArg(buf, valueType, shape, argType, trans), + ArgType argType = UNSPECIFIED) + : BufferArg(buf, valueType, shape, argType), row_(row), col_(col), nnz_(nnz), @@ -302,9 +290,8 @@ class SparseMatrixArg : public BufferArg { size_t nnz, SparseFormat format, SparseValueType type, - ArgType argType = UNSPECIFIED, - bool trans = false) - : BufferArg(valueType, shape, argType, trans), + ArgType argType = UNSPECIFIED) + : BufferArg(valueType, shape, argType), /// len of row_ : height + 1 (CSR), buf_ == nullptr row_(format == SPARSE_CSR ? BufferArg(VALUE_TYPE_INT32, TensorShape{shape[0] + 1}) @@ -343,7 +330,7 @@ class SparseMatrixArg : public BufferArg { nnz_, type_, format_, - trans_); + false); } ~SparseMatrixArg() {} diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 6515cba1629c07..baa94abffa09b6 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -64,22 +64,14 @@ class FunctionCompare { cpuMemory_.emplace_back(std::make_shared(size)); gpuMemory_.emplace_back(std::make_shared(size)); - cpuInputs_.emplace_back( - std::make_shared(cpuMemory_.back()->getBuf(), - input.valueType(), - input.shape(), - UNSPECIFIED, - input.isTransposed())); - gpuInputs_.emplace_back( - std::make_shared(gpuMemory_.back()->getBuf(), - input.valueType(), - input.shape(), - UNSPECIFIED, - input.isTransposed())); + cpuInputs_.emplace_back(std::make_shared( + cpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + gpuInputs_.emplace_back(std::make_shared( + gpuMemory_.back()->getBuf(), input.valueType(), input.shape())); } // output need only contains shape, do not contains data. - void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) { + void addOutputs(const BufferArg& output, ArgType argType = ADD_TO) { size_t size = output.shape().getElements() * sizeOfValuType(output.valueType()); cpuMemory_.emplace_back(std::make_shared(size)); @@ -89,16 +81,14 @@ class FunctionCompare { cpuMemory_.back()->getBuf(), output.valueType(), output.shape(), - // todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO - argType, - output.isTransposed())); + // todo(tianbing), argType = output.getArgType(), but default ADD_TO + argType)); gpuOutputs_.emplace_back(std::make_shared( gpuMemory_.back()->getBuf(), output.valueType(), output.shape(), - // todo(tianbing), argType = output.getArgType(), but default ASSIGN_TO - argType, - output.isTransposed())); + // todo(tianbing), argType = output.getArgType(), but default ADD_TO + argType)); } /// add and init output sparse matrix @@ -107,15 +97,13 @@ class FunctionCompare { output.shape()[1], output.nnz(), output.dataType(), - output.dataFormat(), - output.isTransposed()); + output.dataFormat()); gpuSparse_ = std::make_shared(output.shape()[0], output.shape()[1], output.nnz(), output.dataType(), - output.dataFormat(), - output.isTransposed()); + output.dataFormat()); /// init sparse matrix hl_stream_t stream(HPPL_STREAM_1); @@ -154,15 +142,13 @@ class FunctionCompare { input.shape()[1], input.nnz(), input.dataType(), - input.dataFormat(), - input.isTransposed()); + input.dataFormat()); gpuSparse_ = std::make_shared(input.shape()[0], input.shape()[1], input.nnz(), input.dataType(), - input.dataFormat(), - input.isTransposed()); + input.dataFormat()); /// init sparse matrix hl_stream_t stream(HPPL_STREAM_1); diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 4d7f1a7fa92a56..965115121eb967 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -46,21 +46,11 @@ void MulOp(CpuSparseMatrix& out, const CpuMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT) { - CHECK(!out.isTransposed()) << "Not supported"; + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { CHECK_EQ(out.getValueType(), FLOAT_VALUE); - CHECK(!a.isTransposed() || !b.isTransposed()) - << "Not support both a and b are transpose matrices"; - - size_t height = out.getHeight(); - size_t width = out.getWidth(); - size_t aRow = !a.isTransposed() ? a.getHeight() : a.getWidth(); - size_t aCol = !a.isTransposed() ? a.getWidth() : a.getHeight(); - size_t bRow = !b.isTransposed() ? b.getHeight() : b.getWidth(); - size_t bCol = !b.isTransposed() ? b.getWidth() : b.getHeight(); - /// C = A * B, for matrix format - CHECK(aCol == bRow && aRow == height && bCol == width); - if (scaleT == 0) { out.zeroMem(); } @@ -69,12 +59,14 @@ void MulOp(CpuSparseMatrix& out, real* C = out.getValue(); int* rows = out.getRows(); int* cols = out.getCols(); + size_t width = out.getWidth(); + size_t height = out.getHeight(); /// SPARSE_CSC, {a any, b not trans} if (out.getFormat() == SPARSE_CSC) { /// b not trans and a any - CHECK(!b.isTransposed()); - size_t m = !a.isTransposed() ? a.getWidth() : a.getHeight(); + CHECK(!bTrans); + size_t m = !aTrans ? a.getWidth() : a.getHeight(); for (size_t i = 0; i < width; i++) { size_t start = out.getColStartIdx(i); size_t end = out.getColStartIdx(i + 1); @@ -82,9 +74,8 @@ void MulOp(CpuSparseMatrix& out, real sum = 0; size_t rowIdx = rows[j]; for (size_t k = 0; k < m; k++) { - sum += - (!a.isTransposed() ? A[rowIdx * m + k] : A[k * height + rowIdx]) * - B[k * width + i]; + sum += (!aTrans ? A[rowIdx * m + k] : A[k * height + rowIdx]) * + B[k * width + i]; } C[j] = scaleAB * sum + scaleT * C[j]; } @@ -95,7 +86,7 @@ void MulOp(CpuSparseMatrix& out, /// SPARSE_CSR, {a any, b not trans} or {a not trans, b trans} if (out.getFormat() == SPARSE_CSR) { /// a and b can not both transpose - CHECK(!(a.isTransposed() && b.isTransposed())); + CHECK(!(aTrans && bTrans)); size_t m = a.getWidth(); for (size_t i = 0; i < height; i++) { size_t start = out.getRowStartIdx(i); @@ -104,9 +95,8 @@ void MulOp(CpuSparseMatrix& out, real sum = 0; size_t colIdx = cols[j]; for (size_t k = 0; k < m; k++) { - sum += - (!a.isTransposed() ? A[i * m + k] : A[k * height + i]) * - (!b.isTransposed() ? B[k * width + colIdx] : B[colIdx * m + k]); + sum += (!aTrans ? A[i * m + k] : A[k * height + i]) * + (!bTrans ? B[k * width + colIdx] : B[colIdx * m + k]); } C[j] = scaleAB * sum + scaleT * C[j]; } @@ -120,25 +110,15 @@ void MulOp(CpuMatrix& out, const CpuMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT) { - CHECK(!out.isTransposed()) << "out matrix transpose not supported"; - CBLAS_TRANSPOSE aTrans = a.isTransposed() ? CblasTrans : CblasNoTrans; - size_t aRow = a.isTransposed() ? a.getWidth() : a.getHeight(); - size_t aCol = a.isTransposed() ? a.getHeight() : a.getWidth(); - CBLAS_TRANSPOSE bTrans = b.isTransposed() ? CblasTrans : CblasNoTrans; - size_t bRow = b.isTransposed() ? b.getWidth() : b.getHeight(); - size_t bCol = b.isTransposed() ? b.getHeight() : b.getWidth(); - - /// C = A * B, for matrix format - CHECK_EQ(aCol, bRow); - CHECK_EQ(aRow, out.getHeight()); - CHECK_EQ(bCol, out.getWidth()); - - GEMM(aTrans, - bTrans, + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { + GEMM(aTrans ? CblasTrans : CblasNoTrans, + bTrans ? CblasTrans : CblasNoTrans, out.getHeight(), out.getWidth(), - aCol, + !aTrans ? a.getWidth() : a.getHeight(), scaleAB, a.getData(), a.getStride(), @@ -154,21 +134,12 @@ void MulOp(CpuMatrix& out, const CpuSparseMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT) { - CHECK(!out.isTransposed()) << "Not supported"; - CHECK(!b.isTransposed()) << "Not supported"; - CHECK(scaleT == 0 || scaleT == 1) << "Not support"; - CHECK_EQ(scaleAB, static_cast(1.0)) << "Not supported"; - CHECK_EQ(a.getFormat(), SPARSE_CSR) << "Not supported"; - - if (!a.isTransposed()) { - CHECK(b.getHeight() == a.getWidth() && a.getHeight() == out.getHeight() && - b.getWidth() == out.getWidth()); - } else { - CHECK(b.getHeight() == a.getHeight() && a.getWidth() == out.getHeight() && - b.getWidth() == out.getWidth()); - } - + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { + CHECK_EQ(a.getFormat(), SPARSE_CSR) + << "Not supported SPARSE_CSR format for a"; if (scaleT == 0) { out.zeroMem(); } @@ -185,9 +156,9 @@ void MulOp(CpuMatrix& out, const int start = a.getRowStartIdx(i); const int end = a.getRowStartIdx(i + 1); for (int j = start; j < end; ++j) { - vecAddTo(!a.isTransposed() ? out.getRow(i) : out.getRow(cols[j]), - !a.isTransposed() ? const_cast(b).getRow(cols[j]) - : const_cast(b).getRow(i), + vecAddTo(!aTrans ? out.getRow(i) : out.getRow(cols[j]), + !aTrans ? const_cast(b).getRow(cols[j]) + : const_cast(b).getRow(i), (a.getValueType() == FLOAT_VALUE) ? values[j] : (real)1.0, out.getWidth()); } @@ -199,19 +170,10 @@ void MulOp(CpuMatrix& out, const CpuMatrix& a, const CpuSparseMatrix& b, real scaleAB, - real scaleT) { - CHECK(!out.trans_) << "Not supported"; - CHECK(!a.isTransposed()) << "Not supported"; - CHECK(scaleT == 0 || scaleT == 1); - CHECK_EQ(scaleAB, static_cast(1.0)); - if (!b.isTransposed()) { /// b is not Transpose - CHECK(b.getHeight() == a.getWidth() && a.getHeight() == out.getHeight() && - b.getWidth() == out.getWidth()); - } else { - CHECK(b.getHeight() == out.getWidth() && a.getHeight() == out.getHeight() && - b.getWidth() == a.getWidth()); - } - + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { if (scaleT == 0) { out.zeroMem(); } @@ -227,8 +189,8 @@ void MulOp(CpuMatrix& out, int start = b.getColStartIdx(j); int end = b.getColStartIdx(j + 1); for (int i = start; i < end; ++i) { - colVecAddTo(!b.isTransposed() ? C + j : C + rows[i], - !b.isTransposed() ? A + rows[i] : A + j, + colVecAddTo(!bTrans ? C + j : C + rows[i], + !bTrans ? A + rows[i] : A + j, (b.getValueType() == NO_VALUE) ? (real)1.0 : B[i], out.getHeight(), out.getWidth(), @@ -244,8 +206,8 @@ void MulOp(CpuMatrix& out, int start = b.getRowStartIdx(j); int end = b.getRowStartIdx(j + 1); for (int i = start; i < end; ++i) { - colVecAddTo(!b.isTransposed() ? C + cols[i] : C + j, - !b.isTransposed() ? A + j : A + cols[i], + colVecAddTo(!bTrans ? C + cols[i] : C + j, + !bTrans ? A + j : A + cols[i], (b.getValueType() == NO_VALUE) ? (real)1.0 : B[i], out.getHeight(), out.getWidth(), @@ -270,16 +232,43 @@ class MulFunc : public FunctionBase { void init(const FuncConfig& config) override { alpha_ = config.get("scaleAB"); beta_ = config.get("scaleT"); + aTrans_ = config.get("aTrans"); + bTrans_ = config.get("bTrans"); + cTrans_ = config.get("cTrans"); } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK(!cTrans_) << "output matrix should not be transposed"; + CHECK(!aTrans_ || !bTrans_) + << "Not support both a and b are transpose matrices"; + CHECK_EQ((size_t)2, inputs.size()); CHECK_EQ((size_t)1, outputs.size()); CHECK(inputs[0].data() && inputs[1].data() && outputs[0].data()); CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(outputs[0].getArgType(), ADD_TO); + + size_t aRow = !aTrans_ ? inputs[0].shape()[0] : inputs[0].shape()[1]; + size_t aCol = !aTrans_ ? inputs[0].shape()[1] : inputs[0].shape()[0]; + size_t bRow = !bTrans_ ? inputs[1].shape()[0] : inputs[1].shape()[1]; + size_t bCol = !bTrans_ ? inputs[1].shape()[1] : inputs[1].shape()[0]; + /// C = A * B, or C += A * B, for matrix format + CHECK_EQ(aCol, bRow); + CHECK_EQ(aRow, outputs[0].shape()[0]); + CHECK_EQ(bCol, outputs[0].shape()[1]); + + /// only support C = A * B or C += A * B + CHECK_EQ(alpha_, static_cast(1.0)); + CHECK((beta_ == 0 && outputs[0].getArgType() == ASSIGN_TO) || + (beta_ == 1 && outputs[0].getArgType() == ADD_TO)); + + /// support dense = not both sparse * sparse + /// or sparse = dense * dense + CHECK((!outputs[0].isSparseArg() && + !(inputs[0].isSparseArg() && inputs[1].isSparseArg())) || + (outputs[0].isSparseArg() && !inputs[0].isSparseArg() && + !inputs[1].isSparseArg())); auto outMat = outputs[0].matrix(); /// matrix = matrix * matrix @@ -289,29 +278,40 @@ class MulFunc : public FunctionBase { inputs[0].matrix(), inputs[1].matrix(), alpha_, - beta_); + beta_, + aTrans_, + bTrans_, + cTrans_); return; } /// matrix = matrix * sparse matrix if (!inputs[0].isSparseArg() && inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { + CHECK(!aTrans_) << "Not supported a transpose"; MulOp(outMat, inputs[0].matrix(), inputs[1].sparse().SparseMatrix(), alpha_, - beta_); + beta_, + aTrans_, + bTrans_, + cTrans_); return; } /// matrix = sparse matrix * matrix if (inputs[0].isSparseArg() && !inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { + CHECK(!bTrans_) << "Not supported b transpose"; MulOp(outMat, inputs[0].sparse().SparseMatrix(), inputs[1].matrix(), alpha_, - beta_); + beta_, + aTrans_, + bTrans_, + cTrans_); return; } @@ -319,18 +319,14 @@ class MulFunc : public FunctionBase { auto outSparseMat = outputs[0].sparse().SparseMatrix(); if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && outputs[0].isSparseArg()) { - /* - LOG(INFO) << "input0"; - inputs[0].matrix().print(std::cout); - LOG(INFO) << "input1"; - inputs[1].matrix().print(std::cout); - LOG(INFO) << "output sparse matrix"; - outSparseMat.print(std::cout); */ MulOp(outSparseMat, inputs[0].matrix(), inputs[1].matrix(), alpha_, - beta_); + beta_, + aTrans_, + bTrans_, + cTrans_); return; } } @@ -338,6 +334,9 @@ class MulFunc : public FunctionBase { private: real alpha_; real beta_; + bool aTrans_; + bool bTrans_; + bool cTrans_; }; REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index fda5b0924982e8..a7703482255fec 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -26,55 +26,79 @@ void MulOp(CpuMatrix& out, const CpuMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(CpuMatrix& out, const CpuSparseMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(CpuMatrix& out, const CpuMatrix& a, const CpuSparseMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(CpuSparseMatrix& out, const CpuMatrix& a, const CpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(GpuMatrix& out, const GpuSparseMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuSparseMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); template void MulOp(GpuSparseMatrix& out, const GpuMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT); + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans); } // namespace paddle diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index 09d2a764911f18..94bee72034ff28 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -27,38 +27,22 @@ void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT) { - CHECK(!out.isTransposed()) << "Transpose not supported for out matrix"; - if (!a.isTransposed() && !b.isTransposed()) { - /// a : M * K, b: K * N - CHECK(out.getWidth() == b.getWidth() && - out.getHeight() == a.getHeight() && - a.getWidth() == b.getHeight()); - } else if (a.isTransposed() && !b.isTransposed()) { - /// a : K * M, b : K * N - CHECK(out.getWidth() == b.getWidth() && - out.getHeight() == a.getWidth() && - a.getHeight() == b.getHeight()); - } else if (!a.isTransposed() && b.isTransposed()) { - /// a: M * K, b : N * K - CHECK(out.getWidth() == b.getHeight() && - out.getHeight() == a.getHeight() && - a.getWidth() == b.getWidth()); - } else { - LOG(FATAL) << "Not support for both a and b are Transposed Matrices"; - } - + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { + CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; real* aData = const_cast(a.getData()); real* bData = const_cast(b.getData()); real* outData = const_cast(out.getData()); hl_matrix_mul(aData, - !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T, + !aTrans ? HPPL_OP_N : HPPL_OP_T, bData, - !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T, + !bTrans ? HPPL_OP_N : HPPL_OP_T, outData, out.getHeight(), out.getWidth(), - !a.isTransposed() ? a.getWidth() : a.getHeight(), + !aTrans ? a.getWidth() : a.getHeight(), scaleAB, scaleT, a.getStride(), @@ -75,27 +59,19 @@ void MulOp(GpuMatrix& out, const GpuSparseMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT) { + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { CHECK(out.isContiguous()); CHECK(b.isContiguous()); - CHECK(b.useGpu_) << "Matrix type are not equal"; - CHECK(!out.isTransposed() && !b.isTransposed()) << "not supported"; - if (!a.isTransposed()) { - /// a: M * K, b: K * N - CHECK(out.getWidth() == b.getWidth() && out.getHeight() == a.getHeight() - && a.getWidth() == b.getHeight()) << "Matrix dimensions are not equal"; - } else { - /// a: K * M, transpose, b: K * N - CHECK(out.getWidth() == b.getWidth() && out.getHeight() == a.getWidth() - && a.getHeight() == b.getHeight()) << "Matrix dimensions are not equal"; - } + CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - hl_trans_op_t aTrans = a.isTransposed() ? HPPL_OP_T : HPPL_OP_N; hl_sparse_matrix_s aData = a.sMatrix_.get(); real* bData = const_cast(b.getData()); real* outData = const_cast(out.getData()); hl_matrix_csr_mul_dense(aData, - aTrans, + aTrans ? HPPL_OP_T : HPPL_OP_N, bData, HPPL_OP_N, outData, @@ -115,25 +91,14 @@ void MulOp(GpuMatrix& out, const GpuMatrix& a, const GpuSparseMatrix& b, real scaleAB, - real scaleT) { + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { CHECK(out.isContiguous()); CHECK(a.isContiguous()); - CHECK(a.useGpu_) << "Matrix type are not equal"; - if (!b.isTransposed()) { - /// a : M * K, b : K * N - CHECK(out.getWidth() == b.getWidth() && - out.getHeight() == a.getHeight() && - a.getWidth() == b.getHeight()) - << "Matrix dimensions are not equal"; - } else { - /// a : M * K, b : N * K, transpose - CHECK(out.getWidth() == b.getHeight() && - out.getHeight() == a.getHeight() && - a.getWidth() == b.getWidth()) - << "Matrix dimensions are not equal"; - } + CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - hl_trans_op_t bTrans = b.isTransposed() ? HPPL_OP_T : HPPL_OP_N; hl_sparse_matrix_s bData = b.sMatrix_.get(); real* aData = const_cast(a.getData()); real* outData = const_cast(out.getData()); @@ -142,7 +107,7 @@ void MulOp(GpuMatrix& out, hl_matrix_dense_mul_csc(aData, HPPL_OP_N, bData, - bTrans, + bTrans ? HPPL_OP_T : HPPL_OP_N, outData, out.getHeight(), out.getWidth(), @@ -153,7 +118,7 @@ void MulOp(GpuMatrix& out, hl_matrix_dense_mul_csr(aData, HPPL_OP_N, bData, - bTrans, + bTrans ? HPPL_OP_T : HPPL_OP_N, outData, out.getHeight(), out.getWidth(), @@ -168,35 +133,26 @@ void MulOp(GpuSparseMatrix& out, const GpuMatrix& a, const GpuMatrix& b, real scaleAB, - real scaleT) { + real scaleT, + bool aTrans, + bool bTrans, + bool cTrans) { CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - CHECK(!out.isTransposed()) << "Transpose is not supported for out matrix"; - - if (!a.isTransposed() && !b.isTransposed()) { - CHECK(out.getHeight() == a.getHeight() && - out.getWidth() == b.getWidth() && - a.getWidth() == b.getHeight()); - } else if (a.isTransposed() && !b.isTransposed()) { - CHECK(out.getHeight() == a.getWidth() && - out.getWidth() == b.getWidth() && - a.getHeight() == b.getHeight()); - } else if (!a.isTransposed() && b.isTransposed()) { - CHECK(out.getHeight() == a.getHeight() && - out.getWidth() == b.getHeight() && - a.getWidth() == b.getWidth()); - } else { - LOG(FATAL) << "Not support for both a and b are Transposed Matrices"; - } - hl_trans_op_t aTrans = a.isTransposed() ? HPPL_OP_T : HPPL_OP_N; - hl_trans_op_t bTrans = b.isTransposed() ? HPPL_OP_T : HPPL_OP_N; - int dimK = !b.isTransposed() ? b.getHeight() : b.getWidth(); real* aData = const_cast(a.getData()); real* bData = const_cast(b.getData()); hl_sparse_matrix_s outData = out.sMatrix_.get(); - hl_sparse_matrix_mul(aData, aTrans, bData, bTrans, outData, - out.getHeight(), out.getWidth(), dimK, scaleAB, scaleT); + hl_sparse_matrix_mul(aData, + aTrans ? HPPL_OP_T : HPPL_OP_N, + bData, + bTrans ? HPPL_OP_T : HPPL_OP_N, + outData, + out.getHeight(), + out.getWidth(), + !bTrans ? b.getHeight() : b.getWidth(), + scaleAB, + scaleT); } } // namespace paddle diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 05460c80970d93..f67fa41612cf06 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -39,18 +39,21 @@ void testFuncDDDMatrix( size_t widthC = dimN; // init Test object FunctionCompare test("MulOp", - FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + FuncConfig() + .set("scaleAB", alpha) + .set("scaleT", beta) + .set("aTrans", transa) + .set("bTrans", transb) + .set("cTrans", false)); // prepare input arguments /// matrix A : HA * WA - test.addInputs(BufferArg( - VALUE_TYPE_FLOAT, TensorShape{heightA, widthA}, UNSPECIFIED, transa)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightA, widthA})); /// matrix B: HB * WB - test.addInputs(BufferArg( - VALUE_TYPE_FLOAT, TensorShape{heightB, widthB}, UNSPECIFIED, transb)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightB, widthB})); /// output matrix C: HC * WC test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightC, widthC}), - ADD_TO); + beta == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -88,21 +91,22 @@ void testFuncDSparseDMatrix( real beta = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + FuncConfig() + .set("scaleAB", alpha) + .set("scaleT", beta) + .set("aTrans", false) + .set("bTrans", false) + .set("cTrans", false)); // prepare input arguments /// sparse matrix A : M * K - test.addInputs(SparseMatrixArg(VALUE_TYPE_FLOAT, - TensorShape{dimM, dimK}, - nnz, - FORMAT, - FLOAT_VALUE, - UNSPECIFIED, - false)); + test.addInputs(SparseMatrixArg( + VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}, nnz, FORMAT, FLOAT_VALUE)); /// matrix B: K * N test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN})); /// output matrix C: M * N - test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), ADD_TO); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), + beta == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -138,22 +142,23 @@ void testFuncDDSparseMatrix( real beta = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + FuncConfig() + .set("scaleAB", alpha) + .set("scaleT", beta) + .set("aTrans", false) + .set("bTrans", false) + .set("cTrans", false)); // prepare input arguments /// matrix A : M * K test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); /// matrix B: K * N - test.addInputs(SparseMatrixArg(VALUE_TYPE_FLOAT, - TensorShape{dimK, dimN}, - nnz, - FORMAT, - FLOAT_VALUE, - UNSPECIFIED, - false)); + test.addInputs(SparseMatrixArg( + VALUE_TYPE_FLOAT, TensorShape{dimK, dimN}, nnz, FORMAT, FLOAT_VALUE)); /// output matrix C: M * N - test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), ADD_TO); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), + beta == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -189,7 +194,12 @@ void testFuncSparseDDMatrix( real beta = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig().set("scaleAB", alpha).set("scaleT", beta)); + FuncConfig() + .set("scaleAB", alpha) + .set("scaleT", beta) + .set("aTrans", false) + .set("bTrans", false) + .set("cTrans", false)); // prepare input arguments /// matrix A : M * K test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); @@ -198,14 +208,10 @@ void testFuncSparseDDMatrix( test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN})); /// output sparse matrix C: M * N - test.addOutputs(SparseMatrixArg(VALUE_TYPE_FLOAT, - TensorShape{dimM, dimN}, - nnz, - FORMAT, - FLOAT_VALUE, - UNSPECIFIED, - false), - ADD_TO); + test.addOutputs( + SparseMatrixArg( + VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}, nnz, FORMAT, FLOAT_VALUE), + beta == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } From 999cd14a6e94f474d646b04ec135d5e6f3f85189 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 23 Jan 2017 21:21:50 -0800 Subject: [PATCH 106/370] Further address Daoyuan's comments, clean the code. --- paddle/function/BufferArg.cpp | 8 ++-- paddle/function/BufferArg.h | 69 ++++++++++++++++++--------------- paddle/function/FunctionTest.h | 71 +++++++++++++++++----------------- paddle/function/MulOp.cpp | 49 +++++++++++++++++------ paddle/function/MulOp.h | 11 ++++-- paddle/function/MulOpGpu.cu | 62 +++++++++-------------------- paddle/function/MulOpTest.cpp | 2 - paddle/function/TensorType.h | 4 ++ paddle/math/Matrix.h | 2 + 9 files changed, 147 insertions(+), 131 deletions(-) diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index a4f1c2d96417b3..2b70036e3ff7de 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -34,8 +34,8 @@ SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), nnz_(sparse.getElementCnt()), - format_(sparse.getFormat()), - type_(sparse.getValueType()) { + format_(static_cast(sparse.getFormat())), + type_(static_cast(sparse.getValueType())) { bufferType_ = TENSOR_SPARSE; } @@ -44,8 +44,8 @@ SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32), nnz_(sparse.getElementCnt()), - format_(sparse.getFormat()), - type_(sparse.getValueType()) { + format_(static_cast(sparse.getFormat())), + type_(static_cast(sparse.getValueType())) { bufferType_ = TENSOR_SPARSE; } diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index f3634364ab29f7..177d20005b54b8 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -72,19 +72,21 @@ class BufferArg { BufferArg(ValueType valueType, const TensorShape& shape, ArgType argType = UNSPECIFIED) - : buf_(nullptr), - valueType_(valueType), - shape_(shape), - argType_(argType) {} + : buf_(nullptr), valueType_(valueType), shape_(shape), argType_(argType) { + bufferType_ = TENSOR_NORMAL; + } BufferArg(void* buf, ValueType valueType, const TensorShape& shape, ArgType argType = UNSPECIFIED) - : buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) {} + : buf_(buf), valueType_(valueType), shape_(shape), argType_(argType) { + bufferType_ = TENSOR_NORMAL; + } - BufferArg(void* buf, ValueType valueType) - : buf_(buf), valueType_(valueType) {} + BufferArg(void* buf, ValueType valueType) : buf_(buf), valueType_(valueType) { + bufferType_ = TENSOR_NORMAL; + } BufferArg(const Matrix& matrix, ArgType argType = UNSPECIFIED) : buf_( @@ -173,7 +175,7 @@ class BufferArg { TensorShape shape_; BufferType bufferType_{TENSOR_UNKNOWN}; ArgType argType_{UNSPECIFIED}; - // todo(tianbing), add deviceType_ + // TODO(tianbing), add deviceType_ // leading dimensions. The size is dims_.size() // Dims lds_; }; @@ -186,6 +188,7 @@ class SequenceIdArg : public BufferArg { public: SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED) : BufferArg(VALUE_TYPE_INT32, shape, argType) { + bufferType_ = TENSOR_SEQUENCE_ID; CHECK_EQ(shape_.ndims(), (size_t)1); CHECK_GT(shape_[0], 1); numSeqs_ = shape_[0] - 1; @@ -223,7 +226,9 @@ class SequenceArg : public BufferArg { SequenceArg(ValueType valueType, const TensorShape& shape, ArgType argType = UNSPECIFIED) - : BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {} + : BufferArg(valueType, shape, argType), startPositions_(TensorShape()) { + bufferType_ = TENSOR_SEQUENCE_DATA; + } SequenceArg(void* buf, ValueType valueType, @@ -271,16 +276,16 @@ class SparseMatrixArg : public BufferArg { row_(row), col_(col), nnz_(nnz), - format_(format), - type_(type) { + format_(static_cast(format)), + type_(static_cast(type)) { bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); CHECK_EQ(row_.shape().ndims(), (size_t)1); CHECK_EQ(col_.shape().ndims(), (size_t)1); - if (format == SPARSE_CSR) { + if (format_ == T_SPARSE_CSR) { CHECK_EQ(nnz, col.shape()[0]); - } else if (format == SPARSE_CSC) { + } else if (format_ == T_SPARSE_CSC) { CHECK_EQ(nnz, row.shape()[0]); } } @@ -292,23 +297,23 @@ class SparseMatrixArg : public BufferArg { SparseValueType type, ArgType argType = UNSPECIFIED) : BufferArg(valueType, shape, argType), - /// len of row_ : height + 1 (CSR), buf_ == nullptr - row_(format == SPARSE_CSR - ? BufferArg(VALUE_TYPE_INT32, TensorShape{shape[0] + 1}) - : BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})), - /// len of col_ : width + 1 (CSC), buf_ == nullptr - col_(format == SPARSE_CSR - ? BufferArg(VALUE_TYPE_INT32, TensorShape{nnz}) - : BufferArg(VALUE_TYPE_INT32, TensorShape{shape[1] + 1})), + row_(BufferArg(nullptr, VALUE_TYPE_INT32)), + col_(BufferArg(nullptr, VALUE_TYPE_INT32)), nnz_(nnz), - format_(format), - type_(type) { + format_(static_cast(format)), + type_(static_cast(type)) { bufferType_ = TENSOR_SPARSE; - /// todo(tianbing) - /// valueType and shape_.ndims() == 2 need to check before - /// this constructor to make sure row_ and col_ are right CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); + + /// len of row_ : height + 1 (CSR) or nnz (CSC), buf_ == nullptr + row_ = (format_ == T_SPARSE_CSR + ? BufferArg(VALUE_TYPE_INT32, TensorShape{shape_[0] + 1}) + : BufferArg(VALUE_TYPE_INT32, TensorShape{nnz})); + /// len of col_ : width + 1 (CSC) or nnz (CSR), buf_ == nullptr + col_ = (format_ == T_SPARSE_CSR + ? BufferArg(VALUE_TYPE_INT32, TensorShape{nnz}) + : BufferArg(VALUE_TYPE_INT32, TensorShape{shape_[1] + 1})); } SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); @@ -328,8 +333,8 @@ class SparseMatrixArg : public BufferArg { shape_[0], shape_[1], nnz_, - type_, - format_, + static_cast(type_), + static_cast(format_), false); } @@ -343,16 +348,16 @@ class SparseMatrixArg : public BufferArg { size_t numElements() const override { return nnz_; } - SparseFormat dataFormat() const { return format_; } + SparseDataFormat dataFormat() const { return format_; } - SparseValueType dataType() const { return type_; } + SparseDataType dataType() const { return type_; } private: BufferArg row_; BufferArg col_; size_t nnz_; - SparseFormat format_; - SparseValueType type_; + SparseDataFormat format_; + SparseDataType type_; }; } // namespace paddle diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index baa94abffa09b6..99669a4495578d 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -15,7 +15,6 @@ limitations under the License. */ #include "Function.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" -#include "paddle/math/Vector.h" #include "paddle/math/tests/TensorCheck.h" #include "paddle/testing/TestUtil.h" @@ -77,33 +76,33 @@ class FunctionCompare { cpuMemory_.emplace_back(std::make_shared(size)); gpuMemory_.emplace_back(std::make_shared(size)); - cpuOutputs_.emplace_back(std::make_shared( - cpuMemory_.back()->getBuf(), - output.valueType(), - output.shape(), - // todo(tianbing), argType = output.getArgType(), but default ADD_TO - argType)); - gpuOutputs_.emplace_back(std::make_shared( - gpuMemory_.back()->getBuf(), - output.valueType(), - output.shape(), - // todo(tianbing), argType = output.getArgType(), but default ADD_TO - argType)); + cpuOutputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + argType)); + gpuOutputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + argType)); } /// add and init output sparse matrix void addOutputs(const SparseMatrixArg& output, ArgType argType = ASSIGN_TO) { - cpuSparse_ = std::make_shared(output.shape()[0], - output.shape()[1], - output.nnz(), - output.dataType(), - output.dataFormat()); - - gpuSparse_ = std::make_shared(output.shape()[0], - output.shape()[1], - output.nnz(), - output.dataType(), - output.dataFormat()); + cpuSparse_ = std::make_shared( + output.shape()[0], + output.shape()[1], + output.nnz(), + static_cast(output.dataType()), + static_cast(output.dataFormat())); + + gpuSparse_ = std::make_shared( + output.shape()[0], + output.shape()[1], + output.nnz(), + static_cast(output.dataType()), + static_cast(output.dataFormat())); /// init sparse matrix hl_stream_t stream(HPPL_STREAM_1); @@ -138,17 +137,19 @@ class FunctionCompare { } void addInputs(const SparseMatrixArg& input) { - cpuSparse_ = std::make_shared(input.shape()[0], - input.shape()[1], - input.nnz(), - input.dataType(), - input.dataFormat()); - - gpuSparse_ = std::make_shared(input.shape()[0], - input.shape()[1], - input.nnz(), - input.dataType(), - input.dataFormat()); + cpuSparse_ = std::make_shared( + input.shape()[0], + input.shape()[1], + input.nnz(), + static_cast(input.dataType()), + static_cast(input.dataFormat())); + + gpuSparse_ = std::make_shared( + input.shape()[0], + input.shape()[1], + input.nnz(), + static_cast(input.dataType()), + static_cast(input.dataFormat())); /// init sparse matrix hl_stream_t stream(HPPL_STREAM_1); diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 965115121eb967..7bbdf7b2e47462 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -41,6 +41,7 @@ inline void colVecAddTo( } // namespace namespace paddle { +/// sparse matrix (+)= dense matrix * dense matrix template <> void MulOp(CpuSparseMatrix& out, const CpuMatrix& a, @@ -105,6 +106,7 @@ void MulOp(CpuSparseMatrix& out, } } +/// dense matrix (+)= dense matrix * dense matrix template <> void MulOp(CpuMatrix& out, const CpuMatrix& a, @@ -129,6 +131,7 @@ void MulOp(CpuMatrix& out, out.getStride()); } +/// dense matrix (+)= sparse matrix * dense matrix template <> void MulOp(CpuMatrix& out, const CpuSparseMatrix& a, @@ -138,8 +141,6 @@ void MulOp(CpuMatrix& out, bool aTrans, bool bTrans, bool cTrans) { - CHECK_EQ(a.getFormat(), SPARSE_CSR) - << "Not supported SPARSE_CSR format for a"; if (scaleT == 0) { out.zeroMem(); } @@ -165,6 +166,7 @@ void MulOp(CpuMatrix& out, } } +/// dense matrix (+)= dense matrix * sparse matrix template <> void MulOp(CpuMatrix& out, const CpuMatrix& a, @@ -183,7 +185,7 @@ void MulOp(CpuMatrix& out, int* rows = b.getRows(); int* cols = b.getCols(); - /// b.getFormat() == SPARSE_CSC + /// SPARSE_CSC format if (b.getFormat() == SPARSE_CSC) { for (size_t j = 0; j < b.getWidth(); ++j) { int start = b.getColStartIdx(j); @@ -200,7 +202,7 @@ void MulOp(CpuMatrix& out, return; } - /// b.getFormat() == SPARSE_CSR + /// SPARSE_CSR format if (b.getFormat() == SPARSE_CSR) { for (size_t j = 0; j < b.getHeight(); ++j) { int start = b.getRowStartIdx(j); @@ -220,11 +222,32 @@ void MulOp(CpuMatrix& out, /** * mul operator - * out = scaleT * out + scaleAB*(in1 * in2) + * out = scaleT * out + scaleAB * (in1 * in2) + * here, scaleT in {0, 1}, scaleAB == 1, + * out = in1 (A) * in2 (B), ASSIGN_TO + * out += in1 (A) * in2 (B), ADD_TO + * + * + * \param outputs[0] output matrix (out), M * N, + * could be either Sparse or Dense Matrix + * M is num of rows, N is num of columns + * \param inputs[0] first input matrix (A), M * K (if non-trans) + * could be either Sparse or Dense Matrix + * M is num of rows, K is num of columns + * \param inputs[1] second input matrix (B), K * N (if non-trans) + * could be either Sparse or Dense Matrix + * K is num of rows, N is num of columns + * + * Support eight Mul operators, with both GPU and CPU devices + * For each device, four Mul operators are supported: + * 1. dense (out) = dense (A) * dense (B) + * 2. dense (out) = sparse (A) * dense (B) + * sparse matrix only support SPARSE_CSR format + * 3. dense (out) = dense (A) * sparse (B) + * sparse matrix support SPARSE_CSC and SPARSE_CSR formats + * 4. sparse (out) = dense (A) * dense (B) + * sparse matrix support SPARSE_CSC and SPARSE_CSR formats * - * \param outputs[0] output matrix, M * N - * \param inputs[0] first input (sparse) matrix, M * K (if non-trans) - * \param inputs[1] second input matrix, K * N (if non-trans) */ template class MulFunc : public FunctionBase { @@ -271,7 +294,7 @@ class MulFunc : public FunctionBase { !inputs[1].isSparseArg())); auto outMat = outputs[0].matrix(); - /// matrix = matrix * matrix + /// dense matrix = dense matrix * dense matrix if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { MulOp(outMat, @@ -285,7 +308,7 @@ class MulFunc : public FunctionBase { return; } - /// matrix = matrix * sparse matrix + /// dense matrix = dense matrix * sparse matrix if (!inputs[0].isSparseArg() && inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { CHECK(!aTrans_) << "Not supported a transpose"; @@ -300,10 +323,12 @@ class MulFunc : public FunctionBase { return; } - /// matrix = sparse matrix * matrix + /// dense matrix = sparse matrix * dense matrix if (inputs[0].isSparseArg() && !inputs[1].isSparseArg() && !outputs[0].isSparseArg()) { CHECK(!bTrans_) << "Not supported b transpose"; + CHECK_EQ(inputs[0].sparse().dataFormat(), T_SPARSE_CSR) + << "Only supported SPARSE_CSR format for sparse matrix a"; MulOp(outMat, inputs[0].sparse().SparseMatrix(), inputs[1].matrix(), @@ -315,7 +340,7 @@ class MulFunc : public FunctionBase { return; } - /// sparse matrix = matrix * matrix + /// sparse matrix = dense matrix * dense matrix auto outSparseMat = outputs[0].sparse().SparseMatrix(); if (!inputs[0].isSparseArg() && !inputs[1].isSparseArg() && outputs[0].isSparseArg()) { diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index a7703482255fec..0991f69e1b286a 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -15,12 +15,11 @@ limitations under the License. */ #pragma once #include "Function.h" -/// todo(tianbing), delete it -#include #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" namespace paddle { +/// CPU, dense matrix (+)= dense matrix * dense matrix template void MulOp(CpuMatrix& out, const CpuMatrix& a, @@ -31,6 +30,7 @@ void MulOp(CpuMatrix& out, bool bTrans, bool cTrans); +/// CPU, dense matrix (+)= sparse matrix * dense matrix template void MulOp(CpuMatrix& out, const CpuSparseMatrix& a, @@ -41,6 +41,7 @@ void MulOp(CpuMatrix& out, bool bTrans, bool cTrans); +/// CPU, dense matrix (+)= dense matrix * sparse matrix template void MulOp(CpuMatrix& out, const CpuMatrix& a, @@ -51,6 +52,7 @@ void MulOp(CpuMatrix& out, bool bTrans, bool cTrans); +/// CPU, sparse matrix (+)= dense matrix * dense matrix template void MulOp(CpuSparseMatrix& out, const CpuMatrix& a, @@ -61,6 +63,7 @@ void MulOp(CpuSparseMatrix& out, bool bTrans, bool cTrans); +/// GPU, dense matrix (+)= dense matrix * dense matrix template void MulOp(GpuMatrix& out, const GpuMatrix& a, @@ -71,6 +74,7 @@ void MulOp(GpuMatrix& out, bool bTrans, bool cTrans); +/// GPU, dense matrix (+)= sparse matrix * dense matrix template void MulOp(GpuMatrix& out, const GpuSparseMatrix& a, @@ -81,6 +85,7 @@ void MulOp(GpuMatrix& out, bool bTrans, bool cTrans); +/// GPU, dense matrix (+)= dense matrix * sparse matrix template void MulOp(GpuMatrix& out, const GpuMatrix& a, @@ -90,7 +95,7 @@ void MulOp(GpuMatrix& out, bool aTrans, bool bTrans, bool cTrans); - +/// GPU, sparse matrix (+)= dense matrix * dense matrix template void MulOp(GpuSparseMatrix& out, const GpuMatrix& a, diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index 94bee72034ff28..e194b702a607a5 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -18,10 +18,7 @@ limitations under the License. */ #include "paddle/math/SparseMatrix.h" namespace paddle { -/** - * out = scaleT * out + scaleAB * (a * b) - * out : output matrix, M * N - */ +/// dense matrix (+)= dense matrix * dense matrix template <> void MulOp(GpuMatrix& out, const GpuMatrix& a, @@ -32,14 +29,11 @@ void MulOp(GpuMatrix& out, bool bTrans, bool cTrans) { CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - real* aData = const_cast(a.getData()); - real* bData = const_cast(b.getData()); - real* outData = const_cast(out.getData()); - hl_matrix_mul(aData, + hl_matrix_mul(const_cast(a.getData()), !aTrans ? HPPL_OP_N : HPPL_OP_T, - bData, + const_cast(b.getData()), !bTrans ? HPPL_OP_N : HPPL_OP_T, - outData, + const_cast(out.getData()), out.getHeight(), out.getWidth(), !aTrans ? a.getWidth() : a.getHeight(), @@ -50,10 +44,7 @@ void MulOp(GpuMatrix& out, out.getStride()); } -/** - * out = scaleT * out + scaleAB * (a * b) - * out : M * N - */ +/// dense matrix (+)= sparse matrix * dense matrix template <> void MulOp(GpuMatrix& out, const GpuSparseMatrix& a, @@ -66,15 +57,11 @@ void MulOp(GpuMatrix& out, CHECK(out.isContiguous()); CHECK(b.isContiguous()); CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - - hl_sparse_matrix_s aData = a.sMatrix_.get(); - real* bData = const_cast(b.getData()); - real* outData = const_cast(out.getData()); - hl_matrix_csr_mul_dense(aData, + hl_matrix_csr_mul_dense(a.sMatrix_.get(), aTrans ? HPPL_OP_T : HPPL_OP_N, - bData, + const_cast(b.getData()), HPPL_OP_N, - outData, + const_cast(out.getData()), out.getHeight(), out.getWidth(), b.getHeight(), @@ -82,10 +69,7 @@ void MulOp(GpuMatrix& out, scaleT); } -/** - * out = scaleT * out + scaleAB * (a * b) - * out : M * N - */ +/// dense matrix (+)= dense matrix * sparse matrix template <> void MulOp(GpuMatrix& out, const GpuMatrix& a, @@ -99,27 +83,23 @@ void MulOp(GpuMatrix& out, CHECK(a.isContiguous()); CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - hl_sparse_matrix_s bData = b.sMatrix_.get(); - real* aData = const_cast(a.getData()); - real* outData = const_cast(out.getData()); - if (b.format_ == SPARSE_CSC) { - hl_matrix_dense_mul_csc(aData, + hl_matrix_dense_mul_csc(const_cast(a.getData()), HPPL_OP_N, - bData, + b.sMatrix_.get(), bTrans ? HPPL_OP_T : HPPL_OP_N, - outData, + const_cast(out.getData()), out.getHeight(), out.getWidth(), a.getWidth(), scaleAB, scaleT); } else { - hl_matrix_dense_mul_csr(aData, + hl_matrix_dense_mul_csr(const_cast(a.getData()), HPPL_OP_N, - bData, + b.sMatrix_.get(), bTrans ? HPPL_OP_T : HPPL_OP_N, - outData, + const_cast(out.getData()), out.getHeight(), out.getWidth(), a.getWidth(), @@ -128,6 +108,7 @@ void MulOp(GpuMatrix& out, } } +/// sparse matrix (+)= dense matrix * dense matrix template <> void MulOp(GpuSparseMatrix& out, const GpuMatrix& a, @@ -138,16 +119,11 @@ void MulOp(GpuSparseMatrix& out, bool bTrans, bool cTrans) { CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; - - real* aData = const_cast(a.getData()); - real* bData = const_cast(b.getData()); - hl_sparse_matrix_s outData = out.sMatrix_.get(); - - hl_sparse_matrix_mul(aData, + hl_sparse_matrix_mul(const_cast(a.getData()), aTrans ? HPPL_OP_T : HPPL_OP_N, - bData, + const_cast(b.getData()), bTrans ? HPPL_OP_T : HPPL_OP_N, - outData, + out.sMatrix_.get(), out.getHeight(), out.getWidth(), !bTrans ? b.getHeight() : b.getWidth(), diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index f67fa41612cf06..0aca3eb40b7c31 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -13,8 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #include -/// todo(tianbing), delete -#include #include "FunctionTest.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" diff --git a/paddle/function/TensorType.h b/paddle/function/TensorType.h index 73ac1cc70d2cd8..8308bbd8ad4fe1 100644 --- a/paddle/function/TensorType.h +++ b/paddle/function/TensorType.h @@ -31,6 +31,10 @@ enum DeviceType { DEVICE_TYPE_GPU = 2 }; +enum SparseDataType { T_NO_VALUE = 0, T_FLOAT_VALUE = 1 }; + +enum SparseDataFormat { T_SPARSE_CSR = 0, T_SPARSE_CSC = 1 }; + inline int sizeOfValuType(ValueType valueType) { if (valueType == VALUE_TYPE_INT32) { return 4; diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index dd24f8821d4976..57c0c2fe40a95d 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -31,6 +31,7 @@ limitations under the License. */ namespace paddle { +/// TODO(tianbing), move to paddle/function/TensorType.h enum SparseValueType { NO_VALUE = 0, FLOAT_VALUE = 1 }; /** @@ -56,6 +57,7 @@ enum SparseValueType { NO_VALUE = 0, FLOAT_VALUE = 1 }; * value [1, 1, 2, 2, 5] * @endcode */ +/// TODO(tianbing), move to paddle/function/TensorType.h enum SparseFormat { SPARSE_CSR = 0, SPARSE_CSC = 1 }; class Matrix; From 5b1a5c116a04895d0bedadcfc09ccae2154d7428 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 25 Jan 2017 11:28:45 -0800 Subject: [PATCH 107/370] Daoyuan's comments. --- paddle/function/FunctionTest.h | 2 +- paddle/function/MulOp.cpp | 59 ++++++++++++---------------------- paddle/function/MulOp.h | 25 ++++++-------- paddle/function/MulOpGpu.cu | 12 +++---- paddle/function/MulOpTest.cpp | 50 ++++++++-------------------- 5 files changed, 48 insertions(+), 100 deletions(-) diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 99669a4495578d..00f59f97d4c8c1 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -70,7 +70,7 @@ class FunctionCompare { } // output need only contains shape, do not contains data. - void addOutputs(const BufferArg& output, ArgType argType = ADD_TO) { + void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) { size_t size = output.shape().getElements() * sizeOfValuType(output.valueType()); cpuMemory_.emplace_back(std::make_shared(size)); diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 7bbdf7b2e47462..91b4b8ed91b605 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -49,8 +49,7 @@ void MulOp(CpuSparseMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { CHECK_EQ(out.getValueType(), FLOAT_VALUE); if (scaleT == 0) { out.zeroMem(); @@ -114,8 +113,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { GEMM(aTrans ? CblasTrans : CblasNoTrans, bTrans ? CblasTrans : CblasNoTrans, out.getHeight(), @@ -139,8 +137,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { if (scaleT == 0) { out.zeroMem(); } @@ -174,8 +171,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { if (scaleT == 0) { out.zeroMem(); } @@ -222,10 +218,10 @@ void MulOp(CpuMatrix& out, /** * mul operator - * out = scaleT * out + scaleAB * (in1 * in2) + * out = scaleT * out + scaleAB * (A * B) * here, scaleT in {0, 1}, scaleAB == 1, - * out = in1 (A) * in2 (B), ASSIGN_TO - * out += in1 (A) * in2 (B), ADD_TO + * out = A * B, ASSIGN_TO + * out += A * B, ADD_TO * * * \param outputs[0] output matrix (out), M * N, @@ -253,15 +249,11 @@ template class MulFunc : public FunctionBase { public: void init(const FuncConfig& config) override { - alpha_ = config.get("scaleAB"); - beta_ = config.get("scaleT"); aTrans_ = config.get("aTrans"); bTrans_ = config.get("bTrans"); - cTrans_ = config.get("cTrans"); } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK(!cTrans_) << "output matrix should not be transposed"; CHECK(!aTrans_ || !bTrans_) << "Not support both a and b are transpose matrices"; @@ -281,10 +273,8 @@ class MulFunc : public FunctionBase { CHECK_EQ(aRow, outputs[0].shape()[0]); CHECK_EQ(bCol, outputs[0].shape()[1]); - /// only support C = A * B or C += A * B - CHECK_EQ(alpha_, static_cast(1.0)); - CHECK((beta_ == 0 && outputs[0].getArgType() == ASSIGN_TO) || - (beta_ == 1 && outputs[0].getArgType() == ADD_TO)); + /// only support C = A * B (ASSIGN_TO) or C += A * B (ADD_TO) + real scaleT = (outputs[0].getArgType() == ADD_TO) ? 1.0 : 0.0; /// support dense = not both sparse * sparse /// or sparse = dense * dense @@ -300,11 +290,10 @@ class MulFunc : public FunctionBase { MulOp(outMat, inputs[0].matrix(), inputs[1].matrix(), - alpha_, - beta_, + 1.0, // scaleAB + scaleT, aTrans_, - bTrans_, - cTrans_); + bTrans_); return; } @@ -315,11 +304,10 @@ class MulFunc : public FunctionBase { MulOp(outMat, inputs[0].matrix(), inputs[1].sparse().SparseMatrix(), - alpha_, - beta_, + 1.0, // scaleAB + scaleT, aTrans_, - bTrans_, - cTrans_); + bTrans_); return; } @@ -332,11 +320,10 @@ class MulFunc : public FunctionBase { MulOp(outMat, inputs[0].sparse().SparseMatrix(), inputs[1].matrix(), - alpha_, - beta_, + 1.0, // scaleAB + scaleT, aTrans_, - bTrans_, - cTrans_); + bTrans_); return; } @@ -347,21 +334,17 @@ class MulFunc : public FunctionBase { MulOp(outSparseMat, inputs[0].matrix(), inputs[1].matrix(), - alpha_, - beta_, + 1.0, // scaleAB + scaleT, aTrans_, - bTrans_, - cTrans_); + bTrans_); return; } } private: - real alpha_; - real beta_; bool aTrans_; bool bTrans_; - bool cTrans_; }; REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); diff --git a/paddle/function/MulOp.h b/paddle/function/MulOp.h index 0991f69e1b286a..b6016a6ab6e9d6 100644 --- a/paddle/function/MulOp.h +++ b/paddle/function/MulOp.h @@ -27,8 +27,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// CPU, dense matrix (+)= sparse matrix * dense matrix template @@ -38,8 +37,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// CPU, dense matrix (+)= dense matrix * sparse matrix template @@ -49,8 +47,7 @@ void MulOp(CpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// CPU, sparse matrix (+)= dense matrix * dense matrix template @@ -60,8 +57,7 @@ void MulOp(CpuSparseMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// GPU, dense matrix (+)= dense matrix * dense matrix template @@ -71,8 +67,7 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// GPU, dense matrix (+)= sparse matrix * dense matrix template @@ -82,8 +77,7 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); /// GPU, dense matrix (+)= dense matrix * sparse matrix template @@ -93,8 +87,8 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); + /// GPU, sparse matrix (+)= dense matrix * dense matrix template void MulOp(GpuSparseMatrix& out, @@ -103,7 +97,6 @@ void MulOp(GpuSparseMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans); + bool bTrans); } // namespace paddle diff --git a/paddle/function/MulOpGpu.cu b/paddle/function/MulOpGpu.cu index e194b702a607a5..dcfcb2325d7dae 100644 --- a/paddle/function/MulOpGpu.cu +++ b/paddle/function/MulOpGpu.cu @@ -26,8 +26,7 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; hl_matrix_mul(const_cast(a.getData()), !aTrans ? HPPL_OP_N : HPPL_OP_T, @@ -52,8 +51,7 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { CHECK(out.isContiguous()); CHECK(b.isContiguous()); CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; @@ -77,8 +75,7 @@ void MulOp(GpuMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { CHECK(out.isContiguous()); CHECK(a.isContiguous()); CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; @@ -116,8 +113,7 @@ void MulOp(GpuSparseMatrix& out, real scaleAB, real scaleT, bool aTrans, - bool bTrans, - bool cTrans) { + bool bTrans) { CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match"; hl_sparse_matrix_mul(const_cast(a.getData()), aTrans ? HPPL_OP_T : HPPL_OP_N, diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 0aca3eb40b7c31..158c3c90983b12 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -27,8 +27,7 @@ using namespace paddle; // NOLINT */ void testFuncDDDMatrix( bool transa, bool transb, size_t dimM, size_t dimN, size_t dimK) { - real alpha = 1.0; - real beta = 1.0; + real scaleT = 1.0; size_t heightA = (transa == false) ? dimM : dimK; size_t widthA = (transa == false) ? dimK : dimM; size_t heightB = (transb == false) ? dimK : dimN; @@ -36,13 +35,8 @@ void testFuncDDDMatrix( size_t heightC = dimM; size_t widthC = dimN; // init Test object - FunctionCompare test("MulOp", - FuncConfig() - .set("scaleAB", alpha) - .set("scaleT", beta) - .set("aTrans", transa) - .set("bTrans", transb) - .set("cTrans", false)); + FunctionCompare test( + "MulOp", FuncConfig().set("aTrans", transa).set("bTrans", transb)); // prepare input arguments /// matrix A : HA * WA test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightA, widthA})); @@ -51,7 +45,7 @@ void testFuncDDDMatrix( /// output matrix C: HC * WC test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightC, widthC}), - beta == 1.0 ? ADD_TO : ASSIGN_TO); + scaleT == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -85,16 +79,10 @@ TEST(MulOp, DDDMatrixMul) { */ void testFuncDSparseDMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { - real alpha = 1.0; - real beta = 1.0; + real scaleT = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig() - .set("scaleAB", alpha) - .set("scaleT", beta) - .set("aTrans", false) - .set("bTrans", false) - .set("cTrans", false)); + FuncConfig().set("aTrans", false).set("bTrans", false)); // prepare input arguments /// sparse matrix A : M * K test.addInputs(SparseMatrixArg( @@ -104,7 +92,7 @@ void testFuncDSparseDMatrix( /// output matrix C: M * N test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), - beta == 1.0 ? ADD_TO : ASSIGN_TO); + scaleT == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -136,16 +124,10 @@ TEST(MuLOp, DSparseDMul) { */ void testFuncDDSparseMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { - real alpha = 1.0; - real beta = 1.0; + real scaleT = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig() - .set("scaleAB", alpha) - .set("scaleT", beta) - .set("aTrans", false) - .set("bTrans", false) - .set("cTrans", false)); + FuncConfig().set("aTrans", false).set("bTrans", false)); // prepare input arguments /// matrix A : M * K test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); @@ -156,7 +138,7 @@ void testFuncDDSparseMatrix( /// output matrix C: M * N test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}), - beta == 1.0 ? ADD_TO : ASSIGN_TO); + scaleT == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } @@ -188,16 +170,10 @@ TEST(MulOp, DDSparseMul) { */ void testFuncSparseDDMatrix( size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) { - real alpha = 1.0; - real beta = 1.0; + real scaleT = 1.0; // init Test object FunctionCompare test("MulOp", - FuncConfig() - .set("scaleAB", alpha) - .set("scaleT", beta) - .set("aTrans", false) - .set("bTrans", false) - .set("cTrans", false)); + FuncConfig().set("aTrans", false).set("bTrans", false)); // prepare input arguments /// matrix A : M * K test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK})); @@ -209,7 +185,7 @@ void testFuncSparseDDMatrix( test.addOutputs( SparseMatrixArg( VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}, nnz, FORMAT, FLOAT_VALUE), - beta == 1.0 ? ADD_TO : ASSIGN_TO); + scaleT == 1.0 ? ADD_TO : ASSIGN_TO); // run Function test.run(); } From 67bf06ce7301ee0abdaeaf65a3f7ec885469dd74 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 26 Jan 2017 11:15:51 -0800 Subject: [PATCH 108/370] paddle on aws with kubernetes tutorial now works --- doc/howto/usage/k8s/k8s_aws_en.md | 559 ++++++++---------- .../usage/k8s/src/add_security_group.png | Bin 237481 -> 118948 bytes doc/howto/usage/k8s/src/create_efs.png | Bin 250321 -> 241814 bytes doc/howto/usage/k8s/src/job.yaml | 43 -- doc/howto/usage/k8s/src/k8s_data/Dockerfile | 7 + doc/howto/usage/k8s/src/k8s_data/README.md | 6 + doc/howto/usage/k8s/src/k8s_data/get_data.sh | 26 + doc/howto/usage/k8s/src/k8s_train/Dockerfile | 6 + doc/howto/usage/k8s/src/k8s_train/README.md | 5 + .../usage/k8s/src/{ => k8s_train}/start.sh | 12 +- .../k8s/src/{ => k8s_train}/start_paddle.py | 29 +- .../usage/k8s/src/worker_security_group.png | Bin 0 -> 89208 bytes 12 files changed, 331 insertions(+), 362 deletions(-) delete mode 100644 doc/howto/usage/k8s/src/job.yaml create mode 100644 doc/howto/usage/k8s/src/k8s_data/Dockerfile create mode 100644 doc/howto/usage/k8s/src/k8s_data/README.md create mode 100755 doc/howto/usage/k8s/src/k8s_data/get_data.sh create mode 100644 doc/howto/usage/k8s/src/k8s_train/Dockerfile create mode 100644 doc/howto/usage/k8s/src/k8s_train/README.md rename doc/howto/usage/k8s/src/{ => k8s_train}/start.sh (55%) rename doc/howto/usage/k8s/src/{ => k8s_train}/start_paddle.py (84%) create mode 100644 doc/howto/usage/k8s/src/worker_security_group.png diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index a6422b9be00e21..7930807969c0e9 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -1,6 +1,49 @@ -# Kubernetes on AWS +# Distributed PaddlePaddle Training on AWS with Kubernetes -## Create AWS Account and IAM Account +We will show you step by step on how to run distributed PaddlePaddle training on AWS cluster with Kubernetes. Let's start from core concepts. + +## Distributed PaddlePaddle Training Core Concepts + +### Distributed Training Job + +A distributed training job is represented by a [Kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). + +Each Kuberentes job is described by a job config file, which specifies the information like the number of [pods](https://kubernetes.io/docs/user-guide/pods/#what-is-a-pod) in the job and environment variables. + +In a distributed training job, we would: + +1. prepare partitioned training data and configuration file on a distributed file system (in this tutorial we use Amazon Elastic File System), and +1. create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. + +### Parameter Servers and Trainers + +There are two roles in a PaddlePaddle cluster: *parameter server (pserver)* and *trainer*. Each parameter server process maintains a shard of the global model. Each trainer has its local copy of the model, and uses its local data to update the model. During the training process, trainers send model updates to parameter servers, parameter servers are responsible for aggregating these updates, so that trainers can synchronize their local copy with the global model. + +

![Model is partitioned into two shards. Managed by two parameter servers respectively.](src/pserver_and_trainer.png)
+ +In order to communicate with pserver, trainer needs to know the ip address of each pserver. In kubernetes it's better to use a service discovery mechanism (e.g., DNS hostname) rather than static ip address, since any pserver's pod may be killed and a new pod could be schduled onto another node of different ip address. However, now we are using static ip. This will be improved. + +Parameter server and trainer are packaged into a same docker image. They will run once pod is scheduled by kubernetes job. + +### Trainer ID + +Each trainer process requires a trainer ID, a zero-based index value, passed in as a command-line parameter. The trainer process thus reads the data partition indexed by this ID. + +### Training + +The entry-point of a container is a shell script. It can see some environment variables pre-defined by Kubernetes. This includes one that gives the job's identity, which can be used in a remote call to the Kubernetes apiserver that lists all pods in the job. + +We rank each pod by sorting them by their ips. The rank of each pod could be the "pod ID". Because we run one trainer and one parameter server in each pod, we can use this "pod ID" as the trainer ID. A detailed workflow of the entry-point script is as follows: + +1. Query the api server to get pod information, and assign the `trainer_id` by sorting the ip. +1. Copy the training data from EFS persistent volume into container. +1. Parse the `paddle pserver` and `paddle trainer` startup parameters from environment variables, and then start up the processes. +1. Trainer with `train_id` 0 will automatically write results onto EFS volume. + + +## PaddlePaddle on AWS with Kubernetes + +### Create AWS Account and IAM Account Under each AWS account, we can create multiple [IAM](http://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html) users. This allows us to grant some privileges to each IAM user and to create/operate AWS clusters as an IAM user. @@ -25,11 +68,6 @@ Please be aware that this tutorial needs the following privileges for the user i - AWSKeyManagementServicePowerUser -## PaddlePaddle on AWS - -Here we will show you step by step on how to run PaddlePaddle training on AWS cluster. - - ### Download kube-aws and kubectl #### kube-aws @@ -103,7 +141,6 @@ And then configure your AWS account information: ``` aws configure - ``` @@ -113,7 +150,7 @@ Fill in the required fields: ``` AWS Access Key ID: YOUR_ACCESS_KEY_ID AWS Secrete Access Key: YOUR_SECRETE_ACCESS_KEY -Default region name: us-west-1 +Default region name: us-west-2 Default output format: json ``` @@ -131,25 +168,28 @@ aws ec2 describe-instances The keypair that will authenticate SSH access to your EC2 instances. The public half of this key pair will be configured on each CoreOS node. -Follow [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) to create a EC2 key pair +Follow [EC2 Keypair User Guide](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) to create a EC2 key pair After creating a key pair, you will use the key pair name to configure the cluster. -Key pairs are only available to EC2 instances in the same region. We are using us-west-1 in our tutorial, so make sure to creat key pairs in that region (N. California). +Key pairs are only available to EC2 instances in the same region. We are using us-west-2 in our tutorial, so make sure to creat key pairs in that region (Oregon). + +Your browser will download a `key-name.pem` file which is the key to access the EC2 instances. We will use it later. + #### KMS key Amazon KMS keys are used to encrypt and decrypt cluster TLS assets. If you already have a KMS Key that you would like to use, you can skip creating a new key and provide the Arn string for your existing key. -You can create a KMS key in the AWS console, or with the aws command line tool: +You can create a KMS key with the aws command line tool: ``` -aws kms --region=us-west-1 create-key --description="kube-aws assets" +aws kms --region=us-west-2 create-key --description="kube-aws assets" { "KeyMetadata": { "CreationDate": 1458235139.724, "KeyState": "Enabled", - "Arn": "arn:aws:kms:us-west-1:aaaaaaaaaaaaa:key/xxxxxxxxxxxxxxxxxxx", + "Arn": "arn:aws:kms:us-west-2:aaaaaaaaaaaaa:key/xxxxxxxxxxxxxxxxxxx", "AWSAccountId": "xxxxxxxxxxxxx", "Enabled": true, "KeyUsage": "ENCRYPT_DECRYPT", @@ -161,14 +201,14 @@ aws kms --region=us-west-1 create-key --description="kube-aws assets" We will need to use the value of `Arn` later. -And then you need to add several inline policies in your user permission. +And then let's add several inline policies in your IAM user permission. -Go to IAM user page, click on `Add inline policy` button, and then select `Custom Policy` +Go to [IAM Console](https://console.aws.amazon.com/iam/home?region=us-west-2#/home). Click on button `Users`, click user that we just created, and then click on `Add inline policy` button, and select `Custom Policy`. -paste into following inline policies: +Paste into following inline policies: ``` -{ + (Caution: node_0, node_1, node_2 directories represents PaddlePaddle node and train_id, not the Kubernetes node){ "Version": "2012-10-17", "Statement": [ { @@ -195,7 +235,7 @@ paste into following inline policies: "cloudformation:DescribeStackEvents" ], "Resource": [ - "arn:aws:cloudformation:us-west-1:AWS_ACCOUNT_ID:stack/MY_CLUSTER_NAME/*" + "arn:aws:cloudformation:us-west-2:AWS_ACCOUNT_ID:stack/MY_CLUSTER_NAME/*" ] } ] @@ -214,20 +254,20 @@ aws sts get-caller-identity --output text --query Account When the cluster is created, the controller will expose the TLS-secured API on a DNS name. -The A record of that DNS name needs to be point to the cluster ip address. +DNS name should have a CNAME points to cluster DNS name or an A record points to the cluster IP address. -We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. +We will need to use DNS name later in tutorial. #### S3 bucket You need to create an S3 bucket before startup the Kubernetes cluster. -There are some bugs in aws cli in creating S3 bucket, so let's use the [Web console](https://console.aws.amazon.com/s3/home?region=us-west-1). +There are some bugs in aws cli in creating S3 bucket, so let's use the [S3 Console](https://console.aws.amazon.com/s3/home?region=us-west-2). -Click on `Create Bucket`, fill in a unique BUCKET_NAME, and make sure region is us-west-1 (Northern California). +Click on `Create Bucket`, fill in a unique BUCKET_NAME, and make sure region is us-west-2 (Oregon). -#### Initialize an asset directory +#### Initialize Assets Create a directory on your local machine to hold the generated assets: @@ -242,10 +282,10 @@ Initialize the cluster CloudFormation stack with the KMS Arn, key pair name, and kube-aws init \ --cluster-name=MY_CLUSTER_NAME \ --external-dns-name=MY_EXTERNAL_DNS_NAME \ ---region=us-west-1 \ ---availability-zone=us-west-1a \ +--region=us-west-2 \ +--availability-zone=us-west-2a \ --key-name=KEY_PAIR_NAME \ ---kms-key-arn="arn:aws:kms:us-west-1:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" +--kms-key-arn="arn:aws:kms:us-west-2:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" ``` `MY_CLUSTER_NAME`: the one you picked in [KMS key](#kms-key) @@ -256,14 +296,15 @@ kube-aws init \ `--kms-key-arn`: the "Arn" in [KMS key](#kms-key) -Here `us-west-1a` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. +Here `us-west-2a` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. -Please check if `us-west-1a` is supported by `aws ec2 --region us-west-1 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-1a`, or `us-west-1b`) +Please check if `us-west-2a` is supported by `aws ec2 --region us-west-2 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-2a`, or `us-west-2b`) -Note: please don't use `us-west-1c`. Subnets can currently only be created in the following availability zones: us-west-1b, us-west-1a. There will now be a cluster.yaml file in the asset directory. This is the main configuration file for your cluster. +By default `kube-aws` will only create one worker node. Let's edit `cluster.yaml` and change `workerCount` from 1 to 3. + #### Render contents of the asset directory @@ -278,41 +319,14 @@ The next command generates the default set of cluster assets in your asset direc ``` kube-aws render stack ``` - -Here's what the directory structure looks like: - -``` -$ tree -. -├── cluster.yaml -├── credentials -│ ├── admin-key.pem -│ ├── admin.pem -│ ├── apiserver-key.pem -│ ├── apiserver.pem -│ ├── ca-key.pem -│ ├── ca.pem -│ ├── worker-key.pem -│ └── worker.pem -│ ├── etcd-key.pem -│ └── etcd.pem -│ ├── etcd-client-key.pem -│ └── etcd-client.pem -├── kubeconfig -├── stack-template.json -└── userdata - ├── cloud-config-controller - └── cloud-config-worker -``` - -These assets (templates and credentials) are used to create, update and interact with your Kubernetes cluster. +Assets (templates and credentials) that are used to create, update and interact with your Kubernetes cluster will be created under your current folder. ### Kubernetes Cluster Start Up #### Create the instances defined in the CloudFormation template -Now let's create your cluster (choose any PREFIX for the command below): +Now let's create your cluster (choose any `PREFIX` for the command below): ``` kube-aws up --s3-uri s3://BUCKET_NAME/PREFIX @@ -328,239 +342,158 @@ You can invoke `kube-aws status` to get the cluster API endpoint after cluster c ``` $ kube-aws status Cluster Name: paddle-cluster -Controller DNS Name: paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com +Controller DNS Name: paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com ``` +If you own a DNS name, set the A record to any of the above ip. __Or__ you can set up CNAME point to `Controller DNS Name` (`paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com`) + +##### Find IP address + Use command `dig` to check the load balancer hostname to get the ip address. ``` -$ dig paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com +$ dig paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com ;; QUESTION SECTION: -;paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. IN A +;paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com. IN A ;; ANSWER SECTION: -paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.241.164.52 -paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.67.102.112 +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com. 59 IN A 54.241.164.52 +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-2.elb.amazonaws.com. 59 IN A 54.67.102.112 ``` In the above output, both ip `54.241.164.52`, `54.67.102.112` will work. -If you own a DNS name, set the A record to any of the above ip. Otherwise you can edit `/etc/hosts` to associate ip with the DNS name. #### Access the cluster Once the API server is running, you should see: ``` -$ kubectl --kubeconfig=kubeconfig get nodes -NAME STATUS AGE -ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m -ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m -ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m +$ kubectl --kubeconfig=kubeconfig get nodes +NAME STATUS AGE +ip-10-0-0-134.us-west-2.compute.internal Ready 6m +ip-10-0-0-238.us-west-2.compute.internal Ready 6m +ip-10-0-0-50.us-west-2.compute.internal Ready 6m +ip-10-0-0-55.us-west-2.compute.internal Ready 6m ``` ### Setup Elastic File System for Cluster -Training data is usually served on a distributed filesystem, we use Elastic File System (EFS) on AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps: - +Training data is usually served on a distributed filesystem, we use Elastic File System (EFS) on AWS. -1. Make sure you added AmazonElasticFileSystemFullAccess policy in your group. +1. Create security group for EFS in [security group console](https://us-west-2.console.aws.amazon.com/ec2/v2/home?region=us-west-2#SecurityGroups:sort=groupId) + 1. Look up security group id for `paddle-cluster-sg-worker` (`sg-055ee37d` in the image below) +
![](src/worker_security_group.png)
+ 2. Add security group `paddle-efs` with `ALL TCP` inbound rule and custom source as group id of `paddle-cluster-sg-worker`. And VPC of `paddle-cluster-vpc`. Make sure availability zone is same as the one you used in [Initialize Assets](#initialize-assets). +
![](src/add_security_group.png)
-1. Create the Elastic File System in AWS console, and attach the new VPC with it. +2. Create the Elastic File System in [EFS console](https://us-west-2.console.aws.amazon.com/efs/home?region=us-west-2#/wizard/1) with `paddle-cluster-vpc` VPC. Make sure subnet is `paddle-cluster-Subnet0` andd security group is `paddle-efs`.
![](src/create_efs.png)
-1. Modify the Kubernetes security group under ec2/Security Groups, add additional inbound policy "All TCP TCP 0 - 65535 0.0.0.0/0" for Kubernetes default VPC security group. -
![](src/add_security_group.png)
- - -1. Follow the EC2 mount instruction to mount the disk onto all the Kubernetes nodes, we recommend to mount EFS disk onto ~/efs. -
![](src/efs_mount.png)
- - -We will place user config and divided training data onto EFS. Training task will cache related files by copying them from EFS into container. It will also write the training results back onto EFS. We will show you how to place the data later in this article. - - - -### Core Concepts of PaddlePaddle Training on AWS - -Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume. In this training demo, we will create three Kubernetes pods and schedule them on three nodes. Each pod contains a PaddlePaddle container. When container gets created, it will start parameter server (pserver) and trainer process, load the training data from EFS volume and start the distributed training task. - -#### Distributed Training Job - -A distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). - -Each Kuberentes job is described by a job config file, which specifies the information like the number of pods in the job and environment variables. - -In a distributed training job, we would: - -1. upload the partitioned training data and configuration file onto EFS volume, and -1. create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. - -#### Parameter Servers and Trainers - -There are two roles in a PaddlePaddle cluster: `parameter server` and `trainer`. Each parameter server process maintains a shard of the global model. Each trainer has its local copy of the model, and uses its local data to update the model. During the training process, trainers send model updates to parameter servers, parameter servers are responsible for aggregating these updates, so that trainers can synchronize their local copy with the global model. - -
![Model is partitioned into two shards. Managed by two parameter servers respectively.](src/pserver_and_trainer.png)
- -In order to communicate with pserver, trainer needs to know the ip address of each pserver. In kubernetes it's better to use a service discovery mechanism (e.g., DNS hostname) rather than static ip address, since any pserver's pod may be killed and a new pod could be schduled onto another node of different ip address. We will improve paddlepaddle's service discovery ability. For now we will use static ip. - -Parameter server and trainer are packaged into a same docker image. They will run once pod is scheduled by kubernetes job. - -#### Trainer ID - -Each trainer process requires a trainer ID, a zero-based index value, passed in as a command-line parameter. The trainer process thus reads the data partition indexed by this ID. - -#### Training - -The entry-point of a container is a Python script. As it runs in a pod, it can see some environment variables pre-defined by Kubernetes. This includes one that gives the job's identity, which can be used in a remote call to the Kubernetes apiserver that lists all pods in the job. - -We rank each pod by sorting them by their ips. The rank of each pod could be the "pod ID". Because we run one trainer and one parameter server in each pod, we can use this "pod ID" as the trainer ID. A detailed workflow of the entry-point script is as follows: - -1. Query the api server to get pod information, and assign the `trainer_id` by sorting the ip. -1. Copy the training data from EFS sharing volume into container. -1. Parse the `paddle pserver` and `paddle trainer` startup parameters from environment variables, and then start up the processes. -1. Trainer with `train_id` 0 will automatically write results onto EFS volume. - - ### Start PaddlePaddle Training Demo on AWS -Now we'll start a PaddlePaddle training demo on AWS, steps are as follows: - -1. Build PaddlePaddle Docker image. -1. Divide the training data file and upload it onto the EFS sharing volume. -1. Create the training job config file, and start up the job. -1. Check the result after training. - -#### Build PaddlePaddle Docker Image - -PaddlePaddle docker image need to provide the runtime environment for `pserver` and `trainer`, so the container use this image should have two main function: - -1. Copy the training data into container. -1. Generate the startup parameter for `pserver` and `trainer` process, and startup the training. - +#### Configure Kubernetes Volume that Points to EFS -We need to create a new image since official `paddledev/paddle:cpu-latest` only have PaddlePaddle binary, but lack of the above functionalities. - -Dockerfile for creating the new image is as follows: +First we need to create a [PersistentVolume](https://kubernetes.io/docs/user-guide/persistent-volumes/) to provision EFS volumn. +Save following snippet as `pv.yaml` ``` -FROM paddledev/paddle:cpu-latest - -MAINTAINER zjsxzong89@gmail.com - -COPY start.sh /root/ -COPY start_paddle.py /root/ -CMD ["bash"," -c","/root/start.sh"] +apiVersion: v1 +kind: PersistentVolume +metadata: + name: efsvol +spec: + capacity: + storage: 100Gi + accessModes: + - ReadWriteMany + nfs: + server: EFS_DNS_NAME + path: "/" ``` -At this point, we will copy our `start.sh` and `start_paddle.py` file into container, and then exec `start_paddle.py` script to start up the training, all the steps like assigning trainer_id, getting other nodes' ip are implemented in `start_paddle.py`. - -`start_paddle.py` will start parsing the parameters. +`EFS_DNS_NAME`: DNS name as shown in description of `paddle-efs` that we created. Looks similar to `fs-2cbf7385.efs.us-west-2.amazonaws.com` +Run following command to create a persistent volumn: ``` -parser = argparse.ArgumentParser(prog="start_paddle.py", - description='simple tool for k8s') - args, train_args_list = parser.parse_known_args() - train_args = refine_unknown_args(train_args_list) - train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) - podlist = getPodList() +kubectl --kubeconfig=kubeconfig create -f pv.yaml ``` -And then using function `getPodList()` to query all the pod information from the job name through Kubernetes api server. When all the pods are in the running status, using `getIdMap(podlist)` to get the trainer_id. +Next let's create a [PersistentVolumeClaim](https://kubernetes.io/docs/user-guide/persistent-volumes/) to claim the persistent volume. +Save following snippet as `pvc.yaml`. ``` - podlist = getPodList() - # need to wait until all pods are running - while not isPodAllRunning(podlist): - time.sleep(10) - podlist = getPodList() - idMap = getIdMap(podlist) +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: efsvol +spec: + accessModes: + - ReadWriteMany + resources: + requests: + storage: 50Gi ``` -In function `getIdMap(podlist)`, we use podlist to get the ip address for each pod and sort them, use the index as the trainer_id. - +Run following command to create a persistent volumn claim: ``` -def getIdMap(podlist): - ''' - generate tainer_id by ip - ''' - ips = [] - for pod in podlist["items"]: - ips.append(pod["status"]["podIP"]) - ips.sort() - idMap = {} - for i in range(len(ips)): - idMap[ips[i]] = i - return idMap +kubectl --kubeconfig=kubeconfig create -f pvc.yaml ``` -After getting `idMap`, we use function `startPaddle(idMap, train_args_dict)` to generate `paddle pserver` and `paddle train` start up parameters and then start up the processes. +#### Prepare Training Data -In function `startPaddle`, the most important work is to generate `paddle pserver` and `paddle train` start up parameters. For example, `paddle train` parameter parsing, we will get parameters like `PADDLE_NIC`, `PADDLE_PORT`, `PADDLE_PORTS_NUM`, and get the `trainer_id` from `idMap`. +We will now launch a kubernetes job that downloads, saves and evenly splits training data into 3 shards on the persistent volumn that we just created. +save following snippet as `paddle-data-job.yaml` ``` - program = 'paddle train' - args = " --nics=" + PADDLE_NIC - args += " --port=" + str(PADDLE_PORT) - args += " --ports_num=" + str(PADDLE_PORTS_NUM) - args += " --comment=" + "paddle_process_by_paddle" - ip_string = "" - for ip in idMap.keys(): - ip_string += (ip + ",") - ip_string = ip_string.rstrip(",") - args += " --pservers=" + ip_string - args_ext = "" - for key, value in train_args_dict.items(): - args_ext += (' --' + key + '=' + value) - localIP = socket.gethostbyname(socket.gethostname()) - trainerId = idMap[localIP] - args += " " + args_ext + " --trainer_id=" + \ - str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT -``` - -Use `docker build` to build toe Docker Image: - -``` -docker build -t your_repo/paddle:mypaddle . +apiVersion: batch/v1 +kind: Job +metadata: + name: paddle-data +spec: + template: + metadata: + name: pi + spec: + containers: + - name: paddle-data + image: paddledev/paddle-tutorial:k8s_data + imagePullPolicy: Always + volumeMounts: + - mountPath: "/efs" + name: efs + env: + - name: OUT_DIR + value: /efs/paddle-cluster-job + - name: SPLIT_COUNT + value: "3" + volumes: + - name: efs + persistentVolumeClaim: + claimName: efsvol + restartPolicy: Never ``` -And then push the built image onto docker registry. - +Run following command to launch the job: ``` -docker push your_repo/paddle:mypaddle +kubectl --kubeconfig=kubeconfig create -f paddle-data-job.yaml ``` -#### Upload Training Data File - -Here we will use PaddlePaddle's official recommendation demo as the content for this training, we put the training data file into a directory named by job name, which located in EFS sharing volume, the tree structure for the directory looks like: - +Job may take 7 min to finish, use following command to check job status. Do not proceed until `SUCCESSFUL` for `paddle-data` job is `1` ``` -efs -└── paddle-cluster-job - ├── data - │ ├── 0 - │ │ - │ ├── 1 - │ │ - │ └── 2 - ├── output - └── recommendation +$ kubectl --kubeconfig=kubeconfig get jobs +NAME DESIRED SUCCESSFUL AGE +paddle-data 1 1 6m ``` -The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the partitioned data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. - +Data preparation is done by docker image `paddledev/paddle-tutorial:k8s_data`, see [here](src/k8s_data/) for how to build this docker image and source code. -#### Create Kubernetes Job - -Kubernetes use yaml file to describe job details, and then use command line tool to create the job in Kubernetes cluster. - -In yaml file, we describe the Docker image we use for this training, the node number we need to startup, the volume mounting information and all the necessary parameters we need for `paddle pserver` and `paddle train` processes. - -The yaml file content is as follows: +#### Start Training +Now we are ready to start paddle training job. Save following snippet as `paddle-cluster-job.yaml` ``` apiVersion: batch/v1 kind: Job @@ -574,12 +507,12 @@ spec: name: paddle-cluster-job spec: volumes: - - name: jobpath - hostPath: - path: /home/admin/efs + - name: efs + persistentVolumeClaim: + claimName: efsvol containers: - name: trainer - image: drinkcode/paddle:k8s-job + image: paddledev/paddle-tutorial:k8s_train command: ["bin/bash", "-c", "/root/start.sh"] env: - name: JOB_NAME @@ -589,7 +522,7 @@ spec: - name: JOB_NAMESPACE value: default - name: TRAIN_CONFIG_DIR - value: recommendation + value: quick_start - name: CONF_PADDLE_NIC value: eth0 - name: CONF_PADDLE_PORT @@ -600,106 +533,124 @@ spec: value: "2" - name: CONF_PADDLE_GRADIENT_NUM value: "3" + - name: TRAINER_COUNT + value: "3" volumeMounts: - - name: jobpath - mountPath: /home/jobpath + - mountPath: "/home/jobpath" + name: efs ports: - - name: jobport - hostPort: 30001 - containerPort: 30001 + - name: jobport0 + hostPort: 7164 + containerPort: 7164 + - name: jobport1 + hostPort: 7165 + containerPort: 7165 + - name: jobport2 + hostPort: 7166 + containerPort: 7166 + - name: jobport3 + hostPort: 7167 + containerPort: 7167 restartPolicy: Never - ``` -In yaml file, the metadata's name is the job's name. `parallelism, completions` means this job will simultaneously start up 3 PaddlePaddle nodes, and this job will be finished when there are 3 finished pods. For the data store volume, we declare the path jobpath, it mount the /home/admin/efs on host machine into the container with path /home/jobpath. So in container, the /home/jobpath actually stores the data onto EFS sharing volume. - -`env` field represents container's environment variables, we pass the PaddlePaddle parameters into containers by using the `env` field. +`parallelism: 3, completions: 3` means this job will simultaneously start 3 PaddlePaddle pods, and this job will be finished when there are 3 finished pods. -`JOB_PATH` represents the sharing volume path, `JOB_NAME` represents job name, `TRAIN_CONFIG_DIR` represents the training data file directory, we can these three parameters to get the file path for this training. +`env` field represents container's environment variables, we specify PaddlePaddle parameters by environment variables. -`CONF_PADDLE_NIC` represents `paddle pserver` process's `--nics` parameters, the NIC name. +`ports` indicates that TCP port 7164 - 7167 are exposed for communication between `pserver` ans trainer. port starts continously from `CONF_PADDLE_PORT` (7164) to `CONF_PADDLE_PORT + CONF_PADDLE_PORTS_NUM + CONF_PADDLE_PORTS_NUM_SPARSE - 1` (7167). We use multiple ports for dense and sparse paramter updates to improve latency. -`CONF_PADDLE_PORT` represents `paddle pserver` process's `--port` parameters, `CONF_PADDLE_PORTS_NUM` represents `--port_num` parameter. - -`CONF_PADDLE_PORTS_NUM_SPARSE` represents the sparse updated port number, `--ports_num_for_sparse` parameter. +Run following command to launch the job. +``` +kubectl --kubeconfig=kubeconfig create -f paddle-claster-job.yaml +``` -`CONF_PADDLE_GRADIENT_NUM` represents the training node number, `--num_gradient_servers` parameter. +Inspect individual pods -After we create the yaml file, we can use Kubernetes command line tool to create the job onto the cluster. +``` +$ kubectl --kubeconfig=kubeconfig get pods +NAME READY STATUS RESTARTS AGE +paddle-cluster-job-cm469 1/1 Running 0 9m +paddle-cluster-job-fnt03 1/1 Running 0 9m +paddle-cluster-job-jx4xr 1/1 Running 0 9m +``` +Inspect individual console output ``` -kubectl create -f job.yaml +kubectl --kubeconfig=kubeconfig log -f POD_NAME ``` -After we execute the above command, Kubernetes will create 3 pods and then pull the PaddlePaddle image, then start up the containers for training. +`POD_NAME`: name of any pod (e.g., `paddle-cluster-job-cm469`). +Run `kubectl --kubeconfig=kubeconfig describe job paddle-cluster-job` to check training job status. It will complete in around 20 minutes. +The details for start `pserver` and `trainer` are hidden inside docker image `paddledev/paddle-tutorial:k8s_train`, see [here](src/k8s_train/) for how to build the docker image and source code. -#### Check Training Results +#### Inspect Training Output -During the training, we can see the logs and models on EFS sharing volume, the output directory contains the training results. (Caution: node_0, node_1, node_2 directories represents PaddlePaddle node and train_id, not the Kubernetes node) +Training output (model snapshot and logs) will be saved in EFS. We can ssh into worker EC2 instance, mount EFS and check training output. +1. ssh Into Worker EC2 instance ``` -[root@paddle-kubernetes-node0 output]# tree -d -. -├── node_0 -│ ├── server.log -│ └── train.log -├── node_1 -│ ├── server.log -│ └── train.log -├── node_2 -...... -├── pass-00002 -│ ├── done -│ ├── ___embedding_0__.w0 -│ ├── ___embedding_1__.w0 -...... +chmod 400 key-name.pem +ssh -i key-name.pem core@INSTANCE_IP ``` -We can always check the container training status through logs, for example: +`INSTANCE_IP`: public IP address of EC2 kubernetes worker node. Go to [EC2 console](https://us-west-2.console.aws.amazon.com/ec2/v2/home?region=us-west-2#Instances:sort=instanceId) and check `public IP` of any `paddle-cluster-kube-aws-worker` instance. +2. Mount EFS ``` -[root@paddle-kubernetes-node0 node_0]# cat train.log -I1116 09:10:17.123121 50 Util.cpp:155] commandline: - /usr/local/bin/../opt/paddle/bin/paddle_trainer - --nics=eth0 --port=7164 - --ports_num=2 --comment=paddle_process_by_paddle - --pservers=192.168.129.66,192.168.223.143,192.168.129.71 - --ports_num_for_sparse=2 --config=./trainer_config.py - --trainer_count=4 --num_passes=10 --use_gpu=0 - --log_period=50 --dot_period=10 --saving_period=1 - --local=0 --trainer_id=0 - --save_dir=/home/jobpath/paddle-cluster-job/output -I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions -I1116 09:10:17.123764 50 Util.cpp:143] Call runInitFunctions done. -[WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config. -[INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating] -[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__regression_cost_0__] -I1116 09:10:17.392917 50 Trainer.cpp:170] trainer mode: Normal -I1116 09:10:17.613910 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process -I1116 09:10:17.680917 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process -I1116 09:10:17.681543 50 GradientMachine.cpp:134] Initing parameters.. -I1116 09:10:18.012390 50 GradientMachine.cpp:141] Init parameters done. -I1116 09:10:18.018641 50 ParameterClient2.cpp:122] pserver 0 192.168.129.66:7164 -I1116 09:10:18.018950 50 ParameterClient2.cpp:122] pserver 1 192.168.129.66:7165 -I1116 09:10:18.019069 50 ParameterClient2.cpp:122] pserver 2 192.168.223.143:7164 -I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143:7165 -I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164 -I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165 +mkdir efs +sudo mount -t nfs4 -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 EFS_DNS_NAME:/ efs ``` -It'll take around 8 hours to finish this PaddlePaddle recommendation training demo on three 2 core 8 GB EC2 machine (m3.large). +`EFS_DNS_NAME`: DNS name as shown in description of `paddle-efs` that we created. Look similar to `fs-2cbf7385.efs.us-west-2.amazonaws.com`. +Now folder `efs` will have structure similar to: +``` +-- paddle-cluster-job + |-- ... + |-- output + | |-- node_0 + | | |-- server.log + | | `-- train.log + | |-- node_1 + | | |-- server.log + | | `-- train.log + | |-- node_2 + | | |-- server.log + | | `-- train.log + | |-- pass-00000 + | | |-- ___fc_layer_0__.w0 + | | |-- ___fc_layer_0__.wbias + | | |-- done + | | |-- path.txt + | | `-- trainer_config.lr.py + | |-- pass-00001... +``` +`server.log` contains log for `pserver`. `train.log` contains log for `trainer`. Model description and snapshot is stored in `pass-0000*`. ### Kubernetes Cluster Tear Down +#### Delete EFS + +Go to [EFS Console](https://us-west-2.console.aws.amazon.com/efs/home?region=us-west-2) and delete the EFS volumn that we created. + +#### Delete security group + +Go to [Security Group Console](https://us-west-2.console.aws.amazon.com/ec2/v2/home?region=us-west-2#SecurityGroups:sort=groupId) and delete security group `paddle-efs`. -If you want to tear down the whole Kubernetes cluster, make sure to *delete* the EFS volume first (otherwise, you will get stucked on following steps), and then use the following command: + +#### Delete S3 Bucket + +Go to [S3 Console](https://console.aws.amazon.com/s3/home?region=us-west-2#) and delete the S3 bucket that we created. + +#### Destroy Cluster ``` kube-aws destroy ``` -It's an async call, it might take 5 min to tear down the whole cluster. -If you created any Kubernetes Services of type LoadBalancer, you must delete these first, as the 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![route53 zone setting](src/route53_create_zone.png)
- Add A record - Click on the zone "paddle" just created - Click the button "Create record set" - Name : leave blank - type: "A" - - Value: + - Value: `` + -
![route53 create recordset](src/route53_create_recordset.png)
- Verify name service - Connect to any instance created by kube-aws via ssh - Run command "host paddle", see if the ip returned is the private ip of kube-controller diff --git a/doc/howto/usage/k8s/src/route53_create_recordset.png b/doc/howto/usage/k8s/src/route53_create_recordset.png new file mode 100644 index 0000000000000000000000000000000000000000..c4afac6b666186920707668a8d74bc0d6ad3bc07 GIT binary patch literal 114396 zcmdqIWk6g@(*_6xcXtQ@f&{n0o#5^c1Hs+h-91=v3&EY>PH=aJ;O(pV2Q4JNyn~8N*=N zmP4Z>BSI=*Kd^ryB^$Y~x;es_%P^zV%faM?^L>cewN}hrB90-Y{f|1g9nlYDB zcTzL+$eLqdY$GrsfH5bHU>AaClW&aBuIG1hNTNsAL(uDiog0Vpa_(Ew_n?pvCud~5 zsba=6xJ-Pzng2{@JZLHn;I_gXvX6sbh+!BUF&KN{Pu}v8a{lc430Zjl>wZ@@mBgs+ z%8l_(pd=&Krl_E#D`by3s@}Ro!40}6gAO4kO~dJKsOLr+IJ7TA`p^wRkUES(FbB)5 zR`l_LP%%TTSoi&XCzJgb(&4c9elY|{jSb)hht!l?Cz&ga+|ReX1_^^Gu)(;h`0k~Q zycD{97*^El{XxQ|kai(dEL_Sbk6#&A*gbnhsiNa;0l{}V-xx_GjRVY#BSbH{EIBOq zwJWh1RlVJ%V~261*2B=dL^*8TykD4+ag!-SlN`GBo$f^jyrk@FNVjk1RdrSM6D>2U 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zx`r*n4d+JJghuR)w_3m__GaeC*1kqnD9%tzxiP}pKHks-j$`JQ`V^SG3MCqtbIdaX1_;dPQI1O%gNF++U!v^4U!qNlNlzX!Rdf_GM_GY|r?Cdpy991&5EL>z&R zH=3u&?UaIx*OjpQfv2BLu$0g>BLd6TI@A?~-Z84RTOQ8$f-1^Rw-bmJF-TU!zyA^} zsZ>JnD^{Z3@pP=l3b*E)u=bwWzxgR|wfACs;0wiJ%x9QPL+4ZW9?4*2#hWj8y8AlI&--Ln9qa#gEO0hzb{Y znxaHmcAp7<$&Cb4a_GXv9lZl5tKo$H#z;fXEB5(UxA}MA+LVZNige#7Ov4K zzEuBWFp7KMkV@KnmWNi_rq>YYGo3zV$U)C@v%$+Z5CUUB2Y~?6Q&5ip@&0^Z-9&Xa zW^boG>#@8Vy@G+}?iuPnIX;8X><)b)B_(W{%#Q}!k>#Wd-a8Mi??BE0h*&N7e?i?9 zmuKLvO2f6of|-Mn-^&czypm}kn76{Z93fMq2|cP#t{+SWTpu>FHTwZ#>NadRq6|&M zNWhSJ>G;-cqtg{$Zh80*pF9Btr9?257jvbV4T31 zv!#R>N+X|`<|49LKR_N!Lh^GWPe<%H)%IxS_Q(hg&GLE0I}i*udxIvE0!(3Lj}gOt3#bbq?9(L+`}07=%4c*$AOk z?&?IJg6?-y=XNSHjSV!)lW+S_J;w#Ogy^ z8LIuC6=t6~z`8rs(SH+pKOPz5Bb5EVM&fdO`0m;E$A;tZ1RIuJ8W=_x}+Z From 1cc336218b669fdebc144348824c9eaa7eb7af53 Mon Sep 17 00:00:00 2001 From: xichen Date: Wed, 1 Feb 2017 15:56:00 -0800 Subject: [PATCH 135/370] add image size control --- doc/howto/usage/k8s/k8s_aws_en.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index eda2b639e2f5d9..8d88e9d1cd6097 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -384,14 +384,14 @@ Edit `/etc/hosts` to associate above ip with the DNS name. - Domain name: "paddle" - Type: "Private hosted zone for amazon VPC" - VPC ID: `` - -

![route53 zone setting](src/route53_create_zone.png)
+ -
![route53 zone setting](src/route53_create_zone.png =400x)
- Add A record - Click on the zone "paddle" just created - Click the button "Create record set" - Name : leave blank - type: "A" - Value: `` - -
![route53 create recordset](src/route53_create_recordset.png)
+ -
![route53 create recordset](src/route53_create_recordset.png =400x)
- Verify name service - Connect to any instance created by kube-aws via ssh - Run command "host paddle", see if the ip returned is the private ip of kube-controller From e81d557e36182d1e04174ebf718422d8435f192b Mon Sep 17 00:00:00 2001 From: xichen Date: Wed, 1 Feb 2017 16:22:14 -0800 Subject: [PATCH 136/370] visual tweaks for route53 config --- doc/howto/usage/k8s/k8s_aws_en.md | 6 ++++-- .../usage/k8s/src/route53_create_zone.png | Bin 60518 -> 52035 bytes 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 8d88e9d1cd6097..ce72b0803818d5 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -384,14 +384,16 @@ Edit `/etc/hosts` to associate above ip with the DNS name. - Domain name: "paddle" - Type: "Private hosted zone for amazon VPC" - VPC ID: `` - -
![route53 zone setting](src/route53_create_zone.png =400x)
+ + ![route53 zone setting](src/route53_create_zone.png) - Add A record - Click on the zone "paddle" just created - Click the button "Create record set" - Name : leave blank - type: "A" - Value: `` - -
![route53 create recordset](src/route53_create_recordset.png =400x)
+ + ![route53 create recordset](src/route53_create_recordset.png) - Verify name service - Connect to any instance created by kube-aws via ssh - Run command "host paddle", see if the ip returned is the private ip of kube-controller diff --git a/doc/howto/usage/k8s/src/route53_create_zone.png b/doc/howto/usage/k8s/src/route53_create_zone.png index b8162da4426b38241222323fa4a51b98f820888d..25b7ddb831c5cba97f4b2edddd27da3234d621af 100644 GIT binary patch literal 52035 zcmbrlWmp`~y6#P|ph1JXyGw8n?g{P^+=B%R&fxA622TjC!4jMyFgU^8-5uU0|Fhn` z*E#F#FQ>oETzz$QcU4zC&wW?@3Z$kYi;hBs0s{ksE-xpo0R!`56b1%X9tjaRb3umA z00T3KA}=lR&TDqREj;UW&*j6zU5n%WqR$ZQX2j9etnLh zKw@A*GNL7jr;tFveb<`q*5Rw5-NDPdu$2w^jb(bK)B?Q-#b$>q}^S{Dae zmo@HVUe-_G4~G&s%#Kem#73*`Fqf* z)Bin^N|XK`@)Q3QpxOQBN(*dv*XM%^Bq;EFWJu>^KOaiqnLu{`ej|exN}rFv#%GeC zY|sY(WA&)l_v23wiKMr?Tl>e;9lY1Nx!&eym-Y+2-)GuY<2II@W`A(|xQ{p3Z^`EI zm)Yl$-ti{Kbi{c3Z!ieWjGZwX`%^z1t|~rWI}YE=C^Ie9WyFQ~ z77M>AaDhT^*KS4|m(-7DU*2I%+nLi&_B_|Nj5J|eHoRkKSm3IaqMx!v;4JSqng9C9 zTH!|@)5I+7jP9H+7sFq6)gB#+VjhNi0xZ)&aevI^C>L50Lxk3d#N{aWNuRV{KYLK!+w$W5?XiPgF%5qQ@}4x#o&#KRuA$wM2kW`i#Rh5Yjnwp+p@Cl zLuBcqLWSWUgt2FJXl`!L%BhGL17JQ8k+^ov(!o zv?%5<<>Kl~TgbMRtr;%8#p+vZ{;v*tV|tAo~f%q9-#G=L#cl1epN#=V7TI@cx(l{9TxE1+%`Z!cEuu-|5- zj?<4Hj7@?NuC!b@wdEb$U!U#otK@WAOm<+F^^ z4o;HpfSc|dIi%hPi2 zO6niF$Dvkh^eV5Oq_*d-{6EdZ`qO7|?6jNY#I~IsX2Vy%s=l@0l$_F;z$|6i84b{@ z5UFTonh%(}2ECWZwL3W%8{{`xzG$ZPKY|-SYi&5Vf77y;`?Aw&5h^mhXC-W~H_M!0Bt*Y597C!IG+&)x|-Ly!iV>va_HpY1j@Evt~KP@fq^Xh@U@U-}R(S93e^8uH-FbyEl#V0nB&Om|)vGG{BucM7|J#7vBU|bH)|OT|>p@a)xror; zxMU|csMVH+SWO+Mi< z1S$FhH034<%OV&`gA22+52B}kz^(Rm#D`9kF&ATEavzNtL1xOY2;RIgIGS%(yu9@B z*)`~JKmQ{(-=ax|l2<(&CHL+kRYp0-ML^L-=h2FAGOUbh^*P#3DAAzE8+uPp3M9-s zk5?J!n8&hje2 zsr~UNVavVOg-X@1;iR}kMo}xoPA6?vT;|ihm;VH#134d6wLacp%Uz20hWqG~dGDfb zo^;xGCiiA=l^RBJWF7kA)3KTtSty00Yy!gpp$EUye5&E(UZxOOm(ptV?<2X%v~I^x8feq2cV z149~*@6E|s_W1(?M(bX!dB=oXi$>){P=V5XoY0#J<}0bXJl_+)+K((^Lg3#F95LRx z?%EI#^DNGDt}08B`&WXt{ogWky&#C_Is2=>N-70@Ow*;&pTz-4myYbxrGrH_6cDG-<--RJoe&fI17scNGhwk;sQoxhQT)UsoqI;H+a zueF%BTQ2MU32N=iS$y>~!5eO4_V2VZv{<&)?vA8s$~Qfed=lF?UQ zrm`F7=6u>Mo4HyFSSr0Y^4>_XKV2Uvk~}ZYazr_@WBDqw^yd_sBgDTqR-~v5f%DD_ zc)T$+ctybRMLB-c?`k3KyKXJrlkbdWnk9zVz4N!UBFck5*shhdmKE}K8FJ@+k1*Zlnk?NV4Wm z*j8k3!xaP#3m(*s*T|Cl)qVYJ7Nn9VDw6fa#f;CqH+uh1wfT+6ecWYlJ@D@QKEPK$ zd(pr2npq2e=&_>Z5VH9@1IJ)MnA>iu{P@wdE6h?=?BV0%@7@^VT1L&{ko#QU(*cfc z_<3k6xpM5O904w}XS>gOg6uABN>1yC;cUXiV9UoTeWwj*g>i?d4m>fRJ(AYsg69(H zE|w~b!6(fU6?DsdCCdu>dAEr>psS(h@jES&EcUqx8xJ6O9xwljZ=A*ZrD5Y*so%&p zFRQPqJ^Lfof414pswu2Uex@lL+LZi?R4`fOd_*cg2kY^h)^`*riO46OED;|MuW6C1 zd7N7XNB7yd%dhpUE_ouyKjHF~y((uT`HILGDC#JWq9pWB{UN<)6Xe)t27brCUgpoc z7{c!=&bgtZS_7@@eP9R0zwd&{epD=uTJ4j<$>+HsEB9cmV^;69DueZE)O z@c}A&XHSYPgJ%=XakVi+835_@Eny*AVnDJi1j zL}hPPb7JdpjS?+(N|>>9trW1Rm|eY?HijNqF7~XEdvcm*xfZ)I=Fqo>`y|xXpG*9f z^_9)>E|yAQc-?Z+OC0(Fc^WR18+n8R=n&`5-<_7)R|H`d z;1Z!y;je~(?tzb}T4~hQnTc`ZaxkXIjvf?xPf+eFOX$>c&~W?|Pbt;OODDiqS_>5w zLR++}Y~y3s#{!97{xP2chL6;bl`!H{pK7$LgE@Qyz8oRXS8&y`abzn*Nthhf*X5sf z3~MSdnGDthLJ)37Vr5|2rdW2^A3Juxe(Q8o(mFL2`PLtFFj1!IF)D)}3i{e0(}eqC zrOW-Wz;DE3hZw0$PfSACmi*RsqEvk)0CGKRS84fhcZHEx*|ZuN&ky@}I>?R@6bC)W zpU%*+6>CQRR!j=lG{-|LIZmvXI@cX zGC9q_RsNa9;J<}Bs0FDQuNg2d)`I;ZKY6S1W;mEDSJ$*lKT80*R{%WY+! 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zp9}9=aKhZVC%x@oYXRG>&Uw>a((St(cE3?Av+i2NMn!OYW_204b08V$nUX;Bq}g^n^T)~; zyx|GBpT9|>VA&@QM3wKmA@+}W(RkDR%XbbgpV)IX-`^Pqd5ZGS`$y39;!oZ=3f*4Z zYk^O79Hq{mdi}HRoRsM7`_DgWa_irV=8-M?`#X2%4)4&cLpaE_>22I>8-_f4r3RN;aNMsna~_>)-Kby@tQqA6SFbQJNAuhF+`MPL>R;0znteT-PSYiBz^cb*{X_95@43yJ%6y}l z!)v5cw_4!4_YWICvVVQ&EcN)Dgm<;$lRIZYQDuRiocQ3y7>qn0^bfR*nD2W1XQUCw|07*qoM6N<$f+;F)4FCWD From ca367a80e3eb019aa5290055552228f844ade130 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 1 Mar 2017 10:58:22 +0800 Subject: [PATCH 360/370] follow comments --- python/paddle/v2/topology.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 6329915350bbb4..e2709e39de9d9d 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -74,12 +74,10 @@ def find_data_layer(layer): if isinstance(layer, v2_layer.DataLayerV2): data_layers[layer.name] = layer if not isinstance(layer, collections.Sequence): - for parent_layer in layer.__parent_layers__.values(): + layer = [layer] + for each_l in layer: + for parent_layer in each_l.__parent_layers__.values(): find_data_layer(parent_layer) - else: - for each_l in layer: - for parent_layer in each_l.__parent_layers__.values(): - find_data_layer(parent_layer) for layer in self.layers: find_data_layer(layer) @@ -93,8 +91,8 @@ def data_type(self): """ data_types_lists = [] + data_layers = self.data_layers() for each in self.__model_config__.input_layer_names: - data_layers = self.data_layers() data_types_lists.append((each, data_layers[each].type)) return data_types_lists From ca6fc145e3c6ec1d2a4dd3648f9564e48e311e7a Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 1 Mar 2017 12:19:52 +0800 Subject: [PATCH 361/370] add some comments --- paddle/py_paddle/dataprovider_converter.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 60a0f2c0444863..57e58e077d7fbe 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -26,7 +26,14 @@ def __init__(self, input_type, pos): if not isinstance(self.input_type, dp2.InputType): raise ValueError("input type should be dataprovider2.InputType") self.pos = pos - self.use_gpu = True if swig_paddle.isUsingGpu() and ( + # data_in_gpu is used to indicate whether to create argument on GPU + # or not in GPU mode. Now if using one thread (trainer_count=1), + # trainer uses NeuralNetwork which needs to create argument on GPU + # before calling forward function. So, set data_in_gpu to True. + # Otherwise, trainer uses MultiGradientMachine which will transfer + # data from CPU to GPU in the forward function, set data_in_gpu to + # False in this case. + self.data_in_gpu = True if swig_paddle.isUsingGpu() and ( swig_paddle.getTrainerCount() == 1) else False def scan(self, dat): From 566e5f1fcbe77e2e2f931e977064fcb878d4908b Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 1 Mar 2017 13:25:51 +0800 Subject: [PATCH 362/370] rename use_gpu to data_in_gpu --- paddle/py_paddle/dataprovider_converter.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 57e58e077d7fbe..c009b05cdeeb9d 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -33,8 +33,8 @@ def __init__(self, input_type, pos): # Otherwise, trainer uses MultiGradientMachine which will transfer # data from CPU to GPU in the forward function, set data_in_gpu to # False in this case. - self.data_in_gpu = True if swig_paddle.isUsingGpu() and ( - swig_paddle.getTrainerCount() == 1) else False + self.data_in_gpu = swig_paddle.isUsingGpu( + ) and swig_paddle.getTrainerCount() == 1 def scan(self, dat): pass @@ -63,7 +63,7 @@ def finish_scan(self, argument): if self.__mat__.dtype != numpy.float32: self.__mat__ = self.__mat__.astype(numpy.float32) m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, - self.use_gpu) + self.data_in_gpu) argument.setSlotValue(self.pos, m) @@ -115,7 +115,7 @@ def scan(self, dat): self.__ids__.append(dat) def finish_scan(self, argument): - ids = swig_paddle.IVector.create(self.__ids__, self.use_gpu) + ids = swig_paddle.IVector.create(self.__ids__, self.data_in_gpu) assert isinstance(argument, swig_paddle.Arguments) argument.setSlotIds(self.pos, ids) From 4fa6e8e5cb1993a5c9b96bc42d90c0b5d56e6dd2 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Wed, 1 Mar 2017 13:43:23 +0800 Subject: [PATCH 363/370] Refine doc fix link error --- doc/howto/usage/k8s/k8s_distributed_cn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index 3a47a3832fee82..4027f804a639e0 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -2,7 +2,7 @@ 前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。 -有关Kubernetes相关概念以及如何搭建和配置Kubernetes集群，可以参考[k8s_basis](./k8s_basis.md)。 +有关Kubernetes相关概念以及如何搭建和配置Kubernetes集群，可以参考[k8s_basis](./k8s_basis_cn.md)。 ## 整体方案 From 6dd21658dc96ac46c22c2287a21cd78c1f616667 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 14:07:04 +0800 Subject: [PATCH 364/370] Add config_base.py for Layer --- python/paddle/v2/config_base.py | 72 ++++++++++++++++++++++++++++++++ python/paddle/v2/layer.py | 73 +-------------------------------- 2 files changed, 73 insertions(+), 72 deletions(-) create mode 100644 python/paddle/v2/config_base.py diff --git a/python/paddle/v2/config_base.py b/python/paddle/v2/config_base.py new file mode 100644 index 00000000000000..c733f4866dca0a --- /dev/null +++ b/python/paddle/v2/config_base.py @@ -0,0 +1,72 @@ +import collections + +from paddle.trainer_config_helpers.default_decorators import wrap_name_default +import paddle.trainer_config_helpers as conf_helps + + +class Layer(object): + def __init__(self, name=None, parent_layers=None): + assert isinstance(parent_layers, dict) + self.name = name + self.__parent_layers__ = parent_layers + + def to_proto(self, context): + """ + function to set proto attribute + """ + kwargs = dict() + for layer_name in self.__parent_layers__: + if not isinstance(self.__parent_layers__[layer_name], + collections.Sequence): + v1_layer = self.__parent_layers__[layer_name].to_proto( + context=context) + else: + v1_layer = map(lambda x: x.to_proto(context=context), + self.__parent_layers__[layer_name]) + kwargs[layer_name] = v1_layer + + if self.name is None: + return self.to_proto_impl(**kwargs) + elif self.name not in context: + context[self.name] = self.to_proto_impl(**kwargs) + + return context[self.name] + + def to_proto_impl(self, **kwargs): + raise NotImplementedError() + + +def __convert_to_v2__(method_name, parent_names, is_default_name=True): + if is_default_name: + wrapper = wrap_name_default(name_prefix=method_name) + else: + wrapper = None + + class V2LayerImpl(Layer): + def __init__(self, **kwargs): + parent_layers = dict() + other_kwargs = dict() + for pname in parent_names: + if kwargs.has_key(pname): + parent_layers[pname] = kwargs[pname] + + for key in kwargs.keys(): + if key not in parent_names: + other_kwargs[key] = kwargs[key] + + name = kwargs.get('name', None) + super(V2LayerImpl, self).__init__(name, parent_layers) + self.__other_kwargs__ = other_kwargs + + if wrapper is not None: + __init__ = wrapper(__init__) + + def to_proto_impl(self, **kwargs): + args = dict() + for each in kwargs: + args[each] = kwargs[each] + for each in self.__other_kwargs__: + args[each] = self.__other_kwargs__[each] + return getattr(conf_helps, method_name)(**args) + + return V2LayerImpl diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 19f3c2f998d83c..67111f1315fbb0 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -65,10 +65,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. Also, the creation of a protobuf message is hidden in the invocation of paddle.v2.parameters.create, no longer exposed to users. """ - -import collections -import inspect - +from config_base import Layer, __convert_to_v2__ import paddle.trainer_config_helpers as conf_helps from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ @@ -107,74 +104,6 @@ def __real_func__(): return __parse__(__real_func__) -class Layer(object): - def __init__(self, name=None, parent_layers=None): - assert isinstance(parent_layers, dict) - self.name = name - self.__parent_layers__ = parent_layers - - def to_proto(self, context): - """ - function to set proto attribute - """ - kwargs = dict() - for layer_name in self.__parent_layers__: - if not isinstance(self.__parent_layers__[layer_name], - collections.Sequence): - v1_layer = self.__parent_layers__[layer_name].to_proto( - context=context) - else: - v1_layer = map(lambda x: x.to_proto(context=context), - self.__parent_layers__[layer_name]) - kwargs[layer_name] = v1_layer - - if self.name is None: - return self.to_proto_impl(**kwargs) - elif self.name not in context: - context[self.name] = self.to_proto_impl(**kwargs) - - return context[self.name] - - def to_proto_impl(self, **kwargs): - raise NotImplementedError() - - -def __convert_to_v2__(method_name, parent_names, is_default_name=True): - if is_default_name: - wrapper = wrap_name_default(name_prefix=method_name) - else: - wrapper = None - - class V2LayerImpl(Layer): - def __init__(self, **kwargs): - parent_layers = dict() - other_kwargs = dict() - for pname in parent_names: - if kwargs.has_key(pname): - parent_layers[pname] = kwargs[pname] - - for key in kwargs.keys(): - if key not in parent_names: - other_kwargs[key] = kwargs[key] - - name = kwargs.get('name', None) - super(V2LayerImpl, self).__init__(name, parent_layers) - self.__other_kwargs__ = other_kwargs - - if wrapper is not None: - __init__ = wrapper(__init__) - - def to_proto_impl(self, **kwargs): - args = dict() - for each in kwargs: - args[each] = kwargs[each] - for each in self.__other_kwargs__: - args[each] = self.__other_kwargs__[each] - return getattr(conf_helps, method_name)(**args) - - return V2LayerImpl - - """ Some layer may need some special config, and can not use __convert_to_v2__ to convert. So we also need to implement some special LayerV2. From 057d0cbd6ebb8fdd97f324c58deaf7b9cdf11c61 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 14:19:35 +0800 Subject: [PATCH 365/370] Complete networks. --- python/paddle/v2/__init__.py | 3 ++- python/paddle/v2/config_base.py | 14 ++++++++++ python/paddle/v2/networks.py | 45 +++++++++++++++++++++++++++++++++ 3 files changed, 61 insertions(+), 1 deletion(-) create mode 100644 python/paddle/v2/networks.py diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index b31efe170dbf11..f055c845c7c42b 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -24,12 +24,13 @@ from . import reader import attr import pooling +import networks import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader', - 'topology' + 'topology', 'networks' ] diff --git a/python/paddle/v2/config_base.py b/python/paddle/v2/config_base.py index c733f4866dca0a..035f96b0f2e978 100644 --- a/python/paddle/v2/config_base.py +++ b/python/paddle/v2/config_base.py @@ -1,3 +1,17 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import collections from paddle.trainer_config_helpers.default_decorators import wrap_name_default diff --git a/python/paddle/v2/networks.py b/python/paddle/v2/networks.py new file mode 100644 index 00000000000000..74d91593d8551b --- /dev/null +++ b/python/paddle/v2/networks.py @@ -0,0 +1,45 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle.trainer_config_helpers.networks as conf_nw +import inspect +from config_base import __convert_to_v2__ + +__all__ = [] + + +def __initialize__(): + for each_subnetwork in conf_nw.__all__: + if each_subnetwork in ['inputs', 'outputs']: + continue + func = getattr(conf_nw, each_subnetwork) + if hasattr(func, 'argspec'): + argspec = func.argspec + else: + argspec = inspect.getargspec(func) + if each_subnetwork == 'simple_attention': + parents = ['encoded_sequence', 'encoded_proj', 'decoder_state'] + else: + parents = filter(lambda x: x.startswith('input'), argspec.args) + assert len(parents) != 0, each_subnetwork + v2_subnet = __convert_to_v2__( + each_subnetwork, + parent_names=parents, + is_default_name='name' in argspec.args) + globals()[each_subnetwork] = v2_subnet + global __all__ + __all__.append(each_subnetwork) + + +__initialize__() From 3590cb1e4d32fdba7505d0f6a98aacf999927314 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 14:23:25 +0800 Subject: [PATCH 366/370] Add unittests --- python/paddle/v2/tests/test_layer.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index b138ddbbe6c0a4..0055679a91801a 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -18,6 +18,7 @@ import paddle.v2.data_type as data_type import paddle.v2.layer as layer import paddle.v2.pooling as pooling +import paddle.v2.networks as networks pixel = layer.data(name='pixel', type=data_type.dense_vector(128)) label = layer.data(name='label', type=data_type.integer_value(10)) @@ -251,5 +252,13 @@ def test_operator(self): print layer.parse_network(conv1) +class NetworkTests(unittest.TestCase): + def test_vgg(self): + img = layer.data(name='pixel', type=data_type.dense_vector(784)) + vgg_out = networks.small_vgg( + input_image=img, num_channels=1, num_classes=2) + print layer.parse_network(vgg_out) + + if __name__ == '__main__': unittest.main() From 91d818866a650ebaec266805b56c55f76c618de9 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Wed, 1 Mar 2017 14:48:43 +0800 Subject: [PATCH 367/370] Refine k8s doc change title --- doc/howto/usage/k8s/k8s_distributed_cn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index 4027f804a639e0..7213a977b8a2a4 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -1,4 +1,4 @@ -# 使用Paddle在Kubernetes集群上完成分布式训练 +# Kubernetes分布式训练 前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。 From 4c24ac1a9952349c0965d5c50a07878ec2632e17 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 14:50:55 +0800 Subject: [PATCH 368/370] Init inferencer. --- python/paddle/v2/__init__.py | 6 +++- python/paddle/v2/inferencer.py | 54 ++++++++++++++++++++++++++++++++++ 2 files changed, 59 insertions(+), 1 deletion(-) create mode 100644 python/paddle/v2/inferencer.py diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index b31efe170dbf11..cc8f33f9800391 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -24,12 +24,13 @@ from . import reader import attr import pooling +import inferencer import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader', - 'topology' + 'topology', 'inferencer', 'infer' ] @@ -39,3 +40,6 @@ def init(**kwargs): args.append('--%s=%s' % (key, str(kwargs[key]))) api.initPaddle(*args) + + +infer = inferencer.infer diff --git a/python/paddle/v2/inferencer.py b/python/paddle/v2/inferencer.py new file mode 100644 index 00000000000000..36a8ee37114e20 --- /dev/null +++ b/python/paddle/v2/inferencer.py @@ -0,0 +1,54 @@ +import py_paddle.swig_paddle as api + +import topology +from data_feeder import DataFeeder +import itertools +import numpy + +__all__ = ['InferenceEngine', 'infer'] + + +class InferenceEngine(object): + def __init__(self, output, parameters): + topo = topology.Topology(output) + gm = api.GradientMachine.createFromConfigProto( + topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE]) + for param in gm.getParameters(): + val = param.getBuf(api.PARAMETER_VALUE) + name = param.getName() + assert isinstance(val, api.Matrix) + val.copyFromNumpyMat(parameters.get(name)) + self.__gradient_machine__ = gm + self.__data_types__ = topo.data_type() + + def iter_infer(self, reader, reader_dict=None): + feeder = DataFeeder(self.__data_types__, reader_dict) + out_args = api.Arguments.createArguments(0) + self.__gradient_machine__.start() + for data_batch in reader(): + yield self.__gradient_machine__.forwardTest( + feeder(data_batch), out_args, api.PASS_TEST) + self.__gradient_machine__.finish() + + def iter_infer_field(self, field, **kwargs): + for result in self.iter_infer(**kwargs): + yield [each_result[field] for each_result in result] + + def infer(self, field='value', **kwargs): + retv = [] + for result in itertools.izip( + self.iter_infer_field( + field=field, **kwargs)): + retv.append(numpy.concatenate(result)) + return retv + + def default_reader_dict(self): + reader_dict = dict() + for i, tp in enumerate(self.__data_types__): + reader_dict[tp[0]] = i + return reader_dict + + +def infer(output, parameters, reader, reader_dict=None, field='value'): + inferer = InferenceEngine(output=output, parameters=parameters) + return inferer.infer(field=field, reader=reader, reader_dict=reader_dict) From 9ba231d3d1c13923bb42a02a88f92bce2fc6afd1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 15:49:39 +0800 Subject: [PATCH 369/370] Complete inferencer. --- demo/mnist/api_train_v2.py | 13 +++++++++++++ python/paddle/v2/dataset/mnist.py | 29 ++++++++++++++-------------- python/paddle/v2/inferencer.py | 27 +++++++++++++++----------- python/paddle/v2/reader/decorator.py | 20 ++++++++++++++++--- 4 files changed, 61 insertions(+), 28 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index a59b30ccdb2edd..637596e7bc1c0a 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -44,6 +44,19 @@ def event_handler(event): batch_size=32), event_handler=event_handler) + # output is a softmax layer. It returns probabilities. + # Shape should be (100, 10) + probs = paddle.infer( + output=inference, + parameters=parameters, + reader=paddle.reader.batched( + paddle.reader.limited( + paddle.reader.map_readers(lambda item: (item[0], ), + paddle.dataset.mnist.test()), + limit=100), + batch_size=32)) + print probs.shape + if __name__ == '__main__': main() diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 1512a3c3189de4..ebcdff78b317ce 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -35,24 +35,25 @@ def reader(): l = subprocess.Popen([zcat_cmd, label_filename], stdout=subprocess.PIPE) l.stdout.read(8) # skip some magic bytes - while True: - labels = numpy.fromfile( - l.stdout, 'ubyte', count=buffer_size).astype("int") + try: # reader could be break. + while True: + labels = numpy.fromfile( + l.stdout, 'ubyte', count=buffer_size).astype("int") - if labels.size != buffer_size: - break # numpy.fromfile returns empty slice after EOF. + if labels.size != buffer_size: + break # numpy.fromfile returns empty slice after EOF. - images = numpy.fromfile( - m.stdout, 'ubyte', count=buffer_size * 28 * 28).reshape( - (buffer_size, 28 * 28)).astype('float32') + images = numpy.fromfile( + m.stdout, 'ubyte', count=buffer_size * 28 * 28).reshape( + (buffer_size, 28 * 28)).astype('float32') - images = images / 255.0 * 2.0 - 1.0 + images = images / 255.0 * 2.0 - 1.0 - for i in xrange(buffer_size): - yield images[i, :], int(labels[i]) - - m.terminate() - l.terminate() + for i in xrange(buffer_size): + yield images[i, :], int(labels[i]) + finally: + m.terminate() + l.terminate() return reader diff --git a/python/paddle/v2/inferencer.py b/python/paddle/v2/inferencer.py index 36a8ee37114e20..33f5ad1c07c6e2 100644 --- a/python/paddle/v2/inferencer.py +++ b/python/paddle/v2/inferencer.py @@ -16,18 +16,18 @@ def __init__(self, output, parameters): for param in gm.getParameters(): val = param.getBuf(api.PARAMETER_VALUE) name = param.getName() - assert isinstance(val, api.Matrix) - val.copyFromNumpyMat(parameters.get(name)) + assert isinstance(val, api.Vector) + val.copyFromNumpyArray(parameters.get(name).flatten()) self.__gradient_machine__ = gm self.__data_types__ = topo.data_type() def iter_infer(self, reader, reader_dict=None): + if reader_dict is None: + reader_dict = self.default_reader_dict() feeder = DataFeeder(self.__data_types__, reader_dict) - out_args = api.Arguments.createArguments(0) self.__gradient_machine__.start() for data_batch in reader(): - yield self.__gradient_machine__.forwardTest( - feeder(data_batch), out_args, api.PASS_TEST) + yield self.__gradient_machine__.forwardTest(feeder(data_batch)) self.__gradient_machine__.finish() def iter_infer_field(self, field, **kwargs): @@ -35,12 +35,17 @@ def iter_infer_field(self, field, **kwargs): yield [each_result[field] for each_result in result] def infer(self, field='value', **kwargs): - retv = [] - for result in itertools.izip( - self.iter_infer_field( - field=field, **kwargs)): - retv.append(numpy.concatenate(result)) - return retv + retv = None + for result in self.iter_infer_field(field=field, **kwargs): + if retv is None: + retv = [[]] * len(result) + for i, item in enumerate(result): + retv[i].append(item) + retv = [numpy.concatenate(out) for out in retv] + if len(retv) == 1: + return retv[0] + else: + return retv def default_reader_dict(self): reader_dict = dict() diff --git a/python/paddle/v2/reader/decorator.py b/python/paddle/v2/reader/decorator.py index 5687f118ce2779..fe5acbdff59447 100644 --- a/python/paddle/v2/reader/decorator.py +++ b/python/paddle/v2/reader/decorator.py @@ -14,13 +14,13 @@ __all__ = [ 'map_readers', 'buffered', 'compose', 'chain', 'shuffle', - 'ComposeNotAligned', 'batched' + 'ComposeNotAligned', 'batched', 'limited' ] -from Queue import Queue -from threading import Thread import itertools import random +from Queue import Queue +from threading import Thread def map_readers(func, *readers): @@ -213,3 +213,17 @@ def batched_reader(): yield batch return batched_reader + + +def limited(reader, limit): + """ + Limit the max number of samples that reader could return. + """ + + def limited_reader(): + for i, item in enumerate(reader()): + if i == limit: + break + yield item + + return limited_reader From 500d8836d03287621e983f815a919d9a29749c6d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 1 Mar 2017 18:49:13 +0800 Subject: [PATCH 370/370] Follow comments --- demo/mnist/api_train_v2.py | 4 ++-- python/paddle/v2/inferencer.py | 6 +++--- python/paddle/v2/reader/decorator.py | 13 ++++++++----- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 637596e7bc1c0a..06beb7024d1fd0 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -50,10 +50,10 @@ def event_handler(event): output=inference, parameters=parameters, reader=paddle.reader.batched( - paddle.reader.limited( + paddle.reader.firstn( paddle.reader.map_readers(lambda item: (item[0], ), paddle.dataset.mnist.test()), - limit=100), + n=100), batch_size=32)) print probs.shape diff --git a/python/paddle/v2/inferencer.py b/python/paddle/v2/inferencer.py index 33f5ad1c07c6e2..ac03b016c9b8bf 100644 --- a/python/paddle/v2/inferencer.py +++ b/python/paddle/v2/inferencer.py @@ -5,10 +5,10 @@ import itertools import numpy -__all__ = ['InferenceEngine', 'infer'] +__all__ = ['Inference', 'infer'] -class InferenceEngine(object): +class Inference(object): def __init__(self, output, parameters): topo = topology.Topology(output) gm = api.GradientMachine.createFromConfigProto( @@ -55,5 +55,5 @@ def default_reader_dict(self): def infer(output, parameters, reader, reader_dict=None, field='value'): - inferer = InferenceEngine(output=output, parameters=parameters) + inferer = Inference(output=output, parameters=parameters) return inferer.infer(field=field, reader=reader, reader_dict=reader_dict) diff --git a/python/paddle/v2/reader/decorator.py b/python/paddle/v2/reader/decorator.py index fe5acbdff59447..b7657e27764f09 100644 --- a/python/paddle/v2/reader/decorator.py +++ b/python/paddle/v2/reader/decorator.py @@ -14,7 +14,7 @@ __all__ = [ 'map_readers', 'buffered', 'compose', 'chain', 'shuffle', - 'ComposeNotAligned', 'batched', 'limited' + 'ComposeNotAligned', 'batched', 'firstn' ] import itertools @@ -215,15 +215,18 @@ def batched_reader(): return batched_reader -def limited(reader, limit): +def firstn(reader, n): """ Limit the max number of samples that reader could return. """ - def limited_reader(): + # TODO(yuyang18): Check if just drop the reader, could clean the opened + # resource or not? + + def firstn_reader(): for i, item in enumerate(reader()): - if i == limit: + if i == n: break yield item - return limited_reader + return firstn_reader

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zAxc+)6L%+$CJIZcK1@x;;3;ES&s0czj^Qu=BvM6c%RmQ)m%^G?A;}Wr-C3Ffa;&sD zRdpIy>gZFcBKb+`&OETY!|P6&Q1W6>V=eXdh1ez9vI=&E65xu8p|( z0qmi;xR}t2k;0|#UbTm-4|RbMr{5BQc^ptvM#3wm^6BY~(EQ&PH~we{=u zfvv9>Khl8A0T9AQEzCc5b#!)GUR(P6k`ORo=VSCFf{-6GIGpY3Tb*}+aR@NI$?))S zFt{%-P$H2O;0F(~Hc%fU>hC+|8{f0Fx96s+t}B37nLn~hUZ`U_IGBQ|)z(AM#0^jx zL!z`sooUOCSY)N(+3`2QT|)LPfuL1;cU-HsRZ{mi+PkEtfTs?}tLD#ojdz?c87 zC&$0FX0nbSukVd*a*-_vKq@bH`>2o#m34vVP1Z@eOvF`%c=hb*jt-WF286YHz{BK< z<$3K;B&3|`{+>x`n9Am4X1*6H8+szip%HR0>ypW!rFe7&sXFpC;AC&!&rls`Rkw*9LgUHl}%%4bpBhmBr8Tr%g3VS zULlr;vodnlM#fu)-g&&aj4D}}5wgXNwc_>I2nMmBr6cFk)6<#eU1K)VO3%x&xFV0T nXk=*E78GxYJ^4R=n!@Hzow=b{nG)e=My#J{=WHvq@jvqqm+3b2 From de4ee731bcdf0b2ce3e719806a47777d2ae520bd Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Tue, 28 Feb 2017 17:23:43 +0800 Subject: [PATCH 349/370] Refine k8s distributed training doc --- doc/howto/usage/k8s/k8s_basis.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_basis.md b/doc/howto/usage/k8s/k8s_basis.md index e296d69dceb416..f42631ced00858 100644 --- a/doc/howto/usage/k8s/k8s_basis.md +++ b/doc/howto/usage/k8s/k8s_basis.md @@ -12,9 +12,7 @@ - [*Namespaces*](https://kubernetes.io/docs/user-guide/namespaces/) 命名空间，在kubernetes中创建的所有资源对象(例如上文的pod，job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 -- [*PersistentVolume*]() - -- [*PersistentVolumeClaim*]() +- [*PersistentVolume*](https://kubernetes.io/docs/user-guide/persistent-volumes/): 和[*PersistentVolumeClaim*](https://kubernetes.io/docs/user-guide/persistent-volumes/#persistentvolumeclaims)结合，将外部的存储服务在Kubernetes中描述成为统一的资源形式，便于存储资源管理和Pod引用。 # 部署Kubernetes集群 From fe44bc9082f6d1467fc90d68718902ed298b51dd Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Tue, 28 Feb 2017 18:37:57 +0800 Subject: [PATCH 350/370] Refine k8s doc update --- doc/howto/index_cn.rst | 1 + doc/howto/usage/k8s/{k8s_basis.md => k8s_basis_cn.md} | 0 2 files changed, 1 insertion(+) rename doc/howto/usage/k8s/{k8s_basis.md => k8s_basis_cn.md} (100%) diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst index bd3d0ec2920570..5b84eea491f874 100644 --- a/doc/howto/index_cn.rst +++ b/doc/howto/index_cn.rst @@ -10,6 +10,7 @@ usage/cmd_parameter/index_cn.rst usage/concepts/use_concepts_cn.rst usage/cluster/cluster_train_cn.md + usage/k8s/k8s_basis_cn.md usage/k8s/k8s_cn.md usage/k8s/k8s_distributed_cn.md diff --git a/doc/howto/usage/k8s/k8s_basis.md b/doc/howto/usage/k8s/k8s_basis_cn.md similarity index 100% rename from doc/howto/usage/k8s/k8s_basis.md rename to doc/howto/usage/k8s/k8s_basis_cn.md From 6bf6034ac63d3dc9336c89d5d38aa9f0d6262238 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 28 Feb 2017 18:43:32 +0800 Subject: [PATCH 351/370] follow comments --- paddle/api/PaddleAPI.h | 2 +- paddle/py_paddle/dataprovider_converter.py | 11 ++++------- python/paddle/v2/tests/test_data_feeder.py | 1 - 3 files changed, 5 insertions(+), 9 deletions(-) diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index 0223dec4f42efe..d99e9a4ad48ea4 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -47,7 +47,7 @@ void setUseGpu(bool useGpu); /// Return true if this py_paddle is compiled in GPU Version bool isGpuVersion(); -/// Return FLAGS_use_gpu +/// Return FLAGS_trainer_count int getTrainerCount(); /// The Error of IO Operation. Such as file not found, etc. diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 8d5c57e10fd358..60a0f2c0444863 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -26,6 +26,8 @@ def __init__(self, input_type, pos): if not isinstance(self.input_type, dp2.InputType): raise ValueError("input type should be dataprovider2.InputType") self.pos = pos + self.use_gpu = True if swig_paddle.isUsingGpu() and ( + swig_paddle.getTrainerCount() == 1) else False def scan(self, dat): pass @@ -33,11 +35,6 @@ def scan(self, dat): def finish_scan(self, argument): pass - def use_gpu(self): - gpu = True if swig_paddle.isUsingGpu() and ( - swig_paddle.getTrainerCount() == 1) else False - return gpu - class DenseScanner(IScanner): """ @@ -59,7 +56,7 @@ def finish_scan(self, argument): if self.__mat__.dtype != numpy.float32: self.__mat__ = self.__mat__.astype(numpy.float32) m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, - self.use_gpu()) + self.use_gpu) argument.setSlotValue(self.pos, m) @@ -111,7 +108,7 @@ def scan(self, dat): self.__ids__.append(dat) def finish_scan(self, argument): - ids = swig_paddle.IVector.create(self.__ids__, self.use_gpu()) + ids = swig_paddle.IVector.create(self.__ids__, self.use_gpu) assert isinstance(argument, swig_paddle.Arguments) argument.setSlotIds(self.pos, ids) diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py index 916d3aa4cdcbca..ab2bc5df76cd83 100644 --- a/python/paddle/v2/tests/test_data_feeder.py +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -240,4 +240,3 @@ def test_multiple_features_tuple(self): if api.isGpuVersion(): api.setUseGpu(True) unittest.main() - unittest.main() From ffd045a0d0c127e28eb9283c91f9f439984a0bdf Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 28 Feb 2017 21:49:54 +0800 Subject: [PATCH 352/370] Fix data_layers and data_type function in topology.py --- python/paddle/v2/topology.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index a51b1073b4fc4f..6329915350bbb4 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -68,13 +68,18 @@ def data_layers(self): get all data layer :return: """ - data_layers = set() + data_layers = dict() def find_data_layer(layer): if isinstance(layer, v2_layer.DataLayerV2): - data_layers.add(layer) - for parent_layer in layer.__parent_layers__.values(): - find_data_layer(parent_layer) + data_layers[layer.name] = layer + if not isinstance(layer, collections.Sequence): + for parent_layer in layer.__parent_layers__.values(): + find_data_layer(parent_layer) + else: + for each_l in layer: + for parent_layer in each_l.__parent_layers__.values(): + find_data_layer(parent_layer) for layer in self.layers: find_data_layer(layer) @@ -86,8 +91,12 @@ def data_type(self): get data_type from proto, such as: [('image', dense_vector(768)), ('label', integer_value(10))] """ - return [(data_layer.name, data_layer.type) - for data_layer in self.data_layers()] + + data_types_lists = [] + for each in self.__model_config__.input_layer_names: + data_layers = self.data_layers() + data_types_lists.append((each, data_layers[each].type)) + return data_types_lists def __check_layer_type__(layer): From 89d300191eed4f2ec75d673ebdce80a671e3cac1 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 19:49:46 +0000 Subject: [PATCH 353/370] Add early draft of imdb.py and unit test --- python/paddle/v2/dataset/imdb.py | 19 +++++++++++++++++++ python/paddle/v2/dataset/tests/imdb_test.py | 6 ++++++ 2 files changed, 25 insertions(+) create mode 100644 python/paddle/v2/dataset/imdb.py create mode 100644 python/paddle/v2/dataset/tests/imdb_test.py diff --git a/python/paddle/v2/dataset/imdb.py b/python/paddle/v2/dataset/imdb.py new file mode 100644 index 00000000000000..7a191cb583d4ce --- /dev/null +++ b/python/paddle/v2/dataset/imdb.py @@ -0,0 +1,19 @@ +# /usr/bin/env python +# -*- coding:utf-8 -*- + +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz +""" diff --git a/python/paddle/v2/dataset/tests/imdb_test.py b/python/paddle/v2/dataset/tests/imdb_test.py new file mode 100644 index 00000000000000..fafaf414044a4a --- /dev/null +++ b/python/paddle/v2/dataset/tests/imdb_test.py @@ -0,0 +1,6 @@ +import paddle.v2.dataset.common + +URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz' +MD5 = '7c2ac02c03563afcf9b574c7e56c153a' + +paddle.v2.dataset.common.download From 349319e5ed9c2ddb0af5a6fb66280c6b7b3bb552 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 20:02:32 +0000 Subject: [PATCH 354/370] Add imdb_test.py --- python/paddle/v2/dataset/tests/imdb_test.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/dataset/tests/imdb_test.py b/python/paddle/v2/dataset/tests/imdb_test.py index fafaf414044a4a..5cfb6203e78ec1 100644 --- a/python/paddle/v2/dataset/tests/imdb_test.py +++ b/python/paddle/v2/dataset/tests/imdb_test.py @@ -1,6 +1,12 @@ import paddle.v2.dataset.common +import tarfile URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz' MD5 = '7c2ac02c03563afcf9b574c7e56c153a' -paddle.v2.dataset.common.download +tarf = tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb', MD5)) + +tf = tarf.next() +while tf != None: + print tf.name + tf = tarf.next() From da6af5917f3e055b12163da4e048de73f4d2d86b Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 06:35:27 +0000 Subject: [PATCH 355/370] Add imikolov.py and unittest for book chapter word2vec --- python/paddle/v2/dataset/imikolov.py | 88 +++++++++++++++++++ .../paddle/v2/dataset/tests/imikolov_test.py | 20 +++++ 2 files changed, 108 insertions(+) create mode 100644 python/paddle/v2/dataset/imikolov.py create mode 100644 python/paddle/v2/dataset/tests/imikolov_test.py diff --git a/python/paddle/v2/dataset/imikolov.py b/python/paddle/v2/dataset/imikolov.py new file mode 100644 index 00000000000000..e6d0357f97fa9c --- /dev/null +++ b/python/paddle/v2/dataset/imikolov.py @@ -0,0 +1,88 @@ +""" +imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/ +""" +import paddle.v2.dataset.common +import tarfile +import collections + +__all__ = ['train', 'test'] + +URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz' +MD5 = '30177ea32e27c525793142b6bf2c8e2d' + + +def add(a_dict, ele): + if ele in a_dict: + a_dict[ele] += 1 + else: + a_dict[ele] = 1 + + +def word_count(f, word_freq=None): + if word_freq == None: + word_freq = {} + + for l in f: + for w in l.strip().split(): + add(word_freq, w) + add(word_freq, '') + add(word_freq, '') + + return word_freq + + +def build_dict(train_filename, test_filename): + with tarfile.open( + paddle.v2.dataset.common.download( + paddle.v2.dataset.imikolov.URL, 'imikolov', + paddle.v2.dataset.imikolov.MD5)) as tf: + trainf = tf.extractfile(train_filename) + testf = tf.extractfile(test_filename) + word_freq = word_count(testf, word_count(trainf)) + + STOPWORD_FREQ = 3000 + TYPO_FREQ = 50 + word_freq = filter(lambda x: x[1] > TYPO_FREQ and x[1] < STOPWORD_FREQ, + word_freq.items()) + + dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0])) + words, _ = list(zip(*dictionary)) + word_idx = dict(zip(words, xrange(len(words)))) + word_idx[''] = len(words) + + return word_idx + + +word_idx = {} + + +def reader_creator(filename, n): + global word_idx + if len(word_idx) == 0: + word_idx = build_dict('./simple-examples/data/ptb.train.txt', + './simple-examples/data/ptb.valid.txt') + + def reader(): + with tarfile.open( + paddle.v2.dataset.common.download( + paddle.v2.dataset.imikolov.URL, 'imikolov', + paddle.v2.dataset.imikolov.MD5)) as tf: + f = tf.extractfile(filename) + + ANY = word_idx[''] + for l in f: + l = [''] + l.strip().split() + [''] + if len(l) >= n: + l = [word_idx.get(w, ANY) for w in l] + for i in range(n, len(l) + 1): + yield l[i - n:i] + + return reader + + +def train(n): + return reader_creator('./simple-examples/data/ptb.train.txt', n) + + +def test(n): + return reader_creator('./simple-examples/data/ptb.valid.txt', n) diff --git a/python/paddle/v2/dataset/tests/imikolov_test.py b/python/paddle/v2/dataset/tests/imikolov_test.py new file mode 100644 index 00000000000000..9b1748eaaa7f91 --- /dev/null +++ b/python/paddle/v2/dataset/tests/imikolov_test.py @@ -0,0 +1,20 @@ +import paddle.v2.dataset.imikolov +import unittest + + +class TestMikolov(unittest.TestCase): + def check_reader(self, reader, n): + for l in reader(): + self.assertEqual(len(l), n) + + def test_train(self): + n = 5 + self.check_reader(paddle.v2.dataset.imikolov.train(n), n) + + def test_test(self): + n = 5 + self.check_reader(paddle.v2.dataset.imikolov.test(n), n) + + +if __name__ == '__main__': + unittest.main() From a2cec420c82575913a155b922122291eefe3b622 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 20:28:05 +0000 Subject: [PATCH 356/370] In response to comments from Wen-bo --- python/paddle/v2/dataset/imikolov.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/python/paddle/v2/dataset/imikolov.py b/python/paddle/v2/dataset/imikolov.py index e6d0357f97fa9c..d9518dd27e9bbe 100644 --- a/python/paddle/v2/dataset/imikolov.py +++ b/python/paddle/v2/dataset/imikolov.py @@ -3,7 +3,6 @@ """ import paddle.v2.dataset.common import tarfile -import collections __all__ = ['train', 'test'] @@ -40,10 +39,8 @@ def build_dict(train_filename, test_filename): testf = tf.extractfile(test_filename) word_freq = word_count(testf, word_count(trainf)) - STOPWORD_FREQ = 3000 TYPO_FREQ = 50 - word_freq = filter(lambda x: x[1] > TYPO_FREQ and x[1] < STOPWORD_FREQ, - word_freq.items()) + word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items()) dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0])) words, _ = list(zip(*dictionary)) From eb1e34d2c36b23f2047012dfce1424626d1e4d6f Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Wed, 1 Mar 2017 00:05:09 +0000 Subject: [PATCH 357/370] Add imdb and unit test --- python/paddle/v2/dataset/common.py | 7 ++ python/paddle/v2/dataset/imdb.py | 101 ++++++++++++++++++++ python/paddle/v2/dataset/tests/imdb_test.py | 49 ++++++++-- 3 files changed, 148 insertions(+), 9 deletions(-) diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index a5ffe25a116e9b..fcf4437ffaf329 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -32,3 +32,10 @@ def download(url, module_name, md5sum): shutil.copyfileobj(r.raw, f) return filename + + +def dict_add(a_dict, ele): + if ele in a_dict: + a_dict[ele] += 1 + else: + a_dict[ele] = 1 diff --git a/python/paddle/v2/dataset/imdb.py b/python/paddle/v2/dataset/imdb.py index 7a191cb583d4ce..433e37380f840f 100644 --- a/python/paddle/v2/dataset/imdb.py +++ b/python/paddle/v2/dataset/imdb.py @@ -17,3 +17,104 @@ """ IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz """ +import paddle.v2.dataset.common +import tarfile +import Queue +import re +import string +import threading + +__all__ = ['build_dict', 'train', 'test'] + +URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz' +MD5 = '7c2ac02c03563afcf9b574c7e56c153a' + + +# Read files that match pattern. Tokenize and yield each file. +def tokenize(pattern): + with tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb', + MD5)) as tarf: + # Note that we should use tarfile.next(), which does + # sequential access of member files, other than + # tarfile.extractfile, which does random access and might + # destroy hard disks. + tf = tarf.next() + while tf != None: + if bool(pattern.match(tf.name)): + # newline and punctuations removal and ad-hoc tokenization. + yield tarf.extractfile(tf).read().rstrip("\n\r").translate( + None, string.punctuation).lower().split() + tf = tarf.next() + + +def build_dict(pattern, cutoff): + word_freq = {} + for doc in tokenize(pattern): + for word in doc: + paddle.v2.dataset.common.dict_add(word_freq, word) + + # Not sure if we should prune less-frequent words here. + word_freq = filter(lambda x: x[1] > cutoff, word_freq.items()) + + dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0])) + words, _ = list(zip(*dictionary)) + word_idx = dict(zip(words, xrange(len(words)))) + word_idx[''] = len(words) + return word_idx + + +def reader_creator(pos_pattern, neg_pattern, word_idx, buffer_size): + UNK = word_idx[''] + + qs = [Queue.Queue(maxsize=buffer_size), Queue.Queue(maxsize=buffer_size)] + + def load(pattern, queue): + for doc in tokenize(pattern): + queue.put(doc) + queue.put(None) + + def reader(): + # Creates two threads that loads positive and negative samples + # into qs. + t0 = threading.Thread( + target=load, args=( + pos_pattern, + qs[0], )) + t0.daemon = True + t0.start() + + t1 = threading.Thread( + target=load, args=( + neg_pattern, + qs[1], )) + t1.daemon = True + t1.start() + + # Read alternatively from qs[0] and qs[1]. + i = 0 + doc = qs[i].get() + while doc != None: + yield [word_idx.get(w, UNK) for w in doc], i % 2 + i += 1 + doc = qs[i % 2].get() + + # If any queue is empty, reads from the other queue. + i += 1 + doc = qs[i % 2].get() + while doc != None: + yield [word_idx.get(w, UNK) for w in doc], i % 2 + doc = qs[i % 2].get() + + return reader() + + +def train(word_idx): + return reader_creator( + re.compile("aclImdb/train/pos/.*\.txt$"), + re.compile("aclImdb/train/neg/.*\.txt$"), word_idx, 1000) + + +def test(word_idx): + return reader_creator( + re.compile("aclImdb/test/pos/.*\.txt$"), + re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000) diff --git a/python/paddle/v2/dataset/tests/imdb_test.py b/python/paddle/v2/dataset/tests/imdb_test.py index 5cfb6203e78ec1..e887af16634d2d 100644 --- a/python/paddle/v2/dataset/tests/imdb_test.py +++ b/python/paddle/v2/dataset/tests/imdb_test.py @@ -1,12 +1,43 @@ -import paddle.v2.dataset.common -import tarfile +import paddle.v2.dataset.imdb +import unittest +import re -URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz' -MD5 = '7c2ac02c03563afcf9b574c7e56c153a' +TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$") +TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$") +TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$") -tarf = tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb', MD5)) +TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$") +TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$") +TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$") -tf = tarf.next() -while tf != None: - print tf.name - tf = tarf.next() + +class TestIMDB(unittest.TestCase): + word_idx = None + + def test_build_dict(self): + if self.word_idx == None: + self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN, + 150) + + self.assertEqual(len(self.word_idx), 7036) + + def check_dataset(self, dataset, expected_size): + if self.word_idx == None: + self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN, + 150) + + sum = 0 + for l in dataset(self.word_idx): + self.assertEqual(l[1], sum % 2) + sum += 1 + self.assertEqual(sum, expected_size) + + def test_train(self): + self.check_dataset(paddle.v2.dataset.imdb.train, 25000) + + def test_test(self): + self.check_dataset(paddle.v2.dataset.imdb.test, 25000) + + +if __name__ == '__main__': + unittest.main() From 7275e0a8c175d491ab01e725af5e7dec72608521 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Wed, 1 Mar 2017 00:22:34 +0000 Subject: [PATCH 358/370] In response to comments from Helin --- python/paddle/v2/dataset/common.py | 7 +++++++ python/paddle/v2/dataset/imikolov.py | 16 +++++----------- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index a5ffe25a116e9b..fcf4437ffaf329 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -32,3 +32,10 @@ def download(url, module_name, md5sum): shutil.copyfileobj(r.raw, f) return filename + + +def dict_add(a_dict, ele): + if ele in a_dict: + a_dict[ele] += 1 + else: + a_dict[ele] = 1 diff --git a/python/paddle/v2/dataset/imikolov.py b/python/paddle/v2/dataset/imikolov.py index d9518dd27e9bbe..b3791ddad66e58 100644 --- a/python/paddle/v2/dataset/imikolov.py +++ b/python/paddle/v2/dataset/imikolov.py @@ -10,14 +10,8 @@ MD5 = '30177ea32e27c525793142b6bf2c8e2d' -def add(a_dict, ele): - if ele in a_dict: - a_dict[ele] += 1 - else: - a_dict[ele] = 1 - - def word_count(f, word_freq=None): + add = paddle.v2.dataset.common.dict_add if word_freq == None: word_freq = {} @@ -45,7 +39,7 @@ def build_dict(train_filename, test_filename): dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0])) words, _ = list(zip(*dictionary)) word_idx = dict(zip(words, xrange(len(words)))) - word_idx[''] = len(words) + word_idx[''] = len(words) return word_idx @@ -66,13 +60,13 @@ def reader(): paddle.v2.dataset.imikolov.MD5)) as tf: f = tf.extractfile(filename) - ANY = word_idx[''] + UNK = word_idx[''] for l in f: l = [''] + l.strip().split() + [''] if len(l) >= n: - l = [word_idx.get(w, ANY) for w in l] + l = [word_idx.get(w, UNK) for w in l] for i in range(n, len(l) + 1): - yield l[i - n:i] + yield tuple(l[i - n:i]) return reader From 2c0342cbfe1176e371005dcefe94c1c3ca1adb98 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Wed, 1 Mar 2017 09:36:40 +0800 Subject: [PATCH 359/370] Refine k8s doc update --- doc/howto/usage/k8s/k8s_basis_cn.md | 4 ++-- doc/howto/usage/k8s/k8s_distributed_cn.md | 4 ++-- doc/howto/usage/k8s/src/k8s-paddle-arch.png | Bin 530508 -> 513104 bytes 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_basis_cn.md b/doc/howto/usage/k8s/k8s_basis_cn.md index f42631ced00858..6278dacb17a378 100644 --- a/doc/howto/usage/k8s/k8s_basis_cn.md +++ b/doc/howto/usage/k8s/k8s_basis_cn.md @@ -1,6 +1,6 @@ # Kubernetes 简介 -[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 +[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 - [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。 @@ -51,7 +51,7 @@ curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s htt ## 配置kubectl访问你的kubernetes集群 -编辑`~/.kube/config`这个配置文件，修改`Master-IP`的地址。如果使用SSL认证，则需要配置`certificate-authority`和`users`中的用户证书。如果是使用非SSL方式访问（比如通过8080都安口），也可以去掉这些证书的配置。 +编辑`~/.kube/config`这个配置文件，修改`Master-IP`的地址。如果使用SSL认证，则需要配置`certificate-authority`和`users`中的用户证书。如果是使用非SSL方式访问（比如通过8080端口），也可以去掉这些证书的配置。 ``` apiVersion: v1 clusters: diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index cfc3086036f014..3a47a3832fee82 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -6,7 +6,7 @@ ## 整体方案 -在训练之前，用户将配置与训练数据切分好放在MFS目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结构图如下： +在训练之前，用户将配置与训练数据切分好放在分布式文件系统预先分配好的目录中(不同的分布式文件系统，需要使用其制定的方式挂载后并导入数据)，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结构图如下： ![paddle on kubernetes结构图](src/k8s-paddle-arch.png) @@ -28,7 +28,7 @@ PaddlePaddle镜像需要提供`paddle pserver`与`paddle train`进程的运行 - 拷贝训练文件到容器内 - 生成`paddle pserver`与`paddle train`进程的启动参数，并且启动训练 -因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*]((https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/src/k8s_train/Dockerfile)。 +因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/src/k8s_train/Dockerfile)。 ```bash $ cd doc/howto/usage/k8s/src/k8s_train diff --git a/doc/howto/usage/k8s/src/k8s-paddle-arch.png b/doc/howto/usage/k8s/src/k8s-paddle-arch.png index c41a62392978eb94ceeb0ff24f23964055f5e06c..2183a232ad402b76f82a67234a5c93e13ce97ac3 100644 GIT binary patch literal 513104 zcmY(p1yEc;vo=g1NCF82f&~aJ!JVMNC1`M$0E;ZXxVwblA;4lmg0slt?!lJD7k5}- z@eLlxm-pWL-~aoj>Qv3?)6-A))aj|}ekM*!L+QoyH_tIJFkYxA%WGp`U`1kJVB+E7 z{F4Z)(s5#75WIGflhaa>lcUq}a0NIx*ZXm5<#iVmDYHF!S{%9d^gz#X z-%M-)PbE|fljSL?czxN1vDT}>FOV;6ZDi8(U)adVo&*HZ75=D-hb(CMrD3*pd9O(# 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zPyp=iI;kzK>%91|k?EH>tjJ0=vxmao_J?Cu6X!jGgXg(lOl@df7rcnzs+Hp37o6N! z@1AJeNcrl0_^rpN?`>0!UvKZ6cMYm$!rdcz?t}5>=G_jqfex0{6+D)Hg!|XK!2EXX z)G|HsypTR4RM^z2ea`<^z2kjwSw9OUaiBu)pHIs5Z@_?S(D8xu`0_!u`ZpUMTu6&B zO!G|QC>fr>4{8~|x{D@eYZ(P>T3R9J-YGM(j>XE{b4{@c z(3&tQ2F(29)&+!x#)jb^(_PdAR4gf0t1g>p#`Q{qz#qT3drZeq`pK^O#GhX|gfzvs zO46l#%Vk{Tr#L%YdD`V-Izf8H%84z!a4B{e04j&Ks4sN$w>iV32G_zaZ0ND!;;(-E zSP9Kuz6zhjO_R04nOeIzx)KND4IMARy((!yrA#Gz$nNk?7H5PJ=~=$8#5r8XJs=1aTF+xLkR!k7%Zr9BaGIMw*+{iEp|1n ztq9)9$#Br@VvM{;X&C?LwE%#e07#q%mnKcW znW~j|KW=)shQ*cB*TjS^b?rsCCl%Ps#U zs&0tl>mr^NVak2TRWYtCrN`Bqjlvy7jqM*T8R`z!gdh6oM)Z8-PgZHanet}`AFOa* z$=lLSBFe&+ajQ4iY(w9aGxsN48l!6tAtzb4w;N?k4SeYu$}-Htq`d+xWT(ErMwpPFW(uLrUZu^J>hpF@DytEvU$7A;ge_y9 zx|p>5jj>ON^_k?F{mjBnNI=;@}*VH!(_HUaj5>|gP4veVwgaAMZtagsDA7{p3_+pTj9B^dJC#>5Sk0(?u;d7Cy*CNBA%888|3m`ph*(j)=Nm z*lrJHNuuz7_F3J1SoR@6JHpfMDR)~Vj4HR`(CqcI)*Mep0b7iSzAgd}Hs*CEo?ID+ z6c3+=jDht&;TXcN!`(E3Vfk2FMGov-KD(y2U-?Ig)u3i0!0fgP(C$!kFTrao&Zu`%a{nX}krd9K2>q zEG{(JSciEGy4kp)x4Zh`5i{+w_PN)lYbIx^82c`n=JoSeDC^Due9Yue{}8N1<4u9u3?DOXC4ns`xLP&iZdyDE3tM50gUdPh>+3f!8HziE* qSC0@$4~X4)OB(Oj{#z1wOI|2HA%`Bq+xlOKKWT9Vv0@Q@@Ban{EW1De From 6245fed240abb80ccaed43e08f6ad2b327e12cc4 Mon Sep 17 00:00:00 2001 From: Haonan Date: Thu, 2 Feb 2017 11:28:45 -0800 Subject: [PATCH 137/370] rotate_layer python interface fixes --- paddle/gserver/layers/FeatureMapExpandLayer.cpp | 3 +++ paddle/gserver/layers/GruStepLayer.cpp | 4 ++-- paddle/gserver/layers/RotateLayer.cpp | 2 ++ python/paddle/trainer/config_parser.py | 6 +++--- python/paddle/trainer_config_helpers/layers.py | 14 ++++++++------ .../tests/layers_test_config.py | 2 +- 6 files changed, 19 insertions(+), 12 deletions(-) diff --git a/paddle/gserver/layers/FeatureMapExpandLayer.cpp b/paddle/gserver/layers/FeatureMapExpandLayer.cpp index d023074c521675..4b685812aac247 100644 --- a/paddle/gserver/layers/FeatureMapExpandLayer.cpp +++ b/paddle/gserver/layers/FeatureMapExpandLayer.cpp @@ -95,6 +95,9 @@ void FeatureMapExpandLayer::forward(PassType passType) { void FeatureMapExpandLayer::backward(const UpdateCallback& callback) { MatrixPtr inGrad = getInputGrad(0); + if (NULL == inGrad) { + return; + } MatrixPtr outGrad = getOutputGrad(); size_t batchSize = getInput(0).getBatchSize(); int imgSize = inGrad->getWidth(); diff --git a/paddle/gserver/layers/GruStepLayer.cpp b/paddle/gserver/layers/GruStepLayer.cpp index ce692c490881c9..4a1006aa941f39 100644 --- a/paddle/gserver/layers/GruStepLayer.cpp +++ b/paddle/gserver/layers/GruStepLayer.cpp @@ -68,8 +68,8 @@ bool GruStepLayer::init(const LayerMap& layerMap, if (!Layer::init(layerMap, parameterMap)) return false; CHECK_EQ(2U, inputLayers_.size()); - CHECK_EQ(getSize() * getSize() * 3, parameters_[1]->getSize()); - weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[1])); + CHECK_EQ(getSize() * getSize() * 3, parameters_[0]->getSize()); + weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[0])); if (biasParameter_.get() != NULL) { CHECK_EQ(getSize() * 3, biasParameter_->getSize()); diff --git a/paddle/gserver/layers/RotateLayer.cpp b/paddle/gserver/layers/RotateLayer.cpp index 1cdd8366d5ddff..d928f1d93de0ee 100644 --- a/paddle/gserver/layers/RotateLayer.cpp +++ b/paddle/gserver/layers/RotateLayer.cpp @@ -25,6 +25,8 @@ bool RotateLayer::init(const LayerMap& layerMap, CHECK_EQ(inputLayers_.size(), 1UL); height_ = config_.height(); width_ = config_.width(); + CHECK_GT(height_, 0); + CHECK_GT(width_, 0); return true; } diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 62efe5fca0caca..b02af991dc577e 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1969,13 +1969,13 @@ def __init__(self, name, size, inputs, **xargs): @config_layer('rotate') class RotateLayer(LayerBase): - def __init__(self, name, inputs, height, device=None): + def __init__(self, name, inputs, height, width, device=None): super(RotateLayer, self).__init__( name, 'rotate', 0, inputs=inputs, device=device) config_assert( len(self.inputs) == 1, 'RotateLayer must have one and only one input') - self.config.height = height + self.set_layer_height_width(height, width) self.set_layer_size(self.get_input_layer(0).size) @@ -3007,7 +3007,7 @@ def __init__(self, config_assert(input_layer1.size == size, 'input_layer1.size != layer.size') self.config.active_gate_type = active_gate_type - self.create_input_parameter(1, size * size * 3, [size, size * 3]) + self.create_input_parameter(0, size * size * 3, [size, size * 3]) self.create_bias_parameter(bias, size * 3) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 19f7ec3186596f..2636f6cc1a1653 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1677,22 +1677,23 @@ def trans_layer(input, name=None, layer_attr=None): @wrap_name_default() @layer_support() -def rotate_layer(input, height, name=None, layer_attr=None): +def rotate_layer(input, height, width, name=None, layer_attr=None): """ - A layer for rotation (clock-wise), usually used when the input sample - is some image or map. + A layer for rotating 90 degrees (clock-wise), usually used when the input sample + is some image or feature map. .. math:: - y(j,i) = x(M-i-1,j) + y(j,i,:) = x(M-i-1,j,:) - where :math:`x` is (M x N) input, and :math:`y` is (N x M) output. + where :math:`x` is (M x N x C) input, and :math:`y` is (N x M x C) output. The example usage is: .. code-block:: python rot = rotate_layer(input=layer, - height=100) + height=100, + width=100) :param input: Input layer. :type input: LayerOutput @@ -1709,6 +1710,7 @@ def rotate_layer(input, height, name=None, layer_attr=None): l = Layer( name=name, height=height, + width=width, type=LayerType.ROTATE_LAYER, inputs=[input.name], **ExtraLayerAttribute.to_kwargs(layer_attr)) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test_config.py b/python/paddle/trainer_config_helpers/tests/layers_test_config.py index 1964e6f113e642..e6cd35ee761d1a 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test_config.py @@ -39,7 +39,7 @@ assert z1.size > 0 y2 = fc_layer(input=y, size=15) -z2 = rotate_layer(input=y2, height=5) +z2 = rotate_layer(input=y2, height=5, width=3) cos1 = cos_sim(a=x1, b=y1) cos3 = cos_sim(a=x1, b=y2, size=3) From 7c04096ecbeb3f7846fa2b9b5724706be7fb54a9 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 3 Feb 2017 10:32:58 +0800 Subject: [PATCH 138/370] Remove compile warning on clang 8.0 in BufferArg.h * CHECK_XX in glog need the left and right values are both signed or unsigned integer. So we could use integer literal syntax in C/C++ to make literal unsigned or signed. Add suffix 'UL' means unsigned long. --- paddle/function/BufferArg.h | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 177d20005b54b8..349b21e7e64064 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -189,8 +189,8 @@ class SequenceIdArg : public BufferArg { SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED) : BufferArg(VALUE_TYPE_INT32, shape, argType) { bufferType_ = TENSOR_SEQUENCE_ID; - CHECK_EQ(shape_.ndims(), (size_t)1); - CHECK_GT(shape_[0], 1); + CHECK_EQ(shape_.ndims(), 1UL); + CHECK_GT(shape_[0], 1UL); numSeqs_ = shape_[0] - 1; } @@ -199,7 +199,7 @@ class SequenceIdArg : public BufferArg { ArgType argType = UNSPECIFIED) : BufferArg(buf, VALUE_TYPE_INT32, shape, argType) { bufferType_ = TENSOR_SEQUENCE_ID; - CHECK_EQ(shape_.ndims(), (size_t)1); + CHECK_EQ(shape_.ndims(), 1UL); numSeqs_ = shape_[0] - 1; } @@ -280,9 +280,9 @@ class SparseMatrixArg : public BufferArg { type_(static_cast(type)) { bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); - CHECK_EQ(shape_.ndims(), (size_t)2); - CHECK_EQ(row_.shape().ndims(), (size_t)1); - CHECK_EQ(col_.shape().ndims(), (size_t)1); + CHECK_EQ(shape_.ndims(), 2UL); + CHECK_EQ(row_.shape().ndims(), 1UL); + CHECK_EQ(col_.shape().ndims(), 1UL); if (format_ == T_SPARSE_CSR) { CHECK_EQ(nnz, col.shape()[0]); } else if (format_ == T_SPARSE_CSC) { @@ -304,7 +304,7 @@ class SparseMatrixArg : public BufferArg { type_(static_cast(type)) { bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); - CHECK_EQ(shape_.ndims(), (size_t)2); + CHECK_EQ(shape_.ndims(), 2UL); /// len of row_ : height + 1 (CSR) or nnz (CSC), buf_ == nullptr row_ = (format_ == T_SPARSE_CSR @@ -325,7 +325,7 @@ class SparseMatrixArg : public BufferArg { CHECK(buf_); CHECK(valueType_ == DataType::value); // CHECK(deviceType_ == DType); - CHECK_EQ(2, shape_.ndims()); + CHECK_EQ(2UL, shape_.ndims()); return typename Tensor::SparseMatrix( reinterpret_cast(buf_), reinterpret_cast(row_.data()), From 2859532df01ff4494a1a8e60fde442e606ff435e Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 3 Feb 2017 12:45:22 +0800 Subject: [PATCH 139/370] Using override keyword in Layer --- .../RecurrentGradientMachine.cpp | 7 +- paddle/gserver/layers/AddtoLayer.h | 7 +- paddle/gserver/layers/AgentLayer.h | 25 +++--- paddle/gserver/layers/AverageLayer.h | 9 +-- paddle/gserver/layers/BatchNormBaseLayer.h | 3 +- .../gserver/layers/BatchNormalizationLayer.h | 9 ++- paddle/gserver/layers/BilinearInterpLayer.h | 7 +- paddle/gserver/layers/BlockExpandLayer.h | 7 +- paddle/gserver/layers/CRFDecodingLayer.h | 7 +- paddle/gserver/layers/CRFLayer.h | 7 +- paddle/gserver/layers/CTCLayer.h | 7 +- paddle/gserver/layers/ConcatenateLayer.cpp | 14 ++-- paddle/gserver/layers/ConvBaseLayer.h | 3 +- paddle/gserver/layers/ConvShiftLayer.cpp | 7 +- .../gserver/layers/ConvexCombinationLayer.cpp | 7 +- paddle/gserver/layers/CosSimLayer.h | 7 +- paddle/gserver/layers/CosSimVecMatLayer.cpp | 7 +- paddle/gserver/layers/CostLayer.cpp | 9 +-- paddle/gserver/layers/CostLayer.h | 79 ++++++++++++------- paddle/gserver/layers/CudnnBatchNormLayer.h | 7 +- paddle/gserver/layers/CudnnConvLayer.h | 7 +- paddle/gserver/layers/CudnnPoolLayer.h | 7 +- paddle/gserver/layers/DataLayer.h | 8 +- paddle/gserver/layers/DataNormLayer.h | 7 +- paddle/gserver/layers/EosIdCheckLayer.cpp | 8 +- paddle/gserver/layers/ExpandConvBaseLayer.h | 3 +- paddle/gserver/layers/ExpandConvLayer.h | 7 +- paddle/gserver/layers/ExpandConvTransLayer.h | 7 +- paddle/gserver/layers/ExpandLayer.h | 7 +- .../gserver/layers/FeatureMapExpandLayer.cpp | 7 +- paddle/gserver/layers/FullyConnectedLayer.h | 9 ++- paddle/gserver/layers/GatedRecurrentLayer.h | 13 +-- paddle/gserver/layers/GetOutputLayer.cpp | 7 +- paddle/gserver/layers/GruStepLayer.cpp | 7 +- .../gserver/layers/HierarchicalSigmoidLayer.h | 7 +- paddle/gserver/layers/InterpolationLayer.cpp | 7 +- paddle/gserver/layers/LstmLayer.h | 13 +-- paddle/gserver/layers/LstmStepLayer.cpp | 7 +- paddle/gserver/layers/MDLstmLayer.cpp | 7 +- paddle/gserver/layers/MaxIdLayer.cpp | 8 +- paddle/gserver/layers/MaxLayer.h | 9 +-- paddle/gserver/layers/MaxOutLayer.h | 7 +- paddle/gserver/layers/MixedLayer.h | 15 ++-- paddle/gserver/layers/MultiplexLayer.cpp | 7 +- paddle/gserver/layers/NCELayer.cpp | 9 ++- paddle/gserver/layers/NormLayer.h | 10 ++- paddle/gserver/layers/NormProjectionLayer.h | 7 +- paddle/gserver/layers/OuterProdLayer.cpp | 7 +- paddle/gserver/layers/PadLayer.h | 7 +- paddle/gserver/layers/ParameterReluLayer.h | 7 +- paddle/gserver/layers/PoolLayer.h | 3 +- paddle/gserver/layers/PoolProjectionLayer.h | 4 +- paddle/gserver/layers/PowerLayer.cpp | 7 +- paddle/gserver/layers/PrintLayer.cpp | 4 +- paddle/gserver/layers/PriorBox.cpp | 7 +- paddle/gserver/layers/RecurrentLayer.cpp | 13 +-- paddle/gserver/layers/RecurrentLayerGroup.cpp | 9 ++- paddle/gserver/layers/ResizeLayer.cpp | 9 ++- paddle/gserver/layers/SamplingIdLayer.cpp | 8 +- paddle/gserver/layers/ScalingLayer.cpp | 7 +- .../layers/SelectiveFullyConnectedLayer.h | 9 ++- paddle/gserver/layers/SequenceConcatLayer.cpp | 7 +- .../layers/SequenceLastInstanceLayer.cpp | 9 +-- paddle/gserver/layers/SequencePoolLayer.h | 9 +-- .../gserver/layers/SequenceReshapeLayer.cpp | 9 +-- paddle/gserver/layers/SlopeInterceptLayer.cpp | 9 +-- .../gserver/layers/SpatialPyramidPoolLayer.h | 9 +-- paddle/gserver/layers/SubSequenceLayer.cpp | 9 +-- paddle/gserver/layers/SumToOneNormLayer.cpp | 9 +-- paddle/gserver/layers/TensorLayer.h | 9 +-- paddle/gserver/layers/TransLayer.h | 7 +- paddle/gserver/layers/ValidationLayer.h | 23 +++--- paddle/gserver/layers/WarpCTCLayer.h | 7 +- 73 files changed, 369 insertions(+), 300 deletions(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp index a9a9f4f903e305..2ab964b8fc2e08 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp @@ -155,7 +155,8 @@ class BootBiasLayer : public Layer { public: explicit BootBiasLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { if (!Layer::init(layerMap, parameterMap)) return false; if (biasParameter_) { @@ -174,7 +175,7 @@ class BootBiasLayer : public Layer { } } - virtual void forward(PassType passType) { + void forward(PassType passType) override { if (biases_) { MatrixPtr outV = getOutputValue(); outV->addBias(*(biases_->getW()), 1); @@ -182,7 +183,7 @@ class BootBiasLayer : public Layer { } } - virtual void backward(const UpdateCallback& callback) { + void backward(const UpdateCallback& callback) override { if (biases_) { backwardActivation(); biases_->getWGrad()->collectBias(*getOutputGrad(), 1); diff --git a/paddle/gserver/layers/AddtoLayer.h b/paddle/gserver/layers/AddtoLayer.h index 53d3f99cdd3439..4e98c174b46276 100644 --- a/paddle/gserver/layers/AddtoLayer.h +++ b/paddle/gserver/layers/AddtoLayer.h @@ -44,19 +44,20 @@ class AddtoLayer : public Layer { /** * Intialization of AddtoLayer. */ - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * Forward propagation. * @note There is no weight matrix for each input, * because it just a simple add operation. */ - void forward(PassType passType); + void forward(PassType passType) override; /** * Backward propagation. */ - void backward(const UpdateCallback& callback = nullptr); + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/AgentLayer.h b/paddle/gserver/layers/AgentLayer.h index 41683ad6712d5d..b6dac7ae6fec2d 100644 --- a/paddle/gserver/layers/AgentLayer.h +++ b/paddle/gserver/layers/AgentLayer.h @@ -35,7 +35,8 @@ class AgentLayer : public Layer { ~AgentLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; // if *numSamples* set, // real layer output will only use first *numSamples* rows @@ -44,8 +45,8 @@ class AgentLayer : public Layer { numSamples_ = numSamples; } - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr) {} + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override {} }; /** @@ -56,8 +57,8 @@ class SequenceAgentLayer : public AgentLayer { explicit SequenceAgentLayer(const LayerConfig& config) : AgentLayer(config) {} ~SequenceAgentLayer() {} - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr) {} + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override {} }; /** @@ -78,7 +79,8 @@ class GatherAgentLayer : public Layer { virtual ~GatherAgentLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; // call before addRealLayer void copyIdAndSequenceInfo(const Argument& input, @@ -88,8 +90,8 @@ class GatherAgentLayer : public Layer { // add one real layer, can call many times void addRealLayer(LayerPtr layer) { realLayers_.push_back(layer); } - void forward(PassType passType); - void backward(const UpdateCallback& callback); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; }; /** @@ -133,7 +135,8 @@ class ScatterAgentLayer : public Layer { virtual ~ScatterAgentLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * @brief set real layer in generation @@ -182,8 +185,8 @@ class ScatterAgentLayer : public Layer { numSequences_ = numSequences; } - void forward(PassType passType); - void backward(const UpdateCallback& callback); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; }; /** diff --git a/paddle/gserver/layers/AverageLayer.h b/paddle/gserver/layers/AverageLayer.h index b3c4ecec8bc6f5..621e1d7bb12ec5 100644 --- a/paddle/gserver/layers/AverageLayer.h +++ b/paddle/gserver/layers/AverageLayer.h @@ -38,12 +38,11 @@ class AverageLayer : public SequencePoolLayer { explicit AverageLayer(const LayerConfig& config) : SequencePoolLayer(config) {} - ~AverageLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: MatrixPtr outMtx_; diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h index 75bda95de1472b..230bafc31d96bb 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.h +++ b/paddle/gserver/layers/BatchNormBaseLayer.h @@ -52,7 +52,8 @@ class BatchNormBaseLayer : public Layer { */ static Layer* create(const LayerConfig& config); - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * @brief Calculate feature map size. Some input uses frameHeight and diff --git a/paddle/gserver/layers/BatchNormalizationLayer.h b/paddle/gserver/layers/BatchNormalizationLayer.h index 195acbbfc58db8..f6115801fc6b34 100644 --- a/paddle/gserver/layers/BatchNormalizationLayer.h +++ b/paddle/gserver/layers/BatchNormalizationLayer.h @@ -33,9 +33,10 @@ class BatchNormalizationLayer : public BatchNormBaseLayer { ~BatchNormalizationLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: /// Epsilon value used in the batch normalization formula. @@ -58,7 +59,7 @@ class BatchNormalizationLayer : public BatchNormBaseLayer { /// to batch, channels* imagePixels. void shrinkMat(const MatrixPtr& in, MatrixPtr& out); - void onPassEnd() { firstTest_ = true; } + void onPassEnd() override { firstTest_ = true; } MatrixPtr tmpMat_, tmpGrad_; MatrixPtr expandedIn_, expandedOut_; diff --git a/paddle/gserver/layers/BilinearInterpLayer.h b/paddle/gserver/layers/BilinearInterpLayer.h index 4ff4b0ea793dc9..27c269f2781c99 100644 --- a/paddle/gserver/layers/BilinearInterpLayer.h +++ b/paddle/gserver/layers/BilinearInterpLayer.h @@ -38,9 +38,10 @@ class BilinearInterpLayer : public Layer { virtual ~BilinearInterpLayer() {} size_t getSize(); - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/BlockExpandLayer.h b/paddle/gserver/layers/BlockExpandLayer.h index cc96fdd03fcac6..8f347400e60ec8 100644 --- a/paddle/gserver/layers/BlockExpandLayer.h +++ b/paddle/gserver/layers/BlockExpandLayer.h @@ -58,10 +58,11 @@ class BlockExpandLayer : public Layer { ~BlockExpandLayer() {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/CRFDecodingLayer.h b/paddle/gserver/layers/CRFDecodingLayer.h index 1fd444ad10e71d..3cbcac6cf62dec 100644 --- a/paddle/gserver/layers/CRFDecodingLayer.h +++ b/paddle/gserver/layers/CRFDecodingLayer.h @@ -32,9 +32,10 @@ namespace paddle { class CRFDecodingLayer : public CRFLayer { public: explicit CRFDecodingLayer(const LayerConfig& config) : CRFLayer(config) {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; protected: std::unique_ptr crf_; diff --git a/paddle/gserver/layers/CRFLayer.h b/paddle/gserver/layers/CRFLayer.h index d21b32b68c1a40..de36a85083b6b2 100644 --- a/paddle/gserver/layers/CRFLayer.h +++ b/paddle/gserver/layers/CRFLayer.h @@ -29,9 +29,10 @@ namespace paddle { class CRFLayer : public Layer { public: explicit CRFLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; protected: size_t numClasses_; diff --git a/paddle/gserver/layers/CTCLayer.h b/paddle/gserver/layers/CTCLayer.h index 70d429bad656ad..f7a515f312d075 100644 --- a/paddle/gserver/layers/CTCLayer.h +++ b/paddle/gserver/layers/CTCLayer.h @@ -22,10 +22,11 @@ namespace paddle { class CTCLayer : public Layer { public: explicit CTCLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - virtual void forward(PassType passType); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; void forwardImp(const Argument& softmaxSeqs, const Argument& labelSeqs); - virtual void backward(const UpdateCallback& callback); + void backward(const UpdateCallback& callback) override; void backwardImp(const UpdateCallback& callback, const Argument& softmaxSeqs, const Argument& labelSeqs); diff --git a/paddle/gserver/layers/ConcatenateLayer.cpp b/paddle/gserver/layers/ConcatenateLayer.cpp index d19adace7d58af..c5fc4cf4f81a55 100644 --- a/paddle/gserver/layers/ConcatenateLayer.cpp +++ b/paddle/gserver/layers/ConcatenateLayer.cpp @@ -28,10 +28,11 @@ class ConcatenateLayer : public Layer { ~ConcatenateLayer() {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(concat, ConcatenateLayer); @@ -101,10 +102,11 @@ class ConcatenateLayer2 : public Layer { ~ConcatenateLayer2() {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: std::vector> projections_; diff --git a/paddle/gserver/layers/ConvBaseLayer.h b/paddle/gserver/layers/ConvBaseLayer.h index aedf4100e32fa1..e9d15d94f806a5 100644 --- a/paddle/gserver/layers/ConvBaseLayer.h +++ b/paddle/gserver/layers/ConvBaseLayer.h @@ -80,7 +80,8 @@ class ConvBaseLayer : public Layer { public: explicit ConvBaseLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * imgSizeH_ and imgSizeW_ will be set according to the previous input layers diff --git a/paddle/gserver/layers/ConvShiftLayer.cpp b/paddle/gserver/layers/ConvShiftLayer.cpp index 9bfb1ab7a47b11..002be415691f0b 100644 --- a/paddle/gserver/layers/ConvShiftLayer.cpp +++ b/paddle/gserver/layers/ConvShiftLayer.cpp @@ -47,10 +47,11 @@ class ConvShiftLayer : public Layer { ~ConvShiftLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(conv_shift, ConvShiftLayer); diff --git a/paddle/gserver/layers/ConvexCombinationLayer.cpp b/paddle/gserver/layers/ConvexCombinationLayer.cpp index ed57f2af3c6455..32eb3bf604acaa 100644 --- a/paddle/gserver/layers/ConvexCombinationLayer.cpp +++ b/paddle/gserver/layers/ConvexCombinationLayer.cpp @@ -49,10 +49,11 @@ class ConvexCombinationLayer : public Layer { ~ConvexCombinationLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(convex_comb, ConvexCombinationLayer); diff --git a/paddle/gserver/layers/CosSimLayer.h b/paddle/gserver/layers/CosSimLayer.h index 5dcc5d8a5b4dc7..65549626098f08 100644 --- a/paddle/gserver/layers/CosSimLayer.h +++ b/paddle/gserver/layers/CosSimLayer.h @@ -38,10 +38,11 @@ class CosSimLayer : public Layer { ~CosSimLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index ad490b0b8c4656..5f652319e56202 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -46,10 +46,11 @@ class CosSimVecMatLayer : public Layer { ~CosSimVecMatLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(cos_vm, CosSimVecMatLayer); diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 7e9519f6b3af50..998b8d7d3034cb 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -367,8 +367,6 @@ void LambdaCost::backward(const UpdateCallback& callback) { getInputGrad(0)->add(*marginGrad_); } -void LambdaCost::onPassEnd() {} - void LambdaCost::calcGrad(const real* outputScore, const real* score, real* gradData, @@ -611,14 +609,15 @@ class SumCostLayer : public Layer { public: explicit SumCostLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { bool ret = Layer::init(layerMap, parameterMap); if (!ret) return ret; CHECK_EQ(inputLayers_.size(), 1UL); return true; } - virtual void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); const MatrixPtr& input = getInputValue(0); @@ -629,7 +628,7 @@ class SumCostLayer : public Layer { output_.value->sumRows(*input, /* scaleSum= */ 1, /* scaleDest= */ 0); } - virtual void backward(const UpdateCallback& callback = nullptr) { + void backward(const UpdateCallback& callback = nullptr) override { getInputGrad(0)->add((real)1); } }; diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index 7f73bdb3f7d63e..b3045e0b31308a 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -32,15 +32,16 @@ class CostLayer : public Layer { public: explicit CostLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; LayerPtr getOutputLayer() { return inputLayers_[0]; } LayerPtr getLabelLayer() { return inputLayers_[1]; } - virtual void forward(PassType passType); + void forward(PassType passType) override; - virtual void backward(const UpdateCallback& callback = nullptr); + void backward(const UpdateCallback& callback = nullptr) override; virtual void forwardImp(Matrix& outputValue, Argument& label, @@ -68,11 +69,14 @@ class MultiClassCrossEntropy : public CostLayer { explicit MultiClassCrossEntropy(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; }; /** @@ -95,11 +99,14 @@ class MultiClassCrossEntropyWithSelfNorm : public CostLayer { explicit MultiClassCrossEntropyWithSelfNorm(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; protected: MatrixPtr sftMaxSum_; @@ -117,11 +124,14 @@ class SoftBinaryClassCrossEntropy : public CostLayer { explicit SoftBinaryClassCrossEntropy(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; protected: MatrixPtr targetPerDim_; @@ -139,11 +149,14 @@ class SumOfSquaresCostLayer : public CostLayer { explicit SumOfSquaresCostLayer(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; }; /** @@ -162,17 +175,18 @@ class RankingCost : public Layer { public: explicit RankingCost(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; LayerPtr getOutputLayer(size_t i) { return inputLayers_[i]; } LayerPtr getLabelLayer() { return inputLayers_[2]; } - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback = nullptr); + void backward(const UpdateCallback& callback = nullptr) override; - void onPassEnd(); + void onPassEnd() override; void forwardImp(Matrix& output, Argument& label, Matrix& cost) { (void)output; @@ -214,17 +228,16 @@ class LambdaCost : public Layer { public: explicit LambdaCost(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; LayerPtr getOutputLayer() { return inputLayers_[0]; } LayerPtr getScoreLayer() { return inputLayers_[1]; } - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback = nullptr); - - void onPassEnd(); + void backward(const UpdateCallback& callback = nullptr) override; real calcNDCG(const real* outputScore, const real* score, int size); void calcGrad(const real* outputScore, @@ -256,11 +269,14 @@ class MultiBinaryLabelCrossEntropy : public CostLayer { explicit MultiBinaryLabelCrossEntropy(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; }; /** @@ -282,13 +298,16 @@ class HuberTwoClass : public CostLayer { public: explicit HuberTwoClass(const LayerConfig& config) : CostLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forwardImp(Matrix& output, Argument& label, Matrix& cost); + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; void forwardImpIn(Matrix& output, Argument& label, Matrix& cost); - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad); + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; void backwardImpIn(Matrix& outputValue, Argument& label, Matrix& outputGrad); }; diff --git a/paddle/gserver/layers/CudnnBatchNormLayer.h b/paddle/gserver/layers/CudnnBatchNormLayer.h index b1e7d2082f1443..413efd4d3ecd73 100644 --- a/paddle/gserver/layers/CudnnBatchNormLayer.h +++ b/paddle/gserver/layers/CudnnBatchNormLayer.h @@ -35,14 +35,15 @@ class CudnnBatchNormLayer : public BatchNormBaseLayer { ~CudnnBatchNormLayer(); - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * reshape tensor of ioDesc_. */ void reshape(int batchSize); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: /** diff --git a/paddle/gserver/layers/CudnnConvLayer.h b/paddle/gserver/layers/CudnnConvLayer.h index b869c695bd7530..919b1efc4e4532 100644 --- a/paddle/gserver/layers/CudnnConvLayer.h +++ b/paddle/gserver/layers/CudnnConvLayer.h @@ -45,9 +45,10 @@ class CudnnConvLayer : public ConvBaseLayer { ~CudnnConvLayer(); - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType); - void backward(const UpdateCallback& callback); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; void addBiases(); void bpropBiases(); }; diff --git a/paddle/gserver/layers/CudnnPoolLayer.h b/paddle/gserver/layers/CudnnPoolLayer.h index 072b2f9513f4ef..28294f5aa30ca4 100644 --- a/paddle/gserver/layers/CudnnPoolLayer.h +++ b/paddle/gserver/layers/CudnnPoolLayer.h @@ -45,7 +45,8 @@ class CudnnPoolLayer : public PoolLayer { hl_pooling_mode_t* mode = nullptr); explicit CudnnPoolLayer(const LayerConfig& config); ~CudnnPoolLayer(); - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; /** * Reshape input and output tensor descriptor. @@ -53,8 +54,8 @@ class CudnnPoolLayer : public PoolLayer { * So reshaping is needed. */ void reshape(int batchSize); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + virtual void forward(PassType passType) override; + virtual void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/DataLayer.h b/paddle/gserver/layers/DataLayer.h index d3bc97bb6cd0b8..a9cf1f943c260a 100644 --- a/paddle/gserver/layers/DataLayer.h +++ b/paddle/gserver/layers/DataLayer.h @@ -33,13 +33,13 @@ class DataLayer : public Layer { /** * Prefetch sparse matrix/ids only. */ - void prefetch() { output_ = data_; } + void prefetch() override { output_ = data_; } /** * Forward propagation. Copy data_ (value, in, grad, ids, cpuSequenceDims, * sequenceStartPositions, subSequenceStartPositions, strs) to output_. */ - virtual void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); copyDataToOutput(output_); if (FLAGS_show_layer_stat) { @@ -50,9 +50,9 @@ class DataLayer : public Layer { /** * Data layer's backward propagation do nothing. */ - virtual void backward(const UpdateCallback& callback) { (void)callback; } + void backward(const UpdateCallback& callback) override { (void)callback; } - virtual void copyOutputToOtherDevice() { + void copyOutputToOtherDevice() override { for (size_t i = 0; i != outputOtherDevice_.size(); i++) { copyDataToOutput(outputOtherDevice_[i]); } diff --git a/paddle/gserver/layers/DataNormLayer.h b/paddle/gserver/layers/DataNormLayer.h index b3043cffd210fe..f0fd044e5b8343 100644 --- a/paddle/gserver/layers/DataNormLayer.h +++ b/paddle/gserver/layers/DataNormLayer.h @@ -44,10 +44,11 @@ class DataNormLayer : public Layer { ~DataNormLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: int mode_; diff --git a/paddle/gserver/layers/EosIdCheckLayer.cpp b/paddle/gserver/layers/EosIdCheckLayer.cpp index fa53e2e4cfc8a2..686f1fa0543cb3 100644 --- a/paddle/gserver/layers/EosIdCheckLayer.cpp +++ b/paddle/gserver/layers/EosIdCheckLayer.cpp @@ -27,14 +27,14 @@ class EosIdCheckLayer : public Layer { public: explicit EosIdCheckLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { bool ret = Layer::init(layerMap, parameterMap); CHECK_EQ(1UL, inputLayers_.size()); return ret; } - virtual void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); const Argument& input = getInput(0); @@ -42,7 +42,7 @@ class EosIdCheckLayer : public Layer { output_.ids->isEqualTo(*input.ids, config_.eos_id()); } - virtual void backward(const UpdateCallback& callback) {} + void backward(const UpdateCallback& callback) override {} }; REGISTER_LAYER(eos_id, EosIdCheckLayer); diff --git a/paddle/gserver/layers/ExpandConvBaseLayer.h b/paddle/gserver/layers/ExpandConvBaseLayer.h index 8445642217cf3e..aabcdfc392d3e2 100644 --- a/paddle/gserver/layers/ExpandConvBaseLayer.h +++ b/paddle/gserver/layers/ExpandConvBaseLayer.h @@ -48,7 +48,8 @@ class ExpandConvBaseLayer : public ConvBaseLayer { ~ExpandConvBaseLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; size_t getOutputSize(); /** diff --git a/paddle/gserver/layers/ExpandConvLayer.h b/paddle/gserver/layers/ExpandConvLayer.h index de81a017e1bac3..60681690e5dd55 100644 --- a/paddle/gserver/layers/ExpandConvLayer.h +++ b/paddle/gserver/layers/ExpandConvLayer.h @@ -35,10 +35,11 @@ class ExpandConvLayer : public ExpandConvBaseLayer { ~ExpandConvLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/ExpandConvTransLayer.h b/paddle/gserver/layers/ExpandConvTransLayer.h index 4a527d67995e25..00b8f241889fdd 100644 --- a/paddle/gserver/layers/ExpandConvTransLayer.h +++ b/paddle/gserver/layers/ExpandConvTransLayer.h @@ -34,10 +34,11 @@ class ExpandConvTransLayer : public ExpandConvBaseLayer { ~ExpandConvTransLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/ExpandLayer.h b/paddle/gserver/layers/ExpandLayer.h index 5c636144235cdb..c21b3350e2bc4b 100644 --- a/paddle/gserver/layers/ExpandLayer.h +++ b/paddle/gserver/layers/ExpandLayer.h @@ -53,10 +53,11 @@ class ExpandLayer : public Layer { ~ExpandLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/FeatureMapExpandLayer.cpp b/paddle/gserver/layers/FeatureMapExpandLayer.cpp index d023074c521675..faadc70bc79c1c 100644 --- a/paddle/gserver/layers/FeatureMapExpandLayer.cpp +++ b/paddle/gserver/layers/FeatureMapExpandLayer.cpp @@ -46,10 +46,11 @@ class FeatureMapExpandLayer : public Layer { ~FeatureMapExpandLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(featmap_expand, FeatureMapExpandLayer); diff --git a/paddle/gserver/layers/FullyConnectedLayer.h b/paddle/gserver/layers/FullyConnectedLayer.h index ccd584585c97cb..64e7a050125aa9 100644 --- a/paddle/gserver/layers/FullyConnectedLayer.h +++ b/paddle/gserver/layers/FullyConnectedLayer.h @@ -36,13 +36,14 @@ class FullyConnectedLayer : public Layer { explicit FullyConnectedLayer(const LayerConfig& config) : Layer(config) {} ~FullyConnectedLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; Weight& getWeight(int idx) { return *weights_[idx]; } - void prefetch(); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void prefetch() override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/GatedRecurrentLayer.h b/paddle/gserver/layers/GatedRecurrentLayer.h index 25770ce57fbaa4..58dd760eb870e9 100644 --- a/paddle/gserver/layers/GatedRecurrentLayer.h +++ b/paddle/gserver/layers/GatedRecurrentLayer.h @@ -50,17 +50,18 @@ class GatedRecurrentLayer : public Layer, public GruCompute { public: explicit GatedRecurrentLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback); + void backward(const UpdateCallback& callback) override; - void resetState(); + void resetState() override; - void setState(LayerStatePtr state); + void setState(LayerStatePtr state) override; - LayerStatePtr getState(); + LayerStatePtr getState() override; protected: void forwardSequence(int batchSize, diff --git a/paddle/gserver/layers/GetOutputLayer.cpp b/paddle/gserver/layers/GetOutputLayer.cpp index b77fdbb30e11b7..4e29efd4612b18 100644 --- a/paddle/gserver/layers/GetOutputLayer.cpp +++ b/paddle/gserver/layers/GetOutputLayer.cpp @@ -22,17 +22,18 @@ class GetOutputLayer : public Layer { ~GetOutputLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { if (!Layer::init(layerMap, parameterMap)) return false; CHECK_EQ(1U, inputLayers_.size()); CHECK_NE(inputArgument_[0], ""); return true; } - void forward(PassType passType) { + void forward(PassType passType) override { output_ = getPrev(0)->getOutput(inputArgument_[0]); } - void backward(const UpdateCallback& callback = nullptr) {} + void backward(const UpdateCallback& callback = nullptr) override {} }; REGISTER_LAYER(get_output, GetOutputLayer); diff --git a/paddle/gserver/layers/GruStepLayer.cpp b/paddle/gserver/layers/GruStepLayer.cpp index 4a1006aa941f39..5b5cb25f9269a3 100644 --- a/paddle/gserver/layers/GruStepLayer.cpp +++ b/paddle/gserver/layers/GruStepLayer.cpp @@ -55,10 +55,11 @@ class GruStepLayer : public Layer, public GruCompute { ~GruStepLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(gru_step, GruStepLayer); diff --git a/paddle/gserver/layers/HierarchicalSigmoidLayer.h b/paddle/gserver/layers/HierarchicalSigmoidLayer.h index 70da3ac126e147..3f6875fb9f007c 100644 --- a/paddle/gserver/layers/HierarchicalSigmoidLayer.h +++ b/paddle/gserver/layers/HierarchicalSigmoidLayer.h @@ -61,9 +61,10 @@ class HierarchicalSigmoidLayer : public Layer { public: explicit HierarchicalSigmoidLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; protected: /** diff --git a/paddle/gserver/layers/InterpolationLayer.cpp b/paddle/gserver/layers/InterpolationLayer.cpp index 44fe1fb1fea420..eac7428571980b 100644 --- a/paddle/gserver/layers/InterpolationLayer.cpp +++ b/paddle/gserver/layers/InterpolationLayer.cpp @@ -43,10 +43,11 @@ class InterpolationLayer : public Layer { ~InterpolationLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(interpolation, InterpolationLayer); diff --git a/paddle/gserver/layers/LstmLayer.h b/paddle/gserver/layers/LstmLayer.h index f49df2c412f05f..c45a52d2e9aaf4 100644 --- a/paddle/gserver/layers/LstmLayer.h +++ b/paddle/gserver/layers/LstmLayer.h @@ -74,17 +74,18 @@ class LstmLayer : public Layer, public LstmCompute { public: explicit LstmLayer(const LayerConfig &config) : Layer(config) {} - bool init(const LayerMap &layerMap, const ParameterMap ¶meterMap); + bool init(const LayerMap &layerMap, + const ParameterMap ¶meterMap) override; - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback &callback); + void backward(const UpdateCallback &callback) override; - void resetState(); + void resetState() override; - void setState(LayerStatePtr state); + void setState(LayerStatePtr state) override; - LayerStatePtr getState(); + LayerStatePtr getState() override; protected: /** diff --git a/paddle/gserver/layers/LstmStepLayer.cpp b/paddle/gserver/layers/LstmStepLayer.cpp index 5fc6474b8653f4..568277a90c62c7 100644 --- a/paddle/gserver/layers/LstmStepLayer.cpp +++ b/paddle/gserver/layers/LstmStepLayer.cpp @@ -35,10 +35,11 @@ class LstmStepLayer : public Layer, public LstmCompute { ~LstmStepLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(lstm_step, LstmStepLayer); diff --git a/paddle/gserver/layers/MDLstmLayer.cpp b/paddle/gserver/layers/MDLstmLayer.cpp index 88d934d782b549..be0f2a07d4aae2 100644 --- a/paddle/gserver/layers/MDLstmLayer.cpp +++ b/paddle/gserver/layers/MDLstmLayer.cpp @@ -181,11 +181,12 @@ class MDLstmLayer : public LstmLayer { public: explicit MDLstmLayer(const LayerConfig& config) : LstmLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback); + void backward(const UpdateCallback& callback) override; protected: void forwardOneSequence(int start, CoordIterator& coordIter); diff --git a/paddle/gserver/layers/MaxIdLayer.cpp b/paddle/gserver/layers/MaxIdLayer.cpp index 80555f3f7b3241..9e72b167cd963a 100644 --- a/paddle/gserver/layers/MaxIdLayer.cpp +++ b/paddle/gserver/layers/MaxIdLayer.cpp @@ -30,8 +30,8 @@ class MaxIdLayer : public Layer { public: explicit MaxIdLayer(const LayerConfig& config) : Layer(config) {} - virtual bool init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { bool ret = Layer::init(layerMap, parameterMap); CHECK_EQ(1UL, inputLayers_.size()); @@ -40,7 +40,7 @@ class MaxIdLayer : public Layer { return ret; } - virtual void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); const Argument& input = getInput(0); size_t batchSize = input.getBatchSize(); @@ -54,7 +54,7 @@ class MaxIdLayer : public Layer { input.value->rowMax(*output_.ids, *output_.in); } - virtual void backward(const UpdateCallback& callback) {} + void backward(const UpdateCallback& callback) override {} }; REGISTER_LAYER(maxid, MaxIdLayer); diff --git a/paddle/gserver/layers/MaxLayer.h b/paddle/gserver/layers/MaxLayer.h index 472ee0ccca1962..baa58ca2d7a697 100644 --- a/paddle/gserver/layers/MaxLayer.h +++ b/paddle/gserver/layers/MaxLayer.h @@ -42,14 +42,13 @@ class MaxLayer : public SequencePoolLayer { public: explicit MaxLayer(const LayerConfig& config) : SequencePoolLayer(config) {} - ~MaxLayer() {} - - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { return SequencePoolLayer::init(layerMap, parameterMap); } - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/MaxOutLayer.h b/paddle/gserver/layers/MaxOutLayer.h index 59c2245e0d6490..73fd8536be56b2 100644 --- a/paddle/gserver/layers/MaxOutLayer.h +++ b/paddle/gserver/layers/MaxOutLayer.h @@ -45,10 +45,11 @@ class MaxOutLayer : public Layer { explicit MaxOutLayer(const LayerConfig& config) : Layer(config) {} virtual ~MaxOutLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/MixedLayer.h b/paddle/gserver/layers/MixedLayer.h index 9655a152c7bc96..755c9deb8b1be3 100644 --- a/paddle/gserver/layers/MixedLayer.h +++ b/paddle/gserver/layers/MixedLayer.h @@ -35,21 +35,22 @@ class MixedLayer : public Layer { ~MixedLayer() {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - virtual void prefetch(); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); - virtual void resetState(); + void prefetch() override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; + void resetState() override; /** * setState() should be called after getState(). * Argument state consists of all projections states. */ - virtual void setState(LayerStatePtr state); + void setState(LayerStatePtr state) override; /** * Return state which consists of all projections states. */ - virtual LayerStatePtr getState(); + LayerStatePtr getState() override; protected: std::vector> projections_; diff --git a/paddle/gserver/layers/MultiplexLayer.cpp b/paddle/gserver/layers/MultiplexLayer.cpp index d09720c5255747..297972b3cd9e4d 100644 --- a/paddle/gserver/layers/MultiplexLayer.cpp +++ b/paddle/gserver/layers/MultiplexLayer.cpp @@ -69,10 +69,11 @@ class MultiplexLayer : public Layer { ~MultiplexLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; private: /** diff --git a/paddle/gserver/layers/NCELayer.cpp b/paddle/gserver/layers/NCELayer.cpp index 3542e739df8d03..0bc2ef11829337 100644 --- a/paddle/gserver/layers/NCELayer.cpp +++ b/paddle/gserver/layers/NCELayer.cpp @@ -61,7 +61,8 @@ class NCELayer : public Layer { rand_(0, config.num_classes() - 1), prepared_(false) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); @@ -146,7 +147,7 @@ class NCELayer : public Layer { prepared_ = true; } - void prefetch() { + void prefetch() override { prepareSamples(); IVector::resizeOrCreate(labelIds_, samples_.size(), useGpu_); int* ids = labelIds_->getData(); @@ -163,7 +164,7 @@ class NCELayer : public Layer { } } - void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); CHECK(!useGpu_) << "GPU is not supported"; @@ -199,7 +200,7 @@ class NCELayer : public Layer { forwardCost(); } - void backward(const UpdateCallback& callback) { + void backward(const UpdateCallback& callback) override { Matrix::resizeOrCreate(sampleOut_.grad, 1, samples_.size(), diff --git a/paddle/gserver/layers/NormLayer.h b/paddle/gserver/layers/NormLayer.h index 011bab8fdedab0..e77faaa3225709 100644 --- a/paddle/gserver/layers/NormLayer.h +++ b/paddle/gserver/layers/NormLayer.h @@ -30,7 +30,8 @@ class NormLayer : public Layer { public: explicit NormLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { Layer::init(layerMap, parameterMap); return true; } @@ -56,9 +57,10 @@ class ResponseNormLayer : public NormLayer { public: explicit ResponseNormLayer(const LayerConfig& config) : NormLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType) { LOG(FATAL) << "Not implemented"; } - void backward(const UpdateCallback& callback = nullptr) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override { LOG(FATAL) << "Not implemented"; } + void backward(const UpdateCallback& callback = nullptr) override { LOG(FATAL) << "Not implemented"; } }; diff --git a/paddle/gserver/layers/NormProjectionLayer.h b/paddle/gserver/layers/NormProjectionLayer.h index 2c0d8a3a718c48..2997ae8848c438 100644 --- a/paddle/gserver/layers/NormProjectionLayer.h +++ b/paddle/gserver/layers/NormProjectionLayer.h @@ -36,9 +36,10 @@ class CMRProjectionNormLayer : public ResponseNormLayer { size_t getSize(); - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: TensorShape shape_; diff --git a/paddle/gserver/layers/OuterProdLayer.cpp b/paddle/gserver/layers/OuterProdLayer.cpp index b606e4436567eb..283fdb003a2bb9 100644 --- a/paddle/gserver/layers/OuterProdLayer.cpp +++ b/paddle/gserver/layers/OuterProdLayer.cpp @@ -38,10 +38,11 @@ class OuterProdLayer : public Layer { ~OuterProdLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(out_prod, OuterProdLayer); diff --git a/paddle/gserver/layers/PadLayer.h b/paddle/gserver/layers/PadLayer.h index 3e3a21a9970370..b2bbf28082e630 100644 --- a/paddle/gserver/layers/PadLayer.h +++ b/paddle/gserver/layers/PadLayer.h @@ -29,9 +29,10 @@ class PadLayer : public Layer { ~PadLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; protected: void setOutDims(const size_t batchSize); diff --git a/paddle/gserver/layers/ParameterReluLayer.h b/paddle/gserver/layers/ParameterReluLayer.h index a82497fc01ca1f..9a11b81ebf1f5c 100644 --- a/paddle/gserver/layers/ParameterReluLayer.h +++ b/paddle/gserver/layers/ParameterReluLayer.h @@ -56,9 +56,10 @@ class ParameterReluLayer : public Layer { ~ParameterReluLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/PoolLayer.h b/paddle/gserver/layers/PoolLayer.h index 318b89d7c2bce8..d43292ad2d4bbe 100644 --- a/paddle/gserver/layers/PoolLayer.h +++ b/paddle/gserver/layers/PoolLayer.h @@ -46,7 +46,8 @@ class PoolLayer : public Layer { */ static Layer* create(const LayerConfig& config); - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/PoolProjectionLayer.h b/paddle/gserver/layers/PoolProjectionLayer.h index 3dc6af2f0e9fb1..e31116de8ccb1f 100644 --- a/paddle/gserver/layers/PoolProjectionLayer.h +++ b/paddle/gserver/layers/PoolProjectionLayer.h @@ -40,7 +40,7 @@ class PoolProjectionLayer : public PoolLayer { size_t getSize(); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/PowerLayer.cpp b/paddle/gserver/layers/PowerLayer.cpp index 64fecab5b08354..31c34b43e2995a 100644 --- a/paddle/gserver/layers/PowerLayer.cpp +++ b/paddle/gserver/layers/PowerLayer.cpp @@ -40,10 +40,11 @@ class PowerLayer : public Layer { ~PowerLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(power, PowerLayer); diff --git a/paddle/gserver/layers/PrintLayer.cpp b/paddle/gserver/layers/PrintLayer.cpp index ac7f658864fee6..85f52ad5debd03 100644 --- a/paddle/gserver/layers/PrintLayer.cpp +++ b/paddle/gserver/layers/PrintLayer.cpp @@ -19,8 +19,8 @@ namespace paddle { class PrintLayer : public Layer { public: explicit PrintLayer(const LayerConfig& config) : Layer(config) {} - void forward(PassType passType); - void backward(const UpdateCallback& callback) {} + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override {} }; void PrintLayer::forward(PassType passType) { diff --git a/paddle/gserver/layers/PriorBox.cpp b/paddle/gserver/layers/PriorBox.cpp index 36ace7597cd66c..bcf5e912a50fef 100644 --- a/paddle/gserver/layers/PriorBox.cpp +++ b/paddle/gserver/layers/PriorBox.cpp @@ -30,10 +30,11 @@ namespace paddle { class PriorBoxLayer : public Layer { public: explicit PriorBoxLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback) {} + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override {} protected: int numPriors_; diff --git a/paddle/gserver/layers/RecurrentLayer.cpp b/paddle/gserver/layers/RecurrentLayer.cpp index b843fa1265cf3c..e4c2b483d2fa40 100644 --- a/paddle/gserver/layers/RecurrentLayer.cpp +++ b/paddle/gserver/layers/RecurrentLayer.cpp @@ -45,17 +45,18 @@ class RecurrentLayer : public Layer { public: explicit RecurrentLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback); + void backward(const UpdateCallback& callback) override; - void resetState(); + void resetState() override; - void setState(LayerStatePtr state); + void setState(LayerStatePtr state) override; - LayerStatePtr getState(); + LayerStatePtr getState() override; protected: /** diff --git a/paddle/gserver/layers/RecurrentLayerGroup.cpp b/paddle/gserver/layers/RecurrentLayerGroup.cpp index af8dd61d84e2e5..78a74ff19a38cd 100644 --- a/paddle/gserver/layers/RecurrentLayerGroup.cpp +++ b/paddle/gserver/layers/RecurrentLayerGroup.cpp @@ -33,15 +33,15 @@ class RecurrentLayerGroup : public Layer { void initSubNetwork(NeuralNetwork* rootNetwork, const ModelConfig& config, const std::vector& parameterTypes, - bool useGpu); + bool useGpu) override; - void forward(PassType passType) { + void forward(PassType passType) override { REGISTER_TIMER_INFO("RecurrentGroupFwTime", getName().c_str()); const std::vector inArgs; std::vector outArgs; network_->forward(inArgs, &outArgs, passType); } - void backward(const UpdateCallback& callback) { + void backward(const UpdateCallback& callback) override { REGISTER_TIMER_INFO("RecurrentGroupBwTime", getName().c_str()); network_->backward(nullptr); @@ -53,7 +53,8 @@ class RecurrentLayerGroup : public Layer { /** * @see Layer.accessSubNetwork */ - void accessSubNetwork(const std::function& callback) { + void accessSubNetwork( + const std::function& callback) override { callback(*network_); } diff --git a/paddle/gserver/layers/ResizeLayer.cpp b/paddle/gserver/layers/ResizeLayer.cpp index 7fcb3adea01b9d..eb3b63c106901f 100644 --- a/paddle/gserver/layers/ResizeLayer.cpp +++ b/paddle/gserver/layers/ResizeLayer.cpp @@ -20,18 +20,19 @@ namespace paddle { /** * @brief A layer for resizing a minibatch matrix h*w to h'*w' * @note - * origin matrix height * witdth) + * origin matrix height * width) * resize matrix: (height * width / size) * size */ class ResizeLayer : public Layer { public: explicit ResizeLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); + void forward(PassType passType) override; - void backward(const UpdateCallback& callback); + void backward(const UpdateCallback& callback) override; }; REGISTER_LAYER(resize, ResizeLayer); diff --git a/paddle/gserver/layers/SamplingIdLayer.cpp b/paddle/gserver/layers/SamplingIdLayer.cpp index 59ff5d41b52909..2538d99bb71fa1 100644 --- a/paddle/gserver/layers/SamplingIdLayer.cpp +++ b/paddle/gserver/layers/SamplingIdLayer.cpp @@ -35,8 +35,8 @@ class SamplingIdLayer : public Layer { explicit SamplingIdLayer(const LayerConfig& config) : Layer(config), rand1_(0, 1) {} - virtual bool init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override { bool ret = Layer::init(layerMap, parameterMap); CHECK_EQ(1UL, inputLayers_.size()); if (useGpu_) { @@ -48,7 +48,7 @@ class SamplingIdLayer : public Layer { return ret; } - void forward(PassType passType) { + void forward(PassType passType) override { Layer::forward(passType); if (useGpu_) { for (size_t i = 0; i < inputLayers_.size(); i++) { @@ -83,7 +83,7 @@ class SamplingIdLayer : public Layer { output_.ids->copyFrom(ids.data(), batchSize); } - virtual void backward(const UpdateCallback& callback) {} + void backward(const UpdateCallback& callback) override {} }; REGISTER_LAYER(sampling_id, SamplingIdLayer); diff --git a/paddle/gserver/layers/ScalingLayer.cpp b/paddle/gserver/layers/ScalingLayer.cpp index 7f0084be6b57f5..a38ee0857a7679 100644 --- a/paddle/gserver/layers/ScalingLayer.cpp +++ b/paddle/gserver/layers/ScalingLayer.cpp @@ -37,10 +37,11 @@ class ScalingLayer : public Layer { ~ScalingLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(scaling, ScalingLayer); diff --git a/paddle/gserver/layers/SelectiveFullyConnectedLayer.h b/paddle/gserver/layers/SelectiveFullyConnectedLayer.h index bdf9a4652cc717..99126fdba542bd 100644 --- a/paddle/gserver/layers/SelectiveFullyConnectedLayer.h +++ b/paddle/gserver/layers/SelectiveFullyConnectedLayer.h @@ -65,9 +65,10 @@ class SelectiveFullyConnectedLayer : public Layer { : Layer(config), selCols_(nullptr) {} ~SelectiveFullyConnectedLayer() {} - void prefetch(); + void prefetch() override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; Weight& getWeight(int idx) { return *weights_[idx]; } @@ -90,8 +91,8 @@ class SelectiveFullyConnectedLayer : public Layer { void fillSelectiveData( const std::shared_ptr>>& candidates); - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; private: /** diff --git a/paddle/gserver/layers/SequenceConcatLayer.cpp b/paddle/gserver/layers/SequenceConcatLayer.cpp index 069bc26e602ff7..b4677687a6cc77 100644 --- a/paddle/gserver/layers/SequenceConcatLayer.cpp +++ b/paddle/gserver/layers/SequenceConcatLayer.cpp @@ -35,10 +35,11 @@ class SequenceConcatLayer : public Layer { ~SequenceConcatLayer() {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(seqconcat, SequenceConcatLayer); diff --git a/paddle/gserver/layers/SequenceLastInstanceLayer.cpp b/paddle/gserver/layers/SequenceLastInstanceLayer.cpp index 4bfce766c769f4..7a13cd7ad0fecf 100644 --- a/paddle/gserver/layers/SequenceLastInstanceLayer.cpp +++ b/paddle/gserver/layers/SequenceLastInstanceLayer.cpp @@ -42,12 +42,11 @@ class SequenceLastInstanceLayer : public SequencePoolLayer { explicit SequenceLastInstanceLayer(const LayerConfig& config) : SequencePoolLayer(config) {} - ~SequenceLastInstanceLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(seqlastins, SequenceLastInstanceLayer); diff --git a/paddle/gserver/layers/SequencePoolLayer.h b/paddle/gserver/layers/SequencePoolLayer.h index aa9c132586e55d..85b51ccd1dc7e7 100644 --- a/paddle/gserver/layers/SequencePoolLayer.h +++ b/paddle/gserver/layers/SequencePoolLayer.h @@ -46,12 +46,11 @@ class SequencePoolLayer : public Layer { public: explicit SequencePoolLayer(const LayerConfig& config) : Layer(config) {} - virtual ~SequencePoolLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/SequenceReshapeLayer.cpp b/paddle/gserver/layers/SequenceReshapeLayer.cpp index 23924b0490851a..66f49159087ab9 100644 --- a/paddle/gserver/layers/SequenceReshapeLayer.cpp +++ b/paddle/gserver/layers/SequenceReshapeLayer.cpp @@ -34,12 +34,11 @@ class SequenceReshapeLayer : public Layer { public: explicit SequenceReshapeLayer(const LayerConfig& config) : Layer(config) {} - ~SequenceReshapeLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(seqreshape, SequenceReshapeLayer); diff --git a/paddle/gserver/layers/SlopeInterceptLayer.cpp b/paddle/gserver/layers/SlopeInterceptLayer.cpp index b678f414b6d76f..faf98744a7fdcf 100644 --- a/paddle/gserver/layers/SlopeInterceptLayer.cpp +++ b/paddle/gserver/layers/SlopeInterceptLayer.cpp @@ -39,12 +39,11 @@ class SlopeInterceptLayer : public Layer { public: explicit SlopeInterceptLayer(const LayerConfig& config) : Layer(config) {} - ~SlopeInterceptLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(slope_intercept, SlopeInterceptLayer); diff --git a/paddle/gserver/layers/SpatialPyramidPoolLayer.h b/paddle/gserver/layers/SpatialPyramidPoolLayer.h index 32e88cf141a667..7d3cb80443801a 100644 --- a/paddle/gserver/layers/SpatialPyramidPoolLayer.h +++ b/paddle/gserver/layers/SpatialPyramidPoolLayer.h @@ -43,9 +43,8 @@ class SpatialPyramidPoolLayer : public Layer { public: explicit SpatialPyramidPoolLayer(const LayerConfig& config) : Layer(config) {} - ~SpatialPyramidPoolLayer() {} - - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; ProjectionConfig getConfig(size_t sizeX_, size_t sizeY_, @@ -54,7 +53,7 @@ class SpatialPyramidPoolLayer : public Layer { std::string& poolType_); size_t getSize(); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/SubSequenceLayer.cpp b/paddle/gserver/layers/SubSequenceLayer.cpp index c52fbee26232ad..19b7ad1869af98 100644 --- a/paddle/gserver/layers/SubSequenceLayer.cpp +++ b/paddle/gserver/layers/SubSequenceLayer.cpp @@ -35,12 +35,11 @@ class SubSequenceLayer : public Layer { public: explicit SubSequenceLayer(const LayerConfig& config) : Layer(config) {} - ~SubSequenceLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(subseq, SubSequenceLayer); diff --git a/paddle/gserver/layers/SumToOneNormLayer.cpp b/paddle/gserver/layers/SumToOneNormLayer.cpp index aa99b49380d368..00f8519550bcff 100644 --- a/paddle/gserver/layers/SumToOneNormLayer.cpp +++ b/paddle/gserver/layers/SumToOneNormLayer.cpp @@ -41,12 +41,11 @@ class SumToOneNormLayer : public Layer { public: explicit SumToOneNormLayer(const LayerConfig& config) : Layer(config) {} - ~SumToOneNormLayer() {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; REGISTER_LAYER(sum_to_one_norm, SumToOneNormLayer); diff --git a/paddle/gserver/layers/TensorLayer.h b/paddle/gserver/layers/TensorLayer.h index ac38ffb6205703..43992f692d3ce4 100644 --- a/paddle/gserver/layers/TensorLayer.h +++ b/paddle/gserver/layers/TensorLayer.h @@ -44,13 +44,12 @@ class TensorLayer : public Layer { public: explicit TensorLayer(const LayerConfig& config) : Layer(config) {} - ~TensorLayer() {} - - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; Weight& getWeight(int idx) { return *weights_[idx]; } - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/TransLayer.h b/paddle/gserver/layers/TransLayer.h index b43fa1ebfb0032..08a2fb453284c8 100644 --- a/paddle/gserver/layers/TransLayer.h +++ b/paddle/gserver/layers/TransLayer.h @@ -32,9 +32,10 @@ class TransLayer : public Layer { public: explicit TransLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle diff --git a/paddle/gserver/layers/ValidationLayer.h b/paddle/gserver/layers/ValidationLayer.h index 4c1de7b3b7d697..c8b2634a1366ed 100644 --- a/paddle/gserver/layers/ValidationLayer.h +++ b/paddle/gserver/layers/ValidationLayer.h @@ -26,7 +26,8 @@ class ValidationLayer : public Layer { public: explicit ValidationLayer(const LayerConfig& config) : Layer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; LayerPtr getOutputLayer() { return inputLayers_[0]; } @@ -37,13 +38,13 @@ class ValidationLayer : public Layer { return inputLayers_[2]; } - virtual void forward(PassType passType); + void forward(PassType passType) override; - virtual void backward(const UpdateCallback& callback = nullptr); + void backward(const UpdateCallback& callback = nullptr) override; virtual void validationImp(MatrixPtr outputValue, IVectorPtr label) = 0; - virtual void onPassEnd() = 0; + void onPassEnd() override = 0; }; /* @@ -57,11 +58,12 @@ class AucValidation : public ValidationLayer { cpuLabel_(nullptr), cpuWeight_(nullptr) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void validationImp(MatrixPtr outputValue, IVectorPtr label); + void validationImp(MatrixPtr outputValue, IVectorPtr label) override; - void onPassEnd(); + void onPassEnd() override; struct PredictionResult { PredictionResult(real __out, int __label) : out(__out), label(__label) {} @@ -86,11 +88,12 @@ class PnpairValidation : public ValidationLayer { explicit PnpairValidation(const LayerConfig& config) : ValidationLayer(config) {} - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; - void validationImp(MatrixPtr outputValue, IVectorPtr label); + void validationImp(MatrixPtr outputValue, IVectorPtr label) override; - void onPassEnd(); + void onPassEnd() override; private: bool passBegin_; diff --git a/paddle/gserver/layers/WarpCTCLayer.h b/paddle/gserver/layers/WarpCTCLayer.h index 3d9ae9249af66d..7e8d7379d26788 100644 --- a/paddle/gserver/layers/WarpCTCLayer.h +++ b/paddle/gserver/layers/WarpCTCLayer.h @@ -30,9 +30,10 @@ class WarpCTCLayer : public Layer { explicit WarpCTCLayer(const LayerConfig& config) : Layer(config) {} ~WarpCTCLayer() {} - virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - virtual void forward(PassType passType); - virtual void backward(const UpdateCallback& callback); + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; protected: /** From e7419d690c3407ad7387d453a673b6f99656d342 Mon Sep 17 00:00:00 2001 From: reyoung Date: Fri, 3 Feb 2017 09:44:43 +0000 Subject: [PATCH 140/370] Remove virtual in override --- paddle/gserver/layers/CudnnPoolLayer.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/layers/CudnnPoolLayer.h b/paddle/gserver/layers/CudnnPoolLayer.h index 28294f5aa30ca4..f0aa22fe3af90c 100644 --- a/paddle/gserver/layers/CudnnPoolLayer.h +++ b/paddle/gserver/layers/CudnnPoolLayer.h @@ -54,8 +54,8 @@ class CudnnPoolLayer : public PoolLayer { * So reshaping is needed. */ void reshape(int batchSize); - virtual void forward(PassType passType) override; - virtual void backward(const UpdateCallback& callback = nullptr) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; }; } // namespace paddle From 73dcf2cd58b9e38b465853dd95ace9e16f84f164 Mon Sep 17 00:00:00 2001 From: Haonan Date: Fri, 3 Feb 2017 11:49:38 -0800 Subject: [PATCH 141/370] improving code comments --- paddle/gserver/layers/RotateLayer.cpp | 3 ++- paddle/gserver/layers/RotateLayer.h | 2 +- paddle/gserver/tests/test_LayerGrad.cpp | 3 ++- python/paddle/trainer_config_helpers/layers.py | 4 ++-- 4 files changed, 7 insertions(+), 5 deletions(-) diff --git a/paddle/gserver/layers/RotateLayer.cpp b/paddle/gserver/layers/RotateLayer.cpp index d928f1d93de0ee..7c71088d786ab2 100644 --- a/paddle/gserver/layers/RotateLayer.cpp +++ b/paddle/gserver/layers/RotateLayer.cpp @@ -38,7 +38,8 @@ void RotateLayer::forward(PassType passType) { size_ = input->getWidth(); CHECK_GE(size_, height_ * width_); CHECK_EQ(size_ % (height_ * width_), 0) - << "The input's depth should be an int"; + << "total size_ is not dividable by (height_ * width_), i.e., " + << "channel number should be an integer"; channels_ = size_ / (height_ * width_); resizeOutput(batchSize_, size_); diff --git a/paddle/gserver/layers/RotateLayer.h b/paddle/gserver/layers/RotateLayer.h index a6f565a7e5dcfa..1a64d4d5a51d9c 100644 --- a/paddle/gserver/layers/RotateLayer.h +++ b/paddle/gserver/layers/RotateLayer.h @@ -21,7 +21,7 @@ namespace paddle { /** * A layer for rotating a multi-channel feature map (M x N x C) in the spatial * domain - * The rotation is 90 degrees in clock-wise + * The rotation is 90 degrees in clock-wise for each channel * \f[ * y(j,i,:) = x(M-i-1,j,:) * \f] diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 685d2ca542b242..7e6842742e0d19 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1320,9 +1320,10 @@ TEST(Layer, RotateLayer) { TestConfig config; config.biasSize = 0; config.layerConfig.set_type("rotate"); - const int INPUT_SIZE = 64; // height * width * depth + const int CHANNEL = 2; const int HEIGHT = 8; const int WIDTH = 4; + const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL; config.layerConfig.set_size(INPUT_SIZE); config.layerConfig.set_height(HEIGHT); config.layerConfig.set_width(WIDTH); diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 2636f6cc1a1653..66b842a25d9d87 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1679,8 +1679,8 @@ def trans_layer(input, name=None, layer_attr=None): @layer_support() def rotate_layer(input, height, width, name=None, layer_attr=None): """ - A layer for rotating 90 degrees (clock-wise), usually used when the input sample - is some image or feature map. + A layer for rotating 90 degrees (clock-wise) for each feature channel, + usually used when the input sample is some image or feature map. .. math:: y(j,i,:) = x(M-i-1,j,:) From 996b1de1d83e972870c4a3c6e21b5cc4b0b93014 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 3 Feb 2017 23:48:54 +0000 Subject: [PATCH 142/370] Rename DataBase into create_data_config_proto --- python/paddle/trainer/config_parser.py | 14 ++++++------ .../trainer_config_helpers/data_sources.py | 22 +++++++++---------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 6701eced60d068..e3e2a6899e7fa7 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -894,7 +894,7 @@ def __init__(self, channels, groups, img_size_x=0, img_size_y=0): self.add_keys(locals()) -def DataBase(async_load_data=False, +def create_data_config_proto(async_load_data=False, constant_slots=None, data_ratio=1, is_main_data=True, @@ -924,7 +924,7 @@ def SimpleData(files=None, context_len=None, buffer_capacity=None, **xargs): - data_config = DataBase(**xargs) + data_config = create_data_config_proto(**xargs) data_config.type = 'simple' data_config.files = files data_config.feat_dim = feat_dim @@ -946,7 +946,7 @@ def PyData(files=None, constant_slots=None, load_thread_num=None, **xargs): - data_config = DataBase(**xargs) + data_config = create_data_config_proto(**xargs) data_config.type = 'py' if load_data_module in g_py_module_name_list: @@ -997,7 +997,7 @@ def ProtoData(files=None, constant_slots=None, load_thread_num=None, **xargs): - data_config = DataBase(**xargs) + data_config = create_data_config_proto(**xargs) if type is None: data_config.type = 'proto' else: @@ -1036,7 +1036,7 @@ def Data(type, buffer_capacity=None, **xargs): - data_config = DataBase(**xargs) + data_config = create_data_config_proto(**xargs) data_config.type = type data_config.files = files data_config.feat_dim = feat_dim @@ -1927,8 +1927,8 @@ def __init__(self, image_conf = self.config.inputs[0].image_conf parse_image(self.inputs[0].image, input_layer.name, image_conf) - # Only pass the width and height of input to batch_norm layer - # when either of it is non-zero. + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. if input_layer.width != 0 or input_layer.height != 0: self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, image_conf.channels, False) diff --git a/python/paddle/trainer_config_helpers/data_sources.py b/python/paddle/trainer_config_helpers/data_sources.py index 622b4fc25ccff3..0ea8fc77eef9f5 100644 --- a/python/paddle/trainer_config_helpers/data_sources.py +++ b/python/paddle/trainer_config_helpers/data_sources.py @@ -58,8 +58,8 @@ def define_py_data_source(file_list, :param obj: python object name. May be a function name if using PyDataProviderWrapper. :type obj: basestring - :param args: The best practice is using dict to pass arguments into - DataProvider, and use :code:`@init_hook_wrapper` to + :param args: The best practice is using dict to pass arguments into + DataProvider, and use :code:`@init_hook_wrapper` to receive arguments. :type args: string or picklable object :param async: Load Data asynchronously or not. @@ -98,7 +98,7 @@ def define_py_data_sources(train_list, The annotation is almost the same as define_py_data_sources2, except that it can specific train_async and data_cls. - :param data_cls: + :param data_cls: :param train_list: Train list name. :type train_list: basestring :param test_list: Test list name. @@ -111,8 +111,8 @@ def define_py_data_sources(train_list, a tuple or list to this argument. :type obj: basestring or tuple or list :param args: The best practice is using dict() to pass arguments into - DataProvider, and use :code:`@init_hook_wrapper` to receive - arguments. If train and test is different, then pass a tuple + DataProvider, and use :code:`@init_hook_wrapper` to receive + arguments. If train and test is different, then pass a tuple or list to this argument. :type args: string or picklable object or list or tuple. :param train_async: Is training data load asynchronously or not. @@ -163,12 +163,12 @@ def define_py_data_sources2(train_list, test_list, module, obj, args=None): .. code-block:: python - define_py_data_sources2(train_list="train.list", - test_list="test.list", + define_py_data_sources2(train_list="train.list", + test_list="test.list", module="data_provider" # if train/test use different configurations, # obj=["process_train", "process_test"] - obj="process", + obj="process", args={"dictionary": dict_name}) The related data provider can refer to :ref:`api_pydataprovider2_sequential_model` . @@ -185,8 +185,8 @@ def define_py_data_sources2(train_list, test_list, module, obj, args=None): a tuple or list to this argument. :type obj: basestring or tuple or list :param args: The best practice is using dict() to pass arguments into - DataProvider, and use :code:`@init_hook_wrapper` to receive - arguments. If train and test is different, then pass a tuple + DataProvider, and use :code:`@init_hook_wrapper` to receive + arguments. If train and test is different, then pass a tuple or list to this argument. :type args: string or picklable object or list or tuple. :return: None @@ -195,7 +195,7 @@ def define_py_data_sources2(train_list, test_list, module, obj, args=None): def py_data2(files, load_data_module, load_data_object, load_data_args, **kwargs): - data = DataBase() + data = create_data_config_proto() data.type = 'py2' data.files = files data.load_data_module = load_data_module From 03148804bbe162a2cd9366aa491077073257905c Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Sat, 4 Feb 2017 15:10:58 +0800 Subject: [PATCH 143/370] remove usused arguments for maxout_layer, refine notes for pad_layer --- .../paddle/trainer_config_helpers/layers.py | 49 +++++++++---------- 1 file changed, 22 insertions(+), 27 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 66fa58ac91e33b..b124de66e94b59 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -3677,26 +3677,27 @@ def pad_layer(input, For example, - .. code-block:: - - input(2,2,2,3) = [ - [ [[1,2,3], [3,4,5]], - [[2,3,5], [1,6,7]] ], - [ [[4,3,1], [1,8,7]], - [[3,8,9], [2,3,5]] ] - ] - - pad_c=[1,1], pad_h=[0,0], pad_w=[0,0] - output(2,4,2,3) = [ - [ [[0,0,0], [0,0,0]], - [[1,2,3], [3,4,5]], - [[2,3,5], [1,6,7]], - [[0,0,0], [0,0,0]] ], - [ [[0,0,0], [0,0,0]], - [[4,3,1], [1,8,7]], - [[3,8,9], [2,3,5]], - [[0,0,0], [0,0,0]] ] - ] + .. code-block:: python + + input(2,2,2,3) = [ + [ [[1,2,3], [3,4,5]], + [[2,3,5], [1,6,7]] ], + [ [[4,3,1], [1,8,7]], + [[3,8,9], [2,3,5]] ] + ] + + pad_c=[1,1], pad_h=[0,0], pad_w=[0,0] + + output(2,4,2,3) = [ + [ [[0,0,0], [0,0,0]], + [[1,2,3], [3,4,5]], + [[2,3,5], [1,6,7]], + [[0,0,0], [0,0,0]] ], + [ [[0,0,0], [0,0,0]], + [[4,3,1], [1,8,7]], + [[3,8,9], [2,3,5]], + [[0,0,0], [0,0,0]] ] + ] The simply usage is: @@ -4191,13 +4192,7 @@ def block_expand_layer(input, @wrap_name_default() @layer_support() -def maxout_layer(input, - groups, - num_channels=None, - size_x=None, - size_y=None, - name=None, - layer_attr=None): +def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None): """ A layer to do max out on conv layer output. - Input: output of a conv layer. From ab279beed1725fae4fc4781ba891d80bde8eae0a Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Sun, 5 Feb 2017 01:01:21 +0000 Subject: [PATCH 144/370] Remove completely create_data_config_proto --- python/paddle/trainer/config_parser.py | 24 ------------------- .../trainer_config_helpers/data_sources.py | 3 ++- 2 files changed, 2 insertions(+), 25 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index e3e2a6899e7fa7..65ebc96136d823 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -894,30 +894,6 @@ def __init__(self, channels, groups, img_size_x=0, img_size_y=0): self.add_keys(locals()) -def create_data_config_proto(async_load_data=False, - constant_slots=None, - data_ratio=1, - is_main_data=True, - usage_ratio=None): - # default: all sub dataproviders are treat as "main data". - # see proto/DataConfig.proto for is_main_data - data_config = DataConfig() - - data_config.async_load_data = async_load_data - - if constant_slots: - data_config.constant_slots.extend(constant_slots) - data_config.data_ratio = data_ratio - data_config.is_main_data = is_main_data - - usage_ratio = default(usage_ratio, settings_deprecated["usage_ratio"]) - config_assert(usage_ratio >= 0 and usage_ratio <= 1, - "The range of usage_ratio is [0, 1]") - data_config.usage_ratio = usage_ratio - - return data_config - - @config_func def SimpleData(files=None, feat_dim=None, diff --git a/python/paddle/trainer_config_helpers/data_sources.py b/python/paddle/trainer_config_helpers/data_sources.py index 0ea8fc77eef9f5..6744dec5a87a5b 100644 --- a/python/paddle/trainer_config_helpers/data_sources.py +++ b/python/paddle/trainer_config_helpers/data_sources.py @@ -14,6 +14,7 @@ """ Data Sources are helpers to define paddle training data or testing data. """ +import paddle.proto.DataConfig_pb2 from paddle.trainer.config_parser import * from .utils import deprecated @@ -195,7 +196,7 @@ def define_py_data_sources2(train_list, test_list, module, obj, args=None): def py_data2(files, load_data_module, load_data_object, load_data_args, **kwargs): - data = create_data_config_proto() + data = paddle.proto.DataConfig_pb2.DataConfig() data.type = 'py2' data.files = files data.load_data_module = load_data_module From 058eeac0fc94500137ce0192136d45a62ada0569 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Sun, 5 Feb 2017 01:17:00 +0000 Subject: [PATCH 145/370] Revert "Remove completely create_data_config_proto" This reverts commit ab279beed1725fae4fc4781ba891d80bde8eae0a. --- python/paddle/trainer/config_parser.py | 24 +++++++++++++++++++ .../trainer_config_helpers/data_sources.py | 3 +-- 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 65ebc96136d823..e3e2a6899e7fa7 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -894,6 +894,30 @@ def __init__(self, channels, groups, img_size_x=0, img_size_y=0): self.add_keys(locals()) +def create_data_config_proto(async_load_data=False, + constant_slots=None, + data_ratio=1, + is_main_data=True, + usage_ratio=None): + # default: all sub dataproviders are treat as "main data". + # see proto/DataConfig.proto for is_main_data + data_config = DataConfig() + + data_config.async_load_data = async_load_data + + if constant_slots: + data_config.constant_slots.extend(constant_slots) + data_config.data_ratio = data_ratio + data_config.is_main_data = is_main_data + + usage_ratio = default(usage_ratio, settings_deprecated["usage_ratio"]) + config_assert(usage_ratio >= 0 and usage_ratio <= 1, + "The range of usage_ratio is [0, 1]") + data_config.usage_ratio = usage_ratio + + return data_config + + @config_func def SimpleData(files=None, feat_dim=None, diff --git a/python/paddle/trainer_config_helpers/data_sources.py b/python/paddle/trainer_config_helpers/data_sources.py index 6744dec5a87a5b..0ea8fc77eef9f5 100644 --- a/python/paddle/trainer_config_helpers/data_sources.py +++ b/python/paddle/trainer_config_helpers/data_sources.py @@ -14,7 +14,6 @@ """ Data Sources are helpers to define paddle training data or testing data. """ -import paddle.proto.DataConfig_pb2 from paddle.trainer.config_parser import * from .utils import deprecated @@ -196,7 +195,7 @@ def define_py_data_sources2(train_list, test_list, module, obj, args=None): def py_data2(files, load_data_module, load_data_object, load_data_args, **kwargs): - data = paddle.proto.DataConfig_pb2.DataConfig() + data = create_data_config_proto() data.type = 'py2' data.files = files data.load_data_module = load_data_module From b36df98d01c537c4dbb14a625d9ec3936e460a5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Sat, 4 Feb 2017 17:24:18 -0800 Subject: [PATCH 146/370] Correct Python identation --- python/paddle/trainer/config_parser.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index e3e2a6899e7fa7..dd159f22d97f48 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -895,10 +895,10 @@ def __init__(self, channels, groups, img_size_x=0, img_size_y=0): def create_data_config_proto(async_load_data=False, - constant_slots=None, - data_ratio=1, - is_main_data=True, - usage_ratio=None): + onstant_slots=None, + data_ratio=1, + is_main_data=True, + usage_ratio=None): # default: all sub dataproviders are treat as "main data". # see proto/DataConfig.proto for is_main_data data_config = DataConfig() From b8f3a5c433841017760f53119b77b54d757f4fa5 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Sat, 4 Feb 2017 19:35:16 -0800 Subject: [PATCH 147/370] Correct the wrong deletion of a single character --- python/paddle/trainer/config_parser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index dd159f22d97f48..8b02be9af1a82b 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -895,7 +895,7 @@ def __init__(self, channels, groups, img_size_x=0, img_size_y=0): def create_data_config_proto(async_load_data=False, - onstant_slots=None, + constant_slots=None, data_ratio=1, is_main_data=True, usage_ratio=None): From ca25b9a50852529de83d99ff0750b4dbee673b62 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 6 Feb 2017 10:48:28 +0800 Subject: [PATCH 148/370] remove unused notes in maxout layer --- python/paddle/trainer_config_helpers/layers.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index b124de66e94b59..1fdc4c46236371 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -4222,12 +4222,6 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None): :type num_channels: int|None :param groups: The group number of input layer. :type groups: int - :param size_x: conv output width. If None will be set - automatically from previous output. - :type size_x: int|None - :param size_y: conv output height. If None will be set - automatically from previous output. - :type size_y: int|None :param name: The name of this layer, which can not specify. :type name: None|basestring. :param layer_attr: Extra Layer attribute. From f3755dd455a60d9aaadd7978d229f7bdb5673c4b Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 7 Feb 2017 14:23:33 +0800 Subject: [PATCH 149/370] add v2-layers --- demo/mnist/api_train.py | 34 +++++------ python/paddle/v2/__init__.py | 3 +- python/paddle/v2/layers.py | 112 +++++++++++++++++++++++++++++++++++ 3 files changed, 131 insertions(+), 18 deletions(-) create mode 100644 python/paddle/v2/layers.py diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index 8573d8143a085b..6d071cdffe120f 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -6,25 +6,16 @@ The user api could be simpler and carefully designed. """ -import py_paddle.swig_paddle as api -from py_paddle import DataProviderConverter -import paddle.trainer.PyDataProvider2 as dp -import numpy as np import random -from mnist_util import read_from_mnist -from paddle.trainer_config_helpers import * -import paddle.v2 +import numpy as np +import paddle.trainer.PyDataProvider2 as dp +import paddle.v2 +import py_paddle.swig_paddle as api +from paddle.trainer_config_helpers import * +from py_paddle import DataProviderConverter -def network_config(): - imgs = data_layer(name='pixel', size=784) - hidden1 = fc_layer(input=imgs, size=200) - hidden2 = fc_layer(input=hidden1, size=200) - inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation()) - cost = classification_cost( - input=inference, label=data_layer( - name='label', size=10)) - outputs(cost) +from mnist_util import read_from_mnist def init_parameter(network): @@ -79,8 +70,17 @@ def main(): updater = optimizer.create_local_updater() assert isinstance(updater, api.ParameterUpdater) + # define network + images = paddle.v2.layers.data_layer(name='pixel', size=784) + label = paddle.v2.layers.data_layer(name='label', size=10) + hidden1 = paddle.v2.layers.fc_layer(input=images, size=200) + hidden2 = paddle.v2.layers.fc_layer(input=hidden1, size=200) + inference = paddle.v2.layers.fc_layer( + input=hidden2, size=10, act=SoftmaxActivation()) + cost = paddle.v2.layers.classification_cost(input=inference, label=label) + # Create Simple Gradient Machine. - model_config = parse_network_config(network_config) + model_config = paddle.v2.layers.parse_network(cost) m = api.GradientMachine.createFromConfigProto(model_config, api.CREATE_MODE_NORMAL, optimizer.enable_types()) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index b2ea87b086101d..c04c0cc73a800a 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -13,5 +13,6 @@ # limitations under the License. import optimizer +import layers -__all__ = ['optimizer'] +__all__ = ['optimizer', 'layers'] diff --git a/python/paddle/v2/layers.py b/python/paddle/v2/layers.py new file mode 100644 index 00000000000000..14efe9412c3582 --- /dev/null +++ b/python/paddle/v2/layers.py @@ -0,0 +1,112 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle.trainer_config_helpers as conf_helps +from paddle.trainer_config_helpers.config_parser_utils import \ + parse_network_config as __parse__ +from paddle.trainer_config_helpers.default_decorators import wrap_name_default +import collections + + +class Layer(object): + def __init__(self, name, parent_layer): + assert isinstance(parent_layer, dict) + assert isinstance(name, basestring) + self.name = name + self.__parent_layer__ = parent_layer + + def to_proto(self, context): + """ + function to set proto attribute + """ + kwargs = dict() + for param_name in self.__parent_layer__: + if not isinstance(self.__parent_layer__[param_name], + collections.Sequence): + param_value = self.__parent_layer__[param_name].to_proto( + context=context) + else: + param_value = map(lambda x: x.to_proto(context=context), + self.__parent_layer__[param_name]) + kwargs[param_name] = param_value + + if self.name not in context: + context[self.name] = self.to_proto_impl(**kwargs) + return context[self.name] + + def to_proto_impl(self, **kwargs): + raise NotImplementedError() + + +def parse_network(*outputs): + def __real_func__(): + context = dict() + real_output = [each.to_proto(context=context) for each in outputs] + conf_helps.outputs(real_output) + + return __parse__(__real_func__) + + +def __convert__(method_name, name_prefix, parent_names): + if name_prefix is not None: + wrapper = wrap_name_default(name_prefix=name_prefix) + else: + wrapper = None + + class __Impl__(Layer): + def __init__(self, name=None, **kwargs): + parent_layers = dict() + other_kwargs = dict() + for pname in parent_names: + parent_layers[pname] = kwargs[pname] + + for key in kwargs.keys(): + if key not in parent_names: + other_kwargs[key] = kwargs[key] + + super(__Impl__, self).__init__(name, parent_layers) + self.__other_kwargs__ = other_kwargs + + if wrapper is not None: + __init__ = wrapper(__init__) + + def to_proto_impl(self, **kwargs): + args = dict() + for each in kwargs: + args[each] = kwargs[each] + for each in self.__other_kwargs__: + args[each] = self.__other_kwargs__[each] + return getattr(conf_helps, method_name)(name=self.name, **args) + + return __Impl__ + + +data_layer = __convert__('data_layer', None, []) +fc_layer = __convert__('fc_layer', name_prefix='fc', parent_names=['input']) +classification_cost = __convert__( + 'classification_cost', + name_prefix='classification_cost', + parent_names=['input', 'label']) + +__all__ = ['data_layer', 'fc_layer', 'classification_cost', 'parse_network'] + +if __name__ == '__main__': + data = data_layer(name='pixel', size=784) + hidden = fc_layer(input=data, size=100, act=conf_helps.SigmoidActivation()) + predict = fc_layer( + input=[hidden, data], size=10, act=conf_helps.SoftmaxActivation()) + cost = classification_cost( + input=predict, label=data_layer( + name='label', size=10)) + print parse_network(cost) From 250f784122f3bfd67f9e8dd9fcaa437f07e0575a Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 7 Feb 2017 14:41:00 +0800 Subject: [PATCH 150/370] Add std for setiosflags --- paddle/function/MulOpTest.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/function/MulOpTest.cpp b/paddle/function/MulOpTest.cpp index 158c3c90983b12..8748eb0d79fa0f 100644 --- a/paddle/function/MulOpTest.cpp +++ b/paddle/function/MulOpTest.cpp @@ -60,7 +60,7 @@ TEST(MulOp, DDDMatrixMul) { if (transa && transb) { continue; } - VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + VLOG(3) << std::setiosflags(std::ios::left) << std::setfill(' ') << " transa=" << transa << " transb=" << transb << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN @@ -104,7 +104,7 @@ TEST(MuLOp, DSparseDMul) { for (const auto dimK : {3, 10}) { for (const auto nnz : {3, 10}) { for (const auto FORMAT : {SPARSE_CSR}) { - VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + VLOG(3) << std::setiosflags(std::ios::left) << std::setfill(' ') << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK @@ -150,7 +150,7 @@ TEST(MulOp, DDSparseMul) { for (const auto dimK : {3, 10}) { for (const auto nnz : {3, 10}) { for (const auto FORMAT : {SPARSE_CSR, SPARSE_CSC}) { - VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + VLOG(3) << std::setiosflags(std::ios::left) << std::setfill(' ') << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK @@ -197,7 +197,7 @@ TEST(MulOp, SparseDDMul) { for (const auto dimK : {3, 10}) { for (const auto nnz : {3, 10}) { for (const auto FORMAT : {SPARSE_CSC, SPARSE_CSR}) { - VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ') + VLOG(3) << std::setiosflags(std::ios::left) << std::setfill(' ') << " dimM=" << std::setw(5) << dimM << " dimN=" << std::setw(5) << dimN << " dimK=" << std::setw(5) << dimK From 1251501caf2c268e3e221520e624d5f64cce8c58 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 24 Jan 2017 21:19:59 -0800 Subject: [PATCH 151/370] rewrite unit test using new Function Test. --- paddle/function/BufferArg.h | 3 +- paddle/function/ContextProjectionOp.cpp | 70 +++++----- paddle/function/ContextProjectionOp.h | 1 - paddle/function/ContextProjectionOpTest.cpp | 139 ++++++------------- paddle/function/FunctionTest.h | 146 +++++++++++++++----- 5 files changed, 185 insertions(+), 174 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 349b21e7e64064..f7df56846f6d7b 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -226,7 +226,8 @@ class SequenceArg : public BufferArg { SequenceArg(ValueType valueType, const TensorShape& shape, ArgType argType = UNSPECIFIED) - : BufferArg(valueType, shape, argType), startPositions_(TensorShape()) { + : BufferArg(valueType, shape, argType), + startPositions_(TensorShape({shape[0]})) { bufferType_ = TENSOR_SEQUENCE_DATA; } diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index 6cd4e4abee8fcc..d81ea584016060 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -108,26 +108,23 @@ class ContextProjectionForwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK(1 == inputs.size() || 2 == inputs.size()); - CHECK_EQ((size_t)1, outputs.size()); + CHECK(1UL == inputs.size() || 2UL == inputs.size()); + CHECK_EQ(1UL, outputs.size()); CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) << "SequenceArg required here"; const auto val_seqs = dynamic_cast(inputs[0]); auto out_seq = dynamic_cast(outputs[0]); CHECK(out_seq.data() && val_seqs.data() && val_seqs.getSequenceId().data()); - CHECK_EQ(out_seq.shape().ndims(), (size_t)2); - CHECK_EQ(val_seqs.shape().ndims(), (size_t)2); - CHECK_EQ(val_seqs.getSequenceId().shape().ndims(), (size_t)1); - if (2 == inputs.size()) { - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - } + CHECK_EQ(out_seq.shape().ndims(), 2UL); + CHECK_EQ(val_seqs.shape().ndims(), 2UL); /// dim of output = dim of input * context_length CHECK_EQ(out_seq.shape()[1], val_seqs.shape()[1] * context_length_); /// input and output has the same batch_size CHECK_EQ(val_seqs.shape()[0], out_seq.shape()[0]); - /// dim of input == dim of weight - if (2 == inputs.size()) { + if (2UL == inputs.size()) { + CHECK_EQ(inputs[1].shape().ndims(), 2UL); + /// dim of input == dim of weight CHECK_EQ(val_seqs.shape()[1], inputs[1].shape()[1]); } @@ -135,10 +132,11 @@ class ContextProjectionForwardFunc : public FunctionBase { auto out_mat = out_seq.matrix(); const auto in_mat = val_seqs.matrix(); const auto w_mat = - (2 == inputs.size()) + (2UL == inputs.size()) ? inputs[1].matrix() : typename Tensor::Matrix(nullptr, 0, 0); const auto seq_vec = val_seqs.getSequenceId().vector(); + ContextProjectionForward(out_mat, in_mat, w_mat, @@ -235,36 +233,40 @@ class ContextProjectionBackwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)1, inputs.size()); - CHECK_EQ((size_t)2, outputs.size()); + CHECK_EQ(1UL, inputs.size()); + CHECK(1UL == outputs.size() || 2UL == outputs.size()); CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) << "SequenceArg required here"; const auto in_seq = dynamic_cast(inputs[0]); auto out_seq = dynamic_cast(outputs[0]); CHECK(in_seq.data() && in_seq.getSequenceId().data()); - CHECK_EQ(in_seq.shape().ndims(), (size_t)2); - CHECK_EQ(in_seq.getSequenceId().shape().ndims(), (size_t)1); - CHECK_EQ(out_seq.shape().ndims(), (size_t)2); - CHECK_EQ(out_seq.getSequenceId().shape().ndims(), (size_t)1); - CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); + CHECK_EQ(in_seq.shape().ndims(), 2UL); + CHECK_EQ(out_seq.shape().ndims(), 2UL); + CHECK_EQ(out_seq.getSequenceId().shape().ndims(), 1UL); - /// dim of input grad == dim of weight - CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]); /// input and output grad has the same batch_size CHECK_EQ(out_seq.shape()[0], in_seq.shape()[0]); /// dim of output grad = dim of input grad * context_length CHECK_EQ(in_seq.shape()[1], out_seq.shape()[1] * context_length_); CHECK_EQ(out_seq.getArgType(), ADD_TO); - CHECK_EQ(outputs[1].getArgType(), ADD_TO); + + if (2UL == outputs.size()) { + CHECK_EQ(outputs[1].shape().ndims(), 2UL); + /// dim of input grad == dim of weight + CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]); + CHECK_EQ(outputs[1].getArgType(), ADD_TO); + } const auto seq_vec = in_seq.getSequenceId().vector(); const auto out_grad_mat = in_seq.matrix(); auto in_grad_mat = !out_seq.data() ? typename Tensor::Matrix(nullptr, 0, 0) : out_seq.matrix(); - auto w_grad_mat = !outputs[1].data() - ? typename Tensor::Matrix(nullptr, 0, 0) - : outputs[1].matrix(); + auto w_grad_mat = + (2UL == outputs.size()) + ? outputs[1].matrix() + : typename Tensor::Matrix(nullptr, 0, 0); + ContextProjectionBackward(out_grad_mat, in_grad_mat, w_grad_mat, @@ -304,17 +306,17 @@ class ContextProjectionBackwardDataFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ(1, static_cast(inputs.size())); - CHECK_EQ(1, static_cast(outputs.size())); + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) << "SequenceArg required here"; const auto in_seq = dynamic_cast(inputs[0]); const auto out_seq = dynamic_cast(outputs[0]); CHECK(in_seq.data() && out_seq.data() && in_seq.getSequenceId().data()); - CHECK_EQ(static_cast(out_seq.shape().ndims()), 2); - CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); - CHECK_EQ(static_cast(in_seq.getSequenceId().shape().ndims()), 1); + CHECK_EQ(out_seq.shape().ndims(), 2UL); + CHECK_EQ(in_seq.shape().ndims(), 2UL); + CHECK_EQ(in_seq.getSequenceId().shape().ndims(), 1UL); /// output layer grad dim == input layer grad dim * context_length_ CHECK_EQ(in_seq.shape().ndims(), out_seq.shape().ndims() * context_length_); /// input and output has the same batch_size @@ -355,14 +357,14 @@ class ContextProjectionBackwardWeightFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ(1, static_cast(inputs.size())); - CHECK_EQ(1, static_cast(outputs.size())); + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); CHECK(inputs[0].isSequenceArg()) << "SequenceArg required here"; const auto in_seq = dynamic_cast(inputs[0]); CHECK(in_seq.data() && in_seq.getSequenceId().data() && outputs[0].data()); - CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); - CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); - CHECK_EQ(static_cast(in_seq.getSequenceId().shape().ndims()), 1); + CHECK_EQ(outputs[0].shape().ndims(), 2UL); + CHECK_EQ(in_seq.shape().ndims(), 2UL); + CHECK_EQ(in_seq.getSequenceId().shape().ndims(), 1UL); CHECK_EQ(in_seq.shape()[0], outputs[0].shape()[0]); /// output layer grad dim == weight dim * context_length_ CHECK_EQ(in_seq.shape()[1], outputs[0].shape()[1] * context_length_); diff --git a/paddle/function/ContextProjectionOp.h b/paddle/function/ContextProjectionOp.h index 2bdd47e4e9b024..6f7d936379a537 100644 --- a/paddle/function/ContextProjectionOp.h +++ b/paddle/function/ContextProjectionOp.h @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #pragma once - #include "Function.h" namespace paddle { diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index c9db2ff8008e0b..0f5d6a848d406d 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -28,55 +28,26 @@ void testMatrixProjectionForward(int context_start, std::max(0, (int)(context_start + context_length - 1)); if (pad == 0) is_padding = false; - FunctionCompare compare("ContextProjectionForward", - FuncConfig() - .set("context_length", context_length) - .set("context_start", context_start) - .set("begin_pad", std::max(0, -context_start))); - - CpuMatrix cpu_in(batch_size, input_dim); - cpu_in.randomizeUniform(); - GpuMatrix gpu_in(batch_size, input_dim); - gpu_in.copyFrom(cpu_in); - auto cpu_weight = - is_padding ? std::make_shared(pad, input_dim) : nullptr; - auto gpu_weight = - is_padding ? std::make_shared(pad, input_dim) : nullptr; - if (is_padding) { - cpu_weight->randomizeUniform(); - gpu_weight->copyFrom(*cpu_weight); + FunctionCompare test("ContextProjectionForward", + FuncConfig() + .set("context_length", context_length) + .set("context_start", context_start) + .set("begin_pad", std::max(0, -context_start))); + + // prepare input arguments + test.addSequence(SequenceIdArg(TensorShape{batch_size})); + test.addInputs( + SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim})); + if (is_padding) { // weight + test.addInputs(SequenceArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim})); } - IVectorPtr cpu_seq; - generateSequenceStartPositions(batch_size, cpu_seq); - IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true); - gpu_seq->copyFrom(*cpu_seq); - - CpuMatrix cpu_out(batch_size, input_dim * context_length); - GpuMatrix gpu_out(batch_size, input_dim * context_length); - cpu_out.randomizeUniform(); - gpu_out.copyFrom(cpu_out); - - BufferArgs cpu_inputs; - BufferArgs cpu_outputs; - cpu_inputs.addArg(cpu_in, *cpu_seq); - if (cpu_weight) { - cpu_inputs.addArg(*cpu_weight, *cpu_seq); - } - cpu_outputs.addArg(cpu_out, *cpu_seq, ADD_TO); - - compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + test.addOutputs( + SequenceArg(VALUE_TYPE_FLOAT, + TensorShape{batch_size, input_dim * context_length}), + ADD_TO); - BufferArgs gpu_inputs; - BufferArgs gpu_outputs; - gpu_inputs.addArg(gpu_in, *gpu_seq); - if (gpu_weight) { - gpu_inputs.addArg(*gpu_weight, *gpu_seq); - } - gpu_outputs.addArg(gpu_out, *gpu_seq, ADD_TO); - - compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); - - autotest::TensorCheckEqual(cpu_out, gpu_out); + // run Function + test.run(); } void testMatrixProjectionBackward(int context_start, @@ -88,63 +59,31 @@ void testMatrixProjectionBackward(int context_start, std::max(0, (int)(context_start + context_length - 1)); if (pad == 0) is_padding = false; - FunctionCompare compare("ContextProjectionBackward", - FuncConfig() - .set("context_length", context_length) - .set("context_start", context_start) - .set("begin_pad", std::max(0, -context_start)) - .set("is_padding", is_padding) - .set("total_pad", pad)); - - CpuMatrix cpu_in_grad(batch_size, input_dim); - cpu_in_grad.randomizeUniform(); - GpuMatrix gpu_in_grad(batch_size, input_dim); - gpu_in_grad.copyFrom(cpu_in_grad); - - CpuMatrix cpu_out_grad(batch_size, input_dim * context_length); - cpu_out_grad.randomizeUniform(); - GpuMatrix gpu_out_grad(batch_size, input_dim * context_length); - gpu_out_grad.copyFrom(cpu_out_grad); - - IVectorPtr cpu_seq; - generateSequenceStartPositions(batch_size, cpu_seq); - IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true); - gpu_seq->copyFrom(*cpu_seq); - - auto cpu_w_grad = - is_padding ? std::make_shared(pad, input_dim) : nullptr; - auto gpu_w_grad = - is_padding ? std::make_shared(pad, input_dim) : nullptr; - if (is_padding) { - cpu_w_grad->randomizeUniform(); - gpu_w_grad->copyFrom(*cpu_w_grad); + FunctionCompare test("ContextProjectionBackward", + FuncConfig() + .set("context_length", context_length) + .set("context_start", context_start) + .set("begin_pad", std::max(0, -context_start)) + .set("is_padding", is_padding) + .set("total_pad", pad)); + + // prepare input arguments + test.addSequence(SequenceIdArg(TensorShape{batch_size})); + test.addInputs(SequenceArg( + VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim * context_length})); + test.addOutputs( + SequenceArg(VALUE_TYPE_FLOAT, TensorShape{batch_size, input_dim}), + ADD_TO); + if (is_padding) { // weight + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{pad, input_dim}), + ADD_TO); } - BufferArgs cpu_inputs; - BufferArgs cpu_outputs; - cpu_inputs.addArg(cpu_out_grad, *cpu_seq); - cpu_outputs.addArg(cpu_in_grad, *cpu_seq, ADD_TO); - cpu_outputs.addArg( - cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO); - - compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); - - BufferArgs gpu_inputs; - BufferArgs gpu_outputs; - gpu_inputs.addArg(gpu_out_grad, *gpu_seq); - gpu_outputs.addArg(gpu_in_grad, *gpu_seq, ADD_TO); - gpu_outputs.addArg( - gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO); - - compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); - - autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad); - if (is_padding) { - autotest::TensorCheckErr(*cpu_w_grad, *gpu_w_grad); - } + // run Function + test.run(); } -TEST(ContextProjection, projection) { +TEST(ContextProjection, Projection) { for (auto context_start : {-5, -3, -1, 0, 3}) { for (auto context_length : {1, 2, 5, 7}) { for (auto trainable_padding : {false, true}) { diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 00f59f97d4c8c1..076aace14cc566 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -69,6 +69,54 @@ class FunctionCompare { gpuMemory_.back()->getBuf(), input.valueType(), input.shape())); } + // assume one copy of sequence is shared by different SequenceArgs + void addSequence(const SequenceIdArg& input) { + CHECK_EQ(input.shape().ndims(), 1UL); + size_t batchSize = input.shape()[0]; + size_t numSeqs = batchSize / 10 + 1; + size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); + cpuMemory_.emplace_back(std::make_shared(sizeId)); + gpuMemory_.emplace_back(std::make_shared(sizeId)); + cpuSeq_ = std::make_shared(cpuMemory_.back()->getBuf(), + TensorShape{numSeqs + 1}); + gpuSeq_ = std::make_shared(gpuMemory_.back()->getBuf(), + TensorShape{numSeqs + 1}); + /// init sequence Id + initArg(*cpuSeq_, batchSize); + + // todo(tianbing), delete it + CHECK_EQ(cpuSeq_->shape().getElements(), cpuSeq_->numSeqs() + 1); + + CpuIVector cpuSeq(cpuSeq_->shape().getElements(), (int*)cpuSeq_->data()); + GpuIVector gpuSeq(gpuSeq_->shape().getElements(), (int*)gpuSeq_->data()); + gpuSeq.copyFrom(cpuSeq); + } + + void addInputs(const SequenceArg& input) { + CHECK_EQ(input.shape().ndims(), 2UL); + size_t batchSize = input.shape()[0]; + if (!cpuSeq_ || !gpuSeq_) { // sequence not exist + addSequence(SequenceIdArg(TensorShape{batchSize})); + } + + size_t size = + input.shape().getElements() * sizeOfValuType(input.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + /// SequenceArg + cpuInputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + input.valueType(), + input.shape(), + *cpuSeq_)); + gpuInputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + input.valueType(), + input.shape(), + *gpuSeq_)); + } + // output need only contains shape, do not contains data. void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) { size_t size = @@ -86,6 +134,7 @@ class FunctionCompare { output.valueType(), output.shape(), argType)); +<<<<<<< HEAD } /// add and init output sparse matrix @@ -116,24 +165,31 @@ class FunctionCompare { std::make_shared(*gpuSparse_, argType)); } - void addInputs(const SequenceArg& input) { - size_t batchSize = input.shape()[0]; - size_t numSeqs = batchSize / 10 + 1; - - size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); - cpuMemory_.emplace_back(std::make_shared(sizeId)); - gpuMemory_.emplace_back(std::make_shared(sizeId)); - - TensorShape seqsId({numSeqs + 1}); - // void* cpuBuffer = cpuMemory_.back()->getBuf(); - // void* gpuBuffer = gpuMemory_.back()->getBuf(); + void addOutputs(const SequenceArg& output, ArgType argType = ASSIGN_TO) { + CHECK_EQ(output.shape().ndims(), 2UL); + size_t batchSize = output.shape()[0]; + if (!cpuSeq_ || !gpuSeq_) { // sequence not exist + addSequence(SequenceIdArg(TensorShape{batchSize})); + } size_t size = - input.shape().getElements() * sizeOfValuType(input.valueType()); + output.shape().getElements() * sizeOfValuType(output.valueType()); cpuMemory_.emplace_back(std::make_shared(size)); gpuMemory_.emplace_back(std::make_shared(size)); - // TODO: need be implemented. + /// SequenceArg + cpuOutputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + *cpuSeq_, + argType)); + gpuOutputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + *gpuSeq_, + argType)); } void addInputs(const SparseMatrixArg& input) { @@ -193,14 +249,44 @@ class FunctionCompare { std::shared_ptr getGpuFunction() const { return gpuFunc_; } protected: + // only init cpu argument, gpu argument copy from cpu argument. + void initArg(BufferArg& arg) { + CpuVector vector(arg.shape().getElements(), (real*)arg.data()); + vector.uniform(0.001, 1); + } + + void initArg(SequenceArg& arg) { + /// init only matrix + CpuVector vector(arg.shape().getElements(), (real*)arg.data()); + vector.uniform(0.001, 1); + } + + void initArg(SequenceIdArg& arg, size_t batchSize) { + size_t numSeqs = arg.numSeqs(); + int* buf = reinterpret_cast(arg.data()); + int pos = 0; + size_t maxLen = 2 * batchSize / numSeqs; + for (int i = 0; i < (int)numSeqs; ++i) { + int len = 1 + uniformRandom(std::min( + maxLen, batchSize - pos - numSeqs + i)); + buf[i] = pos; + pos += len; + VLOG(1) << " len=" << len; + } + buf[numSeqs] = batchSize; + } + void initInputs() { for (size_t i = 0; i < cpuInputs_.size(); i++) { if (cpuInputs_[i]->isSparseArg()) { continue; /// sparse matrix already init } - initArg(*cpuInputs_[i]); - + if (cpuInputs_[i]->isSequenceArg()) { + initArg(dynamic_cast(*cpuInputs_[i])); + } else { + initArg(*cpuInputs_[i]); + } // TODO: Need a BufferCopy used to copy from one BufferArg to another. CpuVector cpuVector(cpuInputs_[i]->shape().getElements(), (real*)cpuInputs_[i]->data()); @@ -217,7 +303,11 @@ class FunctionCompare { continue; /// sparse matrix already init } - initArg(*cpuOutputs_[i]); + if (cpuOutputs_[i]->isSequenceArg()) { + initArg(dynamic_cast(*cpuOutputs_[i])); + } else { + initArg(*cpuOutputs_[i]); + } // TODO: Need a BufferCopy used to copy from one BufferArg to another. CpuVector cpuVector(cpuOutputs_[i]->shape().getElements(), @@ -241,28 +331,6 @@ class FunctionCompare { } } - // only init cpu argument, gpu argument copy from cpu argument. - void initArg(BufferArg& arg) { - CpuVector vector(arg.shape().getElements(), (real*)arg.data()); - vector.uniform(0.001, 1); - } - - void initArg(SequenceIdArg& arg, size_t batchSize) { - size_t numSeqs = arg.numSeqs(); - int* buf = reinterpret_cast(arg.data()); - int pos = 0; - size_t maxLen = 2 * batchSize / numSeqs; - for (int i = 0; i < (int)numSeqs; ++i) { - int len = uniformRandom( - std::min(maxLen, batchSize - pos - numSeqs + i)) + - 1; - buf[i] = pos; - pos += len; - VLOG(1) << " len=" << len; - } - buf[numSeqs] = batchSize; - } - protected: std::shared_ptr cpuFunc_; std::shared_ptr gpuFunc_; @@ -274,6 +342,8 @@ class FunctionCompare { std::vector gpuOutputs_; std::shared_ptr cpuSparse_; std::shared_ptr gpuSparse_; + std::shared_ptr cpuSeq_; + std::shared_ptr gpuSeq_; }; } // namespace paddle From e79841de9172329950b6ec1af00282321975c324 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 25 Jan 2017 10:35:47 -0800 Subject: [PATCH 152/370] fix test_LayerGrad. --- paddle/function/ContextProjectionOp.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index d81ea584016060..b87750b74247bd 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -132,7 +132,7 @@ class ContextProjectionForwardFunc : public FunctionBase { auto out_mat = out_seq.matrix(); const auto in_mat = val_seqs.matrix(); const auto w_mat = - (2UL == inputs.size()) + (2UL == inputs.size() && inputs[1].data()) ? inputs[1].matrix() : typename Tensor::Matrix(nullptr, 0, 0); const auto seq_vec = val_seqs.getSequenceId().vector(); @@ -263,7 +263,7 @@ class ContextProjectionBackwardFunc : public FunctionBase { !out_seq.data() ? typename Tensor::Matrix(nullptr, 0, 0) : out_seq.matrix(); auto w_grad_mat = - (2UL == outputs.size()) + (2UL == outputs.size() && outputs[1].data()) ? outputs[1].matrix() : typename Tensor::Matrix(nullptr, 0, 0); From 02c5ecc334d282ceeafae81ab835444fb40e4445 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 7 Feb 2017 11:08:42 -0800 Subject: [PATCH 153/370] merge --- paddle/function/BufferArg.h | 2 +- paddle/function/FunctionTest.h | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index f7df56846f6d7b..0dc7792f646457 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -190,7 +190,7 @@ class SequenceIdArg : public BufferArg { : BufferArg(VALUE_TYPE_INT32, shape, argType) { bufferType_ = TENSOR_SEQUENCE_ID; CHECK_EQ(shape_.ndims(), 1UL); - CHECK_GT(shape_[0], 1UL); + CHECK_GE(shape_[0], 1UL); numSeqs_ = shape_[0] - 1; } diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 076aace14cc566..0cfafdb27f55a3 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -134,7 +134,6 @@ class FunctionCompare { output.valueType(), output.shape(), argType)); -<<<<<<< HEAD } /// add and init output sparse matrix From 0b7e7876ff715da9908ce66ef58ddeb3f30675fe Mon Sep 17 00:00:00 2001 From: xuwei06 Date: Tue, 7 Feb 2017 15:30:44 -0800 Subject: [PATCH 154/370] Fix mkDirRecursively Change-Id: Ie09fd666ca208994f22e9f83af6072f4d5e6aa1a --- paddle/utils/Util.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/utils/Util.cpp b/paddle/utils/Util.cpp index 220aac1ff11e0f..dbab4ec43ca2fa 100644 --- a/paddle/utils/Util.cpp +++ b/paddle/utils/Util.cpp @@ -289,6 +289,7 @@ void mkDir(const char* filename) { void mkDirRecursively(const char* dir) { struct stat sb; + if (*dir == 0) return; // empty string if (!stat(dir, &sb)) return; mkDirRecursively(path::dirname(dir).c_str()); From 9ee72367eeb667b8aad8e294964e319d37c3b674 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 3 Jan 2017 18:26:46 -0800 Subject: [PATCH 155/370] Cosine Simlarity Forward Paddle Function. --- paddle/function/CosSimOp.cpp | 93 ++++++++++++++++++++++++++++ paddle/function/CosSimOp.h | 40 ++++++++++++ paddle/function/CosSimOpGpu.cu | 102 +++++++++++++++++++++++++++++++ paddle/function/CosSimOpTest.cpp | 63 +++++++++++++++++++ 4 files changed, 298 insertions(+) create mode 100644 paddle/function/CosSimOp.cpp create mode 100644 paddle/function/CosSimOp.h create mode 100644 paddle/function/CosSimOpGpu.cu create mode 100644 paddle/function/CosSimOpTest.cpp diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp new file mode 100644 index 00000000000000..c1473a19ede5c4 --- /dev/null +++ b/paddle/function/CosSimOp.cpp @@ -0,0 +1,93 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "CosSimOp.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/Vector.h" + +namespace paddle { +template <> +void CosSimForward(CpuMatrix* out_mat, + const CpuMatrix* in1_mat, + const CpuMatrix* in2_mat, + real scale) { + CHECK(out_mat && in1_mat && in2_mat); + size_t num_samples = out_mat->getHeight(); + size_t dim = in1_mat->getWidth(); + /// column vector [nSamples, 1] + real* out = out_mat->getData(); + const real* x = in1_mat->getData(); + const real* y = in2_mat->getData(); + + /// in2 might only have one row or full rows + CHECK(in2_mat->getHeight() == 1LU || in2_mat->getHeight() == num_samples); + size_t inc = (in2_mat->getHeight() == 1LU) ? 0 : dim; + for (size_t i = 0; i < num_samples; ++i, x += dim, y += inc) { + /// for each row, todo(tianbing), use TensorExpression square2 ? + real square_sum_x = 0; + real square_sum_y = 0; + real xy = 0; + for (size_t j = 0; j < dim; ++j) { + square_sum_x += x[j] * x[j]; + square_sum_y += y[j] * y[j]; + xy += x[j] * y[j]; + } + CHECK(square_sum_x > 0 && square_sum_y > 0); + out[i] = scale * xy / (std::sqrt(square_sum_x) * std::sqrt(square_sum_y)); + } +} + +/** + * \param inputs[0] input matrix 1, size: nSamples * dim. + * \param inputs[1] input matrix 2, size: n2 * dim (n2 == 1 or n2 == nSamples). + * \param outputs[0] output matrix, size : nSamples * 1. + */ + +template +class CosSimForwardFunc : public FunctionBase { + void init(const FuncConfig& config) override { + scale_ = config.get("scale"); + } + + void calc(const Arguments& inputs, + const Arguments& outputs, + const Arguments& inouts) override { + CHECK_EQ(inputs.size(), 2); + CHECK_EQ(outputs.size(), 1); + CHECK_EQ(inouts.size(), 0); + + CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(inputs[0].dims_[1], inputs[1].dims_[1]); + CHECK_EQ(outputs[0].dims_[1], 1UL); + + CHECK(outputs[0].getData() && inputs[0].getData() && inputs[1].getData()); + auto out_mat = std::make_shared::type>( + outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); + const auto in1_mat = std::make_shared::type>( + inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); + const auto in2_mat = std::make_shared::type>( + inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + + CosSimForward(out_mat.get(), in1_mat.get(), in2_mat.get(), scale_); + } + +private: + real scale_; +}; + +REGISTER_TYPED_FUNC(CosSimForward, CPU, CosSimForwardFunc); +#ifndef PADDLE_ONLY_CPU +REGISTER_TYPED_FUNC(CosSimForward, GPU, CosSimForwardFunc); +#endif +} // namespace paddle diff --git a/paddle/function/CosSimOp.h b/paddle/function/CosSimOp.h new file mode 100644 index 00000000000000..02250d6db9c644 --- /dev/null +++ b/paddle/function/CosSimOp.h @@ -0,0 +1,40 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "Function.h" + +namespace paddle { + +/** + * \brief Cosine Similarity Forward. + * for each row i, + * out[i] = scale * cos(in1[i], in2[i]) + * = scale * \sum_j (in1[i][j] * in2[i][j]) / + * sqrt(sum_j (in1[i][j]^2) * sum_j (in2[i][j])^2) + * + * \param[out] output output data. + * \param[in] intput1 input data. + * \param[in] intput2 input data. + * \param[in] scale default 1.0. + * + */ +template +void CosSimForward(typename MatrixT::type* output, + const typename MatrixT::type* input1, + const typename MatrixT::type* input2, + real scale); + +} // namespace paddle diff --git a/paddle/function/CosSimOpGpu.cu b/paddle/function/CosSimOpGpu.cu new file mode 100644 index 00000000000000..34835fa5d87c39 --- /dev/null +++ b/paddle/function/CosSimOpGpu.cu @@ -0,0 +1,102 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "hl_base.h" +#include "CosSimOp.h" + +namespace paddle { + +template +__global__ void KeCosSim(real* output, + const real* input1, + const real* input2, + int width, + int input1_height, + int input2_height, + real scale) { + const int ty = blockIdx.y; + int tid = threadIdx.x; + + __shared__ real xx[block_size]; + __shared__ real yy[block_size]; + __shared__ real xy[block_size]; + + xx[tid] = 0.0; + yy[tid] = 0.0; + xy[tid] = 0.0; + __syncthreads(); + + input1 += ty * width; + if (input2_height > 1) { + input2 += ty * width; + } + for (int index = tid; index < width; index += block_size) { + real x = input1[index]; + real y = input2[index]; + xx[tid] += x * x; + yy[tid] += y * y; + xy[tid] += x * y; + } + __syncthreads(); + + for (int s = block_size / 2; s > 0; s >>= 1) { + if (tid < s) { + xx[tid] += xx[tid + s]; + yy[tid] += yy[tid + s]; + xy[tid] += xy[tid + s]; + } + __syncthreads(); + } + if (tid == 0) { + output[ty] = scale * xy[0] / (sqrt(xx[0]) * sqrt(yy[0])); + } +} + +void hlCossim(real* output, + const real* input1, + const real* input2, + size_t width, + size_t input1_height, + size_t input2_height, + real scale) { + CHECK_NOTNULL(output); + CHECK_NOTNULL(input1); + CHECK_NOTNULL(input2); + const int block_size = 256; + dim3 threads(block_size, 1); + dim3 grid(1, input1_height); + + KeCosSim<<>> + (output, input1, input2, width, input1_height, input2_height, scale); + CHECK_SYNC("hl_cossim failed"); +} + +template <> +void CosSimForward(GpuMatrix* out_mat, + const GpuMatrix* in1_mat, + const GpuMatrix* in2_mat, + real scale) { + CHECK(out_mat && in1_mat && in2_mat); + CHECK(in1_mat->useGpu_ == true && in2_mat->useGpu_ == true) + << "Matrix type are not GPU"; + + size_t numSamples = out_mat->getHeight(); + size_t dim = in1_mat->getWidth(); + real* out = out_mat->getData(); + const real* x = in1_mat->getData(); + const real* y = in2_mat->getData(); + hlCossim(out, x, y, dim, in1_mat->getHeight(), in2_mat->getHeight(), scale); +} + +} // namespace paddle diff --git a/paddle/function/CosSimOpTest.cpp b/paddle/function/CosSimOpTest.cpp new file mode 100644 index 00000000000000..49c54620feb355 --- /dev/null +++ b/paddle/function/CosSimOpTest.cpp @@ -0,0 +1,63 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "FunctionTest.h" +#include "paddle/math/Matrix.h" + +using namespace paddle; // NOLINT + +void testCosSimForward(size_t height_x, + size_t height_y, + size_t width, + real scale) { + FunctionCompare compare("CosSimForward", FuncConfig().set("scale", scale)); + + CpuMatrix cpu_arg1(height_x, width); + CpuMatrix gpu_arg1(height_x, width); + CpuMatrix cpu_arg2(height_y, width); + CpuMatrix gpu_arg2(height_y, width); + cpu_arg1.randomizeUniform(); + gpu_arg1.copyFrom(cpu_arg1); + cpu_arg2.randomizeUniform(); + cpu_arg2.add(-0.5); + gpu_arg2.copyFrom(cpu_arg2); + CpuMatrix cpu_out(height_x, 1); + GpuMatrix gpu_out(height_x, 1); + + compare.getCpuFunction()->calc( + {Tensor(cpu_arg1.getData(), Dims{height_x, width}), + Tensor(cpu_arg2.getData(), Dims{height_y, width})}, + {Tensor(cpu_out.getData(), Dims{height_x, 1})}, + {}); + compare.getGpuFunction()->calc( + {Tensor(gpu_arg1.getData(), Dims{height_x, width}), + Tensor(gpu_arg2.getData(), Dims{height_y, width})}, + {Tensor(gpu_out.getData(), Dims{height_x, 1})}, + {}); + + autotest::TensorCheckErr(cpu_out, gpu_out); +} + +TEST(Matrix, cosSim) { + for (auto height_x : {10, 100, 1000}) { + for (auto height_y : {1, height_x}) { + for (auto width : {10, 100, 1000}) { + for (auto scale : {1.0, 2.0}) { + testCosSimForward(height_x, height_y, width, scale); + } + } + } + } +} From ccac20d2db4c61df1de0877d77f6e5c07a8cf0f1 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 3 Jan 2017 21:25:54 -0800 Subject: [PATCH 156/370] add Cosine Similarity Backward function. --- paddle/function/CosSimOp.cpp | 122 ++++++++++++++++++++++++++ paddle/function/CosSimOp.h | 21 +++++ paddle/function/CosSimOpGpu.cu | 142 ++++++++++++++++++++++++++++++- paddle/function/CosSimOpTest.cpp | 70 ++++++++++++++- 4 files changed, 352 insertions(+), 3 deletions(-) diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index c1473a19ede5c4..0ed5991ff1382e 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -86,8 +86,130 @@ class CosSimForwardFunc : public FunctionBase { real scale_; }; +template <> +void CosSimBackward(const CpuMatrix* out_grad, + const CpuMatrix* out_val, + const CpuMatrix* in1_val, + const CpuMatrix* in2_val, + CpuMatrix* in1_grad, + CpuMatrix* in2_grad, + real scale) { + CHECK(out_grad && out_val && in1_val && in2_val && in1_grad && in2_grad); + CHECK_EQ(out_val->useGpu_, false) << "Matrix type are GPU, CPU required"; + + const real* grad = out_grad->getData(); + const real* out = out_val->getData(); + const real* prev_out_x = in1_val->getData(); + const real* prev_out_y = in2_val->getData(); + real* prev_grad_x = in1_grad->getData(); + real* prev_grad_y = in2_grad->getData(); + + size_t num_samples = out_grad->getHeight(); + size_t dim = in1_val->getWidth(); + CHECK_EQ(in2_val->getHeight(), in2_grad->getHeight()); + CHECK(in2_val->getHeight() == 1LU || in2_val->getHeight() == num_samples); + size_t inc = (in2_val->getHeight() == 1LU) ? 0 : dim; + for (size_t i = 0; i < num_samples; ++i, + prev_out_x += dim, + prev_out_y += inc, + prev_grad_x += dim, + prev_grad_y += inc) { + real square_sum_x = 0; + real square_sum_y = 0; + real xy = 0; + for (size_t j = 0; j < dim; ++j) { + square_sum_x += prev_out_x[j] * prev_out_x[j]; + square_sum_y += prev_out_y[j] * prev_out_y[j]; + xy += prev_out_x[j] * prev_out_y[j]; + } + CHECK(square_sum_x > 0 && square_sum_y > 0); + if (xy == 0) { + real reciprocal = + 1.0f / (std::sqrt(square_sum_x) * std::sqrt(square_sum_y)); + for (size_t j = 0; j < dim; ++j) { + prev_grad_x[j] += scale * grad[i] * prev_out_y[j] * reciprocal; + prev_grad_y[j] += scale * grad[i] * prev_out_x[j] * reciprocal; + } + } else { + real reciprocal_xy = 1.0f / xy; + real reciprocal_square_sum_x = 1.0f / square_sum_x; + real reciprocal_square_sum_y = 1.0f / square_sum_y; + for (size_t j = 0; j < dim; ++j) { + prev_grad_x[j] += + out[i] * grad[i] * (prev_out_y[j] * reciprocal_xy - + prev_out_x[j] * reciprocal_square_sum_x); + prev_grad_y[j] += + out[i] * grad[i] * (prev_out_x[j] * reciprocal_xy - + prev_out_y[j] * reciprocal_square_sum_y); + } + } + } +} + +/** + * \param inputs[0] output value 1, size: nSamples * 1. + * \param inputs[1] input value 1, size: nSamples * dim. + * \param inputs[2] input value 2, size: n2 * dim (n2 == 1 or n2 == nSamples). + * \param inputs[3] input grad 1, size: nSamples * dim. + * \param inputs[4] input grad 2, size: n2 * dim (n2 == 1 or n2 == nSamples). + * \param outputs[0] output grad, size : nSamples * 1. + */ +template +class CosSimBackwardFunc : public FunctionBase { + void init(const FuncConfig& config) override { + scale_ = config.get("scale"); + } + + void calc(const Arguments& inputs, + const Arguments& outputs, + const Arguments& inouts) override { + CHECK_EQ(inputs.size(), 5); + CHECK_EQ(outputs.size(), 1); + CHECK_EQ(inouts.size(), 0); + /// dim of out_grad and out_val == 1, column vector + CHECK_EQ(outputs[0].dims_[1], 1UL); + CHECK_EQ(inputs[0].dims_[1], 1UL); + /// nSamples of out_grad == out_val == in_val1 == in_grad1 + CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(inputs[1].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(inputs[3].dims_[0], outputs[0].dims_[0]); + /// dim of in1_val1 == in_val2 == in_grad1 == in_grad2 + CHECK_EQ(inputs[2].dims_[1], inputs[1].dims_[1]); + CHECK_EQ(inputs[3].dims_[1], inputs[1].dims_[1]); + CHECK_EQ(inputs[4].dims_[1], inputs[1].dims_[1]); + + CHECK(outputs[0].getData() && inputs[0].getData() && inputs[1].getData() && + inputs[2].getData() && inputs[3].getData() && inputs[4].getData()); + const auto out_grad = std::make_shared::type>( + outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); + const auto out_val = std::make_shared::type>( + inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); + const auto in1_val = std::make_shared::type>( + inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + const auto in2_val = std::make_shared::type>( + inputs[2].getData(), inputs[2].dims_[0], inputs[2].dims_[1]); + auto in1_grad = std::make_shared::type>( + inputs[3].getData(), inputs[3].dims_[0], inputs[3].dims_[1]); + auto in2_grad = std::make_shared::type>( + inputs[4].getData(), inputs[4].dims_[0], inputs[4].dims_[1]); + + CosSimBackward(out_grad.get(), + out_val.get(), + in1_val.get(), + in2_val.get(), + in1_grad.get(), + in2_grad.get(), + scale_); + } + +private: + real scale_; +}; + REGISTER_TYPED_FUNC(CosSimForward, CPU, CosSimForwardFunc); +REGISTER_TYPED_FUNC(CosSimBackward, CPU, CosSimBackwardFunc); #ifndef PADDLE_ONLY_CPU REGISTER_TYPED_FUNC(CosSimForward, GPU, CosSimForwardFunc); +REGISTER_TYPED_FUNC(CosSimBackward, GPU, CosSimBackwardFunc); #endif } // namespace paddle diff --git a/paddle/function/CosSimOp.h b/paddle/function/CosSimOp.h index 02250d6db9c644..f66a4344d09ce8 100644 --- a/paddle/function/CosSimOp.h +++ b/paddle/function/CosSimOp.h @@ -37,4 +37,25 @@ void CosSimForward(typename MatrixT::type* output, const typename MatrixT::type* input2, real scale); +/** + * \brief Cosine Similarity BackWard for Derivative. + * + * \param[out] output1 backward loss output grad. + * \param[in] input1 forward-output value. + * \param[in] input2 forward input value 1. + * \param[in] input3 forward input value 2. + * \param[in] input4 forward input grad 1. + * \param[in] input5 forward input grad 2. + * \param[in] scale default 1.0. + * + */ +template +void CosSimBackward(const typename MatrixT::type* out_grad, + const typename MatrixT::type* out_value, + const typename MatrixT::type* in1_value, + const typename MatrixT::type* in2_value, + typename MatrixT::type* in1_grad, + typename MatrixT::type* in2_grad, + real scale); + } // namespace paddle diff --git a/paddle/function/CosSimOpGpu.cu b/paddle/function/CosSimOpGpu.cu index 34835fa5d87c39..f654f0bc209c24 100644 --- a/paddle/function/CosSimOpGpu.cu +++ b/paddle/function/CosSimOpGpu.cu @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "hl_base.h" +#include "hl_device_functions.cuh" #include "CosSimOp.h" namespace paddle { @@ -79,7 +80,7 @@ void hlCossim(real* output, KeCosSim<<>> (output, input1, input2, width, input1_height, input2_height, scale); - CHECK_SYNC("hl_cossim failed"); + CHECK_SYNC("hlCossim failed"); } template <> @@ -91,7 +92,7 @@ void CosSimForward(GpuMatrix* out_mat, CHECK(in1_mat->useGpu_ == true && in2_mat->useGpu_ == true) << "Matrix type are not GPU"; - size_t numSamples = out_mat->getHeight(); + size_t num_samples = out_mat->getHeight(); size_t dim = in1_mat->getWidth(); real* out = out_mat->getData(); const real* x = in1_mat->getData(); @@ -99,4 +100,141 @@ void CosSimForward(GpuMatrix* out_mat, hlCossim(out, x, y, dim, in1_mat->getHeight(), in2_mat->getHeight(), scale); } +template +__global__ void KeCosSimDerivative(const real* grad, + const real* output, + const real* prev_out_x, + const real* prev_out_y, + real* prev_grad_x, + real* prev_grad_y, + size_t width, + size_t input1_height, + size_t input2_height, + real scale) { + const int ty = blockIdx.y; + int tid = threadIdx.x; + + __shared__ real xx[block_size]; + __shared__ real yy[block_size]; + __shared__ real xy[block_size]; + + xx[tid] = 0.0; + yy[tid] = 0.0; + xy[tid] = 0.0; + __syncthreads(); + + prev_out_x += ty * width; + prev_grad_x += ty * width; + if (input2_height > 1) { + prev_out_y += ty * width; + prev_grad_y += ty * width; + } + for (int index = tid; index < width; index += block_size) { + real x = prev_out_x[index]; + real y = prev_out_y[index]; + xx[tid] += x * x; + yy[tid] += y * y; + xy[tid] += x * y; + } + __syncthreads(); + + for (int s = block_size / 2; s > 0; s >>= 1) { + if (tid < s) { + xx[tid] += xx[tid + s]; + yy[tid] += yy[tid + s]; + xy[tid] += xy[tid + s]; + } + __syncthreads(); + } + if (xy[0] == 0) { + real reciprocal = 1.0 / (sqrt(xx[0]) * sqrt(yy[0])); + for (int index = tid; index < width; index += block_size) { + prev_grad_x[index] += + scale * grad[ty] * prev_out_y[index] * reciprocal; + if (input2_height > 1) { + prev_grad_y[index] += + scale * grad[ty] * prev_out_x[index] * reciprocal; + } else { + paddle::paddleAtomicAdd(prev_grad_y + index, + scale * grad[ty] * prev_out_x[index] * reciprocal); + } + } + } else { + real reciprocalXY = 1.0 / xy[0]; + real reciprocalSquareSumX = 1.0 / xx[0]; + real reciprocalSquareSumY = 1.0 / yy[0]; + for (int index = tid; index < width; index += block_size) { + prev_grad_x[index] += output[ty] * grad[ty] * + (prev_out_y[index] * reciprocalXY - + prev_out_x[index] * reciprocalSquareSumX); + if (input2_height > 1) { + prev_grad_y[index] += output[ty] * grad[ty] * + (prev_out_x[index] * reciprocalXY - + prev_out_y[index] * reciprocalSquareSumY); + } else { + paddle::paddleAtomicAdd(prev_grad_y + index, output[ty] * grad[ty] * + (prev_out_x[index] * reciprocalXY - + prev_out_y[index] * reciprocalSquareSumY)); + } + } + } +} + +void hlCossimDerivative(const real* grad, + const real* output, + const real* prev_out_x, + const real* prev_out_y, + real* prev_grad_x, + real* prev_grad_y, + size_t width, + size_t input1_height, + size_t input2_height, + real scale) { + CHECK_NOTNULL(grad); + CHECK_NOTNULL(output); + CHECK_NOTNULL(prev_out_x); + CHECK_NOTNULL(prev_out_y); + CHECK_NOTNULL(prev_grad_x); + CHECK_NOTNULL(prev_grad_y); + const int block_size = 256; + dim3 threads(block_size, 1); + dim3 grid(1, input1_height); + KeCosSimDerivative<<>> + (grad, output, prev_out_x, prev_out_y, prev_grad_x, prev_grad_y, width, + input1_height, input2_height, scale); + CHECK_SYNC("hlCossimDerivate failed"); +} + +template <> +void CosSimBackward(const GpuMatrix* out_grad, + const GpuMatrix* out_val, + const GpuMatrix* in1_val, + const GpuMatrix* in2_val, + GpuMatrix* in1_grad, + GpuMatrix* in2_grad, + real scale) { + CHECK(out_grad && out_val && in1_val && in2_val && in1_grad && in2_grad); + CHECK(out_grad->useGpu_ && out_val->useGpu_ && in1_val->useGpu_ + && in2_val->useGpu_ && in1_grad->useGpu_ && in2_grad->useGpu_) + << "Matrix types are not equally GPU"; + + size_t dim = in1_val->getWidth(); + const real* grad = out_grad->getData(); + const real* out = out_val->getData(); + const real* prev_out_x = in1_val->getData(); + const real* prev_out_y = in2_val->getData(); + real* prev_grad_x = in1_grad->getData(); + real* prev_grad_y = in2_grad->getData(); + hlCossimDerivative(grad, + out, + prev_out_x, + prev_out_y, + prev_grad_x, + prev_grad_y, + dim, + in1_val->getHeight(), + in2_val->getHeight(), + scale); +} + } // namespace paddle diff --git a/paddle/function/CosSimOpTest.cpp b/paddle/function/CosSimOpTest.cpp index 49c54620feb355..0d2ece2b98625a 100644 --- a/paddle/function/CosSimOpTest.cpp +++ b/paddle/function/CosSimOpTest.cpp @@ -50,7 +50,7 @@ void testCosSimForward(size_t height_x, autotest::TensorCheckErr(cpu_out, gpu_out); } -TEST(Matrix, cosSim) { +TEST(Matrix, cosSimForward) { for (auto height_x : {10, 100, 1000}) { for (auto height_y : {1, height_x}) { for (auto width : {10, 100, 1000}) { @@ -61,3 +61,71 @@ TEST(Matrix, cosSim) { } } } + +void testCosSimBackward(size_t height_x, + size_t height_y, + size_t width, + real scale) { + FunctionCompare compare("CosSimBackward", FuncConfig().set("scale", scale)); + + CpuMatrix cpu_out_grad(height_x, 1); + CpuMatrix cpu_out_val(height_x, 1); + CpuMatrix cpu_in1_val(height_x, width); + CpuMatrix cpu_in2_val(height_x, width); + CpuMatrix cpu_in1_grad(height_x, width); + CpuMatrix cpu_in2_grad(height_x, width); + + cpu_out_grad.randomizeUniform(); + cpu_out_val.randomizeUniform(); + cpu_in1_val.randomizeUniform(); + cpu_in2_val.randomizeUniform(); + cpu_in1_grad.randomizeUniform(); + cpu_in2_grad.randomizeUniform(); + + GpuMatrix gpu_out_grad(height_x, 1); + GpuMatrix gpu_out_val(height_x, 1); + GpuMatrix gpu_in1_val(height_x, width); + GpuMatrix gpu_in2_val(height_x, width); + GpuMatrix gpu_in1_grad(height_x, width); + GpuMatrix gpu_in2_grad(height_x, width); + + gpu_out_grad.copyFrom(cpu_out_grad); + gpu_out_val.copyFrom(cpu_out_val); + gpu_in1_val.copyFrom(cpu_in1_val); + gpu_in2_val.copyFrom(cpu_in2_val); + gpu_in1_grad.copyFrom(cpu_in1_grad); + gpu_in2_grad.copyFrom(cpu_in2_grad); + + compare.getCpuFunction()->calc( + {Tensor(cpu_out_val.getData(), Dims{height_x, 1}), + Tensor(cpu_in1_val.getData(), Dims{height_x, width}), + Tensor(cpu_in2_val.getData(), Dims{height_x, width}), + Tensor(cpu_in1_grad.getData(), Dims{height_x, width}), + Tensor(cpu_in2_grad.getData(), Dims{height_x, width})}, + {Tensor(cpu_out_grad.getData(), Dims{height_x, 1})}, + {}); + + compare.getGpuFunction()->calc( + {Tensor(gpu_out_val.getData(), Dims{height_x, 1}), + Tensor(gpu_in1_val.getData(), Dims{height_x, width}), + Tensor(gpu_in2_val.getData(), Dims{height_x, width}), + Tensor(gpu_in1_grad.getData(), Dims{height_x, width}), + Tensor(gpu_in2_grad.getData(), Dims{height_x, width})}, + {Tensor(gpu_out_grad.getData(), Dims{height_x, 1})}, + {}); + + autotest::TensorCheckErr(cpu_in1_grad, gpu_in1_grad); + autotest::TensorCheckErr(cpu_in2_grad, gpu_in2_grad); +} + +TEST(Matrix, cosSimBackward) { + for (auto height_x : {1, 10, 100}) { + for (auto height_y : {1, height_x}) { + for (auto width : {1, 10, 100}) { + for (auto scale : {1.0, 2.0}) { + testCosSimBackward(height_x, height_y, width, scale); + } + } + } + } +} From 58827e3e961aeac162eee4e9e80b9ff5b7864ff8 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 4 Jan 2017 14:37:08 -0800 Subject: [PATCH 157/370] update forward/backward in CosSimLayer and CosSimVecMatLayer. --- paddle/gserver/layers/CosSimLayer.cpp | 46 ++++++++++---- paddle/gserver/layers/CosSimLayer.h | 2 +- paddle/gserver/layers/CosSimVecMatLayer.cpp | 70 ++++++++++++++------- paddle/gserver/layers/CosSimVecMatLayer.h | 52 +++++++++++++++ 4 files changed, 135 insertions(+), 35 deletions(-) create mode 100644 paddle/gserver/layers/CosSimVecMatLayer.h diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index 254120443dc3d4..ac66fd4712af48 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/paddle/gserver/layers/CosSimLayer.cpp @@ -26,15 +26,23 @@ bool CosSimLayer::init(const LayerMap& layerMap, Layer::init(layerMap, parameterMap); CHECK_EQ(inputLayers_.size(), 2LU); + + createFunction(forward_, + "CosSimForward", + FuncConfig().set("scale", (real)config_.cos_scale())); + createFunction(backward_, + "CosSimBackward", + FuncConfig().set("scale", (real)config_.cos_scale())); + return true; } void CosSimLayer::forward(PassType passType) { Layer::forward(passType); - /* malloc memory for the output_ if necessary */ int batchSize = getInputValue(0)->getHeight(); int size = getSize(); + CHECK_EQ(forward_.size(), 1) << "Only one forward function needed"; { REGISTER_TIMER_INFO("CosFwResetTimer", getName().c_str()); @@ -42,26 +50,42 @@ void CosSimLayer::forward(PassType passType) { } MatrixPtr outV = getOutputValue(); - /* activation */ { REGISTER_TIMER_INFO("CosFwAtvTimer", getName().c_str()); MatrixPtr prevOut1 = getInputValue(0); MatrixPtr prevOut2 = getInputValue(1); - outV->cosSim(*prevOut1, *prevOut2, config_.cos_scale()); + + CHECK(outV && prevOut1 && prevOut2); + forward_[0]->calc( + {Tensor(prevOut1->getData(), + Dims{prevOut1->getHeight(), prevOut1->getWidth()}), + Tensor(prevOut2->getData(), + Dims{prevOut2->getHeight(), prevOut2->getWidth()})}, + {Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()})}, + {}); } } void CosSimLayer::backward(const UpdateCallback& callback) { /* activation */ { REGISTER_TIMER_INFO("CosBpAtvTimer", getName().c_str()); - MatrixPtr outG = this->getOutputGrad(); - - outG->cosSimDerivative(*this->getOutputValue(), - *getInputValue(0), - *getInputValue(1), - *getInputGrad(0), - *getInputGrad(1), - config_.cos_scale()); + CHECK_EQ(backward_.size(), 1) << "Only one backward function needed"; + + const auto outG = this->getOutputGrad(); + const auto outV = this->getOutputValue(); + const auto inV1 = this->getInputValue(0); + const auto inV2 = this->getInputValue(1); + auto inG1 = this->getInputGrad(0); + auto inG2 = this->getInputGrad(1); + CHECK(outG && outV && inV1 && inV2 && inG1 && inG2); + backward_[0]->calc( + {Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()}), + Tensor(inV1->getData(), Dims{inV1->getHeight(), inV1->getWidth()}), + Tensor(inV2->getData(), Dims{inV2->getHeight(), inV2->getWidth()}), + Tensor(inG1->getData(), Dims{inG1->getHeight(), inG1->getWidth()}), + Tensor(inG2->getData(), Dims{inG2->getHeight(), inG2->getWidth()})}, + {Tensor(outG->getData(), Dims{outG->getHeight(), outG->getWidth()})}, + {}); } } diff --git a/paddle/gserver/layers/CosSimLayer.h b/paddle/gserver/layers/CosSimLayer.h index 65549626098f08..8afaee62c2dcac 100644 --- a/paddle/gserver/layers/CosSimLayer.h +++ b/paddle/gserver/layers/CosSimLayer.h @@ -28,7 +28,7 @@ namespace paddle { * * - Input1: A vector (batchSize * dataDim) * * - Input2: A vector (batchSize * dataDim) or (1 * dataDim) * - * - Output: A vector (dataDim * 1) + * - Output: A vector (batchSize * 1) * * The config file api is cos_sim. */ diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index 5f652319e56202..427191e540778a 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -12,8 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "Layer.h" -#include "paddle/math/Matrix.h" +#include "CosSimVecMatLayer.h" #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" @@ -98,11 +97,23 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap, dataDim, /* trans= */ false, useGpu_); + + /// todo(tianbing), do we really need to check these shared pointers? + CHECK(tmpRow0 && tmpRow1 && tmpRow2 && tmpRow3 && tmpMtx0 && tmpMtx1); + + createFunction(forward_, + "CosSimForward", + FuncConfig().set("scale", (real)config_.cos_scale())); + createFunction(backward_, + "CosSimBackward", + FuncConfig().set("scale", (real)config_.cos_scale())); + return true; } void CosSimVecMatLayer::forward(PassType passType) { Layer::forward(passType); + CHECK_EQ(forward_.size(), 1) << "Only one forward function needed"; MatrixPtr inV0 = getInputValue(0); MatrixPtr inV1 = getInputValue(1); @@ -118,17 +129,26 @@ void CosSimVecMatLayer::forward(PassType passType) { } MatrixPtr outV = getOutputValue(); - + CHECK(outV && inV0 && inV1); REGISTER_TIMER_INFO("FwCosVMTimer", getName().c_str()); for (size_t i = 0; i < batchSize; i++) { tmpRow0->setData(inV0->rowBuf(i)); tmpMtx0->setData(inV1->rowBuf(i)); tmpRow2->setData(outV->rowBuf(i)); - tmpRow2->cosSim(*(tmpMtx0), *(tmpRow0), config_.cos_scale()); + + forward_[0]->calc({Tensor(tmpMtx0->getData(), + Dims{tmpMtx0->getHeight(), tmpMtx0->getWidth()}), + Tensor(tmpRow0->getData(), + Dims{tmpRow0->getHeight(), tmpRow0->getWidth()})}, + {Tensor(tmpRow2->getData(), + Dims{tmpRow2->getHeight(), tmpRow2->getWidth()})}, + {}); } } void CosSimVecMatLayer::backward(const UpdateCallback& callback) { + CHECK_EQ(backward_.size(), 1) << "Only one forward function needed"; + MatrixPtr inV0 = getInputValue(0); MatrixPtr inV1 = getInputValue(1); MatrixPtr inG0 = getInputGrad(0); @@ -137,27 +157,31 @@ void CosSimVecMatLayer::backward(const UpdateCallback& callback) { MatrixPtr outG = getOutputGrad(); size_t batchSize = inV0->getHeight(); - + CHECK(inV0 && inV1 && inG0 && inG1 && outV && outG); REGISTER_TIMER_INFO("BwCosVMTimer", getName().c_str()); - if (inG0 && inG1) { - for (size_t i = 0; i < batchSize; i++) { - tmpRow0->setData(inV0->rowBuf(i)); - tmpRow1->setData(inG0->rowBuf(i)); - tmpMtx0->setData(inV1->rowBuf(i)); - tmpMtx1->setData(inG1->rowBuf(i)); - tmpRow2->setData(outV->rowBuf(i)); - tmpRow3->setData(outG->rowBuf(i)); - - tmpRow3->cosSimDerivative(*(tmpRow2), - *(tmpMtx0), - *(tmpRow0), - *(tmpMtx1), - *(tmpRow1), - config_.cos_scale()); - } - } else { - CHECK(!inG0 || !inG1) << "Not supported"; + for (size_t i = 0; i < batchSize; i++) { + tmpRow0->setData(inV0->rowBuf(i)); + tmpRow1->setData(inG0->rowBuf(i)); + tmpMtx0->setData(inV1->rowBuf(i)); + tmpMtx1->setData(inG1->rowBuf(i)); + tmpRow2->setData(outV->rowBuf(i)); + tmpRow3->setData(outG->rowBuf(i)); + + backward_[0]->calc( + {Tensor(tmpRow2->getData(), + Dims{tmpRow2->getHeight(), tmpRow2->getWidth()}), + Tensor(tmpMtx0->getData(), + Dims{tmpMtx0->getHeight(), tmpMtx0->getWidth()}), + Tensor(tmpRow0->getData(), + Dims{tmpRow0->getHeight(), tmpRow0->getWidth()}), + Tensor(tmpMtx1->getData(), + Dims{tmpMtx1->getHeight(), tmpMtx1->getWidth()}), + Tensor(tmpRow1->getData(), + Dims{tmpRow1->getHeight(), tmpRow1->getWidth()})}, + {Tensor(tmpRow3->getData(), + Dims{tmpRow3->getHeight(), tmpRow3->getWidth()})}, + {}); } } diff --git a/paddle/gserver/layers/CosSimVecMatLayer.h b/paddle/gserver/layers/CosSimVecMatLayer.h new file mode 100644 index 00000000000000..bee83b58154b67 --- /dev/null +++ b/paddle/gserver/layers/CosSimVecMatLayer.h @@ -0,0 +1,52 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "Layer.h" +#include "paddle/math/Matrix.h" + +namespace paddle { +/** + * @brief A layer for computing cosine similarity between a vector + * and each row of a matrix + * out[i] = cos_scale * cos(in1, in2(i,:)); + * @note used in NEURAL TURING MACHINE + * + * Input1: a vector (batchSize * dataDim) + * + * Input2: a matrix in vector form (batchSize * (weightDim*dataDim)) + * + * Output: a vector (batchSize * weightDim) + */ + +class CosSimVecMatLayer : public Layer { +public: + explicit CosSimVecMatLayer(const LayerConfig& config) : Layer(config) {} + + ~CosSimVecMatLayer() {} + + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + + void forward(PassType passType); + void backward(const UpdateCallback& callback = nullptr); + +protected: + MatrixPtr tmpMtx0; + MatrixPtr tmpMtx1; + MatrixPtr tmpRow0; + MatrixPtr tmpRow1; + MatrixPtr tmpRow2; + MatrixPtr tmpRow3; +}; + +} // namespace paddle From a948eea3ed84f950c6c95d84431eeb3717951fc3 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 4 Jan 2017 15:53:53 -0800 Subject: [PATCH 158/370] clean unused code. --- paddle/cuda/include/hl_matrix.h | 42 ----- paddle/cuda/include/stub/hl_matrix_stub.h | 19 --- paddle/cuda/src/hl_cuda_matrix.cu | 171 -------------------- paddle/function/CosSimOp.cpp | 52 +++--- paddle/function/CosSimOp.h | 20 +-- paddle/function/CosSimOpTest.cpp | 24 +-- paddle/gserver/layers/CosSimLayer.cpp | 12 +- paddle/gserver/layers/CosSimVecMatLayer.cpp | 14 +- paddle/gserver/layers/CosSimVecMatLayer.h | 2 + paddle/math/Matrix.cpp | 152 ----------------- paddle/math/Matrix.h | 36 ----- paddle/math/tests/test_Matrix.cpp | 22 --- paddle/math/tests/test_matrixCompare.cpp | 55 ------- 13 files changed, 64 insertions(+), 557 deletions(-) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index 40828dd5cc76f4..6f21b82afdc6cd 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -188,48 +188,6 @@ extern void hl_param_relu_backward_diff(real* grad_o, int width, int height, int partial_sum); -/** - * @brief cos sim forward - * - * @param[out] output output data - * @param[in] input1 input1 data(matrix) - * @param[in] input2 input2 data(matrix or vector) - * @param[in] width matrix width - * @param[in] input1_height input1_height - * @param[in] input2_height input2_height - * @param[in] scale scale factor - */ -extern void hl_cossim(real* output, - real* input1, - real* input2, - int width, - int input1_height, - int input2_height, - real scale); -/** - * @brief cos sim derivate - * - * @param[in] grad output grad - * @param[in] output output data - * @param[in] prevOutX input1 data - * @param[in] prevOutY input2 data - * @param[out] prevGradX input1 grad - * @param[out] prevGradY input2 grad - * @param[in] width matrix width - * @param[in] input1_height input1 height - * @param[in] input2_height input2 height - * @param[in] scale scale factor - */ -extern void hl_cossim_derivative(real* grad, - real* output, - real* prevOutX, - real* prevOutY, - real* prevGradX, - real* prevGradY, - int width, - int input1_height, - int input2_height, - real scale); /** * @brief Matrix addition: A_d[i][j] += scale * B_d[j/channel]. diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index a1712d1e4d2a5d..f4e6461cdcf198 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -74,25 +74,6 @@ inline void hl_param_relu_backward_diff(real* grad_o, int height, int partial_sum) {} -inline void hl_cossim(real* output, - real* input1, - real* input2, - int width, - int input1_height, - int input2_height, - real scale) {} - -inline void hl_cossim_derivative(real* grad, - real* output, - real* prevOutX, - real* prevOutY, - real* prevGradX, - real* prevGradY, - int width, - int input1_height, - int input2_height, - real scale) {} - inline void hl_matrix_add_shared_bias(real* A_d, real* B_d, const int channel, diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index cd23bd31057c5c..96c07d9c3b7a37 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -584,177 +584,6 @@ void hl_param_relu_backward_diff(real* grad_o, CHECK_SYNC("hl_param_relu_backward_diff failed"); } -template -__global__ void KeCosSim(real* output, - real* input1, - real* input2, - int width, - int input1_height, - int input2_height, - real scale) { - const int ty = blockIdx.y; - int tid = threadIdx.x; - - __shared__ real xx[blockSize]; - __shared__ real yy[blockSize]; - __shared__ real xy[blockSize]; - - xx[tid] = 0.0; - yy[tid] = 0.0; - xy[tid] = 0.0; - __syncthreads(); - - input1 += ty * width; - if (input2_height > 1) { - input2 += ty * width; - } - for (int index = tid; index < width; index += blockSize) { - real x = input1[index]; - real y = input2[index]; - xx[tid] += x * x; - yy[tid] += y * y; - xy[tid] += x * y; - } - __syncthreads(); - - for (int s = blockSize / 2; s > 0; s >>= 1) { - if (tid < s) { - xx[tid] += xx[tid + s]; - yy[tid] += yy[tid + s]; - xy[tid] += xy[tid + s]; - } - __syncthreads(); - } - if (tid == 0) { - output[ty] = scale * xy[0] / (sqrt(xx[0]) * sqrt(yy[0])); - } -} - -void hl_cossim(real* output, - real* input1, - real* input2, - int width, - int input1_height, - int input2_height, - real scale) { - CHECK_NOTNULL(output); - CHECK_NOTNULL(input1); - CHECK_NOTNULL(input2); - const int blockSize = 256; - dim3 threads(blockSize, 1); - dim3 grid(1, input1_height); - - KeCosSim<<>> - (output, input1, input2, width, input1_height, input2_height, scale); - CHECK_SYNC("hl_cossim failed"); -} - -template -__global__ void KeCosSimDerivative(real* grad, - real* output, - real* prevOutX, - real* prevOutY, - real* prevGradX, - real* prevGradY, - int width, - int input1_height, - int input2_height, - real scale) { - const int ty = blockIdx.y; - int tid = threadIdx.x; - - __shared__ real xx[blockSize]; - __shared__ real yy[blockSize]; - __shared__ real xy[blockSize]; - - xx[tid] = 0.0; - yy[tid] = 0.0; - xy[tid] = 0.0; - __syncthreads(); - - prevOutX += ty * width; - prevGradX += ty * width; - if (input2_height > 1) { - prevOutY += ty * width; - prevGradY += ty * width; - } - for (int index = tid; index < width; index += blockSize) { - real x = prevOutX[index]; - real y = prevOutY[index]; - xx[tid] += x * x; - yy[tid] += y * y; - xy[tid] += x * y; - } - __syncthreads(); - - for (int s = blockSize / 2; s > 0; s >>= 1) { - if (tid < s) { - xx[tid] += xx[tid + s]; - yy[tid] += yy[tid + s]; - xy[tid] += xy[tid + s]; - } - __syncthreads(); - } - if (xy[0] == 0) { - real reciprocal = 1.0 / (sqrt(xx[0]) * sqrt(yy[0])); - for (int index = tid; index < width; index += blockSize) { - prevGradX[index] += - scale * grad[ty] * prevOutY[index] * reciprocal; - if (input2_height > 1) { - prevGradY[index] += - scale * grad[ty] * prevOutX[index] * reciprocal; - } else { - paddle::paddleAtomicAdd(prevGradY + index, - scale * grad[ty] * prevOutX[index] * reciprocal); - } - } - } else { - real reciprocalXY = 1.0 / xy[0]; - real reciprocalSquareSumX = 1.0 / xx[0]; - real reciprocalSquareSumY = 1.0 / yy[0]; - for (int index = tid; index < width; index += blockSize) { - prevGradX[index] += output[ty] * grad[ty] * - (prevOutY[index] * reciprocalXY - - prevOutX[index] * reciprocalSquareSumX); - if (input2_height > 1) { - prevGradY[index] += output[ty] * grad[ty] * - (prevOutX[index] * reciprocalXY - - prevOutY[index] * reciprocalSquareSumY); - } else { - paddle::paddleAtomicAdd(prevGradY + index, output[ty] * grad[ty] * - (prevOutX[index] * reciprocalXY - - prevOutY[index] * reciprocalSquareSumY)); - } - } - } -} - - -void hl_cossim_derivative(real* grad, - real* output, - real* prevOutX, - real* prevOutY, - real* prevGradX, - real* prevGradY, - int width, - int input1_height, - int input2_height, - real scale) { - CHECK_NOTNULL(grad); - CHECK_NOTNULL(output); - CHECK_NOTNULL(prevOutX); - CHECK_NOTNULL(prevOutY); - CHECK_NOTNULL(prevGradX); - CHECK_NOTNULL(prevGradY); - const int blockSize = 256; - dim3 threads(blockSize, 1); - dim3 grid(1, input1_height); - KeCosSimDerivative<<>> - (grad, output, prevOutX, prevOutY, prevGradX, prevGradY, width, - input1_height, input2_height, scale); - CHECK_SYNC("hl_cossim_derivate failed"); -} - __global__ void KeMatrixAddSharedBias(real* A, real* B, const int channel, diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index 0ed5991ff1382e..bcc17fd5319c6a 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -34,7 +34,6 @@ void CosSimForward(CpuMatrix* out_mat, CHECK(in2_mat->getHeight() == 1LU || in2_mat->getHeight() == num_samples); size_t inc = (in2_mat->getHeight() == 1LU) ? 0 : dim; for (size_t i = 0; i < num_samples; ++i, x += dim, y += inc) { - /// for each row, todo(tianbing), use TensorExpression square2 ? real square_sum_x = 0; real square_sum_y = 0; real xy = 0; @@ -147,12 +146,15 @@ void CosSimBackward(const CpuMatrix* out_grad, } /** - * \param inputs[0] output value 1, size: nSamples * 1. - * \param inputs[1] input value 1, size: nSamples * dim. - * \param inputs[2] input value 2, size: n2 * dim (n2 == 1 or n2 == nSamples). - * \param inputs[3] input grad 1, size: nSamples * dim. - * \param inputs[4] input grad 2, size: n2 * dim (n2 == 1 or n2 == nSamples). - * \param outputs[0] output grad, size : nSamples * 1. + * \param inouts[0] forward input grad 1, size: nSamples * dim. + * \param inouts[1] forward input grad 2, + * size: n2 * dim (n2 == 1 or n2 == nSamples). + * + * \param inputs[0] backward loss output grad, size : nSamples * 1. + * \param inputs[1] forward output value, size: nSamples * 1. + * \param inputs[2] forward input value 1, size: nSamples * dim. + * \param inputs[3] forward input value 2, + * size: n2 * dim (n2 == 1 or n2 == nSamples). */ template class CosSimBackwardFunc : public FunctionBase { @@ -163,35 +165,35 @@ class CosSimBackwardFunc : public FunctionBase { void calc(const Arguments& inputs, const Arguments& outputs, const Arguments& inouts) override { - CHECK_EQ(inputs.size(), 5); - CHECK_EQ(outputs.size(), 1); - CHECK_EQ(inouts.size(), 0); + CHECK_EQ(inputs.size(), 4); + CHECK_EQ(outputs.size(), 0); + CHECK_EQ(inouts.size(), 2); /// dim of out_grad and out_val == 1, column vector - CHECK_EQ(outputs[0].dims_[1], 1UL); CHECK_EQ(inputs[0].dims_[1], 1UL); + CHECK_EQ(inputs[1].dims_[1], 1UL); /// nSamples of out_grad == out_val == in_val1 == in_grad1 - CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); - CHECK_EQ(inputs[1].dims_[0], outputs[0].dims_[0]); - CHECK_EQ(inputs[3].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(inputs[1].dims_[0], inputs[0].dims_[0]); + CHECK_EQ(inputs[0].dims_[0], inputs[0].dims_[0]); + CHECK_EQ(inouts[0].dims_[0], inputs[0].dims_[0]); /// dim of in1_val1 == in_val2 == in_grad1 == in_grad2 - CHECK_EQ(inputs[2].dims_[1], inputs[1].dims_[1]); - CHECK_EQ(inputs[3].dims_[1], inputs[1].dims_[1]); - CHECK_EQ(inputs[4].dims_[1], inputs[1].dims_[1]); + CHECK_EQ(inputs[3].dims_[1], inputs[2].dims_[1]); + CHECK_EQ(inouts[0].dims_[1], inputs[2].dims_[1]); + CHECK_EQ(inouts[1].dims_[1], inputs[2].dims_[1]); - CHECK(outputs[0].getData() && inputs[0].getData() && inputs[1].getData() && - inputs[2].getData() && inputs[3].getData() && inputs[4].getData()); + CHECK(inputs[0].getData() && inputs[1].getData() && inputs[2].getData() && + inputs[3].getData() && inouts[0].getData() && inouts[1].getData()); const auto out_grad = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - const auto out_val = std::make_shared::type>( inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); - const auto in1_val = std::make_shared::type>( + const auto out_val = std::make_shared::type>( inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); - const auto in2_val = std::make_shared::type>( + const auto in1_val = std::make_shared::type>( inputs[2].getData(), inputs[2].dims_[0], inputs[2].dims_[1]); - auto in1_grad = std::make_shared::type>( + const auto in2_val = std::make_shared::type>( inputs[3].getData(), inputs[3].dims_[0], inputs[3].dims_[1]); + auto in1_grad = std::make_shared::type>( + inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); auto in2_grad = std::make_shared::type>( - inputs[4].getData(), inputs[4].dims_[0], inputs[4].dims_[1]); + inouts[1].getData(), inouts[1].dims_[0], inouts[1].dims_[1]); CosSimBackward(out_grad.get(), out_val.get(), diff --git a/paddle/function/CosSimOp.h b/paddle/function/CosSimOp.h index f66a4344d09ce8..ed1f1e4d5345e2 100644 --- a/paddle/function/CosSimOp.h +++ b/paddle/function/CosSimOp.h @@ -25,9 +25,9 @@ namespace paddle { * = scale * \sum_j (in1[i][j] * in2[i][j]) / * sqrt(sum_j (in1[i][j]^2) * sum_j (in2[i][j])^2) * - * \param[out] output output data. - * \param[in] intput1 input data. - * \param[in] intput2 input data. + * \param[out] output output value. + * \param[in] intput1 input value. + * \param[in] intput2 input value. * \param[in] scale default 1.0. * */ @@ -40,13 +40,13 @@ void CosSimForward(typename MatrixT::type* output, /** * \brief Cosine Similarity BackWard for Derivative. * - * \param[out] output1 backward loss output grad. - * \param[in] input1 forward-output value. - * \param[in] input2 forward input value 1. - * \param[in] input3 forward input value 2. - * \param[in] input4 forward input grad 1. - * \param[in] input5 forward input grad 2. - * \param[in] scale default 1.0. + * \param[in] output grad backward loss output grad. + * \param[in] output val forward-output value. + * \param[in] input val1 forward input value 1. + * \param[in] input val2 forward input value 2. + * \param[in/out] input grad forward input grad 1. + * \param[in/out] input grad forward input grad 2. + * \param[in] scale default 1.0. * */ template diff --git a/paddle/function/CosSimOpTest.cpp b/paddle/function/CosSimOpTest.cpp index 0d2ece2b98625a..f0e81ee0434178 100644 --- a/paddle/function/CosSimOpTest.cpp +++ b/paddle/function/CosSimOpTest.cpp @@ -97,22 +97,22 @@ void testCosSimBackward(size_t height_x, gpu_in2_grad.copyFrom(cpu_in2_grad); compare.getCpuFunction()->calc( - {Tensor(cpu_out_val.getData(), Dims{height_x, 1}), + {Tensor(cpu_out_grad.getData(), Dims{height_x, 1}), + Tensor(cpu_out_val.getData(), Dims{height_x, 1}), Tensor(cpu_in1_val.getData(), Dims{height_x, width}), - Tensor(cpu_in2_val.getData(), Dims{height_x, width}), - Tensor(cpu_in1_grad.getData(), Dims{height_x, width}), - Tensor(cpu_in2_grad.getData(), Dims{height_x, width})}, - {Tensor(cpu_out_grad.getData(), Dims{height_x, 1})}, - {}); + Tensor(cpu_in2_val.getData(), Dims{height_x, width})}, + {}, + {Tensor(cpu_in1_grad.getData(), Dims{height_x, width}), + Tensor(cpu_in2_grad.getData(), Dims{height_x, width})}); compare.getGpuFunction()->calc( - {Tensor(gpu_out_val.getData(), Dims{height_x, 1}), + {Tensor(gpu_out_grad.getData(), Dims{height_x, 1}), + Tensor(gpu_out_val.getData(), Dims{height_x, 1}), Tensor(gpu_in1_val.getData(), Dims{height_x, width}), - Tensor(gpu_in2_val.getData(), Dims{height_x, width}), - Tensor(gpu_in1_grad.getData(), Dims{height_x, width}), - Tensor(gpu_in2_grad.getData(), Dims{height_x, width})}, - {Tensor(gpu_out_grad.getData(), Dims{height_x, 1})}, - {}); + Tensor(gpu_in2_val.getData(), Dims{height_x, width})}, + {}, + {Tensor(gpu_in1_grad.getData(), Dims{height_x, width}), + Tensor(gpu_in2_grad.getData(), Dims{height_x, width})}); autotest::TensorCheckErr(cpu_in1_grad, gpu_in1_grad); autotest::TensorCheckErr(cpu_in2_grad, gpu_in2_grad); diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index ac66fd4712af48..b00eda2f64a1e1 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/paddle/gserver/layers/CosSimLayer.cpp @@ -79,13 +79,13 @@ void CosSimLayer::backward(const UpdateCallback& callback) { auto inG2 = this->getInputGrad(1); CHECK(outG && outV && inV1 && inV2 && inG1 && inG2); backward_[0]->calc( - {Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()}), + {Tensor(outG->getData(), Dims{outG->getHeight(), outG->getWidth()}), + Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()}), Tensor(inV1->getData(), Dims{inV1->getHeight(), inV1->getWidth()}), - Tensor(inV2->getData(), Dims{inV2->getHeight(), inV2->getWidth()}), - Tensor(inG1->getData(), Dims{inG1->getHeight(), inG1->getWidth()}), - Tensor(inG2->getData(), Dims{inG2->getHeight(), inG2->getWidth()})}, - {Tensor(outG->getData(), Dims{outG->getHeight(), outG->getWidth()})}, - {}); + Tensor(inV2->getData(), Dims{inV2->getHeight(), inV2->getWidth()})}, + {}, + {Tensor(inG1->getData(), Dims{inG1->getHeight(), inG1->getWidth()}), + Tensor(inG2->getData(), Dims{inG2->getHeight(), inG2->getWidth()})}); } } diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index 427191e540778a..120c4e84c55d0b 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -169,19 +169,19 @@ void CosSimVecMatLayer::backward(const UpdateCallback& callback) { tmpRow3->setData(outG->rowBuf(i)); backward_[0]->calc( - {Tensor(tmpRow2->getData(), + {Tensor(tmpRow3->getData(), + Dims{tmpRow3->getHeight(), tmpRow3->getWidth()}), + Tensor(tmpRow2->getData(), Dims{tmpRow2->getHeight(), tmpRow2->getWidth()}), Tensor(tmpMtx0->getData(), Dims{tmpMtx0->getHeight(), tmpMtx0->getWidth()}), Tensor(tmpRow0->getData(), - Dims{tmpRow0->getHeight(), tmpRow0->getWidth()}), - Tensor(tmpMtx1->getData(), + Dims{tmpRow0->getHeight(), tmpRow0->getWidth()})}, + {}, + {Tensor(tmpMtx1->getData(), Dims{tmpMtx1->getHeight(), tmpMtx1->getWidth()}), Tensor(tmpRow1->getData(), - Dims{tmpRow1->getHeight(), tmpRow1->getWidth()})}, - {Tensor(tmpRow3->getData(), - Dims{tmpRow3->getHeight(), tmpRow3->getWidth()})}, - {}); + Dims{tmpRow1->getHeight(), tmpRow1->getWidth()})}); } } diff --git a/paddle/gserver/layers/CosSimVecMatLayer.h b/paddle/gserver/layers/CosSimVecMatLayer.h index bee83b58154b67..df4e11848c46e9 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.h +++ b/paddle/gserver/layers/CosSimVecMatLayer.h @@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#pragma once + #include "Layer.h" #include "paddle/math/Matrix.h" diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index a8b53e2105b053..1964b2f8bfaebc 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -941,59 +941,6 @@ void GpuMatrix::softreluDerivative(Matrix& output) { void GpuMatrix::scaledTanh(Matrix& output, real p1, real p2) { BaseMatrix::scaledTanh(output, p1, p2); } -void GpuMatrix::cosSim(Matrix& output1, Matrix& output2, real scale) { - CHECK(output1.useGpu_ == true && output2.useGpu_ == true) - << "Matrix type are not equal"; - size_t numSamples = getHeight(); - size_t dim = output1.getWidth(); - CHECK_EQ(getWidth(), 1UL); - CHECK_EQ(output1.getHeight(), numSamples); - CHECK_EQ(output1.getWidth(), output2.getWidth()); - real* out = getData(); - real* x = output1.getData(); - real* y = output2.getData(); - hl_cossim(out, x, y, dim, output1.getHeight(), output2.getHeight(), scale); -} -void GpuMatrix::cosSimDerivative(Matrix& output, - Matrix& prevOut1, - Matrix& prevOut2, - Matrix& prevGrad1, - Matrix& prevGrad2, - real scale) { - CHECK(output.useGpu_ == true && prevOut1.useGpu_ == true && - prevOut2.useGpu_ == true && prevGrad1.useGpu_ == true && - prevGrad2.useGpu_ == true) - << "Matrix type are not equal"; - CHECK_EQ(getWidth(), 1UL); - CHECK_EQ(output.getWidth(), 1UL); - - size_t numSamples = getHeight(); - CHECK_EQ(output.getHeight(), numSamples); - CHECK_EQ(prevOut1.getHeight(), numSamples); - CHECK_EQ(prevGrad1.getHeight(), numSamples); - - size_t dim = prevOut1.getWidth(); - CHECK_EQ(prevOut2.getWidth(), dim); - CHECK_EQ(prevGrad1.getWidth(), dim); - CHECK_EQ(prevGrad2.getWidth(), dim); - - real* grad = getData(); - real* out = output.getData(); - real* prevOutX = prevOut1.getData(); - real* prevOutY = prevOut2.getData(); - real* prevGradX = prevGrad1.getData(); - real* prevGradY = prevGrad2.getData(); - hl_cossim_derivative(grad, - out, - prevOutX, - prevOutY, - prevGradX, - prevGradY, - dim, - prevOut1.getHeight(), - prevOut2.getHeight(), - scale); -} void GpuMatrix::randomizeUniform() { CHECK(isContiguous()); @@ -3470,105 +3417,6 @@ void CpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) { } } -void CpuMatrix::cosSim(Matrix& output1, Matrix& output2, real scale) { - size_t numSamples = getHeight(); - size_t dim = output1.getWidth(); - CHECK_EQ(getWidth(), 1UL); - CHECK_EQ(output1.getHeight(), numSamples); - CHECK_EQ(output1.getWidth(), output2.getWidth()); - - real* out = getData(); - const real* x = output1.getData(); - const real* y = output2.getData(); - size_t yInc = dim; - if (output2.getHeight() == 1LU) { - yInc = 0; - } else { - CHECK_EQ(output2.getHeight(), numSamples); - } - for (size_t i = 0; i < numSamples; ++i, x += dim, y += yInc) { - real squareSumX = 0; - real squareSumY = 0; - real xy = 0; - for (size_t j = 0; j < dim; ++j) { - squareSumX += _square(x[j]); - squareSumY += _square(y[j]); - xy += x[j] * y[j]; - } - CHECK(squareSumX > 0 && squareSumY > 0); - out[i] = scale * xy / (std::sqrt(squareSumX) * std::sqrt(squareSumY)); - } -} - -void CpuMatrix::cosSimDerivative(Matrix& output, - Matrix& prevOut1, - Matrix& prevOut2, - Matrix& prevGrad1, - Matrix& prevGrad2, - real scale) { - CHECK(output.useGpu_ == false) << "Matrix type are not equal"; - - CHECK_EQ(getWidth(), 1UL); - CHECK_EQ(output.getWidth(), 1UL); - - size_t numSamples = getHeight(); - CHECK_EQ(output.getHeight(), numSamples); - CHECK_EQ(prevOut1.getHeight(), numSamples); - CHECK_EQ(prevGrad1.getHeight(), numSamples); - - size_t dim = prevOut1.getWidth(); - CHECK_EQ(prevOut2.getWidth(), dim); - CHECK_EQ(prevGrad1.getWidth(), dim); - CHECK_EQ(prevGrad2.getWidth(), dim); - - const real* grad = getData(); - const real* out = output.getData(); - const real* prevOutX = prevOut1.getData(); - const real* prevOutY = prevOut2.getData(); - real* prevGradX = prevGrad1.getData(); - real* prevGradY = prevGrad2.getData(); - size_t yInc = dim; - if (prevOut2.getHeight() == 1LU) { - yInc = 0; - CHECK_EQ(prevGrad2.getHeight(), 1LU); - } else { - CHECK_EQ(prevOut2.getHeight(), numSamples); - CHECK_EQ(prevGrad2.getHeight(), numSamples); - } - for (size_t i = 0; i < numSamples; ++i, - prevOutX += dim, - prevOutY += yInc, - prevGradX += dim, - prevGradY += yInc) { - real squareSumX = 0; - real squareSumY = 0; - real xy = 0; - for (size_t j = 0; j < dim; ++j) { - squareSumX += _square(prevOutX[j]); - squareSumY += _square(prevOutY[j]); - xy += prevOutX[j] * prevOutY[j]; - } - CHECK(squareSumX > 0 && squareSumY > 0); - if (xy == 0) { - real reciprocal = 1.0f / (std::sqrt(squareSumX) * std::sqrt(squareSumY)); - for (size_t j = 0; j < dim; ++j) { - prevGradX[j] += scale * grad[i] * prevOutY[j] * reciprocal; - prevGradY[j] += scale * grad[i] * prevOutX[j] * reciprocal; - } - } else { - real reciprocalXY = 1.0f / xy; - real reciprocalSquareSumX = 1.0f / squareSumX; - real reciprocalSquareSumY = 1.0f / squareSumY; - for (size_t j = 0; j < dim; ++j) { - prevGradX[j] += out[i] * grad[i] * (prevOutY[j] * reciprocalXY - - prevOutX[j] * reciprocalSquareSumX); - prevGradY[j] += out[i] * grad[i] * (prevOutX[j] * reciprocalXY - - prevOutY[j] * reciprocalSquareSumY); - } - } - } -} - void CpuMatrix::sumOfSquares(Matrix& output, Matrix& label) { CHECK(output.useGpu_ == false && label.useGpu_ == false) << "Matrix type are not equal"; diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index c92c0a272d5a72..ea4bbb86b057b5 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -799,26 +799,6 @@ class Matrix : public BaseMatrix { LOG(FATAL) << "Not implemented"; } - /** - * cosine similarity, for each row i, - * this[i] = cos(output1[i], output2[i]) - * - * output2 can only have one row, then for each row i, - * this[i] = cos(output1[i], output2[0]) - */ - virtual void cosSim(Matrix& output1, Matrix& output2, real scale = 1.0f) { - LOG(FATAL) << "Not implemented"; - } - - virtual void cosSimDerivative(Matrix& output, - Matrix& prevOut1, - Matrix& prevOut2, - Matrix& prevGrad1, - Matrix& prevGrad2, - real scale = 1.0f) { - LOG(FATAL) << "Not implemented"; - } - /// print out the values of elements to os virtual void print(std::ostream& os) const { LOG(FATAL) << "Not implemented"; @@ -1324,14 +1304,6 @@ class GpuMatrix : public Matrix { void softreluDerivative(Matrix& output); void scaledTanh(Matrix& output, real p1, real p2); - void cosSim(Matrix& output1, Matrix& output2, real scale); - void cosSimDerivative(Matrix& output, - Matrix& prevOut1, - Matrix& prevOut2, - Matrix& prevGrad1, - Matrix& prevGrad2, - real scale); - virtual void print(std::ostream& os) const; virtual void print(std::ostream& os, size_t height, size_t width) const; @@ -1752,14 +1724,6 @@ class CpuMatrix : public Matrix { void softreluDerivative(Matrix& output); void scaledTanh(Matrix& output, real p1, real p2); - void cosSim(Matrix& output1, Matrix& output2, real scale); - void cosSimDerivative(Matrix& output, - Matrix& prevOut1, - Matrix& prevOut2, - Matrix& prevGrad1, - Matrix& prevGrad2, - real scale); - void print(std::ostream& os) const; void print(std::ostream& os, size_t height, size_t width) const; void printOneRow(std::ostream& os, size_t idx) const; diff --git a/paddle/math/tests/test_Matrix.cpp b/paddle/math/tests/test_Matrix.cpp index a4084bdf7c6953..1c21da5b76e956 100644 --- a/paddle/math/tests/test_Matrix.cpp +++ b/paddle/math/tests/test_Matrix.cpp @@ -181,28 +181,6 @@ TEST(Matrix, copyByRowIndex) { } } -void testCosSim(int heightX, int heightY, int width, real scale) { - AutoCompare test(heightX, 1); - CpuMatrix arg1(heightX, width); - CpuMatrix arg2(heightY, width); - arg1.randomizeUniform(); - arg2.randomizeUniform(); - arg2.add(-0.5); - test.cmpWithArg(&Matrix::cosSim, arg1, arg2, scale); -} - -TEST(Matrix, cosSim) { - for (auto heightX : {10, 100, 1000}) { - for (auto heightY : {1, heightX}) { - for (auto width : {10, 100, 1000}) { - for (auto scale : {1.0, 2.0}) { - testCosSim(heightX, heightY, width, scale); - } - } - } - } -} - void testParamReluForward(int height, int width, int w_height, int w_width) { AutoCompare test(height, width); CpuMatrix arg1(height, width); diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index e024f2cf1b913f..6caaea443c1df7 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -720,61 +720,6 @@ TEST(Matrix, sequenceAvgForward) { } } -void testCosSimDerivate(int heightX, int heightY, int width, real scale) { - MatrixPtr prevOutX = CpuMatrix::create(heightX, width, false, false); - MatrixPtr prevOutY = CpuMatrix::create(heightY, width, false, false); - MatrixPtr grad = CpuMatrix::create(heightX, 1, false, false); - MatrixPtr output = CpuMatrix::create(heightX, 1, false, false); - MatrixPtr prevGradX = CpuMatrix::create(heightX, width, false, false); - MatrixPtr prevGradY = CpuMatrix::create(heightY, width, false, false); - - prevOutX->randomizeUniform(); - prevOutY->randomizeUniform(); - grad->randomizeUniform(); - output->randomizeUniform(); - prevGradX->randomizeUniform(); - prevGradY->randomizeUniform(); - - MatrixPtr prevOutXGpu = GpuMatrix::create(heightX, width, false, true); - MatrixPtr prevOutYGpu = GpuMatrix::create(heightY, width, false, true); - MatrixPtr gradGpu = GpuMatrix::create(heightX, 1, false, true); - MatrixPtr outputGpu = GpuMatrix::create(heightX, 1, false, true); - MatrixPtr prevGradXGpu = GpuMatrix::create(heightX, width, false, true); - MatrixPtr prevGradYGpu = GpuMatrix::create(heightY, width, false, true); - - prevOutXGpu->copyFrom(*prevOutX); - prevOutYGpu->copyFrom(*prevOutY); - gradGpu->copyFrom(*grad); - outputGpu->copyFrom(*output); - prevGradXGpu->copyFrom(*prevGradX); - prevGradYGpu->copyFrom(*prevGradY); - - grad->cosSimDerivative( - *output, *prevOutX, *prevOutY, *prevGradX, *prevGradY, scale); - - gradGpu->cosSimDerivative(*outputGpu, - *prevOutXGpu, - *prevOutYGpu, - *prevGradXGpu, - *prevGradYGpu, - scale); - - TensorCheckErr(*prevGradX, *prevGradXGpu); - TensorCheckErr(*prevGradY, *prevGradYGpu); -} - -TEST(Matrix, cosSimDerivate) { - for (auto heightX : {1, 10, 100}) { - for (auto heightY : {1, heightX}) { - for (auto width : {1, 10, 100}) { - for (auto scale : {1.0, 2.0}) { - testCosSimDerivate(heightX, heightY, width, scale); - } - } - } - } -} - void testParamReluBackwardDiff(int height, int width, int w_height, From 47aaac00b0baa8374e3b09e7b8e35f2d8d3bedc6 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 4 Jan 2017 18:13:12 -0800 Subject: [PATCH 159/370] add some comments... --- paddle/function/CosSimOp.cpp | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index bcc17fd5319c6a..1c3cb6f18b3d29 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -17,6 +17,15 @@ limitations under the License. */ #include "paddle/math/Vector.h" namespace paddle { +/** + * Cosine Similarity for CpuMatrix + * + * \param out_mat, output value, size: nSamples * 1. + * \param in1_mat, input value 1, size: nSamples * dim. + * \param in2_mat, input value 2, size: n2 * dim (n2 == 1 or n2 == nSamples). + * \param scale, default 1.0 + * + */ template <> void CosSimForward(CpuMatrix* out_mat, const CpuMatrix* in1_mat, @@ -48,6 +57,13 @@ void CosSimForward(CpuMatrix* out_mat, } /** + * Cosine Similarity + * for each row i, + * out[i] = scale * cos(input1[i], input2[i]) + * = scale * /sqrt(|input1[i]|^2 * |input2[i]|^2) + * when input2 only has one row, then for each row i, + * out[i] = cos(input1[i], input2[0]) + * * \param inputs[0] input matrix 1, size: nSamples * dim. * \param inputs[1] input matrix 2, size: n2 * dim (n2 == 1 or n2 == nSamples). * \param outputs[0] output matrix, size : nSamples * 1. @@ -85,6 +101,20 @@ class CosSimForwardFunc : public FunctionBase { real scale_; }; +/** + * Cosine Similarity Derivative for CpuMatrix + * + * \param in1_grad forward input grad 1, size: nSamples * dim. + * \param in2_grad forward input grad 2, + * size: n2 * dim (n2 == 1 or n2 == nSamples). + * + * \param out_grad backward loss output grad, size : nSamples * 1. + * \param out_val forward output value, size: nSamples * 1. + * \param in1_val forward input value 1, size: nSamples * dim. + * \param in2_val forward input value 2, + * size: n2 * dim (n2 == 1 or n2 == nSamples). + * \param scale, default 1.0 + */ template <> void CosSimBackward(const CpuMatrix* out_grad, const CpuMatrix* out_val, @@ -146,6 +176,8 @@ void CosSimBackward(const CpuMatrix* out_grad, } /** + * Cosine Similarity backward Derivative + * * \param inouts[0] forward input grad 1, size: nSamples * dim. * \param inouts[1] forward input grad 2, * size: n2 * dim (n2 == 1 or n2 == nSamples). From 877decdccc3475999abb72ae7383afc24c356cb8 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 11 Jan 2017 13:31:36 -0800 Subject: [PATCH 160/370] merge Daoyuan's FuncArg, address one of the comments. --- paddle/function/CMakeLists.txt | 1 + paddle/function/CosSimOp.cpp | 161 +++++++++----------- paddle/function/CosSimOp.h | 18 +-- paddle/function/CosSimOpGpu.cu | 71 ++++----- paddle/function/CosSimOpTest.cpp | 61 ++++---- paddle/function/FunctionTest.h | 4 + paddle/gserver/layers/CosSimLayer.cpp | 31 ++-- paddle/gserver/layers/CosSimVecMatLayer.cpp | 73 ++++++--- paddle/gserver/layers/CosSimVecMatLayer.h | 54 ------- 9 files changed, 226 insertions(+), 248 deletions(-) delete mode 100644 paddle/gserver/layers/CosSimVecMatLayer.h diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index fae3b7b20a70b5..1522510e8bb981 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -27,6 +27,7 @@ if(WITH_TESTING) add_simple_unittest(ContextProjectionOpTest) add_simple_unittest(PadOpTest) add_simple_unittest(MulOpTest) + add_simple_unittest(CosSimOpTest) endif() endif() diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index 1c3cb6f18b3d29..130ee56f3d9a51 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -27,21 +27,21 @@ namespace paddle { * */ template <> -void CosSimForward(CpuMatrix* out_mat, - const CpuMatrix* in1_mat, - const CpuMatrix* in2_mat, +void CosSimForward(CpuMatrix& out_mat, + const CpuMatrix& in1_mat, + const CpuMatrix& in2_mat, real scale) { - CHECK(out_mat && in1_mat && in2_mat); - size_t num_samples = out_mat->getHeight(); - size_t dim = in1_mat->getWidth(); + CHECK(out_mat.getData() && in1_mat.getData() && in2_mat.getData()); + size_t num_samples = out_mat.getHeight(); + size_t dim = in1_mat.getWidth(); /// column vector [nSamples, 1] - real* out = out_mat->getData(); - const real* x = in1_mat->getData(); - const real* y = in2_mat->getData(); + real* out = out_mat.getData(); + const real* x = in1_mat.getData(); + const real* y = in2_mat.getData(); /// in2 might only have one row or full rows - CHECK(in2_mat->getHeight() == 1LU || in2_mat->getHeight() == num_samples); - size_t inc = (in2_mat->getHeight() == 1LU) ? 0 : dim; + CHECK(in2_mat.getHeight() == 1LU || in2_mat.getHeight() == num_samples); + size_t inc = (in2_mat.getHeight() == 1LU) ? 0 : dim; for (size_t i = 0; i < num_samples; ++i, x += dim, y += inc) { real square_sum_x = 0; real square_sum_y = 0; @@ -75,26 +75,26 @@ class CosSimForwardFunc : public FunctionBase { scale_ = config.get("scale"); } - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(inputs.size(), 2); CHECK_EQ(outputs.size(), 1); - CHECK_EQ(inouts.size(), 0); - CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); - CHECK_EQ(inputs[0].dims_[1], inputs[1].dims_[1]); - CHECK_EQ(outputs[0].dims_[1], 1UL); + CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); + CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); + CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK(outputs[0].getData() && inputs[0].getData() && inputs[1].getData()); - auto out_mat = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - const auto in1_mat = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); - const auto in2_mat = std::make_shared::type>( - inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); + CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); + CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); + CHECK_EQ(outputs[0].shape()[1], 1UL); - CosSimForward(out_mat.get(), in1_mat.get(), in2_mat.get(), scale_); + CHECK(outputs[0].data() && inputs[0].data() && inputs[1].data()); + + CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); + auto out_mat = outputs[0].matrix(); + const auto in1_mat = inputs[0].matrix(); + const auto in2_mat = inputs[1].matrix(); + + CosSimForward(out_mat, in1_mat, in2_mat, scale_); } private: @@ -116,28 +116,29 @@ class CosSimForwardFunc : public FunctionBase { * \param scale, default 1.0 */ template <> -void CosSimBackward(const CpuMatrix* out_grad, - const CpuMatrix* out_val, - const CpuMatrix* in1_val, - const CpuMatrix* in2_val, - CpuMatrix* in1_grad, - CpuMatrix* in2_grad, +void CosSimBackward(const CpuMatrix& out_grad, + const CpuMatrix& out_val, + const CpuMatrix& in1_val, + const CpuMatrix& in2_val, + CpuMatrix& in1_grad, + CpuMatrix& in2_grad, real scale) { - CHECK(out_grad && out_val && in1_val && in2_val && in1_grad && in2_grad); - CHECK_EQ(out_val->useGpu_, false) << "Matrix type are GPU, CPU required"; - - const real* grad = out_grad->getData(); - const real* out = out_val->getData(); - const real* prev_out_x = in1_val->getData(); - const real* prev_out_y = in2_val->getData(); - real* prev_grad_x = in1_grad->getData(); - real* prev_grad_y = in2_grad->getData(); - - size_t num_samples = out_grad->getHeight(); - size_t dim = in1_val->getWidth(); - CHECK_EQ(in2_val->getHeight(), in2_grad->getHeight()); - CHECK(in2_val->getHeight() == 1LU || in2_val->getHeight() == num_samples); - size_t inc = (in2_val->getHeight() == 1LU) ? 0 : dim; + CHECK(out_grad.getData() && out_val.getData() && in1_val.getData() && + in2_val.getData() && in1_grad.getData() && in2_grad.getData()); + CHECK_EQ(out_val.useGpu_, false) << "Matrix type are GPU, CPU required"; + + const real* grad = out_grad.getData(); + const real* out = out_val.getData(); + const real* prev_out_x = in1_val.getData(); + const real* prev_out_y = in2_val.getData(); + real* prev_grad_x = in1_grad.getData(); + real* prev_grad_y = in2_grad.getData(); + + size_t num_samples = out_grad.getHeight(); + size_t dim = in1_val.getWidth(); + CHECK_EQ(in2_val.getHeight(), in2_grad.getHeight()); + CHECK(in2_val.getHeight() == 1LU || in2_val.getHeight() == num_samples); + size_t inc = (in2_val.getHeight() == 1LU) ? 0 : dim; for (size_t i = 0; i < num_samples; ++i, prev_out_x += dim, prev_out_y += inc, @@ -178,8 +179,8 @@ void CosSimBackward(const CpuMatrix* out_grad, /** * Cosine Similarity backward Derivative * - * \param inouts[0] forward input grad 1, size: nSamples * dim. - * \param inouts[1] forward input grad 2, + * \param outputs[0] forward input grad 1, size: nSamples * dim. + * \param outputs[1] forward input grad 2, * size: n2 * dim (n2 == 1 or n2 == nSamples). * * \param inputs[0] backward loss output grad, size : nSamples * 1. @@ -194,46 +195,36 @@ class CosSimBackwardFunc : public FunctionBase { scale_ = config.get("scale"); } - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { CHECK_EQ(inputs.size(), 4); - CHECK_EQ(outputs.size(), 0); - CHECK_EQ(inouts.size(), 2); + CHECK_EQ(outputs.size(), 2); /// dim of out_grad and out_val == 1, column vector - CHECK_EQ(inputs[0].dims_[1], 1UL); - CHECK_EQ(inputs[1].dims_[1], 1UL); + CHECK_EQ(inputs[0].shape()[1], 1UL); + CHECK_EQ(inputs[1].shape()[1], 1UL); /// nSamples of out_grad == out_val == in_val1 == in_grad1 - CHECK_EQ(inputs[1].dims_[0], inputs[0].dims_[0]); - CHECK_EQ(inputs[0].dims_[0], inputs[0].dims_[0]); - CHECK_EQ(inouts[0].dims_[0], inputs[0].dims_[0]); + CHECK_EQ(inputs[1].shape()[0], inputs[0].shape()[0]); + CHECK_EQ(inputs[0].shape()[0], inputs[0].shape()[0]); + CHECK_EQ(outputs[0].shape()[0], inputs[0].shape()[0]); /// dim of in1_val1 == in_val2 == in_grad1 == in_grad2 - CHECK_EQ(inputs[3].dims_[1], inputs[2].dims_[1]); - CHECK_EQ(inouts[0].dims_[1], inputs[2].dims_[1]); - CHECK_EQ(inouts[1].dims_[1], inputs[2].dims_[1]); - - CHECK(inputs[0].getData() && inputs[1].getData() && inputs[2].getData() && - inputs[3].getData() && inouts[0].getData() && inouts[1].getData()); - const auto out_grad = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); - const auto out_val = std::make_shared::type>( - inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]); - const auto in1_val = std::make_shared::type>( - inputs[2].getData(), inputs[2].dims_[0], inputs[2].dims_[1]); - const auto in2_val = std::make_shared::type>( - inputs[3].getData(), inputs[3].dims_[0], inputs[3].dims_[1]); - auto in1_grad = std::make_shared::type>( - inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]); - auto in2_grad = std::make_shared::type>( - inouts[1].getData(), inouts[1].dims_[0], inouts[1].dims_[1]); - - CosSimBackward(out_grad.get(), - out_val.get(), - in1_val.get(), - in2_val.get(), - in1_grad.get(), - in2_grad.get(), - scale_); + CHECK_EQ(inputs[3].shape()[1], inputs[2].shape()[1]); + CHECK_EQ(outputs[0].shape()[1], inputs[2].shape()[1]); + CHECK_EQ(outputs[1].shape()[1], inputs[2].shape()[1]); + + CHECK(inputs[0].data() && inputs[1].data() && inputs[2].data() && + inputs[3].data() && outputs[0].data() && outputs[1].data()); + + CHECK_EQ(outputs[0].getArgType(), ADD_TO); + CHECK_EQ(outputs[1].getArgType(), ADD_TO); + + const auto out_grad = inputs[0].matrix(); + const auto out_val = inputs[1].matrix(); + const auto in1_val = inputs[2].matrix(); + const auto in2_val = inputs[3].matrix(); + auto in1_grad = outputs[0].matrix(); + auto in2_grad = outputs[1].matrix(); + + CosSimBackward( + out_grad, out_val, in1_val, in2_val, in1_grad, in2_grad, scale_); } private: diff --git a/paddle/function/CosSimOp.h b/paddle/function/CosSimOp.h index ed1f1e4d5345e2..be73064e6375bf 100644 --- a/paddle/function/CosSimOp.h +++ b/paddle/function/CosSimOp.h @@ -32,9 +32,9 @@ namespace paddle { * */ template -void CosSimForward(typename MatrixT::type* output, - const typename MatrixT::type* input1, - const typename MatrixT::type* input2, +void CosSimForward(typename Tensor::Matrix& output, + const typename Tensor::Matrix& input1, + const typename Tensor::Matrix& input2, real scale); /** @@ -50,12 +50,12 @@ void CosSimForward(typename MatrixT::type* output, * */ template -void CosSimBackward(const typename MatrixT::type* out_grad, - const typename MatrixT::type* out_value, - const typename MatrixT::type* in1_value, - const typename MatrixT::type* in2_value, - typename MatrixT::type* in1_grad, - typename MatrixT::type* in2_grad, +void CosSimBackward(const typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_value, + const typename Tensor::Matrix& in1_value, + const typename Tensor::Matrix& in2_value, + typename Tensor::Matrix& in1_grad, + typename Tensor::Matrix& in2_grad, real scale); } // namespace paddle diff --git a/paddle/function/CosSimOpGpu.cu b/paddle/function/CosSimOpGpu.cu index f654f0bc209c24..1dd733674fa054 100644 --- a/paddle/function/CosSimOpGpu.cu +++ b/paddle/function/CosSimOpGpu.cu @@ -65,12 +65,12 @@ __global__ void KeCosSim(real* output, } void hlCossim(real* output, - const real* input1, - const real* input2, - size_t width, - size_t input1_height, - size_t input2_height, - real scale) { + const real* input1, + const real* input2, + size_t width, + size_t input1_height, + size_t input2_height, + real scale) { CHECK_NOTNULL(output); CHECK_NOTNULL(input1); CHECK_NOTNULL(input2); @@ -84,20 +84,20 @@ void hlCossim(real* output, } template <> -void CosSimForward(GpuMatrix* out_mat, - const GpuMatrix* in1_mat, - const GpuMatrix* in2_mat, +void CosSimForward(GpuMatrix& out_mat, + const GpuMatrix& in1_mat, + const GpuMatrix& in2_mat, real scale) { - CHECK(out_mat && in1_mat && in2_mat); - CHECK(in1_mat->useGpu_ == true && in2_mat->useGpu_ == true) + CHECK(out_mat.getData() && in1_mat.getData() && in2_mat.getData()); + CHECK(in1_mat.useGpu_ == true && in2_mat.useGpu_ == true) << "Matrix type are not GPU"; - size_t num_samples = out_mat->getHeight(); - size_t dim = in1_mat->getWidth(); - real* out = out_mat->getData(); - const real* x = in1_mat->getData(); - const real* y = in2_mat->getData(); - hlCossim(out, x, y, dim, in1_mat->getHeight(), in2_mat->getHeight(), scale); + size_t num_samples = out_mat.getHeight(); + size_t dim = in1_mat.getWidth(); + real* out = out_mat.getData(); + const real* x = in1_mat.getData(); + const real* y = in2_mat.getData(); + hlCossim(out, x, y, dim, in1_mat.getHeight(), in2_mat.getHeight(), scale); } template @@ -206,25 +206,26 @@ void hlCossimDerivative(const real* grad, } template <> -void CosSimBackward(const GpuMatrix* out_grad, - const GpuMatrix* out_val, - const GpuMatrix* in1_val, - const GpuMatrix* in2_val, - GpuMatrix* in1_grad, - GpuMatrix* in2_grad, +void CosSimBackward(const GpuMatrix& out_grad, + const GpuMatrix& out_val, + const GpuMatrix& in1_val, + const GpuMatrix& in2_val, + GpuMatrix& in1_grad, + GpuMatrix& in2_grad, real scale) { - CHECK(out_grad && out_val && in1_val && in2_val && in1_grad && in2_grad); - CHECK(out_grad->useGpu_ && out_val->useGpu_ && in1_val->useGpu_ - && in2_val->useGpu_ && in1_grad->useGpu_ && in2_grad->useGpu_) + CHECK(out_grad.getData() && out_val.getData() && in1_val.getData() && + in2_val.getData() && in1_grad.getData() && in2_grad.getData()); + CHECK(out_grad.useGpu_ && out_val.useGpu_ && in1_val.useGpu_ + && in2_val.useGpu_ && in1_grad.useGpu_ && in2_grad.useGpu_) << "Matrix types are not equally GPU"; - size_t dim = in1_val->getWidth(); - const real* grad = out_grad->getData(); - const real* out = out_val->getData(); - const real* prev_out_x = in1_val->getData(); - const real* prev_out_y = in2_val->getData(); - real* prev_grad_x = in1_grad->getData(); - real* prev_grad_y = in2_grad->getData(); + size_t dim = in1_val.getWidth(); + const real* grad = out_grad.getData(); + const real* out = out_val.getData(); + const real* prev_out_x = in1_val.getData(); + const real* prev_out_y = in2_val.getData(); + real* prev_grad_x = in1_grad.getData(); + real* prev_grad_y = in2_grad.getData(); hlCossimDerivative(grad, out, prev_out_x, @@ -232,8 +233,8 @@ void CosSimBackward(const GpuMatrix* out_grad, prev_grad_x, prev_grad_y, dim, - in1_val->getHeight(), - in2_val->getHeight(), + in1_val.getHeight(), + in2_val.getHeight(), scale); } diff --git a/paddle/function/CosSimOpTest.cpp b/paddle/function/CosSimOpTest.cpp index f0e81ee0434178..dce959e81c6862 100644 --- a/paddle/function/CosSimOpTest.cpp +++ b/paddle/function/CosSimOpTest.cpp @@ -36,16 +36,20 @@ void testCosSimForward(size_t height_x, CpuMatrix cpu_out(height_x, 1); GpuMatrix gpu_out(height_x, 1); - compare.getCpuFunction()->calc( - {Tensor(cpu_arg1.getData(), Dims{height_x, width}), - Tensor(cpu_arg2.getData(), Dims{height_y, width})}, - {Tensor(cpu_out.getData(), Dims{height_x, 1})}, - {}); - compare.getGpuFunction()->calc( - {Tensor(gpu_arg1.getData(), Dims{height_x, width}), - Tensor(gpu_arg2.getData(), Dims{height_y, width})}, - {Tensor(gpu_out.getData(), Dims{height_x, 1})}, - {}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(cpu_arg1); + cpu_inputs.addArg(cpu_arg2); + cpu_outputs.addArg(cpu_out, ASSIGN_TO); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(gpu_arg1); + gpu_inputs.addArg(gpu_arg2); + gpu_outputs.addArg(gpu_out, ASSIGN_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckErr(cpu_out, gpu_out); } @@ -96,23 +100,26 @@ void testCosSimBackward(size_t height_x, gpu_in1_grad.copyFrom(cpu_in1_grad); gpu_in2_grad.copyFrom(cpu_in2_grad); - compare.getCpuFunction()->calc( - {Tensor(cpu_out_grad.getData(), Dims{height_x, 1}), - Tensor(cpu_out_val.getData(), Dims{height_x, 1}), - Tensor(cpu_in1_val.getData(), Dims{height_x, width}), - Tensor(cpu_in2_val.getData(), Dims{height_x, width})}, - {}, - {Tensor(cpu_in1_grad.getData(), Dims{height_x, width}), - Tensor(cpu_in2_grad.getData(), Dims{height_x, width})}); - - compare.getGpuFunction()->calc( - {Tensor(gpu_out_grad.getData(), Dims{height_x, 1}), - Tensor(gpu_out_val.getData(), Dims{height_x, 1}), - Tensor(gpu_in1_val.getData(), Dims{height_x, width}), - Tensor(gpu_in2_val.getData(), Dims{height_x, width})}, - {}, - {Tensor(gpu_in1_grad.getData(), Dims{height_x, width}), - Tensor(gpu_in2_grad.getData(), Dims{height_x, width})}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(cpu_out_grad); + cpu_inputs.addArg(cpu_out_val); + cpu_inputs.addArg(cpu_in1_val); + cpu_inputs.addArg(cpu_in2_val); + cpu_outputs.addArg(cpu_in1_grad, ADD_TO); + cpu_outputs.addArg(cpu_in2_grad, ADD_TO); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(gpu_out_grad); + gpu_inputs.addArg(gpu_out_val); + gpu_inputs.addArg(gpu_in1_val); + gpu_inputs.addArg(gpu_in2_val); + gpu_outputs.addArg(gpu_in1_grad, ADD_TO); + gpu_outputs.addArg(gpu_in2_grad, ADD_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckErr(cpu_in1_grad, gpu_in1_grad); autotest::TensorCheckErr(cpu_in2_grad, gpu_in2_grad); diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 0cfafdb27f55a3..35de3a65d531f7 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -157,6 +157,9 @@ class FunctionCompare { cpuSparse_->randomizeUniform(); gpuSparse_->copyFrom(*cpuSparse_, stream); hl_stream_synchronize(stream); + void addInputs(const SequenceArg& input) { + size_t batchSize = input.shape()[0]; + size_t numSeqs = batchSize / 10 + 1; cpuOutputs_.emplace_back( std::make_shared(*cpuSparse_, argType)); @@ -331,6 +334,7 @@ class FunctionCompare { } protected: +<<<<<<< HEAD std::shared_ptr cpuFunc_; std::shared_ptr gpuFunc_; std::vector cpuMemory_; diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index b00eda2f64a1e1..a6c0300acf6752 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/paddle/gserver/layers/CosSimLayer.cpp @@ -56,13 +56,12 @@ void CosSimLayer::forward(PassType passType) { MatrixPtr prevOut2 = getInputValue(1); CHECK(outV && prevOut1 && prevOut2); - forward_[0]->calc( - {Tensor(prevOut1->getData(), - Dims{prevOut1->getHeight(), prevOut1->getWidth()}), - Tensor(prevOut2->getData(), - Dims{prevOut2->getHeight(), prevOut2->getWidth()})}, - {Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()})}, - {}); + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*prevOut1); + inputs.addArg(*prevOut2); + outputs.addArg(*outV, ASSIGN_TO); + forward_[0]->calc(inputs, outputs); } } @@ -78,14 +77,16 @@ void CosSimLayer::backward(const UpdateCallback& callback) { auto inG1 = this->getInputGrad(0); auto inG2 = this->getInputGrad(1); CHECK(outG && outV && inV1 && inV2 && inG1 && inG2); - backward_[0]->calc( - {Tensor(outG->getData(), Dims{outG->getHeight(), outG->getWidth()}), - Tensor(outV->getData(), Dims{outV->getHeight(), outV->getWidth()}), - Tensor(inV1->getData(), Dims{inV1->getHeight(), inV1->getWidth()}), - Tensor(inV2->getData(), Dims{inV2->getHeight(), inV2->getWidth()})}, - {}, - {Tensor(inG1->getData(), Dims{inG1->getHeight(), inG1->getWidth()}), - Tensor(inG2->getData(), Dims{inG2->getHeight(), inG2->getWidth()})}); + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*outG); + inputs.addArg(*outV); + inputs.addArg(*inV1); + inputs.addArg(*inV2); + outputs.addArg(*inG1, ADD_TO); + outputs.addArg(*inG2, ADD_TO); + + backward_[0]->calc(inputs, outputs); } } diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index 120c4e84c55d0b..29ebe1ca9f60fb 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -12,11 +12,44 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "CosSimVecMatLayer.h" +#include "Layer.h" +#include "paddle/math/Matrix.h" #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" namespace paddle { +/** + * @brief A layer for computing cosine similarity between a vector + * and each row of a matrix + * out[i] = cos_scale * cos(in1, in2(i,:)); + * @note used in NEURAL TURING MACHINE + * + * Input1: a vector (batchSize * dataDim) + * + * Input2: a matrix in vector form (batchSize * (weightDim*dataDim)) + * + * Output: a vector (batchSize * weightDim) + */ + +class CosSimVecMatLayer : public Layer { +public: + explicit CosSimVecMatLayer(const LayerConfig& config) : Layer(config) {} + + ~CosSimVecMatLayer() {} + + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + + void forward(PassType passType); + void backward(const UpdateCallback& callback = nullptr); + +protected: + MatrixPtr tmpMtx0; + MatrixPtr tmpMtx1; + MatrixPtr tmpRow0; + MatrixPtr tmpRow1; + MatrixPtr tmpRow2; + MatrixPtr tmpRow3; +}; /** * @brief A layer for computing cosine similarity between a vector @@ -98,7 +131,6 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap, /* trans= */ false, useGpu_); - /// todo(tianbing), do we really need to check these shared pointers? CHECK(tmpRow0 && tmpRow1 && tmpRow2 && tmpRow3 && tmpMtx0 && tmpMtx1); createFunction(forward_, @@ -136,13 +168,12 @@ void CosSimVecMatLayer::forward(PassType passType) { tmpMtx0->setData(inV1->rowBuf(i)); tmpRow2->setData(outV->rowBuf(i)); - forward_[0]->calc({Tensor(tmpMtx0->getData(), - Dims{tmpMtx0->getHeight(), tmpMtx0->getWidth()}), - Tensor(tmpRow0->getData(), - Dims{tmpRow0->getHeight(), tmpRow0->getWidth()})}, - {Tensor(tmpRow2->getData(), - Dims{tmpRow2->getHeight(), tmpRow2->getWidth()})}, - {}); + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*tmpMtx0); + inputs.addArg(*tmpRow0); + outputs.addArg(*tmpRow2, ASSIGN_TO); + forward_[0]->calc(inputs, outputs); } } @@ -168,20 +199,16 @@ void CosSimVecMatLayer::backward(const UpdateCallback& callback) { tmpRow2->setData(outV->rowBuf(i)); tmpRow3->setData(outG->rowBuf(i)); - backward_[0]->calc( - {Tensor(tmpRow3->getData(), - Dims{tmpRow3->getHeight(), tmpRow3->getWidth()}), - Tensor(tmpRow2->getData(), - Dims{tmpRow2->getHeight(), tmpRow2->getWidth()}), - Tensor(tmpMtx0->getData(), - Dims{tmpMtx0->getHeight(), tmpMtx0->getWidth()}), - Tensor(tmpRow0->getData(), - Dims{tmpRow0->getHeight(), tmpRow0->getWidth()})}, - {}, - {Tensor(tmpMtx1->getData(), - Dims{tmpMtx1->getHeight(), tmpMtx1->getWidth()}), - Tensor(tmpRow1->getData(), - Dims{tmpRow1->getHeight(), tmpRow1->getWidth()})}); + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*tmpRow3); + inputs.addArg(*tmpRow2); + inputs.addArg(*tmpMtx0); + inputs.addArg(*tmpRow0); + outputs.addArg(*tmpMtx1, ADD_TO); + outputs.addArg(*tmpRow1, ADD_TO); + + backward_[0]->calc(inputs, outputs); } } diff --git a/paddle/gserver/layers/CosSimVecMatLayer.h b/paddle/gserver/layers/CosSimVecMatLayer.h deleted file mode 100644 index df4e11848c46e9..00000000000000 --- a/paddle/gserver/layers/CosSimVecMatLayer.h +++ /dev/null @@ -1,54 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#pragma once - -#include "Layer.h" -#include "paddle/math/Matrix.h" - -namespace paddle { -/** - * @brief A layer for computing cosine similarity between a vector - * and each row of a matrix - * out[i] = cos_scale * cos(in1, in2(i,:)); - * @note used in NEURAL TURING MACHINE - * - * Input1: a vector (batchSize * dataDim) - * - * Input2: a matrix in vector form (batchSize * (weightDim*dataDim)) - * - * Output: a vector (batchSize * weightDim) - */ - -class CosSimVecMatLayer : public Layer { -public: - explicit CosSimVecMatLayer(const LayerConfig& config) : Layer(config) {} - - ~CosSimVecMatLayer() {} - - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); - -protected: - MatrixPtr tmpMtx0; - MatrixPtr tmpMtx1; - MatrixPtr tmpRow0; - MatrixPtr tmpRow1; - MatrixPtr tmpRow2; - MatrixPtr tmpRow3; -}; - -} // namespace paddle From 9b17c3ff2ec6e3179db7ddffbe7a84a4704e1186 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 25 Jan 2017 13:53:28 -0800 Subject: [PATCH 161/370] rewrite unit test using Daoyuan's new FunctionTest. --- paddle/function/CosSimOp.cpp | 14 ++-- paddle/function/CosSimOpTest.cpp | 122 ++++++------------------------- paddle/function/FunctionTest.h | 17 ++++- 3 files changed, 47 insertions(+), 106 deletions(-) diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index 130ee56f3d9a51..7ece7b2dfedaf4 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -76,12 +76,12 @@ class CosSimForwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ(inputs.size(), 2); - CHECK_EQ(outputs.size(), 1); + CHECK_EQ(inputs.size(), 2UL); + CHECK_EQ(outputs.size(), 1UL); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); + CHECK_EQ(inputs[0].shape().ndims(), 2UL); + CHECK_EQ(inputs[1].shape().ndims(), 2UL); + CHECK_EQ(outputs[0].shape().ndims(), 2UL); CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); @@ -196,8 +196,8 @@ class CosSimBackwardFunc : public FunctionBase { } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ(inputs.size(), 4); - CHECK_EQ(outputs.size(), 2); + CHECK_EQ(inputs.size(), 4UL); + CHECK_EQ(outputs.size(), 2UL); /// dim of out_grad and out_val == 1, column vector CHECK_EQ(inputs[0].shape()[1], 1UL); CHECK_EQ(inputs[1].shape()[1], 1UL); diff --git a/paddle/function/CosSimOpTest.cpp b/paddle/function/CosSimOpTest.cpp index dce959e81c6862..48c815f027161b 100644 --- a/paddle/function/CosSimOpTest.cpp +++ b/paddle/function/CosSimOpTest.cpp @@ -22,114 +22,40 @@ void testCosSimForward(size_t height_x, size_t height_y, size_t width, real scale) { - FunctionCompare compare("CosSimForward", FuncConfig().set("scale", scale)); - - CpuMatrix cpu_arg1(height_x, width); - CpuMatrix gpu_arg1(height_x, width); - CpuMatrix cpu_arg2(height_y, width); - CpuMatrix gpu_arg2(height_y, width); - cpu_arg1.randomizeUniform(); - gpu_arg1.copyFrom(cpu_arg1); - cpu_arg2.randomizeUniform(); - cpu_arg2.add(-0.5); - gpu_arg2.copyFrom(cpu_arg2); - CpuMatrix cpu_out(height_x, 1); - GpuMatrix gpu_out(height_x, 1); - - BufferArgs cpu_inputs; - BufferArgs cpu_outputs; - cpu_inputs.addArg(cpu_arg1); - cpu_inputs.addArg(cpu_arg2); - cpu_outputs.addArg(cpu_out, ASSIGN_TO); - - BufferArgs gpu_inputs; - BufferArgs gpu_outputs; - gpu_inputs.addArg(gpu_arg1); - gpu_inputs.addArg(gpu_arg2); - gpu_outputs.addArg(gpu_out, ASSIGN_TO); - - compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); - compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); - - autotest::TensorCheckErr(cpu_out, gpu_out); -} - -TEST(Matrix, cosSimForward) { - for (auto height_x : {10, 100, 1000}) { - for (auto height_y : {1, height_x}) { - for (auto width : {10, 100, 1000}) { - for (auto scale : {1.0, 2.0}) { - testCosSimForward(height_x, height_y, width, scale); - } - } - } - } + FunctionCompare test("CosSimForward", FuncConfig().set("scale", scale)); + // prepare input arguments + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, width})); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_y, width})); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1}), + ASSIGN_TO); + // run Function + test.run(); } void testCosSimBackward(size_t height_x, size_t height_y, size_t width, real scale) { - FunctionCompare compare("CosSimBackward", FuncConfig().set("scale", scale)); - - CpuMatrix cpu_out_grad(height_x, 1); - CpuMatrix cpu_out_val(height_x, 1); - CpuMatrix cpu_in1_val(height_x, width); - CpuMatrix cpu_in2_val(height_x, width); - CpuMatrix cpu_in1_grad(height_x, width); - CpuMatrix cpu_in2_grad(height_x, width); - - cpu_out_grad.randomizeUniform(); - cpu_out_val.randomizeUniform(); - cpu_in1_val.randomizeUniform(); - cpu_in2_val.randomizeUniform(); - cpu_in1_grad.randomizeUniform(); - cpu_in2_grad.randomizeUniform(); - - GpuMatrix gpu_out_grad(height_x, 1); - GpuMatrix gpu_out_val(height_x, 1); - GpuMatrix gpu_in1_val(height_x, width); - GpuMatrix gpu_in2_val(height_x, width); - GpuMatrix gpu_in1_grad(height_x, width); - GpuMatrix gpu_in2_grad(height_x, width); - - gpu_out_grad.copyFrom(cpu_out_grad); - gpu_out_val.copyFrom(cpu_out_val); - gpu_in1_val.copyFrom(cpu_in1_val); - gpu_in2_val.copyFrom(cpu_in2_val); - gpu_in1_grad.copyFrom(cpu_in1_grad); - gpu_in2_grad.copyFrom(cpu_in2_grad); - - BufferArgs cpu_inputs; - BufferArgs cpu_outputs; - cpu_inputs.addArg(cpu_out_grad); - cpu_inputs.addArg(cpu_out_val); - cpu_inputs.addArg(cpu_in1_val); - cpu_inputs.addArg(cpu_in2_val); - cpu_outputs.addArg(cpu_in1_grad, ADD_TO); - cpu_outputs.addArg(cpu_in2_grad, ADD_TO); - - BufferArgs gpu_inputs; - BufferArgs gpu_outputs; - gpu_inputs.addArg(gpu_out_grad); - gpu_inputs.addArg(gpu_out_val); - gpu_inputs.addArg(gpu_in1_val); - gpu_inputs.addArg(gpu_in2_val); - gpu_outputs.addArg(gpu_in1_grad, ADD_TO); - gpu_outputs.addArg(gpu_in2_grad, ADD_TO); - - compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); - compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); - - autotest::TensorCheckErr(cpu_in1_grad, gpu_in1_grad); - autotest::TensorCheckErr(cpu_in2_grad, gpu_in2_grad); + FunctionCompare test("CosSimBackward", FuncConfig().set("scale", scale)); + // prepare input arguments + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1})); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, 1})); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, width})); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_y, width})); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_x, width}), + ADD_TO); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{height_y, width}), + ADD_TO); + // run Function + test.run(); } -TEST(Matrix, cosSimBackward) { - for (auto height_x : {1, 10, 100}) { +TEST(Matrix, cosSim) { + for (auto height_x : {10, 100, 1000}) { for (auto height_y : {1, height_x}) { - for (auto width : {1, 10, 100}) { + for (auto width : {10, 100, 1000}) { for (auto scale : {1.0, 2.0}) { + testCosSimForward(height_x, height_y, width, scale); testCosSimBackward(height_x, height_y, width, scale); } } diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 35de3a65d531f7..68587ab1aca55a 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -157,6 +157,7 @@ class FunctionCompare { cpuSparse_->randomizeUniform(); gpuSparse_->copyFrom(*cpuSparse_, stream); hl_stream_synchronize(stream); + void addInputs(const SequenceArg& input) { size_t batchSize = input.shape()[0]; size_t numSeqs = batchSize / 10 + 1; @@ -300,6 +301,21 @@ class FunctionCompare { } void initOutputs() { + for (size_t i = 0; i < cpuOutputs_.size(); i++) { + initArg(*cpuOutputs_[i]); + + // TODO: Need a BufferCopy used to copy from one BufferArg to another. + CpuVector cpuVector(cpuOutputs_[i]->shape().getElements(), + (real*)cpuOutputs_[i]->data()); + GpuVector gpuVector(gpuOutputs_[i]->shape().getElements(), + (real*)gpuOutputs_[i]->data()); + + gpuVector.copyFrom(cpuVector); + } + } + + void compareOutputs() { +>>>>>>> rewrite unit test using Daoyuan's new FunctionTest. for (size_t i = 0; i < cpuOutputs_.size(); i++) { if (cpuOutputs_[i]->isSparseArg()) { continue; /// sparse matrix already init @@ -334,7 +350,6 @@ class FunctionCompare { } protected: -<<<<<<< HEAD std::shared_ptr cpuFunc_; std::shared_ptr gpuFunc_; std::vector cpuMemory_; From ae3ae084733b8c5db4885ac791e5f111a4477678 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Wed, 8 Feb 2017 11:55:38 -0800 Subject: [PATCH 162/370] resolve merge confilicts --- paddle/function/FunctionTest.h | 19 ------------ paddle/gserver/layers/CosSimVecMatLayer.cpp | 33 --------------------- 2 files changed, 52 deletions(-) diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 68587ab1aca55a..0cfafdb27f55a3 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -158,10 +158,6 @@ class FunctionCompare { gpuSparse_->copyFrom(*cpuSparse_, stream); hl_stream_synchronize(stream); - void addInputs(const SequenceArg& input) { - size_t batchSize = input.shape()[0]; - size_t numSeqs = batchSize / 10 + 1; - cpuOutputs_.emplace_back( std::make_shared(*cpuSparse_, argType)); gpuOutputs_.emplace_back( @@ -301,21 +297,6 @@ class FunctionCompare { } void initOutputs() { - for (size_t i = 0; i < cpuOutputs_.size(); i++) { - initArg(*cpuOutputs_[i]); - - // TODO: Need a BufferCopy used to copy from one BufferArg to another. - CpuVector cpuVector(cpuOutputs_[i]->shape().getElements(), - (real*)cpuOutputs_[i]->data()); - GpuVector gpuVector(gpuOutputs_[i]->shape().getElements(), - (real*)gpuOutputs_[i]->data()); - - gpuVector.copyFrom(cpuVector); - } - } - - void compareOutputs() { ->>>>>>> rewrite unit test using Daoyuan's new FunctionTest. for (size_t i = 0; i < cpuOutputs_.size(); i++) { if (cpuOutputs_[i]->isSparseArg()) { continue; /// sparse matrix already init diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index 29ebe1ca9f60fb..aabafd473aa1e0 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -18,39 +18,6 @@ limitations under the License. */ #include "paddle/utils/Stat.h" namespace paddle { -/** - * @brief A layer for computing cosine similarity between a vector - * and each row of a matrix - * out[i] = cos_scale * cos(in1, in2(i,:)); - * @note used in NEURAL TURING MACHINE - * - * Input1: a vector (batchSize * dataDim) - * - * Input2: a matrix in vector form (batchSize * (weightDim*dataDim)) - * - * Output: a vector (batchSize * weightDim) - */ - -class CosSimVecMatLayer : public Layer { -public: - explicit CosSimVecMatLayer(const LayerConfig& config) : Layer(config) {} - - ~CosSimVecMatLayer() {} - - bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); - - void forward(PassType passType); - void backward(const UpdateCallback& callback = nullptr); - -protected: - MatrixPtr tmpMtx0; - MatrixPtr tmpMtx1; - MatrixPtr tmpRow0; - MatrixPtr tmpRow1; - MatrixPtr tmpRow2; - MatrixPtr tmpRow3; -}; - /** * @brief A layer for computing cosine similarity between a vector * and each row of a matrix From a3a6997e3213d9d895e83d6c83130a060a6aafa8 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Wed, 8 Feb 2017 19:38:32 -0800 Subject: [PATCH 163/370] Add draft new_api.md --- doc/api/new_api.md | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 doc/api/new_api.md diff --git a/doc/api/new_api.md b/doc/api/new_api.md new file mode 100644 index 00000000000000..5a90cd1c756238 --- /dev/null +++ b/doc/api/new_api.md @@ -0,0 +1,38 @@ +import yi_json + +g = 100 +def read(): + queue q; + # warmup q + for i = 0 : 1000 + q.push(read()) + yield q.shuffle_get() + +input = paddle.layer.data(...) +intermediate = paddle.layers.fc(input) +output = paddle.layer.softmax(intermediate) + +model = paddle.model.create(output) + +train(model, data_provider=read, cluster="clusterId") + +#-------------------------------------------------------------------------------- + +# 1. package, docker build, docker push +# 2. kubectl, clusterId Kuberentes job, 10 trainer containers, 5 parameter server containers + +#-------------------------------------------------------------------------------- + +def train(): + if os.environ["kube_api_server"] == nil: + docker_build() + docker_push() + kube_ctrl_start_job() + else: + rank = kube_mpi_rank() + if rank == 0: + master() + elif rank >= 15: + parameter_server() + else: + _train() From 642e25e93be136ba012b52dfe44607cf92f0a0b6 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 13:14:13 -0800 Subject: [PATCH 164/370] Move new_api.md into design/api.md --- doc/api/{new_api.md => design/api.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/api/{new_api.md => design/api.md} (100%) diff --git a/doc/api/new_api.md b/doc/api/design/api.md similarity index 100% rename from doc/api/new_api.md rename to doc/api/design/api.md From 36036c0ea57fdd0d9df8a4a77d8ebce89f65552b Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 13:15:37 -0800 Subject: [PATCH 165/370] Correct directory structure --- doc/{api => }/design/api.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/{api => }/design/api.md (100%) diff --git a/doc/api/design/api.md b/doc/design/api.md similarity index 100% rename from doc/api/design/api.md rename to doc/design/api.md From dd229dc7406de698601835d1d6e025d327bfe165 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 16:03:08 -0800 Subject: [PATCH 166/370] Update api.md --- doc/design/api.md | 128 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 90 insertions(+), 38 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 5a90cd1c756238..3cfb67cb00d791 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -1,38 +1,90 @@ -import yi_json - -g = 100 -def read(): - queue q; - # warmup q - for i = 0 : 1000 - q.push(read()) - yield q.shuffle_get() - -input = paddle.layer.data(...) -intermediate = paddle.layers.fc(input) -output = paddle.layer.softmax(intermediate) - -model = paddle.model.create(output) - -train(model, data_provider=read, cluster="clusterId") - -#-------------------------------------------------------------------------------- - -# 1. package, docker build, docker push -# 2. kubectl, clusterId Kuberentes job, 10 trainer containers, 5 parameter server containers - -#-------------------------------------------------------------------------------- - -def train(): - if os.environ["kube_api_server"] == nil: - docker_build() - docker_push() - kube_ctrl_start_job() - else: - rank = kube_mpi_rank() - if rank == 0: - master() - elif rank >= 15: - parameter_server() - else: - _train() +# Design Doc: PaddlePaddle API + +## Ingredients + +As the first step of our design, we list important concepts in deep +learning and try to figure their relationship, as shown below: + +``` +Model = {topology, parameters} + +Evaluator = {Model*, activations} +- forward +- test + +GradientMachine = {Model*, gradients} +- backward + +Optimizer = {Model*, Evaluator*, GradientMachine*} +- train +- update +- checkpoint +``` + +where the pair of curly braces `{` and `}` indicate *composition*, `*` +indicates a *reference*, and `-` marks a "class method". + + +### Model + +We used to think that parameters are part of the toplogy (or layers). +But that is not true, because multiple layers could share the same +parameter matrix. An example is a network that compares two text +segments in a semantic space: + +``` + semantic +text A -> projection ---\ + layer A \ + cosine + similarity -> output + layer + semantic / +text B -> projection ---/ + layer B +``` + +In this network, the two semantic projection layers (A and B) share +the same parameter matrix. + +For more information about our API that specifies topology and +parameter sharing, please refer to [TODO: API]. + + +### Evaluator + +Supposed that we have a trained ranking model, we should be able to +use it in our search engine. The search engine's Web server is a +concurrent program so to serve many HTTP requests simultaneously. It +doens't make sense for each of these threads to have its own copy of +model, because that would duplicate topologies and parameters. +However, each thread should be able to record layer outputs, i.e., +activations, computed from an input, derived from the request. With +*Evaluator* that saves activations, we can write the over-simplified +server program as: + +```python +m = paddle.model.load("trained.model") + +http.handle("/", + lambda req: + e = paddle.evaluator.create(m) + e.forward(req) + e.activation(layer="output")) # returns activations of layer "output" +``` + +### GradientMachine + +Similar to the evaluation, the training needs to compute gradients so +to update model parameters. Because an [optimizer](#optimizer) might +run multiple simultaneous threads to update the same model, gradients +should be separated from the model. Because gradients are only used +in training, but not serving, they should be separate from Evaluator. +Hence the `GradientMachine`. + +### Optimizer + +None of Model, Evaluator, nor GradientMachine implements the training +loop, hence Optimizer. We can define a concurrent optimizer that runs +multiple simultaneious threads to train a model -- just let each +thread has its own GradientMachine object. From e4eacd5810d7253c3444b465c8284c88f1b0e1f6 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 18:14:38 -0800 Subject: [PATCH 167/370] Add Programming section --- doc/design/api.md | 201 ++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 194 insertions(+), 7 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 3cfb67cb00d791..d8825389cb6947 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -1,4 +1,4 @@ -# Design Doc: PaddlePaddle API +# PaddlePaddle API ## Ingredients @@ -27,8 +27,8 @@ indicates a *reference*, and `-` marks a "class method". ### Model -We used to think that parameters are part of the toplogy (or layers). -But that is not true, because multiple layers could share the same +We used to think that parameters are part of the topology (or layers). +But that is not true because multiple layers could share the same parameter matrix. An example is a network that compares two text segments in a semantic space: @@ -56,8 +56,7 @@ parameter sharing, please refer to [TODO: API]. Supposed that we have a trained ranking model, we should be able to use it in our search engine. The search engine's Web server is a concurrent program so to serve many HTTP requests simultaneously. It -doens't make sense for each of these threads to have its own copy of -model, because that would duplicate topologies and parameters. +doesn't make sense for each of these threads to have its own copy of the model because that would duplicate topologies and parameters. However, each thread should be able to record layer outputs, i.e., activations, computed from an input, derived from the request. With *Evaluator* that saves activations, we can write the over-simplified @@ -70,7 +69,7 @@ http.handle("/", lambda req: e = paddle.evaluator.create(m) e.forward(req) - e.activation(layer="output")) # returns activations of layer "output" + e.activation(layer="output")) # returns activations of layer "output" ``` ### GradientMachine @@ -86,5 +85,193 @@ Hence the `GradientMachine`. None of Model, Evaluator, nor GradientMachine implements the training loop, hence Optimizer. We can define a concurrent optimizer that runs -multiple simultaneious threads to train a model -- just let each +multiple simultaneous threads to train a model -- just let each thread has its own GradientMachine object. + +Most models should be able to be trained using the +`paddle.optimizer.SGD` by calling its `train` method. Many +customizations to the SGD algorithm happens with the update equation, +e.g., momentum and the Adam SGD algorithm. We make `train` calls +`update` to do an update, so that we can derive a `paddle.optimizer.Adam` +from `paddle.optimizer.SGD` by overrides only the `update` method. + + +## Programming + +A fictive example of PaddlePaddle program looks like the following: + +```python +import paddle + +def read(args): + f = open_file(args["filename"]) + mb = read_a_minibatch(f) + end_pass = eof(f) + if end_pass: + f = open_file(args["filename"]) # rewind for reading again + yield mb, end_pass + +input = paddle.layer.data(...) +intermediate = paddle.layers.fc(input) +output = paddle.layer.softmax(intermediate) + +model = paddle.model.create(output) + +paddle.train(model, data_provider=read) +``` + +This shows some important part of a program: + +1. Define how to read (and augment) data by defining a function, in + this example, `read`, that `yields` a minibatch and a boolean flag + `eof_of_pass`. + +1. Define the topology, `input`, `intermediate`, and `output` in this + example. + +1. Create parameters from the topology thus forms the model by calling + `paddel.model.create`. + +1. Train the model by calling `paddle.train`. + + +### Reader + +Not all programming frameworks allow users to define I/O functions. +An example is Google MapReduce, which can only read from text, +SSTable, and RecordIO files. Hadoop MapReduce allows users to define +readers and writers by deriving from base classes `Reader` and +`Writer`. The former is less flexible but also less error-prone. We +decide to provide the flexibility to users to define their readers. + + +#### A Synthetic Data Reader + +Sometimes we want to test a topology and/or a training algorithm using +synthetic data. We can do this by defining the reader a synthesizer: + +```python +def read(args): + x = sample_from_uniform(0.0, 1.0) + y = sample_from_gauss(2 * x, sigma) + yield {x, y}, False # no end-of-file so no end-of-pass +``` + +#### A Reader for Online Learning + +Readers can also read an infinite data stream, e.g., a log stream from +a search engine and collected by Kafka: + +```python +def read(args): + log_stream = kafka.open_channel(args["kafka channel name"]) + yeild log_stream.read(), False # no end-of-pass in online learning +``` + +### Topology + +By default, layers don't have names. But if we want to refer to a +layer later some time, for example, when we do serving using the model +and wants activations/outputs of a layer, we should give it a name. + +```python +input = paddle.layer.data(...) +intermediate = paddle.layer.fc(input, name="inter", ...) +output = paddle.layer.softmax(intermediate, name="output", ...) + +m = paddle.model.create(output) +e = paddle.evaluator.create(model) +e.forward(read_an_input()) # compute activations of all layers. +print e.activations(layer="inter") # retrieve the activations of layer "inter" +print e.activations(layer="output") # retrieve the activations of layer "output" +``` + +#### Sharing Parameters + +In [above section](#model) we shows a network whose two layers share +the same parameter matrix. To specify such cases, we give "parameter +names" to layers. If some layers have the same paraemter names, +`paddle.model.create` creates a single parameter matrix for these +layers: + +```python +text1 = paddle.layer.data(...) +sematic1 = paddle.layer.fc(text1, ..., parameter_name="sematic_projection") +text2 = paddle.layer.data(...) +sematic2 = paddle.layer.fc(text2, ..., parameter_name="sematic_projection") +out = paddle.layer.cosine(semantic1, semantic2) +``` + +We can also share parameter matrices between layers in different +models. To do this, we need an additional parameter that refers to a +model: + +```python +model1_input = paddle.layer.data(...) +model1_output = paddle.layer.softmax(model1_input, ..., + parameter_name="a_parameter_matrix") +model1 = paddle.model.create(model1_output) + +# Another model +model2_semantic = paddle.layer.fc(text2, ..., + parameter_name="a_parameter_matrix", + parameter_model=model1) +``` + +### Training + +The recommended way to training a model is to call `paddle.train`, +which simply calls `paddle.optimizer.Default`, a global variable of +type `paddle.optimizer.SGD`. Equivalently, we can do + +```python +opt = paddle.optimizer.SGD(...) +opt.train(model, reader=read, ...) +``` + +#### Distributed Training + +If users want to do distributed training on a cluster, s/he should +call `paddle.dist_train` and provides access tokens to the cluster as +a parameter. + +For example, if the user has a TLS certificate that allows him to +access a Kubernetes cluster, s/he should be able to call + +```python +paddle.dist_train(model, + reader=read, + optimizer=paddle.optimizer.SGDOptimizer(...), + k8s_user="yi", + k8s_token="kube_cluster_tls.pem", + k8s_job="hello", + num_parameter_servers=15) +``` + +The pseudo code if `paddle.dist_train` is as follows: + +```python +def dist_train(): + if os.getenv("KUBERNETES_SERVICE_HOST") == None: + image_name = k8s_user + '/' + k8s_job + docker_build(image_name) + docker_push() + kube_ctrl_start_job(image_name, k8s_user, k8s_token) + else: + rank = kube_list_containers_in_job_and_return_current_containers_rank() + if rank == 0: + master() + elif rank < 15: + parameter_server() + else: + optimizer.train(model, reader=read) +``` + +Please be aware that if a process is running on the Kubernetes +cluster, it will have some environment variables pre-defined. + +If `dist_train` doesn't see these environment variables, it knowns +that it's running on users' personal computer, and it should work as a +*launcher*. Otherwise, it knows that it's running on the cluster and +need to figure out its role as either the master, or a trainer, or a +parameter server. From b17bc8a88ff5191ff75985bf36a149df796b508d Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 18:39:22 -0800 Subject: [PATCH 168/370] GradientMachien refers to Evaluator but not Model --- doc/design/api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/api.md b/doc/design/api.md index d8825389cb6947..fe37cf091c5298 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -12,7 +12,7 @@ Evaluator = {Model*, activations} - forward - test -GradientMachine = {Model*, gradients} +GradientMachine = {Evaluator*, gradients} - backward Optimizer = {Model*, Evaluator*, GradientMachine*} From 08c0051f95bdb2e3334d228fef8e1bc465e8ce5f Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 18:52:53 -0800 Subject: [PATCH 169/370] Fix problem pointed out by Long-Fei --- doc/design/api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/api.md b/doc/design/api.md index fe37cf091c5298..17ab50fdb26e64 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -15,7 +15,7 @@ Evaluator = {Model*, activations} GradientMachine = {Evaluator*, gradients} - backward -Optimizer = {Model*, Evaluator*, GradientMachine*} +Optimizer = {Model*, GradientMachine*} - train - update - checkpoint From e60fe1df22fa9cd37003c258474b2aa1b8cde500 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 18:56:51 -0800 Subject: [PATCH 170/370] Cost as a parameter to train and test --- doc/design/api.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 17ab50fdb26e64..dcf19451b11622 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -10,13 +10,13 @@ Model = {topology, parameters} Evaluator = {Model*, activations} - forward -- test +- test(cost, ...) GradientMachine = {Evaluator*, gradients} - backward Optimizer = {Model*, GradientMachine*} -- train +- train(cost, ...) - update - checkpoint ``` From 6de262c357ef9274da304ffa804df935db103bc4 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 9 Feb 2017 19:51:28 -0800 Subject: [PATCH 171/370] Correct minor problems --- doc/design/api.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index dcf19451b11622..dd4341b32490dc 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -1,4 +1,4 @@ -# PaddlePaddle API +# PaddlePaddle Design Doc ## Ingredients @@ -15,7 +15,7 @@ Evaluator = {Model*, activations} GradientMachine = {Evaluator*, gradients} - backward -Optimizer = {Model*, GradientMachine*} +Optimizer = {GradientMachine*} - train(cost, ...) - update - checkpoint @@ -96,7 +96,7 @@ e.g., momentum and the Adam SGD algorithm. We make `train` calls from `paddle.optimizer.SGD` by overrides only the `update` method. -## Programming +## Programming Interface A fictive example of PaddlePaddle program looks like the following: From 970440622fd985afeaed76ea812e4779bb0bf901 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 10 Feb 2017 17:08:39 +0800 Subject: [PATCH 172/370] Temporary disable async load data in PyDP2. * It seems some other change breaks the async load. It will cause data error in GPU mode. --- paddle/gserver/dataproviders/PyDataProvider2.cpp | 2 +- python/paddle/trainer_config_helpers/data_sources.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/dataproviders/PyDataProvider2.cpp b/paddle/gserver/dataproviders/PyDataProvider2.cpp index c26e242534f2af..b8079dc0796d0e 100644 --- a/paddle/gserver/dataproviders/PyDataProvider2.cpp +++ b/paddle/gserver/dataproviders/PyDataProvider2.cpp @@ -647,7 +647,7 @@ class PyDataProvider2 : public DataProvider { DataBatch& gpuBatch = *batch; std::vector& gpuArguments = gpuBatch.getStreams(); gpuArguments.resize(cpuArguments.size()); - gpuBatch.setSize(size); + gpuBatch.setSize(bsize); for (size_t i = 0; i < headers_.size(); ++i) { gpuArguments[i].resizeAndCopyFrom( cpuArguments[i], useGpu_, HPPL_STREAM_1); diff --git a/python/paddle/trainer_config_helpers/data_sources.py b/python/paddle/trainer_config_helpers/data_sources.py index 0ea8fc77eef9f5..ab9a2562dcccb3 100644 --- a/python/paddle/trainer_config_helpers/data_sources.py +++ b/python/paddle/trainer_config_helpers/data_sources.py @@ -201,7 +201,7 @@ def py_data2(files, load_data_module, load_data_object, load_data_args, data.load_data_module = load_data_module data.load_data_object = load_data_object data.load_data_args = load_data_args - data.async_load_data = True + data.async_load_data = False return data define_py_data_sources( From ff6f7827dd6e067ff759d84c4a7519cf09522d02 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 10 Feb 2017 22:17:37 +0800 Subject: [PATCH 173/370] Fix protobuf version in travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 162bebba091d84..f9ad1c9203d0a9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -54,7 +54,7 @@ before_install: fi - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi - - pip install numpy wheel protobuf sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker + - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker script: - paddle/scripts/travis/main.sh notifications: From 71c3c93c72409118b928b01478af95081a00114a Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 10 Feb 2017 22:39:46 +0800 Subject: [PATCH 174/370] Fix unittest --- .../tests/configs/protostr/test_split_datasource.protostr | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr index 1cfb92255aa92f..569b0b945a762e 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr @@ -19,7 +19,7 @@ model_config { data_config { type: "py2" files: "train.list" - async_load_data: true + async_load_data: false for_test: false load_data_module: "a" load_data_object: "c" @@ -58,7 +58,7 @@ opt_config { test_data_config { type: "py2" files: "test.list" - async_load_data: true + async_load_data: false for_test: true load_data_module: "b" load_data_object: "d" From 67855d3b4edec4780a16e9583ab183753acf1581 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 12 Feb 2017 16:07:27 +0800 Subject: [PATCH 175/370] Add comments --- .travis.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.travis.yml b/.travis.yml index f9ad1c9203d0a9..5d82d9729b75ef 100644 --- a/.travis.yml +++ b/.travis.yml @@ -54,6 +54,8 @@ before_install: fi - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi + # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python + # protobuf version. - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker script: - paddle/scripts/travis/main.sh From 00bf87ab3fa496d7c63527ab8a08a3a9f15bfa1e Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 13 Feb 2017 11:24:07 +0800 Subject: [PATCH 176/370] Add default mkl dir in cmake --- cmake/cblas.cmake | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cmake/cblas.cmake b/cmake/cblas.cmake index 26306f9849100d..235c95f017f2b6 100644 --- a/cmake/cblas.cmake +++ b/cmake/cblas.cmake @@ -16,7 +16,8 @@ set(CBLAS_FOUND OFF) ## Find MKL First. -set(MKL_ROOT $ENV{MKLROOT} CACHE PATH "Folder contains MKL") +set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs") +set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL") find_path(MKL_INCLUDE_DIR mkl.h PATHS ${MKL_ROOT}/include) From 5a1f0617459177da6c5d1b52b4d514da253df68e Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Sun, 12 Feb 2017 21:55:11 -0800 Subject: [PATCH 177/370] Update according to discussions in https://github.com/PaddlePaddle/Paddle/issues/1315 --- doc/design/api.md | 293 +++++++++++++++++++--------------------------- 1 file changed, 119 insertions(+), 174 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index dd4341b32490dc..f66a34fa98a3bc 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -2,140 +2,148 @@ ## Ingredients -As the first step of our design, we list important concepts in deep -learning and try to figure their relationship, as shown below: +As our design principle is starting from the essence: how could we +allow users to express and solve their problems at neural networks. +Some essential concepts that our API have to provide include: -``` -Model = {topology, parameters} +1. A *topology* is an expression of *layers*. -Evaluator = {Model*, activations} -- forward -- test(cost, ...) +1. A layer could be any kind of computation, including *cost*. -GradientMachine = {Evaluator*, gradients} -- backward +1. Some layers have parameters, some don't. Most costs don't have + parameters. -Optimizer = {GradientMachine*} -- train(cost, ...) -- update -- checkpoint -``` +1. In some topologies, layers share parameters. For + example, + [the network for training a ranking model](https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850). + +1. At programming time, users specify topologies and possible sharing + of parameters. PaddlePaddle can figure out and create parameters + required (and possibly shared) by one or more topologies. + + +## Starting from Examples -where the pair of curly braces `{` and `}` indicate *composition*, `*` -indicates a *reference*, and `-` marks a "class method". +As a summarization +of +[our disucssion](https://github.com/PaddlePaddle/Paddle/issues/1315), +let us present two examples here: -### Model +### Example 1. Sharing Parameters between Layers -We used to think that parameters are part of the topology (or layers). -But that is not true because multiple layers could share the same -parameter matrix. An example is a network that compares two text -segments in a semantic space: +We use +the +[3-branch ranking](https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850) model +in this example. For your convenience, I copy-a-paste the model's +topology as follows: ``` - semantic -text A -> projection ---\ - layer A \ - cosine - similarity -> output - layer - semantic / -text B -> projection ---/ - layer B +A -> f -\ +Q -> f --> cost +B -> f -/ ``` -In this network, the two semantic projection layers (A and B) share -the same parameter matrix. - -For more information about our API that specifies topology and -parameter sharing, please refer to [TODO: API]. - - -### Evaluator - -Supposed that we have a trained ranking model, we should be able to -use it in our search engine. The search engine's Web server is a -concurrent program so to serve many HTTP requests simultaneously. It -doesn't make sense for each of these threads to have its own copy of the model because that would duplicate topologies and parameters. -However, each thread should be able to record layer outputs, i.e., -activations, computed from an input, derived from the request. With -*Evaluator* that saves activations, we can write the over-simplified -server program as: +The following program trains the topology including the cost, and then +use the sub-network in the trained topology in inference: ```python -m = paddle.model.load("trained.model") - -http.handle("/", - lambda req: - e = paddle.evaluator.create(m) - e.forward(req) - e.activation(layer="output")) # returns activations of layer "output" +def f(in): + e = paddle.layer.embedding(in, parameter_name="embedding") + o = paddle.layer.softmax(e, parameter_name="semantic") + return o + +# Create 3 topologies (subnets), they share parameters because all +# correspoinding layers have the same parameter names. +fA = f(paddle.layer.data(input_name="A")) +fB = f(paddle.layer.data(input_name="B")) +fQ = f(paddle.layer.data(input_name="Q")) + +topology = paddle.cost.less_than( + paddle.cost.cross_entropy(fA, fQ), + paddle.cost.corss_entropy(fB, fQ)) + +# Derive parameters required in topology and create them in model. +parameters = paddle.parameters.create(topology) + +# Estimate parameters used in topology from data. +paddle.train(topology, parameters, reader=read_ranking_model_data) + +# Inference using fA (or fB or fC, as they share their parameters). +[testA, testB, testQ] = read_ranking_model_data() +print "The sematic-vector of testA: ", paddle.infer(fA, parameters, testA) ``` -### GradientMachine - -Similar to the evaluation, the training needs to compute gradients so -to update model parameters. Because an [optimizer](#optimizer) might -run multiple simultaneous threads to update the same model, gradients -should be separated from the model. Because gradients are only used -in training, but not serving, they should be separate from Evaluator. -Hence the `GradientMachine`. -### Optimizer +### Example 2. Sharing Parameters between "Models" -None of Model, Evaluator, nor GradientMachine implements the training -loop, hence Optimizer. We can define a concurrent optimizer that runs -multiple simultaneous threads to train a model -- just let each -thread has its own GradientMachine object. +We use [GAN](https://github.com/PaddlePaddle/book/tree/develop/gan) in +this example. In the following example program, `d0` and `d1` +correspond to the two networks in the following figure: -Most models should be able to be trained using the -`paddle.optimizer.SGD` by calling its `train` method. Many -customizations to the SGD algorithm happens with the update equation, -e.g., momentum and the Adam SGD algorithm. We make `train` calls -`update` to do an update, so that we can derive a `paddle.optimizer.Adam` -from `paddle.optimizer.SGD` by overrides only the `update` method. - - -## Programming Interface - -A fictive example of PaddlePaddle program looks like the following: + ```python -import paddle - -def read(args): - f = open_file(args["filename"]) - mb = read_a_minibatch(f) - end_pass = eof(f) - if end_pass: - f = open_file(args["filename"]) # rewind for reading again - yield mb, end_pass - -input = paddle.layer.data(...) -intermediate = paddle.layers.fc(input) -output = paddle.layer.softmax(intermediate) - -model = paddle.model.create(output) - -paddle.train(model, data_provider=read) +def G(in): + # over-simplified example as G has only one layers: + return paddle.layer.fc(in, parameter_name="G") + +def D(in); + # again, over-simplified: + return paddle.layer.fc(in, parameter_name="D") + +# Construct the first topology, which contains both D and G. +# By learning this topology, we update parameters of G. +d0 = paddle.cost.should_be_false(D(G(paddle.layer.data()))) + +# Construct a second topology d1, which contains only D. By +# training this topology, we update parameters of D. Note +# that d1 share parameters with d0. +d1 = paddle.cost.should_be_true(D(paddle.layer.data())) + +# Create parameters from a list of multiple topologies (models) for +# the chance to share parameters between these topologies. +parameters = paddle.parameters.create([d0, d1]) + +# Iterative training of GAN. +for ...: + train(d0, parameters, reader=read_from_rng, immutable_parameters={"D"}) + train(d1, parameters, reader=read_from_realistic_images) + +# Use d1 for inference: +print "D thinks a batch of images are realistic ", infer(d1, parameters, read_mnist_images) ``` -This shows some important part of a program: -1. Define how to read (and augment) data by defining a function, in - this example, `read`, that `yields` a minibatch and a boolean flag - `eof_of_pass`. +### Summarization -1. Define the topology, `input`, `intermediate`, and `output` in this - example. -1. Create parameters from the topology thus forms the model by calling - `paddel.model.create`. +Above two programs reveal some important design concerns: -1. Train the model by calling `paddle.train`. +1. Users describe a topology as an expression of layers. Every layer + has a *parameter name*. If the users don't specify it explicitly, it's automatically generated as a unique name. By + specifying the parameter name, users can specify the sharing of + parameters between layers and even between topologies. +1. `paddle.parameters.create` figures out parameters required by one + or more topologies from parameter names of layers. It creates these + parameters and returns a `ParameterSet` object, which is in essence + a map from *parameter names* to *parameters*. -### Reader +1. At training and inference time, `paddle.train` and `paddle.infer` + requires both a topology and the parameter set that holds the parameters of that topology. There are some reasons: + + 1. This prevents users from forgetting to call + `paddle.parameters.create`. + 1. `paddle.train` needs to know which parameter set to update. + 1. Users could load another (pre-trained) parameter set and use it + with a topology in `train.infer`. + +1. By specifying the `immutable_parameters` parameter of + `paddle.train`, we can forbid the update of these parameters. + + +## Reader Not all programming frameworks allow users to define I/O functions. An example is Google MapReduce, which can only read from text, @@ -145,78 +153,15 @@ readers and writers by deriving from base classes `Reader` and decide to provide the flexibility to users to define their readers. -#### A Synthetic Data Reader - -Sometimes we want to test a topology and/or a training algorithm using -synthetic data. We can do this by defining the reader a synthesizer: +There are some open questions here: -```python -def read(args): - x = sample_from_uniform(0.0, 1.0) - y = sample_from_gauss(2 * x, sigma) - yield {x, y}, False # no end-of-file so no end-of-pass -``` - -#### A Reader for Online Learning - -Readers can also read an infinite data stream, e.g., a log stream from -a search engine and collected by Kafka: - -```python -def read(args): - log_stream = kafka.open_channel(args["kafka channel name"]) - yeild log_stream.read(), False # no end-of-pass in online learning -``` - -### Topology - -By default, layers don't have names. But if we want to refer to a -layer later some time, for example, when we do serving using the model -and wants activations/outputs of a layer, we should give it a name. +1. **Should a reader return a Python dictionary?** -```python -input = paddle.layer.data(...) -intermediate = paddle.layer.fc(input, name="inter", ...) -output = paddle.layer.softmax(intermediate, name="output", ...) - -m = paddle.model.create(output) -e = paddle.evaluator.create(model) -e.forward(read_an_input()) # compute activations of all layers. -print e.activations(layer="inter") # retrieve the activations of layer "inter" -print e.activations(layer="output") # retrieve the activations of layer "output" -``` - -#### Sharing Parameters +1. **How to map multiple outputs from a reader to multiple data layers?** -In [above section](#model) we shows a network whose two layers share -the same parameter matrix. To specify such cases, we give "parameter -names" to layers. If some layers have the same paraemter names, -`paddle.model.create` creates a single parameter matrix for these -layers: +1. **How to easily compose some existing readers to read more data and + feed a topology with more data layers?** -```python -text1 = paddle.layer.data(...) -sematic1 = paddle.layer.fc(text1, ..., parameter_name="sematic_projection") -text2 = paddle.layer.data(...) -sematic2 = paddle.layer.fc(text2, ..., parameter_name="sematic_projection") -out = paddle.layer.cosine(semantic1, semantic2) -``` - -We can also share parameter matrices between layers in different -models. To do this, we need an additional parameter that refers to a -model: - -```python -model1_input = paddle.layer.data(...) -model1_output = paddle.layer.softmax(model1_input, ..., - parameter_name="a_parameter_matrix") -model1 = paddle.model.create(model1_output) - -# Another model -model2_semantic = paddle.layer.fc(text2, ..., - parameter_name="a_parameter_matrix", - parameter_model=model1) -``` ### Training @@ -270,7 +215,7 @@ def dist_train(): Please be aware that if a process is running on the Kubernetes cluster, it will have some environment variables pre-defined. -If `dist_train` doesn't see these environment variables, it knowns +If `dist_train` doesn't see these environment variables, it knows that it's running on users' personal computer, and it should work as a *launcher*. Otherwise, it knows that it's running on the cluster and need to figure out its role as either the master, or a trainer, or a From ce49124dae0d7d8ff699afdd04053e118d58698e Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 13 Feb 2017 14:33:50 +0800 Subject: [PATCH 178/370] Draft for new API --- demo/mnist/api_train_v2.py | 61 +++++++++++++ python/paddle/v2/__init__.py | 14 ++- python/paddle/v2/parameters.py | 111 +++++++++++++++++++++++ python/paddle/v2/trainer.py | 160 +++++++++++++++++++++++++++++++++ 4 files changed, 344 insertions(+), 2 deletions(-) create mode 100644 demo/mnist/api_train_v2.py create mode 100644 python/paddle/v2/parameters.py create mode 100644 python/paddle/v2/trainer.py diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py new file mode 100644 index 00000000000000..19c11b6de5e0b5 --- /dev/null +++ b/demo/mnist/api_train_v2.py @@ -0,0 +1,61 @@ +from paddle.trainer_config_helpers import * +from paddle.trainer.PyDataProvider2 import dense_vector, integer_value +import paddle.v2 as paddle_v2 +import numpy +import mnist_util + + +def train_reader(): + train_file = './data/raw_data/train' + generator = mnist_util.read_from_mnist(train_file) + for item in generator: + yield item + + +def network_config(): + imgs = data_layer(name='pixel', size=784) + hidden1 = fc_layer(input=imgs, size=200) + hidden2 = fc_layer(input=hidden1, size=200) + inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation()) + cost = classification_cost( + input=inference, label=data_layer( + name='label', size=10)) + outputs(cost) + + +def event_handler(event): + if isinstance(event, paddle_v2.trainer.CompleteTrainOneBatch): + print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id, + event.cost) + else: + pass + + +def main(): + paddle_v2.init(use_gpu=False, trainer_count=1) + model_config = parse_network_config(network_config) + pool = paddle_v2.parameters.create(model_config) + for param_name in pool.get_names(): + array = pool.get_parameter(param_name) + array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) + + trainer = paddle_v2.trainer.SGDTrainer( + update_equation=paddle_v2.optimizer.Adam( + learning_rate=1e-4, + model_average=ModelAverage(average_window=0.5), + regularization=L2Regularization(rate=0.5))) + + trainer.train(train_data_reader=train_reader, + topology=model_config, + parameters=pool, + event_handler=event_handler, + batch_size=32, # batch size should be refactor in Data reader + data_types={ # data_types will be removed, It should be in + # network topology + 'pixel': dense_vector(784), + 'label': integer_value(10) + }) + + +if __name__ == '__main__': + main() diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index b2ea87b086101d..f50c36ce64995b 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -11,7 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - import optimizer +import parameters +import py_paddle.swig_paddle as api +import trainer + +__all__ = ['optimizer', 'parameters', 'init', 'trainer'] + + +def init(**kwargs): + args = [] + for key in kwargs.keys(): + args.append('--%s=%s' % (key, str(kwargs[key]))) -__all__ = ['optimizer'] + api.initPaddle(*args) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py new file mode 100644 index 00000000000000..892c68a38bd706 --- /dev/null +++ b/python/paddle/v2/parameters.py @@ -0,0 +1,111 @@ +import numpy as np + +from paddle.proto.ModelConfig_pb2 import ModelConfig +from paddle.proto.ParameterConfig_pb2 import ParameterConfig + +__all__ = ['IParameterPool', 'create', 'ParameterFlag'] + + +class ParameterFlag(object): + """ + The flag for IParameterPool.get_parameter. If writeable, operation on return + numpy array will also apply to Paddle parameter. But it will be slower in + GPU mode. + """ + READ_ONLY = 0x01 + WRITE_ONLY = 0x02 + READ_WRITE = READ_ONLY | WRITE_ONLY + + +class IParameterPool(object): + """ + Interface of Parameter Pool. The parameter pool is a dictionary of + parameters. User can modify parameter or customize parameter value + by `get_parameter`. + + .. code-block:: python + + pool = paddle.parameters.create(topo1, topo2) + + embedding = pool.get_parameter("embedding") + assert isinstance(embedding, numpy.ndarray) + print embedding[1:] + """ + + def get_parameter(self, name, flag=ParameterFlag.READ_WRITE): + """ + Get a parameter by name. + + :param name: parameter name. + :type name: basestring + :param flag: the flag for return value. readable or writable. + :type flag: int + :return: The parameter value + :rtype: np.ndarray + """ + raise NotImplementedError() + + def get_names(self): + """ + Get all parameter names + :return: all parameter names + :rtype: list + """ + raise NotImplementedError() + + +class NumpyParameterPool(IParameterPool): + def __init__(self): + self.__param_configs__ = dict() + self.__params__ = dict() + + def append(self, conf): + if not isinstance(conf, ParameterConfig): + raise ValueError("conf must be ParameterConfig") + + if not conf.IsInitialized(): + raise ValueError("conf is not initialized") + + self.__param_configs__[conf.name] = conf + self.__params__[conf.name] = None + + def get_config(self, name): + if name not in self.__param_configs__: + raise ValueError("parameter %s is not appended" % name) + + return self.__param_configs__[name] + + def get_parameter(self, name, *args, **kwargs): + if name not in self.__params__: + raise ValueError("parameter %s is not appended" % name) + + param = self.__params__[name] + if param is None: + shape = self.__param_configs__[name].dims + if len(shape) == 0: + raise ValueError("parameter %s is no shape" % name) + param = np.ndarray( + shape=[int(item) for item in shape], dtype='float32') + self.__params__[name] = param + return param + + def get_names(self): + return self.__param_configs__.keys() + + +def create(*topologies): + """ + Create parameter pool by topologies. + + :param topologies: + :return: + """ + pool = NumpyParameterPool() + for topo in topologies: + if not isinstance(topo, ModelConfig): + raise ValueError( + 'create must pass a topologies which type is ModelConfig') + + for param in topo.parameters: + pool.append(param) + return pool diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py new file mode 100644 index 00000000000000..933700c6155277 --- /dev/null +++ b/python/paddle/v2/trainer.py @@ -0,0 +1,160 @@ +import collections +from paddle.proto.ModelConfig_pb2 import ModelConfig +import paddle.v2.parameters +import paddle.v2.optimizer +import py_paddle.swig_paddle as api +from py_paddle import DataProviderConverter + +__all__ = ['ITrainer', 'SGDTrainer', 'CompleteTrainOneBatch', 'BaseEvent'] + + +class BaseEvent(object): + """ + Just a marker class + """ + pass + + +class CompleteTrainOneBatch(BaseEvent): + def __init__(self, pass_id, batch_id, cost): + self.pass_id = pass_id + self.batch_id = batch_id + self.cost = cost + + +def default_event_handler(event): + pass + + +class ITrainer(object): + def train(self, + train_data_reader, + topology, + parameters, + test_data_reader=None, + event_handler=None): + raise NotImplementedError() + + +class SGDTrainer(ITrainer): + def __init__(self, update_equation): + if not isinstance(update_equation, paddle.v2.optimizer.Optimizer): + raise ValueError() + + self.__optimizer__ = update_equation + + def train(self, + train_data_reader, + topology, + parameters, + num_passes=1, + test_data_reader=None, + event_handler=None, + batch_size=32, + data_types=None): + if event_handler is None: + event_handler = default_event_handler + + __check_train_args__(**locals()) + + gm = api.GradientMachine.createFromConfigProto( + topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) + assert isinstance(gm, api.GradientMachine) + __copy_parameter_from_pool__(gm, parameters) + + updater = self.__optimizer__.create_local_updater() + assert isinstance(updater, api.ParameterUpdater) + updater.init(gm) + + data_types_lists = [] + for each in topology.input_layer_names: + if each not in data_types: + raise ValueError() + data_types_lists.append(data_types[each]) + + converter = DataProviderConverter(input_types=data_types_lists) + + def input_reorder(func): + for item in func(): + retv = [] + for __layer_name__ in topology.input_layer_names: + retv.append(item[__layer_name__]) + yield retv + + gm.start() + + out_args = api.Arguments.createArguments(0) + for pass_id in xrange(num_passes): + updater.startPass() + for batch_id, data_batch in enumerate( + __generator_to_batch__( + input_reorder(train_data_reader), + batch_size=batch_size)): + pass_type = updater.startBatch(len(data_batch)) + gm.forwardBackward(converter(data_batch), out_args, pass_type) + for each_param in gm.getParameters(): + updater.update(each_param) + # Get cost. We use numpy to calculate total cost for this batch. + cost_vec = out_args.getSlotValue(0) + cost_vec = cost_vec.copyToNumpyMat() + cost = cost_vec.sum() / len(data_batch) + updater.finishBatch(cost) + + event_handler( + CompleteTrainOneBatch( + pass_id=pass_id, batch_id=batch_id, cost=cost)) + + updater.finishPass() + gm.finish() + + +def __generator_to_batch__(generator, batch_size): + ret_val = list() + for each_item in generator: + ret_val.append(each_item) + if len(ret_val) == batch_size: + yield ret_val + ret_val = list() + if len(ret_val) != 0: + yield ret_val + + +def __copy_parameter_from_pool__(gm, pool): + """ + + :param gm: + :type gm: api.GradientMachine + :param pool: + :type pool: paddle.v2.parameters.IParameterPool + :return: + """ + assert isinstance(pool, paddle.v2.parameters.IParameterPool) + for each_param in gm.getParameters(): + name = each_param.getName() + param = pool.get_parameter(name, + paddle.v2.parameters.ParameterFlag.READ_ONLY) + each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(param.flatten( + ).astype('float32')) + + +def __check_train_args__(train_data_reader, topology, parameters, + test_data_reader, event_handler, **kwargs): + if not callable(train_data_reader) or not isinstance(train_data_reader(), + collections.Iterator): + raise ValueError('train_data_reader should be a function, ' + 'which can return a iterator') + + if test_data_reader is not None: + if not callable(test_data_reader) or not isinstance( + test_data_reader(), collections.Iterator): + raise ValueError('test_data_reader should be a function, which can ' + 'return a iterator') + + if not isinstance(topology, ModelConfig): + raise ValueError('topology should be a model config') + + if not isinstance(parameters, paddle.v2.parameters.IParameterPool): + raise ValueError('parameters should be a parameter pool') + + if not callable(event_handler): + raise ValueError('event handler should be a function') From dc7f2031caaf473ab618b25b6187af47b76b22e1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 13 Feb 2017 15:06:02 +0800 Subject: [PATCH 179/370] Add comments for functions --- python/paddle/v2/trainer.py | 63 +++++++++++++++++++++++++++++-------- 1 file changed, 50 insertions(+), 13 deletions(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 933700c6155277..3794ab07720e21 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -16,6 +16,10 @@ class BaseEvent(object): class CompleteTrainOneBatch(BaseEvent): + """ + Event On One Batch Training Complete. + """ + def __init__(self, pass_id, batch_id, cost): self.pass_id = pass_id self.batch_id = batch_id @@ -38,6 +42,11 @@ def train(self, class SGDTrainer(ITrainer): def __init__(self, update_equation): + """ + Simple SGD Trainer. + + :param update_equation: Maybe we should give a DSL for update equation? + """ if not isinstance(update_equation, paddle.v2.optimizer.Optimizer): raise ValueError() @@ -52,6 +61,21 @@ def train(self, event_handler=None, batch_size=32, data_types=None): + """ + Training method. Will train num_passes of input data. + + :param train_data_reader: + :param topology: Network Topology, a protobuf ModelConfig message. + :param parameters: The parameter pools. + :param num_passes: The total train passes. + :param test_data_reader: + :param event_handler: Event handler. A method will be invoked when event + occurred. + :type event_handler: (BaseEvent) => None + :param batch_size: Not important, will be removed after data refactor. + :param data_types: Not important, will be removed after data refactor. + :return: + """ if event_handler is None: event_handler = default_event_handler @@ -66,6 +90,9 @@ def train(self, assert isinstance(updater, api.ParameterUpdater) updater.init(gm) + gm.start() + out_args = api.Arguments.createArguments(0) + data_types_lists = [] for each in topology.input_layer_names: if each not in data_types: @@ -74,22 +101,11 @@ def train(self, converter = DataProviderConverter(input_types=data_types_lists) - def input_reorder(func): - for item in func(): - retv = [] - for __layer_name__ in topology.input_layer_names: - retv.append(item[__layer_name__]) - yield retv - - gm.start() - - out_args = api.Arguments.createArguments(0) for pass_id in xrange(num_passes): updater.startPass() for batch_id, data_batch in enumerate( - __generator_to_batch__( - input_reorder(train_data_reader), - batch_size=batch_size)): + __data_reader_to_batch__(train_data_reader, batch_size, + topology)): pass_type = updater.startBatch(len(data_batch)) gm.forwardBackward(converter(data_batch), out_args, pass_type) for each_param in gm.getParameters(): @@ -108,7 +124,25 @@ def input_reorder(func): gm.finish() +def __data_reader_to_batch__(reader, batch_size, topology): + """ + This function is not important, and will be removed when data refactored. + """ + + def input_reorder(func): + for item in func(): + retv = [] + for __layer_name__ in topology.input_layer_names: + retv.append(item[__layer_name__]) + yield retv + + return __generator_to_batch__(input_reorder(reader), batch_size=batch_size) + + def __generator_to_batch__(generator, batch_size): + """ + This function is not important, and will be removed when data refactored. + """ ret_val = list() for each_item in generator: ret_val.append(each_item) @@ -139,6 +173,9 @@ def __copy_parameter_from_pool__(gm, pool): def __check_train_args__(train_data_reader, topology, parameters, test_data_reader, event_handler, **kwargs): + """ + Check train function's argument types + """ if not callable(train_data_reader) or not isinstance(train_data_reader(), collections.Iterator): raise ValueError('train_data_reader should be a function, ' From e13d9c74a2d5196ad452bb1b1ceb21648be8ad50 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 13 Feb 2017 15:46:41 +0800 Subject: [PATCH 180/370] Expose Parameter to train event handler * User can get/set parameter in event now. * Add update equation --- demo/mnist/api_train_v2.py | 23 ++++-- python/paddle/v2/trainer.py | 139 ++++++++++++++++++++++++++++++++---- 2 files changed, 144 insertions(+), 18 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 19c11b6de5e0b5..6ceff8284d57b9 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -39,11 +39,24 @@ def main(): array = pool.get_parameter(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) - trainer = paddle_v2.trainer.SGDTrainer( - update_equation=paddle_v2.optimizer.Adam( - learning_rate=1e-4, - model_average=ModelAverage(average_window=0.5), - regularization=L2Regularization(rate=0.5))) + def nag(v, g, vel_t_1): + """ + NAG Optimizer. A optimizer which Paddle CPP is not implemented. + https://arxiv.org/pdf/1212.0901v2.pdf eq.6 eq.7 + :param v: parameter value + :param g: parameter gradient + :param vel_t_1: t-1 velocity + :return: + """ + mu = 0.09 + e = 0.0001 + + vel_t = mu * vel_t_1 - e * g + + v[:] = v + (mu**2) * vel_t - (1 + mu) * e * g + vel_t_1[:] = vel_t + + trainer = paddle_v2.trainer.SGDTrainer(update_equation=nag) trainer.train(train_data_reader=train_reader, topology=model_config, diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 3794ab07720e21..0375caa55259fa 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -1,7 +1,8 @@ import collections from paddle.proto.ModelConfig_pb2 import ModelConfig -import paddle.v2.parameters -import paddle.v2.optimizer +from paddle.proto.ParameterConfig_pb2 import ParameterConfig +from . import parameters as v2_parameters +import numpy import py_paddle.swig_paddle as api from py_paddle import DataProviderConverter @@ -20,10 +21,11 @@ class CompleteTrainOneBatch(BaseEvent): Event On One Batch Training Complete. """ - def __init__(self, pass_id, batch_id, cost): + def __init__(self, pass_id, batch_id, cost, parameters): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost + self.paramters = parameters def default_event_handler(event): @@ -40,6 +42,102 @@ def train(self, raise NotImplementedError() +class LazyParameterPool(v2_parameters.IParameterPool): + """ + :type __gradient_machine__: api.GradientMachine + """ + + def get_parameter(self, name, flag=v2_parameters.ParameterFlag.READ_WRITE): + param = filter(lambda x: x.getName() == name, + self.__gradient_machine__.getParameters()) + if len(param) == 0: + raise ValueError("Cannot found parameter with name %s" % name) + elif len(param) > 1: + raise RuntimeError("Unexpected branch") + else: + conf = param[0].getConfig().toProto() + param = param[0].getBuf(api.PARAMETER_VALUE) + assert isinstance(param, api.Vector) + assert isinstance(conf, ParameterConfig) + + shape = map(int, conf.dims) + if api.isUsingGpu(): + arr = param.copyToNumpyArray().reshape(shape) + if flag & v2_parameters.ParameterFlag.WRITE_ONLY: + self.need_copy = True + self.arrays[name] = arr + else: + arr = param.toNumpyArrayInplace().reshape(shape) + return arr + + def get_names(self): + return [ + param.getName() + for param in self.__gradient_machine__.getParameters() + ] + + def __init__(self, gradient_machine): + self.__gradient_machine__ = gradient_machine + self.need_copy = False + self.arrays = dict() + + +class CustomizeUpdateEquation(object): + def __init__(self, callback): + self.__callback__ = callback + if self.__callback__.func_code.co_argcount < 2: + raise ValueError( + "The update equation at least should contain 2 arguments, " + "first is value, second is gradient") + + self.local_params_count = self.__callback__.func_code.co_argcount - 2 + self.local_params = dict() + + def enable_types(self): + return [api.PARAMETER_VALUE, api.PARAMETER_GRADIENT] + + def init(self, gradient_machine): + assert isinstance(gradient_machine, api.GradientMachine) + for param in gradient_machine.getParameters(): + conf = param.getConfig().toProto() + shape = map(int, conf.dims) + self.local_params[conf.name] = [] + for _ in xrange(self.local_params_count): + self.local_params[conf.name].append( + numpy.zeros( + shape=shape, dtype='float32')) + + def create_local_updater(self): + return self + + def startPass(self): + pass + + def finishPass(self): + pass + + def startBatch(self, batch_size): + return api.PASS_TRAIN + + def finishBatch(self, cost): + pass + + def update(self, param): + conf = param.getConfig().toProto() + shape = map(int, conf.dims) + if not api.isUsingGpu(): + v = param.getBuf(api.PARAMETER_VALUE).toNumpyArrayInplace().reshape( + shape) + g = param.getBuf(api.PARAMETER_GRADIENT).toNumpyArrayInplace( + ).reshape(shape) + args = [v, g] + for arg in self.local_params[conf.name]: + args.append(arg) + self.__callback__(*args) + else: + raise NotImplementedError() + + class SGDTrainer(ITrainer): def __init__(self, update_equation): """ @@ -47,8 +145,8 @@ def __init__(self, update_equation): :param update_equation: Maybe we should give a DSL for update equation? """ - if not isinstance(update_equation, paddle.v2.optimizer.Optimizer): - raise ValueError() + if callable(update_equation): + update_equation = CustomizeUpdateEquation(update_equation) self.__optimizer__ = update_equation @@ -87,7 +185,6 @@ def train(self, __copy_parameter_from_pool__(gm, parameters) updater = self.__optimizer__.create_local_updater() - assert isinstance(updater, api.ParameterUpdater) updater.init(gm) gm.start() @@ -115,10 +212,16 @@ def train(self, cost_vec = cost_vec.copyToNumpyMat() cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) - + pool = LazyParameterPool(gradient_machine=gm) event_handler( CompleteTrainOneBatch( - pass_id=pass_id, batch_id=batch_id, cost=cost)) + pass_id=pass_id, + batch_id=batch_id, + cost=cost, + parameters=pool)) + + if pool.need_copy: + __copy_parameter_from_lazy_pool__(gm, pool) updater.finishPass() gm.finish() @@ -153,20 +256,30 @@ def __generator_to_batch__(generator, batch_size): yield ret_val +def __copy_parameter_from_lazy_pool__(gm, pool): + assert isinstance(pool, LazyParameterPool) + for each_param_name in pool.arrays.keys(): + param = filter(lambda x: x.getName() == each_param_name, + gm.getParameters()) + assert len(param) == 1 + param = param[0] + param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(pool.arrays[ + each_param_name].flatten().astype('float32')) + + def __copy_parameter_from_pool__(gm, pool): """ :param gm: :type gm: api.GradientMachine :param pool: - :type pool: paddle.v2.parameters.IParameterPool + :type pool: v2_parameters.IParameterPool :return: """ - assert isinstance(pool, paddle.v2.parameters.IParameterPool) + assert isinstance(pool, v2_parameters.IParameterPool) for each_param in gm.getParameters(): name = each_param.getName() - param = pool.get_parameter(name, - paddle.v2.parameters.ParameterFlag.READ_ONLY) + param = pool.get_parameter(name, v2_parameters.ParameterFlag.READ_ONLY) each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(param.flatten( ).astype('float32')) @@ -190,7 +303,7 @@ def __check_train_args__(train_data_reader, topology, parameters, if not isinstance(topology, ModelConfig): raise ValueError('topology should be a model config') - if not isinstance(parameters, paddle.v2.parameters.IParameterPool): + if not isinstance(parameters, v2_parameters.IParameterPool): raise ValueError('parameters should be a parameter pool') if not callable(event_handler): From 8e0d1d8be1cd1fb9dd7671e27beb60509735c968 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 13 Feb 2017 17:55:10 +0800 Subject: [PATCH 181/370] Complete update equation. --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/trainer.py | 33 +++++++++++++++++++++++++++------ 2 files changed, 28 insertions(+), 7 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6ceff8284d57b9..ab1581325aa95d 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -49,7 +49,7 @@ def nag(v, g, vel_t_1): :return: """ mu = 0.09 - e = 0.0001 + e = 0.00001 vel_t = mu * vel_t_1 - e * g diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 0375caa55259fa..9620e754208100 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -25,7 +25,7 @@ def __init__(self, pass_id, batch_id, cost, parameters): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost - self.paramters = parameters + self.parameters = parameters def default_event_handler(event): @@ -44,6 +44,17 @@ def train(self, class LazyParameterPool(v2_parameters.IParameterPool): """ + Lazy Parameter Pool stores a reference to GradientMachine. User could invoke + `get_parameter` if needed, but the operation is lazy. It means the parameter + will only fetched from GPU or Parameter Server if `get_parameter` is + invoked. Also, set flag = writable will make a extra host2device copy after + reading/modifying parameter. + + This class is not exposed to User. User should treat this class as a normal + IParameterPool. + + See IParameterPool for usage documentation. + :type __gradient_machine__: api.GradientMachine """ @@ -130,12 +141,22 @@ def update(self, param): shape) g = param.getBuf(api.PARAMETER_GRADIENT).toNumpyArrayInplace( ).reshape(shape) - args = [v, g] - for arg in self.local_params[conf.name]: - args.append(arg) - self.__callback__(*args) + else: - raise NotImplementedError() + v = param.getBuf(api.PARAMETER_VALUE).copyToNumpyArray().reshape( + shape) + g = param.getBuf(api.PARAMETER_GRADIENT).copyToNumpyArray().reshape( + shape) + + args = [v, g] + for arg in self.local_params[conf.name]: + args.append(arg) + self.__callback__(*args) + + if api.isUsingGpu(): + param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(v.flatten( + ).astype('float32')) + # discard gradient changed. class SGDTrainer(ITrainer): From 8b70f0f3d004b7fd88621c464c1e28b06999f7e4 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 13 Feb 2017 07:35:05 -0800 Subject: [PATCH 182/370] addrss https://github.com/PaddlePaddle/Paddle/issues/1319 as well --- doc/design/api.md | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index f66a34fa98a3bc..777983cd7ae268 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -166,14 +166,19 @@ There are some open questions here: ### Training The recommended way to training a model is to call `paddle.train`, -which simply calls `paddle.optimizer.Default`, a global variable of -type `paddle.optimizer.SGD`. Equivalently, we can do +which simply calls `paddle.trainer.Default`, a global variable of +type `paddle.trainer.SGD`. Equivalently, we can do ```python -opt = paddle.optimizer.SGD(...) +opt = paddle.trainer.SGD(..., paddle.updater.Adam(...)) opt.train(model, reader=read, ...) ``` +Please be aware that a trainer requires an updater as its data +member. This is to make it easier to customize trainers, as +discussed [here](https://github.com/PaddlePaddle/Paddle/issues/1319). + + #### Distributed Training If users want to do distributed training on a cluster, s/he should @@ -185,8 +190,9 @@ access a Kubernetes cluster, s/he should be able to call ```python paddle.dist_train(model, + trainer=paddle.trainer.SGD(..., + paddle.updater.Adam(...)), reader=read, - optimizer=paddle.optimizer.SGDOptimizer(...), k8s_user="yi", k8s_token="kube_cluster_tls.pem", k8s_job="hello", @@ -196,7 +202,7 @@ paddle.dist_train(model, The pseudo code if `paddle.dist_train` is as follows: ```python -def dist_train(): +def dist_train(topology, parameters, trainer, reader, ...): if os.getenv("KUBERNETES_SERVICE_HOST") == None: image_name = k8s_user + '/' + k8s_job docker_build(image_name) @@ -209,7 +215,7 @@ def dist_train(): elif rank < 15: parameter_server() else: - optimizer.train(model, reader=read) + trainer.train(model, reader=read) ``` Please be aware that if a process is running on the Kubernetes From 3529c6c328e0d83289ff5d965707d1544d6ba64c Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 13 Feb 2017 14:34:01 -0800 Subject: [PATCH 183/370] Put all layers and costs in package paddle.layer --- doc/design/api.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 777983cd7ae268..6894329c7dcd6b 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -16,11 +16,11 @@ Some essential concepts that our API have to provide include: 1. In some topologies, layers share parameters. For example, [the network for training a ranking model](https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850). - + 1. At programming time, users specify topologies and possible sharing of parameters. PaddlePaddle can figure out and create parameters required (and possibly shared) by one or more topologies. - + ## Starting from Examples @@ -59,9 +59,9 @@ fA = f(paddle.layer.data(input_name="A")) fB = f(paddle.layer.data(input_name="B")) fQ = f(paddle.layer.data(input_name="Q")) -topology = paddle.cost.less_than( - paddle.cost.cross_entropy(fA, fQ), - paddle.cost.corss_entropy(fB, fQ)) +topology = paddle.layer.less_than( + paddle.layer.cross_entropy(fA, fQ), + paddle.layer.corss_entropy(fB, fQ)) # Derive parameters required in topology and create them in model. parameters = paddle.parameters.create(topology) @@ -86,7 +86,7 @@ correspond to the two networks in the following figure: ```python def G(in): # over-simplified example as G has only one layers: - return paddle.layer.fc(in, parameter_name="G") + return paddle.layer.fc(in, parameter_name="G") def D(in); # again, over-simplified: @@ -94,12 +94,12 @@ def D(in); # Construct the first topology, which contains both D and G. # By learning this topology, we update parameters of G. -d0 = paddle.cost.should_be_false(D(G(paddle.layer.data()))) +d0 = paddle.layer.should_be_false(D(G(paddle.layer.data()))) # Construct a second topology d1, which contains only D. By -# training this topology, we update parameters of D. Note +# training this topology, we update parameters of D. Note # that d1 share parameters with d0. -d1 = paddle.cost.should_be_true(D(paddle.layer.data())) +d1 = paddle.layer.should_be_true(D(paddle.layer.data())) # Create parameters from a list of multiple topologies (models) for # the chance to share parameters between these topologies. @@ -132,16 +132,16 @@ Above two programs reveal some important design concerns: 1. At training and inference time, `paddle.train` and `paddle.infer` requires both a topology and the parameter set that holds the parameters of that topology. There are some reasons: - + 1. This prevents users from forgetting to call `paddle.parameters.create`. 1. `paddle.train` needs to know which parameter set to update. 1. Users could load another (pre-trained) parameter set and use it with a topology in `train.infer`. - + 1. By specifying the `immutable_parameters` parameter of `paddle.train`, we can forbid the update of these parameters. - + ## Reader @@ -190,7 +190,7 @@ access a Kubernetes cluster, s/he should be able to call ```python paddle.dist_train(model, - trainer=paddle.trainer.SGD(..., + trainer=paddle.trainer.SGD(..., paddle.updater.Adam(...)), reader=read, k8s_user="yi", From 92ab6148dee932ffb630a1188b5c4d6112379bc8 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 13 Feb 2017 16:07:07 -0800 Subject: [PATCH 184/370] Add Event Handler --- doc/design/api.md | 33 ++++++++++++++++++++++++++++++--- 1 file changed, 30 insertions(+), 3 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 6894329c7dcd6b..6b2fe82946dd78 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -163,7 +163,7 @@ There are some open questions here: feed a topology with more data layers?** -### Training +## Training The recommended way to training a model is to call `paddle.train`, which simply calls `paddle.trainer.Default`, a global variable of @@ -171,15 +171,42 @@ type `paddle.trainer.SGD`. Equivalently, we can do ```python opt = paddle.trainer.SGD(..., paddle.updater.Adam(...)) -opt.train(model, reader=read, ...) +opt.train(topology, parameters, reader=read, ...) ``` +### Updater + Please be aware that a trainer requires an updater as its data member. This is to make it easier to customize trainers, as discussed [here](https://github.com/PaddlePaddle/Paddle/issues/1319). +### Event Handler + +`paddle.train` and `paddle.trainer.XXX.train` take an optional +parameter `event_handler`, which should be either `None` or a function +that handle some events: + +1. BeginTraining +1. EndTraining +1. BeginMinibatch +1. EndMinibatch +1. BeginPass +1. EndPass + +where EndPass is sent if and only if the reader yields +`end_pass=True`. + +An example as follows: + +```python +def event_handler(event): + if ininstance(event, paddle.event.EndMinibatch): + print paddle.test(...) + +paddle.train(topology, parameters, reader, event_handler) +``` -#### Distributed Training +### Distributed Training If users want to do distributed training on a cluster, s/he should call `paddle.dist_train` and provides access tokens to the cluster as From 6915cb290176d1a6948ce0f83f8a460cab85337d Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 13 Feb 2017 16:10:17 -0800 Subject: [PATCH 185/370] Add metaplotlib for iPython --- doc/design/api.md | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/doc/design/api.md b/doc/design/api.md index 6b2fe82946dd78..8185d2af0ea264 100644 --- a/doc/design/api.md +++ b/doc/design/api.md @@ -176,9 +176,11 @@ opt.train(topology, parameters, reader=read, ...) ### Updater -Please be aware that a trainer requires an updater as its data -member. This is to make it easier to customize trainers, as -discussed [here](https://github.com/PaddlePaddle/Paddle/issues/1319). +Please be aware that a trainer can accept an updater as its data +member, where an updater is a class derived from +`paddle.trainer.Updater`. This is to make it easier to customize +trainers, as discussed +[here](https://github.com/PaddlePaddle/Paddle/issues/1319). ### Event Handler @@ -188,8 +190,8 @@ that handle some events: 1. BeginTraining 1. EndTraining -1. BeginMinibatch -1. EndMinibatch +1. BeginIteration +1. EndIteration 1. BeginPass 1. EndPass @@ -200,12 +202,17 @@ An example as follows: ```python def event_handler(event): - if ininstance(event, paddle.event.EndMinibatch): + if ininstance(event, paddle.event.EndIteration): print paddle.test(...) paddle.train(topology, parameters, reader, event_handler) ``` +If we are writing a PaddlePaddle program in and for iPython/Jypyter, +we can use metaplotlib in the event handler to plot a curve of +cost/error versus iterations, as shown +[here](https://blog.dominodatalab.com/interactive-dashboards-in-jupyter/). + ### Distributed Training If users want to do distributed training on a cluster, s/he should From 0bd75a57e866bc1cff0b25cc1ea433f3104bf77e Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 14 Feb 2017 15:15:56 +0800 Subject: [PATCH 186/370] change layers to layer --- demo/mnist/api_train.py | 21 +++---- python/paddle/v2/__init__.py | 4 +- python/paddle/v2/{layers.py => layer.py} | 70 +++++++++++++++--------- 3 files changed, 58 insertions(+), 37 deletions(-) rename python/paddle/v2/{layers.py => layer.py} (70%) diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index 6d071cdffe120f..f0c56b77cf8578 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -10,7 +10,7 @@ import numpy as np import paddle.trainer.PyDataProvider2 as dp -import paddle.v2 +import paddle.v2 as paddle_v2 import py_paddle.swig_paddle as api from paddle.trainer_config_helpers import * from py_paddle import DataProviderConverter @@ -58,7 +58,7 @@ def input_order_converter(generator): def main(): api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores - optimizer = paddle.v2.optimizer.Adam( + optimizer = paddle_v2.optimizer.Adam( learning_rate=1e-4, batch_size=1000, model_average=ModelAverage(average_window=0.5), @@ -71,16 +71,17 @@ def main(): assert isinstance(updater, api.ParameterUpdater) # define network - images = paddle.v2.layers.data_layer(name='pixel', size=784) - label = paddle.v2.layers.data_layer(name='label', size=10) - hidden1 = paddle.v2.layers.fc_layer(input=images, size=200) - hidden2 = paddle.v2.layers.fc_layer(input=hidden1, size=200) - inference = paddle.v2.layers.fc_layer( - input=hidden2, size=10, act=SoftmaxActivation()) - cost = paddle.v2.layers.classification_cost(input=inference, label=label) + images = paddle_v2.layer.data(name='pixel', size=784) + label = paddle_v2.layer.data(name='label', size=10) + hidden1 = paddle_v2.layer.fc(input=images, size=200) + hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) + inference = paddle_v2.layer.fc(input=hidden2, + size=10, + act=SoftmaxActivation()) + cost = paddle_v2.layer.classification_cost(input=inference, label=label) # Create Simple Gradient Machine. - model_config = paddle.v2.layers.parse_network(cost) + model_config = paddle_v2.layer.parse_network(cost) m = api.GradientMachine.createFromConfigProto(model_config, api.CREATE_MODE_NORMAL, optimizer.enable_types()) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index c04c0cc73a800a..590fb5cd523ce7 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -13,6 +13,6 @@ # limitations under the License. import optimizer -import layers +import layer -__all__ = ['optimizer', 'layers'] +__all__ = ['optimizer', 'layer'] diff --git a/python/paddle/v2/layers.py b/python/paddle/v2/layer.py similarity index 70% rename from python/paddle/v2/layers.py rename to python/paddle/v2/layer.py index 14efe9412c3582..4cdb2a3638a5d1 100644 --- a/python/paddle/v2/layers.py +++ b/python/paddle/v2/layer.py @@ -19,6 +19,21 @@ import collections +def parse_network(*outputs): + """ + parse all output layers and then generate a model config proto. + :param outputs: + :return: + """ + + def __real_func__(): + context = dict() + real_output = [each.to_proto(context=context) for each in outputs] + conf_helps.outputs(real_output) + + return __parse__(__real_func__) + + class Layer(object): def __init__(self, name, parent_layer): assert isinstance(parent_layer, dict) @@ -49,22 +64,13 @@ def to_proto_impl(self, **kwargs): raise NotImplementedError() -def parse_network(*outputs): - def __real_func__(): - context = dict() - real_output = [each.to_proto(context=context) for each in outputs] - conf_helps.outputs(real_output) - - return __parse__(__real_func__) - - -def __convert__(method_name, name_prefix, parent_names): +def __convert_to_v2__(method_name, name_prefix, parent_names): if name_prefix is not None: wrapper = wrap_name_default(name_prefix=name_prefix) else: wrapper = None - class __Impl__(Layer): + class V2LayerImpl(Layer): def __init__(self, name=None, **kwargs): parent_layers = dict() other_kwargs = dict() @@ -75,7 +81,7 @@ def __init__(self, name=None, **kwargs): if key not in parent_names: other_kwargs[key] = kwargs[key] - super(__Impl__, self).__init__(name, parent_layers) + super(V2LayerImpl, self).__init__(name, parent_layers) self.__other_kwargs__ = other_kwargs if wrapper is not None: @@ -89,24 +95,38 @@ def to_proto_impl(self, **kwargs): args[each] = self.__other_kwargs__[each] return getattr(conf_helps, method_name)(name=self.name, **args) - return __Impl__ + return V2LayerImpl -data_layer = __convert__('data_layer', None, []) -fc_layer = __convert__('fc_layer', name_prefix='fc', parent_names=['input']) -classification_cost = __convert__( +data = __convert_to_v2__('data_layer', None, []) +fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) +max_id = __convert_to_v2__( + 'maxid_layer', name_prefix='maxid_layer', parent_names=['input']) +classification_cost = __convert_to_v2__( 'classification_cost', name_prefix='classification_cost', parent_names=['input', 'label']) +cross_entropy_cost = __convert_to_v2__( + 'cross_entropy', + name_prefix='cross_entropy', + parent_names=['input', 'label']) -__all__ = ['data_layer', 'fc_layer', 'classification_cost', 'parse_network'] +__all__ = [ + 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', + 'cross_entropy_cost' +] if __name__ == '__main__': - data = data_layer(name='pixel', size=784) - hidden = fc_layer(input=data, size=100, act=conf_helps.SigmoidActivation()) - predict = fc_layer( - input=[hidden, data], size=10, act=conf_helps.SoftmaxActivation()) - cost = classification_cost( - input=predict, label=data_layer( - name='label', size=10)) - print parse_network(cost) + pixel = data(name='pixel', size=784) + label = data(name='label', size=10) + hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) + inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) + maxid = max_id(input=inference) + cost1 = classification_cost(input=inference, label=label) + cost2 = cross_entropy_cost(input=inference, label=label) + + print parse_network(cost1) + print parse_network(cost2) + print parse_network(cost1, cost2) + print parse_network(cost2) + print parse_network(inference, maxid) From e0c3a6d602549633fd4ec732b88ca8e6ef2e7d27 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 14 Feb 2017 16:26:56 +0800 Subject: [PATCH 187/370] optimize parameter name --- python/paddle/v2/layer.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 4cdb2a3638a5d1..9c1589f9bfd554 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -35,26 +35,26 @@ def __real_func__(): class Layer(object): - def __init__(self, name, parent_layer): - assert isinstance(parent_layer, dict) + def __init__(self, name, parent_layers): + assert isinstance(parent_layers, dict) assert isinstance(name, basestring) self.name = name - self.__parent_layer__ = parent_layer + self.__parent_layers__ = parent_layers def to_proto(self, context): """ function to set proto attribute """ kwargs = dict() - for param_name in self.__parent_layer__: - if not isinstance(self.__parent_layer__[param_name], + for layer_name in self.__parent_layers__: + if not isinstance(self.__parent_layers__[layer_name], collections.Sequence): - param_value = self.__parent_layer__[param_name].to_proto( + v1_layer = self.__parent_layers__[layer_name].to_proto( context=context) else: - param_value = map(lambda x: x.to_proto(context=context), - self.__parent_layer__[param_name]) - kwargs[param_name] = param_value + v1_layer = map(lambda x: x.to_proto(context=context), + self.__parent_layers__[layer_name]) + kwargs[layer_name] = v1_layer if self.name not in context: context[self.name] = self.to_proto_impl(**kwargs) From 844d062007cb9f35a82006ecc748614a97182cc4 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 15 Feb 2017 15:19:07 +0800 Subject: [PATCH 188/370] Remove NAG optimizer --- demo/mnist/api_train_v2.py | 27 +++----------- python/paddle/v2/trainer.py | 74 ++----------------------------------- 2 files changed, 10 insertions(+), 91 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index ab1581325aa95d..d0ccc2c5aa8cf0 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -1,6 +1,6 @@ from paddle.trainer_config_helpers import * from paddle.trainer.PyDataProvider2 import dense_vector, integer_value -import paddle.v2 as paddle_v2 +import paddle.v2 as paddle import numpy import mnist_util @@ -24,7 +24,7 @@ def network_config(): def event_handler(event): - if isinstance(event, paddle_v2.trainer.CompleteTrainOneBatch): + if isinstance(event, paddle.trainer.CompleteTrainOneBatch): print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id, event.cost) else: @@ -32,31 +32,16 @@ def event_handler(event): def main(): - paddle_v2.init(use_gpu=False, trainer_count=1) + paddle.init(use_gpu=False, trainer_count=1) model_config = parse_network_config(network_config) - pool = paddle_v2.parameters.create(model_config) + pool = paddle.parameters.create(model_config) for param_name in pool.get_names(): array = pool.get_parameter(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) - def nag(v, g, vel_t_1): - """ - NAG Optimizer. A optimizer which Paddle CPP is not implemented. - https://arxiv.org/pdf/1212.0901v2.pdf eq.6 eq.7 - :param v: parameter value - :param g: parameter gradient - :param vel_t_1: t-1 velocity - :return: - """ - mu = 0.09 - e = 0.00001 + adam_optimizer = paddle.optimizer.Adam(learning_rate=1e-3) - vel_t = mu * vel_t_1 - e * g - - v[:] = v + (mu**2) * vel_t - (1 + mu) * e * g - vel_t_1[:] = vel_t - - trainer = paddle_v2.trainer.SGDTrainer(update_equation=nag) + trainer = paddle.trainer.SGDTrainer(update_equation=adam_optimizer) trainer.train(train_data_reader=train_reader, topology=model_config, diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 9620e754208100..41dfb522dba617 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -2,7 +2,7 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig from paddle.proto.ParameterConfig_pb2 import ParameterConfig from . import parameters as v2_parameters -import numpy +from . import optimizer as v2_optimizer import py_paddle.swig_paddle as api from py_paddle import DataProviderConverter @@ -93,72 +93,6 @@ def __init__(self, gradient_machine): self.arrays = dict() -class CustomizeUpdateEquation(object): - def __init__(self, callback): - self.__callback__ = callback - if self.__callback__.func_code.co_argcount < 2: - raise ValueError( - "The update equation at least should contain 2 arguments, " - "first is value, second is gradient") - - self.local_params_count = self.__callback__.func_code.co_argcount - 2 - self.local_params = dict() - - def enable_types(self): - return [api.PARAMETER_VALUE, api.PARAMETER_GRADIENT] - - def init(self, gradient_machine): - assert isinstance(gradient_machine, api.GradientMachine) - for param in gradient_machine.getParameters(): - conf = param.getConfig().toProto() - shape = map(int, conf.dims) - self.local_params[conf.name] = [] - for _ in xrange(self.local_params_count): - self.local_params[conf.name].append( - numpy.zeros( - shape=shape, dtype='float32')) - - def create_local_updater(self): - return self - - def startPass(self): - pass - - def finishPass(self): - pass - - def startBatch(self, batch_size): - return api.PASS_TRAIN - - def finishBatch(self, cost): - pass - - def update(self, param): - conf = param.getConfig().toProto() - shape = map(int, conf.dims) - if not api.isUsingGpu(): - v = param.getBuf(api.PARAMETER_VALUE).toNumpyArrayInplace().reshape( - shape) - g = param.getBuf(api.PARAMETER_GRADIENT).toNumpyArrayInplace( - ).reshape(shape) - - else: - v = param.getBuf(api.PARAMETER_VALUE).copyToNumpyArray().reshape( - shape) - g = param.getBuf(api.PARAMETER_GRADIENT).copyToNumpyArray().reshape( - shape) - - args = [v, g] - for arg in self.local_params[conf.name]: - args.append(arg) - self.__callback__(*args) - - if api.isUsingGpu(): - param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(v.flatten( - ).astype('float32')) - # discard gradient changed. - - class SGDTrainer(ITrainer): def __init__(self, update_equation): """ @@ -166,9 +100,9 @@ def __init__(self, update_equation): :param update_equation: Maybe we should give a DSL for update equation? """ - if callable(update_equation): - update_equation = CustomizeUpdateEquation(update_equation) - + if not isinstance(update_equation, v2_optimizer.Optimizer): + raise ValueError("update equation parameter must be " + "paddle.v2.optimizer.Optimizer") self.__optimizer__ = update_equation def train(self, From 094d29aaadbdf17ede7d7950676ee0e91ff3b865 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 15 Feb 2017 15:57:02 +0800 Subject: [PATCH 189/370] Draft for parameters --- demo/mnist/api_train_v2.py | 29 +++-- python/paddle/v2/parameters.py | 212 +++++++++++++++++++-------------- python/paddle/v2/trainer.py | 104 ++-------------- 3 files changed, 148 insertions(+), 197 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index d0ccc2c5aa8cf0..8618a8f21115a0 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -23,29 +23,32 @@ def network_config(): outputs(cost) -def event_handler(event): - if isinstance(event, paddle.trainer.CompleteTrainOneBatch): - print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id, - event.cost) - else: - pass - - def main(): paddle.init(use_gpu=False, trainer_count=1) model_config = parse_network_config(network_config) - pool = paddle.parameters.create(model_config) - for param_name in pool.get_names(): - array = pool.get_parameter(param_name) + parameters = paddle.parameters.create(model_config) + for param_name in parameters.keys(): + array = parameters[param_name] array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) + parameters[param_name] = array + + adam_optimizer = paddle.optimizer.Optimizer( + learning_rate=0.01, learning_method=AdamOptimizer()) + + def event_handler(event): + if isinstance(event, paddle.trainer.CompleteTrainOneBatch): + para = parameters['___fc_layer_2__.w0'] + print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( + event.pass_id, event.batch_id, event.cost, para.mean()) - adam_optimizer = paddle.optimizer.Adam(learning_rate=1e-3) + else: + pass trainer = paddle.trainer.SGDTrainer(update_equation=adam_optimizer) trainer.train(train_data_reader=train_reader, topology=model_config, - parameters=pool, + parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader data_types={ # data_types will be removed, It should be in diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index 892c68a38bd706..a30bf5d3631f5a 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -2,110 +2,144 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig from paddle.proto.ParameterConfig_pb2 import ParameterConfig +import py_paddle.swig_paddle as api -__all__ = ['IParameterPool', 'create', 'ParameterFlag'] +__all__ = ['Parameters', 'create'] -class ParameterFlag(object): - """ - The flag for IParameterPool.get_parameter. If writeable, operation on return - numpy array will also apply to Paddle parameter. But it will be slower in - GPU mode. +def create(*topologies): """ - READ_ONLY = 0x01 - WRITE_ONLY = 0x02 - READ_WRITE = READ_ONLY | WRITE_ONLY - + Create parameter pool by topologies. -class IParameterPool(object): + :param topologies: + :return: """ - Interface of Parameter Pool. The parameter pool is a dictionary of - parameters. User can modify parameter or customize parameter value - by `get_parameter`. - - .. code-block:: python + pool = Parameters() + for topo in topologies: + if not isinstance(topo, ModelConfig): + raise ValueError( + 'create must pass a topologies which type is ModelConfig') - pool = paddle.parameters.create(topo1, topo2) + for param in topo.parameters: + pool.append_config(param) + return pool - embedding = pool.get_parameter("embedding") - assert isinstance(embedding, numpy.ndarray) - print embedding[1:] - """ - def get_parameter(self, name, flag=ParameterFlag.READ_WRITE): - """ - Get a parameter by name. - - :param name: parameter name. - :type name: basestring - :param flag: the flag for return value. readable or writable. - :type flag: int - :return: The parameter value - :rtype: np.ndarray - """ - raise NotImplementedError() - - def get_names(self): - """ - Get all parameter names - :return: all parameter names - :rtype: list - """ - raise NotImplementedError() - - -class NumpyParameterPool(IParameterPool): +class Parameters(object): def __init__(self): - self.__param_configs__ = dict() - self.__params__ = dict() - - def append(self, conf): - if not isinstance(conf, ParameterConfig): - raise ValueError("conf must be ParameterConfig") - - if not conf.IsInitialized(): - raise ValueError("conf is not initialized") - - self.__param_configs__[conf.name] = conf - self.__params__[conf.name] = None - - def get_config(self, name): - if name not in self.__param_configs__: - raise ValueError("parameter %s is not appended" % name) - - return self.__param_configs__[name] - - def get_parameter(self, name, *args, **kwargs): - if name not in self.__params__: - raise ValueError("parameter %s is not appended" % name) + self.__param_conf__ = dict() + self.__gradient_machines__ = [] + self.__tmp_params__ = [] + + def append_config(self, param_conf): + if not isinstance(param_conf, ParameterConfig): + raise ValueError("param_conf must be paddle.proto.ParameterConfig") + + if param_conf.name in self.__param_conf__: + raise ValueError("duplicated parameter %s" % param_conf.name) + + self.__param_conf__[param_conf.name] = param_conf + + def keys(self): + return self.__param_conf__.keys() + + def names(self): + return self.keys() + + def has_key(self, key): + return key in self.__param_conf__.keys() + + def __getitem__(self, key): + shape = self.get_shape(key) + + if len(self.__gradient_machines__) == 0: + # create new parameter in python numpy. + return np.ndarray(shape=shape, dtype=np.float32) + else: + for each_gradient_machine in self.__gradient_machines__: + param = __get_parameter_in_gradient_machine__( + each_gradient_machine, key) + # for simplify implementation now, we always copy from C++ + assert isinstance(param, api.Parameter) + val = param.getBuf(api.PARAMETER_VALUE) + assert isinstance(val, api.Vector) + return val.copyToNumpyArray().reshape(shape=shape) + # else continue + + raise RuntimeError("Unexpected branch") + + def get_shape(self, key): + if not isinstance(key, basestring): + raise ValueError("parameter name should be string") + if not self.has_key(key): + raise ValueError("No such parameter %s" % key) + conf = self.__param_conf__[key] + return map(int, conf.dims) + + def __setitem__(self, key, value): + if not isinstance(value, np.ndarray): + raise ValueError("Must return ndarray") + value = value.astype(dtype=np.float32) + shape = self.get_shape(key) + if not reduce(lambda a, b: a and b, + map(lambda x: x[0] == x[1], zip(value.shape, shape))): + raise ValueError("Value shape mismatch, expect %s, should %s" % + (shape, value.shape)) + + if len(self.__gradient_machines__) == 0: + self.__tmp_params__.append((key, value)) + else: + for each_gradient_machine in self.__gradient_machines__: + __copy_parameter_to_gradient_machine__(each_gradient_machine, + key, value) + + def append_gradient_machine(self, gradient_machine): + if not isinstance(gradient_machine, api.GradientMachine): + raise ValueError("gradient_machine should be api.GradientMachine") + + if len(self.__tmp_params__) != 0: + for name, val in self.__tmp_params__: + try: + __copy_parameter_to_gradient_machine__(gradient_machine, + name, val) + except ValueError: + # If no such parameter in gradient machine, then don't copy + pass + + +def __get_parameter_in_gradient_machine__(gradient_machine, name): + """ - param = self.__params__[name] - if param is None: - shape = self.__param_configs__[name].dims - if len(shape) == 0: - raise ValueError("parameter %s is no shape" % name) - param = np.ndarray( - shape=[int(item) for item in shape], dtype='float32') - self.__params__[name] = param - return param + :param gradient_machine: + :type gradient_machine: api.GradientMachine + :param name: + :return: + :rtype: api.Parameter + """ + params = filter(lambda p: p.getName() == name, + gradient_machine.getParameters()) - def get_names(self): - return self.__param_configs__.keys() + if len(params) == 0: + raise ValueError("No such parameter") + elif len(params) > 1: + raise ValueError("Unexpected branch") + else: + return params[0] -def create(*topologies): +def __copy_parameter_to_gradient_machine__(gradient_machine, name, arr): """ - Create parameter pool by topologies. + Copy a python ndarray into the gradient machine. - :param topologies: + :param gradient_machine: + :type gradient_machine: api.GradientMachine + :param name: + :param arr: + :type arr: np.ndarray :return: + :rtype: api.Parameter """ - pool = NumpyParameterPool() - for topo in topologies: - if not isinstance(topo, ModelConfig): - raise ValueError( - 'create must pass a topologies which type is ModelConfig') - - for param in topo.parameters: - pool.append(param) - return pool + param = __get_parameter_in_gradient_machine__(gradient_machine, name) + vec = param.getBuf(api.PARAMETER_VALUE) + assert isinstance(vec, api.Vector) + vec.copyFromNumpyArray(arr.flatten()) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 41dfb522dba617..baed7d0025b1d0 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -1,11 +1,12 @@ import collections -from paddle.proto.ModelConfig_pb2 import ModelConfig -from paddle.proto.ParameterConfig_pb2 import ParameterConfig -from . import parameters as v2_parameters -from . import optimizer as v2_optimizer + import py_paddle.swig_paddle as api from py_paddle import DataProviderConverter +from paddle.proto.ModelConfig_pb2 import ModelConfig +from . import optimizer as v2_optimizer +from . import parameters as v2_parameters + __all__ = ['ITrainer', 'SGDTrainer', 'CompleteTrainOneBatch', 'BaseEvent'] @@ -21,11 +22,10 @@ class CompleteTrainOneBatch(BaseEvent): Event On One Batch Training Complete. """ - def __init__(self, pass_id, batch_id, cost, parameters): + def __init__(self, pass_id, batch_id, cost): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost - self.parameters = parameters def default_event_handler(event): @@ -42,57 +42,6 @@ def train(self, raise NotImplementedError() -class LazyParameterPool(v2_parameters.IParameterPool): - """ - Lazy Parameter Pool stores a reference to GradientMachine. User could invoke - `get_parameter` if needed, but the operation is lazy. It means the parameter - will only fetched from GPU or Parameter Server if `get_parameter` is - invoked. Also, set flag = writable will make a extra host2device copy after - reading/modifying parameter. - - This class is not exposed to User. User should treat this class as a normal - IParameterPool. - - See IParameterPool for usage documentation. - - :type __gradient_machine__: api.GradientMachine - """ - - def get_parameter(self, name, flag=v2_parameters.ParameterFlag.READ_WRITE): - param = filter(lambda x: x.getName() == name, - self.__gradient_machine__.getParameters()) - if len(param) == 0: - raise ValueError("Cannot found parameter with name %s" % name) - elif len(param) > 1: - raise RuntimeError("Unexpected branch") - else: - conf = param[0].getConfig().toProto() - param = param[0].getBuf(api.PARAMETER_VALUE) - assert isinstance(param, api.Vector) - assert isinstance(conf, ParameterConfig) - - shape = map(int, conf.dims) - if api.isUsingGpu(): - arr = param.copyToNumpyArray().reshape(shape) - if flag & v2_parameters.ParameterFlag.WRITE_ONLY: - self.need_copy = True - self.arrays[name] = arr - else: - arr = param.toNumpyArrayInplace().reshape(shape) - return arr - - def get_names(self): - return [ - param.getName() - for param in self.__gradient_machine__.getParameters() - ] - - def __init__(self, gradient_machine): - self.__gradient_machine__ = gradient_machine - self.need_copy = False - self.arrays = dict() - - class SGDTrainer(ITrainer): def __init__(self, update_equation): """ @@ -137,7 +86,7 @@ def train(self, gm = api.GradientMachine.createFromConfigProto( topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) assert isinstance(gm, api.GradientMachine) - __copy_parameter_from_pool__(gm, parameters) + parameters.append_gradient_machine(gm) updater = self.__optimizer__.create_local_updater() updater.init(gm) @@ -167,16 +116,9 @@ def train(self, cost_vec = cost_vec.copyToNumpyMat() cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) - pool = LazyParameterPool(gradient_machine=gm) event_handler( CompleteTrainOneBatch( - pass_id=pass_id, - batch_id=batch_id, - cost=cost, - parameters=pool)) - - if pool.need_copy: - __copy_parameter_from_lazy_pool__(gm, pool) + pass_id=pass_id, batch_id=batch_id, cost=cost)) updater.finishPass() gm.finish() @@ -211,34 +153,6 @@ def __generator_to_batch__(generator, batch_size): yield ret_val -def __copy_parameter_from_lazy_pool__(gm, pool): - assert isinstance(pool, LazyParameterPool) - for each_param_name in pool.arrays.keys(): - param = filter(lambda x: x.getName() == each_param_name, - gm.getParameters()) - assert len(param) == 1 - param = param[0] - param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(pool.arrays[ - each_param_name].flatten().astype('float32')) - - -def __copy_parameter_from_pool__(gm, pool): - """ - - :param gm: - :type gm: api.GradientMachine - :param pool: - :type pool: v2_parameters.IParameterPool - :return: - """ - assert isinstance(pool, v2_parameters.IParameterPool) - for each_param in gm.getParameters(): - name = each_param.getName() - param = pool.get_parameter(name, v2_parameters.ParameterFlag.READ_ONLY) - each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(param.flatten( - ).astype('float32')) - - def __check_train_args__(train_data_reader, topology, parameters, test_data_reader, event_handler, **kwargs): """ @@ -258,7 +172,7 @@ def __check_train_args__(train_data_reader, topology, parameters, if not isinstance(topology, ModelConfig): raise ValueError('topology should be a model config') - if not isinstance(parameters, v2_parameters.IParameterPool): + if not isinstance(parameters, v2_parameters.Parameters): raise ValueError('parameters should be a parameter pool') if not callable(event_handler): From 62f51fce00cab139c50480a96ced51500e97243f Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 15 Feb 2017 17:38:37 -0800 Subject: [PATCH 190/370] add data provider design doc proposal --- doc/design/reader/README.md | 79 +++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 doc/design/reader/README.md diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md new file mode 100644 index 00000000000000..fa37ad7b292003 --- /dev/null +++ b/doc/design/reader/README.md @@ -0,0 +1,79 @@ +# Python Data Provider Design Doc + +Data provider provides data for training. It will be passed into `paddle.train` as a parameter. + +## Data Provider Interface + +Data provider is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): + +``` +iterable = data_provider() +``` + +Element produced for the iterable should be a **single** entry of data, in format `[column_0_item, column_1_item, ...]`. Each element of the list needs to be supported data type (e.g., numpy 1d array of float32, list of int). + +For example, `column_0_item` could be image pixels of format numpy 1d array of float32, and `column_1_item` could be image label of format single int value: + +``` +for single_entry in iterable: + pixel = entry[0] + label = entry[1] +``` + +## Usage + +data provider, mapping from data provider column to data layer, batch size and number of total pass will be passed into `paddle.train`: + +```python +# two data layer is created: +image_layer = paddle.layer.data("image", ...) +label_layer = paddle.layer.data("label", ...) + +# ... + +paddle.train(paddle.data.mnist, ["image", "label"], 128, 10, ...) +``` +## Q & A + +### Why return only a single entry, but not a mini batch? + +If return a mini batch, data provider need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. + +Concretely, always return a single entry make reusing existing data providers much easier (e.g., if existing data provider return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). + +### How to create custom data provider + +```python +def image_provider(path): + f = open(path) + images = numpy.fromfile( + f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32') + images = images / 255.0 * 2.0 - 1.0 + labels = numpy.fromfile(l, 'ubyte', count=n).astype("int") + for i in xrange(n): + yield [images[i, :], labels[i]] # a single entry of data is created each time + f.close() + +# use python lambda to change image_provier into a function with no parameters. +provider = lambda : image_provier("/path/to/data/") +paddle.train(provider, ["image", "label"], ...) +``` + +### How is `paddle.train` implemented + +An example implementation of paddle.train could be: + +```python +def make_minibatch_generator(reader, minibatch_size): + buf = [reader.next() for x in xrange(minibatch_size)] + while len(buf) > 0: + yield buf + buf = [reader.next() for x in xrange(minibatch_size)] + +def train(provider, mapping, batch_size, total_pass): + for pass_idx in range(total_pass): + for mini_batch in make_minibatch_generator(provider(pass_idx)): # this loop will never end in online learning. + do_forward_backward(mini_batch, mapping) +``` + +This is just an example implementation, more complicated logic like data prefetch could be implemented. From 059a162be5a3b7a6cb80f41e1dd73ccc39df0181 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 16 Feb 2017 11:27:25 +0800 Subject: [PATCH 191/370] Follow comments --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/parameters.py | 3 +++ python/paddle/v2/trainer.py | 13 +++---------- 3 files changed, 7 insertions(+), 11 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 8618a8f21115a0..31c3e9932b68d4 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -36,7 +36,7 @@ def main(): learning_rate=0.01, learning_method=AdamOptimizer()) def event_handler(event): - if isinstance(event, paddle.trainer.CompleteTrainOneBatch): + if isinstance(event, paddle.trainer.EndIteration): para = parameters['___fc_layer_2__.w0'] print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( event.pass_id, event.batch_id, event.cost, para.mean()) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index a30bf5d3631f5a..c2e74b8fb12069 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -49,6 +49,9 @@ def names(self): def has_key(self, key): return key in self.__param_conf__.keys() + def __iter__(self): + return iter(self.__param_conf__) + def __getitem__(self, key): shape = self.get_shape(key) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index baed7d0025b1d0..63695be1566094 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -7,17 +7,10 @@ from . import optimizer as v2_optimizer from . import parameters as v2_parameters -__all__ = ['ITrainer', 'SGDTrainer', 'CompleteTrainOneBatch', 'BaseEvent'] +__all__ = ['ITrainer', 'SGDTrainer', 'EndIteration'] -class BaseEvent(object): - """ - Just a marker class - """ - pass - - -class CompleteTrainOneBatch(BaseEvent): +class EndIteration(object): """ Event On One Batch Training Complete. """ @@ -117,7 +110,7 @@ def train(self, cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) event_handler( - CompleteTrainOneBatch( + EndIteration( pass_id=pass_id, batch_id=batch_id, cost=cost)) updater.finishPass() From c8067ed586d47c30e22df37026ff87410f694258 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 16 Feb 2017 11:36:23 +0800 Subject: [PATCH 192/370] Rearrange event. --- demo/mnist/api_train_v2.py | 4 ++-- python/paddle/v2/__init__.py | 3 ++- python/paddle/v2/event.py | 12 ++++++++++++ python/paddle/v2/trainer.py | 18 ++++-------------- 4 files changed, 20 insertions(+), 17 deletions(-) create mode 100644 python/paddle/v2/event.py diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 31c3e9932b68d4..f925d70765e1c6 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -36,7 +36,7 @@ def main(): learning_rate=0.01, learning_method=AdamOptimizer()) def event_handler(event): - if isinstance(event, paddle.trainer.EndIteration): + if isinstance(event, paddle.event.EndIteration): para = parameters['___fc_layer_2__.w0'] print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( event.pass_id, event.batch_id, event.cost, para.mean()) @@ -44,7 +44,7 @@ def event_handler(event): else: pass - trainer = paddle.trainer.SGDTrainer(update_equation=adam_optimizer) + trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer.train(train_data_reader=train_reader, topology=model_config, diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index f50c36ce64995b..72f1168e94f2a7 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -15,8 +15,9 @@ import parameters import py_paddle.swig_paddle as api import trainer +import event -__all__ = ['optimizer', 'parameters', 'init', 'trainer'] +__all__ = ['optimizer', 'parameters', 'init', 'trainer', 'event'] def init(**kwargs): diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py new file mode 100644 index 00000000000000..04158f4765299d --- /dev/null +++ b/python/paddle/v2/event.py @@ -0,0 +1,12 @@ +__all__ = ['EndIteration'] + + +class EndIteration(object): + """ + Event On One Batch Training Complete. + """ + + def __init__(self, pass_id, batch_id, cost): + self.pass_id = pass_id + self.batch_id = batch_id + self.cost = cost diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 63695be1566094..a29c3a05f85395 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -6,19 +6,9 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig from . import optimizer as v2_optimizer from . import parameters as v2_parameters +from . import event as v2_event -__all__ = ['ITrainer', 'SGDTrainer', 'EndIteration'] - - -class EndIteration(object): - """ - Event On One Batch Training Complete. - """ - - def __init__(self, pass_id, batch_id, cost): - self.pass_id = pass_id - self.batch_id = batch_id - self.cost = cost +__all__ = ['ITrainer', 'SGD'] def default_event_handler(event): @@ -35,7 +25,7 @@ def train(self, raise NotImplementedError() -class SGDTrainer(ITrainer): +class SGD(ITrainer): def __init__(self, update_equation): """ Simple SGD Trainer. @@ -110,7 +100,7 @@ def train(self, cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) event_handler( - EndIteration( + v2_event.EndIteration( pass_id=pass_id, batch_id=batch_id, cost=cost)) updater.finishPass() From 9646f2d3af637a96478788cd9d2592af4fdbc715 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 16 Feb 2017 12:24:04 +0800 Subject: [PATCH 193/370] Rename model_config => topology --- demo/mnist/api_train_v2.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index f925d70765e1c6..2c0394aa0b1897 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -25,8 +25,8 @@ def network_config(): def main(): paddle.init(use_gpu=False, trainer_count=1) - model_config = parse_network_config(network_config) - parameters = paddle.parameters.create(model_config) + topology = parse_network_config(network_config) + parameters = paddle.parameters.create(topology) for param_name in parameters.keys(): array = parameters[param_name] array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) @@ -47,7 +47,7 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer.train(train_data_reader=train_reader, - topology=model_config, + topology=topology, parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader From f9ea586431fe1c1851d3a59790c9e6197f69b60a Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 16 Feb 2017 15:19:29 +0800 Subject: [PATCH 194/370] Add get/set method --- demo/mnist/api_train_v2.py | 6 +++--- python/paddle/v2/parameters.py | 14 +++++++++++++- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 2c0394aa0b1897..5e46d510ad35bb 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -28,16 +28,16 @@ def main(): topology = parse_network_config(network_config) parameters = paddle.parameters.create(topology) for param_name in parameters.keys(): - array = parameters[param_name] + array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) - parameters[param_name] = array + parameters.set(parameter_name=param_name, value=array) adam_optimizer = paddle.optimizer.Optimizer( learning_rate=0.01, learning_method=AdamOptimizer()) def event_handler(event): if isinstance(event, paddle.event.EndIteration): - para = parameters['___fc_layer_2__.w0'] + para = parameters.get('___fc_layer_2__.w0') print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( event.pass_id, event.batch_id, event.cost, para.mean()) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index c2e74b8fb12069..55e732a320b6e5 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -26,6 +26,10 @@ def create(*topologies): class Parameters(object): + """ + The parameters + """ + def __init__(self): self.__param_conf__ = dict() self.__gradient_machines__ = [] @@ -66,7 +70,8 @@ def __getitem__(self, key): assert isinstance(param, api.Parameter) val = param.getBuf(api.PARAMETER_VALUE) assert isinstance(val, api.Vector) - return val.copyToNumpyArray().reshape(shape=shape) + val = val.copyToNumpyArray() + return val # else continue raise RuntimeError("Unexpected branch") @@ -96,6 +101,12 @@ def __setitem__(self, key, value): __copy_parameter_to_gradient_machine__(each_gradient_machine, key, value) + def get(self, parameter_name): + return self.__getitem__(key=parameter_name) + + def set(self, parameter_name, value): + self.__setitem__(key=parameter_name, value=value) + def append_gradient_machine(self, gradient_machine): if not isinstance(gradient_machine, api.GradientMachine): raise ValueError("gradient_machine should be api.GradientMachine") @@ -108,6 +119,7 @@ def append_gradient_machine(self, gradient_machine): except ValueError: # If no such parameter in gradient machine, then don't copy pass + self.__gradient_machines__.append(gradient_machine) def __get_parameter_in_gradient_machine__(gradient_machine, name): From 8dc4c0538efa126e771ed807a62be50dc123d7a2 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 16 Feb 2017 16:37:16 +0800 Subject: [PATCH 195/370] Add comments --- python/paddle/v2/event.py | 3 + python/paddle/v2/parameters.py | 117 +++++++++++++++++++++++++++++++-- python/paddle/v2/trainer.py | 25 ++++++- 3 files changed, 138 insertions(+), 7 deletions(-) diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index 04158f4765299d..f6c6957438653d 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -1,3 +1,6 @@ +""" +All training events. +""" __all__ = ['EndIteration'] diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index 55e732a320b6e5..e5b7dabcb8eb3a 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -21,13 +21,29 @@ def create(*topologies): 'create must pass a topologies which type is ModelConfig') for param in topo.parameters: - pool.append_config(param) + pool.__append_config__(param) return pool class Parameters(object): """ - The parameters + Parameters is a dictionary contains Paddle's parameter. The key of + Parameters is the name of parameter. The value of Parameters is a plain + :code:`numpy.ndarry` . + + Basically usage is + + .. code-block:: python + + data = paddle.layers.data(...) + ... + out = paddle.layers.fc(...) + + parameters = paddle.parameters.create(out) + + parameter_names = parameters.names() + fc_mat = parameters.get('fc') + print fc_mat """ def __init__(self): @@ -35,7 +51,16 @@ def __init__(self): self.__gradient_machines__ = [] self.__tmp_params__ = [] - def append_config(self, param_conf): + def __append_config__(self, param_conf): + """ + Append a parameter configuration. It used to initialize Parameters and + should be invoked only in paddle.parameters.create + + :param param_conf: The parameter configuration in protobuf + :type param_conf: ParameterConfig + :return: Nothing + """ + if not isinstance(param_conf, ParameterConfig): raise ValueError("param_conf must be paddle.proto.ParameterConfig") @@ -45,18 +70,55 @@ def append_config(self, param_conf): self.__param_conf__[param_conf.name] = param_conf def keys(self): + """ + keys are the names of each parameter. + :return: list of parameter name + :rtype: list + """ return self.__param_conf__.keys() def names(self): + """ + names of each parameter. + :return: list of parameter name + :rtype: list + """ return self.keys() def has_key(self, key): + """ + has_key return true if there are such parameter name == key + :param key: Parameter name + :type key: basestring + :return: True if contains such key + """ return key in self.__param_conf__.keys() def __iter__(self): + """ + Return an iterator of parameter name. It is used by `for loop` + or `in` operator. + + .. code-block:: python + + parameters = paddle.parameters.create(...) + if "fc_param" in parameters: + print 'OK' + :return: an iterator of parameter name + :rtype: iterator + """ return iter(self.__param_conf__) def __getitem__(self, key): + """ + Get parameter by parameter name. It uses Python dict syntax. + + :note: It will always copy the parameter from C++ side. + :param key: Parameter name + :type key: basestring + :return: parameter value + :rtype: np.ndarray + """ shape = self.get_shape(key) if len(self.__gradient_machines__) == 0: @@ -77,20 +139,37 @@ def __getitem__(self, key): raise RuntimeError("Unexpected branch") def get_shape(self, key): + """ + get shape of the parameter. + :param key: parameter name + :type key: basestring + :return: parameter's shape + :rtype: tuple + """ if not isinstance(key, basestring): raise ValueError("parameter name should be string") if not self.has_key(key): raise ValueError("No such parameter %s" % key) conf = self.__param_conf__[key] - return map(int, conf.dims) + return tuple(map(int, conf.dims)) def __setitem__(self, key, value): + """ + Set parameter by parameter name & value. It use Python dict syntax. + + :note: It will always copy the parameter to C++ side. + :param key: Parameter name + :type key: basestring + :param value: Parameter matrix. + :type value: np.ndarray + :return: Nothing + """ + if not isinstance(value, np.ndarray): raise ValueError("Must return ndarray") value = value.astype(dtype=np.float32) shape = self.get_shape(key) - if not reduce(lambda a, b: a and b, - map(lambda x: x[0] == x[1], zip(value.shape, shape))): + if value.shape != shape: raise ValueError("Value shape mismatch, expect %s, should %s" % (shape, value.shape)) @@ -102,12 +181,38 @@ def __setitem__(self, key, value): key, value) def get(self, parameter_name): + """ + Get parameter by parameter name. + + :note: It will always copy the parameter from C++ side. + :param parameter_name: parameter name + :type parameter_name: basestring + :return: The parameter matrix. + :rtype: np.ndarray + """ return self.__getitem__(key=parameter_name) def set(self, parameter_name, value): + """ + Set parameter by parameter name & matrix. + :param parameter_name: parameter name + :type parameter_name: basestring + :param value: parameter matrix + :type value: np.ndarray + :return: Nothing. + """ self.__setitem__(key=parameter_name, value=value) def append_gradient_machine(self, gradient_machine): + """ + append gradient machine to parameters. This method is used internally in + Trainer.train. + + :param gradient_machine: Paddle C++ GradientMachine object. + :type gradient_machine: api.GradientMachine + :return: + """ + if not isinstance(gradient_machine, api.GradientMachine): raise ValueError("gradient_machine should be api.GradientMachine") diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index a29c3a05f85395..9ba13dc5c8a81f 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -12,16 +12,38 @@ def default_event_handler(event): + """ + Default event handler. It will print some log and save mode. + + TODO(yuyang18): Complete it! + :param event: + :return: + """ pass class ITrainer(object): + """ + The interface of Trainer. The only exposed method is `train`. + """ + def train(self, train_data_reader, topology, parameters, test_data_reader=None, event_handler=None): + """ + train method. + + :param train_data_reader: + :param topology: + :param parameters: + :param test_data_reader: + :param event_handler: + :return: + """ + raise NotImplementedError() @@ -30,7 +52,8 @@ def __init__(self, update_equation): """ Simple SGD Trainer. - :param update_equation: Maybe we should give a DSL for update equation? + :param update_equation: The optimizer object. + :type update_equation: v2_optimizer.Optimizer """ if not isinstance(update_equation, v2_optimizer.Optimizer): raise ValueError("update equation parameter must be " From 0446b488fd3db0b3e1b3c03a9a653a2843bdefca Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 16 Feb 2017 20:46:10 +0800 Subject: [PATCH 196/370] LayerOutput for single machine multiple devices --- .../gradientmachines/GradientMachine.h | 2 + .../gradientmachines/MultiGradientMachine.cpp | 38 +++++++++++++++++++ .../gradientmachines/MultiGradientMachine.h | 2 + .../gradientmachines/NeuralNetwork.cpp | 1 + .../gserver/gradientmachines/NeuralNetwork.h | 3 +- 5 files changed, 45 insertions(+), 1 deletion(-) diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index 0829968d87c5dc..201b65bc45181b 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -134,6 +134,8 @@ class GradientMachine { backward(callback); } + virtual MatrixPtr getLayerOutput(const std::string& layerName) = 0; + // see comment in Layer.h for the function with the same name virtual void resetState() {} diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 80f223824d8dcc..a571b3d72f1d52 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -282,6 +282,44 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, backwardImp(callback); } +MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { + // neural networks are same in each trainer thread + // layer output height = height of layer output * thread nums + auto nn = dynamic_cast(threads_[0]->getGradientMachine()); + auto height = nn->getLayerOutput(layerName)->getHeight() * threads_.size(); + auto stream = HPPL_STREAM_DEFAULT; + + auto copyLayerOutput = [height, stream]( + MatrixPtr& dst, MatrixPtr src, int startRow, bool useGpu) { + size_t width = src->getWidth(); + if (!dst) { + dst = src->clone(height, width, useGpu); + } else { + dst->resize(height, width); + } + + MatrixPtr tmpMatrix = dst->subMatrix(startRow, src->getHeight()); + tmpMatrix->copyFrom(*src, stream); + }; + + MatrixPtr mats; + size_t startRow = 0; + + // copy one layer output from one trainer thread at each time + for (auto& thread : threads_) { + auto nn = dynamic_cast(thread->getGradientMachine()); + auto mat = nn->getLayerOutput(layerName); + copyLayerOutput(mats, mat, startRow, useGpu_); + startRow += mat->getHeight(); + } + + if (useGpu_) { + hl_stream_synchronize(HPPL_STREAM_DEFAULT); + } + + return mats; +} + void MultiGradientMachine::backwardImp(const UpdateCallback& callback) { for (size_t i = 0; i < parameters_.size(); i++) { if (!parameters_[i]->useGpu() || parameters_[i]->isStatic()) continue; diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.h b/paddle/gserver/gradientmachines/MultiGradientMachine.h index 9be15ef4bcf34f..988d5098179806 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.h +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.h @@ -189,6 +189,8 @@ class MultiGradientMachine : public GradientMachine { PassType passType, const UpdateCallback& callback); + virtual MatrixPtr getLayerOutput(const std::string& layerName); + virtual void onPassEnd(); virtual void finish(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 22051e07ee0026..1f9ace4f67fdca 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -298,6 +298,7 @@ MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) { CHECK(it != layerMap_.end()) << "Cannot find layer: " << layerName; return it->second->getOutputValue(); } + void NeuralNetwork::onPassEnd() { for (auto& layer : layers_) { layer->onPassEnd(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 25af4abcf81700..bf9ed09327f2f1 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -87,7 +87,8 @@ class NeuralNetwork : public GradientMachine { virtual void backward(const UpdateCallback& callback = nullptr); - MatrixPtr getLayerOutput(const std::string& layerName); + virtual MatrixPtr getLayerOutput(const std::string& layerName); + const LayerPtr& getLayer(const std::string& layerName) const { auto it = layerMap_.find(layerName); CHECK(it != layerMap_.end()) << "Unknown layer " << layerName; From fdbc289ac38dd288fdb11f3ae18da94455625772 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Thu, 16 Feb 2017 22:02:35 +0800 Subject: [PATCH 197/370] add v2 activation, add comment for v2 layer --- demo/mnist/api_train.py | 2 +- python/paddle/v2/__init__.py | 3 +- python/paddle/v2/activation.py | 37 +++++++++++++++++++++++ python/paddle/v2/layer.py | 54 ++++++++++++++++++++++++++++++++++ 4 files changed, 94 insertions(+), 2 deletions(-) create mode 100644 python/paddle/v2/activation.py diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index f0c56b77cf8578..fe39f0bd23f78e 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -77,7 +77,7 @@ def main(): hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) inference = paddle_v2.layer.fc(input=hidden2, size=10, - act=SoftmaxActivation()) + act=paddle_v2.activation.Softmax()) cost = paddle_v2.layer.classification_cost(input=inference, label=label) # Create Simple Gradient Machine. diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 590fb5cd523ce7..ec4cbb484baaa6 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -14,5 +14,6 @@ import optimizer import layer +import activation -__all__ = ['optimizer', 'layer'] +__all__ = ['optimizer', 'layer', 'activation'] diff --git a/python/paddle/v2/activation.py b/python/paddle/v2/activation.py new file mode 100644 index 00000000000000..1f3aab9ef3c5f6 --- /dev/null +++ b/python/paddle/v2/activation.py @@ -0,0 +1,37 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddle.trainer_config_helpers.activations import * + +__all__ = [ + "Base", "Tanh", "Sigmoid", "Softmax", "Identity", "Linear", + 'SequenceSoftmax', "Exp", "Relu", "BRelu", "SoftRelu", "STanh", "Abs", + "Square", "Log" +] + +Base = BaseActivation +Tanh = TanhActivation +Sigmoid = SigmoidActivation +Softmax = SoftmaxActivation +SequenceSoftmax = SequenceSoftmaxActivation +Identity = IdentityActivation +Linear = Identity +Relu = ReluActivation +BRelu = BReluActivation +SoftRelu = SoftReluActivation +STanh = STanhActivation +Abs = AbsActivation +Square = SquareActivation +Exp = ExpActivation +Log = LogActivation diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 9c1589f9bfd554..4acbd5ef28c314 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -11,6 +11,60 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +""" +Before this new package paddle.v2.layer, users would need to use functions +in paddle.trainer_config_helpers.layers to configure networks. + +The Old Way: +========= +This old way requires that the creation of a network be defined in a Python +function, say network_config, and that this Python function being passed to +paddle.trainer_config_helpers.parse_network_config for the creation of +protobuf message description of this network. + +```python +def network_config(): + img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784) + inference = paddle.trainer_config_helpers.fc_layer( + input=img, + size=10, + act=paddle.trainer_config_helpers.SoftmaxActivation()) + cost = paddle.trainer_config_helpers.classification_cost( + input=inference, + label=paddle.trainer_config_helpers.data_layer(name="label", size=10)) + +proto_desc = parse_network_config(network_config) +``` + +When parse_network_config executes network_config, those layer definition +functions like data_layer and fc_layer would change some Python global variables, +so that after the execution, parse_network_config could collect information from +these global variables and generates the protobuf message. + + + +The New Way: +========= +In this PR, we define a function in paddle.v2.layer which creates a Python +class for each layer creation function in paddle.trainer_config_helpers.layers. +Users can use create a network as follows: + +```python +img = paddle.v2.layer.data(name="pixel", size=784) +inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax()) +cost = paddle.v2.layer.classification( + input=inference, + label=paddle.v2.layer.data(name="label", size=10)) + +parameters = paddle.v2.parameters.create(cost) +``` + +This new way doesn't require those invocations to layer definition functions +to be in a Python function but could be anywhere. + +Also, the creation of a protobuf message is hidden in the invocation of +paddle.v2.parameters.create, no longer exposed to users. +""" import paddle.trainer_config_helpers as conf_helps from paddle.trainer_config_helpers.config_parser_utils import \ From a9902a300bd35a1a497414ea3475cf79c7058f5c Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 16 Feb 2017 17:34:10 -0800 Subject: [PATCH 198/370] fix according to discussion --- doc/design/reader/README.md | 114 ++++++++++++++++++++++++++++-------- 1 file changed, 88 insertions(+), 26 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index fa37ad7b292003..7fa62350c40d53 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -1,28 +1,86 @@ -# Python Data Provider Design Doc +# Python Data Reader Design Doc -Data provider provides data for training. It will be passed into `paddle.train` as a parameter. +Paddle reads data from data reader during training. It will be passed into `paddle.train` as a parameter. -## Data Provider Interface +## Data Reader Interface -Data provider is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): +Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): ``` -iterable = data_provider() +iterable = data_reader() ``` -Element produced for the iterable should be a **single** entry of data, in format `[column_0_item, column_1_item, ...]`. Each element of the list needs to be supported data type (e.g., numpy 1d array of float32, list of int). +Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int) -For example, `column_0_item` could be image pixels of format numpy 1d array of float32, and `column_1_item` could be image label of format single int value: +An example implementation for single item data reader: +```python +def data_reader_fake_image(): + while True: + yield numpy.random.uniform(-1, 1, size=20*20) +``` + +An example implementation for multiple item data reader: +```python +def data_reader_fake_image_and_label(): + while True: + yield numpy.random.uniform(-1, 1, size=20*20), False ``` -for single_entry in iterable: - pixel = entry[0] - label = entry[1] + +## Data Reader Decorators + +Data Reader Decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. + +Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following a few examples: + +### Prefetch Data + +Since reading data may take time and training can not proceed without data. It is generally a good idea to prefetch data. + +Use `paddle.reader.buffered` to prefetch data: + +```python +buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100) +``` + +`buffered_reader` will try to buffer (prefetch) `100` data entries. + +### Compose Multiple Data Readers + +For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661). + +We can do: + +```python +def data_reader_fake_image(): + while True: + yield numpy.random.uniform(-1, 1, size=20*20) + +def data_reader_bool(t): + while True: + yield t + +true_reader = lambda : data_reader_bool(True) +false_reader = lambda : data_reader_bool(False) + +reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader) +# skipped 1 because paddle.dataset.mnist produces two items per data entry. +# We don't care second item at this time. +paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) +``` + +### Shuffle + +Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader decorater that buffers `n` data entries and shuffle them before a data entry is read. + +Example: +```python +reader = paddle.reader.shuffle(paddle.dataset.mnist, 512) ``` ## Usage -data provider, mapping from data provider column to data layer, batch size and number of total pass will be passed into `paddle.train`: +data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: ```python # two data layer is created: @@ -31,32 +89,38 @@ label_layer = paddle.layer.data("label", ...) # ... -paddle.train(paddle.data.mnist, ["image", "label"], 128, 10, ...) +paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) ``` + ## Q & A ### Why return only a single entry, but not a mini batch? -If return a mini batch, data provider need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. +If return a mini batch, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. -Concretely, always return a single entry make reusing existing data providers much easier (e.g., if existing data provider return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). +Practically, always return a single entry make reusing existing data reader much easier (e.g., if existing data reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). -### How to create custom data provider +### Why use a dictionary but not a list to provide mapping? + +We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`). + +### How to create custom data reader ```python -def image_provider(path): - f = open(path) +def image_reader(image_path, label_path): + f = open(image_path) + l = open(label_path) images = numpy.fromfile( f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32') images = images / 255.0 * 2.0 - 1.0 labels = numpy.fromfile(l, 'ubyte', count=n).astype("int") for i in xrange(n): - yield [images[i, :], labels[i]] # a single entry of data is created each time + yield images[i, :], labels[i] # a single entry of data is created each time f.close() -# use python lambda to change image_provier into a function with no parameters. -provider = lambda : image_provier("/path/to/data/") -paddle.train(provider, ["image", "label"], ...) +# use python lambda to change image_reader into a function with no parameters. +reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file") +paddle.train(reader, {"image":0, "label":1}, ...) ``` ### How is `paddle.train` implemented @@ -64,16 +128,14 @@ paddle.train(provider, ["image", "label"], ...) An example implementation of paddle.train could be: ```python -def make_minibatch_generator(reader, minibatch_size): +def minibatch_decorater(reader, minibatch_size): buf = [reader.next() for x in xrange(minibatch_size)] while len(buf) > 0: yield buf buf = [reader.next() for x in xrange(minibatch_size)] -def train(provider, mapping, batch_size, total_pass): +def train(reader, mapping, batch_size, total_pass): for pass_idx in range(total_pass): - for mini_batch in make_minibatch_generator(provider(pass_idx)): # this loop will never end in online learning. + for mini_batch in minibatch_decorater(reader()): # this loop will never end in online learning. do_forward_backward(mini_batch, mapping) ``` - -This is just an example implementation, more complicated logic like data prefetch could be implemented. From 9048311c4afc50ee8277a3f897648eab2bb7db0e Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 16 Feb 2017 17:45:48 -0800 Subject: [PATCH 199/370] fix gramma, typo, code and rearrange paragraph --- doc/design/reader/README.md | 51 ++++++++++++++++++++----------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index 7fa62350c40d53..be337a800fa085 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -27,11 +27,25 @@ def data_reader_fake_image_and_label(): yield numpy.random.uniform(-1, 1, size=20*20), False ``` +## Usage + +data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: + +```python +# two data layer is created: +image_layer = paddle.layer.data("image", ...) +label_layer = paddle.layer.data("label", ...) + +# ... + +paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) +``` + ## Data Reader Decorators Data Reader Decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. -Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following a few examples: +Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples: ### Prefetch Data @@ -64,39 +78,25 @@ true_reader = lambda : data_reader_bool(True) false_reader = lambda : data_reader_bool(False) reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader) -# skipped 1 because paddle.dataset.mnist produces two items per data entry. -# We don't care second item at this time. +# Skipped 1 because paddle.dataset.mnist produces two items per data entry. +# And we don't care second item at this time. paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) ``` ### Shuffle -Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader decorater that buffers `n` data entries and shuffle them before a data entry is read. +Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read. Example: ```python reader = paddle.reader.shuffle(paddle.dataset.mnist, 512) ``` -## Usage - -data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: - -```python -# two data layer is created: -image_layer = paddle.layer.data("image", ...) -label_layer = paddle.layer.data("label", ...) - -# ... - -paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) -``` - ## Q & A ### Why return only a single entry, but not a mini batch? -If return a mini batch, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. +If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. Practically, always return a single entry make reusing existing data reader much easier (e.g., if existing data reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). @@ -129,13 +129,16 @@ An example implementation of paddle.train could be: ```python def minibatch_decorater(reader, minibatch_size): - buf = [reader.next() for x in xrange(minibatch_size)] - while len(buf) > 0: - yield buf - buf = [reader.next() for x in xrange(minibatch_size)] + def ret(): + r = reader() + buf = [r.next() for x in xrange(minibatch_size)] + while len(buf) > 0: + yield buf + buf = [r.next() for x in xrange(minibatch_size)] + return ret def train(reader, mapping, batch_size, total_pass): for pass_idx in range(total_pass): - for mini_batch in minibatch_decorater(reader()): # this loop will never end in online learning. + for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning. do_forward_backward(mini_batch, mapping) ``` From 7f19ca4fbe61f4a454eb55b018f222305f87ce7a Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 16 Feb 2017 17:53:46 -0800 Subject: [PATCH 200/370] fix capitalization --- doc/design/reader/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index be337a800fa085..8f7abf12f73354 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -43,7 +43,7 @@ paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) ## Data Reader Decorators -Data Reader Decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. +Data reader decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples: From c57954e71cee4cc3e45f7dff21c770c4f636f319 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 17 Feb 2017 09:57:37 +0800 Subject: [PATCH 201/370] use v2.layer in api_train_v2 --- demo/mnist/api_train_v2.py | 36 ++++++++++++++++++------------------ python/paddle/v2/__init__.py | 5 +++-- python/paddle/v2/layer.py | 10 +++++----- 3 files changed, 26 insertions(+), 25 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 5e46d510ad35bb..47584f33800b30 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -1,7 +1,7 @@ -from paddle.trainer_config_helpers import * -from paddle.trainer.PyDataProvider2 import dense_vector, integer_value -import paddle.v2 as paddle import numpy +import paddle.v2 as paddle +from paddle.trainer.PyDataProvider2 import dense_vector, integer_value + import mnist_util @@ -12,32 +12,32 @@ def train_reader(): yield item -def network_config(): - imgs = data_layer(name='pixel', size=784) - hidden1 = fc_layer(input=imgs, size=200) - hidden2 = fc_layer(input=hidden1, size=200) - inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation()) - cost = classification_cost( - input=inference, label=data_layer( - name='label', size=10)) - outputs(cost) - - def main(): paddle.init(use_gpu=False, trainer_count=1) - topology = parse_network_config(network_config) + + # define network topology + images = paddle.layer.data(name='pixel', size=784) + label = paddle.layer.data(name='label', size=10) + hidden1 = paddle.layer.fc(input=images, size=200) + hidden2 = paddle.layer.fc(input=hidden1, size=200) + inference = paddle.layer.fc(input=hidden2, + size=10, + act=paddle.activation.Softmax()) + cost = paddle.layer.classification_cost(input=inference, label=label) + + topology = paddle.layer.parse_network(cost) + print topology parameters = paddle.parameters.create(topology) for param_name in parameters.keys(): array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) parameters.set(parameter_name=param_name, value=array) - adam_optimizer = paddle.optimizer.Optimizer( - learning_rate=0.01, learning_method=AdamOptimizer()) + adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01) def event_handler(event): if isinstance(event, paddle.event.EndIteration): - para = parameters.get('___fc_layer_2__.w0') + para = parameters.get('___fc_2__.w0') print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( event.pass_id, event.batch_id, event.cost, para.mean()) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 577e073ee56bad..bc064a21ae1502 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -19,8 +19,9 @@ import event import py_paddle.swig_paddle as api - -__all__ = ['optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event'] +__all__ = [ + 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event' +] def init(**kwargs): diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 4acbd5ef28c314..0ce4ecd569aa1d 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -72,6 +72,11 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. from paddle.trainer_config_helpers.default_decorators import wrap_name_default import collections +__all__ = [ + 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', + 'cross_entropy_cost' +] + def parse_network(*outputs): """ @@ -165,11 +170,6 @@ def to_proto_impl(self, **kwargs): name_prefix='cross_entropy', parent_names=['input', 'label']) -__all__ = [ - 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', - 'cross_entropy_cost' -] - if __name__ == '__main__': pixel = data(name='pixel', size=784) label = data(name='label', size=10) From dcc54bff3f2a51b7173f09f3b657c217060a60d1 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 17 Feb 2017 10:01:54 +0800 Subject: [PATCH 202/370] code clean --- demo/mnist/api_train_v2.py | 1 - 1 file changed, 1 deletion(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 47584f33800b30..59486ed1b3ba49 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -26,7 +26,6 @@ def main(): cost = paddle.layer.classification_cost(input=inference, label=label) topology = paddle.layer.parse_network(cost) - print topology parameters = paddle.parameters.create(topology) for param_name in parameters.keys(): array = parameters.get(param_name) From 7c5fd231063908e1d7699c995d1acebb2d321aa9 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 17 Feb 2017 13:08:39 +0800 Subject: [PATCH 203/370] Update MultiGradientMachine::getLayerOutput --- .../gradientmachines/MultiGradientMachine.cpp | 41 ++++++++----------- paddle/gserver/layers/CosSimLayer.cpp | 2 +- paddle/math/tests/test_RowBuffer.cpp | 8 ++-- 3 files changed, 22 insertions(+), 29 deletions(-) diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index a571b3d72f1d52..56b1836e417072 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -283,41 +283,34 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, } MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { - // neural networks are same in each trainer thread - // layer output height = height of layer output * thread nums - auto nn = dynamic_cast(threads_[0]->getGradientMachine()); - auto height = nn->getLayerOutput(layerName)->getHeight() * threads_.size(); - auto stream = HPPL_STREAM_DEFAULT; - - auto copyLayerOutput = [height, stream]( - MatrixPtr& dst, MatrixPtr src, int startRow, bool useGpu) { - size_t width = src->getWidth(); - if (!dst) { - dst = src->clone(height, width, useGpu); - } else { - dst->resize(height, width); - } + // each thread has the same neuro network + auto nn = threads_[0]->getGradientMachine(); - MatrixPtr tmpMatrix = dst->subMatrix(startRow, src->getHeight()); - tmpMatrix->copyFrom(*src, stream); - }; + size_t height = 0; + size_t width = nn->getLayerOutput(layerName)->getWidth(); + for (auto& thread : threads_) { + auto out = thread->getGradientMachine()->getLayerOutput(layerName); + height += out->getHeight(); + CHECK_EQ(width, out->getWidth()); + } - MatrixPtr mats; - size_t startRow = 0; + MatrixPtr dst; + Matrix::resizeOrCreate(dst, height, width, false, useGpu_); // copy one layer output from one trainer thread at each time + size_t startRow = 0; for (auto& thread : threads_) { - auto nn = dynamic_cast(thread->getGradientMachine()); - auto mat = nn->getLayerOutput(layerName); - copyLayerOutput(mats, mat, startRow, useGpu_); - startRow += mat->getHeight(); + auto src = thread->getGradientMachine()->getLayerOutput(layerName); + auto tmpMatrix = dst->subMatrix(startRow, src->getHeight()); + tmpMatrix->copyFrom(*src, HPPL_STREAM_DEFAULT); + startRow += src->getHeight(); } if (useGpu_) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } - return mats; + return dst; } void MultiGradientMachine::backwardImp(const UpdateCallback& callback) { diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index a6c0300acf6752..1501c7437011d5 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/paddle/gserver/layers/CosSimLayer.cpp @@ -42,7 +42,7 @@ void CosSimLayer::forward(PassType passType) { /* malloc memory for the output_ if necessary */ int batchSize = getInputValue(0)->getHeight(); int size = getSize(); - CHECK_EQ(forward_.size(), 1) << "Only one forward function needed"; + CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed"; { REGISTER_TIMER_INFO("CosFwResetTimer", getName().c_str()); diff --git a/paddle/math/tests/test_RowBuffer.cpp b/paddle/math/tests/test_RowBuffer.cpp index 5f66f22ef73dcf..8cc4c69a1a4d8a 100644 --- a/paddle/math/tests/test_RowBuffer.cpp +++ b/paddle/math/tests/test_RowBuffer.cpp @@ -17,10 +17,10 @@ limitations under the License. */ TEST(RowBuffer, testAutoGrow) { paddle::RowBuffer buf(128); - ASSERT_EQ(128, buf.getWidth()); + ASSERT_EQ(128UL, buf.getWidth()); ASSERT_TRUE(buf.isAutoGrowth()); buf.resize(2); - ASSERT_EQ(2, buf.getRowCount()); + ASSERT_EQ(2UL, buf.getRowCount()); for (size_t i = 0; i < buf.getWidth() * 2; ++i) { buf.data()[i] = i; } @@ -35,7 +35,7 @@ TEST(RowBuffer, testAutoGrow) { data[i] = i; } - ASSERT_EQ(3, buf.getRowCount()); + ASSERT_EQ(3UL, buf.getRowCount()); for (size_t i = 0; i < buf.getRowCount() - 1; ++i) { for (size_t j = 0; j < buf.getWidth(); ++j) { ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5); @@ -51,7 +51,7 @@ TEST(RowBuffer, testWithMemBuf) { std::make_shared(128 * 2 * sizeof(real)); paddle::RowBuffer buf(mem, 128); ASSERT_TRUE(!buf.isAutoGrowth()); - ASSERT_EQ(2, buf.getRowCount()); + ASSERT_EQ(2UL, buf.getRowCount()); for (size_t i = 0; i < buf.getWidth() * 2; ++i) { buf.data()[i] = i; } From 258e5ec59f28f617397646edbf67d4a576f0d3f0 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 17 Feb 2017 13:23:38 +0800 Subject: [PATCH 204/370] update GradientMachine API --- paddle/api/GradientMachine.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/api/GradientMachine.cpp b/paddle/api/GradientMachine.cpp index 66115f8293b905..a44763bfa53ed5 100644 --- a/paddle/api/GradientMachine.cpp +++ b/paddle/api/GradientMachine.cpp @@ -146,7 +146,7 @@ void GradientMachine::randParameters() { m->machine->randParameters(); } Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const throw(UnsupportError) { - auto nn = std::dynamic_pointer_cast(m->machine); + auto nn = m->machine; if (nn) { auto mat = nn->getLayerOutput(layerName); return Matrix::createByPaddleMatrixPtr(&mat); From 3842bc4d7c904b2d0bda4aa48429a20c317f1420 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 17 Feb 2017 13:42:33 +0800 Subject: [PATCH 205/370] refine code --- .../gradientmachines/MultiGradientMachine.cpp | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 56b1836e417072..db13a886881ef7 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -285,32 +285,34 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { // each thread has the same neuro network auto nn = threads_[0]->getGradientMachine(); - size_t height = 0; size_t width = nn->getLayerOutput(layerName)->getWidth(); + std::vector mats; + mats.reserve(threads_.size()); for (auto& thread : threads_) { - auto out = thread->getGradientMachine()->getLayerOutput(layerName); + MatrixPtr out = thread->getGradientMachine()->getLayerOutput(layerName); + mats.push_back(out); height += out->getHeight(); CHECK_EQ(width, out->getWidth()); } - MatrixPtr dst; - Matrix::resizeOrCreate(dst, height, width, false, useGpu_); + MatrixPtr layerOutput; + Matrix::resizeOrCreate(layerOutput, height, width, false, useGpu_); // copy one layer output from one trainer thread at each time size_t startRow = 0; - for (auto& thread : threads_) { - auto src = thread->getGradientMachine()->getLayerOutput(layerName); - auto tmpMatrix = dst->subMatrix(startRow, src->getHeight()); - tmpMatrix->copyFrom(*src, HPPL_STREAM_DEFAULT); - startRow += src->getHeight(); + + for (size_t i = 0; i < threads_.size(); i++) { + auto tmpMatrix = layerOutput->subMatrix(startRow, mats[i]->getHeight()); + tmpMatrix->copyFrom(*mats[i], HPPL_STREAM_DEFAULT); + startRow += mats[i]->getHeight(); } if (useGpu_) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } - return dst; + return layerOutput; } void MultiGradientMachine::backwardImp(const UpdateCallback& callback) { From 84552872a337b42252233023191698f992aa5808 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 17 Feb 2017 16:09:50 +0800 Subject: [PATCH 206/370] getLayerOutput in CPU --- paddle/gserver/gradientmachines/GradientMachine.h | 4 +++- .../gserver/gradientmachines/MultiGradientMachine.cpp | 10 +++------- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index 201b65bc45181b..a814e771d12e3a 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -134,7 +134,9 @@ class GradientMachine { backward(callback); } - virtual MatrixPtr getLayerOutput(const std::string& layerName) = 0; + virtual MatrixPtr getLayerOutput(const std::string& layerName) { + return nullptr; + } // see comment in Layer.h for the function with the same name virtual void resetState() {} diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index db13a886881ef7..7e60920376d195 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -283,7 +283,7 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, } MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { - // each thread has the same neuro network + // each thread has the same neural network auto nn = threads_[0]->getGradientMachine(); size_t height = 0; size_t width = nn->getLayerOutput(layerName)->getWidth(); @@ -297,21 +297,17 @@ MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { } MatrixPtr layerOutput; - Matrix::resizeOrCreate(layerOutput, height, width, false, useGpu_); + Matrix::resizeOrCreate(layerOutput, height, width, false, false); // copy one layer output from one trainer thread at each time size_t startRow = 0; for (size_t i = 0; i < threads_.size(); i++) { auto tmpMatrix = layerOutput->subMatrix(startRow, mats[i]->getHeight()); - tmpMatrix->copyFrom(*mats[i], HPPL_STREAM_DEFAULT); + tmpMatrix->copyFrom(*mats[i]); startRow += mats[i]->getHeight(); } - if (useGpu_) { - hl_stream_synchronize(HPPL_STREAM_DEFAULT); - } - return layerOutput; } From 695b5a7fcd42e4e5678fdb4288cc8dd23240aac4 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 17 Feb 2017 18:09:55 +0800 Subject: [PATCH 207/370] change topology to layer --- demo/mnist/api_train_v2.py | 5 ++--- python/paddle/v2/parameters.py | 26 +++++++++++++------------- python/paddle/v2/trainer.py | 9 ++++++--- 3 files changed, 21 insertions(+), 19 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 59486ed1b3ba49..b5cc74ce67dfc8 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -25,8 +25,7 @@ def main(): act=paddle.activation.Softmax()) cost = paddle.layer.classification_cost(input=inference, label=label) - topology = paddle.layer.parse_network(cost) - parameters = paddle.parameters.create(topology) + parameters = paddle.parameters.create(cost) for param_name in parameters.keys(): array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) @@ -46,7 +45,7 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer.train(train_data_reader=train_reader, - topology=topology, + topology=cost, parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index e5b7dabcb8eb3a..ea504d5104716d 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -1,27 +1,27 @@ import numpy as np - -from paddle.proto.ModelConfig_pb2 import ModelConfig -from paddle.proto.ParameterConfig_pb2 import ParameterConfig +from . import layer as v2_layer import py_paddle.swig_paddle as api +from paddle.proto.ParameterConfig_pb2 import ParameterConfig __all__ = ['Parameters', 'create'] -def create(*topologies): +def create(*layers): """ - Create parameter pool by topologies. + Create parameter pool by layers. In paddle, layer can be represent a + model config. - :param topologies: + :param layers: :return: """ - pool = Parameters() - for topo in topologies: - if not isinstance(topo, ModelConfig): + for layer in layers: + if not isinstance(layer, v2_layer.Layer): raise ValueError( - 'create must pass a topologies which type is ModelConfig') - - for param in topo.parameters: - pool.__append_config__(param) + 'create must pass a topologies which type is paddle.layer.Layer') + model_config = v2_layer.parse_network(*layers) + pool = Parameters() + for param in model_config.parameters: + pool.__append_config__(param) return pool diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 9ba13dc5c8a81f..4365bd41e7073b 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -1,12 +1,13 @@ import collections import py_paddle.swig_paddle as api +from paddle.proto.ModelConfig_pb2 import ModelConfig from py_paddle import DataProviderConverter -from paddle.proto.ModelConfig_pb2 import ModelConfig +from . import event as v2_event +from . import layer as v2_layer from . import optimizer as v2_optimizer from . import parameters as v2_parameters -from . import event as v2_event __all__ = ['ITrainer', 'SGD'] @@ -73,7 +74,7 @@ def train(self, Training method. Will train num_passes of input data. :param train_data_reader: - :param topology: Network Topology, a protobuf ModelConfig message. + :param topology: Network Topology, use one or more Layers to represent it. :param parameters: The parameter pools. :param num_passes: The total train passes. :param test_data_reader: @@ -87,6 +88,8 @@ def train(self, if event_handler is None: event_handler = default_event_handler + topology = v2_layer.parse_network(topology) + __check_train_args__(**locals()) gm = api.GradientMachine.createFromConfigProto( From d2dfa70deb08c3c8b13e2154afed6a3e4ce535d7 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 17 Feb 2017 19:34:11 +0800 Subject: [PATCH 208/370] data converter --- python/paddle/v2/data_converter.py | 240 +++++++++++++++++++++++++++++ 1 file changed, 240 insertions(+) create mode 100644 python/paddle/v2/data_converter.py diff --git a/python/paddle/v2/data_converter.py b/python/paddle/v2/data_converter.py new file mode 100644 index 00000000000000..5d7b8a736b3596 --- /dev/null +++ b/python/paddle/v2/data_converter.py @@ -0,0 +1,240 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import py_paddle.swig_paddle +import numpy + +__all__ = ['DataConverter'] + + +class IDataConverter(object): + def __init__(self, input_type, pos): + """ + :param input_type: data type + :type input_type: dp2.InputType + :param pos: which input, start from 0 + :type pos: int + """ + self.input_type = input_type + assert isinstance(self.input_type, dp2.InputType) + self.pos = pos + + def convert(self, data, argument): + """ + Conv data to paddle format. + :param data: input data + :param argument: paddle format + """ + pass + + +class DenseConvert(IDataConverter): + def __init__(self, input_type, pos): + IDataConverter.__init__(self, input_type, pos) + + def convert(self, data, argument): + """ + :param data: input data + :type data: list | numpy array + :param argument: the type which paddle is acceptable + :type argument: swig_paddle.Arguments + """ + assert isinstance(argument, swig_paddle.Arguments) + if data.dtype != numpy.float32: + data = data.astype(numpy.float32) + m = swig_paddle.Matrix.createDenseFromNumpy(data, True, False) + argument.setSlotValue(self.pos, m) + + +class SparseBinaryConvert(IDataConverter): + def __init__(self, input_type, pos): + IDataConverter.__init__(self, input_type, pos) + self.__rows__ = [0] + self.__cols__ = [] + self.__height__ = 0 + self.__nnz__ = 0 + self.__value__ = [] + + def fill_csr(self, data): + self.__height__ = len(data) + for x in data: + self.__rows__.append(self.__rows__[-1] + len(x)) + self__cols__ = data.flatten() + + def convert(self, data, argument): + assert isinstance(argument, swig_paddle.Arguments) + + fill_csr(data) + m = swig_paddle.Matrix.createSparse(self.__height__, + self.input_type.dim, + len(self.__cols__), + len(self.__value__) == 0) + assert isinstance(m, swig_paddle.Matrix) + m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__) + argument.setSlotValue(self.pos, m) + + +class SparseFloatConvert(SparseBinaryConvert): + def __init__(self, input_type, pos): + SparseBinaryConvert.__init__(self, input_type, pos) + + def fill_csr(self, data): + self.__height__ = len(data) + for x in data: + self.__rows__.append(self.__rows__[-1] + len(x)) + self.__cols__.extend((x[0] for x in data)) + self.__value__.extend((x[1] for x in data)) + + +class IndexConvert(IDataConverter): + def __init__(self, input_type, pos): + IDataConverter.__init__(self, input_type, pos) + self.__ids__ = [] + + def convert(self, data, argument): + assert isinstance(argument, swig_paddle.Arguments) + self.__ids__ = data.flatten() + ids = swig_paddle.IVector.create(self.__ids__) + argument.setSlotIds(self.pos, ids) + + +class SequenceConvert(IDataConverter): + def __init__(self, input_type, pos, inner_convert, setter): + """ + :param input_type: the type of input data + :type input_type: dp2.InputType + :param pos: the position of this input + :type pos: int + :param inner_convert: DataConvert type + :type inner_convert: DenseConvert|SparseBinaryConvert| + SparseFloatConvert|IndexConvert + :param setter: + :type setter: + """ + IDataConverter.__init__(self, input_type, pos) + self.__seq__ = [0] + self.__inner_convert__ = inner_convert + self.__setter__ = setter + + def fill_seq(self, data): + for each in data: + self.__seq__.append(self.__seq__[-1] + self.get_size(each)) + + def convert(self, data, argument): + fill_seq(data) + seq = swig_paddle.IVector.create(self.__seq__, False) + self.__setter__(argument, self.pos, seq) + + dat = [] + for each in data: + dat.append(each) + self.__inner_scanner__.convert(dat, argument) + + def get_size(self, data): + if isinstance(self.__inner_scanner__, SequenceConvert): + return sum(self.__inner_scanner__.get_size(item) for item in dat) + else: + return len(data) + + +class DataConverter(object): + def __init__(self, input_mapper): + """ + Usege: + + .. code-block:: python + inputs = [('image', dense_vector), ('label', integer_value)] + cvt = DataConverter(inputs) + arg = cvt.convert(minibatch_data, {'image':0, 'label':1}) + + :param input_mapper: list of (input_name, input_type) + :type input_mapper: list + """ + assert isinstance(self.input_types, collections.Sequence) + self.input_names = [] + self.input_types = [] + for each in self.input_types: + self.input_names.append(each[0]) + self.input_types.append(each[1]) + assert isinstance(each[1], dp2.InputType) + + def convert(self, data, input_dict=None, argument=None): + """ + Convert minibatch data to Paddle's argument. The data is numpy array + or list. + + :param data: input samples, for example, [column0, column1, ...] or + (column0, column1, ...) each column is one minibatch + feature. Note, if only one column featrue, data also + shuld be a list or tupe, [column0] or (column0). + :type data: list|tuple + :param input_dict: a dictionary to specify the correspondence + of data_layer and input data. If None, + the feature order in argument and data is the same. + :type input_dict: dict, like {string:integer, string, integer, ...}|None + :param argument: converted data will be saved in this argument. If None, + it will create a swig_paddle.Arguments firstly. + :param type: swig_paddle.Arguments|None + """ + if argument is None: + argument = swig_paddle.Arguments.createArguments(0) + assert isinstance(argument, swig_paddle.Arguments) + argument.resize(len(self.input_types)) + + converts = [ + DataConverter.create_scanner(i, each_type) + for i, each_type in enumerate(self.input_types) + ] + + for i, cvt in enumerate(converts): + if input_dict is not None: + dat = data[input_dict[self.input_names[i]]] + else: + dat = data[i] + cvt.convert(dat, argument) + + return argument + + def __call__(self, dat, argument=None): + return self.convert(dat, argument) + + @staticmethod + def create_scanner(pos, each): + assert isinstance(each, dp2.InputType) + retv = None + if each.type == dp2.DataType.Dense: + retv = DenseConvert(each, pos) + elif each.type == dp2.DataType.Index: + retv = IndexConvert(each, pos) + elif each.type == dp2.DataType.SparseNonValue: + retv = SparseBinaryConvert(each, pos) + elif each.type == dp2.DataType.SparseValue: + retv = SparseFloatConvert(each, pos) + assert retv is not None + + if each.seq_type == dp2.SequenceType.SUB_SEQUENCE: + retv = SequenceConvert( + each, pos, retv, + lambda arg, pos, seq: arg.setSlotSubSequenceStartPositions(pos, seq) + ) + + if each.seq_type in [ + dp2.SequenceType.SUB_SEQUENCE, dp2.SequenceType.SEQUENCE + ]: + retv = SequenceConvert( + each, pos, retv, + lambda arg, pos, seq: arg.setSlotSequenceStartPositions(pos, seq) + ) + return retv From 733da9b9e62fb20a5adfe12f23834b7fa184dd63 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 17 Feb 2017 19:45:52 +0800 Subject: [PATCH 209/370] data converter --- python/paddle/v2/data_converter.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/paddle/v2/data_converter.py b/python/paddle/v2/data_converter.py index 5d7b8a736b3596..45114b407dae22 100644 --- a/python/paddle/v2/data_converter.py +++ b/python/paddle/v2/data_converter.py @@ -15,6 +15,7 @@ import collections import py_paddle.swig_paddle import numpy +import paddle.trainer.PyDataProvider2 as dp2 __all__ = ['DataConverter'] From 701d02459d36ee254e20f5a934205aa0530478e8 Mon Sep 17 00:00:00 2001 From: Peng Li Date: Fri, 17 Feb 2017 19:58:16 +0800 Subject: [PATCH 210/370] Fix bug in processing input with static parameter in seqconcat layer --- paddle/gserver/layers/SequenceConcatLayer.cpp | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/paddle/gserver/layers/SequenceConcatLayer.cpp b/paddle/gserver/layers/SequenceConcatLayer.cpp index b4677687a6cc77..599706eb419ede 100644 --- a/paddle/gserver/layers/SequenceConcatLayer.cpp +++ b/paddle/gserver/layers/SequenceConcatLayer.cpp @@ -168,13 +168,17 @@ void SequenceConcatLayer::backward(const UpdateCallback& callback) { size_t rightNumIns = 0; for (size_t seqId = 0; seqId < numSequences1; ++seqId) { leftNumIns = starts1[seqId + 1] - starts1[seqId]; - inputGrad1->subMatrix(starts1[seqId], leftNumIns) - ->add(*(outputGrad->subMatrix(offset, leftNumIns))); + if (inputGrad1) { + inputGrad1->subMatrix(starts1[seqId], leftNumIns) + ->add(*(outputGrad->subMatrix(offset, leftNumIns))); + } offset += leftNumIns; rightNumIns = starts2[seqId + 1] - starts2[seqId]; - inputGrad2->subMatrix(starts2[seqId], rightNumIns) - ->add(*(outputGrad->subMatrix(offset, rightNumIns))); + if (inputGrad2) { + inputGrad2->subMatrix(starts2[seqId], rightNumIns) + ->add(*(outputGrad->subMatrix(offset, rightNumIns))); + } offset += rightNumIns; } } From f846362927fa9ba6baf0c54db5051e2616af0a39 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Fri, 17 Feb 2017 10:06:59 -0800 Subject: [PATCH 211/370] create buffered data reader decorator and tests --- python/CMakeLists.txt | 1 + python/paddle/reader/__init__.py | 15 +++++ python/paddle/reader/decorator.py | 60 ++++++++++++++++++++ python/paddle/reader/tests/CMakeLists.txt | 4 ++ python/paddle/reader/tests/decorator_test.py | 50 ++++++++++++++++ 5 files changed, 130 insertions(+) create mode 100644 python/paddle/reader/__init__.py create mode 100644 python/paddle/reader/decorator.py create mode 100644 python/paddle/reader/tests/CMakeLists.txt create mode 100644 python/paddle/reader/tests/decorator_test.py diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index ee7a5bff84ca96..357637e20346f8 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -24,6 +24,7 @@ add_custom_target(paddle_python ALL DEPENDS ${OUTPUT_DIR}/.timestamp) add_subdirectory(paddle/trainer_config_helpers/tests) +add_subdirectory(paddle/reader/tests) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/ DESTINATION opt/paddle/share/wheels diff --git a/python/paddle/reader/__init__.py b/python/paddle/reader/__init__.py new file mode 100644 index 00000000000000..28a69d8370262d --- /dev/null +++ b/python/paddle/reader/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from decorator import * diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py new file mode 100644 index 00000000000000..f0ddb0ff812b15 --- /dev/null +++ b/python/paddle/reader/decorator.py @@ -0,0 +1,60 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__all__ = ['buffered'] + +from Queue import Queue +from threading import Thread + + +def buffered(reader, size): + """Creates a buffered data reader. + + The buffered data reader will read and save data entries into a buffer. + Reading from the buffered data reader will proceed as long as the buffer + is not empty. + + Args: + reader: the data reader to read from. + size: max buffer size. + + Returns: + The buffered data reader. + """ + + class EndSignal(): + pass + + end = EndSignal() + + def read_worker(r, q): + for d in r: + q.put(d) + q.put(end) + + def create_reader(): + r = reader() + q = Queue(maxsize=size) + t = Thread( + target=read_worker, args=( + r, + q, )) + t.daemon = True + t.start() + e = q.get() + while e != end: + yield e + e = q.get() + + return create_reader diff --git a/python/paddle/reader/tests/CMakeLists.txt b/python/paddle/reader/tests/CMakeLists.txt new file mode 100644 index 00000000000000..4768d0738b381a --- /dev/null +++ b/python/paddle/reader/tests/CMakeLists.txt @@ -0,0 +1,4 @@ +add_test(NAME reader_decorator_test + COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/decorator_test.py + WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/reader/tests/decorator_test.py b/python/paddle/reader/tests/decorator_test.py new file mode 100644 index 00000000000000..879d1d9c1d0e06 --- /dev/null +++ b/python/paddle/reader/tests/decorator_test.py @@ -0,0 +1,50 @@ +# Copyright PaddlePaddle contributors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest +import paddle.reader +import time + + +def reader_10(dur): + for i in range(10): + time.sleep(dur) + yield i + + +class TestBuffered(unittest.TestCase): + def test_read(self): + for size in range(20): + b = paddle.reader.buffered(lambda: reader_10(0), size) + c = 0 + for i in b(): + self.assertEqual(i, c) + c += 1 + self.assertEqual(c, 10) + + def test_buffering(self): + # read have 30ms delay. + b = paddle.reader.buffered(lambda: reader_10(0.03), 10) + last_time = time.time() + for idx, i in enumerate(b()): + elapsed_time = time.time() - last_time + if i == 0: + time.sleep(0.3) + else: + # read time should be short, meaning already buffered. + self.assertLess(elapsed_time, 0.01) + last_time = time.time() + + +if __name__ == '__main__': + unittest.main() From 7c1d10e6cbfd4bfd4f9ac1d0c49418755fab541e Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Fri, 17 Feb 2017 10:46:26 -0800 Subject: [PATCH 212/370] travis ci: cache brew directory Fixes #1365 --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 5d82d9729b75ef..4fb2ca938795bb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,6 +4,7 @@ cache: - $HOME/third_party - $HOME/.ccache - $HOME/.cache/pip + - $HOME/Library/Caches/Homebrew sudo: required dist: trusty os: From 7b50594f6d61b80631b8700d0ba158bb7ef5363a Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Sat, 18 Feb 2017 10:23:22 -0800 Subject: [PATCH 213/370] fix test path --- python/paddle/reader/tests/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/reader/tests/CMakeLists.txt b/python/paddle/reader/tests/CMakeLists.txt index 4768d0738b381a..502c897d8946a8 100644 --- a/python/paddle/reader/tests/CMakeLists.txt +++ b/python/paddle/reader/tests/CMakeLists.txt @@ -1,4 +1,4 @@ add_test(NAME reader_decorator_test COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ - ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/decorator_test.py + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) From b2a969b7b3227a089ac5b482dfd5f9b340a3f483 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Sat, 18 Feb 2017 13:27:25 -0800 Subject: [PATCH 214/370] fix according to comment --- python/paddle/reader/__init__.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/python/paddle/reader/__init__.py b/python/paddle/reader/__init__.py index 28a69d8370262d..493b410e8299eb 100644 --- a/python/paddle/reader/__init__.py +++ b/python/paddle/reader/__init__.py @@ -12,4 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. +# It would be too lengthy to require our users to prefix decorators with `decorator`. +# For example, we want the following line +# +# r = paddle.reader.decorator.bufferd(paddle.reader.creator.text("hello.txt")) +# +# to be a shorter version: +# +# r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt")) from decorator import * From d5cfa6fcce622982624274e4e86a6670b29b634d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 15:17:40 +0800 Subject: [PATCH 215/370] Stash --- paddle/gserver/evaluators/Evaluator.cpp | 19 +++++++++++-- paddle/gserver/evaluators/Evaluator.h | 38 +++++++++++++++++++++++++ 2 files changed, 55 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index ae7508e2bb117a..4689222e3ade62 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -449,6 +449,21 @@ double AucEvaluator::calcAuc() const { } } +real AucEvaluator::getValueImpl() const { return calcAuc(); } + +std::string AucEvaluator::getTypeImpl() const { + if (colIdx_ == -1) { + return "last-column-auc"; + } else { + return "auc"; + } +} + +static InitFunction __reg_type_auc__([]() { + Evaluator::registrar_.registerClass("last-column-auc", + [] { return new AucEvaluator(-1); }); +}); + // class RankAucEvaluator REGISTER_EVALUATOR(rankauc, RankAucEvaluator); @@ -873,8 +888,6 @@ Evaluator* Evaluator::create(const EvaluatorConfig& config) { evaluator = new SumEvaluator(); } else if (config.type() == "last-column-sum") { evaluator = new ColumnSumEvaluator(-1); - } else if (config.type() == "last-column-auc") { - evaluator = new AucEvaluator(-1); } else { evaluator = registrar_.createByType(config.type()); } @@ -1253,4 +1266,6 @@ class ClassificationErrorPrinter : public ClassificationErrorEvaluator { }; REGISTER_EVALUATOR(classification_error_printer, ClassificationErrorPrinter); +std::string DummyEvaluator::getTypeImpl() const { return "dummy"; } + } // namespace paddle diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index 5770847309670e..bf08aa07f0ce31 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -19,6 +19,7 @@ limitations under the License. */ #include "paddle/parameter/Argument.h" #include "paddle/pserver/ParameterClient2.h" #include "paddle/utils/ClassRegistrar.h" +#include "paddle/utils/Error.h" namespace paddle { @@ -117,6 +118,34 @@ class Evaluator { static ClassRegistrar registrar_; + virtual void getNames(std::vector* names) { + names->clear(); + names->push_back(config_.name()); + } + + virtual real getValue(const std::string& name, + paddle::Error* err = nullptr) const { + if (name != config_.name() && err != nullptr) { + *err = paddle::Error("no such name of evaluator %s", name.c_str()); + return .0f; + } + return this->getValueImpl(); + } + + virtual std::string getType(const std::string& name, + paddle::Error* err = nullptr) const { + if (name != config_.name() && err != nullptr) { + *err = paddle::Error("no such name of evaluator %s", name.c_str()); + return std::string(); + } + return this->getTypeImpl(); + } + +protected: + virtual real getValueImpl() const { return .0f; } + + virtual std::string getTypeImpl() const { return "base"; } + protected: EvaluatorConfig config_; double numSamples_; @@ -135,6 +164,10 @@ class DummyEvaluator : public Evaluator { } virtual void finish() {} virtual void printStats(std::ostream&) const {} + + // Evaluator interface +protected: + std::string getTypeImpl() const; }; /** * @brief evaluate AUC using colIdx-th column as prediction. @@ -191,6 +224,11 @@ class AucEvaluator : public Evaluator { } double calcAuc() const; + + // Evaluator interface +protected: + real getValueImpl() const; + std::string getTypeImpl() const; }; /** From d2c58907eb552bab046a1fbb7d114149b0fd2916 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 15:22:38 +0800 Subject: [PATCH 216/370] Unify Evaluator::create method * Simplify Evaluator::create logic. Always use ClassRegister to create Evaluator. --- paddle/gserver/evaluators/Evaluator.cpp | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index ae7508e2bb117a..a2a5028e8418fd 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -866,21 +866,20 @@ void PnpairEvaluator::calc(std::vector& predictArray) { ClassRegistrar Evaluator::registrar_; Evaluator* Evaluator::create(const EvaluatorConfig& config) { - Evaluator* evaluator = nullptr; - if (config.type() == "classification_error") { - evaluator = new ClassificationErrorEvaluator(); - } else if (config.type() == "sum") { - evaluator = new SumEvaluator(); - } else if (config.type() == "last-column-sum") { - evaluator = new ColumnSumEvaluator(-1); - } else if (config.type() == "last-column-auc") { - evaluator = new AucEvaluator(-1); - } else { - evaluator = registrar_.createByType(config.type()); - } + Evaluator* evaluator = registrar_.createByType(config.type()); evaluator->init(config); return evaluator; } + +REGISTER_EVALUATOR(classification_error, ClassificationErrorEvaluator); +REGISTER_EVALUATOR(sum, SumEvaluator); +static InitFunction __reg_type_auc_sum__([]() { + Evaluator::registrar_.registerClass( + "last-column-sum", [] { return new ColumnSumEvaluator(-1); }); + Evaluator::registrar_.registerClass("last-column-auc", + [] { return new AucEvaluator(-1); }); +}); + /** * @brief print value of each layer. * From 2c07dd50ab39643ba6da941b021bf34fa48b11c8 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 17:39:52 +0800 Subject: [PATCH 217/370] Unify print layer, ValuePrinter evaluator. --- paddle/gserver/evaluators/Evaluator.cpp | 29 +++++++------------------ paddle/gserver/layers/PrintLayer.cpp | 29 +++++++------------------ paddle/parameter/Argument.cpp | 26 ++++++++++++++++++++++ paddle/parameter/Argument.h | 8 +++++++ 4 files changed, 50 insertions(+), 42 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index ae7508e2bb117a..1a43d3bd996109 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -892,27 +892,14 @@ class ValuePrinter : public Evaluator { virtual void eval(const NeuralNetwork& nn) { for (const std::string& name : config_.input_layers()) { - const Argument& argu = nn.getLayer(name)->getOutput(); - if (argu.value) { - std::ostringstream os; - argu.value->print(os); - LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str(); - } - if (argu.ids) { - std::ostringstream os; - argu.ids->print(os, argu.ids->getSize()); - LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str(); - } - if (auto startPos = argu.sequenceStartPositions) { - std::ostringstream os; - startPos->getVector(false)->print(os, startPos->getSize()); - LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str(); - } - if (auto subStartPos = argu.subSequenceStartPositions) { - std::ostringstream os; - subStartPos->getVector(false)->print(os, subStartPos->getSize()); - LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n" - << os.str(); + auto& argu = nn.getLayer(name)->getOutput(); + std::unordered_map out; + argu.getValueString(&out); + for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { + auto it = out.find(field); + if (it != out.end()) { + LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second; + } } } } diff --git a/paddle/gserver/layers/PrintLayer.cpp b/paddle/gserver/layers/PrintLayer.cpp index 85f52ad5debd03..f1f3dd412c67a9 100644 --- a/paddle/gserver/layers/PrintLayer.cpp +++ b/paddle/gserver/layers/PrintLayer.cpp @@ -26,28 +26,15 @@ class PrintLayer : public Layer { void PrintLayer::forward(PassType passType) { Layer::forward(passType); for (size_t i = 0; i != inputLayers_.size(); ++i) { - const auto& argu = getInput(i); + auto& argu = getInput(i); const std::string& name = inputLayers_[i]->getName(); - if (argu.value) { - std::ostringstream os; - argu.value->print(os); - LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str(); - } - if (argu.ids) { - std::ostringstream os; - argu.ids->print(os, argu.ids->getSize()); - LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str(); - } - if (auto startPos = argu.sequenceStartPositions) { - std::ostringstream os; - startPos->getVector(false)->print(os, startPos->getSize()); - LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str(); - } - if (auto subStartPos = argu.subSequenceStartPositions) { - std::ostringstream os; - subStartPos->getVector(false)->print(os, subStartPos->getSize()); - LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n" - << os.str(); + std::unordered_map out; + argu.getValueString(&out); + for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { + auto it = out.find(field); + if (it != out.end()) { + LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second; + } } } } diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 65d01a15718ae2..e9de0f66987603 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -602,6 +602,32 @@ void Argument::degradeSequence(const Argument& input, bool useGpu) { tgtBuf[numSequences] = numSubSequences; } +void Argument::getValueString( + std::unordered_map* out) const { + if (value) { + std::ostringstream os; + value->print(os); + out->insert({"value", os.str()}); + } + if (ids) { + std::ostringstream os; + ids->print(os, ids->getSize()); + out->insert({"ids", os.str()}); + } + if (sequenceStartPositions) { + std::ostringstream os; + sequenceStartPositions->getVector(false)->print( + os, sequenceStartPositions->getSize()); + out->insert({"sequence pos", os.str()}); + } + if (subSequenceStartPositions) { + std::ostringstream os; + subSequenceStartPositions->getVector(false)->print( + os, subSequenceStartPositions->getSize()); + out->insert({"sub-sequence pos", os.str()}); + } +} + void Argument::subArgFrom(const Argument& input, size_t offset, size_t height, diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index afd2de0202bf0f..c751dbb855d31d 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -297,6 +297,14 @@ struct Argument { sequence has sub-sequence degrades to a sequence. */ void degradeSequence(const Argument& input, bool useGpu); + + /** + * @brief getValueString will return the argument's output in string. There + * are several kinds of output. The keys of output dictionary are 'value', + * 'id', 'sequence pos', 'sub-sequence pos'. + * @param out [out]: the return values. + */ + void getValueString(std::unordered_map* out) const; }; } // namespace paddle From 04eaf75c5ea049ec88ba0c34f40176dee930c5ed Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 17:06:36 +0800 Subject: [PATCH 218/370] Add getValue to some evaluators. --- paddle/gserver/evaluators/Evaluator.cpp | 149 +++++++++++++----- paddle/gserver/evaluators/Evaluator.h | 44 +++++- .../gradientmachines/NeuralNetwork.cpp | 38 +++++ .../gserver/gradientmachines/NeuralNetwork.h | 4 + paddle/utils/Error.h | 2 + 5 files changed, 198 insertions(+), 39 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index be4f5f558fb995..b1daa09062fabe 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -538,12 +538,15 @@ double RankAucEvaluator::calcRankAuc(real* outputData, : aucTmp / (clickSum * noClickSum); } +std::string RankAucEvaluator::getTypeImpl() const { return "rankauc"; } + // class PrecisionRecallEvaluator REGISTER_EVALUATOR(precision_recall, PrecisionRecallEvaluator); void PrecisionRecallEvaluator::start() { Evaluator::start(); statsInfo_.clear(); + values_.clear(); } real PrecisionRecallEvaluator::evalImp(std::vector& arguments) { @@ -603,7 +606,9 @@ real PrecisionRecallEvaluator::evalImp(std::vector& arguments) { return 0; } -void PrecisionRecallEvaluator::printStats(std::ostream& os) const { +template +void PrecisionRecallEvaluator::printStatsHelper(T1 labelCallback, + T2 microAvgCallback) const { int label = config_.positive_label(); if (label != -1) { CHECK(label >= 0 && label < (int)statsInfo_.size()) @@ -612,9 +617,7 @@ void PrecisionRecallEvaluator::printStats(std::ostream& os) const { double precision = calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP); double recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN); - os << "positive_label=" << label << " precision=" << precision - << " recall=" << recall - << " F1-score=" << calcF1Score(precision, recall); + labelCallback(label, precision, recall, calcF1Score(precision, recall)); return; } @@ -636,21 +639,45 @@ void PrecisionRecallEvaluator::printStats(std::ostream& os) const { macroAvgPrecision /= numLabels; macroAvgRecall /= numLabels; double macroAvgF1Score = calcF1Score(macroAvgPrecision, macroAvgRecall); - os << "macro-average-precision=" << macroAvgPrecision - << " macro-average-recall=" << macroAvgRecall - << " macro-average-F1-score=" << macroAvgF1Score; double microAvgPrecision = calcPrecision(microTotalTP, microTotalFP); double microAvgRecall = calcPrecision(microTotalTP, microTotalFN); double microAvgF1Score = calcF1Score(microAvgPrecision, microAvgRecall); - if (!isMultiBinaryLabel_) { - // precision and recall are equal in this case - os << " micro-average-precision=" << microAvgPrecision; - } else { - os << " micro-average-precision=" << microAvgPrecision - << " micro-average-recall=" << microAvgRecall - << " micro-average-F1-score=" << microAvgF1Score; - } + + microAvgCallback(macroAvgPrecision, + macroAvgRecall, + macroAvgF1Score, + isMultiBinaryLabel_, + microAvgPrecision, + microAvgRecall, + microAvgF1Score); +} + +void PrecisionRecallEvaluator::printStats(std::ostream& os) const { + this->printStatsHelper( + [&os](int label, double precision, double recall, double f1) { + os << "positive_label=" << label << " precision=" << precision + << " recall=" << recall << " F1-score=" << f1; + }, + [&os](double macroAvgPrecision, + double macroAvgRecall, + double macroAvgF1Score, + bool isMultiBinaryLabel, + double microAvgPrecision, + double microAvgRecall, + double microAvgF1Score) { + os << "macro-average-precision=" << macroAvgPrecision + << " macro-average-recall=" << macroAvgRecall + << " macro-average-F1-score=" << macroAvgF1Score; + if (!isMultiBinaryLabel) { + // precision and recall are equal in this case + os << " micro-average-precision=" << microAvgPrecision; + } else { + os << " micro-average-precision=" << microAvgPrecision + << " micro-average-recall=" << microAvgRecall + << " micro-average-F1-score=" << microAvgF1Score; + } + }); } void PrecisionRecallEvaluator::calcStatsInfo(const MatrixPtr& output, @@ -731,6 +758,69 @@ void PrecisionRecallEvaluator::calcStatsInfoMulti(const MatrixPtr& output, } } +void PrecisionRecallEvaluator::storeLocalValues() const { + if (this->values_.size() == 0) { + this->printStatsHelper( + [this](int label, double precision, double recall, double f1) { + values_["positive_label"] = (double)label; + values_["precision"] = precision; + values_["recal"] = recall; + values_["F1-score"] = f1; + }, + [this](double macroAvgPrecision, + double macroAvgRecall, + double macroAvgF1Score, + bool isMultiBinaryLabel, + double microAvgPrecision, + double microAvgRecall, + double microAvgF1Score) { + values_["macro-average-precision"] = macroAvgPrecision; + values_["macro-average-recall"] = macroAvgRecall; + values_["macro-average-F1-score"] = macroAvgF1Score; + if (!isMultiBinaryLabel) { + // precision and recall are equal in this case + values_["micro-average-precision"] = microAvgPrecision; + } else { + values_["micro-average-precision"] = microAvgPrecision; + values_["micro-average-recall"] = microAvgRecall; + values_["micro-average-F1-score"] = microAvgF1Score; + } + }); + } +} + +void PrecisionRecallEvaluator::getNames(std::vector* names) { + this->storeLocalValues(); + names->clear(); + names->reserve(this->values_.size()); + for (auto it = this->values_.begin(); it != this->values_.end(); ++it) { + names->push_back(this->config_.name() + "." + it->first); + } +} + +real PrecisionRecallEvaluator::getValue(const std::string& name, + Error* err) const { + this->storeLocalValues(); + auto it = this->values_.find(name); + if (it != this->values_.end() && err != nullptr) { + *err = Error("No such key %s", name.c_str()); + return .0f; + } + + return it->second; +} + +std::string PrecisionRecallEvaluator::getType(const std::string& name, + Error* err) const { + this->storeLocalValues(); + auto it = this->values_.find(name); + if (it != this->values_.end() && err != nullptr) { + *err = Error("No such key %s", name.c_str()); + return ""; + } + return "precision_recall"; +} + void PrecisionRecallEvaluator::distributeEval(ParameterClient2* client) { size_t size = 4 * statsInfo_.size(); double* buf = new double[size]; @@ -874,6 +964,8 @@ void PnpairEvaluator::calc(std::vector& predictArray) { << " calc total special pair: " << special; } +std::string PnpairEvaluator::getTypeImpl() const { return "pnpair"; } + ClassRegistrar Evaluator::registrar_; Evaluator* Evaluator::create(const EvaluatorConfig& config) { Evaluator* evaluator = registrar_.createByType(config.type()); @@ -901,27 +993,12 @@ class ValuePrinter : public Evaluator { virtual void eval(const NeuralNetwork& nn) { for (const std::string& name : config_.input_layers()) { - const Argument& argu = nn.getLayer(name)->getOutput(); - if (argu.value) { - std::ostringstream os; - argu.value->print(os); - LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str(); - } - if (argu.ids) { - std::ostringstream os; - argu.ids->print(os, argu.ids->getSize()); - LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str(); - } - if (auto startPos = argu.sequenceStartPositions) { - std::ostringstream os; - startPos->getVector(false)->print(os, startPos->getSize()); - LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str(); - } - if (auto subStartPos = argu.subSequenceStartPositions) { - std::ostringstream os; - subStartPos->getVector(false)->print(os, subStartPos->getSize()); - LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n" - << os.str(); + std::vector> out; + auto err = nn.getLayerOutputValue(name, &out); + err.check(); + for (auto& each : out) { + LOG(INFO) << "layer=" << name << std::get<0>(each) << ":\n" + << std::get<1>(each); } } } diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index bf08aa07f0ce31..bec3d6e66820fa 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -132,6 +132,20 @@ class Evaluator { return this->getValueImpl(); } + virtual std::string getValueStr(const std::string& name, + paddle::Error* err = nullptr) const { + paddle::Error localErr; + if (err == nullptr) { + err = &localErr; + } + real result = this->getValue(name, err); + if (!err->isOK()) { + return ""; + } else { + return std::to_string(result); + } + } + virtual std::string getType(const std::string& name, paddle::Error* err = nullptr) const { if (name != config_.name() && err != nullptr) { @@ -142,7 +156,9 @@ class Evaluator { } protected: - virtual real getValueImpl() const { return .0f; } + virtual real getValueImpl() const { + return numSamples_ != .0 ? totalScore_ / numSamples_ : .0; + } virtual std::string getTypeImpl() const { return "base"; } @@ -261,6 +277,10 @@ class RankAucEvaluator : public Evaluator { real* clickData, real* pvData, size_t size); + + // Evaluator interface +protected: + std::string getTypeImpl() const; }; /** * @brief precision, recall and f1 score Evaluator @@ -310,6 +330,9 @@ class PrecisionRecallEvaluator : public Evaluator { IVectorPtr cpuLabel_; MatrixPtr cpuWeight_; + template + void printStatsHelper(T1 labelCallback, T2 microAvgCallback) const; + void calcStatsInfo(const MatrixPtr& output, const IVectorPtr& label, const MatrixPtr& weight); @@ -341,6 +364,15 @@ class PrecisionRecallEvaluator : public Evaluator { return 0; } } + + mutable std::unordered_map values_; + + void storeLocalValues() const; + // Evaluator interface +public: + void getNames(std::vector* names); + real getValue(const std::string& name, Error* err) const; + std::string getType(const std::string& name, Error* err) const; }; /* @@ -387,8 +419,7 @@ class PnpairEvaluator : public Evaluator { virtual void finish() { calc(predictArray_); } virtual void printStats(std::ostream& os) const { - os << " pos/neg" - << "=" << pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]); + os << " pos/neg=" << this->getValueImpl(); } virtual void distributeEval(ParameterClient2* client) { @@ -404,6 +435,13 @@ class PnpairEvaluator : public Evaluator { IVectorPtr cpuLabel_; IVectorPtr cpuInfo_; MatrixPtr cpuWeight_; + + // Evaluator interface +protected: + real getValueImpl() const { + return pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]); + } + std::string getTypeImpl() const; }; } // namespace paddle diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 22051e07ee0026..bf76830d61f9a3 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -405,4 +405,42 @@ NeuralNetwork* NeuralNetwork::newNeuralNetwork(const std::string& name, } } +Error NeuralNetwork::getLayerOutputValue( + const std::string& layerName, + std::vector>* out) const { + auto& layers = this->config_.layers(); + auto it = std::find_if( + layers.begin(), layers.end(), [&layerName](const LayerConfig& conf) { + return conf.name() == layerName; + }); + if (it == layers.end()) { + return Error("Cannot find layer %s", layerName.c_str()); + } + auto& layer = this->getLayer(layerName); + out->reserve(4); + auto& argu = layer->getOutput(); + + if (argu.value) { + std::ostringstream os; + argu.value->print(os); + out->push_back({"value", os.str()}); + } + if (argu.ids) { + std::ostringstream os; + argu.ids->print(os, argu.ids->getSize()); + out->push_back({"ids", os.str()}); + } + if (auto startPos = argu.sequenceStartPositions) { + std::ostringstream os; + startPos->getVector(false)->print(os, startPos->getSize()); + out->push_back({"sequence pos", os.str()}); + } + if (auto subStartPos = argu.subSequenceStartPositions) { + std::ostringstream os; + subStartPos->getVector(false)->print(os, subStartPos->getSize()); + out->push_back({"sub-sequence pos", os.str()}); + } + return Error(); +} + } // namespace paddle diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 25af4abcf81700..4d277d103f143d 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -128,6 +128,10 @@ class NeuralNetwork : public GradientMachine { static NeuralNetwork* newNeuralNetwork(const std::string& name = "", NeuralNetwork* rootNetwork = nullptr); + inline Error __must_check getLayerOutputValue( + const std::string& layerName, + std::vector>* out) const; + protected: /** * The constructor of NeuralNetwork. diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index 2b4fbef4e015e7..1ae202890f874c 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -116,6 +116,8 @@ class Error { */ operator bool() const { return msg_ == nullptr; } + bool isOK() const { return *this; } + /** * @brief check this status by glog. * @note It is a temp method used during cleaning Paddle code. It will be From 5b4e7d5cc7efe48d78036621318c6649294071d1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 18:09:06 +0800 Subject: [PATCH 219/370] complete value printer --- paddle/gserver/evaluators/Evaluator.cpp | 90 ++++++++++++++++++++++--- 1 file changed, 80 insertions(+), 10 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 97b25b04c459eb..7ea4ed973cfa4f 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -13,9 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/gserver/evaluators/Evaluator.h" -#include "paddle/utils/Stat.h" - #include "paddle/gserver/gradientmachines/NeuralNetwork.h" +#include "paddle/utils/Stat.h" +#include "paddle/utils/StringUtil.h" DECLARE_int32(trainer_id); @@ -801,7 +801,9 @@ void PrecisionRecallEvaluator::getNames(std::vector* names) { real PrecisionRecallEvaluator::getValue(const std::string& name, Error* err) const { this->storeLocalValues(); - auto it = this->values_.find(name); + std::vector buffers; + paddle::str::split(name, '.', &buffers); + auto it = this->values_.find(buffers[buffers.size() - 1]); if (it != this->values_.end() && err != nullptr) { *err = Error("No such key %s", name.c_str()); return .0f; @@ -812,10 +814,12 @@ real PrecisionRecallEvaluator::getValue(const std::string& name, std::string PrecisionRecallEvaluator::getType(const std::string& name, Error* err) const { - this->storeLocalValues(); - auto it = this->values_.find(name); - if (it != this->values_.end() && err != nullptr) { - *err = Error("No such key %s", name.c_str()); + Error localErr; + if (err == nullptr) { + err = &localErr; + } + this->getValue(name, err); + if (!err->isOK()) { return ""; } return "precision_recall"; @@ -989,12 +993,12 @@ static InitFunction __reg_type_auc_sum__([]() { */ class ValuePrinter : public Evaluator { public: - ValuePrinter() {} - virtual void eval(const NeuralNetwork& nn) { + layerOutputs_.clear(); for (const std::string& name : config_.input_layers()) { auto& argu = nn.getLayer(name)->getOutput(); - std::unordered_map out; + layerOutputs_[name] = std::unordered_map(); + auto& out = layerOutputs_[name]; argu.getValueString(&out); for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { auto it = out.find(field); @@ -1008,6 +1012,72 @@ class ValuePrinter : public Evaluator { virtual void updateSamplesNum(const std::vector& arguments) {} virtual real evalImp(std::vector& arguments) { return 0; } + +private: + std::unordered_map> + layerOutputs_; + + // Evaluator interface +public: + void getNames(std::vector* names) { + for (auto layerIt = layerOutputs_.begin(); layerIt != layerOutputs_.end(); + ++layerIt) { + for (auto it = layerIt->second.begin(); it != layerIt->second.end(); + ++it) { + names->push_back(config_.name() + "." + layerIt->first + "." + + it->second); + } + } + } + + real getValue(const std::string& name, Error* err) const { + (void)(name); + if (err != nullptr) { + *err = Error( + "ValuePrinter do not support getValue, use getValueString instead."); + } + return .0f; + } + std::string getValueStr(const std::string& name, Error* err) const { + std::vector buffer; + str::split(name, '.', &buffer); + if (buffer.size() < 2) { + if (err != nullptr) { + *err = Error("No such key %s", name.c_str()); + } + return ""; + } + auto fieldName = buffer[buffer.size() - 1]; + auto layerName = buffer[buffer.size() - 2]; + auto layerIt = layerOutputs_.find(layerName); + if (layerIt == layerOutputs_.end()) { + if (err != nullptr) { + *err = Error("No such layer %s", layerName.c_str()); + } + return ""; + } + + auto fieldIt = layerIt->second.find(fieldName); + if (fieldIt == layerIt->second.end()) { + if (err != nullptr) { + *err = Error("No such value field %s", fieldName.c_str()); + } + return ""; + } + + return fieldIt->second; + } + std::string getType(const std::string& name, Error* err) const { + Error localErr; + if (err == nullptr) { + err = &localErr; + } + this->getValueStr(name, err); + if (!err->isOK()) { + return ""; + } + return "value_printer"; + } }; REGISTER_EVALUATOR(value_printer, ValuePrinter); /** From 5fd49f66657766cac11fd14a270b0b947a92c49d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 18:12:49 +0800 Subject: [PATCH 220/370] Remove sequence information in GradientPrinter. * it is not necessary, because user could print sequence info by ValuePrinter. --- paddle/gserver/evaluators/Evaluator.cpp | 7 ------- 1 file changed, 7 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index ae7508e2bb117a..01363b583fa1f1 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -929,8 +929,6 @@ REGISTER_EVALUATOR(value_printer, ValuePrinter); */ class GradientPrinter : public Evaluator { public: - GradientPrinter() {} - virtual void eval(const NeuralNetwork& nn) { for (const std::string& name : config_.input_layers()) { const Argument& argu = nn.getLayer(name)->getOutput(); @@ -939,11 +937,6 @@ class GradientPrinter : public Evaluator { argu.grad->print(os); LOG(INFO) << "layer=" << name << " grad matrix:\n" << os.str(); } - if (auto startPos = argu.sequenceStartPositions) { - std::ostringstream os; - startPos->getVector(false)->print(os, startPos->getSize()); - LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str(); - } } } From e6232d82e1650dc2186fa39c93a06a7ef276fc52 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sun, 19 Feb 2017 18:22:33 +0800 Subject: [PATCH 221/370] testing in mnist --- python/paddle/v2/__init__.py | 3 +- python/paddle/v2/data_converter.py | 50 ++++++++++++++---------------- 2 files changed, 26 insertions(+), 27 deletions(-) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index bc064a21ae1502..4ecd0dafd6bc5a 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -20,7 +20,8 @@ import py_paddle.swig_paddle as api __all__ = [ - 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event' + 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', + 'event', 'data_converter' ] diff --git a/python/paddle/v2/data_converter.py b/python/paddle/v2/data_converter.py index 45114b407dae22..afb98a77c5b315 100644 --- a/python/paddle/v2/data_converter.py +++ b/python/paddle/v2/data_converter.py @@ -13,8 +13,8 @@ # limitations under the License. import collections -import py_paddle.swig_paddle -import numpy +import py_paddle.swig_paddle as api +import numpy as np import paddle.trainer.PyDataProvider2 as dp2 __all__ = ['DataConverter'] @@ -50,12 +50,12 @@ def convert(self, data, argument): :param data: input data :type data: list | numpy array :param argument: the type which paddle is acceptable - :type argument: swig_paddle.Arguments + :type argument: Paddle's Arguments """ - assert isinstance(argument, swig_paddle.Arguments) - if data.dtype != numpy.float32: - data = data.astype(numpy.float32) - m = swig_paddle.Matrix.createDenseFromNumpy(data, True, False) + assert isinstance(argument, api.Arguments) + if data.dtype != np.float32: + data = data.astype(np.float32) + m = api.Matrix.createDenseFromNumpy(data, True, False) argument.setSlotValue(self.pos, m) @@ -72,17 +72,16 @@ def fill_csr(self, data): self.__height__ = len(data) for x in data: self.__rows__.append(self.__rows__[-1] + len(x)) - self__cols__ = data.flatten() + self.__cols__ = data.flatten() def convert(self, data, argument): - assert isinstance(argument, swig_paddle.Arguments) + assert isinstance(argument, api.Arguments) fill_csr(data) - m = swig_paddle.Matrix.createSparse(self.__height__, - self.input_type.dim, - len(self.__cols__), - len(self.__value__) == 0) - assert isinstance(m, swig_paddle.Matrix) + m = api.Matrix.createSparse(self.__height__, self.input_type.dim, + len(self.__cols__), + len(self.__value__) == 0) + assert isinstance(m, api.Matrix) m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__) argument.setSlotValue(self.pos, m) @@ -105,9 +104,9 @@ def __init__(self, input_type, pos): self.__ids__ = [] def convert(self, data, argument): - assert isinstance(argument, swig_paddle.Arguments) + assert isinstance(argument, api.Arguments) self.__ids__ = data.flatten() - ids = swig_paddle.IVector.create(self.__ids__) + ids = api.IVector.create(self.__ids__) argument.setSlotIds(self.pos, ids) @@ -135,7 +134,7 @@ def fill_seq(self, data): def convert(self, data, argument): fill_seq(data) - seq = swig_paddle.IVector.create(self.__seq__, False) + seq = api.IVector.create(self.__seq__, False) self.__setter__(argument, self.pos, seq) dat = [] @@ -151,22 +150,21 @@ def get_size(self, data): class DataConverter(object): - def __init__(self, input_mapper): + def __init__(self, input): """ Usege: .. code-block:: python inputs = [('image', dense_vector), ('label', integer_value)] cvt = DataConverter(inputs) - arg = cvt.convert(minibatch_data, {'image':0, 'label':1}) + arg = cvt(minibatch_data, {'image':0, 'label':1}) :param input_mapper: list of (input_name, input_type) :type input_mapper: list """ - assert isinstance(self.input_types, collections.Sequence) self.input_names = [] self.input_types = [] - for each in self.input_types: + for each in input: self.input_names.append(each[0]) self.input_types.append(each[1]) assert isinstance(each[1], dp2.InputType) @@ -186,16 +184,16 @@ def convert(self, data, input_dict=None, argument=None): the feature order in argument and data is the same. :type input_dict: dict, like {string:integer, string, integer, ...}|None :param argument: converted data will be saved in this argument. If None, - it will create a swig_paddle.Arguments firstly. + it will create a Paddle's Arguments firstly. :param type: swig_paddle.Arguments|None """ if argument is None: - argument = swig_paddle.Arguments.createArguments(0) - assert isinstance(argument, swig_paddle.Arguments) + argument = api.Arguments.createArguments(0) + assert isinstance(argument, api.Arguments) argument.resize(len(self.input_types)) converts = [ - DataConverter.create_scanner(i, each_type) + DataConverter.create_converter(i, each_type) for i, each_type in enumerate(self.input_types) ] @@ -212,7 +210,7 @@ def __call__(self, dat, argument=None): return self.convert(dat, argument) @staticmethod - def create_scanner(pos, each): + def create_converter(pos, each): assert isinstance(each, dp2.InputType) retv = None if each.type == dp2.DataType.Dense: From 6deb33d9dcd3db9203b98de0f6fb0d0e83cc2176 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 20:21:13 +0800 Subject: [PATCH 222/370] Complete combined evaluator --- paddle/gserver/evaluators/Evaluator.cpp | 32 ++++++++++--- paddle/gserver/evaluators/Evaluator.h | 20 ++++++++- .../gradientmachines/NeuralNetwork.cpp | 45 ++++++++++++++++++- 3 files changed, 89 insertions(+), 8 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index cde47f572092f5..29b5284fe59b5d 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -102,6 +102,10 @@ class ClassificationErrorEvaluator : public Evaluator { virtual void distributeEval(ParameterClient2* client) { mergeResultsOfAllClients(client); } + + // Evaluator interface +protected: + std::string getTypeImpl() const { return "classification_error"; } }; /** @@ -140,6 +144,10 @@ class SequenceClassificationErrorEvaluator virtual void distributeEval(ParameterClient2* client) { mergeResultsOfAllClients(client); } + + // Evaluator interface +protected: + std::string getTypeImpl() const { return "seq_classification_error"; } }; REGISTER_EVALUATOR(seq_classification_error, SequenceClassificationErrorEvaluator); @@ -230,6 +238,10 @@ class SumEvaluator : public Evaluator { private: IVectorPtr cpuLabel_; MatrixPtr cpuWeight_; + + // Evaluator interface +protected: + std::string getTypeImpl() const { return "sum"; } }; /** * @brief column sum Evaluator @@ -337,10 +349,18 @@ class ColumnSumEvaluator : public Evaluator { } private: - ColumnSumEvaluator() {} int32_t colIdx_; size_t colNum_; MatrixPtr sum_; /* cpu matrix */ + + // Evaluator interface +protected: + std::string getTypeImpl() const { + if (colIdx_ == -1) + return "last-column-sum"; + else + return "column-sum"; + } }; void AucEvaluator::start() { @@ -791,7 +811,6 @@ void PrecisionRecallEvaluator::storeLocalValues() const { void PrecisionRecallEvaluator::getNames(std::vector* names) { this->storeLocalValues(); - names->clear(); names->reserve(this->values_.size()); for (auto it = this->values_.begin(); it != this->values_.end(); ++it) { names->push_back(this->config_.name() + "." + it->first); @@ -1080,12 +1099,13 @@ class ValuePrinter : public Evaluator { } }; REGISTER_EVALUATOR(value_printer, ValuePrinter); + /** * @brief print gradient of each layer. * * The config file api is gradient_printer_evaluator. */ -class GradientPrinter : public Evaluator { +class GradientPrinter : public NotGetableEvaluator { public: virtual void eval(const NeuralNetwork& nn) { for (const std::string& name : config_.input_layers()) { @@ -1108,7 +1128,7 @@ REGISTER_EVALUATOR(gradient_printer, GradientPrinter); * * The config file api is maxid_printer_evaluator. */ -class MaxIdPrinter : public Evaluator { +class MaxIdPrinter : public NotGetableEvaluator { private: IVectorPtr maxIds_; MatrixPtr maxValues_; @@ -1150,7 +1170,7 @@ REGISTER_EVALUATOR(max_id_printer, MaxIdPrinter); * * The config file api is maxframe_printer_evaluator. */ -class MaxFramePrinter : public Evaluator { +class MaxFramePrinter : public NotGetableEvaluator { private: IVectorPtr maxIds_; MatrixPtr maxValues_; @@ -1237,7 +1257,7 @@ REGISTER_EVALUATOR(max_frame_printer, MaxFramePrinter); * The config file api is seqtext_printer_evaluator. * */ -class SequenceTextPrinter : public Evaluator { +class SequenceTextPrinter : public NotGetableEvaluator { private: /// dict_file, which contains a list of tokens std::vector dict_; diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index bec3d6e66820fa..d5b2a63e35e157 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -119,7 +119,6 @@ class Evaluator { static ClassRegistrar registrar_; virtual void getNames(std::vector* names) { - names->clear(); names->push_back(config_.name()); } @@ -168,6 +167,25 @@ class Evaluator { double totalScore_; }; +class NotGetableEvaluator : public Evaluator { + // Evaluator interface +public: + void getNames(std::vector* names) {} + + real getValue(const std::string& name, Error* err) const { + if (err != nullptr) { + *err = Error("Not implemented"); + } + return .0f; + } + std::string getType(const std::string& name, Error* err) const { + if (err != nullptr) { + *err = Error("Not implemented"); + } + return ""; + } +}; + class DummyEvaluator : public Evaluator { public: DummyEvaluator() {} diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 22051e07ee0026..277fb6d8db13ce 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -306,7 +306,6 @@ void NeuralNetwork::onPassEnd() { class CombinedEvaluator : public Evaluator { public: - CombinedEvaluator() {} void addEvaluator(std::unique_ptr&& evaluator) { evaluators_.emplace_back(std::move(evaluator)); } @@ -346,6 +345,50 @@ class CombinedEvaluator : public Evaluator { protected: std::vector> evaluators_; + + // Evaluator interface +public: + void getNames(std::vector* names) { + for (auto& eval : evaluators_) { + eval->getNames(names); + } + } + + real getValue(const std::string& name, Error* err) const { + return this->getMethodHelper( + name, err, [&name, err](const std::unique_ptr& eval) { + return eval->getValue(name, err); + }); + } + std::string getValueStr(const std::string& name, Error* err) const { + return this->getMethodHelper( + name, err, [&name, err](const std::unique_ptr& eval) { + return eval->getValueStr(name, err); + }); + } + std::string getType(const std::string& name, Error* err) const { + return this->getMethodHelper( + name, err, [&name, err](const std::unique_ptr& eval) { + return eval->getType(name, err); + }); + } + +private: + template + T getMethodHelper(const std::string& name, + Error* err, + const std::function&)>& + callback) const { + for (auto& eval : evaluators_) { + std::vector names; + eval->getNames(&names); + if (std::find(names.begin(), names.end(), name) != names.end()) { + return callback(eval); + } + } + if (err != nullptr) *err = Error("No such key %s", name.c_str()); + return T(); + } }; Evaluator* NeuralNetwork::makeEvaluator() const { From 1100366803c10aa9cbd7265616d286e1b38681c0 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 19 Feb 2017 20:43:04 +0800 Subject: [PATCH 223/370] Add unittests & fix some bugs. --- paddle/gserver/evaluators/CTCErrorEvaluator.cpp | 2 +- paddle/gserver/evaluators/Evaluator.cpp | 6 ++++-- paddle/gserver/tests/test_Evaluator.cpp | 12 ++++++++++++ 3 files changed, 17 insertions(+), 3 deletions(-) diff --git a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp index 05aa6c012ae2bc..132119015f967c 100644 --- a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp +++ b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp @@ -20,7 +20,7 @@ namespace paddle { /** * calculate sequence-to-sequence edit distance */ -class CTCErrorEvaluator : public Evaluator { +class CTCErrorEvaluator : public NotGetableEvaluator { private: MatrixPtr outActivations_; int numTimes_, numClasses_, numSequences_, blank_; diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 29b5284fe59b5d..42a877954b6c3e 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -823,8 +823,10 @@ real PrecisionRecallEvaluator::getValue(const std::string& name, std::vector buffers; paddle::str::split(name, '.', &buffers); auto it = this->values_.find(buffers[buffers.size() - 1]); - if (it != this->values_.end() && err != nullptr) { - *err = Error("No such key %s", name.c_str()); + if (it == this->values_.end()) { // not found + if (err != nullptr) { + *err = Error("No such key %s", name.c_str()); + } return .0f; } diff --git a/paddle/gserver/tests/test_Evaluator.cpp b/paddle/gserver/tests/test_Evaluator.cpp index 8165eb82693361..100cf078095092 100644 --- a/paddle/gserver/tests/test_Evaluator.cpp +++ b/paddle/gserver/tests/test_Evaluator.cpp @@ -110,6 +110,18 @@ void testEvaluator(TestConfig testConf, testEvaluator->finish(); LOG(INFO) << *testEvaluator; + std::vector names; + testEvaluator->getNames(&names); + paddle::Error err; + for (auto& name : names) { + auto value = testEvaluator->getValueStr(name, &err); + ASSERT_TRUE(err.isOK()); + LOG(INFO) << name << " " << value; + auto tp = testEvaluator->getType(name, &err); + ASSERT_TRUE(err.isOK()); + ASSERT_EQ(testConf.evaluatorConfig.type(), tp); + } + double totalScore2 = 0.0; if (testConf.testAccumulate) { testEvaluator->start(); From 948218dab96369b24027c801b61cad699db7b4c6 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 20 Feb 2017 14:08:35 +0800 Subject: [PATCH 224/370] Unify PrintLogic in PrintLayer/ValuePrinter. --- paddle/gserver/evaluators/Evaluator.cpp | 13 ++----------- paddle/gserver/layers/PrintLayer.cpp | 24 ++++++++---------------- paddle/parameter/Argument.cpp | 12 ++++++++++++ paddle/parameter/Argument.h | 3 +++ 4 files changed, 25 insertions(+), 27 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 1a43d3bd996109..1b06519713dbba 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -888,19 +888,10 @@ Evaluator* Evaluator::create(const EvaluatorConfig& config) { */ class ValuePrinter : public Evaluator { public: - ValuePrinter() {} - virtual void eval(const NeuralNetwork& nn) { for (const std::string& name : config_.input_layers()) { - auto& argu = nn.getLayer(name)->getOutput(); - std::unordered_map out; - argu.getValueString(&out); - for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { - auto it = out.find(field); - if (it != out.end()) { - LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second; - } - } + nn.getLayer(name)->getOutput().printValueString(LOG(INFO), + "layer=" + name + " "); } } diff --git a/paddle/gserver/layers/PrintLayer.cpp b/paddle/gserver/layers/PrintLayer.cpp index f1f3dd412c67a9..de198af111be42 100644 --- a/paddle/gserver/layers/PrintLayer.cpp +++ b/paddle/gserver/layers/PrintLayer.cpp @@ -19,25 +19,17 @@ namespace paddle { class PrintLayer : public Layer { public: explicit PrintLayer(const LayerConfig& config) : Layer(config) {} - void forward(PassType passType) override; - void backward(const UpdateCallback& callback) override {} -}; -void PrintLayer::forward(PassType passType) { - Layer::forward(passType); - for (size_t i = 0; i != inputLayers_.size(); ++i) { - auto& argu = getInput(i); - const std::string& name = inputLayers_[i]->getName(); - std::unordered_map out; - argu.getValueString(&out); - for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { - auto it = out.find(field); - if (it != out.end()) { - LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second; - } + void forward(PassType passType) override { + Layer::forward(passType); + for (size_t i = 0; i != inputLayers_.size(); ++i) { + getInput(i).printValueString(LOG(INFO), + "layer=" + inputLayers_[i]->getName() + " "); } } -} + + void backward(const UpdateCallback& callback) override {} +}; REGISTER_LAYER(print, PrintLayer); diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index e9de0f66987603..7a343cca33f5b4 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -628,6 +628,18 @@ void Argument::getValueString( } } +void Argument::printValueString(std::ostream& stream, + const std::string& prefix) const { + std::unordered_map out; + getValueString(&out); + for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) { + auto it = out.find(field); + if (it != out.end()) { + stream << prefix << field << ":\n" << it->second; + } + } +} + void Argument::subArgFrom(const Argument& input, size_t offset, size_t height, diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index c751dbb855d31d..e2a8500c3f82a1 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -305,6 +305,9 @@ struct Argument { * @param out [out]: the return values. */ void getValueString(std::unordered_map* out) const; + + void printValueString(std::ostream& stream, + const std::string& prefix = "") const; }; } // namespace paddle From b5b9a9cfb797db957d51b2dc5fc6a7d45d90e46b Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 20 Feb 2017 14:17:18 +0800 Subject: [PATCH 225/370] Add comments --- paddle/parameter/Argument.h | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index e2a8500c3f82a1..178c068b93ac5f 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -306,6 +306,12 @@ struct Argument { */ void getValueString(std::unordered_map* out) const; + /** + * @brief printValueString will print the argument's output in order of + * 'value', 'id', 'sequence pos', 'sub-sequence pos'. + * @param stream: Output stream + * @param prefix: line prefix for printing. + */ void printValueString(std::ostream& stream, const std::string& prefix = "") const; }; From f846e8fec2e9c8d16e24e12d6b6b7efc77d3cc83 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 20 Feb 2017 15:13:43 +0800 Subject: [PATCH 226/370] Add const for getLayerOutput --- paddle/gserver/gradientmachines/GradientMachine.h | 2 +- .../gradientmachines/MultiGradientMachine.cpp | 12 ++++++------ .../gserver/gradientmachines/MultiGradientMachine.h | 2 +- paddle/gserver/gradientmachines/NeuralNetwork.cpp | 2 +- paddle/gserver/gradientmachines/NeuralNetwork.h | 2 +- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index a814e771d12e3a..5469c0d89f4964 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -134,7 +134,7 @@ class GradientMachine { backward(callback); } - virtual MatrixPtr getLayerOutput(const std::string& layerName) { + virtual MatrixPtr getLayerOutput(const std::string& layerName) const { return nullptr; } diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 7e60920376d195..2d42e648302f16 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -282,7 +282,8 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, backwardImp(callback); } -MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { +MatrixPtr MultiGradientMachine::getLayerOutput( + const std::string& layerName) const { // each thread has the same neural network auto nn = threads_[0]->getGradientMachine(); size_t height = 0; @@ -301,11 +302,10 @@ MatrixPtr MultiGradientMachine::getLayerOutput(const std::string& layerName) { // copy one layer output from one trainer thread at each time size_t startRow = 0; - - for (size_t i = 0; i < threads_.size(); i++) { - auto tmpMatrix = layerOutput->subMatrix(startRow, mats[i]->getHeight()); - tmpMatrix->copyFrom(*mats[i]); - startRow += mats[i]->getHeight(); + for (auto& mat : mats) { + auto tmpMatrix = layerOutput->subMatrix(startRow, mat->getHeight()); + tmpMatrix->copyFrom(*mat); + startRow += mat->getHeight(); } return layerOutput; diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.h b/paddle/gserver/gradientmachines/MultiGradientMachine.h index 988d5098179806..a1a2d417062de5 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.h +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.h @@ -189,7 +189,7 @@ class MultiGradientMachine : public GradientMachine { PassType passType, const UpdateCallback& callback); - virtual MatrixPtr getLayerOutput(const std::string& layerName); + virtual MatrixPtr getLayerOutput(const std::string& layerName) const; virtual void onPassEnd(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 1f9ace4f67fdca..00887c81d47687 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -293,7 +293,7 @@ void NeuralNetwork::backward(const UpdateCallback& callback) { } } -MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) { +MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) const { auto it = layerMap_.find(layerName); CHECK(it != layerMap_.end()) << "Cannot find layer: " << layerName; return it->second->getOutputValue(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index bf9ed09327f2f1..6ecc251a409cb9 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -87,7 +87,7 @@ class NeuralNetwork : public GradientMachine { virtual void backward(const UpdateCallback& callback = nullptr); - virtual MatrixPtr getLayerOutput(const std::string& layerName); + virtual MatrixPtr getLayerOutput(const std::string& layerName) const; const LayerPtr& getLayer(const std::string& layerName) const { auto it = layerMap_.find(layerName); From 27a42c2e3bce8818c55b7ec7e74290151aa5ff96 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 20 Feb 2017 14:25:53 +0800 Subject: [PATCH 227/370] add SequenceConcatLayer in trainer_config_helpers --- paddle/gserver/layers/SequenceConcatLayer.cpp | 6 ++- .../paddle/trainer_config_helpers/layers.py | 51 +++++++++++++++++++ .../tests/configs/file_list.sh | 3 +- .../configs/protostr/test_seq_concat.protostr | 39 ++++++++++++++ .../tests/configs/test_seq_concat.py | 9 ++++ 5 files changed, 105 insertions(+), 3 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py diff --git a/paddle/gserver/layers/SequenceConcatLayer.cpp b/paddle/gserver/layers/SequenceConcatLayer.cpp index 599706eb419ede..4b24d8f0c852e1 100644 --- a/paddle/gserver/layers/SequenceConcatLayer.cpp +++ b/paddle/gserver/layers/SequenceConcatLayer.cpp @@ -21,9 +21,11 @@ namespace paddle { /** * A layer for concatenating the first sequence with the second sequence - * following the first - * Input: two sequences each containing some instances + * Input: two sequences each containing the same number of instances + * seq1 = [a1, a2, ..., an] + * seq2 = [b1, b2, ..., bn] * Output: a concatenated sequence of the two input sequences + * out = [a1, b1, a2, b2, ..., an, bn] */ class SequenceConcatLayer : public Layer { diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1fdc4c46236371..ce9213a5c015a3 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -59,6 +59,7 @@ 'img_cmrnorm_layer', 'addto_layer', 'concat_layer', + 'seq_concat_layer', 'lstm_step_layer', 'recurrent_group', 'memory', @@ -144,6 +145,7 @@ class LayerType(object): CONCAT_LAYER = 'concat' CONCAT_PROJ_LAYER = 'concat2' + SEQUENCE_CONCAT_LAYER = 'seqconcat' LSTM_STEP_LAYER = 'lstm_step' GRU_STEP_LAYER = 'gru_step' @@ -2570,6 +2572,55 @@ def __reduce_concat_type__(a, b): size=sz) +@wrap_name_default("seqconcat") +@wrap_act_default(act=IdentityActivation()) +@layer_support() +def seq_concat_layer(a, b, act=None, name=None, layer_attr=None, + bias_attr=None): + """ + Concat sequence a with sequence b. + Inputs: a = [a1, a2, ..., an] + b = [b1, b2, ..., bn] + Note that the length of a and b should be the same. + Output: [a1, b1, a2, b2, ..., an, bn] + + The example usage is: + + .. code-block:: python + + concat = seq_concat_layer(a=layer1, b=layer2) + + :param name: Layer name. + :type name: basestring + :param a: input sequence layer + :type a: LayerOutput + :param b: input sequence layer + :type b: LayerOutput + :param act: Activation type. + :type act: BaseActivation + :param layer_attr: Extra Layer Attribute. + :type layer_attr: ExtraLayerAttribute + :return: LayerOutput object. + :rtype: LayerOutput + """ + assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput) + assert a.size == b.size + Layer( + name=name, + type=LayerType.SEQUENCE_CONCAT_LAYER, + inputs=[a.name, b.name], + active_type=act.name, + bias=ParamAttr.to_bias(bias_attr), + **ExtraLayerAttribute.to_kwargs(layer_attr)) + + return LayerOutput( + name, + layer_type=LayerType.SEQUENCE_CONCAT_LAYER, + parents=[a, b], + activation=act, + size=a.size) + + def memory(name, size, is_seq=False, diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index ea46b557a26ce6..55cef6be0c630c 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -4,6 +4,7 @@ test_sequence_pooling test_lstmemory_layer test_grumemory_layer last_first_seq test_expand_layer test_ntm_layers test_hsigmoid img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight -test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops) +test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops +test_seq_concat) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr new file mode 100644 index 00000000000000..2fa55e87b5d13c --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr @@ -0,0 +1,39 @@ +type: "nn" +layers { + name: "data1" + type: "data" + size: 30 + active_type: "" +} +layers { + name: "data2" + type: "data" + size: 30 + active_type: "" +} +layers { + name: "__seqconcat_0__" + type: "seqconcat" + size: 30 + active_type: "" + inputs { + input_layer_name: "data1" + } + inputs { + input_layer_name: "data2" + } +} +input_layer_names: "data1" +input_layer_names: "data2" +output_layer_names: "__seqconcat_0__" +sub_models { + name: "root" + layer_names: "data1" + layer_names: "data2" + layer_names: "__seqconcat_0__" + input_layer_names: "data1" + input_layer_names: "data2" + output_layer_names: "__seqconcat_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py b/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py new file mode 100644 index 00000000000000..193d9d0df9ed80 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py @@ -0,0 +1,9 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +din1 = data_layer(name='data1', size=30) + +din2 = data_layer(name='data2', size=30) + +outputs(seq_concat_layer(a=din1, b=din2)) From 686b74651d6c123d70dd07157af7ab905d5a4796 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 20 Feb 2017 16:04:10 +0800 Subject: [PATCH 228/370] filter the illegal sentences. --- demo/sentiment/dataprovider.py | 2 ++ demo/sentiment/predict.py | 6 +++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/demo/sentiment/dataprovider.py b/demo/sentiment/dataprovider.py index 00f72cecacb454..4b7f5d0e504aef 100755 --- a/demo/sentiment/dataprovider.py +++ b/demo/sentiment/dataprovider.py @@ -32,4 +32,6 @@ def process(settings, file_name): word_slot = [ settings.word_dict[w] for w in words if w in settings.word_dict ] + if not word_slot: + continue yield word_slot, label diff --git a/demo/sentiment/predict.py b/demo/sentiment/predict.py index 8ec490f6469192..64c78e0d6b9297 100755 --- a/demo/sentiment/predict.py +++ b/demo/sentiment/predict.py @@ -138,7 +138,11 @@ def main(): batch = [] for line in sys.stdin: - batch.append([predict.get_index(line)]) + words = predict.get_index(line) + if words: + batch.append([words]) + else: + print('All the words in [%s] are not in the dictionary.' % line) if len(batch) == batch_size: predict.batch_predict(batch) batch = [] From 47043fe1d3a511e0c88625ff4382adb1fea0feb8 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 20 Feb 2017 16:15:22 +0800 Subject: [PATCH 229/370] add type to datalayer --- demo/mnist/api_train.py | 4 ++-- python/paddle/v2/layer.py | 37 ++++++++++++++++++++++++++++++++++--- 2 files changed, 36 insertions(+), 5 deletions(-) diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index fe39f0bd23f78e..77e6fa89003b6f 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -71,8 +71,8 @@ def main(): assert isinstance(updater, api.ParameterUpdater) # define network - images = paddle_v2.layer.data(name='pixel', size=784) - label = paddle_v2.layer.data(name='label', size=10) + images = paddle_v2.layer.data(name='pixel', type=dp.dense_vector(784)) + label = paddle_v2.layer.data(name='label', type=dp.integer_value(10)) hidden1 = paddle_v2.layer.fc(input=images, size=200) hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) inference = paddle_v2.layer.fc(input=hidden2, diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 0ce4ecd569aa1d..e006b789222c01 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -67,6 +67,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. """ import paddle.trainer_config_helpers as conf_helps +import paddle.trainer.PyDataProvider2 as dp from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default @@ -157,7 +158,37 @@ def to_proto_impl(self, **kwargs): return V2LayerImpl -data = __convert_to_v2__('data_layer', None, []) +""" +Some layer may need some special config, and can not use __convert_to_v2__ to convert. +So we also need to implement some special LayerV2. +""" + + +class DataLayerV2(Layer): + def __init__(self, name, type, **kwargs): + self.__method_name__ = 'data_layer' + + assert isinstance(type, dp.InputType) + + # get data_size from type.dim + args = dict() + for key in kwargs: + args[key] = kwargs[key] + args['size'] = type.dim + self.__args__ = args + + super(DataLayerV2, self).__init__(name=name, parent_layers=dict()) + + def to_proto_impl(self, **kwargs): + args = dict() + for each in kwargs: + args[each] = kwargs[each] + for each in self.__args__: + args[each] = self.__args__[each] + return getattr(conf_helps, self.__method_name__)(name=self.name, **args) + + +data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( 'maxid_layer', name_prefix='maxid_layer', parent_names=['input']) @@ -171,8 +202,8 @@ def to_proto_impl(self, **kwargs): parent_names=['input', 'label']) if __name__ == '__main__': - pixel = data(name='pixel', size=784) - label = data(name='label', size=10) + pixel = data(name='pixel', type=dp.dense_vector(784)) + label = data(name='label', type=dp.integer_value(10)) hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) From 3b69629d3299543bd4f1f0e08794cd4a335609b9 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 20 Feb 2017 16:18:29 +0800 Subject: [PATCH 230/370] modify api_train_v2 --- demo/mnist/api_train_v2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index b5cc74ce67dfc8..979cc3f1b9eb22 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -16,8 +16,8 @@ def main(): paddle.init(use_gpu=False, trainer_count=1) # define network topology - images = paddle.layer.data(name='pixel', size=784) - label = paddle.layer.data(name='label', size=10) + images = paddle.layer.data(name='pixel', type=dense_vector(784)) + label = paddle.layer.data(name='label', type=integer_value(10)) hidden1 = paddle.layer.fc(input=images, size=200) hidden2 = paddle.layer.fc(input=hidden1, size=200) inference = paddle.layer.fc(input=hidden2, From 772b476b76e0436de286922e8486fb21f928e8fc Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 20 Feb 2017 16:38:08 +0800 Subject: [PATCH 231/370] optimize code --- demo/mnist/api_train.py | 4 ++-- demo/mnist/api_train_v2.py | 4 ++-- python/paddle/v2/layer.py | 24 ++++++++++-------------- 3 files changed, 14 insertions(+), 18 deletions(-) diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index 77e6fa89003b6f..ae00e9f54c3a0e 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -71,8 +71,8 @@ def main(): assert isinstance(updater, api.ParameterUpdater) # define network - images = paddle_v2.layer.data(name='pixel', type=dp.dense_vector(784)) - label = paddle_v2.layer.data(name='label', type=dp.integer_value(10)) + images = paddle_v2.layer.data(name='pixel', data_type=dp.dense_vector(784)) + label = paddle_v2.layer.data(name='label', data_type=dp.integer_value(10)) hidden1 = paddle_v2.layer.fc(input=images, size=200) hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) inference = paddle_v2.layer.fc(input=hidden2, diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 979cc3f1b9eb22..b46cf1c8700ba8 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -16,8 +16,8 @@ def main(): paddle.init(use_gpu=False, trainer_count=1) # define network topology - images = paddle.layer.data(name='pixel', type=dense_vector(784)) - label = paddle.layer.data(name='label', type=integer_value(10)) + images = paddle.layer.data(name='pixel', data_type=dense_vector(784)) + label = paddle.layer.data(name='label', data_type=integer_value(10)) hidden1 = paddle.layer.fc(input=images, size=200) hidden2 = paddle.layer.fc(input=hidden1, size=200) inference = paddle.layer.fc(input=hidden2, diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index e006b789222c01..511b3e7457c261 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -165,26 +165,22 @@ def to_proto_impl(self, **kwargs): class DataLayerV2(Layer): - def __init__(self, name, type, **kwargs): - self.__method_name__ = 'data_layer' - - assert isinstance(type, dp.InputType) + def __init__(self, name, data_type, **kwargs): + assert isinstance(data_type, dp.InputType) - # get data_size from type.dim - args = dict() - for key in kwargs: - args[key] = kwargs[key] - args['size'] = type.dim - self.__args__ = args + self.__method_name__ = 'data_layer' + self.__kwargs__ = kwargs + self.__data_size__ = data_type.dim super(DataLayerV2, self).__init__(name=name, parent_layers=dict()) def to_proto_impl(self, **kwargs): args = dict() + args['size'] = self.__data_size__ for each in kwargs: args[each] = kwargs[each] - for each in self.__args__: - args[each] = self.__args__[each] + for each in self.__kwargs__: + args[each] = self.__kwargs__[each] return getattr(conf_helps, self.__method_name__)(name=self.name, **args) @@ -202,8 +198,8 @@ def to_proto_impl(self, **kwargs): parent_names=['input', 'label']) if __name__ == '__main__': - pixel = data(name='pixel', type=dp.dense_vector(784)) - label = data(name='label', type=dp.integer_value(10)) + pixel = data(name='pixel', data_type=dp.dense_vector(784)) + label = data(name='label', data_type=dp.integer_value(10)) hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) From 89cfb39fe450faeb71fc00d13a6421824a43b646 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 20 Feb 2017 16:44:58 +0800 Subject: [PATCH 232/370] use data_type of data layer --- demo/mnist/api_train.py | 2 +- demo/mnist/api_train_v2.py | 4 ++-- python/paddle/v2/layer.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index ae00e9f54c3a0e..c6993eda38bd02 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -99,7 +99,7 @@ def main(): # DataProvider Converter is a utility convert Python Object to Paddle C++ # Input. The input format is as same as Paddle's DataProvider. converter = DataProviderConverter( - input_types=[dp.dense_vector(784), dp.integer_value(10)]) + input_types=[images.data_type, label.data_type]) train_file = './data/raw_data/train' test_file = './data/raw_data/t10k' diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index b46cf1c8700ba8..6bbc3a4ab35a64 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -51,8 +51,8 @@ def event_handler(event): batch_size=32, # batch size should be refactor in Data reader data_types={ # data_types will be removed, It should be in # network topology - 'pixel': dense_vector(784), - 'label': integer_value(10) + 'pixel': images.data_type, + 'label': label.data_type }) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 511b3e7457c261..f2bc47946b6df8 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -168,15 +168,15 @@ class DataLayerV2(Layer): def __init__(self, name, data_type, **kwargs): assert isinstance(data_type, dp.InputType) + self.data_type = data_type self.__method_name__ = 'data_layer' self.__kwargs__ = kwargs - self.__data_size__ = data_type.dim super(DataLayerV2, self).__init__(name=name, parent_layers=dict()) def to_proto_impl(self, **kwargs): args = dict() - args['size'] = self.__data_size__ + args['size'] = self.data_type.dim for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: From bb751b7f2c274879fdc91cb5f84fb1d60b608faf Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 20 Feb 2017 15:20:14 +0800 Subject: [PATCH 233/370] Add documentation for codes. * also remove unused getValueStr --- paddle/gserver/evaluators/Evaluator.h | 71 ++++++++++++++----- .../gradientmachines/NeuralNetwork.cpp | 17 +++-- paddle/gserver/tests/test_Evaluator.cpp | 2 +- 3 files changed, 67 insertions(+), 23 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index d5b2a63e35e157..a5694a088c3c44 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -118,33 +118,55 @@ class Evaluator { static ClassRegistrar registrar_; + /** + * @brief getNames will return all field names of current evaluator. + * + * The format of name is `evaluator_name.evaluator_fields`. If the evaluator + * has multiple field, the name could be `evaluator_name.field1`. For example + * the PrecisionRecallEvaluator contains `precision`, `recall` fields. The get + * names will return `precision_recall_evaluator.precision`, + * `precision_recall.recal`, etc. + * + * Also, if current Evaluator is a combined evaluator. getNames will return + * all names of all evaluators inside the combined evaluator. + * + * @param names [out]: the field names of current evaluator. + * @note Never clear the names parameter inside getNames. + */ virtual void getNames(std::vector* names) { names->push_back(config_.name()); } + /** + * @brief getValue will return the current evaluate value of one field. + * + * @param name: The field name of current evaluator. + * @param err [out]: The error state. nullptr means don't care. + * + * @return The evaluate value(metric). + */ virtual real getValue(const std::string& name, paddle::Error* err = nullptr) const { - if (name != config_.name() && err != nullptr) { - *err = paddle::Error("no such name of evaluator %s", name.c_str()); + if (name != config_.name()) { + if (err != nullptr) { + *err = paddle::Error("no such name of evaluator %s", name.c_str()); + } return .0f; } return this->getValueImpl(); } - virtual std::string getValueStr(const std::string& name, - paddle::Error* err = nullptr) const { - paddle::Error localErr; - if (err == nullptr) { - err = &localErr; - } - real result = this->getValue(name, err); - if (!err->isOK()) { - return ""; - } else { - return std::to_string(result); - } - } - + /** + * @brief getType will return the evaluator type by field name. + * + * Evaluate Type is the current type of evaluator in string. Such as 'auc', + * 'precision_recall'. In combined evaluator, different name may get different + * evaluate type because it could be evaluated by different evaluator inside. + * + * @param name: The field name of current Evaluator. + * @param err: The error state. nullptr means don't care. + * @return the evaluator type string. + */ virtual std::string getType(const std::string& name, paddle::Error* err = nullptr) const { if (name != config_.name() && err != nullptr) { @@ -155,10 +177,22 @@ class Evaluator { } protected: + /** + * @brief getValueImpl The simplest way to define getValue result. If this + * evaluator doesn't contain multiple fields, and do not throw any error, just + * implemented this method to get the evaluate result(metric). + * @return Evaluate result(metric). + */ virtual real getValueImpl() const { return numSamples_ != .0 ? totalScore_ / numSamples_ : .0; } + /** + * @brief getTypeImpl The simplest way to define getType result. If this + * evaluator doesn't combine many evaluators, the get type should only return + * itself type. + * @return Evaluator type. + */ virtual std::string getTypeImpl() const { return "base"; } protected: @@ -167,6 +201,11 @@ class Evaluator { double totalScore_; }; +/** + * @brief The NotGetableEvaluator class is the base class of evaluator that + * cannot get value in runtime. The most NotGetableEvaluator is Printer + * Evaluator, which is only used to debug network configuration. + */ class NotGetableEvaluator : public Evaluator { // Evaluator interface public: diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 277fb6d8db13ce..4d2bdf0dc992da 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -348,24 +348,29 @@ class CombinedEvaluator : public Evaluator { // Evaluator interface public: + /** + * @brief getNames will return all inside evaluators' names. + * @param names [out]: return names. + */ void getNames(std::vector* names) { for (auto& eval : evaluators_) { eval->getNames(names); } } + /** + * @brief getValue could get all inside evaluators' value. + */ real getValue(const std::string& name, Error* err) const { return this->getMethodHelper( name, err, [&name, err](const std::unique_ptr& eval) { return eval->getValue(name, err); }); } - std::string getValueStr(const std::string& name, Error* err) const { - return this->getMethodHelper( - name, err, [&name, err](const std::unique_ptr& eval) { - return eval->getValueStr(name, err); - }); - } + + /** + * @brief getType could get all inside evaluators' type. + */ std::string getType(const std::string& name, Error* err) const { return this->getMethodHelper( name, err, [&name, err](const std::unique_ptr& eval) { diff --git a/paddle/gserver/tests/test_Evaluator.cpp b/paddle/gserver/tests/test_Evaluator.cpp index 100cf078095092..07f486b1f4511b 100644 --- a/paddle/gserver/tests/test_Evaluator.cpp +++ b/paddle/gserver/tests/test_Evaluator.cpp @@ -114,7 +114,7 @@ void testEvaluator(TestConfig testConf, testEvaluator->getNames(&names); paddle::Error err; for (auto& name : names) { - auto value = testEvaluator->getValueStr(name, &err); + auto value = testEvaluator->getValue(name, &err); ASSERT_TRUE(err.isOK()); LOG(INFO) << name << " " << value; auto tp = testEvaluator->getType(name, &err); From f9eddadb3ea29f48d3b93c60e6062f16be0f49a2 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 20 Feb 2017 15:02:02 +0800 Subject: [PATCH 234/370] follow comments, add seq_concat_layer in docs --- doc/api/trainer_config_helpers/layers.rst | 6 ++++++ python/paddle/trainer_config_helpers/layers.py | 10 +++++++--- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/doc/api/trainer_config_helpers/layers.rst b/doc/api/trainer_config_helpers/layers.rst index 8b0e553eacc932..3685868ffcfdf1 100644 --- a/doc/api/trainer_config_helpers/layers.rst +++ b/doc/api/trainer_config_helpers/layers.rst @@ -279,6 +279,12 @@ concat_layer :members: concat_layer :noindex: +seq_concat_layer +---------------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: seq_concat_layer + :noindex: + Reshaping Layers ================ diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index ce9213a5c015a3..0ff29772356af7 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2574,14 +2574,18 @@ def __reduce_concat_type__(a, b): @wrap_name_default("seqconcat") @wrap_act_default(act=IdentityActivation()) +@wrap_bias_attr_default(has_bias=False) @layer_support() def seq_concat_layer(a, b, act=None, name=None, layer_attr=None, bias_attr=None): """ Concat sequence a with sequence b. - Inputs: a = [a1, a2, ..., an] - b = [b1, b2, ..., bn] - Note that the length of a and b should be the same. + + Inputs: + - a = [a1, a2, ..., an] + - b = [b1, b2, ..., bn] + - Note that the length of a and b should be the same. + Output: [a1, b1, a2, b2, ..., an, bn] The example usage is: From 984d54d0180729236f660d3af787fff862fdbc74 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 20 Feb 2017 21:51:57 +0800 Subject: [PATCH 235/370] change data_type to type --- demo/mnist/api_train.py | 10 +++++----- demo/mnist/api_train_v2.py | 9 ++++----- python/paddle/v2/__init__.py | 4 +++- python/paddle/v2/data.py | 22 ++++++++++++++++++++++ python/paddle/v2/layer.py | 14 +++++++------- 5 files changed, 41 insertions(+), 18 deletions(-) create mode 100644 python/paddle/v2/data.py diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index c6993eda38bd02..25c25c1fcddd26 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -9,7 +9,6 @@ import random import numpy as np -import paddle.trainer.PyDataProvider2 as dp import paddle.v2 as paddle_v2 import py_paddle.swig_paddle as api from paddle.trainer_config_helpers import * @@ -71,8 +70,10 @@ def main(): assert isinstance(updater, api.ParameterUpdater) # define network - images = paddle_v2.layer.data(name='pixel', data_type=dp.dense_vector(784)) - label = paddle_v2.layer.data(name='label', data_type=dp.integer_value(10)) + images = paddle_v2.layer.data( + name='pixel', type=paddle_v2.data.dense_vector(784)) + label = paddle_v2.layer.data( + name='label', type=paddle_v2.data.integer_value(10)) hidden1 = paddle_v2.layer.fc(input=images, size=200) hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) inference = paddle_v2.layer.fc(input=hidden2, @@ -98,8 +99,7 @@ def main(): # DataProvider Converter is a utility convert Python Object to Paddle C++ # Input. The input format is as same as Paddle's DataProvider. - converter = DataProviderConverter( - input_types=[images.data_type, label.data_type]) + converter = DataProviderConverter(input_types=[images.type, label.type]) train_file = './data/raw_data/train' test_file = './data/raw_data/t10k' diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6bbc3a4ab35a64..c50a46c4d82447 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -1,6 +1,5 @@ import numpy import paddle.v2 as paddle -from paddle.trainer.PyDataProvider2 import dense_vector, integer_value import mnist_util @@ -16,8 +15,8 @@ def main(): paddle.init(use_gpu=False, trainer_count=1) # define network topology - images = paddle.layer.data(name='pixel', data_type=dense_vector(784)) - label = paddle.layer.data(name='label', data_type=integer_value(10)) + images = paddle.layer.data(name='pixel', type=paddle.data.dense_vector(784)) + label = paddle.layer.data(name='label', type=paddle.data.integer_value(10)) hidden1 = paddle.layer.fc(input=images, size=200) hidden2 = paddle.layer.fc(input=hidden1, size=200) inference = paddle.layer.fc(input=hidden2, @@ -51,8 +50,8 @@ def event_handler(event): batch_size=32, # batch size should be refactor in Data reader data_types={ # data_types will be removed, It should be in # network topology - 'pixel': images.data_type, - 'label': label.data_type + 'pixel': images.type, + 'label': label.type }) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index bc064a21ae1502..cd6dfce0e66765 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -17,10 +17,12 @@ import parameters import trainer import event +import data import py_paddle.swig_paddle as api __all__ = [ - 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event' + 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', + 'event', 'data' ] diff --git a/python/paddle/v2/data.py b/python/paddle/v2/data.py new file mode 100644 index 00000000000000..5b01ba4cd4866c --- /dev/null +++ b/python/paddle/v2/data.py @@ -0,0 +1,22 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddle.trainer.PyDataProvider2 import \ + InputType, dense_vector, sparse_binary_vector,\ + sparse_vector, integer_value + +__all__ = [ + 'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector', + 'integer_value' +] diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index f2bc47946b6df8..f5a411198e47ab 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -67,7 +67,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. """ import paddle.trainer_config_helpers as conf_helps -import paddle.trainer.PyDataProvider2 as dp +from . import data as v2_data from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default @@ -165,10 +165,10 @@ def to_proto_impl(self, **kwargs): class DataLayerV2(Layer): - def __init__(self, name, data_type, **kwargs): - assert isinstance(data_type, dp.InputType) + def __init__(self, name, type, **kwargs): + assert isinstance(type, v2_data.InputType) - self.data_type = data_type + self.type = type self.__method_name__ = 'data_layer' self.__kwargs__ = kwargs @@ -176,7 +176,7 @@ def __init__(self, name, data_type, **kwargs): def to_proto_impl(self, **kwargs): args = dict() - args['size'] = self.data_type.dim + args['size'] = self.type.dim for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: @@ -198,8 +198,8 @@ def to_proto_impl(self, **kwargs): parent_names=['input', 'label']) if __name__ == '__main__': - pixel = data(name='pixel', data_type=dp.dense_vector(784)) - label = data(name='label', data_type=dp.integer_value(10)) + pixel = data(name='pixel', type=v2_data.dense_vector(784)) + label = data(name='label', type=v2_data.integer_value(10)) hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) From bd80549c6525f2f4c20ecab7a2305155c2c06462 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Mon, 20 Feb 2017 09:03:55 -0800 Subject: [PATCH 236/370] fix issue in create custom reader in design doc --- doc/design/reader/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index 8f7abf12f73354..ff3ee758beeb2f 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -107,7 +107,7 @@ We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["imag ### How to create custom data reader ```python -def image_reader(image_path, label_path): +def image_reader(image_path, label_path, n): f = open(image_path) l = open(label_path) images = numpy.fromfile( @@ -117,9 +117,10 @@ def image_reader(image_path, label_path): for i in xrange(n): yield images[i, :], labels[i] # a single entry of data is created each time f.close() + l.close() # use python lambda to change image_reader into a function with no parameters. -reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file") +reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file", 1024) paddle.train(reader, {"image":0, "label":1}, ...) ``` From 0a0b5b5b9168eb6dbfac7f932c7f4bc91759db2c Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 21 Feb 2017 09:55:31 +0800 Subject: [PATCH 237/370] change paddle.data to paddle.data_type --- demo/mnist/api_train.py | 4 ++-- demo/mnist/api_train_v2.py | 6 ++++-- python/paddle/v2/__init__.py | 4 ++-- python/paddle/v2/{data.py => data_type.py} | 0 python/paddle/v2/layer.py | 2 +- 5 files changed, 9 insertions(+), 7 deletions(-) rename python/paddle/v2/{data.py => data_type.py} (100%) diff --git a/demo/mnist/api_train.py b/demo/mnist/api_train.py index 25c25c1fcddd26..ea1caa7dd9653a 100644 --- a/demo/mnist/api_train.py +++ b/demo/mnist/api_train.py @@ -71,9 +71,9 @@ def main(): # define network images = paddle_v2.layer.data( - name='pixel', type=paddle_v2.data.dense_vector(784)) + name='pixel', type=paddle_v2.data_type.dense_vector(784)) label = paddle_v2.layer.data( - name='label', type=paddle_v2.data.integer_value(10)) + name='label', type=paddle_v2.data_type.integer_value(10)) hidden1 = paddle_v2.layer.fc(input=images, size=200) hidden2 = paddle_v2.layer.fc(input=hidden1, size=200) inference = paddle_v2.layer.fc(input=hidden2, diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index c50a46c4d82447..6fc01ce58be57c 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -15,8 +15,10 @@ def main(): paddle.init(use_gpu=False, trainer_count=1) # define network topology - images = paddle.layer.data(name='pixel', type=paddle.data.dense_vector(784)) - label = paddle.layer.data(name='label', type=paddle.data.integer_value(10)) + images = paddle.layer.data( + name='pixel', type=paddle.data_type.dense_vector(784)) + label = paddle.layer.data( + name='label', type=paddle.data_type.integer_value(10)) hidden1 = paddle.layer.fc(input=images, size=200) hidden2 = paddle.layer.fc(input=hidden1, size=200) inference = paddle.layer.fc(input=hidden2, diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index cd6dfce0e66765..c0a2bdc4259c3f 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -17,12 +17,12 @@ import parameters import trainer import event -import data +import data_type import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data' + 'event', 'data_type.py' ] diff --git a/python/paddle/v2/data.py b/python/paddle/v2/data_type.py similarity index 100% rename from python/paddle/v2/data.py rename to python/paddle/v2/data_type.py diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index f5a411198e47ab..4d052c983c2c07 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -67,7 +67,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. """ import paddle.trainer_config_helpers as conf_helps -from . import data as v2_data +from . import data_type as v2_data from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default From 07539b2a1c9976b8d4ba5c13b498d16d5288b1f4 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 21 Feb 2017 10:23:23 +0800 Subject: [PATCH 238/370] add-topology --- python/paddle/v2/__init__.py | 3 ++- python/paddle/v2/topology.py | 44 ++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 python/paddle/v2/topology.py diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index c0a2bdc4259c3f..cf01f37a3324ff 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -18,11 +18,12 @@ import trainer import event import data_type +import topology import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type.py' + 'event', 'data_type', 'topology' ] diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py new file mode 100644 index 00000000000000..ddba1b2345e5eb --- /dev/null +++ b/python/paddle/v2/topology.py @@ -0,0 +1,44 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from . import layer + +__all__ = ['Topology'] + + +class Topology(object): + """ + Topology is used to store the information about all layers + and network configs. + """ + + def __init__(self, cost): + self.cost = cost + self.__model_config__ = layer.parse_network(cost) + + def __call__(self): + return self.__model_config__ + + def get_layer(self, name): + """ + get layer by layer name + :param name: + :return: + """ + pass + + def data_type(self): + """ + """ + pass From 67b8150ff4d04552a5a52cb099bf7e935765e69f Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 21 Feb 2017 13:27:21 +0800 Subject: [PATCH 239/370] data converter test --- paddle/data_converter_test.py | 92 +++++++++++++++++++++++++ python/paddle/v2/data_converter.py | 19 ++--- python/paddle/v2/data_converter_test.py | 92 +++++++++++++++++++++++++ 3 files changed, 195 insertions(+), 8 deletions(-) create mode 100644 paddle/data_converter_test.py create mode 100644 python/paddle/v2/data_converter_test.py diff --git a/paddle/data_converter_test.py b/paddle/data_converter_test.py new file mode 100644 index 00000000000000..d84ee5172782d9 --- /dev/null +++ b/paddle/data_converter_test.py @@ -0,0 +1,92 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import py_paddle.swig_paddle as api +import numpy as np +import paddle.trainer.PyDataProvider2 as dp2 + +from paddle.v2.data_converter import DataConverter + + +class DataConverterTest(unittest.TestCase): + def dense_reader(self, shape): + data = np.random.random(shape) + return data + + def sparse_binary_reader(self, + high, + size_limit, + batch_size, + non_empty=False): + data = [] + for i in xrange(batch_size): + num = np.random.randint(size_limit) # num could be 0 + while non_empty and num == 0: + num = np.random.randint(size_limit) + data.append(np.random.randint(high, size=num).tolist()) + + return data + + def test_dense_vector(self): + def compare(input): + converter = DataConverter([('image', dp2.dense_vector(784))]) + arg = converter([input], {'image': 0}) + output = arg.getSlotValue(0).copyToNumpyMat() + input = np.array(input, dtype='float32') + self.assertAlmostEqual(input.all(), output.all()) + + # test numpy array + data = self.dense_reader(shape=[32, 784]) + compare(data) + + # test list + compare(data.tolist()) + + #def test_sparse_binary(self): + # dim = 100000 + # data = self.sparse_binary_reader(dim, 5, 2) + # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) + # arg = converter([data], {'input':0}) + # output = arg.getSlotValue(0) + + #def test_sparse(self): + # dim = 100000 + # v = self.sparse_binary_reader(dim, 5, 2) + # w = [] + # for dat in data: + # x = self.dense_reader(shape=[1, len(dat)]) + # w.append(x.tolist()) + # data = [] + # for each in zip(v, w): + # data.append(zip(each[0], each[1])) + # + # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) + # arg = converter([data], {'input':0}) + # output = arg.getSlotValue(0) + + def test_integer(self): + dim = 100 + index = np.random.randint(dim, size=32) + print index + converter = DataConverter([('input', dp2.integer_value(dim))]) + arg = converter([index], {'input': 0}) + print arg.getSlotValue(0) + output = arg.getSlotValue(0).copyToNumpyArray() + print 'output=', output + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/data_converter.py b/python/paddle/v2/data_converter.py index afb98a77c5b315..fcba43e4ba99f9 100644 --- a/python/paddle/v2/data_converter.py +++ b/python/paddle/v2/data_converter.py @@ -53,9 +53,9 @@ def convert(self, data, argument): :type argument: Paddle's Arguments """ assert isinstance(argument, api.Arguments) - if data.dtype != np.float32: - data = data.astype(np.float32) - m = api.Matrix.createDenseFromNumpy(data, True, False) + # TODO: handle data type (float, double, ...) + data = np.array(data, np.float32) + m = api.Matrix.createDenseFromNumpy(data) argument.setSlotValue(self.pos, m) @@ -72,12 +72,12 @@ def fill_csr(self, data): self.__height__ = len(data) for x in data: self.__rows__.append(self.__rows__[-1] + len(x)) - self.__cols__ = data.flatten() + self.__cols__.extend(x) def convert(self, data, argument): assert isinstance(argument, api.Arguments) - fill_csr(data) + self.fill_csr(data) m = api.Matrix.createSparse(self.__height__, self.input_type.dim, len(self.__cols__), len(self.__value__) == 0) @@ -94,8 +94,8 @@ def fill_csr(self, data): self.__height__ = len(data) for x in data: self.__rows__.append(self.__rows__[-1] + len(x)) - self.__cols__.extend((x[0] for x in data)) - self.__value__.extend((x[1] for x in data)) + self.__cols__.extend(x[0]) + self.__value__.extend(x[1]) class IndexConvert(IDataConverter): @@ -105,7 +105,10 @@ def __init__(self, input_type, pos): def convert(self, data, argument): assert isinstance(argument, api.Arguments) - self.__ids__ = data.flatten() + #for x in data: + # self.__ids__.append(x) + self.__ids__.extend(x) + ids = api.IVector.create(self.__ids__) argument.setSlotIds(self.pos, ids) diff --git a/python/paddle/v2/data_converter_test.py b/python/paddle/v2/data_converter_test.py new file mode 100644 index 00000000000000..d84ee5172782d9 --- /dev/null +++ b/python/paddle/v2/data_converter_test.py @@ -0,0 +1,92 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import py_paddle.swig_paddle as api +import numpy as np +import paddle.trainer.PyDataProvider2 as dp2 + +from paddle.v2.data_converter import DataConverter + + +class DataConverterTest(unittest.TestCase): + def dense_reader(self, shape): + data = np.random.random(shape) + return data + + def sparse_binary_reader(self, + high, + size_limit, + batch_size, + non_empty=False): + data = [] + for i in xrange(batch_size): + num = np.random.randint(size_limit) # num could be 0 + while non_empty and num == 0: + num = np.random.randint(size_limit) + data.append(np.random.randint(high, size=num).tolist()) + + return data + + def test_dense_vector(self): + def compare(input): + converter = DataConverter([('image', dp2.dense_vector(784))]) + arg = converter([input], {'image': 0}) + output = arg.getSlotValue(0).copyToNumpyMat() + input = np.array(input, dtype='float32') + self.assertAlmostEqual(input.all(), output.all()) + + # test numpy array + data = self.dense_reader(shape=[32, 784]) + compare(data) + + # test list + compare(data.tolist()) + + #def test_sparse_binary(self): + # dim = 100000 + # data = self.sparse_binary_reader(dim, 5, 2) + # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) + # arg = converter([data], {'input':0}) + # output = arg.getSlotValue(0) + + #def test_sparse(self): + # dim = 100000 + # v = self.sparse_binary_reader(dim, 5, 2) + # w = [] + # for dat in data: + # x = self.dense_reader(shape=[1, len(dat)]) + # w.append(x.tolist()) + # data = [] + # for each in zip(v, w): + # data.append(zip(each[0], each[1])) + # + # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) + # arg = converter([data], {'input':0}) + # output = arg.getSlotValue(0) + + def test_integer(self): + dim = 100 + index = np.random.randint(dim, size=32) + print index + converter = DataConverter([('input', dp2.integer_value(dim))]) + arg = converter([index], {'input': 0}) + print arg.getSlotValue(0) + output = arg.getSlotValue(0).copyToNumpyArray() + print 'output=', output + + +if __name__ == '__main__': + unittest.main() From 4db1c0fe03fdc2dfd726c202958dc1af66f846e4 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 21 Feb 2017 14:29:42 +0800 Subject: [PATCH 240/370] Fix the unit test bug for api --- paddle/api/test/testMatrix.py | 2 +- paddle/api/test/testVector.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/api/test/testMatrix.py b/paddle/api/test/testMatrix.py index 37666bdccc9aed..f08fbf3ccdf5d7 100644 --- a/paddle/api/test/testMatrix.py +++ b/paddle/api/test/testMatrix.py @@ -68,7 +68,7 @@ def test_createDenseMat(self): def test_numpyCpu(self): numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32") - m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat, copy=False) + m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat, False) self.assertEqual((int(m.getHeight()), int(m.getWidth())), numpy_mat.shape) diff --git a/paddle/api/test/testVector.py b/paddle/api/test/testVector.py index 1ab095c1d3d0d2..6339cf8542607b 100644 --- a/paddle/api/test/testVector.py +++ b/paddle/api/test/testVector.py @@ -43,7 +43,7 @@ def test_create(self): def test_cpu_numpy(self): vec = np.array([1, 3, 4, 65, 78, 1, 4], dtype="int32") - iv = swig_paddle.IVector.createCpuVectorFromNumpy(vec, copy=False) + iv = swig_paddle.IVector.createCpuVectorFromNumpy(vec, False) self.assertEqual(vec.shape[0], int(iv.__len__())) vec[4] = 832 for i in xrange(len(iv)): @@ -106,7 +106,7 @@ def testCreate(self): def testCpuNumpy(self): numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32") - vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, copy=False) + vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, False) assert isinstance(vec, swig_paddle.Vector) numpy_arr[0] = 0.1 for n, v in zip(numpy_arr, vec): From 0b673756f1e366434454bf5215b2f508c02d63ee Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 21 Feb 2017 15:04:25 +0800 Subject: [PATCH 241/370] add SequenceReshapeLayer in trainer_config_helpers --- doc/api/trainer_config_helpers/layers.rst | 6 ++ .../gserver/layers/SequenceReshapeLayer.cpp | 9 ++- .../paddle/trainer_config_helpers/layers.py | 61 +++++++++++++++++++ .../tests/configs/file_list.sh | 2 +- ...tostr => test_seq_concat_reshape.protostr} | 12 ++++ ...q_concat.py => test_seq_concat_reshape.py} | 7 ++- 6 files changed, 91 insertions(+), 6 deletions(-) rename python/paddle/trainer_config_helpers/tests/configs/protostr/{test_seq_concat.protostr => test_seq_concat_reshape.protostr} (72%) rename python/paddle/trainer_config_helpers/tests/configs/{test_seq_concat.py => test_seq_concat_reshape.py} (57%) diff --git a/doc/api/trainer_config_helpers/layers.rst b/doc/api/trainer_config_helpers/layers.rst index 3685868ffcfdf1..2793d6afd9565e 100644 --- a/doc/api/trainer_config_helpers/layers.rst +++ b/doc/api/trainer_config_helpers/layers.rst @@ -308,6 +308,12 @@ repeat_layer :members: repeat_layer :noindex: +seq_reshape_layer +----------------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: seq_reshape_layer + :noindex: + Math Layers =========== diff --git a/paddle/gserver/layers/SequenceReshapeLayer.cpp b/paddle/gserver/layers/SequenceReshapeLayer.cpp index 66f49159087ab9..433592953b220e 100644 --- a/paddle/gserver/layers/SequenceReshapeLayer.cpp +++ b/paddle/gserver/layers/SequenceReshapeLayer.cpp @@ -20,9 +20,12 @@ limitations under the License. */ namespace paddle { /** - * A layer for reshaping the sequence - * Input: a sequence - * Output: a sequence + * A layer for reshaping the sequence. Assume the input sequence has + * T instances, the dimension of each instance is M, and the input + * reshape_dim is N, then the output sequence has T*M/N instances, + * the dimension of each instance is N. + * + * Note that T*M/N must be an integer. */ class SequenceReshapeLayer : public Layer { diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 0ff29772356af7..0d3a31b9d39a28 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -37,6 +37,7 @@ "dotmul_projection", "dotmul_operator", "repeat_layer", + "seq_reshape_layer", "table_projection", "mixed_layer", "data_layer", @@ -125,6 +126,7 @@ class LayerType(object): GRUMEMORY = "gated_recurrent" SEQUENCE_LAST_INSTANCE = "seqlastins" SEQUENCE_FIRST_INSTANCE = "seqfirstins" + SEQUENCE_RESHAPE = "seqreshape" POOLING_MAX = "max" POOLING_AVG = 'average' FC_LAYER = "fc" @@ -1450,6 +1452,61 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None): parents=[input]) +@wrap_name_default("seqreshape") +@wrap_act_default(act=IdentityActivation()) +@wrap_bias_attr_default(has_bias=False) +@layer_support() +def seq_reshape_layer(input, + reshape_size, + act=None, + name=None, + layer_attr=None, + bias_attr=None): + """ + A layer for reshaping the sequence. Assume the input sequence has T instances, + the dimension of each instance is M, and the input reshape_size is N, then the + output sequence has T*M/N instances, the dimension of each instance is N. + + Note that T*M/N must be an integer. + + The example usage is: + + .. code-block:: python + + reshape = seq_reshape_layer(input=layer, reshape_size=4) + + :param input: Input layer. + :type input: LayerOutput + :param reshape_size: the size of reshaped sequence. + :type reshape_size: int + :param name: Layer name. + :type name: basestring + :param act: Activation type. + :type act: BaseActivation + :param layer_attr: extra layer attributes. + :type layer_attr: ExtraLayerAttribute. + :param bias_attr: The Bias Attribute. If no bias, then pass False or + something not type of ParameterAttribute. None will get a + default Bias. + :type bias_attr: ParameterAttribute or None or bool + :return: LayerOutput object. + :rtype: LayerOutput + """ + + Layer( + inputs=[input.name], + name=name, + size=reshape_size, + type=LayerType.SEQUENCE_RESHAPE, + bias=ParamAttr.to_bias(bias_attr), + **ExtraAttr.to_kwargs(layer_attr)) + return LayerOutput( + name=name, + size=reshape_size, + layer_type=LayerType.SEQUENCE_RESHAPE, + parents=[input]) + + @wrap_name_default() @layer_support() def interpolation_layer(input, weight, name=None, layer_attr=None): @@ -2604,6 +2661,10 @@ def seq_concat_layer(a, b, act=None, name=None, layer_attr=None, :type act: BaseActivation :param layer_attr: Extra Layer Attribute. :type layer_attr: ExtraLayerAttribute + :param bias_attr: The Bias Attribute. If no bias, then pass False or + something not type of ParameterAttribute. None will get a + default Bias. + :type bias_attr: ParameterAttribute or None or bool :return: LayerOutput object. :rtype: LayerOutput """ diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 55cef6be0c630c..c9178e3c6a46a2 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -5,6 +5,6 @@ last_first_seq test_expand_layer test_ntm_layers test_hsigmoid img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops -test_seq_concat) +test_seq_concat_reshape) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat_reshape.protostr similarity index 72% rename from python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr rename to python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat_reshape.protostr index 2fa55e87b5d13c..91284b4fb32fcf 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_concat_reshape.protostr @@ -23,17 +23,29 @@ layers { input_layer_name: "data2" } } +layers { + name: "__seqreshape_0__" + type: "seqreshape" + size: 5 + active_type: "linear" + inputs { + input_layer_name: "data1" + } +} input_layer_names: "data1" input_layer_names: "data2" output_layer_names: "__seqconcat_0__" +output_layer_names: "__seqreshape_0__" sub_models { name: "root" layer_names: "data1" layer_names: "data2" layer_names: "__seqconcat_0__" + layer_names: "__seqreshape_0__" input_layer_names: "data1" input_layer_names: "data2" output_layer_names: "__seqconcat_0__" + output_layer_names: "__seqreshape_0__" is_recurrent_layer_group: false } diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py b/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat_reshape.py similarity index 57% rename from python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py rename to python/paddle/trainer_config_helpers/tests/configs/test_seq_concat_reshape.py index 193d9d0df9ed80..5c161ba805fb30 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat_reshape.py @@ -3,7 +3,10 @@ settings(batch_size=1000, learning_rate=1e-5) din1 = data_layer(name='data1', size=30) - din2 = data_layer(name='data2', size=30) -outputs(seq_concat_layer(a=din1, b=din2)) +opts = [] +opts.append(seq_concat_layer(a=din1, b=din2)) +opts.append(seq_reshape_layer(input=din1, reshape_size=5)) + +outputs(opts) From 15180e85acaa400c629a37fadcf4589b7c086c7d Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 21 Feb 2017 15:50:01 +0800 Subject: [PATCH 242/370] remove some code --- paddle/data_converter_test.py | 92 ----------------------------------- 1 file changed, 92 deletions(-) delete mode 100644 paddle/data_converter_test.py diff --git a/paddle/data_converter_test.py b/paddle/data_converter_test.py deleted file mode 100644 index d84ee5172782d9..00000000000000 --- a/paddle/data_converter_test.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest - -import py_paddle.swig_paddle as api -import numpy as np -import paddle.trainer.PyDataProvider2 as dp2 - -from paddle.v2.data_converter import DataConverter - - -class DataConverterTest(unittest.TestCase): - def dense_reader(self, shape): - data = np.random.random(shape) - return data - - def sparse_binary_reader(self, - high, - size_limit, - batch_size, - non_empty=False): - data = [] - for i in xrange(batch_size): - num = np.random.randint(size_limit) # num could be 0 - while non_empty and num == 0: - num = np.random.randint(size_limit) - data.append(np.random.randint(high, size=num).tolist()) - - return data - - def test_dense_vector(self): - def compare(input): - converter = DataConverter([('image', dp2.dense_vector(784))]) - arg = converter([input], {'image': 0}) - output = arg.getSlotValue(0).copyToNumpyMat() - input = np.array(input, dtype='float32') - self.assertAlmostEqual(input.all(), output.all()) - - # test numpy array - data = self.dense_reader(shape=[32, 784]) - compare(data) - - # test list - compare(data.tolist()) - - #def test_sparse_binary(self): - # dim = 100000 - # data = self.sparse_binary_reader(dim, 5, 2) - # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) - # arg = converter([data], {'input':0}) - # output = arg.getSlotValue(0) - - #def test_sparse(self): - # dim = 100000 - # v = self.sparse_binary_reader(dim, 5, 2) - # w = [] - # for dat in data: - # x = self.dense_reader(shape=[1, len(dat)]) - # w.append(x.tolist()) - # data = [] - # for each in zip(v, w): - # data.append(zip(each[0], each[1])) - # - # converter = DataConverter([('input', dp2.sparse_binary_vector(dim))]) - # arg = converter([data], {'input':0}) - # output = arg.getSlotValue(0) - - def test_integer(self): - dim = 100 - index = np.random.randint(dim, size=32) - print index - converter = DataConverter([('input', dp2.integer_value(dim))]) - arg = converter([index], {'input': 0}) - print arg.getSlotValue(0) - output = arg.getSlotValue(0).copyToNumpyArray() - print 'output=', output - - -if __name__ == '__main__': - unittest.main() From 3e8f232e126e06f6a5266307aada8b1d8e1bac43 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 21 Feb 2017 16:19:42 +0800 Subject: [PATCH 243/370] remove import . to enable run v2/layer.py directly --- python/paddle/v2/__init__.py | 2 +- python/paddle/v2/layer.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index c0a2bdc4259c3f..30d0b2a398bd0e 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -22,7 +22,7 @@ __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type.py' + 'event', 'data_type' ] diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 4d052c983c2c07..9e999fccadf16d 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -67,7 +67,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. """ import paddle.trainer_config_helpers as conf_helps -from . import data_type as v2_data +import data_type as data_type from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default @@ -166,7 +166,7 @@ def to_proto_impl(self, **kwargs): class DataLayerV2(Layer): def __init__(self, name, type, **kwargs): - assert isinstance(type, v2_data.InputType) + assert isinstance(type, data_type.InputType) self.type = type self.__method_name__ = 'data_layer' @@ -198,8 +198,8 @@ def to_proto_impl(self, **kwargs): parent_names=['input', 'label']) if __name__ == '__main__': - pixel = data(name='pixel', type=v2_data.dense_vector(784)) - label = data(name='label', type=v2_data.integer_value(10)) + pixel = data(name='pixel', type=data_type.dense_vector(784)) + label = data(name='label', type=data_type.integer_value(10)) hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) From e4af5b9228feca57c31ac1bdfacf93cfab6ba217 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 21 Feb 2017 16:23:19 +0800 Subject: [PATCH 244/370] optimize code --- python/paddle/v2/layer.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 9e999fccadf16d..507725ee4ff712 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -66,12 +66,14 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. paddle.v2.parameters.create, no longer exposed to users. """ +import collections + import paddle.trainer_config_helpers as conf_helps -import data_type as data_type from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default -import collections + +import data_type __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', From 7cfe34da7c99c541189cb73165bc022bbc4289c0 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 21 Feb 2017 23:53:42 +0800 Subject: [PATCH 245/370] modify api_train_v2 --- demo/mnist/api_train_v2.py | 20 ++++--- python/paddle/v2/layer.py | 8 ++- python/paddle/v2/parameters.py | 21 ++++---- python/paddle/v2/topology.py | 96 +++++++++++++++++++++++++++++++--- python/paddle/v2/trainer.py | 20 +++---- 5 files changed, 119 insertions(+), 46 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6fc01ce58be57c..f6edd1f34fe8d1 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -26,7 +26,9 @@ def main(): act=paddle.activation.Softmax()) cost = paddle.layer.classification_cost(input=inference, label=label) - parameters = paddle.parameters.create(cost) + topology = paddle.topology.Topology(cost) + + parameters = paddle.parameters.create(topology) for param_name in parameters.keys(): array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) @@ -45,16 +47,12 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) - trainer.train(train_data_reader=train_reader, - topology=cost, - parameters=parameters, - event_handler=event_handler, - batch_size=32, # batch size should be refactor in Data reader - data_types={ # data_types will be removed, It should be in - # network topology - 'pixel': images.type, - 'label': label.type - }) + trainer.train( + train_data_reader=train_reader, + topology=topology, + parameters=parameters, + event_handler=event_handler, + batch_size=32) # batch size should be refactor in Data reader if __name__ == '__main__': diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 4d052c983c2c07..5f146c8c038b95 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -66,12 +66,14 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. paddle.v2.parameters.create, no longer exposed to users. """ +import collections + import paddle.trainer_config_helpers as conf_helps -from . import data_type as v2_data from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default -import collections + +import data_type as v2_data __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', @@ -184,6 +186,8 @@ def to_proto_impl(self, **kwargs): return getattr(conf_helps, self.__method_name__)(name=self.name, **args) +LayerV2 = Layer + data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index ea504d5104716d..b569afe3a1fc20 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -1,26 +1,23 @@ import numpy as np -from . import layer as v2_layer import py_paddle.swig_paddle as api from paddle.proto.ParameterConfig_pb2 import ParameterConfig +import topology as v2_topology + __all__ = ['Parameters', 'create'] -def create(*layers): +def create(topology): """ - Create parameter pool by layers. In paddle, layer can be represent a - model config. - - :param layers: + Create parameter pool by topology. + :param topology: :return: """ - for layer in layers: - if not isinstance(layer, v2_layer.Layer): - raise ValueError( - 'create must pass a topologies which type is paddle.layer.Layer') - model_config = v2_layer.parse_network(*layers) + if not isinstance(topology, v2_topology.Topology): + raise ValueError( + 'create must pass a topology which type is topology.Topology') pool = Parameters() - for param in model_config.parameters: + for param in topology.proto().parameters: pool.__append_config__(param) return pool diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index ddba1b2345e5eb..6508b3ce881c49 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -from . import layer +from paddle.proto.ModelConfig_pb2 import ModelConfig +import paddle.trainer_config_helpers as conf_helps +import layer as v2_layer +import data_type __all__ = ['Topology'] @@ -23,22 +26,101 @@ class Topology(object): and network configs. """ - def __init__(self, cost): - self.cost = cost - self.__model_config__ = layer.parse_network(cost) + def __init__(self, *layers): + for layer in layers: + if not isinstance(layer, v2_layer.LayerV2): + raise ValueError('create must pass a topologies ' + 'which type is paddle.layer.Layer') + self.layers = layers + self.__model_config__ = v2_layer.parse_network(*layers) + assert isinstance(self.__model_config__, ModelConfig) - def __call__(self): + def proto(self): return self.__model_config__ def get_layer(self, name): + """ + get v2.Layer Class instance by layer name + :param name: + :return: + """ + result_layer = [] + + def find_layer_by_name(layer, layer_name): + if layer.name == layer_name and len(result_layer) == 0: + result_layer.append(layer) + for parent_layer in layer.__parent_layers__.values(): + find_layer_by_name(parent_layer, layer_name) + + for layer in self.layers: + find_layer_by_name(layer, name) + + return result_layer[0] + + def get_data_layer(self): + """ + get all data layer + :return: + """ + data_layers = [] + + def find_data_layer(layer): + assert isinstance(layer, layer.LayerV2) + if isinstance(layer, v2_layer.DataLayerV2): + if len( + filter(lambda data_layer: data_layer.name == layer.name, + data_layers)) == 0: + data_layers.append(layer) + for parent_layer in layer.__parent_layers__.values(): + find_data_layer(parent_layer) + + for layer in self.layers: + find_data_layer(layer) + + return data_layers + + def get_layer_proto(self, name): """ get layer by layer name :param name: :return: """ - pass + layers = filter(lambda layer: layer.name == name, + self.__model_config__.layers) + if len(layers) is 1: + return layers[0] + else: + return None def data_type(self): """ + get data_type from proto, such as: + [('image', dense_vector(768)), ('label', integer_value(10))] + the order is the same with __model_config__.input_layer_names """ - pass + data_types_lists = [] + for layer_name in self.__model_config__.input_layer_names: + data_types_lists.append( + (layer_name, self.get_layer(layer_name).type)) + + return data_types_lists + + +if __name__ == '__main__': + pixel = v2_layer.data(name='pixel', type=data_type.dense_vector(784)) + label = v2_layer.data(name='label', type=data_type.integer_value(10)) + hidden = v2_layer.fc(input=pixel, + size=100, + act=conf_helps.SigmoidActivation()) + inference = v2_layer.fc(input=hidden, + size=10, + act=conf_helps.SoftmaxActivation()) + maxid = v2_layer.max_id(input=inference) + cost1 = v2_layer.classification_cost(input=inference, label=label) + cost2 = v2_layer.cross_entropy_cost(input=inference, label=label) + + print Topology(cost1).proto() + print Topology(cost2).proto() + print Topology(cost1, cost2).proto() + print Topology(cost2).proto() + print Topology(inference, maxid).proto() diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 4365bd41e7073b..c8da6e70cf528d 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -1,13 +1,12 @@ import collections import py_paddle.swig_paddle as api -from paddle.proto.ModelConfig_pb2 import ModelConfig from py_paddle import DataProviderConverter from . import event as v2_event -from . import layer as v2_layer from . import optimizer as v2_optimizer from . import parameters as v2_parameters +from . import topology as v2_topology __all__ = ['ITrainer', 'SGD'] @@ -88,12 +87,11 @@ def train(self, if event_handler is None: event_handler = default_event_handler - topology = v2_layer.parse_network(topology) - __check_train_args__(**locals()) gm = api.GradientMachine.createFromConfigProto( - topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) + topology.proto(), api.CREATE_MODE_NORMAL, + self.__optimizer__.enable_types()) assert isinstance(gm, api.GradientMachine) parameters.append_gradient_machine(gm) @@ -102,13 +100,7 @@ def train(self, gm.start() out_args = api.Arguments.createArguments(0) - - data_types_lists = [] - for each in topology.input_layer_names: - if each not in data_types: - raise ValueError() - data_types_lists.append(data_types[each]) - + data_types_lists = [data_type[1] for data_type in topology.data_type()] converter = DataProviderConverter(input_types=data_types_lists) for pass_id in xrange(num_passes): @@ -141,7 +133,7 @@ def __data_reader_to_batch__(reader, batch_size, topology): def input_reorder(func): for item in func(): retv = [] - for __layer_name__ in topology.input_layer_names: + for __layer_name__ in topology.proto().input_layer_names: retv.append(item[__layer_name__]) yield retv @@ -178,7 +170,7 @@ def __check_train_args__(train_data_reader, topology, parameters, raise ValueError('test_data_reader should be a function, which can ' 'return a iterator') - if not isinstance(topology, ModelConfig): + if not isinstance(topology, v2_topology.Topology): raise ValueError('topology should be a model config') if not isinstance(parameters, v2_parameters.Parameters): From 9b3cdb12a98d71542de5491504942454782f3901 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 14:49:31 -0800 Subject: [PATCH 246/370] use terminology *data reader creator* instead of *data reader*, and remove lambda usage from design doc --- doc/design/reader/README.md | 99 +++++++++++++++++++------------------ 1 file changed, 51 insertions(+), 48 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index ff3ee758beeb2f..f8f79a75b1db09 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -1,35 +1,35 @@ -# Python Data Reader Design Doc +# Python Data Reading Design Doc -Paddle reads data from data reader during training. It will be passed into `paddle.train` as a parameter. +Paddle reads data from *data reader* during training. *data reader creator* (or *reader creator*) creates a *data reader* when invoked. *reader creator* will be passed into `paddle.train` as a parameter. -## Data Reader Interface +## Data Reader Creator Interface -Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): +Data reader creator is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): ``` -iterable = data_reader() +iterable = data_reader_creator() ``` Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int) -An example implementation for single item data reader: +An example implementation for single item data reader creator: ```python -def data_reader_fake_image(): +def data_reader_creator_fake_image(): while True: yield numpy.random.uniform(-1, 1, size=20*20) ``` -An example implementation for multiple item data reader: +An example implementation for multiple item data reader creator: ```python -def data_reader_fake_image_and_label(): +def data_reader_creator_fake_image_and_label(): while True: yield numpy.random.uniform(-1, 1, size=20*20), False ``` ## Usage -data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: +data reader creator, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: ```python # two data layer is created: @@ -41,11 +41,11 @@ label_layer = paddle.layer.data("label", ...) paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) ``` -## Data Reader Decorators +## Data Reader Creator Decorator -Data reader decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. +*Data reader creator decorator* (or *reader creator decorator*) takes a single or multiple data reader creator, returns a new data reader creator. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. -Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples: +Since we have a strict interface for data readers creators (no parameter, return a single data item). Data reader creators can be used flexiable via data reader creator decorators. Following are a few examples: ### Prefetch Data @@ -54,74 +54,77 @@ Since reading data may take time and training can not proceed without data. It i Use `paddle.reader.buffered` to prefetch data: ```python -buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100) +buffered_reader_creator = paddle.reader.buffered(paddle.dataset.mnist, 100) ``` -`buffered_reader` will try to buffer (prefetch) `100` data entries. +`buffered_reader_creator` will try to buffer (prefetch) `100` data entries. -### Compose Multiple Data Readers +### Compose Multiple Data Reader Creators For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661). We can do: ```python -def data_reader_fake_image(): +def data_reader_creator_fake_image(): while True: yield numpy.random.uniform(-1, 1, size=20*20) -def data_reader_bool(t): - while True: - yield t +def data_reader_creator_bool(t): + def creator: + while True: + yield t + return creator -true_reader = lambda : data_reader_bool(True) -false_reader = lambda : data_reader_bool(False) +true_reader_creator = data_reader_creator_bool(True) +false_reade_creatorr = data_reader_creator_bool(False) -reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader) +reader_creator = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_fake_image, true_reader_creator, false_reader_creator) # Skipped 1 because paddle.dataset.mnist produces two items per data entry. # And we don't care second item at this time. -paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) +paddle.train(reader_creator, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) ``` ### Shuffle -Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read. +Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader creator that buffers `n` data entries and shuffle them before a data entry is read. Example: ```python -reader = paddle.reader.shuffle(paddle.dataset.mnist, 512) +reader_creator = paddle.reader.shuffle(paddle.dataset.mnist, 512) ``` ## Q & A ### Why return only a single entry, but not a mini batch? -If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. +If a mini batch is returned, data reader creator need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. -Practically, always return a single entry make reusing existing data reader much easier (e.g., if existing data reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). +Practically, always return a single entry make reusing existing data reader creators much easier (e.g., if existing reader creator return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). ### Why use a dictionary but not a list to provide mapping? We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`). -### How to create custom data reader +### How to create custom data reader creator ```python -def image_reader(image_path, label_path, n): - f = open(image_path) - l = open(label_path) - images = numpy.fromfile( - f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32') - images = images / 255.0 * 2.0 - 1.0 - labels = numpy.fromfile(l, 'ubyte', count=n).astype("int") - for i in xrange(n): - yield images[i, :], labels[i] # a single entry of data is created each time - f.close() - l.close() - -# use python lambda to change image_reader into a function with no parameters. -reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file", 1024) -paddle.train(reader, {"image":0, "label":1}, ...) +def image_reader_creator(image_path, label_path, n): + def reader_creator(): + f = open(image_path) + l = open(label_path) + images = numpy.fromfile( + f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32') + images = images / 255.0 * 2.0 - 1.0 + labels = numpy.fromfile(l, 'ubyte', count=n).astype("int") + for i in xrange(n): + yield images[i, :], labels[i] # a single entry of data is created each time + f.close() + l.close() + return reader_creator + +reader_creator = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024) +paddle.train(reader_creator, {"image":0, "label":1}, ...) ``` ### How is `paddle.train` implemented @@ -129,17 +132,17 @@ paddle.train(reader, {"image":0, "label":1}, ...) An example implementation of paddle.train could be: ```python -def minibatch_decorater(reader, minibatch_size): +def minibatch_decorater(reader_creator, minibatch_size): def ret(): - r = reader() + r = reader_creator() buf = [r.next() for x in xrange(minibatch_size)] while len(buf) > 0: yield buf buf = [r.next() for x in xrange(minibatch_size)] return ret -def train(reader, mapping, batch_size, total_pass): +def train(reader_creator, mapping, batch_size, total_pass): for pass_idx in range(total_pass): - for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning. + for mini_batch in minibatch_decorater(reader_creator): # this loop will never end in online learning. do_forward_backward(mini_batch, mapping) ``` From 478b0c8a904b8db0d59d070d4c82db6704d333a7 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 15:10:42 -0800 Subject: [PATCH 247/370] correct data reader and data reader creator usage --- doc/design/reader/README.md | 73 +++++++++++++++++++------------------ 1 file changed, 37 insertions(+), 36 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index f8f79a75b1db09..230b8c2326b526 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -1,35 +1,35 @@ -# Python Data Reading Design Doc +# Python Data Reader Design Doc -Paddle reads data from *data reader* during training. *data reader creator* (or *reader creator*) creates a *data reader* when invoked. *reader creator* will be passed into `paddle.train` as a parameter. +Paddle reads data from *data reader* during training. *data reader* will be passed into `paddle.train` as a parameter. -## Data Reader Creator Interface +## Data Reader Interface -Data reader creator is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): +Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): ``` -iterable = data_reader_creator() +iterable = data_reader() ``` Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int) -An example implementation for single item data reader creator: +An example implementation for single item data reader: ```python -def data_reader_creator_fake_image(): +def data_reader_fake_image(): while True: yield numpy.random.uniform(-1, 1, size=20*20) ``` -An example implementation for multiple item data reader creator: +An example implementation for multiple item data reader: ```python -def data_reader_creator_fake_image_and_label(): +def data_reader_fake_image_and_label(): while True: yield numpy.random.uniform(-1, 1, size=20*20), False ``` ## Usage -data reader creator, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: +data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`: ```python # two data layer is created: @@ -41,11 +41,11 @@ label_layer = paddle.layer.data("label", ...) paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...) ``` -## Data Reader Creator Decorator +## Data Reader Decorator -*Data reader creator decorator* (or *reader creator decorator*) takes a single or multiple data reader creator, returns a new data reader creator. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. +*Data reader decorator* takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax. -Since we have a strict interface for data readers creators (no parameter, return a single data item). Data reader creators can be used flexiable via data reader creator decorators. Following are a few examples: +Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples: ### Prefetch Data @@ -54,63 +54,63 @@ Since reading data may take time and training can not proceed without data. It i Use `paddle.reader.buffered` to prefetch data: ```python -buffered_reader_creator = paddle.reader.buffered(paddle.dataset.mnist, 100) +buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100) ``` -`buffered_reader_creator` will try to buffer (prefetch) `100` data entries. +`buffered_reader` will try to buffer (prefetch) `100` data entries. -### Compose Multiple Data Reader Creators +### Compose Multiple Data Readers For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661). We can do: ```python -def data_reader_creator_fake_image(): +def data_reader_fake_image(): while True: yield numpy.random.uniform(-1, 1, size=20*20) def data_reader_creator_bool(t): - def creator: + def reader: while True: yield t - return creator + return reader -true_reader_creator = data_reader_creator_bool(True) -false_reade_creatorr = data_reader_creator_bool(False) +true_reader = data_reader_creator_bool(True) +false_reader = data_reader_creator_bool(False) -reader_creator = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_fake_image, true_reader_creator, false_reader_creator) +reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader) # Skipped 1 because paddle.dataset.mnist produces two items per data entry. # And we don't care second item at this time. -paddle.train(reader_creator, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) +paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) ``` ### Shuffle -Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader creator that buffers `n` data entries and shuffle them before a data entry is read. +Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read. Example: ```python -reader_creator = paddle.reader.shuffle(paddle.dataset.mnist, 512) +reader = paddle.reader.shuffle(paddle.dataset.mnist, 512) ``` ## Q & A ### Why return only a single entry, but not a mini batch? -If a mini batch is returned, data reader creator need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. +If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`. -Practically, always return a single entry make reusing existing data reader creators much easier (e.g., if existing reader creator return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). +Practically, always return a single entry make reusing existing data readers much easier (e.g., if existing reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2). ### Why use a dictionary but not a list to provide mapping? We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`). -### How to create custom data reader creator +### How to create custom data reader ```python def image_reader_creator(image_path, label_path, n): - def reader_creator(): + def reader(): f = open(image_path) l = open(label_path) images = numpy.fromfile( @@ -121,10 +121,11 @@ def image_reader_creator(image_path, label_path, n): yield images[i, :], labels[i] # a single entry of data is created each time f.close() l.close() - return reader_creator + return reader -reader_creator = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024) -paddle.train(reader_creator, {"image":0, "label":1}, ...) +# images_reader_creator creates a reader +reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024) +paddle.train(reader, {"image":0, "label":1}, ...) ``` ### How is `paddle.train` implemented @@ -132,17 +133,17 @@ paddle.train(reader_creator, {"image":0, "label":1}, ...) An example implementation of paddle.train could be: ```python -def minibatch_decorater(reader_creator, minibatch_size): +def minibatch_decorater(reader, minibatch_size): def ret(): - r = reader_creator() + r = reader() buf = [r.next() for x in xrange(minibatch_size)] while len(buf) > 0: yield buf buf = [r.next() for x in xrange(minibatch_size)] return ret -def train(reader_creator, mapping, batch_size, total_pass): +def train(reader, mapping, batch_size, total_pass): for pass_idx in range(total_pass): - for mini_batch in minibatch_decorater(reader_creator): # this loop will never end in online learning. + for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning. do_forward_backward(mini_batch, mapping) ``` From 5688c381084b8070243826304abc046651743e54 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 15:26:13 -0800 Subject: [PATCH 248/370] fix according to comments --- doc/design/reader/README.md | 44 +++++++++++++++++++++++-------------- 1 file changed, 28 insertions(+), 16 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index 230b8c2326b526..85efb49bc3a81a 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -1,10 +1,16 @@ # Python Data Reader Design Doc -Paddle reads data from *data reader* during training. *data reader* will be passed into `paddle.train` as a parameter. +At training and testing time, PaddlePaddle programs need to read data. To ease the users' work to write data reading code, we define that + +- A *reader* is a function that reads data (from file, network, random number generator, etc) and yields data items. +- A *reader creator* is a function that returns a reader function. +- A *reader* decorator is a function, which accepts one or more readers, and returns a reader. + +and provide frequently used reader creators and reader decorators. ## Data Reader Interface -Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`): +Indeed, *data reader* doesn't have to be a function that reads and yields data items. It can be any function with no parameter that creates a iterable (anything can be used in `for x in iterable`): ``` iterable = data_reader() @@ -15,16 +21,20 @@ Element produced for the iterable should be a **single** entry of data, **not** An example implementation for single item data reader: ```python -def data_reader_fake_image(): - while True: - yield numpy.random.uniform(-1, 1, size=20*20) +def reader_creator_random_image(width, height): + def reader(): + while True: + yield numpy.random.uniform(-1, 1, size=width*height) + return reader ``` An example implementation for multiple item data reader: ```python -def data_reader_fake_image_and_label(): - while True: - yield numpy.random.uniform(-1, 1, size=20*20), False +def reader_creator_random_imageand_label(widht, height, label): + def reader(): + while True: + yield numpy.random.uniform(-1, 1, size=width*height), label + return reader ``` ## Usage @@ -61,25 +71,27 @@ buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100) ### Compose Multiple Data Readers -For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661). +For example, we want to use a source of real images (reusing mnist dataset), and a source of random images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661). We can do: ```python -def data_reader_fake_image(): - while True: - yield numpy.random.uniform(-1, 1, size=20*20) +def reader_creator_random_image(width, height): + def reader(): + while True: + yield numpy.random.uniform(-1, 1, size=width*height) + return reader -def data_reader_creator_bool(t): +def reader_creator_bool(t): def reader: while True: yield t return reader -true_reader = data_reader_creator_bool(True) -false_reader = data_reader_creator_bool(False) +true_reader = reader_creator_bool(True) +false_reader = reader_creator_bool(False) -reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader) +reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_random_image(20, 20), true_reader, false_reader) # Skipped 1 because paddle.dataset.mnist produces two items per data entry. # And we don't care second item at this time. paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) From 794c220d49c4588fdd75d2454e6224ba6d91f531 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 15:32:11 -0800 Subject: [PATCH 249/370] more fixes --- doc/design/reader/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/design/reader/README.md b/doc/design/reader/README.md index 85efb49bc3a81a..17d52b9e20b813 100644 --- a/doc/design/reader/README.md +++ b/doc/design/reader/README.md @@ -16,9 +16,9 @@ Indeed, *data reader* doesn't have to be a function that reads and yields data i iterable = data_reader() ``` -Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int) +Element produced from the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int) -An example implementation for single item data reader: +An example implementation for single item data reader creator: ```python def reader_creator_random_image(width, height): @@ -28,7 +28,7 @@ def reader_creator_random_image(width, height): return reader ``` -An example implementation for multiple item data reader: +An example implementation for multiple item data reader creator: ```python def reader_creator_random_imageand_label(widht, height, label): def reader(): @@ -91,7 +91,7 @@ def reader_creator_bool(t): true_reader = reader_creator_bool(True) false_reader = reader_creator_bool(False) -reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_random_image(20, 20), true_reader, false_reader) +reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_random_image(20, 20), true_reader, false_reader) # Skipped 1 because paddle.dataset.mnist produces two items per data entry. # And we don't care second item at this time. paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...) @@ -118,7 +118,7 @@ Practically, always return a single entry make reusing existing data readers muc We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`). -### How to create custom data reader +### How to create custom data reader creator ```python def image_reader_creator(image_path, label_path, n): @@ -145,7 +145,7 @@ paddle.train(reader, {"image":0, "label":1}, ...) An example implementation of paddle.train could be: ```python -def minibatch_decorater(reader, minibatch_size): +def make_minibatch(reader, minibatch_size): def ret(): r = reader() buf = [r.next() for x in xrange(minibatch_size)] @@ -156,6 +156,6 @@ def minibatch_decorater(reader, minibatch_size): def train(reader, mapping, batch_size, total_pass): for pass_idx in range(total_pass): - for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning. + for mini_batch in make_minibatch(reader): # this loop will never end in online learning. do_forward_backward(mini_batch, mapping) ``` From 7fd42b8cce0585d2ff276f2cbd075a6ec5a502d0 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 14:32:36 -0800 Subject: [PATCH 250/370] create reader creator decorators: shuffle, compose, chain --- python/paddle/reader/decorator.py | 139 +++++++++++++++++-- python/paddle/reader/tests/decorator_test.py | 71 +++++++++- 2 files changed, 193 insertions(+), 17 deletions(-) diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py index f0ddb0ff812b15..dcfaf70587084e 100644 --- a/python/paddle/reader/decorator.py +++ b/python/paddle/reader/decorator.py @@ -12,25 +12,142 @@ # See the License for the specific language governing permissions and # limitations under the License. -__all__ = ['buffered'] +__all__ = ['buffered', 'compose', 'chain', 'shuffle', 'ComposeNotAligned'] from Queue import Queue from threading import Thread +import itertools +import random -def buffered(reader, size): - """Creates a buffered data reader. +def shuffle(reader_creator, buf_size): + """Creates a data reader creator whose data output is suffled. - The buffered data reader will read and save data entries into a buffer. - Reading from the buffered data reader will proceed as long as the buffer - is not empty. + Output from the iterator that created by original reader creator will be + buffered into shuffle buffer, and then shuffled. The size of shuffle buffer + is determined by argument buf_size. + + Args: + reader_creator: the original reader creator whose output will be + shuffled. + buf_size: shuffle buffer size. + + Returns: + the new reader creator whose output is shuffled. + """ + + def create_reader_creator(): + buf = [] + for e in reader_creator(): + buf.append(e) + if len(buf) >= buf_size: + random.shuffle(buf) + for b in buf: + yield b + buf = [] + + if len(buf) > 0: + random.shuffle(buf) + for b in buf: + yield b + + return create_reader_creator + + +def chain(*reader_creators): + """Creates a data reader creator whose output is the outputs of input data + reader creators chained together. + + If input reader creators output following data entries: + [0, 0, 0] + [1, 1, 1] + [2, 2, 2] + The chained reader creator will output: + [0, 0, 0, 1, 1, 1, 2, 2, 2] + + Args: + readers_creators: input reader creators + + Returns: + the new data reader creator. + """ + + def create_reader_creator(): + rs = [] + for r in reader_creators: + rs.append(r()) + + for e in itertools.chain(*rs): + yield e + + return create_reader_creator + + +class ComposeNotAligned: + pass + + +def compose(*reader_creators, **kwargs): + """Creates a data reader creator whose output is the combination of input + readers creators. + + If input reader creators output following data entries: + (1, 2) 3 (4, 5) + The composed reader creator will output: + (1, 2, 3, 4, 5) + + Args: + *reader_creators: reader creators that will be composed together. + check_alignment: If True, will check if input reader creators are aligned + correctly. If False, will not check alignment and trailing outputs + will be discarded. Defaults to True. + + Returns: + the new data reader creator. + + Raises: + ComposeNotAligned: outputs of reader creators are not aligned. + Will not raise when check_alignment is set to False. + """ + check_alignment = kwargs.pop('check_alignment', True) + + def make_tuple(x): + if isinstance(x, tuple): + return x + else: + return (x, ) + + def create_reader_creator(): + rs = [] + for r in reader_creators: + rs.append(r()) + if not check_alignment: + for outputs in itertools.izip(*rs): + yield sum(map(make_tuple, outputs), ()) + else: + for outputs in itertools.izip_longest(*rs): + for o in outputs: + if o is None: + # None will be not be present if compose is aligned + raise ComposeNotAligned + yield sum(map(make_tuple, outputs), ()) + + return create_reader_creator + + +def buffered(reader_creator, size): + """Creates a buffered data reader creator. + + The buffered data reader creator will read and save data entries into a + buffer. Reading from the buffered data reader creator will proceed as long + as the buffer is not empty. Args: - reader: the data reader to read from. + reader_creator: the data reader creator to read from. size: max buffer size. Returns: - The buffered data reader. + The buffered data reader creator. """ class EndSignal(): @@ -43,8 +160,8 @@ def read_worker(r, q): q.put(d) q.put(end) - def create_reader(): - r = reader() + def create_reader_creator(): + r = reader_creator() q = Queue(maxsize=size) t = Thread( target=read_worker, args=( @@ -57,4 +174,4 @@ def create_reader(): yield e e = q.get() - return create_reader + return create_reader_creator diff --git a/python/paddle/reader/tests/decorator_test.py b/python/paddle/reader/tests/decorator_test.py index 879d1d9c1d0e06..2830d41bf0a243 100644 --- a/python/paddle/reader/tests/decorator_test.py +++ b/python/paddle/reader/tests/decorator_test.py @@ -17,15 +17,18 @@ def reader_10(dur): - for i in range(10): - time.sleep(dur) - yield i + def reader(): + for i in range(10): + time.sleep(dur) + yield i + + return reader class TestBuffered(unittest.TestCase): def test_read(self): for size in range(20): - b = paddle.reader.buffered(lambda: reader_10(0), size) + b = paddle.reader.buffered(reader_10(0), size) c = 0 for i in b(): self.assertEqual(i, c) @@ -34,7 +37,7 @@ def test_read(self): def test_buffering(self): # read have 30ms delay. - b = paddle.reader.buffered(lambda: reader_10(0.03), 10) + b = paddle.reader.buffered(reader_10(0.03), 10) last_time = time.time() for idx, i in enumerate(b()): elapsed_time = time.time() - last_time @@ -42,9 +45,65 @@ def test_buffering(self): time.sleep(0.3) else: # read time should be short, meaning already buffered. - self.assertLess(elapsed_time, 0.01) + self.assertLess(elapsed_time, 0.05) last_time = time.time() +class TestCompose(unittest.TestCase): + def test_compse(self): + a = reader_10(0) + b = reader_10(0) + c = paddle.reader.compose(a, b) + for idx, e in enumerate(c()): + self.assertEqual(e, (idx, idx)) + + def test_compose_not_aligned(self): + a = reader_10(0) + b = paddle.reader.chain(a, a) + c = paddle.reader.compose(a, b) + total = 0 + with self.assertRaises(paddle.reader.ComposeNotAligned): + for e in c(): + total += 1 + # expecting 10, not 20 + self.assertEqual(total, 10) + + def test_compose_not_aligned_no_check(self): + a = reader_10(0) + b = paddle.reader.chain(a, a) + c = paddle.reader.compose(a, b, check_alignment=False) + total = 0 + for e in c(): + total += 1 + # expecting 10, not 20 + self.assertEqual(total, 10) + + +class TestChain(unittest.TestCase): + def test_chain(self): + a = reader_10(0) + b = reader_10(0) + c = paddle.reader.chain(a, b) + idx = 0 + for e in c(): + self.assertEqual(e, idx % 10) + idx += 1 + self.assertEqual(idx, 20) + + +class TestShuffle(unittest.TestCase): + def test_shuffle(self): + case = [(0, True), (1, True), (10, False), (100, False)] + a = reader_10(0) + for size, checkEq in case: + s = paddle.reader.shuffle(a, size) + total = 0 + for idx, e in enumerate(s()): + if checkEq: + self.assertEqual(idx, e) + total += 1 + self.assertEqual(total, 10) + + if __name__ == '__main__': unittest.main() From 2a21681c00b740627d6f5c37b89550f2990704f4 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 21 Feb 2017 16:21:57 -0800 Subject: [PATCH 251/370] fix according to comments --- python/paddle/reader/decorator.py | 77 ++++++++++---------- python/paddle/reader/tests/decorator_test.py | 37 +++++----- 2 files changed, 56 insertions(+), 58 deletions(-) diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py index dcfaf70587084e..1192b5de90f980 100644 --- a/python/paddle/reader/decorator.py +++ b/python/paddle/reader/decorator.py @@ -20,25 +20,25 @@ import random -def shuffle(reader_creator, buf_size): - """Creates a data reader creator whose data output is suffled. +def shuffle(reader, buf_size): + """Creates a data reader whose data output is suffled. - Output from the iterator that created by original reader creator will be + Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size. Args: - reader_creator: the original reader creator whose output will be + reader: the original reader whose output will be shuffled. buf_size: shuffle buffer size. Returns: - the new reader creator whose output is shuffled. + the new reader whose output is shuffled. """ - def create_reader_creator(): + def data_reader(): buf = [] - for e in reader_creator(): + for e in reader(): buf.append(e) if len(buf) >= buf_size: random.shuffle(buf) @@ -51,62 +51,61 @@ def create_reader_creator(): for b in buf: yield b - return create_reader_creator + return data_reader -def chain(*reader_creators): - """Creates a data reader creator whose output is the outputs of input data - reader creators chained together. +def chain(*readers): + """Creates a data reader whose output is the outputs of input data + readers chained together. - If input reader creators output following data entries: + If input readers output following data entries: [0, 0, 0] [1, 1, 1] [2, 2, 2] - The chained reader creator will output: + The chained reader will output: [0, 0, 0, 1, 1, 1, 2, 2, 2] Args: - readers_creators: input reader creators + readerss: input readers. Returns: - the new data reader creator. + the new data reader. """ - def create_reader_creator(): + def reader(): rs = [] - for r in reader_creators: + for r in readers: rs.append(r()) for e in itertools.chain(*rs): yield e - return create_reader_creator + return reader class ComposeNotAligned: pass -def compose(*reader_creators, **kwargs): - """Creates a data reader creator whose output is the combination of input - readers creators. +def compose(*readers, **kwargs): + """Creates a data reader whose output is the combination of input readers. - If input reader creators output following data entries: + If input readers output following data entries: (1, 2) 3 (4, 5) - The composed reader creator will output: + The composed reader will output: (1, 2, 3, 4, 5) Args: - *reader_creators: reader creators that will be composed together. - check_alignment: If True, will check if input reader creators are aligned + *readers: readers that will be composed together. + check_alignment: If True, will check if input readers are aligned correctly. If False, will not check alignment and trailing outputs will be discarded. Defaults to True. Returns: - the new data reader creator. + the new data reader. Raises: - ComposeNotAligned: outputs of reader creators are not aligned. + ComposeNotAligned: outputs of readers are not aligned. Will not raise when check_alignment is set to False. """ check_alignment = kwargs.pop('check_alignment', True) @@ -117,9 +116,9 @@ def make_tuple(x): else: return (x, ) - def create_reader_creator(): + def reader(): rs = [] - for r in reader_creators: + for r in readers: rs.append(r()) if not check_alignment: for outputs in itertools.izip(*rs): @@ -132,22 +131,22 @@ def create_reader_creator(): raise ComposeNotAligned yield sum(map(make_tuple, outputs), ()) - return create_reader_creator + return reader -def buffered(reader_creator, size): - """Creates a buffered data reader creator. +def buffered(reader, size): + """Creates a buffered data reader. - The buffered data reader creator will read and save data entries into a - buffer. Reading from the buffered data reader creator will proceed as long + The buffered data reader will read and save data entries into a + buffer. Reading from the buffered data reader will proceed as long as the buffer is not empty. Args: - reader_creator: the data reader creator to read from. + reader: the data reader to read from. size: max buffer size. Returns: - The buffered data reader creator. + The buffered data reader. """ class EndSignal(): @@ -160,8 +159,8 @@ def read_worker(r, q): q.put(d) q.put(end) - def create_reader_creator(): - r = reader_creator() + def data_reader(): + r = reader() q = Queue(maxsize=size) t = Thread( target=read_worker, args=( @@ -174,4 +173,4 @@ def create_reader_creator(): yield e e = q.get() - return create_reader_creator + return data_reader diff --git a/python/paddle/reader/tests/decorator_test.py b/python/paddle/reader/tests/decorator_test.py index 2830d41bf0a243..46eec44158cee5 100644 --- a/python/paddle/reader/tests/decorator_test.py +++ b/python/paddle/reader/tests/decorator_test.py @@ -16,9 +16,10 @@ import time -def reader_10(dur): +def reader_creator_10(dur): def reader(): for i in range(10): + # this invocation helps testing paddle.reader.buffer time.sleep(dur) yield i @@ -28,7 +29,7 @@ def reader(): class TestBuffered(unittest.TestCase): def test_read(self): for size in range(20): - b = paddle.reader.buffered(reader_10(0), size) + b = paddle.reader.buffered(reader_creator_10(0), size) c = 0 for i in b(): self.assertEqual(i, c) @@ -37,7 +38,7 @@ def test_read(self): def test_buffering(self): # read have 30ms delay. - b = paddle.reader.buffered(reader_10(0.03), 10) + b = paddle.reader.buffered(reader_creator_10(0.03), 10) last_time = time.time() for idx, i in enumerate(b()): elapsed_time = time.time() - last_time @@ -51,29 +52,29 @@ def test_buffering(self): class TestCompose(unittest.TestCase): def test_compse(self): - a = reader_10(0) - b = reader_10(0) - c = paddle.reader.compose(a, b) - for idx, e in enumerate(c()): + reader = paddle.reader.compose( + reader_creator_10(0), reader_creator_10(0)) + for idx, e in enumerate(reader()): self.assertEqual(e, (idx, idx)) def test_compose_not_aligned(self): - a = reader_10(0) - b = paddle.reader.chain(a, a) - c = paddle.reader.compose(a, b) total = 0 + reader = paddle.reader.compose( + paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)), + reader_creator_10(0)) with self.assertRaises(paddle.reader.ComposeNotAligned): - for e in c(): + for e in reader(): total += 1 # expecting 10, not 20 self.assertEqual(total, 10) def test_compose_not_aligned_no_check(self): - a = reader_10(0) - b = paddle.reader.chain(a, a) - c = paddle.reader.compose(a, b, check_alignment=False) total = 0 - for e in c(): + reader = paddle.reader.compose( + paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)), + reader_creator_10(0), + check_alignment=False) + for e in reader(): total += 1 # expecting 10, not 20 self.assertEqual(total, 10) @@ -81,9 +82,7 @@ def test_compose_not_aligned_no_check(self): class TestChain(unittest.TestCase): def test_chain(self): - a = reader_10(0) - b = reader_10(0) - c = paddle.reader.chain(a, b) + c = paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)) idx = 0 for e in c(): self.assertEqual(e, idx % 10) @@ -94,7 +93,7 @@ def test_chain(self): class TestShuffle(unittest.TestCase): def test_shuffle(self): case = [(0, True), (1, True), (10, False), (100, False)] - a = reader_10(0) + a = reader_creator_10(0) for size, checkEq in case: s = paddle.reader.shuffle(a, size) total = 0 From e19134e0cc0b6f143bb31430765c3fad82d2f54f Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 22 Feb 2017 11:52:37 +0800 Subject: [PATCH 252/370] add cost function in v2.layer --- python/paddle/v2/layer.py | 53 +++++++++++++++++++++++++++++++++------ 1 file changed, 45 insertions(+), 8 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 507725ee4ff712..cd6dd5110a44b2 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -77,7 +77,9 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', - 'cross_entropy_cost' + 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', + 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', + 'sum_cost', 'huber_cost' ] @@ -137,7 +139,8 @@ def __init__(self, name=None, **kwargs): parent_layers = dict() other_kwargs = dict() for pname in parent_names: - parent_layers[pname] = kwargs[pname] + if kwargs.has_key(pname): + parent_layers[pname] = kwargs[pname] for key in kwargs.keys(): if key not in parent_names: @@ -189,27 +192,61 @@ def to_proto_impl(self, **kwargs): data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( - 'maxid_layer', name_prefix='maxid_layer', parent_names=['input']) + 'maxid_layer', name_prefix='maxid', parent_names=['input']) classification_cost = __convert_to_v2__( 'classification_cost', name_prefix='classification_cost', - parent_names=['input', 'label']) + parent_names=['input', 'label', 'weight']) +regression_cost = __convert_to_v2__( + 'regression_cost', + name_prefix='regression_cost', + parent_names=['input', 'label', 'weight']) cross_entropy_cost = __convert_to_v2__( 'cross_entropy', name_prefix='cross_entropy', parent_names=['input', 'label']) +cross_entropy_with_selfnorm_cost = __convert_to_v2__( + 'cross_entropy_with_selfnorm', + name_prefix='cross_entropy_with_selfnorm', + parent_names=['input', 'label']) +multi_binary_label_cross_entropy_cost = __convert_to_v2__( + 'multi_binary_label_cross_entropy', + name_prefix='multi_binary_label_cross_entropy', + parent_names=['input', 'label']) +rank_cost = __convert_to_v2__( + 'rank_cost', + name_prefix='rank_cost', + parent_names=['left', 'right', 'label', 'weight']) +lambda_cost = __convert_to_v2__( + 'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score']) +sum_cost = __convert_to_v2__( + 'sum_cost', name_prefix='sum_cost', parent_names=['input']) +huber_cost = __convert_to_v2__( + 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) if __name__ == '__main__': pixel = data(name='pixel', type=data_type.dense_vector(784)) label = data(name='label', type=data_type.integer_value(10)) + weight = data(name='weight', type=data_type.dense_vector(10)) + score = data(name='score', type=data_type.dense_vector(1)) + hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) cost1 = classification_cost(input=inference, label=label) - cost2 = cross_entropy_cost(input=inference, label=label) + cost2 = classification_cost(input=inference, label=label, weight=weight) + cost3 = cross_entropy_cost(input=inference, label=label) + cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label) + cost5 = regression_cost(input=inference, label=label) + cost6 = regression_cost(input=inference, label=label, weight=weight) + cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label) + cost8 = rank_cost(left=score, right=score, label=score) + cost9 = lambda_cost(input=inference, score=score) + cost10 = sum_cost(input=inference) + cost11 = huber_cost(input=score, label=label) - print parse_network(cost1) - print parse_network(cost2) print parse_network(cost1, cost2) - print parse_network(cost2) + print parse_network(cost3, cost4) + print parse_network(cost5, cost6) + print parse_network(cost7, cost8, cost9, cost10, cost11) print parse_network(inference, maxid) From e00f06afa44211a68e29018603c6f7ab982ccce3 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 14 Feb 2017 09:51:11 -0800 Subject: [PATCH 253/370] Add top-k error --- paddle/gserver/evaluators/Evaluator.cpp | 61 ++++++++++++++++++- paddle/math/Matrix.cpp | 4 +- paddle/parameter/Parameter.cpp | 4 +- .../trainer_config_helpers/evaluators.py | 1 - 4 files changed, 65 insertions(+), 5 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 2bf6ead0dc382c..70f2da45ea18c3 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -76,9 +76,55 @@ class ClassificationErrorEvaluator : public Evaluator { 1, /* trans= */ false, useGpu(arguments[0].deviceId)); + const MatrixPtr errorMat2 = Matrix::create(output->getHeight(), + 1, + /* trans= */ false, false); + // useGpu(arguments[0].deviceId)); errorMat->zeroMem(); + if (label != nullptr) { - errorMat->classificationError(*output, *label); + errorMat->classificationError(output, label); // top-1 error + size_t height = output->getHeight(); + size_t width = 5; // config_.num_results(); + + IVector::resizeOrCreate(maxIds_, height * width, + useGpu(arguments[0].deviceId)); + Matrix::resizeOrCreate(maxValues_, height, width, false, + useGpu(arguments[0].deviceId)); + output->rowMax(*maxIds_, *maxValues_); // top-5 values + + int* ids; + int* lbl; + if (useGpu(arguments[0].deviceId)) { + IVectorPtr dest = IVector::create(maxIds_->getSize(), false); + hl_memcpy_device2host((void*)dest->getData(), + (void*)maxIds_->getData(), + sizeof(int) * maxIds_->getSize()); + ids = dest->getData(); + + IVectorPtr dest2 = IVector::create(label->getSize(), false); + hl_memcpy_device2host((void*)dest2->getData(), + (void*)label->getData(), + sizeof(int) * label->getSize()); + lbl = dest2->getData(); + } else { + ids = maxIds_->getData(); + lbl = label->getData(); + } + + // real* result = errorMat->getData(); + real* result2 = errorMat2->getData(); + for (size_t i = 0; i < height; ++i) { + // result[i] = (ids[i * width] != lbl[i]); // top-1 error + result2[i] = (ids[i * width] != lbl[i]); // initialize top-5 error + for (size_t j = 1; j < width; ++j) { + if (result2[i] == 0.0) { + break; + } + result2[i] = (ids[i * width + j] != lbl[i]); // top-5 error + } + } + totalScore2_ = errorMat2->getSum(); } else if (dynamic_cast(multiBinaryLabel.get()) || dynamic_cast(multiBinaryLabel.get())) { errorMat->classificationErrorMulti( @@ -94,6 +140,13 @@ class ClassificationErrorEvaluator : public Evaluator { return errorMat; } + void printStats(std::ostream& os) const { + os << "top_1_error=" + << (numSamples_ ? totalScore_ / numSamples_ : 0) + << " top_5_error=" + << (numSamples_ ? totalScore2_ / numSamples_ : 0); + } + virtual real evalImp(std::vector& arguments) { MatrixPtr errorMat = calcError(arguments); return errorMat->getSum(); @@ -102,6 +155,12 @@ class ClassificationErrorEvaluator : public Evaluator { virtual void distributeEval(ParameterClient2* client) { mergeResultsOfAllClients(client); } + + +private: + IVectorPtr maxIds_; + MatrixPtr maxValues_; + double totalScore2_; }; /** diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 1964b2f8bfaebc..446dd30567856f 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -732,6 +732,7 @@ void GpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { size_t beam = maxVal.getWidth(); CHECK_EQ(maxIds.getSize(), numSamples * beam); CHECK_EQ(maxVal.getHeight(), numSamples); + CHECK_EQ(maxVal.getWidth(), beam); hl_matrix_top_k(maxVal.getData(), maxVal.getStride(), @@ -3039,7 +3040,7 @@ void CpuMatrix::rowMax(Matrix& max) { max.maxRows(*this); } -/* get beam size of max ids and values */ +/* Get the top k elements of each row of this matrix */ void CpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { CHECK(isContiguous()); CHECK(!maxIds.useGpu() && !maxVal.useGpu()) << "Matrix type are not equal"; @@ -3047,6 +3048,7 @@ void CpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { size_t beam = maxVal.getWidth(); CHECK_EQ(maxIds.getSize(), numSamples * beam); CHECK_EQ(maxVal.getHeight(), numSamples); + CHECK_EQ(maxVal.getWidth(), beam); real* a = getData(); int* s = maxIds.getData(); diff --git a/paddle/parameter/Parameter.cpp b/paddle/parameter/Parameter.cpp index 29d6e20dc16968..9e7b4ea39d4ea6 100644 --- a/paddle/parameter/Parameter.cpp +++ b/paddle/parameter/Parameter.cpp @@ -375,10 +375,10 @@ bool Parameter::load(const std::string& filename) { std::ifstream fs(filename, std::ios_base::binary); if (!fs) { LOG(INFO) << "missing parameters [" << filename << "] while loading model."; - if (isStatic()) { + /*if (isStatic()) { LOG(FATAL) << getName() << " is static but missing, not allowed."; return false; - } + }*/ if (kMissParameterFail == FLAGS_load_missing_parameter_strategy) { LOG(FATAL) << getName() << " missing, not allowed."; return false; diff --git a/python/paddle/trainer_config_helpers/evaluators.py b/python/paddle/trainer_config_helpers/evaluators.py index bd247ea9af9d8d..236128f1bade11 100644 --- a/python/paddle/trainer_config_helpers/evaluators.py +++ b/python/paddle/trainer_config_helpers/evaluators.py @@ -495,7 +495,6 @@ def gradient_printer_evaluator( """ evaluator_base(name=name, type="gradient_printer", input=input) - @evaluator(EvaluatorAttribute.FOR_PRINT) @wrap_name_default() def maxid_printer_evaluator( From 5250b9b21321da5b5b4aa584aa9e4f75ad314036 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 14 Feb 2017 11:14:21 -0800 Subject: [PATCH 254/370] fix classificationError input --- paddle/gserver/evaluators/Evaluator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 70f2da45ea18c3..1c202259831c38 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -83,7 +83,7 @@ class ClassificationErrorEvaluator : public Evaluator { errorMat->zeroMem(); if (label != nullptr) { - errorMat->classificationError(output, label); // top-1 error + errorMat->classificationError(*output, *label); // top-1 error size_t height = output->getHeight(); size_t width = 5; // config_.num_results(); From 043859b5db83da51bfb66c9cab5d276321c6e4b6 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 14 Feb 2017 15:32:07 -0800 Subject: [PATCH 255/370] clean up code --- paddle/gserver/evaluators/Evaluator.cpp | 38 +++++++++---------- .../trainer_config_helpers/evaluators.py | 1 + 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 1c202259831c38..5fbba77b48766d 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -78,45 +78,44 @@ class ClassificationErrorEvaluator : public Evaluator { useGpu(arguments[0].deviceId)); const MatrixPtr errorMat2 = Matrix::create(output->getHeight(), 1, - /* trans= */ false, false); - // useGpu(arguments[0].deviceId)); + /* trans= */ false, + false); + errorMat->zeroMem(); if (label != nullptr) { errorMat->classificationError(*output, *label); // top-1 error size_t height = output->getHeight(); - size_t width = 5; // config_.num_results(); + size_t width = 5; - IVector::resizeOrCreate(maxIds_, height * width, - useGpu(arguments[0].deviceId)); - Matrix::resizeOrCreate(maxValues_, height, width, false, - useGpu(arguments[0].deviceId)); + IVector::resizeOrCreate( + maxIds_, height * width, useGpu(arguments[0].deviceId)); + Matrix::resizeOrCreate( + maxValues_, height, width, false, useGpu(arguments[0].deviceId)); output->rowMax(*maxIds_, *maxValues_); // top-5 values - int* ids; - int* lbl; + int* ids = nullptr; + int* lbl = nullptr; if (useGpu(arguments[0].deviceId)) { IVectorPtr dest = IVector::create(maxIds_->getSize(), false); hl_memcpy_device2host((void*)dest->getData(), - (void*)maxIds_->getData(), - sizeof(int) * maxIds_->getSize()); + (void*)maxIds_->getData(), + sizeof(int) * maxIds_->getSize()); ids = dest->getData(); IVectorPtr dest2 = IVector::create(label->getSize(), false); hl_memcpy_device2host((void*)dest2->getData(), - (void*)label->getData(), - sizeof(int) * label->getSize()); + (void*)label->getData(), + sizeof(int) * label->getSize()); lbl = dest2->getData(); } else { ids = maxIds_->getData(); lbl = label->getData(); } - // real* result = errorMat->getData(); real* result2 = errorMat2->getData(); for (size_t i = 0; i < height; ++i) { - // result[i] = (ids[i * width] != lbl[i]); // top-1 error - result2[i] = (ids[i * width] != lbl[i]); // initialize top-5 error + result2[i] = (ids[i * width] != lbl[i]); // initialize top-5 error for (size_t j = 1; j < width; ++j) { if (result2[i] == 0.0) { break; @@ -141,10 +140,8 @@ class ClassificationErrorEvaluator : public Evaluator { } void printStats(std::ostream& os) const { - os << "top_1_error=" - << (numSamples_ ? totalScore_ / numSamples_ : 0) - << " top_5_error=" - << (numSamples_ ? totalScore2_ / numSamples_ : 0); + os << "top_1_error=" << (numSamples_ ? totalScore_ / numSamples_ : 0) + << " top_5_error=" << (numSamples_ ? totalScore2_ / numSamples_ : 0); } virtual real evalImp(std::vector& arguments) { @@ -156,7 +153,6 @@ class ClassificationErrorEvaluator : public Evaluator { mergeResultsOfAllClients(client); } - private: IVectorPtr maxIds_; MatrixPtr maxValues_; diff --git a/python/paddle/trainer_config_helpers/evaluators.py b/python/paddle/trainer_config_helpers/evaluators.py index 236128f1bade11..bd247ea9af9d8d 100644 --- a/python/paddle/trainer_config_helpers/evaluators.py +++ b/python/paddle/trainer_config_helpers/evaluators.py @@ -495,6 +495,7 @@ def gradient_printer_evaluator( """ evaluator_base(name=name, type="gradient_printer", input=input) + @evaluator(EvaluatorAttribute.FOR_PRINT) @wrap_name_default() def maxid_printer_evaluator( From 413cbb8432255349d9152449597cb3b96d037495 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 14 Feb 2017 16:58:41 -0800 Subject: [PATCH 256/370] fix a variable definition bug --- paddle/gserver/evaluators/Evaluator.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 5fbba77b48766d..1c2479d7280037 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -96,14 +96,14 @@ class ClassificationErrorEvaluator : public Evaluator { int* ids = nullptr; int* lbl = nullptr; + IVectorPtr dest = IVector::create(maxIds_->getSize(), false); + IVectorPtr dest2 = IVector::create(label->getSize(), false); if (useGpu(arguments[0].deviceId)) { - IVectorPtr dest = IVector::create(maxIds_->getSize(), false); hl_memcpy_device2host((void*)dest->getData(), (void*)maxIds_->getData(), sizeof(int) * maxIds_->getSize()); ids = dest->getData(); - IVectorPtr dest2 = IVector::create(label->getSize(), false); hl_memcpy_device2host((void*)dest2->getData(), (void*)label->getData(), sizeof(int) * label->getSize()); From e768721cd9bf2582e391f0b2e7dc4fb790f36e74 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Wed, 15 Feb 2017 15:05:35 -0800 Subject: [PATCH 257/370] fix calculating totalScore2_ bug --- paddle/gserver/evaluators/Evaluator.cpp | 95 +++++++++++-------- paddle/gserver/tests/test_Evaluator.cpp | 1 + proto/ModelConfig.proto | 4 + python/paddle/trainer/config_parser.py | 3 + .../trainer_config_helpers/evaluators.py | 10 ++ .../paddle/trainer_config_helpers/layers.py | 17 ++-- 6 files changed, 85 insertions(+), 45 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 1c2479d7280037..30e91fa222fca5 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -39,6 +39,13 @@ void Evaluator::eval(const NeuralNetwork& nn) { */ class ClassificationErrorEvaluator : public Evaluator { public: + ClassificationErrorEvaluator() : totalScore2_(0) {} + + virtual void start() { + Evaluator::start(); + totalScore2_ = 0; + } + virtual void updateSamplesNum(const std::vector& arguments) { if (3 == arguments.size()) { numSamples_ += arguments[2].value->getSum(); @@ -85,45 +92,47 @@ class ClassificationErrorEvaluator : public Evaluator { if (label != nullptr) { errorMat->classificationError(*output, *label); // top-1 error - size_t height = output->getHeight(); - size_t width = 5; - - IVector::resizeOrCreate( - maxIds_, height * width, useGpu(arguments[0].deviceId)); - Matrix::resizeOrCreate( - maxValues_, height, width, false, useGpu(arguments[0].deviceId)); - output->rowMax(*maxIds_, *maxValues_); // top-5 values - - int* ids = nullptr; - int* lbl = nullptr; - IVectorPtr dest = IVector::create(maxIds_->getSize(), false); - IVectorPtr dest2 = IVector::create(label->getSize(), false); - if (useGpu(arguments[0].deviceId)) { - hl_memcpy_device2host((void*)dest->getData(), - (void*)maxIds_->getData(), - sizeof(int) * maxIds_->getSize()); - ids = dest->getData(); - - hl_memcpy_device2host((void*)dest2->getData(), - (void*)label->getData(), - sizeof(int) * label->getSize()); - lbl = dest2->getData(); - } else { - ids = maxIds_->getData(); - lbl = label->getData(); - } + if (config_.top_k() > 1) { + size_t height = output->getHeight(); + size_t width = config_.top_k(); + + IVector::resizeOrCreate( + maxIds_, height * width, useGpu(arguments[0].deviceId)); + Matrix::resizeOrCreate( + maxValues_, height, width, false, useGpu(arguments[0].deviceId)); + output->rowMax(*maxIds_, *maxValues_); // top-k values + + int* ids = nullptr; + int* lbl = nullptr; + IVectorPtr dest = IVector::create(maxIds_->getSize(), false); + IVectorPtr dest2 = IVector::create(label->getSize(), false); + if (useGpu(arguments[0].deviceId)) { + hl_memcpy_device2host((void*)dest->getData(), + (void*)maxIds_->getData(), + sizeof(int) * maxIds_->getSize()); + ids = dest->getData(); + + hl_memcpy_device2host((void*)dest2->getData(), + (void*)label->getData(), + sizeof(int) * label->getSize()); + lbl = dest2->getData(); + } else { + ids = maxIds_->getData(); + lbl = label->getData(); + } - real* result2 = errorMat2->getData(); - for (size_t i = 0; i < height; ++i) { - result2[i] = (ids[i * width] != lbl[i]); // initialize top-5 error - for (size_t j = 1; j < width; ++j) { - if (result2[i] == 0.0) { - break; + real* result2 = errorMat2->getData(); + for (size_t i = 0; i < height; ++i) { + result2[i] = (ids[i * width] != lbl[i]); // initialize top-k error + for (size_t j = 1; j < width; ++j) { + if (result2[i] == 0.0) { + break; + } + result2[i] = (ids[i * width + j] != lbl[i]); // top-k error } - result2[i] = (ids[i * width + j] != lbl[i]); // top-5 error } + totalScore2_ += errorMat2->getSum(); } - totalScore2_ = errorMat2->getSum(); } else if (dynamic_cast(multiBinaryLabel.get()) || dynamic_cast(multiBinaryLabel.get())) { errorMat->classificationErrorMulti( @@ -140,8 +149,14 @@ class ClassificationErrorEvaluator : public Evaluator { } void printStats(std::ostream& os) const { - os << "top_1_error=" << (numSamples_ ? totalScore_ / numSamples_ : 0) - << " top_5_error=" << (numSamples_ ? totalScore2_ / numSamples_ : 0); + if (config_.top_k() == 1) { + os << config_.name() << "=" + << (numSamples_ ? totalScore_ / numSamples_ : 0); + } else { + os << "top_1_error=" << (numSamples_ ? totalScore_ / numSamples_ : 0) + << " top_" << config_.top_k() + << "_error=" << (numSamples_ ? totalScore2_ / numSamples_ : 0); + } } virtual real evalImp(std::vector& arguments) { @@ -150,7 +165,11 @@ class ClassificationErrorEvaluator : public Evaluator { } virtual void distributeEval(ParameterClient2* client) { - mergeResultsOfAllClients(client); + double data[3] = {totalScore_, totalScore2_, numSamples_}; + client->reduce(data, data, 3, FLAGS_trainer_id, 0); + totalScore_ = data[0]; + totalScore2_ = data[1]; + numSamples_ = data[2]; } private: diff --git a/paddle/gserver/tests/test_Evaluator.cpp b/paddle/gserver/tests/test_Evaluator.cpp index 8165eb82693361..d39c0422715675 100644 --- a/paddle/gserver/tests/test_Evaluator.cpp +++ b/paddle/gserver/tests/test_Evaluator.cpp @@ -129,6 +129,7 @@ void testEvaluatorAll(TestConfig testConf, TEST(Evaluator, classification_error) { TestConfig config; config.evaluatorConfig.set_type("classification_error"); + config.evaluatorConfig.set_top_k(5); config.inputDefs.push_back({INPUT_DATA, "output", 50}); config.inputDefs.push_back({INPUT_LABEL, "label", 50}); diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index be4634d5103c0f..65d5d50277b665 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -475,6 +475,10 @@ message EvaluatorConfig { // Used by ChunkEvaluator // chunk of these types are not counted repeated int32 excluded_chunk_types = 12; + + // Used by ClassificationErrorEvaluator + // top # classification error + optional int32 top_k = 13 [default = 1]; } message LinkConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index d403a6029a3e9d..da937152ee0ce7 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1253,6 +1253,7 @@ def Evaluator( dict_file=None, result_file=None, num_results=None, + top_k=None, delimited=None, excluded_chunk_types=None, ): evaluator = g_config.model_config.evaluators.add() @@ -1280,6 +1281,8 @@ def Evaluator( evaluator.result_file = result_file if num_results is not None: evaluator.num_results = num_results + if top_k is not None: + evaluator.top_k = top_k if delimited is not None: evaluator.delimited = delimited diff --git a/python/paddle/trainer_config_helpers/evaluators.py b/python/paddle/trainer_config_helpers/evaluators.py index bd247ea9af9d8d..567521ee9dbadb 100644 --- a/python/paddle/trainer_config_helpers/evaluators.py +++ b/python/paddle/trainer_config_helpers/evaluators.py @@ -71,6 +71,7 @@ def evaluator_base( result_file=None, num_results=None, delimited=None, + top_k=None, excluded_chunk_types=None, ): """ Evaluator will evaluate the network status while training/testing. @@ -104,12 +105,15 @@ def evaluator_base( :param weight: An input layer which is a weight for each sample. Each evaluator may calculate differently to use this weight. :type weight: LayerOutput. + :param top_k: number k in top-k error rate + :type top_k: int """ # inputs type assertions. assert classification_threshold is None or isinstance( classification_threshold, float) assert positive_label is None or isinstance(positive_label, int) assert num_results is None or isinstance(num_results, int) + assert top_k is None or isinstance(top_k, int) if not isinstance(input, list): input = [input] @@ -130,6 +134,8 @@ def evaluator_base( dict_file=dict_file, result_file=result_file, delimited=delimited, + num_results=num_results, + top_k=top_k, excluded_chunk_types=excluded_chunk_types, ) @@ -139,6 +145,7 @@ def classification_error_evaluator(input, label, name=None, weight=None, + top_k=None, threshold=None): """ Classification Error Evaluator. It will print error rate for classification. @@ -167,6 +174,8 @@ def classification_error_evaluator(input, then means not set weight. The larger weight it is, the more important this sample is. :type weight: LayerOutput + :param top_k: number k in top-k error rate + :type top_k: int :param threshold: The classification threshold. :type threshold: float :return: None. @@ -178,6 +187,7 @@ def classification_error_evaluator(input, input=input, label=label, weight=weight, + top_k=top_k, classification_threshold=threshold, ) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 0d3a31b9d39a28..00aef80691fba0 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2870,8 +2870,8 @@ def gru_step_layer(input, :param name: :param gate_act: :param bias_attr: - :param param_attr: the parameter_attribute for transforming the output_mem - from previous step. + :param param_attr: the parameter_attribute for transforming the output_mem + from previous step. :param layer_attr: :return: LayerOutput object. :rtype: LayerOutput @@ -2882,10 +2882,10 @@ def gru_step_layer(input, Layer( name=name, type=LayerType.GRU_STEP_LAYER, - # The parameter here is for transforming the output_mem. The input has - # already been transformed outside this module so it does not need - # parameter associated with it. - # The parameter here is instead grouped with input is due to + # The parameter here is for transforming the output_mem. The input has + # already been transformed outside this module so it does not need + # parameter associated with it. + # The parameter here is instead grouped with input is due to # backward model compatibility. inputs=[Input(input.name, **param_attr.attr), output_mem.name], bias=ParamAttr.to_bias(bias_attr), @@ -3536,6 +3536,7 @@ def classification_cost(input, label, weight=None, name=None, + top_k=None, evaluator=classification_error_evaluator, layer_attr=None): """ @@ -3550,6 +3551,8 @@ def classification_cost(input, :param weight: The weight affects the cost, namely the scale of cost. It is an optional argument. :type weight: LayerOutput + :param top_k: number k in top-k error rate + :type top_k: int :param evaluator: Evaluator method. :param layer_attr: layer's extra attribute. :type layer_attr: ExtraLayerAttribute @@ -3577,7 +3580,7 @@ def __add_evaluator__(e): assert isinstance(e.for_classification, bool) assert e.for_classification - e(name=e.__name__, input=input, label=label, weight=weight) + e(name=e.__name__, input=input, label=label, weight=weight, top_k=top_k) if not isinstance(evaluator, collections.Sequence): evaluator = [evaluator] From d256512832d734134204ff483504551b3c184a57 Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Mon, 20 Feb 2017 11:18:22 -0800 Subject: [PATCH 258/370] Rewrite code according to reviewer comments --- paddle/gserver/evaluators/Evaluator.cpp | 34 ++++++++----------------- paddle/parameter/Parameter.cpp | 4 --- 2 files changed, 10 insertions(+), 28 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 30e91fa222fca5..f2dd5cf8073f6d 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -102,36 +102,22 @@ class ClassificationErrorEvaluator : public Evaluator { maxValues_, height, width, false, useGpu(arguments[0].deviceId)); output->rowMax(*maxIds_, *maxValues_); // top-k values - int* ids = nullptr; - int* lbl = nullptr; IVectorPtr dest = IVector::create(maxIds_->getSize(), false); IVectorPtr dest2 = IVector::create(label->getSize(), false); - if (useGpu(arguments[0].deviceId)) { - hl_memcpy_device2host((void*)dest->getData(), - (void*)maxIds_->getData(), - sizeof(int) * maxIds_->getSize()); - ids = dest->getData(); - - hl_memcpy_device2host((void*)dest2->getData(), - (void*)label->getData(), - sizeof(int) * label->getSize()); - lbl = dest2->getData(); - } else { - ids = maxIds_->getData(); - lbl = label->getData(); - } + dest->copyFrom(*maxIds_); + dest2->copyFrom(*label); + int* ids = dest->getData(); + int* lbl = dest2->getData(); - real* result2 = errorMat2->getData(); for (size_t i = 0; i < height; ++i) { - result2[i] = (ids[i * width] != lbl[i]); // initialize top-k error - for (size_t j = 1; j < width; ++j) { - if (result2[i] == 0.0) { - break; - } - result2[i] = (ids[i * width + j] != lbl[i]); // top-k error + bool contain = false; + for (size_t j = 0; j < width && !contain; ++j) { + contain = (ids[i * width + j] == lbl[i]); + } + if (!contain) { + totalScore2_ += 1.0; // update top-k error } } - totalScore2_ += errorMat2->getSum(); } } else if (dynamic_cast(multiBinaryLabel.get()) || dynamic_cast(multiBinaryLabel.get())) { diff --git a/paddle/parameter/Parameter.cpp b/paddle/parameter/Parameter.cpp index 9e7b4ea39d4ea6..1ccded81879679 100644 --- a/paddle/parameter/Parameter.cpp +++ b/paddle/parameter/Parameter.cpp @@ -375,10 +375,6 @@ bool Parameter::load(const std::string& filename) { std::ifstream fs(filename, std::ios_base::binary); if (!fs) { LOG(INFO) << "missing parameters [" << filename << "] while loading model."; - /*if (isStatic()) { - LOG(FATAL) << getName() << " is static but missing, not allowed."; - return false; - }*/ if (kMissParameterFail == FLAGS_load_missing_parameter_strategy) { LOG(FATAL) << getName() << " missing, not allowed."; return false; From 8fded24c75265a27498eda2f7fdbeb17b969c33a Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 21 Feb 2017 15:27:56 -0800 Subject: [PATCH 259/370] implement top k classification error in class matrix --- paddle/cuda/include/hl_matrix.h | 13 ---- paddle/cuda/include/hl_top_k.h | 28 ++++++++- paddle/cuda/src/hl_cuda_matrix.cu | 53 ---------------- paddle/cuda/src/hl_top_k.cu | 78 ++++++++++++++++++++++++ paddle/gserver/evaluators/Evaluator.cpp | 52 ++-------------- paddle/gserver/layers/Layer.h | 1 + paddle/math/Matrix.cpp | 80 +++++++++++++++---------- paddle/math/Matrix.h | 9 ++- 8 files changed, 168 insertions(+), 146 deletions(-) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index 6f21b82afdc6cd..eb454c59c1e58c 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -69,19 +69,6 @@ extern void hl_sequence_softmax_forward(real* A_d, const int* index, int numSequence); -/** - * @brief Matrix classification error. - * - * @param[in] A_d input matrix (M x N). - * @param[in] B_d input vector (M x 1). - * @param[out] C_d output vector (M x 1). - * @param[in] dimM matrix height. - * @param[in] dimN matrix width. - * - */ -extern void hl_matrix_classification_error( - real* A_d, int* B_d, real* C_d, int dimM, int dimN); - /** * @brief Matrix cross entropy. * diff --git a/paddle/cuda/include/hl_top_k.h b/paddle/cuda/include/hl_top_k.h index 77949ed295a6ea..79ae0d0e741de0 100644 --- a/paddle/cuda/include/hl_top_k.h +++ b/paddle/cuda/include/hl_top_k.h @@ -58,4 +58,30 @@ extern void hl_sparse_matrix_top_k(real* topVal, int beamSize, int numSamples); -#endif /* HL_TOP_K_H_ */ +/** + * @brief Matrix classification error. + * + * @param[out] topVal top k element. + * @param[in] ldv leading dimension of topVal. + * @param[out] topIds top k index. + * @param[in] src input value. + * @param[in] lds leading dimension of src. + * @param[in] dim width of input value. + * @param[in] topkSize size of top k element. + * @param[in] numSamples height of input value. + * @param[in] label ground truth label. + * @param[out] recResult top-k classification error. + * + */ +extern void hl_matrix_classification_error(real* topVal, + int ldv, + int* topIds, + real* src, + int lds, + int dim, + int topkSize, + int numSamples, + int* label, + real* recResult); + +#endif // HL_TOP_K_H_ diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index 96c07d9c3b7a37..9bcc7fb7de44b2 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -265,59 +265,6 @@ void hl_matrix_softmax_derivative(real *grad_d, CHECK_SYNC("hl_matrix_softmax_derivative failed"); } -template -__global__ void KeMatrixClassificationError(real* in_A, - int* in_B, - real* out_C, - int dimN) { - __shared__ real max_s[blockSize]; - __shared__ int max_l[blockSize]; - const int tid = threadIdx.x; - const int rowId = blockIdx.x; - - max_s[tid] = -1e30f; - in_A += rowId * dimN; - real tmp; - for (int colId = tid; colId < dimN; colId += blockSize) { - tmp = in_A[colId]; - if (max_s[tid] < tmp) { - max_s[tid] = tmp; - max_l[tid] = colId; - } - } - __syncthreads(); - - for (int stride = blockSize/2; stride > 0; stride = stride/2) { - if (tid < stride) { - if (max_s[tid] < max_s[tid + stride]) { - max_s[tid] = max_s[tid + stride]; - max_l[tid] = max_l[tid + stride]; - } - } - __syncthreads(); - } - __syncthreads(); - - if (tid == 0) { - out_C[rowId] = (max_l[0] == in_B[rowId] ? 0 : 1.0f); - } -} - -void hl_matrix_classification_error(real* A_d, - int* B_d, - real* C_d, - int dimM, - int dimN) { - CHECK_NOTNULL(A_d); - CHECK_NOTNULL(B_d); - CHECK_NOTNULL(C_d); - - // each sample is calculated by one block - KeMatrixClassificationError<1024><<< dimM, 1024, 0, STREAM_DEFAULT >>> - (A_d, B_d, C_d, dimN); - CHECK_SYNC("hl_matrix_classification_error"); -} - __global__ void KeMatrixMultiBinaryCrossEntropy(real* output, real* entropy, int* row, diff --git a/paddle/cuda/src/hl_top_k.cu b/paddle/cuda/src/hl_top_k.cu index f0ef0cc3c51f9e..4f0bbfcf4e3aa5 100644 --- a/paddle/cuda/src/hl_top_k.cu +++ b/paddle/cuda/src/hl_top_k.cu @@ -384,3 +384,81 @@ void hl_sparse_matrix_top_k(real* topVal, int ldv, CHECK_SYNC("hl_sparse_matrix_top_k failed"); } +/** + * Each block compute one sample. + * In a block: + * 1. every thread get top maxLength value; + * 2. merge to shTopK, block reduce and get max value; + * 3. go to the second setp, until one thread's topK value is null; + * 4. go to the first setp, until get the topK value. + */ +template +__global__ void KeMatrixTopKClassificationError(real* topVal, int ldv, + int * topIds, + real* src, int lds, + int dim, + int beamSize, + int* label, + real* recResult) { + __shared__ Pair shTopK[blockSize]; + __shared__ int maxId[blockSize / 2]; + const int tid = threadIdx.x; + const int warp = threadIdx.x / 32; + src += blockIdx.x * lds; + topVal += blockIdx.x * ldv; + topIds += blockIdx.x * beamSize; + + Pair topK[maxLength]; // NOLINT + int beam = maxLength; + Pair max; + bool isEmpty = false; + bool firstStep = true; + int topkSize = beamSize; + + for (int k = 0; k < maxLength; k++) { + topK[k].set(-HL_FLOAT_MAX, -1); + } + + while (beamSize) { + threadGetTopK + (topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid); + + shTopK[tid] = topK[0]; + blockReduce + (shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp); + } + + __syncthreads(); + if (tid == 0) { + for (int i = 0; i < topkSize; i++) { + if (*--topIds == label[blockIdx.x]) { + recResult[blockIdx.x] = 0; + break; + } + recResult[blockIdx.x] = 1.0f; + } + } +} + +void hl_matrix_classification_error(real* topVal, int ldv, + int* topIds, + real* src, int lds, + int dim, + int topkSize, + int numSamples, + int* label, + real* recResult) { + CHECK_NOTNULL(topVal); + CHECK_NOTNULL(topIds); + CHECK_NOTNULL(src); + + if (topkSize > dim) topkSize = dim; + + dim3 threads(256, 1); + dim3 grid(numSamples, 1); + KeMatrixTopKClassificationError<5, 256> + <<< grid, threads, 0, STREAM_DEFAULT >>> + (topVal, ldv, topIds, src, lds, dim, topkSize, label, recResult); + + CHECK_SYNC("hl_matrix_top_k classification error failed"); +} diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index f2dd5cf8073f6d..cd8d1e9ecbfd83 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -39,12 +39,13 @@ void Evaluator::eval(const NeuralNetwork& nn) { */ class ClassificationErrorEvaluator : public Evaluator { public: + /* ClassificationErrorEvaluator() : totalScore2_(0) {} virtual void start() { Evaluator::start(); totalScore2_ = 0; - } + } */ virtual void updateSamplesNum(const std::vector& arguments) { if (3 == arguments.size()) { @@ -83,42 +84,11 @@ class ClassificationErrorEvaluator : public Evaluator { 1, /* trans= */ false, useGpu(arguments[0].deviceId)); - const MatrixPtr errorMat2 = Matrix::create(output->getHeight(), - 1, - /* trans= */ false, - false); errorMat->zeroMem(); if (label != nullptr) { - errorMat->classificationError(*output, *label); // top-1 error - if (config_.top_k() > 1) { - size_t height = output->getHeight(); - size_t width = config_.top_k(); - - IVector::resizeOrCreate( - maxIds_, height * width, useGpu(arguments[0].deviceId)); - Matrix::resizeOrCreate( - maxValues_, height, width, false, useGpu(arguments[0].deviceId)); - output->rowMax(*maxIds_, *maxValues_); // top-k values - - IVectorPtr dest = IVector::create(maxIds_->getSize(), false); - IVectorPtr dest2 = IVector::create(label->getSize(), false); - dest->copyFrom(*maxIds_); - dest2->copyFrom(*label); - int* ids = dest->getData(); - int* lbl = dest2->getData(); - - for (size_t i = 0; i < height; ++i) { - bool contain = false; - for (size_t j = 0; j < width && !contain; ++j) { - contain = (ids[i * width + j] == lbl[i]); - } - if (!contain) { - totalScore2_ += 1.0; // update top-k error - } - } - } + errorMat->classificationError(*output, *label, config_.top_k()); } else if (dynamic_cast(multiBinaryLabel.get()) || dynamic_cast(multiBinaryLabel.get())) { errorMat->classificationErrorMulti( @@ -139,9 +109,8 @@ class ClassificationErrorEvaluator : public Evaluator { os << config_.name() << "=" << (numSamples_ ? totalScore_ / numSamples_ : 0); } else { - os << "top_1_error=" << (numSamples_ ? totalScore_ / numSamples_ : 0) - << " top_" << config_.top_k() - << "_error=" << (numSamples_ ? totalScore2_ / numSamples_ : 0); + os << " top_" << config_.top_k() + << "_error=" << (numSamples_ ? totalScore_ / numSamples_ : 0); } } @@ -151,17 +120,8 @@ class ClassificationErrorEvaluator : public Evaluator { } virtual void distributeEval(ParameterClient2* client) { - double data[3] = {totalScore_, totalScore2_, numSamples_}; - client->reduce(data, data, 3, FLAGS_trainer_id, 0); - totalScore_ = data[0]; - totalScore2_ = data[1]; - numSamples_ = data[2]; + mergeResultsOfAllClients(client); } - -private: - IVectorPtr maxIds_; - MatrixPtr maxValues_; - double totalScore2_; }; /** diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h index 6dfd48fb966181..7c4bea072157aa 100644 --- a/paddle/gserver/layers/Layer.h +++ b/paddle/gserver/layers/Layer.h @@ -311,6 +311,7 @@ class Layer { return *output->second; } else { LOG(FATAL) << "No specific output " << str; + return *((Argument*)nullptr); } } } diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 446dd30567856f..07450bfb0ef709 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -793,19 +793,32 @@ void GpuMatrix::maxoutBackward(Matrix& a, } /*calulate the error of classification */ -void GpuMatrix::classificationError(Matrix& output, IVector& label) { - auto output_ptr = dynamic_cast(&output); - auto label_ptr = dynamic_cast(&label); - CHECK(output_ptr && label_ptr) << "Invalid argument pointer"; - - CHECK(height_ == output_ptr->height_ && width_ == 1) +void GpuMatrix::classificationError(Matrix& output, + IVector& label, + size_t topkSize) { + auto gpuOutput = dynamic_cast(&output); + auto gpuLabel = dynamic_cast(&label); + size_t numSamples = this->getHeight(); + GpuMatrixPtr gpuTopVal = std::make_shared(numSamples, topkSize); + GpuIVectorPtr gpuTopIds = std::make_shared(numSamples * topkSize); + + CHECK(gpuOutput && gpuLabel) << "Invalid argument pointer"; + CHECK(gpuTopVal && gpuTopIds) << "Allocate GPU memory failed"; + CHECK(gpuLabel->getSize() == numSamples) << "Vector size is not equal"; + CHECK(numSamples == gpuOutput->getHeight() && this->getWidth() == 1) << "Matrix dimensions are not equal"; - hl_matrix_classification_error((real*)output_ptr->data_, - (int*)label_ptr->getData(), - data_, - height_, - output_ptr->width_); + size_t dim = gpuOutput->getWidth(); + hl_matrix_classification_error(gpuTopVal->getData(), + gpuTopVal->getStride(), + gpuTopIds->getData(), + gpuOutput->getData(), + gpuOutput->getStride(), + dim, + topkSize, + numSamples, + gpuLabel->getData(), + this->getData()); } /* copy -log(output[i * width + label]) to this->data[i] */ @@ -3200,32 +3213,39 @@ void CpuMatrix::rowNormalizeL1(Matrix& out) { } /* calulate classification error */ -void CpuMatrix::classificationError(Matrix& output, IVector& label) { - CHECK(dynamic_cast(&output)); - CHECK(dynamic_cast(&label)); +void CpuMatrix::classificationError(Matrix& output, + IVector& label, + size_t topkSize) { + size_t numSamples = this->getHeight(); + auto cpuOutput = dynamic_cast(&output); + auto cpuLabel = dynamic_cast(&label); + IVectorPtr cpuTopIds = std::make_shared(numSamples * topkSize); + MatrixPtr cpuTopVal = std::make_shared(numSamples, topkSize); + + CHECK(cpuOutput && cpuLabel) << "Invalid argument pointer"; + CHECK(cpuTopIds && cpuTopVal) << "Allocate cpu memory failed"; + CHECK(cpuLabel->getSize() == numSamples) << "Vector size is not equal"; + CHECK(cpuOutput->getHeight() == numSamples && this->getWidth() == 1) + << "Matrix dimensions are not equal"; - CHECK_EQ(getWidth(), (size_t)1); - size_t numSamples = getHeight(); - CHECK_EQ(label.getSize(), numSamples); - CHECK_EQ(output.getHeight(), numSamples); + // top k matrix classification + cpuOutput->rowMax(*cpuTopIds, *cpuTopVal); - size_t dim = output.getWidth(); - real* out = output.getData(); - int* lbl = label.getData(); - real maxData = 0.0; - int maxIndex = -1; + size_t dim = cpuOutput->getWidth(); + real* result = this->getData(); + int* ids = cpuTopIds->getData(); + int* lbl = cpuLabel->getData(); for (size_t i = 0; i < numSamples; ++i) { CHECK_GE(lbl[i], 0); CHECK_LT((size_t)lbl[i], dim); - maxData = out[i * dim]; - maxIndex = 0; - for (size_t j = 0; j < dim; ++j) { - if (maxData < out[i * dim + j]) { - maxIndex = j; - maxData = out[i * dim + j]; + + for (size_t j = 0; j < topkSize; ++j) { + if (ids[j + i * topkSize] == lbl[i]) { + result[i] = 0; + break; } + result[i] = 1.0f; } - getData()[i] = (maxIndex != lbl[i]); } } diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index ea4bbb86b057b5..d0ba2e93feabfc 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -836,8 +836,11 @@ class Matrix : public BaseMatrix { * output[i] = 1 if row i is an error. * * output[i] = 0 if row i is correct. + * */ - virtual void classificationError(Matrix& output, IVector& label) { + virtual void classificationError(Matrix& output, + IVector& label, + size_t topkSize = 1) { LOG(FATAL) << "Not implemented"; } @@ -1314,7 +1317,7 @@ class GpuMatrix : public Matrix { void check(std::ostream& os, Matrix& refMat, bool printDiff = true); void randomizeUniform(); - void classificationError(Matrix& output, IVector& label); + void classificationError(Matrix& output, IVector& label, size_t topkSize = 1); void convExpand(Matrix& feature, int feaImgHeight, @@ -1739,7 +1742,7 @@ class CpuMatrix : public Matrix { void randomizeUniform(); - void classificationError(Matrix& output, IVector& label); + void classificationError(Matrix& output, IVector& label, size_t topkSize = 1); void addByBitCode(size_t numClasses, const IVector& codes, const Matrix& vec); From 046349dd409c2745f20cecf181d5a58a5b37511f Mon Sep 17 00:00:00 2001 From: Liang Zhao Date: Tue, 21 Feb 2017 22:01:19 -0800 Subject: [PATCH 260/370] Fix definition of hl_matrix_classification_error in hl_matrix_stub.h --- paddle/cuda/include/stub/hl_matrix_stub.h | 12 ++++++++++-- paddle/math/tests/test_matrixCompare.cpp | 19 ++++++++++++------- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index f4e6461cdcf198..127cb7e27983e8 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -35,8 +35,16 @@ inline void hl_sequence_softmax_forward(real* A_d, inline void hl_matrix_softmax_derivative( real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN) {} -inline void hl_matrix_classification_error( - real* A_d, int* B_d, real* C_d, int dimM, int dimN) {} +inline void hl_matrix_classification_error(real* topVal, + int ldv, + int* topIds, + real* src, + int lds, + int dim, + int topkSize, + int numSamples, + int* label, + real* recResult) {} inline void hl_matrix_cross_entropy( real* A_d, real* C_d, int* label_d, int dimM, int dimN) {} diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 6caaea443c1df7..08b64c1bb6f5d3 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -764,7 +764,7 @@ TEST(Matrix, paramReluBackwardDiff) { } } -void testClassificationError(int numSamples, int dim) { +void testClassificationError(int numSamples, int dim, int topkSize) { MatrixPtr cpuError = std::make_shared(numSamples, 1); MatrixPtr gpuError = std::make_shared(numSamples, 1); MatrixPtr cpuOutput = std::make_shared(numSamples, dim); @@ -777,17 +777,22 @@ void testClassificationError(int numSamples, int dim) { gpuOutput->copyFrom(*cpuOutput); gpuLabel->copyFrom(*cpuLabel); - cpuError->classificationError(*cpuOutput, *cpuLabel); - gpuError->classificationError(*gpuOutput, *gpuLabel); + cpuError->classificationError(*cpuOutput, *cpuLabel, topkSize); + gpuError->classificationError(*gpuOutput, *gpuLabel, topkSize); TensorCheckEqual(*cpuError, *gpuError); } TEST(Matrix, classificationError) { - for (auto numSamples : {1, 10, 100, 1000, 70000}) { - for (auto dim : {1, 10, 100, 1000}) { - VLOG(3) << " numSamples=" << numSamples << " dim=" << dim; - testClassificationError(numSamples, dim); + for (auto numSamples : {1, 5, 31, 90, 150, 300}) { + for (auto dim : + {1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) { + for (auto topkSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) { + if (topkSize > dim) continue; + VLOG(3) << " sample= " << numSamples << " topkSize= " << topkSize + << " dim= " << dim; + testClassificationError(numSamples, dim, topkSize); + } } } } From bb625337e37c06cec40954abb83fde8b2716d44a Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 22 Feb 2017 15:28:02 +0800 Subject: [PATCH 261/370] add some comments --- python/paddle/v2/data_feeder.py | 41 +++++++++++++++++++++++++++++++++ python/paddle/v2/trainer.py | 1 - 2 files changed, 41 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py index 2a0b6bbeb563f7..83a4efef9e7a7b 100644 --- a/python/paddle/v2/data_feeder.py +++ b/python/paddle/v2/data_feeder.py @@ -15,5 +15,46 @@ from py_paddle import DataProviderConverter __all__ = ['DataFeeder'] +""" +DataFeeder converts the data returned by paddle.reader into a data structure +of Arguments which is defined in the API. The paddle.reader usually returns +a list of mini-batch data. Each item in the list is a tuple or list, which is +one sample with multiple features. DataFeeder converts this mini-batch data +into Arguments in order to feed it to C++ interface. + +The example usage: + + data_types = [paddle.data_type.dense_vector(784), + paddle.data_type.integer_value(10)] + feeder = DataFeeder(input_types=data_types) + minibatch_data = [ + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample + ] + + # or + # minibatch_data = [ + # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample + # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample + # ] + arg = feeder(minibatch_data) + + +Args: + input_types: A list of input data types. It's length is equal to the length + of data returned by paddle.reader. Each item specifies the type + of each feature. + mintbatch_data: A list of mini-batch data. Each item is a list or tuple, + for example: + [ + (feature_0, feature_1, feature_2, ...), # first sample + (feature_0, feature_1, feature_2, ...), # second sample + ... + ] + +Returns: + An Arguments object contains this mini-batch data with multiple features. + The Arguments definition is in the API. +""" DataFeeder = DataProviderConverter diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 7480a3fb84bbd2..5709c7e886c323 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -89,7 +89,6 @@ def train(self, event_handler = default_event_handler topology = v2_layer.parse_network(topology) - print topology __check_train_args__(**locals()) From fbf864362dc1bd716a8db1f4441afe488fe3d74b Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 22 Feb 2017 16:02:58 +0800 Subject: [PATCH 262/370] Update python getLayerOutputs --- demo/image_classification/prediction.py | 2 +- demo/model_zoo/resnet/classify.py | 2 +- paddle/api/Arguments.cpp | 7 ++++ paddle/api/GradientMachine.cpp | 5 ++- paddle/api/PaddleAPI.h | 5 +-- paddle/api/Trainer.cpp | 8 ++--- .../gradientmachines/GradientMachine.h | 4 +-- .../gradientmachines/MultiGradientMachine.cpp | 32 +++++-------------- .../gradientmachines/MultiGradientMachine.h | 4 ++- .../gradientmachines/NeuralNetwork.cpp | 6 ++-- .../gserver/gradientmachines/NeuralNetwork.h | 2 +- paddle/gserver/layers/CosSimLayer.cpp | 2 +- paddle/py_paddle/util.py | 2 +- 13 files changed, 35 insertions(+), 46 deletions(-) diff --git a/demo/image_classification/prediction.py b/demo/image_classification/prediction.py index 9a86aafcb2fa4d..49c0ff600c40e0 100755 --- a/demo/image_classification/prediction.py +++ b/demo/image_classification/prediction.py @@ -126,7 +126,7 @@ def forward(self, data, output_layer): # For oversampling, average predictions across crops. # If not, the shape of output[name]: (1, class_number), # the mean is also applicable. - return output[output_layer].mean(0) + return output[output_layer]['value'].mean(0) def predict(self, image=None, output_layer=None): assert isinstance(image, basestring) diff --git a/demo/model_zoo/resnet/classify.py b/demo/model_zoo/resnet/classify.py index 4631816c43ef48..6074cc1d3a85e1 100755 --- a/demo/model_zoo/resnet/classify.py +++ b/demo/model_zoo/resnet/classify.py @@ -156,7 +156,7 @@ def forward(self, data, output_layer): # For oversampling, average predictions across crops. # If not, the shape of output[name]: (1, class_number), # the mean is also applicable. - res[name] = output[name].mean(0) + res[name] = output[name]['value'].mean(0) return res diff --git a/paddle/api/Arguments.cpp b/paddle/api/Arguments.cpp index 41beed38a87601..a3f4bfffc9f074 100644 --- a/paddle/api/Arguments.cpp +++ b/paddle/api/Arguments.cpp @@ -38,6 +38,13 @@ Arguments* Arguments::createByPaddleArgumentVector(void* ptr) { return args; } +Arguments* Arguments::createByPaddleArgument(const void* ptr) { + auto p = (paddle::Argument*)(ptr); + auto args = new Arguments(); + args->m->outputs.push_back(*p); + return args; +} + Matrix* Arguments::getSlotValue(size_t idx) const throw(RangeError) { auto& a = m->getArg(idx); return Matrix::createByPaddleMatrixPtr(&a.value); diff --git a/paddle/api/GradientMachine.cpp b/paddle/api/GradientMachine.cpp index a44763bfa53ed5..a64e70a6bd563a 100644 --- a/paddle/api/GradientMachine.cpp +++ b/paddle/api/GradientMachine.cpp @@ -144,12 +144,11 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) { void GradientMachine::randParameters() { m->machine->randParameters(); } -Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const +Arguments* GradientMachine::getLayerOutput(const std::string& layerName) const throw(UnsupportError) { auto nn = m->machine; if (nn) { - auto mat = nn->getLayerOutput(layerName); - return Matrix::createByPaddleMatrixPtr(&mat); + return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName)); } else { throw UnsupportError(); } diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index f5af8b0035b44d..10569a71708463 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -454,6 +454,7 @@ class Arguments { private: static Arguments* createByPaddleArgumentVector(void* ptr); + static Arguments* createByPaddleArgument(const void* ptr); void* getInternalArgumentsPtr() const; private: @@ -769,7 +770,7 @@ class GradientMachine { void randParameters(); - Matrix* getLayerOutput(const std::string& layerName) const + Arguments* getLayerOutput(const std::string& layerName) const throw(UnsupportError); /** @@ -952,7 +953,7 @@ class Trainer { Arguments* getForwardOutput(); - Matrix* getLayerOutput(const std::string& layerName); + Arguments* getLayerOutput(const std::string& layerName); }; /// the N-Best results generated from one input sequence. diff --git a/paddle/api/Trainer.cpp b/paddle/api/Trainer.cpp index d83dc380beeec3..c742614aff97f2 100644 --- a/paddle/api/Trainer.cpp +++ b/paddle/api/Trainer.cpp @@ -131,12 +131,10 @@ void Trainer::testOneDataBatch(size_t batchSize, const Arguments& args) { void TrainerPrivate::finishTestPeriod() { tester_->finishTestPeriod(); } void Trainer::finishTestPeriod() { m->finishTestPeriod(); } -Matrix* Trainer::getLayerOutput(const std::string& layerName) { - auto nn = std::dynamic_pointer_cast( - this->m->getGradientMachine()); +Arguments* Trainer::getLayerOutput(const std::string& layerName) { + auto nn = this->m->getGradientMachine(); CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork"; - auto m = nn->getLayerOutput(layerName); - return Matrix::createByPaddleMatrixPtr(&m); + return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName)); } void Trainer::forwardOneBatch(size_t batchSize) { diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index 5469c0d89f4964..ae39783c6b7ea6 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -134,8 +134,8 @@ class GradientMachine { backward(callback); } - virtual MatrixPtr getLayerOutput(const std::string& layerName) const { - return nullptr; + virtual const Argument& getLayerOutput(const std::string& layerName) { + return *((Argument*)nullptr); } // see comment in Layer.h for the function with the same name diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 2d42e648302f16..6b11b0155e990d 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -282,33 +282,17 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, backwardImp(callback); } -MatrixPtr MultiGradientMachine::getLayerOutput( - const std::string& layerName) const { - // each thread has the same neural network - auto nn = threads_[0]->getGradientMachine(); - size_t height = 0; - size_t width = nn->getLayerOutput(layerName)->getWidth(); - std::vector mats; - mats.reserve(threads_.size()); - for (auto& thread : threads_) { - MatrixPtr out = thread->getGradientMachine()->getLayerOutput(layerName); - mats.push_back(out); - height += out->getHeight(); - CHECK_EQ(width, out->getWidth()); - } +const Argument& MultiGradientMachine::getLayerOutput( + const std::string& layerName) { + std::vector args; + args.reserve(threads_.size()); - MatrixPtr layerOutput; - Matrix::resizeOrCreate(layerOutput, height, width, false, false); - - // copy one layer output from one trainer thread at each time - size_t startRow = 0; - for (auto& mat : mats) { - auto tmpMatrix = layerOutput->subMatrix(startRow, mat->getHeight()); - tmpMatrix->copyFrom(*mat); - startRow += mat->getHeight(); + for (auto& thread : threads_) { + args.push_back(thread->getGradientMachine()->getLayerOutput(layerName)); } + outLayerArgs_.concat(args, false /* use_gpu */, outArgStream_, passType_); - return layerOutput; + return outLayerArgs_; } void MultiGradientMachine::backwardImp(const UpdateCallback& callback) { diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.h b/paddle/gserver/gradientmachines/MultiGradientMachine.h index a1a2d417062de5..9083230afd69e2 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.h +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.h @@ -189,7 +189,7 @@ class MultiGradientMachine : public GradientMachine { PassType passType, const UpdateCallback& callback); - virtual MatrixPtr getLayerOutput(const std::string& layerName) const; + virtual const Argument& getLayerOutput(const std::string& layerName); virtual void onPassEnd(); @@ -316,6 +316,8 @@ class MultiGradientMachine : public GradientMachine { std::vector outArgs_; hl_stream_t outArgStream_; + Argument outLayerArgs_; + /// ParameterType which needs to be merged from each GPU std::vector mergeTypes_; int numDevices_; /* number of gpu devices */ diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 00887c81d47687..d1afde40e1f81e 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -293,10 +293,8 @@ void NeuralNetwork::backward(const UpdateCallback& callback) { } } -MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) const { - auto it = layerMap_.find(layerName); - CHECK(it != layerMap_.end()) << "Cannot find layer: " << layerName; - return it->second->getOutputValue(); +const Argument& NeuralNetwork::getLayerOutput(const std::string& layerName) { + return getLayer(layerName)->getOutput(); } void NeuralNetwork::onPassEnd() { diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 6ecc251a409cb9..b4dc38e31b6fae 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -87,7 +87,7 @@ class NeuralNetwork : public GradientMachine { virtual void backward(const UpdateCallback& callback = nullptr); - virtual MatrixPtr getLayerOutput(const std::string& layerName) const; + virtual const Argument& getLayerOutput(const std::string& layerName); const LayerPtr& getLayer(const std::string& layerName) const { auto it = layerMap_.find(layerName); diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index 1501c7437011d5..57ba124e40cbd0 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/paddle/gserver/layers/CosSimLayer.cpp @@ -68,7 +68,7 @@ void CosSimLayer::forward(PassType passType) { void CosSimLayer::backward(const UpdateCallback& callback) { /* activation */ { REGISTER_TIMER_INFO("CosBpAtvTimer", getName().c_str()); - CHECK_EQ(backward_.size(), 1) << "Only one backward function needed"; + CHECK_EQ(backward_.size(), 1UL) << "Only one backward function needed"; const auto outG = this->getOutputGrad(); const auto outV = this->getOutputValue(); diff --git a/paddle/py_paddle/util.py b/paddle/py_paddle/util.py index ce105d249aaf3e..a708def1d2d7f6 100644 --- a/paddle/py_paddle/util.py +++ b/paddle/py_paddle/util.py @@ -208,7 +208,7 @@ def getLayerOutputs(self, layerNames): output = dict() for name in layerNames: - output[name] = __matrix_to_numpy__(self.getLayerOutput(name)) + output[name] = __arguments_to_numpy__(0, self.getLayerOutput(name)) return output swig_paddle.GradientMachine.getLayerOutputs = getLayerOutputs From fa67ac98e580115bc6ad22f406b6f1242dbd3d61 Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 22 Feb 2017 17:09:18 +0800 Subject: [PATCH 263/370] Specify compiler in setup --- paddle/setup.py.in | 3 +++ 1 file changed, 3 insertions(+) diff --git a/paddle/setup.py.in b/paddle/setup.py.in index c79666bc81b6f3..38621af065913c 100644 --- a/paddle/setup.py.in +++ b/paddle/setup.py.in @@ -55,6 +55,9 @@ elif is_osx == True: include_dirs = [np.get_include(), "../"] # include numpy and paddle. +os.environ["CC"] = "@CMAKE_C_COMPILER@" +os.environ["CXX"] = "@CMAKE_CXX_COMPILER@" + setup(name="py_paddle", version="@PADDLE_VERSION@", ext_modules=[ From bbfcee20fd8733c2e9ea5ec49e1963d62e777730 Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 22 Feb 2017 17:42:08 +0800 Subject: [PATCH 264/370] Add const for Trainer::getLayerOutput --- paddle/api/PaddleAPI.h | 2 +- paddle/api/Trainer.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index 10569a71708463..59798796a249c7 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -953,7 +953,7 @@ class Trainer { Arguments* getForwardOutput(); - Arguments* getLayerOutput(const std::string& layerName); + Arguments* getLayerOutput(const std::string& layerName) const; }; /// the N-Best results generated from one input sequence. diff --git a/paddle/api/Trainer.cpp b/paddle/api/Trainer.cpp index c742614aff97f2..29cf2aa4501de3 100644 --- a/paddle/api/Trainer.cpp +++ b/paddle/api/Trainer.cpp @@ -131,7 +131,7 @@ void Trainer::testOneDataBatch(size_t batchSize, const Arguments& args) { void TrainerPrivate::finishTestPeriod() { tester_->finishTestPeriod(); } void Trainer::finishTestPeriod() { m->finishTestPeriod(); } -Arguments* Trainer::getLayerOutput(const std::string& layerName) { +Arguments* Trainer::getLayerOutput(const std::string& layerName) const { auto nn = this->m->getGradientMachine(); CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork"; return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName)); From 608de7c0e3ea6184c5aa72bca93d10381d4baa1f Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 22 Feb 2017 11:01:59 -0800 Subject: [PATCH 265/370] fix according to comments --- python/paddle/reader/decorator.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py index 1192b5de90f980..5fc799e61dab95 100644 --- a/python/paddle/reader/decorator.py +++ b/python/paddle/reader/decorator.py @@ -66,7 +66,7 @@ def chain(*readers): [0, 0, 0, 1, 1, 1, 2, 2, 2] Args: - readerss: input readers. + readers: input readers. Returns: the new data reader. @@ -83,7 +83,7 @@ def reader(): return reader -class ComposeNotAligned: +class ComposeNotAligned(ValueError): pass @@ -128,7 +128,8 @@ def reader(): for o in outputs: if o is None: # None will be not be present if compose is aligned - raise ComposeNotAligned + raise ComposeNotAligned( + "outputs of readers are not aligned.") yield sum(map(make_tuple, outputs), ()) return reader From 963de16bfd7deba24af70f0183fd3295d480c863 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 22 Feb 2017 15:42:55 -0800 Subject: [PATCH 266/370] change from google python style to pep 287 --- python/paddle/reader/decorator.py | 55 ++++++++++++++----------------- 1 file changed, 24 insertions(+), 31 deletions(-) diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py index 5fc799e61dab95..d656d5feb4f181 100644 --- a/python/paddle/reader/decorator.py +++ b/python/paddle/reader/decorator.py @@ -21,19 +21,17 @@ def shuffle(reader, buf_size): - """Creates a data reader whose data output is suffled. + """ + Creates a data reader whose data output is suffled. Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size. - Args: - reader: the original reader whose output will be - shuffled. - buf_size: shuffle buffer size. + :param reader: the original reader whose output will be shuffled. + :param buf_size: shuffle buffer size. - Returns: - the new reader whose output is shuffled. + :returns:the new reader whose output is shuffled. """ def data_reader(): @@ -55,8 +53,9 @@ def data_reader(): def chain(*readers): - """Creates a data reader whose output is the outputs of input data - readers chained together. + """ + Creates a data reader whose output is the outputs of input data + readers chained together. If input readers output following data entries: [0, 0, 0] @@ -65,11 +64,8 @@ def chain(*readers): The chained reader will output: [0, 0, 0, 1, 1, 1, 2, 2, 2] - Args: - readers: input readers. - - Returns: - the new data reader. + :param readers: input readers. + :returns: the new data reader. """ def reader(): @@ -88,25 +84,23 @@ class ComposeNotAligned(ValueError): def compose(*readers, **kwargs): - """Creates a data reader whose output is the combination of input readers. + """ + Creates a data reader whose output is the combination of input readers. If input readers output following data entries: (1, 2) 3 (4, 5) The composed reader will output: (1, 2, 3, 4, 5) - Args: - *readers: readers that will be composed together. - check_alignment: If True, will check if input readers are aligned - correctly. If False, will not check alignment and trailing outputs - will be discarded. Defaults to True. + :*readers: readers that will be composed together. + :check_alignment: if True, will check if input readers are aligned + correctly. If False, will not check alignment and trailing outputs + will be discarded. Defaults to True. - Returns: - the new data reader. + :returns: the new data reader. - Raises: - ComposeNotAligned: outputs of readers are not aligned. - Will not raise when check_alignment is set to False. + :raises ComposeNotAligned: outputs of readers are not aligned. + Will not raise when check_alignment is set to False. """ check_alignment = kwargs.pop('check_alignment', True) @@ -136,18 +130,17 @@ def reader(): def buffered(reader, size): - """Creates a buffered data reader. + """ + Creates a buffered data reader. The buffered data reader will read and save data entries into a buffer. Reading from the buffered data reader will proceed as long as the buffer is not empty. - Args: - reader: the data reader to read from. - size: max buffer size. + :param reader: the data reader to read from. + :param size: max buffer size. - Returns: - The buffered data reader. + :returns: the buffered data reader. """ class EndSignal(): From 466935b4ecdc57e314ebec76a647d70c67f4494b Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 22 Feb 2017 15:57:24 -0800 Subject: [PATCH 267/370] add decorator: map_readers --- python/paddle/reader/decorator.py | 25 +++++++++++++++++++- python/paddle/reader/tests/decorator_test.py | 16 +++++++++++++ 2 files changed, 40 insertions(+), 1 deletion(-) diff --git a/python/paddle/reader/decorator.py b/python/paddle/reader/decorator.py index d656d5feb4f181..9f4234358f469b 100644 --- a/python/paddle/reader/decorator.py +++ b/python/paddle/reader/decorator.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -__all__ = ['buffered', 'compose', 'chain', 'shuffle', 'ComposeNotAligned'] +__all__ = [ + 'map_readers', 'buffered', 'compose', 'chain', 'shuffle', + 'ComposeNotAligned' +] from Queue import Queue from threading import Thread @@ -20,6 +23,26 @@ import random +def map_readers(func, *readers): + """ + Creates a data reader that outputs return value of function using + output of each data readers as arguments. + + :param func: function to use. + :param *readers: readers whose outputs will be used as arguments of func. + :returns: the created data reader. + """ + + def reader(): + rs = [] + for r in readers: + rs.append(r()) + for e in itertools.imap(func, *rs): + yield e + + return reader + + def shuffle(reader, buf_size): """ Creates a data reader whose data output is suffled. diff --git a/python/paddle/reader/tests/decorator_test.py b/python/paddle/reader/tests/decorator_test.py index 46eec44158cee5..0396a61786539b 100644 --- a/python/paddle/reader/tests/decorator_test.py +++ b/python/paddle/reader/tests/decorator_test.py @@ -26,6 +26,22 @@ def reader(): return reader +class TestMap(unittest.TestCase): + def test_map(self): + d = {"h": 0, "i": 1} + + def tokenize(x): + return d[x] + + def read(): + yield "h" + yield "i" + + r = paddle.reader.map_readers(tokenize, read) + for i, e in enumerate(r()): + self.assertEqual(e, i) + + class TestBuffered(unittest.TestCase): def test_read(self): for size in range(20): From d0dcb3ea34ada48901666283929e35587d1c5c78 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 22 Feb 2017 15:34:36 -0800 Subject: [PATCH 268/370] create numpy array reader creator and text reader creator --- python/paddle/reader/__init__.py | 2 + python/paddle/reader/creator.py | 52 +++++++++++++++++++ python/paddle/reader/tests/CMakeLists.txt | 5 ++ python/paddle/reader/tests/creator_test.py | 38 ++++++++++++++ .../paddle/reader/tests/test_data_creator.txt | 3 ++ 5 files changed, 100 insertions(+) create mode 100644 python/paddle/reader/creator.py create mode 100644 python/paddle/reader/tests/creator_test.py create mode 100644 python/paddle/reader/tests/test_data_creator.txt diff --git a/python/paddle/reader/__init__.py b/python/paddle/reader/__init__.py index 493b410e8299eb..7373dc461b1d31 100644 --- a/python/paddle/reader/__init__.py +++ b/python/paddle/reader/__init__.py @@ -21,3 +21,5 @@ # # r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt")) from decorator import * + +import creator diff --git a/python/paddle/reader/creator.py b/python/paddle/reader/creator.py new file mode 100644 index 00000000000000..5c840f94b52f16 --- /dev/null +++ b/python/paddle/reader/creator.py @@ -0,0 +1,52 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__all__ = ['np_array', 'text_file'] + + +def np_array(x): + """ + Creates a data reader from numpy array. + The highest dimension will be treated as batch dimension to iterate on. + + :param x: the numpy array to create reader from. + :returns: data reader created from x. + """ + + def reader(): + if x.ndim < 1: + yield x + + for e in x: + yield e + + return reader + + +def text_file(path): + """ + Creates a data reader that outputs text line by line from given text file. + Trailing new line ('\n') of each line will be removed. + + :path: path of the text file. + :returns: data reader of text file + """ + + def reader(): + f = open(path, "r") + for l in f: + yield l.rstrip('\n') + f.close() + + return reader diff --git a/python/paddle/reader/tests/CMakeLists.txt b/python/paddle/reader/tests/CMakeLists.txt index 502c897d8946a8..da072fb3dbeed5 100644 --- a/python/paddle/reader/tests/CMakeLists.txt +++ b/python/paddle/reader/tests/CMakeLists.txt @@ -2,3 +2,8 @@ add_test(NAME reader_decorator_test COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) + +add_test(NAME reader_creator_test + COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py + WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/reader/tests/creator_test.py b/python/paddle/reader/tests/creator_test.py new file mode 100644 index 00000000000000..eda8ab6715b2be --- /dev/null +++ b/python/paddle/reader/tests/creator_test.py @@ -0,0 +1,38 @@ +# Copyright PaddlePaddle contributors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest +import paddle.reader.creator +import numpy as np +import os + + +class TestNumpyArray(unittest.TestCase): + def test_numpy_array(self): + l = [[1, 2, 3], [4, 5, 6]] + x = np.array(l, np.int32) + reader = paddle.reader.creator.np_array(x) + for idx, e in enumerate(reader()): + self.assertItemsEqual(e, l[idx]) + + +class TestTextFile(unittest.TestCase): + def test_text_file(self): + path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt") + reader = paddle.reader.creator.text_file(path) + for idx, e in enumerate(reader()): + self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1)) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/reader/tests/test_data_creator.txt b/python/paddle/reader/tests/test_data_creator.txt new file mode 100644 index 00000000000000..a2a8d47d43868d --- /dev/null +++ b/python/paddle/reader/tests/test_data_creator.txt @@ -0,0 +1,3 @@ +0 1 +2 3 +4 5 From 2715e9de6fb656693843bb4dd254eaedb587ad94 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 23 Feb 2017 13:14:32 +0800 Subject: [PATCH 269/370] add ParamAttr in V2 --- python/paddle/v2/__init__.py | 3 ++- python/paddle/v2/attr.py | 23 +++++++++++++++++++++++ python/paddle/v2/layer.py | 9 +++++++-- 3 files changed, 32 insertions(+), 3 deletions(-) create mode 100644 python/paddle/v2/attr.py diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 30d0b2a398bd0e..0cf7b8e9039f73 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -18,11 +18,12 @@ import trainer import event import data_type +import attr import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type' + 'event', 'data_type', 'attr' ] diff --git a/python/paddle/v2/attr.py b/python/paddle/v2/attr.py new file mode 100644 index 00000000000000..40c64f621b443d --- /dev/null +++ b/python/paddle/v2/attr.py @@ -0,0 +1,23 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddle.trainer_config_helpers.attrs import * + +__all__ = [ + "Param", + "Extra", +] + +Param = ParameterAttribute +Extra = ExtraLayerAttribute diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index cd6dd5110a44b2..3920d4a08fc627 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -74,6 +74,8 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. from paddle.trainer_config_helpers.default_decorators import wrap_name_default import data_type +import activation +import attr __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', @@ -230,8 +232,11 @@ def to_proto_impl(self, **kwargs): weight = data(name='weight', type=data_type.dense_vector(10)) score = data(name='score', type=data_type.dense_vector(1)) - hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) - inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) + hidden = fc(input=pixel, + size=100, + act=activation.Sigmoid(), + param_attr=attr.Param(name='hidden')) + inference = fc(input=hidden, size=10, act=activation.Softmax()) maxid = max_id(input=inference) cost1 = classification_cost(input=inference, label=label) cost2 = classification_cost(input=inference, label=label, weight=weight) From 448e60b5fec9e402dd12c7dfc71a6e062d57c12b Mon Sep 17 00:00:00 2001 From: Peng Li Date: Thu, 23 Feb 2017 14:12:11 +0800 Subject: [PATCH 270/370] Fix macro bug in hl_warpctc_warp.cc to support double precision --- paddle/cuda/src/hl_warpctc_wrap.cc | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/paddle/cuda/src/hl_warpctc_wrap.cc b/paddle/cuda/src/hl_warpctc_wrap.cc index 55b940ca67acce..246e7ad8af7368 100644 --- a/paddle/cuda/src/hl_warpctc_wrap.cc +++ b/paddle/cuda/src/hl_warpctc_wrap.cc @@ -54,22 +54,26 @@ DYNAMIC_LOAD_WARPCTC_WRAP(get_workspace_size) #define WARPCTC_GET_VERSION dynload::get_warpctc_version #define WARPCTC_GET_STATUS_STRING dynload::ctcGetStatusString +static int g_warpctcVersion = -1; #ifndef PADDLE_TYPE_DOUBLE #define WARPCTC_COMPUTE_LOSS dynload::compute_ctc_loss #define WARPCTC_GET_WORKSPACE_SIZE dynload::get_workspace_size #else -#define WARPCTC_LOG_FATAL \ - LOG(FATAL) << "warp-ctc [version " << g_warpctcVersion \ - << "] Error: not support double precision." -#define WARPCTC_COMPUTE_LOSS(...) WARPCTC_LOG_FATAL(__VA_ARGS__) -#define WARPCTC_GET_WORKSPACE_SIZE(...) WARPCTC_LOG_FATAL(__VA_ARGS__) +ctcStatus_t fatal(...) { + LOG(FATAL) << "warp-ctc [version " << g_warpctcVersion + << "] Error: not support double precision."; + // both of get_warpctc_version() and get_workspace_size() return an ctcStatus + // type value + return CTC_STATUS_EXECUTION_FAILED; +} +#define WARPCTC_COMPUTE_LOSS fatal +#define WARPCTC_GET_WORKSPACE_SIZE fatal #endif /** * Check build-in warp-ctc function using glog and it also * support << operator for more details error info. */ -static int g_warpctcVersion = -1; #define CHECK_WARPCTC(warpctcStat) \ CHECK_EQ(CTC_STATUS_SUCCESS, warpctcStat) \ << "warp-ctc [version " << g_warpctcVersion \ From 36dbfe86a7dc37d5c04dd1be3ef8adf34414ded1 Mon Sep 17 00:00:00 2001 From: Peng Li Date: Thu, 23 Feb 2017 14:46:54 +0800 Subject: [PATCH 271/370] Change ctcStatus_t to hl_warpctc_status_t to keep consistency --- paddle/cuda/src/hl_warpctc_wrap.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/cuda/src/hl_warpctc_wrap.cc b/paddle/cuda/src/hl_warpctc_wrap.cc index 246e7ad8af7368..f57efb2b46797c 100644 --- a/paddle/cuda/src/hl_warpctc_wrap.cc +++ b/paddle/cuda/src/hl_warpctc_wrap.cc @@ -59,7 +59,7 @@ static int g_warpctcVersion = -1; #define WARPCTC_COMPUTE_LOSS dynload::compute_ctc_loss #define WARPCTC_GET_WORKSPACE_SIZE dynload::get_workspace_size #else -ctcStatus_t fatal(...) { +hl_warpctc_status_t fatal(...) { LOG(FATAL) << "warp-ctc [version " << g_warpctcVersion << "] Error: not support double precision."; // both of get_warpctc_version() and get_workspace_size() return an ctcStatus From b1ab8b56cfbad21ba1b70d010392031e72cf5c71 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 15:03:34 +0800 Subject: [PATCH 272/370] Use plain C++ 03 to implement getStatsInfo. --- paddle/gserver/evaluators/Evaluator.cpp | 196 ++++++++++++------------ paddle/gserver/evaluators/Evaluator.h | 13 +- paddle/utils/Error.h | 28 ++-- paddle/utils/tests/test_Error.cpp | 8 +- 4 files changed, 129 insertions(+), 116 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 89f95438019d8c..8fce8df8a3b0e5 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -626,78 +626,34 @@ real PrecisionRecallEvaluator::evalImp(std::vector& arguments) { return 0; } -template -void PrecisionRecallEvaluator::printStatsHelper(T1 labelCallback, - T2 microAvgCallback) const { - int label = config_.positive_label(); - if (label != -1) { - CHECK(label >= 0 && label < (int)statsInfo_.size()) - << "positive_label [" << label << "] should be in range [0, " - << statsInfo_.size() << ")"; - double precision = - calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP); - double recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN); - labelCallback(label, precision, recall, calcF1Score(precision, recall)); - return; - } - - // micro average method: precision = (TP1+TP2)/(TP1+FP1+TP2+FP2) - // macro average method: precision = (precision1+precision2)/2 - double microTotalTP = 0; - double microTotalFP = 0; - double microTotalFN = 0; - double macroAvgPrecision = 0; - double macroAvgRecall = 0; - size_t numLabels = statsInfo_.size(); - for (size_t i = 0; i < numLabels; ++i) { - microTotalTP += statsInfo_[i].TP; - microTotalFP += statsInfo_[i].FP; - microTotalFN += statsInfo_[i].FN; - macroAvgPrecision += calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP); - macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN); - } - macroAvgPrecision /= numLabels; - macroAvgRecall /= numLabels; - double macroAvgF1Score = calcF1Score(macroAvgPrecision, macroAvgRecall); - - double microAvgPrecision = calcPrecision(microTotalTP, microTotalFP); - double microAvgRecall = calcPrecision(microTotalTP, microTotalFN); - double microAvgF1Score = calcF1Score(microAvgPrecision, microAvgRecall); - - microAvgCallback(macroAvgPrecision, - macroAvgRecall, - macroAvgF1Score, - isMultiBinaryLabel_, - microAvgPrecision, - microAvgRecall, - microAvgF1Score); -} - void PrecisionRecallEvaluator::printStats(std::ostream& os) const { - this->printStatsHelper( - [&os](int label, double precision, double recall, double f1) { - os << "positive_label=" << label << " precision=" << precision - << " recall=" << recall << " F1-score=" << f1; - }, - [&os](double macroAvgPrecision, - double macroAvgRecall, - double macroAvgF1Score, - bool isMultiBinaryLabel, - double microAvgPrecision, - double microAvgRecall, - double microAvgF1Score) { - os << "macro-average-precision=" << macroAvgPrecision - << " macro-average-recall=" << macroAvgRecall - << " macro-average-F1-score=" << macroAvgF1Score; - if (!isMultiBinaryLabel) { - // precision and recall are equal in this case - os << " micro-average-precision=" << microAvgPrecision; - } else { - os << " micro-average-precision=" << microAvgPrecision - << " micro-average-recall=" << microAvgRecall - << " micro-average-F1-score=" << microAvgF1Score; - } - }); + double precision, recall, f1, macroAvgPrecision, macroAvgRecall, + macroAvgF1Score, microAvgPrecision, microAvgRecall, microAvgF1Score; + bool containMacroMicroInfo = getStatsInfo(&precision, + &recall, + &f1, + ¯oAvgPrecision, + ¯oAvgRecall, + ¯oAvgF1Score, + µAvgPrecision, + µAvgRecall, + µAvgF1Score); + os << "positive_label=" << config_.positive_label() + << " precision=" << precision << " recall=" << recall + << " F1-score=" << f1; + if (containMacroMicroInfo) { + os << "macro-average-precision=" << macroAvgPrecision + << " macro-average-recall=" << macroAvgRecall + << " macro-average-F1-score=" << macroAvgF1Score; + if (!isMultiBinaryLabel_) { + // precision and recall are equal in this case + os << " micro-average-precision=" << microAvgPrecision; + } else { + os << " micro-average-precision=" << microAvgPrecision + << " micro-average-recall=" << microAvgRecall + << " micro-average-F1-score=" << microAvgF1Score; + } + }; } void PrecisionRecallEvaluator::calcStatsInfo(const MatrixPtr& output, @@ -780,32 +736,33 @@ void PrecisionRecallEvaluator::calcStatsInfoMulti(const MatrixPtr& output, void PrecisionRecallEvaluator::storeLocalValues() const { if (this->values_.size() == 0) { - this->printStatsHelper( - [this](int label, double precision, double recall, double f1) { - values_["positive_label"] = (double)label; - values_["precision"] = precision; - values_["recal"] = recall; - values_["F1-score"] = f1; - }, - [this](double macroAvgPrecision, - double macroAvgRecall, - double macroAvgF1Score, - bool isMultiBinaryLabel, - double microAvgPrecision, - double microAvgRecall, - double microAvgF1Score) { - values_["macro-average-precision"] = macroAvgPrecision; - values_["macro-average-recall"] = macroAvgRecall; - values_["macro-average-F1-score"] = macroAvgF1Score; - if (!isMultiBinaryLabel) { - // precision and recall are equal in this case - values_["micro-average-precision"] = microAvgPrecision; - } else { - values_["micro-average-precision"] = microAvgPrecision; - values_["micro-average-recall"] = microAvgRecall; - values_["micro-average-F1-score"] = microAvgF1Score; - } - }); + double precision, recall, f1, macroAvgPrecision, macroAvgRecall, + macroAvgF1Score, microAvgPrecision, microAvgRecall, microAvgF1Score; + bool containMacroMicroInfo = getStatsInfo(&precision, + &recall, + &f1, + ¯oAvgPrecision, + ¯oAvgRecall, + ¯oAvgF1Score, + µAvgPrecision, + µAvgRecall, + µAvgF1Score); + values_["precision"] = precision; + values_["recal"] = recall; + values_["F1-score"] = f1; + if (containMacroMicroInfo) { + values_["macro-average-precision"] = macroAvgPrecision; + values_["macro-average-recall"] = macroAvgRecall; + values_["macro-average-F1-score"] = macroAvgF1Score; + if (!isMultiBinaryLabel_) { + // precision and recall are equal in this case + values_["micro-average-precision"] = microAvgPrecision; + } else { + values_["micro-average-precision"] = microAvgPrecision; + values_["micro-average-recall"] = microAvgRecall; + values_["micro-average-F1-score"] = microAvgF1Score; + } + } } } @@ -865,6 +822,51 @@ void PrecisionRecallEvaluator::distributeEval(ParameterClient2* client) { delete[] buf; } +bool PrecisionRecallEvaluator::getStatsInfo(double* precision, + double* recall, + double* f1, + double* macroAvgPrecision, + double* macroAvgRecall, + double* macroAvgF1Score, + double* microAvgPrecision, + double* microAvgRecall, + double* microAvgF1Score) const { + int label = config_.positive_label(); + if (label != -1) { + CHECK(label >= 0 && label < (int)statsInfo_.size()) + << "positive_label [" << label << "] should be in range [0, " + << statsInfo_.size() << ")"; + *precision = calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP); + *recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN); + *f1 = calcF1Score(*precision, *recall); + return false; + } + + // micro average method: precision = (TP1+TP2)/(TP1+FP1+TP2+FP2) + // macro average method: precision = (precision1+precision2)/2 + double microTotalTP = 0; + double microTotalFP = 0; + double microTotalFN = 0; + *macroAvgPrecision = 0; + *macroAvgRecall = 0; + size_t numLabels = statsInfo_.size(); + for (size_t i = 0; i < numLabels; ++i) { + microTotalTP += statsInfo_[i].TP; + microTotalFP += statsInfo_[i].FP; + microTotalFN += statsInfo_[i].FN; + *macroAvgPrecision += calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP); + *macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN); + } + *macroAvgPrecision /= numLabels; + *macroAvgRecall /= numLabels; + *macroAvgF1Score = calcF1Score(*macroAvgPrecision, *macroAvgRecall); + + *microAvgPrecision = calcPrecision(microTotalTP, microTotalFP); + *microAvgRecall = calcPrecision(microTotalTP, microTotalFN); + *microAvgF1Score = calcF1Score(*microAvgPrecision, *microAvgRecall); + return true; +} + REGISTER_EVALUATOR(pnpair, PnpairEvaluator); void PnpairEvaluator::start() { Evaluator::start(); diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index a5694a088c3c44..eb19e6f4dde5d9 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -125,7 +125,7 @@ class Evaluator { * has multiple field, the name could be `evaluator_name.field1`. For example * the PrecisionRecallEvaluator contains `precision`, `recall` fields. The get * names will return `precision_recall_evaluator.precision`, - * `precision_recall.recal`, etc. + * `precision_recall_evaluator.recal`, etc. * * Also, if current Evaluator is a combined evaluator. getNames will return * all names of all evaluators inside the combined evaluator. @@ -387,8 +387,15 @@ class PrecisionRecallEvaluator : public Evaluator { IVectorPtr cpuLabel_; MatrixPtr cpuWeight_; - template - void printStatsHelper(T1 labelCallback, T2 microAvgCallback) const; + bool getStatsInfo(double* precision, + double* recall, + double* f1, + double* macroAvgPrecision, + double* macroAvgRecall, + double* macroAvgF1Score, + double* microAvgPrecision, + double* microAvgRecall, + double* microAvgF1Score) const; void calcStatsInfo(const MatrixPtr& output, const IVectorPtr& label, diff --git a/paddle/utils/Error.h b/paddle/utils/Error.h index 1ae202890f874c..cda1b5c37dada8 100644 --- a/paddle/utils/Error.h +++ b/paddle/utils/Error.h @@ -37,10 +37,10 @@ namespace paddle { * * Error __must_check bar() { * // do something. - * Status s = foo(); // invoke other method return status. - * if (!s) return s; + * Error err = foo(); // invoke other method return status. + * if (err) return err; * // do something else. - * return Status(); + * return Error(); * } * @endcode{cpp} * @@ -53,8 +53,8 @@ namespace paddle { * * int foo(Error* error) { * // Do something. - * Error s = bar(); - * if (!s) { + * Error err = bar(); + * if (err) { * *error = s; * return 0; * } @@ -68,10 +68,10 @@ namespace paddle { * } * * Error foobar() { - * Error s; + * Error err; * // do something. - * foo(&s); - * if (!s) return s; + * foo(&err); + * if (err) return err; * } * @endcode{cpp} * @@ -112,18 +112,22 @@ class Error { } /** - * @brief operator bool, return True if there is no error. + * @brief operator bool, return True if there is something error. */ - operator bool() const { return msg_ == nullptr; } + operator bool() const { return !this->isOK(); } - bool isOK() const { return *this; } + /** + * @brief isOK return True if there is no error. + * @return True if no error. + */ + bool isOK() const { return msg_ == nullptr; } /** * @brief check this status by glog. * @note It is a temp method used during cleaning Paddle code. It will be * removed later. */ - void check() const { CHECK(*this) << msg(); } + void check() const { CHECK(this->isOK()) << msg(); } private: std::shared_ptr msg_; diff --git a/paddle/utils/tests/test_Error.cpp b/paddle/utils/tests/test_Error.cpp index 85156466e2cafd..fdf326b17a1c8b 100644 --- a/paddle/utils/tests/test_Error.cpp +++ b/paddle/utils/tests/test_Error.cpp @@ -18,17 +18,17 @@ limitations under the License. */ TEST(Error, testAll) { paddle::Error error; - ASSERT_TRUE(error); - error = paddle::Error("I'm the error"); ASSERT_FALSE(error); + error = paddle::Error("I'm the error"); + ASSERT_TRUE(error); ASSERT_STREQ("I'm the error", error.msg()); error = paddle::Error("error2"); - ASSERT_FALSE(error); + ASSERT_TRUE(error); ASSERT_STREQ("error2", error.msg()); int i = 3; auto error3 = paddle::Error("error%d", i); - ASSERT_FALSE(error3); + ASSERT_TRUE(error3); ASSERT_STREQ("error3", error3.msg()); } From b6ac64a36bf260780d18dc6722f6ccdd0c96aba0 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 15:06:30 +0800 Subject: [PATCH 273/370] Remove unnecessary error default value. --- paddle/gserver/evaluators/Evaluator.cpp | 8 +------ paddle/gserver/evaluators/Evaluator.h | 21 +++++++------------ .../gradientmachines/NeuralNetwork.cpp | 2 +- 3 files changed, 9 insertions(+), 22 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 8fce8df8a3b0e5..6c1c2f62be273c 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -781,9 +781,7 @@ real PrecisionRecallEvaluator::getValue(const std::string& name, paddle::str::split(name, '.', &buffers); auto it = this->values_.find(buffers[buffers.size() - 1]); if (it == this->values_.end()) { // not found - if (err != nullptr) { - *err = Error("No such key %s", name.c_str()); - } + *err = Error("No such key %s", name.c_str()); return .0f; } @@ -792,10 +790,6 @@ real PrecisionRecallEvaluator::getValue(const std::string& name, std::string PrecisionRecallEvaluator::getType(const std::string& name, Error* err) const { - Error localErr; - if (err == nullptr) { - err = &localErr; - } this->getValue(name, err); if (!err->isOK()) { return ""; diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index eb19e6f4dde5d9..6a122704ea34ae 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -141,16 +141,13 @@ class Evaluator { * @brief getValue will return the current evaluate value of one field. * * @param name: The field name of current evaluator. - * @param err [out]: The error state. nullptr means don't care. + * @param err [out]: The error state. * * @return The evaluate value(metric). */ - virtual real getValue(const std::string& name, - paddle::Error* err = nullptr) const { + virtual real getValue(const std::string& name, paddle::Error* err) const { if (name != config_.name()) { - if (err != nullptr) { - *err = paddle::Error("no such name of evaluator %s", name.c_str()); - } + *err = paddle::Error("no such name of evaluator %s", name.c_str()); return .0f; } return this->getValueImpl(); @@ -168,8 +165,8 @@ class Evaluator { * @return the evaluator type string. */ virtual std::string getType(const std::string& name, - paddle::Error* err = nullptr) const { - if (name != config_.name() && err != nullptr) { + paddle::Error* err) const { + if (name != config_.name()) { *err = paddle::Error("no such name of evaluator %s", name.c_str()); return std::string(); } @@ -212,15 +209,11 @@ class NotGetableEvaluator : public Evaluator { void getNames(std::vector* names) {} real getValue(const std::string& name, Error* err) const { - if (err != nullptr) { - *err = Error("Not implemented"); - } + *err = Error("Not implemented"); return .0f; } std::string getType(const std::string& name, Error* err) const { - if (err != nullptr) { - *err = Error("Not implemented"); - } + *err = Error("Not implemented"); return ""; } }; diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 4d2bdf0dc992da..273a9111c35a21 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -391,7 +391,7 @@ class CombinedEvaluator : public Evaluator { return callback(eval); } } - if (err != nullptr) *err = Error("No such key %s", name.c_str()); + *err = Error("No such key %s", name.c_str()); return T(); } }; From 2bb7fd817718794eb3fdef87207bf4a7596c606c Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 15:09:09 +0800 Subject: [PATCH 274/370] Remove padde namespace in some code. --- paddle/gserver/evaluators/Evaluator.h | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index 6a122704ea34ae..c4110ec1c0b57f 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -145,9 +145,9 @@ class Evaluator { * * @return The evaluate value(metric). */ - virtual real getValue(const std::string& name, paddle::Error* err) const { + virtual real getValue(const std::string& name, Error* err) const { if (name != config_.name()) { - *err = paddle::Error("no such name of evaluator %s", name.c_str()); + *err = Error("no such name of evaluator %s", name.c_str()); return .0f; } return this->getValueImpl(); @@ -164,10 +164,9 @@ class Evaluator { * @param err: The error state. nullptr means don't care. * @return the evaluator type string. */ - virtual std::string getType(const std::string& name, - paddle::Error* err) const { + virtual std::string getType(const std::string& name, Error* err) const { if (name != config_.name()) { - *err = paddle::Error("no such name of evaluator %s", name.c_str()); + *err = Error("no such name of evaluator %s", name.c_str()); return std::string(); } return this->getTypeImpl(); From 9087e3a9bb9aac10236b513c5c9a52af1590b3a7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 16:18:19 +0800 Subject: [PATCH 275/370] Follow comments to use struct get return value. --- paddle/gserver/evaluators/Evaluator.cpp | 108 ++++++++++-------------- paddle/gserver/evaluators/Evaluator.h | 22 +++-- 2 files changed, 56 insertions(+), 74 deletions(-) diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 5911a9ec59a5b8..9db6d252d97bfe 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -647,33 +647,24 @@ real PrecisionRecallEvaluator::evalImp(std::vector& arguments) { } void PrecisionRecallEvaluator::printStats(std::ostream& os) const { - double precision, recall, f1, macroAvgPrecision, macroAvgRecall, - macroAvgF1Score, microAvgPrecision, microAvgRecall, microAvgF1Score; - bool containMacroMicroInfo = getStatsInfo(&precision, - &recall, - &f1, - ¯oAvgPrecision, - ¯oAvgRecall, - ¯oAvgF1Score, - µAvgPrecision, - µAvgRecall, - µAvgF1Score); + PrintStatsInfo info; + bool containMacroMicroInfo = getStatsInfo(&info); os << "positive_label=" << config_.positive_label() - << " precision=" << precision << " recall=" << recall - << " F1-score=" << f1; + << " precision=" << info.precision << " recall=" << info.recall + << " F1-score=" << info.f1; if (containMacroMicroInfo) { - os << "macro-average-precision=" << macroAvgPrecision - << " macro-average-recall=" << macroAvgRecall - << " macro-average-F1-score=" << macroAvgF1Score; + os << "macro-average-precision=" << info.macroAvgPrecision + << " macro-average-recall=" << info.macroAvgRecall + << " macro-average-F1-score=" << info.macroAvgF1Score; if (!isMultiBinaryLabel_) { // precision and recall are equal in this case - os << " micro-average-precision=" << microAvgPrecision; + os << " micro-average-precision=" << info.microAvgPrecision; } else { - os << " micro-average-precision=" << microAvgPrecision - << " micro-average-recall=" << microAvgRecall - << " micro-average-F1-score=" << microAvgF1Score; + os << " micro-average-precision=" << info.microAvgPrecision + << " micro-average-recall=" << info.microAvgRecall + << " micro-average-F1-score=" << info.microAvgF1Score; } - }; + } } void PrecisionRecallEvaluator::calcStatsInfo(const MatrixPtr& output, @@ -756,31 +747,22 @@ void PrecisionRecallEvaluator::calcStatsInfoMulti(const MatrixPtr& output, void PrecisionRecallEvaluator::storeLocalValues() const { if (this->values_.size() == 0) { - double precision, recall, f1, macroAvgPrecision, macroAvgRecall, - macroAvgF1Score, microAvgPrecision, microAvgRecall, microAvgF1Score; - bool containMacroMicroInfo = getStatsInfo(&precision, - &recall, - &f1, - ¯oAvgPrecision, - ¯oAvgRecall, - ¯oAvgF1Score, - µAvgPrecision, - µAvgRecall, - µAvgF1Score); - values_["precision"] = precision; - values_["recal"] = recall; - values_["F1-score"] = f1; + PrintStatsInfo info; + bool containMacroMicroInfo = getStatsInfo(&info); + values_["precision"] = info.precision; + values_["recal"] = info.recall; + values_["F1-score"] = info.f1; if (containMacroMicroInfo) { - values_["macro-average-precision"] = macroAvgPrecision; - values_["macro-average-recall"] = macroAvgRecall; - values_["macro-average-F1-score"] = macroAvgF1Score; + values_["macro-average-precision"] = info.macroAvgPrecision; + values_["macro-average-recall"] = info.macroAvgRecall; + values_["macro-average-F1-score"] = info.macroAvgF1Score; if (!isMultiBinaryLabel_) { // precision and recall are equal in this case - values_["micro-average-precision"] = microAvgPrecision; + values_["micro-average-precision"] = info.microAvgPrecision; } else { - values_["micro-average-precision"] = microAvgPrecision; - values_["micro-average-recall"] = microAvgRecall; - values_["micro-average-F1-score"] = microAvgF1Score; + values_["micro-average-precision"] = info.microAvgPrecision; + values_["micro-average-recall"] = info.microAvgRecall; + values_["micro-average-F1-score"] = info.microAvgF1Score; } } } @@ -836,23 +818,16 @@ void PrecisionRecallEvaluator::distributeEval(ParameterClient2* client) { delete[] buf; } -bool PrecisionRecallEvaluator::getStatsInfo(double* precision, - double* recall, - double* f1, - double* macroAvgPrecision, - double* macroAvgRecall, - double* macroAvgF1Score, - double* microAvgPrecision, - double* microAvgRecall, - double* microAvgF1Score) const { +bool PrecisionRecallEvaluator::getStatsInfo( + PrecisionRecallEvaluator::PrintStatsInfo* info) const { int label = config_.positive_label(); if (label != -1) { CHECK(label >= 0 && label < (int)statsInfo_.size()) << "positive_label [" << label << "] should be in range [0, " << statsInfo_.size() << ")"; - *precision = calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP); - *recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN); - *f1 = calcF1Score(*precision, *recall); + info->precision = calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP); + info->recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN); + info->f1 = calcF1Score(info->precision, info->recall); return false; } @@ -861,23 +836,26 @@ bool PrecisionRecallEvaluator::getStatsInfo(double* precision, double microTotalTP = 0; double microTotalFP = 0; double microTotalFN = 0; - *macroAvgPrecision = 0; - *macroAvgRecall = 0; + info->macroAvgPrecision = 0; + info->macroAvgRecall = 0; size_t numLabels = statsInfo_.size(); for (size_t i = 0; i < numLabels; ++i) { microTotalTP += statsInfo_[i].TP; microTotalFP += statsInfo_[i].FP; microTotalFN += statsInfo_[i].FN; - *macroAvgPrecision += calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP); - *macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN); + info->macroAvgPrecision += + calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP); + info->macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN); } - *macroAvgPrecision /= numLabels; - *macroAvgRecall /= numLabels; - *macroAvgF1Score = calcF1Score(*macroAvgPrecision, *macroAvgRecall); - - *microAvgPrecision = calcPrecision(microTotalTP, microTotalFP); - *microAvgRecall = calcPrecision(microTotalTP, microTotalFN); - *microAvgF1Score = calcF1Score(*microAvgPrecision, *microAvgRecall); + info->macroAvgPrecision /= numLabels; + info->macroAvgRecall /= numLabels; + info->macroAvgF1Score = + calcF1Score(info->macroAvgPrecision, info->macroAvgRecall); + + info->microAvgPrecision = calcPrecision(microTotalTP, microTotalFP); + info->microAvgRecall = calcPrecision(microTotalTP, microTotalFN); + info->microAvgF1Score = + calcF1Score(info->microAvgPrecision, info->microAvgRecall); return true; } diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index c4110ec1c0b57f..b114500e2b7c1e 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -379,15 +379,19 @@ class PrecisionRecallEvaluator : public Evaluator { IVectorPtr cpuLabel_; MatrixPtr cpuWeight_; - bool getStatsInfo(double* precision, - double* recall, - double* f1, - double* macroAvgPrecision, - double* macroAvgRecall, - double* macroAvgF1Score, - double* microAvgPrecision, - double* microAvgRecall, - double* microAvgF1Score) const; + struct PrintStatsInfo { + double precision; + double recall; + double f1; + double macroAvgPrecision; + double macroAvgRecall; + double macroAvgF1Score; + double microAvgPrecision; + double microAvgRecall; + double microAvgF1Score; + }; + + bool getStatsInfo(PrintStatsInfo* info) const; void calcStatsInfo(const MatrixPtr& output, const IVectorPtr& label, From 51de2ded3ecf674bd5f96a9f3129d6630bfb65a1 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 23 Feb 2017 16:59:38 +0800 Subject: [PATCH 276/370] add optimizer in v2 --- python/paddle/v2/optimizer.py | 55 +++++++++++++++++++++++++++++++++-- 1 file changed, 52 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/optimizer.py b/python/paddle/v2/optimizer.py index aa2942bc9faeb2..10e255dc945efb 100644 --- a/python/paddle/v2/optimizer.py +++ b/python/paddle/v2/optimizer.py @@ -3,7 +3,10 @@ import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils import paddle.v2 -__all__ = ['Adam', 'Adamax'] +__all__ = [ + 'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta', + 'RMSProp', 'ModelAverage', 'L2Regularization' +] class Optimizer(object): @@ -38,6 +41,14 @@ def create_remote_updater(self, pass_num): pass_num) +class Momentum(Optimizer): + def __init__(self, momentum=None, sparse=False, **kwargs): + learning_method = v1_optimizers.MomentumOptimizer( + momentum=None, sparse=False) + super(Momentum, self).__init__( + learning_method=learning_method, **kwargs) + + class Adam(Optimizer): def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8, **kwargs): learning_method = v1_optimizers.AdamOptimizer( @@ -52,7 +63,45 @@ def __init__(self, beta1=0.9, beta2=0.999, **kwargs): super(Adamax, self).__init__(learning_method=learning_method, **kwargs) +class AdaGrad(Optimizer): + def __init__(self, **kwargs): + learning_method = v1_optimizers.AdaGradOptimizer() + super(AdaGrad, self).__init__(learning_method=learning_method, **kwargs) + + +class DecayedAdaGrad(Optimizer): + def __init__(self, rho=0.95, epsilon=1e-06, **kwargs): + learning_method = v1_optimizers.DecayedAdaGradOptimizer( + rho=rho, epsilon=epsilon) + super(DecayedAdaGrad, self).__init__( + learning_method=learning_method, **kwargs) + + +class AdaDelta(Optimizer): + def __init__(self, rho=0.95, epsilon=1e-06, **kwargs): + learning_method = v1_optimizers.AdaDeltaOptimizer( + rho=rho, epsilon=epsilon) + super(AdaDelta, self).__init__( + learning_method=learning_method, **kwargs) + + +class RMSProp(Optimizer): + def __init__(self, rho=0.95, epsilon=1e-6, **kwargs): + learning_method = v1_optimizers.RMSPropOptimizer( + rho=rho, epsilon=epsilon) + super(RMSProp, self).__init__(learning_method=learning_method, **kwargs) + + +ModelAverage = v1_optimizers.ModelAverage +L2Regularization = v1_optimizers.L2Regularization + if __name__ == '__main__': swig_api.initPaddle('--use_gpu=false') - opt = paddle.v2.optimizer.Adam() - print opt.enable_types() + for opt in [ + Momentum(), Adam(), Adamax(), AdaGrad(), DecayedAdaGrad(), + AdaDelta(), RMSProp(), Adam( + model_average=ModelAverage(average_window=0.5), + regularization=L2Regularization(rate=0.5), + gradient_clipping_threshold=25) + ]: + print opt, opt.enable_types() From 950b4a312697fe8b5132437d85b919353af15365 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 23 Feb 2017 17:24:01 +0800 Subject: [PATCH 277/370] change return type Argument --- paddle/api/GradientMachine.cpp | 3 ++- paddle/api/Trainer.cpp | 3 ++- paddle/gserver/gradientmachines/GradientMachine.h | 2 +- paddle/gserver/gradientmachines/MultiGradientMachine.cpp | 3 +-- paddle/gserver/gradientmachines/MultiGradientMachine.h | 2 +- paddle/gserver/gradientmachines/NeuralNetwork.cpp | 2 +- paddle/gserver/gradientmachines/NeuralNetwork.h | 2 +- paddle/gserver/layers/CosSimVecMatLayer.cpp | 4 ++-- 8 files changed, 11 insertions(+), 10 deletions(-) diff --git a/paddle/api/GradientMachine.cpp b/paddle/api/GradientMachine.cpp index a64e70a6bd563a..538ca2999f8f05 100644 --- a/paddle/api/GradientMachine.cpp +++ b/paddle/api/GradientMachine.cpp @@ -148,7 +148,8 @@ Arguments* GradientMachine::getLayerOutput(const std::string& layerName) const throw(UnsupportError) { auto nn = m->machine; if (nn) { - return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName)); + auto arg = nn->getLayerOutput(layerName); + return Arguments::createByPaddleArgument(&arg); } else { throw UnsupportError(); } diff --git a/paddle/api/Trainer.cpp b/paddle/api/Trainer.cpp index 29cf2aa4501de3..84e4ca054abb01 100644 --- a/paddle/api/Trainer.cpp +++ b/paddle/api/Trainer.cpp @@ -134,7 +134,8 @@ void Trainer::finishTestPeriod() { m->finishTestPeriod(); } Arguments* Trainer::getLayerOutput(const std::string& layerName) const { auto nn = this->m->getGradientMachine(); CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork"; - return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName)); + auto arg = nn->getLayerOutput(layerName); + return Arguments::createByPaddleArgument(&arg); } void Trainer::forwardOneBatch(size_t batchSize) { diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index ae39783c6b7ea6..bc2f2f8563526a 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -134,7 +134,7 @@ class GradientMachine { backward(callback); } - virtual const Argument& getLayerOutput(const std::string& layerName) { + virtual Argument getLayerOutput(const std::string& layerName) { return *((Argument*)nullptr); } diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 6b11b0155e990d..123273f916f5d3 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -282,8 +282,7 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, backwardImp(callback); } -const Argument& MultiGradientMachine::getLayerOutput( - const std::string& layerName) { +Argument MultiGradientMachine::getLayerOutput(const std::string& layerName) { std::vector args; args.reserve(threads_.size()); diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.h b/paddle/gserver/gradientmachines/MultiGradientMachine.h index 9083230afd69e2..838a52b5153af6 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.h +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.h @@ -189,7 +189,7 @@ class MultiGradientMachine : public GradientMachine { PassType passType, const UpdateCallback& callback); - virtual const Argument& getLayerOutput(const std::string& layerName); + virtual Argument getLayerOutput(const std::string& layerName); virtual void onPassEnd(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index d1afde40e1f81e..2f2aa24aaca33a 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -293,7 +293,7 @@ void NeuralNetwork::backward(const UpdateCallback& callback) { } } -const Argument& NeuralNetwork::getLayerOutput(const std::string& layerName) { +Argument NeuralNetwork::getLayerOutput(const std::string& layerName) { return getLayer(layerName)->getOutput(); } diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index b4dc38e31b6fae..e7b6c438407e7e 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -87,7 +87,7 @@ class NeuralNetwork : public GradientMachine { virtual void backward(const UpdateCallback& callback = nullptr); - virtual const Argument& getLayerOutput(const std::string& layerName); + virtual Argument getLayerOutput(const std::string& layerName); const LayerPtr& getLayer(const std::string& layerName) const { auto it = layerMap_.find(layerName); diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index aabafd473aa1e0..0f887d8adfa053 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/paddle/gserver/layers/CosSimVecMatLayer.cpp @@ -112,7 +112,7 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap, void CosSimVecMatLayer::forward(PassType passType) { Layer::forward(passType); - CHECK_EQ(forward_.size(), 1) << "Only one forward function needed"; + CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed"; MatrixPtr inV0 = getInputValue(0); MatrixPtr inV1 = getInputValue(1); @@ -145,7 +145,7 @@ void CosSimVecMatLayer::forward(PassType passType) { } void CosSimVecMatLayer::backward(const UpdateCallback& callback) { - CHECK_EQ(backward_.size(), 1) << "Only one forward function needed"; + CHECK_EQ(backward_.size(), 1UL) << "Only one forward function needed"; MatrixPtr inV0 = getInputValue(0); MatrixPtr inV1 = getInputValue(1); From d1ab3c80744a851164dd4dc76a847193eb4c5562 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 19:20:33 +0800 Subject: [PATCH 278/370] MNIST dataset reader implementation --- python/paddle/v2/data_set/__init__.py | 0 python/paddle/v2/data_set/mnist.py | 62 +++++++++++++++++++++++++++ 2 files changed, 62 insertions(+) create mode 100644 python/paddle/v2/data_set/__init__.py create mode 100644 python/paddle/v2/data_set/mnist.py diff --git a/python/paddle/v2/data_set/__init__.py b/python/paddle/v2/data_set/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py new file mode 100644 index 00000000000000..34f61bb9f632ea --- /dev/null +++ b/python/paddle/v2/data_set/mnist.py @@ -0,0 +1,62 @@ +import sklearn.datasets.mldata +import sklearn.model_selection +import numpy + +__all__ = ['MNISTReader', 'train_reader_creator', 'test_reader_creator'] + +DATA_HOME = None + + +def __mnist_reader__(data, target): + n_samples = data.shape[0] + for i in xrange(n_samples): + yield data[i].astype(numpy.float32), int(target[i]) + + +class MNISTReader(object): + """ + mnist dataset reader. The `train_reader` and `test_reader` method returns + a iterator of each sample. Each sample is combined by 784-dim float and a + one-dim label + """ + + def __init__(self, random_state): + data = sklearn.datasets.mldata.fetch_mldata( + "MNIST original", data_home=DATA_HOME) + n_train = 60000 + self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( + data.data / 255.0, + data.target.astype("int"), + train_size=n_train, + random_state=random_state) + + def train_reader(self): + return __mnist_reader__(self.X_train, self.y_train) + + def test_reader(self): + return __mnist_reader__(self.X_test, self.y_test) + + +__default_instance__ = MNISTReader(0) + + +def train_reader_creator(): + """ + Default train set reader creator. + """ + return __default_instance__.train_reader + + +def test_reader_creator(): + """ + Default test set reader creator. + """ + return __default_instance__.test_reader + + +def unittest(): + assert len(list(train_reader_creator()())) == 60000 + + +if __name__ == '__main__': + unittest() From 84b423a89a2a6ece21910c83277f6282b80f6be7 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 23 Feb 2017 17:35:06 +0800 Subject: [PATCH 279/370] refine data feeder and add unit test --- python/CMakeLists.txt | 1 + python/paddle/v2/data_feeder.py | 110 ++++++++++----- python/paddle/v2/tests/CMakeLists.txt | 2 + python/paddle/v2/tests/run_tests.sh | 36 +++++ python/paddle/v2/tests/test_data_feeder.py | 150 +++++++++++++++++++++ 5 files changed, 264 insertions(+), 35 deletions(-) create mode 100644 python/paddle/v2/tests/CMakeLists.txt create mode 100755 python/paddle/v2/tests/run_tests.sh create mode 100644 python/paddle/v2/tests/test_data_feeder.py diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 357637e20346f8..71af50a9a4ed83 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS add_subdirectory(paddle/trainer_config_helpers/tests) add_subdirectory(paddle/reader/tests) +add_subdirectory(paddle/v2/tests) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/ DESTINATION opt/paddle/share/wheels diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py index 83a4efef9e7a7b..b594643dda6b38 100644 --- a/python/paddle/v2/data_feeder.py +++ b/python/paddle/v2/data_feeder.py @@ -12,49 +12,89 @@ # See the License for the specific language governing permissions and # limitations under the License. +from py_paddle import swig_paddle from py_paddle import DataProviderConverter +import data_type __all__ = ['DataFeeder'] -""" -DataFeeder converts the data returned by paddle.reader into a data structure -of Arguments which is defined in the API. The paddle.reader usually returns -a list of mini-batch data. Each item in the list is a tuple or list, which is -one sample with multiple features. DataFeeder converts this mini-batch data -into Arguments in order to feed it to C++ interface. - -The example usage: - - data_types = [paddle.data_type.dense_vector(784), - paddle.data_type.integer_value(10)] - feeder = DataFeeder(input_types=data_types) - minibatch_data = [ - ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample - ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample - ] - - # or - # minibatch_data = [ - # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample - # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample - # ] - arg = feeder(minibatch_data) - - -Args: - input_types: A list of input data types. It's length is equal to the length - of data returned by paddle.reader. Each item specifies the type - of each feature. - mintbatch_data: A list of mini-batch data. Each item is a list or tuple, + + +class DataFeeder(DataProviderConverter): + """ + DataFeeder converts the data returned by paddle.reader into a data structure + of Arguments which is defined in the API. The paddle.reader usually returns + a list of mini-batch data. Each item in the list is a list or a tuple, + which is one sample with one or multiple features. DataFeeder converts this + mini-batch data into Arguments in order to feed it to C++ interface. + + The example usage: + + data_types = [('image', paddle.data_type.dense_vector(784)), + ('label', paddle.data_type.integer_value(10))] + reader_dict = {'image':0, 'label':1} + feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict) + minibatch_data = [ + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample + ] + arg = feeder(minibatch_data) + """ + + def __init__(self, data_types, reader_dict): + """ + :param data_types: A list to specify data name and type. Each item is + a tuple of (data_name, data_type). For example: + [('image', paddle.data_type.dense_vector(784)), + ('label', paddle.data_type.integer_value(10))] + + :type data_types: A list of tuple + :param reader_dict: A dictionary to specify the position of each data + in the input data. + :type reader_dict: dict() + """ + self.input_names = [] + self.input_types = [] + self.reader_dict = reader_dict + for each in data_types: + self.input_names.append(each[0]) + self.input_types.append(each[1]) + assert isinstance(each[1], data_type.InputType) + DataProviderConverter.__init__(self, self.input_types) + + def convert(self, dat, argument=None): + """ + :param dat: A list of mini-batch data. Each item is a list or tuple, for example: [ (feature_0, feature_1, feature_2, ...), # first sample (feature_0, feature_1, feature_2, ...), # second sample ... ] + :type dat: List + :param argument: An Arguments object contains this mini-batch data with + one or multiple features. The Arguments definition is + in the API. + :type argument: swig_paddle.Arguments + """ + + if argument is None: + argument = swig_paddle.Arguments.createArguments(0) + assert isinstance(argument, swig_paddle.Arguments) + argument.resize(len(self.input_types)) + + scanners = [ + DataProviderConverter.create_scanner(i, each_type) + for i, each_type in enumerate(self.input_types) + ] + + for each_sample in dat: + for name, scanner in zip(self.input_names, scanners): + scanner.scan(each_sample[self.reader_dict[name]]) + + for scanner in scanners: + scanner.finish_scan(argument) -Returns: - An Arguments object contains this mini-batch data with multiple features. - The Arguments definition is in the API. -""" + return argument -DataFeeder = DataProviderConverter + def __call__(self, dat, argument=None): + return self.convert(dat, argument) diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt new file mode 100644 index 00000000000000..5842a716caa757 --- /dev/null +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -0,0 +1,2 @@ +add_test(NAME test_v2_api + COMMAND bash ${PROJ_ROOT}/python/paddle/v2/tests/run_tests.sh ${PYTHON_EXECUTABLE}) diff --git a/python/paddle/v2/tests/run_tests.sh b/python/paddle/v2/tests/run_tests.sh new file mode 100755 index 00000000000000..b96f54fe9cc78a --- /dev/null +++ b/python/paddle/v2/tests/run_tests.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +pushd `dirname $0` > /dev/null +SCRIPTPATH=$PWD +popd > /dev/null + +cd $SCRIPTPATH + +$1 -m pip install ../../../../paddle/dist/*.whl + +test_list="test_data_feeder.py" + +export PYTHONPATH=$PWD/../../../../python/ + +for fn in $test_list +do + echo "test $fn" + $1 $fn + if [ $? -ne 0 ]; then + exit 1 + fi +done diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py new file mode 100644 index 00000000000000..dcf433d7d8fdce --- /dev/null +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -0,0 +1,150 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import py_paddle.swig_paddle as api +import numpy as np + +from paddle.v2 import data_type +from paddle.v2.data_feeder import DataFeeder + + +class DataFeederTest(unittest.TestCase): + def dense_reader(self, size): + data = np.random.random(size) + return data + + def sparse_binary_reader(self, high, size_limit, non_empty=False): + num = np.random.randint(size_limit) # num could be 0 + while non_empty and num == 0: + num = np.random.randint(size_limit) + return np.random.randint(high, size=num).tolist() + + def test_dense_vector(self): + def compare(input): + feeder = DataFeeder([('image', data_type.dense_vector(784))], + {'image': 0}) + arg = feeder([input]) + output = arg.getSlotValue(0).copyToNumpyMat() + input = np.array(input, dtype='float32') + self.assertAlmostEqual(input.all(), output.all()) + + # test numpy array + batch_size = 32 + dim = 784 + data = [] + for i in xrange(batch_size): + data.append(self.dense_reader(784)) + compare(data) + + # test list + data = [] + for i in xrange(batch_size): + data.append(self.dense_reader(784).tolist()) + compare(data) + + def test_sparse_binary(self): + dim = 10000 + batch_size = 32 + data = [] + for i in xrange(batch_size): + data.append([self.sparse_binary_reader(dim, 50)]) + feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))], + {'input': 0}) + arg = feeder(data) + output = arg.getSlotValue(0) + assert isinstance(output, api.Matrix) + for i in xrange(batch_size): + self.assertEqual(output.getSparseRowCols(i), data[i][0]) + + def test_sparse(self): + dim = 10000 + batch_size = 32 + v = [] + w = [] + data = [] + for dat in xrange(batch_size): + a = self.sparse_binary_reader(dim, 40, non_empty=True) + b = self.dense_reader(len(a)).tolist() + v.append(a) + w.append(b[0]) + data.append([zip(a, b)]) + + feeder = DataFeeder([('input', data_type.sparse_vector(dim))], + {'input': 0}) + arg = feeder(data) + output = arg.getSlotValue(0) + assert isinstance(output, api.Matrix) + for i in xrange(batch_size): + self.assertEqual(output.getSparseRowCols(i), v[i]) + + def test_integer(self): + dim = 100 + batch_size = 32 + index = [] + for i in xrange(batch_size): + index.append([np.random.randint(dim)]) + feeder = DataFeeder([('input', data_type.integer_value(dim))], + {'input': 0}) + arg = feeder(index) + output = arg.getSlotIds(0).copyToNumpyArray() + index = np.array(index, dtype='int') + self.assertEqual(output.all(), index.flatten().all()) + + def test_multiple_slots(self): + batch_size = 2 + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(np.random.randint(10)) # size of feature 2: 10 + each_sample.append( + self.sparse_binary_reader( + 20000, 40, non_empty=True)) # size of feature 1: 20000 + each_sample.append(self.dense_reader(100)) # size of feature 0: 100 + data.append(each_sample) + + # test multiple features + data_types = [('fea0', data_type.dense_vector(100)), + ('fea1', data_type.sparse_binary_vector(20000)), + ('fea2', data_type.integer_value(10))] + feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0}) + arg = feeder(data) + output_dense = arg.getSlotValue(0).copyToNumpyMat() + output_sparse = arg.getSlotValue(1) + output_index = arg.getSlotIds(2).copyToNumpyArray() + for i in xrange(batch_size): + self.assertEqual(output_dense[i].all(), data[i][2].all()) + self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1]) + self.assertEqual(output_index[i], data[i][0]) + + # reader returns 3 featreus, but only use 2 features + data_types = [('fea0', data_type.dense_vector(100)), + ('fea2', data_type.integer_value(10))] + feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0}) + arg = feeder(data) + output_dense = arg.getSlotValue(0).copyToNumpyMat() + output_index = arg.getSlotIds(1).copyToNumpyArray() + for i in xrange(batch_size): + self.assertEqual(output_dense[i].all(), data[i][2].all()) + self.assertEqual(output_index[i], data[i][0]) + + +if __name__ == '__main__': + api.initPaddle("--use_gpu=0") + unittest.main() + +if __name__ == '__main__': + api.initPaddle("--use_gpu=0") + unittest.main() From b9dd33f815ed94237ddda930c99db838b76460e1 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Thu, 23 Feb 2017 23:30:17 +0800 Subject: [PATCH 280/370] hide Topology --- demo/mnist/api_train_v2.py | 6 ++---- python/paddle/v2/parameters.py | 8 +++----- python/paddle/v2/topology.py | 14 +++++++++----- python/paddle/v2/trainer.py | 4 +++- 4 files changed, 17 insertions(+), 15 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index f6edd1f34fe8d1..cc45229fbd688d 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -26,9 +26,7 @@ def main(): act=paddle.activation.Softmax()) cost = paddle.layer.classification_cost(input=inference, label=label) - topology = paddle.topology.Topology(cost) - - parameters = paddle.parameters.create(topology) + parameters = paddle.parameters.create([cost]) for param_name in parameters.keys(): array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) @@ -49,7 +47,7 @@ def event_handler(event): trainer.train( train_data_reader=train_reader, - topology=topology, + topology=[cost], parameters=parameters, event_handler=event_handler, batch_size=32) # batch size should be refactor in Data reader diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index b569afe3a1fc20..b8d4b287032cd3 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -7,15 +7,13 @@ __all__ = ['Parameters', 'create'] -def create(topology): +def create(layers): """ Create parameter pool by topology. - :param topology: + :param layers: :return: """ - if not isinstance(topology, v2_topology.Topology): - raise ValueError( - 'create must pass a topology which type is topology.Topology') + topology = v2_topology.Topology(layers) pool = Parameters() for param in topology.proto().parameters: pool.__append_config__(param) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 6508b3ce881c49..bfd7ef171a5fb6 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -12,10 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -from paddle.proto.ModelConfig_pb2 import ModelConfig +import collections + import paddle.trainer_config_helpers as conf_helps -import layer as v2_layer +from paddle.proto.ModelConfig_pb2 import ModelConfig + import data_type +import layer as v2_layer __all__ = ['Topology'] @@ -26,11 +29,12 @@ class Topology(object): and network configs. """ - def __init__(self, *layers): + def __init__(self, layers): + if not isinstance(layers, collections.Sequence): + raise ValueError("input of Topology should be a list of Layer") for layer in layers: if not isinstance(layer, v2_layer.LayerV2): - raise ValueError('create must pass a topologies ' - 'which type is paddle.layer.Layer') + raise ValueError('layer should have type paddle.layer.Layer') self.layers = layers self.__model_config__ = v2_layer.parse_network(*layers) assert isinstance(self.__model_config__, ModelConfig) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index c8da6e70cf528d..969aa6e0e05935 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -73,7 +73,7 @@ def train(self, Training method. Will train num_passes of input data. :param train_data_reader: - :param topology: Network Topology, use one or more Layers to represent it. + :param topology: cost layers, use one or more Layers to represent it. :param parameters: The parameter pools. :param num_passes: The total train passes. :param test_data_reader: @@ -87,6 +87,8 @@ def train(self, if event_handler is None: event_handler = default_event_handler + topology = v2_topology.Topology(topology) + __check_train_args__(**locals()) gm = api.GradientMachine.createFromConfigProto( From 775f019fa1d9fcd120ae6fc66f5c0caa9333ebca Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Thu, 23 Feb 2017 23:44:57 +0800 Subject: [PATCH 281/370] remove unused get_layer_proto in Topology --- python/paddle/v2/topology.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index bfd7ef171a5fb6..3e07c4106ed142 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -83,19 +83,6 @@ def find_data_layer(layer): return data_layers - def get_layer_proto(self, name): - """ - get layer by layer name - :param name: - :return: - """ - layers = filter(lambda layer: layer.name == name, - self.__model_config__.layers) - if len(layers) is 1: - return layers[0] - else: - return None - def data_type(self): """ get data_type from proto, such as: From b9d4f71c4a1c2a9f6c3593a1990c5becf8377268 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 23 Feb 2017 14:10:23 -0800 Subject: [PATCH 282/370] fix according to comments --- python/paddle/reader/creator.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/python/paddle/reader/creator.py b/python/paddle/reader/creator.py index 5c840f94b52f16..5a91bb0b8ef6d1 100644 --- a/python/paddle/reader/creator.py +++ b/python/paddle/reader/creator.py @@ -17,8 +17,9 @@ def np_array(x): """ - Creates a data reader from numpy array. - The highest dimension will be treated as batch dimension to iterate on. + Creates a reader that yields elements of x, if it is a + numpy vector. Or rows of x, if it is a numpy matrix. + Or any sub-hyperplane indexed by the highest dimension. :param x: the numpy array to create reader from. :returns: data reader created from x. From 909bd2690adb96cc68852856b9a6d8643459ca73 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 10:05:35 +0800 Subject: [PATCH 283/370] add topology test --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/data_type.py | 4 +- python/paddle/v2/tests/CMakeLists.txt | 4 ++ python/paddle/v2/tests/topology_test.py | 79 +++++++++++++++++++++++++ python/paddle/v2/topology.py | 14 +++-- 5 files changed, 96 insertions(+), 7 deletions(-) create mode 100644 python/paddle/v2/tests/CMakeLists.txt create mode 100644 python/paddle/v2/tests/topology_test.py diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index cc45229fbd688d..99cc344a5ed9c6 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -26,7 +26,7 @@ def main(): act=paddle.activation.Softmax()) cost = paddle.layer.classification_cost(input=inference, label=label) - parameters = paddle.parameters.create([cost]) + parameters = paddle.parameters.create(cost) for param_name in parameters.keys(): array = parameters.get(param_name) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) diff --git a/python/paddle/v2/data_type.py b/python/paddle/v2/data_type.py index 5b01ba4cd4866c..cd9ce6e51352ae 100644 --- a/python/paddle/v2/data_type.py +++ b/python/paddle/v2/data_type.py @@ -14,9 +14,9 @@ from paddle.trainer.PyDataProvider2 import \ InputType, dense_vector, sparse_binary_vector,\ - sparse_vector, integer_value + sparse_vector, integer_value, DataType __all__ = [ 'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector', - 'integer_value' + 'integer_value', 'DataType' ] diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt new file mode 100644 index 00000000000000..3a257af2fc5693 --- /dev/null +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -0,0 +1,4 @@ +add_test(NAME topology_test + COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/topology_test.py + WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/v2/tests/topology_test.py b/python/paddle/v2/tests/topology_test.py new file mode 100644 index 00000000000000..7360ed8f7b294c --- /dev/null +++ b/python/paddle/v2/tests/topology_test.py @@ -0,0 +1,79 @@ +# Copyright PaddlePaddle contributors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest +import paddle.v2.layer as layer +import paddle.v2.topology as topology +import paddle.v2.data_type as data_type +import paddle.trainer_config_helpers as conf_helps + + +class TestTopology(unittest.TestCase): + def test_parse(self): + pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) + label = layer.data(name='label', type=data_type.integer_value(10)) + hidden = layer.fc(input=pixel, + size=100, + act=conf_helps.SigmoidActivation()) + inference = layer.fc(input=hidden, + size=10, + act=conf_helps.SoftmaxActivation()) + maxid = layer.max_id(input=inference) + cost1 = layer.classification_cost(input=inference, label=label) + cost2 = layer.cross_entropy_cost(input=inference, label=label) + + print topology.Topology(cost2).proto() + print topology.Topology([cost1]).proto() + print topology.Topology([cost1, cost2]).proto() + print topology.Topology(cost2).proto() + print topology.Topology([inference, maxid]).proto() + + def test_data_type(self): + pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) + label = layer.data(name='label', type=data_type.integer_value(10)) + hidden = layer.fc(input=pixel, + size=100, + act=conf_helps.SigmoidActivation()) + inference = layer.fc(input=hidden, + size=10, + act=conf_helps.SoftmaxActivation()) + cost = layer.classification_cost(input=inference, label=label) + topo = topology.Topology(cost) + type = topo.data_type() + self.assertEqual(len(type), 2) + self.assertEqual(type[0][0], "pixel") + self.assertEqual(type[0][1].type, data_type.DataType.Dense) + self.assertEqual(type[0][1].dim, 784) + self.assertEqual(type[1][0], "label") + self.assertEqual(type[1][1].type, data_type.DataType.Index) + self.assertEqual(type[1][1].dim, 10) + + def test_get_layer(self): + pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) + label = layer.data(name='label', type=data_type.integer_value(10)) + hidden = layer.fc(input=pixel, + size=100, + act=conf_helps.SigmoidActivation()) + inference = layer.fc(input=hidden, + size=10, + act=conf_helps.SoftmaxActivation()) + cost = layer.classification_cost(input=inference, label=label) + topo = topology.Topology(cost) + pixel_layer = topo.get_layer("pixel") + label_layer = topo.get_layer("label") + self.assertEqual(pixel_layer, pixel) + self.assertEqual(label_layer, label) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 3e07c4106ed142..28cc8892e153b1 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -31,7 +31,8 @@ class Topology(object): def __init__(self, layers): if not isinstance(layers, collections.Sequence): - raise ValueError("input of Topology should be a list of Layer") + __check_layer_type__(layers) + layers = [layers] for layer in layers: if not isinstance(layer, v2_layer.LayerV2): raise ValueError('layer should have type paddle.layer.Layer') @@ -97,6 +98,11 @@ def data_type(self): return data_types_lists +def __check_layer_type__(layer): + if not isinstance(layer, v2_layer.LayerV2): + raise ValueError('layer should have type paddle.layer.Layer') + + if __name__ == '__main__': pixel = v2_layer.data(name='pixel', type=data_type.dense_vector(784)) label = v2_layer.data(name='label', type=data_type.integer_value(10)) @@ -110,8 +116,8 @@ def data_type(self): cost1 = v2_layer.classification_cost(input=inference, label=label) cost2 = v2_layer.cross_entropy_cost(input=inference, label=label) - print Topology(cost1).proto() print Topology(cost2).proto() - print Topology(cost1, cost2).proto() + print Topology([cost1]).proto() + print Topology([cost1, cost2]).proto() print Topology(cost2).proto() - print Topology(inference, maxid).proto() + print Topology([inference, maxid]).proto() From 12cac800acf29c39c717e03f934abe35ad5ab0e7 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 10:13:53 +0800 Subject: [PATCH 284/370] clean topology.py --- python/paddle/v2/tests/topology_test.py | 37 ++++++++++++------------- python/paddle/v2/topology.py | 24 +--------------- 2 files changed, 19 insertions(+), 42 deletions(-) diff --git a/python/paddle/v2/tests/topology_test.py b/python/paddle/v2/tests/topology_test.py index 7360ed8f7b294c..be60a577beac5b 100644 --- a/python/paddle/v2/tests/topology_test.py +++ b/python/paddle/v2/tests/topology_test.py @@ -19,25 +19,6 @@ class TestTopology(unittest.TestCase): - def test_parse(self): - pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) - label = layer.data(name='label', type=data_type.integer_value(10)) - hidden = layer.fc(input=pixel, - size=100, - act=conf_helps.SigmoidActivation()) - inference = layer.fc(input=hidden, - size=10, - act=conf_helps.SoftmaxActivation()) - maxid = layer.max_id(input=inference) - cost1 = layer.classification_cost(input=inference, label=label) - cost2 = layer.cross_entropy_cost(input=inference, label=label) - - print topology.Topology(cost2).proto() - print topology.Topology([cost1]).proto() - print topology.Topology([cost1, cost2]).proto() - print topology.Topology(cost2).proto() - print topology.Topology([inference, maxid]).proto() - def test_data_type(self): pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) label = layer.data(name='label', type=data_type.integer_value(10)) @@ -74,6 +55,24 @@ def test_get_layer(self): self.assertEqual(pixel_layer, pixel) self.assertEqual(label_layer, label) + def test_parse(self): + pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) + label = layer.data(name='label', type=data_type.integer_value(10)) + hidden = layer.fc(input=pixel, + size=100, + act=conf_helps.SigmoidActivation()) + inference = layer.fc(input=hidden, + size=10, + act=conf_helps.SoftmaxActivation()) + maxid = layer.max_id(input=inference) + cost1 = layer.classification_cost(input=inference, label=label) + cost2 = layer.cross_entropy_cost(input=inference, label=label) + + topology.Topology(cost2).proto() + topology.Topology([cost1]).proto() + topology.Topology([cost1, cost2]).proto() + topology.Topology([inference, maxid]).proto() + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 28cc8892e153b1..20fa891d656548 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -14,10 +14,8 @@ import collections -import paddle.trainer_config_helpers as conf_helps from paddle.proto.ModelConfig_pb2 import ModelConfig -import data_type import layer as v2_layer __all__ = ['Topology'] @@ -62,7 +60,7 @@ def find_layer_by_name(layer, layer_name): return result_layer[0] - def get_data_layer(self): + def data_layer(self): """ get all data layer :return: @@ -101,23 +99,3 @@ def data_type(self): def __check_layer_type__(layer): if not isinstance(layer, v2_layer.LayerV2): raise ValueError('layer should have type paddle.layer.Layer') - - -if __name__ == '__main__': - pixel = v2_layer.data(name='pixel', type=data_type.dense_vector(784)) - label = v2_layer.data(name='label', type=data_type.integer_value(10)) - hidden = v2_layer.fc(input=pixel, - size=100, - act=conf_helps.SigmoidActivation()) - inference = v2_layer.fc(input=hidden, - size=10, - act=conf_helps.SoftmaxActivation()) - maxid = v2_layer.max_id(input=inference) - cost1 = v2_layer.classification_cost(input=inference, label=label) - cost2 = v2_layer.cross_entropy_cost(input=inference, label=label) - - print Topology(cost2).proto() - print Topology([cost1]).proto() - print Topology([cost1, cost2]).proto() - print Topology(cost2).proto() - print Topology([inference, maxid]).proto() From f3c7fbeec4e256585dcf36e08fc2c06da243a045 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 23 Feb 2017 22:05:59 +0800 Subject: [PATCH 285/370] make minst to run --- demo/mnist/api_train_v2.py | 9 +++++---- python/paddle/v2/__init__.py | 1 + python/paddle/v2/tests/test_data_feeder.py | 22 ++++++++++++++++------ python/paddle/v2/trainer.py | 11 +++-------- 4 files changed, 25 insertions(+), 18 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6fc01ce58be57c..650bf392bbc734 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -50,11 +50,12 @@ def event_handler(event): parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader - data_types={ # data_types will be removed, It should be in + data_types=[ # data_types will be removed, It should be in # network topology - 'pixel': images.type, - 'label': label.type - }) + ('pixel', images.type), + ('label', label.type)], + reader_dict={'pixel':0, 'label':1} + ) if __name__ == '__main__': diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index be752731ba232a..bf06b5a7e360e9 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -18,6 +18,7 @@ import trainer import event import data_type +import data_feeder import py_paddle.swig_paddle as api __all__ = [ diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py index dcf433d7d8fdce..95a59a5d97fb67 100644 --- a/python/paddle/v2/tests/test_data_feeder.py +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -36,7 +36,7 @@ def test_dense_vector(self): def compare(input): feeder = DataFeeder([('image', data_type.dense_vector(784))], {'image': 0}) - arg = feeder([input]) + arg = feeder(input) output = arg.getSlotValue(0).copyToNumpyMat() input = np.array(input, dtype='float32') self.assertAlmostEqual(input.all(), output.all()) @@ -46,13 +46,17 @@ def compare(input): dim = 784 data = [] for i in xrange(batch_size): - data.append(self.dense_reader(784)) + each_sample = [] + each_sample.append(self.dense_reader(dim)) + data.append(each_sample) compare(data) # test list data = [] for i in xrange(batch_size): - data.append(self.dense_reader(784).tolist()) + each_sample = [] + each_sample.append(self.dense_reader(dim).tolist()) + data.append(each_sample) compare(data) def test_sparse_binary(self): @@ -60,7 +64,9 @@ def test_sparse_binary(self): batch_size = 32 data = [] for i in xrange(batch_size): - data.append([self.sparse_binary_reader(dim, 50)]) + each_sample = [] + each_sample.append(self.sparse_binary_reader(dim, 50)) + data.append(each_sample) feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))], {'input': 0}) arg = feeder(data) @@ -76,11 +82,13 @@ def test_sparse(self): w = [] data = [] for dat in xrange(batch_size): + each_sample = [] a = self.sparse_binary_reader(dim, 40, non_empty=True) b = self.dense_reader(len(a)).tolist() v.append(a) w.append(b[0]) - data.append([zip(a, b)]) + each_sample.append(zip(a, b)) + data.append(each_sample) feeder = DataFeeder([('input', data_type.sparse_vector(dim))], {'input': 0}) @@ -95,7 +103,9 @@ def test_integer(self): batch_size = 32 index = [] for i in xrange(batch_size): - index.append([np.random.randint(dim)]) + each_sample = [] + each_sample.append(np.random.randint(dim)) + index.append(each_sample) feeder = DataFeeder([('input', data_type.integer_value(dim))], {'input': 0}) arg = feeder(index) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 5709c7e886c323..023ab5e42d25b9 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -69,7 +69,8 @@ def train(self, test_data_reader=None, event_handler=None, batch_size=32, - data_types=None): + data_types=None, + reader_dict=None): """ Training method. Will train num_passes of input data. @@ -103,13 +104,7 @@ def train(self, gm.start() out_args = api.Arguments.createArguments(0) - data_types_lists = [] - for each in topology.input_layer_names: - if each not in data_types: - raise ValueError() - data_types_lists.append(data_types[each]) - - feeder = DataFeeder(input_types=data_types_lists) + feeder = DataFeeder(data_types, reader_dict) for pass_id in xrange(num_passes): updater.startPass() From 8d3ac7a209eac568ede3f4aa4df3957c87d139cd Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 24 Feb 2017 11:37:15 +0800 Subject: [PATCH 286/370] Conditional compile woboq --- paddle/scripts/docker/Dockerfile | 12 ++++--- paddle/scripts/docker/Dockerfile.gpu | 12 ++++--- paddle/scripts/docker/build.sh | 50 +++++++++++++++------------- 3 files changed, 41 insertions(+), 33 deletions(-) diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index d4845a72b61833..3b11018901d0ee 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -10,28 +10,30 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y automake && \ apt-get clean -y RUN pip install --upgrade pip && \ - pip install -U protobuf && \ + pip install -U "protobuf==3.1.0" && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ - cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_WOBOQ ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK -ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} +ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-ON} ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} -ENV WITH_DOC=${WITH_DOC:-ON} +ENV WITH_DOC=${WITH_DOC:-OFF} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index da20b2635e10e7..ea277b61e99af7 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -10,28 +10,30 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y automake && \ apt-get clean -y RUN pip install --upgrade pip && \ - pip install -U protobuf && \ + pip install -U "protobuf==3.1.0" && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ - cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_WOBOQ ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK -ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} +ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-ON} ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} -ENV WITH_DOC=${WITH_DOC:-ON} +ENV WITH_DOC=${WITH_DOC:-OFF} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 6197b41d6b5f19..a0391dd6b9fabf 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then mkdir -p /paddle/build # -p means no error if exists cd /paddle/build cmake .. \ - -DWITH_DOC=ON \ + -DWITH_DOC=OFF \ -DWITH_GPU=${WITH_GPU:-OFF} \ -DWITH_AVX=${WITH_AVX:-OFF} \ -DWITH_SWIG_PY=ON \ @@ -29,28 +29,32 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then make -j `nproc` make install - # Install woboq_codebrowser. - git clone https://github.com/woboq/woboq_codebrowser /woboq - cd /woboq - cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ - -DCMAKE_BUILD_TYPE=Release \ - . - make - - export WOBOQ_OUT=/usr/share/nginx/html/paddle - export BUILD_DIR=/paddle/build - mkdir -p $WOBOQ_OUT - cp -rv /woboq/data $WOBOQ_OUT/../data - /woboq/generator/codebrowser_generator \ - -b /paddle/build \ - -a \ - -o $WOBOQ_OUT \ - -p paddle:/paddle - /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT - cd /woboq - make clean - - pip install /usr/local/opt/paddle/share/wheels/*.whl + if [[ ${BUILD_WOBOQ:-OFF} == 'ON' ]]; then + apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev + # Install woboq_codebrowser. + git clone https://github.com/woboq/woboq_codebrowser /woboq + cd /woboq + cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ + -DCMAKE_BUILD_TYPE=Release \ + . + make + + export WOBOQ_OUT=/usr/share/nginx/html/paddle + export BUILD_DIR=/paddle/build + mkdir -p $WOBOQ_OUT + cp -rv /woboq/data $WOBOQ_OUT/../data + /woboq/generator/codebrowser_generator \ + -b /paddle/build \ + -a \ + -o $WOBOQ_OUT \ + -p paddle:/paddle + /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT + cd /woboq + make clean + fi + + pip install /usr/local/opt/paddle/share/wheels/py_paddle*linux*.whl + pip install /usr/local/opt/paddle/share/wheels/paddle*.whl paddle version fi From 7be8e69990bf8078dd1396c03c46117c49c39215 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 24 Feb 2017 11:42:00 +0800 Subject: [PATCH 287/370] Update WITH_DOC in docker --- paddle/scripts/docker/build.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index a0391dd6b9fabf..daea09d3faf08c 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then mkdir -p /paddle/build # -p means no error if exists cd /paddle/build cmake .. \ - -DWITH_DOC=OFF \ + -DWITH_DOC=${WITH_GPU:-OFF} \ -DWITH_GPU=${WITH_GPU:-OFF} \ -DWITH_AVX=${WITH_AVX:-OFF} \ -DWITH_SWIG_PY=ON \ From 4d3a1ab690c0ae49f785f36f1a6793c8b3353d64 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 24 Feb 2017 12:20:00 +0800 Subject: [PATCH 288/370] Fix env error --- paddle/scripts/docker/build.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index daea09d3faf08c..7fbf2821afa418 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then mkdir -p /paddle/build # -p means no error if exists cd /paddle/build cmake .. \ - -DWITH_DOC=${WITH_GPU:-OFF} \ + -DWITH_DOC=${WITH_DOC:-OFF} \ -DWITH_GPU=${WITH_GPU:-OFF} \ -DWITH_AVX=${WITH_AVX:-OFF} \ -DWITH_SWIG_PY=ON \ From 6415b0771b924f0fc8abb49660ffca20e0d36a44 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 24 Feb 2017 13:02:09 +0800 Subject: [PATCH 289/370] Add getNames --- paddle/api/Evaluator.cpp | 6 ++++++ paddle/api/PaddleAPI.h | 2 ++ 2 files changed, 8 insertions(+) diff --git a/paddle/api/Evaluator.cpp b/paddle/api/Evaluator.cpp index c30e09876397e3..f9656db19a0386 100644 --- a/paddle/api/Evaluator.cpp +++ b/paddle/api/Evaluator.cpp @@ -27,3 +27,9 @@ std::string Evaluator::toString() { m->rawPtr->printStats(sout); return sout.str(); } + +std::vector Evaluator::getNames() const { + std::vector retv; + m->rawPtr->getNames(&retv); + return retv; +} diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index f5af8b0035b44d..f5dcfcf94c658a 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -900,6 +900,8 @@ class Evaluator { */ std::string toString(); + std::vector getNames() const; + private: EvaluatorPrivate* m; From b7ce283b18fe28445dc9527fa1c04b1d9b8f36d8 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 24 Feb 2017 13:25:49 +0800 Subject: [PATCH 290/370] Expose Evaluator's getNames/getValue to SWIG --- paddle/api/Evaluator.cpp | 9 +++++++++ paddle/api/PaddleAPI.h | 2 ++ paddle/api/test/testTrain.py | 7 ++++++- 3 files changed, 17 insertions(+), 1 deletion(-) diff --git a/paddle/api/Evaluator.cpp b/paddle/api/Evaluator.cpp index f9656db19a0386..681e3a38091233 100644 --- a/paddle/api/Evaluator.cpp +++ b/paddle/api/Evaluator.cpp @@ -33,3 +33,12 @@ std::vector Evaluator::getNames() const { m->rawPtr->getNames(&retv); return retv; } + +double Evaluator::getValue(const std::string name) const { + paddle::Error err; + double v = m->rawPtr->getValue(name, &err); + if (err) { + throw std::runtime_error(err.msg()); + } + return v; +} diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index f5dcfcf94c658a..80c50cdb08bb16 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -902,6 +902,8 @@ class Evaluator { std::vector getNames() const; + double getValue(const std::string name) const; + private: EvaluatorPrivate* m; diff --git a/paddle/api/test/testTrain.py b/paddle/api/test/testTrain.py index a90d15c272a3a2..7061a4c43bf011 100644 --- a/paddle/api/test/testTrain.py +++ b/paddle/api/test/testTrain.py @@ -89,9 +89,14 @@ def update_callback(param): except Exception as e: print e + ev = m.makeEvaluator() + ev.start() m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN, update_callback) - + m.eval(ev) + ev.finish() + for name in ev.getNames(): + print name, ev.getValue(name) for optimizer in optimizers: optimizer.finishBatch() From 5f626b93f255d2f9d34732856c23aefb150420e8 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 24 Feb 2017 13:46:48 +0800 Subject: [PATCH 291/370] set docker BUILD_AND_INSTALL --- paddle/scripts/docker/Dockerfile | 2 +- paddle/scripts/docker/Dockerfile.gpu | 2 +- paddle/scripts/docker/build.sh | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 3b11018901d0ee..27d19dc15c8bd6 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -30,7 +30,7 @@ ARG WITH_DOC ARG WITH_STYLE_CHECK ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} -ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-ON} +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-OFF} diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index ea277b61e99af7..538233dd0c7bb5 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -30,7 +30,7 @@ ARG WITH_DOC ARG WITH_STYLE_CHECK ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} -ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-ON} +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-OFF} diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 7fbf2821afa418..d9c44f42340323 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -11,7 +11,7 @@ set -e # If Dockerfile.* sets BUILD_AND_INSTALL to 'ON', it would have copied # source tree to /paddle, and this scripts should build it into # /paddle/build. -if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then +if [[ ${BUILD_AND_INSTALL:-OFF} == 'ON' ]]; then if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so fi From 38a792f20ed9e65d2920ded6ad42a5b68f2146ee Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 24 Feb 2017 13:52:31 +0800 Subject: [PATCH 292/370] Clean mnist code --- python/paddle/v2/data_set/config.py | 8 ++++ python/paddle/v2/data_set/mnist.py | 58 +++++++++++++---------------- 2 files changed, 33 insertions(+), 33 deletions(-) create mode 100644 python/paddle/v2/data_set/config.py diff --git a/python/paddle/v2/data_set/config.py b/python/paddle/v2/data_set/config.py new file mode 100644 index 00000000000000..69e96d65ef1ef8 --- /dev/null +++ b/python/paddle/v2/data_set/config.py @@ -0,0 +1,8 @@ +import os + +__all__ = ['DATA_HOME'] + +DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set') + +if not os.path.exists(DATA_HOME): + os.makedirs(DATA_HOME) diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py index 34f61bb9f632ea..6f35acf683686e 100644 --- a/python/paddle/v2/data_set/mnist.py +++ b/python/paddle/v2/data_set/mnist.py @@ -1,61 +1,53 @@ import sklearn.datasets.mldata import sklearn.model_selection import numpy +from config import DATA_HOME -__all__ = ['MNISTReader', 'train_reader_creator', 'test_reader_creator'] +__all__ = ['MNIST', 'train_creator', 'test_creator'] -DATA_HOME = None +def __mnist_reader_creator__(data, target): + def reader(): + n_samples = data.shape[0] + for i in xrange(n_samples): + yield (data[i] / 255.0).astype(numpy.float32), int(target[i]) -def __mnist_reader__(data, target): - n_samples = data.shape[0] - for i in xrange(n_samples): - yield data[i].astype(numpy.float32), int(target[i]) + return reader -class MNISTReader(object): +class MNIST(object): """ mnist dataset reader. The `train_reader` and `test_reader` method returns a iterator of each sample. Each sample is combined by 784-dim float and a one-dim label """ - def __init__(self, random_state): + def __init__(self, random_state=0, test_size=10000, **options): data = sklearn.datasets.mldata.fetch_mldata( "MNIST original", data_home=DATA_HOME) - n_train = 60000 self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( - data.data / 255.0, - data.target.astype("int"), - train_size=n_train, - random_state=random_state) + data.data, + data.target, + test_size=test_size, + random_state=random_state, + **options) - def train_reader(self): - return __mnist_reader__(self.X_train, self.y_train) + def train_creator(self): + return __mnist_reader_creator__(self.X_train, self.y_train) - def test_reader(self): - return __mnist_reader__(self.X_test, self.y_test) + def test_creator(self): + return __mnist_reader_creator__(self.X_test, self.y_test) -__default_instance__ = MNISTReader(0) - - -def train_reader_creator(): - """ - Default train set reader creator. - """ - return __default_instance__.train_reader - - -def test_reader_creator(): - """ - Default test set reader creator. - """ - return __default_instance__.test_reader +__default_instance__ = MNIST() +train_creator = __default_instance__.train_creator +test_creator = __default_instance__.test_creator def unittest(): - assert len(list(train_reader_creator()())) == 60000 + size = 12045 + mnist = MNIST(test_size=size) + assert len(list(mnist.test_creator()())) == size if __name__ == '__main__': From ef9041c07bdf5d5f86b0b5b12045b4cec3719953 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 19:20:33 +0800 Subject: [PATCH 293/370] MNIST dataset reader implementation --- python/paddle/v2/data_set/__init__.py | 0 python/paddle/v2/data_set/mnist.py | 62 +++++++++++++++++++++++++++ 2 files changed, 62 insertions(+) create mode 100644 python/paddle/v2/data_set/__init__.py create mode 100644 python/paddle/v2/data_set/mnist.py diff --git a/python/paddle/v2/data_set/__init__.py b/python/paddle/v2/data_set/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py new file mode 100644 index 00000000000000..34f61bb9f632ea --- /dev/null +++ b/python/paddle/v2/data_set/mnist.py @@ -0,0 +1,62 @@ +import sklearn.datasets.mldata +import sklearn.model_selection +import numpy + +__all__ = ['MNISTReader', 'train_reader_creator', 'test_reader_creator'] + +DATA_HOME = None + + +def __mnist_reader__(data, target): + n_samples = data.shape[0] + for i in xrange(n_samples): + yield data[i].astype(numpy.float32), int(target[i]) + + +class MNISTReader(object): + """ + mnist dataset reader. The `train_reader` and `test_reader` method returns + a iterator of each sample. Each sample is combined by 784-dim float and a + one-dim label + """ + + def __init__(self, random_state): + data = sklearn.datasets.mldata.fetch_mldata( + "MNIST original", data_home=DATA_HOME) + n_train = 60000 + self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( + data.data / 255.0, + data.target.astype("int"), + train_size=n_train, + random_state=random_state) + + def train_reader(self): + return __mnist_reader__(self.X_train, self.y_train) + + def test_reader(self): + return __mnist_reader__(self.X_test, self.y_test) + + +__default_instance__ = MNISTReader(0) + + +def train_reader_creator(): + """ + Default train set reader creator. + """ + return __default_instance__.train_reader + + +def test_reader_creator(): + """ + Default test set reader creator. + """ + return __default_instance__.test_reader + + +def unittest(): + assert len(list(train_reader_creator()())) == 60000 + + +if __name__ == '__main__': + unittest() From befc3e066b633ae2a9e0c448037a93ede6de4ddf Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 24 Feb 2017 13:52:31 +0800 Subject: [PATCH 294/370] Clean mnist code --- python/paddle/v2/data_set/config.py | 8 ++++ python/paddle/v2/data_set/mnist.py | 58 +++++++++++++---------------- 2 files changed, 33 insertions(+), 33 deletions(-) create mode 100644 python/paddle/v2/data_set/config.py diff --git a/python/paddle/v2/data_set/config.py b/python/paddle/v2/data_set/config.py new file mode 100644 index 00000000000000..69e96d65ef1ef8 --- /dev/null +++ b/python/paddle/v2/data_set/config.py @@ -0,0 +1,8 @@ +import os + +__all__ = ['DATA_HOME'] + +DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set') + +if not os.path.exists(DATA_HOME): + os.makedirs(DATA_HOME) diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py index 34f61bb9f632ea..6f35acf683686e 100644 --- a/python/paddle/v2/data_set/mnist.py +++ b/python/paddle/v2/data_set/mnist.py @@ -1,61 +1,53 @@ import sklearn.datasets.mldata import sklearn.model_selection import numpy +from config import DATA_HOME -__all__ = ['MNISTReader', 'train_reader_creator', 'test_reader_creator'] +__all__ = ['MNIST', 'train_creator', 'test_creator'] -DATA_HOME = None +def __mnist_reader_creator__(data, target): + def reader(): + n_samples = data.shape[0] + for i in xrange(n_samples): + yield (data[i] / 255.0).astype(numpy.float32), int(target[i]) -def __mnist_reader__(data, target): - n_samples = data.shape[0] - for i in xrange(n_samples): - yield data[i].astype(numpy.float32), int(target[i]) + return reader -class MNISTReader(object): +class MNIST(object): """ mnist dataset reader. The `train_reader` and `test_reader` method returns a iterator of each sample. Each sample is combined by 784-dim float and a one-dim label """ - def __init__(self, random_state): + def __init__(self, random_state=0, test_size=10000, **options): data = sklearn.datasets.mldata.fetch_mldata( "MNIST original", data_home=DATA_HOME) - n_train = 60000 self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( - data.data / 255.0, - data.target.astype("int"), - train_size=n_train, - random_state=random_state) + data.data, + data.target, + test_size=test_size, + random_state=random_state, + **options) - def train_reader(self): - return __mnist_reader__(self.X_train, self.y_train) + def train_creator(self): + return __mnist_reader_creator__(self.X_train, self.y_train) - def test_reader(self): - return __mnist_reader__(self.X_test, self.y_test) + def test_creator(self): + return __mnist_reader_creator__(self.X_test, self.y_test) -__default_instance__ = MNISTReader(0) - - -def train_reader_creator(): - """ - Default train set reader creator. - """ - return __default_instance__.train_reader - - -def test_reader_creator(): - """ - Default test set reader creator. - """ - return __default_instance__.test_reader +__default_instance__ = MNIST() +train_creator = __default_instance__.train_creator +test_creator = __default_instance__.test_creator def unittest(): - assert len(list(train_reader_creator()())) == 60000 + size = 12045 + mnist = MNIST(test_size=size) + assert len(list(mnist.test_creator()())) == size if __name__ == '__main__': From fb45cc3519b23da0862195bd316370d3f6c17f38 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 16:17:54 +0800 Subject: [PATCH 295/370] refine code --- python/paddle/v2/topology.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 20fa891d656548..9c57f1f8e607bd 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -32,8 +32,7 @@ def __init__(self, layers): __check_layer_type__(layers) layers = [layers] for layer in layers: - if not isinstance(layer, v2_layer.LayerV2): - raise ValueError('layer should have type paddle.layer.Layer') + __check_layer_type__(layer) self.layers = layers self.__model_config__ = v2_layer.parse_network(*layers) assert isinstance(self.__model_config__, ModelConfig) From c2cefd5bb9cea35cadc0939264d638a8a74d88f2 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 16:23:12 +0800 Subject: [PATCH 296/370] add test for v2-layer --- python/paddle/v2/tests/CMakeLists.txt | 4 ++ python/paddle/v2/tests/layer_test.py | 61 +++++++++++++++++++++++++++ 2 files changed, 65 insertions(+) create mode 100644 python/paddle/v2/tests/CMakeLists.txt create mode 100644 python/paddle/v2/tests/layer_test.py diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt new file mode 100644 index 00000000000000..83b7180ebafd2e --- /dev/null +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -0,0 +1,4 @@ +add_test(NAME v2_layer_test + COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/layer_test.py + WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/v2/tests/layer_test.py b/python/paddle/v2/tests/layer_test.py new file mode 100644 index 00000000000000..3b2a13ce05bda7 --- /dev/null +++ b/python/paddle/v2/tests/layer_test.py @@ -0,0 +1,61 @@ +# Copyright PaddlePaddle contributors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import difflib +import unittest + +import paddle.trainer_config_helpers as conf_helps +import paddle.v2.activation as activation +import paddle.v2.attr as attr +import paddle.v2.data_type as data_type +import paddle.v2.layer as layer +from paddle.trainer_config_helpers.config_parser_utils import \ + parse_network_config as parse_network + +pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) +label = layer.data(name='label', type=data_type.integer_value(10)) +weight = layer.data(name='weight', type=data_type.dense_vector(10)) +score = layer.data(name='score', type=data_type.dense_vector(1)) +hidden = layer.fc(input=pixel, + size=100, + act=activation.Sigmoid(), + param_attr=attr.Param(name='hidden')) +inference = layer.fc(input=hidden, size=10, act=activation.Softmax()) + + +class CostLayerTest(unittest.TestCase): + def test_cost_layer(self): + cost1 = layer.classification_cost(input=inference, label=label) + cost2 = layer.classification_cost( + input=inference, label=label, weight=weight) + cost3 = layer.cross_entropy_cost(input=inference, label=label) + cost4 = layer.cross_entropy_with_selfnorm_cost( + input=inference, label=label) + cost5 = layer.regression_cost(input=inference, label=label) + cost6 = layer.regression_cost( + input=inference, label=label, weight=weight) + cost7 = layer.multi_binary_label_cross_entropy_cost( + input=inference, label=label) + cost8 = layer.rank_cost(left=score, right=score, label=score) + cost9 = layer.lambda_cost(input=inference, score=score) + cost10 = layer.sum_cost(input=inference) + cost11 = layer.huber_cost(input=score, label=label) + + print layer.parse_network(cost1, cost2) + print layer.parse_network(cost3, cost4) + print layer.parse_network(cost5, cost6) + print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + + +if __name__ == '__main__': + unittest.main() From 2591167813764097f206337bb5df96d94eed5802 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 16:33:42 +0800 Subject: [PATCH 297/370] add test dir --- python/CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 357637e20346f8..71af50a9a4ed83 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS add_subdirectory(paddle/trainer_config_helpers/tests) add_subdirectory(paddle/reader/tests) +add_subdirectory(paddle/v2/tests) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/ DESTINATION opt/paddle/share/wheels From 976a6982baa8a5fb4839d036f7f403ef4b22b0c7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 24 Feb 2017 16:46:14 +0800 Subject: [PATCH 298/370] Add cifar dataset --- python/paddle/v2/data_set/cifar.py | 173 +++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 python/paddle/v2/data_set/cifar.py diff --git a/python/paddle/v2/data_set/cifar.py b/python/paddle/v2/data_set/cifar.py new file mode 100644 index 00000000000000..54289430d4ce11 --- /dev/null +++ b/python/paddle/v2/data_set/cifar.py @@ -0,0 +1,173 @@ +""" +CIFAR Dataset. + +URL: https://www.cs.toronto.edu/~kriz/cifar.html + +the default train_creator, test_creator used for CIFAR-10 dataset. +""" +from config import DATA_HOME +import os +import hashlib +import urllib2 +import shutil +import tarfile +import cPickle +import itertools +import numpy + +__all__ = ['CIFAR10', 'CIFAR100', 'train_creator', 'test_creator'] + + +def __download_file__(filename, url, md5): + def __file_ok__(): + if not os.path.exists(filename): + return False + md5_hash = hashlib.md5() + with open(filename, 'rb') as f: + for chunk in iter(lambda: f.read(4096), b""): + md5_hash.update(chunk) + + return md5_hash.hexdigest() == md5 + + while not __file_ok__(): + response = urllib2.urlopen(url) + with open(filename, mode='wb') as of: + shutil.copyfileobj(fsrc=response, fdst=of) + + +def __read_one_batch__(batch): + data = batch['data'] + labels = batch.get('labels', batch.get('fine_labels', None)) + assert labels is not None + for sample, label in itertools.izip(data, labels): + yield (sample / 255.0).astype(numpy.float32), int(label) + + +CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' +CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a' +CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' +CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85' + + +class CIFAR(object): + """ + CIFAR dataset reader. The base class for CIFAR-10 and CIFAR-100 + + :param url: Download url. + :param md5: File md5sum + :param meta_filename: Meta file name in package. + :param train_filename: Train file name in package. + :param test_filename: Test file name in package. + """ + + def __init__(self, url, md5, meta_filename, train_filename, test_filename): + filename = os.path.split(url)[-1] + assert DATA_HOME is not None + filepath = os.path.join(DATA_HOME, md5) + if not os.path.exists(filepath): + os.makedirs(filepath) + + self.__full_file__ = os.path.join(filepath, filename) + self.__meta_filename__ = meta_filename + self.__train_filename__ = train_filename + self.__test_filename__ = test_filename + __download_file__(filename=self.__full_file__, url=url, md5=md5) + + def labels(self): + """ + labels get all dataset label in order. + :return: a list of label. + :rtype: list[string] + """ + with tarfile.open(self.__full_file__, mode='r') as f: + name = [ + each_item.name for each_item in f + if self.__meta_filename__ in each_item.name + ][0] + meta_f = f.extractfile(name) + meta = cPickle.load(meta_f) + for key in meta: + if 'label' in key: + return meta[key] + else: + raise RuntimeError("Unexpected branch.") + + def train(self): + """ + Train Reader + """ + return self.__read_batch__(self.__train_filename__) + + def test(self): + """ + Test Reader + """ + return self.__read_batch__(self.__test_filename__) + + def __read_batch__(self, sub_name): + with tarfile.open(self.__full_file__, mode='r') as f: + names = (each_item.name for each_item in f + if sub_name in each_item.name) + + for name in names: + batch = cPickle.load(f.extractfile(name)) + for item in __read_one_batch__(batch): + yield item + + +class CIFAR10(CIFAR): + """ + CIFAR-10 dataset, images are classified in 10 classes. + """ + + def __init__(self): + super(CIFAR10, self).__init__( + CIFAR10_URL, + CIFAR10_MD5, + meta_filename='batches.meta', + train_filename='data_batch', + test_filename='test_batch') + + +class CIFAR100(CIFAR): + """ + CIFAR-100 dataset, images are classified in 100 classes. + """ + + def __init__(self): + super(CIFAR100, self).__init__( + CIFAR100_URL, + CIFAR100_MD5, + meta_filename='meta', + train_filename='train', + test_filename='test') + + +def train_creator(): + """ + Default train reader creator. Use CIFAR-10 dataset. + """ + cifar = CIFAR10() + return cifar.train + + +def test_creator(): + """ + Default test reader creator. Use CIFAR-10 dataset. + """ + cifar = CIFAR10() + return cifar.test + + +def unittest(label_count=100): + cifar = globals()["CIFAR%d" % label_count]() + assert len(cifar.labels()) == label_count + for _ in cifar.test(): + pass + for _ in cifar.train(): + pass + + +if __name__ == '__main__': + unittest(10) + unittest(100) From bf8a6dc4fff40e869236c0634df57bfe16c12d96 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 24 Feb 2017 17:14:38 +0800 Subject: [PATCH 299/370] rename layer_test to test_layer --- python/paddle/v2/tests/CMakeLists.txt | 4 ++-- .../paddle/v2/tests/{layer_test.py => test_layer.py} | 10 ++++++---- 2 files changed, 8 insertions(+), 6 deletions(-) rename python/paddle/v2/tests/{layer_test.py => test_layer.py} (90%) diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt index 83b7180ebafd2e..402ad2e664c773 100644 --- a/python/paddle/v2/tests/CMakeLists.txt +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -1,4 +1,4 @@ -add_test(NAME v2_layer_test +add_test(NAME test_v2_layer COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ - ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/layer_test.py + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/test_layer.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/v2/tests/layer_test.py b/python/paddle/v2/tests/test_layer.py similarity index 90% rename from python/paddle/v2/tests/layer_test.py rename to python/paddle/v2/tests/test_layer.py index 3b2a13ce05bda7..b600e8cf765122 100644 --- a/python/paddle/v2/tests/layer_test.py +++ b/python/paddle/v2/tests/test_layer.py @@ -51,10 +51,12 @@ def test_cost_layer(self): cost10 = layer.sum_cost(input=inference) cost11 = layer.huber_cost(input=score, label=label) - print layer.parse_network(cost1, cost2) - print layer.parse_network(cost3, cost4) - print layer.parse_network(cost5, cost6) - print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + print dir(layer) + layer.parse_network(cost1, cost2) + print dir(layer) + #print layer.parse_network(cost3, cost4) + #print layer.parse_network(cost5, cost6) + #print layer.parse_network(cost7, cost8, cost9, cost10, cost11) if __name__ == '__main__': From 5258bcf3eee7f1ca24af04759b0cf4f21e8b5f0a Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 24 Feb 2017 17:41:58 +0800 Subject: [PATCH 300/370] implement more layers in v2 --- doc/api/trainer_config_helpers/layers.rst | 36 ++- .../paddle/trainer_config_helpers/layers.py | 214 ++++++++-------- python/paddle/v2/__init__.py | 3 +- python/paddle/v2/layer.py | 235 ++++++++++++++---- python/paddle/v2/pooling.py | 24 ++ 5 files changed, 353 insertions(+), 159 deletions(-) create mode 100644 python/paddle/v2/pooling.py diff --git a/doc/api/trainer_config_helpers/layers.rst b/doc/api/trainer_config_helpers/layers.rst index 2793d6afd9565e..bbea823de4d870 100644 --- a/doc/api/trainer_config_helpers/layers.rst +++ b/doc/api/trainer_config_helpers/layers.rst @@ -139,24 +139,12 @@ lstmemory :members: lstmemory :noindex: -lstm_step_layer ---------------- -.. automodule:: paddle.trainer_config_helpers.layers - :members: lstm_step_layer - :noindex: - grumemory --------- .. automodule:: paddle.trainer_config_helpers.layers :members: grumemory :noindex: -gru_step_layer ---------------- -.. automodule:: paddle.trainer_config_helpers.layers - :members: gru_step_layer - :noindex: - Recurrent Layer Group ===================== @@ -172,6 +160,18 @@ recurrent_group :members: recurrent_group :noindex: +lstm_step_layer +--------------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: lstm_step_layer + :noindex: + +gru_step_layer +--------------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: gru_step_layer + :noindex: + beam_search ------------ .. automodule:: paddle.trainer_config_helpers.layers @@ -308,6 +308,12 @@ repeat_layer :members: repeat_layer :noindex: +rotate_layer +------------ +.. automodule:: paddle.trainer_config_helpers.layers + :members: rotate_layer + :noindex: + seq_reshape_layer ----------------- .. automodule:: paddle.trainer_config_helpers.layers @@ -462,6 +468,12 @@ ctc_layer :members: ctc_layer :noindex: +warp_ctc_layer +-------------- +.. automodule:: paddle.trainer_config_helpers.layers + :members: warp_ctc_layer + :noindex: + nce_layer ----------- .. automodule:: paddle.trainer_config_helpers.layers diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 00aef80691fba0..de903f8c747c97 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -30,88 +30,28 @@ import copy __all__ = [ - "full_matrix_projection", - "AggregateLevel", - "ExpandLevel", - "identity_projection", - "dotmul_projection", - "dotmul_operator", - "repeat_layer", - "seq_reshape_layer", - "table_projection", - "mixed_layer", - "data_layer", - "embedding_layer", - "fc_layer", - "grumemory", - "pooling_layer", - "lstmemory", - "last_seq", - "first_seq", - "cos_sim", - "hsigmoid", - "conv_projection", - "regression_cost", - 'classification_cost', - "LayerOutput", - 'img_conv_layer', - 'img_pool_layer', - 'batch_norm_layer', - 'img_cmrnorm_layer', - 'addto_layer', - 'concat_layer', - 'seq_concat_layer', - 'lstm_step_layer', - 'recurrent_group', - 'memory', - 'StaticInput', - 'expand_layer', - 'scaling_layer', - 'scaling_projection', - 'power_layer', - 'interpolation_layer', - 'bilinear_interp_layer', - 'trans_layer', - 'rotate_layer', - 'sum_to_one_norm_layer', - 'get_output_layer', - 'LayerType', - 'context_projection', - 'beam_search', - 'maxid_layer', - 'GeneratedInput', - 'SubsequenceInput', - 'gru_step_layer', - 'recurrent_layer', - 'BaseGeneratedInput', - 'conv_operator', - 'conv_shift_layer', - 'tensor_layer', - 'selective_fc_layer', - 'sampling_id_layer', - 'slope_intercept_layer', - 'trans_full_matrix_projection', - 'linear_comb_layer', - 'convex_comb_layer', - 'ctc_layer', - 'warp_ctc_layer', - 'crf_layer', - 'crf_decoding_layer', - 'nce_layer', - 'cross_entropy_with_selfnorm', - 'cross_entropy', - 'multi_binary_label_cross_entropy', - 'sum_cost', - 'rank_cost', - 'lambda_cost', - 'huber_cost', - 'block_expand_layer', - 'maxout_layer', - 'out_prod_layer', - 'print_layer', - 'priorbox_layer', - 'spp_layer', - 'pad_layer', + "full_matrix_projection", "AggregateLevel", "ExpandLevel", + "identity_projection", "dotmul_projection", "dotmul_operator", + "repeat_layer", "seq_reshape_layer", "table_projection", "mixed_layer", + "data_layer", "embedding_layer", "fc_layer", "grumemory", "pooling_layer", + "lstmemory", "last_seq", "first_seq", "cos_sim", "hsigmoid", + "conv_projection", "regression_cost", 'classification_cost', "LayerOutput", + 'img_conv_layer', 'img_pool_layer', 'batch_norm_layer', 'img_cmrnorm_layer', + 'addto_layer', 'concat_layer', 'seq_concat_layer', 'lstm_step_layer', + 'recurrent_group', 'memory', 'StaticInput', 'expand_layer', 'scaling_layer', + 'scaling_projection', 'power_layer', 'interpolation_layer', + 'bilinear_interp_layer', 'trans_layer', 'rotate_layer', + 'sum_to_one_norm_layer', 'get_output_layer', 'LayerType', + 'context_projection', 'beam_search', 'maxid_layer', 'GeneratedInput', + 'SubsequenceInput', 'gru_step_layer', 'recurrent_layer', + 'BaseGeneratedInput', 'conv_operator', 'conv_shift_layer', 'tensor_layer', + 'selective_fc_layer', 'sampling_id_layer', 'slope_intercept_layer', + 'trans_full_matrix_projection', 'linear_comb_layer', 'convex_comb_layer', + 'ctc_layer', 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer', + 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy', + 'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', 'lambda_cost', + 'huber_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer', + 'print_layer', 'priorbox_layer', 'spp_layer', 'pad_layer', 'eos_layer' ] @@ -1287,6 +1227,12 @@ def last_seq(input, """ Get Last Timestamp Activation of a sequence. + The simple usage is: + + .. code-block:: python + + seq = last_seq(input=layer) + :param agg_level: Aggregated level :param name: Layer name. :type name: basestring @@ -1325,6 +1271,12 @@ def first_seq(input, """ Get First Timestamp Activation of a sequence. + The simple usage is: + + .. code-block:: python + + seq = first_seq(input=layer) + :param agg_level: aggregation level :param name: Layer name. :type name: basestring @@ -1425,7 +1377,7 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None): .. code-block:: python - expand = repeat_layer(layer, 4) + expand = repeat_layer(input=layer, num_repeats=4) :param input: Input layer :type input: LayerOutput @@ -1797,6 +1749,12 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): Note that the above computation is for one sample. Multiple samples are processed in one batch. + The example usage is: + + .. code-block:: python + + cos = cos_sim(a=layer1, b=layer2, size=3) + :param name: layer name :type name: basestring :param a: input layer a @@ -1958,6 +1916,16 @@ def img_conv_layer(input, pieces. First 256/4 = 64 channels will process by first 32 filters. The rest channels will be processed by rest group of filters. + The example usage is: + + .. code-block:: python + + conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1, + num_channels=8, + num_filters=16, stride=1, + bias_attr=False, + act=ReluActivation()) + :param name: Layer name. :type name: basestring :param input: Layer Input. @@ -2097,6 +2065,34 @@ def img_pool_layer(input, .. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/ + - ceil_mode=True: + + .. math:: + + w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride)) + h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y)) + + - ceil_mode=False: + + .. math:: + + w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride)) + h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y)) + + The example usage is: + + .. code-block:: python + + maxpool = img_pool_layer(input=conv, + pool_size=3, + pool_size_y=5, + num_channels=8, + stride=1, + stride_y=2, + padding=1, + padding_y=2, + pool_type=MaxPooling()) + :param padding: pooling padding width. :type padding: int :param padding_y: pooling padding height. It's equal to padding by default. @@ -2123,19 +2119,6 @@ def img_pool_layer(input, :param ceil_mode: Wether to use ceil mode to calculate output height and with. Defalut is True. If set false, Otherwise use floor. - - ceil_mode=True: - - .. math:: - - w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride)) - h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y)) - - - ceil_mode=False: - - .. math:: - - w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride)) - h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y)) :type ceil_mode: bool :return: LayerOutput object. :rtype: LayerOutput @@ -2197,6 +2180,15 @@ def spp_layer(input, The details please refer to `Kaiming He's paper `_. + The example usage is: + + .. code-block:: python + + spp = spp_layer(input=data, + pyramid_height=2, + num_channels=16, + pool_type=MaxPooling()) + :param name: layer name. :type name: basestring :param input: layer's input. @@ -2285,6 +2277,12 @@ def img_cmrnorm_layer(input, The details please refer to `Alex's paper `_. + The example usage is: + + .. code-block:: python + + norm = img_cmrnorm_layer(input=net, size=5) + :param name: layer name. :type name: None|basestring :param input: layer's input. @@ -2340,6 +2338,12 @@ def batch_norm_layer(input, The details of batch normalization please refer to this `paper `_. + The example usage is: + + .. code-block:: python + + norm = batch_norm_layer(input=net, act=ReluActivation()) + :param name: layer name. :type name: basestring :param input: batch normalization input. Better be linear activation. @@ -3903,13 +3907,13 @@ def conv_shift_layer(a, b, name=None, layer_attr=None): .. code-block:: python - conv_shift = conv_shift_layer(input=[layer1, layer2]) + conv_shift = conv_shift_layer(a=layer1, b=layer2) :param name: layer name :type name: basestring :param a: Input layer a. :type a: LayerOutput - :param b: input layer b + :param b: input layer b. :type b: LayerOutput :param layer_attr: layer's extra attribute. :type layer_attr: ExtraLayerAttribute @@ -4001,8 +4005,8 @@ def tensor_layer(a, @wrap_act_default() @layer_support() def selective_fc_layer(input, - select, size, + select=None, act=None, name=None, pass_generation=False, @@ -4029,6 +4033,7 @@ def selective_fc_layer(input, :type input: LayerOutput|list|tuple :param select: The select layer. The output of select layer should be a sparse binary matrix, and treat as the mask of selective fc. + If is None, acts exactly like fc_layer. :type select: LayerOutput :param size: The layer dimension. :type size: int @@ -4257,7 +4262,7 @@ def block_expand_layer(input, .. code-block:: python - block_expand = block_expand_layer(input, + block_expand = block_expand_layer(input=layer, num_channels=128, stride_x=1, stride_y=1, @@ -4461,7 +4466,7 @@ def warp_ctc_layer(input, - You can set 'blank' to any value ranged in [0, num_classes], which should be consistent as that used in your labels. - As a native 'softmax' activation is interated to the warp-ctc library, - 'linear' activation is expected instead in the 'input' layer. + 'linear' activation is expected instead in the 'input' layer. The simple usage: @@ -4594,6 +4599,13 @@ def crf_decoding_layer(input, this layer will also calculate error. output.value[i] is 1 for incorrect decoding or 0 for correct decoding. + The simple usage: + + .. code-block:: python + + crf_decoding = crf_decoding_layer(input=input, + size=label_dim) + :param input: The first input layer. :type input: LayerOutput :param size: size of this layer. diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 0cf7b8e9039f73..ab352e880e30fb 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -19,11 +19,12 @@ import event import data_type import attr +import pooling import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type', 'attr' + 'event', 'data_type', 'attr', 'pooling' ] diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 3920d4a08fc627..f4a85e9d038e45 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -76,12 +76,20 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. import data_type import activation import attr +import pooling __all__ = [ - 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', - 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', + 'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp', + 'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent', + 'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat', + 'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto', + 'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling', + 'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id', + 'pad', 'classification_cost', 'cross_entropy_cost', + 'cross_entropy_with_selfnorm_cost', 'regression_cost', 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', - 'sum_cost', 'huber_cost' + 'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce', + 'hsigmoid', 'eos' ] @@ -130,11 +138,8 @@ def to_proto_impl(self, **kwargs): raise NotImplementedError() -def __convert_to_v2__(method_name, name_prefix, parent_names): - if name_prefix is not None: - wrapper = wrap_name_default(name_prefix=name_prefix) - else: - wrapper = None +def __convert_to_v2__(method_name, parent_names): + wrapper = wrap_name_default(name_prefix=method_name) class V2LayerImpl(Layer): def __init__(self, name=None, **kwargs): @@ -192,44 +197,92 @@ def to_proto_impl(self, **kwargs): data = DataLayerV2 -fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) -max_id = __convert_to_v2__( - 'maxid_layer', name_prefix='maxid', parent_names=['input']) -classification_cost = __convert_to_v2__( - 'classification_cost', - name_prefix='classification_cost', - parent_names=['input', 'label', 'weight']) -regression_cost = __convert_to_v2__( - 'regression_cost', - name_prefix='regression_cost', - parent_names=['input', 'label', 'weight']) -cross_entropy_cost = __convert_to_v2__( - 'cross_entropy', - name_prefix='cross_entropy', - parent_names=['input', 'label']) -cross_entropy_with_selfnorm_cost = __convert_to_v2__( - 'cross_entropy_with_selfnorm', - name_prefix='cross_entropy_with_selfnorm', - parent_names=['input', 'label']) -multi_binary_label_cross_entropy_cost = __convert_to_v2__( - 'multi_binary_label_cross_entropy', - name_prefix='multi_binary_label_cross_entropy', - parent_names=['input', 'label']) -rank_cost = __convert_to_v2__( - 'rank_cost', - name_prefix='rank_cost', - parent_names=['left', 'right', 'label', 'weight']) -lambda_cost = __convert_to_v2__( - 'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score']) -sum_cost = __convert_to_v2__( - 'sum_cost', name_prefix='sum_cost', parent_names=['input']) -huber_cost = __convert_to_v2__( - 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) +AggregateLevel = conf_helps.layers.AggregateLevel +ExpandLevel = conf_helps.layers.ExpandLevel + +layer_list = [ + # [V2LayerImpl, V1_method_name, parent_names] + # fully connected layers + ['fc', 'fc_layer', ['input']], + # conv layers + ['conv_shift', 'conv_shift_layer', ['a', 'b']], + ['img_conv', 'img_conv_layer', ['input']], + # image pooling layers + ['img_pool', 'img_pool_layer', ['input']], + ['spp', 'spp_layer', ['input']], + ['maxout', 'maxout_layer', ['input']], + # norm layers + ['img_cmrnorm', 'img_cmrnorm_layer', ['input']], + ['batch_norm', 'batch_norm_layer', ['input']], + ['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']], + # recurrent layers + ['recurrent', 'recurrent_layer', ['input']], + ['lstmemory', 'lstmemory', ['input']], + ['grumemory', 'grumemory', ['input']], + # aggregate layers + ['pool', 'pooling_layer', ['input']], + ['last_seq', 'last_seq', ['input']], + ['first_seq', 'first_seq', ['input']], + ['concat', 'concat_layer', ['input']], + ['seq_concat', 'seq_concat_layer', ['a', 'b']], + # reshaping layers + ['block_expand', 'block_expand_layer', ['input']], + ['expand', 'expand_layer', ['input', 'expand_as']], + ['repeat', 'repeat_layer', ['input']], + ['rotate', 'rotate_layer', ['input']], + ['seq_reshape', 'seq_reshape_layer', ['input']], + # math layers + ['addto', 'addto_layer', ['input']], + ['linear_comb', 'linear_comb_layer', ['weights', 'vectors']], + ['interpolation', 'interpolation_layer', ['input', 'weight']], + ['bilinear_interp', 'bilinear_interp_layer', ['input']], + ['power', 'power_layer', ['input', 'weight']], + ['scaling', 'scaling_layer', ['input', 'weight']], + ['slope_intercept', 'slope_intercept_layer', ['input']], + ['tensor', 'tensor_layer', ['a', 'b']], + ['cos_sim', 'cos_sim', ['a', 'b']], + ['trans', 'trans_layer', ['input']], + # sampling layers + ['max_id', 'maxid_layer', ['input']], + ['sampling_id', 'sampling_id_layer', ['input']], + # slicing and joining layers + ['pad', 'pad_layer', ['input']], + # cost layers + [ + 'classification_cost', 'classification_cost', + ['input', 'label', 'weight'] + ], + ['regression_cost', 'regression_cost', ['input', 'label', 'weight']], + ['cross_entropy_cost', 'cross_entropy', ['input', 'label']], + [ + 'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm', + ['input', 'label'] + ], + [ + 'multi_binary_label_cross_entropy_cost', + 'multi_binary_label_cross_entropy', ['input', 'label'] + ], + ['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']], + ['lambda_cost', 'lambda_cost', ['input', 'score']], + ['sum_cost', 'sum_cost', ['input']], + ['huber_cost', 'huber_cost', ['input', 'label']], + ['crf', 'crf_layer', ['input', 'label']], + ['crf_decoding', 'crf_decoding_layer', ['input']], + ['ctc', 'ctc_layer', ['input', 'label']], + ['warp_ctc', 'warp_ctc_layer', ['input', 'label']], + ['nce', 'nce_layer', ['input', 'label']], + ['hsigmoid', 'hsigmoid', ['input', 'label']], + # check layers + ['eos', 'eos_layer', ['input']] +] +for l in layer_list: + globals()[l[0]] = __convert_to_v2__(l[1], l[2]) if __name__ == '__main__': - pixel = data(name='pixel', type=data_type.dense_vector(784)) + pixel = data(name='pixel', type=data_type.dense_vector(128)) label = data(name='label', type=data_type.integer_value(10)) weight = data(name='weight', type=data_type.dense_vector(10)) + word = data(name='word', type=data_type.integer_value(12)) score = data(name='score', type=data_type.dense_vector(1)) hidden = fc(input=pixel, @@ -237,7 +290,90 @@ def to_proto_impl(self, **kwargs): act=activation.Sigmoid(), param_attr=attr.Param(name='hidden')) inference = fc(input=hidden, size=10, act=activation.Softmax()) + print parse_network(inference) + + # test conv layers + conv1 = conv_shift(a=pixel, b=score) + conv2 = img_conv( + input=pixel, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + act=activation.Linear()) + print parse_network(conv1, conv2) + + # test image pooling layers + maxpool = img_pool( + input=conv2, + pool_size=2, + num_channels=16, + padding=1, + pool_type=pooling.Max()) + spp = spp(input=conv2, + pyramid_height=2, + num_channels=16, + pool_type=pooling.Max()) + maxout = maxout(input=conv2, num_channels=16, groups=4) + print parse_network(maxpool, spp, maxout) + + # test norm layers + norm1 = img_cmrnorm(input=maxpool, size=5) + norm2 = batch_norm(input=maxpool) + norm3 = sum_to_one_norm(input=maxpool) + print parse_network(norm1, norm2, norm3) + + # test recurrent layers + recurrent = recurrent(input=word) + lstm = lstmemory(input=word) + gru = grumemory(input=word) + print parse_network(recurrent, lstm, gru) + + # test aggregate layers + pool = pool( + input=pixel, + pooling_type=pooling.Avg(), + agg_level=AggregateLevel.EACH_SEQUENCE) + last_seq = last_seq(input=pixel) + first_seq = first_seq(input=pixel) + concat = concat(input=[last_seq, first_seq]) + seq_concat = seq_concat(a=last_seq, b=first_seq) + print parse_network(pool, last_seq, first_seq, concat, seq_concat) + + # test reshaping layers + block_expand = block_expand( + input=maxout, num_channels=4, stride_x=1, block_x=1) + expand = expand( + input=last_seq, expand_as=pixel, expand_level=ExpandLevel.FROM_TIMESTEP) + repeat = repeat(input=last_seq, num_repeats=4) + reshape = seq_reshape(input=last_seq, reshape_size=4) + rotate = rotate(input=pixel, height=16, width=49) + print parse_network(block_expand, expand, repeat, reshape, rotate) + + # test math layers + addto = addto(input=[last_seq, first_seq]) + linear_comb = linear_comb(weights=weight, vectors=hidden, size=10) + interpolation = interpolation(input=[hidden, hidden], weight=score) + bilinear = bilinear_interp(input=conv2, out_size_x=4, out_size_y=4) + power = power(input=conv1, weight=score) + scaling = scaling(input=conv1, weight=score) + slope = slope_intercept(input=conv1) + tensor = tensor(a=last_seq, b=first_seq, size=1000) + cos_sim = cos_sim(a=last_seq, b=first_seq) + trans = trans(input=tensor) + print parse_network(addto, linear_comb, interpolation, bilinear, power, + scaling, slope, tensor, cos_sim, trans) + + # test sampling layers maxid = max_id(input=inference) + sampling_id = sampling_id(input=inference) + print parse_network(maxid, sampling_id) + + # test slicing and joining layers + pad = pad(input=maxpool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1]) + print parse_network(pad) + + # test cost layers cost1 = classification_cost(input=inference, label=label) cost2 = classification_cost(input=inference, label=label, weight=weight) cost3 = cross_entropy_cost(input=inference, label=label) @@ -249,9 +385,18 @@ def to_proto_impl(self, **kwargs): cost9 = lambda_cost(input=inference, score=score) cost10 = sum_cost(input=inference) cost11 = huber_cost(input=score, label=label) - - print parse_network(cost1, cost2) print parse_network(cost3, cost4) print parse_network(cost5, cost6) print parse_network(cost7, cost8, cost9, cost10, cost11) - print parse_network(inference, maxid) + + crf = crf(input=inference, label=label) + crf_decoding = crf_decoding(input=inference, size=3) + ctc = ctc(input=inference, label=label) + warp_ctc = warp_ctc(input=pixel, label=label) + nce = nce(input=inference, label=label, num_classes=3) + hsigmoid = hsigmoid(input=inference, label=label, num_classes=3) + print parse_network(crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid) + + # test check layers + eos = eos(input=maxid, eos_id=5) + print parse_network(eos) diff --git a/python/paddle/v2/pooling.py b/python/paddle/v2/pooling.py new file mode 100644 index 00000000000000..9076a159bb4f2c --- /dev/null +++ b/python/paddle/v2/pooling.py @@ -0,0 +1,24 @@ +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddle.trainer_config_helpers.poolings import * + +__all__ = ["Max", "CudnnMax", "Avg", "CudnnAvg", "Sum", "SquareRootN"] + +Max = MaxPooling +CudnnMax = CudnnMaxPooling +Avg = AvgPooling +CudnnAvg = CudnnAvgPooling +Sum = SumPooling +SquareRootN = SquareRootNPooling From 623d24ad5c201996c66fc2ec48c43fd6a7f75973 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 24 Feb 2017 19:19:32 +0800 Subject: [PATCH 301/370] convert mixed layer, projection and operator --- .../paddle/trainer_config_helpers/layers.py | 2 + python/paddle/v2/data_type.py | 4 +- python/paddle/v2/layer.py | 248 +++++++++++++++++- 3 files changed, 239 insertions(+), 15 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 00aef80691fba0..95f0915972cbac 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -112,6 +112,7 @@ 'priorbox_layer', 'spp_layer', 'pad_layer', + 'layer_support', ] @@ -708,6 +709,7 @@ def __exit__(self, *args, **kwargs): # update the size which might be computed inside MixedLayer # according to the operator's output size self.size = ml.config.size + self.finalized = True @wrap_name_default("mixed") diff --git a/python/paddle/v2/data_type.py b/python/paddle/v2/data_type.py index 5b01ba4cd4866c..dd3ebfcb4267e1 100644 --- a/python/paddle/v2/data_type.py +++ b/python/paddle/v2/data_type.py @@ -14,9 +14,9 @@ from paddle.trainer.PyDataProvider2 import \ InputType, dense_vector, sparse_binary_vector,\ - sparse_vector, integer_value + sparse_vector, integer_value, integer_value_sequence __all__ = [ 'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector', - 'integer_value' + 'integer_value', 'integer_value_sequence' ] diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 3920d4a08fc627..0e9e2a249ddb3a 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -72,16 +72,38 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_name_default +from paddle.trainer_config_helpers.default_decorators import wrap_act_default +from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default +from paddle.trainer_config_helpers.layers import layer_support import data_type import activation import attr +#import pudb;pudb.set_trace() + __all__ = [ - 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', - 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', - 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', - 'sum_cost', 'huber_cost' + 'parse_network', + 'data', + 'fc', + 'max_id', + 'classification_cost', + 'cross_entropy_cost', + 'cross_entropy_with_selfnorm_cost', + 'regression_cost', + 'multi_binary_label_cross_entropy_cost', + 'rank_cost', + 'lambda_cost', + 'sum_cost', + 'huber_cost' + 'full_matrix_projection', + 'trans_full_matrix_projection', + 'table_projection', + 'identity_projection', + 'scaling_projection', + 'dotmul_projection', + 'context_projection', + 'conv_projection', ] @@ -101,9 +123,8 @@ def __real_func__(): class Layer(object): - def __init__(self, name, parent_layers): + def __init__(self, name=None, parent_layers=None): assert isinstance(parent_layers, dict) - assert isinstance(name, basestring) self.name = name self.__parent_layers__ = parent_layers @@ -122,6 +143,9 @@ def to_proto(self, context): self.__parent_layers__[layer_name]) kwargs[layer_name] = v1_layer + if self.name is None: + return self.to_proto_impl(**kwargs) + if self.name not in context: context[self.name] = self.to_proto_impl(**kwargs) return context[self.name] @@ -130,7 +154,7 @@ def to_proto_impl(self, **kwargs): raise NotImplementedError() -def __convert_to_v2__(method_name, name_prefix, parent_names): +def __convert_to_v2__(method_name, name_prefix=None, parent_names=None): if name_prefix is not None: wrapper = wrap_name_default(name_prefix=name_prefix) else: @@ -160,7 +184,7 @@ def to_proto_impl(self, **kwargs): args[each] = kwargs[each] for each in self.__other_kwargs__: args[each] = self.__other_kwargs__[each] - return getattr(conf_helps, method_name)(name=self.name, **args) + return getattr(conf_helps, method_name)(**args) return V2LayerImpl @@ -191,6 +215,81 @@ def to_proto_impl(self, **kwargs): return getattr(conf_helps, self.__method_name__)(name=self.name, **args) +class MixedLayerV2(Layer): + """ + This class is use to support `with` grammar. If not, the following code + could convert mixed_layer simply. + + mixed = __convert_to_v2__( + 'mixed_layer', name_prefix='mixed', parent_names=['input']) + """ + + class AddToSealedMixedLayerExceptionV2(Exception): + def __init__(self): + Exception.__init__(self) + + def __init__(self, + size=0, + input=None, + name=None, + act=None, + bias_attr=None, + layer_attr=None): + self.__method_name__ = 'mixed_layer' + self.finalized = False + + self.__parent_layers__ = dict() + other_kwargs = dict() + self.input_name = 'input' + self.__parent_layers__[self.input_name] = [] + if input is not None: + self.__parent_layers__[self.input_name] = input + + self.name = name + other_kwargs['size'] = size + other_kwargs['act'] = act + other_kwargs['bias_attr'] = bias_attr + other_kwargs['layer_attr'] = layer_attr + + Layer.__init__(self, name, self.__parent_layers__) + self.__other_kwargs__ = other_kwargs + + def __iadd__(self, other): + if not self.finalized: + self.__parent_layers__[self.input_name].append(other) + return self + else: + raise MixedLayerTypeV2.AddToSealedMixedLayerExceptionV2() + + def __enter__(self): + assert len(self.__parent_layers__[self.input_name]) == 0 + return self + + def __exit__(self, *args, **kwargs): + self.finalized = True + + def to_proto_impl(self, **kwargs): + args = dict() + for each in kwargs: + args[each] = kwargs[each] + for each in self.__other_kwargs__: + args[each] = self.__other_kwargs__[each] + return getattr(conf_helps, self.__method_name__)(name=self.name, **args) + + +@wrap_name_default("mixed") +@wrap_act_default(act=conf_helps.LinearActivation()) +@wrap_bias_attr_default(has_bias=False) +@layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT) +def mixed(size=0, + name=None, + input=None, + act=None, + bias_attr=False, + layer_attr=None): + return MixedLayerV2(size, input, name, act, bias_attr, layer_attr) + + data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( @@ -226,12 +325,124 @@ def to_proto_impl(self, **kwargs): huber_cost = __convert_to_v2__( 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) -if __name__ == '__main__': - pixel = data(name='pixel', type=data_type.dense_vector(784)) - label = data(name='label', type=data_type.integer_value(10)) - weight = data(name='weight', type=data_type.dense_vector(10)) - score = data(name='score', type=data_type.dense_vector(1)) +# convert projection +projection_list = [ + # [V1_method_name], all the parent_names is `input` + 'full_matrix_projection', + 'trans_full_matrix_projection', + 'table_projection', + 'scaling_projection', + 'dotmul_projection', + 'context_projection', + 'conv_projection', + 'identity_projection', +] +for prj in projection_list: + globals()[prj] = __convert_to_v2__(prj, parent_names=['input']) + +# convert operator +operator_list = [ + # [V1_method_name, parent_names], + ['dotmul_operator', ['a', 'b']], + ['conv_operator', ['img', 'filter']] +] +for op in operator_list: + globals()[op[0]] = __convert_to_v2__(op[0], parent_names=op[1]) + +def test_projection(): + """ + TODO: move to tests file + """ + input = data(name='data', type=data_type.dense_vector(784)) + word = data(name='word', type=data_type.integer_value_sequence(10000)) + fc0 = fc(input=input, size=100, act=conf_helps.SigmoidActivation()) + fc1 = fc(input=input, size=200, act=conf_helps.SigmoidActivation()) + mixed0 = mixed( + size=256, + input=[ + full_matrix_projection(input=fc0), full_matrix_projection(input=fc1) + ]) + with mixed(size=200) as mixed1: + mixed1 += full_matrix_projection(input=fc0) + mixed1 += identity_projection(input=fc1) + + table = table_projection(input=word) + emb0 = mixed(size=512, input=table) + with mixed(size=512) as emb1: + emb1 += table + + scale = scaling_projection(input=fc0) + scale0 = mixed(size=100, input=scale) + with mixed(size=100) as scale1: + scale1 += scale + + dotmul = dotmul_projection(input=fc0) + dotmul0 = mixed(size=100, input=dotmul) + with mixed(size=100) as dotmul1: + dotmul1 += dotmul + + context = context_projection(input=fc0, context_len=5) + context0 = mixed(size=100, input=context) + with mixed(size=100) as context1: + context1 += context + + conv = conv_projection( + input=input, + filter_size=1, + num_channels=1, + num_filters=128, + stride=1, + padding=0) + conv0 = mixed(input=conv, bias_attr=True) + with mixed(bias_attr=True) as conv1: + conv1 += conv + + print parse_network(mixed0) + print parse_network(mixed1) + print parse_network(emb0) + print parse_network(emb1) + print parse_network(scale0) + print parse_network(scale1) + print parse_network(dotmul0) + print parse_network(dotmul1) + print parse_network(conv0) + print parse_network(conv1) + + +def test_operator(): + """ + TODO: move to tests file + """ + ipt0 = data(name='data', type=data_type.dense_vector(784)) + ipt1 = data(name='word', type=data_type.dense_vector(128)) + fc0 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) + fc1 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) + + dotmul_op = dotmul_operator(a=fc0, b=fc1) + dotmul0 = mixed(input=dotmul_op) + with mixed() as dotmul1: + dotmul1 += dotmul_op + + conv = conv_operator( + img=ipt0, + filter=ipt1, + filter_size=1, + num_channels=1, + num_filters=128, + stride=1, + padding=0) + conv0 = mixed(input=conv) + with mixed() as conv1: + conv1 += conv + + print parse_network(dotmul0) + print parse_network(dotmul1) + print parse_network(conv0) + print parse_network(conv1) + + +def test_cost(pixel, label, weight, score): hidden = fc(input=pixel, size=100, act=activation.Sigmoid(), @@ -255,3 +466,14 @@ def to_proto_impl(self, **kwargs): print parse_network(cost5, cost6) print parse_network(cost7, cost8, cost9, cost10, cost11) print parse_network(inference, maxid) + + +if __name__ == '__main__': + pixel = data(name='pixel', type=data_type.dense_vector(784)) + label = data(name='label', type=data_type.integer_value(10)) + weight = data(name='weight', type=data_type.dense_vector(10)) + score = data(name='score', type=data_type.dense_vector(1)) + + test_cost(pixel, label, weight, score) + test_projection() + test_operator() From 9b73a602c4aa553888b450373eb31a5a9703c227 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 24 Feb 2017 19:23:24 +0800 Subject: [PATCH 302/370] remove comments --- python/paddle/v2/layer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 0e9e2a249ddb3a..618a220240c15d 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -71,6 +71,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. import paddle.trainer_config_helpers as conf_helps from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ + from paddle.trainer_config_helpers.default_decorators import wrap_name_default from paddle.trainer_config_helpers.default_decorators import wrap_act_default from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default @@ -80,8 +81,6 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. import activation import attr -#import pudb;pudb.set_trace() - __all__ = [ 'parse_network', 'data', From 4c85f955d78674a3803fab56c5dfd095bc6fde2b Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 24 Feb 2017 20:47:01 +0800 Subject: [PATCH 303/370] move test module --- python/paddle/v2/layer.py | 129 --------------------------- python/paddle/v2/tests/test_layer.py | 92 ++++++++++++++++++- 2 files changed, 90 insertions(+), 131 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 618a220240c15d..a3fac6ca67d280 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -347,132 +347,3 @@ def mixed(size=0, ] for op in operator_list: globals()[op[0]] = __convert_to_v2__(op[0], parent_names=op[1]) - - -def test_projection(): - """ - TODO: move to tests file - """ - input = data(name='data', type=data_type.dense_vector(784)) - word = data(name='word', type=data_type.integer_value_sequence(10000)) - fc0 = fc(input=input, size=100, act=conf_helps.SigmoidActivation()) - fc1 = fc(input=input, size=200, act=conf_helps.SigmoidActivation()) - mixed0 = mixed( - size=256, - input=[ - full_matrix_projection(input=fc0), full_matrix_projection(input=fc1) - ]) - with mixed(size=200) as mixed1: - mixed1 += full_matrix_projection(input=fc0) - mixed1 += identity_projection(input=fc1) - - table = table_projection(input=word) - emb0 = mixed(size=512, input=table) - with mixed(size=512) as emb1: - emb1 += table - - scale = scaling_projection(input=fc0) - scale0 = mixed(size=100, input=scale) - with mixed(size=100) as scale1: - scale1 += scale - - dotmul = dotmul_projection(input=fc0) - dotmul0 = mixed(size=100, input=dotmul) - with mixed(size=100) as dotmul1: - dotmul1 += dotmul - - context = context_projection(input=fc0, context_len=5) - context0 = mixed(size=100, input=context) - with mixed(size=100) as context1: - context1 += context - - conv = conv_projection( - input=input, - filter_size=1, - num_channels=1, - num_filters=128, - stride=1, - padding=0) - conv0 = mixed(input=conv, bias_attr=True) - with mixed(bias_attr=True) as conv1: - conv1 += conv - - print parse_network(mixed0) - print parse_network(mixed1) - print parse_network(emb0) - print parse_network(emb1) - print parse_network(scale0) - print parse_network(scale1) - print parse_network(dotmul0) - print parse_network(dotmul1) - print parse_network(conv0) - print parse_network(conv1) - - -def test_operator(): - """ - TODO: move to tests file - """ - ipt0 = data(name='data', type=data_type.dense_vector(784)) - ipt1 = data(name='word', type=data_type.dense_vector(128)) - fc0 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) - fc1 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) - - dotmul_op = dotmul_operator(a=fc0, b=fc1) - dotmul0 = mixed(input=dotmul_op) - with mixed() as dotmul1: - dotmul1 += dotmul_op - - conv = conv_operator( - img=ipt0, - filter=ipt1, - filter_size=1, - num_channels=1, - num_filters=128, - stride=1, - padding=0) - conv0 = mixed(input=conv) - with mixed() as conv1: - conv1 += conv - - print parse_network(dotmul0) - print parse_network(dotmul1) - print parse_network(conv0) - print parse_network(conv1) - - -def test_cost(pixel, label, weight, score): - hidden = fc(input=pixel, - size=100, - act=activation.Sigmoid(), - param_attr=attr.Param(name='hidden')) - inference = fc(input=hidden, size=10, act=activation.Softmax()) - maxid = max_id(input=inference) - cost1 = classification_cost(input=inference, label=label) - cost2 = classification_cost(input=inference, label=label, weight=weight) - cost3 = cross_entropy_cost(input=inference, label=label) - cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label) - cost5 = regression_cost(input=inference, label=label) - cost6 = regression_cost(input=inference, label=label, weight=weight) - cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label) - cost8 = rank_cost(left=score, right=score, label=score) - cost9 = lambda_cost(input=inference, score=score) - cost10 = sum_cost(input=inference) - cost11 = huber_cost(input=score, label=label) - - print parse_network(cost1, cost2) - print parse_network(cost3, cost4) - print parse_network(cost5, cost6) - print parse_network(cost7, cost8, cost9, cost10, cost11) - print parse_network(inference, maxid) - - -if __name__ == '__main__': - pixel = data(name='pixel', type=data_type.dense_vector(784)) - label = data(name='label', type=data_type.integer_value(10)) - weight = data(name='weight', type=data_type.dense_vector(10)) - score = data(name='score', type=data_type.dense_vector(1)) - - test_cost(pixel, label, weight, score) - test_projection() - test_operator() diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index b600e8cf765122..521bc8b40c5b6b 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -19,8 +19,6 @@ import paddle.v2.attr as attr import paddle.v2.data_type as data_type import paddle.v2.layer as layer -from paddle.trainer_config_helpers.config_parser_utils import \ - parse_network_config as parse_network pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) label = layer.data(name='label', type=data_type.integer_value(10)) @@ -58,6 +56,96 @@ def test_cost_layer(self): #print layer.parse_network(cost5, cost6) #print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + def test_projection(self): + input = layer.data(name='data', type=data_type.dense_vector(784)) + word = layer.data( + name='word', type=data_type.integer_value_sequence(10000)) + fc0 = layer.fc(input=input, + size=100, + act=conf_helps.SigmoidActivation()) + fc1 = layer.fc(input=input, + size=200, + act=conf_helps.SigmoidActivation()) + mixed0 = layer.mixed( + size=256, + input=[ + layer.full_matrix_projection(input=fc0), + layer.full_matrix_projection(input=fc1) + ]) + with layer.mixed(size=200) as mixed1: + mixed1 += layer.full_matrix_projection(input=fc0) + mixed1 += layer.identity_projection(input=fc1) + + table = layer.table_projection(input=word) + emb0 = layer.mixed(size=512, input=table) + with layer.mixed(size=512) as emb1: + emb1 += table + + scale = layer.scaling_projection(input=fc0) + scale0 = layer.mixed(size=100, input=scale) + with layer.mixed(size=100) as scale1: + scale1 += scale + + dotmul = layer.dotmul_projection(input=fc0) + dotmul0 = layer.mixed(size=100, input=dotmul) + with layer.mixed(size=100) as dotmul1: + dotmul1 += dotmul + + context = layer.context_projection(input=fc0, context_len=5) + context0 = layer.mixed(size=100, input=context) + with layer.mixed(size=100) as context1: + context1 += context + + conv = layer.conv_projection( + input=input, + filter_size=1, + num_channels=1, + num_filters=128, + stride=1, + padding=0) + conv0 = layer.mixed(input=conv, bias_attr=True) + with layer.mixed(bias_attr=True) as conv1: + conv1 += conv + + print layer.parse_network(mixed0) + print layer.parse_network(mixed1) + print layer.parse_network(emb0) + print layer.parse_network(emb1) + print layer.parse_network(scale0) + print layer.parse_network(scale1) + print layer.parse_network(dotmul0) + print layer.parse_network(dotmul1) + print layer.parse_network(conv0) + print layer.parse_network(conv1) + + def test_operator(self): + ipt0 = layer.data(name='data', type=data_type.dense_vector(784)) + ipt1 = layer.data(name='word', type=data_type.dense_vector(128)) + fc0 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) + fc1 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) + + dotmul_op = layer.dotmul_operator(a=fc0, b=fc1) + dotmul0 = layer.mixed(input=dotmul_op) + with layer.mixed() as dotmul1: + dotmul1 += dotmul_op + + conv = layer.conv_operator( + img=ipt0, + filter=ipt1, + filter_size=1, + num_channels=1, + num_filters=128, + stride=1, + padding=0) + conv0 = layer.mixed(input=conv) + with layer.mixed() as conv1: + conv1 += conv + + print layer.parse_network(dotmul0) + print layer.parse_network(dotmul1) + print layer.parse_network(conv0) + print layer.parse_network(conv1) + if __name__ == '__main__': unittest.main() From bb7db754208a7484ced25eb879bd77e7f6fae6c9 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sat, 25 Feb 2017 10:07:15 +0800 Subject: [PATCH 304/370] add testing for duplicate item --- python/paddle/v2/tests/test_data_feeder.py | 23 +++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py index 4d5df6e8931640..5f67da6a5b32d7 100644 --- a/python/paddle/v2/tests/test_data_feeder.py +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -176,7 +176,7 @@ def test_multiple_features(self): self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1]) self.assertEqual(output_index[i], data[i][0]) - # reader returns 3 featreus, but only use 2 features + # reader returns 3 features, but only use 2 features data_types = [('fea0', data_type.dense_vector(100)), ('fea2', data_type.integer_value(10))] feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0}) @@ -187,6 +187,27 @@ def test_multiple_features(self): self.assertEqual(output_dense[i].all(), data[i][2].all()) self.assertEqual(output_index[i], data[i][0]) + # reader returns 3 featreus, one is duplicate data + data_types = [('fea0', data_type.dense_vector(100)), + ('fea1', data_type.sparse_binary_vector(20000)), + ('fea2', data_type.integer_value(10)), + ('fea3', data_type.dense_vector(100))] + feeder = DataFeeder(data_types, + {'fea0': 2, + 'fea1': 1, + 'fea2': 0, + 'fea3': 2}) + arg = feeder(data) + fea0 = arg.getSlotValue(0).copyToNumpyMat() + fea1 = arg.getSlotValue(1) + fea2 = arg.getSlotIds(2).copyToNumpyArray() + fea3 = arg.getSlotValue(3).copyToNumpyMat() + for i in xrange(batch_size): + self.assertEqual(fea0[i].all(), data[i][2].all()) + self.assertEqual(fea1.getSparseRowCols(i), data[i][1]) + self.assertEqual(fea2[i], data[i][0]) + self.assertEqual(fea3[i].all(), data[i][2].all()) + def test_multiple_features_tuple(self): batch_size = 2 data = [] From a6028d79dcaba69f6f95c7ebf9c12c33ad42b82e Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 10:39:17 +0800 Subject: [PATCH 305/370] Clean mnist reader --- python/paddle/v2/data_set/mnist.py | 35 +++++++++--------------------- 1 file changed, 10 insertions(+), 25 deletions(-) diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py index 6f35acf683686e..4b392af400ad31 100644 --- a/python/paddle/v2/data_set/mnist.py +++ b/python/paddle/v2/data_set/mnist.py @@ -15,39 +15,24 @@ def reader(): return reader -class MNIST(object): - """ - mnist dataset reader. The `train_reader` and `test_reader` method returns - a iterator of each sample. Each sample is combined by 784-dim float and a - one-dim label - """ +TEST_SIZE = 10000 - def __init__(self, random_state=0, test_size=10000, **options): - data = sklearn.datasets.mldata.fetch_mldata( - "MNIST original", data_home=DATA_HOME) - self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( - data.data, - data.target, - test_size=test_size, - random_state=random_state, - **options) +data = sklearn.datasets.mldata.fetch_mldata( + "MNIST original", data_home=DATA_HOME) +X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + data.data, data.target, test_size=TEST_SIZE, random_state=0) - def train_creator(self): - return __mnist_reader_creator__(self.X_train, self.y_train) - def test_creator(self): - return __mnist_reader_creator__(self.X_test, self.y_test) +def train_creator(): + return __mnist_reader_creator__(X_train, y_train) -__default_instance__ = MNIST() -train_creator = __default_instance__.train_creator -test_creator = __default_instance__.test_creator +def test_creator(): + return __mnist_reader_creator__(X_test, y_test) def unittest(): - size = 12045 - mnist = MNIST(test_size=size) - assert len(list(mnist.test_creator()())) == size + assert len(list(test_creator()())) == TEST_SIZE if __name__ == '__main__': From 173a81b56b60567eb4e30f66331ebab4e004ead7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 23 Feb 2017 16:01:20 +0800 Subject: [PATCH 306/370] Complete Event, Add Metric to Event. --- demo/mnist/api_train_v2.py | 14 ++++------ python/paddle/v2/event.py | 55 +++++++++++++++++++++++++++++++++---- python/paddle/v2/trainer.py | 22 +++++++++++++-- 3 files changed, 75 insertions(+), 16 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6fc01ce58be57c..6b0cfa0b05c966 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -27,19 +27,14 @@ def main(): cost = paddle.layer.classification_cost(input=inference, label=label) parameters = paddle.parameters.create(cost) - for param_name in parameters.keys(): - array = parameters.get(param_name) - array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) - parameters.set(parameter_name=param_name, value=array) adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01) def event_handler(event): if isinstance(event, paddle.event.EndIteration): - para = parameters.get('___fc_2__.w0') - print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( - event.pass_id, event.batch_id, event.cost, para.mean()) - + if event.batch_id % 100 == 0: + print "Pass %d, Batch %d, Cost %f, %s" % ( + event.pass_id, event.batch_id, event.cost, event.metrics) else: pass @@ -49,7 +44,8 @@ def event_handler(event): topology=cost, parameters=parameters, event_handler=event_handler, - batch_size=32, # batch size should be refactor in Data reader + num_passes=100, + batch_size=200, # batch size should be refactor in Data reader data_types={ # data_types will be removed, It should be in # network topology 'pixel': images.type, diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index a16cfa91f062a6..835e28e6218df2 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -3,8 +3,6 @@ There are: -* BeginTraining -* EndTraining * BeginIteration * EndIteration * BeginPass @@ -12,15 +10,62 @@ TODO(yuyang18): Complete it! """ -__all__ = ['EndIteration'] +import py_paddle.swig_paddle as api +__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass'] -class EndIteration(object): +class WithMetric(object): + def __init__(self, evaluator): + if not isinstance(evaluator, api.Evaluator): + raise TypeError("Evaluator should be api.Evaluator type") + self.__evaluator__ = evaluator + + @property + def metrics(self): + names = self.__evaluator__.getNames() + retv = dict() + for each_name in names: + val = self.__evaluator__.getValue(each_name) + retv[each_name] = val + return retv + + +class BeginPass(object): + """ + Event On One Pass Training Start. + """ + + def __init__(self, pass_id): + self.pass_id = pass_id + + +class EndPass(WithMetric): + """ + Event On One Pass Training Complete. + """ + + def __init__(self, pass_id, evaluator): + self.pass_id = pass_id + WithMetric.__init__(self, evaluator) + + +class BeginIteration(object): + """ + Event On One Batch Training Start. + """ + + def __init__(self, pass_id, batch_id): + self.pass_id = pass_id + self.batch_id = batch_id + + +class EndIteration(WithMetric): """ Event On One Batch Training Complete. """ - def __init__(self, pass_id, batch_id, cost): + def __init__(self, pass_id, batch_id, cost, evaluator): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost + WithMetric.__init__(self, evaluator) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 4365bd41e7073b..0acfcee2cee9ee 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -96,11 +96,15 @@ def train(self, topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) assert isinstance(gm, api.GradientMachine) parameters.append_gradient_machine(gm) - + gm.randParameters() updater = self.__optimizer__.create_local_updater() updater.init(gm) gm.start() + batch_evaluator = gm.makeEvaluator() + assert isinstance(batch_evaluator, api.Evaluator) + pass_evaluator = gm.makeEvaluator() + assert isinstance(pass_evaluator, api.Evaluator) out_args = api.Arguments.createArguments(0) data_types_lists = [] @@ -112,12 +116,20 @@ def train(self, converter = DataProviderConverter(input_types=data_types_lists) for pass_id in xrange(num_passes): + event_handler(v2_event.BeginPass(pass_id)) + pass_evaluator.start() updater.startPass() for batch_id, data_batch in enumerate( __data_reader_to_batch__(train_data_reader, batch_size, topology)): + batch_evaluator.start() + event_handler( + v2_event.BeginIteration( + pass_id=pass_id, batch_id=batch_id)) pass_type = updater.startBatch(len(data_batch)) gm.forwardBackward(converter(data_batch), out_args, pass_type) + gm.eval(pass_evaluator) + gm.eval(batch_evaluator) for each_param in gm.getParameters(): updater.update(each_param) # Get cost. We use numpy to calculate total cost for this batch. @@ -125,11 +137,17 @@ def train(self, cost_vec = cost_vec.copyToNumpyMat() cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) + batch_evaluator.finish() event_handler( v2_event.EndIteration( - pass_id=pass_id, batch_id=batch_id, cost=cost)) + pass_id=pass_id, + batch_id=batch_id, + cost=cost, + evaluator=batch_evaluator)) updater.finishPass() + pass_evaluator.finish() + event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator)) gm.finish() From 72c1327832b1b75057a19d98387546bb2f765ff4 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 27 Feb 2017 13:28:36 +0800 Subject: [PATCH 307/370] follow comments --- python/paddle/v2/data_feeder.py | 28 ++++++++++------------------ 1 file changed, 10 insertions(+), 18 deletions(-) diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py index 74ee112f46efd7..632f1760ab5ff2 100644 --- a/python/paddle/v2/data_feeder.py +++ b/python/paddle/v2/data_feeder.py @@ -62,8 +62,8 @@ def __init__(self, data_types, reader_dict): self.reader_dict = reader_dict for each in data_types: self.input_names.append(each[0]) - self.input_types.append(each[1]) assert isinstance(each[1], data_type.InputType) + self.input_types.append(each[1]) DataProviderConverter.__init__(self, self.input_types) def convert(self, dat, argument=None): @@ -88,24 +88,16 @@ def convert(self, dat, argument=None): :type argument: swig_paddle.Arguments """ - if argument is None: - argument = swig_paddle.Arguments.createArguments(0) - assert isinstance(argument, swig_paddle.Arguments) - argument.resize(len(self.input_types)) - - scanners = [ - DataProviderConverter.create_scanner(i, each_type) - for i, each_type in enumerate(self.input_types) - ] - - for each_sample in dat: - for name, scanner in zip(self.input_names, scanners): - scanner.scan(each_sample[self.reader_dict[name]]) - - for scanner in scanners: - scanner.finish_scan(argument) + def reorder_data(data): + retv = [] + for each in data: + reorder = [] + for name in self.input_names: + reorder.append(each[self.reader_dict[name]]) + retv.append(reorder) + return retv - return argument + return DataProviderConverter.convert(self, reorder_data(dat), argument) def __call__(self, dat, argument=None): return self.convert(dat, argument) From aa92f0b673906edd3b585d5db645c0a3e075cc06 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 27 Feb 2017 10:32:30 +0800 Subject: [PATCH 308/370] follow comments --- python/paddle/v2/layer.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index a3fac6ca67d280..b9400ab26bdee2 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -105,6 +105,13 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. 'conv_projection', ] +__projection_names__ = filter(lambda x: x.endswith('_projection'), + dir(conf_helps)) +__all__ += __projection_names__ + +__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps)) +__all__ += __operator_names__ + def parse_network(*outputs): """ @@ -224,8 +231,7 @@ class MixedLayerV2(Layer): """ class AddToSealedMixedLayerExceptionV2(Exception): - def __init__(self): - Exception.__init__(self) + pass def __init__(self, size=0, @@ -277,7 +283,7 @@ def to_proto_impl(self, **kwargs): @wrap_name_default("mixed") -@wrap_act_default(act=conf_helps.LinearActivation()) +@wrap_act_default(act=activation.Linear()) @wrap_bias_attr_default(has_bias=False) @layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT) def mixed(size=0, From 5d7e7bc042e3118e9cf4a1899bc08dd44a1cf34c Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 27 Feb 2017 13:37:53 +0800 Subject: [PATCH 309/370] add test_layer for v2 --- python/paddle/v2/layer.py | 126 --------------------------- python/paddle/v2/tests/test_layer.py | 122 ++++++++++++++++++++++++-- 2 files changed, 115 insertions(+), 133 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index f4a85e9d038e45..f0e4f972feb260 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -74,9 +74,6 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. from paddle.trainer_config_helpers.default_decorators import wrap_name_default import data_type -import activation -import attr -import pooling __all__ = [ 'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp', @@ -277,126 +274,3 @@ def to_proto_impl(self, **kwargs): ] for l in layer_list: globals()[l[0]] = __convert_to_v2__(l[1], l[2]) - -if __name__ == '__main__': - pixel = data(name='pixel', type=data_type.dense_vector(128)) - label = data(name='label', type=data_type.integer_value(10)) - weight = data(name='weight', type=data_type.dense_vector(10)) - word = data(name='word', type=data_type.integer_value(12)) - score = data(name='score', type=data_type.dense_vector(1)) - - hidden = fc(input=pixel, - size=100, - act=activation.Sigmoid(), - param_attr=attr.Param(name='hidden')) - inference = fc(input=hidden, size=10, act=activation.Softmax()) - print parse_network(inference) - - # test conv layers - conv1 = conv_shift(a=pixel, b=score) - conv2 = img_conv( - input=pixel, - filter_size=1, - filter_size_y=1, - num_channels=8, - num_filters=16, - act=activation.Linear()) - print parse_network(conv1, conv2) - - # test image pooling layers - maxpool = img_pool( - input=conv2, - pool_size=2, - num_channels=16, - padding=1, - pool_type=pooling.Max()) - spp = spp(input=conv2, - pyramid_height=2, - num_channels=16, - pool_type=pooling.Max()) - maxout = maxout(input=conv2, num_channels=16, groups=4) - print parse_network(maxpool, spp, maxout) - - # test norm layers - norm1 = img_cmrnorm(input=maxpool, size=5) - norm2 = batch_norm(input=maxpool) - norm3 = sum_to_one_norm(input=maxpool) - print parse_network(norm1, norm2, norm3) - - # test recurrent layers - recurrent = recurrent(input=word) - lstm = lstmemory(input=word) - gru = grumemory(input=word) - print parse_network(recurrent, lstm, gru) - - # test aggregate layers - pool = pool( - input=pixel, - pooling_type=pooling.Avg(), - agg_level=AggregateLevel.EACH_SEQUENCE) - last_seq = last_seq(input=pixel) - first_seq = first_seq(input=pixel) - concat = concat(input=[last_seq, first_seq]) - seq_concat = seq_concat(a=last_seq, b=first_seq) - print parse_network(pool, last_seq, first_seq, concat, seq_concat) - - # test reshaping layers - block_expand = block_expand( - input=maxout, num_channels=4, stride_x=1, block_x=1) - expand = expand( - input=last_seq, expand_as=pixel, expand_level=ExpandLevel.FROM_TIMESTEP) - repeat = repeat(input=last_seq, num_repeats=4) - reshape = seq_reshape(input=last_seq, reshape_size=4) - rotate = rotate(input=pixel, height=16, width=49) - print parse_network(block_expand, expand, repeat, reshape, rotate) - - # test math layers - addto = addto(input=[last_seq, first_seq]) - linear_comb = linear_comb(weights=weight, vectors=hidden, size=10) - interpolation = interpolation(input=[hidden, hidden], weight=score) - bilinear = bilinear_interp(input=conv2, out_size_x=4, out_size_y=4) - power = power(input=conv1, weight=score) - scaling = scaling(input=conv1, weight=score) - slope = slope_intercept(input=conv1) - tensor = tensor(a=last_seq, b=first_seq, size=1000) - cos_sim = cos_sim(a=last_seq, b=first_seq) - trans = trans(input=tensor) - print parse_network(addto, linear_comb, interpolation, bilinear, power, - scaling, slope, tensor, cos_sim, trans) - - # test sampling layers - maxid = max_id(input=inference) - sampling_id = sampling_id(input=inference) - print parse_network(maxid, sampling_id) - - # test slicing and joining layers - pad = pad(input=maxpool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1]) - print parse_network(pad) - - # test cost layers - cost1 = classification_cost(input=inference, label=label) - cost2 = classification_cost(input=inference, label=label, weight=weight) - cost3 = cross_entropy_cost(input=inference, label=label) - cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label) - cost5 = regression_cost(input=inference, label=label) - cost6 = regression_cost(input=inference, label=label, weight=weight) - cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label) - cost8 = rank_cost(left=score, right=score, label=score) - cost9 = lambda_cost(input=inference, score=score) - cost10 = sum_cost(input=inference) - cost11 = huber_cost(input=score, label=label) - print parse_network(cost3, cost4) - print parse_network(cost5, cost6) - print parse_network(cost7, cost8, cost9, cost10, cost11) - - crf = crf(input=inference, label=label) - crf_decoding = crf_decoding(input=inference, size=3) - ctc = ctc(input=inference, label=label) - warp_ctc = warp_ctc(input=pixel, label=label) - nce = nce(input=inference, label=label, num_classes=3) - hsigmoid = hsigmoid(input=inference, label=label, num_classes=3) - print parse_network(crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid) - - # test check layers - eos = eos(input=maxid, eos_id=5) - print parse_network(eos) diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index b600e8cf765122..2f139866e8096a 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -19,18 +19,106 @@ import paddle.v2.attr as attr import paddle.v2.data_type as data_type import paddle.v2.layer as layer +import paddle.v2.pooling as pooling from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as parse_network -pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) +pixel = layer.data(name='pixel', type=data_type.dense_vector(128)) label = layer.data(name='label', type=data_type.integer_value(10)) weight = layer.data(name='weight', type=data_type.dense_vector(10)) score = layer.data(name='score', type=data_type.dense_vector(1)) + hidden = layer.fc(input=pixel, size=100, act=activation.Sigmoid(), param_attr=attr.Param(name='hidden')) inference = layer.fc(input=hidden, size=10, act=activation.Softmax()) +conv = layer.img_conv( + input=pixel, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + act=activation.Linear()) + + +class ImageLayerTest(unittest.TestCase): + def test_conv_layer(self): + conv_shift = layer.conv_shift(a=pixel, b=score) + print layer.parse_network(conv, conv_shift) + + def test_pooling_layer(self): + maxpool = layer.img_pool( + input=conv, + pool_size=2, + num_channels=16, + padding=1, + pool_type=pooling.Max()) + spp = layer.spp(input=conv, + pyramid_height=2, + num_channels=16, + pool_type=pooling.Max()) + maxout = layer.maxout(input=conv, num_channels=16, groups=4) + print layer.parse_network(maxpool, spp, maxout) + + def test_norm_layer(self): + norm1 = layer.img_cmrnorm(input=conv, size=5) + norm2 = layer.batch_norm(input=conv) + norm3 = layer.sum_to_one_norm(input=conv) + print layer.parse_network(norm1, norm2, norm3) + + +class AggregateLayerTest(unittest.TestCase): + def test_aggregate_layer(self): + pool = layer.pool( + input=pixel, + pooling_type=pooling.Avg(), + agg_level=layer.AggregateLevel.EACH_SEQUENCE) + last_seq = layer.last_seq(input=pixel) + first_seq = layer.first_seq(input=pixel) + concat = layer.concat(input=[last_seq, first_seq]) + seq_concat = layer.seq_concat(a=last_seq, b=first_seq) + print layer.parse_network(pool, last_seq, first_seq, concat, seq_concat) + + +class MathLayerTest(unittest.TestCase): + def test_math_layer(self): + addto = layer.addto(input=[pixel, pixel]) + linear_comb = layer.linear_comb(weights=weight, vectors=hidden, size=10) + interpolation = layer.interpolation( + input=[hidden, hidden], weight=score) + bilinear = layer.bilinear_interp(input=conv, out_size_x=4, out_size_y=4) + power = layer.power(input=pixel, weight=score) + scaling = layer.scaling(input=pixel, weight=score) + slope = layer.slope_intercept(input=pixel) + tensor = layer.tensor(a=pixel, b=pixel, size=1000) + cos_sim = layer.cos_sim(a=pixel, b=pixel) + trans = layer.trans(input=tensor) + print layer.parse_network(addto, linear_comb, interpolation, power, + scaling, slope, tensor, cos_sim, trans) + + +class ReshapeLayerTest(unittest.TestCase): + def test_reshape_layer(self): + block_expand = layer.block_expand( + input=conv, num_channels=4, stride_x=1, block_x=1) + expand = layer.expand( + input=weight, + expand_as=pixel, + expand_level=layer.ExpandLevel.FROM_TIMESTEP) + repeat = layer.repeat(input=pixel, num_repeats=4) + reshape = layer.seq_reshape(input=pixel, reshape_size=4) + rotate = layer.rotate(input=pixel, height=16, width=49) + print layer.parse_network(block_expand, expand, repeat, reshape, rotate) + + +class RecurrentLayerTest(unittest.TestCase): + def test_recurrent_layer(self): + word = layer.data(name='word', type=data_type.integer_value(12)) + recurrent = layer.recurrent(input=word) + lstm = layer.lstmemory(input=word) + gru = layer.grumemory(input=word) + print layer.parse_network(recurrent, lstm, gru) class CostLayerTest(unittest.TestCase): @@ -51,12 +139,32 @@ def test_cost_layer(self): cost10 = layer.sum_cost(input=inference) cost11 = layer.huber_cost(input=score, label=label) - print dir(layer) - layer.parse_network(cost1, cost2) - print dir(layer) - #print layer.parse_network(cost3, cost4) - #print layer.parse_network(cost5, cost6) - #print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + print layer.parse_network(cost1, cost2) + print layer.parse_network(cost3, cost4) + print layer.parse_network(cost5, cost6) + print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + + crf = layer.crf(input=inference, label=label) + crf_decoding = layer.crf_decoding(input=inference, size=3) + ctc = layer.ctc(input=inference, label=label) + warp_ctc = layer.warp_ctc(input=pixel, label=label) + nce = layer.nce(input=inference, label=label, num_classes=3) + hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3) + + print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce, + hsigmoid) + + +class OtherLayerTest(unittest.TestCase): + def test_sampling_layer(self): + maxid = layer.max_id(input=inference) + sampling_id = layer.sampling_id(input=inference) + eos = layer.eos(input=maxid, eos_id=5) + print layer.parse_network(maxid, sampling_id, eos) + + def test_slicing_joining_layer(self): + pad = layer.pad(input=conv, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1]) + print layer.parse_network(pad) if __name__ == '__main__': From bc074d0e581bb0804d11246c3d87ccaa1a5abc50 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 27 Feb 2017 13:43:41 +0800 Subject: [PATCH 310/370] minor change --- python/paddle/v2/data_feeder.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py index 632f1760ab5ff2..2a16d46dda47f8 100644 --- a/python/paddle/v2/data_feeder.py +++ b/python/paddle/v2/data_feeder.py @@ -58,13 +58,13 @@ def __init__(self, data_types, reader_dict): :type reader_dict: dict() """ self.input_names = [] - self.input_types = [] + input_types = [] self.reader_dict = reader_dict for each in data_types: self.input_names.append(each[0]) assert isinstance(each[1], data_type.InputType) - self.input_types.append(each[1]) - DataProviderConverter.__init__(self, self.input_types) + input_types.append(each[1]) + DataProviderConverter.__init__(self, input_types) def convert(self, dat, argument=None): """ @@ -98,6 +98,3 @@ def reorder_data(data): return retv return DataProviderConverter.convert(self, reorder_data(dat), argument) - - def __call__(self, dat, argument=None): - return self.convert(dat, argument) From a219455516f77b735c93621db16d26e8f230f5e7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 13:45:47 +0800 Subject: [PATCH 311/370] Rise type error because of python. --- python/paddle/v2/trainer.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 023ab5e42d25b9..f65c631f7bfdd4 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -164,20 +164,20 @@ def __check_train_args__(train_data_reader, topology, parameters, """ if not callable(train_data_reader) or not isinstance(train_data_reader(), collections.Iterator): - raise ValueError('train_data_reader should be a function, ' - 'which can return a iterator') + raise TypeError('train_data_reader should be a function, ' + 'which can return a iterator') if test_data_reader is not None: if not callable(test_data_reader) or not isinstance( test_data_reader(), collections.Iterator): - raise ValueError('test_data_reader should be a function, which can ' - 'return a iterator') + raise TypeError('test_data_reader should be a function, which can ' + 'return a iterator') if not isinstance(topology, ModelConfig): - raise ValueError('topology should be a model config') + raise TypeError('topology should be a model config') if not isinstance(parameters, v2_parameters.Parameters): - raise ValueError('parameters should be a parameter pool') + raise TypeError('parameters should be a parameter pool') if not callable(event_handler): - raise ValueError('event handler should be a function') + raise TypeError('event handler should be a function') From c26431bacf25a6053999af791ab66d17d3c62c1b Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 14:03:34 +0800 Subject: [PATCH 312/370] Reordered package for feature usage. --- python/CMakeLists.txt | 4 +-- python/paddle/v2/__init__.py | 3 ++- python/paddle/v2/dataset/__init__.py | 3 +++ python/paddle/{ => v2}/reader/__init__.py | 0 python/paddle/{ => v2}/reader/creator.py | 0 python/paddle/{ => v2}/reader/decorator.py | 0 .../{ => v2}/reader/tests/CMakeLists.txt | 0 python/paddle/v2/reader/tests/__init__.py | 0 .../{ => v2}/reader/tests/creator_test.py | 10 ++++--- .../{ => v2}/reader/tests/decorator_test.py | 27 ++++++++++--------- .../reader/tests/test_data_creator.txt | 0 python/paddle/v2/trainer.py | 6 ++--- python/setup.py.in | 4 ++- 13 files changed, 33 insertions(+), 24 deletions(-) rename python/paddle/{ => v2}/reader/__init__.py (100%) rename python/paddle/{ => v2}/reader/creator.py (100%) rename python/paddle/{ => v2}/reader/decorator.py (100%) rename python/paddle/{ => v2}/reader/tests/CMakeLists.txt (100%) create mode 100644 python/paddle/v2/reader/tests/__init__.py rename python/paddle/{ => v2}/reader/tests/creator_test.py (89%) rename python/paddle/{ => v2}/reader/tests/decorator_test.py (81%) rename python/paddle/{ => v2}/reader/tests/test_data_creator.txt (100%) diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 71af50a9a4ed83..48e0a1993d07f8 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -4,7 +4,7 @@ set(OUTPUT_DIR file(GLOB TRAINER_PY_FILES . ./paddle/trainer/*.py) file(GLOB HELPERS_PY_FILES . ./paddle/trainer_config_helpers/*.py) file(GLOB UTILS_PY_FILES . ./paddle/utils/*.py) -file(GLOB V2_PY_FILES . ./paddle/v2/*.py) +file(GLOB_RECURSE V2_PY_FILES ./paddle/v2/ *.py) set(PY_FILES paddle/__init__.py ${TRAINER_PY_FILES} @@ -24,7 +24,7 @@ add_custom_target(paddle_python ALL DEPENDS ${OUTPUT_DIR}/.timestamp) add_subdirectory(paddle/trainer_config_helpers/tests) -add_subdirectory(paddle/reader/tests) +add_subdirectory(paddle/v2/reader/tests) add_subdirectory(paddle/v2/tests) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/ diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 49d1983a2a422b..1122bcb5e45727 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -19,12 +19,13 @@ import event import data_type import data_feeder +from . import dataset import attr import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type', 'attr', 'data_feeder' + 'event', 'data_type', 'attr', 'data_feeder', 'dataset' ] diff --git a/python/paddle/v2/dataset/__init__.py b/python/paddle/v2/dataset/__init__.py index e69de29bb2d1d6..9647e9850332b7 100644 --- a/python/paddle/v2/dataset/__init__.py +++ b/python/paddle/v2/dataset/__init__.py @@ -0,0 +1,3 @@ +import mnist + +__all__ = ['mnist'] diff --git a/python/paddle/reader/__init__.py b/python/paddle/v2/reader/__init__.py similarity index 100% rename from python/paddle/reader/__init__.py rename to python/paddle/v2/reader/__init__.py diff --git a/python/paddle/reader/creator.py b/python/paddle/v2/reader/creator.py similarity index 100% rename from python/paddle/reader/creator.py rename to python/paddle/v2/reader/creator.py diff --git a/python/paddle/reader/decorator.py b/python/paddle/v2/reader/decorator.py similarity index 100% rename from python/paddle/reader/decorator.py rename to python/paddle/v2/reader/decorator.py diff --git a/python/paddle/reader/tests/CMakeLists.txt b/python/paddle/v2/reader/tests/CMakeLists.txt similarity index 100% rename from python/paddle/reader/tests/CMakeLists.txt rename to python/paddle/v2/reader/tests/CMakeLists.txt diff --git a/python/paddle/v2/reader/tests/__init__.py b/python/paddle/v2/reader/tests/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/python/paddle/reader/tests/creator_test.py b/python/paddle/v2/reader/tests/creator_test.py similarity index 89% rename from python/paddle/reader/tests/creator_test.py rename to python/paddle/v2/reader/tests/creator_test.py index eda8ab6715b2be..9f8d7133b8694a 100644 --- a/python/paddle/reader/tests/creator_test.py +++ b/python/paddle/v2/reader/tests/creator_test.py @@ -11,17 +11,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import os import unittest -import paddle.reader.creator + import numpy as np -import os + +import paddle.v2.reader.creator class TestNumpyArray(unittest.TestCase): def test_numpy_array(self): l = [[1, 2, 3], [4, 5, 6]] x = np.array(l, np.int32) - reader = paddle.reader.creator.np_array(x) + reader = paddle.v2.reader.creator.np_array(x) for idx, e in enumerate(reader()): self.assertItemsEqual(e, l[idx]) @@ -29,7 +31,7 @@ def test_numpy_array(self): class TestTextFile(unittest.TestCase): def test_text_file(self): path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt") - reader = paddle.reader.creator.text_file(path) + reader = paddle.v2.reader.creator.text_file(path) for idx, e in enumerate(reader()): self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1)) diff --git a/python/paddle/reader/tests/decorator_test.py b/python/paddle/v2/reader/tests/decorator_test.py similarity index 81% rename from python/paddle/reader/tests/decorator_test.py rename to python/paddle/v2/reader/tests/decorator_test.py index 0396a61786539b..734154b9790a4d 100644 --- a/python/paddle/reader/tests/decorator_test.py +++ b/python/paddle/v2/reader/tests/decorator_test.py @@ -11,9 +11,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import unittest -import paddle.reader import time +import unittest + +import paddle.v2.reader def reader_creator_10(dur): @@ -37,7 +38,7 @@ def read(): yield "h" yield "i" - r = paddle.reader.map_readers(tokenize, read) + r = paddle.v2.reader.map_readers(tokenize, read) for i, e in enumerate(r()): self.assertEqual(e, i) @@ -45,7 +46,7 @@ def read(): class TestBuffered(unittest.TestCase): def test_read(self): for size in range(20): - b = paddle.reader.buffered(reader_creator_10(0), size) + b = paddle.v2.reader.buffered(reader_creator_10(0), size) c = 0 for i in b(): self.assertEqual(i, c) @@ -54,7 +55,7 @@ def test_read(self): def test_buffering(self): # read have 30ms delay. - b = paddle.reader.buffered(reader_creator_10(0.03), 10) + b = paddle.v2.reader.buffered(reader_creator_10(0.03), 10) last_time = time.time() for idx, i in enumerate(b()): elapsed_time = time.time() - last_time @@ -68,17 +69,17 @@ def test_buffering(self): class TestCompose(unittest.TestCase): def test_compse(self): - reader = paddle.reader.compose( + reader = paddle.v2.reader.compose( reader_creator_10(0), reader_creator_10(0)) for idx, e in enumerate(reader()): self.assertEqual(e, (idx, idx)) def test_compose_not_aligned(self): total = 0 - reader = paddle.reader.compose( - paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)), + reader = paddle.v2.reader.compose( + paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)), reader_creator_10(0)) - with self.assertRaises(paddle.reader.ComposeNotAligned): + with self.assertRaises(paddle.v2.reader.ComposeNotAligned): for e in reader(): total += 1 # expecting 10, not 20 @@ -86,8 +87,8 @@ def test_compose_not_aligned(self): def test_compose_not_aligned_no_check(self): total = 0 - reader = paddle.reader.compose( - paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)), + reader = paddle.v2.reader.compose( + paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)), reader_creator_10(0), check_alignment=False) for e in reader(): @@ -98,7 +99,7 @@ def test_compose_not_aligned_no_check(self): class TestChain(unittest.TestCase): def test_chain(self): - c = paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)) + c = paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)) idx = 0 for e in c(): self.assertEqual(e, idx % 10) @@ -111,7 +112,7 @@ def test_shuffle(self): case = [(0, True), (1, True), (10, False), (100, False)] a = reader_creator_10(0) for size, checkEq in case: - s = paddle.reader.shuffle(a, size) + s = paddle.v2.reader.shuffle(a, size) total = 0 for idx, e in enumerate(s()): if checkEq: diff --git a/python/paddle/reader/tests/test_data_creator.txt b/python/paddle/v2/reader/tests/test_data_creator.txt similarity index 100% rename from python/paddle/reader/tests/test_data_creator.txt rename to python/paddle/v2/reader/tests/test_data_creator.txt diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index f65c631f7bfdd4..89415787eb2e36 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -62,7 +62,7 @@ def __init__(self, update_equation): self.__optimizer__ = update_equation def train(self, - train_data_reader, + train_reader_creator, topology, parameters, num_passes=1, @@ -74,7 +74,7 @@ def train(self, """ Training method. Will train num_passes of input data. - :param train_data_reader: + :param train_reader_creator: :param topology: Network Topology, use one or more Layers to represent it. :param parameters: The parameter pools. :param num_passes: The total train passes. @@ -109,7 +109,7 @@ def train(self, for pass_id in xrange(num_passes): updater.startPass() for batch_id, data_batch in enumerate( - __data_reader_to_batch__(train_data_reader, batch_size, + __data_reader_to_batch__(train_reader_creator, batch_size, topology)): pass_type = updater.startBatch(len(data_batch)) gm.forwardBackward(feeder(data_batch), out_args, pass_type) diff --git a/python/setup.py.in b/python/setup.py.in index 1e1324eea825ab..68ca35265cf132 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -5,7 +5,9 @@ packages=['paddle', 'paddle.trainer', 'paddle.trainer_config_helpers', 'paddle.utils', - 'paddle.v2'] + 'paddle.v2', + 'paddle.v2.dataset', + 'paddle.v2.reader'] setup(name='paddle', version='${PADDLE_VERSION}', From 46d2ca575b98fef6d2506a66651c51400f189617 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 14:44:59 +0800 Subject: [PATCH 313/370] Combine Reader=>Feeder together. --- demo/mnist/api_train_v2.py | 38 +++++++++--------- python/paddle/v2/__init__.py | 3 +- python/paddle/v2/reader/decorator.py | 24 ++++++++++- python/paddle/v2/trainer.py | 59 +++++----------------------- 4 files changed, 52 insertions(+), 72 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 650bf392bbc734..67e36e6b201f38 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -1,15 +1,6 @@ import numpy import paddle.v2 as paddle -import mnist_util - - -def train_reader(): - train_file = './data/raw_data/train' - generator = mnist_util.read_from_mnist(train_file) - for item in generator: - yield item - def main(): paddle.init(use_gpu=False, trainer_count=1) @@ -45,17 +36,24 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) - trainer.train(train_data_reader=train_reader, - topology=cost, - parameters=parameters, - event_handler=event_handler, - batch_size=32, # batch size should be refactor in Data reader - data_types=[ # data_types will be removed, It should be in - # network topology - ('pixel', images.type), - ('label', label.type)], - reader_dict={'pixel':0, 'label':1} - ) + reader = paddle.reader.batched( + paddle.reader.shuffle( + paddle.dataset.mnist.train_creator(), buf_size=8192), + batch_size=32) + + trainer.train( + train_reader=paddle.reader.batched( + paddle.reader.shuffle(paddle.dataset.mnist.train_creator(), + buf_size=8192), batch_size=32), + topology=cost, + parameters=parameters, + event_handler=event_handler, + data_types=[ # data_types will be removed, It should be in + # network topology + ('pixel', images.type), + ('label', label.type)], + reader_dict={'pixel': 0, 'label': 1} + ) if __name__ == '__main__': diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 1122bcb5e45727..cc130caa15f645 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -20,12 +20,13 @@ import data_type import data_feeder from . import dataset +from . import reader import attr import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type', 'attr', 'data_feeder', 'dataset' + 'event', 'data_type', 'attr', 'data_feeder', 'dataset', 'reader' ] diff --git a/python/paddle/v2/reader/decorator.py b/python/paddle/v2/reader/decorator.py index 9f4234358f469b..d83e2f4577f20b 100644 --- a/python/paddle/v2/reader/decorator.py +++ b/python/paddle/v2/reader/decorator.py @@ -14,7 +14,7 @@ __all__ = [ 'map_readers', 'buffered', 'compose', 'chain', 'shuffle', - 'ComposeNotAligned' + 'ComposeNotAligned', 'batched' ] from Queue import Queue @@ -191,3 +191,25 @@ def data_reader(): e = q.get() return data_reader + + +def batched(reader, batch_size): + """ + Create a batched reader. + :param reader: the data reader to read from. + :param batch_size: batch_size + :return: the batched reader. + """ + + def __impl__(): + r = reader() + batch = [] + for instance in r: + batch.append(instance) + if len(batch) == batch_size: + yield batch + batch = [] + if batch: + yield batch + + return __impl__ diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 89415787eb2e36..9b45ee69f39f3f 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -29,7 +29,7 @@ class ITrainer(object): """ def train(self, - train_data_reader, + train_reader_creator, topology, parameters, test_data_reader=None, @@ -37,7 +37,7 @@ def train(self, """ train method. - :param train_data_reader: + :param train_reader_creator: :param topology: :param parameters: :param test_data_reader: @@ -62,27 +62,23 @@ def __init__(self, update_equation): self.__optimizer__ = update_equation def train(self, - train_reader_creator, + train_reader, topology, parameters, num_passes=1, - test_data_reader=None, event_handler=None, - batch_size=32, data_types=None, reader_dict=None): """ Training method. Will train num_passes of input data. - :param train_reader_creator: + :param train_reader: :param topology: Network Topology, use one or more Layers to represent it. :param parameters: The parameter pools. :param num_passes: The total train passes. - :param test_data_reader: :param event_handler: Event handler. A method will be invoked when event occurred. :type event_handler: (BaseEvent) => None - :param batch_size: Not important, will be removed after data refactor. :param data_types: Not important, will be removed after data refactor. :return: """ @@ -108,9 +104,7 @@ def train(self, for pass_id in xrange(num_passes): updater.startPass() - for batch_id, data_batch in enumerate( - __data_reader_to_batch__(train_reader_creator, batch_size, - topology)): + for batch_id, data_batch in enumerate(train_reader()): pass_type = updater.startBatch(len(data_batch)) gm.forwardBackward(feeder(data_batch), out_args, pass_type) for each_param in gm.getParameters(): @@ -128,51 +122,16 @@ def train(self, gm.finish() -def __data_reader_to_batch__(reader, batch_size, topology): - """ - This function is not important, and will be removed when data refactored. - """ - - def input_reorder(func): - for item in func(): - retv = [] - for __layer_name__ in topology.input_layer_names: - retv.append(item[__layer_name__]) - yield retv - - return __generator_to_batch__(input_reorder(reader), batch_size=batch_size) - - -def __generator_to_batch__(generator, batch_size): - """ - This function is not important, and will be removed when data refactored. - """ - ret_val = list() - for each_item in generator: - ret_val.append(each_item) - if len(ret_val) == batch_size: - yield ret_val - ret_val = list() - if len(ret_val) != 0: - yield ret_val - - -def __check_train_args__(train_data_reader, topology, parameters, - test_data_reader, event_handler, **kwargs): +def __check_train_args__(train_reader, topology, parameters, event_handler, + **kwargs): """ Check train function's argument types """ - if not callable(train_data_reader) or not isinstance(train_data_reader(), - collections.Iterator): + if not callable(train_reader) or not isinstance(train_reader(), + collections.Iterator): raise TypeError('train_data_reader should be a function, ' 'which can return a iterator') - if test_data_reader is not None: - if not callable(test_data_reader) or not isinstance( - test_data_reader(), collections.Iterator): - raise TypeError('test_data_reader should be a function, which can ' - 'return a iterator') - if not isinstance(topology, ModelConfig): raise TypeError('topology should be a model config') From f02698675d0ce833bee27057aec17b86e46a00c0 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 14:51:37 +0800 Subject: [PATCH 314/370] Remove test code --- demo/mnist/api_train_v2.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 67e36e6b201f38..d5eeb053188300 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -36,11 +36,6 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) - reader = paddle.reader.batched( - paddle.reader.shuffle( - paddle.dataset.mnist.train_creator(), buf_size=8192), - batch_size=32) - trainer.train( train_reader=paddle.reader.batched( paddle.reader.shuffle(paddle.dataset.mnist.train_creator(), From f3f24604350cedb62bc05b02449958b4e9aca4d4 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 15:08:58 +0800 Subject: [PATCH 315/370] Make unittest ok. --- python/paddle/v2/reader/tests/CMakeLists.txt | 12 ++----- python/paddle/v2/reader/tests/run_tests.sh | 35 ++++++++++++++++++++ 2 files changed, 38 insertions(+), 9 deletions(-) create mode 100755 python/paddle/v2/reader/tests/run_tests.sh diff --git a/python/paddle/v2/reader/tests/CMakeLists.txt b/python/paddle/v2/reader/tests/CMakeLists.txt index da072fb3dbeed5..a51f700406b48f 100644 --- a/python/paddle/v2/reader/tests/CMakeLists.txt +++ b/python/paddle/v2/reader/tests/CMakeLists.txt @@ -1,9 +1,3 @@ -add_test(NAME reader_decorator_test - COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ - ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py - WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) - -add_test(NAME reader_creator_test - COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ - ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py - WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) +add_test(NAME reader_tests + COMMAND bash ${PROJ_ROOT}/python/paddle/v2/reader/tests/run_tests.sh + ${PYTHON_EXECUTABLE}) diff --git a/python/paddle/v2/reader/tests/run_tests.sh b/python/paddle/v2/reader/tests/run_tests.sh new file mode 100755 index 00000000000000..a544a5636021bc --- /dev/null +++ b/python/paddle/v2/reader/tests/run_tests.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +pushd `dirname $0` > /dev/null +SCRIPTPATH=$PWD +popd > /dev/null + +cd $SCRIPTPATH +$1 -m pip install ../../../../../paddle/dist/*.whl + +test_list="creator_test.py decorator_test.py" + +export PYTHONPATH=$PWD/../../../../../python/ + +for fn in $test_list +do + echo "test $fn" + $1 $fn + if [ $? -ne 0 ]; then + exit 1 + fi +done From dcc332a89001a01fb982938cc6c408b50ebd895a Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 15:12:53 +0800 Subject: [PATCH 316/370] Follow qq's comments --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/trainer.py | 22 +++++++--------------- 2 files changed, 8 insertions(+), 16 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index d5eeb053188300..4d95780f06cd79 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -37,7 +37,7 @@ def event_handler(event): trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer.train( - train_reader=paddle.reader.batched( + reader=paddle.reader.batched( paddle.reader.shuffle(paddle.dataset.mnist.train_creator(), buf_size=8192), batch_size=32), topology=cost, diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 9b45ee69f39f3f..cbeef0306b739c 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -28,19 +28,13 @@ class ITrainer(object): The interface of Trainer. The only exposed method is `train`. """ - def train(self, - train_reader_creator, - topology, - parameters, - test_data_reader=None, - event_handler=None): + def train(self, reader, topology, parameters, event_handler=None): """ train method. - :param train_reader_creator: + :param reader: :param topology: :param parameters: - :param test_data_reader: :param event_handler: :return: """ @@ -62,7 +56,7 @@ def __init__(self, update_equation): self.__optimizer__ = update_equation def train(self, - train_reader, + reader, topology, parameters, num_passes=1, @@ -72,7 +66,7 @@ def train(self, """ Training method. Will train num_passes of input data. - :param train_reader: + :param reader: :param topology: Network Topology, use one or more Layers to represent it. :param parameters: The parameter pools. :param num_passes: The total train passes. @@ -104,7 +98,7 @@ def train(self, for pass_id in xrange(num_passes): updater.startPass() - for batch_id, data_batch in enumerate(train_reader()): + for batch_id, data_batch in enumerate(reader()): pass_type = updater.startBatch(len(data_batch)) gm.forwardBackward(feeder(data_batch), out_args, pass_type) for each_param in gm.getParameters(): @@ -122,13 +116,11 @@ def train(self, gm.finish() -def __check_train_args__(train_reader, topology, parameters, event_handler, - **kwargs): +def __check_train_args__(reader, topology, parameters, event_handler, **kwargs): """ Check train function's argument types """ - if not callable(train_reader) or not isinstance(train_reader(), - collections.Iterator): + if not callable(reader) or not isinstance(reader(), collections.Iterator): raise TypeError('train_data_reader should be a function, ' 'which can return a iterator') From b59f35ef7ea97b74cc500bf5b28aed410c64e941 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 27 Feb 2017 14:22:49 +0800 Subject: [PATCH 317/370] follow some comments --- python/paddle/v2/layer.py | 61 ++++++++-------------------- python/paddle/v2/tests/test_layer.py | 14 +++---- 2 files changed, 21 insertions(+), 54 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index b9400ab26bdee2..e7d986d4c423e6 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -82,27 +82,10 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. import attr __all__ = [ - 'parse_network', - 'data', - 'fc', - 'max_id', - 'classification_cost', - 'cross_entropy_cost', - 'cross_entropy_with_selfnorm_cost', - 'regression_cost', - 'multi_binary_label_cross_entropy_cost', - 'rank_cost', - 'lambda_cost', - 'sum_cost', - 'huber_cost' - 'full_matrix_projection', - 'trans_full_matrix_projection', - 'table_projection', - 'identity_projection', - 'scaling_projection', - 'dotmul_projection', - 'context_projection', - 'conv_projection', + 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', + 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', + 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', + 'sum_cost', 'huber_cost' ] __projection_names__ = filter(lambda x: x.endswith('_projection'), @@ -167,7 +150,7 @@ def __convert_to_v2__(method_name, name_prefix=None, parent_names=None): wrapper = None class V2LayerImpl(Layer): - def __init__(self, name=None, **kwargs): + def __init__(self, **kwargs): parent_layers = dict() other_kwargs = dict() for pname in parent_names: @@ -178,6 +161,7 @@ def __init__(self, name=None, **kwargs): if key not in parent_names: other_kwargs[key] = kwargs[key] + name = kwargs['name'] if kwargs.has_key('name') else None super(V2LayerImpl, self).__init__(name, parent_layers) self.__other_kwargs__ = other_kwargs @@ -242,32 +226,30 @@ def __init__(self, layer_attr=None): self.__method_name__ = 'mixed_layer' self.finalized = False - - self.__parent_layers__ = dict() - other_kwargs = dict() - self.input_name = 'input' - self.__parent_layers__[self.input_name] = [] + self.__inputs__ = [] if input is not None: - self.__parent_layers__[self.input_name] = input + self.__inputs__ = input - self.name = name + other_kwargs = dict() + other_kwargs['name'] = name other_kwargs['size'] = size other_kwargs['act'] = act other_kwargs['bias_attr'] = bias_attr other_kwargs['layer_attr'] = layer_attr - Layer.__init__(self, name, self.__parent_layers__) + parent_layers = {"input": self.__inputs__} + super(MixedLayerV2, self).__init__(name, parent_layers) self.__other_kwargs__ = other_kwargs def __iadd__(self, other): if not self.finalized: - self.__parent_layers__[self.input_name].append(other) + self.__inputs__.append(other) return self else: raise MixedLayerTypeV2.AddToSealedMixedLayerExceptionV2() def __enter__(self): - assert len(self.__parent_layers__[self.input_name]) == 0 + assert len(self.__inputs__) == 0 return self def __exit__(self, *args, **kwargs): @@ -279,7 +261,7 @@ def to_proto_impl(self, **kwargs): args[each] = kwargs[each] for each in self.__other_kwargs__: args[each] = self.__other_kwargs__[each] - return getattr(conf_helps, self.__method_name__)(name=self.name, **args) + return getattr(conf_helps, self.__method_name__)(**args) @wrap_name_default("mixed") @@ -331,18 +313,7 @@ def mixed(size=0, 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) # convert projection -projection_list = [ - # [V1_method_name], all the parent_names is `input` - 'full_matrix_projection', - 'trans_full_matrix_projection', - 'table_projection', - 'scaling_projection', - 'dotmul_projection', - 'context_projection', - 'conv_projection', - 'identity_projection', -] -for prj in projection_list: +for prj in __projection_names__: globals()[prj] = __convert_to_v2__(prj, parent_names=['input']) # convert operator diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index 521bc8b40c5b6b..77423e4aa187dd 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -32,7 +32,7 @@ class CostLayerTest(unittest.TestCase): - def test_cost_layer(self): + def not_test_cost_layer(self): cost1 = layer.classification_cost(input=inference, label=label) cost2 = layer.classification_cost( input=inference, label=label, weight=weight) @@ -60,12 +60,8 @@ def test_projection(self): input = layer.data(name='data', type=data_type.dense_vector(784)) word = layer.data( name='word', type=data_type.integer_value_sequence(10000)) - fc0 = layer.fc(input=input, - size=100, - act=conf_helps.SigmoidActivation()) - fc1 = layer.fc(input=input, - size=200, - act=conf_helps.SigmoidActivation()) + fc0 = layer.fc(input=input, size=100, act=activation.Sigmoid()) + fc1 = layer.fc(input=input, size=200, act=activation.Sigmoid()) mixed0 = layer.mixed( size=256, input=[ @@ -121,8 +117,8 @@ def test_projection(self): def test_operator(self): ipt0 = layer.data(name='data', type=data_type.dense_vector(784)) ipt1 = layer.data(name='word', type=data_type.dense_vector(128)) - fc0 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) - fc1 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation()) + fc0 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid()) + fc1 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid()) dotmul_op = layer.dotmul_operator(a=fc0, b=fc1) dotmul0 = layer.mixed(input=dotmul_op) From 739ff181a7a1196a5de0ebba3674688b4296a174 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 16:30:16 +0800 Subject: [PATCH 318/370] V2.testing complete --- demo/mnist/api_train_v2.py | 31 +++++----- python/paddle/v2/event.py | 10 +++- python/paddle/v2/trainer.py | 115 +++++++++++++++++++++++++----------- 3 files changed, 104 insertions(+), 52 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index c787a6122ad6c6..45a70bc84afa29 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -21,28 +21,29 @@ def main(): adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01) + trainer = paddle.trainer.SGD(topology=cost, + parameters=parameters, + update_equation=adam_optimizer) + def event_handler(event): if isinstance(event, paddle.event.EndIteration): - if event.batch_id % 100 == 0: - print "Pass %d, Batch %d, Cost %f, %s" % ( - event.pass_id, event.batch_id, event.cost, event.metrics) + if event.batch_id % 1000 == 0: + result = trainer.test(reader=paddle.reader.batched( + paddle.dataset.mnist.test_creator(), batch_size=256)) + + print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % ( + event.pass_id, event.batch_id, event.cost, event.metrics, + result.metrics) + else: pass - trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer.train( reader=paddle.reader.batched( - paddle.reader.shuffle(paddle.dataset.mnist.train_creator(), - buf_size=8192), batch_size=32), - topology=cost, - parameters=parameters, - event_handler=event_handler, - data_types=[ # data_types will be removed, It should be in - # network topology - ('pixel', images.type), - ('label', label.type)], - reader_dict={'pixel': 0, 'label': 1} - ) + paddle.reader.shuffle( + paddle.dataset.mnist.train_creator(), buf_size=8192), + batch_size=32), + event_handler=event_handler) if __name__ == '__main__': diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index 835e28e6218df2..a78bcf076cc65e 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -11,7 +11,10 @@ TODO(yuyang18): Complete it! """ import py_paddle.swig_paddle as api -__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass'] + +__all__ = [ + 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult' +] class WithMetric(object): @@ -30,6 +33,11 @@ def metrics(self): return retv +class TestResult(WithMetric): + def __init__(self, evaluator): + super(TestResult, self).__init__(evaluator) + + class BeginPass(object): """ Event On One Pass Training Start. diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index f3a323db4fcfae..77232d5ac5e411 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -23,6 +23,13 @@ def default_event_handler(event): pass +def __bfs_travel_topology__(callback, *topologies): + for each_layer in topologies: + callback(each_layer) + __bfs_travel_topology__(callback, + *each_layer.__parent_layers__.values()) + + class ITrainer(object): """ The interface of Trainer. The only exposed method is `train`. @@ -43,26 +50,49 @@ def train(self, reader, topology, parameters, event_handler=None): class SGD(ITrainer): - def __init__(self, update_equation): + def __init__(self, topology, parameters, update_equation): """ Simple SGD Trainer. :param update_equation: The optimizer object. :type update_equation: v2_optimizer.Optimizer """ + if not isinstance(parameters, v2_parameters.Parameters): + raise TypeError('parameters should be parameters') + if not isinstance(update_equation, v2_optimizer.Optimizer): - raise ValueError("update equation parameter must be " - "paddle.v2.optimizer.Optimizer") + raise TypeError("update equation parameter must be " + "paddle.v2.optimizer.Optimizer") self.__optimizer__ = update_equation + self.__topology__ = topology + self.__parameters__ = parameters + self.__topology_in_proto__ = v2_layer.parse_network(topology) + data_types = dict() + + def __travel__(l): + if hasattr(l, 'type'): + data_types[l.name] = l.type + + if not isinstance(topology, collections.Sequence): + topology = [topology] + __bfs_travel_topology__(__travel__, *topology) + self.__data_types__ = [ + (iname, data_types[iname]) + for iname in self.__topology_in_proto__.input_layer_names + ] + + if not isinstance(self.__topology_in_proto__, ModelConfig): + raise TypeError('topology should be a model config') - def train(self, - reader, - topology, - parameters, - num_passes=1, - event_handler=None, - data_types=None, - reader_dict=None): + gm = api.GradientMachine.createFromConfigProto( + self.__topology_in_proto__, api.CREATE_MODE_NORMAL, + self.__optimizer__.enable_types()) + assert isinstance(gm, api.GradientMachine) + parameters.append_gradient_machine(gm) + self.__gradient_machine__ = gm + self.__gradient_machine__.randParameters() + + def train(self, reader, num_passes=1, event_handler=None, reader_dict=None): """ Training method. Will train num_passes of input data. @@ -79,26 +109,21 @@ def train(self, if event_handler is None: event_handler = default_event_handler - topology = v2_layer.parse_network(topology) + if reader_dict is None: + reader_dict = self.default_reader_dict() __check_train_args__(**locals()) - - gm = api.GradientMachine.createFromConfigProto( - topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) - assert isinstance(gm, api.GradientMachine) - parameters.append_gradient_machine(gm) - gm.randParameters() updater = self.__optimizer__.create_local_updater() - updater.init(gm) + updater.init(self.__gradient_machine__) - gm.start() - batch_evaluator = gm.makeEvaluator() + self.__gradient_machine__.start() + batch_evaluator = self.__gradient_machine__.makeEvaluator() assert isinstance(batch_evaluator, api.Evaluator) - pass_evaluator = gm.makeEvaluator() + pass_evaluator = self.__gradient_machine__.makeEvaluator() assert isinstance(pass_evaluator, api.Evaluator) out_args = api.Arguments.createArguments(0) - feeder = DataFeeder(data_types, reader_dict) + feeder = DataFeeder(self.__data_types__, reader_dict) for pass_id in xrange(num_passes): event_handler(v2_event.BeginPass(pass_id)) @@ -106,16 +131,18 @@ def train(self, updater.startPass() for batch_id, data_batch in enumerate(reader()): pass_type = updater.startBatch(len(data_batch)) - gm.forwardBackward(feeder(data_batch), out_args, pass_type) + self.__gradient_machine__.forwardBackward( + feeder(data_batch), out_args, pass_type) batch_evaluator.start() event_handler( v2_event.BeginIteration( pass_id=pass_id, batch_id=batch_id)) pass_type = updater.startBatch(len(data_batch)) - gm.forwardBackward(feeder(data_batch), out_args, pass_type) - gm.eval(pass_evaluator) - gm.eval(batch_evaluator) - for each_param in gm.getParameters(): + self.__gradient_machine__.forwardBackward( + feeder(data_batch), out_args, pass_type) + self.__gradient_machine__.eval(pass_evaluator) + self.__gradient_machine__.eval(batch_evaluator) + for each_param in self.__gradient_machine__.getParameters(): updater.update(each_param) # Get cost. We use numpy to calculate total cost for this batch. cost_vec = out_args.getSlotValue(0) @@ -133,10 +160,32 @@ def train(self, updater.finishPass() pass_evaluator.finish() event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator)) - gm.finish() + self.__gradient_machine__.finish() + + def default_reader_dict(self): + reader_dict = dict() + for i, tp in enumerate(self.__data_types__): + reader_dict[tp[0]] = i + return reader_dict + def test(self, reader, reader_dict=None): + if reader_dict is None: + reader_dict = self.default_reader_dict() + + feeder = DataFeeder(self.__data_types__, reader_dict) + evaluator = self.__gradient_machine__.makeEvaluator() + out_args = api.Arguments.createArguments(0) + evaluator.start() + for data_batch in reader(): + self.__gradient_machine__.forward( + feeder(data_batch), out_args, api.PASS_TEST) + self.__gradient_machine__.eval(evaluator) -def __check_train_args__(reader, topology, parameters, event_handler, **kwargs): + evaluator.finish() + return v2_event.TestResult(evaluator=evaluator) + + +def __check_train_args__(reader, event_handler, **kwargs): """ Check train function's argument types """ @@ -144,11 +193,5 @@ def __check_train_args__(reader, topology, parameters, event_handler, **kwargs): raise TypeError('train_data_reader should be a function, ' 'which can return a iterator') - if not isinstance(topology, ModelConfig): - raise TypeError('topology should be a model config') - - if not isinstance(parameters, v2_parameters.Parameters): - raise TypeError('parameters should be a parameter pool') - if not callable(event_handler): raise TypeError('event handler should be a function') From d25173c0248a2e62db250ba481297c2c01ce1605 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 27 Feb 2017 16:30:53 +0800 Subject: [PATCH 319/370] follow comments --- python/paddle/v2/layer.py | 6 +++--- python/paddle/v2/tests/test_layer.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index e7d986d4c423e6..d5fe06542bc19e 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -134,9 +134,9 @@ def to_proto(self, context): if self.name is None: return self.to_proto_impl(**kwargs) - - if self.name not in context: + elif self.name not in context: context[self.name] = self.to_proto_impl(**kwargs) + return context[self.name] def to_proto_impl(self, **kwargs): @@ -161,7 +161,7 @@ def __init__(self, **kwargs): if key not in parent_names: other_kwargs[key] = kwargs[key] - name = kwargs['name'] if kwargs.has_key('name') else None + name = kwargs.get('name', None) super(V2LayerImpl, self).__init__(name, parent_layers) self.__other_kwargs__ = other_kwargs diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index 77423e4aa187dd..bf1c344202f457 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -32,7 +32,7 @@ class CostLayerTest(unittest.TestCase): - def not_test_cost_layer(self): + def test_cost_layer(self): cost1 = layer.classification_cost(input=inference, label=label) cost2 = layer.classification_cost( input=inference, label=label, weight=weight) From 692df52ef801ce79180ada37dc0fbae94282ae34 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 16:34:54 +0800 Subject: [PATCH 320/370] Fix unittest --- .travis.yml | 2 +- python/setup.py.in | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 4fb2ca938795bb..3993dc31fdbf75 100644 --- a/.travis.yml +++ b/.travis.yml @@ -57,7 +57,7 @@ before_install: - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python # protobuf version. - - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker + - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker 'scikit-learn>=0.18.0' script: - paddle/scripts/travis/main.sh notifications: diff --git a/python/setup.py.in b/python/setup.py.in index 68ca35265cf132..fbee7bca90e3ad 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -15,5 +15,8 @@ setup(name='paddle', packages=packages, package_dir={ '': '${CMAKE_CURRENT_SOURCE_DIR}' - } + }, + install_requires = [ + 'scikit-learn>=0.18.0' + ] ) From 434ada47ef0bb039192d4de5d969fc70ae033a0b Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 17:05:18 +0800 Subject: [PATCH 321/370] Up to date --- python/paddle/v2/{data_set => dataset}/cifar.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename python/paddle/v2/{data_set => dataset}/cifar.py (100%) diff --git a/python/paddle/v2/data_set/cifar.py b/python/paddle/v2/dataset/cifar.py similarity index 100% rename from python/paddle/v2/data_set/cifar.py rename to python/paddle/v2/dataset/cifar.py From 0bcc4d48defeb00f191c04d868098523965bc0d2 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 17:19:29 +0800 Subject: [PATCH 322/370] Simplize cifar --- python/paddle/v2/dataset/cifar.py | 170 ++++++++++-------------------- 1 file changed, 53 insertions(+), 117 deletions(-) diff --git a/python/paddle/v2/dataset/cifar.py b/python/paddle/v2/dataset/cifar.py index 54289430d4ce11..9a999de7e02aad 100644 --- a/python/paddle/v2/dataset/cifar.py +++ b/python/paddle/v2/dataset/cifar.py @@ -15,33 +15,10 @@ import itertools import numpy -__all__ = ['CIFAR10', 'CIFAR100', 'train_creator', 'test_creator'] - - -def __download_file__(filename, url, md5): - def __file_ok__(): - if not os.path.exists(filename): - return False - md5_hash = hashlib.md5() - with open(filename, 'rb') as f: - for chunk in iter(lambda: f.read(4096), b""): - md5_hash.update(chunk) - - return md5_hash.hexdigest() == md5 - - while not __file_ok__(): - response = urllib2.urlopen(url) - with open(filename, mode='wb') as of: - shutil.copyfileobj(fsrc=response, fdst=of) - - -def __read_one_batch__(batch): - data = batch['data'] - labels = batch.get('labels', batch.get('fine_labels', None)) - assert labels is not None - for sample, label in itertools.izip(data, labels): - yield (sample / 255.0).astype(numpy.float32), int(label) - +__all__ = [ + 'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator', + 'test_creator' +] CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a' @@ -49,125 +26,84 @@ def __read_one_batch__(batch): CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85' -class CIFAR(object): - """ - CIFAR dataset reader. The base class for CIFAR-10 and CIFAR-100 - - :param url: Download url. - :param md5: File md5sum - :param meta_filename: Meta file name in package. - :param train_filename: Train file name in package. - :param test_filename: Test file name in package. - """ +def __read_batch__(filename, sub_name): + def reader(): + def __read_one_batch_impl__(batch): + data = batch['data'] + labels = batch.get('labels', batch.get('fine_labels', None)) + assert labels is not None + for sample, label in itertools.izip(data, labels): + yield (sample / 255.0).astype(numpy.float32), int(label) - def __init__(self, url, md5, meta_filename, train_filename, test_filename): - filename = os.path.split(url)[-1] - assert DATA_HOME is not None - filepath = os.path.join(DATA_HOME, md5) - if not os.path.exists(filepath): - os.makedirs(filepath) - - self.__full_file__ = os.path.join(filepath, filename) - self.__meta_filename__ = meta_filename - self.__train_filename__ = train_filename - self.__test_filename__ = test_filename - __download_file__(filename=self.__full_file__, url=url, md5=md5) - - def labels(self): - """ - labels get all dataset label in order. - :return: a list of label. - :rtype: list[string] - """ - with tarfile.open(self.__full_file__, mode='r') as f: - name = [ - each_item.name for each_item in f - if self.__meta_filename__ in each_item.name - ][0] - meta_f = f.extractfile(name) - meta = cPickle.load(meta_f) - for key in meta: - if 'label' in key: - return meta[key] - else: - raise RuntimeError("Unexpected branch.") - - def train(self): - """ - Train Reader - """ - return self.__read_batch__(self.__train_filename__) - - def test(self): - """ - Test Reader - """ - return self.__read_batch__(self.__test_filename__) - - def __read_batch__(self, sub_name): - with tarfile.open(self.__full_file__, mode='r') as f: + with tarfile.open(filename, mode='r') as f: names = (each_item.name for each_item in f if sub_name in each_item.name) for name in names: batch = cPickle.load(f.extractfile(name)) - for item in __read_one_batch__(batch): + for item in __read_one_batch_impl__(batch): yield item + return reader -class CIFAR10(CIFAR): - """ - CIFAR-10 dataset, images are classified in 10 classes. - """ - def __init__(self): - super(CIFAR10, self).__init__( - CIFAR10_URL, - CIFAR10_MD5, - meta_filename='batches.meta', - train_filename='data_batch', - test_filename='test_batch') +def download(url, md5): + filename = os.path.split(url)[-1] + assert DATA_HOME is not None + filepath = os.path.join(DATA_HOME, md5) + if not os.path.exists(filepath): + os.makedirs(filepath) + __full_file__ = os.path.join(filepath, filename) + def __file_ok__(): + if not os.path.exists(__full_file__): + return False + md5_hash = hashlib.md5() + with open(__full_file__, 'rb') as f: + for chunk in iter(lambda: f.read(4096), b""): + md5_hash.update(chunk) + + return md5_hash.hexdigest() == md5 + + while not __file_ok__(): + response = urllib2.urlopen(url) + with open(__full_file__, mode='wb') as of: + shutil.copyfileobj(fsrc=response, fdst=of) + return __full_file__ + + +def cifar_100_train_creator(): + fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5) + return __read_batch__(fn, 'train') -class CIFAR100(CIFAR): - """ - CIFAR-100 dataset, images are classified in 100 classes. - """ - def __init__(self): - super(CIFAR100, self).__init__( - CIFAR100_URL, - CIFAR100_MD5, - meta_filename='meta', - train_filename='train', - test_filename='test') +def cifar_100_test_creator(): + fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5) + return __read_batch__(fn, 'test') def train_creator(): """ Default train reader creator. Use CIFAR-10 dataset. """ - cifar = CIFAR10() - return cifar.train + fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5) + return __read_batch__(fn, 'data_batch') def test_creator(): """ Default test reader creator. Use CIFAR-10 dataset. """ - cifar = CIFAR10() - return cifar.test + fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5) + return __read_batch__(fn, 'test_batch') -def unittest(label_count=100): - cifar = globals()["CIFAR%d" % label_count]() - assert len(cifar.labels()) == label_count - for _ in cifar.test(): +def unittest(): + for _ in train_creator()(): pass - for _ in cifar.train(): + for _ in test_creator()(): pass if __name__ == '__main__': - unittest(10) - unittest(100) + unittest() From de9012a50450da19a9227bc28d5bb042ac66fcf7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 20:32:44 +0800 Subject: [PATCH 323/370] Add MovieLens Dataset --- python/paddle/v2/dataset/cifar.py | 35 +------- python/paddle/v2/dataset/config.py | 30 ++++++- python/paddle/v2/dataset/movielens.py | 120 ++++++++++++++++++++++++++ 3 files changed, 153 insertions(+), 32 deletions(-) create mode 100644 python/paddle/v2/dataset/movielens.py diff --git a/python/paddle/v2/dataset/cifar.py b/python/paddle/v2/dataset/cifar.py index 9a999de7e02aad..2ac71c6effe9d5 100644 --- a/python/paddle/v2/dataset/cifar.py +++ b/python/paddle/v2/dataset/cifar.py @@ -5,16 +5,14 @@ the default train_creator, test_creator used for CIFAR-10 dataset. """ -from config import DATA_HOME -import os -import hashlib -import urllib2 -import shutil -import tarfile import cPickle import itertools +import tarfile + import numpy +from config import download + __all__ = [ 'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator', 'test_creator' @@ -47,31 +45,6 @@ def __read_one_batch_impl__(batch): return reader -def download(url, md5): - filename = os.path.split(url)[-1] - assert DATA_HOME is not None - filepath = os.path.join(DATA_HOME, md5) - if not os.path.exists(filepath): - os.makedirs(filepath) - __full_file__ = os.path.join(filepath, filename) - - def __file_ok__(): - if not os.path.exists(__full_file__): - return False - md5_hash = hashlib.md5() - with open(__full_file__, 'rb') as f: - for chunk in iter(lambda: f.read(4096), b""): - md5_hash.update(chunk) - - return md5_hash.hexdigest() == md5 - - while not __file_ok__(): - response = urllib2.urlopen(url) - with open(__full_file__, mode='wb') as of: - shutil.copyfileobj(fsrc=response, fdst=of) - return __full_file__ - - def cifar_100_train_creator(): fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5) return __read_batch__(fn, 'train') diff --git a/python/paddle/v2/dataset/config.py b/python/paddle/v2/dataset/config.py index 69e96d65ef1ef8..02a009f09c71cc 100644 --- a/python/paddle/v2/dataset/config.py +++ b/python/paddle/v2/dataset/config.py @@ -1,8 +1,36 @@ +import hashlib import os +import shutil +import urllib2 -__all__ = ['DATA_HOME'] +__all__ = ['DATA_HOME', 'download'] DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set') if not os.path.exists(DATA_HOME): os.makedirs(DATA_HOME) + + +def download(url, md5): + filename = os.path.split(url)[-1] + assert DATA_HOME is not None + filepath = os.path.join(DATA_HOME, md5) + if not os.path.exists(filepath): + os.makedirs(filepath) + __full_file__ = os.path.join(filepath, filename) + + def __file_ok__(): + if not os.path.exists(__full_file__): + return False + md5_hash = hashlib.md5() + with open(__full_file__, 'rb') as f: + for chunk in iter(lambda: f.read(4096), b""): + md5_hash.update(chunk) + + return md5_hash.hexdigest() == md5 + + while not __file_ok__(): + response = urllib2.urlopen(url) + with open(__full_file__, mode='wb') as of: + shutil.copyfileobj(fsrc=response, fdst=of) + return __full_file__ diff --git a/python/paddle/v2/dataset/movielens.py b/python/paddle/v2/dataset/movielens.py new file mode 100644 index 00000000000000..314329e91cadf8 --- /dev/null +++ b/python/paddle/v2/dataset/movielens.py @@ -0,0 +1,120 @@ +import zipfile +from config import download +import re +import random +import functools + +__all__ = ['train_creator', 'test_creator'] + + +class MovieInfo(object): + def __init__(self, index, categories, title): + self.index = int(index) + self.categories = categories + self.title = title + + def value(self): + return [ + self.index, [CATEGORIES_DICT[c] for c in self.categories], + [MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()] + ] + + +class UserInfo(object): + def __init__(self, index, gender, age, job_id): + self.index = int(index) + self.is_male = gender == 'M' + self.age = [1, 18, 25, 35, 45, 50, 56].index(int(age)) + self.job_id = int(job_id) + + def value(self): + return [self.index, 0 if self.is_male else 1, self.age, self.job_id] + + +MOVIE_INFO = None +MOVIE_TITLE_DICT = None +CATEGORIES_DICT = None +USER_INFO = None + + +def __initialize_meta_info__(): + fn = download( + url='http://files.grouplens.org/datasets/movielens/ml-1m.zip', + md5='c4d9eecfca2ab87c1945afe126590906') + global MOVIE_INFO + if MOVIE_INFO is None: + pattern = re.compile(r'^(.*)\((\d+)\)$') + with zipfile.ZipFile(file=fn) as package: + for info in package.infolist(): + assert isinstance(info, zipfile.ZipInfo) + MOVIE_INFO = dict() + title_word_set = set() + categories_set = set() + with package.open('ml-1m/movies.dat') as movie_file: + for i, line in enumerate(movie_file): + movie_id, title, categories = line.strip().split('::') + categories = categories.split('|') + for c in categories: + categories_set.add(c) + title = pattern.match(title).group(1) + MOVIE_INFO[int(movie_id)] = MovieInfo( + index=movie_id, categories=categories, title=title) + for w in title.split(): + title_word_set.add(w.lower()) + + global MOVIE_TITLE_DICT + MOVIE_TITLE_DICT = dict() + for i, w in enumerate(title_word_set): + MOVIE_TITLE_DICT[w] = i + + global CATEGORIES_DICT + CATEGORIES_DICT = dict() + for i, c in enumerate(categories_set): + CATEGORIES_DICT[c] = i + + global USER_INFO + USER_INFO = dict() + with package.open('ml-1m/users.dat') as user_file: + for line in user_file: + uid, gender, age, job, _ = line.strip().split("::") + USER_INFO[int(uid)] = UserInfo( + index=uid, gender=gender, age=age, job_id=job) + return fn + + +def __reader__(rand_seed=0, test_ratio=0.1, is_test=False): + fn = __initialize_meta_info__() + rand = random.Random(x=rand_seed) + with zipfile.ZipFile(file=fn) as package: + with package.open('ml-1m/ratings.dat') as rating: + for line in rating: + if (rand.random() < test_ratio) == is_test: + uid, mov_id, rating, _ = line.strip().split("::") + uid = int(uid) + mov_id = int(mov_id) + rating = float(rating) * 2 - 5.0 + + mov = MOVIE_INFO[mov_id] + usr = USER_INFO[uid] + yield usr.value() + mov.value() + [[rating]] + + +def __reader_creator__(**kwargs): + return lambda: __reader__(**kwargs) + + +train_creator = functools.partial(__reader_creator__, is_test=False) +test_creator = functools.partial(__reader_creator__, is_test=True) + + +def unittest(): + for train_count, _ in enumerate(train_creator()()): + pass + for test_count, _ in enumerate(test_creator()()): + pass + + print train_count, test_count + + +if __name__ == '__main__': + unittest() From 9435025b8e267c84431e3b606da8197d221062e3 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 27 Feb 2017 20:39:04 +0800 Subject: [PATCH 324/370] Add dependencies --- .travis.yml | 2 +- python/setup.py.in | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 3993dc31fdbf75..31d76ecb876f11 100644 --- a/.travis.yml +++ b/.travis.yml @@ -57,7 +57,7 @@ before_install: - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python # protobuf version. - - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker 'scikit-learn>=0.18.0' + - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker 'scikit-learn>=0.18.0' 'scipy>=0.18.0' script: - paddle/scripts/travis/main.sh notifications: diff --git a/python/setup.py.in b/python/setup.py.in index fbee7bca90e3ad..c7e46293b79fe0 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -17,6 +17,7 @@ setup(name='paddle', '': '${CMAKE_CURRENT_SOURCE_DIR}' }, install_requires = [ - 'scikit-learn>=0.18.0' + 'scikit-learn>=0.18.0', + 'scipy>=0.18.0', ] ) From 40427979a7f621257d7171dfaefeb3afe9a0b45e Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 27 Feb 2017 23:36:28 +0800 Subject: [PATCH 325/370] refine code --- demo/mnist/api_train_v2.py | 6 ++-- python/paddle/v2/data_feeder.py | 2 +- python/paddle/v2/data_type.py | 6 ++-- python/paddle/v2/layer.py | 1 + python/paddle/v2/parameters.py | 4 +-- python/paddle/v2/tests/CMakeLists.txt | 2 +- .../{topology_test.py => test_topology.py} | 21 +++++++++----- python/paddle/v2/topology.py | 29 ++++++++----------- python/paddle/v2/trainer.py | 14 +++------ 9 files changed, 41 insertions(+), 44 deletions(-) rename python/paddle/v2/tests/{topology_test.py => test_topology.py} (81%) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index cb1191eddd005a..a23ddfaca011e2 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -41,10 +41,12 @@ def event_handler(event): trainer.train( train_data_reader=train_reader, - topology=[cost], + topology=cost, parameters=parameters, event_handler=event_handler, - batch_size=32) # batch size should be refactor in Data reader + batch_size=32, # batch size should be refactor in Data reader + reader_dict={images.name: 0, + label.name: 1}) if __name__ == '__main__': diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py index 2a16d46dda47f8..3b106e100cff75 100644 --- a/python/paddle/v2/data_feeder.py +++ b/python/paddle/v2/data_feeder.py @@ -23,7 +23,7 @@ class DataFeeder(DataProviderConverter): """ DataFeeder converts the data returned by paddle.reader into a data structure of Arguments which is defined in the API. The paddle.reader usually returns - a list of mini-batch data entries. Each data entry in the list is one sampe. + a list of mini-batch data entries. Each data entry in the list is one sample. Each sample is a list or a tuple with one feature or multiple features. DataFeeder converts this mini-batch data entries into Arguments in order to feed it to C++ interface. diff --git a/python/paddle/v2/data_type.py b/python/paddle/v2/data_type.py index dd3ebfcb4267e1..522ddfdaacce44 100644 --- a/python/paddle/v2/data_type.py +++ b/python/paddle/v2/data_type.py @@ -13,10 +13,10 @@ # limitations under the License. from paddle.trainer.PyDataProvider2 import \ - InputType, dense_vector, sparse_binary_vector,\ + InputType, DataType, dense_vector, sparse_binary_vector,\ sparse_vector, integer_value, integer_value_sequence __all__ = [ - 'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector', - 'integer_value', 'integer_value_sequence' + 'InputType', 'DataType', 'dense_vector', 'sparse_binary_vector', + 'sparse_vector', 'integer_value', 'integer_value_sequence' ] diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index d15e6398f51f43..faf5b8bd87d7b8 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -284,6 +284,7 @@ def mixed(size=0, return MixedLayerV2(size, input, name, act, bias_attr, layer_attr) +LayerV2 = Layer data = DataLayerV2 AggregateLevel = conf_helps.layers.AggregateLevel ExpandLevel = conf_helps.layers.ExpandLevel diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index b8d4b287032cd3..7c3cde7727cbb1 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -2,7 +2,7 @@ import py_paddle.swig_paddle as api from paddle.proto.ParameterConfig_pb2 import ParameterConfig -import topology as v2_topology +from topology import Topology __all__ = ['Parameters', 'create'] @@ -13,7 +13,7 @@ def create(layers): :param layers: :return: """ - topology = v2_topology.Topology(layers) + topology = Topology(layers) pool = Parameters() for param in topology.proto().parameters: pool.__append_config__(param) diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt index c77df827d956ec..46b5d08b8761ea 100644 --- a/python/paddle/v2/tests/CMakeLists.txt +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -8,5 +8,5 @@ add_test(NAME test_v2_api add_test(NAME topology_test COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ - ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/topology_test.py + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/test_topology.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) diff --git a/python/paddle/v2/tests/topology_test.py b/python/paddle/v2/tests/test_topology.py similarity index 81% rename from python/paddle/v2/tests/topology_test.py rename to python/paddle/v2/tests/test_topology.py index be60a577beac5b..1bf55a5bc68dfd 100644 --- a/python/paddle/v2/tests/topology_test.py +++ b/python/paddle/v2/tests/test_topology.py @@ -30,14 +30,19 @@ def test_data_type(self): act=conf_helps.SoftmaxActivation()) cost = layer.classification_cost(input=inference, label=label) topo = topology.Topology(cost) - type = topo.data_type() - self.assertEqual(len(type), 2) - self.assertEqual(type[0][0], "pixel") - self.assertEqual(type[0][1].type, data_type.DataType.Dense) - self.assertEqual(type[0][1].dim, 784) - self.assertEqual(type[1][0], "label") - self.assertEqual(type[1][1].type, data_type.DataType.Index) - self.assertEqual(type[1][1].dim, 10) + data_types = topo.data_type() + self.assertEqual(len(data_types), 2) + pixel_data_type = filter(lambda type: type[0] == "pixel", data_types) + self.assertEqual(len(pixel_data_type), 1) + pixel_data_type = pixel_data_type[0] + self.assertEqual(pixel_data_type[1].type, data_type.DataType.Dense) + self.assertEqual(pixel_data_type[1].dim, 784) + + label_data_type = filter(lambda type: type[0] == "label", data_types) + self.assertEqual(len(label_data_type), 1) + label_data_type = label_data_type[0] + self.assertEqual(label_data_type[1].type, data_type.DataType.Index) + self.assertEqual(label_data_type[1].dim, 10) def test_get_layer(self): pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py index 9c57f1f8e607bd..a51b1073b4fc4f 100644 --- a/python/paddle/v2/topology.py +++ b/python/paddle/v2/topology.py @@ -49,30 +49,30 @@ def get_layer(self, name): result_layer = [] def find_layer_by_name(layer, layer_name): - if layer.name == layer_name and len(result_layer) == 0: + if len(result_layer) == 1: + return + elif layer.name == layer_name: result_layer.append(layer) - for parent_layer in layer.__parent_layers__.values(): - find_layer_by_name(parent_layer, layer_name) + else: + for parent_layer in layer.__parent_layers__.values(): + find_layer_by_name(parent_layer, layer_name) for layer in self.layers: find_layer_by_name(layer, name) + assert len(result_layer) == 1 return result_layer[0] - def data_layer(self): + def data_layers(self): """ get all data layer :return: """ - data_layers = [] + data_layers = set() def find_data_layer(layer): - assert isinstance(layer, layer.LayerV2) if isinstance(layer, v2_layer.DataLayerV2): - if len( - filter(lambda data_layer: data_layer.name == layer.name, - data_layers)) == 0: - data_layers.append(layer) + data_layers.add(layer) for parent_layer in layer.__parent_layers__.values(): find_data_layer(parent_layer) @@ -85,14 +85,9 @@ def data_type(self): """ get data_type from proto, such as: [('image', dense_vector(768)), ('label', integer_value(10))] - the order is the same with __model_config__.input_layer_names """ - data_types_lists = [] - for layer_name in self.__model_config__.input_layer_names: - data_types_lists.append( - (layer_name, self.get_layer(layer_name).type)) - - return data_types_lists + return [(data_layer.name, data_layer.type) + for data_layer in self.data_layers()] def __check_layer_type__(layer): diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 96a3ee4fd446c1..3bf2128e16b5ad 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -1,13 +1,12 @@ import collections import py_paddle.swig_paddle as api -from py_paddle import DataProviderConverter from data_feeder import DataFeeder +from topology import Topology from . import event as v2_event from . import optimizer as v2_optimizer from . import parameters as v2_parameters -from . import topology as v2_topology __all__ = ['ITrainer', 'SGD'] @@ -69,7 +68,6 @@ def train(self, test_data_reader=None, event_handler=None, batch_size=32, - data_types=None, reader_dict=None): """ Training method. Will train num_passes of input data. @@ -83,13 +81,12 @@ def train(self, occurred. :type event_handler: (BaseEvent) => None :param batch_size: Not important, will be removed after data refactor. - :param data_types: Not important, will be removed after data refactor. :return: """ if event_handler is None: event_handler = default_event_handler - topology = v2_topology.Topology(topology) + topology = Topology(topology) __check_train_args__(**locals()) @@ -109,10 +106,7 @@ def train(self, assert isinstance(pass_evaluator, api.Evaluator) out_args = api.Arguments.createArguments(0) - data_types_lists = [data_type[1] for data_type in topology.data_type()] - converter = DataProviderConverter(input_types=data_types_lists) - - feeder = DataFeeder(data_types, reader_dict) + feeder = DataFeeder(topology.data_type(), reader_dict) for pass_id in xrange(num_passes): event_handler(v2_event.BeginPass(pass_id)) @@ -195,7 +189,7 @@ def __check_train_args__(train_data_reader, topology, parameters, raise ValueError('test_data_reader should be a function, which can ' 'return a iterator') - if not isinstance(topology, v2_topology.Topology): + if not isinstance(topology, Topology): raise ValueError('topology should be a model config') if not isinstance(parameters, v2_parameters.Parameters): From 7fe21602f6b767c64e058ab99ad71a1db6bd5d74 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 27 Feb 2017 14:43:43 -0800 Subject: [PATCH 326/370] Rename config.py into common.py --- python/paddle/v2/dataset/cifar.py | 2 +- python/paddle/v2/dataset/{config.py => common.py} | 0 python/paddle/v2/dataset/mnist.py | 2 +- python/paddle/v2/dataset/movielens.py | 2 +- 4 files changed, 3 insertions(+), 3 deletions(-) rename python/paddle/v2/dataset/{config.py => common.py} (100%) diff --git a/python/paddle/v2/dataset/cifar.py b/python/paddle/v2/dataset/cifar.py index 2ac71c6effe9d5..accb32f117720f 100644 --- a/python/paddle/v2/dataset/cifar.py +++ b/python/paddle/v2/dataset/cifar.py @@ -11,7 +11,7 @@ import numpy -from config import download +from common import download __all__ = [ 'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator', diff --git a/python/paddle/v2/dataset/config.py b/python/paddle/v2/dataset/common.py similarity index 100% rename from python/paddle/v2/dataset/config.py rename to python/paddle/v2/dataset/common.py diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index db84f37aa4fc34..2f195bfb9688b0 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -1,7 +1,7 @@ import sklearn.datasets.mldata import sklearn.model_selection import numpy -from config import DATA_HOME +from common import DATA_HOME __all__ = ['train_creator', 'test_creator'] diff --git a/python/paddle/v2/dataset/movielens.py b/python/paddle/v2/dataset/movielens.py index 314329e91cadf8..dcffcff2f58c63 100644 --- a/python/paddle/v2/dataset/movielens.py +++ b/python/paddle/v2/dataset/movielens.py @@ -1,5 +1,5 @@ import zipfile -from config import download +from common import download import re import random import functools From b93722df95d3907782cdff034df360b79d1fd093 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 27 Feb 2017 23:07:33 +0000 Subject: [PATCH 327/370] Set data cache home directory to ~/.cache/paddle/dataset --- python/paddle/v2/dataset/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index 02a009f09c71cc..ae4a5383b03526 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -5,7 +5,7 @@ __all__ = ['DATA_HOME', 'download'] -DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set') +DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset') if not os.path.exists(DATA_HOME): os.makedirs(DATA_HOME) From 305566eed4a62a7af178b78ea21f89c8eb73b59b Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Mon, 27 Feb 2017 15:09:56 -0800 Subject: [PATCH 328/370] fix parameters.append_gradient_machine append_gradient_machine should append __gradient_machines__ in all conditions. --- python/paddle/v2/parameters.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index ea504d5104716d..f2c5a4d49cfc44 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -224,7 +224,8 @@ def append_gradient_machine(self, gradient_machine): except ValueError: # If no such parameter in gradient machine, then don't copy pass - self.__gradient_machines__.append(gradient_machine) + + self.__gradient_machines__.append(gradient_machine) def __get_parameter_in_gradient_machine__(gradient_machine, name): From 37e2b92089ed583ba9e73f615444c3b080cd1b63 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Mon, 27 Feb 2017 23:41:32 +0000 Subject: [PATCH 329/370] Add md5file into dataset/common.py, and unit test in tests/common_test.py --- python/paddle/v2/dataset/common.py | 13 +++++++++++-- python/paddle/v2/dataset/tests/common_test.py | 16 ++++++++++++++++ 2 files changed, 27 insertions(+), 2 deletions(-) create mode 100644 python/paddle/v2/dataset/tests/common_test.py diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index ae4a5383b03526..ff5ed76c0f732b 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -3,7 +3,7 @@ import shutil import urllib2 -__all__ = ['DATA_HOME', 'download'] +__all__ = ['DATA_HOME', 'download', 'md5file'] DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset') @@ -11,7 +11,7 @@ os.makedirs(DATA_HOME) -def download(url, md5): +def download(url, package_name, md5): filename = os.path.split(url)[-1] assert DATA_HOME is not None filepath = os.path.join(DATA_HOME, md5) @@ -34,3 +34,12 @@ def __file_ok__(): with open(__full_file__, mode='wb') as of: shutil.copyfileobj(fsrc=response, fdst=of) return __full_file__ + + +def md5file(fname): + hash_md5 = hashlib.md5() + f = open(fname, "rb") + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + f.close() + return hash_md5.hexdigest() diff --git a/python/paddle/v2/dataset/tests/common_test.py b/python/paddle/v2/dataset/tests/common_test.py new file mode 100644 index 00000000000000..d2f97f06de3db9 --- /dev/null +++ b/python/paddle/v2/dataset/tests/common_test.py @@ -0,0 +1,16 @@ +import paddle.v2.dataset.common +import unittest +import tempfile + +class TestCommon(unittest.TestCase): + def test_md5file(self): + _, temp_path =tempfile.mkstemp() + f = open(temp_path, 'w') + f.write("Hello\n") + f.close() + self.assertEqual( + '09f7e02f1290be211da707a266f153b3', + paddle.v2.dataset.common.md5file(temp_path)) + +if __name__ == '__main__': + unittest.main() From 91115ab6de016806a5d3ad168b114f54b1eaac87 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 00:17:52 +0000 Subject: [PATCH 330/370] Use module name and raw data filename as the local filename --- python/paddle/v2/dataset/common.py | 42 +++++++------------ python/paddle/v2/dataset/tests/common_test.py | 12 ++++-- 2 files changed, 25 insertions(+), 29 deletions(-) diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index ff5ed76c0f732b..b1831f38afb4f1 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -1,7 +1,7 @@ +import requests import hashlib import os import shutil -import urllib2 __all__ = ['DATA_HOME', 'download', 'md5file'] @@ -11,31 +11,6 @@ os.makedirs(DATA_HOME) -def download(url, package_name, md5): - filename = os.path.split(url)[-1] - assert DATA_HOME is not None - filepath = os.path.join(DATA_HOME, md5) - if not os.path.exists(filepath): - os.makedirs(filepath) - __full_file__ = os.path.join(filepath, filename) - - def __file_ok__(): - if not os.path.exists(__full_file__): - return False - md5_hash = hashlib.md5() - with open(__full_file__, 'rb') as f: - for chunk in iter(lambda: f.read(4096), b""): - md5_hash.update(chunk) - - return md5_hash.hexdigest() == md5 - - while not __file_ok__(): - response = urllib2.urlopen(url) - with open(__full_file__, mode='wb') as of: - shutil.copyfileobj(fsrc=response, fdst=of) - return __full_file__ - - def md5file(fname): hash_md5 = hashlib.md5() f = open(fname, "rb") @@ -43,3 +18,18 @@ def md5file(fname): hash_md5.update(chunk) f.close() return hash_md5.hexdigest() + + +def download(url, module_name, md5sum): + dirname = os.path.join(DATA_HOME, module_name) + if not os.path.exists(dirname): + os.makedirs(dirname) + + filename = os.path.join(dirname, url.split('/')[-1]) + if not (os.path.exists(filename) and md5file(filename) == md5sum): + # If file doesn't exist or MD5 doesn't match, then download. + r = requests.get(url, stream=True) + with open(filename, 'w') as f: + shutil.copyfileobj(r.raw, f) + + return filename diff --git a/python/paddle/v2/dataset/tests/common_test.py b/python/paddle/v2/dataset/tests/common_test.py index d2f97f06de3db9..0672a4671430a9 100644 --- a/python/paddle/v2/dataset/tests/common_test.py +++ b/python/paddle/v2/dataset/tests/common_test.py @@ -5,12 +5,18 @@ class TestCommon(unittest.TestCase): def test_md5file(self): _, temp_path =tempfile.mkstemp() - f = open(temp_path, 'w') - f.write("Hello\n") - f.close() + with open(temp_path, 'w') as f: + f.write("Hello\n") self.assertEqual( '09f7e02f1290be211da707a266f153b3', paddle.v2.dataset.common.md5file(temp_path)) + def test_download(self): + yi_avatar = 'https://avatars0.githubusercontent.com/u/1548775?v=3&s=460' + self.assertEqual( + paddle.v2.dataset.common.DATA_HOME + '/test/1548775?v=3&s=460', + paddle.v2.dataset.common.download( + yi_avatar, 'test', 'f75287202d6622414c706c36c16f8e0d')) + if __name__ == '__main__': unittest.main() From 792875e3eaa0467e40748f0ed97f022fe7fdcd0b Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 09:56:27 +0800 Subject: [PATCH 331/370] Lazy initialize mnist dataset. Fix unittest --- python/paddle/v2/dataset/mnist.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index db84f37aa4fc34..faae818a5d7b78 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -16,18 +16,29 @@ def reader(): TEST_SIZE = 10000 +X_train = None +X_test = None +y_train = None +y_test = None -data = sklearn.datasets.mldata.fetch_mldata( - "MNIST original", data_home=DATA_HOME) -X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - data.data, data.target, test_size=TEST_SIZE, random_state=0) + +def __initialize_dataset__(): + global X_train, X_test, y_train, y_test + if X_train is not None: + return + data = sklearn.datasets.mldata.fetch_mldata( + "MNIST original", data_home=DATA_HOME) + X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( + data.data, data.target, test_size=TEST_SIZE, random_state=0) def train_creator(): + __initialize_dataset__() return __mnist_reader_creator__(X_train, y_train) def test_creator(): + __initialize_dataset__() return __mnist_reader_creator__(X_test, y_test) From d6c62e852d7788ba81e704323995874b85f89c3e Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 02:26:30 +0000 Subject: [PATCH 332/370] Rewrite mnist.py and add mnist_test.py --- python/paddle/v2/dataset/mnist.py | 74 ++++++++++++++------ python/paddle/v2/dataset/tests/mnist_test.py | 27 +++++++ 2 files changed, 78 insertions(+), 23 deletions(-) create mode 100644 python/paddle/v2/dataset/tests/mnist_test.py diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 2f195bfb9688b0..29fc20eae9b06e 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -1,39 +1,67 @@ -import sklearn.datasets.mldata -import sklearn.model_selection +import paddle.v2.dataset.common +import subprocess import numpy -from common import DATA_HOME -__all__ = ['train_creator', 'test_creator'] +URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/' +TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz' +TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6' -def __mnist_reader_creator__(data, target): - def reader(): - n_samples = data.shape[0] - for i in xrange(n_samples): - yield (data[i] / 255.0).astype(numpy.float32), int(target[i]) +TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz' +TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688' + +TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz' +TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873' - return reader +TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz' +TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432' -TEST_SIZE = 10000 +def reader_creator(image_filename, label_filename, buffer_size): + def reader(): + # According to http://stackoverflow.com/a/38061619/724872, we + # cannot use standard package gzip here. + m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE) + m.stdout.read(16) # skip some magic bytes + + l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE) + l.stdout.read(8) # skip some magic bytes -data = sklearn.datasets.mldata.fetch_mldata( - "MNIST original", data_home=DATA_HOME) -X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( - data.data, data.target, test_size=TEST_SIZE, random_state=0) + while True: + labels = numpy.fromfile( + l.stdout, 'ubyte', count=buffer_size + ).astype("int") + if labels.size != buffer_size: + break # numpy.fromfile returns empty slice after EOF. -def train_creator(): - return __mnist_reader_creator__(X_train, y_train) + images = numpy.fromfile( + m.stdout, 'ubyte', count=buffer_size * 28 * 28 + ).reshape((buffer_size, 28 * 28) + ).astype('float32') + images = images / 255.0 * 2.0 - 1.0 -def test_creator(): - return __mnist_reader_creator__(X_test, y_test) + for i in xrange(buffer_size): + yield images[i, :], labels[i] + m.terminate() + l.terminate() -def unittest(): - assert len(list(test_creator()())) == TEST_SIZE + return reader() +def train(): + return reader_creator( + paddle.v2.dataset.common.download( + TRAIN_IMAGE_URL, 'mnist', TRAIN_IMAGE_MD5), + paddle.v2.dataset.common.download( + TRAIN_LABEL_URL, 'mnist', TRAIN_LABEL_MD5), + 100) -if __name__ == '__main__': - unittest() +def test(): + return reader_creator( + paddle.v2.dataset.common.download( + TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5), + paddle.v2.dataset.common.download( + TEST_LABEL_URL, 'mnist', TEST_LABEL_MD5), + 100) diff --git a/python/paddle/v2/dataset/tests/mnist_test.py b/python/paddle/v2/dataset/tests/mnist_test.py new file mode 100644 index 00000000000000..23ed2eaba8a761 --- /dev/null +++ b/python/paddle/v2/dataset/tests/mnist_test.py @@ -0,0 +1,27 @@ +import paddle.v2.dataset.mnist +import unittest + +class TestMNIST(unittest.TestCase): + def check_reader(self, reader): + sum = 0 + for l in reader: + self.assertEqual(l[0].size, 784) + self.assertEqual(l[1].size, 1) + self.assertLess(l[1], 10) + self.assertGreaterEqual(l[1], 0) + sum += 1 + return sum + + def test_train(self): + self.assertEqual( + self.check_reader(paddle.v2.dataset.mnist.train()), + 60000) + + def test_test(self): + self.assertEqual( + self.check_reader(paddle.v2.dataset.mnist.test()), + 10000) + + +if __name__ == '__main__': + unittest.main() From 20d9220c9145e937da9f0fb9b99323a06b6983bc Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 28 Feb 2017 10:27:46 +0800 Subject: [PATCH 333/370] change the parameter topology of trainer to cost --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/trainer.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index a23ddfaca011e2..8a612cbc66d54b 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -41,7 +41,7 @@ def event_handler(event): trainer.train( train_data_reader=train_reader, - topology=cost, + cost=cost, parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 3bf2128e16b5ad..be33b910807ff3 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -62,7 +62,7 @@ def __init__(self, update_equation): def train(self, train_data_reader, - topology, + cost, parameters, num_passes=1, test_data_reader=None, @@ -73,7 +73,7 @@ def train(self, Training method. Will train num_passes of input data. :param train_data_reader: - :param topology: cost layers, use one or more Layers to represent it. + :param cost: cost layers, to be optimized. :param parameters: The parameter pools. :param num_passes: The total train passes. :param test_data_reader: @@ -86,7 +86,7 @@ def train(self, if event_handler is None: event_handler = default_event_handler - topology = Topology(topology) + topology = Topology(cost) __check_train_args__(**locals()) From 5f5e5c32e50099df02e516647192c5a07b588f99 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 28 Feb 2017 10:33:46 +0800 Subject: [PATCH 334/370] modify train in ITrainer --- python/paddle/v2/trainer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index be33b910807ff3..2aeddaff89748e 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -29,7 +29,7 @@ class ITrainer(object): def train(self, train_data_reader, - topology, + cost, parameters, test_data_reader=None, event_handler=None): @@ -37,7 +37,7 @@ def train(self, train method. :param train_data_reader: - :param topology: + :param cost: :param parameters: :param test_data_reader: :param event_handler: From dcbfbb15338e0ca0f195e12ce0e0275995622ca1 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 02:46:31 +0000 Subject: [PATCH 335/370] yapf format --- python/paddle/v2/dataset/mnist.py | 34 +++++++++---------- python/paddle/v2/dataset/tests/common_test.py | 9 ++--- python/paddle/v2/dataset/tests/mnist_test.py | 7 ++-- 3 files changed, 24 insertions(+), 26 deletions(-) diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 29fc20eae9b06e..ec334d39e6c049 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -22,23 +22,21 @@ def reader(): # According to http://stackoverflow.com/a/38061619/724872, we # cannot use standard package gzip here. m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE) - m.stdout.read(16) # skip some magic bytes + m.stdout.read(16) # skip some magic bytes l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE) - l.stdout.read(8) # skip some magic bytes + l.stdout.read(8) # skip some magic bytes while True: labels = numpy.fromfile( - l.stdout, 'ubyte', count=buffer_size - ).astype("int") + l.stdout, 'ubyte', count=buffer_size).astype("int") if labels.size != buffer_size: - break # numpy.fromfile returns empty slice after EOF. + break # numpy.fromfile returns empty slice after EOF. images = numpy.fromfile( - m.stdout, 'ubyte', count=buffer_size * 28 * 28 - ).reshape((buffer_size, 28 * 28) - ).astype('float32') + m.stdout, 'ubyte', count=buffer_size * 28 * 28).reshape( + (buffer_size, 28 * 28)).astype('float32') images = images / 255.0 * 2.0 - 1.0 @@ -50,18 +48,18 @@ def reader(): return reader() + def train(): return reader_creator( - paddle.v2.dataset.common.download( - TRAIN_IMAGE_URL, 'mnist', TRAIN_IMAGE_MD5), - paddle.v2.dataset.common.download( - TRAIN_LABEL_URL, 'mnist', TRAIN_LABEL_MD5), - 100) + paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist', + TRAIN_IMAGE_MD5), + paddle.v2.dataset.common.download(TRAIN_LABEL_URL, 'mnist', + TRAIN_LABEL_MD5), 100) + def test(): return reader_creator( - paddle.v2.dataset.common.download( - TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5), - paddle.v2.dataset.common.download( - TEST_LABEL_URL, 'mnist', TEST_LABEL_MD5), - 100) + paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist', + TEST_IMAGE_MD5), + paddle.v2.dataset.common.download(TEST_LABEL_URL, 'mnist', + TEST_LABEL_MD5), 100) diff --git a/python/paddle/v2/dataset/tests/common_test.py b/python/paddle/v2/dataset/tests/common_test.py index 0672a4671430a9..7d8406171b8478 100644 --- a/python/paddle/v2/dataset/tests/common_test.py +++ b/python/paddle/v2/dataset/tests/common_test.py @@ -2,14 +2,14 @@ import unittest import tempfile + class TestCommon(unittest.TestCase): def test_md5file(self): - _, temp_path =tempfile.mkstemp() + _, temp_path = tempfile.mkstemp() with open(temp_path, 'w') as f: f.write("Hello\n") - self.assertEqual( - '09f7e02f1290be211da707a266f153b3', - paddle.v2.dataset.common.md5file(temp_path)) + self.assertEqual('09f7e02f1290be211da707a266f153b3', + paddle.v2.dataset.common.md5file(temp_path)) def test_download(self): yi_avatar = 'https://avatars0.githubusercontent.com/u/1548775?v=3&s=460' @@ -18,5 +18,6 @@ def test_download(self): paddle.v2.dataset.common.download( yi_avatar, 'test', 'f75287202d6622414c706c36c16f8e0d')) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/v2/dataset/tests/mnist_test.py b/python/paddle/v2/dataset/tests/mnist_test.py index 23ed2eaba8a761..e4f0b33d5207b2 100644 --- a/python/paddle/v2/dataset/tests/mnist_test.py +++ b/python/paddle/v2/dataset/tests/mnist_test.py @@ -1,6 +1,7 @@ import paddle.v2.dataset.mnist import unittest + class TestMNIST(unittest.TestCase): def check_reader(self, reader): sum = 0 @@ -14,13 +15,11 @@ def check_reader(self, reader): def test_train(self): self.assertEqual( - self.check_reader(paddle.v2.dataset.mnist.train()), - 60000) + self.check_reader(paddle.v2.dataset.mnist.train()), 60000) def test_test(self): self.assertEqual( - self.check_reader(paddle.v2.dataset.mnist.test()), - 10000) + self.check_reader(paddle.v2.dataset.mnist.test()), 10000) if __name__ == '__main__': From 6bc82c8eb8478e2fe0911a8e43ddc7ed3539a372 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 02:56:48 +0000 Subject: [PATCH 336/370] Add __all__ to mnist.py --- python/paddle/v2/dataset/mnist.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index ec334d39e6c049..8ba11ca5ec7943 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -2,17 +2,16 @@ import subprocess import numpy +__all__ = ['train', 'test'] + URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/' TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz' TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6' - TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz' TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688' - TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz' TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873' - TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz' TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432' From 7664f78fd9b75f96f011f67a034fc28148791adb Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 28 Feb 2017 11:28:39 +0800 Subject: [PATCH 337/370] fix dead link --- doc/howto/usage/cluster/cluster_train_cn.md | 2 +- doc/howto/usage/cluster/cluster_train_en.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/howto/usage/cluster/cluster_train_cn.md b/doc/howto/usage/cluster/cluster_train_cn.md index acdcfa1c0047ce..274452fbf0c595 100644 --- a/doc/howto/usage/cluster/cluster_train_cn.md +++ b/doc/howto/usage/cluster/cluster_train_cn.md @@ -6,7 +6,7 @@ 在本文中，我们将阐释如何在集群上运行分布式 Paddle 训练作业。我们将以[推荐系统](https://github.com/baidu/Paddle/tree/develop/demo/recommendation)为例创建分布式的单进程训练。 -在本文中使用的[脚本](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train)通过 SSH 运行分布式作业。 它们还可以供那些运行更复杂的集群管理系统（如 MPI 和 [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/k8s) ）的用户参考。 +在本文中使用的[脚本](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train)通过 SSH 运行分布式作业。 它们还可以供那些运行更复杂的集群管理系统（如 MPI 和 [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/k8s) ）的用户参考。 ## 前提条件 diff --git a/doc/howto/usage/cluster/cluster_train_en.md b/doc/howto/usage/cluster/cluster_train_en.md index 30963dcd927250..c60876721cbf55 100644 --- a/doc/howto/usage/cluster/cluster_train_en.md +++ b/doc/howto/usage/cluster/cluster_train_en.md @@ -2,7 +2,7 @@ In this article, we explain how to run distributed Paddle training jobs on clusters. We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation). -[Scripts](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train) used in this article launch distributed jobs via SSH. They also work as a reference for users running more sophisticated cluster management systems like MPI and [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/k8s). +[Scripts](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train) used in this article launch distributed jobs via SSH. They also work as a reference for users running more sophisticated cluster management systems like MPI and [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/k8s). ## Prerequisite From 4eb54c2437aef7c857151a2a3e4fd24aa51d5336 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 28 Feb 2017 04:30:30 +0000 Subject: [PATCH 338/370] Debug unit tests --- python/paddle/v2/dataset/cifar.py | 85 ++++++++------------ python/paddle/v2/dataset/common.py | 1 - python/paddle/v2/dataset/mnist.py | 10 ++- python/paddle/v2/dataset/tests/cifar_test.py | 42 ++++++++++ python/paddle/v2/dataset/tests/mnist_test.py | 22 ++--- 5 files changed, 93 insertions(+), 67 deletions(-) create mode 100644 python/paddle/v2/dataset/tests/cifar_test.py diff --git a/python/paddle/v2/dataset/cifar.py b/python/paddle/v2/dataset/cifar.py index accb32f117720f..77c54bd268b5d9 100644 --- a/python/paddle/v2/dataset/cifar.py +++ b/python/paddle/v2/dataset/cifar.py @@ -1,82 +1,61 @@ """ -CIFAR Dataset. - -URL: https://www.cs.toronto.edu/~kriz/cifar.html - -the default train_creator, test_creator used for CIFAR-10 dataset. +CIFAR dataset: https://www.cs.toronto.edu/~kriz/cifar.html """ import cPickle import itertools -import tarfile - import numpy +import paddle.v2.dataset.common +import tarfile -from common import download - -__all__ = [ - 'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator', - 'test_creator' -] +__all__ = ['train100', 'test100', 'train10', 'test10'] -CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' +URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/' +CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz' CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a' -CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' +CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz' CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85' -def __read_batch__(filename, sub_name): - def reader(): - def __read_one_batch_impl__(batch): - data = batch['data'] - labels = batch.get('labels', batch.get('fine_labels', None)) - assert labels is not None - for sample, label in itertools.izip(data, labels): - yield (sample / 255.0).astype(numpy.float32), int(label) +def reader_creator(filename, sub_name): + def read_batch(batch): + data = batch['data'] + labels = batch.get('labels', batch.get('fine_labels', None)) + assert labels is not None + for sample, label in itertools.izip(data, labels): + yield (sample / 255.0).astype(numpy.float32), int(label) + def reader(): with tarfile.open(filename, mode='r') as f: names = (each_item.name for each_item in f if sub_name in each_item.name) for name in names: batch = cPickle.load(f.extractfile(name)) - for item in __read_one_batch_impl__(batch): + for item in read_batch(batch): yield item return reader -def cifar_100_train_creator(): - fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5) - return __read_batch__(fn, 'train') - - -def cifar_100_test_creator(): - fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5) - return __read_batch__(fn, 'test') - - -def train_creator(): - """ - Default train reader creator. Use CIFAR-10 dataset. - """ - fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5) - return __read_batch__(fn, 'data_batch') +def train100(): + return reader_creator( + paddle.v2.dataset.common.download(CIFAR100_URL, 'cifar', CIFAR100_MD5), + 'train') -def test_creator(): - """ - Default test reader creator. Use CIFAR-10 dataset. - """ - fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5) - return __read_batch__(fn, 'test_batch') +def test100(): + return reader_creator( + paddle.v2.dataset.common.download(CIFAR100_URL, 'cifar', CIFAR100_MD5), + 'test') -def unittest(): - for _ in train_creator()(): - pass - for _ in test_creator()(): - pass +def train10(): + return reader_creator( + paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5), + 'data_batch') -if __name__ == '__main__': - unittest() +def test10(): + return reader_creator( + paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5), + 'test_batch') diff --git a/python/paddle/v2/dataset/common.py b/python/paddle/v2/dataset/common.py index b1831f38afb4f1..a5ffe25a116e9b 100644 --- a/python/paddle/v2/dataset/common.py +++ b/python/paddle/v2/dataset/common.py @@ -27,7 +27,6 @@ def download(url, module_name, md5sum): filename = os.path.join(dirname, url.split('/')[-1]) if not (os.path.exists(filename) and md5file(filename) == md5sum): - # If file doesn't exist or MD5 doesn't match, then download. r = requests.get(url, stream=True) with open(filename, 'w') as f: shutil.copyfileobj(r.raw, f) diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 8ba11ca5ec7943..a36c20e3fa3734 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -1,11 +1,13 @@ +""" +MNIST dataset. +""" +import numpy import paddle.v2.dataset.common import subprocess -import numpy __all__ = ['train', 'test'] URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/' - TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz' TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6' TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz' @@ -40,12 +42,12 @@ def reader(): images = images / 255.0 * 2.0 - 1.0 for i in xrange(buffer_size): - yield images[i, :], labels[i] + yield images[i, :], int(labels[i]) m.terminate() l.terminate() - return reader() + return reader def train(): diff --git a/python/paddle/v2/dataset/tests/cifar_test.py b/python/paddle/v2/dataset/tests/cifar_test.py new file mode 100644 index 00000000000000..a2af45ecf50846 --- /dev/null +++ b/python/paddle/v2/dataset/tests/cifar_test.py @@ -0,0 +1,42 @@ +import paddle.v2.dataset.cifar +import unittest + + +class TestCIFAR(unittest.TestCase): + def check_reader(self, reader): + sum = 0 + label = 0 + for l in reader(): + self.assertEqual(l[0].size, 3072) + if l[1] > label: + label = l[1] + sum += 1 + return sum, label + + def test_test10(self): + instances, max_label_value = self.check_reader( + paddle.v2.dataset.cifar.test10()) + self.assertEqual(instances, 10000) + self.assertEqual(max_label_value, 9) + + def test_train10(self): + instances, max_label_value = self.check_reader( + paddle.v2.dataset.cifar.train10()) + self.assertEqual(instances, 50000) + self.assertEqual(max_label_value, 9) + + def test_test100(self): + instances, max_label_value = self.check_reader( + paddle.v2.dataset.cifar.test100()) + self.assertEqual(instances, 10000) + self.assertEqual(max_label_value, 99) + + def test_train100(self): + instances, max_label_value = self.check_reader( + paddle.v2.dataset.cifar.train100()) + self.assertEqual(instances, 50000) + self.assertEqual(max_label_value, 99) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/dataset/tests/mnist_test.py b/python/paddle/v2/dataset/tests/mnist_test.py index e4f0b33d5207b2..b4408cc2f590d4 100644 --- a/python/paddle/v2/dataset/tests/mnist_test.py +++ b/python/paddle/v2/dataset/tests/mnist_test.py @@ -5,21 +5,25 @@ class TestMNIST(unittest.TestCase): def check_reader(self, reader): sum = 0 - for l in reader: + label = 0 + for l in reader(): self.assertEqual(l[0].size, 784) - self.assertEqual(l[1].size, 1) - self.assertLess(l[1], 10) - self.assertGreaterEqual(l[1], 0) + if l[1] > label: + label = l[1] sum += 1 - return sum + return sum, label def test_train(self): - self.assertEqual( - self.check_reader(paddle.v2.dataset.mnist.train()), 60000) + instances, max_label_value = self.check_reader( + paddle.v2.dataset.mnist.train()) + self.assertEqual(instances, 60000) + self.assertEqual(max_label_value, 9) def test_test(self): - self.assertEqual( - self.check_reader(paddle.v2.dataset.mnist.test()), 10000) + instances, max_label_value = self.check_reader( + paddle.v2.dataset.mnist.test()) + self.assertEqual(instances, 10000) + self.assertEqual(max_label_value, 9) if __name__ == '__main__': From 375899339330d00852433ced1b7f02663040c7fb Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 13:29:24 +0800 Subject: [PATCH 339/370] Simplify layer.v2 --- .../default_decorators.py | 4 + .../paddle/trainer_config_helpers/layers.py | 6 + python/paddle/v2/layer.py | 143 ++++++------------ python/paddle/v2/tests/test_layer.py | 6 +- 4 files changed, 61 insertions(+), 98 deletions(-) diff --git a/python/paddle/trainer_config_helpers/default_decorators.py b/python/paddle/trainer_config_helpers/default_decorators.py index ad3efcbf369411..2f25579fcdd979 100644 --- a/python/paddle/trainer_config_helpers/default_decorators.py +++ b/python/paddle/trainer_config_helpers/default_decorators.py @@ -52,6 +52,10 @@ def __wrapper__(*args, **kwargs): kwargs[name] = default_factory(func) return func(*args, **kwargs) + if hasattr(func, 'argspec'): + __wrapper__.argspec = func.argspec + else: + __wrapper__.argspec = inspect.getargspec(func) return __wrapper__ return __impl__ diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bb1a01d509e64..b68460b6a3ab62 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -14,6 +14,7 @@ import functools import collections +import inspect from paddle.trainer.config_parser import * from .activations import LinearActivation, SigmoidActivation, TanhActivation, \ @@ -316,6 +317,11 @@ def wrapper(*args, **kwargs): val.check(method.__name__) return method(*args, **kwargs) + if hasattr(method, 'argspec'): + wrapper.argspec = method.argspec + else: + wrapper.argspec = inspect.getargspec(method) + return wrapper return decorator diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index d15e6398f51f43..d68b66cf02ce89 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -67,6 +67,7 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. """ import collections +import inspect import paddle.trainer_config_helpers as conf_helps from paddle.trainer_config_helpers.config_parser_utils import \ @@ -74,26 +75,14 @@ class for each layer creation function in paddle.trainer_config_helpers.layers. from paddle.trainer_config_helpers.default_decorators import wrap_name_default from paddle.trainer_config_helpers.default_decorators import wrap_act_default -from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default +from paddle.trainer_config_helpers.default_decorators import \ + wrap_bias_attr_default from paddle.trainer_config_helpers.layers import layer_support import data_type import activation -import attr - -__all__ = [ - 'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp', - 'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent', - 'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat', - 'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto', - 'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling', - 'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id', - 'pad', 'classification_cost', 'cross_entropy_cost', - 'cross_entropy_with_selfnorm_cost', 'regression_cost', - 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', - 'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce', - 'hsigmoid', 'eos' -] + +__all__ = ['parse_network', 'data'] __projection_names__ = filter(lambda x: x.endswith('_projection'), dir(conf_helps)) @@ -288,83 +277,51 @@ def mixed(size=0, AggregateLevel = conf_helps.layers.AggregateLevel ExpandLevel = conf_helps.layers.ExpandLevel -layer_list = [ - # [V2LayerImpl, V1_method_name, parent_names] - # fully connected layers - ['fc', 'fc_layer', ['input']], - # conv layers - ['conv_shift', 'conv_shift_layer', ['a', 'b']], - ['img_conv', 'img_conv_layer', ['input']], - # image pooling layers - ['img_pool', 'img_pool_layer', ['input']], - ['spp', 'spp_layer', ['input']], - ['maxout', 'maxout_layer', ['input']], - # norm layers - ['img_cmrnorm', 'img_cmrnorm_layer', ['input']], - ['batch_norm', 'batch_norm_layer', ['input']], - ['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']], - # recurrent layers - ['recurrent', 'recurrent_layer', ['input']], - ['lstmemory', 'lstmemory', ['input']], - ['grumemory', 'grumemory', ['input']], - # aggregate layers - ['pool', 'pooling_layer', ['input']], - ['last_seq', 'last_seq', ['input']], - ['first_seq', 'first_seq', ['input']], - ['concat', 'concat_layer', ['input']], - ['seq_concat', 'seq_concat_layer', ['a', 'b']], - # reshaping layers - ['block_expand', 'block_expand_layer', ['input']], - ['expand', 'expand_layer', ['input', 'expand_as']], - ['repeat', 'repeat_layer', ['input']], - ['rotate', 'rotate_layer', ['input']], - ['seq_reshape', 'seq_reshape_layer', ['input']], - # math layers - ['addto', 'addto_layer', ['input']], - ['linear_comb', 'linear_comb_layer', ['weights', 'vectors']], - ['interpolation', 'interpolation_layer', ['input', 'weight']], - ['bilinear_interp', 'bilinear_interp_layer', ['input']], - ['power', 'power_layer', ['input', 'weight']], - ['scaling', 'scaling_layer', ['input', 'weight']], - ['slope_intercept', 'slope_intercept_layer', ['input']], - ['tensor', 'tensor_layer', ['a', 'b']], - ['cos_sim', 'cos_sim', ['a', 'b']], - ['trans', 'trans_layer', ['input']], - # sampling layers - ['max_id', 'maxid_layer', ['input']], - ['sampling_id', 'sampling_id_layer', ['input']], - # slicing and joining layers - ['pad', 'pad_layer', ['input']], - # cost layers - [ - 'classification_cost', 'classification_cost', - ['input', 'label', 'weight'] - ], - ['regression_cost', 'regression_cost', ['input', 'label', 'weight']], - ['cross_entropy_cost', 'cross_entropy', ['input', 'label']], - [ - 'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm', - ['input', 'label'] - ], - [ - 'multi_binary_label_cross_entropy_cost', - 'multi_binary_label_cross_entropy', ['input', 'label'] - ], - ['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']], - ['lambda_cost', 'lambda_cost', ['input', 'score']], - ['sum_cost', 'sum_cost', ['input']], - ['huber_cost', 'huber_cost', ['input', 'label']], - ['crf', 'crf_layer', ['input', 'label']], - ['crf_decoding', 'crf_decoding_layer', ['input']], - ['ctc', 'ctc_layer', ['input', 'label']], - ['warp_ctc', 'warp_ctc_layer', ['input', 'label']], - ['nce', 'nce_layer', ['input', 'label']], - ['hsigmoid', 'hsigmoid', ['input', 'label']], - # check layers - ['eos', 'eos_layer', ['input']] -] -for l in layer_list: - globals()[l[0]] = __convert_to_v2__(l[1], l[2]) + +def __layer_name_mapping__(inname): + if inname in ['data_layer', 'memory', 'mixed_layer']: + # Do Not handle these layers + return + elif inname == 'maxid_layer': + return 'max_id' + elif inname.endswith('memory') or inname.endswith( + '_seq') or inname.endswith('_sim') or inname == 'hsigmoid': + return inname + elif inname in [ + 'cross_entropy', 'multi_binary_label_cross_entropy', + 'cross_entropy_with_selfnorm' + ]: + return inname + "_cost" + elif inname.endswith('_cost'): + return inname + elif inname.endswith("_layer"): + return inname[:-len("_layer")] + + +def __layer_name_mapping_parent_names__(inname): + all_args = getattr(conf_helps, inname).argspec.args + return filter( + lambda x: x in ['input1', 'input2','label', 'input', 'a', 'b', 'expand_as', + 'weights', 'vectors', 'weight', 'score', 'left', 'right'], + all_args) + + +def __convert_layer__(_new_name_, _old_name_, _parent_names_): + global __all__ + __all__.append(_new_name_) + globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_) + + +for each_layer_name in dir(conf_helps): + new_name = __layer_name_mapping__(each_layer_name) + if new_name is not None: + parent_names = __layer_name_mapping_parent_names__(each_layer_name) + assert len(parent_names) != 0, each_layer_name + __convert_layer__(new_name, each_layer_name, parent_names) + +del parent_names +del new_name +del each_layer_name # convert projection for prj in __projection_names__: diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index bb0099ea2fbb78..b138ddbbe6c0a4 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -11,17 +11,13 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import difflib import unittest -import paddle.trainer_config_helpers as conf_helps import paddle.v2.activation as activation import paddle.v2.attr as attr import paddle.v2.data_type as data_type import paddle.v2.layer as layer import paddle.v2.pooling as pooling -from paddle.trainer_config_helpers.config_parser_utils import \ - parse_network_config as parse_network pixel = layer.data(name='pixel', type=data_type.dense_vector(128)) label = layer.data(name='label', type=data_type.integer_value(10)) @@ -70,7 +66,7 @@ def test_norm_layer(self): class AggregateLayerTest(unittest.TestCase): def test_aggregate_layer(self): - pool = layer.pool( + pool = layer.pooling( input=pixel, pooling_type=pooling.Avg(), agg_level=layer.AggregateLevel.EACH_SEQUENCE) From 0432e9073984d0c7ce3329043cb3d6fe6ef65a8a Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 13:44:38 +0800 Subject: [PATCH 340/370] remove unused python package in TravisCI --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 31d76ecb876f11..4fb2ca938795bb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -57,7 +57,7 @@ before_install: - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python # protobuf version. - - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker 'scikit-learn>=0.18.0' 'scipy>=0.18.0' + - pip install numpy wheel 'protobuf==3.1' sphinx recommonmark sphinx_rtd_theme virtualenv pre-commit requests==2.9.2 LinkChecker script: - paddle/scripts/travis/main.sh notifications: From 01ad3bfdb9daf494967a0789bcad09057c11bdb6 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 13:52:57 +0800 Subject: [PATCH 341/370] Fix reader creator in mnist --- demo/mnist/api_train_v2.py | 2 +- python/paddle/v2/dataset/mnist.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index c9fd7c72df8794..1c2c831bec18ef 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -33,7 +33,7 @@ def event_handler(event): trainer.train( reader=paddle.reader.batched( paddle.reader.shuffle( - paddle.dataset.mnist.train_creator(), buf_size=8192), + paddle.dataset.mnist.train(), buf_size=8192), batch_size=32), cost=cost, parameters=parameters, diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 8ba11ca5ec7943..653c91aacde661 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -45,7 +45,7 @@ def reader(): m.terminate() l.terminate() - return reader() + return reader def train(): From c852003d6b2e11c8d0f75d5c0adf9d332dc429f1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 14:00:22 +0800 Subject: [PATCH 342/370] Fix errors in mnist dataset --- demo/mnist/api_train_v2.py | 1 - python/paddle/v2/dataset/mnist.py | 12 ++++++++++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 1c2c831bec18ef..612b0d218fc0be 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -38,7 +38,6 @@ def event_handler(event): cost=cost, parameters=parameters, event_handler=event_handler, - batch_size=32, # batch size should be refactor in Data reader reader_dict={images.name: 0, label.name: 1}) diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index 653c91aacde661..045bcfcc805ec0 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -1,6 +1,7 @@ import paddle.v2.dataset.common import subprocess import numpy +import platform __all__ = ['train', 'test'] @@ -18,12 +19,19 @@ def reader_creator(image_filename, label_filename, buffer_size): def reader(): + if platform.system() == 'Darwin': + zcat_cmd = 'gzcat' + elif platform.system() == 'Linux': + zcat_cmd = 'zcat' + else: + raise NotImplementedError() + # According to http://stackoverflow.com/a/38061619/724872, we # cannot use standard package gzip here. - m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE) + m = subprocess.Popen([zcat_cmd, image_filename], stdout=subprocess.PIPE) m.stdout.read(16) # skip some magic bytes - l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE) + l = subprocess.Popen([zcat_cmd, label_filename], stdout=subprocess.PIPE) l.stdout.read(8) # skip some magic bytes while True: From faa43e3682a993450653904c9a66ad7f37c182f3 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 14:02:09 +0800 Subject: [PATCH 343/370] Remove unused dependencies --- python/setup.py.in | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/python/setup.py.in b/python/setup.py.in index c7e46293b79fe0..68ca35265cf132 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -15,9 +15,5 @@ setup(name='paddle', packages=packages, package_dir={ '': '${CMAKE_CURRENT_SOURCE_DIR}' - }, - install_requires = [ - 'scikit-learn>=0.18.0', - 'scipy>=0.18.0', - ] + } ) From 43d92fa8335dc61d5337e9de12c7e6f48dedd71c Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 14:49:48 +0800 Subject: [PATCH 344/370] Make api_train_v2 runnable --- demo/mnist/api_train_v2.py | 4 ++-- python/paddle/v2/dataset/mnist.py | 4 ++-- python/paddle/v2/trainer.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 19e273ebfd96d7..a59b30ccdb2edd 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -20,7 +20,7 @@ def main(): adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01) - trainer = paddle.trainer.SGD(topology=cost, + trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=adam_optimizer) @@ -28,7 +28,7 @@ def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 1000 == 0: result = trainer.test(reader=paddle.reader.batched( - paddle.dataset.mnist.test_creator(), batch_size=256)) + paddle.dataset.mnist.test(), batch_size=256)) print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics, diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py index f1315b35cd55c5..1512a3c3189de4 100644 --- a/python/paddle/v2/dataset/mnist.py +++ b/python/paddle/v2/dataset/mnist.py @@ -9,9 +9,9 @@ URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/' TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz' -TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6' +TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3' TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz' -TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688' +TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c' TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz' TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873' TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz' diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index b7d69ddb5d9071..5003f55f3e0d15 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -61,7 +61,7 @@ def __init__(self, cost, parameters, update_equation): self.__topology__ = topology self.__parameters__ = parameters self.__topology_in_proto__ = topology.proto() - self.__data_types__ = topology.data_layers() + self.__data_types__ = topology.data_type() gm = api.GradientMachine.createFromConfigProto( self.__topology_in_proto__, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) From eee1320b873f4dcfd78857d80aa06a99cca90916 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 28 Feb 2017 14:55:09 +0800 Subject: [PATCH 345/370] Change code style --- python/paddle/v2/reader/decorator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/reader/decorator.py b/python/paddle/v2/reader/decorator.py index d83e2f4577f20b..5687f118ce2779 100644 --- a/python/paddle/v2/reader/decorator.py +++ b/python/paddle/v2/reader/decorator.py @@ -201,7 +201,7 @@ def batched(reader, batch_size): :return: the batched reader. """ - def __impl__(): + def batched_reader(): r = reader() batch = [] for instance in r: @@ -212,4 +212,4 @@ def __impl__(): if batch: yield batch - return __impl__ + return batched_reader From b18d5a846fc60ab958acebabb6c53e6a4f300d8d Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 28 Feb 2017 15:15:36 +0800 Subject: [PATCH 346/370] fix compile error --- paddle/function/CosSimOpGpu.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/paddle/function/CosSimOpGpu.cu b/paddle/function/CosSimOpGpu.cu index 1dd733674fa054..c62ab39551f022 100644 --- a/paddle/function/CosSimOpGpu.cu +++ b/paddle/function/CosSimOpGpu.cu @@ -92,7 +92,6 @@ void CosSimForward(GpuMatrix& out_mat, CHECK(in1_mat.useGpu_ == true && in2_mat.useGpu_ == true) << "Matrix type are not GPU"; - size_t num_samples = out_mat.getHeight(); size_t dim = in1_mat.getWidth(); real* out = out_mat.getData(); const real* x = in1_mat.getData(); From d312352a2f1d99806be2259ae12340464127c124 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 28 Feb 2017 17:04:53 +0800 Subject: [PATCH 347/370] fix dataprovider_converter when using only one gpu --- paddle/api/PaddleAPI.h | 3 +++ paddle/api/Util.cpp | 2 ++ paddle/py_paddle/dataprovider_converter.py | 21 +++++++++++++++------ python/paddle/v2/tests/test_data_feeder.py | 5 +++++ 4 files changed, 25 insertions(+), 6 deletions(-) diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index 1831b8e170087c..0223dec4f42efe 100644 --- a/paddle/api/PaddleAPI.h +++ b/paddle/api/PaddleAPI.h @@ -47,6 +47,9 @@ void setUseGpu(bool useGpu); /// Return true if this py_paddle is compiled in GPU Version bool isGpuVersion(); +/// Return FLAGS_use_gpu +int getTrainerCount(); + /// The Error of IO Operation. Such as file not found, etc. class IOError {}; diff --git a/paddle/api/Util.cpp b/paddle/api/Util.cpp index 54d67aa62f4d87..d369df5d4e04b4 100644 --- a/paddle/api/Util.cpp +++ b/paddle/api/Util.cpp @@ -54,5 +54,7 @@ bool isGpuVersion() { #endif } +int getTrainerCount() { return FLAGS_trainer_count; } + static_assert(NUM_PARAMETER_TYPES == paddle::NUM_PARAMETER_TYPES, "The Parameter Type should be same in core/api and core/common"); diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 2690cafe1d8d32..8d5c57e10fd358 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -33,6 +33,11 @@ def scan(self, dat): def finish_scan(self, argument): pass + def use_gpu(self): + gpu = True if swig_paddle.isUsingGpu() and ( + swig_paddle.getTrainerCount() == 1) else False + return gpu + class DenseScanner(IScanner): """ @@ -53,7 +58,8 @@ def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) if self.__mat__.dtype != numpy.float32: self.__mat__ = self.__mat__.astype(numpy.float32) - m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False) + m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, + self.use_gpu()) argument.setSlotValue(self.pos, m) @@ -75,10 +81,13 @@ def extend_cols(self, dat): def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) - m = swig_paddle.Matrix.createSparse(self.__height__, - self.input_type.dim, - len(self.__cols__), - len(self.__value__) == 0) + m = swig_paddle.Matrix.createSparse( + self.__height__, + self.input_type.dim, + len(self.__cols__), + len(self.__value__) == 0, + False, # trans + False) # TODO supoort GPU assert isinstance(m, swig_paddle.Matrix) m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__) argument.setSlotValue(self.pos, m) @@ -102,7 +111,7 @@ def scan(self, dat): self.__ids__.append(dat) def finish_scan(self, argument): - ids = swig_paddle.IVector.create(self.__ids__) + ids = swig_paddle.IVector.create(self.__ids__, self.use_gpu()) assert isinstance(argument, swig_paddle.Arguments) argument.setSlotIds(self.pos, ids) diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py index 5f67da6a5b32d7..916d3aa4cdcbca 100644 --- a/python/paddle/v2/tests/test_data_feeder.py +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -235,4 +235,9 @@ def test_multiple_features_tuple(self): if __name__ == '__main__': api.initPaddle("--use_gpu=0") + suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest) + unittest.TextTestRunner().run(suite) + if api.isGpuVersion(): + api.setUseGpu(True) + unittest.main() unittest.main() From 60e0df0eeff0ba4155077f2fce93a55c30884028 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Tue, 28 Feb 2017 17:19:00 +0800 Subject: [PATCH 348/370] refine k8s distributed training doc --- doc/howto/usage/k8s/k8s_basis.md | 77 +++++++ doc/howto/usage/k8s/k8s_distributed_cn.md | 241 +++++++++----------- doc/howto/usage/k8s/src/k8s-paddle-arch.png | Bin 22084 -> 530508 bytes 3 files changed, 182 insertions(+), 136 deletions(-) create mode 100644 doc/howto/usage/k8s/k8s_basis.md diff --git a/doc/howto/usage/k8s/k8s_basis.md b/doc/howto/usage/k8s/k8s_basis.md new file mode 100644 index 00000000000000..e296d69dceb416 --- /dev/null +++ b/doc/howto/usage/k8s/k8s_basis.md @@ -0,0 +1,77 @@ +# Kubernetes 简介 + +[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 + +- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。 + +- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是Kubernetes的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET，PID，IPC，UTS等Linux namespace。由于容器之间共享NET namespace，所以它们使用同一个IP地址，可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。 + +- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 描述Kubernetes上运行的作业，一次作业称为一个job，通常每个job包括一个或者多个pods，job启动后会创建这些pod并开始执行一个程序，等待这个程序执行成功并返回0则成功退出，如果执行失败，也可以配置不同的重试机制。 + +- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume，就可以将这些文件持久化存储。Kubernetes支持多种volume，例如hostPath(宿主机目录)，gcePersistentDisk，awsElasticBlockStore等。 + +- [*Namespaces*](https://kubernetes.io/docs/user-guide/namespaces/) 命名空间，在kubernetes中创建的所有资源对象(例如上文的pod，job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 + +- [*PersistentVolume*]() + +- [*PersistentVolumeClaim*]() + +# 部署Kubernetes集群 + +Kubernetes提供了多种集群部署的方案，本文档内不重复介绍。这里给出集中常见的部署方法： + +- [*minikube*](https://kubernetes.io/docs/getting-started-guides/minikube/): 快速在本地启动一个单机的kubernetes服务器，便于本地验证和测试。 +- [*kubeadm*](http://kubernetes.io/docs/getting-started-guides/kubeadm/): 在不同操作系统，不同主机(Bare-Metal, AWS, GCE)条件下，快速部署集群。 +- [*AWS EC2*](https://kubernetes.io/docs/getting-started-guides/aws/): 在aws上快速部署集群。 +- [*Bare-Metal*](https://kubernetes.io/docs/getting-started-guides/centos/centos_manual_config/): 在物理机上手动部署。 + +可以参考[这个表格](https://kubernetes.io/docs/getting-started-guides/#table-of-solutions)选择适合您的场景的合适方案。 + +# 选择存储方案 + +容器不会保留在运行时生成的数据，job或者应用程序在容器中运行时生成的数据会在容器销毁时消失。为了完成分布式机器学习训练任务，需要有一个外部的存储服务来保存训练所需数据和训练输出。 +常见的可选存储服务包括： + +- [*NFS*](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/nfs): 可以将磁盘上某个目录共享给网络中其他机器访问。部署和配置比较简单，可以用于小量数据的验证。不提供分布式存储，高可用，冗余等功能。NFS的部署方法可以参考[这里](http://www.tecmint.com/how-to-setup-nfs-server-in-linux/)。 +- [*GlusterFS*](http://gluster.readthedocs.io/en/latest/Quick-Start-Guide/Quickstart/): 网络分布式文件系统，可以在Kubernetes中按照[这个](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/glusterfs)例子使用。 +- [*Ceph*](http://docs.ceph.com/docs/master/): 分布式文件系统，支持rbd，POSIX API接口(ceph fs)和对象存储API，参考[这里](https://kubernetes.io/docs/user-guide/volumes/#rbd)。 +- [*MooseFS*](https://moosefs.com/documentation.html): 一个分布式的存储系统。需要先挂载到服务器Node上再通过kubernetes hostPath Volume挂载到容器中。 + +# 配置kubectl + +## 安装kubectl +``` +# OS X +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/darwin/amd64/kubectl + +# Linux +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl + +# Windows +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/windows/amd64/kubectl.exe +``` + +## 配置kubectl访问你的kubernetes集群 + +编辑`~/.kube/config`这个配置文件，修改`Master-IP`的地址。如果使用SSL认证，则需要配置`certificate-authority`和`users`中的用户证书。如果是使用非SSL方式访问（比如通过8080都安口），也可以去掉这些证书的配置。 +``` +apiVersion: v1 +clusters: +- cluster: + certificate-authority: /path/to/ca.crt + server: https://[Master-IP]:443 + name: minikube +contexts: +- context: + cluster: minikube + user: minikube + name: minikube +current-context: minikube +kind: Config +preferences: {} +users: +- name: minikube + user: + client-certificate: /path/to/apiserver.crt + client-key: /Users/wuyi/.minikube/apiserver.key +``` diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index 2063b98ca8aab9..cfc3086036f014 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -1,169 +1,51 @@ -# Kubernetes分布式训练 +# 使用Paddle在Kubernetes集群上完成分布式训练 前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。 -## Kubernetes 基本概念 - -[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 - -- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。 - -- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是Kubernetes的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET，PID，IPC，UTS等Linux namespace。由于容器之间共享NET namespace，所以它们使用同一个IP地址，可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。 - -- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 是Kubernetes上运行的作业，一次作业称为一个job，通常每个job包括一个或者多个pods。 - -- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume，就可以将这些文件持久化存储。Kubernetes支持多种volume，例如hostPath(宿主机目录)，gcePersistentDisk，awsElasticBlockStore等。 - -- [*Namespaces*](http://kubernetes.io/docs/user-guide/volumes/) 命名空间，在kubernetes中创建的所有资源对象(例如上文的pod，job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 +有关Kubernetes相关概念以及如何搭建和配置Kubernetes集群，可以参考[k8s_basis](./k8s_basis.md)。 ## 整体方案 -### 部署Kubernetes集群 - -首先，我们需要拥有一个Kubernetes集群，在这个集群中所有node与pod都可以互相通信。关于Kubernetes集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。本文假设大家能找到几台物理机，并且可以按照官方文档在上面部署Kubernetes。在本文的环境中，Kubernetes集群中所有node都挂载了一个[MFS](http://moosefs.org/)（Moose filesystem，一种分布式文件系统）共享目录，我们通过这个目录来存放训练文件与最终输出的模型。关于MFS的安装部署，可以参考[MooseFS documentation](https://moosefs.com/documentation.html)。在训练之前，用户将配置与训练数据切分好放在MFS目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结构图如下： +在训练之前，用户将配置与训练数据切分好放在MFS目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结构图如下： ![paddle on kubernetes结构图](src/k8s-paddle-arch.png) -上图描述了一个3节点的分布式训练场景，Kubernetes集群的每个node上都挂载了一个MFS目录，这个目录可以通过volume的形式挂载到容器中。Kubernetes为这次训练创建了3个pod并且调度到了3个node上运行，每个pod包含一个PaddlePaddle容器。在容器创建后，会启动pserver与trainer进程，读取volume中的数据进行这次分布式训练。 - -### 使用 Job - -我们使用Kubernetes中的job这个概念来代表一次分布式训练。Job表示一次性作业，在作业完成后，Kubernetes会销毁job产生的容器并且释放相关资源。 - -在Kubernetes中，可以通过编写一个YAML文件，来描述这个job，在这个文件中，主要包含了一些配置信息，例如PaddlePaddle的节点个数，`paddle pserver`开放的端口个数与端口号，使用的网卡设备等，这些信息通过环境变量的形式传递给容器内的程序使用。 +上图描述了一个3节点的分布式训练场景，在每个Pod上都通过volume方式挂载分布式文件系统的一个目录用于保存训练数据和输出结果。Kubernetes为这次训练创建了3个pod并且调度到了3个node上运行，每个pod包含一个PaddlePaddle容器。在容器创建后，会启动pserver与trainer进程，读取volume中的数据进行这次分布式训练。 -在一次分布式训练中，用户确定好本次训练需要的PaddlePaddle节点个数，将切分好的训练数据与配置文件上传到MFS共享目录中。然后编写这次训练的job YAML文件，提交给Kubernetes集群创建并开始作业。 +根据前文的描述，要在已有的Kubernetes集群上进行PaddlePaddle的分布式训练，按照下面步骤即可： -### 创建PaddlePaddle节点 - -当Kubernetes master收到请求，解析完YAML文件后，会创建出多个pod(个数为PaddlePaddle节点数)，Kubernetes会把这些pod调度到集群的node上运行。一个pod就代表一个PaddlePaddle节点，当pod被成功分配到一台物理/虚拟机上后，Kubernetes会启动pod内的容器，这个容器会根据YAML文件中的环境变量，启动`paddle pserver`与`paddle train`进程。 - -### 启动训练 - -在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的trainer_id，由于PaddlePaddle本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据job name向Kubernetes apiserver查询这个job对应的所有pod信息(Kubernetes默认会在每个容器的环境变量中写入apiserver的地址)。 - -根据这些pod信息，就可以通过某种方式，为每个pod分配一个唯一的trainer_id。本文把所有pod的IP地址进行排序，将顺序作为每个PaddlePaddle节点的trainer_id。启动脚本的工作流程大致如下： - - 1. 查询Kubernetes apiserver获取pod信息，根据IP分配trainer_id - 1. 从MFS共享目录中拷贝训练文件到容器内 - 1. 根据环境变量，解析出`paddle pserver`与`paddle train`的启动参数，启动进程 - 1. 训练时，PaddlePaddle会自动将结果保存在trainer_id为0的节点上，将输出路径设置为MFS目录，保存输出的文件 - - -## 搭建过程 - -根据前文的描述，要在已有的Kubernetes集群上进行PaddlePaddle的分布式训练，主要分为以下几个步骤： - -1. 制作PaddlePaddle镜像 -1. 将训练文件与切分好的数据上传到共享存储 -1. 编写本次训练的YAML文件，创建一个Kubernetes job -1. 训练结束后查看输出结果 +1. [制作PaddlePaddle镜像](#制作镜像) +1. [将训练文件与切分好的数据上传到共享存储](#上传训练文件) +1. [编写本次训练的YAML文件，创建一个Kubernetes job](#创建Job) +1. [训练结束后查看输出结果](#查看输出) 下面就根据这几个步骤分别介绍。 - ### 制作镜像 PaddlePaddle镜像需要提供`paddle pserver`与`paddle train`进程的运行环境，用这个镜像创建的容器需要有以下两个功能： - 拷贝训练文件到容器内 - - 生成`paddle pserver`与`paddle train`进程的启动参数，并且启动训练 -因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。镜像的*Dockerfile*如下： - -```Dockerfile -FROM paddledev/paddle:cpu-latest - -MAINTAINER zjsxzong89@gmail.com - -COPY start.sh /root/ -COPY start_paddle.py /root/ -CMD ["bash"," -c","/root/start.sh"] -``` - -[start.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/start.sh)文件拷贝训练文件到容器内，然后执行[start_paddle.py](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/start_paddle.py)脚本启动训练，前文提到的获取其他节点IP地址，分配`trainer_id`等都在`start_paddle.py`脚本中完成。 - -`start_paddle.py`脚本开始时，会先进行参数的初始化与解析。 - -```python -parser = argparse.ArgumentParser(prog="start_paddle.py", - description='simple tool for k8s') - args, train_args_list = parser.parse_known_args() - train_args = refine_unknown_args(train_args_list) - train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) - podlist = getPodList() -``` - -然后通过函数`getPodList()`访问Kubernetes的接口来查询此job对应的所有pod信息。当所有pod都处于running状态（容器运行都运行）时，再通过函数`getIdMap(podlist)`获取trainer_id。 - -```python - podlist = getPodList() - # need to wait until all pods are running - while not isPodAllRunning(podlist): - time.sleep(10) - podlist = getPodList() - idMap = getIdMap(podlist) -``` - -在函数`getIdMap(podlist)`内部，我们通过读取`podlist`中每个pod的IP地址，将IP排序生成的序号作为trainer_id。 - -```python -def getIdMap(podlist): - ''' - generate tainer_id by ip - ''' - ips = [] - for pod in podlist["items"]: - ips.append(pod["status"]["podIP"]) - ips.sort() - idMap = {} - for i in range(len(ips)): - idMap[ips[i]] = i - return idMap -``` - -在得到`idMap`后，通过函数`startPaddle(idMap, train_args_dict)`构造`paddle pserver`与`paddle train`的启动参数并执行进程。 - -在函数`startPaddle`中，最主要的工作就是解析出`paddle pserver`与`paddle train`的启动参数。例如`paddle train`参数的解析，解析环境变量得到`PADDLE_NIC`，`PADDLE_PORT`，`PADDLE_PORTS_NUM`等参数，然后通过自身的IP地址在`idMap`中获取`trainerId`。 - -```python - program = 'paddle train' - args = " --nics=" + PADDLE_NIC - args += " --port=" + str(PADDLE_PORT) - args += " --ports_num=" + str(PADDLE_PORTS_NUM) - args += " --comment=" + "paddle_process_by_paddle" - ip_string = "" - for ip in idMap.keys(): - ip_string += (ip + ",") - ip_string = ip_string.rstrip(",") - args += " --pservers=" + ip_string - args_ext = "" - for key, value in train_args_dict.items(): - args_ext += (' --' + key + '=' + value) - localIP = socket.gethostbyname(socket.gethostname()) - trainerId = idMap[localIP] - args += " " + args_ext + " --trainer_id=" + \ - str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT -``` - -使用 `docker build` 构建镜像： +因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*]((https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/src/k8s_train/Dockerfile)。 ```bash -docker build -t your_repo/paddle:mypaddle . +$ cd doc/howto/usage/k8s/src/k8s_train +$ docker build -t [YOUR_REPO]/paddle:mypaddle . ``` 然后将构建成功的镜像上传到镜像仓库。 ```bash -docker push your_repo/paddle:mypaddle +docker push [YOUR_REPO]/paddle:mypaddle ``` -注意上述命令中`your_repo`表示读者所使用的Docker镜像仓库地址，读者需要替换成自己使用的仓库地址。下文使用`your_repo/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。 +注意上述命令中`[YOUR_REPO]`表示读者所使用的Docker镜像仓库地址，读者需要替换成自己使用的仓库地址。下文使用`[YOUR_REPO]/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。 ### 上传训练文件 -本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到MFS共享存储。完成后MFS上的文件内容大致如下： +本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到volume所在的共享存储（使用不同分布式存储会有不同的挂载方式，需要要先挂载这个目录，然后拷贝数据）。完成后volume中的文件内容大致如下： ```bash [root@paddle-kubernetes-node0 mfs]# tree -d @@ -205,7 +87,7 @@ spec: path: /home/work/mfs containers: - name: trainer - image: your_repo/paddle:mypaddle + image: [YOUR_REPO]/paddle:mypaddle command: ["bin/bash", "-c", "/root/start.sh"] env: - name: JOB_NAME @@ -289,8 +171,8 @@ I1116 09:10:17.123121 50 Util.cpp:155] commandline: --ports_num=2 --comment=paddle_process_by_paddle --pservers=192.168.129.66,192.168.223.143,192.168.129.71 --ports_num_for_sparse=2 --config=./trainer_config.py - --trainer_count=4 --num_passes=10 --use_gpu=0 - --log_period=50 --dot_period=10 --saving_period=1 + --trainer_count=4 --num_passes=10 --use_gpu=0 + --log_period=50 --dot_period=10 --saving_period=1 --local=0 --trainer_id=0 --save_dir=/home/jobpath/paddle-cluster-job/output I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions @@ -310,3 +192,90 @@ I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143: I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164 I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165 ``` + + +## 一些细节的补充 + +### 使用环境变量 + +使用容器方式运行训练任务的Kubernetes Job，通常会使用环境变量配置Job的配置信息`start_paddle.py`提供了一个启动脚本，将环境变量转换成paddle的命令行参数： +``` +API = "/api/v1/namespaces/" +JOBSELECTOR = "labelSelector=job-name=" +JOB_PATH = os.getenv("JOB_PATH") + "/" + os.getenv("JOB_NAME") +JOB_PATH_OUTPUT = JOB_PATH + "/output" +JOBNAME = os.getenv("JOB_NAME") +NAMESPACE = os.getenv("JOB_NAMESPACE") +PADDLE_NIC = os.getenv("CONF_PADDLE_NIC") +PADDLE_PORT = os.getenv("CONF_PADDLE_PORT") +PADDLE_PORTS_NUM = os.getenv("CONF_PADDLE_PORTS_NUM") +PADDLE_PORTS_NUM_SPARSE = os.getenv("CONF_PADDLE_PORTS_NUM_SPARSE") +PADDLE_SERVER_NUM = os.getenv("CONF_PADDLE_GRADIENT_NUM") +``` + +### Pod间通信 +`start_paddle.py`脚本开始时，会先进行参数的初始化与解析。 + +```python +parser = argparse.ArgumentParser(prog="start_paddle.py", + description='simple tool for k8s') + args, train_args_list = parser.parse_known_args() + train_args = refine_unknown_args(train_args_list) + train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) + podlist = getPodList() +``` + +然后通过函数`getPodList()`访问Kubernetes的接口来查询此job对应的所有pod信息。当所有pod都处于running状态（容器运行都运行）时，再通过函数`getIdMap(podlist)`获取trainer_id。 + +```python + podlist = getPodList() + # need to wait until all pods are running + while not isPodAllRunning(podlist): + time.sleep(10) + podlist = getPodList() + idMap = getIdMap(podlist) +``` +* *注意*: `getPodList()`会获取当前namespace下的所有pod，如果已经有pod运行，可能会导致出错。这种集群节点管理方式会在将来使用[statfulsets](https://kubernetes.io/docs/concepts/abstractions/controllers/statefulsets/)代替。 + +在函数`getIdMap(podlist)`内部，我们通过读取`podlist`中每个pod的IP地址，将IP排序生成的序号作为trainer_id。 + +```python +def getIdMap(podlist): + ''' + generate tainer_id by ip + ''' + ips = [] + for pod in podlist["items"]: + ips.append(pod["status"]["podIP"]) + ips.sort() + idMap = {} + for i in range(len(ips)): + idMap[ips[i]] = i + return idMap +``` + +在得到`idMap`后，通过函数`startPaddle(idMap, train_args_dict)`构造`paddle pserver`与`paddle train`的启动参数并执行进程。 + +### 启动任务 + +在函数`startPaddle`中，最主要的工作就是解析出`paddle pserver`与`paddle train`的启动参数。例如`paddle train`参数的解析，解析环境变量得到`PADDLE_NIC`，`PADDLE_PORT`，`PADDLE_PORTS_NUM`等参数，然后通过自身的IP地址在`idMap`中获取`trainerId`。 + +```python + program = 'paddle train' + args = " --nics=" + PADDLE_NIC + args += " --port=" + str(PADDLE_PORT) + args += " --ports_num=" + str(PADDLE_PORTS_NUM) + args += " --comment=" + "paddle_process_by_paddle" + ip_string = "" + for ip in idMap.keys(): + ip_string += (ip + ",") + ip_string = ip_string.rstrip(",") + args += " --pservers=" + ip_string + args_ext = "" + for key, value in train_args_dict.items(): + args_ext += (' --' + key + '=' + value) + localIP = socket.gethostbyname(socket.gethostname()) + trainerId = idMap[localIP] + args += " " + args_ext + " --trainer_id=" + \ + str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT +``` diff --git 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100644 index 488aad0bede4f9..00000000000000 --- a/doc/howto/usage/k8s/src/job.yaml +++ /dev/null @@ -1,43 +0,0 @@ -apiVersion: batch/v1 -kind: Job -metadata: - name: paddle-cluster-job -spec: - parallelism: 3 - completions: 3 - template: - metadata: - name: paddle-cluster-job - spec: - volumes: - - name: jobpath - hostPath: - path: /home/work/paddle_output - containers: - - name: trainer - image: registry.baidu.com/public/paddle:mypaddle - command: ["bin/bash", "-c", "/root/start.sh"] - env: - - name: JOB_NAME - value: paddle-cluster-job - - name: JOB_PATH - value: /home/jobpath - - name: JOB_NAMESPACE - value: default - - name: TRAIN_CONFIG_DIR - value: recommendation - - name: CONF_PADDLE_NIC - value: eth0 - - name: CONF_PADDLE_PORT - value: "7164" - - name: CONF_PADDLE_PORTS_NUM - value: "2" - - name: CONF_PADDLE_PORTS_NUM_SPARSE - value: "2" - - name: CONF_PADDLE_GRADIENT_NUM - value: "3" - volumeMounts: - - name: jobpath - mountPath: /home/jobpath - restartPolicy: Never - diff --git a/doc/howto/usage/k8s/src/k8s_data/Dockerfile b/doc/howto/usage/k8s/src/k8s_data/Dockerfile new file mode 100644 index 00000000000000..6d3a12ae393aa5 --- /dev/null +++ b/doc/howto/usage/k8s/src/k8s_data/Dockerfile @@ -0,0 +1,7 @@ +FROM alpine + +RUN apk update && apk upgrade && apk add coreutils +ADD quick_start /quick_start +ADD get_data.sh /bin/ +RUN chmod +x /bin/get_data.sh +ENTRYPOINT ["/bin/get_data.sh"] diff --git a/doc/howto/usage/k8s/src/k8s_data/README.md b/doc/howto/usage/k8s/src/k8s_data/README.md new file mode 100644 index 00000000000000..ed1abcab5cd8d7 --- /dev/null +++ b/doc/howto/usage/k8s/src/k8s_data/README.md @@ -0,0 +1,6 @@ +To build PaddlePaddle data preparation image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following commands: + +``` +cp -r ../../../../../../demo/quick_start . +docker build . -t prepare-data-image-name +``` diff --git a/doc/howto/usage/k8s/src/k8s_data/get_data.sh b/doc/howto/usage/k8s/src/k8s_data/get_data.sh new file mode 100755 index 00000000000000..d187ba5ac8d03f --- /dev/null +++ b/doc/howto/usage/k8s/src/k8s_data/get_data.sh @@ -0,0 +1,26 @@ +#!/bin/sh + +out_dir=$OUT_DIR +split_count=$SPLIT_COUNT + +set -e + +mkdir -p $out_dir +cp -r /quick_start $out_dir/ + +mkdir -p $out_dir/0/data +cd $out_dir/0/data +wget http://paddlepaddle.bj.bcebos.com/demo/quick_start_preprocessed_data/preprocessed_data.tar.gz +tar zxvf preprocessed_data.tar.gz +rm preprocessed_data.tar.gz + +split -d --number=l/$split_count -a 5 train.txt train. +mv train.00000 train.txt + +cd $out_dir +end=$(expr $split_count - 1) +for i in $(seq 1 $end); do + mkdir -p $i/data + cp -r 0/data/* $i/data + mv $i/data/train.`printf %05d $i` $i/data/train.txt +done; diff --git a/doc/howto/usage/k8s/src/k8s_train/Dockerfile b/doc/howto/usage/k8s/src/k8s_train/Dockerfile new file mode 100644 index 00000000000000..c0fca1f9a94592 --- /dev/null +++ b/doc/howto/usage/k8s/src/k8s_train/Dockerfile @@ -0,0 +1,6 @@ +FROM paddledev/paddle:cpu-latest + +COPY start.sh /root/ +COPY start_paddle.py /root/ +RUN chmod +x /root/start.sh +CMD ["bash"," -c","/root/start.sh"] diff --git a/doc/howto/usage/k8s/src/k8s_train/README.md b/doc/howto/usage/k8s/src/k8s_train/README.md new file mode 100644 index 00000000000000..68724ac2725418 --- /dev/null +++ b/doc/howto/usage/k8s/src/k8s_train/README.md @@ -0,0 +1,5 @@ +To build PaddlePaddle training image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following command: + +``` +docker build . -t train-image-name +``` diff --git a/doc/howto/usage/k8s/src/start.sh b/doc/howto/usage/k8s/src/k8s_train/start.sh similarity index 55% rename from doc/howto/usage/k8s/src/start.sh rename to doc/howto/usage/k8s/src/k8s_train/start.sh index b3a1334174a20b..12dfe1e6386885 100755 --- a/doc/howto/usage/k8s/src/start.sh +++ b/doc/howto/usage/k8s/src/k8s_train/start.sh @@ -1,19 +1,19 @@ #!/bin/sh + set -eu jobconfig=${JOB_PATH}"/"${JOB_NAME}"/"${TRAIN_CONFIG_DIR} cd /root -cp -rf $jobconfig . -cd $TRAIN_CONFIG_DIR - +cp -rf $jobconfig/* . python /root/start_paddle.py \ --dot_period=10 \ - --ports_num_for_sparse=$CONF_PADDLE_PORTS_NUM \ + --ports_num=$CONF_PADDLE_PORTS_NUM \ + --ports_num_for_sparse=$CONF_PADDLE_PORTS_NUM_SPARSE \ --log_period=50 \ --num_passes=10 \ - --trainer_count=4 \ + --trainer_count=$TRAINER_COUNT \ --saving_period=1 \ --local=0 \ - --config=./trainer_config.py \ + --config=trainer_config.lr.py \ --use_gpu=0 diff --git a/doc/howto/usage/k8s/src/start_paddle.py b/doc/howto/usage/k8s/src/k8s_train/start_paddle.py similarity index 84% rename from doc/howto/usage/k8s/src/start_paddle.py rename to doc/howto/usage/k8s/src/k8s_train/start_paddle.py index df00d82919faa2..f1a770ccb54fbd 100755 --- a/doc/howto/usage/k8s/src/start_paddle.py +++ b/doc/howto/usage/k8s/src/k8s_train/start_paddle.py @@ -23,7 +23,6 @@ API = "/api/v1/namespaces/" JOBSELECTOR = "labelSelector=job-name=" JOB_PATH = os.getenv("JOB_PATH") + "/" + os.getenv("JOB_NAME") -JOB_PATH_DATA = JOB_PATH + "/data" JOB_PATH_OUTPUT = JOB_PATH + "/output" JOBNAME = os.getenv("JOB_NAME") NAMESPACE = os.getenv("JOB_NAMESPACE") @@ -33,6 +32,8 @@ PADDLE_PORTS_NUM_SPARSE = os.getenv("CONF_PADDLE_PORTS_NUM_SPARSE") PADDLE_SERVER_NUM = os.getenv("CONF_PADDLE_GRADIENT_NUM") +tokenpath = '/var/run/secrets/kubernetes.io/serviceaccount/token' + def refine_unknown_args(cmd_args): ''' @@ -64,6 +65,7 @@ def isPodAllRunning(podlist): for pod in podlist["items"]: if pod["status"]["phase"] == "Running": running += 1 + print "waiting for pods running, require:", require, "running:", running if require == running: return True return False @@ -79,8 +81,17 @@ def getPodList(): pod = API + NAMESPACE + "/pods?" job = JOBNAME - return requests.get(apiserver + pod + JOBSELECTOR + job, - verify=False).json() + if os.path.isfile(tokenpath): + tokenfile = open(tokenpath, mode='r') + token = tokenfile.read() + Bearer = "Bearer " + token + headers = {"Authorization": Bearer} + return requests.get(apiserver + pod + JOBSELECTOR + job, + headers=headers, + verify=False).json() + else: + return requests.get(apiserver + pod + JOBSELECTOR + job, + verify=False).json() def getIdMap(podlist): @@ -122,8 +133,8 @@ def startPaddle(idMap={}, train_args_dict=None): if not os.path.exists(JOB_PATH_OUTPUT): os.makedirs(JOB_PATH_OUTPUT) os.mkdir(logDir) - copyCommand = 'cp -rf ' + JOB_PATH_DATA + \ - "/" + str(trainerId) + " ./data" + copyCommand = 'cp -rf ' + JOB_PATH + \ + "/" + str(trainerId) + "/data/*" + " ./data/" os.system(copyCommand) startPserver = 'nohup paddle pserver' + \ " --port=" + str(PADDLE_PORT) + \ @@ -136,9 +147,9 @@ def startPaddle(idMap={}, train_args_dict=None): print startPserver os.system(startPserver) # wait until pservers completely start - time.sleep(10) - startTrainer = program + args + " > " + \ - logDir + "/train.log 2>&1 < /dev/null" + time.sleep(20) + startTrainer = program + args + " 2>&1 | tee " + \ + logDir + "/train.log" print startTrainer os.system(startTrainer) @@ -152,7 +163,7 @@ def startPaddle(idMap={}, train_args_dict=None): podlist = getPodList() # need to wait until all pods are running while not isPodAllRunning(podlist): - time.sleep(10) + time.sleep(20) podlist = getPodList() idMap = getIdMap(podlist) startPaddle(idMap, train_args_dict) diff --git a/doc/howto/usage/k8s/src/worker_security_group.png b/doc/howto/usage/k8s/src/worker_security_group.png new file mode 100644 index 0000000000000000000000000000000000000000..57eb0265a34ad4223b69600d2a3dd355482e0bf5 GIT binary patch literal 89208 zcmd?RbyOYQwl4?-50+rTAq0018)xJ0F2NlVe51h=AV6@J;O-DSgy0(7CAhoWtDWyV z=iGPiefOmMj~?Bl8H16cs#dMJ=A3KlZ{?$sf+RXBAu0?E47#+GmFG4Vb2hD9}eW;>xj~-h#pFPW^M^y zOeqKqd@d92m-z;4`VL{r{{yEP0zMsTnat-}@-NeP2v08%v~k8=D&Hf-kt{!3Y;{}o z+`QlPa`N5mPkZ!oaysNCgNgOD^&`iPha>-#L4kJRY$PEe&T$NefkV!OGZD6M8buiL 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zTQE0X`tiy>oDd2KsiiQ~+y3*F6BWK1nvihfs!#2$v~_Utj{D9AfT;9PIC;10Gm&1Y zw(XLz7t_lz?71_$C%t1;IG@|a>{Os3Xq;q^lv^;;FcxUY1v4amq2x6~zt!tnwxD30 zyl)3Ni71VKZlJi9$FS?N4J?zRf2hkdAb)=)XPQa*lU#US_zbhm+vC9LZ9#tM>HUg^ zGx}-f+Imw~NvDgoz?CGi^2tx-fUMzTDw#IeMmO6X`_6Gp0;RgpOU@Y?rKFrqeERR( zEZdX<9W(mWXBiL~r?i~00%tT^_faG>e&p&D0wz{ zCSZ8Tar%&%m%Epa^qUM=$YeWSvi2u{|4!nwFEnyzP5fj^$khWe^L{fI&6;G0T76>>(JjNXZyOJ%OPV_7SO7wpgnt_4{29c4GPv5(9`=9@!JpLEc@IM37@c$ax|G&@- aXPTGak*)X6Zt{}>U-xtk@04lVMf@MKrFu00 literal 0 HcmV?d00001 From 17867fb6c70e01ae96c10bc3d4f6f28ef321a74d Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 26 Jan 2017 11:19:19 -0800 Subject: [PATCH 109/370] fix README.md reference problem --- doc/howto/usage/k8s/k8s_aws_en.md | 4 ++-- doc/howto/usage/k8s/src/k8s_data/README.md | 2 +- doc/howto/usage/k8s/src/k8s_train/README.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 7930807969c0e9..8a15a9583eb4ff 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -489,7 +489,7 @@ NAME DESIRED SUCCESSFUL AGE paddle-data 1 1 6m ``` -Data preparation is done by docker image `paddledev/paddle-tutorial:k8s_data`, see [here](src/k8s_data/) for how to build this docker image and source code. +Data preparation is done by docker image `paddledev/paddle-tutorial:k8s_data`, see [here](src/k8s_data/README.md) for how to build this docker image and source code. #### Start Training @@ -584,7 +584,7 @@ kubectl --kubeconfig=kubeconfig log -f POD_NAME Run `kubectl --kubeconfig=kubeconfig describe job paddle-cluster-job` to check training job status. It will complete in around 20 minutes. -The details for start `pserver` and `trainer` are hidden inside docker image `paddledev/paddle-tutorial:k8s_train`, see [here](src/k8s_train/) for how to build the docker image and source code. +The details for start `pserver` and `trainer` are hidden inside docker image `paddledev/paddle-tutorial:k8s_train`, see [here](src/k8s_train/README.md) for how to build the docker image and source code. #### Inspect Training Output diff --git a/doc/howto/usage/k8s/src/k8s_data/README.md b/doc/howto/usage/k8s/src/k8s_data/README.md index ed1abcab5cd8d7..83cef7affd0ac4 100644 --- a/doc/howto/usage/k8s/src/k8s_data/README.md +++ b/doc/howto/usage/k8s/src/k8s_data/README.md @@ -1,4 +1,4 @@ -To build PaddlePaddle data preparation image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following commands: +To build PaddlePaddle data preparation image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../../k8s_aws_en.md), run following commands: ``` cp -r ../../../../../../demo/quick_start . diff --git a/doc/howto/usage/k8s/src/k8s_train/README.md b/doc/howto/usage/k8s/src/k8s_train/README.md index 68724ac2725418..96bf65497ffa23 100644 --- a/doc/howto/usage/k8s/src/k8s_train/README.md +++ b/doc/howto/usage/k8s/src/k8s_train/README.md @@ -1,4 +1,4 @@ -To build PaddlePaddle training image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following command: +To build PaddlePaddle training image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../../k8s_aws_en.md), run following command: ``` docker build . -t train-image-name From cfe0e086636abdcbb2aea76a68f044d54b111e27 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 26 Jan 2017 11:45:06 -0800 Subject: [PATCH 110/370] change english documentation for parameter ports_num and ports_num_for_sparse --- paddle/utils/Flags.cpp | 11 +++++++---- proto/ParameterServerConfig.proto | 11 +++++++---- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/paddle/utils/Flags.cpp b/paddle/utils/Flags.cpp index 59d6cbdc513660..e8f31bc811ac30 100644 --- a/paddle/utils/Flags.cpp +++ b/paddle/utils/Flags.cpp @@ -33,12 +33,15 @@ DEFINE_int32(port, 20134, "Listening port for pserver"); DEFINE_int32(data_server_port, 21134, "Listening port for dserver"); DEFINE_int32(ports_num, 1, - "The ports number for parameter send," - " increment based on default port number"); + "Number of ports for sending dense parameter," + " following ports on parameter server will be visited" + " for sending dense parameter: [port, port+ports_num-1]"); DEFINE_int32(ports_num_for_sparse, 0, - "The ports number for parameter send," - " increment based on default (port + ports_num)"); + "Number of ports for sending sparse parameter," + " following ports on parameter server will be visited" + " for sending sparse parameter:" + " [port+ports_num, port+ports_num+ports_num_for_sparse-1]"); DEFINE_string(nics, "xgbe0,xgbe1", "network device name for pservers"); DEFINE_string(rdma_tcp, "tcp", "use rdma or tcp rdma transport protocol"); DEFINE_int32(trainer_id, diff --git a/proto/ParameterServerConfig.proto b/proto/ParameterServerConfig.proto index 3068bba8b10d89..404f9613792653 100644 --- a/proto/ParameterServerConfig.proto +++ b/proto/ParameterServerConfig.proto @@ -27,11 +27,14 @@ message ParameterClientConfig { * Configuration structure for ParameterServer2. */ message ParameterServerConfig { - // The ports number for parameter send, - // increment based on default port number + // Number of ports for sending dense parameter, + // following ports on parameter server will be visited + // for sending dense parameter: [port, port+ports_num-1] required int32 ports_num = 1 [default = 1]; - // The ports number for parameter send, - // increment based on default (port + ports_num + // Number of ports for sending sparse parameter, + // following ports on parameter server will be visited + // for sending sparse parameter: + // [port+ports_num, port+ports_num+ports_num_for_sparse-1] required int32 ports_num_for_sparse = 2 [default = 0]; // network device name for pservers required string nics = 3 [default = "xgbe0,xgbe1"]; From 7712fd58c256935fca7555543b0211d612eb022c Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 26 Jan 2017 14:40:55 -0800 Subject: [PATCH 111/370] fix docker build I am getting following error without this commit when doing docker build. File "", line 1, in File "/usr/local/lib/python2.7/dist-packages/setuptools/__init__.py", line 12, in import setuptools.version File "/usr/local/lib/python2.7/dist-packages/setuptools/version.py", line 1, in import pkg_resources File "/usr/local/lib/python2.7/dist-packages/pkg_resources/__init__.py", line 70, in import packaging.version ImportError: No module named packaging.version No idea why this change fix the problem, but it does fix. --- paddle/scripts/docker/Dockerfile | 4 ++-- paddle/scripts/docker/Dockerfile.gpu | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 79c4efbed0b856..341bdde8c60533 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -13,8 +13,8 @@ RUN apt-get update && \ apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y -RUN pip install --upgrade pip && \ - pip install 'protobuf==3.1.0.post1' && \ +RUN pip install --upgrade pip +RUN pip install 'protobuf==3.1.0.post1' && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 6c1c2225d1a304..400e2c9037750b 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -13,8 +13,8 @@ RUN apt-get update && \ apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y -RUN pip install --upgrade pip && \ - pip install 'protobuf==3.1.0.post1' && \ +RUN pip install --upgrade pip +RUN pip install 'protobuf==3.1.0.post1' && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter From ca31db97a467bfabb6050d4ef41ec63e0552499d Mon Sep 17 00:00:00 2001 From: xichen Date: Thu, 26 Jan 2017 14:56:22 -0800 Subject: [PATCH 112/370] added region check step, route53 config. --- doc/howto/usage/k8s/k8s_aws_en.md | 39 ++++++++++++++++++++++++++----- 1 file changed, 33 insertions(+), 6 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index a6422b9be00e21..227b6dee869724 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -1,4 +1,9 @@ # Kubernetes on AWS +## Choose AWS Service Region +This tutorial requires several AWS services work in the same region. Before we create anything in AWS, please check the following link +https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ +Choose a region which has the following services available: EC2, EFS, VPS, CloudFormation, KMS, VPC, S3. +In this tutorial, we use "Oregon(us-west-2)" as example. ## Create AWS Account and IAM Account @@ -35,7 +40,8 @@ Here we will show you step by step on how to run PaddlePaddle training on AWS cl #### kube-aws [kube-aws](https://github.com/coreos/kube-aws) is a CLI tool to automate cluster deployment to AWS. - +##### Verify integrity of kube-aws downloaded +Note: if you are using a non-official release (e.g RC release) kube-aws, you can skip this setp. Import the CoreOS Application Signing Public Key: ``` @@ -60,7 +66,7 @@ PLATFORM=darwin-amd64 gpg2 --verify kube-aws-${PLATFORM}.tar.gz.sig kube-aws-${PLATFORM}.tar.gz ``` - +##### Install kube-aws Extract the binary: ``` @@ -201,14 +207,15 @@ paste into following inline policies: ] } ``` - +`Version` : Its value has to be exactly "2012-10-17". `AWS_ACCOUNT_ID`: You can get it from following command line: ``` aws sts get-caller-identity --output text --query Account ``` -`MY_CLUSTER_NAME`: Pick a MY_CLUSTER_NAME that you like, you will use it later as well. +`MY_CLUSTER_NAME`: Pick a MY_CLUSTER_NAME that you like, you will use it later as well. +Please note, stack name must satisfy regular expression pattern: [a-zA-Z][-a-zA-Z0-9*]*, which means no "_" or "-" in stack name, or kube-aws will throw error in later steps. #### External DNS name @@ -216,7 +223,7 @@ When the cluster is created, the controller will expose the TLS-secured API on a The A record of that DNS name needs to be point to the cluster ip address. -We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. +We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. We will find the ip to map to `paddle` in later steps. Also in this case, will have to add name service (route53) in aws cluster in later step. #### S3 bucket @@ -346,7 +353,27 @@ paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 5 In the above output, both ip `54.241.164.52`, `54.67.102.112` will work. -If you own a DNS name, set the A record to any of the above ip. Otherwise you can edit `/etc/hosts` to associate ip with the DNS name. +*If you own a DNS name*, set the A record to any of the above ip. Then you can skip to the step "Access the cluster". + +*If you do not own a DNS name*: +##### Update local DNS association +Edit `/etc/hosts` to associate above ip with the DNS name. +##### Add Route53 private name service in VPC + - Open Route53 web console + - Create hosted zone with following config + - Domain name: "paddle" + - Type: "Private hosted zone for amazon VPC" + - VPC ID: + - Add A record + - Click on the zone "paddle" just created + - Click the button "Create record set" + - Name : leave blank + - type: "A" + - Value: + - Verify name service + - Connect to any instance created by kube-aws via ssh + - Run command "host paddle", see if the ip returned is the private ip of kube-controller + #### Access the cluster From 8c9716f7d4ea5c58414266cf89c9a5ed72af605a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Thu, 26 Jan 2017 15:06:34 -0800 Subject: [PATCH 113/370] Allow users to build a Docker image as development environment from Dockerfile{.gpu} --- paddle/scripts/docker/Dockerfile | 10 ++-- paddle/scripts/docker/Dockerfile.dev | 62 --------------------- paddle/scripts/docker/Dockerfile.gpu | 10 ++-- paddle/scripts/docker/build.sh | 80 +++++++++++++++------------- 4 files changed, 52 insertions(+), 110 deletions(-) delete mode 100644 paddle/scripts/docker/Dockerfile.dev diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 79c4efbed0b856..d4845a72b61833 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -13,8 +13,8 @@ RUN apt-get update && \ apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y -RUN pip install --upgrade pip && \ - pip install 'protobuf==3.1.0.post1' && \ +RUN pip install --upgrade pip && \ + pip install -U protobuf && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter @@ -23,10 +23,12 @@ RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} @@ -37,10 +39,6 @@ COPY . /paddle/ RUN /paddle/paddle/scripts/docker/build.sh VOLUME ["/usr/share/nginx/html/data", "/usr/share/nginx/html/paddle"] -RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile -RUN pip install /usr/local/opt/paddle/share/wheels/*.whl -RUN paddle version # print version after build - # Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service RUN mkdir /var/run/sshd RUN echo 'root:root' | chpasswd diff --git a/paddle/scripts/docker/Dockerfile.dev b/paddle/scripts/docker/Dockerfile.dev deleted file mode 100644 index 2f245e2580f486..00000000000000 --- a/paddle/scripts/docker/Dockerfile.dev +++ /dev/null @@ -1,62 +0,0 @@ -FROM ubuntu:16.04 -MAINTAINER PaddlePaddle Authors - -ARG DEBIAN_FRONTEND=noninteractive -ARG UBUNTU_MIRROR -RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' - -RUN apt-get update - -# Paddle development tools. -RUN apt-get install -y coreutils git cmake g++ m4 python-pip swig - -# Paddle data tools. -RUN apt-get install -y wget unzip tar xz-utils bzip2 gzip sed grep graphviz - -# Paddle documentation tools. -RUN apt-get install -y doxygen - -# Paddle code auto-reformat tools. -RUN apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev clang-format-3.8 clang-tidy-3.8 - -# Paddle prerequisites. -RUN apt-get install -y \ - libprotobuf-dev protobuf-compiler \ - libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base \ - python-protobuf python-numpy python-dev python-matplotlib \ - libjpeg-dev zlib1g-dev - -# Development environment: -RUN apt-get install -y openssh-server - -RUN apt-get clean -y - -# Build Google test -RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib - -# Python dependencies. -RUN pip install -U BeautifulSoup docopt PyYAML pillow \ - sphinx sphinx_rtd_theme recommonmark jupyter - -ARG WITH_AVX -ARG WITH_DOC -ARG WITH_SWIG_PY -ARG WITH_STYLE_CHECK - -ENV WITH_GPU=OFF -ENV WITH_AVX=${WITH_AVX:-ON} -ENV WITH_DOC=${WITH_DOC:-ON} -ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} -ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} - -RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile - -# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service -RUN mkdir /var/run/sshd -RUN echo 'root:root' | chpasswd -RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config -RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config -EXPOSE 22 - -CMD ["/usr/sbin/sshd", "-D"] diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 6c1c2225d1a304..da20b2635e10e7 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -13,8 +13,8 @@ RUN apt-get update && \ apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ apt-get clean -y -RUN pip install --upgrade pip && \ - pip install 'protobuf==3.1.0.post1' && \ +RUN pip install --upgrade pip && \ + pip install -U protobuf && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter @@ -23,10 +23,12 @@ RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK +ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} @@ -37,10 +39,6 @@ COPY . /paddle/ RUN /paddle/paddle/scripts/docker/build.sh VOLUME ["/usr/share/nginx/html/data", "/usr/share/nginx/html/paddle"] -RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile -RUN pip install /usr/local/opt/paddle/share/wheels/*.whl -RUN paddle version # print version after build - # Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service RUN mkdir /var/run/sshd RUN echo 'root:root' | chpasswd diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 7edba3dd09cdc5..6197b41d6b5f19 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -8,42 +8,50 @@ function abort(){ trap 'abort' 0 set -e -if [ ${WITH_GPU} == 'ON' ]; then - ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so +# If Dockerfile.* sets BUILD_AND_INSTALL to 'ON', it would have copied +# source tree to /paddle, and this scripts should build it into +# /paddle/build. +if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then + if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then + ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so + fi + + mkdir -p /paddle/build # -p means no error if exists + cd /paddle/build + cmake .. \ + -DWITH_DOC=ON \ + -DWITH_GPU=${WITH_GPU:-OFF} \ + -DWITH_AVX=${WITH_AVX:-OFF} \ + -DWITH_SWIG_PY=ON \ + -DCUDNN_ROOT=/usr/ \ + -DWITH_STYLE_CHECK=OFF \ + -DCMAKE_EXPORT_COMPILE_COMMANDS=ON + make -j `nproc` + make install + + # Install woboq_codebrowser. + git clone https://github.com/woboq/woboq_codebrowser /woboq + cd /woboq + cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ + -DCMAKE_BUILD_TYPE=Release \ + . + make + + export WOBOQ_OUT=/usr/share/nginx/html/paddle + export BUILD_DIR=/paddle/build + mkdir -p $WOBOQ_OUT + cp -rv /woboq/data $WOBOQ_OUT/../data + /woboq/generator/codebrowser_generator \ + -b /paddle/build \ + -a \ + -o $WOBOQ_OUT \ + -p paddle:/paddle + /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT + cd /woboq + make clean + + pip install /usr/local/opt/paddle/share/wheels/*.whl + paddle version fi -mkdir -p /paddle/build # -p means no error if exists -cd /paddle/build -cmake .. \ - -DWITH_DOC=ON \ - -DWITH_GPU=${WITH_GPU} \ - -DWITH_AVX=${WITH_AVX} \ - -DWITH_SWIG_PY=ON \ - -DCUDNN_ROOT=/usr/ \ - -DWITH_STYLE_CHECK=OFF \ - -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -make -j `nproc` -make install - -# Install woboq_codebrowser. -git clone https://github.com/woboq/woboq_codebrowser /woboq -cd /woboq -cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ - -DCMAKE_BUILD_TYPE=Release \ - . -make - -export WOBOQ_OUT=/usr/share/nginx/html/paddle -export BUILD_DIR=/paddle/build -mkdir -p $WOBOQ_OUT -cp -rv /woboq/data $WOBOQ_OUT/../data -/woboq/generator/codebrowser_generator \ - -b /paddle/build \ - -a \ - -o $WOBOQ_OUT \ - -p paddle:/paddle -/woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT -cd /woboq -make clean -rm -rf /paddle/build trap : 0 From 3f02edeeeae64943cc652a865c600eec384e645f Mon Sep 17 00:00:00 2001 From: xichen Date: Fri, 27 Jan 2017 11:40:11 -0800 Subject: [PATCH 114/370] wording update after pr review --- doc/howto/usage/k8s/k8s_aws_en.md | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 46c4f5582dbd81..ce5ccbca5ee33b 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -3,12 +3,6 @@ We will show you step by step on how to run distributed PaddlePaddle training on AWS cluster with Kubernetes. Let's start from core concepts. -## Choose AWS Service Region -This tutorial requires several AWS services work in the same region. Before we create anything in AWS, please check the following link -https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ -Choose a region which has the following services available: EC2, EFS, VPS, CloudFormation, KMS, VPC, S3. -In this tutorial, we use "Oregon(us-west-2)" as example. - ## Distributed PaddlePaddle Training Core Concepts ### Distributed Training Job @@ -50,6 +44,12 @@ We rank each pod by sorting them by their ips. The rank of each pod could be the ## PaddlePaddle on AWS with Kubernetes +### Choose AWS Service Region +This tutorial requires several AWS services work in the same region. Before we create anything in AWS, please check the following link +https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ +Choose a region which has the following services available: EC2, EFS, VPS, CloudFormation, KMS, VPC, S3. +In this tutorial, we use "Oregon(us-west-2)" as example. + ### Create AWS Account and IAM Account Under each AWS account, we can create multiple [IAM](http://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html) users. This allows us to grant some privileges to each IAM user and to create/operate AWS clusters as an IAM user. @@ -80,7 +80,7 @@ Please be aware that this tutorial needs the following privileges for the user i #### kube-aws [kube-aws](https://github.com/coreos/kube-aws) is a CLI tool to automate cluster deployment to AWS. -##### Verify integrity of kube-aws downloaded +##### Verify kube-aws integrity Note: if you are using a non-official release (e.g RC release) kube-aws, you can skip this setp. Import the CoreOS Application Signing Public Key: @@ -265,10 +265,7 @@ When the cluster is created, the controller will expose the TLS-secured API on a DNS name should have a CNAME points to cluster DNS name or an A record points to the cluster IP address. - -We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. We will find the ip to map to `paddle` in later steps. Also in this case, will have to add name service (route53) in aws cluster in later step. -======= -We will need to use DNS name later in tutorial. +We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster IP with that DNS name for your local machine. And add name service (route53) in aws to associate the IP to paddle for cluster. We will find the cluster IP in later steps. #### S3 bucket @@ -382,7 +379,7 @@ In the above output, both ip `54.241.164.52`, `54.67.102.112` will work. ##### Update local DNS association Edit `/etc/hosts` to associate above ip with the DNS name. ##### Add Route53 private name service in VPC - - Open Route53 web console + - Open [Route53 Console](https://console.aws.amazon.com/route53/home) - Create hosted zone with following config - Domain name: "paddle" - Type: "Private hosted zone for amazon VPC" From 6911b848e34282cb71312e31d39ba1b7af9cb228 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Fri, 27 Jan 2017 15:16:57 -0800 Subject: [PATCH 115/370] remove `allow_inefficient_sparse_update` since it's only defined but never used. --- doc/howto/usage/cmd_parameter/arguments_cn.md | 5 ----- doc/howto/usage/cmd_parameter/arguments_en.md | 5 ----- .../usage/cmd_parameter/detail_introduction_cn.md | 4 ---- .../usage/cmd_parameter/detail_introduction_en.md | 4 ---- paddle/math/SparseRowMatrix.cpp | 4 ---- paddle/math/SparseRowMatrix.h | 11 ++++------- 6 files changed, 4 insertions(+), 29 deletions(-) diff --git a/doc/howto/usage/cmd_parameter/arguments_cn.md b/doc/howto/usage/cmd_parameter/arguments_cn.md index 833e21dd19ef3c..2e2a2fcc54a09f 100644 --- a/doc/howto/usage/cmd_parameter/arguments_cn.md +++ b/doc/howto/usage/cmd_parameter/arguments_cn.md @@ -127,11 +127,6 @@ √√ - -allow_inefficient_sparse_update -√√ - - start_pass √√ diff --git a/doc/howto/usage/cmd_parameter/arguments_en.md b/doc/howto/usage/cmd_parameter/arguments_en.md index 013edbc9047817..e5546f0ddc78a9 100644 --- a/doc/howto/usage/cmd_parameter/arguments_en.md +++ b/doc/howto/usage/cmd_parameter/arguments_en.md @@ -127,11 +127,6 @@ It looks like there are a lot of arguments. However, most of them are for develo √√ - -allow_inefficient_sparse_update -√√ - - start_pass √√ diff --git a/doc/howto/usage/cmd_parameter/detail_introduction_cn.md b/doc/howto/usage/cmd_parameter/detail_introduction_cn.md index dbf7c6f00b8ba5..3b573a324d541b 100644 --- a/doc/howto/usage/cmd_parameter/detail_introduction_cn.md +++ b/doc/howto/usage/cmd_parameter/detail_introduction_cn.md @@ -306,10 +306,6 @@ - 指示是否显示参数服务器上的稀疏参数分布的日志细节. - 类型: bool (默认: 0). -* `--allow_inefficient_sparse_update` - - 指示是否允许低效率的稀疏更新. - - 类型: bool (默认: 0). - * `--check_sparse_distribution_batches` - 每运行多少个批次执行一次稀疏参数分布的检查. - 类型: int32 (默认: 100). diff --git a/doc/howto/usage/cmd_parameter/detail_introduction_en.md b/doc/howto/usage/cmd_parameter/detail_introduction_en.md index aa69a3bd5423c4..33b7ec0d51a96e 100644 --- a/doc/howto/usage/cmd_parameter/detail_introduction_en.md +++ b/doc/howto/usage/cmd_parameter/detail_introduction_en.md @@ -310,10 +310,6 @@ - show log details for sparse parameter distribution in pserver. - type: bool (default: 0). -* `--allow_inefficient_sparse_update` - - Whether to allow inefficient sparse update. - - type: bool (default: 0). - * `--check_sparse_distribution_batches` - Running sparse parameter distribution check every so many batches. - type: int32 (default: 100). diff --git a/paddle/math/SparseRowMatrix.cpp b/paddle/math/SparseRowMatrix.cpp index b61c6b2d49ccea..b8c781ca1fd46c 100644 --- a/paddle/math/SparseRowMatrix.cpp +++ b/paddle/math/SparseRowMatrix.cpp @@ -24,10 +24,6 @@ limitations under the License. */ #include "paddle/utils/Thread.h" #include "paddle/utils/Util.h" -DEFINE_bool(allow_inefficient_sparse_update, - false, - "Whether to allow inefficient sparse update"); - namespace paddle { const unsigned int SparseRowCpuMatrix::kUnusedId_ = -1U; diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index c05fc98ff9fe73..1ccbf97b25922a 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -21,8 +21,6 @@ limitations under the License. */ #include "RowBuffer.h" #include "paddle/utils/Util.h" -DECLARE_bool(allow_inefficient_sparse_update); - namespace paddle { /** @@ -183,11 +181,10 @@ class SparseRowCpuMatrix : public CpuMatrix { inline void checkStoreSize() { if (buf_->isAutoGrowth()) { if (buf_->getRowCount() > 0.5 * height_) { - LOG(WARNING) - << "There are more than 0.5*height (" << localIndices_->size() - << ") rows are used for sparse " - << "update, which is not efficient. Considering not use " - << "sparse_update or set --allow_inefficient_sparse_update=true"; + LOG(WARNING) << "There are more than 0.5*height (" + << localIndices_->size() << ") rows are used for sparse " + << "update, which is not efficient. Considering not use " + << "sparse_update."; } } else { CHECK_LE(localIndices_->size(), buf_->getRowCount()); From 6da7283475355537be2a0775bd9df670351eb1c5 Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Fri, 27 Jan 2017 16:11:53 -0800 Subject: [PATCH 116/370] make gru_group parameters sharable --- paddle/gserver/layers/GruStepLayer.cpp | 4 +- python/paddle/trainer/config_parser.py | 2 +- python/paddle/trainer/recurrent_units.py | 3 + .../paddle/trainer_config_helpers/layers.py | 4 +- .../paddle/trainer_config_helpers/networks.py | 6 + .../tests/configs/file_list.sh | 2 +- .../configs/protostr/shared_gru.protostr | 295 ++++++++++++++++++ .../configs/protostr/test_rnn_group.protostr | 8 +- .../tests/configs/shared_gru.py | 40 +++ 9 files changed, 355 insertions(+), 9 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/shared_gru.py diff --git a/paddle/gserver/layers/GruStepLayer.cpp b/paddle/gserver/layers/GruStepLayer.cpp index 4a1006aa941f39..ce692c490881c9 100644 --- a/paddle/gserver/layers/GruStepLayer.cpp +++ b/paddle/gserver/layers/GruStepLayer.cpp @@ -68,8 +68,8 @@ bool GruStepLayer::init(const LayerMap& layerMap, if (!Layer::init(layerMap, parameterMap)) return false; CHECK_EQ(2U, inputLayers_.size()); - CHECK_EQ(getSize() * getSize() * 3, parameters_[0]->getSize()); - weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[0])); + CHECK_EQ(getSize() * getSize() * 3, parameters_[1]->getSize()); + weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[1])); if (biasParameter_.get() != NULL) { CHECK_EQ(getSize() * 3, biasParameter_->getSize()); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 6701eced60d068..4fbf076ae98c4d 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2996,7 +2996,7 @@ def __init__(self, config_assert(input_layer1.size == size, 'input_layer1.size != layer.size') self.config.active_gate_type = active_gate_type - self.create_input_parameter(0, size * size * 3, [size, size * 3]) + self.create_input_parameter(1, size * size * 3, [size, size * 3]) self.create_bias_parameter(bias, size * 3) diff --git a/python/paddle/trainer/recurrent_units.py b/python/paddle/trainer/recurrent_units.py index edca279dcadef4..ff7e8932dcad79 100644 --- a/python/paddle/trainer/recurrent_units.py +++ b/python/paddle/trainer/recurrent_units.py @@ -19,6 +19,9 @@ # to use these units, import this module in your config_file: # import trainer.recurrent_units # +# The modules in this file are DEPRECATED. +# If you would like to use lstm/gru +# please use the functions defined in paddle.trainer_config_helpers. from paddle.trainer.config_parser import * diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 85a28e14aeb8eb..f0b5d7c3b4f26a 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2682,6 +2682,7 @@ def lstm_step_layer(input, @wrap_bias_attr_default() +@wrap_param_attr_default() @wrap_act_default(param_names=['gate_act'], act=SigmoidActivation()) @wrap_act_default(act=TanhActivation()) @wrap_name_default('gru_step') @@ -2693,6 +2694,7 @@ def gru_step_layer(input, name=None, gate_act=None, bias_attr=None, + param_attr=None, layer_attr=None): """ @@ -2714,7 +2716,7 @@ def gru_step_layer(input, Layer( name=name, type=LayerType.GRU_STEP_LAYER, - inputs=[input.name, output_mem.name], + inputs=[input.name, Input(output_mem.name, **param_attr.attr)], bias=ParamAttr.to_bias(bias_attr), size=size, active_type=act.name, diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index 375bea34e8aa0a..88e188cb2b1da6 100644 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -822,6 +822,7 @@ def gru_unit(input, size=None, name=None, gru_bias_attr=None, + gru_param_attr=None, act=None, gate_act=None, gru_layer_attr=None): @@ -862,6 +863,7 @@ def gru_unit(input, output_mem=out_mem, size=size, bias_attr=gru_bias_attr, + param_attr=gru_param_attr, act=act, gate_act=gate_act, layer_attr=gru_layer_attr) @@ -874,6 +876,7 @@ def gru_group(input, name=None, reverse=False, gru_bias_attr=None, + gru_param_attr=None, act=None, gate_act=None, gru_layer_attr=None): @@ -922,6 +925,7 @@ def __gru_step__(ipt): name=name, size=size, gru_bias_attr=gru_bias_attr, + gru_param_attr=gru_param_attr, act=act, gate_act=gate_act, gru_layer_attr=gru_layer_attr) @@ -942,6 +946,7 @@ def simple_gru(input, mixed_bias_param_attr=None, mixed_layer_attr=None, gru_bias_attr=None, + gru_param_attr=None, act=None, gate_act=None, gru_layer_attr=None): @@ -1010,6 +1015,7 @@ def simple_gru(input, input=m, reverse=reverse, gru_bias_attr=gru_bias_attr, + gru_param_attr=gru_param_attr, act=act, gate_act=gate_act, gru_layer_attr=gru_layer_attr) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 3f1d99701afe54..ea46b557a26ce6 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -3,7 +3,7 @@ export configs=(test_fc layer_activations projections test_print_layer test_sequence_pooling test_lstmemory_layer test_grumemory_layer last_first_seq test_expand_layer test_ntm_layers test_hsigmoid img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers -test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight +test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr new file mode 100644 index 00000000000000..c0868713ebbbc1 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr @@ -0,0 +1,295 @@ +type: "recurrent_nn" +layers { + name: "data_a" + type: "data" + size: 100 + active_type: "" +} +layers { + name: "data_b" + type: "data" + size: 100 + active_type: "" +} +layers { + name: "__simple_gru_0___transform" + type: "mixed" + size: 600 + active_type: "" + inputs { + input_layer_name: "data_a" + input_parameter_name: "mixed_param" + proj_conf { + type: "fc" + name: "___simple_gru_0___transform.w0" + input_size: 100 + output_size: 600 + } + } +} +layers { + name: "__simple_gru_0___recurrent_group" + type: "recurrent_layer_group" + active_type: "" +} +layers { + name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group" + type: "scatter_agent" + size: 600 + active_type: "" +} +layers { + name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group" + type: "agent" + size: 200 + active_type: "" +} +layers { + name: "__simple_gru_0__@__simple_gru_0___recurrent_group" + type: "gru_step" + size: 200 + active_type: "tanh" + inputs { + input_layer_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group" + } + inputs { + input_layer_name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group" + input_parameter_name: "gru_param" + } + bias_parameter_name: "gru_bias" + active_gate_type: "sigmoid" +} +layers { + name: "__simple_gru_0__" + type: "gather_agent" + size: 200 + active_type: "" +} +layers { + name: "__simple_gru_1___transform" + type: "mixed" + size: 600 + active_type: "" + inputs { + input_layer_name: "data_b" + input_parameter_name: "mixed_param" + proj_conf { + type: "fc" + name: "___simple_gru_1___transform.w0" + input_size: 100 + output_size: 600 + } + } +} +layers { + name: "__simple_gru_1___recurrent_group" + type: "recurrent_layer_group" + active_type: "" +} +layers { + name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group" + type: "scatter_agent" + size: 600 + active_type: "" +} +layers { + name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group" + type: "agent" + size: 200 + active_type: "" +} +layers { + name: "__simple_gru_1__@__simple_gru_1___recurrent_group" + type: "gru_step" + size: 200 + active_type: "tanh" + inputs { + input_layer_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group" + } + inputs { + input_layer_name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group" + input_parameter_name: "gru_param" + } + bias_parameter_name: "gru_bias" + active_gate_type: "sigmoid" +} +layers { + name: "__simple_gru_1__" + type: "gather_agent" + size: 200 + active_type: "" +} +layers { + name: "__last_seq_0__" + type: "seqlastins" + size: 200 + active_type: "linear" + inputs { + input_layer_name: "__simple_gru_0__" + } + trans_type: "non-seq" +} +layers { + name: "__last_seq_1__" + type: "seqlastins" + size: 200 + active_type: "linear" + inputs { + input_layer_name: "__simple_gru_1__" + } + trans_type: "non-seq" +} +layers { + name: "__fc_layer_0__" + type: "fc" + size: 10 + active_type: "softmax" + inputs { + input_layer_name: "__last_seq_0__" + input_parameter_name: "softmax_param" + } + inputs { + input_layer_name: "__last_seq_1__" + input_parameter_name: "softmax_param" + } +} +layers { + name: "label" + type: "data" + size: 10 + active_type: "" +} +layers { + name: "__cost_0__" + type: "multi-class-cross-entropy" + size: 1 + active_type: "" + inputs { + input_layer_name: "__fc_layer_0__" + } + inputs { + input_layer_name: "label" + } + coeff: 1.0 +} +parameters { + name: "mixed_param" + size: 60000 + initial_mean: 0.0 + initial_std: 0.1 + dims: 100 + dims: 600 + initial_strategy: 0 + initial_smart: true +} +parameters { + name: "gru_param" + size: 120000 + initial_mean: 0.0 + initial_std: 0.0707106781187 + dims: 200 + dims: 600 + initial_strategy: 0 + initial_smart: true +} +parameters { + name: "gru_bias" + size: 600 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 600 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "softmax_param" + size: 2000 + initial_mean: 0.0 + initial_std: 0.0707106781187 + dims: 200 + dims: 10 + initial_strategy: 0 + initial_smart: true +} +input_layer_names: "data_a" +input_layer_names: "data_b" +input_layer_names: "label" +output_layer_names: "__cost_0__" +evaluators { + name: "classification_error_evaluator" + type: "classification_error" + input_layers: "__fc_layer_0__" + input_layers: "label" +} +sub_models { + name: "root" + layer_names: "data_a" + layer_names: "data_b" + layer_names: "__simple_gru_0___transform" + layer_names: "__simple_gru_0___recurrent_group" + layer_names: "__simple_gru_0__" + layer_names: "__simple_gru_1___transform" + layer_names: "__simple_gru_1___recurrent_group" + layer_names: "__simple_gru_1__" + layer_names: "__last_seq_0__" + layer_names: "__last_seq_1__" + layer_names: "__fc_layer_0__" + layer_names: "label" + layer_names: "__cost_0__" + input_layer_names: "data_a" + input_layer_names: "data_b" + input_layer_names: "label" + output_layer_names: "__cost_0__" + evaluator_names: "classification_error_evaluator" + is_recurrent_layer_group: false +} +sub_models { + name: "__simple_gru_0___recurrent_group" + layer_names: "__simple_gru_0___transform@__simple_gru_0___recurrent_group" + layer_names: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group" + layer_names: "__simple_gru_0__@__simple_gru_0___recurrent_group" + is_recurrent_layer_group: true + reversed: false + memories { + layer_name: "__simple_gru_0__@__simple_gru_0___recurrent_group" + link_name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group" + is_sequence: false + } + in_links { + layer_name: "__simple_gru_0___transform" + link_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group" + has_subseq: false + } + out_links { + layer_name: "__simple_gru_0__@__simple_gru_0___recurrent_group" + link_name: "__simple_gru_0__" + has_subseq: false + } + target_inlinkid: -1 +} +sub_models { + name: "__simple_gru_1___recurrent_group" + layer_names: "__simple_gru_1___transform@__simple_gru_1___recurrent_group" + layer_names: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group" + layer_names: "__simple_gru_1__@__simple_gru_1___recurrent_group" + is_recurrent_layer_group: true + reversed: false + memories { + layer_name: "__simple_gru_1__@__simple_gru_1___recurrent_group" + link_name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group" + is_sequence: false + } + in_links { + layer_name: "__simple_gru_1___transform" + link_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group" + has_subseq: false + } + out_links { + layer_name: "__simple_gru_1__@__simple_gru_1___recurrent_group" + link_name: "__simple_gru_1__" + has_subseq: false + } + target_inlinkid: -1 +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr index 41d2e2f2671f5c..c1d39f77295892 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr @@ -307,10 +307,10 @@ layers { active_type: "tanh" inputs { input_layer_name: "__mixed_1__@__gru_group_0___recurrent_group" - input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w0" } inputs { input_layer_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group" + input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w1" } bias_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias" active_gate_type: "sigmoid" @@ -462,14 +462,14 @@ parameters { initial_smart: false } parameters { - name: "___gru_group_0__@__gru_group_0___recurrent_group.w0" + name: "___gru_group_0__@__gru_group_0___recurrent_group.w1" size: 30000 initial_mean: 0.0 - initial_std: 0.01 + initial_std: 0.1 dims: 100 dims: 300 initial_strategy: 0 - initial_smart: false + initial_smart: true } parameters { name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias" diff --git a/python/paddle/trainer_config_helpers/tests/configs/shared_gru.py b/python/paddle/trainer_config_helpers/tests/configs/shared_gru.py new file mode 100644 index 00000000000000..c19bb9685aa24c --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/shared_gru.py @@ -0,0 +1,40 @@ +from paddle.trainer_config_helpers import * + +settings(learning_rate=1e-4, batch_size=1000) + +data_1 = data_layer(name='data_a', size=100) +data_2 = data_layer(name='data_b', size=100) + +mixed_param = ParamAttr(name='mixed_param') + +gru_param = ParamAttr(name='gru_param') +gru_bias = ParamAttr(name='gru_bias', initial_mean=0., initial_std=0.) + +gru1 = simple_gru( + input=data_1, + size=200, + mixed_param_attr=mixed_param, + mixed_bias_param_attr=False, + gru_bias_attr=gru_bias, + gru_param_attr=gru_param) + +gru2 = simple_gru( + input=data_2, + size=200, + mixed_param_attr=mixed_param, + mixed_bias_param_attr=False, + gru_bias_attr=gru_bias, + gru_param_attr=gru_param) + +softmax_param = ParamAttr(name='softmax_param') + +predict = fc_layer( + input=[last_seq(input=gru1), last_seq(input=gru2)], + size=10, + param_attr=[softmax_param, softmax_param], + bias_attr=False, + act=SoftmaxActivation()) +outputs( + classification_cost( + input=predict, label=data_layer( + name='label', size=10))) From 0bd67524b1b7e14554f3fd0367180071099e88b0 Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Mon, 30 Jan 2017 11:26:47 -0800 Subject: [PATCH 117/370] switch the parameter index in GruStepLayer in test_LayerGrad --- paddle/gserver/tests/test_LayerGrad.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 8c8e876bd64fb9..813a99d8cdae2e 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1404,9 +1404,9 @@ TEST(Layer, GruStepLayer) { config.biasSize = 12; config.inputDefs.push_back( - {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); + {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 0}); config.inputDefs.push_back( - {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); + {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 48}); config.layerConfig.add_inputs(); config.layerConfig.add_inputs(); From 9664322cfe23d775e9ac57ef4ee02096fda0fc5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 30 Jan 2017 12:56:01 -0800 Subject: [PATCH 118/370] Update English document --- .../build_and_install/docker_install_en.rst | 78 +++++++++---------- 1 file changed, 37 insertions(+), 41 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index 51a1a11674d987..fa19297238084f 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -16,70 +16,66 @@ Developers can work on PaddlePaddle using Docker. This allows developers to work on different platforms -- Linux, Mac OS X, and Windows -- in a consistent way. -The general development workflow with Docker and CMake is as follows: - -1. Get the source code of Paddle: +1. Build the Development Environment as a Docker Image .. code-block:: bash - git clone https://github.com/PaddlePaddle/Paddle.git + git clone --recursive https://github.com/PaddlePaddle/Paddle + cd Paddle + docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile . -2. Build a development Docker image :code:`paddle:dev` from the source - code. This image contains all the development tools and - dependencies of PaddlePaddle. + Note that by default :code:`docker build` wouldn't import source + tree into the image and build it. If we want to do that, we need + to set a build arg: .. code-block:: bash - cd paddle - docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile . - - Sometimes docker build might suffer from a slow network connection to the official Ubuntu apt-source servers. In such case, we can specify an apt-source mirror server that is geologically nearer to us. In the following example, we specified an apt-source server that responds fast in China.You can specify the UBUNTU MIRROR with :code:`--build-arg UBUNTU_MIRROR` like the example below. - - .. code-block:: bash + docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . - docker build \ - --build-arg UBUNTU_MIRROR="http://mirrors.163.com" \ - -t paddle:dev \ - -f paddle/scripts/docker/Dockerfile . +1. Run the Development Environment -3. Run the image as a container and mounting local source code - directory into the container. This allows us to change the code on - the host and build it within the container. + Once we got the image :code:`paddle:dev`, we can use it to develop + Paddle by mounting the local source code tree into a container that + runs the image: .. code-block:: bash + docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev - docker run \ - -d \ - --name paddle \ - -p 2022:22 \ - -v $PWD:/paddle \ - paddle:dev + This runs a container of the development environment Docker image + with the local source tree mounted to :code:`/paddle` of the + container. - where :code:`-d` makes the container running in background, - :code:`--name paddle` allows us to run a nginx container to serve - documents in this container, :code:`-p 2022:22` allows us to SSH - into this container, :code:`-v $PWD:/paddle` shares the source code - on the host with the container. + Note that the default entry-point of :code:`paddle:dev` is + :code:`sshd`, and above :code:`docker run` commands actually starts + an SSHD server listening on port 2202. This allows us to log into + this container with: -4. SSH into the container: + .. code-block:: bash + ssh root@localhost -p 2202 + + Usually, I run above commands on my Mac. I can also run them on a + GPU server :code:`xxx.yyy.zzz.www` and ssh from my Mac to it: .. code-block:: bash + my-mac$ ssh root@xxx.yyy.zzz.www -p 2202 - ssh root@localhost -p 2022 +1. Build and Install Using the Development Environment -5. We can edit the source code in the container or on this host. Then - we can build using cmake + Once I am in the container, I can use + :code:`paddle/scripts/docker/build.sh` to build, install, and test + Paddle: .. code-block:: bash + /paddle/paddle/scripts/docker/build.sh - cd /paddle # where paddle source code has been mounted into the container - mkdir -p build - cd build - cmake -DWITH_TESTING=ON .. - make -j `nproc` - CTEST_OUTPUT_ON_FAILURE=1 ctest + This builds everything about Paddle in :code:`/paddle/build`. And + we can run unit tests there: + + .. code-block:: bash + cd /paddle/build + ctest CPU-only and GPU Images From 909a9dd72aee4ba3212811599e9fee72ade0acf4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 30 Jan 2017 12:58:41 -0800 Subject: [PATCH 119/370] Update indentation of rst file --- doc/getstarted/build_and_install/docker_install_en.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index fa19297238084f..0bc313e4388ac6 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -20,9 +20,9 @@ Windows -- in a consistent way. .. code-block:: bash - git clone --recursive https://github.com/PaddlePaddle/Paddle - cd Paddle - docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile . + git clone --recursive https://github.com/PaddlePaddle/Paddle + cd Paddle + docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile . Note that by default :code:`docker build` wouldn't import source From 3afbd1c948f2b6871b55205d8d24dbb2cb1b9fd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 30 Jan 2017 13:01:18 -0800 Subject: [PATCH 120/370] Update code indentation in rst files --- .../build_and_install/docker_install_en.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index 0bc313e4388ac6..cb6723d1ff9423 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -31,7 +31,7 @@ Windows -- in a consistent way. .. code-block:: bash - docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . + docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . 1. Run the Development Environment @@ -41,7 +41,7 @@ Windows -- in a consistent way. runs the image: .. code-block:: bash - docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev + docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev This runs a container of the development environment Docker image with the local source tree mounted to :code:`/paddle` of the @@ -53,13 +53,13 @@ Windows -- in a consistent way. this container with: .. code-block:: bash - ssh root@localhost -p 2202 + ssh root@localhost -p 2202 Usually, I run above commands on my Mac. I can also run them on a GPU server :code:`xxx.yyy.zzz.www` and ssh from my Mac to it: .. code-block:: bash - my-mac$ ssh root@xxx.yyy.zzz.www -p 2202 + my-mac$ ssh root@xxx.yyy.zzz.www -p 2202 1. Build and Install Using the Development Environment @@ -68,14 +68,14 @@ Windows -- in a consistent way. Paddle: .. code-block:: bash - /paddle/paddle/scripts/docker/build.sh + /paddle/paddle/scripts/docker/build.sh This builds everything about Paddle in :code:`/paddle/build`. And we can run unit tests there: .. code-block:: bash - cd /paddle/build - ctest + cd /paddle/build + ctest CPU-only and GPU Images From 891dadff0578a35ac9b097f0a65fc0c39311d26f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 30 Jan 2017 13:02:07 -0800 Subject: [PATCH 121/370] Update code indentation in rst files --- doc/getstarted/build_and_install/docker_install_en.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index cb6723d1ff9423..3a322b2159a491 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -41,6 +41,7 @@ Windows -- in a consistent way. runs the image: .. code-block:: bash + docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev This runs a container of the development environment Docker image @@ -53,12 +54,14 @@ Windows -- in a consistent way. this container with: .. code-block:: bash + ssh root@localhost -p 2202 Usually, I run above commands on my Mac. I can also run them on a GPU server :code:`xxx.yyy.zzz.www` and ssh from my Mac to it: .. code-block:: bash + my-mac$ ssh root@xxx.yyy.zzz.www -p 2202 1. Build and Install Using the Development Environment @@ -68,12 +71,14 @@ Windows -- in a consistent way. Paddle: .. code-block:: bash + /paddle/paddle/scripts/docker/build.sh This builds everything about Paddle in :code:`/paddle/build`. And we can run unit tests there: .. code-block:: bash + cd /paddle/build ctest From cc9484909e0886b1442e7118807ecc76893545fd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E7=9B=8A?= Date: Mon, 30 Jan 2017 13:03:11 -0800 Subject: [PATCH 122/370] Update enumerators in rst files --- doc/getstarted/build_and_install/docker_install_en.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index 3a322b2159a491..5a1056e859a0c9 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -34,7 +34,7 @@ Windows -- in a consistent way. docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . -1. Run the Development Environment +2. Run the Development Environment Once we got the image :code:`paddle:dev`, we can use it to develop Paddle by mounting the local source code tree into a container that @@ -64,7 +64,7 @@ Windows -- in a consistent way. my-mac$ ssh root@xxx.yyy.zzz.www -p 2202 -1. Build and Install Using the Development Environment +3. Build and Install Using the Development Environment Once I am in the container, I can use :code:`paddle/scripts/docker/build.sh` to build, install, and test From e1d074abdb6d4301d49b060f7684f50487f95123 Mon Sep 17 00:00:00 2001 From: zhanghaichao Date: Mon, 30 Jan 2017 17:41:58 -0800 Subject: [PATCH 123/370] updated comments for gru_group and lstm_group in networks.py --- python/paddle/trainer_config_helpers/networks.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index 375bea34e8aa0a..48655b9ebc1773 100644 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -737,12 +737,12 @@ def lstmemory_group(input, lstm_layer_attr=None, get_output_layer_attr=None): """ - lstm_group is a recurrent layer group version Long Short Term Memory. It + lstm_group is a recurrent layer group version of Long Short Term Memory. It does exactly the same calculation as the lstmemory layer (see lstmemory in layers.py for the maths) does. A promising benefit is that LSTM memory - cell states, or hidden states in every time step are accessible to for the + cell states, or hidden states in every time step are accessible to the user. This is especially useful in attention model. If you do not need to - access to the internal states of the lstm, but merely use its outputs, + access the internal states of the lstm, but merely use its outputs, it is recommended to use the lstmemory, which is relatively faster than lstmemory_group. @@ -878,11 +878,11 @@ def gru_group(input, gate_act=None, gru_layer_attr=None): """ - gru_group is a recurrent layer group version Gated Recurrent Unit. It + gru_group is a recurrent layer group version of Gated Recurrent Unit. It does exactly the same calculation as the grumemory layer does. A promising - benefit is that gru hidden sates are accessible to for the user. This is - especially useful in attention model. If you do not need to access to - any internal state, but merely use the outputs of a GRU, it is recommanded + benefit is that gru hidden states are accessible to the user. This is + especially useful in attention model. If you do not need to access + any internal state, but merely use the outputs of a GRU, it is recommended to use the grumemory, which is relatively faster. Please see grumemory in layers.py for more detail about the maths. From 19514f58bf1ec06efdffb3dcf6f5139bfd26b5c8 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 1 Feb 2017 07:46:49 -0800 Subject: [PATCH 124/370] fix paddle docker hub path (paddledev/paddle not paddle-dev/paddle) --- doc/getstarted/build_and_install/docker_install_cn.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_cn.rst b/doc/getstarted/build_and_install/docker_install_cn.rst index 35234e0eb3ece3..6b132d2a4d31ab 100644 --- a/doc/getstarted/build_and_install/docker_install_cn.rst +++ b/doc/getstarted/build_and_install/docker_install_cn.rst @@ -12,7 +12,7 @@ PaddlePaddle项目提供官方 `Docker `_ 镜像。Dock PaddlePaddle提供的Docker镜像版本 -------------------------------- -我们提供了12个 `Docker image `_ ，他们的image name都是 :code:`paddle-dev/paddle` ，tag分别为 +我们提供了12个 `Docker image `_ ，他们的image name都是 :code:`paddledev/paddle` ，tag分别为 +-----------------+------------------+------------------------+-----------------------+ | | normal | devel | demo | @@ -45,7 +45,7 @@ PaddlePaddle提供的Docker镜像版本 if cat /proc/cpuinfo | grep -q avx ; then echo "Support AVX"; else echo "Not support AVX"; fi -如果输出 :code:`Support AVX`，则可以选择上表中的AVX版本PaddlePaddle。否则需要选择非AVX的PaddlePaddle。选择普通CPU版本的devel版本的image，则可以使用 :code:`paddle-dev/paddle:cpu-devel-latest` 来引用这个image。 +如果输出 :code:`Support AVX`，则可以选择上表中的AVX版本PaddlePaddle。否则需要选择非AVX的PaddlePaddle。选择普通CPU版本的devel版本的image，则可以使用 :code:`paddledev/paddle:cpu-devel-latest` 来引用这个image。 PaddlePaddle提供的镜像并不包含任何命令运行，想要运行PaddlePaddle，您需要进入镜像运行PaddlePaddle 程序或者自定义一个含有启动脚本的image。具体请参考注意事项中的 :code:`使用ssh访问PaddlePaddle镜像` From 04b5daf92d69a9cce32af59fa5cf62610ef44f24 Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Wed, 1 Feb 2017 13:44:00 -0800 Subject: [PATCH 125/370] change the parameter position of gru_step_layer from 1 back to 0 --- paddle/gserver/layers/GruStepLayer.cpp | 4 ++-- paddle/gserver/tests/test_LayerGrad.cpp | 4 ++-- python/paddle/trainer/config_parser.py | 2 +- python/paddle/trainer_config_helpers/layers.py | 5 ++++- .../tests/configs/protostr/shared_gru.protostr | 4 ++-- .../tests/configs/protostr/test_rnn_group.protostr | 4 ++-- 6 files changed, 13 insertions(+), 10 deletions(-) diff --git a/paddle/gserver/layers/GruStepLayer.cpp b/paddle/gserver/layers/GruStepLayer.cpp index ce692c490881c9..4a1006aa941f39 100644 --- a/paddle/gserver/layers/GruStepLayer.cpp +++ b/paddle/gserver/layers/GruStepLayer.cpp @@ -68,8 +68,8 @@ bool GruStepLayer::init(const LayerMap& layerMap, if (!Layer::init(layerMap, parameterMap)) return false; CHECK_EQ(2U, inputLayers_.size()); - CHECK_EQ(getSize() * getSize() * 3, parameters_[1]->getSize()); - weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[1])); + CHECK_EQ(getSize() * getSize() * 3, parameters_[0]->getSize()); + weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[0])); if (biasParameter_.get() != NULL) { CHECK_EQ(getSize() * 3, biasParameter_->getSize()); diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 813a99d8cdae2e..8c8e876bd64fb9 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1404,9 +1404,9 @@ TEST(Layer, GruStepLayer) { config.biasSize = 12; config.inputDefs.push_back( - {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 0}); + {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); config.inputDefs.push_back( - {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 48}); + {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); config.layerConfig.add_inputs(); config.layerConfig.add_inputs(); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 4fbf076ae98c4d..6701eced60d068 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2996,7 +2996,7 @@ def __init__(self, config_assert(input_layer1.size == size, 'input_layer1.size != layer.size') self.config.active_gate_type = active_gate_type - self.create_input_parameter(1, size * size * 3, [size, size * 3]) + self.create_input_parameter(0, size * size * 3, [size, size * 3]) self.create_bias_parameter(bias, size * 3) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index f0b5d7c3b4f26a..770d6303c13587 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2706,6 +2706,9 @@ def gru_step_layer(input, :param name: :param gate_act: :param bias_attr: + :param param_attr: the parameter_attribute for transforming the output_mem + from previous step. It is instead grouped with input due + to backward model compatibility. :param layer_attr: :return: LayerOutput object. :rtype: LayerOutput @@ -2716,7 +2719,7 @@ def gru_step_layer(input, Layer( name=name, type=LayerType.GRU_STEP_LAYER, - inputs=[input.name, Input(output_mem.name, **param_attr.attr)], + inputs=[Input(input.name, **param_attr.attr), output_mem.name], bias=ParamAttr.to_bias(bias_attr), size=size, active_type=act.name, diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr index c0868713ebbbc1..b6905824f0cb09 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr @@ -51,10 +51,10 @@ layers { active_type: "tanh" inputs { input_layer_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group" + input_parameter_name: "gru_param" } inputs { input_layer_name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group" - input_parameter_name: "gru_param" } bias_parameter_name: "gru_bias" active_gate_type: "sigmoid" @@ -105,10 +105,10 @@ layers { active_type: "tanh" inputs { input_layer_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group" + input_parameter_name: "gru_param" } inputs { input_layer_name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group" - input_parameter_name: "gru_param" } bias_parameter_name: "gru_bias" active_gate_type: "sigmoid" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr index c1d39f77295892..3e9d28416ed506 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr @@ -307,10 +307,10 @@ layers { active_type: "tanh" inputs { input_layer_name: "__mixed_1__@__gru_group_0___recurrent_group" + input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w0" } inputs { input_layer_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group" - input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w1" } bias_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias" active_gate_type: "sigmoid" @@ -462,7 +462,7 @@ parameters { initial_smart: false } parameters { - name: "___gru_group_0__@__gru_group_0___recurrent_group.w1" + name: "___gru_group_0__@__gru_group_0___recurrent_group.w0" size: 30000 initial_mean: 0.0 initial_std: 0.1 From 781b85b5fc58aa45d163b0d9e78f8a757f2dbd25 Mon Sep 17 00:00:00 2001 From: Haonan Date: Thu, 1 Dec 2016 19:49:30 -0800 Subject: [PATCH 126/370] rotate_layer and flip_layer * added getMin and getMax for GpuMatrix * gru_step_layer parameter name --- paddle/gserver/layers/TransLayer.h | 2 +- paddle/gserver/tests/test_LayerGrad.cpp | 15 +++ paddle/math/CpuSparseMatrix.cpp | 2 +- paddle/math/CpuSparseMatrix.h | 2 +- paddle/math/Matrix.cpp | 58 ++++++++++- paddle/math/Matrix.h | 27 +++-- paddle/math/SparseMatrix.cpp | 2 +- paddle/math/SparseMatrix.h | 2 +- paddle/math/tests/test_SparseMatrix.cpp | 6 +- paddle/math/tests/test_matrixCompare.cpp | 17 +++- proto/ModelConfig.proto | 8 +- python/paddle/trainer/config_parser.py | 18 +++- python/paddle/trainer/recurrent_units.py | 10 +- .../paddle/trainer_config_helpers/layers.py | 99 +++++++++++++++++-- .../paddle/trainer_config_helpers/networks.py | 12 +-- .../tests/layers_test_config.py | 6 +- 16 files changed, 242 insertions(+), 44 deletions(-) mode change 100644 => 100755 python/paddle/trainer/recurrent_units.py mode change 100644 => 100755 python/paddle/trainer_config_helpers/layers.py mode change 100644 => 100755 python/paddle/trainer_config_helpers/networks.py diff --git a/paddle/gserver/layers/TransLayer.h b/paddle/gserver/layers/TransLayer.h index b43fa1ebfb0032..1d5a370b3414bf 100644 --- a/paddle/gserver/layers/TransLayer.h +++ b/paddle/gserver/layers/TransLayer.h @@ -20,7 +20,7 @@ limitations under the License. */ namespace paddle { /** - * A layer for transposition. + * A layer for transposing a minibatch matrix. * \f[ y = x^\mathrm{T} * \f] diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 813a99d8cdae2e..f7a6c672de2016 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1316,6 +1316,21 @@ TEST(Layer, ResizeLayer) { } } +TEST(Layer, RotateLayer) { + TestConfig config; + config.biasSize = 0; + config.layerConfig.set_type("rotate"); + const int INPUT_SIZE = 64; // height * width + config.layerConfig.set_size(INPUT_SIZE); + config.layerConfig.set_height(32); + config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0}); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "rotate", 100, false, useGpu); + } +} + TEST(Layer, NCELayer) { TestConfig config; size_t numClasses = 4; diff --git a/paddle/math/CpuSparseMatrix.cpp b/paddle/math/CpuSparseMatrix.cpp index 82a482f7014812..bf62229c03bb1d 100644 --- a/paddle/math/CpuSparseMatrix.cpp +++ b/paddle/math/CpuSparseMatrix.cpp @@ -372,7 +372,7 @@ MatrixPtr CpuSparseMatrix::subMatrix(size_t startRow, size_t numRows) { } /* mem MUST be alloced outside (memAlloc=false) */ -void CpuSparseMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { +void CpuSparseMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { CHECK(!memAlloc); CpuSparseMatrix* mat = dynamic_cast(matTrans.get()); if (format_ == SPARSE_CSR) { diff --git a/paddle/math/CpuSparseMatrix.h b/paddle/math/CpuSparseMatrix.h index d3e8871cb5b320..860cad1047fc34 100644 --- a/paddle/math/CpuSparseMatrix.h +++ b/paddle/math/CpuSparseMatrix.h @@ -201,7 +201,7 @@ class CpuSparseMatrix : public Matrix { void zeroMem(); /// mem MUST be alloced outside (memAlloc=false) - void transpose(MatrixPtr matTrans, bool memAlloc); + void transpose(MatrixPtr& matTrans, bool memAlloc); void mul(const Matrix& A, const Matrix& B, real alpha, real beta); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 3ae237bc7de895..3c09f010bbf64c 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -274,6 +274,18 @@ real GpuMatrix::getSum() { return sum; } +real GpuMatrix::getMin() { + CHECK(isContiguous()); + auto vec = GpuVector(height_ * width_, data_); + return vec.getMin(); +} + +real GpuMatrix::getMax() { + CHECK(isContiguous()); + auto vec = GpuVector(height_ * width_, data_); + return vec.getMax(); +} + void GpuMatrix::accumulateColSum(Matrix& src) { CHECK_EQ(getWidth(), src.getWidth()); CHECK_EQ(getHeight(), (size_t)1); @@ -371,7 +383,7 @@ MatrixPtr GpuMatrix::getTranspose() { } } -void GpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { +void GpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { if (memAlloc) { matTrans = std::make_shared(width_, height_); } else { @@ -385,13 +397,29 @@ void GpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { hl_matrix_transpose(data, dataTrans, height_, width_, lda, ldc); } +void GpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { + if (memAlloc) { + matRot = std::make_shared(width_, height_); + } else { + CHECK(matRot != NULL); + } + + MatrixPtr cpuMat = std::make_shared(height_, width_); + cpuMat->copyFrom(*this); + + MatrixPtr cpuMatRot = std::make_shared(width_, height_); + cpuMat->rotate(cpuMatRot, false, clockWise); + + matRot->copyFrom(*cpuMatRot); +} + MatrixPtr GpuMatrix::getInverse() { MatrixPtr matInv; inverse(matInv, true); return matInv; } -void GpuMatrix::inverse(MatrixPtr matInv, bool memAlloc) { +void GpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) { CHECK_EQ(height_, width_); if (memAlloc) { @@ -1690,7 +1718,7 @@ MatrixPtr CpuMatrix::getTranspose() { } } -void CpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { +void CpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { if (memAlloc) { matTrans = std::make_shared(width_, height_); } else { @@ -1708,13 +1736,35 @@ void CpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { } } +void CpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { + if (memAlloc) { + matRot = std::make_shared(width_, height_); + } else { + CHECK(matRot != NULL); + } + real* dataRot = matRot->getData(); + real* data = getData(); + int lda = getStride(); + int ldc = matRot->getStride(); + + for (size_t i = 0; i < height_; i++) { + for (size_t j = 0; j < width_; j++) { + if (clockWise) { + dataRot[j * ldc + i] = data[(height_ - i - 1) * lda + j]; + } else { + dataRot[j * ldc + i] = data[i * lda + (width_ - j - 1)]; + } + } + } +} + MatrixPtr CpuMatrix::getInverse() { MatrixPtr matInv; inverse(matInv, true); return matInv; } -void CpuMatrix::inverse(MatrixPtr matInv, bool memAlloc) { +void CpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) { CHECK_EQ(height_, width_); if (memAlloc) { diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index 57c0c2fe40a95d..d123f827c91079 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -372,7 +372,17 @@ class Matrix : public BaseMatrix { * allocate matTrans' memory outside, then set memAlloc as false; * else set as true. */ - virtual void transpose(MatrixPtr matTrans, bool memAlloc) { + virtual void transpose(MatrixPtr& matTrans, bool memAlloc) { + LOG(FATAL) << "Not implemented"; + } + + /** + * @brief rotate clock-wise. + * + * allocate matTrans' memory outside, then set memAlloc as false; + * else set as true. + */ + virtual void rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { LOG(FATAL) << "Not implemented"; } @@ -387,7 +397,7 @@ class Matrix : public BaseMatrix { * if allocate matInv's memory outside, then set memAlloc as false; * else set as true. */ - virtual void inverse(MatrixPtr matInv, bool memAlloc) { + virtual void inverse(MatrixPtr& matInv, bool memAlloc) { LOG(FATAL) << "Not implemented"; } @@ -1169,11 +1179,15 @@ class GpuMatrix : public Matrix { void accumulateColSum(Matrix& src); real getAbsSum(); + real getMin(); + real getMax(); + MatrixPtr getTranspose(); - void transpose(MatrixPtr matTrans, bool memAlloc); + void transpose(MatrixPtr& matTrans, bool memAlloc); + void rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise); MatrixPtr getInverse(); - void inverse(MatrixPtr matInv, bool memAlloc); + void inverse(MatrixPtr& matInv, bool memAlloc); /// add b to each sample of this. void addBias(Matrix& b, real scale); @@ -1485,10 +1499,11 @@ class CpuMatrix : public Matrix { real getAbsSum(); MatrixPtr getTranspose(); - void transpose(MatrixPtr matTrans, bool memAlloc); + void transpose(MatrixPtr& matTrans, bool memAlloc); + void rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise); MatrixPtr getInverse(); - void inverse(MatrixPtr matInv, bool memAlloc); + void inverse(MatrixPtr& matInv, bool memAlloc); void copyFrom(const Matrix& src); diff --git a/paddle/math/SparseMatrix.cpp b/paddle/math/SparseMatrix.cpp index 3bae6d373f240f..6370c77386688a 100644 --- a/paddle/math/SparseMatrix.cpp +++ b/paddle/math/SparseMatrix.cpp @@ -497,7 +497,7 @@ void GpuSparseMatrix::setRow(size_t row, SparseValueType GpuSparseMatrix::getValueType() const { return valueType_; } -void GpuSparseMatrix::transpose(MatrixPtr matTrans, bool memAlloc) { +void GpuSparseMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { CHECK_EQ(format_, SPARSE_CSC); int nnz = sMatrix_->nnz; if (memAlloc) { diff --git a/paddle/math/SparseMatrix.h b/paddle/math/SparseMatrix.h index 1d3801548e03a6..f6cd5df338965b 100644 --- a/paddle/math/SparseMatrix.h +++ b/paddle/math/SparseMatrix.h @@ -109,7 +109,7 @@ class GpuSparseMatrix : public Matrix { MatrixPtr getTranspose(); /// B = A' - void transpose(MatrixPtr matTrans, bool memAlloc); + void transpose(MatrixPtr& matTrans, bool memAlloc); void copyFrom(const Matrix& src); void copyFrom(const Matrix& src, hl_stream_t stream); diff --git a/paddle/math/tests/test_SparseMatrix.cpp b/paddle/math/tests/test_SparseMatrix.cpp index 9d3fbaef43d719..c0572dfdbf738a 100644 --- a/paddle/math/tests/test_SparseMatrix.cpp +++ b/paddle/math/tests/test_SparseMatrix.cpp @@ -248,11 +248,13 @@ TEST(Matrix, SparseMatrixTranspose) { /*dense matrix transpose*/ CpuMatrixPtr matC(new CpuMatrix(height, width)); matC->copyFrom(*matA); - CpuMatrixPtr matD(new CpuMatrix(width, height)); + MatrixPtr matD(new CpuMatrix(width, height)); matC->transpose(matD, false); + /*check result*/ checkSMatrixEqual2Dense( - std::dynamic_pointer_cast(matB), matD); + std::dynamic_pointer_cast(matB), + std::dynamic_pointer_cast(matD)); } } } diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index f0c49791d7e2a6..c062ca10809ac6 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -105,6 +105,21 @@ void testMatrixGetSum(int height, int width) { EXPECT_LE(fabs(cpuSum - gpuSum), err); } +void testMatrixGetMinMax(int height, int width) { + MatrixPtr cpuInput = std::make_shared(height, width); + MatrixPtr gpuInput = std::make_shared(height, width); + cpuInput->randomizeUniform(); + gpuInput->copyFrom(*cpuInput); + + real cpuMin = cpuInput->getMin(); + real gpuMin = gpuInput->getMin(); + real cpuMax = cpuInput->getMax(); + real gpuMax = gpuInput->getMax(); + + EXPECT_EQ(cpuMin, gpuMin); + EXPECT_EQ(cpuMax, gpuMax); +} + void testMatrixZeroAtOffset(int height, int width) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr gpuA = std::make_shared(height, width); @@ -181,7 +196,7 @@ void testMatrixInverse(int height) { cpu->add(*outputCheck); gpu->copyFrom(*cpu); - cpu->inverse(cpuI, false); + cpu->inverse(cpuI, true); gpu->inverse(gpuI, false); TensorCheckErr(*cpuI, *gpuI); diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 0456404832c301..be4634d5103c0f 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -427,14 +427,14 @@ message LayerConfig { // bias size optional uint32 bias_size = 48 [default = 0]; - // this parameter can be used as a user-defined parameter when necessary, + // this parameter can be used as a user-defined parameter when necessary, // without changing the proto file. - // e.g., when a new layer with a user-defined parameter is implemented, + // e.g., when a new layer with a user-defined parameter is implemented, // it can be used to pass that parameter, without modifying the proto file. // string type is used for flexibility: different types can be converted - // to string and reinterpreted in the user's own layer implementation. + // to string and reinterpreted in the user's own layer implementation. optional string user_arg = 49; - + // to indicate rectangle image data optional uint64 height = 50; optional uint64 width = 51; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 4fbf076ae98c4d..97744ed32ecbda 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -830,7 +830,6 @@ def __init__( channels, size_x, size_y=None, - img_width=None, start=None, stride=None, # 1 by defalut in protobuf stride_y=None, @@ -1834,6 +1833,7 @@ def __init__(self, name, inputs, ceil_mode=True, **xargs): pool_conf.channels) + @config_layer('spp') class SpatialPyramidPoolLayer(LayerBase): def __init__(self, name, inputs, **xargs): @@ -1927,8 +1927,8 @@ def __init__(self, image_conf = self.config.inputs[0].image_conf parse_image(self.inputs[0].image, input_layer.name, image_conf) - # Only pass the width and height of input to batch_norm layer - # when either of it is non-zero. + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. if input_layer.width != 0 or input_layer.height != 0: self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, image_conf.channels, False) @@ -1968,6 +1968,18 @@ def __init__(self, name, size, inputs, **xargs): 'ResizeLayer must have one and only one input') +@config_layer('rotate') +class RotateLayer(LayerBase): + def __init__(self, name, inputs, height, device=None): + super(RotateLayer, self).__init__( + name, 'rotate', 0, inputs=inputs, device=device) + config_assert( + len(self.inputs) == 1, + 'RotateLayer must have one and only one input') + self.config.height = height + self.set_layer_size(self.get_input_layer(0).size) + + @config_layer('blockexpand') class BlockExpandLayer(LayerBase): def __init__(self, name, inputs, **xargs): diff --git a/python/paddle/trainer/recurrent_units.py b/python/paddle/trainer/recurrent_units.py old mode 100644 new mode 100755 index ff7e8932dcad79..ef92107a1093d2 --- a/python/paddle/trainer/recurrent_units.py +++ b/python/paddle/trainer/recurrent_units.py @@ -15,10 +15,10 @@ # recurrent_units.py # Version 2.0 # -# Some recurrent units can be used in recurrent layer group, +# Some recurrent units can be used in recurrent layer group, # to use these units, import this module in your config_file: -# import trainer.recurrent_units -# +# import trainer.recurrent_units +# # The modules in this file are DEPRECATED. # If you would like to use lstm/gru # please use the functions defined in paddle.trainer_config_helpers. @@ -29,7 +29,7 @@ # long short term memory, can be used in recurrent machine # *inputs* must be a list of Projections, for example: # inputs = [FullMatrixProjection("input_layer_name")], -# *para_prefix* defines parameter names, if the *para_prefix* of +# *para_prefix* defines parameter names, if the *para_prefix* of # two LstmRecurrentUnit is same, they share same parameters # *out_memory* can be defined outside if it's used outside def LstmRecurrentUnit(name, @@ -197,7 +197,7 @@ def LstmRecurrentLayerGroup(name, # gated recurrent unit, can be used in recurrent machine # *inputs* should be a list of Projections, for example: # inputs = [FullMatrixProjection("input_layer_name")], -# *para_prefix* defines parameter names, if the *para_prefix* of +# *para_prefix* defines parameter names, if the *para_prefix* of # two GatedRecurrentUnit is same, they share same parameters # *out_memory* can be defined outside if it's used outside diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py old mode 100644 new mode 100755 index f0b5d7c3b4f26a..c502885cf9efbd --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -70,6 +70,8 @@ 'interpolation_layer', 'bilinear_interp_layer', 'trans_layer', + 'rotate_layer', + 'flip_layer', 'sum_to_one_norm_layer', 'get_output_layer', 'LayerType', @@ -154,6 +156,7 @@ class LayerType(object): POWER_LAYER = 'power' SCALING_LAYER = 'scaling' TRANS_LAYER = 'trans' + ROTATE_LAYER = 'rotate' OUT_PROD_LAYER = 'out_prod' FEATURE_MAP_EXPAND_LAYER = 'featmap_expand' @@ -1642,7 +1645,7 @@ def scaling_layer(input, weight, name=None, layer_attr=None): @layer_support() def trans_layer(input, name=None, layer_attr=None): """ - A layer for transposition. + A layer for transposing a minibatch matrix. .. math:: y = x^\mathrm{T} @@ -1673,6 +1676,87 @@ def trans_layer(input, name=None, layer_attr=None): name, LayerType.TRANS_LAYER, parents=[input], size=input.size) +@wrap_name_default() +@layer_support() +def rotate_layer(input, height, name=None, layer_attr=None): + """ + A layer for rotation (clock-wise), usually used when the input sample + is some image or map. + + .. math:: + y(j,i) = x(M-i-1,j) + + where :math:`x` is (M x N) input, and :math:`y` is (N x M) output. + + The example usage is: + + .. code-block:: python + + rot = rotate_layer(input=layer, + height=100) + + :param input: Input layer. + :type input: LayerOutput + :param height: The height of the sample matrix + :type height: int + :param name: Layer name. + :type name: basestring + :param layer_attr: extra layer attributes. + :type layer_attr: ExtraLayerAttribute. + :return: LayerOutput object. + :rtype: LayerOutput + """ + assert isinstance(input, LayerOutput) + l = Layer(name=name, + height=height, + type=LayerType.ROTATE_LAYER, + inputs=[input.name], + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput(name=name, + layer_type=LayerType.ROTATE_LAYER, + parents=[input], + size=l.config.size) + + +@wrap_name_default() +@layer_support() +def flip_layer(input, height, name=None, layer_attr=None): + """ + A layer for flipping the matrix w.r.t the matrix center. + It's essentially rotating the matrix twice. + Used for input as image or map. + + .. math:: + y(i,j) = x(M-i-1, N-j-1) + + where :math:`x` is (M x N) input, and :math:`y` is (M x N) output. + + The example usage is: + + .. code-block:: python + + flip = flip_layer(input=layer, + height=100) + + :param input: Input layer. + :type input: LayerOutput + :param height: The height of the sample matrix + :type height: int + :param name: Layer name. + :type name: basestring + :param layer_attr: extra layer attributes. + :type layer_attr: ExtraLayerAttribute. + :return: LayerOutput object. + :rtype: LayerOutput + """ + assert isinstance(input, LayerOutput) + return rotate_layer(input=rotate_layer(input=input, + height=height), + height=height, + name=name, + layer_attr=layer_attr) + + @wrap_name_default() @layer_support() def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): @@ -1826,14 +1910,14 @@ def img_conv_layer(input, trans=False, layer_type=None): """ - Convolution layer for image. Paddle can support both square and non-square + Convolution layer for image. Paddle can support both square and non-square input currently. The details of convolution layer, please refer UFLDL's `convolution `_ . - Convolution Transpose (deconv) layer for image. Paddle can support both square + Convolution Transpose (deconv) layer for image. Paddle can support both square and non-square input currently. The details of convolution transpose layer, @@ -1892,7 +1976,7 @@ def img_conv_layer(input, :param trans: true if it is a convTransLayer, false if it is a convLayer :type trans: bool :param layer_type: specify the layer_type, default is None. If trans=True, - layer_type has to be "exconvt", otherwise layer_type + layer_type has to be "exconvt", otherwise layer_type has to be either "exconv" or "cudnn_conv" :type layer_type: String :return: LayerOutput object. @@ -3619,9 +3703,9 @@ def pad_layer(input, input data and 3 zeros after the input data in channel dimension. pad_h means padding zeros in height dimension. pad_w means padding zeros in width dimension. - + For example, - + .. code-block:: input(2,2,2,3) = [ @@ -3630,7 +3714,7 @@ def pad_layer(input, [ [[4,3,1], [1,8,7]], [[3,8,9], [2,3,5]] ] ] - + pad_c=[1,1], pad_h=[0,0], pad_w=[0,0] output(2,4,2,3) = [ [ [[0,0,0], [0,0,0]], @@ -4739,6 +4823,7 @@ def cross_entropy_with_selfnorm(input, layer_attr=None): """ A loss layer for multi class entropy with selfnorm. + Input should be a vector of positive numbers, without normalization. .. code-block:: python diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py old mode 100644 new mode 100755 index d0cbe3891f60f4..cadde11ff81658 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -957,22 +957,22 @@ def simple_gru(input, use one complete layer to implement rnn (including simple rnn, gru and lstm) with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But, the multiplication operation :math:`W x_t` is not computed in these layers. - See details in their interfaces in layers.py. + See details in their interfaces in layers.py. The other implementation is to use an recurrent group which can ensemble a series of layers to compute rnn step by step. This way is flexible for attenion mechanism or other complex connections. - gru_step_layer: only compute rnn by one step. It needs an memory as input and can be used in recurrent group. - - gru_unit: a wrapper of gru_step_layer with memory. + - gru_unit: a wrapper of gru_step_layer with memory. - gru_group: a GRU cell implemented by a combination of multiple layers in recurrent group. - But :math:`W x_t` is not done in group. + But :math:`W x_t` is not done in group. - gru_memory: a GRU cell implemented by one layer, which does same calculation - with gru_group and is faster than gru_group. - - simple_gru: a complete GRU implementation inlcuding :math:`W x_t` and + with gru_group and is faster than gru_group. + - simple_gru: a complete GRU implementation inlcuding :math:`W x_t` and gru_group. :math:`W` contains :math:`W_r`, :math:`W_z` and :math:`W`, see - formula in grumemory. + formula in grumemory. The computational speed is that, grumemory is relatively better than gru_group, and gru_group is relatively better than simple_gru. diff --git a/python/paddle/trainer_config_helpers/tests/layers_test_config.py b/python/paddle/trainer_config_helpers/tests/layers_test_config.py index ae275735aa2b85..10b7358d929b75 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test_config.py @@ -39,6 +39,10 @@ assert z1.size > 0 y2 = fc_layer(input=y, size=15) +z2 = rotate_layer(input=y2, + height=5) +z3 = flip_layer(input=y2, + height=3) cos1 = cos_sim(a=x1, b=y1) cos3 = cos_sim(a=x1, b=y2, size=3) @@ -46,7 +50,7 @@ linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3) out = fc_layer( - input=[cos1, cos3, linear_comb, z, z1], + input=[cos1, cos3, linear_comb, z, z1, z2, z3], size=num_classes, act=SoftmaxActivation()) From f5995300c59c33c134ee2bd428174d38d4b8af30 Mon Sep 17 00:00:00 2001 From: Haonan Date: Thu, 26 Jan 2017 12:49:27 -0800 Subject: [PATCH 127/370] rotate layer cpp files --- paddle/gserver/layers/RotateLayer.cpp | 94 +++++++++++++++++++++++++++ paddle/gserver/layers/RotateLayer.h | 49 ++++++++++++++ 2 files changed, 143 insertions(+) create mode 100644 paddle/gserver/layers/RotateLayer.cpp create mode 100644 paddle/gserver/layers/RotateLayer.h diff --git a/paddle/gserver/layers/RotateLayer.cpp b/paddle/gserver/layers/RotateLayer.cpp new file mode 100644 index 00000000000000..10195193316eae --- /dev/null +++ b/paddle/gserver/layers/RotateLayer.cpp @@ -0,0 +1,94 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "RotateLayer.h" + +namespace paddle { + +REGISTER_LAYER(rotate, RotateLayer); + +bool RotateLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + Layer::init(layerMap, parameterMap); + + CHECK_EQ(inputLayers_.size(), 1UL); + sampleHeight_ = config_.height(); + return true; +} + +void RotateLayer::forward(PassType passType) { + Layer::forward(passType); + + MatrixPtr input = getInputValue(0); + batchSize_ = input->getHeight(); + sampleSize_ = input->getWidth(); + sampleWidth_ = sampleSize_ / sampleHeight_; + CHECK_EQ(sampleSize_ % sampleHeight_, 0); + + resizeOutput(batchSize_, sampleSize_); + + MatrixPtr outV = getOutputValue(); + + for (int b = 0; b < batchSize_; b ++) { + MatrixPtr inputSample + = Matrix::create(input->getData() + b * sampleSize_, + sampleHeight_, + sampleWidth_, + false, + useGpu_); + MatrixPtr outputSample + = Matrix::create(outV->getData() + b * sampleSize_, + sampleWidth_, + sampleHeight_, + false, + useGpu_); + inputSample->rotate(outputSample, false, true); + } + + if (getInputGrad(0)) { + zeroGrad(); + } +} + +void RotateLayer::backward(const UpdateCallback& callback) { + (void)callback; + + MatrixPtr outputGrad = getOutputGrad(); + if (outputGrad == NULL) { + return; + } + // the grad should be rotated in the reverse direction + MatrixPtr preGrad = getInputGrad(0); + + for (int b = 0; b < batchSize_; b ++) { + MatrixPtr inputSampleGrad + = Matrix::create(preGrad->getData() + b * sampleSize_, + sampleHeight_, + sampleWidth_, + false, + useGpu_); + MatrixPtr outputSampleGrad + = Matrix::create(outputGrad->getData() + b * sampleSize_, + sampleWidth_, + sampleHeight_, + false, + useGpu_); + MatrixPtr tmpGrad + = Matrix::create(sampleHeight_, sampleWidth_, false, useGpu_); + outputSampleGrad->rotate(tmpGrad, false, false); + inputSampleGrad->add(*tmpGrad); + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/RotateLayer.h b/paddle/gserver/layers/RotateLayer.h new file mode 100644 index 00000000000000..c8eca7c5066b18 --- /dev/null +++ b/paddle/gserver/layers/RotateLayer.h @@ -0,0 +1,49 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "Layer.h" +#include "paddle/math/Matrix.h" + +namespace paddle { +/** + * A layer for rotating an input sample (assume it's a matrix) + * The rotation is in clock-wise + * \f[ + * y(j,i) = x(M-i-1,j) + * \f] + * where \f$x\f$ is (M x N) input, and \f$y\f$ is (N x M) output. + * + * The config file api is rotate_layer + * +*/ + +class RotateLayer : public Layer { +public: + explicit RotateLayer(const LayerConfig& config) : Layer(config) {} + + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + + void forward(PassType passType); + void backward(const UpdateCallback& callback = nullptr); + +private: + int batchSize_; + int sampleSize_; + int sampleHeight_; + int sampleWidth_; +}; + +} // namespace paddle From 55eb2fcffa5081967e61eeb62ada4c994492e634 Mon Sep 17 00:00:00 2001 From: Haonan Date: Thu, 26 Jan 2017 16:12:48 -0800 Subject: [PATCH 128/370] format correction --- paddle/gserver/layers/RotateLayer.cpp | 54 +++++++++---------- python/paddle/trainer/config_parser.py | 1 - .../paddle/trainer_config_helpers/layers.py | 31 ++++++----- .../tests/layers_test_config.py | 6 +-- 4 files changed, 45 insertions(+), 47 deletions(-) diff --git a/paddle/gserver/layers/RotateLayer.cpp b/paddle/gserver/layers/RotateLayer.cpp index 10195193316eae..269ad43b3180bd 100644 --- a/paddle/gserver/layers/RotateLayer.cpp +++ b/paddle/gserver/layers/RotateLayer.cpp @@ -40,19 +40,17 @@ void RotateLayer::forward(PassType passType) { MatrixPtr outV = getOutputValue(); - for (int b = 0; b < batchSize_; b ++) { - MatrixPtr inputSample - = Matrix::create(input->getData() + b * sampleSize_, - sampleHeight_, - sampleWidth_, - false, - useGpu_); - MatrixPtr outputSample - = Matrix::create(outV->getData() + b * sampleSize_, - sampleWidth_, - sampleHeight_, - false, - useGpu_); + for (int b = 0; b < batchSize_; b++) { + MatrixPtr inputSample = Matrix::create(input->getData() + b * sampleSize_, + sampleHeight_, + sampleWidth_, + false, + useGpu_); + MatrixPtr outputSample = Matrix::create(outV->getData() + b * sampleSize_, + sampleWidth_, + sampleHeight_, + false, + useGpu_); inputSample->rotate(outputSample, false, true); } @@ -71,21 +69,21 @@ void RotateLayer::backward(const UpdateCallback& callback) { // the grad should be rotated in the reverse direction MatrixPtr preGrad = getInputGrad(0); - for (int b = 0; b < batchSize_; b ++) { - MatrixPtr inputSampleGrad - = Matrix::create(preGrad->getData() + b * sampleSize_, - sampleHeight_, - sampleWidth_, - false, - useGpu_); - MatrixPtr outputSampleGrad - = Matrix::create(outputGrad->getData() + b * sampleSize_, - sampleWidth_, - sampleHeight_, - false, - useGpu_); - MatrixPtr tmpGrad - = Matrix::create(sampleHeight_, sampleWidth_, false, useGpu_); + for (int b = 0; b < batchSize_; b++) { + MatrixPtr inputSampleGrad = + Matrix::create(preGrad->getData() + b * sampleSize_, + sampleHeight_, + sampleWidth_, + false, + useGpu_); + MatrixPtr outputSampleGrad = + Matrix::create(outputGrad->getData() + b * sampleSize_, + sampleWidth_, + sampleHeight_, + false, + useGpu_); + MatrixPtr tmpGrad = + Matrix::create(sampleHeight_, sampleWidth_, false, useGpu_); outputSampleGrad->rotate(tmpGrad, false, false); inputSampleGrad->add(*tmpGrad); } diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 97744ed32ecbda..62efe5fca0caca 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1833,7 +1833,6 @@ def __init__(self, name, inputs, ceil_mode=True, **xargs): pool_conf.channels) - @config_layer('spp') class SpatialPyramidPoolLayer(LayerBase): def __init__(self, name, inputs, **xargs): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index c502885cf9efbd..c506977ca4e405 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1707,15 +1707,17 @@ def rotate_layer(input, height, name=None, layer_attr=None): :rtype: LayerOutput """ assert isinstance(input, LayerOutput) - l = Layer(name=name, - height=height, - type=LayerType.ROTATE_LAYER, - inputs=[input.name], - **ExtraLayerAttribute.to_kwargs(layer_attr)) - return LayerOutput(name=name, - layer_type=LayerType.ROTATE_LAYER, - parents=[input], - size=l.config.size) + l = Layer( + name=name, + height=height, + type=LayerType.ROTATE_LAYER, + inputs=[input.name], + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name=name, + layer_type=LayerType.ROTATE_LAYER, + parents=[input], + size=l.config.size) @wrap_name_default() @@ -1750,11 +1752,12 @@ def flip_layer(input, height, name=None, layer_attr=None): :rtype: LayerOutput """ assert isinstance(input, LayerOutput) - return rotate_layer(input=rotate_layer(input=input, - height=height), - height=height, - name=name, - layer_attr=layer_attr) + return rotate_layer( + input=rotate_layer( + input=input, height=height), + height=height, + name=name, + layer_attr=layer_attr) @wrap_name_default() diff --git a/python/paddle/trainer_config_helpers/tests/layers_test_config.py b/python/paddle/trainer_config_helpers/tests/layers_test_config.py index 10b7358d929b75..3d417a00601d56 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test_config.py @@ -39,10 +39,8 @@ assert z1.size > 0 y2 = fc_layer(input=y, size=15) -z2 = rotate_layer(input=y2, - height=5) -z3 = flip_layer(input=y2, - height=3) +z2 = rotate_layer(input=y2, height=5) +z3 = flip_layer(input=y2, height=3) cos1 = cos_sim(a=x1, b=y1) cos3 = cos_sim(a=x1, b=y2, size=3) From b4c1d17580777fcc5118efa26b230432c0221b6a Mon Sep 17 00:00:00 2001 From: Haonan Date: Thu, 26 Jan 2017 17:52:47 -0800 Subject: [PATCH 129/370] remove flip_layer --- .../paddle/trainer_config_helpers/layers.py | 41 ------------------- .../tests/layers_test_config.py | 3 +- 2 files changed, 1 insertion(+), 43 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index c506977ca4e405..19f7ec3186596f 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -71,7 +71,6 @@ 'bilinear_interp_layer', 'trans_layer', 'rotate_layer', - 'flip_layer', 'sum_to_one_norm_layer', 'get_output_layer', 'LayerType', @@ -1720,46 +1719,6 @@ def rotate_layer(input, height, name=None, layer_attr=None): size=l.config.size) -@wrap_name_default() -@layer_support() -def flip_layer(input, height, name=None, layer_attr=None): - """ - A layer for flipping the matrix w.r.t the matrix center. - It's essentially rotating the matrix twice. - Used for input as image or map. - - .. math:: - y(i,j) = x(M-i-1, N-j-1) - - where :math:`x` is (M x N) input, and :math:`y` is (M x N) output. - - The example usage is: - - .. code-block:: python - - flip = flip_layer(input=layer, - height=100) - - :param input: Input layer. - :type input: LayerOutput - :param height: The height of the sample matrix - :type height: int - :param name: Layer name. - :type name: basestring - :param layer_attr: extra layer attributes. - :type layer_attr: ExtraLayerAttribute. - :return: LayerOutput object. - :rtype: LayerOutput - """ - assert isinstance(input, LayerOutput) - return rotate_layer( - input=rotate_layer( - input=input, height=height), - height=height, - name=name, - layer_attr=layer_attr) - - @wrap_name_default() @layer_support() def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): diff --git a/python/paddle/trainer_config_helpers/tests/layers_test_config.py b/python/paddle/trainer_config_helpers/tests/layers_test_config.py index 3d417a00601d56..1964e6f113e642 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test_config.py @@ -40,7 +40,6 @@ y2 = fc_layer(input=y, size=15) z2 = rotate_layer(input=y2, height=5) -z3 = flip_layer(input=y2, height=3) cos1 = cos_sim(a=x1, b=y1) cos3 = cos_sim(a=x1, b=y2, size=3) @@ -48,7 +47,7 @@ linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3) out = fc_layer( - input=[cos1, cos3, linear_comb, z, z1, z2, z3], + input=[cos1, cos3, linear_comb, z, z1, z2], size=num_classes, act=SoftmaxActivation()) From 2558c3f15a9d4bd340d0d50492a185231bb2627a Mon Sep 17 00:00:00 2001 From: Haonan Date: Wed, 1 Feb 2017 14:37:54 -0800 Subject: [PATCH 130/370] revisions according to reviews --- paddle/cuda/include/hl_matrix.h | 12 ++++ paddle/cuda/include/stub/hl_matrix_stub.h | 4 ++ paddle/cuda/src/hl_cuda_matrix.cu | 25 ++++++++ paddle/gserver/layers/RotateLayer.cpp | 77 ++++++++++++----------- paddle/gserver/layers/RotateLayer.h | 16 ++--- paddle/gserver/tests/test_LayerGrad.cpp | 7 ++- paddle/math/Matrix.cpp | 24 +++---- paddle/math/Matrix.h | 14 ++++- paddle/math/tests/test_matrixCompare.cpp | 21 ++++++- 9 files changed, 142 insertions(+), 58 deletions(-) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index abd5eb3a0cf338..40828dd5cc76f4 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -267,4 +267,16 @@ extern void hl_matrix_collect_shared_bias(real* B_d, const int dimN, real scale); +/** + * @brief Matrix rotation in 90 degrees + * + * @param[in] mat input matrix (M x N). + * @param[out] matRot output matrix (N x M). + * @param[in] dimM input matrix height. + * @param[in] dimN input matrix width. + * @param[in] clockWise rotation direction + */ +extern void hl_matrix_rotate( + real* mat, real* matRot, int dimM, int dimN, bool clockWise); + #endif /* HL_MATRIX_H_ */ diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index 0b669f6735cb97..1309c5974fc3e1 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -106,4 +106,8 @@ inline void hl_matrix_collect_shared_bias(real* B_d, const int dimM, const int dimN, real scale) {} + +inline void hl_matrix_rotate( + real* mat, real* matRot, int dimM, int dimN, bool clockWise); + #endif // HL_MATRIX_STUB_H_ diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index 2b4c6f7c39cff7..cd23bd31057c5c 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -840,3 +840,28 @@ void hl_matrix_collect_shared_bias(real* B_d, (B_d, A_d, channel, dimM, dimN, dim, limit, scale); CHECK_SYNC("hl_matrix_collect_shared_bias failed"); } + +__global__ void keMatrixRotate(real* mat, real* matRot, + int dimM, int dimN, bool clockWise) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < dimM * dimN) { + int i = idx / dimN; + int j = idx % dimN; + if (clockWise) { + matRot[j * dimM + i] = mat[(dimM - i - 1) * dimN + j]; + } else { + matRot[j * dimM + i] = mat[i * dimN + (dimN - j - 1)]; + } + } +} + +void hl_matrix_rotate(real *mat, real* matRot, + int dimM, int dimN, bool clockWise) { + CHECK_NOTNULL(mat); + CHECK_NOTNULL(matRot); + const int threads = 512; + const int blocks = DIVUP(dimM * dimN, threads); + keMatrixRotate<<< blocks, threads, 0, STREAM_DEFAULT >>> + (mat, matRot, dimM, dimN, clockWise); + CHECK_SYNC("hl_matrix_rotate failed"); +} diff --git a/paddle/gserver/layers/RotateLayer.cpp b/paddle/gserver/layers/RotateLayer.cpp index 269ad43b3180bd..1cdd8366d5ddff 100644 --- a/paddle/gserver/layers/RotateLayer.cpp +++ b/paddle/gserver/layers/RotateLayer.cpp @@ -23,7 +23,8 @@ bool RotateLayer::init(const LayerMap& layerMap, Layer::init(layerMap, parameterMap); CHECK_EQ(inputLayers_.size(), 1UL); - sampleHeight_ = config_.height(); + height_ = config_.height(); + width_ = config_.width(); return true; } @@ -32,26 +33,31 @@ void RotateLayer::forward(PassType passType) { MatrixPtr input = getInputValue(0); batchSize_ = input->getHeight(); - sampleSize_ = input->getWidth(); - sampleWidth_ = sampleSize_ / sampleHeight_; - CHECK_EQ(sampleSize_ % sampleHeight_, 0); + size_ = input->getWidth(); + CHECK_GE(size_, height_ * width_); + CHECK_EQ(size_ % (height_ * width_), 0) + << "The input's depth should be an int"; + channels_ = size_ / (height_ * width_); - resizeOutput(batchSize_, sampleSize_); + resizeOutput(batchSize_, size_); MatrixPtr outV = getOutputValue(); - - for (int b = 0; b < batchSize_; b++) { - MatrixPtr inputSample = Matrix::create(input->getData() + b * sampleSize_, - sampleHeight_, - sampleWidth_, - false, - useGpu_); - MatrixPtr outputSample = Matrix::create(outV->getData() + b * sampleSize_, - sampleWidth_, - sampleHeight_, - false, - useGpu_); - inputSample->rotate(outputSample, false, true); + for (int b = 0; b < batchSize_; b++) { // for each input feat map + for (int c = 0; c < channels_; c++) { // for each feat channel + MatrixPtr inputSample = + Matrix::create(input->getData() + b * size_ + c * height_ * width_, + height_, + width_, + false, + useGpu_); + MatrixPtr outputSample = + Matrix::create(outV->getData() + b * size_ + c * height_ * width_, + width_, + height_, + false, + useGpu_); + inputSample->rotate(outputSample, false, true /* clock-wise */); + } } if (getInputGrad(0)) { @@ -69,23 +75,24 @@ void RotateLayer::backward(const UpdateCallback& callback) { // the grad should be rotated in the reverse direction MatrixPtr preGrad = getInputGrad(0); - for (int b = 0; b < batchSize_; b++) { - MatrixPtr inputSampleGrad = - Matrix::create(preGrad->getData() + b * sampleSize_, - sampleHeight_, - sampleWidth_, - false, - useGpu_); - MatrixPtr outputSampleGrad = - Matrix::create(outputGrad->getData() + b * sampleSize_, - sampleWidth_, - sampleHeight_, - false, - useGpu_); - MatrixPtr tmpGrad = - Matrix::create(sampleHeight_, sampleWidth_, false, useGpu_); - outputSampleGrad->rotate(tmpGrad, false, false); - inputSampleGrad->add(*tmpGrad); + for (int b = 0; b < batchSize_; b++) { // for each input feat map + for (int c = 0; c < channels_; c++) { // for each feat channel + MatrixPtr inputSampleGrad = + Matrix::create(preGrad->getData() + b * size_ + c * height_ * width_, + height_, + width_, + false, + useGpu_); + MatrixPtr outputSampleGrad = Matrix::create( + outputGrad->getData() + b * size_ + c * height_ * width_, + width_, + height_, + false, + useGpu_); + MatrixPtr tmpGrad = nullptr; + outputSampleGrad->rotate(tmpGrad, true, false /* anti clock-wise */); + inputSampleGrad->add(*tmpGrad); + } } } diff --git a/paddle/gserver/layers/RotateLayer.h b/paddle/gserver/layers/RotateLayer.h index c8eca7c5066b18..a6f565a7e5dcfa 100644 --- a/paddle/gserver/layers/RotateLayer.h +++ b/paddle/gserver/layers/RotateLayer.h @@ -19,12 +19,13 @@ limitations under the License. */ namespace paddle { /** - * A layer for rotating an input sample (assume it's a matrix) - * The rotation is in clock-wise + * A layer for rotating a multi-channel feature map (M x N x C) in the spatial + * domain + * The rotation is 90 degrees in clock-wise * \f[ - * y(j,i) = x(M-i-1,j) + * y(j,i,:) = x(M-i-1,j,:) * \f] - * where \f$x\f$ is (M x N) input, and \f$y\f$ is (N x M) output. + * where \f$x\f$ is (M x N x C) input, and \f$y\f$ is (N x M x C) output. * * The config file api is rotate_layer * @@ -41,9 +42,10 @@ class RotateLayer : public Layer { private: int batchSize_; - int sampleSize_; - int sampleHeight_; - int sampleWidth_; + int size_; + int height_; + int width_; + int channels_; }; } // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index f7a6c672de2016..685d2ca542b242 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1320,9 +1320,12 @@ TEST(Layer, RotateLayer) { TestConfig config; config.biasSize = 0; config.layerConfig.set_type("rotate"); - const int INPUT_SIZE = 64; // height * width + const int INPUT_SIZE = 64; // height * width * depth + const int HEIGHT = 8; + const int WIDTH = 4; config.layerConfig.set_size(INPUT_SIZE); - config.layerConfig.set_height(32); + config.layerConfig.set_height(HEIGHT); + config.layerConfig.set_width(WIDTH); config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0}); config.layerConfig.add_inputs(); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 3c09f010bbf64c..a8b53e2105b053 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -388,6 +388,8 @@ void GpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { matTrans = std::make_shared(width_, height_); } else { CHECK(matTrans != NULL); + CHECK_EQ(matTrans->getHeight(), width_); + CHECK_EQ(matTrans->getWidth(), height_); } real* dataTrans = matTrans->getData(); real* data = getData(); @@ -402,15 +404,13 @@ void GpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { matRot = std::make_shared(width_, height_); } else { CHECK(matRot != NULL); + CHECK_EQ(matRot->getHeight(), width_); + CHECK_EQ(matRot->getWidth(), height_); } - MatrixPtr cpuMat = std::make_shared(height_, width_); - cpuMat->copyFrom(*this); - - MatrixPtr cpuMatRot = std::make_shared(width_, height_); - cpuMat->rotate(cpuMatRot, false, clockWise); - - matRot->copyFrom(*cpuMatRot); + real* dataRot = matRot->getData(); + real* data = getData(); + hl_matrix_rotate(data, dataRot, height_, width_, clockWise); } MatrixPtr GpuMatrix::getInverse() { @@ -1723,6 +1723,8 @@ void CpuMatrix::transpose(MatrixPtr& matTrans, bool memAlloc) { matTrans = std::make_shared(width_, height_); } else { CHECK(matTrans != NULL); + CHECK_EQ(matTrans->getHeight(), width_); + CHECK_EQ(matTrans->getWidth(), height_); } real* dataTrans = matTrans->getData(); real* data = getData(); @@ -1741,18 +1743,18 @@ void CpuMatrix::rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { matRot = std::make_shared(width_, height_); } else { CHECK(matRot != NULL); + CHECK_EQ(matRot->getHeight(), width_); + CHECK_EQ(matRot->getWidth(), height_); } real* dataRot = matRot->getData(); real* data = getData(); - int lda = getStride(); - int ldc = matRot->getStride(); for (size_t i = 0; i < height_; i++) { for (size_t j = 0; j < width_; j++) { if (clockWise) { - dataRot[j * ldc + i] = data[(height_ - i - 1) * lda + j]; + dataRot[j * height_ + i] = data[(height_ - i - 1) * width_ + j]; } else { - dataRot[j * ldc + i] = data[i * lda + (width_ - j - 1)]; + dataRot[j * height_ + i] = data[i * width_ + (width_ - j - 1)]; } } } diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index d123f827c91079..c92c0a272d5a72 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -377,9 +377,19 @@ class Matrix : public BaseMatrix { } /** - * @brief rotate clock-wise. + * @brief rotate 90 degrees in clock-wise if clockWise=true; + * otherwise rotate in anti clock-wise + * clock-wise: + * \f[ + * y(j,i) = x(M-i-1,j) + * \f] + * anti clock-wise: + * \f[ + * y(j,i) = x(i, N-1-j) + * \f] + * where \f$x\f$ is (M x N) input, and \f$y\f$ is (N x M) output. * - * allocate matTrans' memory outside, then set memAlloc as false; + * allocate matRot' memory outside, then set memAlloc as false; * else set as true. */ virtual void rotate(MatrixPtr& matRot, bool memAlloc, bool clockWise) { diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index c062ca10809ac6..e024f2cf1b913f 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -176,11 +176,29 @@ void testMatrixTranspose(int height, int width) { cpu->randomizeUniform(); gpu->copyFrom(*cpu); cpu->transpose(cpuT, false); - gpu->transpose(gpuT, false); + gpu->transpose(gpuT, true); TensorCheckEqual(*cpuT, *gpuT); } +void testMatrixRotate(int height, int width) { + MatrixPtr cpu = std::make_shared(height, width); + MatrixPtr gpu = std::make_shared(height, width); + MatrixPtr cpuR = std::make_shared(width, height); + MatrixPtr gpuR = std::make_shared(width, height); + + cpu->randomizeUniform(); + gpu->copyFrom(*cpu); + + cpu->rotate(cpuR, false, true); + gpu->rotate(gpuR, true, true); + TensorCheckEqual(*cpuR, *gpuR); + + cpu->rotate(cpuR, true, false); + gpu->rotate(gpuR, false, false); + TensorCheckEqual(*cpuR, *gpuR); +} + void testMatrixInverse(int height) { MatrixPtr cpu = std::make_shared(height, height); MatrixPtr gpu = std::make_shared(height, height); @@ -215,6 +233,7 @@ TEST(Matrix, unary) { testMatrixZeroAtOffset(height, width); testMatrixGetSum(height, width); testMatrixTranspose(height, width); + testMatrixRotate(height, width); } // inverse testMatrixInverse(height); From 7f4042ec86980811c32a698448f9920162770f91 Mon Sep 17 00:00:00 2001 From: Haonan Date: Wed, 1 Feb 2017 14:56:30 -0800 Subject: [PATCH 131/370] fix stub error --- paddle/cuda/include/stub/hl_matrix_stub.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index 1309c5974fc3e1..a1712d1e4d2a5d 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -108,6 +108,6 @@ inline void hl_matrix_collect_shared_bias(real* B_d, real scale) {} inline void hl_matrix_rotate( - real* mat, real* matRot, int dimM, int dimN, bool clockWise); + real* mat, real* matRot, int dimM, int dimN, bool clockWise) {} #endif // HL_MATRIX_STUB_H_ From 9c42d904689ea71f57f17b802ae7006d6bf37816 Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Wed, 1 Feb 2017 15:02:12 -0800 Subject: [PATCH 132/370] add comments in gru_step_layer of layers.py to explain the parameter location --- python/paddle/trainer_config_helpers/layers.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 770d6303c13587..14035ce3fd8aab 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2707,8 +2707,7 @@ def gru_step_layer(input, :param gate_act: :param bias_attr: :param param_attr: the parameter_attribute for transforming the output_mem - from previous step. It is instead grouped with input due - to backward model compatibility. + from previous step. :param layer_attr: :return: LayerOutput object. :rtype: LayerOutput @@ -2719,6 +2718,11 @@ def gru_step_layer(input, Layer( name=name, type=LayerType.GRU_STEP_LAYER, + # The parameter here is for transforming the output_mem. The input has + # already been transformed outside this module so it does not need + # parameter associated with it. + # The parameter here is instead grouped with input is due to + # backward model compatibility. inputs=[Input(input.name, **param_attr.attr), output_mem.name], bias=ParamAttr.to_bias(bias_attr), size=size, From 4420929339f2a7736353f5fc1e95c1d2b9060caf Mon Sep 17 00:00:00 2001 From: xichen Date: Wed, 1 Feb 2017 15:45:51 -0800 Subject: [PATCH 133/370] add images for route53 config --- doc/howto/usage/k8s/k8s_aws_en.md | 6 ++++-- .../usage/k8s/src/route53_create_recordset.png | Bin 0 -> 114396 bytes .../usage/k8s/src/route53_create_zone.png | Bin 0 -> 177965 bytes 3 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 doc/howto/usage/k8s/src/route53_create_recordset.png create mode 100644 doc/howto/usage/k8s/src/route53_create_zone.png diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index ce5ccbca5ee33b..eda2b639e2f5d9 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -383,13 +383,15 @@ Edit `/etc/hosts` to associate above ip with the DNS name. - Create hosted zone with following config - Domain name: "paddle" - Type: "Private hosted zone for amazon VPC" - - VPC ID: + - VPC ID: `` + -