Split byref op

paddle/fluid/operators/detail/sendrecvop_utils.cc

@@ -82,7 +82,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
         platform::CPUPlace cpu;
         auto& gpu_dev_ctx =
             static_cast<const platform::CUDADeviceContext&>(ctx);
-        auto copy_size = tensor.memory_size();
+        auto copy_size = tensor.numel() * framework::SizeOfType(tensor.type());
         payload = memory::Alloc(cpu, copy_size);
 
         memory::Copy(cpu, payload,
@@ -99,7 +99,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
       } else {
         payload = tensor.data<void>();
       }
-      payload_size = tensor.memory_size();
+      payload_size = tensor.numel() * framework::SizeOfType(tensor.type());
       e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber, payload_size);
     } break;
     case framework::proto::VarType_Type_SELECTED_ROWS: {
@@ -118,7 +118,8 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
         platform::CPUPlace cpu;
         auto& gpu_dev_ctx =
             static_cast<const platform::CUDADeviceContext&>(ctx);
-        auto copy_size = tensor->memory_size();
+        auto copy_size =
+            tensor->numel() * framework::SizeOfType(tensor->type());
         payload = memory::Alloc(cpu, copy_size);
         memory::Copy(cpu, payload,
                      boost::get<platform::CUDAPlace>(tensor->place()),
@@ -133,7 +134,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
       } else {
         payload = slr->mutable_value()->data<void>();
       }
-      payload_size = tensor->memory_size();
+      payload_size = tensor->numel() * framework::SizeOfType(tensor->type());
       e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber, payload_size);
     } break;
     default:

paddle/fluid/operators/split_byref_op.cc

@@ -0,0 +1,101 @@
+/* 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. */
+
+#include "paddle/fluid/operators/split_byref_op.h"
+#include "paddle/fluid/operators/split_op.h"
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class SplitByrefOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SplitOp should not be null.");
+    PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(), 1UL,
+                      "Outputs(Out) of SplitOp should not be empty.");
+    auto in_dims = ctx->GetInputDim("X");
+    auto outs_names = ctx->Outputs("Out");
+    size_t num = static_cast<size_t>(ctx->Attrs().Get<int>("num"));
+    std::vector<int> sections = static_cast<std::vector<int>>(
+        ctx->Attrs().Get<std::vector<int>>("sections"));
+    const size_t outs_number = outs_names.size();
+    std::vector<framework::DDim> outs_dims;
+    outs_dims.reserve(outs_number);
+
+    if (num > 0) {
+      int64_t in_axis_dim = in_dims[0];
+      PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
+                        "tensor split does not result"
+                        " in an equal division");
+      size_t out_axis_dim = in_axis_dim / num;
+      for (size_t i = 0; i < outs_number; ++i) {
+        auto dim = in_dims;
+        dim[0] = out_axis_dim;
+        outs_dims.push_back(dim);
+      }
+    } else if (sections.size() > 0) {
+      PADDLE_ENFORCE_EQ(sections.size(), outs_number,
+                        "tensor split sections size"
+                        "should be equal to output size.");
+      for (size_t i = 0; i < outs_number; ++i) {
+        auto dim = in_dims;
+        dim[0] = sections[i];
+        outs_dims.push_back(dim);
+      }
+    }
+    ctx->SetOutputsDim("Out", outs_dims);
+  }
+};
+
+class SplitByrefOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SplitByrefOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "(Tensor) Input tensor of the split operator.");
+    AddOutput("Out", "(Tensor) Output tensors of the split operator.")
+        .AsDuplicable();
+    AddComment(R"DOC(
+SplitByref operator
+
+Split source tensor to sevaral tensors by axis 0. No copy in this operator
+is performed, output tensor shares the same blocks of memory.
+)DOC");
+    AddAttr<std::vector<int>>("sections",
+                              "(vector<int>) "
+                              "the length of each output along the "
+                              "specified axis.")
+        .SetDefault(std::vector<int>{});
+    AddAttr<int>("num",
+                 "(int, default 0)"
+                 "Number of sub-tensors. This must evenly divide "
+                 "Input.dims()[axis]")
+        .SetDefault(0);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+// NOTE: concat op default axis must be 0!
+USE_CPU_ONLY_OP(concat);
+
+REGISTER_OPERATOR(split_byref, ops::SplitByrefOp, ops::SplitByrefOpMaker,
+                  ops::SplitGradMaker);
+REGISTER_OP_CPU_KERNEL(
+    split_byref, ops::SplitByrefOpKernel<paddle::platform::CPUPlace, float>);

paddle/fluid/operators/split_byref_op.cu.cc

@@ -0,0 +1,19 @@
+/* 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. */
+
+#include "paddle/fluid/operators/split_byref_op.h"
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    split_byref,
+    ops::SplitByrefOpKernel<paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/split_byref_op.h

@@ -0,0 +1,43 @@
+/* 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
+
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class SplitByrefOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<framework::Tensor>("X");
+    auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    auto place = ctx.GetPlace();
+
+    size_t row_offset = 0;
+    for (size_t i = 0; i < outs.size(); ++i) {
+      // NOTE: no need to call mutable_data here to allocate memory.
+      auto* out = outs[i];
+      VLOG(3) << "spliting by ref: " << row_offset << " " << out->dims()[0];
+      *out = std::move(in->Slice(row_offset, row_offset + out->dims()[0]));
+      row_offset += out->dims()[0];
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/split_op.cc

@@ -108,21 +108,6 @@ This operator splits the input tensor into multiple sub-tensors.
   }
 };
 
-class SplitGradMaker : public framework::SingleGradOpDescMaker {
- public:
-  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
-
- protected:
-  std::unique_ptr<framework::OpDesc> Apply() const override {
-    auto op = new framework::OpDesc();
-    op->SetType("concat");
-    op->SetInput("X", OutputGrad("Out"));
-    op->SetOutput("Out", InputGrad("X"));
-    op->SetAttrMap(Attrs());
-    return std::unique_ptr<framework::OpDesc>(op);
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 

paddle/fluid/operators/split_op.h

@@ -44,5 +44,20 @@ class SplitOpKernel : public framework::OpKernel<T> {
   }
 };
 
+class SplitGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto op = new framework::OpDesc();
+    op->SetType("concat");
+    op->SetInput("X", OutputGrad("Out"));
+    op->SetOutput("Out", InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(op);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

python/paddle/fluid/distribute_transpiler.py

@@ -824,7 +824,7 @@ def _append_split_op(self, program, gradblocks):
                 for v in splited_vars:
                     sections.append(v.shape[0])
                 program.global_block().append_op(
-                    type="split",
+                    type="split_byref",
                     inputs={"X": orig_var},
                     outputs={"Out": splited_vars},
                     attrs={"sections": sections}  # assume split evenly

python/paddle/fluid/tests/unittests/test_split_op.py

@@ -19,7 +19,7 @@
 
 class TestSplitOp(OpTest):
     def setUp(self):
-        self.op_type = "split"
+        self._set_op_type()
         axis = 1
         x = np.random.random((4, 5, 6)).astype('float32')
         out = np.split(x, [2, 3], axis)
@@ -28,12 +28,20 @@ def setUp(self):
         self.outputs = {'Out': [('out%d' % i, out[i]) \
             for i in xrange(len(out))]}
 
+    def _set_op_type(self):
+        self.op_type = "split"
+
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad(self):
         self.check_grad(['X'], ['out0', 'out1', 'out2'])
 
 
+class TestSplitByrefOp(OpTest):
+    def _set_op_type(self):
+        self.op_type = "split_byref"
+
+
 if __name__ == '__main__':
     unittest.main()