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Fix bugs of training #667

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merged 5 commits into from
Apr 8, 2020
Merged

Fix bugs of training #667

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57695ff
fix bugs of training
XJDKC Apr 8, 2020
b3a3344
add warning for AsType
XJDKC Apr 8, 2020
373dac1
re-add the accidentally deleted code
XJDKC Apr 8, 2020
917c455
fix and pass all the test cases in TestSoftmaxCrossEntropy
XJDKC Apr 8, 2020
9e77f54
pass all the test cases on CPP side
XJDKC Apr 8, 2020
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4 changes: 2 additions & 2 deletions src/core/device/device.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -50,8 +50,6 @@ void Device::RunGraph(bool serial) {
bool previous_state = graph_enabled_;
graph_enabled_ = false;

// graph_->Debug();

if (serial) {
// sequential execution
graph_->RunInSerial();
Expand All @@ -60,6 +58,8 @@ void Device::RunGraph(bool serial) {
graph_->RunGraph();
}

// graph_->Debug();

graph_enabled_ = previous_state;
}

Expand Down
105 changes: 85 additions & 20 deletions src/core/scheduler/scheduler.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,10 @@

#include "singa/core/scheduler.h"

#include <algorithm>
#include <functional>
#include <iomanip>
#include <sstream>
#include <thread>
#include <unordered_set>

Expand Down Expand Up @@ -99,39 +102,97 @@ void Graph::Reset() {
}

void Graph::Debug() {
if (dirty_) Analysis();

size_t max_in_num = 0, max_out_num = 0, max_next_num = 0, max_free_num = 0;
for (auto &it : nodes_) {
max_in_num = std::max(max_in_num, it->in_edges_.size());
max_out_num = std::max(max_out_num, it->out_edges_.size());
}

for (auto &it : next_nodes_) {
max_next_num = std::max(max_next_num, it.size());
}

for (auto &it : free_blocks_) {
max_free_num = std::max(max_free_num, it.size());
}

int w = 2;
std::stringstream ss;
ss << "begin nodes:[";
for (size_t i = 0; i < begin_nodes_.size(); ++i) {
ss << begin_nodes_[i]->id_;
}
ss << "]" << std::endl;

size_t size = 0;
for (size_t i = 0; i < nodes_.size(); ++i) {
printf("OP[%2lu]: ", i);
printf("Inputs: ");
ss << "OP[" << std::setw(w) << i;
auto node = nodes_[i];
for (size_t j = 0; j < node->in_edges_.size(); ++j) {
printf("%d\t", blocks_[node->in_edges_[j]->blk_]->id_);

ss << "] Inputs:[";
size = node->in_edges_.size();
for (size_t j = 0; j < max_in_num; ++j) {
if (j < size)
ss << std::setw(w) << blocks_[node->in_edges_[j]->blk_]->id_ << " ";
else
ss << std::setw(w + 1) << " ";
}
for (size_t j = node->in_edges_.size(); j < 3; ++j) {
printf("\t");

ss << "] Outputs:[";
size = node->out_edges_.size();
for (size_t j = 0; j < max_out_num; ++j) {
if (j < size)
ss << std::setw(w) << blocks_[node->out_edges_[j]->blk_]->id_ << " ";
else
ss << std::setw(w + 1) << " ";
}
printf("Outputs: ");
for (size_t j = 0; j < node->out_edges_.size(); ++j) {
printf("%d\t", blocks_[node->out_edges_[j]->blk_]->id_);

ss << "] Next nodes:[";
size = next_nodes_[i].size();
for (size_t j = 0; j < max_next_num; ++j) {
if (j < size)
ss << std::setw(w) << next_nodes_[i][j]->id_ << " ";
else
ss << std::setw(w + 1) << " ";
}

ss << "] Free blocks:[";
size = free_blocks_[i].size();
for (size_t j = 0; j < max_free_num; ++j) {
if (j < size)
ss << std::setw(w) << blocks_[free_blocks_[i][j]]->id_ << " ";
else
ss << std::setw(w + 1) << " ";
}
printf("\n");
ss << "]" << std::endl;
}

std::vector<BlkInfo *> blkInfos;
blkInfos.resize(blocks_.size());

for (auto it : blocks_) {
auto blkInfo = it.second;
printf("Block[%2d]: addr[%p] graph_ref[%d] ref_count[%d] ", blkInfo->id_,
blkInfo->blk_, blkInfo->graph_ref_, it.first->ref_count());
blkInfos[it.second->id_] = it.second;
}

for (auto it : blkInfos) {
auto blkInfo = it;
ss << "Block[" << std::setw(w) << blkInfo->id_ << "] addr[" << std::setw(w)
<< blkInfo->blk_ << "] graph_ref[" << std::setw(w) << blkInfo->graph_ref_
<< "] ref_count[" << std::setw(w) << blkInfo->blk_->ref_count() << "] ";
switch (blkInfo->type_) {
case BlockType::kInput:
printf("type[input] ");
ss << "type[input] ";
break;
case BlockType::kParam:
printf("type[param] ");
ss << "type[param] ";
break;
case BlockType::kInter:
printf("type[inter] ");
ss << "type[inter] ";
break;
case BlockType::kEnd:
printf("type[_end_] ");
ss << "type[_end_] ";
break;
default:
break;
Expand All @@ -140,14 +201,16 @@ void Graph::Debug() {
if (blkInfo->write_node_) {
id = blkInfo->write_node_->id_;
}
printf(" write_node[%2d]", id);
ss << " write_node[" << std::setw(w) << id << "]";
id = -1;
if (blkInfo->last_node_) {
id = blkInfo->last_node_->id_;
}
printf(" last_node[%2d]", id);
printf("\n");
ss << " last_node[" << std::setw(w) << id << "]" << std::endl;
;
}

printf("%s", ss.str().c_str());
}

void Graph::RunGraph() {
Expand Down Expand Up @@ -358,6 +421,8 @@ void Graph::Analysis() {
}

dirty_ = false;

// Debug();
}

void Graph::FreeLoop() {
Expand Down
15 changes: 11 additions & 4 deletions src/core/tensor/tensor.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,9 @@ Tensor Resize(const Tensor &in, const Shape &shape) {

// return new tensor
Tensor Tensor::AsType(const DataType type) {
CHECK(block() && block()->initialized() == true)
<< "the data of the tensor needs be initialized before casting to "
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this cannot fix the error. need to update the test case to reverse the two lines.

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Done.

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There seem to be many such cases in unit testing

"another type";
if (data_type_ != type) {
Tensor &thisRef = *this;
Tensor ret(shape_, device_, type);
Expand Down Expand Up @@ -1466,16 +1469,20 @@ void Mult(const Tensor &A, const Tensor &B, Tensor *out) {
template <typename SType>
void Mult(const SType alpha, const Tensor &A, const Tensor &B, const SType beta,
Tensor *C) {
Tensor fakeC;
vector<Block *> read_blocks = {A.block(), B.block()};
// if (beta) read_blocks.push_back(C->block());
if (beta) {
fakeC = *C;
read_blocks.push_back(C->block());
}
if (B.nDim() == 1u) {
CHECK_EQ(A.shape().size(), 2u);
TYPE_LANG_SWITCH(A.data_type(), DType, A.device()->lang(), Lang, {
auto a = TypeCast<SType, DType>(alpha);
auto b = TypeCast<SType, DType>(beta);
Tensor &CRef = *C;
C->device()->Exec(
[a, A, b, B, CRef](Context *ctx) mutable {
[a, A, b, B, CRef, fakeC](Context *ctx) mutable {
GEMV<DType, Lang>(a, A, B, b, &CRef, ctx);
},
read_blocks, {C->block()});
Expand All @@ -1488,7 +1495,7 @@ void Mult(const SType alpha, const Tensor &A, const Tensor &B, const SType beta,
auto b = TypeCast<SType, DType>(beta);
Tensor &CRef = *C;
C->device()->Exec(
[a, A, b, B, CRef](Context *ctx) mutable {
[a, A, b, B, CRef, fakeC](Context *ctx) mutable {
GEMM<DType, Lang>(a, A, B, b, &CRef, ctx);
},
read_blocks, {C->block()});
Expand Down Expand Up @@ -1523,7 +1530,7 @@ void Mult(const SType alpha, const Tensor &A, const Tensor &B, const SType beta,

Tensor &CRef = *C;
C->device()->Exec(
[a, A_tmp, b, B_tmp, CRef](Context *ctx) mutable {
[a, A_tmp, b, B_tmp, CRef, fakeC](Context *ctx) mutable {
GEMMBatched<DType, Lang>(a, A_tmp, B_tmp, b, &CRef, ctx);
},
read_blocks, {C->block()});
Expand Down
10 changes: 6 additions & 4 deletions test/singa/test_cross_entropy.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,10 @@ class TestSoftmaxCrossEntropy : public ::testing::Test {

TEST_F(TestSoftmaxCrossEntropy, CppForward) {
p.CopyDataFromHostPtr(pdat, 8);
t.AsType(singa::kInt);
EXPECT_TRUE(p.block()->initialized());
t.CopyDataFromHostPtr(tdat, 2);
t.AsType(singa::kInt);


singa::SoftmaxCrossEntropy cross_entropy;
const Tensor& loss = cross_entropy.Forward(singa::kEval, p, t);
Expand All @@ -56,8 +58,8 @@ TEST_F(TestSoftmaxCrossEntropy, CppForward) {

TEST_F(TestSoftmaxCrossEntropy, CppForwardAryTarget) {
p.CopyDataFromHostPtr(pdat, 8);
ta.AsType(singa::kInt);
ta.CopyDataFromHostPtr(tary, 8);
ta.AsType(singa::kInt);

singa::SoftmaxCrossEntropy cross_entropy;
const Tensor& loss = cross_entropy.Forward(singa::kEval, p, ta);
Expand All @@ -70,8 +72,8 @@ TEST_F(TestSoftmaxCrossEntropy, CppForwardAryTarget) {

TEST_F(TestSoftmaxCrossEntropy, CppBackward) {
p.CopyDataFromHostPtr(pdat, 8);
t.AsType(singa::kInt);
t.CopyDataFromHostPtr(tdat, 2);
t.AsType(singa::kInt);

singa::SoftmaxCrossEntropy cross_entropy;
cross_entropy.Forward(singa::kTrain, p, t);
Expand All @@ -90,8 +92,8 @@ TEST_F(TestSoftmaxCrossEntropy, CppBackward) {

TEST_F(TestSoftmaxCrossEntropy, CppBackwardAryTarget) {
p.CopyDataFromHostPtr(pdat, 8);
ta.AsType(singa::kInt);
ta.CopyDataFromHostPtr(tary, 8);
ta.AsType(singa::kInt);

singa::SoftmaxCrossEntropy cross_entropy;
cross_entropy.Forward(singa::kTrain, p, ta);
Expand Down
10 changes: 9 additions & 1 deletion test/singa/test_platform.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -28,8 +28,10 @@ using singa::Platform;
TEST(Platform, CreateMultDevice) {
int n = Platform::GetNumGPUs();
auto devs = Platform::CreateCudaGPUs(n);
for (int i = 0; i < devs.size(); i++) {
for (size_t i = 0; i < devs.size(); i++) {
auto b = devs[i]->NewBlock(512 + 512 * (2 - i));
// for lazy allocation
b->mutable_data();
EXPECT_EQ(512 + 512 * (2 - i), devs[i]->GetAllocatedMem());
devs[i]->FreeBlock(b);
}
Expand All @@ -54,6 +56,8 @@ TEST(Platform, CreateDevice) {
size_t size[] = {128, 256, 3, 24};
{
auto ptr = dev->NewBlock(size[0]);
// for lazy allocation
ptr->mutable_data();
auto allocated = dev->GetAllocatedMem();
EXPECT_LE(size[0], allocated);
dev->FreeBlock(ptr);
Expand All @@ -63,9 +67,13 @@ TEST(Platform, CreateDevice) {
auto ptr0 = dev->NewBlock(size[0]);
auto ptr1 = dev->NewBlock(size[1]);
auto ptr2 = dev->NewBlock(size[2]);
ptr0->mutable_data();
ptr1->mutable_data();
ptr2->mutable_data();
auto allocated = dev->GetAllocatedMem();
EXPECT_LE(size[0] + size[1] + size[2], allocated);
auto ptr3 = dev->NewBlock(size[3]);
ptr3->mutable_data();
allocated = dev->GetAllocatedMem();
EXPECT_LE(size[0] + size[1] + size[2] + size[3], allocated);
dev->FreeBlock(ptr0);
Expand Down
2 changes: 1 addition & 1 deletion test/singa/test_snapshot.cc
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Expand Up @@ -79,8 +79,8 @@ TEST(Snapshot, ReadIntTest) {
singa::Snapshot int_snapshot_write(prefix + ".int",
singa::Snapshot::kWrite);
singa::Tensor int_param(singa::Shape{4});
int_param.AsType(singa::kInt);
int_param.CopyDataFromHostPtr(int_data, 4);
int_param.AsType(singa::kInt);
int_snapshot_write.Write("IntParam", int_param);
}

Expand Down