Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 02842cfc 编写于 作者: C chengduoZH
浏览文件
操作

enhance broadcast_op_handle and gather_op_handle

上级 b0267ac9
隐藏空白更改
内联 并排
Showing with 266 addition and 239 deletion
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 02842cfc
......@@ -18,45 +18,74 @@ namespace paddle {
namespace framework {
namespace details {
Tensor *GetTensorFromVar(Variable *in_var) {
if (in_var->IsType<LoDTensor>()) {
return in_var->GetMutable<LoDTensor>();
} else if (in_var->IsType<SelectedRows>()) {
return in_var->GetMutable<SelectedRows>()->mutable_value();
} else {
PADDLE_THROW("Var should be LoDTensor or SelectedRows");
}
return nullptr;
}
BroadcastOpHandle::BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places)
: local_scopes_(local_scopes), places_(places) {}
void BroadcastOpHandle::RunImpl() {
PADDLE_ENFORCE_EQ(this->inputs_.size(), 1,
// the input may have dummy var.
std::vector<VarHandle *> in_var_handle;
for (auto *in : inputs_) {
auto *out_handle = dynamic_cast<VarHandle *>(in);
if (out_handle) {
in_var_handle.push_back(out_handle);
}
}
PADDLE_ENFORCE_EQ(in_var_handle.size(), 1,
"The number of input should be one.");
// the output may have dummy var.
std::vector<VarHandle *> out_var_handles;
for (auto *out : outputs_) {
auto *out_handle = dynamic_cast<VarHandle *>(out);
if (out_handle) {
out_var_handles.push_back(out_handle);
}
}
PADDLE_ENFORCE_EQ(
this->outputs_.size(), places_.size(),
out_var_handles.size(), places_.size(),
"The number of output should equal to the number of places.");
// Wait input done, this Wait is asynchronous operation
auto in_var_handle = static_cast<VarHandle *>(this->inputs_[0]);
auto &in_place = in_var_handle->place_;
if (inputs_[0]->generated_op_) {
inputs_[0]->generated_op_->Wait(dev_ctxes_[in_place]);
for (auto *out : outputs_) {
auto out_handle = static_cast<VarHandle *>(out);
auto &out_p = out_handle->place_;
inputs_[0]->generated_op_->Wait(dev_ctxes_[out_p]);
auto &in_place = in_var_handle[0]->place_;
if (in_var_handle[0]->generated_op_) {
in_var_handle[0]->generated_op_->Wait(dev_ctxes_[in_place]);
for (auto *out : out_var_handles) {
auto &out_p = out->place_;
if (platform::is_same_place(in_place, out_p)) continue;
in_var_handle[0]->generated_op_->Wait(dev_ctxes_[out_p]);
}
}
auto in_scope_idx = in_var_handle->scope_idx_;
//
auto in_scope_idx = in_var_handle[0]->scope_idx_;
PADDLE_ENFORCE_LT(in_scope_idx, local_scopes_.size(),
"The input(%s) is not in the local_scopes.",
in_var_handle->name_);
auto in_var = local_scopes_[in_scope_idx]->FindVar(in_var_handle->name_);
in_var_handle[0]->name_);
auto in_var = local_scopes_[in_scope_idx]->FindVar(in_var_handle[0]->name_);
Tensor *in_tensor = GetTensorFromVar(in_var);
for (auto *out : outputs_) {
auto out_handle = static_cast<VarHandle *>(out);
auto &out_p = out_handle->place_;
auto out_scope_idx = out_handle->scope_idx_;
for (auto *out : out_var_handles) {
auto &out_p = out->place_;
auto out_scope_idx = out->scope_idx_;
PADDLE_ENFORCE_LT(out_scope_idx, local_scopes_.size(),
"%s is not in the local_scopes ", out_handle->name_);
"%s is not in the local_scopes ", out->name_);
auto *s = local_scopes_[out_scope_idx];
auto out_var = s->FindVar(out_handle->name_);
auto out_var = s->FindVar(out->name_);
PADDLE_ENFORCE_EQ(out_p.which(), in_place.which(),
"The place of input and output should be the same.");
......@@ -89,7 +118,7 @@ void BroadcastOpHandle::RunImpl() {
auto dst_gpu_place = boost::get<platform::CUDAPlace>(out_p);
void *dst_ptr = out_tensor->mutable_data(out_p);
void *src_ptr = in_tensor->data<void>();
int64_t size = in_tensor->numel();
int64_t size = in_tensor->numel() * SizeOfType(in_tensor->type());
memory::Copy(
dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
reinterpret_cast<platform::CUDADeviceContext *>(dev_ctxes_[out_p])
......
paddle/fluid/framework/details/broadcast_op_handle_test.cc
浏览文件 @ 02842cfc
......@@ -27,8 +27,20 @@ namespace p = paddle::platform;
// test data amount
const f::DDim kDims = {20, 20};
class BroadcastTester : public ::testing::Test {
public:
struct TestBroadcastOpHandle {
std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
std::vector<Scope*> local_scopes_;
Scope g_scope_;
std::unique_ptr<OpHandleBase> op_handle_;
std::vector<std::unique_ptr<VarHandleBase>> vars_;
std::vector<p::Place> gpu_list_;
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
}
void InitCtxOnGpu(bool use_gpu) {
if (use_gpu) {
#ifdef PADDLE_WITH_CUDA
......@@ -57,61 +69,56 @@ class BroadcastTester : public ::testing::Test {
}
}
void BroadcastInitOp(int input_scope_idx) {
void InitBroadcastOp(size_t input_scope_idx) {
for (size_t j = 0; j < gpu_list_.size(); ++j) {
local_scope_.push_back(&g_scope_.NewScope());
local_scope_[j]->Var("out");
local_scopes_.push_back(&(g_scope_.NewScope()));
local_scopes_[j]->Var("out");
}
local_scope_[input_scope_idx]->Var("input");
local_scopes_[input_scope_idx]->Var("input");
bc_op_handle_ = new f::details::BroadcastOpHandle(local_scope_, gpu_list_);
op_handle_.reset(new BroadcastOpHandle(local_scopes_, gpu_list_));
f::details::VarHandle* in_var_handle = new f::details::VarHandle();
vars_.emplace_back(new VarHandle());
VarHandle* in_var_handle = static_cast<VarHandle*>(vars_.back().get());
in_var_handle->place_ = gpu_list_[input_scope_idx];
in_var_handle->name_ = "input";
in_var_handle->version_ = 1;
in_var_handle->scope_idx_ = input_scope_idx;
in_var_handle->generated_op_ = nullptr;
bc_op_handle_->AddInput(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add dummy var
vars_.emplace_back(new DummyVarHandle());
DummyVarHandle* dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
dummy_var_handle->generated_op_ = nullptr;
op_handle_->AddInput(dummy_var_handle);
for (size_t j = 0; j < gpu_list_.size(); ++j) {
bc_op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j];
f::details::VarHandle* out_var_handle = new f::details::VarHandle();
op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j].get();
vars_.emplace_back(new VarHandle());
VarHandle* out_var_handle = static_cast<VarHandle*>(vars_.back().get());
out_var_handle->place_ = gpu_list_[j];
out_var_handle->name_ = "out";
out_var_handle->version_ = 2;
out_var_handle->scope_idx_ = j;
bc_op_handle_->AddOutput(out_var_handle);
}
}
void BroadcastOpDestroy() {
for (auto in : bc_op_handle_->inputs_) {
delete in;
}
for (auto out : bc_op_handle_->outputs_) {
delete out;
op_handle_->AddOutput(out_var_handle);
}
delete bc_op_handle_;
for (size_t j = 0; j < ctxs_.size(); ++j) {
delete ctxs_[j];
}
}
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
// add dummy var
vars_.emplace_back(new DummyVarHandle());
DummyVarHandle* out_dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
out_dummy_var_handle->generated_op_ = nullptr;
op_handle_->AddOutput(out_dummy_var_handle);
}
void TestBroadcastLodTensor() {
int input_scope_idx = 0;
BroadcastInitOp(input_scope_idx);
auto in_var = local_scope_[input_scope_idx]->Var("input");
void TestBroadcastLodTensor(size_t input_scope_idx) {
auto in_var = local_scopes_[input_scope_idx]->Var("input");
auto in_lod_tensor = in_var->GetMutable<f::LoDTensor>();
in_lod_tensor->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
std::vector<float> send_vector(f::product(kDims), input_scope_idx + 12);
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k;
}
......@@ -120,13 +127,13 @@ class BroadcastTester : public ::testing::Test {
send_vector, *(ctxs_[input_scope_idx]), in_lod_tensor);
in_lod_tensor->set_lod(lod);
bc_op_handle_->Run(false);
op_handle_->Run(false);
WaitAll();
p::CPUPlace cpu_place;
for (size_t j = 0; j < gpu_list_.size(); ++j) {
auto out_var = local_scope_[j]->Var("out");
auto out_var = local_scopes_[j]->Var("out");
auto out_tensor = out_var->Get<f::LoDTensor>();
PADDLE_ENFORCE_EQ(out_tensor.lod(), lod, "lod is not equal.");
......@@ -134,42 +141,37 @@ class BroadcastTester : public ::testing::Test {
f::TensorCopy(out_tensor, cpu_place, *(ctxs_[j]), &result_tensor);
float* ct = result_tensor.mutable_data<float>(cpu_place);
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], send_vector[j], 1e-5);
for (int64_t i = 0; i < f::product(kDims); ++i) {
ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
}
}
BroadcastOpDestroy();
}
void TestBroadcastSelectedRows() {
int input_scope_idx = 0;
BroadcastInitOp(input_scope_idx);
auto in_var = local_scope_[input_scope_idx]->Var("input");
void TestBroadcastSelectedRows(size_t input_scope_idx) {
auto in_var = local_scopes_[input_scope_idx]->Var("input");
auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
auto value = in_selected_rows->mutable_value();
value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
int height = kDims[0] * 2;
int height = static_cast<int>(kDims[0]) * 2;
std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
in_selected_rows->set_height(height);
in_selected_rows->set_rows(rows);
std::vector<float> send_vector(f::product(kDims));
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k;
}
paddle::framework::TensorFromVector<float>(
send_vector, *(ctxs_[input_scope_idx]), value);
bc_op_handle_->Run(false);
op_handle_->Run(false);
WaitAll();
p::CPUPlace cpu_place;
for (size_t j = 0; j < gpu_list_.size(); ++j) {
auto out_var = local_scope_[j]->Var("out");
auto out_var = local_scopes_[j]->Var("out");
auto& out_select_rows = out_var->Get<f::SelectedRows>();
auto rt = out_select_rows.value();
......@@ -183,41 +185,44 @@ class BroadcastTester : public ::testing::Test {
f::TensorCopy(rt, cpu_place, *(ctxs_[j]), &result_tensor);
float* ct = result_tensor.data<float>();
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], send_vector[j], 1e-5);
for (int64_t i = 0; i < f::product(kDims); ++i) {
ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
}
}
BroadcastOpDestroy();
}
public:
f::Scope g_scope_;
std::vector<p::DeviceContext*> ctxs_;
std::vector<f::Scope*> local_scope_;
std::vector<p::Place> gpu_list_;
f::details::BroadcastOpHandle* bc_op_handle_;
};
TEST_F(BroadcastTester, TestCPUBroadcastTestLodTensor) {
InitCtxOnGpu(false);
TestBroadcastLodTensor();
TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
TestBroadcastOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(false);
test_op.InitBroadcastOp(input_scope_idx);
test_op.TestBroadcastLodTensor(input_scope_idx);
}
TEST_F(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
InitCtxOnGpu(false);
TestBroadcastSelectedRows();
TEST(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
TestBroadcastOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(false);
test_op.InitBroadcastOp(input_scope_idx);
test_op.TestBroadcastSelectedRows(input_scope_idx);
}
#ifdef PADDLE_WITH_CUDA
TEST_F(BroadcastTester, TestGPUBroadcastTestLodTensor) {
InitCtxOnGpu(true);
TestBroadcastLodTensor();
TEST(BroadcastTester, TestGPUBroadcastTestLodTensor) {
TestBroadcastOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(true);
test_op.InitBroadcastOp(input_scope_idx);
test_op.TestBroadcastLodTensor(input_scope_idx);
}
TEST_F(BroadcastTester, TestGPUBroadcastTestSelectedRows) {
InitCtxOnGpu(true);
TestBroadcastSelectedRows();
TEST(BroadcastTester, TestGPUBroadcastTestSelectedRows) {
TestBroadcastOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(true);
test_op.InitBroadcastOp(input_scope_idx);
test_op.TestBroadcastSelectedRows(input_scope_idx);
}
#endif
......
paddle/fluid/framework/details/gather_op_handle.cc
浏览文件 @ 02842cfc
......@@ -23,32 +23,54 @@ GatherOpHandle::GatherOpHandle(const std::vector<Scope *> &local_scopes,
: local_scopes_(local_scopes), places_(places) {}
void GatherOpHandle::RunImpl() {
// the input may have dummy var.
std::vector<VarHandle *> in_var_handles;
for (auto *in : inputs_) {
auto *in_handle = dynamic_cast<VarHandle *>(in);
if (in_handle) {
in_var_handles.push_back(in_handle);
}
}
PADDLE_ENFORCE_EQ(
this->inputs_.size(), places_.size(),
"The number of inputs should be equal to the number of place.");
PADDLE_ENFORCE_EQ(this->outputs_.size(), 1,
in_var_handles.size(), places_.size(),
"The number of output should equal to the number of places.");
// the output may have dummy var.
std::vector<VarHandle *> out_var_handles;
for (auto *out : outputs_) {
auto *out_handle = dynamic_cast<VarHandle *>(out);
if (out_handle) {
out_var_handles.push_back(out_handle);
}
}
PADDLE_ENFORCE_EQ(out_var_handles.size(), 1,
"The number of output should be one.");
auto in_0_handle = static_cast<VarHandle *>(inputs_[0]);
auto in_0_handle = static_cast<VarHandle *>(in_var_handles[0]);
auto pre_in_var =
local_scopes_[in_0_handle->scope_idx_]->FindVar(in_0_handle->name_);
auto pre_place = in_0_handle->place_;
PADDLE_ENFORCE(pre_in_var->IsType<framework::SelectedRows>(),
"Currently, gather_op only can gather SelectedRows.");
auto pre_place = in_0_handle->place_;
PADDLE_ENFORCE_EQ(out_var_handles[0]->place_.which(), pre_place.which(),
"The place of input and output should be the same.");
// Wait input done, this Wait is asynchronous operation
for (auto *in : inputs_) {
if (inputs_[0]->generated_op_) {
auto &p = static_cast<VarHandle *>(in)->place_;
in->generated_op_->Wait(dev_ctxes_[p]);
for (auto *in : in_var_handles) {
if (in->generated_op_) {
in->generated_op_->Wait(dev_ctxes_[in->place_]);
}
}
std::vector<int64_t> out_rows;
std::vector<Tensor *> in_tensors;
std::vector<Tensor> in_tensors;
std::vector<platform::Place> in_places;
auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
// gather the inputs
for (auto *in : inputs_) {
for (auto *in : in_var_handles) {
auto in_handle = static_cast<VarHandle *>(in);
auto in_p = in_handle->place_;
in_places.push_back(in_p);
......@@ -58,63 +80,46 @@ void GatherOpHandle::RunImpl() {
"The place of input should be the same.");
auto *s = local_scopes_[in_handle->scope_idx_];
auto in_var = s->FindVar(in_handle->name_);
PADDLE_ENFORCE_EQ(in_var->Type(), pre_in_var->Type(),
auto &in_sr = in_var->Get<framework::SelectedRows>();
PADDLE_ENFORCE_EQ(in_sr.value().type(), pre_in.value().type(),
"The type of input is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(),
"The height of inputs is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(), ,
"The dims of inputs is not consistent.");
if (in_var->IsType<framework::SelectedRows>()) {
auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
auto &in_sr = in_var->Get<framework::SelectedRows>();
auto in_sr_rows = in_sr.rows();
out_rows.insert(out_rows.begin(), in_sr_rows.begin(), in_sr_rows.end());
PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(),
"The height of inputs is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(), ,
"The dims of inputs is not consistent.");
} else if (in_var->IsType<framework::LoDTensor>()) {
auto &pre_in = pre_in_var->Get<framework::LoDTensor>();
auto &in_lodtensor = in_var->Get<framework::LoDTensor>();
PADDLE_ENFORCE_EQ(in_lodtensor.lod(), pre_in.lod(),
"The lod of inputs is not consistent.");
PADDLE_ENFORCE_EQ(in_lodtensor.dims(), pre_in.dims(),
"The dims of inputs is not consistent.");
} else {
PADDLE_THROW("Var should be LoDTensor or SelectedRows.");
}
in_tensors.push_back(GetTensorFromVar(in_var));
pre_in_var = in_var;
auto in_sr_rows = in_sr.rows();
out_rows.insert(out_rows.end(), in_sr_rows.begin(), in_sr_rows.end());
in_tensors.emplace_back(in_sr.value());
}
// write the output
auto out_handle = static_cast<VarHandle *>(this->outputs_[0]);
auto &out_place = out_handle->place_;
auto out_scope_idx = out_handle->scope_idx_;
auto out_var = local_scopes_[out_scope_idx]->FindVar(out_handle->name_);
PADDLE_ENFORCE_EQ(out_place.which(), pre_place.which(),
"The place of input and output should be the same.");
if (pre_in_var->IsType<framework::SelectedRows>()) {
auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
auto out = out_var->GetMutable<framework::SelectedRows>();
out->set_height(pre_in.height());
out->set_rows(out_rows);
size_t rows = out_rows.size();
DDim out_dim = pre_in.GetCompleteDims();
out_dim[0] = static_cast<int64_t>(rows);
out->mutable_value()->Resize(out_dim);
out->mutable_value()->mutable_data(out_place, pre_in.value().type());
auto out_tensor = out->mutable_value();
// copy
int s = 0, e = 0;
for (size_t j = 0; j < in_tensors.size(); ++j) {
e += in_tensors[j]->dims()[0];
auto sub_out = out_tensor->Slice(s, e);
paddle::framework::TensorCopy(*(in_tensors[j]), out_place,
*(dev_ctxes_[in_places[j]]), &sub_out);
s = e;
}
} else if (pre_in_var->IsType<framework::LoDTensor>()) {
PADDLE_THROW("Currently, Var only can be SelectedRows.");
} else {
PADDLE_THROW("Var should be SelectedRows.");
auto &out_place = out_var_handles[0]->place_;
auto out_scope_idx = out_var_handles[0]->scope_idx_;
auto out_var =
local_scopes_[out_scope_idx]->FindVar(out_var_handles[0]->name_);
auto out = out_var->GetMutable<framework::SelectedRows>();
out->set_height(pre_in.height());
out->set_rows(out_rows);
size_t rows = out_rows.size();
DDim out_dim = pre_in.GetCompleteDims();
out_dim[0] = static_cast<int64_t>(rows);
out->mutable_value()->Resize(out_dim);
out->mutable_value()->mutable_data(out_place, pre_in.value().type());
Tensor *out_tensor = out->mutable_value();
// copy
int s = 0, e = 0;
for (size_t j = 0; j < in_tensors.size(); ++j) {
e += in_tensors[j].dims()[0];
auto sub_out = out_tensor->Slice(s, e);
paddle::framework::TensorCopy(in_tensors[j], out_place,
*(dev_ctxes_[in_places[j]]), &sub_out);
s = e;
}
}
......
paddle/fluid/framework/details/gather_op_handle_test.cc
浏览文件 @ 02842cfc
......@@ -26,14 +26,26 @@ namespace p = paddle::platform;
// test data amount
const f::DDim kDims = {20, 20};
class GatherTester : public ::testing::Test {
public:
struct TestGatherOpHandle {
std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
std::vector<Scope*> local_scopes_;
Scope g_scope_;
std::unique_ptr<OpHandleBase> op_handle_;
std::vector<std::unique_ptr<VarHandleBase>> vars_;
std::vector<p::Place> gpu_list_;
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
}
void InitCtxOnGpu(bool use_gpu) {
if (use_gpu) {
#ifdef PADDLE_WITH_CUDA
int count = p::GetCUDADeviceCount();
if (count <= 1) {
LOG(WARNING) << "Cannot test multi-gpu Gather, because the CUDA "
LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
"device count is "
<< count;
exit(0);
......@@ -56,57 +68,51 @@ class GatherTester : public ::testing::Test {
}
}
void InitGatherOp(int input_scope_idx) {
void InitGatherOp(size_t input_scope_idx) {
for (size_t j = 0; j < gpu_list_.size(); ++j) {
local_scope_.push_back(&g_scope_.NewScope());
local_scope_[j]->Var("input");
local_scopes_.push_back(&(g_scope_.NewScope()));
local_scopes_[j]->Var("out");
}
local_scope_[input_scope_idx]->Var("out");
gather_op_handle_ = new f::details::GatherOpHandle(local_scope_, gpu_list_);
f::details::VarHandle* out_var_handle = new f::details::VarHandle();
out_var_handle->place_ = gpu_list_[input_scope_idx];
out_var_handle->name_ = "out";
out_var_handle->version_ = 2;
out_var_handle->scope_idx_ = input_scope_idx;
out_var_handle->generated_op_ = gather_op_handle_;
gather_op_handle_->AddOutput(out_var_handle);
local_scopes_[input_scope_idx]->Var("input");
op_handle_.reset(new GatherOpHandle(local_scopes_, gpu_list_));
// add input
for (size_t j = 0; j < gpu_list_.size(); ++j) {
gather_op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j];
f::details::VarHandle* in_var_handle = new f::details::VarHandle();
op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j].get();
vars_.emplace_back(new VarHandle());
VarHandle* in_var_handle = static_cast<VarHandle*>(vars_.back().get());
in_var_handle->place_ = gpu_list_[j];
in_var_handle->name_ = "input";
in_var_handle->version_ = 1;
in_var_handle->scope_idx_ = j;
in_var_handle->generated_op_ = nullptr;
gather_op_handle_->AddInput(in_var_handle);
}
}
void GatherOpDestroy() {
for (auto in : gather_op_handle_->inputs_) {
delete in;
}
for (auto out : gather_op_handle_->outputs_) {
delete out;
}
delete gather_op_handle_;
for (size_t j = 0; j < ctxs_.size(); ++j) {
delete ctxs_[j];
op_handle_->AddInput(in_var_handle);
}
}
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
}
// add dummy var
vars_.emplace_back(new DummyVarHandle());
DummyVarHandle* in_dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
in_dummy_var_handle->generated_op_ = nullptr;
op_handle_->AddInput(in_dummy_var_handle);
// add output
vars_.emplace_back(new VarHandle());
VarHandle* out_var_handle = static_cast<VarHandle*>(vars_.back().get());
out_var_handle->place_ = gpu_list_[input_scope_idx];
out_var_handle->name_ = "out";
out_var_handle->version_ = 2;
out_var_handle->scope_idx_ = input_scope_idx;
op_handle_->AddOutput(out_var_handle);
void TestGatherSelectedRows() {
int output_scope_idx = 0;
InitGatherOp(output_scope_idx);
// add dummy var
vars_.emplace_back(new DummyVarHandle());
DummyVarHandle* dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
op_handle_->AddOutput(dummy_var_handle);
}
void TestGatherSelectedRows(size_t output_scope_idx) {
int height = kDims[0] * 2;
std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
......@@ -117,7 +123,7 @@ class GatherTester : public ::testing::Test {
for (size_t input_scope_idx = 0; input_scope_idx < gpu_list_.size();
++input_scope_idx) {
auto in_var = local_scope_[input_scope_idx]->Var("input");
auto in_var = local_scopes_[input_scope_idx]->Var("input");
auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
auto value = in_selected_rows->mutable_value();
value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
......@@ -130,13 +136,21 @@ class GatherTester : public ::testing::Test {
value->Resize(kDims);
}
gather_op_handle_->Run(false);
auto out_var = local_scopes_[output_scope_idx]->Var("out");
auto out_selected_rows = out_var->GetMutable<f::SelectedRows>();
auto in_var = local_scopes_[output_scope_idx]->Var("input");
auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
out_selected_rows->mutable_value()->ShareDataWith(
in_selected_rows->value());
op_handle_->Run(false);
WaitAll();
p::CPUPlace cpu_place;
auto out_var = local_scope_[output_scope_idx]->Var("out");
auto& out_select_rows = out_var->Get<f::SelectedRows>();
auto rt = out_select_rows.value();
......@@ -152,28 +166,25 @@ class GatherTester : public ::testing::Test {
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], send_vector[j % send_vector.size()], 1e-5);
}
GatherOpDestroy();
}
public:
f::Scope g_scope_;
std::vector<p::DeviceContext*> ctxs_;
std::vector<f::Scope*> local_scope_;
std::vector<p::Place> gpu_list_;
f::details::GatherOpHandle* gather_op_handle_;
};
TEST_F(GatherTester, TestCPUGatherTestSelectedRows) {
InitCtxOnGpu(false);
TestGatherSelectedRows();
TEST(GatherTester, TestCPUGatherTestSelectedRows) {
TestGatherOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(false);
test_op.InitGatherOp(input_scope_idx);
test_op.TestGatherSelectedRows(input_scope_idx);
}
#ifdef PADDLE_WITH_CUDA
TEST_F(GatherTester, TestGPUGatherTestSelectedRows) {
InitCtxOnGpu(true);
TestGatherSelectedRows();
TEST(GatherTester, TestGPUGatherTestSelectedRows) {
TestGatherOpHandle test_op;
size_t input_scope_idx = 0;
test_op.InitCtxOnGpu(false);
test_op.InitGatherOp(input_scope_idx);
test_op.TestGatherSelectedRows(input_scope_idx);
}
#endif
} // namespace details
......
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 02842cfc
......@@ -17,21 +17,6 @@
namespace paddle {
namespace framework {
namespace details {
// GetTensorFromVar is used in broadcast_op handle and gather_op handle, so it
// should be placed in a commonplace. I don't find an appropriate place, so I
// temporarily place it in op_handle_base.
Tensor *GetTensorFromVar(Variable *in_var) {
if (in_var->IsType<LoDTensor>()) {
return in_var->GetMutable<LoDTensor>();
} else if (in_var->IsType<SelectedRows>()) {
return in_var->GetMutable<SelectedRows>()->mutable_value();
} else {
PADDLE_THROW("Var should be LoDTensor or SelectedRows");
}
return nullptr;
}
std::string OpHandleBase::DebugString() const {
std::stringstream ss;
ss << "(";
......
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ 02842cfc
......@@ -17,9 +17,6 @@
#include <vector>
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/macros.h"
......@@ -27,11 +24,6 @@ namespace paddle {
namespace framework {
namespace details {
// GetTensorFromVar is used in broadcast_op handle and gather_op handle, so it
// should be placed in a commonplace. I don't find an appropriate place, so I
// temporarily place it in op_handle.
Tensor *GetTensorFromVar(Variable *in_var);
constexpr char kLocalExecScopeName[] = "@LCOAL_SCOPE@";
class OpHandleBase {
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或