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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_fetch_op_handle

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fix_gru_py
yuyang18 7 years ago
parent e383ea20dc 9707aa6bde
commit 624caee56b
24 changed files with 529 additions and 316 deletions
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6
cmake/inference_lib.cmake
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@ -70,6 +70,12 @@ copy(glog_lib
DSTS ${dst_dir} ${dst_dir}/lib
)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/boost/")
copy(boost_lib
SRCS ${BOOST_INCLUDE_DIR}/boost
DSTS ${dst_dir}
)
if(NOT PROTOBUF_FOUND)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
copy(protobuf_lib

36
paddle/fluid/framework/details/build_strategy.h
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@ -0,0 +1,36 @@
// Copyright (c) 2018 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
namespace paddle {
namespace framework {
namespace details {
struct BuildStrategy {
enum class ReduceStrategy { kAllReduce = 0, kReduce = 1 };
enum class GradientScaleStrategy {
kCoeffNumDevice = 0,
kOne = 1,
kCustomized = 2,
};
ReduceStrategy reduce_{ReduceStrategy::kAllReduce};
GradientScaleStrategy gradient_scale_{GradientScaleStrategy::kCoeffNumDevice};
};
} // namespace details
} // namespace framework
} // namespace paddle

29
paddle/fluid/framework/details/execution_strategy.h
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@ -0,0 +1,29 @@
// Copyright (c) 2018 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
namespace paddle {
namespace framework {
namespace details {
struct ExecutionStrategy {
size_t num_threads_{0};
bool use_event_{true};
bool allow_op_delay_{false};
};
} // namespace details
} // namespace framework
} // namespace paddle

47
paddle/fluid/framework/details/multi_devices_graph_builder.cc
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@ -37,31 +37,26 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
platform::NCCLContextMap *nccl_ctxs, bool use_default_grad_scale,
bool balance_parameter_opt_between_cards)
platform::NCCLContextMap *nccl_ctxs, const BuildStrategy &strategy)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
nccl_ctxs_(nccl_ctxs),
balance_parameter_opt_between_cards_(
balance_parameter_opt_between_cards) {
strategy_(strategy) {
#else
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes, bool use_default_grad_scale,
bool balance_parameter_opt_between_cards)
const std::vector<Scope *> &local_scopes, const BuildStrategy &strategy)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
balance_parameter_opt_between_cards_(
balance_parameter_opt_between_cards) {
strategy_(strategy) {
#endif
for (auto &p : params) {
grad_names_.insert(GradVarName(p));
}
use_default_grad_scale_ = use_default_grad_scale;
}
void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
@ -146,7 +141,8 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
CreateComputationalOps(&result, *op, 1);
} else if (IsScaleLossOp(*op)) {
// user can customize loss@grad if not use_default_grad_scale_
if (use_default_grad_scale_) {
if (strategy_.gradient_scale_ !=
BuildStrategy::GradientScaleStrategy::kCustomized) {
CreateScaleLossGradOp(&result);
}
is_forwarding = false;
@ -165,19 +161,22 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
// broadcast, and each gradient is only broadcast once.
for (auto &og : op->OutputArgumentNames()) {
if (IsParameterGradientOnce(og, &og_has_been_broadcast)) {
if (balance_parameter_opt_between_cards_) {
CreateReduceOp(&result, og, cur_device_id);
var_name_on_devices[cur_device_id].emplace(og);
bcast_var_name_set[cur_device_id].emplace(
og.substr(0, og.size() - strlen(kGradVarSuffix)));
cur_device_id = (cur_device_id + 1) % places_.size();
} else {
if (IsSparseGradient(var_types, og)) {
CreateReduceOp(&result, og, 0);
CreateBroadcastOp(&result, og, 0);
} else {
InsertNCCLAllReduceOp(&result, og);
}
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
CreateReduceOp(&result, og, cur_device_id);
var_name_on_devices[cur_device_id].emplace(og);
bcast_var_name_set[cur_device_id].emplace(
og.substr(0, og.size() - strlen(kGradVarSuffix)));
cur_device_id = (cur_device_id + 1) % places_.size();
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
if (IsSparseGradient(var_types, og)) {
CreateReduceOp(&result, og, 0);
CreateBroadcastOp(&result, og, 0);
} else {
InsertNCCLAllReduceOp(&result, og);
}
break;
}
}
}
@ -303,7 +302,7 @@ bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
int MultiDevSSAGraphBuilder::GetOpDeviceID(
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const {
if (!balance_parameter_opt_between_cards_) {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}

12
paddle/fluid/framework/details/multi_devices_graph_builder.h
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@ -17,6 +17,7 @@
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
namespace paddle {
@ -36,15 +37,13 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
platform::NCCLContextMap *nccl_ctxs,
bool use_default_grad_scale,
bool balance_parameter_opt_between_cards);
const BuildStrategy &strategy);
#else
MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
bool use_default_grad_scale,
bool balance_parameter_opt_between_cards);
const BuildStrategy &strategy);
#endif
std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
@ -62,8 +61,6 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nccl_ctxs_;
#endif
bool balance_parameter_opt_between_cards_;
bool use_default_grad_scale_;
bool IsScaleLossOp(const OpDesc &op) const;
@ -105,6 +102,9 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
bool IsSparseGradient(
const std::unordered_map<std::string, proto::VarType::Type> &var_types,
const std::string &og) const;
private:
BuildStrategy strategy_;
};
} // namespace details
} // namespace framework

17
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
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@ -18,18 +18,17 @@ namespace paddle {
namespace framework {
namespace details {
ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
size_t num_threads, bool use_event,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<SSAGraph> &&graph, bool allow_op_delay)
std::unique_ptr<SSAGraph> &&graph)
: SSAGraphExecutor(std::move(graph)),
pool_(num_threads >= 2 ? new ::ThreadPool(num_threads) : nullptr),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
local_scopes_(local_scopes),
places_(places),
fetch_ctxs_(places),
use_event_(use_event),
running_ops_(0),
allow_op_delay_(allow_op_delay) {}
strategy_(strategy) {}
FeedFetchList ThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
@ -86,7 +85,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
//
// NOTE: DelayedOps have a lower priority. It will be scheduled after all
// ready_ops have been performed.
if (ready_ops.empty() && allow_op_delay_ && running_ops_ == 0) {
if (ready_ops.empty() && strategy_.allow_op_delay_ && running_ops_ == 0) {
run_all_ops(delayed_ops);
} else {
run_all_ops(ready_ops);
@ -113,7 +112,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto &deps = pending_ops[op];
--deps;
if (deps == 0) {
if (op->IsMultiDeviceTransfer() && allow_op_delay_) {
if (op->IsMultiDeviceTransfer() && strategy_.allow_op_delay_) {
delayed_ops.insert(op);
} else {
ready_ops.insert(op);
@ -191,7 +190,7 @@ void ThreadedSSAGraphExecutor::RunOp(
auto op_run = [ready_var_q, op, this] {
try {
VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
op->Run(use_event_);
op->Run(strategy_.use_event_);
VLOG(10) << op << " " << op->Name() << " Done ";
running_ops_--;
ready_var_q->Extend(op->Outputs());

11
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
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@ -23,6 +23,7 @@
#include <functional>
#include "ThreadPool.h" // ThreadPool in thrird party
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
@ -34,11 +35,10 @@ namespace details {
class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
public:
ThreadedSSAGraphExecutor(size_t num_threads, bool use_event,
ThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<SSAGraph> &&graph,
bool allow_op_delay);
std::unique_ptr<SSAGraph> &&graph);
// Run a SSAGraph by a thread pool
// Use topological sort algorithm
@ -55,10 +55,8 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
const bool use_event_;
std::unique_ptr<platform::EnforceNotMet> exception_;
std::atomic<int> running_ops_;
bool allow_op_delay_;
void InsertPendingOp(std::unordered_map<OpHandleBase *, size_t> *pending_ops,
OpHandleBase *op_instance) const;
@ -74,6 +72,9 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars, FeedFetchList *fetch_data);
private:
ExecutionStrategy strategy_;
};
} // namespace details

17
paddle/fluid/framework/parallel_executor.cc
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@ -52,13 +52,12 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
}
ParallelExecutor::ParallelExecutor(
size_t num_threads, bool use_event,
const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &params,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, const std::vector<Scope *> &local_scopes, bool allow_op_delay,
bool use_default_grad_scale, bool balance_parameter_opt_between_cards,
Scope *scope, const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy,
size_t num_trainers, size_t trainer_id)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
@ -100,18 +99,16 @@ ParallelExecutor::ParallelExecutor(
#ifdef PADDLE_WITH_CUDA
details::MultiDevSSAGraphBuilder builder(
member_->places_, loss_var_name, params, member_->local_scopes_,
member_->nccl_ctxs_.get(), use_default_grad_scale,
balance_parameter_opt_between_cards);
member_->nccl_ctxs_.get(), build_strategy);
#else
details::MultiDevSSAGraphBuilder builder(
member_->places_, loss_var_name, params, member_->local_scopes_,
use_default_grad_scale, balance_parameter_opt_between_cards);
details::MultiDevSSAGraphBuilder builder(member_->places_, loss_var_name,
params, member_->local_scopes_,
build_strategy);
#endif
auto graph = builder.Build(main_program);
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
num_threads, use_event, member_->local_scopes_, places, std::move(graph),
allow_op_delay));
exec_strategy, member_->local_scopes_, places, std::move(graph)));
// Step 3. Create vars in each scope;
for (auto *var : main_program.Block(0).AllVars()) {

37
paddle/fluid/framework/parallel_executor.h
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@ -14,57 +14,60 @@ limitations under the License. */
#pragma once
#include <paddle/fluid/framework/details/build_strategy.h>
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
class ParallelExecutorPrivate;
using details::BuildStrategy;
using details::ExecutionStrategy;
class ParallelExecutor {
DISABLE_COPY_AND_ASSIGN(ParallelExecutor);
public:
explicit ParallelExecutor(size_t num_threads, bool use_event,
const std::vector<platform::Place>& places,
const std::unordered_set<std::string>& params,
const std::unordered_set<std::string>& bcast_vars,
const ProgramDesc& main_program,
const std::string& loss_var_name, Scope* scope,
const std::vector<Scope*>& local_scopes,
bool allow_op_delay, bool use_default_grad_scale,
bool balance_parameter_opt_between_cards,
explicit ParallelExecutor(const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &params,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program,
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy,
size_t num_trainers = 1, size_t trainer_id = 0);
~ParallelExecutor();
std::vector<Scope*>& GetLocalScopes();
std::vector<Scope *> &GetLocalScopes();
/**
* Feed tensors to local scopes. The size of tensors should be equal to the
* size of local scopes.
*/
void FeedTensorsIntoLocalScopes(
const std::vector<std::unordered_map<std::string, LoDTensor>>& tensors);
const std::vector<std::unordered_map<std::string, LoDTensor>> &tensors);
void FeedAndSplitTensorIntoLocalScopes(
const std::unordered_map<std::string, LoDTensor>& tensors);
const std::unordered_map<std::string, LoDTensor> &tensors);
void Run(const std::vector<std::string>& fetch_tensors,
const std::string& fetched_var_name);
void Run(const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name);
void BCastParamsToGPUs(const std::unordered_set<std::string>& vars) const;
void BCastParamsToGPUs(const std::unordered_set<std::string> &vars) const;
private:
ParallelExecutorPrivate* member_;
ParallelExecutorPrivate *member_;
};
} // namespace framework

1
paddle/fluid/inference/analysis/device.h
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@ -11,6 +11,7 @@ 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
namespace paddle {
namespace inference {

1
paddle/fluid/inference/analysis/node.h
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@ -19,6 +19,7 @@ limitations under the License. */
*/
#pragma once
#include <limits>
#include <memory>
#include <string>
#include <unordered_map>

36
paddle/fluid/operators/load_combine_op.cc
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@ -12,7 +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 <fstream>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
@ -31,6 +31,7 @@ class LoadCombineOp : public framework::OperatorBase {
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin),
@ -59,17 +60,25 @@ class LoadCombineOp : public framework::OperatorBase {
// Get data from fin to tensor
DeserializeFromStream(fin, tensor, dev_ctx);
if (platform::is_gpu_place(place)) {
// copy CPU to GPU
framework::LoDTensor cpu_tensor;
cpu_tensor.ShareDataWith(*tensor);
cpu_tensor.set_lod(tensor->lod());
// reset tensor
auto in_dtype = framework::ToDataType(tensor->type());
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
tensor->set_lod(cpu_tensor.lod());
TensorCopy(cpu_tensor, place, dev_ctx, tensor);
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
}
@ -82,6 +91,13 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"Out",
"(vector) The output LoDTensors that will be read from the input file.")
.AsDuplicable();
AddAttr<bool>(
"load_as_fp16",
"(boolean, default false)"
"If true, the tensor will be first loaded and then "
"converted to float16 data type. Otherwise, the tensor will be "
"directly loaded without data type conversion.")
.SetDefault(false);
AddAttr<std::string>("file_path",
"(string) "
"LoDTensors will be loaded from \"file_path\".")

90
paddle/fluid/operators/save_load_combine_op_test.cc
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@ -139,8 +139,9 @@ TEST(SaveLoadCombineOp, CPU) {
CheckValues<int, int>(expect4, actual4, expect_lod4, actual_lod4, numel4);
}
// FP16 version of SaveLoadCombineOp Test
TEST(SaveLoadCombineFP16Op, CPU) {
// FP16 version of SaveLoadCombineOp Test, only altering the saving aspect
// to save as FP16.
TEST(SaveCombineFP16Op, CPU) {
paddle::framework::Scope scope;
paddle::platform::CPUPlace place;
@ -169,7 +170,7 @@ TEST(SaveLoadCombineFP16Op, CPU) {
20, 50, lod4, "test_var4", place, &scope, &expect_lod4);
// Set attributes
std::string filename = "check_tensor_fp16.ls";
std::string filename = "check_tensor_fp16_save.ls";
paddle::framework::AttributeMap attrs;
attrs.insert({"file_path", std::string(filename)});
attrs.insert({"save_as_fp16", true});
@ -216,6 +217,89 @@ TEST(SaveLoadCombineFP16Op, CPU) {
actual_lod4, numel4);
}
// FP16 version of SaveLoadCombineOp Test, only altering the loading aspect
// to load tensors with FP16 precision.
TEST(LoadCombineFP16Op, CPU) {
paddle::framework::Scope scope;
paddle::platform::CPUPlace place;
std::vector<int> lod1 = {0, 1, 2, 3, 10};
int numel1 = 100;
paddle::framework::LoD expect_lod1;
float* expect1 = CreateForSaveCombineOp<float, paddle::platform::float16>(
10, 10, lod1, "test_var1", place, &scope, &expect_lod1);
std::vector<int> lod2 = {0, 2, 5, 10};
int numel2 = 200;
paddle::framework::LoD expect_lod2;
float* expect2 = CreateForSaveCombineOp<float, paddle::platform::float16>(
10, 20, lod2, "test_var2", place, &scope, &expect_lod2);
std::vector<int> lod3 = {0, 20};
int numel3 = 4000;
paddle::framework::LoD expect_lod3;
float* expect3 = CreateForSaveCombineOp<float, paddle::platform::float16>(
20, 200, lod3, "test_var3", place, &scope, &expect_lod3);
std::vector<int> lod4 = {0, 1, 20};
int numel4 = 1000;
paddle::framework::LoD expect_lod4;
float* expect4 = CreateForSaveCombineOp<float, paddle::platform::float16>(
20, 50, lod4, "test_var4", place, &scope, &expect_lod4);
// Set attributes
std::string filename = "check_tensor_fp16_load.ls";
paddle::framework::AttributeMap attrs;
attrs.insert({"file_path", std::string(filename)});
// Run the save_combine_op
auto save_combine_op = paddle::framework::OpRegistry::CreateOp(
"save_combine",
{{"X", {"test_var1", "test_var2", "test_var3", "test_var4"}}}, {}, attrs);
save_combine_op->Run(scope, place);
// Set up output vars
auto load_var1 = scope.Var("out_var1");
auto load_var2 = scope.Var("out_var2");
auto load_var3 = scope.Var("out_var3");
auto load_var4 = scope.Var("out_var4");
attrs.insert({"load_as_fp16", true});
// Run the load_combine_op
auto load_combine_op = paddle::framework::OpRegistry::CreateOp(
"load_combine", {},
{{"Out", {"out_var1", "out_var2", "out_var3", "out_var4"}}}, attrs);
load_combine_op->Run(scope, place);
auto* target1 = load_var1->GetMutable<paddle::framework::LoDTensor>();
auto* target2 = load_var2->GetMutable<paddle::framework::LoDTensor>();
auto* target3 = load_var3->GetMutable<paddle::framework::LoDTensor>();
auto* target4 = load_var4->GetMutable<paddle::framework::LoDTensor>();
paddle::framework::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4;
paddle::platform::float16* actual1 =
GetValuesAfterLoadCombineOp<paddle::platform::float16>(target1, scope,
&actual_lod1);
paddle::platform::float16* actual2 =
GetValuesAfterLoadCombineOp<paddle::platform::float16>(target2, scope,
&actual_lod2);
paddle::platform::float16* actual3 =
GetValuesAfterLoadCombineOp<paddle::platform::float16>(target3, scope,
&actual_lod3);
paddle::platform::float16* actual4 =
GetValuesAfterLoadCombineOp<paddle::platform::float16>(target4, scope,
&actual_lod4);
CheckValues<float, paddle::platform::float16>(expect1, actual1, expect_lod1,
actual_lod1, numel1);
CheckValues<float, paddle::platform::float16>(expect2, actual2, expect_lod2,
actual_lod2, numel2);
CheckValues<float, paddle::platform::float16>(expect3, actual3, expect_lod3,
actual_lod3, numel3);
CheckValues<float, paddle::platform::float16>(expect4, actual4, expect_lod4,
actual_lod4, numel4);
}
// Test with original SaveLoadTest
TEST(SaveLoadTestWithCombineOp, CPU) {
paddle::framework::Scope scope;

2
paddle/fluid/platform/nccl_helper.h
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@ -53,7 +53,7 @@ class NCCLGroupGuard {
}
inline ~NCCLGroupGuard() {
PADDLE_ENFORCE(dynload::ncclGroupEnd());
CHECK_EQ(dynload::ncclGroupEnd(), ncclSuccess);
NCCLMutex().unlock();
}
};

72
paddle/fluid/pybind/pybind.cc
Unescape View File

@ -494,23 +494,61 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("reset_profiler", platform::ResetProfiler);
py::class_<ParallelExecutor>(m, "ParallelExecutor")
.def("__init__",
[](ParallelExecutor &self, size_t num_threads, bool use_event,
const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &params,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, std::vector<Scope *> &local_scopes,
bool allow_op_delay, bool use_default_grad_scale,
bool balance_parameter_opt_between_cards, size_t num_trainers,
size_t trainer_id) {
new (&self) ParallelExecutor(
num_threads, use_event, places, params, bcast_vars,
main_program, loss_var_name, scope, local_scopes,
allow_op_delay, use_default_grad_scale,
balance_parameter_opt_between_cards, num_trainers, trainer_id);
})
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy>(pe, "ExecutionStrategy")
.def(py::init())
.def_property(
"num_threads",
[](const ExecutionStrategy &self) { return self.num_threads_; },
[](ExecutionStrategy &self, size_t num_threads) {
self.num_threads_ = num_threads;
})
.def_property(
"use_event",
[](const ExecutionStrategy &self) { return self.use_event_; },
[](ExecutionStrategy &self, bool use_event) {
self.use_event_ = use_event;
})
.def_property(
"allow_op_delay",
[](const ExecutionStrategy &self) { return self.allow_op_delay_; },
[](ExecutionStrategy &self, bool allow_op_delay) {
self.allow_op_delay_ = allow_op_delay;
});
py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
.value("Reduce", BuildStrategy::ReduceStrategy::kReduce)
.value("AllReduce", BuildStrategy::ReduceStrategy::kAllReduce);
py::enum_<BuildStrategy::GradientScaleStrategy>(build_strategy,
"GradientScaleStrategy")
.value("CoeffNumDevice",
BuildStrategy::GradientScaleStrategy::kCoeffNumDevice)
.value("One", BuildStrategy::GradientScaleStrategy::kOne)
.value("Customized", BuildStrategy::GradientScaleStrategy::kCustomized);
build_strategy.def(py::init())
.def_property(
"reduce_strategy",
[](const BuildStrategy &self) { return self.reduce_; },
[](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
self.reduce_ = strategy;
})
.def_property(
"gradient_scale_strategy",
[](const BuildStrategy &self) { return self.gradient_scale_; },
[](BuildStrategy &self,
BuildStrategy::GradientScaleStrategy strategy) {
self.gradient_scale_ = strategy;
});
pe.def(py::init<const std::vector<platform::Place> &,
const std::unordered_set<std::string> &,
const std::unordered_set<std::string> &, const ProgramDesc &,
const std::string &, Scope *, std::vector<Scope *> &,
const ExecutionStrategy &, const BuildStrategy &, size_t,
size_t>())
.def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element

56
python/paddle/fluid/__init__.py
Unescape View File

@ -44,42 +44,44 @@ import transpiler
from param_attr import ParamAttr, WeightNormParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
from transpiler import DistributeTranspiler, SimpleDistributeTranspiler, InferenceTranspiler, memory_optimize, release_memory
from transpiler import DistributeTranspiler, SimpleDistributeTranspiler, \
InferenceTranspiler, memory_optimize, release_memory
from concurrency import (Go, make_channel, channel_send, channel_recv,
channel_close, Select)
import clip
import profiler
import unique_name
import recordio_writer
from parallel_executor import ParallelExecutor
import parallel_executor
from parallel_executor import *
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ +\
trainer.__all__ + inferencer.__all__ + transpiler.__all__ + [
'io',
'initializer',
'layers',
'transpiler'
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'profiler',
'unique_name',
'recordio_writer',
'ParallelExecutor',
]
__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
parallel_executor.__all__ + [
'io',
'initializer',
'layers',
'transpiler'
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'profiler',
'unique_name',
'recordio_writer',
]
def __bootstrap__():

20
python/paddle/fluid/inferencer.py
Unescape View File

@ -13,29 +13,35 @@
# limitations under the License.
import core
import framework
import executor
import framework
import io
import unique_name
from trainer import check_and_get_place
__all__ = ['Inferencer', ]
class Inferencer(object):
def __init__(self, param_path, place=None):
def __init__(self, infer_func, param_path, place=None):
"""
:param param_path: the path where the inference model is saved by fluid.io.save_inference_model
:param infer_func: a function that will return predict Variable
:param param_path: the path where the inference model is saved by fluid.io.save_params
:param place: place to do the inference
"""
self.param_path = param_path
self.scope = core.Scope()
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
self.exe = executor.Executor(check_and_get_place(place))
with executor.scope_guard(self.scope):
# load params from param_path into scope
[self.inference_program, _,
self.fetch_targets] = io.load_inference_model(
executor=self.exe, dirname=param_path)
io.load_params(self.exe, param_path, self.inference_program)
def infer(self, inputs, return_numpy=True):
"""
@ -51,7 +57,7 @@ class Inferencer(object):
with executor.scope_guard(self.scope):
results = self.exe.run(self.inference_program,
feed=inputs,
fetch_list=self.fetch_targets,
fetch_list=[self.predict_var],
return_numpy=return_numpy)
return results

77
python/paddle/fluid/parallel_executor.py
Unescape View File

@ -19,7 +19,10 @@ import executor
import warnings
import sys
__all__ = ['ParallelExecutor']
__all__ = ['ParallelExecutor', 'ExecutionStrategy', 'BuildStrategy']
ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
class ParallelExecutor(object):
@ -27,13 +30,12 @@ class ParallelExecutor(object):
use_cuda,
loss_name=None,
main_program=None,
num_threads=None,
allow_op_delay=False,
share_vars_from=None,
use_default_grad_scale=True,
balance_parameter_opt_between_cards=False,
exec_strategy=None,
build_strategy=None,
num_trainers=1,
trainer_id=0):
trainer_id=0,
**kwargs):
"""
ParallelExecutor can run program in parallel.
@ -42,21 +44,8 @@ class ParallelExecutor(object):
loss_name(str, default None): The loss name must set in training.
main_program(Program, default None): The program that need to run,
if not provided, then default_main_program will be used.
num_threads(int, default None): How many threads are used for
training.
allow_op_delay(bool, default False): Whether to delay and buffer
some operators together for scheduling or not, which may
improve performance in some cases, default False.
share_vars_from(ParallelExecutor, default None): If provied,
it will share variables from the specified ParallelExecutor.
use_default_grad_scale(bool, default True): If set True, a default
scale value equal to `1./device_count` would be multiplied to
gradients of each device and scaled gradients would be
aggregated. Otherwise, a customized scale value should be fed
to the network.
balance_parameter_opt_between_cards(bool, default True): Whether
updating different gradients on different cards. Currently, it
is not recommended.
num_trainers(int, default 1): If greater than 1, NCCL will be
initialized with multpile rank of nodes, each node should have
same number of GPUs. Distributed training will be enabled then.
@ -83,6 +72,25 @@ class ParallelExecutor(object):
train_loss, = train_exe.run([loss.name], feed=feed_dict)
test_loss, = test_exe.run([loss.name], feed=feed_dict)
"""
if len(kwargs) != 0:
err_msg = ""
for key in kwargs:
if key in dir(ExecutionStrategy):
err_msg += \
"Setting {0} by constructor is deprecated. Use " \
"strategy=ExecutionStrategy(); strategy.{0}=xxx; " \
"pe=ParallelExecutor(exec_strategy=strategy) " \
"instead.\n ".format(key)
elif key in dir(BuildStrategy):
err_msg += \
"Setting {0} by constructor is deprecated. Use " \
"strategy=BuildStrategy(); See help(" \
"paddle.fluid.ParallelExecutor.BuildStrategy) \n".format(
key)
else:
err_msg += "Setting {0} by constructor is deprecated. Use strategy.\n".format(
key)
raise ValueError(err_msg)
self._places = []
self._act_places = []
@ -100,15 +108,25 @@ class ParallelExecutor(object):
self._places.append(p)
assert self._places, "no place for execution"
if num_threads is None:
if exec_strategy is None:
exec_strategy = ExecutionStrategy()
if use_cuda:
exec_strategy.use_event = True
else:
exec_strategy.use_event = False
if exec_strategy.num_threads == 0:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads = len(self._places) * 2
exec_strategy.num_threads = len(self._places) * 2
else:
num_threads = min(
exec_strategy.num_threads = min(
len(self._places) * 2, multiprocessing.cpu_count())
if build_strategy is None:
build_strategy = BuildStrategy()
main = main_program
main = main if main else framework.default_main_program()
scope = executor.global_scope()
@ -127,23 +145,14 @@ class ParallelExecutor(object):
]
self.executor = core.ParallelExecutor(
num_threads,
True if use_cuda else False, # use_event
self._places,
set([
p.name for p in main.global_block().iter_parameters()
if not p.stop_gradient
]),
set(self.persistable_vars),
main.desc,
loss_name if loss_name else '',
scope,
local_scopes,
allow_op_delay,
use_default_grad_scale,
balance_parameter_opt_between_cards,
num_trainers,
trainer_id)
set(self.persistable_vars), main.desc, loss_name
if loss_name else '', scope, local_scopes, exec_strategy,
build_strategy, num_trainers, trainer_id)
self.scope = scope
def run(self, fetch_list, feed=None, feed_dict=None):

20
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
Unescape View File

@ -48,12 +48,11 @@ def linear():
return avg_loss
def train(use_cuda, save_dirname):
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
train_func=linear,
infer_func=inference_program,
train_func=train_program,
place=place,
optimizer=fluid.optimizer.SGD(learning_rate=0.001))
@ -72,11 +71,7 @@ def train(use_cuda, save_dirname):
'''
if float(test_metrics[0]) < 20.0:
if save_dirname is not None:
# NOT clear yet
# fluid.io.save_inference_model(save_dirname, ['x'], [y_predict])
# trainer.save_params(save_dirname)
# https://github.com/PaddlePaddle/Paddle/pull/10445
trainer.save_inference_model(save_dirname)
trainer.save_params(save_dirname)
return
trainer.train(
@ -87,12 +82,13 @@ def train(use_cuda, save_dirname):
# infer
def infer(use_cuda, save_dirname=None):
def infer(use_cuda, inference_program, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_dirname, place=place)
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
@ -108,8 +104,8 @@ def main(use_cuda):
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(use_cuda, save_dirname)
infer(use_cuda, save_dirname)
train(use_cuda, linear, save_dirname)
infer(use_cuda, inference_program, save_dirname)
class TestFitALine(unittest.TestCase):

73
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
Unescape View File

@ -53,48 +53,40 @@ def train_program():
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return avg_cost
acc = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, acc]
def train(use_cuda, save_dirname):
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
trainer = fluid.Trainer(
train_func=train_program,
infer_func=inference_program,
place=place,
optimizer=optimizer)
train_func=train_program, place=place, optimizer=optimizer)
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
# if (event.epoch + 1) % 10 == 0:
# trainer.save_params(save_dirname)
trainer.save_inference_model(save_dirname)
# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
test_metrics = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]
# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if float(acc) > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
train_reader = paddle.batch(
paddle.reader.shuffle(
@ -108,10 +100,11 @@ def train(use_cuda, save_dirname):
feed_order=['img', 'label'])
def infer(use_cuda, save_dirname=None):
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_dirname, place=place)
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
@ -126,8 +119,14 @@ def main(use_cuda):
save_dirname = "recognize_digits_conv.inference.model"
# call train() with is_local argument to run distributed train
train(use_cuda=use_cuda, save_dirname=save_dirname)
infer(use_cuda=use_cuda, save_dirname=save_dirname)
train(
use_cuda=use_cuda,
train_program=train_program,
save_dirname=save_dirname)
infer(
use_cuda=use_cuda,
inference_program=inference_program,
save_dirname=save_dirname)
if __name__ == '__main__':

72
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
Unescape View File

@ -40,47 +40,40 @@ def train_program():
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return avg_cost
acc = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, acc]
def train(use_cuda, save_dirname):
def train(use_cuda, train_program, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
trainer = fluid.Trainer(
train_func=train_program,
infer_func=inference_program,
place=place,
optimizer=optimizer)
train_func=train_program, place=place, optimizer=optimizer)
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
# if (event.epoch + 1) % 10 == 0:
trainer.save_inference_model(save_dirname)
# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
test_metrics = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]
# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if float(acc) > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
train_reader = paddle.batch(
paddle.reader.shuffle(
@ -94,10 +87,11 @@ def train(use_cuda, save_dirname):
feed_order=['img', 'label'])
def infer(use_cuda, save_dirname=None):
def infer(use_cuda, inference_program, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_dirname, place=place)
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
@ -112,8 +106,14 @@ def main(use_cuda):
save_dirname = "recognize_digits_mlp.inference.model"
# call train() with is_local argument to run distributed train
train(use_cuda=use_cuda, save_dirname=save_dirname)
infer(use_cuda=use_cuda, save_dirname=save_dirname)
train(
use_cuda=use_cuda,
train_program=train_program,
save_dirname=save_dirname)
infer(
use_cuda=use_cuda,
inference_program=inference_program,
save_dirname=save_dirname)
if __name__ == '__main__':

20
python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py
Unescape View File

@ -90,7 +90,7 @@ def train_program(is_sparse):
return avg_cost
def train(use_cuda, is_sparse, save_path):
def train(use_cuda, train_program, save_path):
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
test_reader = paddle.batch(
@ -105,23 +105,21 @@ def train(use_cuda, is_sparse, save_path):
print("loss= ", avg_cost)
if avg_cost < 5.0:
trainer.save_inference_model(save_path)
trainer.save_params(save_path)
return
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
trainer = fluid.Trainer(
partial(train_program, is_sparse),
partial(inference_program, is_sparse),
fluid.optimizer.SGD(learning_rate=0.001),
place=place)
train_program, fluid.optimizer.SGD(learning_rate=0.001), place=place)
trainer.train(
reader=train_reader, num_epochs=1, event_handler=event_handler)
def infer(use_cuda, is_sparse, save_path):
def infer(use_cuda, inference_program, save_path):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(param_path=save_path, place=place)
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=save_path, place=place)
lod = [0, 1]
first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
@ -144,9 +142,9 @@ def main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "word2vec.inference.model"
train(use_cuda, is_sparse, save_path)
infer(use_cuda, is_sparse, save_path)
save_path = "word2vec.params"
train(use_cuda, partial(train_program, is_sparse), save_path)
infer(use_cuda, partial(inference_program, is_sparse), save_path)
if __name__ == '__main__':

61
python/paddle/fluid/tests/unittests/test_parallel_executor.py
Unescape View File

@ -232,14 +232,18 @@ class TestParallelExecutorBase(unittest.TestCase):
place = fluid.CUDAPlace(0)
startup_exe = fluid.Executor(place)
startup_exe.run(startup)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.allow_op_delay = allow_op_delay
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce if balance_parameter_opt_between_cards else fluid.BuildStrategy.ReduceStrategy.AllReduce
if use_parallel_executor:
exe = fluid.ParallelExecutor(
True,
loss_name=loss.name,
allow_op_delay=allow_op_delay,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
exec_strategy=exec_strategy,
build_strategy=build_strategy)
else:
exe = fluid.Executor(place=place)
@ -548,7 +552,7 @@ class TestTransformer(TestParallelExecutorBase):
class ParallelExecutorTestingDuringTraining(unittest.TestCase):
def check_network_convergence(self, balance_parameter_opt_between_cards):
def check_network_convergence(self, build_strategy=None):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
@ -571,15 +575,13 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
use_cuda=True,
loss_name=loss.name,
main_program=main,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
build_strategy=build_strategy)
test_exe = fluid.ParallelExecutor(
use_cuda=True,
main_program=test_program,
share_vars_from=train_exe,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
build_strategy=build_strategy)
for i in xrange(5):
test_loss, = test_exe.run([loss.name], feed=feed_dict)
@ -594,10 +596,14 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
str(test_loss))
def test_parallel_testing(self):
self.check_network_convergence(False)
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(build_strategy)
def test_parallel_testing_with_new_strategy(self):
self.check_network_convergence(True)
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(build_strategy)
import paddle.dataset.conll05 as conll05
@ -617,7 +623,7 @@ embedding_name = 'emb'
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
is_sparse, balance_parameter_opt_between_cards, **ignored):
is_sparse, **ignored):
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
@ -686,9 +692,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
class TestCRFModel(unittest.TestCase):
def check_network_convergence(self,
is_sparse,
balance_parameter_opt_between_cards=False):
def check_network_convergence(self, is_sparse, build_strategy=None):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
@ -739,8 +743,7 @@ class TestCRFModel(unittest.TestCase):
pe = fluid.ParallelExecutor(
use_cuda=True,
loss_name=avg_cost.name,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
build_strategy=build_strategy)
feeder = fluid.DataFeeder(
feed_list=[
@ -756,19 +759,29 @@ class TestCRFModel(unittest.TestCase):
pe.run(feed=feeder.feed(cur_batch),
fetch_list=[avg_cost.name]))[0]
def test_update_sparse_parameter(self):
self.check_network_convergence(is_sparse=True)
def test_update_sparse_parameter_all_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(
is_sparse=True, build_strategy=build_strategy)
def test_update_dense_parameter(self):
self.check_network_convergence(is_sparse=False)
def test_update_dense_parameter_all_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(
is_sparse=False, build_strategy=build_strategy)
def test_update_sparse_parameter_with_new_strategy(self):
def test_update_sparse_parameter_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(
is_sparse=True, balance_parameter_opt_between_cards=True)
is_sparse=True, build_strategy=build_strategy)
def test_update_dense_parameter_with_new_strategy(self):
def test_update_dense_parameter_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(
is_sparse=False, balance_parameter_opt_between_cards=True)
is_sparse=False, build_strategy=build_strategy)
# test fetch all the variables of global_block

32
python/paddle/fluid/trainer.py
Unescape View File

@ -92,19 +92,13 @@ class Trainer(object):
place: The device place of this trainer.
"""
def __init__(self,
train_func,
infer_func,
optimizer,
param_path=None,
place=None):
def __init__(self, train_func, optimizer, param_path=None, place=None):
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
if not isinstance(optimizer, opt_module.Optimizer):
raise TypeError("The optimizer should be an instance of Optimizer")
self.infer_func = infer_func
self.scope = core.Scope()
self.startup_program = framework.Program()
@ -178,9 +172,9 @@ class Trainer(object):
def train(self,
num_epochs,
event_handler,
reader=None,
parallel=False,
feed_order=None):
reader,
feed_order,
parallel=False):
"""
Train the model.
@ -208,7 +202,7 @@ class Trainer(object):
self._train_by_executor(num_epochs, event_handler, reader, feed_order)
def test(self, reader, feed_order=None):
def test(self, reader, feed_order):
"""
Test the model on given test data
@ -226,15 +220,6 @@ class Trainer(object):
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, model_path):
inference_program = framework.Program()
with framework.program_guard(inference_program):
with unique_name.guard():
predict_var = self.infer_func()
predict_var = self.train_program.block(0).var(predict_var.name)
exe = executor.Executor(self.place)
io.save_inference_model(model_path, [], [predict_var], exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
@ -291,12 +276,7 @@ def build_feed_var_list(program, feed_order):
if not isinstance(program, framework.Program):
raise TypeError("The 'program' should be an object of Program")
if feed_order is None:
feed_var_list = [
var for var in program.global_block().vars.itervalues()
if var.is_data
]
elif isinstance(feed_order, list):
if isinstance(feed_order, list):
feed_var_list = [
program.global_block().var(var_name) for var_name in feed_order
]

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