未验证 提交 c8744d11 编写于 作者: Y Yan Chunwei 提交者: GitHub

fea/infer executor and concurrency performance issue bug fix (#13451)

- add naive executor
- fix concurrency performance issue
上级 10a13f9c
......@@ -52,6 +52,7 @@ ExternalProject_Add(
PREFIX ${ANAKIN_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS ${CMAKE_ARGS_PREFIX}
-DUSE_LOGGER=YES
-DUSE_X86_PLACE=YES
-DBUILD_WITH_UNIT_TEST=NO
-DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
......
......@@ -56,9 +56,9 @@ else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
......@@ -141,12 +141,15 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
if(WITH_DISTRIBUTE)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass elementwise_add_op)
endif()
if (NOT WIN32)
......
......@@ -28,9 +28,9 @@ cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph grap
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(fc_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif()
if (WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif ()
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
pass_library(fc_lstm_fuse_pass inference)
......@@ -49,6 +49,6 @@ cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_r
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
if(WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif()
if (WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif ()
// 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.
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/string/pretty_log.h"
namespace paddle {
namespace framework {
// These code can be shared with Executor.
static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
if (var_type == proto::VarType::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == proto::VarType::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == proto::VarType::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
} else if (var_type == proto::VarType::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == proto::VarType::LOD_TENSOR_ARRAY) {
var->GetMutable<LoDTensorArray>();
} else if (var_type == proto::VarType::PLACE_LIST) {
var->GetMutable<platform::PlaceList>();
} else if (var_type == proto::VarType::READER) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::CHANNEL) {
var->GetMutable<ChannelHolder>();
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else {
PADDLE_THROW(
"Variable type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, RAW]",
var_type);
}
}
void NaiveExecutor::Prepare(Scope *parent_scope,
const ProgramDesc &program_desc, int block_id,
bool with_feed_fetch_ops) {
if (!parent_scope) {
scope_ = new framework::Scope;
} else {
scope_ = &parent_scope->NewScope();
}
CreateVariables(program_desc, scope_, block_id);
CreateOps(program_desc, block_id, with_feed_fetch_ops);
}
void NaiveExecutor::Run() {
for (auto &op : ops_) {
VLOG(4) << "run " << op->Type();
op->Run(*scope_, place_);
}
}
void NaiveExecutor::CreateVariables(const ProgramDesc &desc, Scope *scope,
int block_id) {
PADDLE_ENFORCE(scope);
auto &global_block = desc.Block(block_id);
const Scope *ancestor_scope = scope;
while (ancestor_scope->parent()) {
ancestor_scope = ancestor_scope->parent();
}
if (ancestor_scope != scope) {
for (auto &var : global_block.AllVars()) {
if (var->Name() == framework::kEmptyVarName) {
continue;
}
// Create persistable vars in ancestor scope.
if (var->Persistable()) {
auto *ptr = const_cast<Scope *>(ancestor_scope)->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else { // Create temporary variables in local scope.
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto &var : global_block.AllVars()) {
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
}
void NaiveExecutor::CreateOps(const ProgramDesc &desc, int block_id,
bool with_feed_fetch_ops) {
for (const auto &op_desc : desc.Block(block_id).AllOps()) {
if (!with_feed_fetch_ops &&
(op_desc->Type() == "feed" || op_desc->Type() == "fetch")) {
string::PrettyLogEndl(string::Style::detail(), "--- skip [%s], %s -> %s",
op_desc->Input("X")[0], op_desc->Type(),
op_desc->Output("Out")[0]);
continue;
}
ops_.emplace_back(OpRegistry::CreateOp(*op_desc));
}
}
LoDTensor *NaiveExecutor::FindTensor(const std::string &name) {
PADDLE_ENFORCE(scope_, "Need to init scope first");
auto *var = scope_->FindVar(name);
PADDLE_ENFORCE(var, "No variable [%s] in the scope");
auto *tensor = const_cast<LoDTensor *>(&var->Get<LoDTensor>());
return tensor;
}
void NaiveExecutor::CleanFeedFetchOps() {
std::vector<std::unique_ptr<OperatorBase>> ops;
for (auto &op : ops_) {
if (op->Type() != "feed" && op->Type() != "fetch") {
ops.emplace_back(std::move(op));
}
}
ops_.swap(ops);
}
} // namespace framework
} // namespace paddle
// 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
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
/*
* Simple, intuitive and effective. Only single thread is supported, and
* currently designed for inference.
*/
class NaiveExecutor {
public:
explicit NaiveExecutor(const platform::Place& place) : place_(place) {}
// Create child scope.
// Create variables.
// @with_feed_fetch_ops: whether to work with the feed and fetch operators.
void Prepare(Scope* parent_scope, const ProgramDesc& program_desc,
int block_id, bool with_feed_fetch_ops);
// Run all the operators.
void Run();
// Get an tensor to operating directly, without the need for feed_ops.
LoDTensor* FindTensor(const std::string& name);
Scope* scope() { return scope_; }
void CleanFeedFetchOps();
protected:
void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
void CreateOps(const ProgramDesc& desc, int block_id,
bool with_feed_fetch_ops);
private:
const platform::Place place_;
// Catch the required resource to avoid recreate.
std::vector<std::unique_ptr<OperatorBase>> ops_;
Scope* scope_;
};
} // namespace framework
} // namespace paddle
// 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.
#include "paddle/fluid/framework/naive_executor.h"
#include <gtest/gtest.h>
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
TEST(NaiveExecutor, Basic) {
ProgramDesc program;
auto* main_block = program.MutableBlock(0);
auto* a = main_block->Var("a"); // input
auto* b = main_block->Var("b"); // input
auto* c = main_block->Var("c"); // input
a->SetType(proto::VarType::LOD_TENSOR);
b->SetType(proto::VarType::LOD_TENSOR);
c->SetType(proto::VarType::LOD_TENSOR);
auto* add = main_block->AppendOp();
add->SetType("elementwise_add");
add->SetInput("X", {"a"});
add->SetInput("Y", {"b"});
add->SetOutput("Out", {"c"});
auto place = platform::CPUPlace();
NaiveExecutor exe(place);
exe.Prepare(nullptr, program, 0, false /*with feed fetch ops*/);
auto* a_tensor = exe.FindTensor("a");
auto* b_tensor = exe.FindTensor("b");
auto* c_tensor = exe.FindTensor("c");
a_tensor->Resize({1, 4});
b_tensor->Resize({1, 4});
c_tensor->Resize({1, 4});
b_tensor->mutable_data<float>(place);
a_tensor->mutable_data<float>(place);
float a_arr[] = {0, 1, 2, 3};
float b_arr[] = {0.0, .1, .2, .3};
std::copy_n(a_arr, 4, a_tensor->mutable_data<float>(place));
std::copy_n(b_arr, 4, b_tensor->mutable_data<float>(place));
exe.Run();
auto* c_data = c_tensor->mutable_data<float>(place);
for (int i = 0; i < 4; i++) {
EXPECT_NEAR(c_data[i], 1.1 * i, 1e-3);
}
}
} // namespace framework
} // namespace paddle
USE_OP(elementwise_add);
......@@ -154,9 +154,15 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
platform::SetDeviceId(dev_id);
#endif
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
}
RunImpl(scope, place);
if (VLOG_IS_ON(3)) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
......
......@@ -20,6 +20,13 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/string/printf.h"
// The mutex is not needed by training and inference, only for distribution.
#if PADDLE_WITH_DISTRIBUTE
#define WITH_LOCK 1
#else
#define WITH_LOCK 0
#endif
DEFINE_bool(benchmark, false,
"Doing memory benchmark. It will make deleting scope synchronized, "
"and add some memory usage logs."
......@@ -49,18 +56,24 @@ int64_t GetEagerDeletionThreshold() {
Scope::~Scope() { DropKids(); }
Scope& Scope::NewScope() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable* Scope::Var(const std::string& name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = new_name;
......@@ -69,29 +82,39 @@ Variable* Scope::Var(std::string* name) {
}
Variable* Scope::FindVar(const std::string& name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindVarInternal(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindScopeInternal(var);
}
void Scope::DropKids() {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
for (Scope* s : kids_) delete s;
kids_.clear();
}
bool Scope::HasKid(const Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
return it != this->kids_.end();
}
std::vector<std::string> Scope::LocalVarNames() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -101,7 +124,9 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -114,7 +139,9 @@ void Scope::DeleteScope(Scope* scope) const {
}
void Scope::EraseVars(const std::vector<std::string>& var_names) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......@@ -127,12 +154,16 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
......
......@@ -53,7 +53,7 @@ if(NOT APPLE)
endif()
if(WITH_TESTING)
# tests/book depends the models that generated by python/paddle/fluid/tests/book
# tests/book depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
if(WITH_INFERENCE_API_TEST)
add_subdirectory(tests/api)
......
cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
set(analysis_deps
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
analyzer.cc
......
......@@ -18,10 +18,10 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${GLOB_PASS_LIB})
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
endif()
function(inference_api_test TARGET_NAME)
......@@ -43,8 +43,10 @@ function(inference_api_test TARGET_NAME)
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
DEPS paddle_inference_api)
......@@ -52,18 +54,22 @@ cc_test(test_paddle_inference_api
inference_api_test(test_api_impl SRC api_impl_tester.cc
ARGS test_word2vec test_image_classification)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book)
if(WITH_GPU AND TENSORRT_FOUND)
cc_library(paddle_inference_tensorrt_subgraph_engine
SRCS api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter)
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
endif()
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml scope zero_copy_tensor_dummy)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber scope)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endfunction()
......
......@@ -16,11 +16,15 @@
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -28,8 +32,11 @@ DECLARE_bool(profile);
namespace paddle {
using contrib::AnalysisConfig;
bool AnalysisPredictor::Init(
const std::shared_ptr<framework::Scope>& parent_scope) {
const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program) {
VLOG(3) << "Predictor::init()";
#if !defined(_WIN32)
if (FLAGS_profile) {
......@@ -43,7 +50,8 @@ bool AnalysisPredictor::Init(
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
LOG(WARNING) << "ir optimize only supports CPU currently";
LOG(WARNING) << "ir optimize only supports CPU currently, enable_ir_optim "
"is turned false.";
config_.enable_ir_optim = false;
} else {
place_ = paddle::platform::CPUPlace();
......@@ -56,37 +64,134 @@ bool AnalysisPredictor::Init(
scope_.reset(new paddle::framework::Scope());
}
executor_.reset(new paddle::framework::Executor(place_));
executor_.reset(new paddle::framework::NaiveExecutor(place_));
// Initialize the inference program
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
if (!program) {
if (!LoadProgramDesc()) return false;
OptimizeInferenceProgram();
} else {
LOG(ERROR) << "fail to load inference model from " << config_.model_dir;
inference_program_ = program;
}
executor_->Prepare(scope_.get(), *inference_program_, 0,
config_.use_feed_fetch_ops);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
return true;
}
bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size) {
VLOG(3) << "Predictor::predict";
inference::Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
return false;
}
// Run the inference program
// if share variables, we need not create variables
executor_->Run();
OptimizeInferenceProgram();
if (config_._use_mkldnn) {
executor_->EnableMKLDNN(*inference_program_);
// get fetch variable
if (!GetFetch(output_data, scope)) {
LOG(ERROR) << "fail to get fetches";
return false;
}
ctx_ = executor_->Prepare(*inference_program_, 0);
VLOG(3) << "predict cost: " << timer.toc() << "ms";
return true;
}
VLOG(5) << "to create variables";
PADDLE_ENFORCE(scope_.get());
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feeds_.size()) {
LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
<< inputs.size();
return false;
}
// Cache the inputs memory for better concurrency performance.
feed_tensors_.resize(inputs.size());
for (size_t i = 0; i < inputs.size(); ++i) {
auto &input = feed_tensors_[i];
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
lod.emplace_back(level);
}
input.set_lod(lod);
int idx = -1;
if (config_.specify_input_name) {
idx = feed_names_[inputs[i].name];
} else {
idx = boost::get<int>(feeds_[i]->GetAttr("col"));
}
framework::SetFeedVariable(scope, input, "feed", idx);
}
return true;
}
template <typename T>
void AnalysisPredictor::GetFetchOne(const framework::LoDTensor &fetch,
PaddleTensor *output) {
// set shape.
auto shape = framework::vectorize(fetch.dims());
output->shape.assign(shape.begin(), shape.end());
// set data.
const T *data = fetch.data<T>();
int num_elems = inference::VecReduceToInt(shape);
output->data.Resize(num_elems * sizeof(T));
// The fetched tensor output by fetch op, should always in CPU memory, so just
// copy.
memcpy(output->data.data(), data, num_elems * sizeof(T));
// set lod
output->lod.clear();
for (auto &level : fetch.lod()) {
output->lod.emplace_back(level.begin(), level.end());
}
}
bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::get_fetch";
outputs->resize(fetchs_.size());
for (size_t i = 0; i < fetchs_.size(); ++i) {
int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
PADDLE_ENFORCE((size_t)idx == i);
framework::LoDTensor &fetch =
framework::GetFetchVariable(*scope, "fetch", idx);
auto type = fetch.type();
auto output = &(outputs->at(i));
if (type == typeid(float)) {
GetFetchOne<float>(fetch, output);
output->dtype = PaddleDType::FLOAT32;
} else if (type == typeid(int64_t)) {
GetFetchOne<int64_t>(fetch, output);
output->dtype = PaddleDType::INT64;
} else {
LOG(ERROR) << "unknown type, only support float32 and int64 now.";
}
}
return true;
}
......@@ -107,6 +212,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
new std::string(config_.prog_file));
argument_.fluid_model_param_path.reset(new std::string(config_.param_file));
}
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
PADDLE_ENFORCE(
......@@ -127,9 +233,8 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
const contrib::AnalysisConfig& config) {
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
if (config.use_gpu) {
// 1. GPU memeroy
......@@ -150,15 +255,90 @@ CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
}
std::unique_ptr<PaddlePredictor> predictor(new AnalysisPredictor(config));
if (!dynamic_cast<AnalysisPredictor*>(predictor.get())->Init(nullptr)) {
if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
return nullptr;
}
return predictor;
}
void AnalysisPredictor::PrepareFeedFetch() {
for (auto *op : inference_program_->Block(0).AllOps()) {
if (op->Type() == "feed") {
int idx = boost::get<int>(op->GetAttr("col"));
if (feeds_.size() <= static_cast<size_t>(idx)) {
feeds_.resize(idx + 1);
}
feeds_[idx] = op;
feed_names_[op->Output("Out")[0]] = idx;
} else if (op->Type() == "fetch") {
int idx = boost::get<int>(op->GetAttr("col"));
if (fetchs_.size() <= static_cast<size_t>(idx)) {
fetchs_.resize(idx + 1);
}
fetchs_[idx] = op;
}
}
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = true;
res->SetName(name);
return res;
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = false;
res->SetName(name);
return res;
}
bool AnalysisPredictor::ZeroCopyRun() {
executor_->Run();
return true;
}
bool AnalysisPredictor::LoadProgramDesc() {
// Initialize the inference program
std::unique_ptr<framework::Executor> tmp_exe(
new framework::Executor(platform::CPUPlace()));
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.prog_file, config_.param_file);
} else {
LOG(ERROR) << string::Sprintf(
"not valid model path '%s' or program path '%s'.", config_.model_dir,
config_.param_file);
return false;
}
return true;
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
auto *x = new AnalysisPredictor(config_);
x->Init(scope_, inference_program_);
return std::unique_ptr<PaddlePredictor>(x);
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<contrib::AnalysisConfig>(
const contrib::AnalysisConfig& config) {
const contrib::AnalysisConfig &config) {
return CreatePaddlePredictor<contrib::AnalysisConfig,
PaddleEngineKind::kAnalysis>(config);
}
......
......@@ -12,42 +12,81 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
using inference::analysis::Argument;
using inference::analysis::Analyzer;
using framework::proto::ProgramDesc;
using framework::NaiveExecutor;
using contrib::AnalysisConfig;
/* This predictor is based on the original native predictor with IR and Analysis
* support. It will optimize IR and Parameters in the runtime.
* TODO(Superjomn) Replace the Navive predictor?
*/
class AnalysisPredictor : public NativePaddlePredictor {
class AnalysisPredictor : public PaddlePredictor {
public:
explicit AnalysisPredictor(const contrib::AnalysisConfig& config)
: NativePaddlePredictor(config), config_(config) {}
explicit AnalysisPredictor(const AnalysisConfig &config) : config_(config) {}
bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
bool Init(const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program = nullptr);
bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data,
int batch_size = -1) override {
return NativePaddlePredictor::Run(inputs, output_data, batch_size);
}
bool Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size = -1) override;
std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string &name) override;
std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string &name) override;
bool ZeroCopyRun() override;
void PrepareFeedFetch();
void OptimizeInferenceProgram();
Argument& analysis_argument() { return argument_; }
Argument &analysis_argument() { return argument_; }
std::unique_ptr<PaddlePredictor> Clone() override;
framework::Scope *scope() { return executor_->scope(); }
framework::ProgramDesc &program() { return *inference_program_; }
protected:
bool LoadProgramDesc();
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
bool GetFetch(std::vector<PaddleTensor> *output_data,
framework::Scope *scope);
template <typename T>
void GetFetchOne(const framework::LoDTensor &fetchs,
PaddleTensor *output_data);
private:
contrib::AnalysisConfig config_;
Argument argument_;
std::unique_ptr<NaiveExecutor> executor_;
platform::Place place_;
std::shared_ptr<framework::Scope> scope_;
framework::Scope *sub_scope_{nullptr};
std::shared_ptr<framework::ProgramDesc> inference_program_;
std::vector<framework::OpDesc *> feeds_;
std::map<std::string, size_t> feed_names_;
std::vector<framework::OpDesc *> fetchs_;
// Memory buffer for feed inputs. The temporary LoDTensor will cause serious
// concurrency problems, so cache them.
std::vector<framework::LoDTensor> feed_tensors_;
};
} // namespace paddle
// 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.
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(dirname, "", "dirname to tests.");
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
TEST(AnalysisPredictor, ZeroCopy) {
AnalysisConfig config;
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.use_feed_fetch_ops = false;
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto w0 = predictor->GetInputTensor("firstw");
auto w1 = predictor->GetInputTensor("secondw");
auto w2 = predictor->GetInputTensor("thirdw");
auto w3 = predictor->GetInputTensor("forthw");
w0->Reshape({4, 1});
w1->Reshape({4, 1});
w2->Reshape({4, 1});
w3->Reshape({4, 1});
auto* w0_data = w0->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w1_data = w1->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w2_data = w2->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w3_data = w3->mutable_data<int64_t>(PaddlePlace::kCPU);
for (int i = 0; i < 4; i++) {
w0_data[i] = i;
w1_data[i] = i;
w2_data[i] = i;
w3_data[i] = i;
}
predictor->ZeroCopyRun();
auto out = predictor->GetOutputTensor("fc_1.tmp_2");
PaddlePlace place;
int size = 0;
auto* out_data = out->data<float>(&place, &size);
LOG(INFO) << "output size: " << size / sizeof(float);
LOG(INFO) << "output_data: " << out_data;
}
} // namespace inference
} // namespace paddle
/* 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. */
// 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.
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle_inference_api.h"
namespace paddle {
......@@ -26,7 +32,7 @@ int PaddleDtypeSize(PaddleDType dtype) {
}
}
PaddleBuf::PaddleBuf(PaddleBuf&& other)
PaddleBuf::PaddleBuf(PaddleBuf &&other)
: data_(other.data_),
length_(other.length_),
memory_owned_(other.memory_owned_) {
......@@ -35,9 +41,9 @@ PaddleBuf::PaddleBuf(PaddleBuf&& other)
other.length_ = 0;
}
PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
PaddleBuf::PaddleBuf(const PaddleBuf &other) { *this = other; }
PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
if (!other.memory_owned_) {
data_ = other.data_;
length_ = other.length_;
......@@ -51,7 +57,7 @@ PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
return *this;
}
PaddleBuf& PaddleBuf::operator=(PaddleBuf&& other) {
PaddleBuf &PaddleBuf::operator=(PaddleBuf &&other) {
// only the buffer with external memory can be copied
data_ = other.data_;
length_ = other.length_;
......@@ -75,7 +81,7 @@ void PaddleBuf::Resize(size_t length) {
}
}
void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Reset(void *data, size_t length) {
Free();
memory_owned_ = false;
data_ = data;
......@@ -85,7 +91,7 @@ void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Free() {
if (memory_owned_ && data_) {
PADDLE_ENFORCE_GT(length_, 0);
free(static_cast<char*>(data_));
free(static_cast<char *>(data_));
data_ = nullptr;
length_ = 0;
}
......
......@@ -145,7 +145,7 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
VLOG(4) << "Run prepared context";
executor_->RunPreparedContext(ctx_.get(), scope,
false, /* don't create local scope each time*/
false /* don't create variable eatch time */);
false /* don't create variable each time */);
VLOG(4) << "Finish prepared context";
// get fetch variable
if (!GetFetch(output_data, scope)) {
......
/* 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
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
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. */
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
......@@ -30,6 +30,8 @@
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -52,6 +54,8 @@ class NativePaddlePredictor : public PaddlePredictor {
~NativePaddlePredictor() override;
framework::Scope *scope() { return sub_scope_ ? sub_scope_ : scope_.get(); }
protected:
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
......
......@@ -43,7 +43,7 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
......@@ -110,7 +110,7 @@ void MainImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
const bool is_combined = false;
std::vector<std::vector<int64_t>> feed_target_shapes =
......@@ -214,7 +214,7 @@ void MainThreadsImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<framework::LoDTensor> jobs(num_jobs);
......
// 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.
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(input_or_output_,
"Can't reshape the output tensor, it is readonly");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(shape));
}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
switch (static_cast<int>(place)) {
case static_cast<int>(PaddlePlace::kCPU): {
return tensor->mutable_data<T>(platform::CPUPlace());
}
case static_cast<int>(PaddlePlace::kGPU): {
return tensor->mutable_data<T>(platform::CUDAPlace());
}
default:
PADDLE_THROW("Unsupported place: %d", static_cast<int>(place));
break;
}
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
auto *res = tensor->data<T>();
if (platform::is_cpu_place(tensor->place())) {
*place = PaddlePlace::kCPU;
} else if (platform::is_gpu_place(tensor->place())) {
*place = PaddlePlace::kGPU;
} else {
*place = PaddlePlace::kUNK;
}
*size = tensor->numel();
return res;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data<float>(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data<int64_t>(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
return tensor;
}
std::vector<int64_t> ZeroCopyTensor::shape() {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
PADDLE_ENFORCE(tensor, "not found tensor called %s in the scope", name_);
return framework::vectorize(tensor->dims());
}
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
framework::LoD lod;
for (auto &level : x) {
lod.emplace_back(level);
}
tensor->set_lod(lod);
}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
std::vector<std::vector<size_t>> res;
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
for (auto &level : tensor->lod()) {
res.emplace_back(level);
}
return res;
}
} // namespace paddle
// 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.
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
return nullptr;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const { return nullptr; }
std::vector<int64_t> ZeroCopyTensor::shape() { return {}; }
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
return std::vector<std::vector<size_t>>();
}
} // namespace paddle
......@@ -21,8 +21,10 @@
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace inference {
......@@ -93,6 +95,20 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
template <typename T>
static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
int size{0};
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
int c = 0;
for (const auto &f : data) {
for (T v : f) {
ptr[c++] = v;
}
}
return size;
}
static std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
......@@ -138,5 +154,127 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
}
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
ss << "\n---- tensor ---" << '\n';
ss << "lod: [";
for (const auto &level : tensor.lod()) {
ss << "[ ";
for (auto i : level) {
ss << i << ", ";
}
ss << "]";
}
ss << "]\n";
ss << "shape: [";
int size = 1;
for (int i = 0; i < tensor.dims().size(); i++) {
int dim = tensor.dims()[i];
ss << dim << ", ";
size *= dim;
}
ss << "]\n";
ss << "data: ";
for (int i = 0; i < std::min(20, size); i++) {
ss << tensor.data<T>()[i] << " ";
}
ss << "\n";
return ss.str();
}
static bool CompareLoD(const framework::LoD &a, const framework::LoD &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("lod size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
auto &al = a[i];
auto &bl = b[i];
if (al.size() != bl.size()) {
LOG(ERROR) << string::Sprintf("level size %d != %d", al.size(),
bl.size());
return false;
}
}
return true;
}
static bool CompareShape(const std::vector<int64_t> &a,
const std::vector<int64_t> &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("shape size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) {
LOG(ERROR) << string::Sprintf("shape %d-th element not match %d != %d", i,
a[i], b[i]);
return false;
}
}
return true;
}
static bool CompareTensorData(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
auto a_shape = framework::vectorize(a.dims());
auto b_shape = framework::vectorize(b.dims());
size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
[](int a, int b) { return a * b; });
size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
[](int a, int b) { return a * b; });
if (a_size != b_size) {
LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",
a_size, b_size);
}
for (size_t i = 0; i < a_size; i++) {
if (a.type() == typeid(float)) {
const auto *a_data = a.data<float>();
const auto *b_data = b.data<float>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
} else if (a.type() == typeid(int64_t)) {
const auto *a_data = a.data<int64_t>();
const auto *b_data = b.data<int64_t>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
}
}
return true;
}
static bool CompareTensor(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
if (!CompareLoD(a.lod(), b.lod())) {
return false;
}
if (!CompareShape(framework::vectorize(a.dims()),
framework::vectorize(b.dims()))) {
return false;
}
if (!CompareTensorData(a, b)) {
return false;
}
return true;
}
} // namespace inference
} // namespace paddle
......@@ -101,6 +101,40 @@ struct PaddleTensor {
std::vector<std::vector<size_t>> lod; // Tensor+LoD equals LoDTensor
};
enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
// Tensor without copy, currently only supports AnalysisPredictor.
class ZeroCopyTensor {
public:
void Reshape(const std::vector<int>& shape);
// Get the memory in CPU or GPU with specific data type, should Reshape first
// to tell the data size.
// Once can directly call this data to feed the data.
// This is for write the input tensor.
template <typename T>
T* mutable_data(PaddlePlace place);
// Get the memory directly, will return the place and memory size by pointer.
// This is for reading the output tensor.
template <typename T>
T* data(PaddlePlace* place, int* size);
std::vector<int64_t> shape();
void SetLoD(const std::vector<std::vector<size_t>>& x);
std::vector<std::vector<size_t>> lod() const;
protected:
ZeroCopyTensor(void* scope) : scope_{scope} {}
void SetName(const std::string& name) { name_ = name; }
void* FindTensor() const;
private:
std::string name_;
bool input_or_output_;
friend class AnalysisPredictor;
void* scope_{nullptr};
};
/*
* A simple Inference API for Paddle.
*/
......@@ -120,6 +154,19 @@ class PaddlePredictor {
std::vector<PaddleTensor>* output_data,
int batch_size = -1) = 0;
// Zero copy input and output optimization.
// Get the input or output tensors, and operate on their memory directly,
// without copy.
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string& name) {
return nullptr;
}
virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string& name) {
return nullptr;
}
virtual bool ZeroCopyRun() { return false; }
// Clone a predictor that share the model weights, the Cloned predictor should
// be thread-safe.
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
......@@ -218,7 +265,12 @@ struct AnalysisConfig : public NativeConfig {
IrPassMode ir_mode{IrPassMode::kExclude};
std::vector<std::string> ir_passes;
// NOTE this is just for internal development, please not use it.
// NOT stable yet.
bool use_feed_fetch_ops{true};
// NOTE this is just for internal development, please not use it. NOT
// stable
// yet.
bool _use_mkldnn{false};
};
......
......@@ -18,6 +18,8 @@ namespace paddle {
namespace inference {
namespace analysis {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<int64_t> data;
std::vector<size_t> lod;
......@@ -78,6 +80,7 @@ struct DataRecord {
}
}
}
DataRecord NextBatch() {
DataRecord data;
data.data = batched_datas[batch_iter];
......@@ -155,7 +158,9 @@ TEST(Analyzer_LAC, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_TRUE(fuse_statis.count("fc_gru_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
......
......@@ -16,6 +16,7 @@
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> word_data_all, mention_data_all;
......@@ -145,7 +146,9 @@ TEST(Analyzer_Chinese_ner, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_TRUE(fuse_statis.count("fc_gru_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
......
......@@ -12,12 +12,16 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/tests/api/tester_helper.h"
DEFINE_bool(with_precision_check, true, "turn on test");
namespace paddle {
namespace inference {
using namespace framework; // NOLINT
using namespace contrib; // NOLINT
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
......@@ -29,10 +33,12 @@ struct DataRecord {
size_t batch_iter{0};
size_t batch_size{1};
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
......@@ -101,6 +107,7 @@ struct DataRecord {
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
......@@ -149,7 +156,55 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
void PrepareZeroCopyInputs(ZeroCopyTensor *lod_attention_tensor,
ZeroCopyTensor *cell_init_tensor,
ZeroCopyTensor *data_tensor,
ZeroCopyTensor *hidden_init_tensor,
ZeroCopyTensor *week_tensor,
ZeroCopyTensor *minute_tensor,
DataRecord *data_record, int batch_size) {
auto one_batch = data_record->NextBatch();
std::vector<int> rnn_link_data_shape(
{static_cast<int>(one_batch.rnn_link_data.size()),
static_cast<int>(one_batch.rnn_link_data.front().size())});
lod_attention_tensor->Reshape({1, 2});
lod_attention_tensor->SetLoD({one_batch.lod1, one_batch.lod2});
cell_init_tensor->Reshape({batch_size, 15});
cell_init_tensor->SetLoD({one_batch.lod3});
hidden_init_tensor->Reshape({batch_size, 15});
hidden_init_tensor->SetLoD({one_batch.lod3});
data_tensor->Reshape(rnn_link_data_shape);
data_tensor->SetLoD({one_batch.lod1});
week_tensor->Reshape(
{static_cast<int>(one_batch.rnn_week_datas.size()),
static_cast<int>(one_batch.rnn_week_datas.front().size())});
week_tensor->SetLoD({one_batch.lod3});
minute_tensor->Reshape(
{static_cast<int>(one_batch.rnn_minute_datas.size()),
static_cast<int>(one_batch.rnn_minute_datas.front().size())});
minute_tensor->SetLoD({one_batch.lod3});
// assign data
float arr0[] = {0, 0};
std::vector<float> zeros(batch_size * 15, 0);
std::copy_n(arr0, 2,
lod_attention_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(arr0, 2, data_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(zeros.begin(), zeros.size(),
cell_init_tensor->mutable_data<float>(PaddlePlace::kCPU));
std::copy_n(zeros.begin(), zeros.size(),
hidden_init_tensor->mutable_data<float>(PaddlePlace::kCPU));
ZeroCopyTensorAssignData(data_tensor, one_batch.rnn_link_data);
ZeroCopyTensorAssignData(week_tensor, one_batch.rnn_week_datas);
ZeroCopyTensorAssignData(minute_tensor, one_batch.rnn_minute_datas);
}
void SetConfig(AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
......@@ -187,7 +242,9 @@ TEST(Analyzer_rnn1, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
......@@ -214,7 +271,229 @@ TEST(Analyzer_rnn1, multi_thread) {
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(cfg, input_slots_all, &outputs, 4 /* num_threads */);
TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
}
bool CompareTensors(framework::Scope &a_scope, framework::Scope &b_scope,
const std::vector<std::string> &tensors) {
for (auto &x : tensors) {
auto *a_var = a_scope.FindVar(x);
auto *b_var = b_scope.FindVar(x);
if (a_var && b_var) {
if (a_var->Type() == typeid(framework::LoDTensor) ||
a_var->Type() == typeid(framework::Tensor)) {
LOG(INFO) << "comparing tensor " << x;
auto &a_t = a_var->Get<framework::LoDTensor>();
auto &b_t = b_var->Get<framework::LoDTensor>();
if (!inference::CompareTensor(a_t, b_t)) {
LOG(ERROR) << string::Sprintf("tensor %s not match in two scopes", x);
}
} else {
LOG(INFO) << "skip no tensor " << x;
}
} else {
LOG(INFO) << "skip tensor " << x;
}
}
return true;
}
// Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
// on the complex RNN1 model.
TEST(Analyzer_rnn1, ZeroCopy) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
PaddlePlace place;
int output_size{0};
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
config.use_feed_fetch_ops = true;
auto native_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
config.use_feed_fetch_ops = true; // the analysis predictor needs feed/fetch.
auto analysis_predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
NEW_TENSOR(data_lod_attention);
NEW_TENSOR(cell_init);
NEW_TENSOR(data);
NEW_TENSOR(week);
NEW_TENSOR(minute);
NEW_TENSOR(hidden_init);
// Prepare data for AnalysisPredictor
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
PrepareZeroCopyInputs(data_lod_attention_tensor.get(), cell_init_tensor.get(),
data_tensor.get(), hidden_init_tensor.get(),
week_tensor.get(), minute_tensor.get(), &data,
FLAGS_batch_size);
// Prepare data for NativePredictor
std::vector<std::vector<PaddleTensor>> native_inputs;
SetInput(&native_inputs);
std::vector<PaddleTensor> native_outputs;
std::vector<PaddleTensor> analysis_outputs;
auto output_tensor = predictor->GetOutputTensor("final_output.tmp_1");
// Run analysis predictor
int num_ops;
auto fuse_statis = GetFuseStatis(predictor.get(), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_EQ(fuse_statis.at("fc_fuse"), 1);
ASSERT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
ASSERT_EQ(fuse_statis.at("seq_concat_fc_fuse"), 1);
ASSERT_EQ(num_ops,
13); // After graph optimization, only 13 operators exists.
Timer timer;
double total_time{0};
double native_total_time{0};
double analysis_total_time{0.};
for (int i = 0; i < FLAGS_repeat; i++) {
timer.tic();
predictor->ZeroCopyRun();
total_time += timer.toc();
}
auto *output_data = output_tensor->data<float>(&place, &output_size);
ASSERT_GT(output_size, 0); // more than one output!
for (int i = 0; i < FLAGS_repeat; i++) {
// Run native predictor.
timer.tic();
ASSERT_TRUE(native_predictor->Run(native_inputs.front(), &native_outputs));
native_total_time += timer.toc();
}
for (int i = 0; i < FLAGS_repeat; i++) {
timer.tic();
ASSERT_TRUE(
analysis_predictor->Run(native_inputs.front(), &analysis_outputs));
analysis_total_time += timer.toc();
}
if (!FLAGS_with_precision_check) {
return;
}
int native_output_size = VecReduceToInt(native_outputs.front().shape);
EXPECT_EQ(native_output_size, output_size);
// Compare tensors between analysis and zerocopy
auto *p0 = static_cast<AnalysisPredictor *>(predictor.get());
auto *p1 = static_cast<AnalysisPredictor *>(analysis_predictor.get());
auto *p2 = static_cast<NativePaddlePredictor *>(native_predictor.get());
std::vector<std::string> tensor_names;
for (auto &var_desc : p0->program().Block(0).AllVars()) {
tensor_names.push_back(var_desc->Name());
}
LOG(INFO) << "Comparing tensors";
ASSERT_TRUE(
CompareTensors(*p0->scope(), *p1->scope(), {"final_output.tmp_1"}));
ASSERT_TRUE(
CompareTensors(*p0->scope(), *p2->scope(), {"final_output.tmp_1"}));
LOG(INFO) << "output1 " << inference::LoDTensorSummary<float>(
p0->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
LOG(INFO) << "output2 " << inference::LoDTensorSummary<float>(
p1->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
LOG(INFO) << "output3 " << inference::LoDTensorSummary<float>(
p2->scope()
->FindVar("final_output.tmp_1")
->Get<framework::LoDTensor>());
for (int i = 0; i < output_size; i++) {
LOG(INFO) << output_data[i] << " "
<< static_cast<float *>(native_outputs.front().data.data())[i]
<< " "
<< static_cast<float *>(analysis_outputs.front().data.data())[i];
EXPECT_NEAR(output_data[i],
static_cast<float *>(native_outputs.front().data.data())[i],
1e-3);
}
LOG(INFO) << "batch_size: " << FLAGS_batch_size;
LOG(INFO) << "zero average time: "
<< total_time / (FLAGS_repeat * FLAGS_batch_size);
LOG(INFO) << "analysis average time: "
<< analysis_total_time / (FLAGS_repeat * FLAGS_batch_size);
LOG(INFO) << "native average time: "
<< native_total_time / (FLAGS_repeat * FLAGS_batch_size);
}
TEST(Analyzer_rnn1, ZeroCopyMultiThread) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
auto base_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
double total_time_of_threads{0};
std::vector<std::thread> threads;
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
for (int tid = 0; tid < FLAGS_num_threads; tid++) {
predictors.emplace_back(CreatePaddlePredictor<AnalysisConfig>(config));
}
for (int tid = 0; tid < FLAGS_num_threads; tid++) {
threads.emplace_back([config, &total_time_of_threads, &predictors, tid] {
// auto predictor = base_predictor->Clone();
auto &predictor = predictors[tid];
NEW_TENSOR(data_lod_attention);
NEW_TENSOR(cell_init);
NEW_TENSOR(data);
NEW_TENSOR(week);
NEW_TENSOR(minute);
NEW_TENSOR(hidden_init);
// Prepare data for AnalysisPredictor
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
Timer timer;
double total_time{0};
for (int i = 0; i < FLAGS_repeat; i++) {
PrepareZeroCopyInputs(data_lod_attention_tensor.get(),
cell_init_tensor.get(), data_tensor.get(),
hidden_init_tensor.get(), week_tensor.get(),
minute_tensor.get(), &data, FLAGS_batch_size);
timer.tic();
predictor->ZeroCopyRun();
total_time += timer.toc();
}
total_time_of_threads += total_time;
LOG(INFO) << "thread time: " << total_time / FLAGS_repeat;
});
}
for (auto &t : threads) {
t.join();
}
LOG(INFO) << "average time: "
<< total_time_of_threads / FLAGS_num_threads / FLAGS_repeat;
}
} // namespace inference
......
......@@ -182,7 +182,8 @@ TEST(Analyzer_seq_conv1, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto fuse_statis = GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
GetFuseStatis(predictor.get(), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
......
......@@ -19,6 +19,7 @@ limitations under the License. */
namespace paddle {
namespace inference {
namespace analysis {
using contrib::AnalysisConfig;
struct Record {
std::vector<float> data;
......@@ -114,7 +115,8 @@ TEST(Analyzer_vis, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
GetFuseStatis(cfg, &num_ops);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
GetFuseStatis(predictor.get(), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
......
......@@ -86,11 +86,9 @@ std::unique_ptr<PaddlePredictor> CreateTestPredictor(
size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); }
std::unordered_map<std::string, int> GetFuseStatis(AnalysisConfig config,
std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
int *num_ops) {
auto predictor = CreateTestPredictor(config);
AnalysisPredictor *analysis_predictor =
dynamic_cast<AnalysisPredictor *>(predictor.get());
auto *analysis_predictor = static_cast<AnalysisPredictor *>(predictor);
auto &fuse_statis = analysis_predictor->analysis_argument()
.Get<std::unordered_map<std::string, int>>(
framework::ir::kFuseStatisAttr);
......
......@@ -36,6 +36,8 @@ namespace memory {
using BuddyAllocator = detail::BuddyAllocator;
BuddyAllocator* GetCPUBuddyAllocator() {
// We tried thread_local for inference::RNN1 model, but that not works much
// for multi-thread test.
static std::once_flag init_flag;
static detail::BuddyAllocator* a = nullptr;
......@@ -48,6 +50,25 @@ BuddyAllocator* GetCPUBuddyAllocator() {
return a;
}
// We compared the NaiveAllocator with BuddyAllocator in CPU memory allocation,
// seems they are almost the same overhead.
struct NaiveAllocator {
void* Alloc(size_t size) { return malloc(size); }
void Free(void* p) {
PADDLE_ENFORCE(p);
free(p);
}
static NaiveAllocator* Instance() {
static NaiveAllocator x;
return &x;
}
private:
std::mutex lock_;
};
template <>
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
......
......@@ -56,13 +56,13 @@ struct Style {
};
template <typename... Args>
static void PrettyLogEndl(const std::string& style, const char* fmt,
const Args&... args) {
static void PrettyLogEndl(const std::string &style, const char *fmt,
const Args &... args) {
std::cerr << style << Sprintf(fmt, args...) << reset() << std::endl;
}
template <typename... Args>
static void PrettyLog(const std::string& style, const char* fmt,
const Args&... args) {
static void PrettyLog(const std::string &style, const char *fmt,
const Args &... args) {
std::cerr << style << Sprintf(fmt, args...) << reset();
}
......
......@@ -28,7 +28,6 @@ list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/Paddl
list(REMOVE_ITEM TEST_OPS op_test) # op_test is a helper python file, not a test
list(REMOVE_ITEM TEST_OPS decorators) # decorators is a helper python file, not a test
if(APPLE)
if(NOT WITH_DISTRIBUTE)
list(REMOVE_ITEM TEST_OPS test_desc_clone)
......
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