提交 405b2486 编写于 作者: T Tao Luo

support loading from memory

test=develop
上级 461ca35b
......@@ -97,7 +97,7 @@ void ExecutorThreadWorker::SetDevice() {
static unsigned concurrency_cap = std::thread::hardware_concurrency();
int thread_id = this->thread_id_;
if (thread_id < concurrency_cap) {
if ((unsigned)thread_id < concurrency_cap) {
unsigned proc = thread_id;
cpu_set_t mask;
......
......@@ -103,6 +103,7 @@ struct Argument {
// Model specified with program and parameters files.
DECL_ARGUMENT_FIELD(model_program_path, ModelProgramPath, std::string);
DECL_ARGUMENT_FIELD(model_params_path, ModelParamsPath, std::string);
DECL_ARGUMENT_FIELD(is_memory_load, IsMemoryLoad, bool);
// The overall graph to work on.
DECL_ARGUMENT_UNIQUE_FIELD(main_graph, MainGraph, framework::ir::Graph);
......
......@@ -46,7 +46,7 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
argument->model_params_path_valid()) {
auto program =
LoadModel(argument->model_program_path(), argument->model_params_path(),
argument->scope_ptr(), place);
argument->scope_ptr(), place, argument->is_memory_load());
argument->SetMainProgram(program.release());
} else {
PADDLE_THROW(
......@@ -68,9 +68,10 @@ std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
const std::string &program_path, const std::string &params_path,
framework::Scope *scope, const platform::Place &place) {
framework::Scope *scope, const platform::Place &place,
bool is_memory_load) {
framework::Executor exe(place);
return Load(&exe, scope, program_path, params_path);
return Load(&exe, scope, program_path, params_path, is_memory_load);
}
std::string IrGraphBuildPass::repr() const { return "ir-graph-build-pass"; }
......
......@@ -24,7 +24,7 @@ namespace inference {
namespace analysis {
/*
* Load program and parameter to memory from the disk.
* Load program and parameter to memory from the disk or directly from memory.
*/
class IrGraphBuildPass : public AnalysisPass {
public:
......@@ -38,7 +38,8 @@ class IrGraphBuildPass : public AnalysisPass {
const platform::Place &place);
std::unique_ptr<framework::ProgramDesc> LoadModel(
const std::string &program_path, const std::string &params_path,
framework::Scope *scope, const platform::Place &place);
framework::Scope *scope, const platform::Place &place,
bool is_memory_load);
std::string model_binary_str_;
};
......
......@@ -53,6 +53,7 @@ contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
use_tensorrt_ = other.use_tensorrt_;
tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
is_memory_load_ = other.is_memory_load_;
if (use_gpu) {
pass_builder_.reset(new GpuPassStrategy(
......@@ -80,6 +81,8 @@ contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
use_tensorrt_ = other.use_tensorrt_;
tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
is_memory_load_ = other.is_memory_load_;
pass_builder_ = std::move(other.pass_builder_);
}
......@@ -102,4 +105,12 @@ void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
pass_builder()->InsertPass(1, "tensorrt_subgraph_pass");
}
void contrib::AnalysisConfig::SetProgBufferAndParamBuffer(
const char *prog_buffer, size_t prog_buffer_size, const char *param_buffer,
size_t param_buffer_size) {
prog_file = std::string(prog_buffer, prog_buffer + prog_buffer_size);
param_file = std::string(param_buffer, param_buffer + param_buffer_size);
is_memory_load_ = true;
}
} // namespace paddle
......@@ -304,20 +304,20 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
// NOTE All the members in AnalysisConfig should be copied to Argument.
void AnalysisPredictor::OptimizeInferenceProgram() {
LOG(INFO) << "optimization program";
status_program_optimized_ = true;
argument_.SetUseGPU(config_.use_gpu);
argument_.SetGPUDeviceId(config_.device);
argument_.SetIsMemoryLoad(config_.is_memory_load_);
// Analyze inference_program
if (!config_.model_dir.empty()) {
argument_.SetModelDir(config_.model_dir);
} else {
PADDLE_ENFORCE(
!config_.param_file.empty(),
"Either model_dir or (param_file, prog_file) should be set.");
PADDLE_ENFORCE(!config_.prog_file.empty());
} else if (!config_.param_file.empty() && !config_.prog_file.empty()) {
argument_.SetModelProgramPath(config_.prog_file);
argument_.SetModelParamsPath(config_.param_file);
} else {
PADDLE_THROW("Either model_dir or (param_file, prog_file) should be set.");
}
if (config_.use_gpu && config_.use_tensorrt_) {
......@@ -448,20 +448,23 @@ bool AnalysisPredictor::LoadProgramDesc() {
return false;
}
// Create ProgramDesc
framework::proto::ProgramDesc proto;
if (!config_.is_memory_load()) {
std::string pb_content;
// Read binary
std::ifstream fin(filename, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
fin.seekg(0, std::ios::end);
pb_content.resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(pb_content.at(0)), pb_content.size());
fin.close();
// Create ProgramDesc
framework::proto::ProgramDesc proto;
proto.ParseFromString(pb_content);
} else {
proto.ParseFromString(config_.prog_file);
}
inference_program_.reset(new framework::ProgramDesc(proto));
return true;
}
......@@ -469,6 +472,7 @@ bool AnalysisPredictor::LoadProgramDesc() {
bool AnalysisPredictor::LoadParameters() {
PADDLE_ENFORCE_NOT_NULL(inference_program_.get(),
"The inference program should be loaded first.");
const auto &global_block = inference_program_->MutableBlock(0);
// create a temporary program to load parameters.
......
......@@ -52,10 +52,15 @@ struct AnalysisConfig : public NativeConfig {
bool use_tensorrt() const { return use_tensorrt_; }
void EnableMKLDNN();
// NOTE this is just for internal development, please not use it.
// NOT stable yet.
bool use_mkldnn() const { return use_mkldnn_; }
// Specify the memory buffer of program and parameter
void SetProgBufferAndParamBuffer(const char* prog_buffer,
size_t prog_buffer_size,
const char* program_buffer,
size_t program_buffer_size);
bool is_memory_load() const { return is_memory_load_; }
friend class ::paddle::AnalysisPredictor;
protected:
......@@ -64,6 +69,7 @@ struct AnalysisConfig : public NativeConfig {
int tensorrt_workspace_size_;
int tensorrt_max_batchsize_;
std::unique_ptr<PassStrategy> pass_builder_;
bool is_memory_load_{false};
};
// Configurations for Anakin engine.
......
......@@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/operators/impl/load_combine.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/pybind/pybind.h"
......@@ -69,7 +70,8 @@ bool IsPersistable(const framework::VarDesc* var) {
void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
const framework::ProgramDesc& main_program,
const std::string& dirname,
const std::string& param_filename) {
const std::string& param_filename,
bool is_memory_load = false) {
const framework::BlockDesc& global_block = main_program.Block(0);
framework::ProgramDesc* load_program = new framework::ProgramDesc();
......@@ -108,6 +110,7 @@ void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
op->SetType("load_combine");
op->SetOutput("Out", paramlist);
op->SetAttr("file_path", {param_filename});
op->SetAttr("is_memory_load", {is_memory_load});
op->CheckAttrs();
}
......@@ -130,16 +133,23 @@ std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
"model version %ld is not supported.",
main_program->Version());
LoadPersistables(executor, scope, *main_program, dirname, "");
// is_memory_load is false in seperate parameters.
LoadPersistables(executor, scope, *main_program, dirname, "",
false /* is_memory_load */);
return main_program;
}
std::unique_ptr<framework::ProgramDesc> Load(
framework::Executor* executor, framework::Scope* scope,
const std::string& prog_filename, const std::string& param_filename) {
std::string model_filename = prog_filename;
std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
framework::Scope* scope,
const std::string& prog_filename,
const std::string& param_filename,
bool is_memory_load = false) {
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
if (!is_memory_load) {
ReadBinaryFile(prog_filename, &program_desc_str);
} else {
program_desc_str = prog_filename;
}
std::unique_ptr<framework::ProgramDesc> main_program(
new framework::ProgramDesc(program_desc_str));
......@@ -147,10 +157,18 @@ std::unique_ptr<framework::ProgramDesc> Load(
"model version %ld is not supported.",
main_program->Version());
LoadPersistables(executor, scope, *main_program, "", param_filename);
LoadPersistables(executor, scope, *main_program, "", param_filename,
is_memory_load);
return main_program;
}
std::unique_ptr<framework::ProgramDesc> Load(
framework::Executor* executor, framework::Scope* scope,
const std::string& prog_filename, const std::string& param_filename) {
return Load(executor, scope, prog_filename, param_filename,
false /* is_memory_load */);
}
void SaveVars(const framework::Scope& scope,
const std::vector<std::string>& vars, const std::string& dirname,
bool predicate) {
......
......@@ -30,7 +30,7 @@ void Init(const std::vector<std::string> argv);
void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
const framework::ProgramDesc& main_program,
const std::string& dirname,
const std::string& param_filename);
const std::string& param_filename, bool is_memory_load);
std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
framework::Scope* scope,
......@@ -41,6 +41,12 @@ std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
const std::string& prog_filename,
const std::string& param_filename);
std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
framework::Scope* scope,
const std::string& prog_filename,
const std::string& param_filename,
bool is_memory_load);
// Save the variables from a scope to disk.
void SaveVars(const framework::Scope& scope,
const std::vector<std::string>& vars, const std::string& dirname,
......
......@@ -93,9 +93,17 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
void SetConfig(contrib::AnalysisConfig *cfg, bool memory_load = false) {
if (memory_load) {
std::string buffer_prog, buffer_param;
ReadBinaryFile(FLAGS_infer_model + "/__model__", &buffer_prog);
ReadBinaryFile(FLAGS_infer_model + "/param", &buffer_param);
cfg->SetProgBufferAndParamBuffer(&buffer_prog[0], buffer_prog.size(),
&buffer_param[0], buffer_param.size());
} else {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
}
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
......@@ -114,9 +122,9 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
}
// Easy for profiling independently.
TEST(Analyzer_Chinese_ner, profile) {
void profile(bool memory_load = false) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
SetConfig(&cfg, memory_load);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
......@@ -138,6 +146,12 @@ TEST(Analyzer_Chinese_ner, profile) {
}
}
TEST(Analyzer_Chinese_ner, profile) { profile(); }
TEST(Analyzer_Chinese_ner, profile_memory_load) {
profile(true /* memory_load */);
}
// Check the fuse status
TEST(Analyzer_Chinese_ner, fuse_statis) {
contrib::AnalysisConfig cfg;
......
......@@ -49,8 +49,6 @@ std::ostream &operator<<(std::ostream &os, const NativeConfig &config) {
os << GenSpaces(num_spaces) << "device: " << config.device << "\n";
os << GenSpaces(num_spaces)
<< "fraction_of_gpu_memory: " << config.fraction_of_gpu_memory << "\n";
os << GenSpaces(num_spaces) << "prog_file: " << config.prog_file << "\n";
os << GenSpaces(num_spaces) << "param_file: " << config.param_file << "\n";
os << GenSpaces(num_spaces)
<< "specify_input_name: " << config.specify_input_name << "\n";
os << GenSpaces(num_spaces)
......@@ -65,6 +63,13 @@ std::ostream &operator<<(std::ostream &os,
os << GenSpaces(num_spaces) << "contrib::AnalysisConfig {\n";
num_spaces++;
os << *reinterpret_cast<const NativeConfig *>(&config);
if (!config.is_memory_load()) {
os << GenSpaces(num_spaces) << "prog_file: " << config.prog_file << "\n";
os << GenSpaces(num_spaces) << "param_file: " << config.param_file << "\n";
} else {
os << GenSpaces(num_spaces)
<< "prog_file and param_file: load from memory \n";
}
os << GenSpaces(num_spaces) << "enable_ir_optim: " << config.enable_ir_optim
<< "\n";
os << GenSpaces(num_spaces)
......
cc_library(load_combine_impl SRCS load_combine.cc DEPS scope lod_tensor device_context op_registry data_type_transform)
// 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/operators/impl/load_combine.h"
namespace paddle {
namespace operators {
namespace impl {
void LoadParamsFromStream(const std::vector<std::string> &out_var_names,
const paddle::platform::Place &place,
bool load_as_fp16, std::istream *buffer,
const paddle::framework::Scope *scope) {
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
for (size_t i = 0; i < out_var_names.size(); i++) {
auto *out_var = scope->FindVar(out_var_names[i]);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_names[i]);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, *dev_ctx);
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(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
}
} // namespace impl
} // namespace operators
} // 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 <string>
#include <vector>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
namespace impl {
// Load parameters from a single stream.
void LoadParamsFromStream(const std::vector<std::string> &out_var_names,
const platform::Place &place, bool load_as_fp16,
std::istream *buffer, const framework::Scope *scope);
} // namespace impl
} // namespace operators
} // namespace paddle
......@@ -32,16 +32,26 @@ class LoadCombineOp : public framework::OperatorBase {
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),
"Cannot open file %s for load_combine op", filename);
auto is_memory_load = Attr<bool>("is_memory_load");
auto out_var_names = Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
if (!is_memory_load) {
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::Scope &scope, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
......@@ -54,11 +64,10 @@ class LoadCombineOp : public framework::OperatorBase {
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot read more from file %s",
filename);
PADDLE_ENFORCE(static_cast<bool>(buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(fin, tensor, dev_ctx);
DeserializeFromStream(*buffer, tensor, dev_ctx);
auto in_dtype = framework::ToDataType(tensor->type());
auto out_dtype =
......@@ -103,11 +112,17 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"LoDTensors will be loaded from \"file_path\".")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<bool>("is_memory_load",
"(boolean, default false)"
"If true, file_path is in memory, and LoDTensors will be "
"loaded directly from memory")
.SetDefault(false);
AddComment(R"DOC(
LoadCombine Operator.
LoadCombine operator loads LoDTensor variables from a file. The file should
contain one or more LoDTensors serialized using the SaveCombine operator. The
LoadCombine operator loads LoDTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
serialized using the SaveCombine operator. The
LoadCombine operator applies a deserialization strategy to appropriately load
the LodTensors, and this strategy complements the serialization strategy used
in the SaveCombine operator. Hence, the LoadCombine operator is tightly coupled
......
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