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@ -33,6 +33,7 @@
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#include "paddle/fluid/inference/utils/singleton.h"
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#include "paddle/fluid/memory/memcpy.h"
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#include "paddle/fluid/platform/cpu_helper.h"
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#include "paddle/fluid/platform/gpu_info.h"
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#include "paddle/fluid/platform/profiler.h"
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DECLARE_bool(profile);
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@ -59,8 +60,8 @@ bool AnalysisPredictor::Init(
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if (FLAGS_profile) {
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LOG(WARNING) << "Profiler is actived, might affect the performance";
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LOG(INFO) << "You can turn off by set gflags '-profile false'";
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auto tracking_device = config_.use_gpu ? platform::ProfilerState::kAll
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: platform::ProfilerState::kCPU;
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auto tracking_device = config_.use_gpu() ? platform::ProfilerState::kAll
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: platform::ProfilerState::kCPU;
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platform::EnableProfiler(tracking_device);
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}
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@ -112,7 +113,7 @@ bool AnalysisPredictor::PrepareProgram(
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// Optimize the program, and load parameters and modify them in the
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// scope_.
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// This will change the scope_ address.
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if (config_.enable_ir_optim) {
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if (config_.ir_optim()) {
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status_ir_optim_enabled_ = true;
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OptimizeInferenceProgram();
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} else {
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@ -140,9 +141,9 @@ bool AnalysisPredictor::PrepareProgram(
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return true;
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}
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bool AnalysisPredictor::CreateExecutor() {
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if (config_.use_gpu) {
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if (config_.use_gpu_) {
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status_use_gpu_ = true;
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place_ = paddle::platform::CUDAPlace(config_.device);
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place_ = paddle::platform::CUDAPlace(config_.device_id_);
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} else {
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place_ = paddle::platform::CPUPlace();
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}
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@ -151,7 +152,7 @@ bool AnalysisPredictor::CreateExecutor() {
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}
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bool AnalysisPredictor::PrepareExecutor() {
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executor_->Prepare(sub_scope_, *inference_program_, 0,
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config_.use_feed_fetch_ops);
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config_.use_feed_fetch_ops_);
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PADDLE_ENFORCE_NOT_NULL(sub_scope_);
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@ -250,7 +251,7 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
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}
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input.set_lod(lod);
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int idx = -1;
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if (config_.specify_input_name) {
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if (config_.specify_input_name_) {
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auto name = inputs[i].name;
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if (feed_names_.find(name) == feed_names_.end()) {
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LOG(ERROR) << "feed names from program do not have name: [" << name
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@ -314,22 +315,22 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
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void AnalysisPredictor::OptimizeInferenceProgram() {
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status_program_optimized_ = true;
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argument_.SetUseGPU(config_.use_gpu);
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argument_.SetGPUDeviceId(config_.device);
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argument_.SetUseGPU(config_.use_gpu());
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argument_.SetGPUDeviceId(config_.gpu_device_id());
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argument_.SetModelFromMemory(config_.model_from_memory_);
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// Analyze inference_program
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if (!config_.model_dir.empty()) {
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argument_.SetModelDir(config_.model_dir);
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if (!config_.model_dir().empty()) {
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argument_.SetModelDir(config_.model_dir());
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} else {
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PADDLE_ENFORCE(
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!config_.param_file.empty(),
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!config_.params_file().empty(),
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"Either model_dir or (param_file, prog_file) should be set.");
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PADDLE_ENFORCE(!config_.prog_file.empty());
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argument_.SetModelProgramPath(config_.prog_file);
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argument_.SetModelParamsPath(config_.param_file);
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PADDLE_ENFORCE(!config_.prog_file().empty());
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argument_.SetModelProgramPath(config_.prog_file());
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argument_.SetModelParamsPath(config_.params_file());
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}
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if (config_.use_gpu && config_.use_tensorrt_) {
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if (config_.use_gpu() && config_.tensorrt_engine_enabled()) {
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argument_.SetUseTensorRT(true);
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argument_.SetTensorRtWorkspaceSize(config_.tensorrt_workspace_size_);
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argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
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@ -341,7 +342,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
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}
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auto passes = config_.pass_builder()->AllPasses();
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if (!config_.enable_ir_optim) passes.clear();
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if (!config_.ir_optim()) passes.clear();
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argument_.SetIrAnalysisPasses(passes);
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argument_.SetScopeNotOwned(const_cast<framework::Scope *>(scope_.get()));
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Analyzer().Run(&argument_);
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@ -358,18 +359,26 @@ template <>
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std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
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AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
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VLOG(3) << "create AnalysisConfig";
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if (config.use_gpu) {
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if (config.use_gpu()) {
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// 1. GPU memeroy
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PADDLE_ENFORCE_GT(
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config.fraction_of_gpu_memory, 0.f,
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"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
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PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
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PADDLE_ENFORCE_GT(config.memory_pool_init_size_mb(), 0.f);
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PADDLE_ENFORCE_GE(config.gpu_device_id(), 0, "Invalid device id %d",
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config.gpu_device_id());
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std::vector<std::string> flags;
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if (config.fraction_of_gpu_memory >= 0.0f ||
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config.fraction_of_gpu_memory <= 0.95f) {
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float fraction_of_gpu_memory = config.fraction_of_gpu_memory_for_pool();
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if (fraction_of_gpu_memory > 0.95f) {
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LOG(ERROR)
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<< "Allocate too much memory for the GPU memory pool, assigned "
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<< config.memory_pool_init_size_mb() << " MB";
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LOG(ERROR)
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<< "Try to shink the value by setting AnalysisConfig::EnableGpu(...)";
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}
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if (fraction_of_gpu_memory >= 0.0f || fraction_of_gpu_memory <= 0.95f) {
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flags.push_back("dummpy");
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std::string flag = "--fraction_of_gpu_memory_to_use=" +
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std::to_string(config.fraction_of_gpu_memory);
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std::to_string(fraction_of_gpu_memory);
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flags.push_back(flag);
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VLOG(3) << "set flag: " << flag;
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framework::InitGflags(flags);
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@ -443,22 +452,22 @@ bool AnalysisPredictor::ZeroCopyRun() {
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bool AnalysisPredictor::LoadProgramDesc() {
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// Initialize the inference program
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std::string filename;
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if (!config_.model_dir.empty()) {
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filename = config_.model_dir + "/__model__";
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} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
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if (!config_.model_dir().empty()) {
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filename = config_.model_dir() + "/__model__";
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} else if (!config_.prog_file().empty() && !config_.params_file().empty()) {
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// All parameters are saved in a single file.
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// The file names should be consistent with that used
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// in Python API `fluid.io.save_inference_model`.
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filename = config_.prog_file;
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filename = config_.prog_file();
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} else {
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if (config_.model_dir.empty() && config_.prog_file.empty()) {
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if (config_.model_dir().empty() && config_.prog_file().empty()) {
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LOG(ERROR)
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<< "Either model_dir or (prog_file, param_file) should be set.";
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return false;
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}
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LOG(ERROR) << string::Sprintf(
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"not valid model path '%s' or program path '%s'.", config_.model_dir,
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config_.param_file);
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"not valid model path '%s' or program path '%s'.", config_.model_dir(),
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config_.params_file());
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return false;
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}
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@ -478,7 +487,7 @@ bool AnalysisPredictor::LoadProgramDesc() {
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proto.ParseFromString(pb_content);
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} else {
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proto.ParseFromString(config_.prog_file);
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proto.ParseFromString(config_.prog_file());
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}
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inference_program_.reset(new framework::ProgramDesc(proto));
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return true;
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@ -508,27 +517,27 @@ bool AnalysisPredictor::LoadParameters() {
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new_var->SetLoDLevel(var->GetLoDLevel());
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new_var->SetPersistable(true);
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if (!config_.param_file.empty()) {
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if (!config_.params_file().empty()) {
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params.push_back(new_var->Name());
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} else {
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// append_op
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framework::OpDesc *op = load_block->AppendOp();
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op->SetType("load");
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op->SetOutput("Out", {new_var->Name()});
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op->SetAttr("file_path", {config_.model_dir + "/" + new_var->Name()});
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op->SetAttr("file_path", {config_.model_dir() + "/" + new_var->Name()});
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op->CheckAttrs();
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}
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}
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}
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if (!config_.param_file.empty()) {
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if (!config_.params_file().empty()) {
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// sort paramlist to have consistent ordering
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std::sort(params.begin(), params.end());
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// append just the load_combine op
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framework::OpDesc *op = load_block->AppendOp();
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op->SetType("load_combine");
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op->SetOutput("Out", params);
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op->SetAttr("file_path", {config_.param_file});
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op->SetAttr("file_path", {config_.params_file()});
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op->CheckAttrs();
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}
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Yan Chunwei
6 years ago
committed by
GitHub