// 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_pass_builder.h" // NOLINT namespace paddle { PassStrategy *contrib::AnalysisConfig::pass_builder() const { PADDLE_ENFORCE( pass_builder_.get(), "Should call constructor first, that will init the pass_builder_."); return pass_builder_.get(); } contrib::AnalysisConfig::AnalysisConfig(bool use_gpu) { this->use_gpu = use_gpu; if (use_gpu) { pass_builder_.reset(new GpuPassStrategy); } else { pass_builder_.reset(new CpuPassStrategy); } } contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) { // fields from Config model_dir = other.model_dir; // fields from NativeConfig use_gpu = other.use_gpu; device = other.device; fraction_of_gpu_memory = other.fraction_of_gpu_memory; prog_file = other.prog_file; param_file = other.param_file; specify_input_name = other.specify_input_name; cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_; // fields from this. enable_ir_optim = other.enable_ir_optim; // For mkldnn use_mkldnn_ = other.use_mkldnn_; mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_; use_feed_fetch_ops = other.use_feed_fetch_ops; use_tensorrt_ = other.use_tensorrt_; tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_; tensorrt_workspace_size_ = other.tensorrt_workspace_size_; tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_; model_from_memory_ = other.model_from_memory_; if (use_gpu) { pass_builder_.reset(new GpuPassStrategy( *static_cast(other.pass_builder()))); } else { pass_builder_.reset(new CpuPassStrategy( *static_cast(other.pass_builder()))); } } contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) { // fields from Config model_dir = other.model_dir; // fields from NativeConfig use_gpu = other.use_gpu; device = other.device; fraction_of_gpu_memory = other.fraction_of_gpu_memory; prog_file = other.prog_file; param_file = other.param_file; specify_input_name = other.specify_input_name; cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_; // fields from this. enable_ir_optim = other.enable_ir_optim; // For mkldnn use_mkldnn_ = other.use_mkldnn_; mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_; use_feed_fetch_ops = other.use_feed_fetch_ops; use_tensorrt_ = other.use_tensorrt_; tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_; tensorrt_workspace_size_ = other.tensorrt_workspace_size_; tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_; model_from_memory_ = other.model_from_memory_; pass_builder_ = std::move(other.pass_builder_); } void contrib::AnalysisConfig::EnableMKLDNN() { #ifdef PADDLE_WITH_MKLDNN pass_builder()->EnableMKLDNN(); use_mkldnn_ = true; #else LOG(ERROR) << "Please compile with MKLDNN first to use MKLDNN"; use_mkldnn_ = false; #endif } void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size, int max_batch_size, int min_subgraph_size) { use_tensorrt_ = true; tensorrt_workspace_size_ = workspace_size; tensorrt_max_batchsize_ = max_batch_size; tensorrt_min_subgraph_size_ = min_subgraph_size; // Append after the conv+affine_channel fuse pass. pass_builder()->InsertPass(3, "tensorrt_subgraph_pass"); } void contrib::AnalysisConfig::SetModelBuffer(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); model_from_memory_ = true; } } // namespace paddle