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// 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.
/*
* This file defines the class Argument, which is the input and output of the
* analysis module. All the fields that needed either by Passes or PassManagers
* are contained in Argument.
*
* TODO(Superjomn) Find some way better to contain the fields when it grow too
* big.
*/
#pragma once
#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_analysis_config.h"
#include "paddle/fluid/platform/variant.h"
namespace paddle {
namespace inference {
namespace analysis {
using framework::ir::Graph;
#ifdef PADDLE_WITH_MKLDNN
using VarQuantScale =
std::unordered_map<std::string, std::pair<bool, framework::LoDTensor>>;
#endif
/*
* The argument definition of both Pass and PassManagers.
*
* All the fields should be registered here for clearness.
*/
struct Argument {
Argument() = default;
explicit Argument(const std::string& model_dir) { SetModelDir(model_dir); }
using unique_ptr_t = std::unique_ptr<void, std::function<void(void*)>>;
using fusion_statis_t = std::unordered_map<std::string, int>;
using input_shape_t = std::map<std::string, std::vector<int>>;
bool Has(const std::string& key) const { return valid_fields_.count(key); }
// If we set the model using config.SetModelBuffer,
// the model and parameter will occupy additional CPU resources.
// Use this interface to release these resources.
void PartiallyRelease() {
if (Has("model_program_path")) {
if (Has("model_from_memory") && model_from_memory()) {
model_program_path().clear();
model_program_path().shrink_to_fit();
model_params_path().clear();
model_params_path().shrink_to_fit();
}
}
}
#define DECL_ARGUMENT_FIELD(field__, Field, type__) \
public: \
type__& field__() { \
PADDLE_ENFORCE_EQ( \
Has(#field__), true, \
platform::errors::PreconditionNotMet("There is no such field")); \
return field__##_; \
} \
void Set##Field(const type__& x) { \
field__##_ = x; \
valid_fields_.insert(#field__); \
} \
DECL_ARGUMENT_FIELD_VALID(field__); \
type__* field__##_ptr() { return &field__##_; } \
\
private: \
type__ field__##_;
#define DECL_ARGUMENT_FIELD_VALID(field__) \
bool field__##_valid() { return Has(#field__); }
#define DECL_ARGUMENT_UNIQUE_FIELD(field__, Field, type__) \
public: \
type__& field__() { \
PADDLE_ENFORCE_NOT_NULL(field__##_, platform::errors::PreconditionNotMet( \
"filed should not be null.")); \
PADDLE_ENFORCE_EQ( \
Has(#field__), true, \
platform::errors::PreconditionNotMet("There is no such field")); \
return *static_cast<type__*>(field__##_.get()); \
} \
void Set##Field(type__* x) { \
field__##_ = \
unique_ptr_t(x, [](void* x) { delete static_cast<type__*>(x); }); \
valid_fields_.insert(#field__); \
} \
void Set##Field##NotOwned(type__* x) { \
valid_fields_.insert(#field__); \
field__##_ = unique_ptr_t(x, [](void* x) {}); \
} \
DECL_ARGUMENT_FIELD_VALID(field__); \
type__* field__##_ptr() { \
PADDLE_ENFORCE_EQ( \
Has(#field__), true, \
platform::errors::PreconditionNotMet("There is no such field")); \
return static_cast<type__*>(field__##_.get()); \
} \
type__* Release##Field() { \
PADDLE_ENFORCE_EQ( \
Has(#field__), true, \
platform::errors::PreconditionNotMet("There is no such field")); \
valid_fields_.erase(#field__); \
return static_cast<type__*>(field__##_.release()); \
} \
\
private: \
unique_ptr_t field__##_;
DECL_ARGUMENT_FIELD(predictor_id, PredictorID, int);
// Model path
DECL_ARGUMENT_FIELD(model_dir, ModelDir, std::string);
// 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(model_from_memory, ModelFromMemory, bool);
DECL_ARGUMENT_FIELD(optim_cache_dir, OptimCacheDir, std::string);
DECL_ARGUMENT_FIELD(enable_analysis_optim, EnableAnalysisOptim, bool);
// The overall graph to work on.
DECL_ARGUMENT_UNIQUE_FIELD(main_graph, MainGraph, framework::ir::Graph);
// The overall Scope to work on.
DECL_ARGUMENT_UNIQUE_FIELD(scope, Scope, framework::Scope);
// The default program, loaded from disk.
DECL_ARGUMENT_UNIQUE_FIELD(main_program, MainProgram, framework::ProgramDesc);
// The ir passes to perform in analysis phase.
DECL_ARGUMENT_FIELD(ir_analysis_passes, IrAnalysisPasses,
std::vector<std::string>);
DECL_ARGUMENT_FIELD(analysis_passes, AnalysisPasses,
std::vector<std::string>);
// whether to mute all logs in inference.
DECL_ARGUMENT_FIELD(disable_logs, DisableLogs, bool);
// Pass a set of op types to enable its mkldnn kernel
DECL_ARGUMENT_FIELD(mkldnn_enabled_op_types, MKLDNNEnabledOpTypes,
std::unordered_set<std::string>);
// The cache capacity of different input shapes for mkldnn.
DECL_ARGUMENT_FIELD(mkldnn_cache_capacity, MkldnnCacheCapacity, int);
#ifdef PADDLE_WITH_MKLDNN
// A set of op types to enable their quantized kernels
DECL_ARGUMENT_FIELD(quantize_enabled_op_types, QuantizeEnabledOpTypes,
std::unordered_set<std::string>);
// A set of op IDs to exclude from enabling their quantized kernels
DECL_ARGUMENT_FIELD(quantize_excluded_op_ids, QuantizeExcludedOpIds,
std::unordered_set<int>);
// Scales for variables to be quantized
DECL_ARGUMENT_FIELD(quant_var_scales, QuantVarScales, VarQuantScale);
// A set of op types to enable their bfloat16 kernels
DECL_ARGUMENT_FIELD(bfloat16_enabled_op_types, Bfloat16EnabledOpTypes,
std::unordered_set<std::string>);
#endif
// Passed from config.
DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
DECL_ARGUMENT_FIELD(use_fc_padding, UseFcPadding, bool);
DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
// Usually use for trt dynamic shape.
// TRT will select the best kernel according to opt shape
// Setting the disable_trt_plugin_fp16 to true means that TRT plugin will not
// run fp16.
DECL_ARGUMENT_FIELD(min_input_shape, MinInputShape, input_shape_t);
DECL_ARGUMENT_FIELD(max_input_shape, MaxInputShape, input_shape_t);
DECL_ARGUMENT_FIELD(optim_input_shape, OptimInputShape, input_shape_t);
DECL_ARGUMENT_FIELD(disable_trt_plugin_fp16, CloseTrtPluginFp16, bool);
DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
DECL_ARGUMENT_FIELD(tensorrt_use_dla, TensorRtUseDLA, bool);
DECL_ARGUMENT_FIELD(tensorrt_dla_core, TensorRtDLACore, int);
DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
DECL_ARGUMENT_FIELD(tensorrt_min_subgraph_size, TensorRtMinSubgraphSize, int);
DECL_ARGUMENT_FIELD(tensorrt_disabled_ops, TensorRtDisabledOPs,
std::vector<std::string>);
DECL_ARGUMENT_FIELD(tensorrt_precision_mode, TensorRtPrecisionMode,
AnalysisConfig::Precision);
DECL_ARGUMENT_FIELD(tensorrt_use_static_engine, TensorRtUseStaticEngine,
bool);
DECL_ARGUMENT_FIELD(tensorrt_use_calib_mode, TensorRtUseCalibMode, bool);
DECL_ARGUMENT_FIELD(tensorrt_use_oss, TensorRtUseOSS, bool);
DECL_ARGUMENT_FIELD(tensorrt_shape_range_info_path,
TensorRtShapeRangeInfoPath, std::string);
DECL_ARGUMENT_FIELD(tensorrt_tuned_dynamic_shape, TensorRtTunedDynamicShape,
bool);
DECL_ARGUMENT_FIELD(tensorrt_allow_build_at_runtime,
TensorRtAllowBuildAtRuntime, bool);
DECL_ARGUMENT_FIELD(use_dlnne, UseDlnne, bool);
DECL_ARGUMENT_FIELD(dlnne_min_subgraph_size, DlnneMinSubgraphSize, int);
DECL_ARGUMENT_FIELD(dlnne_max_batch_size, DlnneMaxBatchSize, int);
DECL_ARGUMENT_FIELD(dlnne_workspace_size, DlnneWorkspaceSize, int);
DECL_ARGUMENT_FIELD(lite_passes_filter, LitePassesFilter,
std::vector<std::string>);
DECL_ARGUMENT_FIELD(lite_ops_filter, LiteOpsFilter, std::vector<std::string>);
DECL_ARGUMENT_FIELD(lite_precision_mode, LitePrecisionMode,
AnalysisConfig::Precision);
DECL_ARGUMENT_FIELD(lite_zero_copy, LiteZeroCopy, bool);
DECL_ARGUMENT_FIELD(use_xpu, UseXpu, bool);
DECL_ARGUMENT_FIELD(xpu_l3_workspace_size, XpuL3WorkspaceSize, int);
DECL_ARGUMENT_FIELD(xpu_locked, XpuLocked, bool);
DECL_ARGUMENT_FIELD(xpu_autotune, XpuAutotune, bool);
DECL_ARGUMENT_FIELD(xpu_autotune_file, XpuAutotuneFile, std::string);
DECL_ARGUMENT_FIELD(xpu_precision, XpuPrecision, std::string);
DECL_ARGUMENT_FIELD(xpu_adaptive_seqlen, XpuAdaptiveSeqlen, bool);
// Memory optimized related.
DECL_ARGUMENT_FIELD(enable_memory_optim, EnableMemoryOptim, bool);
// Indicate which kind of sort algorithm is used for operators, the memory
// optimization relays on the sort algorithm.
DECL_ARGUMENT_FIELD(memory_optim_sort_kind, MemoryOptimSortKind, int);
// The program transformed by IR analysis phase.
DECL_ARGUMENT_UNIQUE_FIELD(ir_analyzed_program, IrAnalyzedProgram,
framework::proto::ProgramDesc);
DECL_ARGUMENT_FIELD(fusion_statis, FusionStatis, fusion_statis_t);
// Only used in paddle-lite subgraph.
DECL_ARGUMENT_FIELD(cpu_math_library_num_threads, CpuMathLibraryNumThreads,
int);
private:
std::unordered_set<std::string> valid_fields_;
};
#define ARGUMENT_CHECK_FIELD(argument__, fieldname__) \
PADDLE_ENFORCE_EQ( \
argument__->Has(#fieldname__), true, \
platform::errors::PreconditionNotMet( \
"the argument field [%s] should be set", #fieldname__));
} // namespace analysis
} // namespace inference
} // namespace paddle