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d09d6ead
编写于
1月 08, 2019
作者:
Y
Yan Chunwei
提交者:
GitHub
1月 08, 2019
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电子邮件补丁
差异文件
make inference api work with Doxygen (#15195)
上级
c562be20
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
227 addition
and
97 deletion
+227
-97
paddle/fluid/inference/api/analysis_predictor.h
paddle/fluid/inference/api/analysis_predictor.h
+5
-2
paddle/fluid/inference/api/api_impl.h
paddle/fluid/inference/api/api_impl.h
+0
-1
paddle/fluid/inference/api/paddle_analysis_config.h
paddle/fluid/inference/api/paddle_analysis_config.h
+96
-7
paddle/fluid/inference/api/paddle_api.h
paddle/fluid/inference/api/paddle_api.h
+104
-72
paddle/fluid/inference/api/paddle_pass_builder.h
paddle/fluid/inference/api/paddle_pass_builder.h
+22
-15
未找到文件。
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @
d09d6ead
...
...
@@ -35,8 +35,11 @@ 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.
/** \brief 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
PaddlePredictor
{
...
...
paddle/fluid/inference/api/api_impl.h
浏览文件 @
d09d6ead
...
...
@@ -19,7 +19,6 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
...
...
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @
d09d6ead
...
...
@@ -19,6 +19,8 @@
#include <unordered_set>
#include <vector>
/*! \file */
// Here we include some header files with relative paths, for that in deploy,
// the abstract path of this header file will be changed.
#include "paddle_api.h" // NOLINT
...
...
@@ -41,49 +43,125 @@ struct AnalysisConfig {
explicit
AnalysisConfig
(
const
std
::
string
&
prog_file
,
const
std
::
string
&
params_file
);
// Model path related.
/** Set model with a directory.
*/
void
SetModel
(
const
std
::
string
&
model_dir
)
{
model_dir_
=
model_dir
;
}
/** Set model with two specific pathes for program and parameters.
*/
void
SetModel
(
const
std
::
string
&
prog_file_path
,
const
std
::
string
&
params_file_path
);
/** Set program file path.
*/
void
SetProgFile
(
const
std
::
string
&
x
)
{
prog_file_
=
x
;
}
/** Set parameter composed file path.
*/
void
SetParamsFile
(
const
std
::
string
&
x
)
{
params_file_
=
x
;
}
/** Get the model directory path.
*/
const
std
::
string
&
model_dir
()
const
{
return
model_dir_
;
}
/** Get the program file path.
*/
const
std
::
string
&
prog_file
()
const
{
return
prog_file_
;
}
/** Get the composed parameters file.
*/
const
std
::
string
&
params_file
()
const
{
return
params_file_
;
}
// GPU related.
/**
* \brief Turn on GPU.
* @param memory_pool_init_size_mb initial size of the GPU memory pool in MB.
* @param device_id the GPU card to use (default is 0).
*/
void
EnableUseGpu
(
uint64_t
memory_pool_init_size_mb
,
int
device_id
=
0
);
/** Turn off the GPU.
*/
void
DisableGpu
();
/** A bool state telling whether the GPU is turned on.
*/
bool
use_gpu
()
const
{
return
use_gpu_
;
}
/** Get the GPU device id.
*/
int
gpu_device_id
()
const
{
return
device_id_
;
}
/** Get the initial size in MB of the GPU memory pool.
*/
int
memory_pool_init_size_mb
()
const
{
return
memory_pool_init_size_mb_
;
}
/** Get the proportion of the initial memory pool size compared to the device.
*/
float
fraction_of_gpu_memory_for_pool
()
const
;
// Determine whether to perform graph optimization.
/** \brief Control whether to perform IR graph optimization.
*
* If turned off, the AnalysisConfig will act just like a NativeConfig.
*/
void
SwitchIrOptim
(
int
x
=
true
)
{
enable_ir_optim_
=
x
;
}
/** A boolean state tell whether the ir graph optimization is actived.
*/
bool
ir_optim
()
const
{
return
enable_ir_optim_
;
}
/** \brief INTERNAL Determine whether to use the feed and fetch operators.
* Just for internal development, not stable yet.
* When ZeroCopyTensor is used, this should turned off.
*/
void
SwitchUseFeedFetchOps
(
int
x
=
true
)
{
use_feed_fetch_ops_
=
x
;
}
/** A boolean state telling whether to use the feed and fetch operators.
*/
bool
use_feed_fetch_ops_enabled
()
const
{
return
use_feed_fetch_ops_
;
}
/** \brief Control whether to specify the inputs' names.
*
* The PaddleTensor type has a `name` member, assign it with the corresponding
* variable name. This is used only when the input PaddleTensors passed to the
* `PaddlePredictor.Run(...)` cannot follow the order in the training phase.
*/
void
SwitchSpecifyInputNames
(
bool
x
=
true
)
{
specify_input_name_
=
x
;
}
/** A boolean state tell whether the input PaddleTensor names specified should
* be used to reorder the inputs in `PaddlePredictor.Run(...)`.
*/
bool
specify_input_name
()
const
{
return
specify_input_name_
;
}
/**
* \brief Turn on the TensorRT engine.
*
* The TensorRT engine will accelerate some subgraphes in the original Fluid
* computation graph. In some models such as TensorRT50, GoogleNet and so on,
* it gains significant performance acceleration.
*
* @param workspace_size the memory size(in byte) used for TensorRT workspace.
* @param max_batch_size the maximum batch size of this prediction task,
* better set as small as possible, or performance loss.
* @param min_subgrpah_size the minimum TensorRT subgraph size needed, if a
* subgraph is less than this, it will not transfer to TensorRT engine.
*/
void
EnableTensorRtEngine
(
int
workspace_size
=
1
<<
20
,
int
max_batch_size
=
1
,
int
min_subgraph_size
=
3
);
/** A boolean state telling whether the TensorRT engine is used.
*/
bool
tensorrt_engine_enabled
()
const
{
return
use_tensorrt_
;
}
/** Control whther to debug IR graph analysis phase.
*/
void
SwitchIrDebug
(
int
x
=
true
)
{
ir_debug_
=
x
;
}
/** Turn on MKLDNN.
*/
void
EnableMKLDNN
();
/** A boolean state telling whether to use the MKLDNN.
*/
bool
mkldnn_enabled
()
const
{
return
use_mkldnn_
;
}
// Set and get the number of cpu math library threads.
/** Set and get the number of cpu math library threads.
*/
void
SetCpuMathLibraryNumThreads
(
int
cpu_math_library_num_threads
);
/** An int state telling how many threads are used in the CPU math library.
*/
int
cpu_math_library_num_threads
()
const
{
return
cpu_math_library_num_threads_
;
}
/** Transform the AnalysisConfig to NativeConfig.
*/
NativeConfig
ToNativeConfig
()
const
{
NativeConfig
config
;
config
.
model_dir
=
model_dir_
;
...
...
@@ -95,19 +173,30 @@ struct AnalysisConfig {
config
.
specify_input_name
=
specify_input_name_
;
return
config
;
}
/** Specify the operator type list to use MKLDNN acceleration.
* @param op_list the operator type list.
*/
void
SetMKLDNNOp
(
std
::
unordered_set
<
std
::
string
>
op_list
)
{
mkldnn_enabled_op_types_
=
op_list
;
}
// Specify the memory buffer of program and parameter
/** Specify the memory buffer of program and parameter
* @param prog_buffer the memory buffer of program.
* @param prog_buffer_size the size of the data.
* @param params_buffer the memory buffer of the composed parameters file.
* @param params_buffer_size the size of the commposed parameters data.
*/
void
SetModelBuffer
(
const
char
*
prog_buffer
,
size_t
prog_buffer_size
,
const
char
*
program_buffer
,
size_t
program_buffer_size
);
const
char
*
params_buffer
,
size_t
params_buffer_size
);
/** A boolean state telling whether the model is set from the CPU memory.
*/
bool
model_from_memory
()
const
{
return
model_from_memory_
;
}
friend
class
::
paddle
::
AnalysisPredictor
;
// NOTE just for developer, not an official API, easily to be broken.
// Get a pass builder for customize the passes in IR analysis phase.
/** NOTE just for developer, not an official API, easily to be broken.
* Get a pass builder for customize the passes in IR analysis phase.
*/
PassStrategy
*
pass_builder
()
const
;
protected:
...
...
paddle/fluid/inference/api/paddle_api.h
浏览文件 @
d09d6ead
...
...
@@ -13,61 +13,76 @@
// limitations under the License.
#pragma once
/*! \file paddle_api.h
*/
#include <cassert>
#include <memory>
#include <string>
#include <vector>
/*! \namespace paddle
*/
namespace
paddle
{
// Data type.
/** paddle data type.
*/
enum
PaddleDType
{
FLOAT32
,
INT64
,
// TODO(Superjomn) support more data types if needed.
};
/*
* Memory menage for PaddleTensor.
* The PaddleBuf holds a buffer for data input or output. The memory can be
* allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
* should be reused for better performance.
/**
*\brief Memory menager for PaddleTensor.
*
* For user allocated memory, the following API can be used:
* - PaddleBuf(void* data, size_t length) to set an external memory by
* specifying
* the memory address and length.
* - Reset(void* data, size_t length) to reset the PaddleBuf with an external
* memory.
* ATTENTION, for user allocated memory, deallocation should be done by users
* externally after the program finished. The PaddleBuf won't do any allocation
* or deallocation.
*The PaddleBuf holds a buffer for data input or output. The memory can be
*allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
*should be reused for better performance.
*
* To have the PaddleBuf allocate and manage the memory:
* - PaddleBuf(size_t length) will allocate a memory of size `length`.
* - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* if the allocated memory is larger than `length`, nothing will done.
*For user allocated memory, the following API can be used:
*- PaddleBuf(void* data, size_t length) to set an external memory by
*specifying
* the memory address and length.
*- Reset(void* data, size_t length) to reset the PaddleBuf with an external
*memory.
*ATTENTION, for user allocated memory, deallocation should be done by users
*externally after the program finished. The PaddleBuf won't do any allocation
*or deallocation.
*
*To have the PaddleBuf allocate and manage the memory:
*- PaddleBuf(size_t length) will allocate a memory of size `length`.
*- Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* if the allocated memory is larger than `length`, nothing will done.
*/
class
PaddleBuf
{
public:
// PaddleBuf allocate memory internally, and manage it.
/** PaddleBuf allocate memory internally, and manage it.
*/
explicit
PaddleBuf
(
size_t
length
)
:
data_
(
new
char
[
length
]),
length_
(
length
),
memory_owned_
(
true
)
{}
// Set external memory, the PaddleBuf won't manage it.
/** Set external memory, the PaddleBuf won't manage it.
*/
PaddleBuf
(
void
*
data
,
size_t
length
)
:
data_
(
data
),
length_
(
length
),
memory_owned_
{
false
}
{}
// Copy only available when memory is managed externally.
/** Copy only available when memory is managed externally.
*/
explicit
PaddleBuf
(
const
PaddleBuf
&
);
// Resize the memory.
/** Resize the memory.
*/
void
Resize
(
size_t
length
);
// Reset to external memory, with address and length set.
/** Reset to external memory, with address and length set.
*/
void
Reset
(
void
*
data
,
size_t
length
);
// Tell whether the buffer is empty.
/** Tell whether the buffer is empty.
*/
bool
empty
()
const
{
return
length_
==
0
;
}
// Get the memory address.
/** Get the memory address.
*/
void
*
data
()
const
{
return
data_
;
}
// Get the memory length.
/** Get the memory length.
*/
size_t
length
()
const
{
return
length_
;
}
~
PaddleBuf
()
{
Free
();
}
...
...
@@ -83,7 +98,8 @@ class PaddleBuf {
bool
memory_owned_
{
true
};
};
// Basic input and output data structure for PaddlePredictor.
/** Basic input and output data structure for PaddlePredictor.
*/
struct
PaddleTensor
{
PaddleTensor
()
=
default
;
std
::
string
name
;
// variable name.
...
...
@@ -94,19 +110,22 @@ struct PaddleTensor {
};
enum
class
PaddlePlace
{
kUNK
=
-
1
,
kCPU
,
kGPU
};
// Tensor without copy, currently only supports AnalysisPredictor.
/** 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.
/** 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.
/** 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
)
const
;
...
...
@@ -128,8 +147,7 @@ class ZeroCopyTensor {
void
*
scope_
{
nullptr
};
};
/*
* A simple Inference API for Paddle.
/** A simple Inference API for Paddle.
*/
class
PaddlePredictor
{
public:
...
...
@@ -138,18 +156,20 @@ class PaddlePredictor {
PaddlePredictor
(
const
PaddlePredictor
&
)
=
delete
;
PaddlePredictor
&
operator
=
(
const
PaddlePredictor
&
)
=
delete
;
// Predict an record.
// The caller should be responsible for allocating and releasing the memory of
// `inputs`. `inputs` should be available until Run returns. Caller should be
// responsible for the output tensor's buffer, either allocated or passed from
// outside.
/** Predict an record.
* The caller should be responsible for allocating and releasing the memory of
* `inputs`. `inputs` should be available until Run returns. Caller should be
* responsible for the output tensor's buffer, either allocated or passed from
* outside.
*/
virtual
bool
Run
(
const
std
::
vector
<
PaddleTensor
>&
inputs
,
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.
/** 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
;
...
...
@@ -160,16 +180,19 @@ class PaddlePredictor {
}
virtual
bool
ZeroCopyRun
()
{
return
false
;
}
// Clone a predictor that share the model weights, the Cloned predictor should
// be thread-safe.
/** Clone a predictor that share the model weights, the Cloned predictor
* should be thread-safe.
*/
virtual
std
::
unique_ptr
<
PaddlePredictor
>
Clone
()
=
0
;
// Destroy the Predictor.
/** Destroy the Predictor.
*/
virtual
~
PaddlePredictor
()
=
default
;
// The common configs for all the predictors.
/** The common configs for all the predictors.
*/
struct
Config
{
std
::
string
model_dir
;
// path to the model directory.
std
::
string
model_dir
;
/*!< path to the model directory. */
};
};
...
...
@@ -177,17 +200,21 @@ struct NativeConfig : public PaddlePredictor::Config {
// GPU related fields.
bool
use_gpu
{
false
};
int
device
{
0
};
float
fraction_of_gpu_memory
{
-
1.
f
};
// Change to a float in (0,1] if needed.
float
fraction_of_gpu_memory
{
-
1.
f
};
/*!< Change to a float in (0,1] if needed. */
// Specify the exact path of program and parameter files.
std
::
string
prog_file
;
std
::
string
param_file
;
// Specify the variable's name of each input if input tensors don't follow the
// `feeds` and `fetches` of the phase `save_inference_model`.
/** Specify the variable's name of each input if input tensors don't follow
* the
* `feeds` and `fetches` of the phase `save_inference_model`.
*/
bool
specify_input_name
{
false
};
// Set and get the number of cpu math library threads.
/** Set and get the number of cpu math library threads.
*/
void
SetCpuMathLibraryNumThreads
(
int
cpu_math_library_num_threads
)
{
cpu_math_library_num_threads_
=
cpu_math_library_num_threads
;
}
...
...
@@ -201,28 +228,33 @@ struct NativeConfig : public PaddlePredictor::Config {
int
cpu_math_library_num_threads_
{
1
};
};
// A factory to help create different predictors.
//
// Usage:
//
// NativeConfig config;
// ... // change the configs.
// auto native_predictor = CreatePaddlePredictor(config);
//
// FOR EXTENSION DEVELOPER:
// Different predictors are designated by config type. Similar configs can be
// merged, but there shouldn't be a huge config containing different fields for
// more than one kind of predictors.
/*! \fn std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT&
* config);
*
* \brief A factory to help create different predictors.
*
* Usage:
*
* NativeConfig config;
* ... // change the configs.
* auto native_predictor = CreatePaddlePredictor(config);
*
* FOR EXTENSION DEVELOPER:
* Different predictors are designated by config type. Similar configs can be
* merged, but there shouldn't be a huge config containing different fields for
* more than one kind of predictors.
*/
template
<
typename
ConfigT
>
std
::
unique_ptr
<
PaddlePredictor
>
CreatePaddlePredictor
(
const
ConfigT
&
config
);
// NOTE The following APIs are too trivial, we will discard it in the following
// versions.
/** NOTE The following APIs are too trivial, we will discard it in the following
* versions.
*/
enum
class
PaddleEngineKind
{
kNative
=
0
,
// Use the native Fluid facility.
kAutoMixedTensorRT
,
// Automatically mix Fluid with TensorRT.
kAnalysis
,
// More optimization.
kAnakin
// Use Anakin for inference, not mature yet.
kNative
=
0
,
/*!< Use the native Fluid facility. */
kAutoMixedTensorRT
,
/*!< Automatically mix Fluid with TensorRT. */
kAnalysis
,
/*!< More optimization. */
kAnakin
/*!< Use Anakin for inference, not mature yet. */
};
template
<
typename
ConfigT
,
PaddleEngineKind
engine
>
...
...
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @
d09d6ead
...
...
@@ -18,30 +18,39 @@
#include <string>
#include <vector>
/*! \file */
/*! \namespace paddle */
namespace
paddle
{
/*
* This is a pass builder based on string. It is part of inference API.
/*
* This is a pass builder based on string. It is part of inference API.
*/
class
PaddlePassBuilder
{
public:
explicit
PaddlePassBuilder
(
const
std
::
vector
<
std
::
string
>
&
passes
)
:
passes_
(
passes
)
{}
/** Append a pass to the end of the passes. */
void
AppendPass
(
const
std
::
string
&
pass_type
);
/** Insert a pass to a specific position.
* @param idx the position to insert.
* @param pass_type the pass key.
*/
void
InsertPass
(
size_t
idx
,
const
std
::
string
&
pass_type
);
/
/ Delete the `idx`-th pass.
/
** Delete the `idx`-th pass. */
void
DeletePass
(
size_t
idx
);
/
/ Delete all the passes that has type `pass_type`.
/
** Delete all the passes that has type `pass_type`. */
void
DeletePass
(
const
std
::
string
&
pass_type
);
// Visualize the computation graph after each pass by generating a DOT
// language file, one can draw them with the Graphviz toolkit.
/** Visualize the computation graph after each pass by generating a DOT
* language file, one can draw them with the Graphviz toolkit.
*/
void
TurnOnDebug
();
/
/ Human-readible information.
/
** Human-readible information. */
std
::
string
DebugString
();
const
std
::
vector
<
std
::
string
>
&
AllPasses
()
const
{
return
passes_
;
}
...
...
@@ -50,16 +59,16 @@ class PaddlePassBuilder {
std
::
vector
<
std
::
string
>
passes_
;
};
/*
* Pass strategy to help control the IR passes.
/**Pass strategy to help control the IR passes.
*/
class
PassStrategy
:
public
PaddlePassBuilder
{
public:
explicit
PassStrategy
(
const
std
::
vector
<
std
::
string
>
&
passes
)
:
PaddlePassBuilder
(
passes
)
{}
// The MKLDNN control exists in both CPU and GPU mode, because there can be
// still some CPU kernels running in CPU mode.
/** The MKLDNN control exists in both CPU and GPU mode, because there can be
* still some CPU kernels running in CPU mode.
*/
virtual
void
EnableMKLDNN
()
=
0
;
bool
use_gpu
()
const
{
return
use_gpu_
;
}
...
...
@@ -70,8 +79,7 @@ class PassStrategy : public PaddlePassBuilder {
bool
use_gpu_
{
false
};
};
/*
* The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
/** The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
*/
class
CpuPassStrategy
:
public
PassStrategy
{
public:
...
...
@@ -117,8 +125,7 @@ class CpuPassStrategy : public PassStrategy {
CpuPassStrategy
(
const
CpuPassStrategy
&
other
)
:
PassStrategy
(
other
.
passes_
)
{}
};
/*
* The GPU passes strategy, it is used in
/** The GPU passes strategy, it is used in AnalysisPredictor with GPU mode.
*/
class
GpuPassStrategy
:
public
PassStrategy
{
public:
...
...
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