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b0e7681f
编写于
8月 14, 2022
作者:
X
xiaoxiaohehe001
提交者:
GitHub
8月 14, 2022
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电子邮件补丁
差异文件
Revert "[Paddle Inference] Support cuda_graph. (#44878)" (#45115)
This reverts commit
84bf5c31
.
上级
e96dae8b
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
15 addition
and
193 deletion
+15
-193
paddle/fluid/framework/inference_cached_ops.h
paddle/fluid/framework/inference_cached_ops.h
+0
-29
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+15
-162
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+0
-1
paddle/fluid/inference/api/paddle_pass_builder.cc
paddle/fluid/inference/api/paddle_pass_builder.cc
+0
-1
未找到文件。
paddle/fluid/framework/inference_cached_ops.h
已删除
100644 → 0
浏览文件 @
e96dae8b
/* Copyright (c) 2022 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>
namespace
paddle
{
namespace
framework
{
// cached ops will be captured to accelerate gpu performance.
// 1. op will generate a cudaGraph to record inner gpu kernels
// 2. inner gpu kernels can be launched by calling the cudagraphExecutor
// only once.
std
::
vector
<
std
::
string
>
cached_gpu_ops
{
"conv2d_fusion"
,
"depthwise_conv2d"
};
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @
b0e7681f
...
...
@@ -21,7 +21,6 @@ limitations under the License. */
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/details/nan_inf_utils.h"
#include "paddle/fluid/framework/inference_cached_ops.h"
#include "paddle/fluid/framework/op_call_stack.h"
#include "paddle/fluid/framework/phi_utils.h"
#include "paddle/fluid/framework/shape_inference.h"
...
...
@@ -710,12 +709,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
return
in
[
0
]
!=
nullptr
;
}
size_t
InputsSize
()
const
{
auto
&
op_proto
=
paddle
::
framework
::
OpInfoMap
::
Instance
().
Get
(
op_
.
Type
()).
proto_
;
return
op_proto
->
inputs
().
size
();
}
bool
HasOutput
(
const
std
::
string
&
name
)
const
override
{
// has only one output
const
auto
&
outs
=
ctx_
.
outputs
;
...
...
@@ -1207,86 +1200,7 @@ struct OperatorWithKernel::CacheImpl {
return
infer_shape_ctx_
.
get
();
}
bool
updateInputsShapesDimCache
()
{
bool
flag
=
false
;
size_t
inputs_size
=
std
::
min
(
kernel_ctx_
->
InputsSize
(),
infer_shape_ctx_
->
InputsSize
());
for
(
size_t
i
=
0
;
i
<
inputs_size
;
i
++
)
{
const
std
::
string
&
in_name
=
infer_shape_ctx_
->
GetInputNameByIdx
(
i
);
if
(
!
infer_shape_ctx_
->
HasInputs
(
in_name
))
continue
;
if
(
!
inputs_dim_caches
.
count
(
in_name
)
||
infer_shape_ctx_
->
GetInputsDim
(
in_name
)
!=
inputs_dim_caches
[
in_name
])
{
inputs_dim_caches
[
in_name
]
=
infer_shape_ctx_
->
GetInputsDim
(
in_name
);
flag
=
true
;
}
}
#if defined(PADDLE_WITH_CUDA)
if
(
flag
)
discardCudaGraphCache
();
#endif
return
flag
;
}
bool
cudaGraphEnabled
(
bool
need_prepare_data
,
bool
need_prepare_phi_data
,
const
std
::
string
&
op_type
)
const
{
#if defined(PADDLE_WITH_CUDA)
return
std
::
count
(
cached_gpu_ops
.
begin
(),
cached_gpu_ops
.
end
(),
op_type
)
&&
!
need_prepare_data
&&
!
need_prepare_phi_data
;
#else
return
false
;
#endif
}
bool
cacheEnabled
(
bool
run_phi_kernel
,
bool
need_prepare_data
,
bool
need_prepare_phi_data
,
const
std
::
string
&
op_type
)
const
{
#if defined(PADDLE_WITH_CUDA)
if
(
cudaGraphEnabled
(
need_prepare_data
,
need_prepare_phi_data
,
op_type
))
return
true
;
#endif
return
(
run_phi_kernel
&&
!
need_prepare_data
&&
!
need_prepare_phi_data
);
}
#if defined(PADDLE_WITH_CUDA)
void
startCudaGraphCapture
()
{
phi
::
GPUContext
*
ctx
=
static_cast
<
phi
::
GPUContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
platform
::
CUDAPlace
(
0
)));
auto
stream
=
ctx
->
stream
();
cudaStreamBeginCapture
(
stream
,
cudaStreamCaptureModeGlobal
);
}
void
endCudaGraphCapture
()
{
phi
::
GPUContext
*
ctx
=
static_cast
<
phi
::
GPUContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
platform
::
CUDAPlace
(
0
)));
auto
stream
=
ctx
->
stream
();
cudaGraph_t
graph_
;
cudaStreamEndCapture
(
stream
,
&
graph_
);
cudaGraphInstantiate
(
&
graph_instance_
,
graph_
,
NULL
,
NULL
,
0
);
graph_generated
=
true
;
}
void
runCudaGraph
()
{
phi
::
GPUContext
*
ctx
=
static_cast
<
phi
::
GPUContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
platform
::
CUDAPlace
(
0
)));
auto
stream
=
ctx
->
stream
();
cudaGraphLaunch
(
graph_instance_
,
stream
);
}
bool
cudaGraphGenerated
()
{
return
graph_generated
;
}
void
discardCudaGraphCache
()
{
graph_generated
=
false
;
}
private:
bool
graph_generated
{
false
};
cudaGraphExec_t
graph_instance_
;
#endif
private:
std
::
map
<
std
::
string
,
std
::
vector
<
DDim
>>
inputs_dim_caches
;
std
::
unique_ptr
<
phi
::
KernelContext
>
kernel_ctx_
;
std
::
unique_ptr
<
RuntimeInferShapeContext
>
infer_shape_ctx_
;
};
...
...
@@ -1496,74 +1410,8 @@ void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
this
->
Info
().
infer_shape_
(
&
infer_shape_ctx
);
}
void
OperatorWithKernel
::
InitOpCache
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
&
scope
)
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
cache_update_mutex_
);
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
&
scope
)
{
runtime_ctx_
.
reset
(
new
RuntimeContext
(
Inputs
(),
Outputs
(),
scope
));
pre_scope_
=
&
scope
;
}
}
impl_
=
new
CacheImpl
(
new
phi
::
KernelContext
(),
new
RuntimeInferShapeContext
(
*
this
,
*
runtime_ctx_
.
get
()));
RunImpl
(
scope
,
place
,
runtime_ctx_
.
get
());
if
(
impl_
->
cacheEnabled
(
run_phi_kernel_
,
need_prepare_data_
,
need_prepare_phi_data_
,
Type
()))
{
impl_
->
updateInputsShapesDimCache
();
}
}
void
OperatorWithKernel
::
RunImpl
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
// function name: runOpCache()
// effect: reuse cacheImpl to accelerate inference period
auto
runOpCache
=
[
&
]()
{
#if defined(PADDLE_WITH_CUDA)
if
(
impl_
->
cudaGraphEnabled
(
need_prepare_data_
,
need_prepare_phi_data_
,
Type
()))
{
// cudaGraph cache
if
(
impl_
->
updateInputsShapesDimCache
())
{
if
(
!
all_kernels_must_compute_runtime_shape_
)
this
->
Info
().
infer_shape_
(
impl_
->
getRuntimeInferShapeContext
());
(
*
phi_kernel_
)(
impl_
->
getKernelContext
());
}
else
if
(
!
impl_
->
cudaGraphGenerated
())
{
impl_
->
startCudaGraphCapture
();
impl_
->
getKernelContext
();
RunImpl
(
scope
,
place
,
runtime_ctx_
.
get
());
impl_
->
endCudaGraphCapture
();
}
else
{
if
(
!
all_kernels_must_compute_runtime_shape_
)
this
->
Info
().
infer_shape_
(
impl_
->
getRuntimeInferShapeContext
());
impl_
->
runCudaGraph
();
}
return
;
}
#endif
// common cache
if
(
!
all_kernels_must_compute_runtime_shape_
)
this
->
Info
().
infer_shape_
(
impl_
->
getRuntimeInferShapeContext
());
(
*
phi_kernel_
)(
impl_
->
getKernelContext
());
};
// function name: updateRuntimeContext
// effect: update runtime_ctx from current scope.
auto
updateRuntimeContext
=
[
&
](
const
Scope
&
scope
)
{
const
Scope
*
cur_scope
=
&
scope
;
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
cur_scope
)
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
cache_update_mutex_
);
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
cur_scope
)
{
runtime_ctx_
.
reset
(
new
RuntimeContext
(
Inputs
(),
Outputs
(),
scope
));
pre_scope_
=
cur_scope
;
}
}
};
// To reduce the elapsed time of HasAttr, we use bool variable to record the
// result of HasAttr.
if
(
!
enable_cache_runtime_context_
&&
HasAttr
(
kEnableCacheRuntimeContext
))
...
...
@@ -1576,18 +1424,20 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
RuntimeContext
ctx
(
Inputs
(),
Outputs
(),
scope
);
RunImpl
(
scope
,
place
,
&
ctx
);
pre_scope_
=
cur_scope
;
}
else
if
(
run_phi_kernel_
&&
impl_
!=
nullptr
&&
!
need_prepare_data_
&&
!
need_prepare_phi_data_
)
{
if
(
!
all_kernels_must_compute_runtime_shape_
)
this
->
Info
().
infer_shape_
(
impl_
->
getRuntimeInferShapeContext
());
(
*
phi_kernel_
)(
impl_
->
getKernelContext
());
}
else
{
if
(
!
impl_
)
{
InitOpCache
(
scope
,
place
);
}
else
if
(
impl_
->
cacheEnabled
(
run_phi_kernel_
,
need_prepare_data_
,
need_prepare_phi_data_
,
Type
()))
{
runOpCache
();
}
else
{
updateRuntimeContext
(
scope
);
RunImpl
(
scope
,
place
,
runtime_ctx_
.
get
());
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
cur_scope
)
{
std
::
lock_guard
<
std
::
mutex
>
lock
(
cache_update_mutex_
);
if
(
runtime_ctx_
.
get
()
==
nullptr
||
pre_scope_
!=
cur_scope
)
{
runtime_ctx_
.
reset
(
new
RuntimeContext
(
Inputs
(),
Outputs
(),
scope
));
pre_scope_
=
cur_scope
;
}
}
RunImpl
(
scope
,
place
,
runtime_ctx_
.
get
());
}
}
...
...
@@ -1852,6 +1702,9 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
phi
::
KernelContext
phi_kernel_context
;
if
(
enable_cache_runtime_context_
&&
!
need_prepare_phi_data_
&&
!
need_prepare_data_
)
{
impl_
=
new
CacheImpl
(
new
phi
::
KernelContext
(),
new
RuntimeInferShapeContext
(
*
this
,
*
runtime_ctx
));
BuildPhiKernelContext
(
*
runtime_ctx
,
dev_ctx
,
impl_
->
getKernelContext
());
(
*
phi_kernel_
)(
impl_
->
getKernelContext
());
}
else
{
...
...
paddle/fluid/framework/operator.h
浏览文件 @
b0e7681f
...
...
@@ -712,7 +712,6 @@ class OperatorWithKernel : public OperatorBase {
// used for IndicateOrPromoteVarDataTypes
Tensor
*
GetTensorFormInputSafely
(
const
ExecutionContext
&
ctx
,
const
std
::
string
&
name
)
const
;
void
InitOpCache
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
;
protected:
mutable
std
::
unique_ptr
<
OpKernelType
>
kernel_type_
;
...
...
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @
b0e7681f
...
...
@@ -165,7 +165,6 @@ const std::vector<std::string> kGpuLowerPrecisionPasses{
"gpu_cpu_map_matmul_v2_to_matmul_pass"
,
"fc_fuse_pass"
,
"fc_elementwise_layernorm_fuse_pass"
,
"runtime_context_cache_pass"
,
};
const
std
::
vector
<
std
::
string
>
kTrtLowerPrecisionPasses
{
...
...
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