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5857fb30
编写于
11月 21, 2018
作者:
H
hjchen2
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into develop
test=develop
上级
3e3599f3
b8f36bd0
变更
40
展开全部
隐藏空白更改
内联
并排
Showing
40 changed file
with
1201 addition
and
229 deletion
+1201
-229
.gitignore
.gitignore
+1
-0
AUTHORS.md
AUTHORS.md
+1
-0
cmake/inference_lib.cmake
cmake/inference_lib.cmake
+2
-2
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+1
-0
paddle/fluid/inference/analysis/CMakeLists.txt
paddle/fluid/inference/analysis/CMakeLists.txt
+4
-3
paddle/fluid/inference/analysis/analyzer_tester.cc
paddle/fluid/inference/analysis/analyzer_tester.cc
+2
-0
paddle/fluid/inference/analysis/argument.h
paddle/fluid/inference/analysis/argument.h
+1
-0
paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
+2
-0
paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc
...le/fluid/inference/analysis/passes/ir_graph_build_pass.cc
+18
-6
paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h
paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h
+5
-3
paddle/fluid/inference/api/CMakeLists.txt
paddle/fluid/inference/api/CMakeLists.txt
+4
-5
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+2
-2
paddle/fluid/inference/api/paddle_pass_builder.h
paddle/fluid/inference/api/paddle_pass_builder.h
+5
-1
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+1
-1
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
+89
-53
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
+9
-7
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
+1
-0
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
+64
-0
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
+111
-0
paddle/fluid/inference/tests/api/CMakeLists.txt
paddle/fluid/inference/tests/api/CMakeLists.txt
+14
-10
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+27
-10
paddle/fluid/inference/tests/api/trt_models_tester.cc
paddle/fluid/inference/tests/api/trt_models_tester.cc
+0
-2
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
+1
-0
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
+1
-0
paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc
.../fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc
+201
-0
paddle/fluid/operators/elementwise/elementwise_op.h
paddle/fluid/operators/elementwise/elementwise_op.h
+14
-0
paddle/fluid/operators/math/jit_code.h
paddle/fluid/operators/math/jit_code.h
+36
-0
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+9
-0
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+41
-0
paddle/fluid/operators/math/pooling.cu
paddle/fluid/operators/math/pooling.cu
+36
-0
paddle/fluid/operators/math/pooling.h
paddle/fluid/operators/math/pooling.h
+13
-0
paddle/fluid/operators/stack_op.h
paddle/fluid/operators/stack_op.h
+18
-6
paddle/fluid/platform/init.cc
paddle/fluid/platform/init.cc
+1
-0
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+4
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+160
-102
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+4
-0
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+3
-1
python/paddle/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.py
...e/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.py
+263
-0
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
+31
-13
python/requirements.txt
python/requirements.txt
+1
-1
未找到文件。
.gitignore
浏览文件 @
5857fb30
python/paddle/fluid/tests/unittests/reader_reset_test.recordio
paddle/operators/check_t.save
paddle/operators/check_tensor.ls
paddle/operators/tensor.save
...
...
AUTHORS.md
浏览文件 @
5857fb30
...
...
@@ -42,6 +42,7 @@
| QiJune | Jun Qi |
| qingqing01 | Qing-Qing Dang |
| reyoung | Yang Yu |
| Sand3r- | Michal Gallus |
| Superjom | Chun-Wei Yan |
| tensor-tang | Jian Tang |
| tianbingsz | Tian-Bing Xu |
...
...
cmake/inference_lib.cmake
浏览文件 @
5857fb30
...
...
@@ -166,8 +166,8 @@ copy(framework_lib DEPS ${framework_lib_deps}
set
(
module
"memory"
)
copy
(
memory_lib
SRCS
${
src_dir
}
/
${
module
}
/*.h
${
src_dir
}
/
${
module
}
/detail/*.h
DSTS
${
dst_dir
}
/
${
module
}
${
dst_dir
}
/
${
module
}
/detail
SRCS
${
src_dir
}
/
${
module
}
/*.h
${
src_dir
}
/
${
module
}
/detail/*.h
${
src_dir
}
/
${
module
}
/allocation/*.h
DSTS
${
dst_dir
}
/
${
module
}
${
dst_dir
}
/
${
module
}
/detail
${
dst_dir
}
/
${
module
}
/allocation
)
set
(
inference_deps paddle_fluid_shared paddle_fluid
)
...
...
paddle/fluid/framework/operator.h
浏览文件 @
5857fb30
...
...
@@ -100,6 +100,7 @@ class OperatorBase {
const
std
::
string
&
Type
()
const
{
return
type_
;
}
bool
HasAttr
(
const
std
::
string
&
name
)
const
{
return
attrs_
.
count
(
name
);
}
template
<
typename
T
>
inline
const
T
&
Attr
(
const
std
::
string
&
name
)
const
{
PADDLE_ENFORCE
(
attrs_
.
count
(
name
)
!=
0
,
"%s should be in AttributeMap"
,
...
...
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @
5857fb30
...
...
@@ -7,16 +7,17 @@ set(analysis_deps # analysis_deps can be extended accross the project
add_subdirectory
(
ir_passes
)
add_subdirectory
(
passes
)
cc_library
(
ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass
${
INFER_IR_PASSES
}
)
cc_library
(
analysis_helper SRCS helper.cc DEPS framework_proto proto_desc graph paddle_fluid_api
)
cc_library
(
ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass
${
INFER_IR_PASSES
}
analysis_helper
)
cc_library
(
argument SRCS argument.cc DEPS scope proto_desc
)
cc_library
(
analysis_pass SRCS analysis_pass.cc DEPS proto_desc
)
cc_library
(
analysis SRCS
analyzer.cc
helper.cc
analysis_pass
DEPS
${
analysis_deps
}
DEPS
${
analysis_deps
}
analysis_helper
)
cc_test
(
test_dot SRCS dot_tester.cc DEPS analysis
)
...
...
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @
5857fb30
...
...
@@ -30,6 +30,7 @@ TEST(Analyzer, analysis_without_tensorrt) {
Argument
argument
;
argument
.
SetModelDir
(
FLAGS_inference_model_dir
);
argument
.
SetIrAnalysisPasses
({
"infer_clean_graph_pass"
});
argument
.
SetUseGPU
(
false
);
Analyzer
analyser
;
analyser
.
Run
(
&
argument
);
...
...
@@ -41,6 +42,7 @@ TEST(Analyzer, analysis_with_tensorrt) {
argument
.
SetTensorRtWorkspaceSize
(
1
<<
20
);
argument
.
SetModelDir
(
FLAGS_inference_model_dir
);
argument
.
SetIrAnalysisPasses
({
"infer_clean_graph_pass"
});
argument
.
SetUseGPU
(
false
);
Analyzer
analyser
;
analyser
.
Run
(
&
argument
);
...
...
paddle/fluid/inference/analysis/argument.h
浏览文件 @
5857fb30
...
...
@@ -116,6 +116,7 @@ struct Argument {
std
::
vector
<
std
::
string
>
);
DECL_ARGUMENT_FIELD
(
use_gpu
,
UseGPU
,
bool
);
DECL_ARGUMENT_FIELD
(
gpu_device_id
,
GPUDeviceId
,
int
);
DECL_ARGUMENT_FIELD
(
use_tensorrt
,
UseTensorRT
,
bool
);
DECL_ARGUMENT_FIELD
(
tensorrt_node_teller
,
TensorRtNodeTeller
,
std
::
function
<
bool
(
const
framework
::
ir
::
Node
*
)
>
);
...
...
paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
浏览文件 @
5857fb30
...
...
@@ -4,4 +4,6 @@ set(analysis_deps ${analysis_deps}
subgraph_detector tensorrt_subgraph_pass
CACHE INTERNAL
""
)
set
(
pass_file
${
PADDLE_BINARY_DIR
}
/paddle/fluid/inference/api/paddle_inference_pass.h
)
file
(
APPEND
${
pass_file
}
"USE_PASS(tensorrt_subgraph_pass);
\n
"
)
set
(
INFER_IR_PASSES
${
INFER_IR_PASSES
}
tensorrt_subgraph_pass CACHE INTERNAL
""
)
paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc
浏览文件 @
5857fb30
...
...
@@ -30,15 +30,28 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
if
(
!
argument
->
scope_valid
())
{
argument
->
SetScope
(
new
framework
::
Scope
);
}
PADDLE_ENFORCE
(
argument
->
use_gpu_valid
());
// The load program should run on the same device with the inference program,
// so that the parameters will on the same device, or they will keep copying
// between difference devices.
platform
::
Place
place
;
if
(
argument
->
use_gpu
())
{
PADDLE_ENFORCE
(
argument
->
gpu_device_id_valid
());
place
=
platform
::
CUDAPlace
(
argument
->
gpu_device_id
());
}
else
{
place
=
platform
::
CPUPlace
();
}
if
(
argument
->
model_dir_valid
())
{
auto
program
=
LoadModel
(
argument
->
model_dir
(),
argument
->
scope_ptr
());
auto
program
=
LoadModel
(
argument
->
model_dir
(),
argument
->
scope_ptr
(),
place
);
argument
->
SetMainProgram
(
program
.
release
());
}
else
if
(
argument
->
model_program_path_valid
()
&&
argument
->
model_params_path_valid
())
{
auto
program
=
LoadModel
(
argument
->
model_program_path
(),
argument
->
model_params_path
(),
argument
->
scope_ptr
());
argument
->
scope_ptr
()
,
place
);
argument
->
SetMainProgram
(
program
.
release
());
}
else
{
PADDLE_THROW
(
...
...
@@ -52,16 +65,15 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
}
std
::
unique_ptr
<
framework
::
ProgramDesc
>
IrGraphBuildPass
::
LoadModel
(
const
std
::
string
&
path
,
framework
::
Scope
*
scope
)
{
platform
::
CPUPlace
place
;
const
std
::
string
&
path
,
framework
::
Scope
*
scope
,
const
platform
::
Place
&
place
)
{
framework
::
Executor
exe
(
place
);
return
Load
(
&
exe
,
scope
,
path
);
}
std
::
unique_ptr
<
framework
::
ProgramDesc
>
IrGraphBuildPass
::
LoadModel
(
const
std
::
string
&
program_path
,
const
std
::
string
&
params_path
,
framework
::
Scope
*
scope
)
{
platform
::
CPUPlace
place
;
framework
::
Scope
*
scope
,
const
platform
::
Place
&
place
)
{
framework
::
Executor
exe
(
place
);
return
Load
(
&
exe
,
scope
,
program_path
,
params_path
);
}
...
...
paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h
浏览文件 @
5857fb30
...
...
@@ -17,6 +17,7 @@
#include <string>
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
inference
{
...
...
@@ -32,11 +33,12 @@ class IrGraphBuildPass : public AnalysisPass {
std
::
string
repr
()
const
override
;
private:
std
::
unique_ptr
<
framework
::
ProgramDesc
>
LoadModel
(
const
std
::
string
&
path
,
framework
::
Scope
*
scope
);
std
::
unique_ptr
<
framework
::
ProgramDesc
>
LoadModel
(
const
std
::
string
&
path
,
framework
::
Scope
*
scope
,
const
platform
::
Place
&
place
);
std
::
unique_ptr
<
framework
::
ProgramDesc
>
LoadModel
(
const
std
::
string
&
program_path
,
const
std
::
string
&
params_path
,
framework
::
Scope
*
scope
);
framework
::
Scope
*
scope
,
const
platform
::
Place
&
place
);
std
::
string
model_binary_str_
;
};
...
...
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @
5857fb30
...
...
@@ -27,11 +27,10 @@ endif()
cc_library
(
reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope
)
cc_library
(
analysis_config SRCS analysis_config.cc DEPS lod_tensor paddle_pass_builder
)
cc_library
(
paddle_pass_builder SRCS paddle_pass_builder.cc
)
cc_library
(
paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder
)
cc_library
(
analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder
)
cc_library
(
zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api
)
cc_library
(
zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api
)
cc_library
(
analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager
)
cc_library
(
zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce
)
cc_library
(
zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc
)
cc_library
(
paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor
)
cc_test
(
test_paddle_inference_api
SRCS api_tester.cc
...
...
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
5857fb30
...
...
@@ -285,6 +285,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
status_program_optimized_
=
true
;
argument_
.
SetUseGPU
(
config_
.
use_gpu
);
argument_
.
SetGPUDeviceId
(
config_
.
device
);
// Analyze inference_program
if
(
!
config_
.
model_dir
.
empty
())
{
argument_
.
SetModelDir
(
config_
.
model_dir
);
...
...
@@ -491,8 +492,7 @@ bool AnalysisPredictor::LoadParameters() {
}
// Use NaiveExecutor to Load parameters.
platform
::
CPUPlace
place
;
framework
::
NaiveExecutor
e
(
place
);
framework
::
NaiveExecutor
e
(
place_
);
e
.
Prepare
(
scope_
.
get
(),
*
load_program
,
0
,
false
);
e
.
Run
();
VLOG
(
3
)
<<
"get "
<<
scope_
->
LocalVarNames
().
size
()
<<
" vars after load"
;
...
...
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @
5857fb30
...
...
@@ -116,8 +116,12 @@ class CpuPassStrategy : public PassStrategy {
class
GpuPassStrategy
:
public
PassStrategy
{
public:
GpuPassStrategy
()
:
PassStrategy
({})
{
// TODO(NHZlX) Problem with Data synchronization between GPU and CPU
// When running in GPU mode, the parameters are all on GPU. But the
// opearations of "conv_bn_fuse_pass" are on CPU.
passes_
.
assign
({
"infer_clean_graph_pass"
,
"conv_bn_fuse_pass"
,
"infer_clean_graph_pass"
,
// "infer_clean_graph_pass", "conv_bn_fuse_pass",
});
}
...
...
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
5857fb30
...
...
@@ -18,7 +18,7 @@ nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
nv_test
(
test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine conv_op conv_transpose_op SERIAL
)
nv_test
(
test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine pool_op SERIAL
)
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine pool_op
tensorrt_plugin
SERIAL
)
nv_test
(
test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OPERATOR_DEPS
}
tensorrt_engine tensorrt_plugin
elementwise_add_op elementwise_mul_op SERIAL
)
...
...
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @
5857fb30
...
...
@@ -13,25 +13,57 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
void
DealCeilMode
(
const
nvinfer1
::
Dims
&
input_shape
,
std
::
vector
<
int
>
ksize
,
std
::
vector
<
int
>
strides
,
std
::
vector
<
int
>
paddings
,
nvinfer1
::
DimsHW
*
pre_pad
,
nvinfer1
::
DimsHW
*
post_pad
,
int
input_dims
)
{
int
input_height
=
input_shape
.
d
[
input_dims
-
2
];
int
input_width
=
input_shape
.
d
[
input_dims
-
1
];
int
floor_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
;
int
ceil_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
;
int
floor_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
;
int
ceil_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
;
if
(
floor_h_output_size
!=
ceil_h_output_size
)
{
post_pad
->
h
()
=
strides
[
0
]
-
1
;
}
if
(
floor_w_output_size
!=
ceil_w_output_size
)
{
post_pad
->
w
()
=
strides
[
1
]
-
1
;
}
}
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class
Pool2dOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
3
)
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
40
)
<<
"convert a fluid pool2d op to tensorrt pool2d layer without bias"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"X"
).
size
(),
1
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Output
(
"Out"
).
size
(),
1
);
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
nvinfer1
::
Dims
input_shape
=
input1
->
getDimensions
();
int
input_dims
=
input_shape
.
nbDims
;
PADDLE_ENFORCE_EQ
(
input_dims
,
3UL
);
bool
global_pooling
=
boost
::
get
<
bool
>
(
op_desc
.
GetAttr
(
"global_pooling"
));
std
::
string
pool_type
=
...
...
@@ -44,23 +76,6 @@ class Pool2dOpConverter : public OpConverter {
boost
::
get
<
std
::
vector
<
int
>>
(
op_desc
.
GetAttr
(
"paddings"
));
bool
ceil_mode
=
boost
::
get
<
bool
>
(
op_desc
.
GetAttr
(
"ceil_mode"
));
nvinfer1
::
Dims
input_shape
=
input1
->
getDimensions
();
int
nbDims
=
input_shape
.
nbDims
;
nvinfer1
::
DimsHW
nv_ksize
(
ksize
[
0
],
ksize
[
1
]);
nvinfer1
::
DimsHW
nv_strides
(
strides
[
0
],
strides
[
1
]);
nvinfer1
::
DimsHW
nv_paddings
(
paddings
[
0
],
paddings
[
1
]);
if
(
global_pooling
==
true
)
{
nv_ksize
.
d
[
0
]
=
input_shape
.
d
[
nbDims
-
2
];
nv_ksize
.
d
[
1
]
=
input_shape
.
d
[
nbDims
-
1
];
nv_strides
.
h
()
=
1
;
nv_strides
.
w
()
=
1
;
nv_paddings
.
h
()
=
0
;
nv_paddings
.
w
()
=
0
;
}
PADDLE_ENFORCE_EQ
(
input1
->
getDimensions
().
nbDims
,
3UL
);
nvinfer1
::
PoolingType
nv_pool_type
=
nvinfer1
::
PoolingType
::
kMAX
;
if
(
pool_type
==
"max"
)
{
nv_pool_type
=
nvinfer1
::
PoolingType
::
kMAX
;
...
...
@@ -70,42 +85,63 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW
(
"TensorRT unsupported pooling type!"
);
}
if
(
ceil_mode
)
{
nvinfer1
::
DimsHW
pre_pad
(
0
,
0
);
nvinfer1
::
DimsHW
post_pad
(
0
,
0
);
int
input_height
=
input_shape
.
d
[
nbDims
-
2
];
int
input_width
=
input_shape
.
d
[
nbDims
-
1
];
int
floor_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
;
int
ceil_h_output_size
=
(
input_height
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
;
int
floor_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
;
int
ceil_w_output_size
=
(
input_width
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
;
if
(
floor_h_output_size
!=
ceil_h_output_size
)
{
post_pad
.
h
()
=
strides
[
0
]
-
1
;
nvinfer1
::
DimsHW
nv_ksize
(
ksize
[
0
],
ksize
[
1
]);
nvinfer1
::
DimsHW
nv_strides
(
strides
[
0
],
strides
[
1
]);
nvinfer1
::
DimsHW
nv_paddings
(
paddings
[
0
],
paddings
[
1
]);
nvinfer1
::
ILayer
*
layer
=
nullptr
;
if
(
global_pooling
==
true
)
{
nv_ksize
.
d
[
0
]
=
input_shape
.
d
[
input_dims
-
2
];
nv_ksize
.
d
[
1
]
=
input_shape
.
d
[
input_dims
-
1
];
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
layer
,
"pool layer could not be created."
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
layer
->
setName
((
"pool2d (Output: "
+
output_name
+
")"
).
c_str
());
layer
->
getOutput
(
0
)
->
setName
(
output_name
.
c_str
());
engine_
->
SetITensor
(
output_name
,
layer
->
getOutput
(
0
));
if
(
test_mode
)
{
engine_
->
DeclareOutput
(
output_name
);
}
return
;
}
if
(
floor_w_output_size
!=
ceil_w_output_size
)
{
post_pad
.
w
()
=
strides
[
1
]
-
1
;
if
(
pool_type
==
"max"
)
{
nvinfer1
::
DimsHW
pre_pad
(
paddings
[
0
],
paddings
[
1
]);
nvinfer1
::
DimsHW
post_pad
(
paddings
[
0
],
paddings
[
1
]);
if
(
ceil_mode
)
{
// If ceil mode is true, we will pad the appropriate size to the input.
DealCeilMode
(
input_shape
,
ksize
,
strides
,
paddings
,
&
pre_pad
,
&
post_pad
,
input_dims
);
auto
*
pad_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Padding
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
pre_pad
,
post_pad
);
PADDLE_ENFORCE_NOT_NULL
(
pad_layer
,
"pad layer in poolOp converter could not be created."
);
input1
=
pad_layer
->
getOutput
(
0
);
}
auto
*
pool_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
pool_layer
,
"pool layer could not be created."
);
pool_layer
->
setStride
(
nv_strides
);
pool_layer
->
setPadding
(
nv_paddings
);
layer
=
pool_layer
;
}
else
{
// Average pooling needs to exclude the padding pixels from the average
// mean.
// It is not supported well by TRT, we use a plugin here.
std
::
vector
<
int
>
input_shape_v
;
for
(
int
i
=
0
;
i
<
input_dims
;
i
++
)
{
input_shape_v
.
push_back
(
input_shape
.
d
[
i
]);
}
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Padding
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
pre_pad
,
post_pad
);
input1
=
layer
->
getOutput
(
0
)
;
plugin
::
AvgPoolPlugin
*
plugin
=
new
plugin
::
AvgPoolPlugin
(
ceil_mode
,
ksize
,
strides
,
paddings
,
input_shape_v
);
auto
*
avg_pool_layer
=
engine_
->
AddPlugin
(
&
input1
,
1
,
plugin
);
layer
=
avg_pool_layer
;
}
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Pooling
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
input1
),
nv_pool_type
,
nv_ksize
);
PADDLE_ENFORCE_NOT_NULL
(
layer
,
"pool layer could not be created."
);
layer
->
setStride
(
nv_strides
);
layer
->
setPadding
(
nv_paddings
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
layer
->
setName
((
"pool2d (Output: "
+
output_name
+
")"
).
c_str
());
...
...
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @
5857fb30
...
...
@@ -20,20 +20,21 @@ namespace paddle {
namespace
inference
{
namespace
tensorrt
{
void
test_pool2d
(
bool
global_pooling
,
bool
ceil_mode
)
{
void
test_pool2d
(
bool
global_pooling
,
bool
ceil_mode
,
std
::
string
pool_type
=
"max"
)
{
framework
::
Scope
scope
;
std
::
unordered_set
<
std
::
string
>
parameters
;
TRTConvertValidation
validator
(
5
,
parameters
,
scope
,
1
<<
15
);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator
.
DeclInputVar
(
"pool2d-X"
,
nvinfer1
::
Dims3
(
3
,
13
,
14
));
validator
.
DeclInputVar
(
"pool2d-X"
,
nvinfer1
::
Dims3
(
3
,
6
,
7
));
if
(
global_pooling
)
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
1
,
1
));
else
if
(
ceil_mode
)
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
6
,
7
));
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
3
,
4
));
else
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
6
,
6
));
validator
.
DeclOutputVar
(
"pool2d-Out"
,
nvinfer1
::
Dims3
(
3
,
3
,
3
));
// Prepare Op description
framework
::
OpDesc
desc
;
...
...
@@ -41,10 +42,10 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
desc
.
SetInput
(
"X"
,
{
"pool2d-X"
});
desc
.
SetOutput
(
"Out"
,
{
"pool2d-Out"
});
std
::
vector
<
int
>
ksize
({
3
,
3
});
std
::
vector
<
int
>
ksize
({
2
,
2
});
std
::
vector
<
int
>
strides
({
2
,
2
});
std
::
vector
<
int
>
paddings
({
0
,
0
});
std
::
string
pooling_t
=
"max"
;
std
::
string
pooling_t
=
pool_type
;
desc
.
SetAttr
(
"pooling_type"
,
pooling_t
);
desc
.
SetAttr
(
"ksize"
,
ksize
);
...
...
@@ -63,7 +64,8 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
TEST
(
Pool2dOpConverter
,
normal
)
{
test_pool2d
(
false
,
false
);
}
TEST
(
Pool2dOpConverter
,
test_global_pooling
)
{
test_pool2d
(
true
,
false
);
}
TEST
(
Pool2dOpConverter
,
test_ceil_mode
)
{
test_pool2d
(
false
,
true
);
}
TEST
(
Pool2dOpConverter
,
max_ceil_test
)
{
test_pool2d
(
false
,
true
);
}
TEST
(
Pool2dOpConverter
,
avg_ceil_test
)
{
test_pool2d
(
false
,
true
,
"avg"
);
}
}
// namespace tensorrt
}
// namespace inference
...
...
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
浏览文件 @
5857fb30
nv_library
(
tensorrt_plugin
SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
avg_pool_op_plugin.cu
DEPS enforce tensorrt_engine
)
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu
0 → 100644
浏览文件 @
5857fb30
// 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/inference/tensorrt/plugin/avg_pool_op_plugin.h"
#include "paddle/fluid/operators/math/pooling.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
namespace
plugin
{
nvinfer1
::
Dims
AvgPoolPlugin
::
getOutputDimensions
(
int
index
,
const
nvinfer1
::
Dims
*
inputDims
,
int
nbInputs
)
{
assert
(
nbInputs
==
1
);
assert
(
index
==
0
);
assert
(
inputDims
[
0
].
nbDims
==
3
);
nvinfer1
::
Dims
const
&
input_dims
=
inputDims
[
0
];
nvinfer1
::
Dims
output_dims
=
input_dims
;
output_dims
.
d
[
1
]
=
output_shape_
[
1
];
output_dims
.
d
[
2
]
=
output_shape_
[
2
];
return
output_dims
;
}
int
AvgPoolPlugin
::
enqueue
(
int
batchSize
,
const
void
*
const
*
inputs
,
void
**
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
{
auto
const
&
input_dims
=
this
->
getInputDims
(
0
);
int
input_size
=
0
;
float
const
*
idata
=
reinterpret_cast
<
float
const
*>
(
inputs
[
0
]);
float
**
odatas
=
reinterpret_cast
<
float
**>
(
outputs
);
paddle
::
operators
::
math
::
AvgPool
<
float
>
pool_process
;
paddle
::
operators
::
math
::
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
AvgPool
<
float
>
,
float
>
pool2d_forward
;
std
::
vector
<
int
>
input_shape
=
input_shape_
;
std
::
vector
<
int
>
output_shape
=
output_shape_
;
input_shape
.
insert
(
input_shape
.
begin
(),
batchSize
);
output_shape
.
insert
(
output_shape
.
begin
(),
batchSize
);
pool2d_forward
(
idata
,
input_shape
,
output_shape
,
ksize_
,
strides_
,
paddings_
,
pool_process
,
true
,
odatas
[
0
],
stream
);
return
cudaGetLastError
()
!=
cudaSuccess
;
}
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h
0 → 100644
浏览文件 @
5857fb30
// 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.
#pragma once
#include <cassert>
#include <vector>
#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
namespace
plugin
{
class
AvgPoolPlugin
:
public
PluginTensorRT
{
private:
bool
ceil_mode_
;
std
::
vector
<
int
>
ksize_
;
std
::
vector
<
int
>
strides_
;
std
::
vector
<
int
>
paddings_
;
std
::
vector
<
int
>
input_shape_
;
std
::
vector
<
int
>
output_shape_
;
protected:
size_t
getSerializationSize
()
override
{
return
SerializedSize
(
ceil_mode_
)
+
SerializedSize
(
ksize_
)
+
SerializedSize
(
strides_
)
+
SerializedSize
(
paddings_
)
+
SerializedSize
(
input_shape_
)
+
getBaseSerializationSize
();
}
// TRT will call this func when we need to serialize the configuration of
// tensorrt.
// It should not be called by users.
void
serialize
(
void
*
buffer
)
override
{
serializeBase
(
buffer
);
SerializeValue
(
&
buffer
,
ceil_mode_
);
SerializeValue
(
&
buffer
,
ksize_
);
SerializeValue
(
&
buffer
,
strides_
);
SerializeValue
(
&
buffer
,
paddings_
);
SerializeValue
(
&
buffer
,
input_shape_
);
}
public:
AvgPoolPlugin
(
bool
ceil_mode
,
std
::
vector
<
int
>
ksize
,
std
::
vector
<
int
>
strides
,
std
::
vector
<
int
>
paddings
,
std
::
vector
<
int
>
input_shape
)
:
ceil_mode_
(
ceil_mode
),
ksize_
(
ksize
),
strides_
(
strides
),
paddings_
(
paddings
),
input_shape_
(
input_shape
)
{
int
output_h
,
output_w
;
output_shape_
=
input_shape_
;
if
(
!
ceil_mode_
)
{
output_h
=
(
input_shape
[
1
]
-
ksize_
[
0
]
+
2
*
paddings_
[
0
])
/
strides_
[
0
]
+
1
;
output_w
=
(
input_shape
[
2
]
-
ksize_
[
1
]
+
2
*
paddings_
[
1
])
/
strides_
[
1
]
+
1
;
}
else
{
output_h
=
(
input_shape
[
1
]
-
ksize_
[
0
]
+
2
*
paddings_
[
0
]
+
strides_
[
0
]
-
1
)
/
strides_
[
0
]
+
1
;
output_w
=
(
input_shape
[
2
]
-
ksize_
[
1
]
+
2
*
paddings_
[
1
]
+
strides_
[
1
]
-
1
)
/
strides_
[
1
]
+
1
;
}
output_shape_
[
1
]
=
output_h
;
output_shape_
[
2
]
=
output_w
;
}
// It was used for tensorrt deserialization.
// It should not be called by users.
AvgPoolPlugin
(
void
const
*
serialData
,
size_t
serialLength
)
{
deserializeBase
(
serialData
,
serialLength
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
ceil_mode_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
ksize_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
strides_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
paddings_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
input_shape_
);
}
AvgPoolPlugin
*
clone
()
const
override
{
return
new
AvgPoolPlugin
(
ceil_mode_
,
ksize_
,
strides_
,
paddings_
,
input_shape_
);
}
const
char
*
getPluginType
()
const
override
{
return
"avg_pool"
;
}
int
getNbOutputs
()
const
override
{
return
1
;
}
nvinfer1
::
Dims
getOutputDimensions
(
int
index
,
const
nvinfer1
::
Dims
*
inputs
,
int
nbInputDims
)
override
;
int
initialize
()
override
{
return
0
;
}
int
enqueue
(
int
batchSize
,
const
void
*
const
*
inputs
,
void
**
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
override
;
};
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @
5857fb30
set
(
INFERENCE_EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
)
if
(
WITH_GPU AND TENSORRT_FOUND
)
set
(
INFERENCE_EXTRA_DEPS
${
INFERENCE_EXTRA_DEPS
}
analysis
${
analysis_deps
}
ir_pass_manager analysis_predictor
)
endif
()
function
(
download_model install_dir model_name
)
if
(
NOT EXISTS
${
install_dir
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
${
model_name
}
)
...
...
@@ -27,14 +31,14 @@ function(inference_analysis_api_test_with_fake_data target install_dir filename
endfunction
()
# RNN1
if
(
NOT APPLE
)
if
(
NOT APPLE
AND WITH_MKLML
)
set
(
RNN1_INSTALL_DIR
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/rnn1"
)
download_model_and_data
(
${
RNN1_INSTALL_DIR
}
"rnn1%2Fmodel.tar.gz"
"rnn1%2Fdata.txt.tar.gz"
)
inference_analysis_api_test
(
test_analyzer_rnn1
${
RNN1_INSTALL_DIR
}
analyzer_rnn1_tester.cc
)
else
()
# TODO: fix this test on MACOS, the reason is that
# fusion_seqexpand_concat_fc_op is not supported on MACOS
message
(
WARNING
"These tests has been disabled in OSX before being fixed:
\n
test_analyzer_rnn1"
)
# TODO: fix this test on MACOS
and OPENBLAS
, the reason is that
# fusion_seqexpand_concat_fc_op is not supported on MACOS
and OPENBLAS
message
(
WARNING
"These tests has been disabled in OSX
or WITH_MKL=OFF
before being fixed:
\n
test_analyzer_rnn1"
)
endif
()
# RNN2
...
...
@@ -75,11 +79,11 @@ endif()
inference_analysis_api_test
(
test_analyzer_ocr
${
OCR_INSTALL_DIR
}
analyzer_vis_tester.cc
)
# resnet50
inference_analysis_api_test_with_fake_data
(
test_analyzer_resnet50
inference_analysis_api_test_with_fake_data
(
test_analyzer_resnet50
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/resnet50"
analyzer_resnet50_tester.cc
"resnet50_model.tar.gz"
)
# mobilenet with depthwise_conv op
inference_analysis_api_test_with_fake_data
(
test_analyzer_mobilenet
inference_analysis_api_test_with_fake_data
(
test_analyzer_mobilenet
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/mobilenet_depthwise_conv"
analyzer_resnet50_tester.cc
"mobilenet_model.tar.gz"
)
# anakin
...
...
@@ -89,15 +93,15 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
set
(
ANAKIN_RNN1_INSTALL_DIR
"
${
ANAKIN_INSTALL_DIR
}
/rnn1"
)
inference_download
(
${
ANAKIN_RNN1_INSTALL_DIR
}
${
INFERENCE_URL
}
"anakin_test%2Fditu_rnn.anakin2.model.bin"
)
inference_download
(
${
ANAKIN_RNN1_INSTALL_DIR
}
${
INFERENCE_URL
}
"anakin_test%2Fditu_rnn_data.txt"
)
cc_test
(
test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=
${
ANAKIN_RNN1_INSTALL_DIR
}
/anakin_test%2Fditu_rnn.anakin2.model.bin
cc_test
(
test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=
${
ANAKIN_RNN1_INSTALL_DIR
}
/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=
${
ANAKIN_RNN1_INSTALL_DIR
}
/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared SERIAL
)
# anakin mobilenet
if
(
WITH_GPU
)
set
(
ANAKIN_MOBILENET_INSTALL_DIR
"
${
ANAKIN_INSTALL_DIR
}
/mobilenet"
)
inference_download
(
${
ANAKIN_MOBILENET_INSTALL_DIR
}
${
INFERENCE_URL
}
"mobilenet_v2.anakin.bin"
)
cc_test
(
test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
cc_test
(
test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
ARGS --model=
${
ANAKIN_MOBILENET_INSTALL_DIR
}
/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared dynload_cuda SERIAL
)
endif
()
...
...
@@ -109,6 +113,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
inference_download_and_uncompress
(
${
TRT_MODEL_INSTALL_DIR
}
${
INFERENCE_URL
}
/tensorrt_test
"trt_test_models.tar.gz"
)
endif
()
inference_analysis_test
(
test_trt_models SRCS trt_models_tester.cc
EXTRA_DEPS
${
INFERENCE_EXTRA_DEPS
}
analysis
${
analysis_deps
}
ir_pass_manager analysis_predictor
EXTRA_DEPS
${
INFERENCE_EXTRA_DEPS
}
ARGS --infer_model=
${
TRT_MODEL_INSTALL_DIR
}
/trt_test_models SERIAL
)
endif
()
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
5857fb30
...
...
@@ -222,19 +222,36 @@ void TestMultiThreadPrediction(
// The inputs of each thread are all the same.
std
::
vector
<
PaddleTensor
>
outputs_tid
;
auto
&
predictor
=
predictors
[
tid
];
LOG
(
INFO
)
<<
"running thread "
<<
tid
;
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
const
auto
&
input
:
inputs
)
{
ASSERT_TRUE
(
predictor
->
Run
(
input
,
&
outputs_tid
));
// warmup run
LOG
(
INFO
)
<<
"Running thread "
<<
tid
<<
", warm up run..."
;
{
Timer
warmup_timer
;
warmup_timer
.
tic
();
predictor
->
Run
(
inputs
[
0
],
outputs
,
batch_size
);
PrintTime
(
batch_size
,
1
,
num_threads
,
tid
,
warmup_timer
.
toc
(),
1
);
#if !defined(_WIN32)
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
#endif
}
auto
time
=
timer
.
toc
();
total_time
+=
time
;
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
time
/
num_times
,
inputs
.
size
());
LOG
(
INFO
)
<<
"Thread "
<<
tid
<<
" run "
<<
num_times
<<
" times..."
;
{
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
const
auto
&
input
:
inputs
)
{
ASSERT_TRUE
(
predictor
->
Run
(
input
,
&
outputs_tid
));
}
}
auto
time
=
timer
.
toc
();
total_time
+=
time
;
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
time
/
num_times
,
inputs
.
size
());
}
});
}
for
(
int
i
=
0
;
i
<
num_threads
;
++
i
)
{
...
...
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @
5857fb30
...
...
@@ -145,5 +145,3 @@ TEST(TensorRT_mobilenet, analysis) {
}
// namespace inference
}
// namespace paddle
USE_PASS
(
tensorrt_subgraph_pass
);
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
浏览文件 @
5857fb30
...
...
@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/memory/allocation/best_fit_allocator.h"
#include <random>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
...
...
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
浏览文件 @
5857fb30
...
...
@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <random>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
...
...
paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc
0 → 100644
浏览文件 @
5857fb30
/* Copyright (c) 2016 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 <mkldnn/include/mkldnn.hpp>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "xbyak.h"
#include "xbyak_util.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
DataLayout
;
using
mkldnn
::
memory
;
static
mkldnn
::
memory
::
format
StringToMKLDNNFormat
(
std
::
string
&
format
)
{
std
::
transform
(
format
.
begin
(),
format
.
end
(),
format
.
begin
(),
::
tolower
);
if
(
!
format
.
compare
(
"nchw"
))
{
return
memory
::
format
::
nchw
;
}
else
if
(
!
format
.
compare
(
"nchw16c"
))
{
return
memory
::
format
::
nChw16c
;
}
else
if
(
!
format
.
compare
(
"nchw8c"
))
{
return
memory
::
format
::
nChw8c
;
}
else
if
(
!
format
.
compare
(
"nhwc"
))
{
return
memory
::
format
::
nhwc
;
}
else
{
return
memory
::
format
::
any
;
}
}
static
void
UpdateDataFormat
(
const
framework
::
ExecutionContext
&
ctx
,
framework
::
Tensor
*
tensor
,
const
char
*
attribute
)
{
if
(
ctx
.
op
().
HasAttr
(
attribute
))
{
auto
format_as_string
=
ctx
.
Attr
<
std
::
string
>
(
attribute
);
auto
format
=
StringToMKLDNNFormat
(
format_as_string
);
if
(
format
!=
memory
::
format
::
any
)
{
tensor
->
set_format
(
format
);
}
}
}
template
<
typename
T
>
static
void
ReorderInput
(
framework
::
Tensor
*
tensor
,
const
platform
::
Place
&
place
,
const
mkldnn
::
engine
&
engine
,
bool
isFourDim
)
{
using
platform
::
to_void_cast
;
auto
dims
=
paddle
::
framework
::
vectorize2int
(
tensor
->
dims
());
framework
::
Tensor
out_tensor
;
out_tensor
.
Resize
(
tensor
->
dims
());
out_tensor
.
set_format
(
isFourDim
?
memory
::
format
::
nchw
:
memory
::
format
::
nc
);
out_tensor
.
set_layout
(
tensor
->
layout
());
mkldnn
::
memory
input_memory
=
{
{{
dims
,
platform
::
MKLDNNGetDataType
<
T
>
(),
tensor
->
format
()},
engine
},
to_void_cast
<
T
>
(
tensor
->
data
<
T
>
())};
mkldnn
::
memory
output_memory
=
{
{{
dims
,
platform
::
MKLDNNGetDataType
<
T
>
(),
out_tensor
.
format
()},
engine
},
to_void_cast
<
T
>
(
out_tensor
.
mutable_data
<
T
>
(
place
))};
platform
::
Reorder
(
input_memory
,
output_memory
);
tensor
->
ShareDataWith
(
out_tensor
);
}
template
<
typename
T
>
class
ElementwiseMulMKLDNNKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
Tensor
=
framework
::
Tensor
;
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
y_data
=
y
->
data
<
T
>
();
T
*
z_data
=
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x_dims
=
x
->
dims
();
auto
y_dims_untrimmed
=
y
->
dims
();
auto
x_int_dims
=
paddle
::
framework
::
vectorize2int
(
x_dims
);
UpdateDataFormat
(
ctx
,
(
Tensor
*
)
x
,
"x_data_format"
);
UpdateDataFormat
(
ctx
,
(
Tensor
*
)
y
,
"y_data_format"
);
Xbyak
::
util
::
Cpu
cpu
;
const
bool
is_avx512_enabled
=
cpu
.
has
(
Xbyak
::
util
::
Cpu
::
tAVX512F
);
const
bool
are_dims_divisable
=
!
(
x_int_dims
[
1
]
%
16
);
const
bool
is_x_format_correct
=
x
->
format
()
==
memory
::
format
::
nChw16c
;
const
bool
is_y_format_correct
=
y
->
format
()
==
memory
::
format
::
nc
;
if
(
is_x_format_correct
&&
is_y_format_correct
&&
are_dims_divisable
&&
is_avx512_enabled
)
{
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims_untrimmed
,
axis
,
&
pre
,
&
n
,
&
post
);
if
(
post
==
1
)
{
PADDLE_THROW
(
"Not implemented when post is 1"
);
}
else
{
// Just check whether it works for RE-Resnext.
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
"X should have 4 dimensions"
);
int
n
=
x_dims
[
0
];
int
c
=
x_dims
[
1
];
int
h
=
x_dims
[
2
];
int
w
=
x_dims
[
3
];
PADDLE_ENFORCE
(
y_dims_untrimmed
[
0
]
==
n
&&
y_dims_untrimmed
[
1
]
==
c
,
"Y should be in nc format"
);
constexpr
int
simd_width
=
16
;
int
C
=
c
/
simd_width
;
const
auto
&
multiply
=
math
::
jitkernel
::
KernelPool
::
Instance
()
.
template
Get
<
math
::
jitkernel
::
EltwiseMulnChw16cNCKernel
<
T
>
>
(
n
);
#pragma omp parallel for collapse(2)
for
(
int
ni
=
0
;
ni
<
n
;
ni
++
)
{
for
(
int
ci
=
0
;
ci
<
C
;
ci
++
)
{
auto
ptr_x
=
x_data
+
ni
*
C
*
h
*
w
*
simd_width
+
ci
*
h
*
w
*
simd_width
;
auto
ptr_y
=
y_data
+
ni
*
C
*
simd_width
+
ci
*
simd_width
;
auto
ptr_z
=
z_data
+
ni
*
C
*
h
*
w
*
simd_width
+
ci
*
h
*
w
*
simd_width
;
multiply
->
Compute
(
ptr_x
,
ptr_y
,
ptr_z
,
h
,
w
);
}
}
}
z
->
set_layout
(
DataLayout
::
kMKLDNN
);
z
->
set_format
(
x
->
format
());
}
else
{
// Fallback to naive version:
const
bool
are_inputs_in_same_format
=
x
->
format
()
==
y
->
format
();
const
bool
is_x_nchw
=
x
->
format
()
==
memory
::
format
::
nchw
;
const
bool
is_x_nc
=
x
->
format
()
==
memory
::
format
::
nc
;
const
bool
is_y_nchw
=
y
->
format
()
==
memory
::
format
::
nchw
;
const
bool
is_y_nc
=
y
->
format
()
==
memory
::
format
::
nc
;
if
(
!
are_inputs_in_same_format
)
{
using
platform
::
MKLDNNDeviceContext
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
if
(
!
(
is_x_nchw
||
is_x_nc
))
ReorderInput
<
T
>
((
Tensor
*
)
x
,
ctx
.
GetPlace
(),
mkldnn_engine
,
x
->
dims
().
size
()
==
4
);
if
(
!
(
is_y_nchw
||
is_y_nc
))
ReorderInput
<
T
>
((
Tensor
*
)
y
,
ctx
.
GetPlace
(),
mkldnn_engine
,
y
->
dims
().
size
()
==
4
);
}
auto
mul_func
=
[](
T
a
,
T
b
)
->
T
{
return
a
*
b
;
};
TransformFunctor
<
decltype
(
mul_func
),
T
,
paddle
::
platform
::
CPUDeviceContext
,
T
>
functor
(
x
,
y
,
z
,
ctx
.
template
device_context
<
paddle
::
platform
::
CPUDeviceContext
>(),
mul_func
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims_untrimmed
.
size
()
:
axis
);
PADDLE_ENFORCE
(
axis
>=
0
&&
axis
<
x_dims
.
size
(),
"Axis should be in range [0, x_dims)"
);
auto
y_dims
=
trim_trailing_singular_dims
(
y_dims_untrimmed
);
axis
=
(
y_dims
.
size
()
==
0
)
?
x_dims
.
size
()
:
axis
;
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
&
pre
,
&
n
,
&
post
);
if
(
post
==
1
)
{
functor
.
RunRowWise
(
n
,
pre
);
}
else
{
functor
.
RunMidWise
(
n
,
pre
,
post
);
}
z
->
set_layout
(
DataLayout
::
kMKLDNN
);
z
->
set_format
(
x
->
format
());
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_KERNEL
(
elementwise_mul
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
ElementwiseMulMKLDNNKernel
<
float
>
)
paddle/fluid/operators/elementwise/elementwise_op.h
浏览文件 @
5857fb30
...
...
@@ -97,6 +97,20 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
.
EqualGreaterThan
(
-
1
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false). Used by MKLDNN."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"x_data_format"
,
"(string, default NCHW) Only used in mkldnn"
"An optional string from:
\"
NHWC
\"
,
\"
NCHW
\"
,
\"
NCHW16C
\"
,
\"
NCHW8C
\"
. "
"Defaults to
\"\"
. Specify the data format of the output data, "
"the input will be transformed automatically. "
)
.
SetDefault
(
""
);
AddAttr
<
std
::
string
>
(
"y_data_format"
,
"(string, default
\"\"
) Only used in mkldnn"
"An optional string from:
\"
NHWC
\"
,
\"
NCHW
\"
,
\"
NCHW16C
\"
,
\"
NCHW8C
\"
. "
"Defaults to
\"\"
. Specify the data format of the output data, "
"the input will be transformed automatically. "
)
.
SetDefault
(
""
);
AddComment
(
string
::
Sprintf
(
R"DOC(
Elementwise %s Operator
...
...
paddle/fluid/operators/math/jit_code.h
浏览文件 @
5857fb30
...
...
@@ -322,6 +322,42 @@ class VActJitCode : public JitCode {
ymm_t
ymm_dst
=
ymm_t
(
1
);
};
#ifdef PADDLE_WITH_MKLDNN
struct
EltwiseMulnChw16cNC
:
public
Xbyak
::
CodeGenerator
{
explicit
EltwiseMulnChw16cNC
(
size_t
code_size
=
256
*
1024
)
:
Xbyak
::
CodeGenerator
(
code_size
)
{
// RDI is ptr x_input
// RSI is ptr y_input
// RDX is ptr output
// RCX is height
// r8 is width
push
(
rbx
);
xor_
(
rax
,
rax
);
xor_
(
r10
,
r10
);
vmovups
(
zmm3
,
ptr
[
rsi
]);
L
(
"h_loop"
);
xor_
(
rbx
,
rbx
);
L
(
"w_loop"
);
vmovups
(
zmm2
,
ptr
[
rdi
+
rax
]);
vmulps
(
zmm1
,
zmm2
,
zmm3
);
vmovups
(
ptr
[
rdx
+
rax
],
zmm1
);
add
(
rax
,
64
);
inc
(
rbx
);
cmp
(
r8
,
rbx
);
jnz
(
"w_loop"
);
inc
(
r10
);
cmp
(
r10
,
rcx
);
jnz
(
"h_loop"
);
pop
(
rbx
);
ret
();
}
};
#endif
}
// namespace gen
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
5857fb30
...
...
@@ -95,6 +95,15 @@ class VAddBiasKernel : public Kernel {
void
(
*
Compute
)(
const
T
*
,
const
T
*
,
T
*
,
int
);
};
#ifdef PADDLE_WITH_MKLDNN
template
<
typename
T
>
class
EltwiseMulnChw16cNCKernel
:
public
Kernel
{
public:
// nChw16c = nChw16c .* NC
void
(
*
Compute
)(
const
float
*
,
const
float
*
,
float
*
,
int
,
int
);
};
#endif
template
<
typename
T
>
class
VActKernel
:
public
Kernel
{
public:
...
...
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @
5857fb30
...
...
@@ -226,6 +226,44 @@ bool VAddKernelImpl<double>::useMKL(int d) {
}
#endif
#ifdef PADDLE_WITH_MKLDNN
/* EltwiseMul for nChw16c & NC inputs JitKernel */
template
<
typename
T
>
class
EltwiseMulnChw16cNCKernelImpl
:
public
math
::
jitkernel
::
EltwiseMulnChw16cNCKernel
<
T
>
{
public:
JITKERNEL_DECLARE_STATIC_FUNC
;
explicit
EltwiseMulnChw16cNCKernelImpl
(
int
d
)
:
EltwiseMulnChw16cNCKernel
<
T
>
()
{
using
mul_func_t
=
void
(
*
)(
const
float
*
,
const
float
*
,
float
*
,
int
,
int
);
#ifdef PADDLE_WITH_XBYAK
if
(
useJIT
(
d
))
{
// roughly estimate the size of code
size_t
sz
=
96
+
d
/
YMM_FLOAT_BLOCK
*
4
*
8
;
sz
=
sz
>
4096
?
sz
:
4096
;
jitcode_
.
reset
(
new
gen
::
EltwiseMulnChw16cNC
(
sz
));
this
->
Compute
=
(
mul_func_t
)
jitcode_
->
getCode
();
return
;
}
#endif
PADDLE_THROW
(
"This kernel shouldn't be used in Non-Xbyak, Non-MKL-DNN "
"environemnt"
);
}
#ifdef PADDLE_WITH_XBYAK
private:
std
::
unique_ptr
<
gen
::
EltwiseMulnChw16cNC
>
jitcode_
{
nullptr
};
};
template
<
>
bool
EltwiseMulnChw16cNCKernelImpl
<
float
>::
useJIT
(
int
d
)
{
return
true
;
}
#endif
#endif
/* VAddRelu JitKernel */
template
<
typename
T
>
class
VAddReluKernelImpl
:
public
VAddReluKernel
<
T
>
{
...
...
@@ -394,6 +432,9 @@ REGISTER_JITKERNEL(vscal, VScalKernel);
REGISTER_JITKERNEL
(
vaddbias
,
VAddBiasKernel
);
REGISTER_JITKERNEL
(
vrelu
,
VReluKernel
);
REGISTER_JITKERNEL
(
videntity
,
VIdentityKernel
);
#ifdef PADDLE_WITH_MKLDNN
REGISTER_JITKERNEL
(
eltwise_mul_nchw16c
,
EltwiseMulnChw16cNCKernel
);
#endif
}
// namespace jitkernel
}
// namespace math
...
...
paddle/fluid/operators/math/pooling.cu
浏览文件 @
5857fb30
...
...
@@ -153,6 +153,37 @@ __global__ void KernelMaxPool2DGrad(
}
}
template
<
typename
PoolProcess
,
typename
T
>
void
Pool2dDirectCUDAFunctor
<
PoolProcess
,
T
>::
operator
()(
const
T
*
input
,
const
std
::
vector
<
int
>&
input_shape
,
const
std
::
vector
<
int
>&
output_shape
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
T
*
output
,
cudaStream_t
stream
)
{
const
int
batch_size
=
input_shape
[
0
];
const
int
input_channels
=
input_shape
[
1
];
const
int
input_height
=
input_shape
[
2
];
const
int
input_width
=
input_shape
[
3
];
const
int
output_channels
=
output_shape
[
1
];
const
int
output_height
=
output_shape
[
2
];
const
int
output_width
=
output_shape
[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_width
=
strides
[
1
];
const
int
padding_height
=
paddings
[
0
];
const
int
padding_width
=
paddings
[
1
];
int
nthreads
=
batch_size
*
output_channels
*
output_height
*
output_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelPool2D
<
PoolProcess
,
T
><<<
grid
,
threads
,
0
,
stream
>>>
(
nthreads
,
input
,
input_channels
,
input_height
,
input_width
,
output_height
,
output_width
,
ksize_height
,
ksize_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
,
pool_compute
,
exclusive
,
output
);
}
/*
* All tensors are in NCHW format.
* Ksize, strides, paddings are two elements. These two elements represent
...
...
@@ -291,6 +322,11 @@ class MaxPool2dGradFunctor<platform::CUDADeviceContext, T> {
}
};
template
class
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
MaxPool
<
float
>,
float
>
;
template
class
Pool2dDirectCUDAFunctor
<
paddle
::
operators
::
math
::
AvgPool
<
float
>,
float
>
;
template
class
MaxPool2dGradFunctor
<
platform
::
CUDADeviceContext
,
float
>;
template
class
MaxPool2dGradFunctor
<
platform
::
CUDADeviceContext
,
double
>;
...
...
paddle/fluid/operators/math/pooling.h
浏览文件 @
5857fb30
...
...
@@ -82,6 +82,19 @@ class AvgPoolGrad {
* This is different from average pooling. So we rewrite the max_pool_grad:
* MaxPool2dGradFunctor, MaxPool3dGradFunctor.
*/
#ifdef PADDLE_WITH_CUDA
template
<
typename
PoolProcess
,
typename
T
>
class
Pool2dDirectCUDAFunctor
{
public:
void
operator
()(
const
T
*
input
,
const
std
::
vector
<
int
>&
input_shape
,
const
std
::
vector
<
int
>&
output_shape
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
T
*
output
,
cudaStream_t
stream
);
};
#endif
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
class
Pool2dFunctor
{
public:
...
...
paddle/fluid/operators/stack_op.h
浏览文件 @
5857fb30
...
...
@@ -147,20 +147,32 @@ class StackKernel : public framework::OpKernel<T> {
auto
&
dim
=
x
[
0
]
->
dims
();
for
(
auto
i
=
0
;
i
<
axis
;
++
i
)
pre
*=
dim
[
i
];
for
(
auto
i
=
axis
;
i
<
dim
.
size
();
++
i
)
post
*=
dim
[
i
];
int
total_num
=
pre
*
n
*
post
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
#ifdef __NVCC__
int
total_num
=
pre
*
n
*
post
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
thrust
::
device_vector
<
const
T
*>
device_x_vec
(
x_datas
);
auto
x_data_arr
=
device_x_vec
.
data
().
get
();
#else
auto
x_data_arr
=
x_datas
.
data
();
#endif
StackFunctorForRange
(
dev_ctx
,
x_data_arr
,
y_data
,
total_num
,
n
,
post
);
#ifdef __NVCC__
// Wait() must be called because device_x_vec may be destructed before
// kernel ends
dev_ctx
.
Wait
();
#else
auto
x_data_arr
=
x_datas
.
data
();
size_t
x_offset
=
0
;
size_t
y_offset
=
0
;
for
(
int
i
=
0
;
i
<
pre
;
i
++
)
{
for
(
int
j
=
0
;
j
<
n
;
j
++
)
{
std
::
memcpy
(
y_data
+
y_offset
,
x_data_arr
[
j
]
+
x_offset
,
post
*
sizeof
(
T
));
y_offset
+=
post
;
}
x_offset
+=
post
;
}
#endif
}
};
...
...
paddle/fluid/platform/init.cc
浏览文件 @
5857fb30
...
...
@@ -38,6 +38,7 @@ std::once_flag p2p_init_flag;
void
InitGflags
(
std
::
vector
<
std
::
string
>
argv
)
{
std
::
call_once
(
gflags_init_flag
,
[
&
]()
{
FLAGS_logtostderr
=
true
;
argv
.
insert
(
argv
.
begin
(),
"dummy"
);
int
argc
=
argv
.
size
();
char
**
arr
=
new
char
*
[
argv
.
size
()];
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
5857fb30
...
...
@@ -359,6 +359,9 @@ All parameter, weight, gradient are variables in Paddle.
return
self
.
GetMutable
<
platform
::
Communicator
>
();
},
py
::
return_value_policy
::
reference
)
#endif
#ifndef _WIN32
.
def
(
"get_reader"
,
[](
Variable
&
self
)
->
framework
::
ReaderHolder
*
{
PADDLE_ENFORCE
(
self
.
IsType
<
framework
::
ReaderHolder
>
());
...
...
@@ -366,7 +369,7 @@ All parameter, weight, gradient are variables in Paddle.
},
py
::
return_value_policy
::
reference
)
#endif
;
;
// NOLINT
#if !defined(_WIN32)
py
::
class_
<
framework
::
ReaderHolder
>
(
m
,
"Reader"
,
""
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
5857fb30
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
5857fb30
...
...
@@ -45,6 +45,10 @@ if(APPLE)
list
(
REMOVE_ITEM TEST_OPS test_dist_se_resnext
)
list
(
REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass
)
endif
()
if
(
NOT WITH_MKLML
)
# this op is not support on openblas
list
(
REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op
)
endif
()
function
(
py_test_modules TARGET_NAME
)
if
(
WITH_TESTING
)
...
...
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
5857fb30
...
...
@@ -362,7 +362,9 @@ class OpTest(unittest.TestCase):
else
:
return
[]
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
()
and
core
.
op_support_gpu
(
self
.
op_type
):
cpu_only
=
self
.
_cpu_only
if
hasattr
(
self
,
'_cpu_only'
)
else
False
if
core
.
is_compiled_with_cuda
()
and
core
.
op_support_gpu
(
self
.
op_type
)
\
and
not
cpu_only
:
places
.
append
(
core
.
CUDAPlace
(
0
))
return
places
...
...
python/paddle/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.py
0 → 100644
浏览文件 @
5857fb30
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
test_elementwise_mul_op
import
*
class
TestElementwiseMulMKLDNNOp_BroadcastNCHW16c
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
x
=
x
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
self
.
y
=
np
.
random
.
rand
(
1
,
16
).
astype
(
self
.
dtype
)
self
.
out
=
x
*
self
.
y
.
reshape
(
1
,
16
,
1
,
1
)
self
.
out
=
self
.
out
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_BroadcastNCHW16c
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw16c"
self
.
attrs
[
"y_data_format"
]
=
"nc"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
@
unittest
.
skip
(
"Not implemented yet."
)
# TODO(mgallus): enable when implemented.
class
TestElementwiseMulMKLDNNOp_BroadcastNCHW8c
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
x
=
np
.
random
.
rand
(
1
,
8
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
x
=
x
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
8
,
2
,
2
)
self
.
y
=
np
.
random
.
rand
(
1
,
8
).
astype
(
self
.
dtype
)
self
.
out
=
x
*
self
.
y
.
reshape
(
1
,
8
,
1
,
1
)
self
.
out
=
self
.
out
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
8
,
2
,
2
)
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_BroadcastNCHW8c
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw8c"
self
.
attrs
[
"y_data_format"
]
=
"nc"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackNCHW
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
y
=
np
.
random
.
rand
(
1
,
16
).
astype
(
self
.
dtype
)
self
.
out
=
self
.
x
*
self
.
y
.
reshape
(
1
,
16
,
1
,
1
)
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackNCHW16C
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
x
=
x
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
y
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
y
=
y
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
self
.
out
=
self
.
x
*
self
.
y
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_FallbackNCHW16C
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw16c"
self
.
attrs
[
"y_data_format"
]
=
"nchw16c"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackNoReorders
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
x
=
x
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
y
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
y
=
y
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
self
.
out
=
self
.
x
*
self
.
y
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_FallbackNoReorders
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw16c"
self
.
attrs
[
"y_data_format"
]
=
"nchw16c"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackWithReorder1
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
y
=
y
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
self
.
out
=
self
.
x
*
y
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_FallbackWithReorder1
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw"
self
.
attrs
[
"y_data_format"
]
=
"nchw16c"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackWithReorder2
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
self
.
y
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
x
=
np
.
random
.
rand
(
1
,
16
,
2
,
2
).
astype
(
self
.
dtype
)
self
.
x
=
x
.
transpose
(
0
,
2
,
3
,
1
).
reshape
(
1
,
16
,
2
,
2
)
self
.
out
=
x
*
self
.
y
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_FallbackWithReorder2
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nchw16c"
self
.
attrs
[
"y_data_format"
]
=
"nchw"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
class
TestElementwiseMulMKLDNNOp_FallbackNoReorders2
(
ElementwiseMulOp
):
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
rand
(
1
,
16
).
astype
(
self
.
dtype
)
self
.
y
=
np
.
random
.
rand
(
1
,
16
).
astype
(
self
.
dtype
)
self
.
out
=
self
.
x
*
self
.
y
def
setUp
(
self
):
super
(
TestElementwiseMulMKLDNNOp_FallbackNoReorders2
,
self
).
setUp
()
self
.
attrs
[
"x_data_format"
]
=
"nc"
self
.
attrs
[
"y_data_format"
]
=
"nc"
self
.
_cpu_only
=
True
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
init_axis
(
self
):
self
.
axis
=
0
def
test_check_grad_normal
(
self
):
pass
def
test_check_grad_ingore_x
(
self
):
pass
def
test_check_grad_ingore_y
(
self
):
pass
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
浏览文件 @
5857fb30
...
...
@@ -21,13 +21,24 @@ from paddle.fluid.op import Operator
class
ElementwiseMulOp
(
OpTest
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
False
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
dtype
=
np
.
float32
self
.
axis
=
-
1
self
.
init_dtype
()
self
.
init_input_output
()
self
.
init_kernel_type
()
self
.
init_axis
()
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float64"
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float64"
)
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
self
.
x
),
'Y'
:
OpTest
.
np_dtype_to_fluid_dtype
(
self
.
y
)
}
self
.
outputs
=
{
'Out'
:
np
.
multiply
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
self
.
outputs
=
{
'Out'
:
self
.
out
}
self
.
attrs
=
{
'axis'
:
self
.
axis
,
'use_mkldnn'
:
self
.
use_mkldnn
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -41,6 +52,17 @@ class ElementwiseMulOp(OpTest):
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
no_grad_set
=
set
(
'Y'
))
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
self
.
dtype
)
self
.
y
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
self
.
dtype
)
self
.
out
=
np
.
multiply
(
self
.
x
,
self
.
y
)
def
init_dtype
(
self
):
pass
def
init_axis
(
self
):
pass
class
TestElementwiseMulOp_scalar
(
ElementwiseMulOp
):
def
setUp
(
self
):
...
...
@@ -63,17 +85,13 @@ class TestElementwiseMulOp_Vector(ElementwiseMulOp):
class
TestElementwiseMulOp_broadcast_0
(
ElementwiseMulOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
rand
(
2
,
3
,
4
).
astype
(
np
.
float64
),
'Y'
:
np
.
random
.
rand
(
2
).
astype
(
np
.
float64
)
}
def
init_input_output
(
self
):
self
.
x
=
np
.
random
.
rand
(
2
,
3
,
4
).
astype
(
self
.
dtype
)
self
.
y
=
np
.
random
.
rand
(
2
).
astype
(
self
.
dtype
)
self
.
out
=
self
.
x
*
self
.
y
.
reshape
(
2
,
1
,
1
)
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
)
}
def
init_axis
(
self
):
self
.
axis
=
0
class
TestElementwiseMulOp_broadcast_1
(
ElementwiseMulOp
):
...
...
python/requirements.txt
浏览文件 @
5857fb30
requests==2.9.2
numpy>=1.12
,<=1.14 #TODO:change to ">=1.12" when numpy fix bug in 1.15 and higher version
numpy>=1.12
protobuf==3.1
recordio>=0.1.0
matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib
...
...
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