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体验新版 GitCode,发现更多精彩内容 >>
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ff052c0e
编写于
8月 21, 2018
作者:
N
nhzlx
浏览文件
操作
浏览文件
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差异文件
merge develop
上级
c6a5c4b0
3ae97aab
变更
44
隐藏空白更改
内联
并排
Showing
44 changed file
with
1340 addition
and
239 deletion
+1340
-239
CMakeLists.txt
CMakeLists.txt
+5
-4
cmake/configure.cmake
cmake/configure.cmake
+16
-11
cmake/cudnn.cmake
cmake/cudnn.cmake
+19
-2
cmake/external/anakin.cmake
cmake/external/anakin.cmake
+7
-6
cmake/flags.cmake
cmake/flags.cmake
+5
-0
cmake/simd.cmake
cmake/simd.cmake
+13
-1
paddle/fluid/framework/CMakeLists.txt
paddle/fluid/framework/CMakeLists.txt
+7
-2
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+2
-0
paddle/fluid/framework/details/execution_strategy.h
paddle/fluid/framework/details/execution_strategy.h
+3
-0
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
...uid/framework/details/fast_threaded_ssa_graph_executor.cc
+175
-0
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
...luid/framework/details/fast_threaded_ssa_graph_executor.h
+64
-0
paddle/fluid/framework/details/op_handle_base.cc
paddle/fluid/framework/details/op_handle_base.cc
+10
-0
paddle/fluid/framework/details/op_handle_base.h
paddle/fluid/framework/details/op_handle_base.h
+2
-0
paddle/fluid/framework/ir/graph.cc
paddle/fluid/framework/ir/graph.cc
+11
-2
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+9
-2
paddle/fluid/framework/program_desc.cc
paddle/fluid/framework/program_desc.cc
+21
-13
paddle/fluid/framework/program_desc.h
paddle/fluid/framework/program_desc.h
+2
-0
paddle/fluid/framework/program_desc_test.cc
paddle/fluid/framework/program_desc_test.cc
+25
-1
paddle/fluid/inference/api/CMakeLists.txt
paddle/fluid/inference/api/CMakeLists.txt
+3
-3
paddle/fluid/inference/api/high_level_api_cn.md
paddle/fluid/inference/api/high_level_api_cn.md
+3
-3
paddle/fluid/inference/tensorrt/CMakeLists.txt
paddle/fluid/inference/tensorrt/CMakeLists.txt
+1
-1
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+15
-4
paddle/fluid/operators/conditional_block_op.cc
paddle/fluid/operators/conditional_block_op.cc
+42
-30
paddle/fluid/operators/crf_decoding_op.h
paddle/fluid/operators/crf_decoding_op.h
+194
-0
paddle/fluid/operators/elementwise_op_function.h
paddle/fluid/operators/elementwise_op_function.h
+3
-0
paddle/fluid/operators/fc_op.cc
paddle/fluid/operators/fc_op.cc
+7
-24
paddle/fluid/operators/fusion_lstm_op.cc
paddle/fluid/operators/fusion_lstm_op.cc
+354
-0
paddle/fluid/operators/fusion_lstm_op.h
paddle/fluid/operators/fusion_lstm_op.h
+42
-0
paddle/fluid/operators/math/fc_compute.h
paddle/fluid/operators/math/fc_compute.h
+43
-0
paddle/fluid/operators/nccl/CMakeLists.txt
paddle/fluid/operators/nccl/CMakeLists.txt
+1
-1
paddle/fluid/operators/squeeze_op.cc
paddle/fluid/operators/squeeze_op.cc
+2
-10
paddle/fluid/operators/unsqueeze_op.cc
paddle/fluid/operators/unsqueeze_op.cc
+2
-11
paddle/fluid/platform/dynload/CMakeLists.txt
paddle/fluid/platform/dynload/CMakeLists.txt
+1
-1
paddle/fluid/platform/enforce.h
paddle/fluid/platform/enforce.h
+3
-3
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+12
-2
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+2
-2
python/paddle/fluid/tests/test_if_else_op.py
python/paddle/fluid/tests/test_if_else_op.py
+15
-3
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
...ddle/fluid/tests/unittests/parallel_executor_test_base.py
+4
-1
python/paddle/fluid/tests/unittests/test_fc_op.py
python/paddle/fluid/tests/unittests/test_fc_op.py
+27
-7
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
+151
-0
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
...dle/fluid/tests/unittests/test_parallel_executor_mnist.py
+9
-5
python/paddle/fluid/tests/unittests/test_squeeze_op.py
python/paddle/fluid/tests/unittests/test_squeeze_op.py
+1
-45
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
+1
-34
python/paddle/fluid/trainer.py
python/paddle/fluid/trainer.py
+6
-5
未找到文件。
CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -204,6 +204,11 @@ include(external/snappy) # download snappy
include
(
external/snappystream
)
include
(
external/threadpool
)
include
(
flags
)
# set paddle compile flags
include
(
cudnn
)
# set cudnn libraries, must before configure
include
(
cupti
)
include
(
configure
)
# add paddle env configuration
if
(
WITH_GPU
)
include
(
cuda
)
include
(
tensorrt
)
...
...
@@ -212,15 +217,11 @@ elseif()
set
(
WITH_ANAKIN OFF CACHE STRING
"Anakin is used in GPU only now."
FORCE
)
endif
()
include
(
cudnn
)
# set cudnn libraries, must before configure
include
(
cupti
)
include
(
configure
)
# add paddle env configuration
include
(
generic
)
# simplify cmake module
include
(
package
)
# set paddle packages
include
(
ccache
)
# set ccache for compilation
include
(
util
)
# set unittest and link libs
include
(
rdma
)
# set rdma libraries
include
(
flags
)
# set paddle compile flags
include
(
version
)
# set PADDLE_VERSION
include
(
coveralls
)
# set code coverage
include
(
inference_lib
)
# add paddle fluid inference libraries
...
...
cmake/configure.cmake
浏览文件 @
ff052c0e
...
...
@@ -50,16 +50,16 @@ if(NOT WITH_PROFILER)
endif
(
NOT WITH_PROFILER
)
if
(
NOT CMAKE_CROSSCOMPILING
)
if
(
WITH_AVX AND AVX_FOUND
)
if
(
WITH_AVX AND AVX512F_FOUND
)
set
(
SIMD_FLAG
${
AVX512F_FLAG
}
)
elseif
(
WITH_AVX AND AVX2_FOUND
)
set
(
SIMD_FLAG
${
AVX2_FLAG
}
)
elseif
(
WITH_AVX AND AVX_FOUND
)
set
(
SIMD_FLAG
${
AVX_FLAG
}
)
elseif
(
SSE3_FOUND
)
set
(
SIMD_FLAG
${
SSE3_FLAG
}
)
endif
()
endif
()
if
(
UNIX AND NOT APPLE
)
# except apple from nix*Os family
set
(
LINUX TRUE
)
endif
(
UNIX AND NOT APPLE
)
if
(
NOT WITH_GOLANG
)
add_definitions
(
-DPADDLE_WITHOUT_GOLANG
)
...
...
@@ -103,15 +103,20 @@ if(WITH_GPU)
endif
()
if
(
WITH_ANAKIN
)
if
(
${
CUDA_VERSION_MAJOR
}
VERSION_LESS 8
)
message
(
FATAL_ERROR
"Anakin needs CUDA >= 8.0 to compile"
)
message
(
WARNING
"Anakin needs CUDA >= 8.0 to compile. Force WITH_ANAKIN=OFF"
)
set
(
WITH_ANAKIN OFF CACHE STRING
"Anakin is valid only when CUDA >= 8.0."
FORCE
)
endif
()
if
(
${
CUDNN_MAJOR_VERSION
}
VERSION_LESS 7
)
message
(
FATAL_ERROR
"Anakin needs CUDNN >= 7.0 to compile"
)
message
(
WARNING
"Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF"
)
set
(
WITH_ANAKIN OFF CACHE STRING
"Anakin is valid only when CUDNN >= 7.0."
FORCE
)
endif
()
set
(
ENV{CUDNN_INCLUDE_DIR}
${
CUDNN_INCLUDE_DIR
}
)
set
(
ENV{CUDNN_LIBRARY}
${
CUDNN_LIBRARY
}
)
message
(
STATUS
"cudnn include header is
${
CUDNN_INCLUDE_DIR
}
/cudnn.h"
)
message
(
STATUS
"cudnn library is
${
CUDNN_LIBRARY
}
"
)
endif
()
if
(
WITH_ANAKIN
)
# NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR
# is a softlink to real cudnn.h directory
set
(
ENV{CUDNN_INCLUDE_DIR}
"
${
CUDNN_INCLUDE_DIR
}
/"
)
get_filename_component
(
CUDNN_LIBRARY_DIR
${
CUDNN_LIBRARY
}
DIRECTORY
)
set
(
ENV{CUDNN_LIBRARY}
${
CUDNN_LIBRARY_DIR
}
)
endif
()
elseif
(
WITH_AMD_GPU
)
add_definitions
(
-DPADDLE_WITH_HIP
)
...
...
cmake/cudnn.cmake
浏览文件 @
ff052c0e
...
...
@@ -25,8 +25,25 @@ list(APPEND CUDNN_CHECK_LIBRARY_DIRS
$ENV{CUDNN_ROOT}
$ENV{CUDNN_ROOT}/lib64
$ENV{CUDNN_ROOT}/lib
/usr/lib
)
find_library
(
CUDNN_LIBRARY NAMES libcudnn.so libcudnn.dylib
# libcudnn_static.a
/usr/lib
${
CUDA_TOOLKIT_ROOT_DIR
}
${
CUDA_TOOLKIT_ROOT_DIR
}
/lib/x64
)
set
(
CUDNN_LIB_NAME
""
)
if
(
LINUX
)
set
(
CUDNN_LIB_NAME
"libcudnn.so"
)
endif
(
LINUX
)
if
(
WIN32
)
# only support cudnn7
set
(
CUDNN_LIB_NAME
"cudnn.lib"
"cudnn64_7.dll"
)
endif
(
WIN32
)
if
(
Apple
)
set
(
CUDNN_LIB_NAME
"libcudnn.dylib"
"libcudnn.so"
)
endif
(
Apple
)
find_library
(
CUDNN_LIBRARY NAMES
${
CUDNN_LIB_NAME
}
# libcudnn_static.a
PATHS
${
CUDNN_CHECK_LIBRARY_DIRS
}
${
CUDNN_INCLUDE_DIR
}
${
__libpath_hist
}
NO_DEFAULT_PATH
DOC
"Path to cuDNN library."
)
...
...
cmake/external/anakin.cmake
浏览文件 @
ff052c0e
...
...
@@ -19,17 +19,17 @@ execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-cer
include_directories
(
${
ANAKIN_INCLUDE
}
)
include_directories
(
${
ANAKIN_INCLUDE
}
/saber/
)
set
(
ANAKIN_COMPILE_EXTRA_FLAGS
set
(
ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=format-extra-args -Wno-format-extra-args
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=switch -Wno-switch
-Wno-error=return-type -Wno-return-type
-Wno-error=return-type -Wno-return-type
-Wno-error=non-virtual-dtor -Wno-non-virtual-dtor
-Wno-sign-compare
-Wno-reorder
-Wno-reorder
-Wno-error=cpp
)
ExternalProject_Add
(
...
...
@@ -47,6 +47,7 @@ ExternalProject_Add(
-DPROTOBUF_ROOT=
${
THIRD_PARTY_PATH
}
/install/protobuf
-DMKLML_ROOT=
${
THIRD_PARTY_PATH
}
/install/mklml
-DCUDNN_ROOT=
${
CUDNN_ROOT
}
-DCUDNN_INCLUDE_DIR=
${
CUDNN_INCLUDE_DIR
}
${
EXTERNAL_OPTIONAL_ARGS
}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=
${
ANAKIN_INSTALL_DIR
}
)
...
...
cmake/flags.cmake
浏览文件 @
ff052c0e
...
...
@@ -142,6 +142,11 @@ else()
${
GPU_COMMON_FLAGS
}
)
endif
()
if
(
UNIX AND NOT APPLE
)
# except apple from nix*Os family
set
(
LINUX TRUE
)
endif
(
UNIX AND NOT APPLE
)
foreach
(
flag
${
COMMON_FLAGS
}
)
safe_set_cflag
(
CMAKE_C_FLAGS
${
flag
}
)
...
...
cmake/simd.cmake
浏览文件 @
ff052c0e
...
...
@@ -10,6 +10,7 @@ if(CMAKE_COMPILER_IS_GNUCC OR CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID
set
(
SSE3_FLAG
"-msse3"
)
set
(
AVX_FLAG
"-mavx"
)
set
(
AVX2_FLAG
"-mavx2"
)
set
(
AVX512F_FLAG
"-mavx512f"
)
elseif
(
MSVC
)
set
(
MMX_FLAG
"/arch:MMX"
)
set
(
SSE2_FLAG
"/arch:SSE2"
)
...
...
@@ -81,5 +82,16 @@ int main()
return 0;
}"
AVX2_FOUND
)
# Check AVX512F
set
(
CMAKE_REQUIRED_FLAGS
${
AVX512F_FLAG
}
)
set
(
AVX512F_FOUND_EXITCODE 1 CACHE STRING
"Result from TRY_RUN"
FORCE
)
CHECK_CXX_SOURCE_RUNS
(
"
#include <immintrin.h>
int main()
{
__m512i a = _mm512_undefined_epi32();
return 0;
}"
AVX512F_FOUND
)
set
(
CMAKE_REQUIRED_FLAGS
${
CMAKE_REQUIRED_FLAGS_RETAINED
}
)
mark_as_advanced
(
MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND
)
mark_as_advanced
(
MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND
AVX512F_FOUND
)
paddle/fluid/framework/CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -99,8 +99,13 @@ else()
cc_library
(
executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method
)
endif
()
cc_library
(
parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_pass multi_devices_graph_print_pass multi_devices_graph_check_pass
)
if
(
NOT WIN32
)
cc_library
(
parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fast_threaded_ssa_graph_executor
)
endif
()
# NOT WIN32
cc_library
(
prune SRCS prune.cc DEPS framework_proto
)
cc_test
(
prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context
)
...
...
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -42,3 +42,5 @@ cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_b
cc_library
(
scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor
)
#cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
# device_context reduce_op_handle )
cc_library
(
fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context
)
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @
ff052c0e
...
...
@@ -19,10 +19,13 @@ namespace framework {
namespace
details
{
struct
ExecutionStrategy
{
enum
ExecutorType
{
kDefault
=
0
,
kExperimental
=
1
};
size_t
num_threads_
{
0
};
bool
use_cuda_
{
true
};
bool
allow_op_delay_
{
false
};
size_t
num_iteration_per_drop_scope_
{
100
};
ExecutorType
type_
{
kDefault
};
};
}
// namespace details
...
...
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
0 → 100644
浏览文件 @
ff052c0e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
FastThreadedSSAGraphExecutor
::
FastThreadedSSAGraphExecutor
(
const
ExecutionStrategy
&
strategy
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
std
::
vector
<
platform
::
Place
>
&
places
,
std
::
unique_ptr
<
ir
::
Graph
>
&&
graph
)
:
strategy_
(
strategy
),
local_scopes_
(
local_scopes
),
places_
(
places
),
graph_
(
std
::
move
(
graph
)),
pool_
(
strategy
.
num_threads_
+
1
),
// add one more thread for generate op_deps
fetch_ctxs_
(
places
)
{
auto
&
ops
=
graph_
->
Get
<
details
::
GraphOps
>
(
"ops"
);
for
(
auto
&
op
:
ops
)
{
int
dep
=
static_cast
<
int
>
(
op
->
NotReadyInputSize
());
op_deps_
.
emplace
(
op
.
get
(),
dep
);
if
(
dep
==
0
)
{
bootstrap_ops_
.
emplace_back
(
op
.
get
());
}
}
PrepareAtomicOpDeps
();
}
FeedFetchList
FastThreadedSSAGraphExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
{
std
::
unique_ptr
<
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>>
op_deps
=
atomic_op_deps_
.
get
();
PrepareAtomicOpDeps
();
paddle
::
framework
::
FeedFetchList
fetches
;
fetches
.
resize
(
fetch_tensors
.
size
());
std
::
unordered_map
<
std
::
string
,
std
::
vector
<
VarHandleBase
*>>
fetched_vars
;
std
::
vector
<
std
::
unique_ptr
<
ir
::
Node
>>
fetch_nodes
;
std
::
vector
<
std
::
unique_ptr
<
FetchOpHandle
>>
fetch_ops
;
for
(
auto
&
fetch_var_name
:
fetch_tensors
)
{
for
(
auto
&
var_map
:
graph_
->
Get
<
details
::
GraphVars
>
(
"vars"
))
{
auto
it
=
var_map
.
find
(
fetch_var_name
);
if
(
it
!=
var_map
.
end
())
{
fetched_vars
[
fetch_var_name
].
push_back
(
it
->
second
.
rbegin
()
->
get
());
}
}
}
for
(
size_t
i
=
0
;
i
<
fetch_tensors
.
size
();
++
i
)
{
auto
&
var_name
=
fetch_tensors
[
i
];
auto
fetched_var_it
=
fetched_vars
.
find
(
var_name
);
PADDLE_ENFORCE
(
fetched_var_it
!=
fetched_vars
.
end
(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)"
);
auto
&
vars
=
fetched_var_it
->
second
;
fetch_nodes
.
emplace_back
(
new
ir
::
Node
(
"fetch"
,
ir
::
Node
::
Type
::
kOperation
));
auto
*
op
=
new
FetchOpHandle
(
fetch_nodes
.
back
().
get
(),
&
fetches
,
i
,
&
local_scopes_
);
fetch_ops
.
emplace_back
(
op
);
for
(
auto
&
p
:
places_
)
{
op
->
SetDeviceContext
(
p
,
fetch_ctxs_
.
Get
(
p
));
}
for
(
auto
*
var
:
vars
)
{
op
->
AddInput
(
var
);
}
(
*
op_deps
)[
op
]
=
static_cast
<
int
>
(
op
->
NotReadyInputSize
());
}
size_t
num_complete
=
0
;
remaining_
=
0
;
BlockingQueue
<
size_t
>
complete_q
;
for
(
auto
op
:
bootstrap_ops_
)
{
RunOpAsync
(
op_deps
.
get
(),
op
,
&
complete_q
);
}
while
(
num_complete
!=
op_deps
->
size
())
{
size_t
num_comp
=
complete_q
.
Pop
();
if
(
num_comp
==
-
1UL
)
{
int
remaining
=
0
;
while
(
true
)
{
remaining
=
remaining_
;
if
(
remaining
==
0
)
{
break
;
}
for
(
int
i
=
0
;
i
<
remaining
;
++
i
)
{
complete_q
.
Pop
();
}
}
exception_
.
ReThrow
();
}
num_complete
+=
num_comp
;
}
// Wait FetchOps.
if
(
!
fetch_ops
.
empty
())
{
fetch_ops
.
clear
();
}
return
fetches
;
}
void
FastThreadedSSAGraphExecutor
::
RunOpAsync
(
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>
*
op_deps
,
OpHandleBase
*
op
,
BlockingQueue
<
size_t
>
*
complete_q
)
{
++
remaining_
;
this
->
pool_
.
enqueue
([
=
]
{
OpHandleBase
*
op_to_run
=
op
;
size_t
complete
=
0
;
while
(
op_to_run
!=
nullptr
)
{
try
{
op_to_run
->
Run
(
strategy_
.
use_cuda_
);
++
complete
;
}
catch
(...)
{
exception_
.
Catch
(
std
::
current_exception
());
--
remaining_
;
complete_q
->
Push
(
-
1UL
);
return
;
}
auto
&
outputs
=
op_to_run
->
Outputs
();
op_to_run
=
nullptr
;
for
(
auto
&
output
:
outputs
)
{
for
(
auto
&
pending_op
:
output
->
PendingOps
())
{
std
::
atomic
<
int
>
&
deps
=
op_deps
->
at
(
pending_op
);
if
(
deps
.
fetch_sub
(
1
)
==
1
)
{
// pending_op ready
if
(
op_to_run
==
nullptr
)
{
op_to_run
=
pending_op
;
}
else
{
this
->
RunOpAsync
(
op_deps
,
pending_op
,
complete_q
);
}
}
}
}
}
--
remaining_
;
complete_q
->
Push
(
complete
);
});
}
void
FastThreadedSSAGraphExecutor
::
PrepareAtomicOpDeps
()
{
atomic_op_deps_
=
pool_
.
enqueue
([
&
]
{
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>
*
op_deps
=
new
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>
;
for
(
auto
&
pair
:
op_deps_
)
{
(
*
op_deps
)[
pair
.
first
]
=
pair
.
second
;
}
return
std
::
unique_ptr
<
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>>
(
op_deps
);
});
}
const
ir
::
Graph
&
FastThreadedSSAGraphExecutor
::
Graph
()
const
{
return
*
graph_
;
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
0 → 100644
浏览文件 @
ff052c0e
// 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 <string>
#include <vector>
#include "ThreadPool.h"
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
details
{
class
OpHandleBase
;
class
FastThreadedSSAGraphExecutor
:
public
SSAGraphExecutor
{
public:
FastThreadedSSAGraphExecutor
(
const
ExecutionStrategy
&
strategy
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
std
::
vector
<
platform
::
Place
>
&
places
,
std
::
unique_ptr
<
ir
::
Graph
>
&&
graph
);
FeedFetchList
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
override
;
const
ir
::
Graph
&
Graph
()
const
override
;
private:
ExecutionStrategy
strategy_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
platform
::
Place
>
places_
;
std
::
unique_ptr
<
ir
::
Graph
>
graph_
;
std
::
unordered_map
<
OpHandleBase
*
,
int
>
op_deps_
;
std
::
vector
<
OpHandleBase
*>
bootstrap_ops_
;
::
ThreadPool
pool_
;
platform
::
DeviceContextPool
fetch_ctxs_
;
std
::
atomic
<
int
>
remaining_
;
void
RunOpAsync
(
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>
*
op_deps
,
OpHandleBase
*
op
,
BlockingQueue
<
size_t
>
*
complete_q
);
void
PrepareAtomicOpDeps
();
std
::
future
<
std
::
unique_ptr
<
std
::
unordered_map
<
OpHandleBase
*
,
std
::
atomic
<
int
>>>>
atomic_op_deps_
;
ExceptionHolder
exception_
;
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @
ff052c0e
...
...
@@ -158,6 +158,16 @@ void OpHandleBase::RunAndRecordEvent(platform::Place p,
#endif
}
size_t
OpHandleBase
::
NotReadyInputSize
()
const
{
std
::
unordered_set
<
VarHandleBase
*>
res
;
for
(
auto
*
var
:
inputs_
)
{
if
(
var
->
GeneratedOp
()
!=
nullptr
)
{
res
.
emplace
(
var
);
}
}
return
res
.
size
();
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @
ff052c0e
...
...
@@ -81,6 +81,8 @@ class OpHandleBase {
return
res
.
size
();
}
size_t
NotReadyInputSize
()
const
;
const
std
::
vector
<
VarHandleBase
*>
&
Outputs
()
const
{
return
outputs_
;
}
size_t
NoDummyInputSize
()
const
;
...
...
paddle/fluid/framework/ir/graph.cc
浏览文件 @
ff052c0e
...
...
@@ -117,7 +117,15 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
}
// For output args, always create a new var.
for
(
auto
&
each_var_name
:
op
->
OutputArgumentNames
())
{
ir
::
Node
*
var
=
CreateVarNode
(
all_vars
.
at
(
each_var_name
));
ir
::
Node
*
var
=
nullptr
;
if
(
all_vars
.
count
(
each_var_name
)
!=
0
)
{
var
=
CreateVarNode
(
all_vars
.
at
(
each_var_name
));
}
else
{
// Operation output vars can be @EMPTY@. For example, while_grad
// can have multi @EMPTY@ outputs with no VarDesc.
// TODO(panyx0718): Add a test.
var
=
CreateEmptyNode
(
each_var_name
,
ir
::
Node
::
Type
::
kVariable
);
}
var_nodes
[
each_var_name
].
push_back
(
var
);
node
->
outputs
.
push_back
(
var
);
var
->
inputs
.
push_back
(
node
);
...
...
@@ -208,7 +216,8 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
// Add write after write dependence
ir
::
Node
*
upstream_op
=
(
*
it_old
)
->
inputs
.
empty
()
?
nullptr
:
(
*
it_old
)
->
inputs
[
0
];
if
(
upstream_op
)
{
// TODO(zcd): Add a test.
if
(
upstream_op
&&
upstream_op
!=
write_op
)
{
ir
::
Node
*
dep_var
=
CreateControlDepVar
();
write_op
->
inputs
.
push_back
(
dep_var
);
upstream_op
->
outputs
.
push_back
(
dep_var
);
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
ff052c0e
...
...
@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
...
...
@@ -193,8 +194,14 @@ ParallelExecutor::ParallelExecutor(
member_
->
local_scopes_
,
member_
->
use_cuda_
,
build_strategy
);
#endif
member_
->
executor_
.
reset
(
new
details
::
ThreadedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
places
,
std
::
move
(
graph
)));
if
(
exec_strategy
.
type_
==
ExecutionStrategy
::
kDefault
)
{
member_
->
executor_
.
reset
(
new
details
::
ThreadedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
places
,
std
::
move
(
graph
)));
}
else
{
member_
->
executor_
.
reset
(
new
details
::
FastThreadedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
places
,
std
::
move
(
graph
)));
}
member_
->
executor_
.
reset
(
new
details
::
ScopeBufferedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
std
::
move
(
var_infos
),
member_
->
places_
,
std
::
move
(
member_
->
executor_
)));
...
...
paddle/fluid/framework/program_desc.cc
浏览文件 @
ff052c0e
...
...
@@ -55,11 +55,20 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
auto
all_ops
=
blocks_
[
block_id
]
->
AllOps
();
for
(
size_t
op_id
=
0
;
op_id
<
all_ops
.
size
();
++
op_id
)
{
auto
&
op
=
all_ops
[
op_id
];
for
(
const
std
::
string
&
attr_name
:
op
->
AttrNames
())
{
if
(
op
->
GetAttrType
(
attr_name
)
==
proto
::
AttrType
::
BLOCK
)
{
int
sub_block_id
=
o
.
Block
(
block_id
).
Op
(
op_id
)
->
GetBlockAttrId
(
attr_name
);
op
->
SetBlockAttr
(
attr_name
,
MutableBlock
(
sub_block_id
));
}
else
if
(
op
->
GetAttrType
(
attr_name
)
==
proto
::
AttrType
::
BLOCKS
)
{
std
::
vector
<
int
>
sub_block_ids
=
o
.
Block
(
block_id
).
Op
(
op_id
)
->
GetBlocksAttrIds
(
attr_name
);
std
::
vector
<
BlockDesc
*>
block_descs
;
for
(
int
block_id
:
sub_block_ids
)
{
block_descs
.
push_back
(
MutableBlock
(
block_id
));
}
op
->
SetBlocksAttr
(
attr_name
,
block_descs
);
}
}
}
...
...
@@ -68,24 +77,16 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
ProgramDesc
::
ProgramDesc
(
const
proto
::
ProgramDesc
&
desc
)
{
desc_
=
desc
;
for
(
auto
&
block_desc
:
*
desc_
.
mutable_blocks
())
{
blocks_
.
emplace_back
(
new
BlockDesc
(
this
,
&
block_desc
));
}
for
(
auto
&
block
:
blocks_
)
{
for
(
auto
*
op
:
block
->
AllOps
())
{
for
(
const
auto
&
attr
:
op
->
Proto
()
->
attrs
())
{
if
(
attr
.
type
()
==
proto
::
AttrType
::
BLOCK
)
{
size_t
blk_idx
=
attr
.
block_idx
();
op
->
SetBlockAttr
(
attr
.
name
(),
this
->
MutableBlock
(
blk_idx
));
}
}
}
}
InitFromProto
();
}
ProgramDesc
::
ProgramDesc
(
const
std
::
string
&
binary_str
)
{
PADDLE_ENFORCE
(
desc_
.
ParseFromString
(
binary_str
),
"Fail to parse program_desc from binary string."
);
InitFromProto
();
}
void
ProgramDesc
::
InitFromProto
()
{
for
(
auto
&
block_desc
:
*
desc_
.
mutable_blocks
())
{
blocks_
.
emplace_back
(
new
BlockDesc
(
this
,
&
block_desc
));
}
...
...
@@ -95,6 +96,13 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
if
(
attr
.
type
()
==
proto
::
AttrType
::
BLOCK
)
{
size_t
blk_idx
=
attr
.
block_idx
();
op
->
SetBlockAttr
(
attr
.
name
(),
this
->
MutableBlock
(
blk_idx
));
}
else
if
(
attr
.
type
()
==
proto
::
AttrType
::
BLOCKS
)
{
auto
blks_idx
=
attr
.
blocks_idx
();
std
::
vector
<
BlockDesc
*>
block_descs
;
for
(
int
blk_idx
:
blks_idx
)
{
block_descs
.
push_back
(
this
->
MutableBlock
(
blk_idx
));
}
op
->
SetBlocksAttr
(
attr
.
name
(),
block_descs
);
}
}
}
...
...
paddle/fluid/framework/program_desc.h
浏览文件 @
ff052c0e
...
...
@@ -76,6 +76,8 @@ class ProgramDesc {
void
SetFetchHolderName
(
const
std
::
string
&
fetch_holder_name
);
private:
void
InitFromProto
();
proto
::
ProgramDesc
desc_
;
std
::
vector
<
std
::
unique_ptr
<
BlockDesc
>>
blocks_
;
...
...
paddle/fluid/framework/program_desc_test.cc
浏览文件 @
ff052c0e
...
...
@@ -42,6 +42,19 @@ TEST(ProgramDesc, copy_ctor) {
out
->
SetType
(
proto
::
VarType
::
LOD_TENSOR
);
op
->
SetOutput
(
"Y"
,
{
out
->
Name
()});
BlockDesc
*
new_block
=
program
.
AppendBlock
(
*
global_block
);
op
=
new_block
->
AppendOp
();
op
->
SetType
(
"mul"
);
op
=
global_block
->
AppendOp
();
op
->
SetType
(
"op_with_subblock"
);
op
->
SetAttr
(
"sub_block"
,
new_block
);
std
::
vector
<
BlockDesc
*>
sub_blocks
;
sub_blocks
.
push_back
(
program
.
AppendBlock
(
*
global_block
));
sub_blocks
.
push_back
(
program
.
AppendBlock
(
*
global_block
));
op
->
SetAttr
(
"sub_blocks"
,
sub_blocks
);
ProgramDesc
program_copy
(
program
);
auto
*
global_block_copy
=
program_copy
.
MutableBlock
(
0
);
...
...
@@ -64,6 +77,8 @@ TEST(ProgramDesc, copy_ctor) {
assert_same_var
(
"Y"
,
y
);
assert_same_var
(
"Out"
,
out
);
bool
found_sub_block
=
false
;
bool
found_sub_blocks
=
false
;
for
(
size_t
i
=
0
;
i
<
global_block
->
OpSize
();
++
i
)
{
auto
op_origin
=
global_block
->
Op
(
i
);
auto
op_copy
=
global_block_copy
->
Op
(
i
);
...
...
@@ -74,8 +89,17 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ
(
op_copy
->
Proto
()
->
SerializeAsString
(),
op_origin
->
Proto
()
->
SerializeAsString
());
}
if
(
op
->
Type
()
==
"op_with_subblock"
)
{
ASSERT_EQ
(
1
,
op
->
GetBlockAttrId
(
"sub_block"
));
found_sub_block
=
true
;
ASSERT_EQ
(
2
,
op
->
GetBlocksAttrIds
(
"sub_blocks"
).
size
());
found_sub_blocks
=
true
;
}
}
ASSERT_TRUE
(
found_sub_block
);
ASSERT_TRUE
(
found_sub_blocks
);
// Not check block's protostr are same it because the order of vars could be
// different and it is correct.
}
...
...
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -62,13 +62,13 @@ endif()
if
(
WITH_ANAKIN AND WITH_GPU
)
# only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
nv
_library
(
inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber
)
#nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin
)
cc
_library
(
inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber
)
cc_library
(
inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber
)
function
(
anakin_target target_name
)
target_compile_options
(
${
target_name
}
BEFORE PUBLIC
${
ANAKIN_COMPILE_EXTRA_FLAGS
}
)
endfunction
()
anakin_target
(
inference_anakin_api
)
#
anakin_target(inference_anakin_api_shared)
anakin_target
(
inference_anakin_api_shared
)
if
(
WITH_TESTING
)
cc_test
(
inference_anakin_test SRCS api_anakin_engine_tester.cc
ARGS --model=
${
ANAKIN_SOURCE_DIR
}
/mobilenet_v2.anakin.bin
...
...
paddle/fluid/inference/api/high_level_api_cn.md
浏览文件 @
ff052c0e
...
...
@@ -65,13 +65,13 @@ config.model_dir = "xxx";
config
.
use_gpu
=
false
;
// 创建一个原生的 PaddlePredictor
auto
predictor
=
paddle
::
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
paddle
::
CreatePaddlePredictor
<
paddle
::
NativeConfig
,
paddle
::
PaddleEngineKind
::
kNative
>
(
config
);
// 创建输入 tensor
int64_t
data
[
4
]
=
{
1
,
2
,
3
,
4
};
paddle
::
PaddleTensor
tensor
{.
name
=
""
,
.
shape
=
std
::
vector
<
int
>
({
4
,
1
}),
.
data
=
PaddleBuf
(
data
,
sizeof
(
data
)),
.
dtype
=
PaddleDType
::
INT64
};
.
data
=
paddle
::
PaddleBuf
(
data
,
sizeof
(
data
)),
.
dtype
=
paddle
::
PaddleDType
::
INT64
};
// 创建输出 tensor,输出 tensor 的内存可以复用
std
::
vector
<
paddle
::
PaddleTensor
>
outputs
;
// 执行预测
...
...
paddle/fluid/inference/tensorrt/CMakeLists.txt
浏览文件 @
ff052c0e
nv_library
(
tensorrt_engine SRCS engine.cc DEPS framework_proto
)
nv_library
(
tensorrt_engine SRCS engine.cc DEPS framework_proto
device_context
)
nv_test
(
test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader
)
nv_test
(
test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine
)
add_subdirectory
(
convert
)
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -84,6 +84,15 @@ function(op_library TARGET)
message
(
FATAL_ERROR
"The op library
${
TARGET
}
should contains at least one .cc file"
)
endif
()
#remove windows unsupported op
if
(
WIN32
)
foreach
(
windows_unsupport_op
"nccl_op"
"gen_nccl_id_op"
)
if
(
"
${
TARGET
}
"
STREQUAL
"
${
windows_unsupport_op
}
"
)
return
()
endif
()
endforeach
()
endif
(
WIN32
)
list
(
LENGTH op_library_DEPS op_library_DEPS_len
)
if
(
${
op_library_DEPS_len
}
GREATER 0
)
set
(
DEPS_OPS
${
TARGET
}
${
DEPS_OPS
}
PARENT_SCOPE
)
...
...
@@ -181,19 +190,19 @@ function(op_library TARGET)
endfunction
()
add_subdirectory
(
math
)
if
(
NOT WIN32
)
add_subdirectory
(
nccl
)
if
(
WITH_GPU
)
op_library
(
nccl_op DEPS nccl_common
)
file
(
APPEND
${
pybind_file
}
"USE_CUDA_ONLY_OP(ncclAllReduce);
\n
"
)
else
()
set
(
DEPS_OPS
${
DEPS_OPS
}
nccl_op
)
endif
()
endif
()
# NOT WIN32
set
(
DISTRIBUTE_DEPS
""
)
if
(
WITH_DISTRIBUTE
)
add_subdirectory
(
distributed
)
set
(
DISTRIBUTE_DEPS
""
)
if
(
WITH_GRPC
)
set
(
DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node
)
...
...
@@ -222,7 +231,7 @@ if(WITH_DISTRIBUTE)
#set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
#cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
# listen_and_serv_op sum_op executor SERIAL)
if
(
WITH_GPU
)
if
(
WITH_GPU
AND NOT WIN32
)
set_source_files_properties
(
test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_test
(
test_send_nccl_id SRCS test_send_nccl_id.cc DEPS listen_and_serv_op
${
DISTRIBUTE_DEPS
}
executor SERIAL
)
if
(
WITH_GRPC
)
...
...
@@ -233,7 +242,7 @@ if(WITH_DISTRIBUTE)
set_source_files_properties
(
gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
else
()
set
(
DEPS_OPS
${
DEPS_OPS
}
gen_nccl_id_op
)
endif
()
endif
()
# WITH_GPU AND NOT WIN32
else
()
set
(
DEPS_OPS
${
DEPS_OPS
}
checkpoint_notify_op prefetch_op recv_op listen_and_serv_op send_op send_barrier_op fetch_barrier_op gen_nccl_id_op
)
endif
()
...
...
@@ -331,5 +340,7 @@ cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_sea
cc_test
(
strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory
)
cc_test
(
save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op
)
cc_test
(
save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op
)
if
(
NOT WIN32
)
nv_test
(
nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context
)
endif
()
nv_test
(
dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor
)
paddle/fluid/operators/conditional_block_op.cc
浏览文件 @
ff052c0e
...
...
@@ -29,9 +29,9 @@ class ConditionalOp : public framework::OperatorBase {
protected:
std
::
vector
<
const
framework
::
LoDTensor
*>
InputTensors
(
const
framework
::
Scope
&
scope
)
const
{
const
framework
::
Scope
&
scope
,
const
std
::
string
&
in_name
)
const
{
std
::
vector
<
const
framework
::
LoDTensor
*>
retv
;
auto
xs
=
Inputs
(
"X"
);
auto
xs
=
Inputs
(
in_name
);
retv
.
resize
(
xs
.
size
(),
nullptr
);
std
::
transform
(
xs
.
begin
(),
xs
.
end
(),
retv
.
begin
(),
...
...
@@ -81,12 +81,18 @@ class ConditionalBlockOp : public ConditionalOp {
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
dev_place
)
const
override
{
auto
xs
=
InputTensors
(
scope
);
bool
need_run
;
if
(
Attr
<
bool
>
(
"is_scalar_condition"
))
{
// When is_scalar_condition is True, the conditional variable is a scalar,
// whether need to execute the operators in sub-block depends on the
// conditional variable (Cond).
auto
xs
=
InputTensors
(
scope
,
"Cond"
);
need_run
=
ScalarCondition
(
xs
);
}
else
{
// When is_scalar_condition is False, the conditional variable maybe a
// vector or tensor, whether need to execute the operators in sub-block
// depends on the input variables (Input).
auto
xs
=
InputTensors
(
scope
,
"Input"
);
need_run
=
std
::
all_of
(
xs
.
begin
(),
xs
.
end
(),
[](
const
framework
::
LoDTensor
*
t
)
{
return
t
->
numel
()
!=
0
;
});
...
...
@@ -110,11 +116,11 @@ class ConditionalBlockOp : public ConditionalOp {
class
ConditionalBlockOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"
X
"
,
"The conditional variable of this operator. If
X
is empty, the "
AddInput
(
"
Cond
"
,
"The conditional variable of this operator. If
Cond
is empty, the "
"whole sub-block will not be executed."
)
.
AsDuplicable
();
AddInput
(
"
Params
"
,
"The input variables of the sub-block."
).
AsDuplicable
();
AddInput
(
"
Input
"
,
"The input variables of the sub-block."
).
AsDuplicable
();
AddOutput
(
"Out"
,
"The output variables of the sub-block."
).
AsDuplicable
();
AddOutput
(
"Scope"
,
"(std::vector<Scope*>) The step scope of conditional block. To "
...
...
@@ -123,13 +129,18 @@ class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddAttr
<
framework
::
BlockDesc
*>
(
"sub_block"
,
"The step block of conditional block operator"
);
AddAttr
<
bool
>
(
"is_scalar_condition"
,
"
the input X
is used as scalar "
"condition"
)
"
The conditional variable (Cond)
is used as scalar "
"condition
.
"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(Conditional block operator
Run the sub-block if X is not empty. Params is the other inputs and Out is the
outputs of the sub-block.
If `is_scalar_condition` is True, the conditional variable (Cond) is a scalar,
run the operators in sub-block if Cond is True.
If `is_scalar_condition` is False, the conditional variable (Cond) is a vector or
tensor, run the operators in sub-block if all of input variables are not empty.
)DOC"
);
}
};
...
...
@@ -145,12 +156,12 @@ class ConditionalBlockGradOp : public ConditionalOp {
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
dev_place
)
const
override
{
auto
xs
=
this
->
InputTensors
(
scope
);
bool
need_run
;
if
(
Attr
<
bool
>
(
"is_scalar_condition"
))
{
auto
xs
=
this
->
InputTensors
(
scope
,
"Cond"
);
need_run
=
ScalarCondition
(
xs
);
}
else
{
auto
xs
=
this
->
InputTensors
(
scope
,
"Input"
);
need_run
=
std
::
all_of
(
xs
.
begin
(),
xs
.
end
(),
[](
const
framework
::
LoDTensor
*
t
)
{
return
t
->
numel
()
!=
0
;
});
...
...
@@ -166,11 +177,11 @@ class ConditionalBlockGradOp : public ConditionalOp {
auto
*
block
=
Attr
<
framework
::
BlockDesc
*>
(
"sub_block"
);
exec
.
Run
(
*
block
->
Program
(),
&
cur_scope
,
block
->
ID
(),
false
);
AssignLocalGradientToGlobal
(
dev_place
,
cur_scope
,
Inputs
(
"
Params
"
),
Outputs
(
framework
::
GradVarName
(
"
Params
"
)));
AssignLocalGradientToGlobal
(
dev_place
,
cur_scope
,
Inputs
(
"
Input
"
),
Outputs
(
framework
::
GradVarName
(
"
Input
"
)));
AssignLocalGradientToGlobal
(
dev_place
,
cur_scope
,
Inputs
(
"
X
"
),
Outputs
(
framework
::
GradVarName
(
"
X
"
)));
AssignLocalGradientToGlobal
(
dev_place
,
cur_scope
,
Inputs
(
"
Cond
"
),
Outputs
(
framework
::
GradVarName
(
"
Cond
"
)));
}
}
...
...
@@ -199,15 +210,15 @@ class ConditionalBlockGradOp : public ConditionalOp {
class
ConditionalBlockGradInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
PADDLE_ENFORCE
(
context
->
HasInputs
(
"
X
"
));
if
(
context
->
HasInputs
(
"
Params
"
))
{
PADDLE_ENFORCE
(
context
->
HasOutputs
(
framework
::
GradVarName
(
"
Params
"
)));
context
->
SetOutputsDim
(
framework
::
GradVarName
(
"
Params
"
),
context
->
GetInputsDim
(
"
Params
"
));
PADDLE_ENFORCE
(
context
->
HasInputs
(
"
Cond
"
));
if
(
context
->
HasInputs
(
"
Input
"
))
{
PADDLE_ENFORCE
(
context
->
HasOutputs
(
framework
::
GradVarName
(
"
Input
"
)));
context
->
SetOutputsDim
(
framework
::
GradVarName
(
"
Input
"
),
context
->
GetInputsDim
(
"
Input
"
));
}
if
(
context
->
HasOutputs
(
framework
::
GradVarName
(
"
X
"
)))
{
context
->
SetOutputsDim
(
framework
::
GradVarName
(
"
X
"
),
context
->
GetInputsDim
(
"
X
"
));
if
(
context
->
HasOutputs
(
framework
::
GradVarName
(
"
Cond
"
)))
{
context
->
SetOutputsDim
(
framework
::
GradVarName
(
"
Cond
"
),
context
->
GetInputsDim
(
"
Cond
"
));
}
}
};
...
...
@@ -220,14 +231,15 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
grad_op
=
new
framework
::
OpDesc
();
grad_op
->
SetType
(
"conditional_block_grad"
);
grad_op
->
SetInput
(
"
X"
,
Input
(
"X
"
));
grad_op
->
SetInput
(
"
Params"
,
Input
(
"Params
"
));
grad_op
->
SetInput
(
"
Cond"
,
Input
(
"Cond
"
));
grad_op
->
SetInput
(
"
Input"
,
Input
(
"Input
"
));
grad_op
->
SetInput
(
"Out"
,
Output
(
"Out"
));
grad_op
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
OutputGrad
(
"Out"
));
grad_op
->
SetInput
(
"Scope"
,
Output
(
"Scope"
));
grad_op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
,
false
));
grad_op
->
SetOutput
(
framework
::
GradVarName
(
"Params"
),
InputGrad
(
"Params"
,
false
));
grad_op
->
SetOutput
(
framework
::
GradVarName
(
"Cond"
),
InputGrad
(
"Cond"
,
false
));
grad_op
->
SetOutput
(
framework
::
GradVarName
(
"Input"
),
InputGrad
(
"Input"
,
false
));
grad_op
->
SetBlockAttr
(
"sub_block"
,
this
->
grad_block_
[
0
]);
grad_op
->
SetAttr
(
"is_scalar_condition"
,
GetAttr
(
"is_scalar_condition"
));
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
grad_op
);
...
...
paddle/fluid/operators/crf_decoding_op.h
浏览文件 @
ff052c0e
...
...
@@ -85,6 +85,199 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
int
*
track_value
=
track
.
mutable_data
<
int
>
(
emission_dims
,
platform
::
CPUPlace
());
#ifdef __AVX__
// It use the AVX or AVX512 instruction to deal the data as the vector of 8 or
// 16 elements per iteration. Then it can implement the parallel processing.
// Only optimize for float type.
#ifdef __AVX512F__
size_t
step_size
=
16
;
#else
size_t
step_size
=
8
;
#endif
if
(
std
::
is_same
<
T
,
float
>::
value
&&
(
tag_num
>=
step_size
))
{
size_t
steps
=
tag_num
/
step_size
;
size_t
remain
=
tag_num
%
step_size
;
int
last_offset
=
static_cast
<
int
>
(
remain
)
-
static_cast
<
int
>
(
step_size
);
// Setup the alpha initial value.
size_t
i_offset
=
0
;
for
(
size_t
i
=
0
;
i
<=
steps
;
++
i
)
{
#ifdef __AVX512F__
// Declare the variable for the content of weights, input and alpha
// values.
__m512
w_content
,
x_content
,
alpha_content
;
// Load the relevant data into the variables from un-aligned address.
w_content
=
_mm512_loadu_ps
((
const
float
*
)(
w
+
i_offset
));
x_content
=
_mm512_loadu_ps
((
const
float
*
)(
x
+
i_offset
));
alpha_content
=
_mm512_add_ps
(
w_content
,
x_content
);
// Save the alpha value.
_mm512_storeu_ps
(
reinterpret_cast
<
float
*>
(
alpha_value
+
i_offset
),
alpha_content
);
#else
// Declare the variable for the content of weights, input and alpha
// values.
__m256
w_content
,
x_content
,
alpha_content
;
// Load the relevant data into the variables from un-aligned address.
w_content
=
_mm256_loadu_ps
((
const
float
*
)(
w
+
i_offset
));
x_content
=
_mm256_loadu_ps
((
const
float
*
)(
x
+
i_offset
));
alpha_content
=
_mm256_add_ps
(
w_content
,
x_content
);
// Save the alpha value.
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
alpha_value
+
i_offset
),
alpha_content
);
#endif
i_offset
+=
step_size
;
if
(
i
==
steps
-
1
)
{
if
(
remain
>
0
)
{
i_offset
+=
last_offset
;
}
else
{
break
;
}
}
}
// Use the column-major strategy to get the location of maximum score.
size_t
seq_offset
=
0
;
for
(
size_t
k
=
1
;
k
<
seq_len
;
++
k
)
{
size_t
j_offset
=
0
;
for
(
size_t
j
=
0
;
j
<=
steps
;
++
j
)
{
#ifdef __AVX512F__
// Initialize the variables of maximum score and location.
__m512
max_score
=
_mm512_set1_ps
(
-
std
::
numeric_limits
<
T
>::
max
());
__m512i
max_j
=
_mm512_setzero_si512
();
#else
// Initialize the variables of maximum score and location.
__m256
max_score
=
_mm256_set1_ps
(
-
std
::
numeric_limits
<
T
>::
max
());
__m256i
max_j
=
_mm256_set1_epi32
(
0
);
#endif
// Calculate the offset of transition_weights.
size_t
trans_offset
=
state_trans_base_idx
*
tag_num
+
j_offset
;
for
(
size_t
i
=
0
;
i
<
tag_num
;
++
i
)
{
#ifdef __AVX512F__
// Initalize the content of alpha variable with related offset.
__m512
alpha_content
=
_mm512_set1_ps
(
*
(
const
float
*
)(
alpha_value
+
seq_offset
+
i
));
// Obtain the content of weights from un-aligned address.
__m512
w_content
=
_mm512_loadu_ps
((
const
float
*
)(
w
+
trans_offset
));
__m512
score_v
=
_mm512_add_ps
(
alpha_content
,
w_content
);
__mmask16
mask
=
_mm512_cmp_ps_mask
(
score_v
,
max_score
,
_CMP_GT_OS
);
// According to the mask value, it update the index of the max_score
// location.
max_j
=
_mm512_mask_set1_epi32
(
max_j
,
mask
,
i
);
// Update the max_score value.
max_score
=
_mm512_max_ps
(
max_score
,
score_v
);
#else
// Initalize the content of alpha variable with related offset.
__m256
alpha_content
=
_mm256_broadcast_ss
(
(
const
float
*
)(
alpha_value
+
seq_offset
+
i
));
// Obtain the content of weights from un-aligned address.
__m256
w_content
=
_mm256_loadu_ps
((
const
float
*
)(
w
+
trans_offset
));
__m256
score_v
=
_mm256_add_ps
(
alpha_content
,
w_content
);
__m256
mask
=
_mm256_cmp_ps
(
score_v
,
max_score
,
_CMP_GT_OS
);
#ifdef __AVX2__
// According to the mask value, it update the index of the max_score
// location.
max_j
=
_mm256_or_si256
(
_mm256_andnot_si256
((
__m256i
)
mask
,
max_j
),
_mm256_and_si256
((
__m256i
)
mask
,
_mm256_set1_epi32
(
i
)));
#else
__m128i
lo_max_j
=
_mm256_extractf128_si256
(
max_j
,
0
);
__m128i
hi_max_j
=
_mm256_extractf128_si256
(
max_j
,
1
);
__m128i
lo_mask
=
_mm256_extractf128_si256
((
__m256i
)
mask
,
0
);
__m128i
hi_mask
=
_mm256_extractf128_si256
((
__m256i
)
mask
,
1
);
lo_max_j
=
_mm_andnot_si128
(
lo_mask
,
lo_max_j
);
hi_max_j
=
_mm_andnot_si128
(
hi_mask
,
hi_max_j
);
lo_mask
=
_mm_and_si128
(
lo_mask
,
_mm_set1_epi32
(
i
));
hi_mask
=
_mm_and_si128
(
hi_mask
,
_mm_set1_epi32
(
i
));
lo_max_j
=
_mm_or_si128
(
lo_mask
,
lo_max_j
);
hi_max_j
=
_mm_or_si128
(
hi_mask
,
hi_max_j
);
// According to the mask value, it update the index of the max_score
// location.
max_j
=
_mm256_insertf128_si256
(
max_j
,
lo_max_j
,
0
);
max_j
=
_mm256_insertf128_si256
(
max_j
,
hi_max_j
,
1
);
#endif
// Update the max_score value.
max_score
=
_mm256_max_ps
(
max_score
,
score_v
);
#endif
trans_offset
+=
tag_num
;
}
#ifdef __AVX512F__
// Update the alpha and track values.
__m512
x_content
=
_mm512_loadu_ps
(
(
const
float
*
)(
x
+
seq_offset
+
tag_num
+
j_offset
));
max_score
=
_mm512_add_ps
(
max_score
,
x_content
);
_mm512_storeu_ps
(
reinterpret_cast
<
float
*>
(
alpha_value
+
seq_offset
+
tag_num
+
j_offset
),
max_score
);
_mm512_storeu_si512
(
reinterpret_cast
<
__m512i
*>
(
track_value
+
seq_offset
+
tag_num
+
j_offset
),
max_j
);
#else
// Update the alpha and track values.
__m256
x_content
=
_mm256_loadu_ps
(
(
const
float
*
)(
x
+
seq_offset
+
tag_num
+
j_offset
));
max_score
=
_mm256_add_ps
(
max_score
,
x_content
);
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
alpha_value
+
seq_offset
+
tag_num
+
j_offset
),
max_score
);
_mm256_storeu_si256
(
reinterpret_cast
<
__m256i
*>
(
track_value
+
seq_offset
+
tag_num
+
j_offset
),
max_j
);
#endif
// Calculate the offset of next step
j_offset
+=
step_size
;
if
(
j
==
steps
-
1
)
{
if
(
remain
>
0
)
{
j_offset
+=
last_offset
;
}
else
{
break
;
}
}
}
seq_offset
+=
tag_num
;
}
}
else
{
for
(
size_t
i
=
0
;
i
<
tag_num
;
++
i
)
alpha_value
[
i
]
=
w
[
i
]
+
x
[
i
];
for
(
size_t
k
=
1
;
k
<
seq_len
;
++
k
)
{
for
(
size_t
i
=
0
;
i
<
tag_num
;
++
i
)
{
T
max_score
=
-
std
::
numeric_limits
<
T
>::
max
();
int
max_j
=
0
;
for
(
size_t
j
=
0
;
j
<
tag_num
;
++
j
)
{
T
score
=
alpha_value
[(
k
-
1
)
*
tag_num
+
j
]
+
w
[(
j
+
state_trans_base_idx
)
*
tag_num
+
i
];
if
(
score
>
max_score
)
{
max_score
=
score
;
max_j
=
j
;
}
}
alpha_value
[
k
*
tag_num
+
i
]
=
max_score
+
x
[
k
*
tag_num
+
i
];
track_value
[
k
*
tag_num
+
i
]
=
max_j
;
}
}
}
#else
for
(
size_t
i
=
0
;
i
<
tag_num
;
++
i
)
alpha_value
[
i
]
=
w
[
i
]
+
x
[
i
];
for
(
size_t
k
=
1
;
k
<
seq_len
;
++
k
)
{
...
...
@@ -105,6 +298,7 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
}
}
#endif
T
max_score
=
-
std
::
numeric_limits
<
T
>::
max
();
int
max_i
=
0
;
for
(
size_t
i
=
0
;
i
<
tag_num
;
++
i
)
{
...
...
paddle/fluid/operators/elementwise_op_function.h
浏览文件 @
ff052c0e
...
...
@@ -80,6 +80,9 @@ inline framework::DDim trim_trailing_singular_dims(
for
(
int
i
=
0
;
i
<
actual_dims_size
;
++
i
)
{
trim_dims
[
i
]
=
dims
[
i
];
}
if
(
trim_dims
.
size
()
==
0
)
{
return
framework
::
DDim
(
framework
::
make_dim
());
}
framework
::
DDim
actual_dims
=
framework
::
make_ddim
(
trim_dims
);
return
actual_dims
;
}
...
...
paddle/fluid/operators/fc_op.cc
浏览文件 @
ff052c0e
...
...
@@ -15,8 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fc_op.h"
#include <vector>
#include "paddle/fluid/operators/math/blas.h"
DECLARE_int32
(
paddle_num_threads
);
#include "paddle/fluid/operators/math/fc_compute.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -110,13 +109,8 @@ void FCOpMaker::Make() {
AddComment
(
R"DOC(
Fully Connected Operator.
The fully connected operation calculates the output based on the input, weights and bias
attribute
.
The fully connected operation calculates the output based on the input, weights and bias.
The size of each dimension of the parameters checked in the infer-shape.
The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
Additional parametrs are use_mkldnn and bias_attr.
The input(X) size and output(Out) size may be diffrent.
The fully connected layer only supports MKLDNN version
)DOC"
);
}
...
...
@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> {
auto
in_dims
=
input
->
dims
();
auto
w_dims
=
w
->
dims
();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
dev_ctx
);
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
w_data
=
w
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
ctx
);
math
::
FCCompute
<
platform
::
CPUDeviceContext
,
T
>
(
blas
,
in_dims
[
0
],
w_dims
[
1
],
w_dims
[
0
],
input_data
,
w_data
,
output_data
,
bias
?
bias
->
data
<
T
>
()
:
NULL
);
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
in_dims
[
0
],
w_dims
[
1
],
w_dims
[
0
],
static_cast
<
T
>
(
1
),
input_data
,
w_data
,
static_cast
<
T
>
(
0
),
output_data
);
if
(
bias
)
{
const
T
*
bias_data
=
bias
->
data
<
T
>
();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for
(
int
bs
=
0
;
bs
<
in_dims
[
0
];
bs
++
)
{
blas
.
AXPY
(
w_dims
[
1
],
static_cast
<
T
>
(
1
),
bias_data
,
output_data
+
bs
*
w_dims
[
1
]);
}
}
// TODO(TJ): fuse act
}
};
...
...
paddle/fluid/operators/fusion_lstm_op.cc
0 → 100644
浏览文件 @
ff052c0e
/* 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 "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
namespace
paddle
{
namespace
operators
{
void
FusionLSTMOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"WeightX"
),
"Input(WeightX) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"WeightH"
),
"Input(WeightH) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Bias"
),
"Input(Bias) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"XX"
),
"Output(XX) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Hidden"
),
"Output(Hidden) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Cell"
),
"Output(Cell) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchedGate"
),
"Output(BatchedGate) of LSTM should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BatchCellPreAct"
),
"Output(BatchedGate) of LSTM should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank must be 2."
);
if
(
ctx
->
HasInput
(
"H0"
))
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"C0"
),
"Input(Cell) and Input(Hidden) of LSTM should not "
"be null at the same time."
);
auto
h_dims
=
ctx
->
GetInputDim
(
"H0"
);
auto
c_dims
=
ctx
->
GetInputDim
(
"C0"
);
PADDLE_ENFORCE
(
h_dims
==
c_dims
,
"The dimension of Input(H0) and Input(C0) "
"should be the same."
);
}
auto
wx_dims
=
ctx
->
GetInputDim
(
"WeightX"
);
PADDLE_ENFORCE_EQ
(
wx_dims
.
size
(),
2
,
"The rank of Input(WeightX) should be 2."
);
PADDLE_ENFORCE_EQ
(
wx_dims
[
0
],
x_dims
[
1
],
"The first dimension of Input(WeightX) "
"should be %d."
,
x_dims
[
1
]);
int
frame_size
=
wx_dims
[
1
]
/
4
;
auto
wh_dims
=
ctx
->
GetInputDim
(
"WeightH"
);
PADDLE_ENFORCE_EQ
(
wh_dims
.
size
(),
2
,
"The rank of Input(WeightH) should be 2."
);
PADDLE_ENFORCE_EQ
(
wh_dims
[
0
],
frame_size
,
"The first dimension of Input(WeightH) "
"should be %d."
,
frame_size
);
PADDLE_ENFORCE_EQ
(
wh_dims
[
1
],
4
*
frame_size
,
"The second dimension of Input(WeightH) "
"should be 4 * %d."
,
frame_size
);
auto
b_dims
=
ctx
->
GetInputDim
(
"Bias"
);
PADDLE_ENFORCE_EQ
(
b_dims
.
size
(),
2
,
"The rank of Input(Bias) should be 2."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
0
],
1
,
"The first dimension of Input(Bias) should be 1."
);
PADDLE_ENFORCE
(
!
ctx
->
Attrs
().
Get
<
bool
>
(
"use_peepholes"
),
"Do not support peephole yet."
);
PADDLE_ENFORCE_EQ
(
b_dims
[
1
],
4
*
frame_size
,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection"
,
frame_size
);
framework
::
DDim
out_dims
({
x_dims
[
0
],
frame_size
});
ctx
->
SetOutputDim
(
"Hidden"
,
out_dims
);
ctx
->
SetOutputDim
(
"Cell"
,
out_dims
);
ctx
->
SetOutputDim
(
"BatchedGate"
,
{
x_dims
[
0
],
wx_dims
[
1
]});
ctx
->
SetOutputDim
(
"BatchCellPreAct"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
"Hidden"
);
ctx
->
ShareLoD
(
"X"
,
"Cell"
);
int
xx_width
=
x_dims
[
1
]
>
wx_dims
[
1
]
?
wx_dims
[
1
]
:
x_dims
[
1
];
ctx
->
SetOutputDim
(
"XX"
,
{
x_dims
[
0
],
xx_width
});
ctx
->
ShareLoD
(
"X"
,
"XX"
);
}
framework
::
OpKernelType
FusionLSTMOp
::
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
void
FusionLSTMOpMaker
::
Make
()
{
AddInput
(
"X"
,
"(LoDTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x."
);
AddInput
(
"WeightX"
,
"(Tensor) the learnable weights of X."
" - The shape is (M x 4D), where M is the dim size of x, D is the "
"hidden size. "
" - Weight = {W_cx, W_ix, W_fx, W_ox}"
);
AddInput
(
"WeightH"
,
"(Tensor) same as LSTMOp, the learnable hidden-hidden weights."
" - The shape is (D x 4D), where D is the hidden size. "
" - Weight = {W_ch, W_ih, W_fh, W_oh}"
);
AddInput
(
"Bias"
,
"(Tensor) the learnable weights. Almost same as LSTMOp"
"Note: we should add the fc bias into this (1x4D) in bias."
"input-hidden bias weight and peephole connections weight if "
"setting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}."
);
AddInput
(
"H0"
,
"(Tensor, optional) (same as LSTMOp) the initial hidden state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size and D is the hidden size."
)
.
AsDispensable
();
AddInput
(
"C0"
,
"(Tensor, optional) (same as LSTMOp) (the initial cell state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time."
)
.
AsDispensable
();
AddOutput
(
"Hidden"
,
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`."
);
AddOutput
(
"Cell"
,
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`."
);
AddOutput
(
"XX"
,
"(LoDTensor) the result after X * WeightX (size is T x 4D)"
" or batched_X (size is T x M), this will be automatically chosen,"
" where T is the total time steps in this mini-batch,"
" D is the hidden size, M is the dim size of x input."
)
.
AsIntermediate
();
AddOutput
(
"BatchedGate"
,
"(LoDTensor) (same as LSTMOp)."
).
AsIntermediate
();
AddOutput
(
"BatchCellPreAct"
,
"(LoDTensor) (same as LSTMOp)."
)
.
AsIntermediate
();
AddAttr
<
bool
>
(
"use_peepholes"
,
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"is_reverse"
,
"(bool, defalut: False) "
"whether to compute reversed LSTM."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"gate_activation"
,
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default."
)
.
SetDefault
(
"sigmoid"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
std
::
string
>
(
"cell_activation"
,
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
std
::
string
>
(
"candidate_activation"
,
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddComment
(
R"DOC(
Fusion Long-Short Term Memory (LSTM) Operator.
This operator fuse the X into LSTM, more details can refer to LSTM op.
)DOC"
);
}
template
<
typename
DeviceContext
,
typename
T
>
inline
void
ReorderInitState
(
const
DeviceContext
&
ctx
,
const
framework
::
Tensor
&
src
,
framework
::
Vector
<
size_t
>
index_lod
,
framework
::
Tensor
*
dst
,
bool
indexed_src
)
{
math
::
CopyMatrixRowsFunctor
<
DeviceContext
,
T
>
row_shuffle
;
dst
->
mutable_data
<
T
>
(
src
.
dims
(),
ctx
.
GetPlace
());
// TODO(TJ): check mem copy perf
row_shuffle
(
ctx
,
src
,
index_lod
,
dst
,
indexed_src
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
FuisonLSTMKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
wx
=
ctx
.
Input
<
Tensor
>
(
"WeightX"
);
auto
*
wh
=
ctx
.
Input
<
Tensor
>
(
"WeightH"
);
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
hidden_t0
=
ctx
.
Input
<
Tensor
>
(
"H0"
);
auto
*
cell_t0
=
ctx
.
Input
<
Tensor
>
(
"C0"
);
auto
*
xx
=
ctx
.
Output
<
LoDTensor
>
(
"XX"
);
auto
*
batched_gate
=
ctx
.
Output
<
LoDTensor
>
(
"BatchedGate"
);
auto
*
hidden_out
=
ctx
.
Output
<
LoDTensor
>
(
"Hidden"
);
auto
*
cell_out
=
ctx
.
Output
<
LoDTensor
>
(
"Cell"
);
bool
is_reverse
=
ctx
.
Attr
<
bool
>
(
"is_reverse"
);
T
*
xx_data
=
xx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batched_gate_data
=
batched_gate
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
hidden_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
cell_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
wx_data
=
wx
->
data
<
T
>
();
auto
x_dims
=
x
->
dims
();
auto
wx_dims
=
wx
->
dims
();
math
::
LoDTensor2BatchFunctor
<
DeviceContext
,
T
>
to_batch
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
if
(
x_dims
[
1
]
>
wx_dims
[
1
])
{
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
x_dims
[
0
],
wx_dims
[
1
],
x_dims
[
1
],
x_data
,
wx_data
,
xx_data
,
bias
->
data
<
T
>
());
to_batch
(
dev_ctx
,
*
xx
,
batched_gate
,
true
,
is_reverse
);
}
else
{
to_batch
(
dev_ctx
,
*
x
,
xx
,
true
,
is_reverse
);
batched_gate
->
set_lod
(
xx
->
lod
());
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
x_dims
[
0
],
wx_dims
[
1
],
x_dims
[
1
],
xx_data
,
wx_data
,
batched_gate_data
,
bias
->
data
<
T
>
());
}
int
frame_size
=
static_cast
<
int
>
(
wx_dims
[
1
]
/
4
);
framework
::
DDim
out_dims
({
x_dims
[
0
],
frame_size
});
math
::
LstmMetaValue
<
T
>
lstm_value
;
// no peephole
lstm_value
.
check_ig
=
nullptr
;
lstm_value
.
check_fg
=
nullptr
;
lstm_value
.
check_og
=
nullptr
;
lstm_value
.
prev_state_value
=
nullptr
;
Tensor
ordered_c0
;
framework
::
Vector
<
size_t
>
order
(
batched_gate
->
lod
()[
2
]);
if
(
cell_t0
)
{
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState
<
DeviceContext
,
T
>
(
dev_ctx
,
*
cell_t0
,
order
,
&
ordered_c0
,
true
);
lstm_value
.
prev_state_value
=
ordered_c0
.
data
<
T
>
();
}
// Use the local variable as here.
LoDTensor
batch_hidden
,
batch_cell
;
auto
*
batch_cell_pre_act
=
ctx
.
Output
<
LoDTensor
>
(
"BatchCellPreAct"
);
batch_hidden
.
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
batch_cell
.
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
batch_cell_pre_act
->
mutable_data
<
T
>
(
out_dims
,
ctx
.
GetPlace
());
auto
batch_starts
=
batched_gate
->
lod
()[
0
];
size_t
max_seq_len
=
batch_starts
.
size
()
-
1
;
auto
gate_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"gate_activation"
));
auto
cell_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"cell_activation"
));
auto
cand_act
=
math
::
detail
::
GetActivationType
(
ctx
.
Attr
<
std
::
string
>
(
"candidate_activation"
));
for
(
size_t
n
=
0
;
n
<
max_seq_len
;
n
++
)
{
int
bstart
=
static_cast
<
int
>
(
batch_starts
[
n
]);
int
bend
=
static_cast
<
int
>
(
batch_starts
[
n
+
1
]);
Tensor
gate_t
=
batched_gate
->
Slice
(
bstart
,
bend
);
Tensor
out_t
=
batch_hidden
.
Slice
(
bstart
,
bend
);
Tensor
cell_t
=
batch_cell
.
Slice
(
bstart
,
bend
);
Tensor
cell_pre_act_t
=
batch_cell_pre_act
->
Slice
(
bstart
,
bend
);
int
cur_batch_size
=
bend
-
bstart
;
if
(
n
>
0
)
{
int
pre_h_start
=
static_cast
<
int
>
(
batch_starts
[
n
-
1
]);
int
pre_h_end
=
pre_h_start
+
cur_batch_size
;
auto
pre_hidden_t
=
batch_hidden
.
Slice
(
pre_h_start
,
pre_h_end
);
// TODO(TJ): use gemm directly
blas
.
MatMul
(
pre_hidden_t
,
false
,
*
wh
,
false
,
static_cast
<
T
>
(
1.0
),
&
gate_t
,
static_cast
<
T
>
(
1.0
));
}
else
if
(
hidden_t0
)
{
// TODO(TJ): move h0 outside for
// If n == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros, the calculation W_h * H0 will be skiped.
// If n == 0 and there is initialized hidden state, calculate W_h * H0.
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor
ordered_h0
;
ReorderInitState
<
DeviceContext
,
T
>
(
dev_ctx
,
*
hidden_t0
,
order
,
&
ordered_h0
,
true
);
// TODO(TJ): use gemm directly
blas
.
MatMul
(
ordered_h0
,
false
,
*
wh
,
false
,
static_cast
<
T
>
(
1.0
),
&
gate_t
,
static_cast
<
T
>
(
1.0
));
}
lstm_value
.
gate_value
=
gate_t
.
data
<
T
>
();
lstm_value
.
output_value
=
out_t
.
data
<
T
>
();
lstm_value
.
state_value
=
cell_t
.
data
<
T
>
();
lstm_value
.
state_active_value
=
cell_pre_act_t
.
data
<
T
>
();
math
::
LstmUnitFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
lstm_value
,
frame_size
,
cur_batch_size
,
gate_act
,
cell_act
,
cand_act
);
lstm_value
.
prev_state_value
=
lstm_value
.
state_value
;
}
math
::
Batch2LoDTensorFunctor
<
DeviceContext
,
T
>
to_seq
;
batch_hidden
.
set_lod
(
batched_gate
->
lod
());
// restore the output hidden in LoDTensor from the batch hidden
to_seq
(
dev_ctx
,
batch_hidden
,
hidden_out
);
batch_cell
.
set_lod
(
batched_gate
->
lod
());
// restore the output cell state in LoDTensor from the batch cell
to_seq
(
dev_ctx
,
batch_cell
,
cell_out
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
fusion_lstm
,
ops
::
FusionLSTMOp
,
ops
::
FusionLSTMOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OP_CPU_KERNEL
(
fusion_lstm
,
ops
::
FuisonLSTMKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
FuisonLSTMKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/fusion_lstm_op.h
0 → 100644
浏览文件 @
ff052c0e
/* 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. */
#pragma once
// #include <string>
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
Tensor
=
framework
::
Tensor
;
class
FusionLSTMOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
;
};
class
FusionLSTMOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
;
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/fc_compute.h
0 → 100644
浏览文件 @
ff052c0e
/* 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. */
#pragma once
#include "paddle/fluid/operators/math/blas.h"
DECLARE_int32
(
paddle_num_threads
);
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
DeviceContext
,
typename
T
>
inline
void
FCCompute
(
const
BlasT
<
DeviceContext
,
T
>&
blas
,
const
int
M
,
const
int
N
,
const
int
K
,
const
T
*
X
,
const
T
*
W
,
T
*
Y
,
const
T
*
B
=
NULL
)
{
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
M
,
N
,
K
,
static_cast
<
T
>
(
1
),
X
,
W
,
static_cast
<
T
>
(
0
),
Y
);
if
(
B
)
{
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for
(
int
i
=
0
;
i
<
M
;
i
++
)
{
blas
.
AXPY
(
N
,
static_cast
<
T
>
(
1
),
B
,
Y
+
i
*
N
);
}
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/nccl/CMakeLists.txt
浏览文件 @
ff052c0e
if
(
WITH_GPU
)
if
(
WITH_GPU
AND NOT WIN32
)
nv_library
(
nccl_common SRCS nccl_gpu_common.cc DEPS device_context operator
)
endif
()
paddle/fluid/operators/squeeze_op.cc
浏览文件 @
ff052c0e
...
...
@@ -23,9 +23,9 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of Squeeze
Op
should not be null."
);
"Input(X) of Squeeze
operator
should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of Squeeze
Op
should not be null."
);
"Output(Out) of Squeeze
operator
should not be null."
);
const
auto
&
x_dims
=
ctx
->
GetInputDim
(
"X"
);
// Check input tensor dims (<6) Eigen limit.
...
...
@@ -107,7 +107,6 @@ class SqueezeOp : public framework::OperatorBase {
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
out_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
// Invoke Reshape Op
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
Input
(
"X"
)}},
{
"Shape"
,
{}}},
...
...
@@ -125,12 +124,6 @@ class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
"(std::vector<int>). List of integers,"
" indicating the dimensions to squeeze."
)
.
SetDefault
({});
AddAttr
<
bool
>
(
"inplace"
,
"(default: false) Squeeze the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x)."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Squeeze Operator.
...
...
@@ -180,7 +173,6 @@ class SqueezeGradOp : public framework::OperatorBase {
auto
x_dims
=
scope
.
FindVar
(
Input
(
"X"
))
->
Get
<
framework
::
LoDTensor
>
().
dims
();
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
x_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
dout_name
}},
{
"Shape"
,
{}}},
{{
"Out"
,
{
dx_name
}}},
...
...
paddle/fluid/operators/unsqueeze_op.cc
浏览文件 @
ff052c0e
...
...
@@ -23,9 +23,9 @@ class UnsqueezeOpInferShape : public framework::InferShapeBase {
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of Unsqueeze
Op
should not be null."
);
"Input(X) of Unsqueeze
operator
should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of Unsqueeze
Op
should not be null."
);
"Output(Out) of Unsqueeze
operator
should not be null."
);
const
auto
&
axes
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"axes"
);
const
auto
&
x_dims
=
ctx
->
GetInputDim
(
"X"
);
...
...
@@ -95,7 +95,6 @@ class UnsqueezeOp : public framework::OperatorBase {
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
out_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
// Invoke Reshape op.
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
Input
(
"X"
)}},
{
"Shape"
,
{}}},
...
...
@@ -126,13 +125,6 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
" within [1, 6] dimensions (Eigen limit)."
);
}
});
AddAttr
<
bool
>
(
"inplace"
,
"(default: false) Unsqueeze the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x)."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Unsqueeze Operator.
...
...
@@ -168,7 +160,6 @@ class UnsqueezeGradOp : public framework::OperatorBase {
framework
::
AttributeMap
attrs
;
attrs
[
"shape"
]
=
framework
::
vectorize2int
(
x_dims
);
attrs
[
"inplace"
]
=
Attr
<
bool
>
(
"inplace"
);
auto
reshape_op
=
framework
::
OpRegistry
::
CreateOp
(
"reshape"
,
{{
"X"
,
{
dout_name
}},
{
"Shape"
,
{}}},
{{
"Out"
,
{
dx_name
}}},
...
...
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @
ff052c0e
...
...
@@ -3,7 +3,7 @@ cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags enforce)
list
(
APPEND CUDA_SRCS cublas.cc cudnn.cc curand.cc
)
# There is no macOS version of NCCL.
if
(
NOT APPLE
)
if
(
NOT APPLE
AND NOT WIN32
)
list
(
APPEND CUDA_SRCS nccl.cc
)
endif
()
...
...
paddle/fluid/platform/enforce.h
浏览文件 @
ff052c0e
...
...
@@ -44,7 +44,7 @@ limitations under the License. */
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/dynload/curand.h"
#if
ndef __APPLE__
#if
!defined(__APPLE__) and !defined(_WIN32)
#include "paddle/fluid/platform/dynload/nccl.h"
#endif // __APPLE__
#endif // PADDLE_WITH_CUDA
...
...
@@ -205,7 +205,7 @@ inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
#endif
}
#if
ndef __APPLE__
#if
!defined(__APPLE__) and !defined(_WIN32)
template
<
typename
...
Args
>
inline
typename
std
::
enable_if
<
sizeof
...(
Args
)
!=
0
,
void
>::
type
throw_on_error
(
ncclResult_t
stat
,
const
Args
&
...
args
)
{
...
...
@@ -221,7 +221,7 @@ inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
#endif
}
}
#endif // __APPLE__
#endif // __APPLE__
and windows
#endif // PADDLE_WITH_CUDA
template
<
typename
T
>
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
ff052c0e
...
...
@@ -596,8 +596,8 @@ All parameter, weight, gradient are variables in Paddle.
// -- python binds for parallel executor.
py
::
class_
<
ParallelExecutor
>
pe
(
m
,
"ParallelExecutor"
);
py
::
class_
<
ExecutionStrategy
>
(
pe
,
"ExecutionStrategy"
)
.
def
(
py
::
init
())
py
::
class_
<
ExecutionStrategy
>
exec_strategy
(
pe
,
"ExecutionStrategy"
);
exec_strategy
.
def
(
py
::
init
())
.
def_property
(
"num_threads"
,
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
num_threads_
;
},
...
...
@@ -624,6 +624,16 @@ All parameter, weight, gradient are variables in Paddle.
[](
ExecutionStrategy
&
self
,
size_t
num_iteration_per_drop_scope
)
{
self
.
num_iteration_per_drop_scope_
=
num_iteration_per_drop_scope
;
});
exec_strategy
.
def_property
(
"use_experimental_executor"
,
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
type_
==
ExecutionStrategy
::
kExperimental
;
},
[](
ExecutionStrategy
&
self
,
bool
experimental
)
{
self
.
type_
=
experimental
?
ExecutionStrategy
::
kExperimental
:
ExecutionStrategy
::
kDefault
;
});
py
::
class_
<
BuildStrategy
>
build_strategy
(
pe
,
"BuildStrategy"
);
py
::
enum_
<
BuildStrategy
::
ReduceStrategy
>
(
build_strategy
,
"ReduceStrategy"
)
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
ff052c0e
...
...
@@ -1272,8 +1272,8 @@ class ConditionalBlock(object):
parent_block
.
append_op
(
type
=
'conditional_block'
,
inputs
=
{
'
X
'
:
self
.
inputs
,
'
Params
'
:
param_list
,
'
Cond
'
:
self
.
inputs
,
'
Input
'
:
param_list
,
},
outputs
=
{
'Out'
:
out_list
,
'Scope'
:
[
step_scope
]},
...
...
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @
ff052c0e
...
...
@@ -30,7 +30,8 @@ import numpy as np
class
TestMNISTIfElseOp
(
unittest
.
TestCase
):
def
test_raw_api
(
self
):
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def
not_test_raw_api
(
self
):
prog
=
Program
()
startup_prog
=
Program
()
with
program_guard
(
prog
,
startup_prog
):
...
...
@@ -91,7 +92,8 @@ class TestMNISTIfElseOp(unittest.TestCase):
return
self
.
assertFalse
(
True
)
def
test_ifelse
(
self
):
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def
not_test_ifelse
(
self
):
prog
=
Program
()
startup_prog
=
Program
()
with
program_guard
(
prog
,
startup_prog
):
...
...
@@ -153,6 +155,13 @@ class TestIfElse(unittest.TestCase):
self
.
cond_value
=
0.5
self
.
data
=
np
.
random
.
rand
(
25
,
1
).
astype
(
np
.
float32
)
def
numpy_cal
(
self
):
s1
=
self
.
data
[
np
.
where
(
self
.
data
<
self
.
cond_value
)]
res
=
np
.
sum
(
np
.
exp
(
s1
))
s2
=
self
.
data
[
np
.
where
(
self
.
data
>=
self
.
cond_value
)]
res
+=
np
.
sum
(
np
.
tanh
(
s2
))
return
res
def
compare_ifelse_op_and_numpy
(
self
,
place
):
self
.
set_test_case
()
...
...
@@ -166,10 +175,12 @@ class TestIfElse(unittest.TestCase):
ie
=
layers
.
IfElse
(
ifcond
)
with
ie
.
true_block
():
true_target
=
ie
.
input
(
src
)
true_target
=
fluid
.
layers
.
exp
(
true_target
)
ie
.
output
(
true_target
)
with
ie
.
false_block
():
false_target
=
ie
.
input
(
src
)
false_target
=
fluid
.
layers
.
tanh
(
false_target
)
ie
.
output
(
false_target
)
if_out
=
ie
()
out
=
layers
.
reduce_sum
(
if_out
)
...
...
@@ -180,7 +191,8 @@ class TestIfElse(unittest.TestCase):
o1
,
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'data'
:
self
.
data
},
fetch_list
=
[
out
])
o2
=
np
.
sum
(
self
.
data
)
o2
=
self
.
numpy_cal
()
self
.
assertTrue
(
np
.
allclose
(
o1
,
o2
,
atol
=
1e-8
),
...
...
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @
ff052c0e
...
...
@@ -38,7 +38,8 @@ class TestParallelExecutorBase(unittest.TestCase):
seed
=
None
,
use_parallel_executor
=
True
,
use_reduce
=
False
,
optimizer
=
fluid
.
optimizer
.
Adam
):
optimizer
=
fluid
.
optimizer
.
Adam
,
use_fast_executor
=
False
):
def
run_executor
(
exe
,
feed
,
fetch_list
,
program
=
None
):
if
isinstance
(
exe
,
fluid
.
ParallelExecutor
):
res
=
exe
.
run
(
fetch_list
=
fetch_list
,
feed
=
feed
)
...
...
@@ -71,6 +72,8 @@ class TestParallelExecutorBase(unittest.TestCase):
startup_exe
.
run
(
startup
)
exec_strategy
=
fluid
.
ExecutionStrategy
()
exec_strategy
.
allow_op_delay
=
allow_op_delay
if
use_fast_executor
:
exec_strategy
.
use_experimental_executor
=
True
build_strategy
=
fluid
.
BuildStrategy
()
build_strategy
.
reduce_strategy
=
fluid
.
BuildStrategy
.
ReduceStrategy
.
Reduce
\
...
...
python/paddle/fluid/tests/unittests/test_fc_op.py
浏览文件 @
ff052c0e
...
...
@@ -64,27 +64,47 @@ class TestFCOp(OpTest):
self
.
check_output
()
class
TestFCOp
BiasBoth
(
TestFCOp
):
class
TestFCOp
NoBias
(
TestFCOp
):
def
init_shapes
(
self
,
mb
,
ic
,
oc
,
h
,
w
):
for
with_bias
in
{
True
,
False
}:
self
.
with_bias
=
with_bias
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
self
.
with_bias
=
False
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
class
TestFCOp1
(
TestFCOpBiasBoth
):
class
TestFCOpWithBias
(
TestFCOp
):
def
init_shapes
(
self
,
mb
,
ic
,
oc
,
h
,
w
):
self
.
with_bias
=
True
self
.
matrix
=
MatrixGenerate
(
mb
,
ic
,
oc
,
h
,
w
)
class
TestFCOp1
(
TestFCOpNoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
2
,
8
,
10
,
1
,
1
)
class
TestFCOp2
(
TestFCOp
BiasBoth
):
class
TestFCOp2
(
TestFCOp
NoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
4
,
5
,
6
,
2
,
2
)
class
TestFCOp4
(
TestFCOp
BiasBoth
):
class
TestFCOp4
(
TestFCOp
NoBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
1
,
32
,
64
,
3
,
3
)
class
TestFCOpWithBias1
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
3
,
8
,
10
,
2
,
1
)
class
TestFCOpWithBias2
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
4
,
5
,
6
,
2
,
2
)
class
TestFCOpWithBias3
(
TestFCOpWithBias
):
def
init_op_type
(
self
):
self
.
init_shapes
(
1
,
64
,
32
,
3
,
3
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
0 → 100644
浏览文件 @
ff052c0e
# 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
from
test_lstm_op
import
lstm
,
ACTIVATION
def
fc
(
x
,
w
,
b
):
return
np
.
dot
(
x
,
w
)
+
b
def
fusion_lstm
(
x
,
# T x M
lod
,
# 1 x N
wx
=
None
,
# M x 4D
bx
=
None
,
# 1 x 4D
h0
=
None
,
# N x D
c0
=
None
,
# N x D
w_h
=
None
,
# D x 4D
w_b
=
None
,
# 1 x 4D
w_c
=
None
,
# 1 x 3D
is_reverse
=
False
,
act_gate
=
None
,
act_cell
=
None
,
act_cand
=
None
):
return
lstm
(
fc
(
x
,
wx
,
bx
),
lod
,
h0
,
c0
,
w_h
,
w_b
,
w_c
,
is_reverse
,
act_gate
,
act_cell
,
act_cand
)
class
TestLstmOp
(
OpTest
):
def
set_argument
(
self
):
self
.
lod
=
[[
2
,
3
,
2
]]
def
setUp
(
self
):
self
.
op_type
=
'fusion_lstm'
self
.
lod
=
[[
2
,
3
,
2
]]
self
.
M
=
8
self
.
D
=
16
self
.
has_initial_state
=
False
self
.
is_reverse
=
False
self
.
act_gate
=
'sigmoid'
self
.
act_cell
=
'tanh'
self
.
act_cand
=
'tanh'
self
.
use_peepholes
=
False
self
.
set_argument
()
T
=
sum
(
self
.
lod
[
0
])
bs
=
len
(
self
.
lod
[
0
])
x
=
np
.
random
.
normal
(
size
=
(
T
,
self
.
M
)).
astype
(
'float64'
)
if
self
.
has_initial_state
:
h0
=
np
.
random
.
normal
(
size
=
(
bs
,
self
.
D
)).
astype
(
'float64'
)
c0
=
np
.
random
.
normal
(
size
=
(
bs
,
self
.
D
)).
astype
(
'float64'
)
else
:
h0
=
np
.
zeros
((
bs
,
self
.
D
)).
astype
(
'float64'
)
c0
=
np
.
zeros
((
bs
,
self
.
D
)).
astype
(
'float64'
)
wh
=
np
.
random
.
normal
(
size
=
(
self
.
D
,
4
*
self
.
D
)).
astype
(
'float64'
)
if
self
.
use_peepholes
:
b
=
np
.
random
.
normal
(
size
=
(
1
,
7
*
self
.
D
)).
astype
(
'float64'
)
else
:
b
=
np
.
random
.
normal
(
size
=
(
1
,
4
*
self
.
D
)).
astype
(
'float64'
)
w_b
=
np
.
copy
(
b
[:,
0
:
4
*
self
.
D
])
w_c
=
b
[:,
4
*
self
.
D
:]
if
self
.
use_peepholes
else
None
# this is the weight of fc
wx
=
np
.
random
.
normal
(
size
=
(
self
.
M
,
4
*
self
.
D
)).
astype
(
'float64'
)
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx
=
np
.
random
.
normal
(
size
=
(
1
,
4
*
self
.
D
)).
astype
(
'float64'
)
b
[
0
,
0
:
4
*
self
.
D
]
+=
bx
[
0
,
:]
h
,
c
=
fusion_lstm
(
x
,
self
.
lod
,
wx
,
bx
,
h0
,
c0
,
wh
,
w_b
,
w_c
,
self
.
is_reverse
,
ACTIVATION
[
self
.
act_gate
],
ACTIVATION
[
self
.
act_cell
],
ACTIVATION
[
self
.
act_cand
])
self
.
inputs
=
{
'X'
:
(
x
,
self
.
lod
),
'WeightX'
:
wx
,
'WeightH'
:
wh
,
'Bias'
:
b
}
if
self
.
has_initial_state
:
self
.
inputs
[
'H0'
]
=
h0
self
.
inputs
[
'C0'
]
=
c0
self
.
outputs
=
{
'Hidden'
:
(
h
,
self
.
lod
),
'Cell'
:
(
c
,
self
.
lod
),
}
self
.
attrs
=
{
'use_peepholes'
:
self
.
use_peepholes
,
'is_reverse'
:
self
.
is_reverse
,
'gate_activation'
:
self
.
act_gate
,
'cell_activation'
:
self
.
act_cell
,
'candidate_activation'
:
self
.
act_cand
}
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-8
)
class
TestLstmOpInitReverse
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
has_initial_state
=
True
self
.
is_reverse
=
True
class
TestLstmOpMD1
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
36
self
.
D
=
8
class
TestLstmOpMD2
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
8
self
.
D
=
8
class
TestLstmOpMD3
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
M
=
15
self
.
D
=
3
class
TestLstmOpBS1
(
TestLstmOp
):
def
set_argument
(
self
):
self
.
lod
=
[[
3
]]
self
.
D
=
16
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @
ff052c0e
...
...
@@ -183,7 +183,9 @@ class TestMNIST(TestParallelExecutorBase):
use_parallel_executor
=
True
)
self
.
assertAlmostEquals
(
np
.
mean
(
parallel_first_loss
),
single_first_loss
,
delta
=
1e-6
)
np
.
mean
(
parallel_first_loss
),
single_first_loss
,
delta
=
1e-6
,
)
self
.
assertAlmostEquals
(
np
.
mean
(
parallel_last_loss
),
single_last_loss
,
delta
=
1e-6
)
...
...
@@ -191,7 +193,7 @@ class TestMNIST(TestParallelExecutorBase):
self
.
check_simple_fc_parallel_accuracy
(
True
)
self
.
check_simple_fc_parallel_accuracy
(
False
)
def
check_batchnorm_fc_convergence
(
self
,
use_cuda
):
def
check_batchnorm_fc_convergence
(
self
,
use_cuda
,
use_fast_executor
):
if
use_cuda
and
not
core
.
is_compiled_with_cuda
():
return
...
...
@@ -203,11 +205,13 @@ class TestMNIST(TestParallelExecutorBase):
fc_with_batchnorm
,
feed_dict
=
{
"image"
:
img
,
"label"
:
label
},
use_cuda
=
use_cuda
)
use_cuda
=
use_cuda
,
use_fast_executor
=
use_fast_executor
)
def
test_batchnorm_fc
(
self
):
self
.
check_batchnorm_fc_convergence
(
True
)
self
.
check_batchnorm_fc_convergence
(
False
)
for
use_cuda
in
(
False
,
True
):
for
use_fast_executor
in
(
False
,
True
):
self
.
check_batchnorm_fc_convergence
(
use_cuda
,
use_fast_executor
)
def
test_batchnorm_fc_with_new_strategy
(
self
):
# FIXME(zcd): close this test temporally.
...
...
python/paddle/fluid/tests/unittests/test_squeeze_op.py
浏览文件 @
ff052c0e
...
...
@@ -41,7 +41,7 @@ class TestSqueezeOp(OpTest):
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
False
}
self
.
attrs
=
{
"axes"
:
self
.
axes
}
# Correct: There is mins axis.
...
...
@@ -68,49 +68,5 @@ class TestSqueezeOp3(TestSqueezeOp):
self
.
new_shape
=
(
3
,
5
,
1
,
4
)
# Correct: Inplace.
class
TestSqueezeOpInplace1
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
2
)
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. There is mins axis.
class
TestSqueezeOpInplace2
(
TestSqueezeOp
):
def
inti_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
(
0
,
-
2
)
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. No axes input.
class
TestSqueezeOpInplace3
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
1
,
3
,
1
,
5
)
self
.
axes
=
()
self
.
new_shape
=
(
3
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inpalce. Just part of axes be squeezed.
class
TestSqueezeOpInplace4
(
TestSqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
1
,
5
,
1
,
4
,
1
)
self
.
axes
=
(
1
,
-
1
)
self
.
new_shape
=
(
3
,
5
,
1
,
4
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
浏览文件 @
ff052c0e
...
...
@@ -41,7 +41,7 @@ class TestUnsqueezeOp(OpTest):
self
.
new_shape
=
(
3
,
1
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
False
}
self
.
attrs
=
{
"axes"
:
self
.
axes
}
# Correct: Single input index.
...
...
@@ -76,38 +76,5 @@ class TestUnsqueezeOp4(TestUnsqueezeOp):
self
.
new_shape
=
(
3
,
1
,
1
,
2
,
5
,
1
)
# Correct: Inplace.
class
TestUnsqueezeOpInplace1
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
5
)
self
.
axes
=
(
0
,
2
)
self
.
new_shape
=
(
1
,
3
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. There is mins index.
class
TestUnsqueezeOpInplace2
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
5
)
self
.
axes
=
(
0
,
-
2
)
self
.
new_shape
=
(
1
,
3
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
# Correct: Inplace. There is duplicated axis.
class
TestUnsqueezeOpInplace3
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
2
,
5
)
self
.
axes
=
(
0
,
3
,
3
)
self
.
new_shape
=
(
1
,
3
,
2
,
1
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
,
"inplace"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/trainer.py
浏览文件 @
ff052c0e
...
...
@@ -285,11 +285,12 @@ class Trainer(object):
self
.
_load_checkpoint
()
if
param_path
and
os
.
path
.
isdir
(
param_path
):
# load params from param_path into scope
io
.
load_persistables
(
executor
=
exe
,
dirname
=
param_path
,
main_program
=
self
.
startup_program
)
with
self
.
_prog_and_scope_guard
():
# load params from param_path into scope
io
.
load_persistables
(
executor
=
exe
,
dirname
=
param_path
,
main_program
=
self
.
startup_program
)
def
_transpile_nccl2_dist
(
self
):
# PADDLE_TRAINER_IPS
...
...
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