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体验新版 GitCode,发现更多精彩内容 >>
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238b24bf
编写于
12月 17, 2018
作者:
Q
Qiao Longfei
浏览文件
操作
浏览文件
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差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into optimize-adam
上级
763e8fdf
363bf8a4
变更
63
展开全部
隐藏空白更改
内联
并排
Showing
63 changed file
with
3137 addition
and
480 deletion
+3137
-480
README.md
README.md
+81
-0
benchmark/fluid/fluid_benchmark.py
benchmark/fluid/fluid_benchmark.py
+3
-1
cmake/external/brpc.cmake
cmake/external/brpc.cmake
+12
-8
cmake/external/gtest.cmake
cmake/external/gtest.cmake
+7
-3
cmake/external/leveldb.cmake
cmake/external/leveldb.cmake
+2
-2
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-0
paddle/fluid/framework/CMakeLists.txt
paddle/fluid/framework/CMakeLists.txt
+6
-3
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+9
-2
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+3
-3
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+2
-0
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse.cc
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse.cc
+106
-0
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.cc
...fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.cc
+105
-0
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h
.../fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h
+33
-0
paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc
.../fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc
+104
-0
paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.h
...e/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.h
+33
-0
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+112
-1
paddle/fluid/framework/ir/graph_pattern_detector.h
paddle/fluid/framework/ir/graph_pattern_detector.h
+45
-0
paddle/fluid/inference/api/analysis_predictor_tester.cc
paddle/fluid/inference/api/analysis_predictor_tester.cc
+6
-1
paddle/fluid/inference/api/paddle_pass_builder.h
paddle/fluid/inference/api/paddle_pass_builder.h
+4
-1
paddle/fluid/inference/io.cc
paddle/fluid/inference/io.cc
+1
-1
paddle/fluid/inference/tests/api/trt_models_tester.cc
paddle/fluid/inference/tests/api/trt_models_tester.cc
+24
-1
paddle/fluid/operators/controlflow/CMakeLists.txt
paddle/fluid/operators/controlflow/CMakeLists.txt
+1
-1
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+3
-1
paddle/fluid/operators/cudnn_lstm_op.cu.cc
paddle/fluid/operators/cudnn_lstm_op.cu.cc
+2
-0
paddle/fluid/operators/distributed/CMakeLists.txt
paddle/fluid/operators/distributed/CMakeLists.txt
+19
-12
paddle/fluid/operators/distributed/brpc_client.cc
paddle/fluid/operators/distributed/brpc_client.cc
+313
-58
paddle/fluid/operators/distributed/brpc_client.h
paddle/fluid/operators/distributed/brpc_client.h
+82
-17
paddle/fluid/operators/distributed/brpc_rdma_pool.cc
paddle/fluid/operators/distributed/brpc_rdma_pool.cc
+84
-0
paddle/fluid/operators/distributed/brpc_rdma_pool.h
paddle/fluid/operators/distributed/brpc_rdma_pool.h
+56
-0
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc
+196
-0
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h
+49
-0
paddle/fluid/operators/distributed/brpc_serde_test.cc
paddle/fluid/operators/distributed/brpc_serde_test.cc
+175
-0
paddle/fluid/operators/distributed/brpc_server.cc
paddle/fluid/operators/distributed/brpc_server.cc
+235
-29
paddle/fluid/operators/distributed/brpc_variable_response.cc
paddle/fluid/operators/distributed/brpc_variable_response.cc
+73
-0
paddle/fluid/operators/distributed/brpc_variable_response.h
paddle/fluid/operators/distributed/brpc_variable_response.h
+67
-0
paddle/fluid/operators/distributed/grpc_client.cc
paddle/fluid/operators/distributed/grpc_client.cc
+1
-2
paddle/fluid/operators/distributed/grpc_serde.cc
paddle/fluid/operators/distributed/grpc_serde.cc
+0
-7
paddle/fluid/operators/distributed/rpc_server.h
paddle/fluid/operators/distributed/rpc_server.h
+4
-0
paddle/fluid/operators/distributed/sendrecvop_utils.cc
paddle/fluid/operators/distributed/sendrecvop_utils.cc
+1
-1
paddle/fluid/operators/distributed/sendrecvop_utils.h
paddle/fluid/operators/distributed/sendrecvop_utils.h
+7
-0
paddle/fluid/operators/distributed_ops/CMakeLists.txt
paddle/fluid/operators/distributed_ops/CMakeLists.txt
+2
-2
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
+4
-3
paddle/fluid/operators/distributed_ops/send_op.cc
paddle/fluid/operators/distributed_ops/send_op.cc
+2
-0
paddle/fluid/operators/math/pooling.cc
paddle/fluid/operators/math/pooling.cc
+153
-62
paddle/fluid/operators/math/pooling.cu
paddle/fluid/operators/math/pooling.cu
+268
-147
paddle/fluid/operators/math/pooling.h
paddle/fluid/operators/math/pooling.h
+22
-10
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+62
-3
paddle/fluid/operators/pool_op.h
paddle/fluid/operators/pool_op.h
+10
-6
paddle/fluid/operators/pool_with_index_op.cc
paddle/fluid/operators/pool_with_index_op.cc
+35
-3
paddle/fluid/operators/pool_with_index_op.h
paddle/fluid/operators/pool_with_index_op.h
+8
-4
paddle/fluid/operators/spp_op.h
paddle/fluid/operators/spp_op.h
+3
-3
paddle/fluid/platform/device_context.cc
paddle/fluid/platform/device_context.cc
+1
-0
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+9
-0
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+12
-0
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+200
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+7
-3
python/paddle/fluid/tests/unittests/ngraph/CMakeLists.txt
python/paddle/fluid/tests/unittests/ngraph/CMakeLists.txt
+6
-0
python/paddle/fluid/tests/unittests/ngraph/__init__.py
python/paddle/fluid/tests/unittests/ngraph/__init__.py
+13
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+23
-0
python/paddle/fluid/tests/unittests/test_pool2d_op.py
python/paddle/fluid/tests/unittests/test_pool2d_op.py
+65
-26
python/paddle/fluid/tests/unittests/test_pool3d_op.py
python/paddle/fluid/tests/unittests/test_pool3d_op.py
+86
-35
python/paddle/fluid/tests/unittests/test_pool_max_op.py
python/paddle/fluid/tests/unittests/test_pool_max_op.py
+77
-18
未找到文件。
README.md
浏览文件 @
238b24bf
...
...
@@ -19,6 +19,15 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our
[
release announcement
](
https://github.com/PaddlePaddle/Paddle/releases
)
to track the latest feature of PaddlePaddle.
欢迎来到 PaddlePaddle GitHub
PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台,最初由百度科学家和工程师共同开发,目的是将深度学习技术应用到百度的众多产品中。
我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
跟进PaddlePaddle最新特性请参考我们的
[
版本说明
](
https://github.com/PaddlePaddle/Paddle/releases
)
### Latest PaddlePaddle Release: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### Install Latest Stable Release:
```
...
...
@@ -34,6 +43,23 @@ pip install paddlepaddle-gpu==1.2.0.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### 安装最新稳定版本:
```
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.2.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.2.0.post85
# 其他平台上的安装指引请参考 http://paddlepaddle.org/
```
## Features
-
**Flexibility**
...
...
@@ -74,10 +100,38 @@ pip install paddlepaddle-gpu==1.2.0.post85
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
## 特点
-
**灵活性**
PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型,例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
-
**高效性**
为了高效使用异步计算资源,PaddlePaddle对框架的不同层进行优化,包括计算、存储、架构和通信。下面是一些样例:
- 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
- 通过MKL-DNN库优化CNN网络
- 高度优化循环网络,无需执行 `padding` 操作即可处理 **变长** 序列
- 针对高维稀疏数据模型,优化了局部和分布式训练。
-
**稳定性**
有了 PaddlePaddle,使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
-
**连接产品**
另外,PaddlePaddle 的设计也易于部署。在百度,PaddlePaddle 已经部署到含有巨大用户量的产品和服务上,包括广告点击率(CTR)预测、大规模图像分类、光学字符识别(OCR)、搜索排序,计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中,产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力,为您的产品创造新的影响力和效果。
## Installation
It is recommended to read
[
this doc
](
http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html
)
on our website.
## 安装
推荐阅读官网上的
[
安装说明
](
http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html
)
## Documentation
We provide
[
English
](
http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html
)
and
...
...
@@ -99,10 +153,37 @@ We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarte
We appreciate your contributions!
## 文档
我们提供
[
英文
](
http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html
)
和
[
中文
](
http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html
)
文档
-
[
深度学习101
](
https://github.com/PaddlePaddle/book
)
或许您想从这个在线交互式书籍开始,可以在Jupyter Notebook中运行
-
[
分布式训练
](
http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html
)
可以在MPI集群上运行分布式训练任务
-
[
Python API
](
http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html
)
新的API支持代码更少更简洁的程序
-
[
贡献方式
](
http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html
)
欢迎您的贡献!
## Ask Questions
You are welcome to submit questions and bug reports as
[
Github Issues
](
https://github.com/PaddlePaddle/Paddle/issues
)
.
## 答疑
欢迎您将问题和bug报告以
[
Github Issues
](
https://github.com/PaddlePaddle/Paddle/issues
)
的形式提交
## Copyright and License
PaddlePaddle is provided under the
[
Apache-2.0 license
](
LICENSE
)
.
## 版权和许可证
PaddlePaddle由
[
Apache-2.0 license
](
LICENSE
)
提供
benchmark/fluid/fluid_benchmark.py
浏览文件 @
238b24bf
...
...
@@ -81,9 +81,11 @@ def dist_transpile(trainer_id, args, train_prog, startup_prog):
# the role, should be either PSERVER or TRAINER
training_role
=
os
.
getenv
(
"PADDLE_TRAINING_ROLE"
)
config
=
distribute_transpiler
.
DistributeTranspilerConfig
()
config
=
fluid
.
DistributeTranspilerConfig
()
config
.
slice_var_up
=
not
args
.
no_split_var
config
.
min_block_size
=
1048576
t
=
distribute_transpiler
.
DistributeTranspiler
(
config
=
config
)
t
.
transpile
(
trainer_id
,
# NOTE: *MUST* use train_prog, for we are using with guard to
...
...
cmake/external/brpc.cmake
浏览文件 @
238b24bf
...
...
@@ -14,14 +14,16 @@
INCLUDE
(
ExternalProject
)
find_library
(
SSL_LIBRARY NAMES ssl
)
find_package
(
OpenSSL REQUIRED
)
message
(
STATUS
"ssl:"
${
OPENSSL_SSL_LIBRARY
}
)
message
(
STATUS
"crypto:"
${
OPENSSL_CRYPTO_LIBRARY
}
)
ADD_LIBRARY
(
ssl SHARED IMPORTED GLOBAL
)
SET_PROPERTY
(
TARGET ssl PROPERTY IMPORTED_LOCATION
${
SSL_LIBRARY
}
)
SET_PROPERTY
(
TARGET ssl PROPERTY IMPORTED_LOCATION
${
OPENSSL_
SSL_LIBRARY
}
)
find_library
(
CRYPTO_LIBRARY NAMES crypto
)
ADD_LIBRARY
(
crypto SHARED IMPORTED GLOBAL
)
SET_PROPERTY
(
TARGET crypto PROPERTY IMPORTED_LOCATION
${
CRYPTO_LIBRARY
}
)
SET_PROPERTY
(
TARGET crypto PROPERTY IMPORTED_LOCATION
${
OPENSSL_CRYPTO_LIBRARY
}
)
SET
(
BRPC_SOURCES_DIR
${
THIRD_PARTY_PATH
}
/brpc
)
SET
(
BRPC_INSTALL_DIR
${
THIRD_PARTY_PATH
}
/install/brpc
)
...
...
@@ -31,14 +33,15 @@ SET(BRPC_LIBRARIES "${BRPC_INSTALL_DIR}/lib/libbrpc.a" CACHE FILEPATH "brpc libr
INCLUDE_DIRECTORIES
(
${
BRPC_INCLUDE_DIR
}
)
# Reference https://stackoverflow.com/questions/45414507/pass-a-list-of-prefix-paths-to-externalproject-add-in-cmake-args
set
(
prefix_path
"
${
THIRD_PARTY_PATH
}
/install/gflags|
${
THIRD_PARTY_PATH
}
/install/leveldb|
${
THIRD_PARTY_PATH
}
/install/snappy|
${
THIRD_PARTY_PATH
}
/install/gtest|
${
THIRD_PARTY_PATH
}
/install/protobuf|
${
THIRD_PARTY_PATH
}
/install/zlib"
)
set
(
prefix_path
"
${
THIRD_PARTY_PATH
}
/install/gflags|
${
THIRD_PARTY_PATH
}
/install/leveldb|
${
THIRD_PARTY_PATH
}
/install/snappy|
${
THIRD_PARTY_PATH
}
/install/gtest|
${
THIRD_PARTY_PATH
}
/install/protobuf|
${
THIRD_PARTY_PATH
}
/install/zlib
|
${
THIRD_PARTY_PATH
}
/install/glog
"
)
# If minimal .a is need, you can set WITH_DEBUG_SYMBOLS=OFF
ExternalProject_Add
(
extern_brpc
${
EXTERNAL_PROJECT_LOG_ARGS
}
# TODO(gongwb): change to de newst repo when they changed.
GIT_REPOSITORY
"https://github.com/gongweibao/brpc"
GIT_TAG
"
7dc04defad1fd4173aae170c3fcbde131b65155a
"
GIT_TAG
"
e9b67ec1b7458f2af5fae76451afe1e27e01b4b4
"
PREFIX
${
BRPC_SOURCES_DIR
}
UPDATE_COMMAND
""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=
${
CMAKE_CXX_COMPILER
}
...
...
@@ -50,7 +53,7 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=
${
THIRD_PARTY_BUILD_TYPE
}
-DCMAKE_PREFIX_PATH=
${
prefix_path
}
-D
BRPC_
WITH_GLOG=ON
-DWITH_GLOG=ON
-DIOBUF_WITH_HUGE_BLOCK=ON
-DBRPC_WITH_RDMA=
${
WITH_BRPC_RDMA
}
${
EXTERNAL_OPTIONAL_ARGS
}
...
...
@@ -65,5 +68,6 @@ ADD_LIBRARY(brpc STATIC IMPORTED GLOBAL)
SET_PROPERTY
(
TARGET brpc PROPERTY IMPORTED_LOCATION
${
BRPC_LIBRARIES
}
)
ADD_DEPENDENCIES
(
brpc extern_brpc
)
add_definitions
(
-DBRPC_WITH_GLOG
)
LIST
(
APPEND external_project_dependencies brpc
)
cmake/external/gtest.cmake
浏览文件 @
238b24bf
...
...
@@ -12,8 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF
(
WITH_TESTING
)
ENABLE_TESTING
()
#FIXME:(gongwb) Move brpc's gtest dependency.
IF
(
WITH_TESTING
OR
(
WITH_DISTRIBUTE AND NOT WITH_GRPC
))
IF
(
WITH_TESTING
)
ENABLE_TESTING
()
ENDIF
(
WITH_TESTING
)
INCLUDE
(
ExternalProject
)
SET
(
GTEST_SOURCES_DIR
${
THIRD_PARTY_PATH
}
/gtest
)
...
...
@@ -76,4 +80,4 @@ IF(WITH_TESTING)
ADD_DEPENDENCIES
(
gtest_main extern_gtest
)
LIST
(
APPEND external_project_dependencies gtest gtest_main
)
ENDIF
(
WITH_TESTING
)
ENDIF
(
WITH_TESTING
OR
(
WITH_DISTRIBUTE AND NOT WITH_GRPC
)
)
cmake/external/leveldb.cmake
浏览文件 @
238b24bf
...
...
@@ -24,8 +24,8 @@ ExternalProject_Add(
extern_leveldb
${
EXTERNAL_PROJECT_LOG_ARGS
}
PREFIX
${
LEVELDB_SOURCES_DIR
}
URL
"https://github.com/google/leveldb/archive/v1.18.tar.gz
"
URL_MD5
"73770de34a2a5ab34498d2e05b2b7fa0"
GIT_REPOSITORY
"https://github.com/google/leveldb
"
GIT_TAG v1.18
CONFIGURE_COMMAND
""
BUILD_COMMAND CXXFLAGS=-fPIC make -j
${
NUM_OF_PROCESSOR
}
libleveldb.a
INSTALL_COMMAND mkdir -p
${
LEVELDB_INSTALL_DIR
}
/lib/
...
...
paddle/fluid/API.spec
浏览文件 @
238b24bf
...
...
@@ -77,6 +77,8 @@ paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name']
paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.adaptive_pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
paddle.fluid.layers.adaptive_pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False))
paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
...
...
paddle/fluid/framework/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -169,9 +169,12 @@ cc_library(variable_helper SRCS variable_helper.cc DEPS lod_tensor)
cc_library
(
naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper
)
if
(
WITH_DISTRIBUTE
)
cc_library
(
executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass variable_helper
)
set
(
DISTRIBUTE_COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
set_source_files_properties
(
executor.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_library
(
executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog
lod_rank_table feed_fetch_method sendrecvop_rpc
${
GLOB_DISTRIBUTE_DEPS
}
graph_to_program_pass variable_helper
)
set
(
DISTRIBUTE_COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
set_source_files_properties
(
executor.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
else
()
if
(
WITH_NGRAPH
)
if
(
NOT WIN32
)
...
...
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -12,12 +12,19 @@ cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc
cc_library
(
variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows
)
if
(
WITH_DISTRIBUTE
)
if
(
NOT WITH_GRPC
)
set
(
DISTRIBUTE_COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
set_source_files_properties
(
reduce_op_handle.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
endif
()
endif
()
if
(
WITH_GPU
)
nv_library
(
all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
dynload_cuda variable_visitor
)
if
(
WITH_DISTRIBUTE
)
nv_library
(
reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim dynload_cuda selected_rows_functor sendrecvop_
g
rpc
)
ddim dynload_cuda selected_rows_functor sendrecvop_rpc
)
else
()
nv_library
(
reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim dynload_cuda selected_rows_functor
)
...
...
@@ -30,7 +37,7 @@ else()
variable_visitor
)
if
(
WITH_DISTRIBUTE
)
cc_library
(
reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim selected_rows_functor sendrecvop_
g
rpc
)
ddim selected_rows_functor sendrecvop_rpc
)
else
()
cc_library
(
reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim selected_rows_functor
)
...
...
paddle/fluid/framework/executor.cc
浏览文件 @
238b24bf
...
...
@@ -157,9 +157,9 @@ void Executor::Close() {
#ifdef PADDLE_WITH_DISTRIBUTE
// TODO(typhoonzero): complete message will need to use real trainer_id,
// except 0.
::
paddle
::
operators
::
distributed
::
RPCClient
::
GetInstance
<
::
paddle
::
operators
::
distributed
::
GRPCClient
>
(
0
)
->
SendComplete
();
auto
client
=
paddle
::
operators
::
distributed
::
RPCClient
::
GetInstance
<
RPCCLIENT_T
>
(
0
);
client
->
SendComplete
();
#endif
}
...
...
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -42,6 +42,8 @@ pass_library(multi_batch_merge_pass base)
pass_library
(
conv_bn_fuse_pass inference
)
pass_library
(
seqconv_eltadd_relu_fuse_pass inference
)
pass_library
(
is_test_pass base
)
pass_library
(
conv_elementwise_add_act_fuse_pass inference
)
pass_library
(
conv_elementwise_add2_act_fuse_pass inference
)
if
(
WITH_MKLDNN
)
pass_library
(
mkldnn_placement_pass base
)
pass_library
(
depthwise_conv_mkldnn_pass base
)
...
...
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse.cc
0 → 100644
浏览文件 @
238b24bf
// 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 <string>
#include "paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
#define GET_NODES \
GET_IR_NODE(conv_op); \
GET_IR_NODE(conv_out); \
GET_IR_NODE(conv_filter); \
GET_IR_NODE(elementwise_add_op); \
GET_IR_NODE(elementwise_add_in_y); \
GET_IR_NODE(elementwise_add_out); \
GET_IR_NODE(elementwise_add_op_1); \
GET_IR_NODE(elementwise_add_in_y_1); \
GET_IR_NODE(elementwise_add_out_1); \
GET_IR_NODE(act_op); \
GET_IR_NODE(act_out);
// Inherient the basic infomation from `base_desc`, and modify some fields.
framework
::
proto
::
OpDesc
PrepareOpDesc
(
const
framework
::
proto
::
OpDesc
&
base_desc
,
const
std
::
string
&
bias
,
const
std
::
string
&
bias1
,
const
std
::
string
&
activation
,
const
std
::
string
&
output
)
{
auto
proto
=
base_desc
;
framework
::
OpDesc
desc
(
proto
,
nullptr
);
desc
.
SetInput
(
"Bias"
,
{
bias
});
desc
.
SetInput
(
"ResidualData"
,
{
bias1
});
desc
.
SetAttr
(
"activation"
,
activation
);
desc
.
SetOutput
(
"Output"
,
{
output
});
desc
.
SetAttr
(
"is_test"
,
true
);
desc
.
SetAttr
(
"use_cudnn"
,
false
);
return
*
desc
.
Proto
();
}
std
::
unique_ptr
<
ir
::
Graph
>
ConvElementwiseAddActFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
const
std
::
string
pattern_name
=
"conv_elementwise_add_act_fuse"
;
FusePassBase
::
Init
(
pattern_name
,
graph
.
get
());
GraphPatternDetector
gpd
;
auto
*
x
=
gpd
.
mutable_pattern
()
->
NewNode
(
"x"
)
->
AsInput
()
->
assert_is_op_input
(
"conv2d"
,
"Input"
);
patterns
::
ConvElementwiseaddAct
pattern
(
gpd
.
mutable_pattern
(),
pattern_name
);
pattern
(
x
);
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
GET_NODES
;
auto
base_op_desc
=
*
conv_op
->
Op
()
->
Proto
();
std
::
string
bias_name
=
elementwise_add_in_y
->
Name
();
std
::
string
bias1_name
=
elementwise_add_in_y_1
->
Name
();
std
::
string
act_op_type
=
act_op
->
Op
()
->
Type
();
std
::
string
act_op_out
=
act_out
->
Name
();
auto
new_op_proto
=
PrepareOpDesc
(
base_op_desc
,
bias_name
,
bias1_name
,
act_op_type
,
act_op_out
);
framework
::
OpDesc
new_op_desc
(
new_op_proto
,
nullptr
);
// Create a new node for the fused op.
auto
new_conv_op
=
graph
->
CreateOpNode
(
&
new_op_desc
);
// Link inputs and outputs.
PADDLE_ENFORCE
(
subgraph
.
count
(
x
));
auto
*
conv_in_node
=
subgraph
.
at
(
x
);
IR_NODE_LINK_TO
(
conv_in_node
,
new_conv_op
);
// Input
IR_NODE_LINK_TO
(
conv_filter
,
new_conv_op
);
// Filter
IR_NODE_LINK_TO
(
elementwise_add_in_y
,
new_conv_op
);
// Bias
IR_NODE_LINK_TO
(
elementwise_add_in_y_1
,
new_conv_op
);
// ResidualData
IR_NODE_LINK_TO
(
new_conv_op
,
act_out
);
// Output
// Delete the unneeded nodes.
GraphSafeRemoveNodes
(
graph
.
get
(),
{
conv_op
,
elementwise_add_op
,
elementwise_add_op_1
,
elementwise_add_out
});
};
gpd
(
graph
.
get
(),
handler
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
conv_elementwise_add2_act_fuse_pass
,
paddle
::
framework
::
ir
::
ConvElementwiseAdd2ActFusePass
);
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.cc
0 → 100644
浏览文件 @
238b24bf
// 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/ir/conv_elementwise_add2_act_fuse_pass.h"
#include <string>
namespace
paddle
{
namespace
framework
{
namespace
ir
{
#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
#define GET_NODES \
GET_IR_NODE(conv_op); \
GET_IR_NODE(conv_out); \
GET_IR_NODE(conv_filter); \
GET_IR_NODE(elementwise_add_op); \
GET_IR_NODE(elementwise_add_in_y); \
GET_IR_NODE(elementwise_add_out); \
GET_IR_NODE(elementwise_add_op_1); \
GET_IR_NODE(elementwise_add_in_y_1); \
GET_IR_NODE(elementwise_add_out_1); \
GET_IR_NODE(act_op); \
GET_IR_NODE(act_out);
// Inherient the basic infomation from `base_desc`, and modify some fields.
framework
::
proto
::
OpDesc
PrepareOpDesc
(
const
framework
::
proto
::
OpDesc
&
base_desc
,
const
std
::
string
&
bias
,
const
std
::
string
&
bias1
,
const
std
::
string
&
activation
,
const
std
::
string
&
output
)
{
auto
proto
=
base_desc
;
framework
::
OpDesc
desc
(
proto
,
nullptr
);
desc
.
SetInput
(
"Bias"
,
{
bias
});
desc
.
SetInput
(
"ResidualData"
,
{
bias1
});
desc
.
SetAttr
(
"activation"
,
activation
);
desc
.
SetOutput
(
"Output"
,
{
output
});
desc
.
SetAttr
(
"is_test"
,
true
);
return
*
desc
.
Proto
();
}
std
::
unique_ptr
<
ir
::
Graph
>
ConvElementwiseAdd2ActFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
const
std
::
string
pattern_name
=
"conv_elementwise_add_act_fuse"
;
FusePassBase
::
Init
(
pattern_name
,
graph
.
get
());
GraphPatternDetector
gpd
;
auto
*
x
=
gpd
.
mutable_pattern
()
->
NewNode
(
"x"
)
->
AsInput
()
->
assert_is_op_input
(
"conv2d"
,
"Input"
);
patterns
::
ConvElementwiseadd2Act
pattern
(
gpd
.
mutable_pattern
(),
pattern_name
);
pattern
(
x
);
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
GET_NODES
;
auto
base_op_desc
=
*
conv_op
->
Op
()
->
Proto
();
std
::
string
bias_name
=
elementwise_add_in_y
->
Name
();
std
::
string
bias1_name
=
elementwise_add_in_y_1
->
Name
();
std
::
string
act_op_type
=
act_op
->
Op
()
->
Type
();
std
::
string
act_op_out
=
act_out
->
Name
();
auto
new_op_proto
=
PrepareOpDesc
(
base_op_desc
,
bias_name
,
bias1_name
,
act_op_type
,
act_op_out
);
framework
::
OpDesc
new_op_desc
(
new_op_proto
,
nullptr
);
// Create a new node for the fused op.
graph
->
CreateOpNode
(
&
new_op_desc
);
// Link inputs and outputs.
PADDLE_ENFORCE
(
subgraph
.
count
(
x
));
auto
*
conv_in_node
=
subgraph
.
at
(
x
);
IR_NODE_LINK_TO
(
conv_in_node
,
conv_op
);
// Input
IR_NODE_LINK_TO
(
conv_filter
,
conv_op
);
// Filter
IR_NODE_LINK_TO
(
conv_op
,
conv_out
);
// Output
IR_NODE_LINK_TO
(
elementwise_add_in_y
,
conv_op
);
// Bias
IR_NODE_LINK_TO
(
elementwise_add_in_y_1
,
conv_op
);
// Bias
// Delete the unneeded nodes.
GraphSafeRemoveNodes
(
graph
.
get
(),
{
conv_op
,
elementwise_add_op
,
elementwise_add_op_1
,
elementwise_add_out
});
};
gpd
(
graph
.
get
(),
handler
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
conv_elementwise_add2_act_fuse_pass
,
paddle
::
framework
::
ir
::
ConvElementwiseAdd2ActFusePass
);
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h
0 → 100644
浏览文件 @
238b24bf
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
class
ConvElementwiseAdd2ActFusePass
:
public
FusePassBase
{
public:
virtual
~
ConvElementwiseAdd2ActFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc
0 → 100644
浏览文件 @
238b24bf
// 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/ir/conv_elementwise_add_act_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
#define GET_NODES \
GET_IR_NODE(conv_op); \
GET_IR_NODE(conv_out); \
GET_IR_NODE(conv_filter); \
GET_IR_NODE(elementwise_add_op); \
GET_IR_NODE(elementwise_add_in_y); \
GET_IR_NODE(elementwise_add_out); \
GET_IR_NODE(act_op); \
GET_IR_NODE(act_out);
// Inherient the basic infomation from `base_desc`, and modify some fields.
framework
::
proto
::
OpDesc
PrepareOpDesc
(
const
framework
::
proto
::
OpDesc
&
base_desc
,
const
std
::
string
&
bias
,
const
std
::
string
&
activation
,
const
std
::
string
&
output
)
{
auto
proto
=
base_desc
;
framework
::
OpDesc
desc
(
proto
,
nullptr
);
desc
.
SetType
(
"conv2d_fusion"
);
desc
.
SetInput
(
"Bias"
,
{
bias
});
desc
.
SetInput
(
"ResidualData"
,
{});
desc
.
SetAttr
(
"activation"
,
activation
);
desc
.
SetOutput
(
"Output"
,
{
output
});
desc
.
SetAttr
(
"is_test"
,
true
);
desc
.
SetAttr
(
"use_cudnn"
,
false
);
desc
.
Flush
();
return
*
desc
.
Proto
();
}
std
::
unique_ptr
<
ir
::
Graph
>
ConvElementwiseAddActFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
const
std
::
string
pattern_name
=
"conv_elementwise_add_act_fuse"
;
FusePassBase
::
Init
(
pattern_name
,
graph
.
get
());
GraphPatternDetector
gpd
;
auto
*
x
=
gpd
.
mutable_pattern
()
->
NewNode
(
"x"
)
->
assert_is_op_input
(
"conv2d"
,
"Input"
)
->
AsInput
();
patterns
::
ConvElementwiseaddAct
pattern
(
gpd
.
mutable_pattern
(),
pattern_name
);
pattern
(
x
);
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
GET_NODES
;
auto
base_op_desc
=
*
conv_op
->
Op
()
->
Proto
();
std
::
string
bias_name
=
elementwise_add_in_y
->
Name
();
std
::
string
act_op_type
=
act_op
->
Op
()
->
Type
();
std
::
string
act_op_out
=
act_out
->
Name
();
auto
new_op_proto
=
PrepareOpDesc
(
base_op_desc
,
bias_name
,
act_op_type
,
act_op_out
);
framework
::
OpDesc
new_op_desc
(
new_op_proto
,
nullptr
);
// Create a new node for the fused op.
auto
*
new_conv_op
=
graph
->
CreateOpNode
(
&
new_op_desc
);
// Link inputs and outputs.
PADDLE_ENFORCE
(
subgraph
.
count
(
x
));
auto
*
conv_in_node
=
subgraph
.
at
(
x
);
IR_NODE_LINK_TO
(
conv_in_node
,
new_conv_op
);
// Input
IR_NODE_LINK_TO
(
conv_filter
,
new_conv_op
);
// Filter
IR_NODE_LINK_TO
(
elementwise_add_in_y
,
new_conv_op
);
// Bias
IR_NODE_LINK_TO
(
new_conv_op
,
act_out
);
// Output
// Delete the unneeded nodes.
GraphSafeRemoveNodes
(
graph
.
get
(),
{
conv_op
,
conv_out
,
elementwise_add_op
,
elementwise_add_out
,
act_op
});
};
gpd
(
graph
.
get
(),
handler
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
conv_elementwise_add_act_fuse_pass
,
paddle
::
framework
::
ir
::
ConvElementwiseAddActFusePass
);
paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.h
0 → 100644
浏览文件 @
238b24bf
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
class
ConvElementwiseAddActFusePass
:
public
FusePassBase
{
public:
virtual
~
ConvElementwiseAddActFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
238b24bf
...
...
@@ -17,6 +17,7 @@
#include <string>
#include <vector>
#include "graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
...
...
@@ -25,6 +26,7 @@
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/string/pretty_log.h"
#include "paddle/fluid/string/printf.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
...
...
@@ -104,7 +106,7 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
for
(
auto
&
node
:
GraphTraits
::
DFS
(
graph
))
{
for
(
const
auto
&
pdnode
:
pattern_
.
nodes
())
{
if
(
pdnode
->
Tell
(
&
node
))
{
VLOG
(
4
)
<<
"
pdnode "
<<
pdnode
->
name
()
<<
" marked"
;
VLOG
(
4
)
<<
"
Node "
<<
node
.
Name
()
<<
" marked as "
<<
pdnode
->
name
()
;
pdnodes2nodes_
[
pdnode
.
get
()].
insert
(
&
node
);
}
}
...
...
@@ -1099,6 +1101,115 @@ PDNode *patterns::ElementwiseAdd::operator()(PDNode *x_var, PDNode *y_var) {
return
out_var
;
}
std
::
unordered_set
<
std
::
string
>
conv_act_set
({
"identity"
,
"sigmoid"
,
"relu"
,
"relu6"
,
"relux"
,
"tanh"
,
"band_pass"
});
PDNode
*
patterns
::
ConvElementwiseaddAct
::
operator
()(
PDNode
*
conv_in
)
{
conv_in
->
AsInput
();
auto
conv_op
=
pattern
->
NewNode
(
conv_op_repr
())
->
assert_is_op
(
"conv2d"
);
auto
conv_out
=
pattern
->
NewNode
(
conv_out_repr
())
->
assert_is_op_output
(
"conv2d"
)
->
assert_is_op_input
(
"elementwise_add"
,
"X"
)
->
AsIntermediate
();
auto
conv_filter
=
pattern
->
NewNode
(
conv_filter_repr
())
->
assert_is_op_input
(
"conv2d"
,
"Filter"
)
->
AsInput
();
auto
elementwise_add_op
=
pattern
->
NewNode
(
elementwise_add_op_repr
())
->
assert_is_op
(
"elementwise_add"
);
auto
elementwise_add_in_y
=
pattern
->
NewNode
(
elementwise_add_in_y_repr
())
->
assert_is_op_input
(
"elementwise_add"
,
"Y"
)
->
AsInput
();
auto
elementwise_add_out
=
pattern
->
NewNode
(
elementwise_add_out_repr
())
->
assert_is_op_output
(
"elementwise_add"
)
->
AsIntermediate
();
auto
act_op
=
pattern
->
NewNode
(
act_op_repr
())
->
assert_is_op
()
->
assert_more
([
&
](
Node
*
node
)
{
auto
op_type
=
node
->
Name
();
return
conv_act_set
.
count
(
op_type
);
});
auto
act_out
=
pattern
->
NewNode
(
act_out_repr
())
->
assert_is_var
()
// is activation op's output.
->
assert_more
([
&
](
Node
*
node
)
{
for
(
auto
*
in_op
:
node
->
inputs
)
{
if
(
conv_act_set
.
count
(
in_op
->
Name
()))
{
return
true
;
}
}
return
false
;
})
->
AsOutput
();
conv_op
->
LinksFrom
({
conv_in
,
conv_filter
});
conv_out
->
LinksFrom
({
conv_op
});
elementwise_add_op
->
LinksFrom
({
conv_out
,
elementwise_add_in_y
})
.
LinksTo
({
elementwise_add_out
});
act_op
->
LinksFrom
({
elementwise_add_out
}).
LinksTo
({
act_out
});
return
act_out
;
}
PDNode
*
patterns
::
ConvElementwiseadd2Act
::
operator
()(
PDNode
*
conv_in
)
{
auto
conv_op
=
pattern
->
NewNode
(
conv_op_repr
())
->
assert_is_op
(
"conv2d"
);
auto
conv_filter
=
pattern
->
NewNode
(
conv_filter_repr
())
->
assert_is_op_input
(
"conv2d"
,
"Filter"
)
->
AsInput
();
auto
conv_out
=
pattern
->
NewNode
(
conv_out_repr
())
->
assert_is_op_output
(
"conv2d"
)
->
assert_is_op_input
(
"elementwise_add"
,
"X"
)
->
AsIntermediate
();
auto
elementwise_add_op
=
pattern
->
NewNode
(
elementwise_add_op_repr
())
->
assert_is_op
(
"elementwise_add"
);
auto
elementwise_add_in_y
=
pattern
->
NewNode
(
elementwise_add_in_y_repr
())
->
assert_is_op_input
(
"elementwise_add"
,
"Y"
)
->
AsInput
();
auto
elementwise_add_out
=
pattern
->
NewNode
(
elementwise_add_out_repr
())
->
assert_is_op_output
(
"elementwise_add"
)
->
assert_is_op_input
(
"elementwise_add"
,
"X"
)
->
AsIntermediate
();
auto
elementwise_add_op_1
=
pattern
->
NewNode
(
elementwise_add_op_1_repr
())
->
assert_is_op
(
"elementwise_add"
);
auto
elementwise_add_in_y_1
=
pattern
->
NewNode
(
elementwise_add_in_y_1_repr
())
->
assert_is_op_input
(
"elementwise_add"
,
"Y"
)
->
AsInput
();
auto
elementwise_add_out_1
=
pattern
->
NewNode
(
elementwise_add_out_1_repr
())
->
assert_is_op_output
(
"elementwise_add"
)
->
AsIntermediate
();
auto
act_op
=
pattern
->
NewNode
(
act_op_repr
())
->
assert_is_op
()
->
assert_more
([
&
](
Node
*
node
)
{
auto
op_type
=
node
->
Name
();
return
conv_act_set
.
count
(
op_type
);
});
auto
act_out
=
pattern
->
NewNode
(
act_out_repr
())
->
assert_is_var
()
// is activation op's output.
->
assert_more
([
&
](
Node
*
node
)
{
for
(
auto
*
in_op
:
node
->
inputs
)
{
if
(
conv_act_set
.
count
(
in_op
->
Name
()))
{
return
true
;
}
}
return
false
;
})
->
AsOutput
();
conv_op
->
LinksFrom
({
conv_in
,
conv_filter
}).
LinksTo
({
conv_out
});
elementwise_add_op
->
LinksFrom
({
conv_out
,
elementwise_add_in_y
})
.
LinksTo
({
elementwise_add_out
});
elementwise_add_op_1
->
LinksFrom
(
{
elementwise_add_out
,
elementwise_add_in_y_1
});
act_op
->
LinksFrom
({
elementwise_add_out_1
}).
LinksTo
({
act_out
});
return
act_out
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @
238b24bf
...
...
@@ -671,6 +671,51 @@ struct ElementwiseAdd : public PatternBase {
PATTERN_DECL_NODE
(
elementwise_add_y
);
PATTERN_DECL_NODE
(
elementwise_add_out
);
};
// Conv + ElementwiseAdd + an activation
// This pattern can futher fuse the conv related ops after the conv+bn fusion.
struct
ConvElementwiseaddAct
:
public
PatternBase
{
ConvElementwiseaddAct
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
)
:
PatternBase
(
pattern
,
name_scope
,
"conv_elementwiseadd_act"
)
{}
PDNode
*
operator
()(
PDNode
*
conv_in
);
PATTERN_DECL_NODE
(
conv_op
);
PATTERN_DECL_NODE
(
conv_out
);
PATTERN_DECL_NODE
(
conv_filter
);
PATTERN_DECL_NODE
(
elementwise_add_op
);
PATTERN_DECL_NODE
(
elementwise_add_in_y
);
// input
PATTERN_DECL_NODE
(
elementwise_add_out
);
PATTERN_DECL_NODE
(
act_op
);
PATTERN_DECL_NODE
(
act_out
);
};
// Conv + ElementwiseAdd + ElementwiseAdd + Activation
struct
ConvElementwiseadd2Act
:
public
PatternBase
{
ConvElementwiseadd2Act
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
)
:
PatternBase
(
pattern
,
name_scope
,
"conv_elementwiseadd2_elementwiseadd_act"
)
{}
PDNode
*
operator
()(
PDNode
*
conv_in
);
PATTERN_DECL_NODE
(
conv_op
);
PATTERN_DECL_NODE
(
conv_filter
);
PATTERN_DECL_NODE
(
conv_out
);
PATTERN_DECL_NODE
(
elementwise_add_op
);
PATTERN_DECL_NODE
(
elementwise_add_in_y
);
// input
PATTERN_DECL_NODE
(
elementwise_add_out
);
PATTERN_DECL_NODE
(
elementwise_add_op_1
);
PATTERN_DECL_NODE
(
elementwise_add_in_y_1
);
// input
PATTERN_DECL_NODE
(
elementwise_add_out_1
);
PATTERN_DECL_NODE
(
act_op
);
PATTERN_DECL_NODE
(
act_out
);
};
}
// namespace patterns
// Link two ir::Nodes from each other.
...
...
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @
238b24bf
...
...
@@ -55,7 +55,12 @@ TEST(AnalysisPredictor, analysis_off) {
}
TEST
(
AnalysisPredictor
,
analysis_on
)
{
AnalysisConfig
config
(
false
);
#ifdef PADDLE_WITH_CUDA
AnalysisConfig
config
(
true
);
config
.
fraction_of_gpu_memory
=
0.15
;
#else
AnalysisConfig
config
;
#endif
config
.
model_dir
=
FLAGS_dirname
;
config
.
enable_ir_optim
=
true
;
...
...
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @
238b24bf
...
...
@@ -118,7 +118,10 @@ class GpuPassStrategy : public PassStrategy {
public:
GpuPassStrategy
()
:
PassStrategy
({})
{
passes_
.
assign
({
"infer_clean_graph_pass"
,
"conv_bn_fuse_pass"
,
"infer_clean_graph_pass"
,
//
"conv_bn_fuse_pass"
,
//
"conv_elementwise_add_act_fuse_pass"
,
//
"conv_elementwise_add2_act_fuse_pass"
,
//
});
}
...
...
paddle/fluid/inference/io.cc
浏览文件 @
238b24bf
...
...
@@ -79,7 +79,7 @@ void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
for
(
auto
*
var
:
global_block
.
AllVars
())
{
if
(
IsPersistable
(
var
))
{
VLOG
(
3
)
<<
"persistable variable's name: "
<<
var
->
Name
();
VLOG
(
4
)
<<
"persistable variable's name: "
<<
var
->
Name
();
framework
::
VarDesc
*
new_var
=
load_block
->
Var
(
var
->
Name
());
new_var
->
SetShape
(
var
->
GetShape
());
...
...
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @
238b24bf
...
...
@@ -78,6 +78,7 @@ void profile(std::string model_dir, bool use_analysis, bool use_tensorrt) {
std
::
vector
<
PaddleTensor
>
outputs
;
if
(
use_analysis
||
use_tensorrt
)
{
contrib
::
AnalysisConfig
config
(
true
);
config
.
pass_builder
()
->
TurnOnDebug
();
SetConfig
<
contrib
::
AnalysisConfig
>
(
&
config
,
model_dir
,
true
,
use_tensorrt
,
FLAGS_batch_size
);
TestPrediction
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
config
),
...
...
@@ -141,9 +142,31 @@ TEST(TensorRT_resnext50, profile) {
profile
(
model_dir
,
/* use_analysis */
true
,
FLAGS_use_tensorrt
);
}
TEST
(
resnext50
,
compare_analysis_native
)
{
std
::
string
model_dir
=
FLAGS_infer_model
+
"/resnext50"
;
compare
(
model_dir
,
false
/*use tensorrt*/
);
}
TEST
(
TensorRT_mobilenet
,
analysis
)
{
std
::
string
model_dir
=
FLAGS_infer_model
+
"/"
+
"mobilenet"
;
compare
(
model_dir
,
/* use_tensorrt */
false
);
compare
(
model_dir
,
false
/* use_tensorrt */
);
}
TEST
(
AnalysisPredictor
,
use_gpu
)
{
std
::
string
model_dir
=
FLAGS_infer_model
+
"/"
+
"mobilenet"
;
AnalysisConfig
config
(
true
);
config
.
model_dir
=
model_dir
;
config
.
fraction_of_gpu_memory
=
0.15
;
config
.
pass_builder
()
->
TurnOnDebug
();
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
inputs_all
;
auto
predictor
=
CreatePaddlePredictor
(
config
);
SetFakeImageInput
(
&
inputs_all
,
model_dir
,
false
,
"__model__"
,
""
);
std
::
vector
<
PaddleTensor
>
outputs
;
for
(
auto
&
input
:
inputs_all
)
{
ASSERT_TRUE
(
predictor
->
Run
(
input
,
&
outputs
));
}
}
}
// namespace inference
...
...
paddle/fluid/operators/controlflow/CMakeLists.txt
浏览文件 @
238b24bf
include
(
operators
)
register_operators
()
register_operators
(
DEPS naive_executor
)
file
(
APPEND
${
pybind_file
}
"USE_OP(less_than);
\n
USE_OP(logical_and);
\n
USE_NO_KERNEL_OP(read_from_array);
\n
"
)
paddle/fluid/operators/conv_op.cc
浏览文件 @
238b24bf
...
...
@@ -44,7 +44,9 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std
::
vector
<
int
>
dilations
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"dilations"
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"Conv intput should be 4-D or 5-D tensor."
);
"Conv intput should be 4-D or 5-D tensor, get %u"
,
in_dims
.
size
());
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
filter_dims
.
size
(),
"Conv input dimension and filter dimension should be the same."
);
...
...
paddle/fluid/operators/cudnn_lstm_op.cu.cc
浏览文件 @
238b24bf
...
...
@@ -300,9 +300,11 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
}
CudnnRNNCache
*
cudnn_rnn_cache
=
nullptr
;
if
(
cache_var
->
IsInitialized
())
{
// const_cast is usually bad.
cudnn_rnn_cache
=
const_cast
<
framework
::
Variable
*>
(
cache_var
)
->
GetMutable
<
CudnnRNNCache
>
();
}
else
{
// const_cast is usually bad.
cudnn_rnn_cache
=
const_cast
<
framework
::
Variable
*>
(
cache_var
)
->
GetMutable
<
CudnnRNNCache
>
();
std
::
random_device
rnd
;
...
...
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -12,7 +12,7 @@ configure_file(send_recv.proto.in ${CMAKE_CURRENT_SOURCE_DIR}/send_recv.proto @O
set
(
DISTRIBUTE_COMPILE_FLAGS
"-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor"
)
if
(
WITH_GRPC
)
grpc_library
(
sendrecvop_
g
rpc SRCS grpc_bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
grpc_library
(
sendrecvop_rpc SRCS grpc_bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
request_handler_impl.cc rpc_client.cc rpc_server.cc grpc_server.cc variable_response.cc grpc_variable_response.cc grpc_serde.cc collective_client.cc collective_server.cc
PROTO send_recv.proto
DEPS lod_tensor selected_rows_functor memory
)
...
...
@@ -20,36 +20,43 @@ if(WITH_GRPC)
set_source_files_properties
(
grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_test
(
grpc_serde_test SRCS grpc_serde_test.cc
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_
g
rpc scope profiler math_function SERIAL
)
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_rpc scope profiler math_function SERIAL
)
cc_test
(
rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_
g
rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_sparse_table_op SERIAL
)
DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_sparse_table_op SERIAL
)
cc_test
(
varhandle_test SRCS varhandle_test.cc DEPS profiler
)
if
(
WITH_GPU
)
cc_test
(
collective_server_test SRCS collective_server_test.cc
DEPS sendrecvop_
g
rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
selected_rows_functor scope math_function SERIAL
)
endif
()
cc_library
(
parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_
g
rpc memory
)
cc_library
(
parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_rpc memory
)
else
()
set_source_files_properties
(
brpc_server.cc brpc_client.cc rpc_server_test.cc brpc_serde_test.cc
brpc_variable_response.cc brpc_sendrecvop_utils.cc brpc_rdma_pool.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
set_source_files_properties
(
brpc_server.cc parameter_prefetch.cc brpc_client.cc rpc_server_test.cc brpc_serde_test.cc
brpc_variable_response.cc brpc_sendrecvop_utils.cc brpc_rdma_pool.cc collective_server.cc collective_server_test.cc
collective_client.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
brpc_library
(
sendrecvop_
b
rpc SRCS brpc_client.cc brpc_server.cc rpc_server.cc rpc_client.cc request_handler_impl.cc brpc_sendrecvop_utils.cc
brpc_variable_response.cc variable_response.cc sendrecvop_utils.cc brpc_rdma_pool.cc
brpc_library
(
sendrecvop_rpc SRCS brpc_client.cc brpc_server.cc rpc_server.cc rpc_client.cc request_handler_impl.cc brpc_sendrecvop_utils.cc
brpc_variable_response.cc variable_response.cc sendrecvop_utils.cc brpc_rdma_pool.cc
collective_client.cc collective_server.cc
PROTO send_recv.proto
DEPS lod_tensor selected_rows memory
)
cc_library
(
parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_
b
rpc memory
)
cc_library
(
parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_rpc memory
)
set
(
brpc_test_depends sendrecvop_brpc brpc ssl crypto protobuf leveldb gflags glog executor proto_desc lookup_table_op snappystream snappy
)
set
(
brpc_test_depends sendrecvop_rpc brpc ssl crypto protobuf leveldb gflags glog executor
proto_desc lookup_sparse_table_op snappystream snappy zlib
)
cc_test
(
b
rpc_server_test SRCS rpc_server_test.cc
cc_test
(
rpc_server_test SRCS rpc_server_test.cc
DEPS
${
brpc_test_depends
}
SERIAL
)
cc_test
(
brpc_serde_test SRCS brpc_serde_test.cc
DEPS
${
brpc_test_depends
}
SERIAL
)
if
(
WITH_GPU
)
cc_test
(
collective_server_test SRCS collective_server_test.cc
DEPS
${
brpc_test_depends
}
selected_rows_functor scope math_function SERIAL
)
endif
()
endif
()
paddle/fluid/operators/distributed/brpc_client.cc
浏览文件 @
238b24bf
...
...
@@ -14,135 +14,316 @@
#include "paddle/fluid/operators/distributed/brpc_client.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
#include "paddle/fluid/platform/profiler.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
DEFINE_int32
(
brpc_channel_num
,
24
,
"Number of channels to send requests connected to one server"
);
DEFINE_int32
(
timeout_ms
,
30000
,
"RPC timeout in milliseconds"
);
DEFINE_int32
(
max_retry
,
3
,
"Max retries(not including the first RPC)"
);
BRPCClient
::~
BRPCClient
()
{
Wait
();
}
void
HandleSendResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VoidMessage
*
response
)
{
void
HandleSendResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VoidMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
)
{
// std::unique_ptr makes sure cntl/response will be deleted before returning.
std
::
unique_ptr
<
brpc
::
Controller
>
cntl_guard
(
cntl
);
std
::
unique_ptr
<
sendrecv
::
VoidMessage
>
response_guard
(
response
);
// this channel can be used by other now.
ch_ptr
->
Push
(
ch_ctx
);
if
(
cntl
->
Failed
())
{
LOG
(
WARNING
)
<<
"Fail to send EchoRequest, "
<<
cntl
->
ErrorText
();
LOG
(
FATAL
)
<<
"Fail to send SendVar: "
<<
var_h
->
name
()
<<
", error text: "
<<
cntl
->
ErrorText
();
var_h
->
Finish
(
false
);
cls
->
DecreaseReqCount
();
return
;
}
LOG
(
INFO
)
<<
"Received response from "
<<
cntl
->
remote_side
()
<<
" latency="
<<
cntl
->
latency_us
()
<<
"us"
;
var_h
->
Finish
(
true
);
cls
->
DecreaseReqCount
();
VLOG
(
4
)
<<
"HandleSendResponse from: "
<<
cntl
->
remote_side
()
<<
", varname: "
<<
var_h
->
name
()
<<
", latency: "
<<
cntl
->
latency_us
()
<<
"us"
;
VLOG
(
4
)
<<
"Finish HandleSendResponse"
;
}
bool
BRPCClient
::
AsyncSendVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
VarHandlePtr
BRPCClient
::
AsyncSendVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
const
platform
::
DeviceContext
*
p_ctx
=
&
ctx
;
const
std
::
string
ep_val
=
ep
;
const
std
::
string
var_name_val
=
var_name
;
const
framework
::
Scope
*
p_scope
=
&
scope
;
const
auto
ch_ptr
=
GetChannel
(
ep_val
);
const
std
::
string
method
=
"SendRPC"
;
VarHandlePtr
var_h
(
new
VarHandle
(
ep
,
method
,
var_name_val
,
p_ctx
,
p_scope
));
framework
::
AsyncIO
([
=
]
{
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VoidMessage
*
response
=
new
sendrecv
::
VoidMessage
();
cntl
->
set_timeout_ms
(
time_out
);
framework
::
AsyncIO
(
[
var_name_val
,
p_ctx
,
ep_val
,
p_scope
,
time_out
,
ch_ptr
,
this
]
{
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VoidMessage
*
response
=
new
sendrecv
::
VoidMessage
();
cntl
->
set_timeout_ms
(
time_out
);
auto
*
var
=
p_scope
->
FindVar
(
var_name_val
);
sendrecv
::
VariableMessage
request
;
distributed
::
SerializeToIOBuf
(
var_name_val
,
var
,
*
p_ctx
,
&
request
,
&
cntl
->
request_attachment
(),
""
,
false
,
trainer_id_
);
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleSendResponse
,
cntl
,
response
);
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleSendResponse
,
cntl
,
response
,
var_h
,
ch_ptr
,
ch_ctx
,
this
);
sendrecv
::
VariableMessage
request
;
ch_ctx
->
stub
->
SendVariable
(
cntl
,
&
request
,
response
,
done
);
});
platform
::
RecordRPCEvent
record_event
(
method
,
p_ctx
);
ch_ctx
->
stub
->
SendVariable
(
cntl
,
&
request
,
response
,
done
);
if
(
UNLIKELY
(
platform
::
IsProfileEnabled
()))
{
var_h
->
Wait
();
}
});
req_count_
++
;
return
true
;
return
var_h
;
}
void
HandleFetchBarrierResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VariableMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
)
{
// std::unique_ptr makes sure cntl/response will be deleted before returning.
std
::
unique_ptr
<
brpc
::
Controller
>
cntl_guard
(
cntl
);
std
::
unique_ptr
<
sendrecv
::
VariableMessage
>
response_guard
(
response
);
// this channel can be used other now.
ch_ptr
->
Push
(
ch_ctx
);
if
(
cntl
->
Failed
())
{
LOG
(
FATAL
)
<<
"Fail to get HandleFetchBarrierResponse: "
<<
var_h
->
name
()
<<
", error text: "
<<
cntl
->
ErrorText
();
var_h
->
Finish
(
false
);
cls
->
DecreaseReqCount
();
return
;
}
var_h
->
Finish
(
true
);
cls
->
DecreaseReqCount
();
VLOG
(
4
)
<<
"HandleFetchBarrierResponse from: "
<<
cntl
->
remote_side
()
<<
", varname: "
<<
var_h
->
name
()
<<
", latency: "
<<
cntl
->
latency_us
()
<<
"us"
;
VLOG
(
4
)
<<
"Finish HandleFetchBarrierResponse"
;
}
void
HandleGetResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VariableMessage
*
response
)
{
sendrecv
::
VariableMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
)
{
// std::unique_ptr makes sure cntl/response will be deleted before returning.
std
::
unique_ptr
<
brpc
::
Controller
>
cntl_guard
(
cntl
);
std
::
unique_ptr
<
sendrecv
::
VariableMessage
>
response_guard
(
response
);
// this channel can be used other now.
ch_ptr
->
Push
(
ch_ctx
);
if
(
cntl
->
Failed
())
{
LOG
(
WARNING
)
<<
"Fail to send EchoRequest, "
<<
cntl
->
ErrorText
();
LOG
(
FATAL
)
<<
"Fail to GetVar: "
<<
var_h
->
name
()
<<
", error text: "
<<
cntl
->
ErrorText
();
cls
->
DecreaseReqCount
();
var_h
->
Finish
(
false
);
return
;
}
LOG
(
INFO
)
<<
"Received response from "
<<
cntl
->
remote_side
()
<<
" latency="
<<
cntl
->
latency_us
()
<<
"us"
;
// framework::Variable* outvar = nullptr;
// DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, &outvar);
VLOG
(
4
)
<<
"HandleGetResponse from: "
<<
cntl
->
remote_side
()
<<
", varname: "
<<
var_h
->
name
()
<<
", latency: "
<<
cntl
->
latency_us
()
<<
"us"
;
framework
::
Variable
*
outvar
=
nullptr
;
int
trainer_id
;
distributed
::
DeserializeFromIOBuf
(
*
response
,
cntl
->
response_attachment
(),
*
var_h
->
ctx
(),
var_h
->
scope
(),
&
outvar
,
&
trainer_id
);
VLOG
(
4
)
<<
"Finish HandleGetResponse"
;
cls
->
DecreaseReqCount
();
var_h
->
Finish
(
true
);
}
bool
BRPCClient
::
AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
VarHandlePtr
BRPCClient
::
_AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
const
std
::
string
&
method_name
,
int64_t
time_out
)
{
const
platform
::
DeviceContext
*
p_ctx
=
&
ctx
;
const
std
::
string
ep_val
=
ep
;
const
std
::
string
var_name_val
=
var_name
;
const
framework
::
Scope
*
p_scope
=
&
scope
;
const
auto
ch
=
GetChannel
(
ep_val
);
const
auto
ch_ptr
=
GetChannel
(
ep_val
);
const
std
::
string
method
=
"GetRPC"
;
VarHandlePtr
var_h
(
new
VarHandle
(
ep
,
method
,
var_name_val
,
p_ctx
,
p_scope
));
framework
::
AsyncIO
([
=
]
{
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VariableMessage
*
response
=
new
sendrecv
::
VariableMessage
();
cntl
->
set_timeout_ms
(
time_out
);
framework
::
AsyncIO
(
[
var_name_val
,
ep_val
,
p_scope
,
p_ctx
,
time_out
,
ch
,
this
]
{});
sendrecv
::
VariableMessage
req
;
req
.
set_varname
(
var_name_val
);
req
.
set_trainer_id
(
trainer_id_
);
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleGetResponse
,
cntl
,
response
,
var_h
,
ch_ptr
,
ch_ctx
,
this
);
platform
::
RecordRPCEvent
record_event
(
method
,
p_ctx
);
if
(
method_name
==
"GetMonomerVariable"
)
{
ch_ctx
->
stub
->
GetMonomerVariable
(
cntl
,
&
req
,
response
,
done
);
}
else
{
ch_ctx
->
stub
->
GetVariable
(
cntl
,
&
req
,
response
,
done
);
}
if
(
UNLIKELY
(
platform
::
IsProfileEnabled
()))
{
var_h
->
Wait
();
}
});
req_count_
++
;
return
true
;
return
var_h
;
}
VarHandlePtr
BRPCClient
::
AsyncGetMonomerVariable
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
return
_AsyncGetVar
(
ep
,
ctx
,
scope
,
var_name
,
"GetMonomerVariable"
,
time_out
);
}
VarHandlePtr
BRPCClient
::
AsyncGetMonomerBarrier
(
const
std
::
string
&
ep
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
return
AsyncSendMessage
(
ep
,
"GetMonomerBarrier"
,
var_name
,
time_out
);
}
bool
BRPCClient
::
AsyncPrefetchVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
in_var_name
,
const
std
::
string
&
out_var_name
,
int64_t
time_out
)
{
VarHandlePtr
BRPCClient
::
AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
)
{
return
_AsyncGetVar
(
ep
,
ctx
,
scope
,
var_name
,
"GetVariable"
,
time_out
);
}
VarHandlePtr
BRPCClient
::
AsyncPrefetchVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
in_var_name
,
const
std
::
string
&
out_var_name
,
const
std
::
string
&
table_name
,
int64_t
time_out
)
{
const
platform
::
DeviceContext
*
p_ctx
=
&
ctx
;
const
std
::
string
ep_val
=
ep
;
const
std
::
string
in_var_name_val
=
in_var_name
;
const
std
::
string
out_var_name_val
=
out_var_name
;
const
std
::
string
table_name_val
=
table_name
;
const
framework
::
Scope
*
p_scope
=
&
scope
;
const
auto
ch
=
GetChannel
(
ep_val
);
const
auto
ch_ptr
=
GetChannel
(
ep_val
);
const
std
::
string
method
=
"PrefetchRPC"
;
VarHandlePtr
var_h
(
new
VarHandle
(
ep
,
method
,
out_var_name_val
,
p_ctx
,
p_scope
));
framework
::
AsyncIO
([
=
]
{
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VariableMessage
*
response
=
new
sendrecv
::
VariableMessage
();
cntl
->
set_timeout_ms
(
time_out
);
auto
*
var
=
p_scope
->
FindVar
(
in_var_name_val
);
sendrecv
::
VariableMessage
req
;
distributed
::
SerializeToIOBuf
(
in_var_name_val
,
var
,
*
p_ctx
,
&
req
,
&
cntl
->
request_attachment
(),
out_var_name_val
,
false
,
0
,
table_name_val
);
platform
::
RecordRPCEvent
record_event
(
method
,
p_ctx
);
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleGetResponse
,
cntl
,
response
,
var_h
,
ch_ptr
,
ch_ctx
,
this
);
framework
::
AsyncIO
([
in_var_name_val
,
out_var_name_val
,
ep_val
,
p_scope
,
p_ctx
,
time_out
,
ch
,
this
]
{});
ch_ctx
->
stub
->
PrefetchVariable
(
cntl
,
&
req
,
response
,
done
);
if
(
UNLIKELY
(
platform
::
IsProfileEnabled
()))
{
var_h
->
Wait
();
}
});
req_count_
++
;
return
true
;
return
var_h
;
}
void
BRPCClient
::
AsyncSendBatchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
)
{
req_count_
++
;
VarHandlePtr
BRPCClient
::
AsyncSendBatchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
)
{
return
AsyncSendMessage
(
ep
,
"BatchBarrierRPC"
,
BATCH_BARRIER_MESSAGE
,
time_out
);
}
void
BRPCClient
::
AsyncSendFetchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
)
{
VarHandlePtr
BRPCClient
::
AsyncSendFetchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
)
{
auto
ch_ptr
=
GetChannel
(
ep
);
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VariableMessage
*
response
=
new
sendrecv
::
VariableMessage
();
cntl
->
set_timeout_ms
(
time_out
);
sendrecv
::
VariableMessage
req
;
req
.
set_varname
(
FETCH_BARRIER_MESSAGE
);
const
std
::
string
method
=
"FetchBarrierRPC"
;
// var handle
VarHandlePtr
var_h
(
new
VarHandle
(
ep
,
method
,
FETCH_BARRIER_MESSAGE
,
nullptr
,
nullptr
));
platform
::
RecordRPCEvent
record_event
(
method
,
nullptr
);
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleFetchBarrierResponse
,
cntl
,
response
,
var_h
,
ch_ptr
,
ch_ctx
,
this
);
ch_ctx
->
stub
->
GetVariable
(
cntl
,
&
req
,
response
,
done
);
req_count_
++
;
if
(
UNLIKELY
(
platform
::
IsProfileEnabled
()))
{
var_h
->
Wait
();
}
return
var_h
;
}
void
BRPCClient
::
Wait
()
{
std
::
unique_lock
<
std
::
mutex
>
lk
(
sync_mutex_
);
sync_cond_
.
wait
(
lk
,
[
this
]
{
return
req_count_
==
0
;
});
bool
BRPCClient
::
Wait
()
{
VLOG
(
9
)
<<
"begin to brpcclient wait"
;
{
std
::
unique_lock
<
std
::
mutex
>
lk
(
sync_mutex_
);
sync_cond_
.
wait
(
lk
,
[
this
]
{
return
req_count_
==
0
;
});
}
VLOG
(
9
)
<<
"end to brpcclient wait"
;
return
true
;
}
ChannelQueuePtr
BRPCClient
::
GetChannel
(
const
std
::
string
&
ep
)
{
VLOG
(
4
)
<<
"begin to GetChannel:"
<<
ep
;
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
chan_mutex_
);
auto
it
=
channels_
.
find
(
ep
);
if
(
it
!=
channels_
.
end
())
{
VLOG
(
4
)
<<
"end to GetChannel:"
<<
ep
;
return
it
->
second
;
}
}
...
...
@@ -150,12 +331,20 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
ChannelQueuePtr
q
(
new
framework
::
BlockingQueue
<
ChannelContextPtr
>
());
brpc
::
ChannelOptions
options
;
#ifdef PADDLE_WITH_BRPC_RDMA
options
.
use_rdma
=
true
;
#endif
options
.
protocol
=
"baidu_std"
;
options
.
connection_type
=
"pooled"
;
options
.
connect_timeout_ms
=
100
;
// don't use pooled type. the server can't afford that.
options
.
connection_type
=
"single"
;
options
.
connect_timeout_ms
=
1000
;
options
.
timeout_ms
=
FLAGS_timeout_ms
/*milliseconds*/
;
options
.
max_retry
=
FLAGS_max_retry
;
for
(
int
i
=
0
;
i
<
FLAGS_brpc_channel_num
;
++
i
)
{
VLOG
(
1
)
<<
"create "
<<
brpc_channel_num_per_server_
<<
" brpc channels to pserver:"
<<
ep
;
for
(
int
i
=
0
;
i
<
brpc_channel_num_per_server_
;
++
i
)
{
std
::
shared_ptr
<
ChannelContext
>
c
(
new
ChannelContext
());
if
(
c
->
channel
.
Init
(
ep
.
c_str
(),
&
options
)
!=
0
)
{
LOG
(
FATAL
)
<<
"Fail to initialize channel"
;
...
...
@@ -172,9 +361,75 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
channels_
[
ep
]
=
q
;
}
VLOG
(
4
)
<<
"end to GetChannel:"
<<
ep
;
return
q
;
}
VarHandlePtr
BRPCClient
::
AsyncSendComplete
(
const
std
::
string
&
ep
,
int64_t
time_out
)
{
return
AsyncSendMessage
(
ep
,
"SendCompleteRPC"
,
COMPLETE_MESSAGE
,
time_out
);
}
void
BRPCClient
::
SendComplete
()
{
for
(
auto
&
kv
:
channels_
)
{
AsyncSendComplete
(
kv
.
first
);
}
}
VarHandlePtr
BRPCClient
::
AsyncSendVarMessage
(
const
std
::
string
&
ep
,
const
std
::
string
&
method_name
,
const
sendrecv
::
VariableMessage
&
req
,
int64_t
time_out
)
{
auto
ch_ptr
=
GetChannel
(
ep
);
auto
ch_ctx
=
ch_ptr
->
Pop
();
brpc
::
Controller
*
cntl
=
new
brpc
::
Controller
();
sendrecv
::
VoidMessage
*
response
=
new
sendrecv
::
VoidMessage
();
cntl
->
set_timeout_ms
(
time_out
);
platform
::
RecordRPCEvent
record_event
(
method_name
,
nullptr
);
VarHandlePtr
var_h
(
new
VarHandle
(
ep
,
method_name
,
req
.
varname
(),
nullptr
,
nullptr
));
google
::
protobuf
::
Closure
*
done
=
brpc
::
NewCallback
(
&
HandleSendResponse
,
cntl
,
response
,
var_h
,
ch_ptr
,
ch_ctx
,
this
);
if
(
method_name
==
"CheckPointNotifyRPC"
)
{
ch_ctx
->
stub
->
CheckpointNotify
(
cntl
,
&
req
,
response
,
done
);
}
else
if
(
method_name
==
"GetMonomerBarrier"
)
{
ch_ctx
->
stub
->
GetMonomerBarrier
(
cntl
,
&
req
,
response
,
done
);
}
else
{
ch_ctx
->
stub
->
SendVariable
(
cntl
,
&
req
,
response
,
done
);
}
req_count_
++
;
if
(
UNLIKELY
(
platform
::
IsProfileEnabled
()))
{
var_h
->
Wait
();
}
return
var_h
;
}
VarHandlePtr
BRPCClient
::
AsyncSendMessage
(
const
std
::
string
&
ep
,
const
std
::
string
&
method_name
,
const
std
::
string
&
message
,
int64_t
time_out
)
{
sendrecv
::
VariableMessage
req
;
req
.
set_varname
(
message
);
return
AsyncSendVarMessage
(
ep
,
method_name
,
req
,
time_out
);
}
VarHandlePtr
BRPCClient
::
AsyncCheckpointNotify
(
const
std
::
string
&
ep
,
const
std
::
string
&
dir
,
int64_t
time_out
)
{
sendrecv
::
VariableMessage
req
;
req
.
set_varname
(
CHECKPOINT_SAVE_MESSAGE
);
req
.
set_out_varname
(
dir
);
return
AsyncSendVarMessage
(
ep
,
"CheckPointNotifyRPC"
,
req
,
time_out
);
}
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/distributed/brpc_client.h
浏览文件 @
238b24bf
...
...
@@ -31,6 +31,8 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
#include "paddle/fluid/operators/distributed/rpc_client.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/platform/macros.h" // for DISABLE_COPY_AND_ASSIGN
...
...
@@ -53,33 +55,94 @@ class BRPCClient : public RPCClient {
BRPCClient
()
{}
virtual
~
BRPCClient
();
bool
AsyncSendVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncSendVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
bool
AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
bool
AsyncPrefetchVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
in_var_name
,
const
std
::
string
&
out_var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncGetMonomerBarrier
(
const
std
::
string
&
ep
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
void
AsyncSendBatchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncGetMonomerVariable
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
void
AsyncSendFetchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncPrefetchVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
in_var_name
,
const
std
::
string
&
out_var_name
,
const
std
::
string
&
table_name
=
""
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
void
Wait
()
override
;
VarHandlePtr
AsyncSendBatchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncSendFetchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
VarHandlePtr
AsyncCheckpointNotify
(
const
std
::
string
&
ep
,
const
std
::
string
&
dir
,
int64_t
time_out
=
FLAGS_rpc_deadline
)
override
;
bool
Wait
()
override
;
void
SendComplete
()
override
;
private:
VarHandlePtr
_AsyncGetVar
(
const
std
::
string
&
ep
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
&
scope
,
const
std
::
string
&
var_name
,
const
std
::
string
&
method_name
,
int64_t
time_out
=
FLAGS_rpc_deadline
);
void
Proceed
();
ChannelQueuePtr
GetChannel
(
const
std
::
string
&
ep
);
VarHandlePtr
AsyncSendComplete
(
const
std
::
string
&
ep
,
int64_t
time_out
=
FLAGS_rpc_deadline
);
VarHandlePtr
AsyncSendMessage
(
const
std
::
string
&
ep
,
const
std
::
string
&
method_name
,
const
std
::
string
&
message
,
int64_t
time_out
);
VarHandlePtr
AsyncSendVarMessage
(
const
std
::
string
&
ep
,
const
std
::
string
&
method_name
,
const
sendrecv
::
VariableMessage
&
req
,
int64_t
time_out
);
friend
void
HandleSendResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VoidMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
);
friend
void
HandleGetResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VariableMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
);
friend
void
HandleFetchBarrierResponse
(
brpc
::
Controller
*
cntl
,
sendrecv
::
VariableMessage
*
response
,
VarHandlePtr
var_h
,
ChannelQueuePtr
ch_ptr
,
ChannelContextPtr
ch_ctx
,
BRPCClient
*
cls
);
void
DecreaseReqCount
()
{
if
(
--
req_count_
<=
0
)
{
sync_cond_
.
notify_all
();
}
}
private:
std
::
unordered_map
<
std
::
string
,
ChannelQueuePtr
>
channels_
;
...
...
@@ -88,6 +151,8 @@ class BRPCClient : public RPCClient {
std
::
condition_variable
sync_cond_
;
std
::
atomic
<
int64_t
>
req_count_
{
0
};
static
constexpr
int
brpc_channel_num_per_server_
=
4
;
// mutex for GetChannel thread safety
std
::
mutex
chan_mutex_
;
DISABLE_COPY_AND_ASSIGN
(
BRPCClient
);
...
...
paddle/fluid/operators/distributed/brpc_rdma_pool.cc
0 → 100644
浏览文件 @
238b24bf
// 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.
#ifdef PADDLE_WITH_BRPC_RDMA
#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
#include "brpc/channel.h"
#include "brpc/rdma/rdma_helper.h"
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
RdmaMemPool
&
RdmaMemPool
::
Instance
()
{
static
RdmaMemPool
*
g_rdma_mem_pool
=
new
RdmaMemPool
();
return
*
g_rdma_mem_pool
;
}
void
*
RdmaMemPool
::
Find
(
const
std
::
string
&
varname
,
int64_t
size
)
{
pthread_rwlock_rdlock
(
&
access_
);
auto
it
=
pool_
.
find
(
varname
);
if
(
it
==
pool_
.
end
())
{
pthread_rwlock_unlock
(
&
access_
);
return
nullptr
;
}
auto
info
=
it
->
second
;
if
(
info
.
data_size
!=
size
)
{
pthread_rwlock_unlock
(
&
access_
);
PADDLE_ENFORCE
(
false
,
"var:%s size:%ld != %ld"
,
varname
,
size
,
info
.
data_size
);
return
nullptr
;
}
pthread_rwlock_unlock
(
&
access_
);
return
info
.
data
;
}
void
RdmaMemPool
::
Register
(
const
std
::
string
&
varname
,
void
*
data
,
int64_t
data_size
)
{
void
*
old
=
Find
(
varname
,
data_size
);
if
(
old
!=
nullptr
)
{
if
(
data
!=
old
)
{
PADDLE_ENFORCE
(
false
,
"var:%s data:%ld != %ld"
,
varname
,
data
,
old
);
}
VLOG
(
7
)
<<
"Find on rdma:"
<<
varname
<<
" data:"
<<
data
<<
" data_size:"
<<
data_size
;
return
;
}
VarInfo
info
;
info
.
data
=
data
;
info
.
data_size
=
data_size
;
pthread_rwlock_wrlock
(
&
access_
);
pool_
[
varname
]
=
info
;
pthread_rwlock_unlock
(
&
access_
);
if
(
brpc
::
rdma
::
RegisterMemoryForRdma
(
data
,
data_size
))
{
LOG
(
FATAL
)
<<
"register "
<<
varname
<<
" data:"
<<
data
<<
" data_size:"
<<
data_size
<<
" error"
;
}
VLOG
(
4
)
<<
"register on rdma:"
<<
varname
<<
" data:"
<<
data
<<
" data_size:"
<<
data_size
;
}
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
#endif
paddle/fluid/operators/distributed/brpc_rdma_pool.h
0 → 100644
浏览文件 @
238b24bf
// 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
#ifdef PADDLE_WITH_BRPC_RDMA
#include <pthread.h> // NOLINT
#include <string>
#include <unordered_map>
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
/*
* This class is used to avoid duplicated registion of brpc::rdma.
*/
class
RdmaMemPool
{
public:
static
RdmaMemPool
&
Instance
();
RdmaMemPool
()
:
access_
(
PTHREAD_RWLOCK_INITIALIZER
)
{}
virtual
~
RdmaMemPool
()
{
pthread_rwlock_destroy
(
&
access_
);
}
void
Register
(
const
std
::
string
&
varname
,
void
*
data
,
int64_t
size
);
void
*
Find
(
const
std
::
string
&
varname
,
int64_t
size
);
private:
struct
VarInfo
{
void
*
data
;
int64_t
data_size
;
VarInfo
()
:
data
(
nullptr
),
data_size
(
0
)
{}
};
private:
std
::
unordered_map
<
std
::
string
,
VarInfo
>
pool_
;
pthread_rwlock_t
access_
;
};
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
#endif
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc
0 → 100644
浏览文件 @
238b24bf
/* 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. */
#ifdef PADDLE_WITH_CUDA
#include <nccl.h>
#endif
#include <sys/time.h>
#include <thread> // NOLINT
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/platform/profiler.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
class
IOBufWriter
{
public:
static
void
Append
(
butil
::
IOBuf
*
iobuf
,
int
k
,
const
char
*
v
,
int64_t
vlen
)
{
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
k
),
4
);
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
vlen
),
8
);
iobuf
->
append
(
v
,
vlen
);
}
static
void
AppendTCPZeroCopy
(
butil
::
IOBuf
*
iobuf
,
int
k
,
const
char
*
v
,
int64_t
vlen
,
bool
in_cuda_pinned
,
void
(
*
destroy
)(
void
*
),
void
*
user_data
)
{
VLOG
(
7
)
<<
"AppendTCPZeroCopy "
<<
" k:"
<<
k
<<
" data:"
<<
static_cast
<
void
*>
(
const_cast
<
char
*>
(
v
))
<<
" data_size:"
<<
vlen
<<
" in_cuda_pinned:"
<<
in_cuda_pinned
;
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
k
),
4
);
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
vlen
),
8
);
// FIXME(gongwb): use append_zerocopy
/*
if (in_cuda_pinned) {
iobuf->append_zerocopy(v, vlen, IOBufWriter::FreeMemory);
} else {
iobuf->append_zerocopy(v, vlen, nullptr);
}
*/
iobuf
->
append
(
v
,
vlen
);
destroy
(
user_data
);
}
#ifdef PADDLE_WITH_BRPC_RDMA
static
void
AppendRdmaZeroCopy
(
const
std
::
string
varname
,
butil
::
IOBuf
*
iobuf
,
int
k
,
const
char
*
v
,
int64_t
vlen
,
bool
in_cuda_pinned
,
void
(
*
destroy
)(
void
*
),
void
*
user_data
)
{
VLOG
(
7
)
<<
"AppendRdmaZeroCopy varname:"
<<
varname
<<
" k:"
<<
k
<<
" data:"
<<
static_cast
<
void
*>
(
const_cast
<
char
*>
(
v
))
<<
" data_size:"
<<
vlen
<<
" in_cuda_pinned:"
<<
in_cuda_pinned
;
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
k
),
4
);
iobuf
->
append
(
reinterpret_cast
<
char
*>
(
&
vlen
),
8
);
RdmaMemPool
::
Instance
().
Register
(
varname
,
static_cast
<
void
*>
(
const_cast
<
char
*>
(
v
)),
vlen
);
// FIXME(gongwb): use append_zerocopy
// iobuf->append_zerocopy(v, vlen, nullptr);
iobuf
->
append
(
v
,
vlen
);
destroy
(
user_data
);
return
;
}
#endif
static
void
AppendZeroCopy
(
const
std
::
string
varname
,
butil
::
IOBuf
*
iobuf
,
int
k
,
const
char
*
v
,
int64_t
vlen
,
bool
in_cuda_pinned
,
void
(
*
destroy
)(
void
*
),
void
*
user_data
)
{
#ifdef PADDLE_WITH_BRPC_RDMA
IOBufWriter
::
AppendRdmaZeroCopy
(
varname
,
iobuf
,
k
,
v
,
vlen
,
in_cuda_pinned
,
destroy
,
user_data
);
#else
IOBufWriter
::
AppendTCPZeroCopy
(
iobuf
,
k
,
v
,
vlen
,
in_cuda_pinned
,
destroy
,
user_data
);
#endif
}
};
void
SerializeToIOBuf
(
const
std
::
string
&
name
,
framework
::
Variable
*
var
,
const
platform
::
DeviceContext
&
ctx
,
VarMsg
*
request
,
butil
::
IOBuf
*
iobuf
,
const
std
::
string
&
out_varname
,
bool
var_is_not_stable
,
int
trainer_id
,
const
std
::
string
&
table_name
)
{
std
::
unique_ptr
<
TensorPayload
>
payload
;
request
->
set_varname
(
name
);
request
->
set_trainer_id
(
trainer_id
);
// Note: normally the profiler is enabled in 1 trainer, hence only
// 1 trainer returns true for ShouldSendProfileState(). It tells PS
// servers the trainer's profiling state so that PS can follow the
// trainer.
if
(
platform
::
ShouldSendProfileState
())
{
if
(
platform
::
IsProfileEnabled
())
{
request
->
set_profile
(
platform
::
kEnableProfiler
);
}
else
{
request
->
set_profile
(
platform
::
kDisableProfiler
);
}
}
if
(
!
out_varname
.
empty
())
{
request
->
set_out_varname
(
out_varname
);
}
if
(
!
table_name
.
empty
())
{
request
->
set_table_name
(
table_name
);
}
if
(
var
->
IsType
<
framework
::
LoDTensor
>
())
{
request
->
set_type
(
::
sendrecv
::
LOD_TENSOR
);
payload
.
reset
(
new
TensorPayload
(
GetTensorPayload
(
var
,
ctx
,
request
)));
}
else
if
(
var
->
IsType
<
framework
::
SelectedRows
>
())
{
request
->
set_type
(
::
sendrecv
::
SELECTED_ROWS
);
payload
.
reset
(
new
TensorPayload
(
GetSelectedRowsPayload
(
var
,
ctx
,
request
)));
#ifdef PADDLE_WITH_CUDA
}
else
if
(
var
->
IsType
<
ncclUniqueId
>
())
{
request
->
set_type
(
::
sendrecv
::
NCCL_ID
);
const
ncclUniqueId
&
uid
=
var
->
Get
<
ncclUniqueId
>
();
// TODO(gongwb): use append_zero to avoid data copy.
IOBufWriter
::
Append
(
iobuf
,
sendrecv
::
VariableMessage
::
kSerializedFieldNumber
,
uid
.
internal
,
NCCL_UNIQUE_ID_BYTES
);
return
;
#endif
}
else
{
PADDLE_THROW
(
"Serialize does not support type: %s"
,
typeid
(
var
->
Type
()).
name
());
}
PADDLE_ENFORCE_NOT_NULL
(
payload
);
// FIXME(gongwb): it seems that can use zero copy.
if
(
var_is_not_stable
)
{
IOBufWriter
::
Append
(
iobuf
,
::
sendrecv
::
VariableMessage
::
kSerializedFieldNumber
,
static_cast
<
const
char
*>
(
payload
->
ptr
()),
payload
->
memory_size
());
}
else
{
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
#ifdef PADDLE_WITH_CUDA
IOBufWriter
::
AppendZeroCopy
(
name
,
iobuf
,
::
sendrecv
::
VariableMessage
::
kSerializedFieldNumber
,
static_cast
<
const
char
*>
(
payload
->
ptr
()),
payload
->
memory_size
(),
true
,
SerializeDestroyCallback
,
static_cast
<
void
*>
(
payload
.
get
()));
payload
.
release
();
#endif
}
else
{
IOBufWriter
::
AppendZeroCopy
(
name
,
iobuf
,
::
sendrecv
::
VariableMessage
::
kSerializedFieldNumber
,
static_cast
<
const
char
*>
(
payload
->
ptr
()),
payload
->
memory_size
(),
false
,
SerializeDestroyCallback
,
static_cast
<
void
*>
(
payload
.
get
()));
payload
.
release
();
}
}
if
(
var
->
IsType
<
framework
::
SelectedRows
>
())
{
auto
*
slr
=
var
->
GetMutable
<
framework
::
SelectedRows
>
();
size_t
rows_memory_size
=
slr
->
rows
().
size
()
*
framework
::
SizeOfType
(
typeid
(
int64_t
));
IOBufWriter
::
Append
(
iobuf
,
::
sendrecv
::
VariableMessage
::
kRowsFieldNumber
,
reinterpret_cast
<
const
char
*>
(
slr
->
rows
().
data
()),
static_cast
<
int64_t
>
(
rows_memory_size
));
}
}
void
DeserializeFromIOBuf
(
const
::
sendrecv
::
VariableMessage
&
meta
,
const
butil
::
IOBuf
&
iobuf
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
*
scope
,
framework
::
Variable
**
var
,
int
*
trainer_id
)
{
operators
::
distributed
::
BRPCVariableResponse
resp
(
scope
,
&
ctx
);
PADDLE_ENFORCE
(
resp
.
Parse
(
iobuf
,
meta
)
==
0
,
"parse iobuf to tensor error!"
);
*
var
=
resp
.
GetVar
();
*
trainer_id
=
resp
.
GetTrainerId
();
}
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h
0 → 100644
浏览文件 @
238b24bf
/* 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 <sys/time.h>
#include <iostream>
#include <string>
#include <vector>
#include "brpc/channel.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
void
SerializeToIOBuf
(
const
std
::
string
&
name
,
framework
::
Variable
*
var
,
const
platform
::
DeviceContext
&
ctx
,
VarMsg
*
request
,
butil
::
IOBuf
*
iobuf
,
const
std
::
string
&
out_varname
,
bool
var_is_not_stable
,
const
int
trainer_id
=
0
,
const
std
::
string
&
table_name
=
std
::
string
());
void
DeserializeFromIOBuf
(
const
VarMsg
&
meta
,
const
butil
::
IOBuf
&
iobuf
,
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Scope
*
scope
,
framework
::
Variable
**
var
,
int
*
trainer_id
);
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/distributed/brpc_serde_test.cc
0 → 100644
浏览文件 @
238b24bf
/* 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 <unistd.h>
#include <string>
#include <thread> // NOLINT
#include "brpc/channel.h"
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/printf.h"
namespace
framework
=
paddle
::
framework
;
namespace
platform
=
paddle
::
platform
;
namespace
operators
=
paddle
::
operators
;
namespace
math
=
paddle
::
operators
::
math
;
namespace
memory
=
paddle
::
memory
;
void
RunSerdeTestSelectedRows
(
platform
::
Place
place
)
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
ctx
=
*
pool
.
Get
(
place
);
butil
::
IOBuf
iobuf
;
sendrecv
::
VariableMessage
msg
;
int
tensor_numel
=
564
*
128
;
// serialize var to IOBuf
{
framework
::
Variable
var
;
auto
*
slr
=
var
.
GetMutable
<
framework
::
SelectedRows
>
();
slr
->
set_height
(
1000
);
auto
*
tensor
=
slr
->
mutable_value
();
auto
*
rows
=
slr
->
mutable_rows
();
tensor
->
Resize
(
framework
::
make_ddim
({
564
,
128
}));
tensor
->
mutable_data
<
float
>
(
place
);
math
::
set_constant
(
ctx
,
tensor
,
32.7
);
for
(
int
i
=
0
;
i
<
564
;
++
i
)
rows
->
push_back
(
i
);
operators
::
distributed
::
SerializeToIOBuf
(
"myvar"
,
&
var
,
ctx
,
&
msg
,
&
iobuf
,
""
,
false
);
}
// desrialize
{
framework
::
Scope
scope
;
scope
.
Var
(
"myvar"
);
operators
::
distributed
::
BRPCVariableResponse
resp
(
&
scope
,
&
ctx
);
EXPECT_EQ
(
resp
.
Parse
(
iobuf
,
msg
),
0
);
framework
::
Variable
*
var2
=
resp
.
GetVar
();
auto
*
slr2
=
var2
->
GetMutable
<
framework
::
SelectedRows
>
();
auto
*
tensor2
=
slr2
->
mutable_value
();
auto
*
rows2
=
slr2
->
mutable_rows
();
float
*
tensor_data2
=
nullptr
;
framework
::
Tensor
tmp_tensor
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
platform
::
CPUPlace
cpu
;
framework
::
TensorCopy
(
*
tensor2
,
cpu
,
&
tmp_tensor
);
tensor_data2
=
tmp_tensor
.
data
<
float
>
();
}
else
{
tensor_data2
=
const_cast
<
float
*>
(
tensor2
->
data
<
float
>
());
}
const
int64_t
*
rows_data2
=
rows2
->
data
();
for
(
int
i
=
0
;
i
<
tensor_numel
;
++
i
)
{
EXPECT_FLOAT_EQ
(
tensor_data2
[
i
],
32.7
);
}
for
(
size_t
i
=
0
;
i
<
rows2
->
size
();
++
i
)
{
EXPECT_EQ
(
rows_data2
[
i
],
static_cast
<
int64_t
>
(
i
));
}
EXPECT_EQ
(
slr2
->
height
(),
1000
);
}
}
void
RunTestLodTensor
(
platform
::
Place
place
)
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
ctx
=
*
pool
.
Get
(
place
);
// serialize var to ByteBuffer
butil
::
IOBuf
iobuf
;
sendrecv
::
VariableMessage
msg
;
int
tensor_numel
=
512
*
8
*
4
*
2
;
{
framework
::
Variable
var
;
auto
*
tensor
=
var
.
GetMutable
<
framework
::
LoDTensor
>
();
tensor
->
Resize
(
framework
::
make_ddim
({
512
,
8
,
4
,
2
}));
framework
::
LoD
lod
;
lod
.
push_back
(
framework
::
Vector
<
size_t
>
({
1
,
3
,
8
}));
tensor
->
set_lod
(
lod
);
tensor
->
mutable_data
<
float
>
(
place
);
math
::
set_constant
(
ctx
,
tensor
,
31.9
);
operators
::
distributed
::
SerializeToIOBuf
(
"myvar"
,
&
var
,
ctx
,
&
msg
,
&
iobuf
,
""
,
false
);
}
// check sendrecv::VariableMessage meta data
{
EXPECT_EQ
(
msg
.
varname
(),
"myvar"
);
EXPECT_EQ
(
msg
.
type
(),
0
);
EXPECT_EQ
(
msg
.
dims
()[
0
],
512
);
EXPECT_EQ
(
msg
.
dims
()[
1
],
8
);
EXPECT_EQ
(
msg
.
dims
()[
2
],
4
);
EXPECT_EQ
(
msg
.
dims
()[
3
],
2
);
EXPECT_EQ
(
msg
.
lod_level
(),
1
);
EXPECT_EQ
(
msg
.
lod
(
0
).
lod_data
(
0
),
1
);
EXPECT_EQ
(
msg
.
lod
(
0
).
lod_data
(
1
),
3
);
EXPECT_EQ
(
msg
.
lod
(
0
).
lod_data
(
2
),
8
);
}
// deserialize
{
framework
::
Scope
scope
;
scope
.
Var
(
"myvar"
);
operators
::
distributed
::
BRPCVariableResponse
resp
(
&
scope
,
&
ctx
);
EXPECT_EQ
(
resp
.
Parse
(
iobuf
,
msg
),
0
);
framework
::
Variable
*
var2
=
resp
.
GetVar
();
auto
tensor2
=
var2
->
Get
<
framework
::
LoDTensor
>
();
float
*
tensor_data2
=
nullptr
;
framework
::
Tensor
tmp_tensor
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
platform
::
CPUPlace
cpu
;
framework
::
TensorCopy
(
tensor2
,
cpu
,
&
tmp_tensor
);
tensor_data2
=
tmp_tensor
.
data
<
float
>
();
}
else
{
tensor_data2
=
const_cast
<
float
*>
(
tensor2
.
data
<
float
>
());
}
for
(
int
i
=
0
;
i
<
tensor_numel
;
++
i
)
EXPECT_FLOAT_EQ
(
tensor_data2
[
i
],
31.9
);
}
}
TEST
(
LodTensor
,
Run
)
{
platform
::
CPUPlace
place
;
RunTestLodTensor
(
place
);
#ifdef PADDLE_WITH_CUDA
platform
::
CUDAPlace
gpu
(
0
);
RunTestLodTensor
(
gpu
);
#endif
}
TEST
(
SelectedRows
,
Run
)
{
platform
::
CPUPlace
place
;
RunSerdeTestSelectedRows
(
place
);
#ifdef PADDLE_WITH_CUDA
platform
::
CUDAPlace
gpu
;
RunSerdeTestSelectedRows
(
gpu
);
#endif
}
paddle/fluid/operators/distributed/brpc_server.cc
浏览文件 @
238b24bf
...
...
@@ -13,84 +13,287 @@
// limitations under the License.
#include "paddle/fluid/operators/distributed/brpc_server.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
namespace
sendrecv
{
typedef
std
::
unordered_map
<
std
::
string
,
paddle
::
operators
::
distributed
::
RequestHandler
*>
namespace
distributed
=
paddle
::
operators
::
distributed
;
typedef
std
::
unordered_map
<
std
::
string
,
distributed
::
RequestHandler
*>
HandlerMap
;
class
BRPCServiceImpl
:
public
SendRecvService
{
public:
explicit
BRPCServiceImpl
(
const
HandlerMap
&
rpc_call_map
)
:
request_send_h_
(
nullptr
),
request_get_h_
(
nullptr
),
request_prefetch_h_
(
nullptr
)
{
auto
it
=
rpc_call_map
.
find
(
paddle
::
operators
::
distributed
::
kRequestSend
);
explicit
BRPCServiceImpl
(
const
HandlerMap
&
rpc_call_map
,
distributed
::
RPCServer
*
rpc_server
)
:
rpc_server_
(
rpc_server
)
{
VLOG
(
3
)
<<
"BRPCServiceImpl size: "
<<
rpc_call_map
.
size
();
auto
it
=
rpc_call_map
.
find
(
distributed
::
kRequestSend
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_send_h_
=
it
->
second
;
send_threads_
.
reset
(
new
paddle
::
framework
::
ThreadPool
(
rpc_server_
->
GetThreadNum
(
distributed
::
kRequestSend
)));
}
it
=
rpc_call_map
.
find
(
paddle
::
operators
::
distributed
::
kRequestSend
);
it
=
rpc_call_map
.
find
(
distributed
::
kRequestGet
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_get_h_
=
it
->
second
;
get_threads_
.
reset
(
new
paddle
::
framework
::
ThreadPool
(
rpc_server_
->
GetThreadNum
(
distributed
::
kRequestGet
)));
}
it
=
rpc_call_map
.
find
(
paddle
::
operators
::
distributed
::
kRequestPrefetch
);
it
=
rpc_call_map
.
find
(
distributed
::
kRequestPrefetch
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_prefetch_h_
=
it
->
second
;
prefetch_threads_
.
reset
(
new
paddle
::
framework
::
ThreadPool
(
rpc_server_
->
GetThreadNum
(
distributed
::
kRequestPrefetch
)));
}
it
=
rpc_call_map
.
find
(
distributed
::
kRequestCheckpoint
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_checkpoint_h_
=
it
->
second
;
checkpoint_notify_threads_
.
reset
(
new
paddle
::
framework
::
ThreadPool
(
rpc_server_
->
GetThreadNum
(
distributed
::
kRequestPrefetch
)));
}
it
=
rpc_call_map
.
find
(
distributed
::
kRequestGetMonomerVariable
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_get_monomer_handler_h_
=
it
->
second
;
}
it
=
rpc_call_map
.
find
(
distributed
::
kRequestGetMonomerBarrier
);
if
(
it
!=
rpc_call_map
.
end
())
{
request_get_monomer_barrier_handler_h_
=
it
->
second
;
}
}
virtual
~
BRPCServiceImpl
()
{}
void
SendVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VoidMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
send_threads_
->
Run
(
[
=
]
{
_SendVariable
(
cntl_butil
,
request
,
response
,
done
);
});
}
void
_SendVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VoidMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
{
PADDLE_ENFORCE
(
request_send_h_
!=
nullptr
,
"RequestSend handler should be registed first!"
);
brpc
::
ClosureGuard
done_guard
(
done
);
paddle
::
framework
::
Scope
*
local_scope
=
request_send_h_
->
scope
();
paddle
::
framework
::
Variable
*
outvar
=
nullptr
;
paddle
::
framework
::
Variable
*
invar
=
nullptr
;
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
std
::
string
varname
=
request
->
varname
();
VLOG
(
3
)
<<
"RequestSend var_name:"
<<
varname
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
if
(
!
request_send_h_
->
sync_mode
())
{
local_scope
=
&
request_send_h_
->
scope
()
->
NewScope
();
invar
=
local_scope
->
Var
(
varname
);
}
else
{
invar
=
local_scope
->
FindVar
(
varname
);
}
distributed
::
BRPCVariableResponse
resp
(
request_send_h_
->
scope
(),
request_send_h_
->
dev_ctx
(),
!
request_send_h_
->
sync_mode
());
PADDLE_ENFORCE
(
resp
.
Parse
(
cntl
->
request_attachment
(),
*
request
)
==
0
,
"parse iobuf to tensor error!"
);
request_send_h_
->
Handle
(
varname
,
local_scope
,
invar
,
&
outvar
);
auto
scope
=
resp
.
GetMutableLocalScope
();
auto
invar
=
resp
.
GetVar
();
int
trainer_id
=
request
->
trainer_id
();
paddle
::
framework
::
Variable
*
outvar
=
nullptr
;
if
(
!
request_send_h_
->
sync_mode
())
{
request_send_h_
->
scope
()
->
DeleteScope
(
local_scope
);
}
request_send_h_
->
Handle
(
varname
,
scope
,
invar
,
&
outvar
,
trainer_id
);
}
void
GetVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VariableMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
get_threads_
->
Run
(
[
=
]
{
_GetVariable
(
cntl_butil
,
request
,
response
,
done
);
});
}
void
_GetVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VariableMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
{
PADDLE_ENFORCE
(
request_get_h_
!=
nullptr
,
"RequestGet handler should be registed first!"
);
}
brpc
::
ClosureGuard
done_guard
(
done
);
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
std
::
string
varname
=
request
->
varname
();
VLOG
(
3
)
<<
"RequestGet varname:"
<<
varname
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
auto
scope
=
request_get_h_
->
scope
();
auto
invar
=
scope
->
FindVar
(
varname
);
int
trainer_id
=
request
->
trainer_id
();
paddle
::
framework
::
Variable
*
outvar
=
nullptr
;
request_get_h_
->
Handle
(
varname
,
scope
,
invar
,
&
outvar
,
trainer_id
);
if
(
outvar
)
{
distributed
::
SerializeToIOBuf
(
varname
,
outvar
,
*
request_get_h_
->
dev_ctx
(),
response
,
&
cntl
->
response_attachment
(),
""
,
false
);
}
}
void
PrefetchVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VariableMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
prefetch_threads_
->
Run
(
[
=
]
{
_PrefetchVariable
(
cntl_butil
,
request
,
response
,
done
);
});
}
void
_PrefetchVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VariableMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
{
PADDLE_ENFORCE
(
request_prefetch_h_
!=
nullptr
,
"kRequestPrefetch handler should be registed first!"
);
brpc
::
ClosureGuard
done_guard
(
done
);
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
// prefetch process...
std
::
string
in_var_name
=
request
->
varname
();
std
::
string
out_var_name
=
request
->
out_varname
();
VLOG
(
3
)
<<
"RequestPrefetch, in_var_name: "
<<
in_var_name
<<
", out_var_name: "
<<
out_var_name
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
distributed
::
BRPCVariableResponse
resp
(
request_prefetch_h_
->
scope
(),
request_prefetch_h_
->
dev_ctx
(),
true
);
PADDLE_ENFORCE
(
resp
.
Parse
(
cntl
->
request_attachment
(),
*
request
)
==
0
,
"parse iobuf to tensor error!"
);
auto
scope
=
resp
.
GetMutableLocalScope
();
auto
invar
=
scope
->
FindVar
(
in_var_name
);
std
::
string
table_name
=
request
->
table_name
();
int
trainer_id
=
request
->
trainer_id
();
paddle
::
framework
::
Variable
*
outvar
=
scope
->
Var
(
out_var_name
);
request_prefetch_h_
->
Handle
(
in_var_name
,
scope
,
invar
,
&
outvar
,
trainer_id
,
out_var_name
,
table_name
);
distributed
::
SerializeToIOBuf
(
out_var_name
,
outvar
,
*
request_prefetch_h_
->
dev_ctx
(),
response
,
&
cntl
->
response_attachment
(),
""
,
true
);
}
void
CheckpointNotify
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VoidMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
checkpoint_notify_threads_
->
Run
(
[
=
]
{
_CheckpointNotify
(
cntl_butil
,
request
,
response
,
done
);
});
}
void
_CheckpointNotify
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VoidMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
{
PADDLE_ENFORCE
(
request_checkpoint_h_
!=
nullptr
,
"kRequestCheckpointNotify handler should be registed first!"
);
brpc
::
ClosureGuard
done_guard
(
done
);
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
distributed
::
BRPCVariableResponse
resp
(
request_checkpoint_h_
->
scope
(),
request_checkpoint_h_
->
dev_ctx
());
auto
scope
=
resp
.
GetMutableLocalScope
();
std
::
string
checkpoint_notify
=
request
->
varname
();
std
::
string
checkpoint_dir
=
request
->
out_varname
();
int
trainer_id
=
request
->
trainer_id
();
VLOG
(
4
)
<<
"RequestCheckpointNotify notify: "
<<
checkpoint_notify
<<
", dir: "
<<
checkpoint_dir
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
request_checkpoint_h_
->
Handle
(
checkpoint_notify
,
scope
,
nullptr
,
nullptr
,
trainer_id
,
checkpoint_dir
);
}
void
GetMonomerVariable
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VariableMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
PADDLE_ENFORCE
(
request_get_monomer_handler_h_
!=
nullptr
,
"kRequestGetMonomerVariable handler should be registed first!"
);
brpc
::
ClosureGuard
done_guard
(
done
);
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
// proc request.
std
::
string
varname
=
request
->
varname
();
VLOG
(
3
)
<<
"GetMonomerVariable "
<<
varname
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
rpc_server_
->
WaitVarCond
(
varname
);
distributed
::
MonomerHandle
h
=
rpc_server_
->
GetMonomer
(
varname
);
auto
scope
=
h
.
scope_
;
auto
invar
=
scope
->
FindVar
(
varname
);
paddle
::
framework
::
Variable
*
outvar
=
nullptr
;
request_get_monomer_handler_h_
->
Handle
(
varname
,
scope
,
invar
,
&
outvar
,
request
->
trainer_id
());
if
(
outvar
)
{
distributed
::
SerializeToIOBuf
(
varname
,
outvar
,
*
h
.
dev_ctx_
,
response
,
&
cntl
->
response_attachment
(),
""
,
false
);
}
}
void
GetMonomerBarrier
(
google
::
protobuf
::
RpcController
*
cntl_butil
,
const
VariableMessage
*
request
,
VoidMessage
*
response
,
google
::
protobuf
::
Closure
*
done
)
override
{
PADDLE_ENFORCE
(
request_get_monomer_barrier_handler_h_
!=
nullptr
,
"RequestGetMonomerBarrier handler should be registed first!"
);
brpc
::
ClosureGuard
done_guard
(
done
);
brpc
::
Controller
*
cntl
=
static_cast
<
brpc
::
Controller
*>
(
cntl_butil
);
std
::
string
varname
=
request
->
varname
();
VLOG
(
3
)
<<
"RequestGetMonomerBarrier var_name:"
<<
varname
<<
", trainer_id:"
<<
request
->
trainer_id
()
<<
", from:"
<<
cntl
->
remote_side
();
rpc_server_
->
WaitVarCond
(
varname
);
distributed
::
MonomerHandle
h
=
rpc_server_
->
GetMonomer
(
varname
);
paddle
::
framework
::
Scope
*
scope
=
nullptr
;
paddle
::
framework
::
Variable
*
invar
=
nullptr
;
paddle
::
framework
::
Variable
*
outvar
=
nullptr
;
request_get_monomer_barrier_handler_h_
->
Handle
(
varname
,
scope
,
invar
,
&
outvar
,
request
->
trainer_id
());
}
private:
paddle
::
operators
::
distributed
::
RequestHandler
*
request_send_h_
;
paddle
::
operators
::
distributed
::
RequestHandler
*
request_get_h_
;
paddle
::
operators
::
distributed
::
RequestHandler
*
request_prefetch_h_
;
distributed
::
RequestHandler
*
request_send_h_
{
nullptr
};
distributed
::
RequestHandler
*
request_get_h_
{
nullptr
};
distributed
::
RequestHandler
*
request_prefetch_h_
{
nullptr
};
distributed
::
RequestHandler
*
request_checkpoint_h_
{
nullptr
};
distributed
::
RequestHandler
*
request_get_monomer_handler_h_
{
nullptr
};
distributed
::
RequestHandler
*
request_get_monomer_barrier_handler_h_
{
nullptr
};
distributed
::
RPCServer
*
rpc_server_
{
nullptr
};
// FIXME(gongwb): brpc should support process one rpce use one threadpool.
std
::
unique_ptr
<
paddle
::
framework
::
ThreadPool
>
send_threads_
;
std
::
unique_ptr
<
paddle
::
framework
::
ThreadPool
>
get_threads_
;
std
::
unique_ptr
<
paddle
::
framework
::
ThreadPool
>
prefetch_threads_
;
std
::
unique_ptr
<
paddle
::
framework
::
ThreadPool
>
checkpoint_notify_threads_
;
};
}
// namespace sendrecv
...
...
@@ -100,7 +303,7 @@ namespace distributed {
void
AsyncBRPCServer
::
StartServer
()
{
// Instance of your service.
sendrecv
::
BRPCServiceImpl
service_impl
(
rpc_call_map_
);
sendrecv
::
BRPCServiceImpl
service_impl
(
rpc_call_map_
,
this
);
// Add the service into server. Notice the second parameter, because the
// service is put on stack, we don't want server to delete it, otherwise
...
...
@@ -111,6 +314,9 @@ void AsyncBRPCServer::StartServer() {
}
brpc
::
ServerOptions
options
;
#ifdef PADDLE_WITH_BRPC_RDMA
options
.
use_rdma
=
true
;
#endif
options
.
idle_timeout_sec
=
idle_timeout_s_
;
options
.
max_concurrency
=
max_concurrency_
;
if
(
server_
.
Start
(
bind_address_
.
c_str
(),
&
options
)
!=
0
)
{
...
...
paddle/fluid/operators/distributed/brpc_variable_response.cc
0 → 100644
浏览文件 @
238b24bf
// 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/operators/distributed/brpc_variable_response.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
namespace
pb
=
::
google
::
protobuf
;
using
vr
=
::
sendrecv
::
VariableMessage
;
int
BRPCVariableResponse
::
Parse
(
Source
*
source
)
{
pb
::
io
::
ZeroCopyInputStream
*
input_stream
=
source
->
contents
();
pb
::
io
::
CodedInputStream
input
(
input_stream
);
input
.
SetTotalBytesLimit
(
INT_MAX
,
INT_MAX
);
while
(
1
)
{
unsigned
int
tag
=
0
;
if
(
!
input
.
ReadLittleEndian32
(
&
tag
))
{
break
;
}
uint64_t
num_bytes
=
0
;
if
(
!
input
.
ReadLittleEndian64
(
&
num_bytes
))
{
break
;
}
int
field
=
static_cast
<
int
>
(
tag
);
int
ret
=
field
==
0
?
-
1
:
field
;
switch
(
field
)
{
case
vr
::
kSerializedFieldNumber
:
{
if
(
!
ProcSerializedField
(
field
,
&
input
,
num_bytes
))
{
return
ret
;
}
break
;
}
case
vr
::
kRowsFieldNumber
:
{
PADDLE_ENFORCE
((
meta_
.
type
()
==
sendrecv
::
SELECTED_ROWS
||
meta_
.
type
()
==
sendrecv
::
LOD_TENSOR
)
&&
meta_
.
varname
()
!=
""
,
"meta info should be got first!"
);
if
(
!
CopySelectRowsData
(
&
input
,
*
dev_ctx_
,
num_bytes
))
{
return
ret
;
}
break
;
}
default:
{
PADDLE_ENFORCE
(
false
,
"not surpported %u fieldnumber"
,
field
);
return
ret
;
}
}
}
return
0
;
}
}
// namespace distributed
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/distributed/brpc_variable_response.h
0 → 100644
浏览文件 @
238b24bf
// 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 "brpc/channel.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
namespace
paddle
{
namespace
operators
{
namespace
distributed
{
class
BRPCSourceWrapper
:
public
Source
{
public:
explicit
BRPCSourceWrapper
(
const
butil
::
IOBuf
&
iobuf
)
:
source_
(
iobuf
)
{}
::
google
::
protobuf
::
io
::
ZeroCopyInputStream
*
contents
()
override
{
return
&
source_
;
}
private:
butil
::
IOBufAsZeroCopyInputStream
source_
;
};
class
BRPCVariableResponse
:
public
VariableResponse
{
public:
BRPCVariableResponse
(
const
framework
::
Scope
*
scope
,
const
platform
::
DeviceContext
*
dev_ctx
,
bool
create_scope
=
false
)
:
VariableResponse
(
scope
,
dev_ctx
,
create_scope
)
{}
virtual
~
BRPCVariableResponse
()
{}
// parse attachment from iobuf
int
Parse
(
Source
*
source
)
override
;
int
Parse
(
const
butil
::
IOBuf
&
iobuf
,
const
sendrecv
::
VariableMessage
&
meta
)
{
BRPCSourceWrapper
wrapper
(
iobuf
);
return
VariableResponse
::
Parse
(
&
wrapper
,
meta
);
}
};
};
// namespace distributed
};
// namespace operators
};
// namespace paddle
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @
238b24bf
...
...
@@ -293,8 +293,7 @@ VarHandlePtr GRPCClient::AsyncGetMonomerBarrier(const std::string& ep,
const
auto
ch
=
GetChannel
(
ep
);
BatchBarrierProcessor
*
s
=
new
BatchBarrierProcessor
(
ch
);
const
std
::
string
method
=
"SendMonomerFetchBarrierRPC"
;
VarHandlePtr
h
(
new
VarHandle
(
ep
,
method
,
FETCH_BARRIER_MESSAGE
,
nullptr
,
nullptr
));
VarHandlePtr
h
(
new
VarHandle
(
ep
,
method
,
var_name
,
nullptr
,
nullptr
));
s
->
Prepare
(
h
,
time_out
);
VLOG
(
30
)
<<
s
->
GetVarHandlePtr
()
->
String
()
<<
" begin"
;
...
...
paddle/fluid/operators/distributed/grpc_serde.cc
浏览文件 @
238b24bf
...
...
@@ -32,13 +32,6 @@ namespace paddle {
namespace
operators
{
namespace
distributed
{
static
void
SerializeDestroyCallback
(
void
*
payload
)
{
if
(
payload
!=
nullptr
)
{
auto
*
shared_payload
=
reinterpret_cast
<
TensorPayload
*>
(
payload
);
delete
shared_payload
;
}
}
void
SerializeToByteBuffer
(
const
std
::
string
&
name
,
framework
::
Variable
*
var
,
const
platform
::
DeviceContext
&
ctx
,
::
grpc
::
ByteBuffer
*
msg
,
const
std
::
string
&
out_name
,
...
...
paddle/fluid/operators/distributed/rpc_server.h
浏览文件 @
238b24bf
...
...
@@ -75,6 +75,10 @@ class RPCServer {
void
RegisterRPC
(
const
std
::
string
&
rpc_name
,
RequestHandler
*
handler
,
int
thread_num
=
5
);
int
GetThreadNum
(
const
std
::
string
&
rpc_name
)
{
return
rpc_thread_num_
[
rpc_name
];
}
// Wait util all the clients have reached the barrier for one
// rpc method. This function should be called in the
// RequestHandler if you want to run the server/client in a
...
...
paddle/fluid/operators/distributed/sendrecvop_utils.cc
浏览文件 @
238b24bf
...
...
@@ -18,6 +18,7 @@ limitations under the License. */
#include <thread> // NOLINT
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/platform/port.h"
...
...
@@ -45,7 +46,6 @@ static TensorPayload GetCommunicationAllocationFromTensor(
memory
::
Copy
(
cuda_pinned
,
result
->
ptr
(),
boost
::
get
<
platform
::
CUDAPlace
>
(
tensor
.
place
()),
tensor
.
data
<
void
>
(),
copy_size
,
gpu_dev_ctx
.
stream
());
ctx
.
Wait
();
return
TensorPayload
(
result
);
#else
...
...
paddle/fluid/operators/distributed/sendrecvop_utils.h
浏览文件 @
238b24bf
...
...
@@ -50,6 +50,13 @@ class TensorPayload final {
size_t
memory_size_
;
};
inline
void
SerializeDestroyCallback
(
void
*
payload
)
{
if
(
payload
!=
nullptr
)
{
auto
*
shared_payload
=
reinterpret_cast
<
TensorPayload
*>
(
payload
);
delete
shared_payload
;
}
}
TensorPayload
GetTensorPayload
(
framework
::
Variable
*
var
,
const
platform
::
DeviceContext
&
ctx
,
VarMsg
*
request
);
...
...
paddle/fluid/operators/distributed_ops/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -2,9 +2,9 @@ include(operators)
set
(
DISTRIBUTE_DEPS
""
)
if
(
WITH_GRPC
)
set
(
DISTRIBUTE_DEPS sendrecvop_
g
rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node
)
set
(
DISTRIBUTE_DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node
)
else
()
set
(
DISTRIBUTE_DEPS sendrecvop_
b
rpc brpc leveldb snappystream snappy protobuf ssl crypto zlib node
)
set
(
DISTRIBUTE_DEPS sendrecvop_rpc brpc leveldb snappystream snappy protobuf ssl crypto zlib node
)
if
(
WITH_BRPC_RDMA
)
find_library
(
IBVERBS_LIBRARY NAMES ibverbs
)
ADD_LIBRARY
(
ibverbs SHARED IMPORTED GLOBAL
)
...
...
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
浏览文件 @
238b24bf
...
...
@@ -26,10 +26,11 @@ limitations under the License. */
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/distributed_ops/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_int32
(
rpc_send_thread_num
,
5
,
"number of threads for rpc send"
);
DEFINE_int32
(
rpc_get_thread_num
,
5
,
"number of threads for rpc get"
);
DEFINE_int32
(
rpc_prefetch_thread_num
,
5
,
"number of threads for rpc prefetch"
);
DEFINE_int32
(
rpc_send_thread_num
,
12
,
"number of threads for rpc send"
);
DEFINE_int32
(
rpc_get_thread_num
,
12
,
"number of threads for rpc get"
);
DEFINE_int32
(
rpc_prefetch_thread_num
,
12
,
"number of threads for rpc prefetch"
);
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/distributed_ops/send_op.cc
浏览文件 @
238b24bf
...
...
@@ -58,7 +58,9 @@ class SendOp : public framework::OperatorBase {
}
if
(
sync_send
)
{
for
(
size_t
i
=
0
;
i
<
rets
.
size
();
i
++
)
{
VLOG
(
7
)
<<
"before sync_send "
<<
ins
[
i
]
<<
"from "
<<
epmap
[
i
];
PADDLE_ENFORCE
(
rets
[
i
]
->
Wait
(),
"internal error in RPCClient"
);
VLOG
(
7
)
<<
"after sync_send "
<<
ins
[
i
]
<<
"from "
<<
epmap
[
i
];
}
}
}
...
...
paddle/fluid/operators/math/pooling.cc
浏览文件 @
238b24bf
...
...
@@ -31,7 +31,7 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
,
bool
exclusive
,
framework
::
Tensor
*
output
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -51,16 +51,28 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
T
ele
=
pool_process
.
initial
();
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
...
...
@@ -68,8 +80,9 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
pool_process
.
compute
(
input_data
[
h
*
input_width
+
w
],
&
ele
);
}
}
int
pool_size
=
exclusive
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
int
pool_size
=
(
exclusive
||
adaptive
)
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
pool_process
.
finalize
(
static_cast
<
T
>
(
pool_size
),
&
ele
);
output_data
[
ph
*
output_width
+
pw
]
=
ele
;
}
...
...
@@ -94,7 +107,7 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -115,18 +128,31 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
int
pool_size
=
exclusive
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
pool_size
=
(
exclusive
||
adaptive
)
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
float
scale
=
1.0
/
pool_size
;
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
...
...
@@ -251,7 +277,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
,
bool
exclusive
,
framework
::
Tensor
*
output
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -276,20 +302,38 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
T
ele
=
pool_process
.
initial
();
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
...
...
@@ -302,7 +346,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
}
}
int
pool_size
=
exclusive
(
exclusive
||
adaptive
)
?
(
dend
-
dstart
)
*
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_depth
*
ksize_height
*
ksize_width
;
pool_process
.
finalize
(
static_cast
<
T
>
(
pool_size
),
&
ele
);
...
...
@@ -330,7 +374,7 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -356,24 +400,41 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
pool_size
=
exclusive
(
exclusive
||
adaptive
)
?
(
dend
-
dstart
)
*
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_depth
*
ksize_height
*
ksize_width
;
float
scale
=
1.0
/
pool_size
;
...
...
@@ -517,8 +578,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -538,16 +599,28 @@ class MaxPool2dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
T1
*
output_data
=
output
->
mutable_data
<
T1
>
(
context
.
GetPlace
());
T2
*
mask_data
=
mask
->
mutable_data
<
T2
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
T1
ele
=
static_cast
<
T1
>
(
-
FLT_MAX
);
int
index
=
-
1
;
...
...
@@ -584,7 +657,7 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUDeviceContext, T1, T2> {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_height
=
input_grad
->
dims
()[
2
];
...
...
@@ -637,8 +710,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -663,20 +736,38 @@ class MaxPool3dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
T1
*
output_data
=
output
->
mutable_data
<
T1
>
(
context
.
GetPlace
());
T2
*
mask_data
=
mask
->
mutable_data
<
T2
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
T1
ele
=
static_cast
<
T1
>
(
-
FLT_MAX
);
...
...
@@ -718,7 +809,7 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUDeviceContext, T1, T2> {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_depth
=
input_grad
->
dims
()[
2
];
...
...
paddle/fluid/operators/math/pooling.cu
浏览文件 @
238b24bf
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/pooling.h
浏览文件 @
238b24bf
...
...
@@ -68,6 +68,18 @@ class AvgPoolGrad {
}
};
/* used for adaptive pool to calculate start and end index of each divided grid
*/
HOSTDEVICE
inline
int
AdaptStartIndex
(
int
ph
,
int
input_size
,
int
output_size
)
{
return
static_cast
<
int
>
(
floor
(
static_cast
<
double
>
(
ph
*
input_size
)
/
output_size
));
}
HOSTDEVICE
inline
int
AdaptEndIndex
(
int
ph
,
int
input_size
,
int
output_size
)
{
return
static_cast
<
int
>
(
ceil
(
static_cast
<
double
>
((
ph
+
1
)
*
input_size
)
/
output_size
));
}
/*
* \brief Getting pooling results, and calculating gradient.
*
...
...
@@ -102,7 +114,7 @@ class Pool2dFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
output
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
);
};
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
...
...
@@ -114,7 +126,7 @@ class Pool2dGradFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
template
<
typename
DeviceContext
,
class
T
>
...
...
@@ -136,7 +148,7 @@ class Pool3dFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
output
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
);
};
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
...
...
@@ -148,7 +160,7 @@ class Pool3dGradFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
template
<
typename
DeviceContext
,
class
T
>
...
...
@@ -176,8 +188,8 @@ class MaxPool2dWithIndexFunctor {
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
template
<
typename
DeviceContext
,
typename
T1
,
typename
T2
>
...
...
@@ -187,7 +199,7 @@ class MaxPool2dWithIndexGradFunctor {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
...
...
@@ -197,8 +209,8 @@ class MaxPool3dWithIndexFunctor {
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
template
<
typename
DeviceContext
,
typename
T1
,
typename
T2
>
...
...
@@ -208,7 +220,7 @@ class MaxPool3dWithIndexGradFunctor {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
...
...
paddle/fluid/operators/pool_op.cc
浏览文件 @
238b24bf
...
...
@@ -52,6 +52,7 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
ceil_mode
=
ctx
->
Attrs
().
Get
<
bool
>
(
"ceil_mode"
);
bool
adaptive
=
ctx
->
Attrs
().
Get
<
bool
>
(
"adaptive"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D tensor."
);
...
...
@@ -72,9 +73,13 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
"Paddings size and pooling size should be the same."
);
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
in_x_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
PoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
],
ceil_mode
));
if
(
adaptive
)
{
output_shape
.
insert
(
output_shape
.
end
(),
ksize
.
begin
(),
ksize
.
end
());
}
else
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
PoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
],
ceil_mode
));
}
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
ShareLoD
(
"X"
,
"Out"
);
...
...
@@ -186,6 +191,14 @@ void Pool2dOpMaker::Make() {
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
...
...
@@ -264,6 +277,14 @@ Example:
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
hstart = floor(i * H_{in} / H_{out})
hend = ceil((i + 1) * H_{in} / H_{out})
wstart = floor(j * W_{in} / W_{out})
wend = ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC"
);
}
...
...
@@ -325,6 +346,13 @@ void Pool3dOpMaker::Make() {
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"use_cudnn"
,
...
...
@@ -376,6 +404,37 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = true:
$$
dstart = i * strides[0] - paddings[0]
dend = dstart + ksize[0]
hstart = j * strides[1] - paddings[1]
hend = hstart + ksize[1]
wstart = k * strides[2] - paddings[2]
wend = wstart + ksize[2]
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = false:
$$
dstart = max(0, i * strides[0] - paddings[0])
dend = min(D, dstart + ksize[0])
hstart = max(0, j * strides[1] - paddings[1])
hend = min(H, hstart + ksize[1])
wstart = max(0, k * strides[2] - paddings[2])
wend = min(W, wstart + ksize[2])
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
dstart = floor(i * D_{in} / D_{out})
dend = ceil((i + 1) * D_{in} / D_{out})
hstart = floor(j * H_{in} / H_{out})
hend = ceil((j + 1) * H_{in} / H_{out})
wstart = floor(k * W_{in} / W_{out})
wend = ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
)DOC"
);
}
...
...
paddle/fluid/operators/pool_op.h
浏览文件 @
238b24bf
...
...
@@ -70,6 +70,7 @@ class PoolKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
exclusive
=
context
.
Attr
<
bool
>
(
"exclusive"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
paddings
[
i
]
=
0
;
...
...
@@ -85,7 +86,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
true
,
out
);
true
,
false
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool2dFunctor
<
...
...
@@ -93,7 +94,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
out
);
exclusive
,
adaptive
,
out
);
}
}
break
;
case
3
:
{
...
...
@@ -103,14 +104,14 @@ class PoolKernel : public framework::OpKernel<T> {
pool3d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
true
,
out
);
true
,
false
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool3dFunctor
<
DeviceContext
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
pool3d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
out
);
exclusive
,
adaptive
,
out
);
}
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
...
@@ -133,6 +134,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
exclusive
=
context
.
Attr
<
bool
>
(
"exclusive"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
...
...
@@ -159,7 +161,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool2d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool2d_backward
(
dev_ctx
,
*
in_x
,
*
out
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
in_x_grad
);
paddings
,
pool_process
,
exclusive
,
adaptive
,
in_x_grad
);
}
}
break
;
case
3
:
{
...
...
@@ -174,7 +177,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool3d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool3d_backward
(
dev_ctx
,
*
in_x
,
*
out
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
in_x_grad
);
paddings
,
pool_process
,
exclusive
,
adaptive
,
in_x_grad
);
}
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
...
paddle/fluid/operators/pool_with_index_op.cc
浏览文件 @
238b24bf
...
...
@@ -40,6 +40,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
std
::
vector
<
int
>
ksize
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
adaptive
=
ctx
->
Attrs
().
Get
<
bool
>
(
"adaptive"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D tensor."
);
...
...
@@ -60,9 +61,13 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
"Paddings size and pooling size should be the same."
);
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
in_x_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
MaxPoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
if
(
adaptive
)
{
output_shape
.
insert
(
output_shape
.
end
(),
ksize
.
begin
(),
ksize
.
end
());
}
else
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
MaxPoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
SetOutputDim
(
"Mask"
,
framework
::
make_ddim
(
output_shape
));
...
...
@@ -133,6 +138,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default:false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling "
"instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int>, default {1, 1}), strides(height, "
"width) of pooling operator."
)
...
...
@@ -169,6 +182,12 @@ Example:
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For adaptive = true:
$$
H_{out} = ksize[0] W_{out} = ksize[1]
$$
)DOC"
);
}
...
...
@@ -209,6 +228,14 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling "
"instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int>, default {1,1,1}), strides(depth, "
"height, width) of pooling operator."
)
...
...
@@ -246,6 +273,11 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For adaptive = true:
$$
D_{out} = ksize[0] H_{out} = ksize[1] W_{out} = ksize[2]
$$
)DOC"
);
}
...
...
paddle/fluid/operators/pool_with_index_op.h
浏览文件 @
238b24bf
...
...
@@ -36,6 +36,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
...
...
@@ -50,13 +51,15 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
paddle
::
operators
::
math
::
MaxPool2dWithIndexFunctor
<
DeviceContext
,
T1
,
T2
>
pool2d_forward
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
out
,
mask
);
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
adaptive
,
out
,
mask
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexFunctor
<
DeviceContext
,
T1
,
T2
>
pool3d_forward
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
out
,
mask
);
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
adaptive
,
out
,
mask
);
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
...
...
@@ -75,6 +78,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
paddings
[
i
]
=
0
;
...
...
@@ -93,14 +97,14 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
T1
,
T2
>
pool2d_backward
;
pool2d_backward
(
device_ctx
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
,
in_x_grad
);
paddings
,
adaptive
,
in_x_grad
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexGradFunctor
<
DeviceContext
,
T1
,
T2
>
pool3d_backward
;
pool3d_backward
(
device_ctx
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
,
in_x_grad
);
paddings
,
adaptive
,
in_x_grad
);
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
...
...
paddle/fluid/operators/spp_op.h
浏览文件 @
238b24bf
...
...
@@ -56,13 +56,13 @@ class SppKernel : public framework::OpKernel<T> {
math
::
Pool2dFunctor
<
DeviceContext
,
math
::
MaxPool
<
T
>
,
T
>
pool_forward
;
math
::
MaxPool
<
T
>
max_process
;
pool_forward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
kernel_size
,
strides
,
paddings
,
max_process
,
true
,
kernel_size
,
strides
,
paddings
,
max_process
,
true
,
false
,
&
out_level
);
}
else
if
(
pooling_type
==
"avg"
)
{
math
::
Pool2dFunctor
<
DeviceContext
,
math
::
AvgPool
<
T
>
,
T
>
pool_forward
;
math
::
AvgPool
<
T
>
avg_process
;
pool_forward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
false
,
&
out_level
);
}
// flatten pooling output shape
...
...
@@ -156,7 +156,7 @@ class SppGradKernel : public framework::OpKernel<T> {
math
::
AvgPoolGrad
<
T
>
avg_process
;
pool_backward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
*&
out_level
,
*&
outgrad_level
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
in_x_grad
);
paddings
,
avg_process
,
true
,
false
,
in_x_grad
);
}
}
}
...
...
paddle/fluid/platform/device_context.cc
浏览文件 @
238b24bf
...
...
@@ -3,6 +3,7 @@ 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.
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
238b24bf
...
...
@@ -81,6 +81,14 @@ bool IsCompiledWithCUDA() {
#endif
}
bool
IsCompiledWithBrpc
()
{
#if defined(PADDLE_WITH_BRPC) || defined(PADDLE_WITH_BRPC_RDMA)
return
true
;
#else
return
false
;
#endif
}
bool
IsCompiledWithDIST
()
{
#ifdef PADDLE_WITH_DISTRIBUTE
return
true
;
...
...
@@ -631,6 +639,7 @@ All parameter, weight, gradient are variables in Paddle.
[](
bool
init_p2p
)
{
framework
::
InitDevices
(
init_p2p
);
});
m
.
def
(
"is_compiled_with_cuda"
,
IsCompiledWithCUDA
);
m
.
def
(
"is_compiled_with_brpc"
,
IsCompiledWithBrpc
);
m
.
def
(
"is_compiled_with_dist"
,
IsCompiledWithDIST
);
#ifdef PADDLE_WITH_CUDA
m
.
def
(
"is_float16_supported"
,
[](
const
platform
::
CUDAPlace
&
place
)
->
bool
{
...
...
paddle/scripts/paddle_build.sh
浏览文件 @
238b24bf
...
...
@@ -517,6 +517,18 @@ function assert_api_spec_approvals() {
fi
fi
done
HAS_CONST_CAST
=
`
git diff
-U0
upstream/
$BRANCH
|grep
-o
-m
1
"const_cast"
||
true
`
if
[
${
HAS_CONST_CAST
}
]
&&
[
"
${
GIT_PR_ID
}
"
!=
""
]
;
then
APPROVALS
=
`
curl
-H
"Authorization: token
${
GITHUB_API_TOKEN
}
"
https://api.github.com/repos/PaddlePaddle/Paddle/pulls/
${
GIT_PR_ID
}
/reviews?per_page
=
10000 |
\
python
${
PADDLE_ROOT
}
/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433
`
echo
"current pr
${
GIT_PR_ID
}
got approvals:
${
APPROVALS
}
"
if
[
"
${
APPROVALS
}
"
==
"FALSE"
]
;
then
echo
"You must have at least 2 approvals for the const_cast"
exit
1
fi
fi
}
...
...
python/paddle/fluid/__init__.py
浏览文件 @
238b24bf
...
...
@@ -152,6 +152,7 @@ def __bootstrap__():
'enable_cublas_tensor_op_math'
,
'conv_workspace_size_limit'
,
'cudnn_exhaustive_search'
,
'selected_gpus'
]
core
.
init_gflags
([
sys
.
argv
[
0
]]
+
[
"--tryfromenv="
+
","
.
join
(
read_env_flags
)])
core
.
init_glog
(
sys
.
argv
[
0
])
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
238b24bf
...
...
@@ -52,6 +52,8 @@ __all__ = [
'softmax'
,
'pool2d'
,
'pool3d'
,
'adaptive_pool2d'
,
'adaptive_pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
...
...
@@ -2500,6 +2502,204 @@ def pool3d(input,
return
pool_out
@
templatedoc
(
op_type
=
"pool2d"
)
def
adaptive_pool2d
(
input
,
pool_size
,
pool_type
=
"max"
,
require_index
=
False
,
name
=
None
):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The pooling result.
Raises:
ValueError: 'pool_type' is not 'max' nor 'avg'.
ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimentions
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(m):
# for j in range(n):
# hstart = floor(i * H / m)
# hend = ceil((i + 1) * H / m)
# wstart = floor(i * W / n)
# wend = ceil((i + 1) * W / n)
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out = fluid.layers.adaptive_pool2d(
input=data,
pool_size=[3, 3],
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
pool_type
==
"avg"
and
require_index
:
raise
ValueError
(
"invalid setting 'require_index' true when 'pool_type' is 'avg'."
)
def
_is_list_or_tuple_
(
data
):
return
(
isinstance
(
data
,
list
)
or
isinstance
(
data
,
tuple
))
if
not
_is_list_or_tuple_
(
pool_size
)
or
len
(
pool_size
)
!=
2
:
raise
ValueError
(
"'pool_size' should be a list or tuple with length as 2."
)
if
pool_type
==
"max"
:
l_type
=
'max_pool2d_with_index'
else
:
l_type
=
"pool2d"
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Out"
:
pool_out
}
if
pool_type
==
"max"
:
mask
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
[
"Mask"
]
=
mask
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"adaptive"
:
True
,
})
return
(
pool_out
,
mask
)
if
require_index
else
pool_out
@
templatedoc
(
op_type
=
"pool3d"
)
def
adaptive_pool3d
(
input
,
pool_size
,
pool_type
=
"max"
,
require_index
=
False
,
name
=
None
):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (Depth, Height, Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The pooling result.
Raises:
ValueError: 'pool_type' is not 'max' nor 'avg'.
ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(l):
# for j in range(m):
# for k in range(n):
# dstart = floor(i * D / l)
# dend = ceil((i + 1) * D / l)
# hstart = floor(j * H / m)
# hend = ceil((j + 1) * H / m)
# wstart = floor(k * W / n)
# wend = ceil((k + 1) * W / n)
# output[:, :, i, j, k] =
# avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d(
input=data,
pool_size=[3, 3],
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
pool_type
==
"avg"
and
require_index
:
raise
ValueError
(
"invalid setting 'require_index' true when 'pool_type' is 'avg'."
)
def
_is_list_or_tuple_
(
data
):
return
(
isinstance
(
data
,
list
)
or
isinstance
(
data
,
tuple
))
if
not
_is_list_or_tuple_
(
pool_size
)
or
len
(
pool_size
)
!=
3
:
raise
ValueError
(
"'pool_size' should be a list or tuple with length as 3."
)
if
pool_type
==
"max"
:
l_type
=
'max_pool3d_with_index'
else
:
l_type
=
"pool3d"
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Out"
:
pool_out
}
if
pool_type
==
"max"
:
mask
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
[
"Mask"
]
=
mask
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"adaptive"
:
True
,
})
return
(
pool_out
,
mask
)
if
require_index
else
pool_out
def
batch_norm
(
input
,
act
=
None
,
is_test
=
False
,
...
...
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
238b24bf
...
...
@@ -63,9 +63,9 @@ function(py_test_modules TARGET_NAME)
set
(
multiValueArgs MODULES DEPS ENVS
)
cmake_parse_arguments
(
py_test_modules
"
${
options
}
"
"
${
oneValueArgs
}
"
"
${
multiValueArgs
}
"
${
ARGN
}
)
add_test
(
NAME
${
TARGET_NAME
}
COMMAND
${
CMAKE_COMMAND
}
-E env PYTHONPATH=
${
PADDLE_BINARY_DIR
}
/python
${
py_test_modules_ENVS
}
${
PYTHON_EXECUTABLE
}
${
PADDLE_SOURCE_DIR
}
/tools/test_runner.py
${
py_test_modules_MODULES
}
WORKING_DIRECTORY
${
CMAKE_CURRENT_BINARY_DIR
}
)
COMMAND
${
CMAKE_COMMAND
}
-E env PYTHONPATH=
${
PADDLE_BINARY_DIR
}
/python
${
py_test_modules_ENVS
}
${
PYTHON_EXECUTABLE
}
${
PADDLE_SOURCE_DIR
}
/tools/test_runner.py
${
py_test_modules_MODULES
}
WORKING_DIRECTORY
${
CMAKE_CURRENT_BINARY_DIR
}
)
if
(
py_test_modules_SERIAL
)
set_property
(
TEST
${
TARGET_NAME
}
PROPERTY RUN_SERIAL 1
)
endif
()
...
...
@@ -111,3 +111,7 @@ py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executo
if
(
NOT APPLE
)
py_test_modules
(
test_image_classification_resnet MODULES test_image_classification_resnet SERIAL
)
endif
()
if
(
WITH_NGRAPH
)
add_subdirectory
(
ngraph
)
endif
()
python/paddle/fluid/tests/unittests/ngraph/CMakeLists.txt
0 → 100644
浏览文件 @
238b24bf
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
foreach
(
TEST_OP
${
TEST_OPS
}
)
py_test_modules
(
${
TEST_OP
}
MODULES
${
TEST_OP
}
ENVS FLAGS_use_ngraph=true
)
endforeach
(
TEST_OP
)
python/paddle/fluid/tests/unittests/ngraph/__init__.py
0 → 100644
浏览文件 @
238b24bf
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
238b24bf
...
...
@@ -233,6 +233,29 @@ class TestBook(unittest.TestCase):
pool_stride
=
[
1
,
2
],
pool_padding
=
(
2
,
1
)))
def
test_adaptive_pool2d
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
self
.
assertIsNotNone
(
layers
.
adaptive_pool2d
(
x
,
[
3
,
3
],
pool_type
=
'avg'
))
pool
,
mask
=
layers
.
adaptive_pool2d
(
x
,
[
3
,
3
],
require_index
=
True
)
self
.
assertIsNotNone
(
pool
)
self
.
assertIsNotNone
(
mask
)
def
test_adaptive_pool3d
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
244
,
224
,
224
],
dtype
=
'float32'
)
self
.
assertIsNotNone
(
layers
.
adaptive_pool3d
(
x
,
[
3
,
3
,
3
],
pool_type
=
'avg'
))
pool
,
mask
=
layers
.
adaptive_pool3d
(
x
,
[
3
,
3
,
3
],
require_index
=
True
)
self
.
assertIsNotNone
(
pool
)
self
.
assertIsNotNone
(
mask
)
def
test_lstm_unit
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @
238b24bf
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
...
...
@@ -21,29 +22,47 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
def
adaptive_start_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
floor
(
index
*
input_size
/
output_size
))
def
adaptive_end_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
ceil
((
index
+
1
)
*
input_size
/
output_size
))
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
...
...
@@ -56,27 +75,37 @@ def avg_pool2D_forward_naive(x,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
field_size
=
((
r_end
-
r_start
)
*
(
c_end
-
c_start
))
if
exclusive
\
else
(
ksize
[
0
]
*
ksize
[
1
])
field_size
=
((
r_end
-
r_start
)
*
(
c_end
-
c_start
))
\
if
(
exclusive
or
adaptive
)
else
(
ksize
[
0
]
*
ksize
[
1
])
out
[:,
:,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
))
/
field_size
return
out
...
...
@@ -93,12 +122,13 @@ class TestPool2D_Op(OpTest):
self
.
init_pool_type
()
self
.
init_ceil_mode
()
self
.
init_exclusive
()
self
.
init_adaptive
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
,
self
.
exclusive
).
astype
(
self
.
dtype
)
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
...
...
@@ -112,7 +142,8 @@ class TestPool2D_Op(OpTest):
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
...
...
@@ -159,6 +190,9 @@ class TestPool2D_Op(OpTest):
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestPool2D_Op
):
def
init_test_case
(
self
):
...
...
@@ -315,5 +349,10 @@ class TestCUDNNAvgInclude(TestCase2):
self
.
exclusive
=
False
class
TestAvgPoolAdaptive
(
TestCase1
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @
238b24bf
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
...
...
@@ -21,35 +22,59 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
def
adaptive_start_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
floor
(
index
*
input_size
/
output_size
))
def
adaptive_end_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
ceil
((
index
+
1
)
*
input_size
/
output_size
))
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
//
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
//
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
,
4
))
...
...
@@ -62,33 +87,49 @@ def avg_pool3D_forward_naive(x,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
//
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
//
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
field_size
=
(
d_end
-
d_start
)
*
(
h_end
-
h_start
)
*
(
w_end
-
w_start
)
\
if
exclusive
else
ksize
[
0
]
*
ksize
[
1
]
*
ksize
[
2
]
if
(
exclusive
or
adaptive
)
else
ksize
[
0
]
*
ksize
[
1
]
*
ksize
[
2
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
,
4
))
/
field_size
return
out
...
...
@@ -105,13 +146,14 @@ class TestPool3d_Op(OpTest):
self
.
init_pool_type
()
self
.
init_ceil_mode
()
self
.
init_exclusive
()
self
.
init_adaptive
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
output
=
self
.
pool3D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
,
self
.
exclusive
).
astype
(
self
.
dtype
)
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
...
...
@@ -124,7 +166,8 @@ class TestPool3d_Op(OpTest):
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
...
...
@@ -171,6 +214,9 @@ class TestPool3d_Op(OpTest):
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestPool3d_Op
):
def
init_test_case
(
self
):
...
...
@@ -353,5 +399,10 @@ class TestCUDNNAvgInclude(TestCUDNNCase3):
self
.
exclusive
=
False
class
TestAvgPoolAdaptive
(
TestCase1
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool_max_op.py
浏览文件 @
238b24bf
...
...
@@ -13,33 +13,62 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
):
def
adaptive_start_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
floor
(
index
*
input_size
/
output_size
))
def
adaptive_end_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
ceil
((
index
+
1
)
*
input_size
/
output_size
))
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
,
adaptive
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
:
ksize
=
[
D
,
H
,
W
]
paddings
=
[
0
,
0
,
0
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
//
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
,
4
))
...
...
@@ -58,23 +87,37 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
return
out
,
mask
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
):
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
:
ksize
=
[
H
,
W
]
paddings
=
[
0
,
0
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
...
...
@@ -95,10 +138,12 @@ class TestMaxPoolWithIndex_Op(OpTest):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
init_global
()
self
.
init_adaptive
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
,
mask
=
self
.
pool_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
self
.
paddings
,
self
.
global_pool
,
self
.
adaptive
)
output
=
output
.
astype
(
"float32"
)
mask
=
mask
.
astype
(
"int32"
)
...
...
@@ -107,6 +152,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'global_pooling'
:
self
.
global_pool
,
'adaptive'
:
self
.
adaptive
,
}
self
.
inputs
=
{
'X'
:
input
}
...
...
@@ -129,6 +175,9 @@ class TestMaxPoolWithIndex_Op(OpTest):
def
init_global
(
self
):
self
.
global_pool
=
False
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestMaxPoolWithIndex_Op
):
def
init_global
(
self
):
...
...
@@ -190,5 +239,15 @@ class TestCase7(TestCase6):
self
.
global_pool
=
False
class
TestCastAdaptive2d
(
TestCase6
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
class
TestCastAdaptive3d
(
TestMaxPoolWithIndex_Op
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
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