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“1010e39bdf738029fcb78b0d388a91dfdebdda2f”上不存在“paddle/fluid/operators/reduce_ops/reduce_mean_op.h”
提交 1457be3e 编写于 作者: Y yi.wu
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fix_api_reference_docs

上级 e2433207 431491a2
隐藏空白更改
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Showing with 2952 addition and 1126 deletion
CMakeLists.txt
浏览文件 @ 1457be3e
......@@ -55,12 +55,13 @@ option(WITH_FLUID_ONLY "Compile PaddlePaddle fluid only" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with grpc distributed support" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
......@@ -147,7 +148,16 @@ include(external/any) # download libn::any
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/grpc)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
else()
include(external/leveldb)
include(external/brpc)
endif()
endif()
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
......
benchmark/fluid/README.md
浏览文件 @ 1457be3e
......@@ -24,10 +24,12 @@ Currently supported `--model` argument include:
* Run the following command to start a benchmark job locally:
```bash
python fluid_benchmark.py --model mnist --device GPU
python fluid_benchmark.py --model mnist --device GPU
```
You can choose to use GPU/CPU training. With GPU training, you can specify
`--gpus <gpu_num>` to run multi GPU training.
You can set async mode parameter server. With async mode, you can specify
`--async_mode` to train model asynchronous.
* Run distributed training with parameter servers:
* see [run_fluid_benchmark.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/fluid/run_fluid_benchmark.sh) as an example.
* start parameter servers:
......
benchmark/fluid/args.py 0 → 100644
浏览文件 @ 1457be3e
# 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.
import argparse
__all__ = ['parse_args', ]
BENCHMARK_MODELS = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
# args related to learning rate
parser.add_argument(
'--learning_rate', type=float, default=0.001, help='The learning rate.')
# TODO(wuyi): add "--use_fake_data" option back.
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
# this option is available only for vgg and resnet.
parser.add_argument(
'--cpus',
type=int,
default=1,
help='If cpus > 1, will use ParallelDo to run, else use Executor.')
parser.add_argument(
'--data_set',
type=str,
default='flowers',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_true',
help='If set, do not test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
parser.add_argument(
'--no_split_var',
action='store_true',
default=False,
help='Whether split variables into blocks when update_method is pserver')
parser.add_argument(
'--async_mode',
action='store_true',
default=False,
help='Whether start pserver in async mode to support ASGD')
parser.add_argument(
'--use_reader_op',
action='store_true',
help='Whether to use reader op, and must specify the data path if set this to true.'
)
parser.add_argument(
'--data_path',
type=str,
default="",
help='Directory that contains all the training recordio files.')
args = parser.parse_args()
return args
benchmark/fluid/fluid_benchmark.py
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......@@ -24,108 +24,7 @@ import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler
BENCHMARK_MODELS = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size',
type=int,
default=32,
help='The batch size on each gpu.')
parser.add_argument(
'--learning_rate', type=float, default=0.001, help='The learning rate.')
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations',
type=int,
default=80,
help='The number of minibatches, set to -1 to run all batches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
# this option is available only for vgg and resnet.
parser.add_argument(
'--cpus',
type=int,
default=1,
help='If cpus > 1, will use ParallelDo to run, else use Executor.')
parser.add_argument(
'--data_set',
type=str,
default='flowers',
choices=['cifar10', 'flowers', 'imagenet'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_true',
help='If set, do not test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
parser.add_argument(
'--use_reader_op',
action='store_true',
help='Whether to use reader op, and must specify the data path if set this to true.'
)
parser.add_argument(
'--data_path',
type=str,
default="",
help='Directory that contains all the training recordio files.')
args = parser.parse_args()
return args
from args import *
def append_nccl2_prepare(trainer_id):
......@@ -160,7 +59,7 @@ def append_nccl2_prepare(trainer_id):
"nccl-based dist train.")
def dist_transpile(trainer_id):
def dist_transpile(trainer_id, args):
if trainer_id < 0:
return None, None
......@@ -182,7 +81,12 @@ def dist_transpile(trainer_id):
training_role = os.getenv("PADDLE_TRAINING_ROLE")
t = distribute_transpiler.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
t.transpile(
trainer_id,
pservers=pserver_endpoints,
trainers=trainers,
sync_mode=not args.async_mode,
slice_var_up=not args.no_split_var)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
......@@ -417,7 +321,7 @@ def main():
fluid.memory_optimize(fluid.default_main_program())
if args.update_method == "pserver":
train_prog, startup_prog = dist_transpile(trainer_id)
train_prog, startup_prog = dist_transpile(trainer_id, args)
if not train_prog:
raise Exception(
"Must configure correct environments to run dist train.")
......
benchmark/fluid/models/stacked_dynamic_lstm.py
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......@@ -104,8 +104,9 @@ def get_model(args):
loss = fluid.layers.mean(x=loss)
# add acc
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
shape=[1], dtype='int64'))
shape=[1], dtype='int64'), total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
......
benchmark/fluid/models/vgg.py
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......@@ -82,7 +82,8 @@ def get_model(args):
data_file, batch_size=args.batch_size))
images, label = fluid.layers.read_file(data_file)
else:
images = fluid.layers.data(name='data', shape=dshape, dtype='float32')
images = fluid.layers.data(
name='data', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
......
cmake/configure.cmake
浏览文件 @ 1457be3e
......@@ -118,6 +118,10 @@ endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
if(WITH_DISTRIBUTE)
add_definitions(-DPADDLE_WITH_DISTRIBUTE)
endif()
if(WITH_GOLANG)
# we need to symlink Paddle directory into GOPATH. If we
# don't do it and we have code that depends on Paddle, go
......@@ -166,3 +170,7 @@ if(WITH_GOLANG)
endif()
endif(WITH_GOLANG)
if(WITH_GRPC)
add_definitions(-DPADDLE_WITH_GRPC)
endif(WITH_GRPC)
cmake/external/brpc.cmake 0 → 100644
浏览文件 @ 1457be3e
# 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(ExternalProject)
SET(BRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/brpc)
SET(BRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/brpc)
SET(BRPC_INCLUDE_DIR "${BRPC_INSTALL_DIR}/include" CACHE PATH "brpc include directory." FORCE)
SET(BRPC_LIBRARIES "${BRPC_INSTALL_DIR}/lib/libbrpc.a" CACHE FILEPATH "brpc library." FORCE)
INCLUDE_DIRECTORIES(${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")
# If minimal .a is need, you can set WITH_DEBUG_SYMBOLS=OFF
ExternalProject_Add(
extern_brpc
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/brpc/brpc"
GIT_TAG "6d153dd7ff00f960ae6895c9c5fff0ce9f07aff2"
PREFIX ${BRPC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${BRPC_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=${BRPC_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_PREFIX_PATH=${prefix_path}
-DBRPC_WITH_GLOG=ON
${EXTERNAL_OPTIONAL_ARGS}
LIST_SEPARATOR |
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${BRPC_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR:PATH=${BRPC_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
ADD_DEPENDENCIES(extern_brpc protobuf leveldb gflags glog gtest snappy)
ADD_LIBRARY(brpc STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET brpc PROPERTY IMPORTED_LOCATION ${BRPC_LIBRARIES})
ADD_DEPENDENCIES(brpc extern_brpc)
LIST(APPEND external_project_dependencies brpc)
cmake/external/leveldb.cmake 0 → 100644
浏览文件 @ 1457be3e
# 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(ExternalProject)
SET(LEVELDB_SOURCES_DIR ${THIRD_PARTY_PATH}/leveldb)
SET(LEVELDB_INSTALL_DIR ${THIRD_PARTY_PATH}/install/leveldb)
SET(LEVELDB_INCLUDE_DIR "${LEVELDB_INSTALL_DIR}/include" CACHE PATH "leveldb include directory." FORCE)
SET(LEVELDB_LIBRARIES "${LEVELDB_INSTALL_DIR}/lib/libleveldb.a" CACHE FILEPATH "leveldb library." FORCE)
INCLUDE_DIRECTORIES(${LEVELDB_INCLUDE_DIR})
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"
CONFIGURE_COMMAND ""
BUILD_COMMAND CXXFLAGS=-fPIC make -j ${NUM_OF_PROCESSOR} libleveldb.a
INSTALL_COMMAND mkdir -p ${LEVELDB_INSTALL_DIR}/lib/
&& cp ${LEVELDB_SOURCES_DIR}/src/extern_leveldb/libleveldb.a ${LEVELDB_LIBRARIES}
&& cp -r ${LEVELDB_SOURCES_DIR}/src/extern_leveldb/include ${LEVELDB_INSTALL_DIR}/
BUILD_IN_SOURCE 1
)
ADD_DEPENDENCIES(extern_leveldb snappy)
ADD_LIBRARY(leveldb STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET leveldb PROPERTY IMPORTED_LOCATION ${LEVELDB_LIBRARIES})
ADD_DEPENDENCIES(leveldb extern_leveldb)
LIST(APPEND external_project_dependencies leveldb)
cmake/generic.cmake
浏览文件 @ 1457be3e
......@@ -610,3 +610,21 @@ function(grpc_library TARGET_NAME)
COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
cc_library("${TARGET_NAME}" SRCS "${grpc_library_SRCS}" DEPS "${TARGET_NAME}_grpc" "${TARGET_NAME}_proto" "${grpc_library_DEPS}")
endfunction()
function(brpc_library TARGET_NAME)
set(oneValueArgs PROTO)
set(multiValueArgs SRCS DEPS)
set(options "")
cmake_parse_arguments(brpc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
message(STATUS "generating brpc ${brpc_library_PROTO}")
get_filename_component(ABS_PROTO ${brpc_library_PROTO} ABSOLUTE)
get_filename_component(PROTO_WE ${brpc_library_PROTO} NAME_WE)
get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
protobuf_generate_cpp(brpc_proto_srcs brpc_proto_hdrs "${ABS_PROTO}")
cc_library("${TARGET_NAME}_proto" SRCS "${brpc_proto_srcs}")
cc_library("${TARGET_NAME}" SRCS "${brpc_library_SRCS}" DEPS "${TARGET_NAME}_proto" "${brpc_library_DEPS}")
endfunction()
doc/survey/dynamic_graph.md 0 → 100644
浏览文件 @ 1457be3e
# Automatic Differentiation with the Tape
## Automatic Differentiation
A key challenge in the field of deep learning is to automatically derive the backward pass from the forward pass described algorithmically by researchers. Such a derivation, or a transformation of the forward pass program, has been long studied before the recent prosperity of deep learning in the field known as [automatic differentiation](https://arxiv.org/pdf/1502.05767.pdf).
## The Tape
Given the forward pass program (usually in Python in practices), there are two strategies to derive the backward pass:
1. from the forward pass program itself, or
1. from the execution trace of the forward pass program, which is often known as the *tape*.
This article surveys systems that follow the latter strategy.
## Dynamic Network
When we train a deep learning model, the tape changes every iteration as the input data change, so we have to re-derive the backward pass every iteration. This is known as *dynamic network*.
Deep learning systems that utilize the idea of dynamic network gained their popularities in recent years. This article surveys two representative systems: [PyTorch](https://pytorch.org/) and [DyNet](https://dynet.readthedocs.io/en/latest/).
## An Overview
Both frameworks record a ‘tape’ of the computation and interpreting (or run-time compiling) a transformation of the tape played back in reverse. This tape is a different kind of entity than the original program.[[link]](http://www.bcl.hamilton.ie/~barak/papers/toplas-reverse.pdf)
Consider the following code feedforward model.
```python
x = Variable(randn(20, 1)))
label = Variable(randint(1))
W_1, W_2 = Variable(randn(20, 20)), Variable(randn(10, 20))
h = matmul(W_1, x)
pred = matmul(W_2, x)
loss = softmax(pred, label)
loss.backward()
```
### 1) Dynet uses List to encode the Tape
During the forward execution, a list of operators, in this case `matmul`, `matmul` and `softmax`, are recorded in the tape, along with the necessary information needed to do the backward such as pointers to the inputs and outputs. Then the tape is played in reverse order at `loss.backward()`.
<details>
<summary></summary>
digraph g {
graph [
rankdir = "LR"
];
node [
fontsize = "16"
shape = "ellipse"
];
edge [];
"node0" [
label = "<f0> type: matmul | <f1> input: W_1, x | <f2> output: h"
shape = "record"
];
"node1" [
label = "<f0> type: matmul | <f1> input: W_2, h | <f2> output: pred"
shape = "record"
];
"node2" [
label = "<f0> type: softmax | <f1> input: pred, label | <f2> output: loss"
shape = "record"
];
"node0":f0 -> "node1":f0 [];
"node1":f0 -> "node2":f0 [];
}
</details>
![Alt text](https://g.gravizo.com/svg?digraph%20g%20{%20graph%20[%20rankdir%20=%20%22LR%22%20];%20node%20[%20fontsize%20=%20%2216%22%20shape%20=%20%22ellipse%22%20];%20edge%20[];%20%22node0%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20%3Cf1%3E%20input:%20W_1,%20x%20|%20%3Cf2%3E%20output:%20h%22%20shape%20=%20%22record%22%20];%20%22node1%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20%3Cf1%3E%20input:%20W_2,%20h%20|%20%3Cf2%3E%20output:%20pred%22%20shape%20=%20%22record%22%20];%20%22node2%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20softmax%20|%20%3Cf1%3E%20input:%20pred,%20label%20|%20%3Cf2%3E%20output:%20loss%22%20shape%20=%20%22record%22%20];%20%22node0%22:f0%20-%3E%20%22node1%22:f0%20[%20id%20=%200%20];%20%22node1%22:f0%20-%3E%20%22node2%22:f0%20[%20id%20=%201%20];%20})
### 2) Pytorch uses Node Graph to encode the Tape
The graph is composed of `Variable`s and `Function`s. During the forward execution, a `Variable` records its creator function, e.g. `h.creator = matmul`. And a Function records its inputs' previous/dependent functions `prev_func` through `creator`, e.g. `matmul.prev_func = matmul1`. At `loss.backward()`, a topological sort is performed on all `prev_func`s. Then the grad op is performed by the sorted order.
<details>
<summary></summary>
digraph g {
graph [
rankdir = "LR"
];
subgraph function {
node [
fontsize = "16"
style = filled
shape = "record"
];
"matmul0" [ label = "<f0> type: matmul | prev_func: None" ];
"matmul1" [ label = "<f0> type: matmul | prev_func: matmul" ];
"softmax" [ label = "<f0> type: softmax | prev_func: matmul" ];
}
subgraph variable {
node [
fontsize = "16"
shape = "Mrecord"
style = filled
fillcolor = white
];
"x" [ label = "<f0> x | <f1> creator: None" ];
"label" [ label = "<f0> label | <f1> creator: None" ];
"W_1" [ label = "<f0> W_1 | <f1> creator: None" ];
"W_2" [ label = "<f0> W_2 | <f1> creator: None" ];
"h" [ label = "<f0> h | <f1> creator: None" ];
"pred" [ label = "<f0> pred | <f1> creator: matmul" ];
"loss" [ label = "<f0> loss | <f1> creator: softmax" ];
}
subgraph data_flow {
"x":f0 -> "matmul0":f0;
"W_1":f0 -> "matmul0":f0;
"matmul0":f0 -> "h":f0;
"h":f0 -> "matmul1":f0;
"W_2":f0 -> "matmul1":f0;
"matmul1":f0 -> "pred":f0;
"pred":f0 -> "softmax":f0;
"label":f0 -> "softmax":f0;
"softmax":f0 -> "loss":f0;
}
subgraph prev_func {
edge [color="red", arrowsize="0.6", penwidth="1", constraint=false];
"matmul1":f1 -> "matmul0":f0;
"softmax":f1 -> "matmul1":f0;
label = "prev_func";
}
}
</details>
![Alt text](https://g.gravizo.com/svg?digraph%20g%20{%20graph%20[%20rankdir%20=%20%22LR%22%20];%20subgraph%20function%20{%20node%20[%20fontsize%20=%20%2216%22%20style%20=%20filled%20shape%20=%20%22record%22%20];%20%22matmul0%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20prev_func:%20None%22%20];%20%22matmul1%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20prev_func:%20matmul%22%20];%20%22softmax%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20softmax%20|%20prev_func:%20matmul%22%20];%20}%20subgraph%20variable%20{%20node%20[%20fontsize%20=%20%2216%22%20shape%20=%20%22Mrecord%22%20style%20=%20filled%20fillcolor%20=%20white%20];%20%22x%22%20[%20label%20=%20%22%3Cf0%3E%20x%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22label%22%20[%20label%20=%20%22%3Cf0%3E%20label%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22W_1%22%20[%20label%20=%20%22%3Cf0%3E%20W_1%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22W_2%22%20[%20label%20=%20%22%3Cf0%3E%20W_2%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22h%22%20[%20label%20=%20%22%3Cf0%3E%20h%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22pred%22%20[%20label%20=%20%22%3Cf0%3E%20pred%20|%20%3Cf1%3E%20creator:%20matmul%22%20];%20%22loss%22%20[%20label%20=%20%22%3Cf0%3E%20loss%20|%20%3Cf1%3E%20creator:%20softmax%22%20];%20}%20subgraph%20data_flow%20{%20%22x%22:f0%20-%3E%20%22matmul0%22:f0;%20%22W_1%22:f0%20-%3E%20%22matmul0%22:f0;%20%22matmul0%22:f0%20-%3E%20%22h%22:f0;%20%22h%22:f0%20-%3E%20%22matmul1%22:f0;%20%22W_2%22:f0%20-%3E%20%22matmul1%22:f0;%20%22matmul1%22:f0%20-%3E%20%22pred%22:f0;%20%22pred%22:f0%20-%3E%20%22softmax%22:f0;%20%22label%22:f0%20-%3E%20%22softmax%22:f0;%20%22softmax%22:f0%20-%3E%20%22loss%22:f0;%20}%20subgraph%20prev_func%20{%20edge%20[color=%22red%22,%20arrowsize=%220.6%22,%20penwidth=%221%22,%20constraint=false];%20%22matmul1%22:f1%20-%3E%20%22matmul0%22:f0;%20%22softmax%22:f1%20-%3E%20%22matmul1%22:f0;%20label%20=%20%22prev_func%22;%20}%20})
Chainer and Autograd uses the similar techniques to record the forward pass. For details please refer to the appendix.
## Design choices
### 1) Dynet's List vs Pytorch's Node Graph
What's good about List:
1. It avoids a topological sort. One only needs to traverse the list of operators in reverse and calling the corresponding backward operator.
1. It promises effient data parallelism implementations. One could count the time of usage of a certain variable during the construction list. Then in the play back, one knows the calculation of a variable has completed. This enables communication and computation overlapping.
What's good about Node Graph:
1. More flexibility. PyTorch users can mix and match independent graphs however they like, in whatever threads they like (without explicit synchronization). An added benefit of structuring graphs this way is that when a portion of the graph becomes dead, it is automatically freed. [[2]](https://openreview.net/pdf?id=BJJsrmfCZ) Consider the following example, Pytorch only does backward on SmallNet while Dynet does both BigNet and SmallNet.
```python
result = BigNet(data)
loss = SmallNet(data)
loss.backward()
```
### 2) Dynet's Lazy evaluation vs Pytorch's Immediate evaluation
Dynet builds the list in a symbolic matter. Consider the following example
```python
for epoch in range(num_epochs):
for in_words, out_label in training_data:
dy.renew_cg()
W = dy.parameter(W_p)
b = dy.parameter(b_p)
score_sym = dy.softmax(W*dy.concatenate([E[in_words[0]],E[in_words[1]]])+b)
loss_sym = dy.pickneglogsoftmax(score_sym, out_label)
loss_val = loss_sym.value()
loss_sym.backward()
```
The computation of `lookup`, `concat`, `matmul` and `softmax` didn't happen until the call of `loss_sym.value()`. This defered execution is useful because it allows some graph-like optimization possible, e.g. kernel fusion.
Pytorch chooses immediate evaluation. It avoids ever materializing a "forward graph"/"tape" (no need to explicitly call `dy.renew_cg()` to reset the list), recording only what is necessary to differentiate the computation, i.e. `creator` and `prev_func`.
## What can fluid learn from them?
TBD
# Appendix
### Overview
| Framework | Has Tape | Core in C++ | First Release Date |
|-----------|----------|-------------|--------------------|
| Autograd | No | No | Mar 5, 2015 |
| Chainer | No | No | Jun 5, 2015 |
| Pytorch | No | Yes | Aug 31, 2016 |
| Dynet | Yes | Yes | Oct 12, 2016 |
### Source Code
#### Autograd
[Backward code](https://github.com/HIPS/autograd/blob/442205dfefe407beffb33550846434baa90c4de7/autograd/core.py#L8-L40). In the forward pass, a graph of VJPNode is constructed.
```python
# User API
def make_grad(fun, x):
start_node = VJPNode.new_root()
end_value, end_node = trace(start_node, fun, x)
return backward_pass(g, end_node), end_value
# trace the forward pass by creating VJPNodes
def trace(start_node, fun, x):
with trace_stack.new_trace() as t:
start_box = new_box(x, t, start_node)
end_box = fun(start_box)
return end_box._value, end_box._node
def backward_pass(g, end_node):
outgrads = {end_node : (g, False)}
for node in toposort(end_node):
outgrad = outgrads.pop(node)
ingrads = node.vjp(outgrad[0])
for parent, ingrad in zip(node.parents, ingrads):
outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad)
return outgrad[0]
# Every VJPNode corresponds to a op_grad
class VJPNode(Node):
__slots__ = ['parents', 'vjp']
def __init__(self, value, fun, args, kwargs, parent_argnums, parents):
self.parents = parents
vjpmaker = primitive_vjps[fun]
self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
```
#### Chainer
Example Code
```python
# (1) Function Set definition, creates FunctionNode
model = FunctionSet(
l1=F.Linear(784, 100),
l2=F.Linear(100, 100),
l3=F.Linear(100, 10)).to_gpu()
# (2) Optimizer Setup
opt = optimizers.SGD()
opt.setup(model)
# (3) Forward computation
def forward(x, t):
h1 = F.relu(model.l1(x))
h2 = F.relu(model.l2(h1))
y = model.l3(h2)
return F.softmax_cross_entropy(y, t)
# (4) Training loop
for epoch in xrange(n_epoch):
for i in xrange(0, N, b_size):
x = Variable(to_gpu(...))
t = Variable(to_gpu(...))
opt.zero_grads()
loss = forward(x, t)
loss.backward()
opt.update()
```
In `forward(x, t)`, a graph of [`VariableNode`](https://github.com/chainer/chainer/blob/master/chainer/variable.py#L110) and [`FunctionNode`](https://github.com/chainer/chainer/blob/a69103a4aa59d5b318f39b01dbcb858d465b89cf/chainer/function_node.py#L19) is constructed. Every output's `VariableNode.creator` is pointed to the `FunctionNode`.
```python
class FunctionNode(object):
...
def apply(self, inputs):
outputs = self.forward(inputs)
ret = tuple([variable.Variable(y, requires_grad=requires_grad)
for y in outputs])
# Topological ordering
self.rank = max([x.rank for x in inputs]) if input_vars else 0
# Add backward edges
for y in ret:
y.creator_node = self
self.inputs = tuple([x.node for x in input_vars])
self.outputs = tuple([y.node for y in ret])
return ret
```
`loss.backward()` will calculate the accumulated gradient of all variables. All the backward of `FunctionNode`s will be called based on the topological order.
```python
class VariableNode(object):
...
def backward(self, retain_grad, loss_scale):
if self.creator_node is None:
return
cand_funcs = []
seen_set = set()
grads = {}
# Initialize error by 1, if this is a loss variable
if self.data.size == 1 and self._grad_var is None:
self.grad = numpy.ones_like(self.data)
grads[self._node] = self._grad_var
def add_cand(cand):
if cand not in seen_set:
# Negate since heapq is min-heap. This is a global variable
heapq.heappush(cand_funcs, (-cand.rank, len(seen_set), cand))
seen_set.add(cand)
add_cand(self.creator_node)
while cand_funcs:
_, _, func = heapq.heappop(cand_funcs)
gxs = func.backward_accumulate(func.inputs, func.outputs, func.outputs.grad)
for x, gx in enumerate(gxs):
if x in grads:
grads[x] += gx
else:
grads[x] = gx
if x.creator_node is not None:
add_cand(x.creator_node)
```
#### PyTorch
Example Code
```python
x = Variable(torch.ones(5, 5))
y = Variable(torch.ones(5, 5) * 4)
z = x ** 2 + x * 2 + x * y + y
z.backward(torch.ones(5, 5))
```
The trace is done by `Variable.creator` and `Function.previous_functions`.
```python
class Variable(object):
def __init__(self, tensor, creator=None, requires_grad=True):
if creator is None:
creator = Leaf(self, requires_grad)
self.data = tensor
self.creator = creator
self._grad = None
def backward(self, gradient=None):
if gradient is None:
if self.data.numel() != 1:
raise RuntimeError('backward should be called only on a scalar (i.e. 1-element tensor) or with gradient w.r.t. the variable')
gradient = self.data.new(1).fill_(1)
self._execution_engine.run_backward(self, gradient)
class Function(obejct):
# ...
def _do_forward(self, *input):
unpacked_input = tuple(arg.data for arg in input)
raw_output = self.forward(*unpacked_input)
# mark output.creator = self for backward trace
output = tuple(Variable(tensor, self) for tensor in raw_output)
self.previous_functions = [(arg.creator, id(arg)) for arg in input]
self.output_ids = {id(var): i for i, var in enumerate(output)}
return output
def _do_backward(self, grad_output):
return self.backwaerd(grad_output)
```
The [backward](https://github.com/pytorch/pytorch/blob/v0.1.1/torch/autograd/engine.py) is similar to Autograd.
#### DyNet
Example code
```python
model = dy.model()
W_p = model.add_parameters((20, 100))
b_p = model.add_parameters(20)
E = model.add_lookup_parameters((20000, 50))
for epoch in range(num_epochs):
for in_words, out_label in training_data:
dy.renew_cg() # init tape
W = dy.parameter(W_p)
b = dy.parameter(b_p)
score_sym = dy.softmax(W*dy.concatenate([E[in_words[0]],E[in_words[1]]])+b)
loss_sym = dy.pickneglogsoftmax(score_sym, out_label)
loss_val = loss_sym.value()
loss_sym.backward()
```
[forward](https://github.com/clab/dynet/blob/740a9626a13a2732544de142e256ad0d0a166658/dynet/exec.cc#L84-L158), [backward](https://github.com/clab/dynet/blob/740a9626a13a2732544de142e256ad0d0a166658/dynet/exec.cc#L166-L284). The trace is done by creating a tape of expressions in every iteration. Backward is done by traverse the tape in the reverse order.
```c++
void SimpleExecutionEngine::backward(VariableIndex from_where, bool full) {
...
for (int i = num_nodes - 1; i >= 0; --i) {
// each node corresponds to an op
node->backward(xs, node_fx, node_dEdfx, ai, node_dEdxai);
}
...
}
```
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 1457be3e
......@@ -83,11 +83,16 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
framework_proto glog lod_rank_table feed_fetch_method)
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 grpc++_unsecure grpc_unsecure gpr)
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()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method)
endif()
cc_library(parallel_executor SRCS parallel_executor.cc DEPS graph_builder_factory threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS ssa_graph_builder_factory threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 1457be3e
......@@ -8,18 +8,19 @@ cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place
cc_library(ssa_graph SRCS ssa_graph.cc DEPS var_handle op_handle_base)
cc_library(ssa_graph_builder SRCS ssa_graph_builder.cc DEPS ssa_graph)
cc_library(ssa_graph_printer SRCS ssa_graph_printer.cc DEPS ssa_graph_builder)
cc_library(ssa_graph_checker SRCS ssa_graph_checker.cc DEPS ssa_graph_builder)
cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
if(WITH_GPU)
nv_library(nccl_all_reduce_op_handle SRCS nccl_all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
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)
set(multi_devices_graph_builder_deps nccl_all_reduce_op_handle)
nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim dynload_cuda)
nv_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor dynload_cuda)
else()
set(multi_devices_graph_builder_deps)
cc_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
variable_visitor)
cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim)
cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
endif()
......@@ -28,10 +29,10 @@ cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope d
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
scale_loss_grad_op_handle rpc_op_handle ${multi_devices_graph_builder_deps} reduce_op_handle broadcast_op_handle)
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle)
cc_library(graph_builder_factory SRCS graph_builder_factory.cc DEPS multi_devices_graph_builder ssa_graph_printer)
cc_library(ssa_graph_builder_factory SRCS ssa_graph_builder_factory.cc DEPS multi_devices_graph_builder ssa_graph_printer ssa_graph_checker)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ssa_graph framework_proto)
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
......
paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc → paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 1457be3e
......@@ -13,25 +13,33 @@
// limitations under the License.
#include <algorithm>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
namespace paddle {
namespace framework {
namespace details {
NCCLAllReduceOpHandle::NCCLAllReduceOpHandle(
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap &ctxs)
#ifdef PADDLE_WITH_CUDA
AllReduceOpHandle::AllReduceOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs)
: local_scopes_(local_scopes), places_(places), nccl_ctxs_(ctxs) {
for (auto &p : places_) {
this->dev_ctxes_[p] = nccl_ctxs_.DevCtx(p);
if (nccl_ctxs_) {
for (auto &p : places_) {
this->dev_ctxes_[p] = nccl_ctxs_->DevCtx(p);
}
}
}
#else
AllReduceOpHandle::AllReduceOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places)
: local_scopes_(local_scopes), places_(places) {}
#endif
void NCCLAllReduceOpHandle::RunImpl() {
void AllReduceOpHandle::RunImpl() {
if (NoDummyInputSize() == 1) {
return; // No need to all reduce when GPU count = 1;
} else {
......@@ -58,6 +66,8 @@ void NCCLAllReduceOpHandle::RunImpl() {
}
if (platform::is_gpu_place(lod_tensors[0]->place())) {
#ifdef PADDLE_WITH_CUDA
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
int dtype = -1;
size_t numel = 0;
std::vector<std::function<void()>> all_reduce_calls;
......@@ -75,7 +85,7 @@ void NCCLAllReduceOpHandle::RunImpl() {
}
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_.at(dev_id);
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
all_reduce_calls.emplace_back([=] {
......@@ -90,22 +100,25 @@ void NCCLAllReduceOpHandle::RunImpl() {
call();
}
});
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
} else { // Special handle CPU only Operator's gradient. Like CRF
auto &trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->Var()
->FindVar(out_var_handles[0]->name_)
->GetMutable<framework::LoDTensor>();
// Reduce All Tensor to trg in CPU
ReduceLoDTensor func(lod_tensors, &trg);
VisitDataType(ToDataType(lod_tensors[0]->type()), func);
for (size_t i = 0; i < local_scopes_.size(); ++i) {
for (size_t i = 1; i < local_scopes_.size(); ++i) {
auto &scope =
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(in_var_handles[i]->name_);
auto *var = scope.FindVar(out_var_handles[i]->name_);
auto *dev_ctx = dev_ctxes_[p];
RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
......@@ -118,7 +131,7 @@ void NCCLAllReduceOpHandle::RunImpl() {
}
}
std::string NCCLAllReduceOpHandle::Name() const { return "nccl_all_reduce"; }
std::string AllReduceOpHandle::Name() const { return "all_reduce"; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/nccl_all_reduce_op_handle.h → paddle/fluid/framework/details/all_reduce_op_handle.h
浏览文件 @ 1457be3e
......@@ -20,17 +20,23 @@
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
namespace details {
struct NCCLAllReduceOpHandle : public OpHandleBase {
NCCLAllReduceOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap &ctxs);
struct AllReduceOpHandle : public OpHandleBase {
#ifdef PADDLE_WITH_CUDA
AllReduceOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs);
#else
AllReduceOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
#endif
std::string Name() const override;
// Delay and buffer nccl_all_reduce together can significantly increase
......@@ -43,7 +49,9 @@ struct NCCLAllReduceOpHandle : public OpHandleBase {
private:
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
const platform::NCCLContextMap &nccl_ctxs_;
#ifdef PADDLE_WITH_CUDA
const platform::NCCLContextMap *nccl_ctxs_;
#endif
};
} // namespace details
......
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ 1457be3e
......@@ -20,7 +20,7 @@ namespace details {
struct ExecutionStrategy {
size_t num_threads_{0};
bool use_event_{true};
bool use_cuda_{true};
bool allow_op_delay_{false};
size_t num_iteration_per_drop_scope_{100};
};
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 1457be3e
......@@ -17,6 +17,7 @@
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
......@@ -26,10 +27,6 @@
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/scope.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#endif
namespace paddle {
namespace framework {
namespace details {
......@@ -89,7 +86,7 @@ std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainSendVars(
for (auto *op : program.Block(0).AllOps()) {
// TODO(Yancey1989): use a graceful method to find send op,
// instead of the the hard code string
if (op->Type() == "send_vars") {
if (op->Type() == "send") {
auto op_vars = op->InputArgumentNames();
send_vars.reserve(send_vars.size() +
std::distance(op_vars.begin(), op_vars.end()));
......@@ -243,7 +240,7 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
CreateReduceOp(&result, g_name, 0);
CreateBroadcastOp(&result, g_name, 0);
} else {
InsertNCCLAllReduceOp(&result, g_name);
InsertAllReduceOp(&result, g_name);
}
break;
}
......@@ -286,6 +283,19 @@ bool MultiDevSSAGraphBuilder::IsSparseGradient(
return false;
}
void MultiDevSSAGraphBuilder::SetCommunicationContext(
OpHandleBase *op_handle, const platform::Place &p) const {
#ifdef PADDLE_WITH_CUDA
if (nccl_ctxs_ == nullptr) {
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
}
#else
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
#endif
}
void MultiDevSSAGraphBuilder::CreateBroadcastOp(SSAGraph *result,
const std::string &p_name,
size_t src_dev_id) const {
......@@ -300,15 +310,12 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(SSAGraph *result,
op_handle->AddInput(in);
for (size_t i = 0; i < places_.size(); ++i) {
auto &vars = result->vars_.at(i).at(p_name);
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
auto &vars = result->vars_.at(i).at(p_name);
auto *out_var = new VarHandle(vars.size(), i, p_name, p);
vars.emplace_back(out_var);
op_handle->AddOutput(out_var);
#ifndef ADDLE_WITH_CUDA
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
#endif
}
}
......@@ -320,15 +327,19 @@ void MultiDevSSAGraphBuilder::CreateComputationalOp(SSAGraph *result,
CreateOpHandleIOs(result, op, dev_id);
}
void MultiDevSSAGraphBuilder::InsertNCCLAllReduceOp(
SSAGraph *result, const std::string &og) const {
void MultiDevSSAGraphBuilder::InsertAllReduceOp(SSAGraph *result,
const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
result->ops_.emplace_back(
new NCCLAllReduceOpHandle(local_scopes_, places_, *nccl_ctxs_));
new AllReduceOpHandle(local_scopes_, places_, nccl_ctxs_));
#else
result->ops_.emplace_back(new AllReduceOpHandle(local_scopes_, places_));
#endif
auto *op_handle = result->ops_.back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
auto &vars = result->vars_[i][og];
PADDLE_ENFORCE(!vars.empty());
auto &prev_grad = vars.back();
......@@ -338,9 +349,6 @@ void MultiDevSSAGraphBuilder::InsertNCCLAllReduceOp(
vars.emplace_back(var);
op_handle->AddOutput(var);
}
#else
PADDLE_ENFORCE("Not implemented");
#endif
}
bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
......@@ -379,7 +387,9 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(SSAGraph *result) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
#ifdef PADDLE_WITH_CUDA
auto *communication_dev_ctx = nccl_ctxs_->DevCtx(places_[i]);
auto *communication_dev_ctx =
nccl_ctxs_ ? nccl_ctxs_->DevCtx(places_[i])
: platform::DeviceContextPool::Instance().Get(places_[i]);
#else
auto *communication_dev_ctx =
platform::DeviceContextPool::Instance().Get(platform::CPUPlace());
......@@ -424,12 +434,9 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(SSAGraph *result,
auto *op_handle = result->ops_.back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &vars = result->vars_[i][og];
#ifndef PADDLE_WITH_CUDA
auto &p = places_[i];
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
#endif
SetCommunicationContext(op_handle, p);
auto &vars = result->vars_[i][og];
PADDLE_ENFORCE(!vars.empty());
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
......@@ -468,17 +475,17 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
new RPCOpHandle(op, local_scopes_[0], op.Type(), places_[0]));
if (op.Type() == "send_barrier") {
ConnectOp(result, result->ops_.back().get(), "send_vars");
ConnectOp(result, result->ops_.back().get(), "send");
} else if (op.Type() == "recv") {
ConnectOp(result, result->ops_.back().get(), "send_barrier");
} else if (op.Type() == "fetch_barrier") {
ConnectOp(result, result->ops_.back().get(), "recv");
} else if (op.Type() == "send_vars") {
} else if (op.Type() == "send") {
// do nothing
} else {
PADDLE_THROW(
"rpc op should be in ["
"send_vars, send_barrier. recv, fetch_barrier]");
"send, send_barrier. recv, fetch_barrier]");
}
// TODO(Yancey1989): schedule rpc op on different place may
......
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ 1457be3e
......@@ -100,7 +100,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const;
void InsertNCCLAllReduceOp(SSAGraph *result, const std::string &og) const;
void InsertAllReduceOp(SSAGraph *result, const std::string &og) const;
void CreateBroadcastOp(SSAGraph *result, const std::string &p_name,
size_t src_dev_id) const;
......@@ -111,6 +111,9 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
private:
BuildStrategy strategy_;
void SetCommunicationContext(OpHandleBase *op_handle,
const platform::Place &p) const;
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 1457be3e
......@@ -39,9 +39,9 @@ OpHandleBase::~OpHandleBase() {
#endif
}
void OpHandleBase::Run(bool use_event) {
void OpHandleBase::Run(bool use_cuda) {
#ifdef PADDLE_WITH_CUDA
if (events_.empty() && use_event) {
if (events_.empty() && use_cuda) {
for (auto &p : dev_ctxes_) {
int dev_id = boost::get<platform::CUDAPlace>(p.first).device;
PADDLE_ENFORCE(cudaSetDevice(dev_id));
......@@ -50,7 +50,7 @@ void OpHandleBase::Run(bool use_event) {
}
}
#else
PADDLE_ENFORCE(!use_event);
PADDLE_ENFORCE(!use_cuda);
#endif
RunImpl();
......
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ 1457be3e
......@@ -36,7 +36,7 @@ class OpHandleBase {
virtual std::string Name() const = 0;
void Run(bool use_event);
void Run(bool use_cuda);
virtual void RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx);
......
paddle/fluid/framework/details/reduce_and_gather.h
浏览文件 @ 1457be3e
......@@ -37,7 +37,9 @@ struct ReduceLoDTensor {
PADDLE_ENFORCE_NE(t0.numel(), 0);
dst_tensor_.Resize(t0.dims());
T *dst = dst_tensor_.mutable_data<T>(platform::CPUPlace());
std::copy(t0.data<T>(), t0.data<T>() + t0.numel(), dst);
if (dst != t0.data<T>()) {
std::copy(t0.data<T>(), t0.data<T>() + t0.numel(), dst);
}
for (size_t i = 1; i < src_tensors_.size(); ++i) {
auto &t = *src_tensors_[i];
......
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @ 1457be3e
......@@ -11,8 +11,8 @@
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
#include <utility>
namespace paddle {
namespace framework {
......
paddle/fluid/framework/details/graph_builder_factory.cc → paddle/fluid/framework/details/ssa_graph_builder_factory.cc
浏览文件 @ 1457be3e
......@@ -12,9 +12,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/graph_builder_factory.h"
#include "paddle/fluid/framework/details/ssa_graph_builder_factory.h"
#include <fstream>
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/ssa_graph_checker.h"
#include "paddle/fluid/framework/details/ssa_graph_printer.h"
namespace paddle {
......@@ -40,6 +41,8 @@ std::unique_ptr<SSAGraphBuilder> SSAGraphBuilderFactory::Create() {
res.reset(new SSAGraghBuilderWithPrinter(
std::move(fout), std::move(graphviz_printer), std::move(res)));
}
res.reset(new SSAGraghBuilderWithChecker(std::move(res)));
return res;
}
} // namespace details
......
paddle/fluid/framework/details/graph_builder_factory.h → paddle/fluid/framework/details/ssa_graph_builder_factory.h
浏览文件 @ 1457be3e
......@@ -40,7 +40,11 @@ class SSAGraphBuilderFactory {
loss_var_name_(loss_var_name),
param_names_(param_names),
local_scopes_(local_scopes),
strategy_(strategy) {}
strategy_(strategy) {
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_ = nullptr;
#endif
}
#ifdef PADDLE_WITH_CUDA
void SetNCCLContextMap(platform::NCCLContextMap* nccl_ctxs) {
......
paddle/fluid/framework/details/ssa_graph_checker.cc 0 → 100644
浏览文件 @ 1457be3e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/ssa_graph.h"
#include <string>
#include "paddle/fluid/framework/details/ssa_graph_checker.h"
namespace paddle {
namespace framework {
namespace details {
bool SSAGraghBuilderWithChecker::IsValidGraph(const SSAGraph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
std::unordered_set<OpHandleBase *> ready_ops;
auto insert_pending_var = [&](VarHandleBase *var) {
pending_vars.insert(var);
if (var->generated_op_ == nullptr) {
ready_vars.emplace(var);
}
};
for (auto &var_map : graph->vars_) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair.get());
}
}
}
for (auto &var : graph->dep_vars_) {
insert_pending_var(var.get());
}
for (auto &op : graph->ops_) {
if (op->Inputs().empty()) {
ready_ops.insert(op.get());
} else {
pending_ops.insert({op.get(), op.get()->NoDupInputSize()});
}
}
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
for (auto out : op->Outputs()) {
ready_vars.emplace(out);
}
}
set.clear();
};
while (!pending_vars.empty()) {
run_all_ops(ready_ops);
if (ready_vars.empty()) {
return false;
}
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->pending_ops_) {
auto &deps = --pending_ops[op];
if (deps == 0) {
ready_ops.insert(op);
}
}
}
ready_vars.clear();
}
return true;
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/ssa_graph_checker.h 0 → 100644
浏览文件 @ 1457be3e
// 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/details/ssa_graph_builder.h"
namespace paddle {
namespace framework {
namespace details {
struct SSAGraph;
class SSAGraghBuilderWithChecker : public SSAGraphBuilder {
public:
explicit SSAGraghBuilderWithChecker(
std::unique_ptr<SSAGraphBuilder>&& builder)
: builder_(std::move(builder)) {}
std::unique_ptr<SSAGraph> Build(const ProgramDesc& program) const override {
auto graph = builder_->Build(program);
PADDLE_ENFORCE(IsValidGraph(graph.get()));
return graph;
}
bool IsValidGraph(const SSAGraph* graph) const;
private:
std::unique_ptr<SSAGraphBuilder> builder_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 1457be3e
......@@ -185,6 +185,7 @@ void ThreadedSSAGraphExecutor::InsertPendingVar(
ready_vars->Push(var);
}
}
void ThreadedSSAGraphExecutor::RunOp(
BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
auto op_run = [ready_var_q, op, this] {
......@@ -192,7 +193,7 @@ void ThreadedSSAGraphExecutor::RunOp(
if (VLOG_IS_ON(10)) {
VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
}
op->Run(strategy_.use_event_);
op->Run(strategy_.use_cuda_);
VLOG(10) << op << " " << op->Name() << " Done ";
running_ops_--;
ready_var_q->Extend(op->Outputs());
......
paddle/fluid/framework/executor.cc
浏览文件 @ 1457be3e
......@@ -20,6 +20,9 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#ifdef PADDLE_WITH_DISTRIBUTE
#include "paddle/fluid/operators/detail/grpc_client.h"
#endif
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -44,6 +47,14 @@ ExecutorPrepareContext::~ExecutorPrepareContext() {
Executor::Executor(const platform::Place& place) : place_(place) {}
#ifdef PADDLE_WITH_DISTRIBUTE
void Executor::Complete() {
::paddle::operators::detail::RPCClient::GetInstance<
::paddle::operators::detail::GRPCClient>()
->SendComplete();
}
#endif
void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
if (var_type == proto::VarType::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
......
paddle/fluid/framework/executor.h
浏览文件 @ 1457be3e
......@@ -44,6 +44,13 @@ class Executor {
explicit Executor(const platform::Place& place);
#ifdef PADDLE_WITH_DISTRIBUTE
/*
* Sending signal to pserver to mark current trainer stop.
*/
void Complete();
#endif
/* @Brief
* Runtime evaluation of the given ProgramDesc under certain Scope
*
......
paddle/fluid/framework/framework.proto
浏览文件 @ 1457be3e
......@@ -71,6 +71,7 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool dispensable = 5 [ default = false ];
optional string reuse = 6;
}
// AttrProto describes the C++ type Attribute.
......
paddle/fluid/framework/op_info.cc
浏览文件 @ 1457be3e
......@@ -17,12 +17,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
static OpInfoMap* g_op_info_map = nullptr;
// C++11 removes the need for manual locking. Concurrent execution shall wait if
// a static local variable is already being initialized.
// https://stackoverflow.com/questions/11711920/how-to-implement-multithread-safe-singleton-in-c11-without-using-mutex
OpInfoMap& OpInfoMap::Instance() {
if (g_op_info_map == nullptr) {
g_op_info_map = new OpInfoMap();
}
static OpInfoMap* g_op_info_map = new OpInfoMap();
return *g_op_info_map;
}
} // namespace framework
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 1457be3e
......@@ -21,6 +21,7 @@ namespace framework {
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
CheckReuseVars();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
......@@ -56,6 +57,24 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
}
}
void OpProtoAndCheckerMaker::CheckReuseVars() {
std::unordered_set<std::string> names;
for (auto& input : proto_->inputs()) {
names.insert(input.name());
}
auto checker = [&](const std::string& name, const std::string& reused) {
PADDLE_ENFORCE(
names.count(reused),
"Output [%s] reuse Input [%s], but the input is not registered.", name,
reused);
};
for (auto& output : proto_->outputs()) {
if (output.has_reuse()) {
checker(output.name(), output.reuse());
}
}
}
void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
OpAttrChecker* attr_checker) {
proto_ = proto;
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 1457be3e
......@@ -14,6 +14,8 @@ limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "glog/logging.h"
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
......@@ -64,6 +66,11 @@ class OpProtoAndCheckerMaker {
var_->set_dispensable(true);
return *this;
}
VariableBuilder &Reuse(const std::string &name) {
var_->set_reuse(name);
return *this;
}
};
VariableBuilder AddInput(const std::string &name, const std::string &comment);
......@@ -89,6 +96,8 @@ class OpProtoAndCheckerMaker {
void CheckNoDuplicatedInOutAttrs();
void Validate();
void CheckReuseVars();
proto::OpProto *proto_;
OpAttrChecker *op_checker_;
bool validated_{false};
......
paddle/fluid/framework/op_proto_maker_test.cc
浏览文件 @ 1457be3e
......@@ -47,3 +47,23 @@ TEST(ProtoMaker, DuplicatedInOut) {
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
}
class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
AddOutput("NoOut", "output of test op").Reuse("NotExists");
}
};
TEST(ProtoMaker, InplaceOutput) {
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
TestInplaceProtoMaker proto_maker;
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
// proto_maker(&op_proto, &op_checker);
// proto_maker.Make();
// ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 1457be3e
......@@ -22,8 +22,8 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/graph_builder_factory.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/ssa_graph_builder_factory.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -43,7 +43,8 @@ class ParallelExecutorPrivate {
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
#endif
bool own_local_scope;
bool own_local_scope_;
bool use_cuda_;
};
std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
......@@ -60,35 +61,40 @@ ParallelExecutor::ParallelExecutor(
size_t num_trainers, size_t trainer_id)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
member_->use_cuda_ = exec_strategy.use_cuda_;
// Step 1. Bcast the params to devs.
// Create local scopes
if (local_scopes.empty()) {
member_->own_local_scope = true;
member_->own_local_scope_ = true;
member_->local_scopes_.emplace_back(member_->global_scope_);
for (size_t i = 1; i < member_->places_.size(); ++i) {
member_->local_scopes_.emplace_back(&scope->NewScope());
}
} else {
member_->own_local_scope = false;
member_->own_local_scope_ = false;
PADDLE_ENFORCE_EQ(member_->places_.size(), local_scopes.size());
for (size_t i = 0; i < member_->places_.size(); ++i) {
member_->local_scopes_.emplace_back(&local_scopes[i]->NewScope());
}
}
if (member_->use_cuda_) {
// Bcast Parameters to all GPUs
#ifdef PADDLE_WITH_CUDA
auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
ncclUniqueId *nccl_id = nullptr;
if (nccl_id_var != nullptr) {
nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
}
member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
member_->places_, nccl_id, num_trainers, trainer_id));
auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
ncclUniqueId *nccl_id = nullptr;
if (nccl_id_var != nullptr) {
nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
}
member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
member_->places_, nccl_id, num_trainers, trainer_id));
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
if (platform::is_gpu_place(places[0]) && member_->local_scopes_.size() != 1 &&
local_scopes.empty()) { // Is CUDA
}
if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
BCastParamsToGPUs(bcast_vars);
}
// Startup Program has been run. All local scopes has correct parameters.
......@@ -108,9 +114,13 @@ ParallelExecutor::ParallelExecutor(
details::SSAGraphBuilderFactory builder_factory(
member_->places_, loss_var_name, params, member_->local_scopes_,
build_strategy);
if (member_->use_cuda_) {
#ifdef PADDLE_WITH_CUDA
builder_factory.SetNCCLContextMap(member_->nccl_ctxs_.get());
builder_factory.SetNCCLContextMap(member_->nccl_ctxs_.get());
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
}
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places,
......@@ -123,7 +133,6 @@ ParallelExecutor::ParallelExecutor(
void ParallelExecutor::BCastParamsToGPUs(
const std::unordered_set<std::string> &vars) const {
#ifdef PADDLE_WITH_CUDA
auto *main_scope = member_->local_scopes_[0];
for (auto &var : vars) {
......@@ -135,6 +144,7 @@ void ParallelExecutor::BCastParamsToGPUs(
auto &main_tensor = main_var->Get<LoDTensor>();
auto &dims = main_tensor.dims();
if (paddle::platform::is_gpu_place(main_tensor.place())) {
#ifdef PADDLE_WITH_CUDA
size_t numel = main_tensor.numel();
ncclDataType_t data_type = platform::ToNCCLDataType(main_tensor.type());
platform::NCCLGroupGuard guard;
......@@ -153,6 +163,10 @@ void ParallelExecutor::BCastParamsToGPUs(
platform::dynload::ncclBcast(buffer, numel, data_type, 0,
nccl_ctx.comm_, nccl_ctx.stream());
}
member_->nccl_ctxs_->WaitAll();
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
} else {
platform::CPUPlace cpu;
for (size_t i = 1; i < member_->places_.size(); ++i) {
......@@ -163,11 +177,7 @@ void ParallelExecutor::BCastParamsToGPUs(
paddle::framework::TensorCopy(main_tensor, cpu, t);
}
}
member_->nccl_ctxs_->WaitAll();
}
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
}
void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
......@@ -213,7 +223,7 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
}
ParallelExecutor::~ParallelExecutor() {
if (member_->own_local_scope) {
if (member_->own_local_scope_) {
for (size_t i = 1; i < member_->local_scopes_.size(); ++i) {
member_->global_scope_->DeleteScope(member_->local_scopes_[i]);
}
......
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 1457be3e
......@@ -64,7 +64,8 @@ class TRTConvertValidation {
TRTConvertValidation(int batch_size,
const std::unordered_set<std::string>& parameters,
framework::Scope& scope, int workspace_size = 1 << 10)
framework::Scope& scope, // NOLINT
int workspace_size = 1 << 10)
: parameters_(parameters), scope_(scope) {
// create engine.
engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 1457be3e
......@@ -186,19 +186,23 @@ endif()
add_subdirectory(detail)
if(WITH_DISTRIBUTE)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
set(DISTRIBUTE_DEPS "")
if(WITH_GRPC)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
else()
set(DISTRIBUTE_DEPS sendrecvop_brpc brpc leveldb snappystream snappy protobuf ssl crypto zlib)
endif()
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
op_library(send_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(prefetch_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(prefetch_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(recv_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(recv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(listen_and_serv_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(listen_and_serv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(send_vars_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_vars_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(send_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
op_library(fetch_barrier_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
......@@ -208,15 +212,18 @@ if(WITH_DISTRIBUTE)
# listen_and_serv_op sum_op executor SERIAL)
if(WITH_GPU)
set_source_files_properties(test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op
listen_and_serv_op executor SERIAL)
op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS listen_and_serv_op executor SERIAL)
if(WITH_GRPC)
op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
else()
op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_brpc)
endif()
set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
endif()
else()
set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op fetch_barrier_op gen_nccl_id_op)
set(DEPS_OPS ${DEPS_OPS} prefetch_op recv_op listen_and_serv_op send_op send_barrier_op fetch_barrier_op gen_nccl_id_op)
endif()
op_library(cross_entropy_op DEPS cross_entropy)
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 1457be3e
......@@ -25,7 +25,7 @@ namespace operators {
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ 1457be3e
......@@ -89,9 +89,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
AddAttr<float>("beta1",
"(float, default 0.9) "
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 1457be3e
......@@ -151,13 +151,15 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training");
"Store the global mean when training")
.Reuse("Mean");
AddOutput("VarianceOut",
"Share memory with Variance. "
"Store the global Variance when training");
"Store the global Variance when training")
.Reuse("Variance");
AddOutput("SavedMean",
"Mean of the current mini batch, "
"will apply to output when training")
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 1457be3e
......@@ -125,7 +125,8 @@ void Conv2DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.");
"The format of output tensor is also NCHW.")
.Reuse("Input");
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
......@@ -220,7 +221,8 @@ void Conv3DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.");
"The format of output tensor is also NCDHW.")
.Reuse("Input");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
......
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @ 1457be3e
......@@ -124,7 +124,8 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
"Tensor<float/double> with shape [N x D].");
AddOutput("Y",
"(Tensor, default Tensor<float>), a 2-D tensor with shape "
"[N x 1]. The cross entropy loss.");
"[N x 1]. The cross entropy loss.")
.Reuse("X");
AddAttr<bool>("soft_label",
"(bool, default false), a flag indicating whether to "
"interpretate the given labels as soft labels.")
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ 1457be3e
if(WITH_DISTRIBUTE)
if(NOT WITH_DISTRIBUTE)
return()
endif()
if(WITH_GRPC)
grpc_library(sendrecvop_grpc SRCS 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 PROTO send_recv.proto DEPS lod_tensor
selected_rows memory)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS grpc_serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc SERIAL)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc
cc_test(grpc_server_test SRCS rpc_server_test.cc DEPS sendrecvop_grpc
grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
proto_desc lookup_table_op SERIAL)
return()
endif()
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(brpc_server.cc brpc_client.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
brpc_library(sendrecvop_brpc SRCS brpc_client.cc brpc_server.cc rpc_server.cc rpc_client.cc request_handler_impl.cc
PROTO send_recv.proto
DEPS lod_tensor selected_rows memory)
find_library(OPENSSL_CRYPTO_LIBRARY_STATIC NAMES libcrypto.so)
ADD_LIBRARY(crypto SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET crypto PROPERTY IMPORTED_LOCATION ${OPENSSL_CRYPTO_LIBRARY_STATIC})
find_library(OPENSSL_SSL_LIBRARY_STATIC NAMES libssl.so)
ADD_LIBRARY(ssl SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET ssl PROPERTY IMPORTED_LOCATION ${OPENSSL_SSL_LIBRARY_STATIC})
cc_test(brpc_server_test SRCS rpc_server_test.cc DEPS sendrecvop_brpc
brpc protobuf leveldb gflags glog
protobuf executor proto_desc lookup_table_op snappystream snappy ssl crypto SERIAL)
paddle/fluid/operators/detail/brpc_client.cc 0 → 100644
浏览文件 @ 1457be3e
// 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/detail/brpc_client.h"
#include "paddle/fluid/framework/threadpool.h"
namespace paddle {
namespace operators {
namespace detail {
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) {
// 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);
if (cntl->Failed()) {
LOG(WARNING) << "Fail to send EchoRequest, " << cntl->ErrorText();
return;
}
LOG(INFO) << "Received response from " << cntl->remote_side()
<< " latency=" << cntl->latency_us() << "us";
}
bool 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);
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);
google::protobuf::Closure* done =
brpc::NewCallback(&HandleSendResponse, cntl, response);
sendrecv::VariableMessage request;
ch_ctx->stub->SendVariable(cntl, &request, response, done);
});
req_count_++;
return true;
}
void HandleGetResponse(brpc::Controller* cntl,
sendrecv::VariableMessage* response) {
// 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);
if (cntl->Failed()) {
LOG(WARNING) << "Fail to send EchoRequest, " << cntl->ErrorText();
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);
}
bool BRPCClient::AsyncGetVar(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 = GetChannel(ep_val);
framework::AsyncIO(
[var_name_val, ep_val, p_scope, p_ctx, time_out, ch, this] {});
req_count_++;
return true;
}
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) {
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 framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
framework::AsyncIO([in_var_name_val, out_var_name_val, ep_val, p_scope, p_ctx,
time_out, ch, this] {});
req_count_++;
return true;
}
void BRPCClient::AsyncSendBatchBarrier(const std::string& ep,
int64_t time_out) {
req_count_++;
}
void BRPCClient::AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out) {
req_count_++;
}
void BRPCClient::Wait() {
std::unique_lock<std::mutex> lk(sync_mutex_);
sync_cond_.wait(lk, [this] { return req_count_ == 0; });
}
ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
{
std::lock_guard<std::mutex> guard(chan_mutex_);
auto it = channels_.find(ep);
if (it != channels_.end()) {
return it->second;
}
}
ChannelQueuePtr q(new framework::BlockingQueue<ChannelContextPtr>());
brpc::ChannelOptions options;
options.protocol = "baidu_std";
options.connection_type = "pooled";
options.connect_timeout_ms = 100;
options.timeout_ms = FLAGS_timeout_ms /*milliseconds*/;
options.max_retry = FLAGS_max_retry;
for (int i = 0; i < FLAGS_brpc_channel_num; ++i) {
std::shared_ptr<ChannelContext> c(new ChannelContext());
if (c->channel.Init(ep.c_str(), &options) != 0) {
LOG(ERROR) << "Fail to initialize channel";
return nullptr;
}
c->stub.reset(new sendrecv::SendRecvService_Stub(
static_cast<google::protobuf::RpcChannel*>(&c->channel)));
q->Push(c);
}
{
std::lock_guard<std::mutex> guard(chan_mutex_);
channels_[ep] = q;
}
return q;
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/brpc_client.h 0 → 100644
浏览文件 @ 1457be3e
/* 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 <time.h>
#include <chrono> // NOLINT
#include <ctime>
#include <functional>
#include <iostream>
#include <map>
#include <mutex> // NOLINT
#include <string>
#include <vector>
#include "brpc/channel.h"
#include "paddle/fluid/framework/blocking_queue.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/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/platform/macros.h" // for DISABLE_COPY_AND_ASSIGN
namespace paddle {
namespace operators {
namespace detail {
struct ChannelContext {
brpc::Channel channel;
std::shared_ptr<sendrecv::SendRecvService_Stub> stub;
};
typedef std::shared_ptr<ChannelContext> ChannelContextPtr;
typedef std::shared_ptr<framework::BlockingQueue<ChannelContextPtr>>
ChannelQueuePtr;
class BRPCClient : public RPCClient {
public:
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 = RPCClient::rpc_time_out) override;
bool AsyncGetVar(const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
int64_t time_out = RPCClient::rpc_time_out) 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 = RPCClient::rpc_time_out) override;
void AsyncSendBatchBarrier(
const std::string& ep,
int64_t time_out = RPCClient::rpc_time_out) override;
void AsyncSendFetchBarrier(
const std::string& ep,
int64_t time_out = RPCClient::rpc_time_out) override;
void Wait() override;
private:
void Proceed();
ChannelQueuePtr GetChannel(const std::string& ep);
private:
std::unordered_map<std::string, ChannelQueuePtr> channels_;
// mutex for Wait client sync
std::mutex sync_mutex_;
std::condition_variable sync_cond_;
std::atomic<int64_t> req_count_{0};
// mutex for GetChannel thread safety
std::mutex chan_mutex_;
DISABLE_COPY_AND_ASSIGN(BRPCClient);
};
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/brpc_server.cc 0 → 100644
浏览文件 @ 1457be3e
// 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/detail/brpc_server.h"
#include "paddle/fluid/operators/detail/request_handler.h"
namespace sendrecv {
typedef std::unordered_map<std::string,
paddle::operators::detail::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::detail::kRequestSend);
if (it != rpc_call_map.end()) {
request_send_h_ = it->second;
}
it = rpc_call_map.find(paddle::operators::detail::kRequestSend);
if (it != rpc_call_map.end()) {
request_get_h_ = it->second;
}
it = rpc_call_map.find(paddle::operators::detail::kRequestPrefetch);
if (it != rpc_call_map.end()) {
request_prefetch_h_ = it->second;
}
}
virtual ~BRPCServiceImpl() {}
void SendVariable(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request, VoidMessage* response,
google::protobuf::Closure* done) override {
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;
std::string varname = request->varname();
if (!request_send_h_->sync_mode()) {
local_scope = &request_send_h_->scope()->NewScope();
invar = local_scope->Var(varname);
} else {
invar = local_scope->FindVar(varname);
}
request_send_h_->Handle(varname, local_scope, invar, &outvar);
if (!request_send_h_->sync_mode()) {
request_send_h_->scope()->DeleteScope(local_scope);
}
}
void GetVariable(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request, VariableMessage* response,
google::protobuf::Closure* done) override {
PADDLE_ENFORCE(request_get_h_ != nullptr,
"RequestGet handler should be registed first!");
}
void PrefetchVariable(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request,
VariableMessage* response,
google::protobuf::Closure* done) override {
PADDLE_ENFORCE(request_prefetch_h_ != nullptr,
"kRequestPrefetch handler should be registed first!");
}
private:
paddle::operators::detail::RequestHandler* request_send_h_;
paddle::operators::detail::RequestHandler* request_get_h_;
paddle::operators::detail::RequestHandler* request_prefetch_h_;
};
} // namespace sendrecv
namespace paddle {
namespace operators {
namespace detail {
void AsyncBRPCServer::StartServer() {
// Instance of your service.
sendrecv::BRPCServiceImpl service_impl(rpc_call_map_);
// 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
// use brpc::SERVER_OWNS_SERVICE.
if (server_.AddService(&service_impl, brpc::SERVER_DOESNT_OWN_SERVICE) != 0) {
LOG(FATAL) << "Fail to add service";
return;
}
brpc::ServerOptions options;
options.idle_timeout_sec = idle_timeout_s_;
options.max_concurrency = max_concurrency_;
if (server_.Start(bind_address_.c_str(), &options) != 0) {
LOG(FATAL) << "Fail to start EchoServer" << bind_address_;
return;
}
butil::EndPoint ep = server_.listen_address();
selected_port_ = ep.port;
{
std::lock_guard<std::mutex> lock(this->mutex_ready_);
ready_ = 1;
}
condition_ready_.notify_all();
server_.Join();
}
void AsyncBRPCServer::ShutDownImpl() { server_.Stop(1000); }
void AsyncBRPCServer::WaitServerReady() {
VLOG(3) << "AsyncGRPCServer is wait server ready";
std::unique_lock<std::mutex> lock(this->mutex_ready_);
condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
VLOG(3) << "AsyncGRPCServer WaitSeverReady";
}
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/brpc_server.h 0 → 100644
浏览文件 @ 1457be3e
/* 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 <condition_variable> // NOLINT
#include <mutex> // NOLINT
#include <string>
#include "brpc/server.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
namespace paddle {
namespace operators {
namespace detail {
class AsyncBRPCServer final : public RPCServer {
public:
explicit AsyncBRPCServer(const std::string& address, int client_num)
: RPCServer(address, client_num), ready_(0) {}
virtual ~AsyncBRPCServer() {}
void StartServer() override;
void WaitServerReady() override;
private:
void ShutDownImpl() override;
brpc::Server server_;
static constexpr int idle_timeout_s_ = -1;
static constexpr int max_concurrency_ = 0;
std::mutex mutex_ready_;
std::condition_variable condition_ready_;
int ready_;
};
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 1457be3e
......@@ -19,6 +19,7 @@ limitations under the License. */
#include <limits>
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
......@@ -33,6 +34,12 @@ void GRPCClient::InitEventLoop() {
client_thread_.reset(new std::thread(std::bind(&GRPCClient::Proceed, this)));
}
void GRPCClient::SendComplete() {
for (auto& it : channels_) {
this->AsyncSendComplete(it.first);
}
}
GRPCClient::~GRPCClient() {
Wait();
cq_.Shutdown();
......@@ -209,6 +216,19 @@ void GRPCClient::AsyncSendFetchBarrier(const std::string& ep,
req_count_++;
}
void GRPCClient::AsyncSendComplete(const std::string& ep, int64_t time_out) {
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
s->Prepare(time_out);
sendrecv::VariableMessage req;
req.set_varname(COMPLETE_MESSAGE);
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
}
void GRPCClient::Wait() {
std::unique_lock<std::mutex> lk(sync_mutex_);
sync_cond_.wait(lk, [this] { return req_count_ == 0; });
......
paddle/fluid/operators/detail/grpc_client.h
浏览文件 @ 1457be3e
......@@ -195,6 +195,8 @@ class GRPCClient : public RPCClient {
void Wait() override;
void SendComplete() override;
protected:
void InitImpl() override;
......@@ -204,6 +206,9 @@ class GRPCClient : public RPCClient {
void Proceed();
void AsyncSendComplete(const std::string& ep,
int64_t time_out = RPCClient::rpc_time_out);
std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
private:
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ 1457be3e
......@@ -41,11 +41,22 @@ class RequestBase {
virtual ~RequestBase() {}
virtual void Process() = 0;
CallStatus Status() { return status_; }
void SetStatus(CallStatus status) { status_ = status; }
CallStatus Status() const {
std::lock_guard<std::mutex> l(status_mu_);
return status_;
}
template <typename T>
void Finish(const T& reply, ServerAsyncResponseWriter<T>* responder) {
std::lock_guard<std::mutex> l(status_mu_);
status_ = FINISH;
responder->Finish(reply, ::grpc::Status::OK,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
}
virtual std::string GetReqName() = 0;
protected:
mutable std::mutex status_mu_;
::grpc::ServerContext ctx_;
GrpcService::AsyncService* service_;
::grpc::ServerCompletionQueue* cq_;
......@@ -80,9 +91,7 @@ class RequestSend final : public RequestBase {
framework::Variable* outvar = nullptr;
request_handler_->Handle(varname, scope, invar, &outvar);
status_ = FINISH;
responder_.Finish(reply_, ::grpc::Status::OK,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
Finish(reply_, &responder_);
}
protected:
......@@ -122,9 +131,7 @@ class RequestGet final : public RequestBase {
SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
&reply_);
}
status_ = FINISH;
responder_.Finish(reply_, ::grpc::Status::OK,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
Finish(reply_, &responder_);
}
protected:
......@@ -155,20 +162,20 @@ class RequestPrefetch final : public RequestBase {
void Process() override {
// prefetch process...
std::string varname = request_->OutVarname();
VLOG(3) << "RequestPrefetch " << varname;
std::string in_var_name = request_->Varname();
std::string out_var_name = request_->OutVarname();
VLOG(3) << "RequestPrefetch, in_var_name: " << in_var_name
<< " out_var_name: " << out_var_name;
auto scope = request_->GetMutableLocalScope();
auto invar = scope->FindVar(varname);
framework::Variable* outvar = nullptr;
auto invar = scope->FindVar(in_var_name);
framework::Variable* outvar = scope->FindVar(out_var_name);
request_handler_->Handle(varname, scope, invar, &outvar);
request_handler_->Handle(in_var_name, scope, invar, &outvar, out_var_name);
SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
SerializeToByteBuffer(out_var_name, outvar, *request_handler_->dev_ctx(),
&reply_);
responder_.Finish(reply_, ::grpc::Status::OK,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
status_ = FINISH;
Finish(reply_, &responder_);
}
protected:
......@@ -282,7 +289,7 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
} else if (rpc_name == kRequestPrefetch) {
b = new RequestPrefetch(&service_, cq.get(), handler, req_id);
} else {
PADDLE_ENFORCE(false, "not surpported rpc");
PADDLE_ENFORCE(false, "not supported rpc");
}
reqs[req_id] = b;
......
paddle/fluid/operators/detail/grpc_server.h
浏览文件 @ 1457be3e
......@@ -53,6 +53,7 @@ class AsyncGRPCServer final : public RPCServer {
void StartServer() override;
private:
// HandleRequest needs to be thread-safe.
void HandleRequest(
::grpc::ServerCompletionQueue* cq, const std::string& rpc_name,
std::function<void(const std::string&, int)> TryToRegisterNewOne);
......
paddle/fluid/operators/detail/macros.h 0 → 100644
浏览文件 @ 1457be3e
// 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_GRPC
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#define RPCSERVER_T detail::AsyncGRPCServer
#define RPCCLIENT_T detail::GRPCClient
#else
#include "paddle/fluid/operators/detail/brpc_client.h"
#include "paddle/fluid/operators/detail/brpc_server.h"
#define RPCSERVER_T detail::AsyncBRPCServer
#define RPCCLIENT_T detail::BRPCClient
#endif
paddle/fluid/operators/detail/request_handler.h
浏览文件 @ 1457be3e
......@@ -28,7 +28,6 @@
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
......@@ -38,6 +37,11 @@ constexpr char kRequestSend[] = "RequestSend";
constexpr char kRequestGet[] = "RequestGet";
constexpr char kRequestPrefetch[] = "RequestPrefetch";
#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
#define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
#define COMPLETE_MESSAGE "COMPLETE@RECV"
class RPCServer;
class RequestHandler {
......@@ -57,9 +61,12 @@ class RequestHandler {
void SetDevCtx(const platform::DeviceContext* dev_ctx) { dev_ctx_ = dev_ctx; }
void SetProgram(framework::ProgramDesc* program) { program_ = program; }
void SetExecutor(framework::Executor* executor) { executor_ = executor; }
// Used for dist lookup table prefetch
void SetPrefetchPreparedCtx(
std::unique_ptr<framework::ExecutorPrepareContext> prepared) {
prefetch_ctx_.reset(prepared.release());
std::unordered_map<
std::string, std::shared_ptr<framework::ExecutorPrepareContext>>* g) {
prefetch_var_name_to_prepared_ctx_ = g;
}
// Used for async.
......@@ -75,9 +82,6 @@ class RequestHandler {
bool sync_mode() { return sync_mode_; }
framework::Scope* scope() { return scope_; }
const platform::DeviceContext* dev_ctx() { return dev_ctx_; }
framework::ExecutorPrepareContext* prefetch_ctx() {
return prefetch_ctx_.get();
}
framework::ProgramDesc* program() { return program_; }
framework::Executor* executor() { return executor_; }
......@@ -96,8 +100,8 @@ class RequestHandler {
// *request_handler_->dev_ctx(), &reply_);
// }
virtual bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var,
framework::Variable** outvar) = 0;
framework::Variable* var, framework::Variable** outvar,
const std::string& out_var_name = "") = 0;
protected:
const bool sync_mode_;
......@@ -106,12 +110,17 @@ class RequestHandler {
framework::Executor* executor_;
framework::Scope* scope_;
framework::ProgramDesc* program_;
std::unique_ptr<framework::ExecutorPrepareContext> prefetch_ctx_;
// used for distribute lookup table prefetch
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>*
prefetch_var_name_to_prepared_ctx_;
// Used for async.
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>*
grad_to_prepared_ctx_;
RPCServer* rpc_server_;
};
......
paddle/fluid/operators/detail/request_handler_impl.cc
浏览文件 @ 1457be3e
......@@ -16,15 +16,12 @@
#include <string>
#include <vector>
#include "paddle/fluid/framework/blocking_queue.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/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/variable_response.h"
namespace paddle {
namespace operators {
......@@ -33,7 +30,8 @@ namespace detail {
bool RequestSendHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
framework::Variable** outvar,
const std::string& out_var_name) {
VLOG(4) << "RequestSendHandler:" << varname;
// Async
......@@ -52,6 +50,9 @@ bool RequestSendHandler::Handle(const std::string& varname,
if (varname == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv batch barrier message";
rpc_server_->IncreaseBatchBarrier(kRequestSend);
} else if (varname == COMPLETE_MESSAGE) {
VLOG(3) << "sync: recv complete message";
rpc_server_->DecreaseClientNum();
} else {
VLOG(3) << "sync: received var_name: " << varname;
if (sync_mode_) {
......@@ -82,7 +83,8 @@ void RequestSendHandler::ResetSparseVarRecorder() {
bool RequestGetHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
framework::Variable** outvar,
const std::string& out_var_name) {
VLOG(4) << "RequestGetHandler:" << varname;
if (varname != FETCH_BARRIER_MESSAGE) {
......@@ -105,13 +107,14 @@ bool RequestGetHandler::Handle(const std::string& varname,
bool RequestPrefetchHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
framework::Variable** outvar,
const std::string& out_var_name) {
VLOG(4) << "RequestPrefetchHandler " << varname;
auto var_desc = program_->Block(0).FindVar(varname);
*outvar = scope->FindVar(varname);
auto var_desc = program_->Block(0).FindVar(out_var_name);
InitializeVariable(*outvar, var_desc->GetType());
executor_->RunPreparedContext(prefetch_ctx_.get(), scope);
executor_->RunPreparedContext(
(*prefetch_var_name_to_prepared_ctx_)[varname].get(), scope);
return true;
}
......
paddle/fluid/operators/detail/request_handler_impl.h
浏览文件 @ 1457be3e
......@@ -29,7 +29,6 @@
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
......@@ -40,7 +39,8 @@ class RequestSendHandler final : public RequestHandler {
explicit RequestSendHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestSendHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
framework::Variable* var, framework::Variable** outvar,
const std::string& out_var_name = "") override;
void ResetSparseVarRecorder();
private:
......@@ -53,7 +53,8 @@ class RequestGetHandler final : public RequestHandler {
explicit RequestGetHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestGetHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
framework::Variable* var, framework::Variable** outvar,
const std::string& out_var_name = "") override;
};
class RequestPrefetchHandler final : public RequestHandler {
......@@ -61,7 +62,8 @@ class RequestPrefetchHandler final : public RequestHandler {
explicit RequestPrefetchHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestPrefetchHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
framework::Variable* var, framework::Variable** outvar,
const std::string& out_var_name = "") override;
};
} // namespace detail
......
paddle/fluid/operators/detail/rpc_client.h
浏览文件 @ 1457be3e
......@@ -26,6 +26,8 @@ namespace detail {
class RPCClient {
public:
RPCClient() {}
virtual ~RPCClient() {}
virtual bool AsyncSendVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
......@@ -51,6 +53,11 @@ class RPCClient {
virtual void AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out = rpc_time_out) = 0;
// SendComplete tells all the server that current trainer have no more data
// to train, so that the pserver can reduce it's barrier count, and continue
// to train with other trainers.
virtual void SendComplete() = 0;
virtual void Wait() = 0;
static constexpr int64_t rpc_time_out = 120 * 1000;
......
paddle/fluid/operators/detail/rpc_server.cc
浏览文件 @ 1457be3e
......@@ -43,7 +43,7 @@ void RPCServer::SavePort() const {
void RPCServer::WaitBarrier(const std::string& rpc_name) {
std::unique_lock<std::mutex> lock(this->mutex_);
barrier_cond_.wait(lock, [=] {
barrier_cond_.wait(lock, [this, &rpc_name] {
return (barrier_counter_[rpc_name] >= client_num_ || exit_flag_.load());
});
......@@ -53,19 +53,23 @@ void RPCServer::WaitBarrier(const std::string& rpc_name) {
void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
VLOG(3) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
int b = 0;
{
std::unique_lock<std::mutex> lock(mutex_);
b = ++barrier_counter_[rpc_name];
}
VLOG(3) << "RPCServer IncreaseBatchBarrier " << rpc_name
<< ", barrier_count:" << b << ", fan_in" << client_num_;
std::unique_lock<std::mutex> lock(mutex_);
b = ++barrier_counter_[rpc_name];
if (b >= client_num_) {
lock.unlock();
barrier_cond_.notify_all();
lock.lock();
}
}
void RPCServer::DecreaseClientNum() {
{
std::unique_lock<std::mutex> lock(mutex_);
client_num_--;
}
barrier_cond_.notify_all();
}
void RPCServer::ResetBarrierCounter() {
VLOG(3) << "RPCServer ResetBarrierCounter ";
std::unique_lock<std::mutex> lock(mutex_);
......
paddle/fluid/operators/detail/rpc_server.h
浏览文件 @ 1457be3e
......@@ -60,7 +60,7 @@ class RPCServer {
void SetCond(const std::string& rpc_name);
void WaitCond(const std::string& rpc_name);
void IncreaseBatchBarrier(const std::string rpc_name);
void DecreaseClientNum();
void ResetBarrierCounter();
protected:
......@@ -79,8 +79,7 @@ class RPCServer {
std::string bind_address_;
std::atomic<int> exit_flag_;
int selected_port_;
const int client_num_;
int client_num_;
std::unordered_map<std::string, RequestHandler*> rpc_call_map_;
std::unordered_map<std::string, int> rpc_thread_num_;
......
paddle/fluid/operators/detail/grpc_server_test.cc → paddle/fluid/operators/detail/rpc_server_test.cc
浏览文件 @ 1457be3e
......@@ -17,15 +17,14 @@ limitations under the License. */
#include <thread> // NOLINT
#include "gtest/gtest.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
namespace framework = paddle::framework;
namespace platform = paddle::platform;
......@@ -33,7 +32,7 @@ namespace detail = paddle::operators::detail;
USE_OP(lookup_table);
std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
std::unique_ptr<detail::RPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
......@@ -99,11 +98,17 @@ void StartServer() {
framework::Executor exe(place);
platform::CPUDeviceContext ctx(place);
auto* block = AppendPrefetchBlcok(&program);
auto prepared = exe.Prepare(program, block->ID());
std::string in_var_name("ids");
std::vector<int> prefetch_block_ids{block->ID()};
auto prepared = exe.Prepare(program, prefetch_block_ids);
InitTensorsOnServer(&scope, &place, 10);
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>
prefetch_var_name_to_prepared;
prefetch_var_name_to_prepared[in_var_name] = prepared[0];
g_req_handler->SetProgram(&program);
g_req_handler->SetPrefetchPreparedCtx(std::move(prepared));
g_req_handler->SetPrefetchPreparedCtx(&prefetch_var_name_to_prepared);
g_req_handler->SetDevCtx(&ctx);
g_req_handler->SetScope(&scope);
g_req_handler->SetExecutor(&exe);
......@@ -112,20 +117,19 @@ void StartServer() {
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
server_thread.join();
}
TEST(PREFETCH, CPU) {
g_req_handler.reset(new detail::RequestPrefetchHandler(true));
g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
std::thread server_thread(StartServer);
g_rpc_service->WaitServerReady();
detail::RPCClient* client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
int port = g_rpc_service->GetSelectedPort();
std::string ep = paddle::string::Sprintf("127.0.0.1:%d", port);
......
paddle/fluid/operators/detail/send_recv.proto
浏览文件 @ 1457be3e
......@@ -14,6 +14,8 @@ limitations under the License. */
syntax = "proto3";
package sendrecv;
// option cc_generic_services = true;
service SendRecvService {
// For parameter server round-robin like hashing, do not split tensors.
// Send and recv only one tensor
......
paddle/fluid/operators/detail/sendrecvop_utils.h
浏览文件 @ 1457be3e
......@@ -32,16 +32,6 @@ namespace paddle {
namespace operators {
namespace detail {
#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
#define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
static int64_t GetTimestamp() {
struct timeval tp;
gettimeofday(&tp, NULL);
return tp.tv_sec * 1000 + tp.tv_usec / 1000;
}
typedef void (*DestroyCallback)(void*);
void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ 1457be3e
......@@ -59,47 +59,48 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() final {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
AddOutput("Out", "The output of elementwise op.");
AddOutput("Out", "The output of elementwise op.").Reuse("X");
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
"for broadcasting Y onto X.")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddComment(string::Sprintf(R"DOC(
Limited Elementwise %s Operator.
Limited Elementwise %s Operator
The equation is:
$$%s$$
$X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be
smaller than or equal to the dimensions of $X$.
- $X$: a tensor of any dimension.
- $Y$: a tensor whose dimensions must be less than or equal to the dimensions of $X$.
There are two cases for this operator:
1. The shape of $Y$ is same with $X$;
2. The shape of $Y$ is a congiguous subsequencet of $X$. The trailing dimensions
of size 1 for $Y$ will be ignored for the consideration of subsequence.
1. The shape of $Y$ is the same with $X$.
2. The shape of $Y$ is a continuous subsequence of $X$.
For case 2:
$Y$ will be broadcasted to match the shape of $X$ and axis should be
set to index of the start dimension to broadcast $Y$ onto $X$.
1. Broadcast $Y$ to match the shape of $X$, where $axis$ is the start dimension index
for broadcasting $Y$ onto $X$.
2. If $axis$ is -1 (default), $axis = rank(X) - rank(Y)$.
3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of
subsequence, such as shape(Y) = (2, 1) => (2).
If axis is -1, it is treated as axis=rank(X)-rank(Y).
For example:
For example
.. code-block:: python
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details)
information. However, the output only shares the LoD information with input $X$.
The inputs $X$ and $Y$ can carry the different LoD information.
But the output only shares the LoD information with the input $X$.
)DOC",
GetName(), GetEquation()));
......
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @ 1457be3e
......@@ -19,9 +19,7 @@ limitations under the License. */
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
......@@ -45,7 +43,7 @@ class FetchBarrierOp : public framework::OperatorBase {
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient::GetInstance<RPCCLIENT_T>();
rpc_client->Wait();
......
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @ 1457be3e
......@@ -21,8 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/platform/nccl_helper.h"
......@@ -61,8 +60,8 @@ class GenNCCLIdOp : public framework::OperatorBase {
std::vector<std::string> endpoint_list =
Attr<std::vector<std::string>>("endpoint_list");
detail::RPCClient* client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
for (auto& ep : endpoint_list) {
VLOG(3) << "sending nccl id to " << ep;
client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
......@@ -78,9 +77,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
// deleter will call GRPC Server's base class's dtor and
// that will cause a wired crash.
detail::RequestSendHandler rpc_h(true);
detail::AsyncGRPCServer rpc_service(endpoint, 1);
rpc_service.RegisterRPC(detail::kRequestSend, &rpc_h);
rpc_h.SetRPCServer(&rpc_service);
std::unique_ptr<detail::RPCServer> rpc_service(
new RPCSERVER_T(endpoint, 1));
rpc_service->RegisterRPC(detail::kRequestSend, &rpc_h);
rpc_h.SetRPCServer(rpc_service.get());
framework::ProgramDesc empty_program;
framework::Executor executor(dev_ctx.GetPlace());
......@@ -90,12 +91,13 @@ class GenNCCLIdOp : public framework::OperatorBase {
rpc_h.SetExecutor(&executor);
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::StartServer, &rpc_service));
rpc_service.SetCond(detail::kRequestSend);
std::bind(&detail::RPCServer::StartServer, rpc_service.get()));
rpc_service->SetCond(detail::kRequestSend);
VLOG(3) << "start getting nccl id from trainer 0...";
rpc_service.WaitBarrier(detail::kRequestSend);
rpc_service->WaitBarrier(detail::kRequestSend);
VLOG(3) << "got nccl id and stop server...";
rpc_service.ShutDown();
rpc_service->ShutDown();
VLOG(3) << "rpc server stopped";
server_thread.join();
}
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 1457be3e
......@@ -19,7 +19,8 @@ limitations under the License. */
#include <thread> // NOLINT
#include <vector>
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -89,19 +90,28 @@ void ListenAndServOp::SavePort() const {
rpc_service_->SavePort();
}
void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
framework::ProgramDesc *program,
framework::Scope *recv_scope,
framework::BlockDesc *prefetch_block) const {
static int64_t GetTimestamp() {
struct timeval tp;
gettimeofday(&tp, NULL);
return tp.tv_sec * 1000 + tp.tv_usec / 1000;
}
void ListenAndServOp::RunSyncLoop(
framework::Executor *executor, framework::ProgramDesc *program,
framework::Scope *recv_scope,
const std::vector<int> &prefetch_block_id_list) const {
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
std::vector<int> block_list;
for (size_t blkid = 1; blkid < num_blocks; ++blkid) {
block_list.push_back(blkid);
std::vector<int> optimize_block_id_list;
for (int blkid = 1; blkid < num_blocks; ++blkid) {
if (std::find(prefetch_block_id_list.begin(), prefetch_block_id_list.end(),
blkid) == prefetch_block_id_list.end()) {
optimize_block_id_list.push_back(blkid);
}
}
auto optimize_prepared = executor->Prepare(*program, block_list);
auto optimize_prepared = executor->Prepare(*program, optimize_block_id_list);
// Insert placeholder for block0 which holds current op itself.
optimize_prepared.insert(
optimize_prepared.begin(),
......@@ -127,21 +137,22 @@ void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
int32_t last_parent_blkid = program->Block(1).Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
double ts = detail::GetTimestamp();
for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
if (blkid != static_cast<size_t>(prefetch_block->ID())) {
if (program->Block(blkid).Parent() != last_parent_blkid) {
ParallelExecuteBlocks(parallel_blkids, executor, optimize_prepared,
program, recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
}
parallel_blkids.push_back(blkid);
double ts = GetTimestamp();
for (size_t i = 1; i < optimize_block_id_list.size(); ++i) {
// skip the first optimize block because it is already in the
// parallel_blkids.
int blkid = optimize_block_id_list[i];
if (program->Block(blkid).Parent() != last_parent_blkid) {
ParallelExecuteBlocks(parallel_blkids, executor, optimize_prepared,
program, recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
}
parallel_blkids.push_back(blkid);
}
ParallelExecuteBlocks(parallel_blkids, executor, optimize_prepared, program,
recv_scope);
VLOG(2) << "run all blocks spent " << detail::GetTimestamp() - ts << "(ms)";
VLOG(2) << "run all blocks spent " << GetTimestamp() - ts << "(ms)";
rpc_service_->SetCond(detail::kRequestGet);
rpc_service_->WaitBarrier(detail::kRequestGet);
......@@ -203,18 +214,19 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
} // while(true)
}
static void FillRequestCtx(detail::RequestHandler *h, framework::Scope *scope,
platform::DeviceContext *dev_ctx,
framework::Executor *executor,
framework::ProgramDesc *program,
framework::ExecutorPrepareContext *prefetch_ctx,
detail::RPCServer *rpc_server) {
static void FillRequestCtx(
detail::RequestHandler *h, framework::Scope *scope,
platform::DeviceContext *dev_ctx, framework::Executor *executor,
framework::ProgramDesc *program,
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>
*prefetch_ctx,
detail::RPCServer *rpc_server) {
h->SetScope(scope);
h->SetDevCtx(dev_ctx);
h->SetExecutor(executor);
h->SetProgram(program);
h->SetPrefetchPreparedCtx(
std::unique_ptr<framework::ExecutorPrepareContext>(prefetch_ctx));
h->SetPrefetchPreparedCtx(prefetch_ctx);
h->SetRPCServer(rpc_server);
}
......@@ -235,8 +247,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
LOG(INFO) << "sync_mode:" << sync_mode << ", fan_in:" << fan_in
<< ", end_point:" << endpoint;
// request_handler_.reset(new detail::GRPCRequestSendHandler(sync_mode));
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, fan_in));
rpc_service_.reset(new RPCSERVER_T(endpoint, fan_in));
request_send_handler_.reset(new detail::RequestSendHandler(sync_mode));
request_get_handler_.reset(new detail::RequestGetHandler(sync_mode));
request_prefetch_handler_.reset(
......@@ -248,17 +260,42 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
request_prefetch_handler_.get());
auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *prefetch_block = Attr<framework::BlockDesc *>(kPrefetchBlock);
auto *program = optimize_block->Program();
framework::Executor executor(dev_place);
// prepare for prefetch
VLOG(3) << "prefetch block id is " << prefetch_block->ID();
auto prefetch_prepared = executor.Prepare(*program, prefetch_block->ID());
std::vector<int> prefetch_block_id_list;
std::unordered_map<int, std::string> block_id_to_prefetch_var_name;
auto prefetch_var_name_to_block_id_str =
Attr<std::vector<std::string>>(kPrefetchVarNameToBlockId);
for (const auto &prefetch_var_name_and_id :
prefetch_var_name_to_block_id_str) {
std::vector<std::string> pieces;
split(prefetch_var_name_and_id, ':', &pieces);
VLOG(3) << "after split, prefetch_var = " << pieces[0]
<< ", id=" << pieces[1];
PADDLE_ENFORCE_EQ(pieces.size(), 2);
int block_id = std::stoi(pieces[1]);
prefetch_block_id_list.push_back(block_id);
block_id_to_prefetch_var_name[block_id] = pieces[0];
}
auto prefetch_prepared = executor.Prepare(*program, prefetch_block_id_list);
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>
prefetch_var_name_to_prepared_ctx;
for (size_t i = 0; i < prefetch_block_id_list.size(); ++i) {
auto block_id = prefetch_block_id_list[i];
auto prefetch_var_name = block_id_to_prefetch_var_name[block_id];
prefetch_var_name_to_prepared_ctx[prefetch_var_name] = prefetch_prepared[i];
}
auto f = std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope,
&dev_ctx, &executor, program, prefetch_prepared.release(),
rpc_service_.get());
&dev_ctx, &executor, program,
&prefetch_var_name_to_prepared_ctx, rpc_service_.get());
f(request_send_handler_.get());
f(request_get_handler_.get());
......@@ -276,7 +313,7 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
// Write to a file of server selected port for python use.
SavePort();
if (sync_mode) {
RunSyncLoop(&executor, program, &recv_scope, prefetch_block);
RunSyncLoop(&executor, program, &recv_scope, prefetch_block_id_list);
} else {
RunAsyncLoop(&executor, program);
}
......@@ -302,8 +339,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("sync_mode", "if works at sync_mode or not").SetDefault(true);
AddAttr<framework::BlockDesc *>(kOptimizeBlock,
"BlockID to run on server side.");
AddAttr<framework::BlockDesc *>(kPrefetchBlock,
"prefetch block to run on server side.");
AddAttr<std::vector<std::string>>(kPrefetchVarNameToBlockId,
"prefetch blocks to run on server side.")
.SetDefault({});
AddAttr<int>("Fanin", "How many clients send to this server.")
.SetDefault(1);
}
......
paddle/fluid/operators/listen_and_serv_op.h
浏览文件 @ 1457be3e
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <atomic>
#include <set>
#include <string>
#include <vector>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
......@@ -30,7 +31,7 @@ namespace paddle {
namespace operators {
constexpr char kOptimizeBlock[] = "OptimizeBlock";
constexpr char kPrefetchBlock[] = "PrefetchBlock";
constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
void RunServer(std::shared_ptr<detail::RPCServer> service);
......@@ -46,7 +47,7 @@ class ListenAndServOp : public framework::OperatorBase {
void RunSyncLoop(framework::Executor* executor,
framework::ProgramDesc* program,
framework::Scope* recv_scope,
framework::BlockDesc* prefetch_block) const;
const std::vector<int>& prefetch_block_id_list) const;
void RunAsyncLoop(framework::Executor* executor,
framework::ProgramDesc* program) const;
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 1457be3e
......@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op");
AddOutput("Out", "The output of mean op").Reuse("X");
AddComment(R"DOC(
Mean Operator.
......
paddle/fluid/operators/norm_op.cc
浏览文件 @ 1457be3e
......@@ -16,40 +16,34 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename AttrType>
class NormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"X",
"(Tensor) The input tensor of norm operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature.");
AddInput("Scale",
"(Tensor) The input tensor of norm operator. "
"The format of input tensor is C * 1.");
AddAttr<AttrType>("epsilon",
"(float, default 1e-10) Constant "
"for numerical stability.")
AddInput("X", "(Tensor) A tensor of rank >= axis.");
AddAttr<int>("axis",
"The axis on which to apply normalization. If axis < 0, "
"the dimension to normalization is rank(X) + axis. -1 is "
"the last dimension.");
AddAttr<float>("epsilon",
"(float, default 1e-10) The epsilon value is used "
"to avoid division by zero.")
.SetDefault(1.0e-10f);
AddOutput("Out",
"(Tensor) The output tensor of norm operator."
"N * M."
"M = C * H * W");
AddOutput("Norm",
"(Tensor) A tensor saved the `sqrt(sum(x) + epsion)` will "
"be used in backward kernel.")
.AsIntermediate();
AddOutput("Out", "(Tensor) A tensor of the same shape as X.");
AddComment(R"DOC(
"Input shape: $(N, C, H, W)$
Scale shape: $(C, 1)$
Output shape: $(N, C, H, W)$
Where
forward
$$
[\frac {x_{1}}{\sqrt{\sum{x_{i}^{2}}}} \frac {x_{2}}{\sqrt{\sum{x_{i}^{2}}}} \frac {x_{3}}{\sqrt{\sum{x_{i}^{2}}}} \cdot \cdot \cdot \frac {x_{n}}{\sqrt{\sum{x_{i}^{2}}}}]
$$
backward
$$
\frac{\frac{\mathrm{d}L }{\mathrm{d}y_{1}} - \frac {x_{1}\sum {\frac{\mathrm{d} L}{\mathrm{d} y_{j}}}x_{j}}{\sum x_{j}^{2}} }{\sqrt{\sum{x_{j}^{2}}}}
$$
)DOC");
Given a tensor, apply 2-normalization along the provided axis.
$$
y = \frac{x}{ \sqrt{\sum {x^2} + epsion }}
$$
where, $\sum {x^2}$ is calculated along the `axis` dimension.
)DOC");
}
};
......@@ -58,15 +52,15 @@ class NormOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of NormOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Scale"),
"Input(Scale) of NormOp"
"should not be null.");
"Input(X) of NormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of NormOp should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", in_x_dims);
auto xdim = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", xdim);
int axis = ctx->Attrs().Get<int>("axis");
if (axis < 0) axis = xdim.size() + axis;
xdim[axis] = 1;
ctx->SetOutputDim("Norm", xdim);
}
};
......@@ -84,12 +78,12 @@ class NormOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(norm, ops::NormOp, ops::NormOpMaker<float>,
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(norm, ops::NormOp, ops::NormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(norm_grad, ops::NormOpGrad);
REGISTER_OP_CPU_KERNEL(
norm, ops::NormKernel<paddle::platform::CPUDeviceContext, float>,
ops::NormKernel<paddle::platform::CPUDeviceContext, double, float>);
REGISTER_OP_CPU_KERNEL(
norm_grad, ops::NormGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::NormGradKernel<paddle::platform::CPUDeviceContext, double, float>);
REGISTER_OP_CPU_KERNEL(norm, ops::NormKernel<CPU, float>,
ops::NormKernel<CPU, double>);
REGISTER_OP_CPU_KERNEL(norm_grad, ops::NormGradKernel<CPU, float>,
ops::NormGradKernel<CPU, double>);
paddle/fluid/operators/norm_op.cu
浏览文件 @ 1457be3e
......@@ -16,9 +16,9 @@ limitations under the License. */
#include "paddle/fluid/operators/norm_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
norm, ops::NormKernel<paddle::platform::CUDADeviceContext, float>,
ops::NormKernel<paddle::platform::CUDADeviceContext, double, float>);
REGISTER_OP_CUDA_KERNEL(
norm_grad, ops::NormGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::NormGradKernel<paddle::platform::CUDADeviceContext, double, float>);
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(norm, ops::NormKernel<CUDA, float>,
ops::NormKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(norm_grad, ops::NormGradKernel<CUDA, float>,
ops::NormGradKernel<CUDA, double>);
paddle/fluid/operators/norm_op.h
浏览文件 @ 1457be3e
......@@ -19,156 +19,110 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T, typename AttrType = T>
inline void GetDims(const framework::DDim& dim, int axis, int* pre, int* n,
int* post) {
*pre = 1;
*post = 1;
*n = dim[axis];
for (int i = 0; i < axis; ++i) {
(*pre) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*post) *= dim[i];
}
}
template <typename DeviceContext, typename T>
class NormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* scale = context.Input<framework::Tensor>("Scale");
auto* out = context.Output<framework::Tensor>("Out");
auto epsilon = static_cast<T>(context.Attr<AttrType>("epsilon"));
out->mutable_data<T>(context.GetPlace());
int batch_size = in_x->dims()[0];
int channels = in_x->dims()[1];
int height = in_x->dims()[2];
int width = in_x->dims()[3];
int fea_len = height * width;
auto* place =
context.template device_context<DeviceContext>().eigen_device();
auto x =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
*in_x, framework::make_ddim({batch_size, fea_len * channels}));
// get square
framework::Tensor x_square;
x_square.mutable_data<T>(in_x->dims(), context.GetPlace());
auto x_square_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
x_square, framework::make_ddim({batch_size, fea_len * channels}));
x_square_eigen.device(*place) = x.square();
auto scale_eigen =
framework::EigenVector<T, Eigen::RowMajor, Eigen::DenseIndex>::Flatten(
*scale);
for (int n = 0; n < batch_size; ++n) {
framework::Tensor in_x_batch = in_x->Slice(n, n + 1);
auto in_x_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
in_x_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor x_square_batch = x_square.Slice(n, n + 1);
auto x_square_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
x_square_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor out_batch = out->Slice(n, n + 1);
auto out_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
out_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor tmp_tensor;
tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
context.GetPlace());
auto tmp = framework::EigenVector<T, Eigen::RowMajor,
Eigen::DenseIndex>::Flatten(tmp_tensor);
// get colsum and sqrt , inverse
auto dim = Eigen::array<int, 1>({{0}});
tmp.device(*place) = x_square_batch_eigen.sum(dim);
tmp.device(*place) = (tmp + epsilon).sqrt().inverse();
Eigen::array<int, 2> broadcast_dim_col;
broadcast_dim_col[1] = 1;
broadcast_dim_col[0] = channels;
out_batch_eigen.device(*place) =
in_x_batch_eigen * (tmp.broadcast(broadcast_dim_col));
Eigen::array<int, 2> broadcast_dim_row;
broadcast_dim_row[1] = fea_len;
broadcast_dim_row[0] = 1;
out_batch_eigen.device(*place) =
out_batch_eigen * (scale_eigen.broadcast(broadcast_dim_row));
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* out_y = ctx.Output<framework::Tensor>("Out");
auto* out_norm = ctx.Output<framework::Tensor>("Norm");
out_y->mutable_data<T>(ctx.GetPlace());
out_norm->mutable_data<T>(ctx.GetPlace());
auto xdim = in_x->dims();
auto ndim = out_norm->dims();
T eps = static_cast<T>(ctx.Attr<float>("epsilon"));
int axis = ctx.Attr<int>("axis");
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 2> norm_shape(pre, post);
auto x_e = framework::EigenVector<T>::Flatten(*in_x);
auto y_e = framework::EigenVector<T>::Flatten(*out_y);
auto norm_e = framework::EigenVector<T>::Flatten(*out_norm);
auto x = x_e.reshape(shape);
auto y = y_e.reshape(shape);
auto norm = norm_e.reshape(norm_shape);
Eigen::DSizes<int, 1> rdim(1);
// y = x / sqrt((sum(x * x) + epsilon))
// norm = sqrt(sum(x * x) + epsilon)
auto sum = x.pow(2).sum(rdim) + eps;
norm.device(*place) = sum.sqrt();
// y = x / norm
Eigen::DSizes<int, 3> rshape(pre, 1, post);
Eigen::DSizes<int, 3> bcast(1, n, 1);
y.device(*place) = x / norm.reshape(rshape).broadcast(bcast);
}
};
template <typename DeviceContext, typename T, typename AttrType = T>
class NormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* scale = context.Input<framework::Tensor>("Scale");
const framework::Tensor* out_grad =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto epsilon = static_cast<T>(context.Attr<AttrType>("epsilon"));
framework::Tensor* in_x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
in_x_grad->mutable_data<T>(context.GetPlace());
int batch_size = in_x->dims()[0];
int channels = in_x->dims()[1];
int height = in_x->dims()[2];
int width = in_x->dims()[3];
int fea_len = height * width;
auto* place =
context.template device_context<DeviceContext>().eigen_device();
auto scale_eigen =
framework::EigenVector<T, Eigen::RowMajor, Eigen::DenseIndex>::Flatten(
*scale);
auto x =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
*in_x, framework::make_ddim({batch_size, fea_len * channels}));
// get square
framework::Tensor x_square;
x_square.mutable_data<T>(in_x->dims(), context.GetPlace());
auto x_square_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
x_square, framework::make_ddim({batch_size, fea_len * channels}));
x_square_eigen.device(*place) = x.square();
for (int n = 0; n < batch_size; ++n) {
framework::Tensor in_x_batch = in_x->Slice(n, n + 1);
auto in_x_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
in_x_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor in_g_batch = in_x_grad->Slice(n, n + 1);
auto in_g_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
in_g_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor x_square_batch = x_square.Slice(n, n + 1);
auto x_square_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
x_square_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor outg_batch = out_grad->Slice(n, n + 1);
auto outg_batch_eigen =
framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
outg_batch, framework::make_ddim({channels, fea_len}));
framework::Tensor tmp_tensor;
tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
context.GetPlace());
auto tmp_eigen =
framework::EigenVector<T, Eigen::RowMajor,
Eigen::DenseIndex>::Flatten(tmp_tensor);
auto dim = Eigen::array<int, 1>({{0}});
tmp_eigen.device(*place) = (in_x_batch_eigen * outg_batch_eigen).sum(dim);
framework::Tensor norm_tmp_tensor;
norm_tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
context.GetPlace());
auto norm_tmp_eigen =
framework::EigenVector<T, Eigen::RowMajor,
Eigen::DenseIndex>::Flatten(norm_tmp_tensor);
norm_tmp_eigen.device(*place) =
(x_square_batch_eigen.sum(dim) + epsilon).sqrt();
Eigen::array<int, 2> broadcast_dim_col;
broadcast_dim_col[1] = 1;
broadcast_dim_col[0] = channels;
in_g_batch_eigen.device(*place) =
in_x_batch_eigen * tmp_eigen.broadcast(broadcast_dim_col);
in_g_batch_eigen.device(*place) =
in_g_batch_eigen /
(norm_tmp_eigen * norm_tmp_eigen).broadcast(broadcast_dim_col);
in_g_batch_eigen.device(*place) = outg_batch_eigen - in_g_batch_eigen;
// outg_batch_eigen + (in_g_batch_eigen * -1);
in_g_batch_eigen.device(*place) =
in_g_batch_eigen / norm_tmp_eigen.broadcast(broadcast_dim_col);
Eigen::array<int, 2> broadcast_dim_row;
broadcast_dim_row[1] = fea_len;
broadcast_dim_row[0] = 1;
in_g_batch_eigen.device(*place) =
in_g_batch_eigen * (scale_eigen.broadcast(broadcast_dim_row));
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* in_norm = ctx.Input<framework::Tensor>("Norm");
auto* in_dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out_dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
out_dx->mutable_data<T>(ctx.GetPlace());
auto xdim = in_x->dims();
int axis = ctx.Attr<int>("axis");
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
auto x_e = framework::EigenVector<T>::Flatten(*in_x);
auto dy_e = framework::EigenVector<T>::Flatten(*in_dy);
auto norm_e = framework::EigenVector<T>::Flatten(*in_norm);
auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 2> norm_shape(pre, post);
auto x = x_e.reshape(shape);
auto dy = dy_e.reshape(shape);
auto norm = norm_e.reshape(norm_shape);
auto dx = dx_e.reshape(shape);
framework::Tensor rsum;
rsum.mutable_data<T>({pre, post}, ctx.GetPlace());
auto sum = framework::EigenTensor<T, 2>::From(rsum);
Eigen::DSizes<int, 1> rdim(1);
Eigen::DSizes<int, 3> bcast(1, n, 1);
Eigen::DSizes<int, 3> rshape(pre, 1, post);
// dx = ( dy/sqrt(sum(x*x)) ) * [1 - x*sum(x) / (sum(x*x) + e)]
// = [dy - dy * x * sum(x) / (sum(x*x) + e)] / sqrt(sum(x*x))
// = [dy - x * sum(x*dy) / (sum(x*x) + e)] / sqrt(sum(x*x))
// 1. sum = sum(x*dy)
sum.device(*place) = (x * dy).sum(rdim);
// 2. dx = x * sum
dx.device(*place) = sum.reshape(rshape).broadcast(bcast) * x;
// 3. dx / (sum(x*x) + e)
// where, norm.pow(2) = sum(x*x) + e, which is calculated in forward.
dx.device(*place) = dx / norm.pow(2).broadcast(bcast);
// 4. [dy - dx] / sqrt(sum(x*x))
dx.device(*place) = (dy - dx) / norm.broadcast(bcast);
}
};
} // namespace operators
......
paddle/fluid/operators/pool_mkldnn_op.cc
浏览文件 @ 1457be3e
......@@ -18,9 +18,14 @@ limitations under the License. */
namespace paddle {
namespace operators {
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::pooling_forward;
using framework::DataLayout;
using mkldnn::memory;
using mkldnn::pooling_backward;
using mkldnn::pooling_forward;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using platform::to_void_cast;
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
......@@ -55,8 +60,9 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const Tensor* input = ctx.Input<Tensor>("X");
Tensor* output = ctx.Output<Tensor>("Out");
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when saving info into device context
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
input->format() != memory::format::format_undef,
"Wrong layout/format set for Input tensor");
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
......@@ -82,6 +88,9 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
auto input_format = input->format();
memory::format output_format{memory::format::format_undef};
const std::string key = gethash(src_tz, pooling_type, ksize, strides,
paddings, ctx.op().Output("Out"));
const std::string key_pool_p = key + "@pool_p";
......@@ -94,16 +103,17 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto pool_p =
std::static_pointer_cast<pooling_forward>(dev_ctx.GetBlob(key_pool_p));
if (pool_p == nullptr) {
// TODO(pzelazko-intel): support more formats
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), input_format);
auto src_md =
platform::MKLDNNMemDesc(src_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
auto dst_md =
platform::MKLDNNMemDesc(dst_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
/* create memory descriptor for pooling without specified format
* ('any') which lets a primitive (pooling in this case) choose
* the memory format preferred for best performance
*/
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::any);
std::shared_ptr<pooling_forward::primitive_desc> pool_pd =
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
pooling_type, mkldnn_engine);
......@@ -116,20 +126,22 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
// save pool_workspace_memory to be referred in backward path
dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
auto pool_src_memory_p = std::make_shared<memory>(
memory::primitive_desc{src_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(input_data)));
dev_ctx.SetBlob(key_pool_src_mem_p, pool_src_memory_p);
auto src_memory = std::make_shared<memory>(pool_pd->src_primitive_desc(),
to_void_cast<T>(input_data));
auto dst_memory =
std::make_shared<memory>(pool_pd->dst_primitive_desc(), output_data);
auto pool_dst_memory_p = std::make_shared<memory>(
memory::primitive_desc{dst_md, mkldnn_engine},
static_cast<void*>(output_data));
dev_ctx.SetBlob(key_pool_dst_mem_p, pool_dst_memory_p);
dev_ctx.SetBlob(key_pool_src_mem_p, src_memory);
dev_ctx.SetBlob(key_pool_dst_mem_p, dst_memory);
pool_p = std::make_shared<pooling_forward>(*pool_pd, *(src_memory.get()),
*(dst_memory.get()),
*workspace_memory);
pool_p = std::make_shared<pooling_forward>(
*pool_pd, *(pool_src_memory_p.get()), *(pool_dst_memory_p.get()),
*workspace_memory);
dev_ctx.SetBlob(key_pool_p, pool_p);
output_format =
(memory::format)dst_memory->get_primitive_desc().desc().data.format;
} else {
// Primitives already exist
auto pool_src_memory_p =
......@@ -140,14 +152,20 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_dst_mem_p));
PADDLE_ENFORCE(pool_dst_memory_p != nullptr,
"Fail to find pooling dst mem_p in device context");
pool_src_memory_p->set_data_handle(
reinterpret_cast<void*>(const_cast<T*>(input_data)));
pool_src_memory_p->set_data_handle(to_void_cast<T>(input_data));
pool_dst_memory_p->set_data_handle(output_data);
output_format = (memory::format)pool_dst_memory_p->get_primitive_desc()
.desc()
.data.format;
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(output_format);
}
private:
......@@ -194,6 +212,13 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
PADDLE_ENFORCE(in_x->layout() == DataLayout::kMKLDNN &&
in_x->format() != memory::format::format_undef,
"Wrong layout/format set for Input X tensor");
PADDLE_ENFORCE(out_grad->layout() == DataLayout::kMKLDNN &&
out_grad->format() != memory::format::format_undef,
"Wrong layout/format set for Input output_grad tensor");
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
......@@ -212,6 +237,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const T* out_grad_data = out_grad->data<T>();
T* in_x_grad_data = in_x_grad->mutable_data<T>(ctx.GetPlace());
memory::format in_x_grad_format{memory::format::format_undef};
std::vector<int> diff_src_tz =
paddle::framework::vectorize2int(in_x_grad->dims());
......@@ -225,39 +251,48 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const std::string key_pool_bwd_p = key + "@pool_bwd_p";
const std::string key_pool_diff_src_mem_p = key + "@pool_diff_src_mem_p";
const std::string key_pool_diff_dst_mem_p = key + "@pool_diff_dst_mem_p";
const std::string key_pool_src_mem_p = key + "@pool_src_mem_p";
const std::string key_pool_dst_mem_p = key + "@pool_dst_mem_p";
const std::string key_pool_pd = key + "@pool_pd";
const std::string key_pool_workspace_memory =
key + "@pool_workspace_memory";
auto user_diff_dst_memory =
memory({{{diff_dst_tz}, memory::data_type::f32, out_grad->format()},
mkldnn_engine},
to_void_cast<T>(out_grad_data));
std::shared_ptr<memory> diff_src_memory;
std::shared_ptr<memory> diff_dst_memory;
auto dst_memory =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_dst_mem_p));
PADDLE_ENFORCE(dst_memory != nullptr,
"Fail to find dst_memory in device context");
primitive reorder_diff_dst;
bool is_diff_dst_reordered = false;
auto pool_bwd_p = std::static_pointer_cast<pooling_backward>(
dev_ctx.GetBlob(key_pool_bwd_p));
if (pool_bwd_p == nullptr) {
auto diff_src_md =
platform::MKLDNNMemDesc(diff_src_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
auto diff_dst_md =
platform::MKLDNNMemDesc(diff_dst_tz, platform::MKLDNNGetDataType<T>(),
mkldnn::memory::format::nchw);
// Retrieve src_memory/dst_memory saved in forward pass
auto src_memory =
std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_src_mem_p));
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find src_memory in device context");
// Retrieve pool_pd/pool_workspace_memory from device context
auto pool_pd =
std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
dev_ctx.GetBlob(key_pool_pd));
PADDLE_ENFORCE(pool_pd != nullptr,
"Fail to find pool_pd in device context");
auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
auto workspace_memory = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_pool_workspace_memory));
PADDLE_ENFORCE(workspace_memory != nullptr,
"Fail to find workspace_memory in device context");
auto pool_diff_src_memory_p = std::make_shared<memory>(memory(
{diff_src_md, mkldnn_engine}, static_cast<void*>(in_x_grad_data)));
dev_ctx.SetBlob(key_pool_diff_src_mem_p, pool_diff_src_memory_p);
auto pool_diff_dst_memory_p = std::make_shared<memory>(
memory({diff_dst_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(out_grad_data))));
dev_ctx.SetBlob(key_pool_diff_dst_mem_p, pool_diff_dst_memory_p);
// create memory descriptors for pooling
auto diff_src_md = src_memory.get()->get_primitive_desc().desc();
auto diff_dst_md = dst_memory.get()->get_primitive_desc().desc();
auto pool_bwd_desc = mkldnn::pooling_backward::desc(
pooling_type == "max" ? mkldnn::algorithm::pooling_max
......@@ -267,35 +302,74 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
pool_bwd_desc, mkldnn_engine, *pool_pd);
// reorder between user_diff_dst and pool diff_dst if needed
diff_dst_memory = std::make_shared<memory>(user_diff_dst_memory);
if (memory::primitive_desc(dst_memory->get_primitive_desc()) !=
user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory =
std::make_shared<memory>(dst_memory.get()->get_primitive_desc());
reorder_diff_dst = reorder(user_diff_dst_memory, *diff_dst_memory);
is_diff_dst_reordered = true;
}
diff_src_memory = std::make_shared<memory>(
pool_bwd_pd.diff_src_primitive_desc(), in_x_grad_data);
dev_ctx.SetBlob(key_pool_diff_src_mem_p, diff_src_memory);
dev_ctx.SetBlob(key_pool_diff_dst_mem_p, diff_dst_memory);
pool_bwd_p = std::make_shared<pooling_backward>(
pool_bwd_pd, *(pool_diff_dst_memory_p.get()), *workspace_memory,
*(pool_diff_src_memory_p));
pool_bwd_pd, *(diff_dst_memory.get()), *workspace_memory,
*(diff_src_memory));
dev_ctx.SetBlob(key_pool_bwd_p, pool_bwd_p);
} else {
// Primitives already exist
auto pool_diff_src_memory_p = std::static_pointer_cast<memory>(
diff_src_memory = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_pool_diff_src_mem_p));
PADDLE_ENFORCE(pool_diff_src_memory_p != nullptr,
PADDLE_ENFORCE(diff_src_memory != nullptr,
"Fail to find pooling src mem_p in device context");
auto pool_diff_dst_memory_p = std::static_pointer_cast<memory>(
diff_dst_memory = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_pool_diff_dst_mem_p));
PADDLE_ENFORCE(pool_diff_dst_memory_p != nullptr,
PADDLE_ENFORCE(diff_dst_memory != nullptr,
"Fail to find pooling dst mem_p in device context");
pool_diff_src_memory_p->set_data_handle(
reinterpret_cast<void*>(in_x_grad_data));
pool_diff_dst_memory_p->set_data_handle(const_cast<T*>(out_grad_data));
diff_src_memory->set_data_handle(reinterpret_cast<void*>(in_x_grad_data));
diff_dst_memory->set_data_handle(const_cast<T*>(out_grad_data));
// reorder between user_diff_dst and pool diff_dst if needed
if (memory::primitive_desc(dst_memory->get_primitive_desc()) !=
user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory =
std::make_shared<memory>(dst_memory.get()->get_primitive_desc());
reorder_diff_dst = reorder(user_diff_dst_memory, *diff_dst_memory);
is_diff_dst_reordered = true;
}
}
in_x_grad_format = (memory::format)diff_src_memory->get_primitive_desc()
.desc()
.data.format;
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{*(pool_bwd_p.get())};
std::vector<mkldnn::primitive> pipeline;
if (is_diff_dst_reordered) {
pipeline.push_back(reorder_diff_dst);
}
pipeline.push_back(*(pool_bwd_p.get()));
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
in_x_grad->set_layout(DataLayout::kMKLDNN);
in_x_grad->set_format(in_x_grad_format);
} // Compute()
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(pool2d, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::PoolMKLDNNOpKernel<float>);
ops::PoolMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(pool2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::PoolMKLDNNGradOpKernel<float>);
ops::PoolMKLDNNGradOpKernel<float>);
paddle/fluid/operators/pool_op.cc
浏览文件 @ 1457be3e
......@@ -151,7 +151,8 @@ void Pool2dOpMaker::Make() {
"The format of output tensor is also 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.");
"and W is the width of the feature.")
.Reuse("X");
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
......@@ -244,7 +245,8 @@ void Pool3dOpMaker::Make() {
"The format of output tensor is also NCDHW, "
"where N is batch size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.");
"width of the feature, respectively.")
.Reuse("X");
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
......
paddle/fluid/operators/prefetch_op.cc
浏览文件 @ 1457be3e
......@@ -18,7 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/send_recv_util.h"
namespace paddle {
......@@ -42,7 +42,7 @@ class PrefetchOp : public framework::OperatorBase {
auto& ctx = *pool.Get(place);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
......
paddle/fluid/operators/recv_op.cc
浏览文件 @ 1457be3e
......@@ -19,8 +19,7 @@ limitations under the License. */
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
......@@ -45,7 +44,7 @@ class RecvOp : public framework::OperatorBase {
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < outs.size(); i++) {
VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
......@@ -78,9 +77,15 @@ This operator can get variables from server side.
}
};
class RecvOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(recv, ops::RecvOp, ops::RecvOpMaker);
REGISTER_OPERATOR(recv, ops::RecvOp, paddle::framework::EmptyGradOpMaker,
ops::RecvOpMaker, ops::RecvOpShapeInference);
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ 1457be3e
......@@ -19,8 +19,8 @@ limitations under the License. */
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
......@@ -45,7 +45,7 @@ class SendBarrierOp : public framework::OperatorBase {
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient::GetInstance<RPCCLIENT_T>();
VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
......
paddle/fluid/operators/send_op.cc
浏览文件 @ 1457be3e
......@@ -16,10 +16,9 @@ limitations under the License. */
#include <ostream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/send_recv_util.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -36,12 +35,9 @@ class SendOp : public framework::OperatorBase {
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
auto ins = Inputs("X");
auto outs = Outputs("Out");
std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
std::vector<std::string> endpoints =
Attr<std::vector<std::string>>("endpoints");
bool sync_mode = Attr<bool>("sync_mode");
std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
int sync_send = Attr<int>("sync_mode");
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
......@@ -50,37 +46,19 @@ class SendOp : public framework::OperatorBase {
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
// TODO(Yancey1989): we need to use an IO threadpool which has
// a larger number of threads than the computing threadpool.
rpc_client->AsyncSendVar(epmap[i], ctx, scope, ins[i]);
} else {
VLOG(3) << "don't send no-initialied variable: " << ins[i];
}
}
rpc_client->Wait();
if (sync_mode) {
for (auto& ep : endpoints) {
VLOG(3) << "batch barrier, ep: " << ep;
rpc_client->AsyncSendBatchBarrier(ep);
}
rpc_client->Wait();
}
if (outs.size() > 0) {
for (size_t i = 0; i < outs.size(); i++) {
VLOG(2) << "getting " << outs[i] << " from " << epmap[i];
rpc_client->AsyncGetVar(epmap[i], ctx, scope, outs[i]);
}
rpc_client->Wait();
// tell pservers that current trainer have called fetch
for (auto& ep : endpoints) {
VLOG(2) << "send fetch barrier, ep: " << ep;
rpc_client->AsyncSendFetchBarrier(ep);
}
if (sync_send) {
rpc_client->Wait();
}
}
......@@ -89,26 +67,22 @@ class SendOp : public framework::OperatorBase {
class SendOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "(Tensor) Input tensor to be sent").AsDuplicable();
AddOutput("Out", "(Tensor) Output tensor to be received from server")
AddInput("X", "(Tensor, SelectedRows) Input variables to be sent")
.AsDuplicable();
AddComment(R"DOC(
Send operator
This operator will send tensor to recv_op at the parameter server.
This operator will send variables to listen_and_serve op at the parameter server.
)DOC");
// TODO(typhoonzero): remove this attr generate de-duplicated vector from
// epmap when initializing.
AddAttr<std::vector<std::string>>("endpoints",
"(string vector, default 127.0.0.1:6164)"
"Server endpoints to send variables to.")
.SetDefault({});
AddAttr<int>("sync_mode",
"(int, default 0)"
"sync send or async send.")
.SetDefault(0);
AddAttr<std::vector<std::string>>("epmap",
"(string vector, default 127.0.0.1:6164)"
"Server endpoints in the order of input "
"variables for mapping")
.SetDefault({});
AddAttr<bool>("sync_mode", "work in sync_mode or not").SetDefault(true);
.SetDefault({"127.0.0.1:6164"});
}
};
......
paddle/fluid/operators/send_vars_op.cc 已删除 100644 → 0
浏览文件 @ e2433207
/* 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 <future> // NOLINT
#include <ostream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/send_recv_util.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
class SendVarsOp : public framework::OperatorBase {
public:
SendVarsOp(const std::string& type, const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
auto ins = Inputs("X");
std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
int sync_send = Attr<int>("sync_send");
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
// TODO(Yancey1989): we need to use an IO threadpool which has
// a larger number of threads than the computing threadpool.
rpc_client->AsyncSendVar(epmap[i], ctx, scope, ins[i]);
} else {
VLOG(3) << "don't send no-initialied variable: " << ins[i];
}
}
if (sync_send) {
rpc_client->Wait();
}
}
};
class SendVarsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "(Tensor, SelectedRows) Input variables to be sent")
.AsDuplicable();
AddComment(R"DOC(
Send operator
This operator will send variables to listen_and_serve op at the parameter server.
)DOC");
AddAttr<int>("sync_send",
"(int, default 0)"
"sync send or async send.")
.SetDefault(0);
AddAttr<std::vector<std::string>>("epmap",
"(string vector, default 127.0.0.1:6164)"
"Server endpoints in the order of input "
"variables for mapping")
.SetDefault({"127.0.0.1:6164"});
}
};
class SendVarsOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(send_vars, ops::SendVarsOp,
paddle::framework::EmptyGradOpMaker, ops::SendVarsOpMaker,
ops::SendVarsOpShapeInference);
paddle/fluid/operators/sgd_op.cc
浏览文件 @ 1457be3e
......@@ -74,7 +74,8 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
AddOutput("ParamOut",
"(Tensor or SelectedRows, same with Param) "
"Output parameter, should share the same memory with Param");
"Output parameter, should share the same memory with Param")
.Reuse("Param");
AddComment(R"DOC(
SGD operator
......
paddle/fluid/operators/slice_op.cc 0 → 100644
浏览文件 @ 1457be3e
/* 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/slice_op.h"
#include <algorithm>
#include <vector>
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class SliceOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input (Input) of slice op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output (Out) of slice op should not be null.");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE(in_dims.size() < 7,
"The rank of input should be less than 7.");
framework::DDim out_dims(in_dims);
auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
PADDLE_ENFORCE_EQ(starts.size(), ends.size());
PADDLE_ENFORCE_EQ(starts.size(), axes.size());
int dim_value, start, end;
for (size_t i = 0; i < axes.size(); ++i) {
dim_value = out_dims[axes[i]];
start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
start = std::max(start, 0);
end = std::max(end, 0);
start = std::min(start, dim_value);
end = std::min(end, dim_value);
start = std::min(start, end);
out_dims[axes[i]] = end - start;
}
ctx->SetOutputDim("Out", out_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
ctx.GetPlace());
}
};
class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input", "Tensor of data to extract slices from.");
AddOutput("Out", "Sliced data tensor.");
AddAttr<std::vector<int>>(
"axes",
"(list<int>) Axes that `starts` and `ends` apply to. It's optional."
"If not present, will be treated as [0, 1, ..., len(`starts`) - 1].");
AddAttr<std::vector<int>>(
"starts",
"(list<int>) Starting indices of corresponding axis in `axes`");
AddAttr<std::vector<int>>(
"ends",
"(list<int>) Starting indices of corresponding axis in `axes`.");
AddComment(R"DOC(
Slice Operator.
Produces a slice of the input tensor along multiple axes. Similar to numpy:
https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Slice uses `axes`, `starts` and `ends` attributes to specify the start and
end dimension for each axis in the list of axes, it uses this information
to slice the input data tensor. If a negative value is passed for any of
the start or end indices, it represents number of elements before the end
of that dimension. If the value passed to start or end is larger than
the n (the number of elements in this dimension), it represents n.
For slicing to the end of a dimension with unknown size, it is recommended
to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1].
Example 1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Example 2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
starts = [0, 1]
ends = [-1, 1000]
Then:
result = [ [2, 3, 4], ]
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(slice, ops::SliceOp, ops::SliceOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
slice, ops::SliceKernel<paddle::platform::CPUDeviceContext, int>,
ops::SliceKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::SliceKernel<paddle::platform::CPUDeviceContext, float>,
ops::SliceKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/slice_op.cu 0 → 100644
浏览文件 @ 1457be3e
/* 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/slice_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
slice, ops::SliceKernel<paddle::platform::CUDADeviceContext, float>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, double>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, int>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/slice_op.h 0 → 100644
浏览文件 @ 1457be3e
/* 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 <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SliceKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
int rank = ctx.Input<framework::Tensor>("Input")->dims().size();
switch (rank) {
case 1:
SliceCompute<1>(ctx);
break;
case 2:
SliceCompute<2>(ctx);
break;
case 3:
SliceCompute<3>(ctx);
break;
case 4:
SliceCompute<4>(ctx);
break;
case 5:
SliceCompute<5>(ctx);
break;
case 6:
SliceCompute<6>(ctx);
break;
}
}
private:
template <size_t D>
void SliceCompute(const framework::ExecutionContext& context) const {
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
auto in = context.Input<framework::Tensor>("Input");
auto out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto out_dims = out->dims();
auto in_dims = in->dims();
auto axes = context.Attr<std::vector<int>>("axes");
auto starts = context.Attr<std::vector<int>>("starts");
auto offsets = Eigen::array<int, D>();
auto extents = Eigen::array<int, D>();
for (size_t i = 0; i < D; ++i) {
offsets[i] = 0;
extents[i] = out_dims[i];
}
int start;
for (size_t i = 0; i < axes.size(); ++i) {
start = starts[i];
if (start < 0) {
start = (start + in_dims[axes[i]]);
}
start = std::max(start, 0);
offsets[axes[i]] = start;
}
auto in_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*in);
auto out_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*out);
out_t.device(place) = in_t.slice(offsets, extents);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 1457be3e
......@@ -83,7 +83,8 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"The input tensor of softmax. "
"2-D with shape [batch_size, input_feature_dimensions].");
AddOutput("Out", "The normalized values with the same shape as X.");
AddOutput("Out", "The normalized values with the same shape as X.")
.Reuse("X");
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......
paddle/fluid/operators/sum_op.cc
浏览文件 @ 1457be3e
......@@ -115,7 +115,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.");
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddComment(R"DOC(
Sum operator.
......
paddle/fluid/operators/test_send_nccl_id.cc
浏览文件 @ 1457be3e
......@@ -20,8 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -29,6 +28,10 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#include "paddle/fluid/string/printf.h"
#ifdef PADDLE_WITH_GRPC
#include "paddle/fluid/operators/send_recv_util.h"
#endif
USE_NO_KERNEL_OP(listen_and_serv);
namespace f = paddle::framework;
......@@ -37,7 +40,7 @@ namespace m = paddle::operators::math;
namespace detail = paddle::operators::detail;
namespace string = paddle::string;
std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
std::unique_ptr<detail::RPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
void StartServer() {
......@@ -58,7 +61,7 @@ void StartServer() {
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
g_rpc_service->SetCond(detail::kRequestSend);
g_rpc_service->WaitBarrier(detail::kRequestSend);
......@@ -68,9 +71,9 @@ void StartServer() {
server_thread.join();
}
TEST(SendNcclId, GrpcServer) {
TEST(SendNcclId, RPCServer) {
g_req_handler.reset(new detail::RequestSendHandler(true));
g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
std::thread server_thread(StartServer);
g_rpc_service->WaitServerReady();
......@@ -87,8 +90,9 @@ TEST(SendNcclId, GrpcServer) {
int port = g_rpc_service->GetSelectedPort();
std::string ep = string::Sprintf("127.0.0.1:%d", port);
detail::RPCClient* client =
detail::RPCClient::GetInstance<detail::GRPCClient>();
detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
LOG(INFO) << "connect to server" << ep;
client->AsyncSendVar(ep, dev_ctx, scope, NCCL_ID_VARNAME);
client->Wait();
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 1457be3e
......@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
AddComment(R"DOC(
Top K operator
......
paddle/fluid/platform/device_context.h
浏览文件 @ 1457be3e
......@@ -11,6 +11,7 @@ limitations under the License. */
#pragma once
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <unordered_map>
#include <vector>
......@@ -100,6 +101,7 @@ class CUDADeviceContext : public DeviceContext {
template <typename Callback>
void RecordEvent(cudaEvent_t ev, Callback callback) {
std::lock_guard<std::mutex> guard(mtx_);
callback();
PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
}
......@@ -116,6 +118,8 @@ class CUDADeviceContext : public DeviceContext {
int compute_capability;
int multi_process;
int max_threads_per_mp;
std::mutex mtx_;
};
template <>
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 1457be3e
......@@ -413,6 +413,9 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::Executor>(m, "Executor")
.def(py::init<const platform::Place &>())
#ifdef PADDLE_WITH_DISTRIBUTE
.def("complete", &Executor::Complete)
#endif
.def("run",
(void (Executor::*)(const ProgramDesc &, Scope *, int, bool, bool)) &
Executor::Run);
......@@ -509,10 +512,10 @@ All parameter, weight, gradient are variables in Paddle.
self.num_threads_ = num_threads;
})
.def_property(
"use_event",
[](const ExecutionStrategy &self) { return self.use_event_; },
[](ExecutionStrategy &self, bool use_event) {
self.use_event_ = use_event;
"use_cuda",
[](const ExecutionStrategy &self) { return self.use_cuda_; },
[](ExecutionStrategy &self, bool use_cuda) {
self.use_cuda_ = use_cuda;
})
.def_property(
"allow_op_delay",
......
paddle/scripts/paddle_build.sh
浏览文件 @ 1457be3e
......@@ -181,6 +181,7 @@ function build() {
============================================
EOF
make clean
make -j `nproc`
make install -j `nproc`
}
......
python/paddle/dataset/flowers.py
浏览文件 @ 1457be3e
......@@ -119,7 +119,8 @@ def reader_creator(data_file,
yield sample, int(label) - 1
if use_xmap:
return xmap_readers(mapper, reader, cpu_count(), buffered_size)
cpu_num = int(os.environ.get('CPU_NUM', cpu_count()))
return xmap_readers(mapper, reader, cpu_num, buffered_size)
else:
return map_readers(mapper, reader)
......
python/paddle/fluid/data_feeder.py
浏览文件 @ 1457be3e
......@@ -15,6 +15,7 @@
from __future__ import print_function
import core
import numpy
import os
import six.moves as six
import multiprocessing
......@@ -150,7 +151,9 @@ class DataFeeder(object):
elif isinstance(self.place, core.CUDAPlace):
return core.get_cuda_device_count()
else:
return multiprocessing.cpu_count()
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
return cpu_num
def decorate_reader(self,
reader,
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 1457be3e
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
All layers just related to the neural network.
All layers just related to the neural network.
"""
from ..layer_helper import LayerHelper
......@@ -95,7 +95,6 @@ def fc(input,
num_flatten_dims=1,
param_attr=None,
bias_attr=None,
use_cudnn=False,
use_mkldnn=False,
act=None,
is_test=False,
......@@ -222,6 +221,7 @@ def embedding(input,
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed (bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output
with zeros whenever lookup encounters it in :attr:`input`. If
......@@ -337,12 +337,12 @@ def dynamic_lstm(input,
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
- The shape is (1 x 7D).
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
......@@ -654,8 +654,9 @@ def dynamic_gru(input,
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
activation(str): The activation for candidate hidden state.
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
h_0 (Variable): The hidden output of the first time step.
Returns:
Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
......@@ -877,11 +878,11 @@ def cos_sim(X, Y):
${comment}
Args:
X(${x_type}): ${x_comment}
Y(${y_type}): ${x_comment}
X (Variable): ${x_comment}
Y (Variable): ${x_comment}
Returns:
A Variable contains the output of this layer.
Variable: the output of cosine(X, Y).
"""
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype)
......@@ -908,15 +909,15 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
unchanged.
Args:
x(variable): The input tensor.
dropout_prob(float): Probability of setting units to zero.
is_test(bool): A flag indicating whether it is in test phrase or not.
seed(int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
x (Variable): The input tensor.
dropout_prob (float): Probability of setting units to zero.
is_test (bool): A flag indicating whether it is in test phrase or not.
seed (int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: A tensor variable.
......@@ -1038,8 +1039,8 @@ def square_error_cost(input, label):
* :math:`Out`: Output value, same shape with :math:`X`.
Args:
input(Variable): Input tensor, has predictions.
label(Variable): Label tensor, has target labels.
input (Variable): Input tensor, has predictions.
label (Variable): Label tensor, has target labels.
Returns:
Variable: The tensor variable storing the element-wise squared error \
......@@ -1075,19 +1076,20 @@ def chunk_eval(input,
num_chunk_types,
excluded_chunk_types=None):
"""
${comment}
This function computes and outputs the precision, recall and
F1-score of chunk detection.
Args:
input(Variable): ${inference_comment}
label(Variable): ${label_comment}
chunk_scheme(${chunk_scheme_type}): ${chunk_scheme_comment}
num_chunk_types(${num_chunk_types_type}): ${num_chunk_types_comment}
excluded_chunk_types(${excluded_chunk_types_type}): ${excluded_chunk_types_comment}
input (Variable): prediction output of the network.
label (Variable): label of the test data set.
chunk_scheme (str): ${chunk_scheme_comment}
num_chunk_types (int): ${num_chunk_types_comment}
excluded_chunk_types (list): ${excluded_chunk_types_comment}
Returns:
tuple: a tuple of variables:
(precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks)
tuple: tuple containing: (precision, recall, f1_score,
num_infer_chunks, num_label_chunks,
num_correct_chunks)
"""
helper = LayerHelper("chunk_eval", **locals())
......@@ -1120,6 +1122,7 @@ def chunk_eval(input,
num_correct_chunks)
@templatedoc()
def sequence_conv(input,
num_filters,
filter_size=3,
......@@ -1132,6 +1135,19 @@ def sequence_conv(input,
This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given
in the input parameters to the function.
Args:
input (Variable): ${x_comment}
num_filters (int): number of filters.
filter_size (int): the filter size (H and W).
filter_stride (int): stride of the filter.
padding (bool): if True, add paddings.
bias_attr (ParamAttr|None): attributes for bias
param_attr (ParamAttr|None): attributes for parameter
act (str): the activation type
Returns:
Variable: output of sequence_conv
"""
# FIXME(dzh) : want to unify the argument of python layer
......@@ -1246,33 +1262,34 @@ def conv2d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
input (Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution and \
......@@ -1430,7 +1447,7 @@ def sequence_pool(input, pool_type):
def sequence_first_step(input):
"""
This funciton get the first step of sequence.
This function gets the first step of sequence.
.. code-block:: text
......@@ -1463,7 +1480,7 @@ def sequence_first_step(input):
def sequence_last_step(input):
"""
This funciton get the last step of sequence.
This function gets the last step of sequence.
.. code-block:: text
......@@ -1507,6 +1524,22 @@ def pool2d(input,
"""
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool2d layer.
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -1564,6 +1597,25 @@ def batch_norm(input,
"""
This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters.
Args:
input (Variable): the input variable.
act (str): activation type
is_test (bool): whether to run batch_norm as test mode.
momentum (float): momentum
epsilon (float): epsilon, default 1e-05
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
data_layout (str): data layout, default NCHW
in_place (bool): if True, do not create tmp variable
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): The name of this layer. It is optional.
moving_mean_name (str): The name of moving mean variable name, optional.
moving_variance_name (str): The name of moving variance name, optional.
do_model_average_for_mean_and_var (bool):
Returns:
Variable: output of batch_norm layer.
"""
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
......@@ -1691,6 +1743,7 @@ def layer_norm(input,
bias_attr(ParamAttr|None): The parameter attribute for the learnable
bias :math:`b`.
act(str): Activation to be applied to the output of layer normalizaiton.
name (str): The name of this layer. It is optional.
Returns:
Variable: A tensor variable with the same shape as the input.
......@@ -1742,6 +1795,17 @@ def layer_norm(input,
def beam_search_decode(ids, scores, name=None):
"""
${beam_search_decode}
Args:
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
name (str): The name of this layer. It is optional.
Returns:
tuple: a tuple of two output variable: sentence_ids, sentence_scores
"""
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
......@@ -1817,46 +1881,46 @@ def conv2d_transpose(input,
W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain two integers, (image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain two integers, (image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
......@@ -1993,6 +2057,17 @@ def sequence_expand(x, y, ref_level=-1, name=None):
def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
'''
This function implements the beam search algorithm.
Args:
pre_ids (Variable): ${pre_ids_comment}
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
beam_size (int): ${beam_size_comment}
end_id (int): ${end_id_comment}
level (int): ${level_comment}
Returns:
tuple: a tuple of beam_search output variables: selected_ids, selected_scores
'''
helper = LayerHelper('beam_search', **locals())
score_type = scores.dtype
......@@ -2488,19 +2563,21 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
The l2 normalize layer normalizes `x` along dimension `axis` using an L2
norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes
output = x / sqrt(max(sum(x**2), epsilon))
.. math::
y = \frac{x}{ \sqrt{\sum {x^2} + epsion }}
For `x` with more dimensions, this layer independently normalizes each 1-D
slice along dimension `axis`.
Args:
x(Variable|list): The input tensor to l2_normalize layer.
axis(int): Dimension along which to normalize the input.
epsilon(float): A lower bound value for `x`'s l2 norm. sqrt(epsilon) will
be used as the divisor if the l2 norm of `x` is less than
sqrt(epsilon).
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
x(Variable|list): The input tensor to l2_normalize layer.
axis(int): The axis on which to apply normalization. If `axis < 0`,
the dimension to normalization is rank(X) + axis. -1 is the
last dimension.
epsilon(float): The epsilon value is used to avoid division by zero,
the defalut value is 1e-10.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
......@@ -2519,46 +2596,17 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
axis = 0
helper = LayerHelper("l2_normalize", **locals())
square = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(type="square", inputs={"X": x}, outputs={"Out": square})
reduced_sum = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_tmp_variable(dtype=x.dtype)
norm = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reduce_sum",
inputs={"X": square},
outputs={"Out": reduced_sum},
type="norm",
inputs={"X": x},
outputs={"Out": out,
"Norm": norm},
attrs={
"dim": [1] if axis is None else [axis],
"keep_dim": True,
"reduce_all": False
"axis": 1 if axis is None else axis,
"epsilon": epsilon,
})
# TODO(caoying) A lower bound value epsilon for the norm is needed to
# imporve the numeric stability of reciprocal. This requires a maximum_op.
rsquare = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reciprocal", inputs={"X": reduced_sum}, outputs={"Out": rsquare})
# TODO(caoying) the current elementwise_mul operator does not support a
# general broadcast rule which broadcasts input(Y) to have the same
# dimension with Input(X) starting from a specified dimension. So this
# exanpsion is requred. Once a general broadcast rule is spported, this
# expanding canbe removed.
rsquare_expanded = helper.create_tmp_variable(dtype=x.dtype)
expand_times = [1] * len(x.shape)
expand_times[axis] = int(x.shape[axis])
helper.append_op(
type="expand",
inputs={"X": rsquare},
outputs={"Out": rsquare_expanded},
attrs={"expand_times": expand_times})
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="elementwise_mul",
inputs={"X": x,
"Y": rsquare_expanded},
outputs={"Out": out})
return out
......@@ -2742,16 +2790,13 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None,
the edit distance will be divided by the length of reference string.
Args:
input(Variable): The indices for hypothesis strings.
label(Variable): The indices for reference strings.
normalized(bool): Indicated whether to normalize the edit distance by
the length of reference string.
ignored_tokens(list of int): Tokens that should be removed before
calculating edit distance.
name (str): The name of this layer. It is optional.
Returns:
Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
......@@ -2842,10 +2887,10 @@ def ctc_greedy_decoder(input, blank, name=None):
where Lp is the sum of all input sequences' length and
num_classes is the true number of classes. (not
including the blank label).
blank(int): the blank label index of Connectionist Temporal
Classification (CTC) loss, which is in thehalf-opened
interval [0, num_classes + 1).
name (str): The name of this layer. It is optional.
Returns:
Variable: CTC greedy decode result. If all the sequences in result were
......@@ -2882,23 +2927,23 @@ def warpctc(input, label, blank=0, norm_by_times=False):
input tensor.
Args:
input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD
information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length.
blank: (int, default: 0), the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times: (bool, default: false), whether to normalize
the gradients by the number of time-step, which is also the
sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op.
input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD
information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length.
blank (int): default 0, the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times (bool): default false, whether to normalize
the gradients by the number of time-step, which is also the
sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op.
Returns:
Variable: The Connectionist Temporal Classification (CTC) loss,
......@@ -2957,9 +3002,9 @@ def sequence_reshape(input, new_dim):
no remainder for each sequence.
Args:
input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
with shape being [N, M] where M for dimension.
new_dim (int): New dimension which the input LoDTensor is reshaped to.
input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
with shape being [N, M] where M for dimension.
new_dim (int): New dimension which the input LoDTensor is reshaped to.
Returns:
Variable: Reshaped LoDTensor according to new dimension.
......@@ -2981,7 +3026,10 @@ def sequence_reshape(input, new_dim):
return out
@autodoc()
# FIXME(wuyi): let docstring_checker.py understand @autodoc.
# For now, the comments in c++ use types like Tensor, but in python side
# the type is often "Variable", and arguments may vary.
@templatedoc(op_type="nce")
def nce(input,
label,
num_total_classes,
......@@ -2989,6 +3037,21 @@ def nce(input,
param_attr=None,
bias_attr=None,
num_neg_samples=None):
"""
${comment}
Args:
input (Variable): input variable.
label (Variable): label.
num_total_classes (int):${num_total_classes_comment}
sample_weight (int): ${sample_weight_comment}
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
num_neg_samples (int): ${num_neg_samples_comment}
Returns:
Variable: output of nce layer.
"""
helper = LayerHelper('nce', **locals())
assert isinstance(input, Variable)
dim = input.shape[1]
......@@ -3046,8 +3109,9 @@ def transpose(x, perm, name=None):
perm[i]-th dimension of `input`.
Args:
input (Variable): (Tensor), A Tensor.
perm (list): A permutation of the dimensions of `input`.
x (Variable): The input Tensor.
perm (list): A permutation of the dimensions of `input`.
name (str): The name of this layer. It is optional.
Returns:
Variable: A transposed Tensor.
......@@ -3280,9 +3344,9 @@ def multiplex(inputs, index):
row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
Args:
inputs (list): A list of variables to gather from. All variables have the
inputs (list): A list of variables to gather from. All variables have the
same shape and the rank is at least 2.
index (Variable): Tensor<int32>, index variable which is a 2-D tensor
index (Variable): Tensor<int32>, index variable which is a 2-D tensor
with shape [M, 1] where M is the batch size.
Returns:
......@@ -3482,7 +3546,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
begin(int): The first value of this counter.
step(int): The increment step between each execution.
Returns(Variable): The global run counter.
Returns:
Variable: The global run counter.
"""
helper = LayerHelper('global_step_counter')
if counter_name is None:
......@@ -3543,7 +3608,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
the corresponding dimension of x.
Args:
input(variable): The input tensor.
x(variable): The input tensor.
shape(list): The new shape. At most one dimension of the new shape can
be -1.
actual_shape(variable): An optional input. If provided, reshape
......@@ -3555,8 +3620,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
inplace(bool): If this flag is set true, a new output tensor is created
whose data is copied from input x, otherwise the output
shares data with input without copying.
name (str): The name of this layer. It is optional.
Returns(variable): The output tensor.
Returns:
Variable: The output tensor.
Examples:
.. code-block:: python
......@@ -4063,7 +4130,6 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
name(str|None): The output variable name.
Returns:
${out_comment}.
"""
......@@ -4082,6 +4148,7 @@ def image_resize_short(input, out_short_len, resample='BILINEAR'):
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_short_len(int): The length of output images' short edge.
resample (str): resample method, default: BILINEAR.
Returns:
out (Variable): The output is a 4-D tensor of the shape
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 1457be3e
......@@ -71,6 +71,7 @@ __all__ = [
'cumsum',
'scatter',
'sum',
'slice',
'polygon_box_transform',
'shape',
'iou_similarity',
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 1457be3e
......@@ -18,6 +18,7 @@ import framework
import executor
import warnings
import sys
import os
__all__ = ['ParallelExecutor', 'ExecutionStrategy', 'BuildStrategy']
......@@ -101,7 +102,9 @@ class ParallelExecutor(object):
p.set_place(self._act_places[-1])
self._places.append(p)
else:
for i in xrange(multiprocessing.cpu_count()):
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
for i in xrange(cpu_num):
p = core.Place()
self._act_places.append(core.CPUPlace())
p.set_place(self._act_places[-1])
......@@ -110,19 +113,17 @@ class ParallelExecutor(object):
if exec_strategy is None:
exec_strategy = ExecutionStrategy()
if use_cuda:
exec_strategy.use_event = True
else:
exec_strategy.use_event = False
exec_strategy.use_cuda = use_cuda
if exec_strategy.num_threads == 0:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
exec_strategy.num_threads = len(self._places) * 2
exec_strategy.num_threads = len(self._places) * 4
else:
exec_strategy.num_threads = min(
len(self._places) * 2, multiprocessing.cpu_count())
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exec_strategy.num_threads = cpu_num
if build_strategy is None:
build_strategy = BuildStrategy()
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 1457be3e
......@@ -41,8 +41,8 @@ function(py_test_modules TARGET_NAME)
endfunction()
list(REMOVE_ITEM TEST_OPS test_warpctc_op)
list(REMOVE_ITEM TEST_OPS test_dist_train)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
#list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
#list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
# TODO(wuyi): this test hungs on CI, will add it back later
list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
foreach(TEST_OP ${TEST_OPS})
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ 1457be3e
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import multiprocessing
import os
import unittest
import paddle.fluid as fluid
import time
......@@ -23,6 +25,7 @@ __all__ = ['TestParallelExecutorBase']
class TestParallelExecutorBase(unittest.TestCase):
def check_network_convergence(self,
method,
use_cuda=True,
memory_opt=True,
iter=50,
batch_size=None,
......@@ -53,7 +56,7 @@ class TestParallelExecutorBase(unittest.TestCase):
adam.minimize(loss)
if memory_opt:
fluid.memory_optimize(main)
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(startup)
exec_strategy = fluid.ExecutionStrategy()
......@@ -64,7 +67,7 @@ class TestParallelExecutorBase(unittest.TestCase):
if use_parallel_executor:
exe = fluid.ParallelExecutor(
True,
use_cuda,
loss_name=loss.name,
exec_strategy=exec_strategy,
build_strategy=build_strategy)
......@@ -72,7 +75,9 @@ class TestParallelExecutorBase(unittest.TestCase):
exe = fluid.Executor(place=place)
if batch_size is not None:
batch_size *= fluid.core.get_cuda_device_count()
batch_size *= fluid.core.get_cuda_device_count(
) if use_cuda else int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
begin = time.time()
first_loss, = run_executor(
exe=exe, feed=feed_dict, fetch_list=[loss.name])
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 1457be3e
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
......@@ -54,10 +55,10 @@ class TestDistTranspiler(TranspilerTest):
delete_ops(trainer.global_block(), optimize_ops)
ops = [op.type for op in trainer.global_block().ops] + [
"split_byref", "send_vars", "send_barrier", "recv", "recv",
"split_byref", "send", "send_barrier", "recv", "recv",
"fetch_barrier", "concat"
]
ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
ops.insert(ops.index("elementwise_add_grad") + 1, "send")
return ops
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 1457be3e
......@@ -387,6 +387,12 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(output)
print(str(program))
def test_l2_normalize(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[8, 7, 10], dtype="float32")
output = layers.l2_normalize(x, axis=1)
def test_maxout(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_listen_and_serv_op.py
浏览文件 @ 1457be3e
......@@ -70,17 +70,18 @@ class TestListenAndServOp(OpTest):
return p.pid
def _wait_ps_ready(self, pid):
retry_times = self.ps_timeout
start_left_time = self.ps_timeout
sleep_time = 0.5
while True:
assert retry_times >= 0, "wait ps ready failed"
time.sleep(0.5)
assert start_left_time >= 0, "wait ps ready failed"
time.sleep(sleep_time)
try:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os.stat("/tmp/paddle.%d.port" % pid)
return
except os.error:
retry_times -= 1
start_left_time -= sleep_time
def test_rpc_interfaces(self):
# TODO(Yancey1989): need to make sure the rpc interface correctly.
......
python/paddle/fluid/tests/unittests/test_norm_op.py
浏览文件 @ 1457be3e
......@@ -17,44 +17,23 @@ import numpy as np
from op_test import OpTest
def norm(input, scale, epsilon):
s0, s1, s2, s3 = input.shape
x_square = input * input
for i in xrange(s0):
input_batch = input[i:i + 1, :, :, :]
input_batch = input_batch.reshape(s1, s2 * s3)
x_square_batch = x_square[i:i + 1, :, :, :]
x_square_batch = x_square_batch.reshape(s1, s2 * s3)
square_colsum = x_square_batch.sum(axis=0) + epsilon
tmp = pow(square_colsum, 0.5)
tmp = np.reciprocal(tmp)
tmp_tile = np.tile(tmp, s1)
tmp_tile = tmp_tile.reshape(s1, s2 * s3)
scale_tile = np.tile(scale, (1, s2 * s3))
scale_tile = scale_tile.reshape(s1, s2 * s3)
out_batch = input_batch * tmp_tile * scale_tile
out_batch = out_batch.reshape(1, s1, s2, s3)
if i == 0:
out = out_batch
else:
out = np.concatenate((out, out_batch), 0)
out.reshape(s0, s1, s2, s3)
return out
def l2_norm(x, axis, epsilon):
x2 = x**2
s = np.sum(x2, axis=axis, keepdims=True)
r = np.sqrt(s + epsilon)
y = x / np.broadcast_to(r, x.shape)
return y, r
class TestNormOp(OpTest):
def setUp(self):
self.op_type = "norm"
self.init_test_case()
input = np.random.random(self.shape).astype("float32")
scale = np.array([10, 10, 10])
self.inputs = {
'X': input.astype('float32'),
'Scale': scale.astype('float32')
}
self.attrs = {'epsilon': self.epsilon}
output = norm(input, scale, self.epsilon)
self.outputs = {'Out': output.astype('float32')}
x = np.random.random(self.shape).astype("float64")
y, norm = l2_norm(x, self.axis, self.epsilon)
self.inputs = {'X': x}
self.attrs = {'epsilon': self.epsilon, 'axis': self.axis}
self.outputs = {'Out': y, 'Norm': norm}
def test_check_output(self):
self.check_output()
......@@ -63,8 +42,23 @@ class TestNormOp(OpTest):
self.check_grad(['X'], 'Out')
def init_test_case(self):
self.shape = [2, 3, 2, 2]
self.epsilon = 1e-6
self.shape = [2, 3, 4, 4]
self.axis = 1
self.epsilon = 1e-8
class TestNormOp2(TestNormOp):
def init_test_case(self):
self.shape = [5, 3, 9, 7]
self.axis = 0
self.epsilon = 1e-8
class TestNormOp3(TestNormOp):
def init_test_case(self):
self.shape = [5, 3, 2, 7]
self.axis = -1
self.epsilon = 1e-8
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_normalization_wrapper.py
浏览文件 @ 1457be3e
......@@ -70,8 +70,9 @@ class TestNormalization(unittest.TestCase):
def l2_normalize(self, data, axis, epsilon):
""" Compute the groundtruth.
"""
output = data * np.reciprocal(
np.sum(np.square(data), axis=axis, keepdims=True))
output = data / np.broadcast_to(
np.sqrt(np.sum(np.square(data), axis=axis, keepdims=True)),
data.shape)
return output
def test_l2_normalize(self):
......
python/paddle/fluid/tests/unittests/test_parallel_executor_crf.py
浏览文件 @ 1457be3e
......@@ -17,6 +17,7 @@ import paddle.fluid as fluid
import unittest
import paddle
import numpy as np
import os
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
......@@ -101,7 +102,11 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
class TestCRFModel(unittest.TestCase):
def check_network_convergence(self, is_sparse, build_strategy=None):
def check_network_convergence(self,
is_sparse,
build_strategy=None,
use_cuda=True):
os.environ['CPU_NUM'] = str(4)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -145,12 +150,12 @@ class TestCRFModel(unittest.TestCase):
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=16)
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
pe = fluid.ParallelExecutor(
use_cuda=True,
use_cuda=use_cuda,
loss_name=avg_cost.name,
build_strategy=build_strategy)
......@@ -172,25 +177,33 @@ class TestCRFModel(unittest.TestCase):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(
is_sparse=True, build_strategy=build_strategy)
is_sparse=True, build_strategy=build_strategy, use_cuda=True)
self.check_network_convergence(
is_sparse=True, build_strategy=build_strategy, use_cuda=False)
def test_update_dense_parameter_all_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(
is_sparse=False, build_strategy=build_strategy)
is_sparse=False, build_strategy=build_strategy, use_cuda=True)
self.check_network_convergence(
is_sparse=False, build_strategy=build_strategy, use_cuda=False)
def test_update_sparse_parameter_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(
is_sparse=True, build_strategy=build_strategy)
is_sparse=True, build_strategy=build_strategy, use_cuda=True)
self.check_network_convergence(
is_sparse=True, build_strategy=build_strategy, use_cuda=False)
def test_update_dense_parameter_reduce(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(
is_sparse=False, build_strategy=build_strategy)
is_sparse=False, build_strategy=build_strategy, use_cuda=True)
self.check_network_convergence(
is_sparse=False, build_strategy=build_strategy, use_cuda=False)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @ 1457be3e
......@@ -18,6 +18,7 @@ import paddle.fluid as fluid
import unittest
import numpy as np
import paddle
import os
def Lenet(data, class_dim):
......@@ -35,7 +36,7 @@ def Lenet(data, class_dim):
class TestFetchOp(unittest.TestCase):
def parallel_exe(self, train_inputs, seed):
def parallel_exe(self, train_inputs, seed, use_cuda):
main = fluid.Program()
startup = fluid.Program()
startup.random_seed = seed
......@@ -59,13 +60,13 @@ class TestFetchOp(unittest.TestCase):
# conv2d_1.b_0@GRAD. Those variables should not be pruned.
# fluid.memory_optimize(main)
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
pe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name, main_program=main)
use_cuda=use_cuda, loss_name=loss.name, main_program=main)
fetch_list = []
all_vars = main.global_block().vars
......@@ -88,14 +89,16 @@ class TestFetchOp(unittest.TestCase):
for i in range(iters):
train_inputs.append(tst_reader_iter.next())
self.parallel_exe(train_inputs, seed=1)
os.environ['CPU_NUM'] = str(4)
self.parallel_exe(train_inputs, seed=1, use_cuda=True)
self.parallel_exe(train_inputs, seed=1, use_cuda=False)
class TestFeedParallel(unittest.TestCase):
def test_main(self):
def parallel_exe(self, use_cuda, seed):
main = fluid.Program()
startup = fluid.Program()
startup.random_seed = 1
startup.random_seed = seed
with fluid.scope_guard(fluid.core.Scope()):
with fluid.program_guard(main, startup):
data = fluid.layers.data(
......@@ -111,15 +114,18 @@ class TestFeedParallel(unittest.TestCase):
regularization=fluid.regularizer.L2Decay(1e-4))
opt.minimize(loss)
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
reader = feeder.decorate_reader(
paddle.batch(
flowers.train(), batch_size=16), multi_devices=True)
exe = fluid.Executor(place)
exe.run(startup)
pe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name, main_program=main)
use_cuda=use_cuda, loss_name=loss.name, main_program=main)
for batch_id, data in enumerate(reader()):
loss_np = np.array(pe.run(feed=data, fetch_list=[loss.name])[0])
......@@ -127,6 +133,11 @@ class TestFeedParallel(unittest.TestCase):
if batch_id == 2:
break
def test_feed_op(self):
os.environ['CPU_NUM'] = str(4)
self.parallel_exe(use_cuda=True, seed=1)
self.parallel_exe(use_cuda=False, seed=1)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @ 1457be3e
......@@ -18,6 +18,7 @@ import numpy as np
import paddle
import paddle.dataset.mnist as mnist
import unittest
import os
MNIST_RECORDIO_FILE = "./mnist_test_pe.recordio"
......@@ -85,6 +86,7 @@ def fc_with_batchnorm(use_feed):
class TestMNIST(TestParallelExecutorBase):
@classmethod
def setUpClass(cls):
os.environ['CPU_NUM'] = str(4)
# Convert mnist to recordio file
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(mnist.train(), batch_size=4)
......@@ -99,9 +101,12 @@ class TestMNIST(TestParallelExecutorBase):
fluid.recordio_writer.convert_reader_to_recordio_file(
MNIST_RECORDIO_FILE, reader, feeder)
def check_simple_fc_convergence(self, balance_parameter_opt_between_cards):
self.check_network_convergence(simple_fc_net)
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
def check_simple_fc_convergence(self,
balance_parameter_opt_between_cards,
use_cuda=True):
self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
img = np.zeros(shape=[32, 784], dtype='float32')
label = np.ones(shape=[32, 1], dtype='int64')
......@@ -109,17 +114,21 @@ class TestMNIST(TestParallelExecutorBase):
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
def test_simple_fc(self):
self.check_simple_fc_convergence(False)
self.check_simple_fc_convergence(False, use_cuda=True)
self.check_simple_fc_convergence(False, use_cuda=False)
def test_simple_fc_with_new_strategy(self):
self.check_simple_fc_convergence(True)
self.check_simple_fc_convergence(True, use_cuda=True)
self.check_simple_fc_convergence(True, use_cuda=False)
def check_simple_fc_parallel_accuracy(self,
balance_parameter_opt_between_cards):
balance_parameter_opt_between_cards,
use_cuda=True):
img = np.zeros(shape=[32, 784], dtype='float32')
label = np.ones(shape=[32, 1], dtype='int64')
single_first_loss, single_last_loss = self.check_network_convergence(
......@@ -127,12 +136,14 @@ class TestMNIST(TestParallelExecutorBase):
seed=1000,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_parallel_executor=False)
parallel_first_loss, parallel_last_loss = self.check_network_convergence(
method=simple_fc_net,
seed=1000,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_parallel_executor=True,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
......@@ -143,28 +154,33 @@ class TestMNIST(TestParallelExecutorBase):
self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
def test_simple_fc_parallel_accuracy(self):
self.check_simple_fc_parallel_accuracy(False)
self.check_simple_fc_parallel_accuracy(False, use_cuda=True)
self.check_simple_fc_parallel_accuracy(False, use_cuda=False)
def test_simple_fc_parallel_accuracy_with_new_strategy(self):
self.check_simple_fc_parallel_accuracy(True)
self.check_simple_fc_parallel_accuracy(True, use_cuda=True)
self.check_simple_fc_parallel_accuracy(True, use_cuda=False)
def check_batchnorm_fc_convergence(self,
balance_parameter_opt_between_cards):
self.check_network_convergence(fc_with_batchnorm)
def check_batchnorm_fc_convergence(
self, balance_parameter_opt_between_cards, use_cuda):
self.check_network_convergence(fc_with_batchnorm, use_cuda=use_cuda)
img = np.zeros(shape=[32, 784], dtype='float32')
label = np.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
def test_batchnorm_fc(self):
self.check_batchnorm_fc_convergence(False)
self.check_batchnorm_fc_convergence(False, use_cuda=True)
self.check_batchnorm_fc_convergence(False, use_cuda=False)
def test_batchnorm_fc_with_new_strategy(self):
self.check_batchnorm_fc_convergence(True)
self.check_batchnorm_fc_convergence(True, use_cuda=True)
self.check_batchnorm_fc_convergence(True, use_cuda=False)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
浏览文件 @ 1457be3e
......@@ -15,6 +15,7 @@
import paddle.fluid as fluid
from parallel_executor_test_base import TestParallelExecutorBase
import unittest
import os
def squeeze_excitation(input, num_channels, reduction_ratio):
......@@ -130,22 +131,30 @@ def SE_ResNeXt50Small(batch_size=2, use_feed=False):
class TestResnet(TestParallelExecutorBase):
def check_resnet_convergence(self, balance_parameter_opt_between_cards):
def check_resnet_convergence(self,
balance_parameter_opt_between_cards,
use_cuda=True,
iter=20):
os.environ['CPU_NUM'] = str(4)
import functools
batch_size = 2
self.check_network_convergence(
functools.partial(
SE_ResNeXt50Small, batch_size=batch_size),
iter=20,
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
balance_parameter_opt_between_cards=balance_parameter_opt_between_cards
)
def test_resnet(self):
self.check_resnet_convergence(False)
self.check_resnet_convergence(False, use_cuda=True)
self.check_resnet_convergence(False, use_cuda=False, iter=5)
def test_resnet_with_new_strategy(self):
self.check_resnet_convergence(True)
self.check_resnet_convergence(True, use_cuda=True)
self.check_resnet_convergence(True, use_cuda=False, iter=5)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_executor_test_while_train.py
浏览文件 @ 1457be3e
......@@ -15,6 +15,7 @@
import paddle.fluid as fluid
import numpy as np
import unittest
import os
def simple_fc_net():
......@@ -35,7 +36,8 @@ def simple_fc_net():
class ParallelExecutorTestingDuringTraining(unittest.TestCase):
def check_network_convergence(self, build_strategy=None):
def check_network_convergence(self, use_cuda, build_strategy=None):
os.environ['CPU_NUM'] = str(4)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -49,19 +51,19 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
image = np.random.normal(size=(batch_size, 784)).astype('float32')
label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
feed_dict = {'image': image, 'label': label}
train_exe = fluid.ParallelExecutor(
use_cuda=True,
use_cuda=use_cuda,
loss_name=loss.name,
main_program=main,
build_strategy=build_strategy)
test_exe = fluid.ParallelExecutor(
use_cuda=True,
use_cuda=use_cuda,
main_program=test_program,
share_vars_from=train_exe,
build_strategy=build_strategy)
......@@ -81,12 +83,18 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
def test_parallel_testing(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
self.check_network_convergence(build_strategy)
self.check_network_convergence(
use_cuda=True, build_strategy=build_strategy)
self.check_network_convergence(
use_cuda=False, build_strategy=build_strategy)
def test_parallel_testing_with_new_strategy(self):
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
self.check_network_convergence(build_strategy)
self.check_network_convergence(
use_cuda=True, build_strategy=build_strategy)
self.check_network_convergence(
use_cuda=False, build_strategy=build_strategy)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
浏览文件 @ 1457be3e
......@@ -19,6 +19,7 @@ from parallel_executor_test_base import TestParallelExecutorBase
import unittest
import paddle
import paddle.dataset.wmt16 as wmt16
import os
WMT16_RECORDIO_FILE = "./wmt16_test_pe.recordio"
......@@ -149,6 +150,7 @@ def transformer(use_feed):
class TestTransformer(TestParallelExecutorBase):
@classmethod
def setUpClass(cls):
os.environ['CPU_NUM'] = str(4)
reader = paddle.batch(
wmt16.train(ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size),
......@@ -167,7 +169,8 @@ class TestTransformer(TestParallelExecutorBase):
@unittest.skip("transformer is buggy in multi gpu")
def test_main(self):
self.check_network_convergence(transformer)
self.check_network_convergence(transformer, use_cuda=True)
self.check_network_convergence(transformer, use_cuda=False)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_simple_dist_transpiler.py
浏览文件 @ 1457be3e
......@@ -59,9 +59,9 @@ class TestSimpleDistTranspiler(TranspilerTest):
delete_ops(trainer.global_block(), optimize_ops)
ops = [op.type for op in trainer.global_block().ops] + [
"send_vars", "send_barrier", "recv", "recv", "fetch_barrier"
"send", "send_barrier", "recv", "recv", "fetch_barrier"
]
ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
ops.insert(ops.index("elementwise_add_grad") + 1, "send")
return ops
def _transpiler_instance(self):
......
python/paddle/fluid/tests/unittests/test_slice_op.py 0 → 100644
浏览文件 @ 1457be3e
# 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.
import unittest
import numpy as np
from op_test import OpTest
class TestSliceOp(OpTest):
def setUp(self):
self.op_type = "slice"
self.config()
self.inputs = {'Input': self.input}
self.outputs = {'Out': self.out}
self.attrs = {
'axes': self.axes,
'starts': self.starts,
'ends': self.ends
}
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32")
self.starts = [1, 0, 2]
self.ends = [3, 3, 4]
self.axes = [0, 1, 2]
self.out = self.input[1:3, 0:3, 2:4, :]
def test_check_output(self):
self.check_output()
class TestCase1(TestSliceOp):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32")
self.starts = [-3, 0, 2]
self.ends = [3, 100, -1]
self.axes = [0, 1, 2]
self.out = self.input[-3:3, 0:100, 2:-1, :]
class TestCase2(TestSliceOp):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32")
self.starts = [-3, 0, 2]
self.ends = [3, 100, -1]
self.axes = [0, 1, 3]
self.out = self.input[-3:3, 0:100, :, 2:-1]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 1457be3e
......@@ -24,9 +24,9 @@ Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send splited variables to server and fetch
params(splited blocks or origin param) from server.
5. append concat_op to merge splited blocks to update local weights.
4. append send_op to send splited variables to server and
5. add recv_op to fetch params(splited blocks or origin param) from server.
6. append concat_op to merge splited blocks to update local weights.
Steps to transpile pserver:
1. create new program for parameter server.
......@@ -317,7 +317,7 @@ class DistributeTranspiler:
program.global_block().insert_op(
index=index + 1,
type="send_vars",
type="send",
inputs={"X": splited_vars},
outputs={},
attrs={
......@@ -515,35 +515,38 @@ class DistributeTranspiler:
grad_to_block_id, None)
# process distributed lookup_table
prefetch_block = None
prefetch_var_name_to_block_id = []
if self.has_distributed_lookup_table:
pserver_index = self.pserver_endpoints.index(endpoint)
table_opt_block = self._create_table_optimize_block(
pserver_index, pserver_program, pre_block_idx, grad_to_block_id)
prefetch_block = self._create_prefetch_block(
prefetch_var_name_to_block_id = self._create_prefetch_block(
pserver_index, pserver_program, table_opt_block)
# NOTE: if has_distributed_lookup_table is False, then prefetch_block will
# not be executed, so it's safe to use optimize_block to hold the place
if self.has_distributed_lookup_table:
assert prefetch_block is not None
assert len(prefetch_var_name_to_block_id) > 0
else:
assert prefetch_block is None
prefetch_block = pserver_program.global_block()
assert len(prefetch_var_name_to_block_id) == 0
attrs = {
"OptimizeBlock": pserver_program.block(1),
"endpoint": endpoint,
"Fanin": self.trainer_num,
"sync_mode": self.sync_mode,
"grad_to_block_id": grad_to_block_id
}
if len(prefetch_var_name_to_block_id) > 0:
attrs['prefetch_var_name_to_block_id'] \
= prefetch_var_name_to_block_id
# step5 append the listen_and_serv op
pserver_program.global_block().append_op(
type="listen_and_serv",
inputs={'X': recv_inputs},
outputs={},
attrs={
"OptimizeBlock": pserver_program.block(1),
"endpoint": endpoint,
"Fanin": self.trainer_num,
"PrefetchBlock": prefetch_block,
"sync_mode": self.sync_mode,
"grad_to_block_id": grad_to_block_id
})
attrs=attrs)
pserver_program.sync_with_cpp()
return pserver_program
......@@ -608,8 +611,15 @@ class DistributeTranspiler:
def _replace_lookup_table_op_with_prefetch(self, program,
pserver_endpoints):
# 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op
self.prefetch_input_vars = None
self.prefetch_output_vars = None
# self.all_prefetch_input_vars =
# [[var0_prefetch_in_pserver0, var0_prefetch_in_pserver1]
# [var1_prefetch_in_pserver0, var1_prefetch_in_pserver1]]
self.all_prefetch_input_vars = []
# self.all_prefetch_input_vars =
# [[var0_prefetch_in_pserver0, var0_prefetch_in_pserver1]
# [var1_prefetch_in_pserver0, var1_prefetch_in_pserver1]]
self.all_prefetch_output_vars = []
continue_search_lookup_table_op = True
while continue_search_lookup_table_op:
......@@ -623,18 +633,19 @@ class DistributeTranspiler:
ids_name = op.input("Ids")
out_name = op.output("Out")
if self.prefetch_input_vars is None:
ids_var = program.global_block().vars[ids_name[0]]
self.prefetch_input_vars = self.create_splited_vars(
source_var=ids_var,
block=program.global_block(),
tag="_prefetch_in_")
if self.prefetch_output_vars is None:
out_var = program.global_block().vars[out_name[0]]
self.prefetch_output_vars = self.create_splited_vars(
source_var=out_var,
block=program.global_block(),
tag="_prefetch_out_")
ids_var = program.global_block().vars[ids_name[0]]
prefetch_input_vars = self.create_splited_vars(
source_var=ids_var,
block=program.global_block(),
tag="_prefetch_in_")
self.all_prefetch_input_vars.append(prefetch_input_vars)
out_var = program.global_block().vars[out_name[0]]
prefetch_output_vars = self.create_splited_vars(
source_var=out_var,
block=program.global_block(),
tag="_prefetch_out_")
self.all_prefetch_output_vars.append(prefetch_output_vars)
# insert split_ids_op
program.global_block().insert_op(
......@@ -646,14 +657,14 @@ class DistributeTranspiler:
for varname in ids_name
]
},
outputs={"Out": self.prefetch_input_vars})
outputs={"Out": prefetch_input_vars})
# insert prefetch_op
program.global_block().insert_op(
index=op_index + 1,
type="prefetch",
inputs={'X': self.prefetch_input_vars},
outputs={"Out": self.prefetch_output_vars},
inputs={'X': prefetch_input_vars},
outputs={"Out": prefetch_output_vars},
attrs={
"epmap": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
......@@ -663,7 +674,7 @@ class DistributeTranspiler:
program.global_block().insert_op(
index=op_index + 2,
type="concat",
inputs={'X': self.prefetch_output_vars},
inputs={'X': prefetch_output_vars},
outputs={
"Out": [
program.global_block().vars[varname]
......@@ -678,7 +689,7 @@ class DistributeTranspiler:
break
def _split_table_grad_and_add_send_vars(self, program, pserver_endpoints):
# 2. add split_ids_op and send_vars_op to send gradient to pservers
# 2. add split_ids_op and send_op to send gradient to pservers
# there should only be one table_name
all_ops = program.global_block().ops
table_grad_name = grad_var_name(self.table_name)
......@@ -695,11 +706,11 @@ class DistributeTranspiler:
outputs={"Out": self.trainer_side_table_grad_list})
program.global_block().insert_op(
index=op_index + 2,
type="send_vars",
type="send",
inputs={'X': self.trainer_side_table_grad_list},
outputs={},
attrs={
"sync_send": True,
"sync_mode": True,
"epmap": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
......@@ -709,30 +720,34 @@ class DistributeTranspiler:
optimize_block):
# STEP: create prefetch block
table_var = pserver_program.global_block().vars[self.table_name]
prefetch_block = pserver_program.create_block(optimize_block.idx)
trainer_ids = self.prefetch_input_vars[pserver_index]
pserver_ids = pserver_program.global_block().create_var(
name=trainer_ids.name,
type=trainer_ids.type,
shape=trainer_ids.shape,
dtype=trainer_ids.dtype)
trainer_out = self.prefetch_output_vars[pserver_index]
pserver_out = pserver_program.global_block().create_var(
name=trainer_out.name,
type=trainer_out.type,
shape=trainer_out.shape,
dtype=trainer_out.dtype)
prefetch_block.append_op(
type="lookup_sparse_table",
inputs={'Ids': pserver_ids,
"W": table_var},
outputs={"Out": pserver_out},
attrs={
"is_sparse": True, # has no effect on lookup_table op
"is_distributed": True,
"padding_idx": -1
})
return prefetch_block
prefetch_var_name_to_block_id = []
for index in range(len(self.all_prefetch_input_vars)):
prefetch_block = pserver_program.create_block(optimize_block.idx)
trainer_ids = self.all_prefetch_input_vars[index][pserver_index]
pserver_ids = pserver_program.global_block().create_var(
name=trainer_ids.name,
type=trainer_ids.type,
shape=trainer_ids.shape,
dtype=trainer_ids.dtype)
trainer_out = self.all_prefetch_output_vars[index][pserver_index]
pserver_out = pserver_program.global_block().create_var(
name=trainer_out.name,
type=trainer_out.type,
shape=trainer_out.shape,
dtype=trainer_out.dtype)
prefetch_block.append_op(
type="lookup_sparse_table",
inputs={'Ids': pserver_ids,
"W": table_var},
outputs={"Out": pserver_out},
attrs={
"is_sparse": True, # has no effect on lookup_table op
"is_distributed": True,
"padding_idx": -1
})
prefetch_var_name_to_block_id.append(trainer_ids.name + ":" + str(
prefetch_block.idx))
return prefetch_var_name_to_block_id
def _create_table_optimize_block(self, pserver_index, pserver_program,
pre_block_idx, grad_to_block_id):
......
python/paddle/v2/dataset/flowers.py
浏览文件 @ 1457be3e
......@@ -119,7 +119,8 @@ def reader_creator(data_file,
yield sample, int(label) - 1
if use_xmap:
return xmap_readers(mapper, reader, cpu_count(), buffered_size)
cpu_num = int(os.environ.get('CPU_NUM', cpu_count()))
return xmap_readers(mapper, reader, cpu_num, buffered_size)
else:
return map_readers(mapper, reader)
......
tools/codestyle/docstring_checker.py
浏览文件 @ 1457be3e
......@@ -126,9 +126,10 @@ class DocstringChecker(BaseChecker):
'W9002':
('Doc string does not end with "." period', symbol + "-end-with",
'Used when a doc string does not end with a period'),
'W9003': ('All args with their types must be mentioned in doc string',
symbol + "-with-all-args",
'Used when not all arguments are in the doc string '),
'W9003':
('All args with their types must be mentioned in doc string %s',
symbol + "-with-all-args",
'Used when not all arguments are in the doc string '),
'W9005': ('Missing docstring or docstring is too short',
symbol + "-missing", 'Add docstring longer >=10'),
'W9006': ('Docstring indent error, use 4 space for indent',
......@@ -178,6 +179,8 @@ class DocstringChecker(BaseChecker):
self.indent_style(node)
def missing_doc_string(self, node):
if node.name.startswith("__") or node.name.startswith("_"):
return True
if node.tolineno - node.fromlineno <= 10:
return True
......@@ -199,12 +202,16 @@ class DocstringChecker(BaseChecker):
doc = node.doc
lines = doc.splitlines()
line_num = 0
for l in lines:
if line_num == 0:
continue
cur_indent = len(l) - len(l.lstrip())
if cur_indent % indent != 0:
self.add_message('W9006', node=node, line=node.fromlineno)
return False
line_num += 1
return True
......@@ -320,15 +327,19 @@ class DocstringChecker(BaseChecker):
return True
parsed_args = doc.args
args_not_documented = set(args) - set(parsed_args)
if len(args) > 0 and len(parsed_args) <= 0:
print "debug:parsed args: ", parsed_args
self.add_message('W9003', node=node, line=node.fromlineno)
self.add_message(
'W9003',
node=node,
line=node.fromlineno,
args=list(args_not_documented))
return False
for t in args:
if t not in parsed_args:
print t, " with (type) not in ", parsed_args
self.add_message('W9003', node=node, line=node.fromlineno)
self.add_message(
'W9003', node=node, line=node.fromlineno, args=[t, ])
return False
return True
tools/codestyle/pylint_pre_commit.hook
浏览文件 @ 1457be3e
......@@ -7,13 +7,13 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
export PYTHONPATH=$DIR:$PYTHONPATH
# The trick to remove deleted files: https://stackoverflow.com/a/2413151
for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'); do
for file in $(git diff --name-status | awk '$1 != "D" {print $2}'); do
pylint --disable=all --load-plugins=docstring_checker \
--enable=doc-string-one-line,doc-string-end-with,doc-string-with-all-args,doc-string-triple-quotes,doc-string-missing,doc-string-indent-error,doc-string-with-returns,doc-string-with-raises $file;
TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
done
#exit $TOTAL_ERRORS
exit $TOTAL_ERRORS
#For now, just warning:
exit 0
#exit 0
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