Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 54002c3b 编写于 作者: Y yuyang18
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/polish_doc

上级 ff55d4c5 94a741d7
展开全部 隐藏空白更改
内联 并排
Showing with 3645 addition and 1318 deletion
CMakeLists.txt
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
# 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
浏览文件 @ 54002c3b
......@@ -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,
......@@ -276,7 +180,7 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
print_train_time(start_time, time.time(), num_samples)
print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))),
# evaluation
if not args.no_test and batch_acc:
if not args.no_test and batch_acc and not args.use_reader_op:
pass_test_acc = test(exe, infer_prog, test_reader, feeder,
batch_acc)
print(", Test Accuracy: %f" % pass_test_acc)
......@@ -373,11 +277,12 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
batch_id += 1
print_train_time(start_time, time.time(), num_samples)
if not args.no_test and batch_acc:
if not args.no_test and batch_acc and not args.use_reader_op:
# we have not implement record io for test
# skip test when use args.use_reader_op
test_acc = test(startup_exe, infer_prog, test_reader, feeder,
batch_acc)
print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
exit(0)
def print_arguments(args):
......@@ -417,7 +322,7 @@ 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/resnet.py
浏览文件 @ 54002c3b
......@@ -199,7 +199,10 @@ def get_model(args):
batched_train_reader = paddle.batch(
paddle.reader.shuffle(
train_reader, buf_size=5120),
batch_size=args.batch_size * args.gpus)
batched_test_reader = paddle.batch(train_reader, batch_size=args.batch_size)
batch_size=args.batch_size * args.gpus,
drop_last=True)
batched_test_reader = paddle.batch(
train_reader, batch_size=args.batch_size, drop_last=True)
return avg_cost, inference_program, optimizer, batched_train_reader, batched_test_reader, batch_acc
return avg_cost, inference_program, optimizer, batched_train_reader,\
batched_test_reader, batch_acc
benchmark/fluid/models/stacked_dynamic_lstm.py
浏览文件 @ 54002c3b
......@@ -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
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
# 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 @ 54002c3b
# 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 ${LEVELDB_LIBRARIES})
ADD_DEPENDENCIES(leveldb extern_leveldb)
LIST(APPEND external_project_dependencies leveldb)
cmake/generic.cmake
浏览文件 @ 54002c3b
......@@ -610,3 +610,21 @@ function(grpc_library TARGET_NAME)
COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
cc_library("${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 @ 54002c3b
# 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/contrib/inference/test_paddle_inference_api_impl.cc
浏览文件 @ 54002c3b
......@@ -109,7 +109,6 @@ void MainWord2Vec(bool use_gpu) {
void MainImageClassification(bool use_gpu) {
int batch_size = 2;
bool use_mkldnn = false;
bool repeat = false;
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
......@@ -134,12 +133,8 @@ void MainImageClassification(bool use_gpu) {
std::vector<framework::LoDTensor*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
TestInference<platform::CPUPlace, false, true>(config.model_dir,
cpu_feeds,
cpu_fetchs1,
repeat,
is_combined,
use_mkldnn);
TestInference<platform::CPUPlace, false, true>(
config.model_dir, cpu_feeds, cpu_fetchs1, repeat, is_combined);
auto predictor = CreatePaddlePredictor(config);
std::vector<PaddleTensor> paddle_tensor_feeds;
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 54002c3b
......@@ -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 cares 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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
// 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 @ 54002c3b
// 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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -20,10 +20,14 @@ 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"
DECLARE_bool(benchmark);
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run");
namespace paddle {
namespace framework {
......@@ -43,6 +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>();
......@@ -115,6 +127,7 @@ void Executor::CreateVariables(const ProgramDesc& pdesc, Scope* scope,
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope, bool create_vars) {
platform::RecordBlock b(block_id);
if (FLAGS_use_mkldnn) EnableMKLDNN(pdesc);
auto ctx = Prepare(pdesc, block_id);
RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
}
......@@ -214,6 +227,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
const std::string& feed_holder_name,
const std::string& fetch_holder_name) {
platform::RecordBlock b(kProgramId);
if (FLAGS_use_mkldnn) EnableMKLDNN(program);
bool has_feed_ops =
has_feed_operators(program.Block(0), *feed_targets, feed_holder_name);
bool has_fetch_ops =
......@@ -225,7 +239,6 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
unique_ptr_of_copy_program.reset(new ProgramDesc(program));
copy_program = unique_ptr_of_copy_program.get();
}
auto* global_block = copy_program->MutableBlock(0);
if (!has_feed_ops) {
......@@ -378,5 +391,19 @@ void Executor::RunPreparedContext(
}
}
void Executor::EnableMKLDNN(const ProgramDesc& program) {
#ifdef PADDLE_WITH_MKLDNN
VLOG(3) << "use_mkldnn=True";
for (size_t bid = 0; bid < program.Size(); ++bid) {
auto* block = const_cast<ProgramDesc&>(program).MutableBlock(bid);
for (auto* op : block->AllOps()) {
if (op->HasAttr("use_mkldnn")) {
op->SetAttr("use_mkldnn", true);
}
}
}
#endif
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/executor.h
浏览文件 @ 54002c3b
......@@ -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
*
......@@ -81,6 +88,8 @@ class Executor {
const std::string& feed_holder_name = "feed",
const std::string& fetch_holder_name = "fetch");
void EnableMKLDNN(const ProgramDesc& program);
private:
const platform::Place place_;
};
......
paddle/fluid/framework/framework.proto
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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/framework/reader.h
浏览文件 @ 54002c3b
......@@ -35,14 +35,15 @@ class ReaderBase {
class DecoratedReader : public ReaderBase {
public:
explicit DecoratedReader(ReaderBase* reader) : ReaderBase(), reader_(reader) {
explicit DecoratedReader(const std::shared_ptr<ReaderBase>& reader)
: ReaderBase(), reader_(reader) {
PADDLE_ENFORCE_NOT_NULL(reader_);
}
void ReInit() override { reader_->ReInit(); }
protected:
ReaderBase* reader_;
std::shared_ptr<ReaderBase> reader_;
};
class FileReader : public ReaderBase {
......@@ -64,7 +65,7 @@ class ReaderHolder {
public:
void Reset(ReaderBase* reader) { reader_.reset(reader); }
ReaderBase* Get() const { return reader_.get(); }
std::shared_ptr<ReaderBase> Get() const { return reader_; }
void ReadNext(std::vector<LoDTensor>* out) {
PADDLE_ENFORCE_NOT_NULL(reader_);
......@@ -76,7 +77,7 @@ class ReaderHolder {
}
private:
std::unique_ptr<ReaderBase> reader_;
std::shared_ptr<ReaderBase> reader_;
};
} // namespace framework
......
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 54002c3b
......@@ -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/inference/tests/book/test_inference_image_classification.cc
浏览文件 @ 54002c3b
......@@ -21,7 +21,6 @@ DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
DEFINE_int32(batch_size, 1, "Batch size of input data");
DEFINE_int32(repeat, 1, "Running the inference program repeat times");
DEFINE_bool(skip_cpu, false, "Skip the cpu test");
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
TEST(inference, image_classification) {
if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
......@@ -59,10 +58,8 @@ TEST(inference, image_classification) {
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: ---";
LOG(INFO) << "Batch size is " << FLAGS_batch_size;
LOG(INFO) << "FLAGS_use_mkldnn: " << FLAGS_use_mkldnn;
TestInference<paddle::platform::CPUPlace, false, true>(
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined,
FLAGS_use_mkldnn);
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
LOG(INFO) << output1.dims();
}
......
paddle/fluid/inference/tests/book/test_inference_nlp.cc
浏览文件 @ 54002c3b
......@@ -27,7 +27,6 @@ limitations under the License. */
DEFINE_string(model_path, "", "Directory of the inference model.");
DEFINE_string(data_file, "", "File of input index data.");
DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_int32(num_threads, 1, "Number of threads should be used");
......@@ -190,9 +189,6 @@ TEST(inference, nlp) {
std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
inference_program = InitProgram(&executor, scope.get(), FLAGS_model_path,
/*model combined*/ false);
if (FLAGS_use_mkldnn) {
EnableMKLDNN(inference_program);
}
// always prepare context
std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
ctx = executor.Prepare(*inference_program, 0);
......
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ 54002c3b
......@@ -22,6 +22,8 @@ limitations under the License. */
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/profiler.h"
DECLARE_bool(use_mkldnn);
template <typename T>
void SetupTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims, T lower, T upper) {
......@@ -133,24 +135,11 @@ std::vector<std::vector<int64_t>> GetFeedTargetShapes(
return feed_target_shapes;
}
void EnableMKLDNN(
const std::unique_ptr<paddle::framework::ProgramDesc>& program) {
for (size_t bid = 0; bid < program->Size(); ++bid) {
auto* block = program->MutableBlock(bid);
for (auto* op : block->AllOps()) {
if (op->HasAttr("use_mkldnn")) {
op->SetAttr("use_mkldnn", true);
}
}
}
}
template <typename Place, bool CreateVars = true, bool PrepareContext = false>
void TestInference(const std::string& dirname,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const int repeat = 1, const bool is_combined = false,
const bool use_mkldnn = false) {
const int repeat = 1, const bool is_combined = false) {
// 1. Define place, executor, scope
auto place = Place();
auto executor = paddle::framework::Executor(place);
......@@ -182,9 +171,6 @@ void TestInference(const std::string& dirname,
"init_program",
paddle::platform::DeviceContextPool::Instance().Get(place));
inference_program = InitProgram(&executor, scope, dirname, is_combined);
if (use_mkldnn) {
EnableMKLDNN(inference_program);
}
}
// Disable the profiler and print the timing information
paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
......@@ -210,7 +196,10 @@ void TestInference(const std::string& dirname,
fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
}
// 6. Run the inference program
// 6. If export Flags_use_mkldnn=True, use mkldnn related ops.
if (FLAGS_use_mkldnn) executor.EnableMKLDNN(*inference_program);
// 7. Run the inference program
{
if (!CreateVars) {
// If users don't want to create and destroy variables every time they
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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/arg_max_op.cc 0 → 100644
浏览文件 @ 54002c3b
/* 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/arg_min_max_op_base.h"
REGISTER_OPERATOR(arg_max, paddle::operators::ArgMinMaxOp,
paddle::operators::ArgMaxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
arg_max,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext, float>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext, double>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
int64_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
int32_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
int16_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext, size_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
uint8_t>);
paddle/fluid/operators/arg_max_op.cu 0 → 100644
浏览文件 @ 54002c3b
/* 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/arg_min_max_op_base.h"
REGISTER_OP_CUDA_KERNEL(
arg_max,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext, float>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
double>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
int64_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
int32_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
int16_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
size_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
uint8_t>);
paddle/fluid/operators/arg_min_max_op_base.h 0 → 100644
浏览文件 @ 54002c3b
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <type_traits>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace operators {
enum ArgMinMaxType { kArgMin, kArgMax };
template <typename DeviceContext, typename T, typename Tout, int64_t Rank,
ArgMinMaxType argMinMaxValue>
struct ArgMinMaxFunctor {};
#define DECLARE_ARG_MIN_MAX_FUNCTOR(eigen_op_type, enum_argminmax_value) \
template <typename DeviceContext, typename T, typename Tout, int64_t Rank> \
struct ArgMinMaxFunctor<DeviceContext, T, Tout, Rank, \
enum_argminmax_value> { \
void operator()(const DeviceContext& ctx, const framework::LoDTensor& in, \
framework::LoDTensor* out, int64_t axis) { \
auto in_eigen = framework::EigenTensor<T, Rank>::From(in); \
auto out_eigen = framework::EigenTensor<Tout, Rank - 1>::From(*out); \
out_eigen.device(*(ctx.eigen_device())) = \
in_eigen.eigen_op_type(axis).template cast<Tout>(); \
} \
}
DECLARE_ARG_MIN_MAX_FUNCTOR(argmin, ArgMinMaxType::kArgMin);
DECLARE_ARG_MIN_MAX_FUNCTOR(argmax, ArgMinMaxType::kArgMax);
template <typename DeviceContext, typename T, typename Tout,
ArgMinMaxType EnumArgMinMaxValue>
class ArgMinMaxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& x = *(ctx.Input<framework::LoDTensor>("X"));
auto& out = *(ctx.Output<framework::LoDTensor>("Out"));
out.mutable_data<Tout>(ctx.GetPlace());
auto axis = ctx.Attr<int64_t>("axis");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
#define CALL_ARG_MINMAX_FUNCTOR(rank) \
ArgMinMaxFunctor<DeviceContext, T, Tout, rank, EnumArgMinMaxValue> \
functor##rank; \
functor##rank(dev_ctx, x, &out, axis)
switch (x.dims().size()) {
case 1:
CALL_ARG_MINMAX_FUNCTOR(1);
break;
case 2:
CALL_ARG_MINMAX_FUNCTOR(2);
break;
case 3:
CALL_ARG_MINMAX_FUNCTOR(3);
break;
case 4:
CALL_ARG_MINMAX_FUNCTOR(4);
break;
case 5:
CALL_ARG_MINMAX_FUNCTOR(5);
break;
case 6:
CALL_ARG_MINMAX_FUNCTOR(6);
break;
default:
PADDLE_THROW(
"%s operator doesn't supports tensors whose ranks are greater "
"than 6.",
(EnumArgMinMaxValue == kArgMin ? "argmin" : "argmax"));
break;
#undef CALL_ARG_MINMAX_FUNCTOR
}
}
};
template <typename DeviceContext, typename T>
using ArgMinKernel =
ArgMinMaxKernel<DeviceContext, T, int64_t, ArgMinMaxType::kArgMin>;
template <typename DeviceContext, typename T>
using ArgMaxKernel =
ArgMinMaxKernel<DeviceContext, T, int64_t, ArgMinMaxType::kArgMax>;
class ArgMinMaxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
const auto& x_dims = ctx->GetInputDim("X");
int64_t axis = ctx->Attrs().Get<int64_t>("axis");
PADDLE_ENFORCE(axis >= -x_dims.size() && axis < x_dims.size(),
"'axis' must be inside [-Rank(X), Rank(X))");
auto x_rank = x_dims.size();
if (axis < 0) axis += x_rank;
std::vector<int64_t> vec;
for (int64_t i = 0; i < axis; i++) vec.push_back(x_dims[i]);
for (int64_t i = axis + 1; i < x_rank; i++) vec.push_back(x_dims[i]);
ctx->SetOutputDim("Out", framework::make_ddim(vec));
}
};
class BaseArgMinMaxOpMaker : public framework::OpProtoAndCheckerMaker {
protected:
virtual const char* OpName() const = 0;
virtual const char* Name() const = 0;
public:
void Make() override {
AddInput("X", "Input tensor.");
AddOutput("Out", "Output tensor.");
AddAttr<int64_t>("axis", "The axis in which to compute the arg indics.");
AddComment(string::Sprintf(R"DOC(
%s Operator.
Computes the indices of the %s elements of the input tensor's element
along the provided axis.
)DOC",
OpName(), Name()));
}
};
class ArgMinOpMaker : public BaseArgMinMaxOpMaker {
protected:
const char* OpName() const override { return "ArgMin"; }
const char* Name() const override { return "min"; }
};
class ArgMaxOpMaker : public BaseArgMinMaxOpMaker {
protected:
const char* OpName() const override { return "ArgMax"; }
const char* Name() const override { return "max"; }
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/arg_min_op.cc 0 → 100644
浏览文件 @ 54002c3b
/* 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/arg_min_max_op_base.h"
REGISTER_OPERATOR(arg_min, paddle::operators::ArgMinMaxOp,
paddle::operators::ArgMinOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
arg_min,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext, float>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext, double>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
int64_t>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
int32_t>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
int16_t>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext, size_t>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
uint8_t>);
paddle/fluid/operators/arg_min_op.cu 0 → 100644
浏览文件 @ 54002c3b
/* 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/arg_min_max_op_base.h"
REGISTER_OP_CUDA_KERNEL(
arg_min,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext, float>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
double>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
int64_t>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
int32_t>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
int16_t>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
size_t>,
paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
uint8_t>);
paddle/fluid/operators/batch_norm_mkldnn_op.cc
浏览文件 @ 54002c3b
......@@ -19,10 +19,17 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using batch_norm_bwd = mkldnn::batch_normalization_backward;
using batch_norm_fwd = mkldnn::batch_normalization_forward;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory;
using platform::to_void_cast;
template <typename T>
using EigenArrayMap =
......@@ -64,21 +71,12 @@ void run_batch_norm_op(Args &&... args) {
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
template <typename T>
inline void *cast_const_to_void(const T *t) {
return static_cast<void *>(const_cast<T *>(t));
}
} // namespace
template <typename T>
class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto data_layout_str = ctx.Attr<std::string>("data_layout");
auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
"MKLDNN batch normalization handles only NCHW data layout");
const float epsilon = ctx.Attr<float>("epsilon");
const float momentum = ctx.Attr<float>("momentum");
const bool is_test = ctx.Attr<bool>("is_test");
......@@ -99,41 +97,53 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *shift = ctx.Input<Tensor>("Bias");
y->mutable_data<T>(ctx.GetPlace());
mean_out->mutable_data<T>(ctx.GetPlace());
variance_out->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
x->format() != memory::format::format_undef,
"Wrong layout/format set for Input x tensor");
const T *x_data = x->data<T>();
const T *mean_data = mean->data<T>();
const T *variance_data = variance->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
T *mean_out_data = mean_out->mutable_data<T>(ctx.GetPlace());
T *variance_out_data = variance_out->mutable_data<T>(ctx.GetPlace());
T *batch_mean_data = nullptr;
T *batch_variance_data = nullptr;
if (!is_test) {
batch_mean->mutable_data<T>(ctx.GetPlace());
batch_variance->mutable_data<T>(ctx.GetPlace());
batch_mean_data = batch_mean->mutable_data<T>(ctx.GetPlace());
batch_variance_data = batch_variance->mutable_data<T>(ctx.GetPlace());
}
auto propagation = is_test == true ? mkldnn::prop_kind::forward_scoring
: mkldnn::prop_kind::forward_training;
auto dims = paddle::framework::vectorize2int(x->dims());
auto src_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto dst_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto src_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine};
auto dst_pd = mkldnn::memory::primitive_desc{dst_md, mkldnn_engine};
auto src = mkldnn::memory{src_pd, cast_const_to_void(x->data<T>())};
auto dst = mkldnn::memory{dst_pd, y->data<T>()};
auto src_tz = paddle::framework::vectorize2int(x->dims());
auto scale_tz = paddle::framework::vectorize2int(scale->dims());
PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
const unsigned int ic = scale_tz[0];
unsigned flags = mkldnn::use_scale_shift;
if (is_test) flags |= mkldnn::use_global_stats;
// create mkldnn memory from input x tensor
auto src_memory =
memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
to_void_cast(x_data));
// create primitive descriptor for batch norm forward
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
auto batch_norm_fwd_desc =
bn_fwd_types::op_desc{propagation, src_md, epsilon, flags};
auto batch_norm_fwd_pd =
bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine};
auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
propagation, src_memory.get_primitive_desc().desc(), epsilon, flags};
std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_fwd_pd =
std::shared_ptr<batch_norm_fwd::primitive_desc>(
new batch_norm_fwd::primitive_desc(batch_norm_fwd_desc,
mkldnn_engine));
const unsigned int ic = dims[1];
// Save the pd to be used in backward pass
const std::string key = ctx.op().Output("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
......@@ -143,73 +153,58 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
shift->data<T>() + ic, &scaleshift_data);
auto scaleshift_memory = mkldnn::memory{
batch_norm_fwd_pd.weights_primitive_desc(), scaleshift_data.data()};
// crate mkldnn memory for weights(scale/shift)
auto scaleshift_memory = memory(batch_norm_fwd_pd->weights_primitive_desc(),
scaleshift_data.data());
if (is_test) {
auto mean_memory = mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(),
cast_const_to_void(mean->data<T>())};
// create mkldnn memory for output y tensor
auto dst_memory = memory(batch_norm_fwd_pd->dst_primitive_desc(), y_data);
if (is_test) {
// create mkldnn memory for stats (as input)
auto mean_memory = memory(batch_norm_fwd_pd->mean_primitive_desc(),
to_void_cast(mean_data));
auto variance_memory =
mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(),
cast_const_to_void(variance->data<T>())};
memory(batch_norm_fwd_pd->variance_primitive_desc(),
to_void_cast(variance_data));
run_batch_norm_op<typename bn_fwd_types::op_type>(
batch_norm_fwd_pd, src, (const mkldnn::primitive::at &)mean_memory,
*batch_norm_fwd_pd, src_memory,
(const mkldnn::primitive::at &)mean_memory,
(const mkldnn::primitive::at &)variance_memory, scaleshift_memory,
dst);
dst_memory);
} else {
// create mkldnn memory for stats (as output)
auto mean_memory =
mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(),
cast_const_to_void(batch_mean->data<T>())};
auto variance_memory =
mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(),
cast_const_to_void(batch_variance->data<T>())};
memory(batch_norm_fwd_pd->mean_primitive_desc(), batch_mean_data);
auto variance_memory = memory(
batch_norm_fwd_pd->variance_primitive_desc(), batch_variance_data);
run_batch_norm_op<bn_fwd_types::op_type>(batch_norm_fwd_pd, src,
scaleshift_memory, dst,
run_batch_norm_op<bn_fwd_types::op_type>(*batch_norm_fwd_pd, src_memory,
scaleshift_memory, dst_memory,
mean_memory, variance_memory);
}
if (!is_test) {
const unsigned int in = dims[0];
const unsigned int sample_size = x->numel() / in / ic;
// saved_xx is use just in this batch of data
EigenVectorArrayMap<T> saved_mean_e(
batch_mean->mutable_data<T>(ctx.GetPlace()), ic);
EigenVectorArrayMap<T> saved_variance_e(
batch_variance->mutable_data<T>(ctx.GetPlace()), ic);
saved_mean_e.setZero();
saved_variance_e.setZero();
const unsigned int x_arr_size = in * ic;
ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, x_arr_size);
for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
saved_mean_e(nc % ic) += x_arr.col(nc).sum();
}
saved_mean_e /= in * sample_size;
for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
saved_variance_e(nc % ic) +=
(x_arr.col(nc) - saved_mean_e(nc % ic)).matrix().squaredNorm();
}
saved_variance_e /= in * sample_size;
ConstEigenVectorArrayMap<T> mean_arr{mean->data<T>(), ic};
ConstEigenVectorArrayMap<T> variance_arr{variance->data<T>(), ic};
EigenVectorArrayMap<T> running_mean_arr(
mean_out->mutable_data<T>(ctx.GetPlace()), ic);
EigenVectorArrayMap<T> running_var_arr(
variance_out->mutable_data<T>(ctx.GetPlace()), ic);
// mkldnn only compute stats for current batch
// so we need compute momentum stats via Eigen lib
EigenVectorArrayMap<T> batch_mean_e(batch_mean_data, ic);
EigenVectorArrayMap<T> batch_variance_e(batch_variance_data, ic);
ConstEigenVectorArrayMap<T> mean_e(mean_data, ic);
ConstEigenVectorArrayMap<T> variance_e{variance_data, ic};
EigenVectorArrayMap<T> running_mean_e(mean_out_data, ic);
EigenVectorArrayMap<T> running_variance_e(variance_out_data, ic);
auto one_minus_momentum = 1. - momentum;
running_mean_arr =
mean_arr * momentum + saved_mean_e * one_minus_momentum;
running_var_arr =
variance_arr * momentum + saved_variance_e * one_minus_momentum;
running_mean_e = mean_e * momentum + batch_mean_e * one_minus_momentum;
running_variance_e =
variance_e * momentum + batch_variance_e * one_minus_momentum;
}
y->set_layout(DataLayout::kMKLDNN);
y->set_format(
(memory::format)dst_memory.get_primitive_desc().desc().data.format);
}
};
......@@ -217,11 +212,6 @@ template <typename T>
class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext &ctx) const override {
auto data_layout_str = ctx.Attr<std::string>("data_layout");
auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
"MKLDNN batch normalization handles only NCHW data layout");
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine();
......@@ -238,88 +228,132 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto *diff_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *diff_shift = ctx.Output<Tensor>(framework::GradVarName("Bias"));
diff_x->mutable_data<T>(ctx.GetPlace());
diff_scale->mutable_data<T>(ctx.GetPlace());
diff_shift->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
diff_y->format() != memory::format::format_undef,
"Wrong layout/format set for Input diff_y tensor");
const T *x_data = x->data<T>();
const T *diff_y_data = diff_y->data<T>();
const T *batch_mean_data = batch_mean->data<T>();
const T *batch_variance_data = batch_variance->data<T>();
const T *scale_data = scale->data<T>();
const T *shift_data = shift->data<T>();
T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
T *diff_scale_data = diff_scale->mutable_data<T>(ctx.GetPlace());
T *diff_shift_data = diff_shift->mutable_data<T>(ctx.GetPlace());
auto src_tz = paddle::framework::vectorize2int(x->dims());
auto diff_src_tz = src_tz;
auto dst_tz = src_tz;
auto diff_dst_tz = dst_tz;
auto scale_tz = paddle::framework::vectorize2int(scale->dims());
PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
const unsigned int ic = scale_tz[0];
// Retrieve bn_fwd_pd from device context
const std::string key = ctx.op().Input("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto batch_norm_fwd_pd =
std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
dev_ctx.GetBlob(key_batch_norm_fwd_pd));
PADDLE_ENFORCE(batch_norm_fwd_pd != nullptr,
"Fail to find batch_norm_fwd_pd in device context");
auto dims = paddle::framework::vectorize2int(x->dims());
unsigned flags = mkldnn::use_scale_shift | !mkldnn::use_global_stats;
using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
auto src_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto dst_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto diff_src_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto diff_dst_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
// create mkldnn memory from input diff_y tensor
auto user_diff_dst_memory =
memory({{{diff_dst_tz}, memory::data_type::f32, diff_y->format()},
mkldnn_engine},
to_void_cast(diff_y_data));
using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
// create mkldnn memory from input x tensor
auto src_memory =
memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
to_void_cast(x_data));
auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
mkldnn::prop_kind::forward_training, src_md, epsilon, flags};
auto batch_norm_fwd_pd =
bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine};
// for diff_dst, try to use same format as dst in forward pass
auto diff_dst_pd = batch_norm_fwd_pd.get()->dst_primitive_desc();
auto diff_dst_md = diff_dst_pd.desc();
// create primitive descriptor for batch norm backward
unsigned flags = mkldnn::use_scale_shift;
auto batch_norm_bwd_desc = bn_bwd_types::op_desc{
mkldnn::prop_kind::backward, diff_dst_md, dst_md, epsilon, flags};
mkldnn::prop_kind::backward, diff_dst_md,
src_memory.get_primitive_desc().desc(), epsilon, flags};
auto batch_norm_bwd_pd = bn_bwd_types::op_prim{
batch_norm_bwd_desc, mkldnn_engine, batch_norm_fwd_pd};
auto src = mkldnn::memory{{src_md, mkldnn_engine},
cast_const_to_void(x->data<T>())};
auto mean = mkldnn::memory{batch_norm_bwd_pd.mean_primitive_desc(),
cast_const_to_void(batch_mean->data<T>())};
auto variance =
mkldnn::memory{batch_norm_bwd_pd.variance_primitive_desc(),
cast_const_to_void(batch_variance->data<T>())};
auto diff_dst = mkldnn::memory{{diff_dst_md, mkldnn_engine},
cast_const_to_void(diff_y->data<T>())};
batch_norm_bwd_desc, mkldnn_engine, *batch_norm_fwd_pd};
// reorder user_diff_dst if it's not in preferred format
auto diff_dst_memory = user_diff_dst_memory;
primitive reorder_diff_dst;
bool is_diff_dst_reordered = false;
if (diff_dst_pd != user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory = memory(diff_dst_pd);
reorder_diff_dst = reorder(user_diff_dst_memory, diff_dst_memory);
is_diff_dst_reordered = true;
}
const unsigned int ic = dims[1];
// create mkldnn memory for input tensors (src/mean/variance)
auto mean_memory = memory(batch_norm_bwd_pd.mean_primitive_desc(),
to_void_cast(batch_mean_data));
auto variance_memory = memory(batch_norm_bwd_pd.variance_primitive_desc(),
to_void_cast(batch_variance_data));
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size);
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
shift->data<T>() + ic, &scaleshift_data);
copy_to_weights(scale_data, scale_data + ic, shift_data, shift_data + ic,
&scaleshift_data);
auto scaleshift_memory = mkldnn::memory{
batch_norm_bwd_pd.weights_primitive_desc(), scaleshift_data.data()};
// create mkldnn memory for input tensors (scale/shift)
auto scaleshift_memory = memory(batch_norm_bwd_pd.weights_primitive_desc(),
scaleshift_data.data());
// create mkldnn memory for output diff weights (combined scale/shift)
std::vector<T> diff_scaleshift_data;
diff_scaleshift_data.reserve(scaleshift_size);
copy_to_weights(diff_scale->data<T>(), diff_scale->data<T>() + ic,
diff_shift->data<T>(), diff_shift->data<T>() + ic,
&diff_scaleshift_data);
auto diff_scaleshift_memory =
mkldnn::memory{batch_norm_bwd_pd.diff_weights_primitive_desc(),
diff_scaleshift_data.data()};
auto diff_src = mkldnn::memory{{diff_src_md, mkldnn_engine},
static_cast<void *>(diff_x->data<T>())};
run_batch_norm_op<bn_bwd_types::op_type>(
batch_norm_bwd_pd, src, mean, variance, diff_dst, scaleshift_memory,
diff_src, diff_scaleshift_memory);
memory(batch_norm_bwd_pd.diff_weights_primitive_desc(),
diff_scaleshift_data.data());
// here assume diff_src is in the same format of src
auto diff_src_memory = memory(src_memory.get_primitive_desc(), diff_x_data);
// finally create batch_norm backward primitive
auto batch_norm_bwd_prim =
batch_norm_bwd(batch_norm_bwd_pd, src_memory, mean_memory,
variance_memory, diff_dst_memory, scaleshift_memory,
diff_src_memory, diff_scaleshift_memory);
// execute optional reorder and batch_norm backward primitive
std::vector<primitive> pipeline;
if (is_diff_dst_reordered) pipeline.push_back(reorder_diff_dst);
pipeline.push_back(batch_norm_bwd_prim);
stream(stream::kind::eager).submit(pipeline).wait();
// copy back diff sacle/shift to output tensors (diff scale/shift)
diff_scaleshift_data.resize(scaleshift_size);
auto it = std::begin(diff_scaleshift_data);
std::copy(it, std::next(it, ic), diff_scale->data<T>());
std::copy(it, std::next(it, ic), diff_scale_data);
std::copy(std::next(it, ic), std::end(diff_scaleshift_data),
diff_shift->data<T>());
diff_shift_data);
// set layout/format of output tensors
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory.get_primitive_desc()
.desc()
.data.format);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(batch_norm, MKLDNN, paddle::platform::CPUPlace,
REGISTER_OP_KERNEL(batch_norm, MKLDNN, ::paddle::platform::CPUPlace,
ops::BatchNormMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, paddle::platform::CPUPlace,
REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::BatchNormMKLDNNGradOpKernel<float>);
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 54002c3b
......@@ -110,19 +110,19 @@ class BatchNormOp : public framework::OperatorWithKernel {
ctx.Input<Tensor>("Variance")->type()),
"Variance input should be of float type");
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::LibraryType library = framework::LibraryType::kPlain;
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library_);
library);
}
};
......@@ -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")
......@@ -368,19 +370,21 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
PADDLE_THROW("can't find Y@GRAD");
}
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
framework::LibraryType library = framework::LibraryType::kPlain;
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout_, library_);
layout, library);
}
};
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
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 @ 54002c3b
// 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 @ 54002c3b
/* 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 @ 54002c3b
// 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 @ 54002c3b
/* 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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
// 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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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/reader/create_batch_reader_op.cc
浏览文件 @ 54002c3b
......@@ -20,7 +20,7 @@ namespace reader {
class BatchReader : public framework::DecoratedReader {
public:
BatchReader(ReaderBase* reader, int batch_size)
BatchReader(const std::shared_ptr<ReaderBase>& reader, int batch_size)
: DecoratedReader(reader), batch_size_(batch_size) {
buffer_.reserve(batch_size_);
}
......
paddle/fluid/operators/reader/create_custom_reader_op.cc
浏览文件 @ 54002c3b
......@@ -22,7 +22,8 @@ namespace reader {
class CustomReader : public framework::DecoratedReader {
public:
CustomReader(ReaderBase* reader, const framework::BlockDesc& sub_block,
CustomReader(const std::shared_ptr<ReaderBase>& reader,
const framework::BlockDesc& sub_block,
const std::vector<std::string>& source_var_names,
const std::vector<std::string>& sink_var_names)
: DecoratedReader(reader),
......
paddle/fluid/operators/reader/create_double_buffer_reader_op.cc
浏览文件 @ 54002c3b
......@@ -34,7 +34,8 @@ static constexpr size_t kChannelSize = 1; // kCacheSize - 2
class DoubleBufferReader : public framework::DecoratedReader {
public:
explicit DoubleBufferReader(
ReaderBase* reader, platform::Place target_place = platform::CPUPlace())
const std::shared_ptr<ReaderBase>& reader,
platform::Place target_place = platform::CPUPlace())
: DecoratedReader(reader), place_(target_place) {
cpu_tensor_cache_.resize(kCacheSize);
gpu_tensor_cache_.resize(kCacheSize);
......
paddle/fluid/operators/reader/create_multi_pass_reader_op.cc
浏览文件 @ 54002c3b
......@@ -21,7 +21,7 @@ namespace reader {
class MultiPassReader : public framework::DecoratedReader {
public:
MultiPassReader(ReaderBase* reader, int pass_num)
MultiPassReader(const std::shared_ptr<ReaderBase>& reader, int pass_num)
: DecoratedReader(reader), pass_num_(pass_num), pass_count_(0) {}
void ReadNext(std::vector<framework::LoDTensor>* out) override {
......
paddle/fluid/operators/reader/create_shuffle_reader_op.cc
浏览文件 @ 54002c3b
......@@ -23,7 +23,8 @@ namespace reader {
class ShuffleReader : public framework::DecoratedReader {
public:
ShuffleReader(ReaderBase* reader, size_t buffer_size, size_t seed = 0)
ShuffleReader(const std::shared_ptr<ReaderBase>& reader, size_t buffer_size,
size_t seed = 0)
: DecoratedReader(reader), buffer_size_(buffer_size), seed_(seed) {
VLOG(10) << "Create shuffle reader of " << reader_;
if (seed_ == 0) {
......
paddle/fluid/operators/reader/create_threaded_reader_op.cc
浏览文件 @ 54002c3b
......@@ -21,7 +21,8 @@ namespace reader {
class ThreadedReader : public framework::DecoratedReader {
public:
explicit ThreadedReader(ReaderBase* reader) : DecoratedReader(reader) {}
explicit ThreadedReader(const std::shared_ptr<ReaderBase>& reader)
: DecoratedReader(reader) {}
void ReadNext(std::vector<framework::LoDTensor>* out) override {
std::lock_guard<std::mutex> lock(mutex_);
......
paddle/fluid/operators/recv_op.cc
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
......@@ -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 @ 54002c3b
此差异已折叠。
paddle/fluid/operators/send_vars_op.cc 已删除 100644 → 0
浏览文件 @ ff55d4c5
此差异已折叠。
paddle/fluid/operators/sgd_op.cc
浏览文件 @ 54002c3b
......@@ -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 @ 54002c3b
此差异已折叠。
paddle/fluid/operators/slice_op.cu 0 → 100644
浏览文件 @ 54002c3b
/* 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 @ 54002c3b
此差异已折叠。
paddle/fluid/operators/sum_op.cc
浏览文件 @ 54002c3b
此差异已折叠。
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 54002c3b
此差异已折叠。
paddle/fluid/platform/cpu_info.cc
浏览文件 @ 54002c3b
此差异已折叠。
paddle/fluid/pybind/pybind.cc
浏览文件 @ 54002c3b
此差异已折叠。
paddle/scripts/paddle_build.sh
浏览文件 @ 54002c3b
此差异已折叠。
paddle/testing/paddle_gtest_main.cc
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/dataset/flowers.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/__init__.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/data_feeder.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/layers/ops.py
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/fluid/tests/unittests/test_slice_op.py 0 → 100644
浏览文件 @ 54002c3b
此差异已折叠。
python/paddle/v2/dataset/flowers.py
浏览文件 @ 54002c3b
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或