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提交 4e9d7ddb 编写于 作者: G guosheng
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into fix-beam_search-dev

上级 f4bdfbc6 6d6996af
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.pre-commit-config.yaml
浏览文件 @ 4e9d7ddb
......@@ -23,7 +23,7 @@ repos:
- id: clang-format-with-version-check
name: clang-format
description: Format files with ClangFormat.
entry: bash ./.clang_format.hook -i
entry: bash ./tools/codestyle/clang_format.hook -i
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
- repo: local
......@@ -52,7 +52,7 @@ repos:
hooks:
- id: copyright_checker
name: copyright_checker
entry: python ./.copyright.hook
entry: python ./tools/codestyle/copyright.hook
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
exclude: (?!.*third_party)^.*$ | (?!.*book)^.*$
Dockerfile
浏览文件 @ 4e9d7ddb
......@@ -76,7 +76,8 @@ RUN easy_install -U pip && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip install pre-commit 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0'
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
#For docstring checker
RUN pip install pylint pytest astroid isort
......
benchmark/fluid/Dockerfile
浏览文件 @ 4e9d7ddb
FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
# Use UBUNTU_MIRROR can speed up apt-get speed.
# ARG UBUNTU_MIRROR
# RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop python-opencv
RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
# exmaple: unset http_proxy && unset https_proxy && python fluid_benchmark.py ...
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
......@@ -14,9 +21,11 @@ RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
RUN chmod +x /usr/bin/paddle_k8s
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl && chmod +x /usr/bin/paddle_k8s
RUN pip install /*.whl && rm -f /*.whl
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD fluid_benchmark.py recordio_converter.py models/ /workspace/
ADD fluid_benchmark.py recordio_converter.py args.py recordio_converter.py run.sh run_fluid_benchmark.sh /workspace/
ADD models/ /workspace/models/
benchmark/fluid/fluid_benchmark.py
浏览文件 @ 4e9d7ddb
......@@ -97,7 +97,7 @@ def dist_transpile(trainer_id, args):
return train_program, fluid.default_startup_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
......@@ -264,8 +264,6 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
break
else:
loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
if args.update_method == "pserver":
exe.bcast_params()
if args.use_reader_op:
num_samples += args.batch_size * args.gpus
else:
......@@ -301,9 +299,18 @@ def print_train_time(start_time, end_time, num_samples):
(num_samples, train_elapsed, examples_per_sec))
def print_paddle_envs():
print('----------- Configuration envs -----------')
for k in os.environ:
if "PADDLE_" in k:
print "ENV %s:%s" % (k, os.environ[k])
print('------------------------------------------------')
def main():
args = parse_args()
print_arguments(args)
print_paddle_envs()
# the unique trainer id, starting from 0, needed by trainer
# only
......
benchmark/fluid/kube_gen_job.py
浏览文件 @ 4e9d7ddb
......@@ -17,6 +17,7 @@ import copy
import argparse
import random
import os
import copy
from kube_templates import pserver, trainer, envs
......@@ -108,10 +109,9 @@ def gen_job():
tn_container["ports"][0]["containerPort"] = spreadport
envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
envs.append({"name": "TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PSERVERS", "value": str(args.pservers)})
envs.append({"name": "PADDLE_TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PADDLE_PSERVERS", "value": str(args.pservers)})
envs.append({"name": "ENTRY", "value": args.entry})
envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
envs.append({"name": "PADDLE_PSERVER_PORT", "value": str(args.port)})
# NOTE: these directories below are cluster specific, please modify
# this settings before you run on your own cluster.
......@@ -166,17 +166,23 @@ def gen_job():
tn["spec"]["template"]["spec"]["volumes"] = volumes
tn_container["volumeMounts"] = volumeMounts
ps_container["env"] = envs
ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
ps_container["env"] = copy.deepcopy(envs)
ps_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "PSERVER"
})
tn_container["env"] = envs
if args.disttype == "pserver":
tn_container["env"].append({
"name": "TRAINING_ROLE",
"name": "PADDLE_TRAINING_ROLE",
"value": "TRAINER"
})
elif args.disttype == "nccl2" or args.disttype == "local":
# NCCL2 have no training role, set to plain WORKER
tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
tn_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "WORKER"
})
os.mkdir(args.jobname)
if args.disttype == "pserver":
......
cmake/external/mkldnn.cmake
浏览文件 @ 4e9d7ddb
......@@ -45,7 +45,8 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-unused-result")
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result")
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
ExternalProject_Add(
......@@ -53,7 +54,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "db3424ad44901513c03a1ea31ccaacdf633fbe9f"
GIT_TAG "a29d8487a63afca3d5b8c5bbdbb473cf8ccc6e51"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
......
doc/fluid/api/gen_doc.sh
浏览文件 @ 4e9d7ddb
#!/bin/bash
python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler metric > layers.rst
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer transpiler
do
python gen_doc.py ${module} > ${module}.rst
done
doc/fluid/api/transpiler.rst 0 → 100644
浏览文件 @ 4e9d7ddb
.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
!DO NOT EDIT THIS FILE MANUALLY!
==========
transpiler
==========
DistributeTranspiler
--------------------
.. autoclass:: paddle.fluid.transpiler.DistributeTranspiler
:members:
:noindex:
InferenceTranspiler
-------------------
.. autoclass:: paddle.fluid.transpiler.InferenceTranspiler
:members:
:noindex:
memory_optimize
---------------
.. autofunction:: paddle.fluid.transpiler.memory_optimize
:noindex:
release_memory
--------------
.. autofunction:: paddle.fluid.transpiler.release_memory
:noindex:
HashName
--------
.. autoclass:: paddle.fluid.transpiler.HashName
:members:
:noindex:
RoundRobin
----------
.. autoclass:: paddle.fluid.transpiler.RoundRobin
:members:
:noindex:
doc/fluid/howto/cluster/fluid_cluster_train_cn.md
浏览文件 @ 4e9d7ddb
......@@ -168,13 +168,13 @@ cd /paddle/python/paddle/fluid/tests/book
第二步，启动Parameter Server：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.2 TRAINERS=2 POD_IP=192.168.1.2 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=PSERVER python test_fit_a_line.py
PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.2 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=192.168.1.2 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=PSERVER python test_fit_a_line.py
```
执行命令后请等待出现提示： ```Server listening on 192.168.1.2:6174 ```, 表示Paramter Server已经正常启动。
第三步，启动Trainer：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.3 TRAINERS=2 POD_IP=192.168.1.3 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=TRAINER python test_fit_a_line.py
PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.3 PADDLE_TRAINERS=2 PADDLE_CURRENT_IPP=192.168.1.3 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=TRAINER python test_fit_a_line.py
```
由于我们定义的Trainer的数量是2个，因此需要在另外一个计算节点上再启动一个Trainer。
......
doc/fluid/howto/cluster/fluid_recordio.md
浏览文件 @ 4e9d7ddb
......@@ -114,8 +114,8 @@ def gen_train_list(file_pattern, trainers, trainer_id):
ret_list.append(f)
return ret_list
trainers = int(os.getenv("TRAINERS"))
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
data_file = fluid.layers.io.open_files(
filenames=gen_train_list("./mnist-[0-9]*.recordio", 2, 0),
thread_num=1,
......
doc/fluid/howto/inference/build_and_install_lib_cn.rst
浏览文件 @ 4e9d7ddb
......@@ -13,6 +13,7 @@ cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository
cuda7.5_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda9.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/fluid.tgz>`_
====================== ========================================
从源码编译
......
doc/v2/faq/build_and_install/index_cn.rst
浏览文件 @ 4e9d7ddb
......@@ -213,3 +213,12 @@ virtualenv本身也是Python的一个包，可以用pip进行安装：
保存并关闭文件。
这样，每次打开终端时就会自动启动名为‘paddle’的Python环境了。
10. 通过pip安装的PaddlePaddle在 :code:`import paddle.fluid` 报找不到 :code:`libmkldnn.so` 或 :code:`libmklml_intel.so`
------------------------------------------------------------------------------------------
出现这种问题的原因是在导入 :code:`paddle.fluid` 时需要加载 :code:`libmkldnn.so` 和 :code:`libmklml_intel.so`，
但是系统没有找到该文件。一般通过pip安装PaddlePaddle时会将 :code:`libmkldnn.so` 和 :code:`libmklml_intel.so`
拷贝到 :code:`/usr/local/lib` 路径下，所以解决办法是将该路径加到 :code:`LD_LIBRARY_PATH` 环境变量下，
即： :code:`export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH` 。
**注意**：如果是在虚拟环境中安装PaddlePaddle， :code:`libmkldnn.so` 和 :code:`libmklml_intel.so` 可能不在 :code:`/usr/local/lib` 路径下。
\ No newline at end of file
paddle/contrib/inference/demo/simple_on_word2vec.cc
浏览文件 @ 4e9d7ddb
......@@ -40,10 +40,9 @@ void Main(bool use_gpu) {
//# 2. Prepare input.
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
// For simplicity, we set all the slots with the same data.
......@@ -55,14 +54,12 @@ void Main(bool use_gpu) {
//# 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
LOG(INFO) << "output buffer size: " << outputs.front().data.length();
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
}
// TODO(Superjomn): this is should be free automatically
free(outputs[0].data.data);
}
}
......@@ -86,10 +83,9 @@ void MainThreads(int num_threads, bool use_gpu) {
for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
// 2. Dummy Input Data
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
std::vector<PaddleTensor> inputs(4, tensor);
std::vector<PaddleTensor> outputs;
......@@ -99,13 +95,13 @@ void MainThreads(int num_threads, bool use_gpu) {
// 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "TID: " << tid << ", "
<< "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
<< "output buffer size: " << outputs.front().data.length();
const size_t num_elements =
outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
}
free(outputs[0].data.data);
}
});
}
......
paddle/contrib/inference/high_level_api.md 0 → 100644
浏览文件 @ 4e9d7ddb
# Inference High-level APIs
This document describes the high-level inference APIs one can use to easily deploy a Paddle model for an application.
The APIs are described in `paddle_inference_api.h`, just one header file, and two libaries `libpaddle_fluid.so` and `libpaddle_fluid_api.so` are needed.
## PaddleTensor
We provide the `PaddleTensor` data structure is to give a general tensor interface.
The definition is
```c++
struct PaddleTensor {
std::string name; // variable name.
std::vector<int> shape;
PaddleBuf data; // blob of data.
PaddleDType dtype;
};
```
The data is stored in a continuous memory `PaddleBuf`, and tensor's data type is specified by a `PaddleDType`.
The `name` field is used to specify the name of input variable,
that is important when there are multiple inputs and need to distiuish which variable to set.
## engine
The inference APIs has two different underlying implementation, currently there are two valid engines:
- the native engine, which is consists of the native operators and framework,
- the Anakin engine, which is a Anakin library embeded.
The native engine takes a native Paddle model as input, and supports any model that trained by Paddle,
but the Anakin engine can only take the Anakin model as input(user need to manully transform the format first) and currently not all Paddle models are supported.
```c++
enum class PaddleEngineKind {
kNative = 0, // Use the native Fluid facility.
kAnakin, // Use Anakin for inference.
};
```
## PaddlePredictor and how to create one
The main interface is `PaddlePredictor`, there are following methods
- `bool Run(const std::vector<PaddleTensor>& inputs, std::vector<PaddleTensor>* output_data)`
- take inputs and output `output_data`
- `Clone` to clone a predictor from an existing one, with model parameter shared.
There is a factory method to help create a predictor, and the user takes the ownership of this object.
```c++
template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
```
By specifying the engine kind and config, one can get an specific implementation.
## Reference
- [paddle_inference_api.h](./paddle_inference_api.h)
- [demos](./demo)
paddle/contrib/inference/paddle_inference_api.cc
浏览文件 @ 4e9d7ddb
......@@ -13,3 +13,53 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/contrib/inference/paddle_inference_api.h"
namespace paddle {
PaddleBuf::PaddleBuf(PaddleBuf&& other)
: data_(other.data_),
length_(other.length_),
memory_owned_(other.memory_owned_) {
other.memory_owned_ = false;
other.data_ = nullptr;
other.length_ = 0;
}
PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
// only the buffer with external memory can be copied
assert(!other.memory_owned_);
data_ = other.data_;
length_ = other.length_;
memory_owned_ = other.memory_owned_;
return *this;
}
void PaddleBuf::Resize(size_t length) {
// Only the owned memory can be reset, the external memory can't be changed.
if (length_ == length) return;
assert(memory_owned_);
Free();
data_ = new char[length];
length_ = length;
memory_owned_ = true;
}
void PaddleBuf::Reset(void* data, size_t length) {
Free();
memory_owned_ = false;
data_ = data;
length_ = length;
}
void PaddleBuf::Free() {
if (memory_owned_ && data_) {
assert(length_ > 0);
delete static_cast<char*>(data_);
data_ = nullptr;
length_ = 0;
}
}
} // namespace paddle
\ No newline at end of file
paddle/contrib/inference/paddle_inference_api.h
浏览文件 @ 4e9d7ddb
......@@ -21,6 +21,7 @@ limitations under the License. */
#pragma once
#include <cassert>
#include <memory>
#include <string>
#include <vector>
......@@ -32,12 +33,38 @@ enum PaddleDType {
INT64,
};
struct PaddleBuf {
void* data; // pointer to the data memory.
size_t length; // number of memory bytes.
class PaddleBuf {
public:
PaddleBuf() = default;
PaddleBuf(PaddleBuf&& other);
// Copy only available when memory is managed externally.
explicit PaddleBuf(const PaddleBuf&);
PaddleBuf& operator=(const PaddleBuf&);
// Do not own the memory.
PaddleBuf(void* data, size_t length)
: data_(data), length_(length), memory_owned_{false} {}
// Own memory.
PaddleBuf(size_t length)
: data_(new char[length]), length_(length), memory_owned_(true) {}
// Resize to `length` bytes.
void Resize(size_t length);
// Reset to external memory.
void Reset(void* data, size_t length);
bool empty() const { return length_ == 0; }
void* data() const { return data_; }
size_t length() const { return length_; }
~PaddleBuf() { Free(); }
private:
void Free();
void* data_{nullptr}; // pointer to the data memory.
size_t length_{0}; // number of memory bytes.
bool memory_owned_{true};
};
struct PaddleTensor {
PaddleTensor() = default;
std::string name; // variable name.
std::vector<int> shape;
// TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
......@@ -67,8 +94,9 @@ class PaddlePredictor {
// Predict an record.
// The caller should be responsible for allocating and releasing the memory of
// `inputs`. `inputs` should be alive until Run returns. caller should be
// responsible for releasing the memory of `output_data`.
// `inputs`. `inputs` should be available until Run returns. Caller should be
// responsible for the output tensor's buffer, either allocated or passed from
// outside.
virtual bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data) = 0;
......@@ -81,8 +109,7 @@ class PaddlePredictor {
// The common configs for all the predictors.
struct Config {
std::string model_dir; // path to the model directory.
bool enable_engine{false}; // Enable to execute (part of) the model on
std::string model_dir; // path to the model directory.
};
};
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc
浏览文件 @ 4e9d7ddb
......@@ -48,7 +48,7 @@ bool PaddleInferenceAnakinPredictor::Run(
auto d_tensor_in_p = executor_.get_in(input.name);
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p,
static_cast<float *>(input.data.data),
static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
LOG(ERROR) << "copy data from CPU to GPU error";
......@@ -65,8 +65,11 @@ bool PaddleInferenceAnakinPredictor::Run(
for (auto &output : *output_data) {
auto *tensor = executor_.get_out(output.name);
output.shape = tensor->shape();
if (output.data.length() < tensor->valid_size() * sizeof(float)) {
output.data.Resize(tensor->valid_size() * sizeof(float));
}
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data,
if (cudaMemcpy(output.data.data(),
tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
......
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc
浏览文件 @ 4e9d7ddb
......@@ -37,28 +37,26 @@ TEST(inference, anakin) {
float data[1 * 3 * 224 * 224] = {1.0f};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "input_0",
.shape = std::vector<int>({1, 3, 224, 224}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::FLOAT32};
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.emplace_back(std::move(tensor));
float data_out[1000];
PaddleBuf buf_out{.data = data_out, .length = sizeof(data)};
PaddleTensor tensor_out{.name = "prob_out",
.shape = std::vector<int>({1000, 1}),
.data = buf_out,
.data = PaddleBuf(),
.dtype = PaddleDType::FLOAT32};
std::vector<PaddleTensor> outputs(1, tensor_out);
std::vector<PaddleTensor> outputs;
outputs.emplace_back(std::move(tensor_out));
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
float* data_o = static_cast<float*>(outputs[0].data.data);
float* data_o = static_cast<float*>(outputs[0].data.data());
for (size_t j = 0; j < 1000; ++j) {
LOG(INFO) << "output[" << j << "]: " << data_o[j];
}
......
paddle/contrib/inference/paddle_inference_api_impl.cc
浏览文件 @ 4e9d7ddb
......@@ -178,8 +178,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr),
inputs[i].data.data,
inputs[i].data.length);
inputs[i].data.data(),
inputs[i].data.length());
feeds->push_back(input);
}
return true;
......@@ -241,10 +241,11 @@ bool NativePaddlePredictor::GetFetch(
}
outputs->at(i).shape = shape;
outputs->at(i).data.length = sizeof(float) * data.size();
outputs->at(i).data.data = malloc(outputs->at(i).data.length);
std::memcpy(
outputs->at(i).data.data, data.data(), outputs->at(i).data.length);
auto &buffer = outputs->at(i).data;
if (buffer.empty() || buffer.length() < sizeof(float) * data.size()) {
buffer.Resize(sizeof(float) * data.size());
}
std::memcpy(buffer.data(), data.data(), buffer.length());
outputs->at(i).dtype = PaddleDType::FLOAT32;
// TODO(panyx0718): support other types? fill tensor name? avoid a copy.
}
......
paddle/contrib/inference/test_paddle_inference_api_impl.cc
浏览文件 @ 4e9d7ddb
......@@ -27,13 +27,12 @@ namespace paddle {
PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
PaddleTensor pt;
pt.data.data = t->data<void>();
if (t->type() == typeid(int64_t)) {
pt.data.length = t->numel() * sizeof(int64_t);
pt.data.Reset(t->data<void>(), t->numel() * sizeof(int64_t));
pt.dtype = PaddleDType::INT64;
} else if (t->type() == typeid(float)) {
pt.data.length = t->numel() * sizeof(float);
pt.data.Reset(t->data<void>(), t->numel() * sizeof(float));
pt.dtype = PaddleDType::FLOAT32;
} else {
LOG(FATAL) << "unsupported type.";
......@@ -79,8 +78,8 @@ void MainWord2Vec(bool use_gpu) {
std::vector<PaddleTensor> outputs;
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
ASSERT_EQ(outputs.size(), 1UL);
size_t len = outputs[0].data.length;
float* data = static_cast<float*>(outputs[0].data.data);
size_t len = outputs[0].data.length();
float* data = static_cast<float*>(outputs[0].data.data());
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
......@@ -103,8 +102,6 @@ void MainWord2Vec(bool use_gpu) {
EXPECT_LT(lod_data[i] - data[i], 1e-3);
EXPECT_GT(lod_data[i] - data[i], -1e-3);
}
free(outputs[0].data.data);
}
void MainImageClassification(bool use_gpu) {
......@@ -143,13 +140,12 @@ void MainImageClassification(bool use_gpu) {
std::vector<PaddleTensor> outputs;
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
ASSERT_EQ(outputs.size(), 1UL);
size_t len = outputs[0].data.length;
float* data = static_cast<float*>(outputs[0].data.data);
size_t len = outputs[0].data.length();
float* data = static_cast<float*>(outputs[0].data.data());
float* lod_data = output1.data<float>();
for (size_t j = 0; j < len / sizeof(float); ++j) {
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
}
free(data);
}
void MainThreadsWord2Vec(bool use_gpu) {
......@@ -192,8 +188,8 @@ void MainThreadsWord2Vec(bool use_gpu) {
// check outputs range
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
const size_t len = local_outputs[0].data.length();
float* data = static_cast<float*>(local_outputs[0].data.data());
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
......@@ -205,7 +201,6 @@ void MainThreadsWord2Vec(bool use_gpu) {
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
......@@ -251,14 +246,13 @@ void MainThreadsImageClassification(bool use_gpu) {
// check outputs correctness
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
const size_t len = local_outputs[0].data.length();
float* data = static_cast<float*>(local_outputs[0].data.data());
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
......
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 4e9d7ddb
......@@ -73,6 +73,9 @@ void BroadcastOpHandle::RunImpl() {
int root_id = boost::get<platform::CUDAPlace>(in_tensor.place()).device;
std::vector<std::function<void()>> broadcast_calls;
int type = platform::ToNCCLDataType(in_tensor.type());
size_t numel = static_cast<size_t>(in_tensor.numel());
for (auto out_var_handle : out_var_handles) {
Variable *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
......@@ -87,13 +90,11 @@ void BroadcastOpHandle::RunImpl() {
send_recv_buffer = const_cast<void *>(in_tensor.data<void>());
out_handle = out_var_handle;
} else {
send_recv_buffer =
VariableVisitor::GetMutableTensor(out_var).mutable_data(
out_var_handle->place_);
send_recv_buffer = VariableVisitor::GetMutableTensor(out_var)
.Resize(in_tensor.dims())
.mutable_data(out_var_handle->place_);
}
int type = platform::ToNCCLDataType(in_tensor.type());
size_t numel = static_cast<size_t>(in_tensor.numel());
broadcast_calls.emplace_back(
[send_recv_buffer, numel, type, root_id, &nccl_ctx] {
PADDLE_ENFORCE(platform::dynload::ncclBcast(
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 4e9d7ddb
......@@ -57,6 +57,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
for (auto &p : params) {
grad_names_.insert(GradVarName(p));
}
balance_vars_.resize(places_.size(), 0);
}
void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
......@@ -140,11 +141,30 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
checker(op.InputArgumentNames(), recv_vars);
}
size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
for (auto var_name : var_names) {
auto var_desc = all_vars_.at(var_name);
PADDLE_ENFORCE_NOT_NULL(var_desc);
auto dim = framework::make_ddim(var_desc->GetShape());
int64_t numel = framework::product(dim);
PADDLE_ENFORCE_GT(numel, 0);
numel_sum += numel;
}
auto smallest =
std::min_element(std::begin(balance_vars_), std::end(balance_vars_));
size_t dev_id =
static_cast<size_t>(std::distance(std::begin(balance_vars_), smallest));
balance_vars_[dev_id] += numel_sum;
return dev_id;
}
std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
const ProgramDesc &program) const {
std::unordered_map<std::string, VarDesc *> all_vars;
for (auto *var : program.Block(0).AllVars()) {
all_vars[var->Name()] = var;
all_vars_.emplace(var->Name(), var);
}
auto graph = new SSAGraph();
......@@ -161,35 +181,16 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
auto send_vars = FindDistTrainSendVars(program);
auto recv_vars = FindDistTrainRecvVars(program);
std::vector<std::unordered_set<std::string>> var_name_on_devices;
std::vector<std::unordered_set<std::string>> bcast_var_name_set;
var_name_on_devices.resize(places_.size());
bcast_var_name_set.resize(places_.size());
size_t cur_device_id = 0;
std::vector<int64_t> balance_grads(places_.size(), 0);
auto get_appropriate_dev = [&](std::string &g_name) -> size_t {
auto var_desc = all_vars.at(g_name);
PADDLE_ENFORCE_NOT_NULL(var_desc);
auto dim = framework::make_ddim(var_desc->GetShape());
int64_t numel = framework::product(dim);
PADDLE_ENFORCE_GE(numel, 0);
auto smallest =
std::min_element(std::begin(balance_grads), std::end(balance_grads));
size_t dev_id =
static_cast<size_t>(std::distance(std::begin(balance_grads), smallest));
balance_grads[dev_id] += numel;
return dev_id;
};
bool is_forwarding = true;
for (auto *op : program.Block(0).AllOps()) {
if (boost::get<int>(
op->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kRPC)) {
// append rpc op if program is distributed trainer main program.
// always use the first device
CreateRPCOp(&result, *op);
} else if (IsDistTrainOp(*op, send_vars, recv_vars)) {
CreateDistTrainOp(&result, *op);
......@@ -199,15 +200,19 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
BuildStrategy::GradientScaleStrategy::kCustomized) {
CreateScaleLossGradOp(&result);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
// It also assumes backward op will always follow the forward op in
// the block.
is_forwarding = false;
} else {
int op_dev_id = GetOpDeviceID(var_name_on_devices, *op);
int op_dev_id = GetOpDeviceID(*op);
if (op_dev_id == -1) { // var on all device
CreateComputationalOps(&result, *op, places_.size());
} else {
CreateComputationalOp(&result, *op, op_dev_id);
for (auto &var_name : op->OutputArgumentNames()) {
var_name_on_devices[op_dev_id].emplace(var_name);
var_name_on_devices_.emplace(var_name, op_dev_id);
}
}
if (!is_forwarding && places_.size() > 1) {
......@@ -230,19 +235,22 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
cur_device_id = get_appropriate_dev(g_name);
cur_device_id = GetAppropriateDeviceID({g_name});
CreateReduceOp(&result, g_name, cur_device_id);
var_name_on_devices[cur_device_id].emplace(g_name);
var_name_on_devices_.emplace(g_name, cur_device_id);
bcast_var_name_set[cur_device_id].emplace(p_name);
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
if (IsSparseGradient(all_vars, g_name)) {
if (IsSparseGradient(g_name)) {
CreateReduceOp(&result, g_name, 0);
CreateBroadcastOp(&result, g_name, 0);
} else {
InsertAllReduceOp(&result, g_name);
}
break;
default:
LOG(FATAL) << "Unknown reduce strategy ";
break;
}
}
} catch (boost::bad_get e) {
......@@ -261,7 +269,7 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
/*
Dependency graph has been constructed. However, there are still data
harzaeds need to be handled.
hazards need to be handled.
*/
PolishGraphToSupportDataHazards(&result);
......@@ -273,11 +281,9 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
return std::unique_ptr<SSAGraph>(graph);
}
bool MultiDevSSAGraphBuilder::IsSparseGradient(
const std::unordered_map<std::string, VarDesc *> &all_vars,
const std::string &og) const {
PADDLE_ENFORCE(all_vars.count(og) != 0);
if (all_vars.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
bool MultiDevSSAGraphBuilder::IsSparseGradient(const std::string &og) const {
PADDLE_ENFORCE(all_vars_.count(og) != 0);
if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
return true;
}
return false;
......@@ -345,7 +351,7 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(SSAGraph *result,
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
auto var = new VarHandle(vars.size() - 1, i, og, p);
auto var = new VarHandle(vars.size(), i, og, p);
vars.emplace_back(var);
op_handle->AddOutput(var);
}
......@@ -363,24 +369,23 @@ bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
return is_pg_once;
}
int MultiDevSSAGraphBuilder::GetOpDeviceID(
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const {
int MultiDevSSAGraphBuilder::GetOpDeviceID(const OpDesc &op) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
int var_dev_id = -1;
for (auto &var_name : op.InputArgumentNames()) {
if (var_dev_id != -1) break;
for (size_t i = 0; i < var_name_on_devices.size(); ++i) {
if (var_name_on_devices[i].count(var_name)) {
var_dev_id = static_cast<int>(i);
break;
}
for (auto &varname : op.InputArgumentNames()) {
int dev_id = GetVarDeviceID(varname);
if (dev_id != -1) {
return dev_id;
}
}
return var_dev_id;
return -1;
}
int MultiDevSSAGraphBuilder::GetVarDeviceID(const std::string &varname) const {
auto got = var_name_on_devices_.find(varname);
return got == var_name_on_devices_.end() ? -1 : got->second;
}
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(SSAGraph *result) const {
......@@ -442,13 +447,14 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(SSAGraph *result,
op_handle->AddInput(prev_grad.get());
}
auto &vars = result->vars_[dst_dev_id][og];
auto var =
new VarHandle(vars.size() - 1, dst_dev_id, og, places_[dst_dev_id]);
auto var = new VarHandle(vars.size(), dst_dev_id, og, places_[dst_dev_id]);
vars.emplace_back(var);
op_handle->AddOutput(var);
return var;
}
// Find the first occurence of `prev_op_name` and make current `op` depend
// on it.
void MultiDevSSAGraphBuilder::ConnectOp(SSAGraph *result, OpHandleBase *op,
const std::string &prev_op_name) const {
for (auto &prev_op : result->ops_) {
......@@ -463,16 +469,66 @@ void MultiDevSSAGraphBuilder::ConnectOp(SSAGraph *result, OpHandleBase *op,
void MultiDevSSAGraphBuilder::CreateDistTrainOp(SSAGraph *result,
const OpDesc &op) const {
CreateComputationalOp(result, op, 0);
int op_dev_id = -1;
if (op.Type() == "split_byref") {
op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
op_dev_id = GetAppropriateDeviceID(op.InputArgumentNames());
for (auto &varname : op.InputArgumentNames()) {
var_name_on_devices_.emplace(varname, op_dev_id);
}
}
for (auto &varname : op.OutputArgumentNames()) {
var_name_on_devices_.emplace(varname, op_dev_id);
}
} else if (op.Type() == "concat") {
op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
} else {
PADDLE_ENFORCE(
"the distribute training related op should be in [split_byref, "
"concat].");
}
PADDLE_ENFORCE(op_dev_id != -1,
"can not find right place for distributed op: %s", op.Type());
CreateComputationalOp(result, op, op_dev_id);
if (op.Type() == "concat") {
ConnectOp(result, result->ops_.back().get(), "fetch_barrier");
}
}
// Create RPC related op handles that connects its in ops and out ops.
void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
const OpDesc &op) const {
result->ops_.emplace_back(
new RPCOpHandle(op, local_scopes_[0], op.Type(), places_[0]));
int op_dev_id = -1;
if (op.Type() == "send") {
op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
// the variable name which contains .block means it was splited by
// split_byref op
// so that we can balance the variable blocks to all the pserver instances.
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
op.InputArgumentNames()[0].find(".block") == std::string::npos) {
op_dev_id = GetAppropriateDeviceID(op.InputArgumentNames());
for (auto &varname : op.InputArgumentNames()) {
var_name_on_devices_.emplace(varname, op_dev_id);
}
}
} else if (op.Type() == "recv") {
op_dev_id = GetAppropriateDeviceID(op.OutputArgumentNames());
for (auto &varname : op.OutputArgumentNames()) {
var_name_on_devices_.emplace(varname, op_dev_id);
}
} else {
// send_barrier and fetch_barrier op can be scheduled on device 0
op_dev_id = 0;
}
PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
op.Type());
result->ops_.emplace_back(new RPCOpHandle(op, local_scopes_[op_dev_id],
op.Type(), places_[op_dev_id]));
if (op.Type() == "send_barrier") {
ConnectOp(result, result->ops_.back().get(), "send");
......@@ -488,9 +544,7 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
"send, send_barrier. recv, fetch_barrier]");
}
// TODO(Yancey1989): schedule rpc op on different place may
// increate throughput
CreateOpHandleIOs(result, op, 0);
CreateOpHandleIOs(result, op, op_dev_id);
}
bool MultiDevSSAGraphBuilder::IsScaleLossOp(const OpDesc &op) const {
......
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ 4e9d7ddb
......@@ -47,10 +47,11 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
#endif
std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
int GetVarDeviceID(const std::string &varname) const override;
private:
void CreateOpHandleIOs(SSAGraph *result, const OpDesc &op,
size_t place_id) const;
size_t device_id) const;
private:
std::string loss_var_name_;
......@@ -96,21 +97,23 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::string &og,
std::unordered_set<std::string> *og_has_been_broadcast) const;
int GetOpDeviceID(
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const;
int GetOpDeviceID(const OpDesc &op) 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;
bool IsSparseGradient(
const std::unordered_map<std::string, VarDesc *> &all_vars,
const std::string &og) const;
bool IsSparseGradient(const std::string &og) const;
size_t GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const;
private:
BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
mutable std::unordered_map<std::string, int> var_name_on_devices_;
mutable std::vector<int64_t> balance_vars_;
void SetCommunicationContext(OpHandleBase *op_handle,
const platform::Place &p) const;
......
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 4e9d7ddb
......@@ -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/op_handle_base.h"
#include <map>
namespace paddle {
namespace framework {
......@@ -122,11 +122,16 @@ void OpHandleBase::RunAndRecordEvent(const std::function<void()> &callback) {
#ifdef PADDLE_WITH_CUDA
if (!events_.empty()) { // Use event
std::function<void()> method = callback;
// NOTE(zcd): device context must be ordered here because RecordEvent
// will use a mutex to ensure the safe of multi-threads.
std::map<platform::DeviceContext *, platform::Place> ordered_ctxes;
for (auto &p : dev_ctxes_) {
ordered_ctxes.emplace(p.second, p.first);
}
for (auto &p : ordered_ctxes) {
method = [method, p, this]() {
static_cast<platform::CUDADeviceContext *>(p.second)->RecordEvent(
events_.at(boost::get<platform::CUDAPlace>(p.first).device),
static_cast<platform::CUDADeviceContext *>(p.first)->RecordEvent(
events_.at(boost::get<platform::CUDAPlace>(p.second).device),
method);
};
}
......
paddle/fluid/framework/details/ssa_graph_builder.h
浏览文件 @ 4e9d7ddb
......@@ -30,6 +30,7 @@ class SSAGraphBuilder {
SSAGraphBuilder() {}
virtual ~SSAGraphBuilder() {}
virtual std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const = 0;
virtual int GetVarDeviceID(const std::string &var_name) const { return -1; }
DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 4e9d7ddb
......@@ -96,6 +96,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
if (timeout) {
std::lock_guard<std::mutex> l(exception_mu_);
if (exception_) {
auto exp = *exception_;
exception_.reset();
......@@ -199,6 +200,7 @@ void ThreadedSSAGraphExecutor::RunOp(
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << "Signal posted";
} catch (platform::EnforceNotMet ex) {
std::lock_guard<std::mutex> l(exception_mu_);
exception_.reset(new platform::EnforceNotMet(ex));
} catch (...) {
LOG(FATAL) << "Unknown exception catched";
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 4e9d7ddb
......@@ -56,6 +56,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
std::mutex exception_mu_;
std::unique_ptr<platform::EnforceNotMet> exception_;
std::atomic<int> running_ops_;
......
paddle/fluid/framework/executor.cc
浏览文件 @ 4e9d7ddb
......@@ -21,7 +21,7 @@ limitations under the License. */
#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"
#include "paddle/fluid/operators/distributed/grpc_client.h"
#endif
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -49,8 +49,8 @@ Executor::Executor(const platform::Place& place) : place_(place) {}
#ifdef PADDLE_WITH_DISTRIBUTE
void Executor::Complete() {
::paddle::operators::detail::RPCClient::GetInstance<
::paddle::operators::detail::GRPCClient>()
::paddle::operators::distributed::RPCClient::GetInstance<
::paddle::operators::distributed::GRPCClient>()
->SendComplete();
}
#endif
......@@ -295,13 +295,14 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
const ProgramDesc& program, int block_id) {
auto* ctx = new ExecutorPrepareContext(program, block_id);
std::unique_ptr<ExecutorPrepareContext> ctx(
new ExecutorPrepareContext(program, block_id));
PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), program.Size());
auto& block = program.Block(block_id);
for (auto& op_desc : block.AllOps()) {
ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
}
return std::unique_ptr<ExecutorPrepareContext>(ctx);
return ctx;
}
std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
......@@ -320,7 +321,8 @@ std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
}
void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
bool create_local_scope, bool create_vars) {
bool create_local_scope, bool create_vars,
bool keep_kids) {
Scope* local_scope = scope;
if (create_vars) {
if (create_local_scope) {
......@@ -343,12 +345,20 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
}
platform::DeviceContextPool::Instance().Get(place_)->Wait();
if (create_vars && create_local_scope) {
if (local_scope != scope) {
scope->DeleteScope(local_scope);
} else {
// Delete the local scopes created in operators.
scope->DropKids();
if (!keep_kids) {
// By default, we should delete all kid scopes after run executor because
// some operators may create local scope when running, such as while_op.
// But when while_op also create a local executor to run it's sub block,
// the sub scopes it created should not be dropped immediately, because
// while_grad_op will use some variables created during while_op run, so
// we need to keep the kids and wait for the outer executor to drop them.
scope->DropKids();
}
}
if (FLAGS_benchmark) {
VLOG(2) << "-------------------------------------------------------";
VLOG(2) << "Memory used after deleting local scope: "
......
paddle/fluid/framework/executor.h
浏览文件 @ 4e9d7ddb
......@@ -78,7 +78,7 @@ class Executor {
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
bool create_local_scope = true,
bool create_vars = true);
bool create_vars = true, bool keep_kids = false);
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
......
paddle/fluid/framework/framework.proto
浏览文件 @ 4e9d7ddb
......@@ -27,6 +27,7 @@ enum AttrType {
BOOLEANS = 7;
BLOCK = 8;
LONG = 9;
BLOCKS = 10;
}
// OpDesc describes an instance of a C++ framework::OperatorBase
......@@ -46,6 +47,7 @@ message OpDesc {
repeated bool bools = 11;
optional int32 block_idx = 12;
optional int64 l = 13;
repeated int32 blocks_idx = 14;
};
message Var {
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 4e9d7ddb
......@@ -51,8 +51,6 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
}
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
PADDLE_ENFORCE(t.type().hash_code() == typeid(float).hash_code());
if (!platform::is_cpu_place(t.place())) {
LoDTensor tt;
framework::TensorCopy(t, platform::CPUPlace(), &tt);
......@@ -70,7 +68,13 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
if (t.type().hash_code() == typeid(float).hash_code()) {
os << t.data<float>()[i] << " ";
} else if (t.type().hash_code() == typeid(int64_t).hash_code()) {
os << t.data<int64_t>()[i] << " ";
} else {
PADDLE_THROW("LoDTensor data type not in [float, int64_t]");
}
}
return os;
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ 4e9d7ddb
......@@ -26,6 +26,20 @@
namespace paddle {
namespace framework {
TEST(LoD, PrintLoDTensor) {
LoDTensor tensor1;
tensor1.mutable_data<float>(platform::CPUPlace());
tensor1.data<float>()[0] = 0.2;
tensor1.data<float>()[1] = 0.5;
LOG(INFO) << tensor1;
LoDTensor tensor2;
tensor2.mutable_data<int64_t>(platform::CPUPlace());
tensor2.data<int64_t>()[0] = 1;
tensor2.data<int64_t>()[1] = 2;
LOG(INFO) << tensor2;
}
TEST(LoD, data) {
LoD lod{{0, 1, 2}};
lod.push_back({0, 2, 4, 5});
......@@ -37,7 +51,7 @@ TEST(LoD, data) {
}
}
TEST(LodExpand, test) {
TEST(LoD, ExpandLoD) {
LoD lod{{0, 2}};
LoDTensor tensor;
tensor.set_lod(lod);
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 4e9d7ddb
......@@ -211,6 +211,12 @@ void OpDesc::SetBlockAttr(const std::string &name, BlockDesc *block) {
need_update_ = true;
}
void OpDesc::SetBlocksAttr(const std::string &name,
std::vector<BlockDesc *> blocks) {
this->attrs_[name] = blocks;
need_update_ = true;
}
void OpDesc::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
......@@ -305,6 +311,13 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(const std::vector<BlockDesc *> &v) const {
std::vector<int> blocks_idx;
for (auto blk : v) {
blocks_idx.push_back(blk->ID());
}
VectorToRepeated(blocks_idx, attr_->mutable_blocks_idx());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
void operator()(int64_t v) const { attr_->set_l(v); }
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 4e9d7ddb
......@@ -77,6 +77,8 @@ class OpDesc {
void SetBlockAttr(const std::string &name, BlockDesc *block);
void SetBlocksAttr(const std::string &name, std::vector<BlockDesc *> blocks);
Attribute GetAttr(const std::string &name) const;
Attribute GetNullableAttr(const std::string &name) const;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 4e9d7ddb
......@@ -110,7 +110,6 @@ ParallelExecutor::ParallelExecutor(
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
details::SSAGraphBuilderFactory builder_factory(
member_->places_, loss_var_name, params, member_->local_scopes_,
build_strategy);
......@@ -122,9 +121,10 @@ ParallelExecutor::ParallelExecutor(
#endif
}
builder_ = builder_factory.Create();
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places,
builder_factory.Create()->Build(main_program)));
builder_->Build(main_program)));
member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, std::move(var_infos),
......@@ -133,10 +133,22 @@ ParallelExecutor::ParallelExecutor(
void ParallelExecutor::BCastParamsToGPUs(
const std::unordered_set<std::string> &vars) const {
auto *main_scope = member_->local_scopes_[0];
// the the initialize bcast, all vars would be bcast from device(0), otherwise
// bcast from the specified device.
bool initialize = builder_.get() == nullptr ? true : false;
for (auto &var : vars) {
auto *main_var = main_scope->FindVar(var);
int var_dev_id =
builder_.get() == nullptr ? -1 : builder_->GetVarDeviceID(var);
if (!initialize && var_dev_id == -1) continue;
framework::Variable *main_var = nullptr;
if (initialize) {
main_var = member_->local_scopes_[0]->FindVar(var);
} else {
main_var = member_->local_scopes_[var_dev_id]->FindVar(var);
}
if (main_var == nullptr || !main_var->IsType<LoDTensor>()) {
continue;
}
......@@ -151,7 +163,8 @@ void ParallelExecutor::BCastParamsToGPUs(
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto place = member_->places_[i];
void *buffer;
if (i == 0) {
if ((initialize && i == 0) || (!initialize && i == var_dev_id)) {
buffer = const_cast<void *>(main_tensor.data<void>());
} else {
auto local_scope = member_->local_scopes_[i];
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 4e9d7ddb
......@@ -19,12 +19,14 @@ limitations under the License. */
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
......@@ -68,6 +70,7 @@ class ParallelExecutor {
private:
ParallelExecutorPrivate *member_;
std::unique_ptr<details::SSAGraphBuilder> builder_;
};
} // namespace framework
......
paddle/fluid/framework/type_defs.h
浏览文件 @ 4e9d7ddb
......@@ -35,7 +35,8 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using Attribute =
boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc*, int64_t>;
std::vector<bool>, BlockDesc*, int64_t,
std::vector<BlockDesc*>>;
using AttributeMap = std::unordered_map<std::string, Attribute>;
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ 4e9d7ddb
......@@ -27,7 +27,7 @@ void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_);
}
} // analysis
} // inference
} // namespace analysis
} // namespace inference
} // paddle
} // namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 4e9d7ddb
......@@ -184,9 +184,9 @@ else()
set(DEPS_OPS ${DEPS_OPS} nccl_op)
endif()
add_subdirectory(detail)
if(WITH_DISTRIBUTE)
add_subdirectory(distributed)
set(DISTRIBUTE_DEPS "")
if(WITH_GRPC)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
......@@ -195,18 +195,11 @@ if(WITH_DISTRIBUTE)
endif()
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
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_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})
set_source_files_properties(fetch_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
foreach(dist_op "prefetch_op" "listen_and_serv_op" "send_op" "recv_op" "send_barrier_op" "fetch_barrier_op")
op_library(${dist_op} DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(${dist_op}.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
endforeach()
#set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
#cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
# listen_and_serv_op sum_op executor SERIAL)
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 4e9d7ddb
......@@ -143,7 +143,7 @@ $$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
__attribute__((unused)) constexpr char TanhShrinkDoc[] = R"DOC(
TanhShrink Activation Operator.
$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC";
......@@ -385,7 +385,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
STanh Activation Operator.
$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
$$out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC");
}
......
paddle/fluid/operators/batch_norm_mkldnn_op.cc
浏览文件 @ 4e9d7ddb
......@@ -21,8 +21,6 @@ namespace operators {
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;
......@@ -31,18 +29,6 @@ using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;
using platform::to_void_cast;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
namespace {
template <typename T>
struct bn_type_traits {
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 4e9d7ddb
......@@ -22,22 +22,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
class BatchNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......
paddle/fluid/operators/batch_norm_op.h
浏览文件 @ 4e9d7ddb
......@@ -19,6 +19,22 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename DeviceContext, typename T>
class BatchNormKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/bilinear_interp_op.cc
浏览文件 @ 4e9d7ddb
......@@ -110,6 +110,7 @@ REGISTER_OPERATOR(bilinear_interp, ops::BilinearInterpOp,
ops::BilinearInterpOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(bilinear_interp_grad, ops::BilinearInterpOpGrad);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>,
ops::BilinearInterpKernel<uint8_t>);
REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
ops::BilinearInterpGradKernel<float>);
paddle/fluid/operators/bilinear_interp_op.h
浏览文件 @ 4e9d7ddb
......@@ -46,8 +46,10 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(output, input, input_t->numel() * sizeof(T));
......@@ -56,24 +58,24 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
T h1lambda = ratio_h * i - h;
T h2lambda = 1 - h1lambda;
float h1lambda = ratio_h * i - h;
float h2lambda = 1.f - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
T w1lambda = ratio_w * j - w;
T w2lambda = 1 - w1lambda;
float w1lambda = ratio_w * j - w;
float w2lambda = 1.f - w1lambda;
// calculate four position for bilinear interpolation
const T* in_pos = &input[k * in_chw + h * in_w + w];
T* out_pos = &output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
// bilinear interpolation
out_pos[0] =
out_pos[0] = static_cast<T>(
h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
h1lambda * (w2lambda * in_pos[hid * in_w] +
w1lambda * in_pos[hid * in_w + wid]);
w1lambda * in_pos[hid * in_w + wid]));
in_pos += in_hw;
out_pos += out_hw;
}
......@@ -117,8 +119,10 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
......@@ -127,22 +131,24 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
T h1lambda = ratio_h * i - h;
T h2lambda = 1 - h1lambda;
float h1lambda = ratio_h * i - h;
float h2lambda = 1 - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
T w1lambda = ratio_w * j - w;
T w2lambda = 1 - w1lambda;
float w1lambda = ratio_w * j - w;
float w2lambda = 1 - w1lambda;
T* in_pos = &d_input[k * in_chw + h * in_w + w];
const T* out_pos = &d_output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
in_pos[0] += h2lambda * w2lambda * out_pos[0];
in_pos[wid] += h2lambda * w1lambda * out_pos[0];
in_pos[hid * in_w] += h1lambda * w2lambda * out_pos[0];
in_pos[hid * in_w + wid] += h1lambda * w1lambda * out_pos[0];
in_pos[0] += static_cast<T>(h2lambda * w2lambda * out_pos[0]);
in_pos[wid] += static_cast<T>(h2lambda * w1lambda * out_pos[0]);
in_pos[hid * in_w] +=
static_cast<T>(h1lambda * w2lambda * out_pos[0]);
in_pos[hid * in_w + wid] +=
static_cast<T>(h1lambda * w1lambda * out_pos[0]);
in_pos += in_hw;
out_pos += out_hw;
}
......
paddle/fluid/operators/detail/macros.h
浏览文件 @ 4e9d7ddb
......@@ -15,13 +15,13 @@
#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
#include "paddle/fluid/operators/distributed/grpc_client.h"
#include "paddle/fluid/operators/distributed/grpc_server.h"
#define RPCSERVER_T distributed::AsyncGRPCServer
#define RPCCLIENT_T distributed::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
#include "paddle/fluid/operators/distributed/brpc_client.h"
#include "paddle/fluid/operators/distributed/brpc_server.h"
#define RPCSERVER_T distributed::AsyncBRPCServer
#define RPCCLIENT_T distributed::BRPCClient
#endif
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ 4e9d7ddb
......@@ -175,12 +175,12 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC");
}
......
paddle/fluid/operators/detail/CMakeLists.txt → paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 4e9d7ddb
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
......
paddle/fluid/operators/detail/brpc_client.cc → paddle/fluid/operators/distributed/brpc_client.cc
浏览文件 @ 4e9d7ddb
......@@ -12,12 +12,12 @@
// 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/operators/distributed/brpc_client.h"
#include "paddle/fluid/framework/threadpool.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
DEFINE_int32(brpc_channel_num, 24,
"Number of channels to send requests connected to one server");
......@@ -175,6 +175,6 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
return q;
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/brpc_client.h → paddle/fluid/operators/distributed/brpc_client.h
浏览文件 @ 4e9d7ddb
......@@ -31,13 +31,13 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/rpc_client.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/platform/macros.h" // for DISABLE_COPY_AND_ASSIGN
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
struct ChannelContext {
brpc::Channel channel;
......@@ -95,6 +95,6 @@ class BRPCClient : public RPCClient {
DISABLE_COPY_AND_ASSIGN(BRPCClient);
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/brpc_server.cc → paddle/fluid/operators/distributed/brpc_server.cc
浏览文件 @ 4e9d7ddb
......@@ -12,13 +12,13 @@
// 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"
#include "paddle/fluid/operators/distributed/brpc_server.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
namespace sendrecv {
typedef std::unordered_map<std::string,
paddle::operators::detail::RequestHandler*>
paddle::operators::distributed::RequestHandler*>
HandlerMap;
class BRPCServiceImpl : public SendRecvService {
......@@ -27,17 +27,17 @@ class BRPCServiceImpl : public SendRecvService {
: request_send_h_(nullptr),
request_get_h_(nullptr),
request_prefetch_h_(nullptr) {
auto it = rpc_call_map.find(paddle::operators::detail::kRequestSend);
auto it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
if (it != rpc_call_map.end()) {
request_send_h_ = it->second;
}
it = rpc_call_map.find(paddle::operators::detail::kRequestSend);
it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
if (it != rpc_call_map.end()) {
request_get_h_ = it->second;
}
it = rpc_call_map.find(paddle::operators::detail::kRequestPrefetch);
it = rpc_call_map.find(paddle::operators::distributed::kRequestPrefetch);
if (it != rpc_call_map.end()) {
request_prefetch_h_ = it->second;
}
......@@ -88,15 +88,15 @@ class BRPCServiceImpl : public SendRecvService {
}
private:
paddle::operators::detail::RequestHandler* request_send_h_;
paddle::operators::detail::RequestHandler* request_get_h_;
paddle::operators::detail::RequestHandler* request_prefetch_h_;
paddle::operators::distributed::RequestHandler* request_send_h_;
paddle::operators::distributed::RequestHandler* request_get_h_;
paddle::operators::distributed::RequestHandler* request_prefetch_h_;
};
} // namespace sendrecv
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
void AsyncBRPCServer::StartServer() {
// Instance of your service.
......@@ -139,6 +139,6 @@ void AsyncBRPCServer::WaitServerReady() {
VLOG(3) << "AsyncGRPCServer WaitSeverReady";
}
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/brpc_server.h → paddle/fluid/operators/distributed/brpc_server.h
浏览文件 @ 4e9d7ddb
......@@ -19,12 +19,12 @@ limitations under the License. */
#include <string>
#include "brpc/server.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class AsyncBRPCServer final : public RPCServer {
public:
......@@ -48,6 +48,6 @@ class AsyncBRPCServer final : public RPCServer {
int ready_;
};
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/bytebuffer_stream.cc → paddle/fluid/operators/distributed/bytebuffer_stream.cc
浏览文件 @ 4e9d7ddb
......@@ -17,11 +17,11 @@ limitations under the License. */
// file and did some modifications so that we can send gRPC
// requests without too much copying of the tensor data.
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
GrpcByteBufferSource::GrpcByteBufferSource() {}
......@@ -83,6 +83,6 @@ google::protobuf::int64 GrpcByteBufferSource::ByteCount() const {
return byte_count_;
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/bytebuffer_stream.h → paddle/fluid/operators/distributed/bytebuffer_stream.h
浏览文件 @ 4e9d7ddb
......@@ -106,7 +106,7 @@ class GrpcBufferReader final
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
// Source provides a way for a particular RPC implementation to provide
// received data to ParseFrom.
class Source {
......@@ -183,6 +183,6 @@ class GrpcByteSource : public Source {
char space_[sizeof(Reader)];
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_client.cc → paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ 4e9d7ddb
......@@ -12,19 +12,20 @@ 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/grpc_client.h"
#include "paddle/fluid/operators/distributed/grpc_client.h"
#include <sys/time.h>
#include <limits>
#include "glog/logging.h" // For VLOG
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
void GRPCClient::InitImpl() { InitEventLoop(); }
......@@ -75,6 +76,9 @@ bool GRPCClient::AsyncSendVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Send";
VLOG(3) << var_h.String() << " begin";
// stub context
SendProcessor* s = new SendProcessor(ch);
......@@ -129,6 +133,9 @@ bool GRPCClient::AsyncGetVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Get";
VLOG(3) << var_h.String() << " begin";
// stub context
GetProcessor* s = new GetProcessor(ch);
......@@ -172,6 +179,9 @@ bool GRPCClient::AsyncPrefetchVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = out_var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Prefetch";
VLOG(3) << var_h.String() << " begin";
// stub context
GetProcessor* s = new GetProcessor(ch);
......@@ -243,10 +253,11 @@ void GRPCClient::Proceed() {
GPR_ASSERT(ok);
PADDLE_ENFORCE(c);
if (c->status_.ok()) {
VLOG(3) << c->var_h_.String() << " process";
c->Process();
} else {
LOG(FATAL) << "var: " << c->var_h_.String()
<< " grpc error:" << c->status_.error_message();
LOG(FATAL) << c->var_h_.String()
<< " meets grpc error:" << c->status_.error_message();
}
delete c;
{
......@@ -276,6 +287,6 @@ std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
return ch;
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_client.h → paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ 4e9d7ddb
......@@ -38,23 +38,27 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/rpc_client.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/platform/macros.h" // for DISABLE_COPY_AND_ASSIGN
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
struct VarHandle {
// RPC endpoint.
std::string ep;
const platform::DeviceContext* ctx;
const framework::Scope* scope;
// Variable name.
std::string name;
// RPC method name.
std::string method;
std::string String() const {
std::ostringstream s;
s << "name:[" << name << "] ep:[" << ep << "]";
s << method << " name:[" << name << "], ep:[" << ep << "]";
return s.str();
}
};
......@@ -226,6 +230,6 @@ class GRPCClient : public RPCClient {
DISABLE_COPY_AND_ASSIGN(GRPCClient);
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_serde_test.cc → paddle/fluid/operators/distributed/grpc_serde_test.cc
浏览文件 @ 4e9d7ddb
......@@ -21,8 +21,8 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/printf.h"
......@@ -50,7 +50,7 @@ void RunSerdeTestSelectedRows(platform::Place place) {
for (int i = 0; i < 564; ++i) rows->push_back(i);
::grpc::ByteBuffer msg;
operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
operators::distributed::SerializeToByteBuffer("myvar", &var, ctx, &msg);
EXPECT_GT(msg.Length(), static_cast<size_t>(0));
// deserialize
......@@ -81,10 +81,10 @@ void RunSerdeTestSelectedRows(platform::Place place) {
// deserialize zero-copy
// framework::Variable var2;
// operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2);
// operators::distributed::DeserializeFromByteBuffer(msg, ctx, &var2);
framework::Scope scope;
scope.Var("myvar");
operators::detail::VariableResponse resp(&scope, &ctx);
operators::distributed::VariableResponse resp(&scope, &ctx);
EXPECT_EQ(resp.Parse(msg), 0);
framework::Variable* var2 = resp.GetVar();
......@@ -128,7 +128,7 @@ void RunTestLodTensor(platform::Place place, int from_type = 0) {
math::set_constant(ctx, tensor, 31.9);
::grpc::ByteBuffer msg;
operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
operators::distributed::SerializeToByteBuffer("myvar", &var, ctx, &msg);
EXPECT_GT(msg.Length(), static_cast<size_t>(0));
// deserialize
......@@ -171,7 +171,7 @@ void RunTestLodTensor(platform::Place place, int from_type = 0) {
// deserialize zero-copy
framework::Scope scope;
scope.Var("myvar");
operators::detail::VariableResponse resp(&scope, &ctx);
operators::distributed::VariableResponse resp(&scope, &ctx);
if (from_type == 0) {
EXPECT_EQ(resp.Parse(msg), 0);
} else {
......
paddle/fluid/operators/detail/grpc_server.cc → paddle/fluid/operators/distributed/grpc_server.cc
浏览文件 @ 4e9d7ddb
......@@ -15,13 +15,13 @@ limitations under the License. */
#include <limits>
#include <string>
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/distributed/grpc_server.h"
using ::grpc::ServerAsyncResponseWriter;
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
enum CallStatus { PROCESS = 0, FINISH };
// reference:
......@@ -41,6 +41,19 @@ class RequestBase {
virtual ~RequestBase() {}
virtual void Process() = 0;
std::string Status2String(const std::string& method) {
std::string status = "Process";
if (status_ == FINISH) {
status = "Finish";
}
std::ostringstream s;
s << method << " name:[" << GetReqName() << "]"
<< ", ep:[" << ctx_.peer() << "]"
<< " " << status << " using req_id:" << req_id_;
return s.str();
}
CallStatus Status() const {
std::lock_guard<std::mutex> l(status_mu_);
return status_;
......@@ -74,7 +87,7 @@ class RequestSend final : public RequestBase {
request_.reset(new VariableResponse(request_handler->scope(),
request_handler->dev_ctx(),
!request_handler->sync_mode()));
int method_id = static_cast<int>(detail::GrpcMethod::kSendVariable);
int method_id = static_cast<int>(distributed::GrpcMethod::kSendVariable);
service_->RequestAsyncUnary(
method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
......@@ -106,7 +119,7 @@ class RequestGet final : public RequestBase {
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
auto method_id = static_cast<int>(detail::GrpcMethod::kGetVariable);
auto method_id = static_cast<int>(distributed::GrpcMethod::kGetVariable);
service_->RequestAsyncUnary(
method_id, &ctx_, &request_, &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
......@@ -150,7 +163,8 @@ class RequestPrefetch final : public RequestBase {
local_scope_(nullptr) {
request_.reset(new VariableResponse(request_handler->scope(),
request_handler->dev_ctx(), true));
int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
int method_id =
static_cast<int>(distributed::GrpcMethod::kPrefetchVariable);
service_->RequestAsyncUnary(
method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
......@@ -271,7 +285,7 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
int req_id) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewSendOne";
LOG(WARNING) << "shutdown, do not TryToRegisterNewSendOne";
return;
}
......@@ -305,14 +319,14 @@ void AsyncGRPCServer::HandleRequest(
bool ok = false;
while (true) {
VLOG(3) << "HandleRequest " << rpc_name << " wait next";
VLOG(4) << "HandleRequest " << rpc_name << " wait next";
if (!cq->Next(&tag, &ok)) {
LOG(INFO) << "CompletionQueue " << rpc_name << " shutdown!";
break;
}
int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
VLOG(3) << "HandleRequest " << rpc_name << ", req_id:" << req_id
VLOG(4) << "HandleRequest " << rpc_name << ", req_id:" << req_id
<< " get next";
auto& reqs = rpc_reqs_[rpc_name];
......@@ -323,22 +337,21 @@ void AsyncGRPCServer::HandleRequest(
base = reqs[req_id];
}
VLOG(3) << base->Status2String(rpc_name);
// reference:
// https://github.com/tensorflow/tensorflow/issues/5596
// https://groups.google.com/forum/#!topic/grpc-io/xftlRy-IQwM
// https://groups.google.com/forum/#!topic/grpc-io/ywATt88Ef_I
if (!ok) {
LOG(WARNING) << "completion queue:" << rpc_name
<< " recv no regular event:argument name["
<< base->GetReqName() << "]";
<< " recv no regular event"
<< " context:" << base->Status2String(rpc_name);
TryToRegisterNewOne(rpc_name, req_id);
delete base;
continue;
}
VLOG(3) << "queue id:" << rpc_name << ", req_id:" << req_id
<< ", status:" << base->Status();
switch (base->Status()) {
case PROCESS: {
base->Process();
......@@ -354,6 +367,6 @@ void AsyncGRPCServer::HandleRequest(
}
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_server.h → paddle/fluid/operators/distributed/grpc_server.h
浏览文件 @ 4e9d7ddb
......@@ -29,17 +29,17 @@ limitations under the License. */
#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/grpc_service.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/grpc_service.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class RequestBase;
......@@ -84,6 +84,6 @@ class AsyncGRPCServer final : public RPCServer {
std::map<std::string, std::vector<RequestBase*>> rpc_reqs_;
};
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/grpc_service.h → paddle/fluid/operators/distributed/grpc_service.h
浏览文件 @ 4e9d7ddb
......@@ -23,7 +23,7 @@
#include <grpc++/impl/codegen/stub_options.h>
#include <grpc++/impl/codegen/sync_stream.h>
#include <grpc++/support/byte_buffer.h>
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -42,24 +42,25 @@ class ServerContext;
// Support parsing/unparsing of tensorflow::VariableResponse.
// Wire-format is identical to RecvVariableResponse.
template <>
class SerializationTraits<paddle::operators::detail::VariableResponse> {
class SerializationTraits<paddle::operators::distributed::VariableResponse> {
public:
static Status Serialize(
const paddle::operators::detail::VariableResponse& msg,
const paddle::operators::distributed::VariableResponse& msg,
grpc_byte_buffer** bp, bool* own_buffer) {
PADDLE_ENFORCE(false, "SerializationTraits::Serialize not implemented!");
return Status();
}
static Status Deserialize(grpc_byte_buffer* buffer,
paddle::operators::detail::VariableResponse* msg,
int max_message_size = INT_MAX) {
static Status Deserialize(
grpc_byte_buffer* buffer,
paddle::operators::distributed::VariableResponse* msg,
int max_message_size = INT_MAX) {
if (buffer == nullptr) {
return Status(StatusCode::INTERNAL, "No payload");
}
Status result = g_core_codegen_interface->ok();
if (result.ok()) {
paddle::operators::detail::GrpcByteSource source(buffer);
paddle::operators::distributed::GrpcByteSource source(buffer);
int ret = msg->Parse(&source);
if (ret != 0) {
result = Status(StatusCode::INTERNAL, "VariableResponse parse error");
......@@ -73,7 +74,7 @@ class SerializationTraits<paddle::operators::detail::VariableResponse> {
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
enum class GrpcMethod {
kSendVariable,
......@@ -118,6 +119,6 @@ class GrpcService final {
};
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/proto_encoder_helper.h → paddle/fluid/operators/distributed/proto_encoder_helper.h
浏览文件 @ 4e9d7ddb
......@@ -26,7 +26,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
char* EncodeVarint32(char* dst, uint32_t v) {
// Operate on characters as unsigneds
......@@ -144,6 +144,6 @@ class ProtoEncodeHelper {
char* limit_; // Just for CHECKs
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/request_handler.h → paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ 4e9d7ddb
......@@ -31,7 +31,7 @@
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
constexpr char kRequestSend[] = "RequestSend";
constexpr char kRequestGet[] = "RequestGet";
......@@ -124,6 +124,6 @@ class RequestHandler {
RPCServer* rpc_server_;
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/request_handler_impl.cc → paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ 4e9d7ddb
......@@ -20,12 +20,12 @@
#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/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
bool RequestSendHandler::Handle(const std::string& varname,
framework::Scope* scope,
......@@ -119,6 +119,6 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
return true;
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/request_handler_impl.h → paddle/fluid/operators/distributed/request_handler_impl.h
浏览文件 @ 4e9d7ddb
......@@ -28,11 +28,11 @@
#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/request_handler.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class RequestSendHandler final : public RequestHandler {
public:
......@@ -66,6 +66,6 @@ class RequestPrefetchHandler final : public RequestHandler {
const std::string& out_var_name = "") override;
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_client.cc → paddle/fluid/operators/distributed/rpc_client.cc
浏览文件 @ 4e9d7ddb
......@@ -12,15 +12,15 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/detail/rpc_client.h"
#include "paddle/fluid/operators/distributed/rpc_client.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
std::once_flag RPCClient::init_flag_;
std::unique_ptr<RPCClient> RPCClient::rpc_client_(nullptr);
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_client.h → paddle/fluid/operators/distributed/rpc_client.h
浏览文件 @ 4e9d7ddb
......@@ -22,7 +22,7 @@
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class RPCClient {
public:
......@@ -84,6 +84,6 @@ class RPCClient {
static std::once_flag init_flag_;
static std::unique_ptr<RPCClient> rpc_client_;
};
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_server.cc → paddle/fluid/operators/distributed/rpc_server.cc
浏览文件 @ 4e9d7ddb
......@@ -17,11 +17,11 @@
#include <limits>
#include <string>
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
void RPCServer::ShutDown() {
LOG(INFO) << "RPCServer ShutDown ";
......@@ -112,6 +112,6 @@ void RPCServer::WaitCond(const std::string& rpc_name) {
lock, [=] { return (cur_cond_.load() == cond || exit_flag_.load()); });
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_server.h → paddle/fluid/operators/distributed/rpc_server.h
浏览文件 @ 4e9d7ddb
......@@ -19,11 +19,11 @@
#include <thread> // NOLINT
#include <utility>
#include <vector>
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class RPCServer {
public:
......@@ -86,6 +86,6 @@ class RPCServer {
friend class RequestHandler;
};
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/rpc_server_test.cc → paddle/fluid/operators/distributed/rpc_server_test.cc
浏览文件 @ 4e9d7ddb
......@@ -22,18 +22,18 @@ limitations under the License. */
#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"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/rpc_client.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace detail = paddle::operators::detail;
namespace distributed = paddle::operators::distributed;
USE_OP(lookup_table);
std::unique_ptr<detail::RPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
std::unique_ptr<distributed::RPCServer> g_rpc_service;
std::unique_ptr<distributed::RequestHandler> g_req_handler;
framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
auto root_block = program->MutableBlock(0);
......@@ -113,19 +113,21 @@ void StartServer() {
g_req_handler->SetScope(&scope);
g_req_handler->SetExecutor(&exe);
g_rpc_service->RegisterRPC(detail::kRequestPrefetch, g_req_handler.get());
g_rpc_service->RegisterRPC(distributed::kRequestPrefetch,
g_req_handler.get());
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
std::bind(&distributed::RPCServer::StartServer, g_rpc_service.get()));
server_thread.join();
}
TEST(PREFETCH, CPU) {
g_req_handler.reset(new detail::RequestPrefetchHandler(true));
g_req_handler.reset(new distributed::RequestPrefetchHandler(true));
g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
std::thread server_thread(StartServer);
g_rpc_service->WaitServerReady();
......
paddle/fluid/operators/detail/sendrecvop_utils.cc → paddle/fluid/operators/distributed/sendrecvop_utils.cc
浏览文件 @ 4e9d7ddb
......@@ -12,7 +12,7 @@ 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/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#ifdef PADDLE_WITH_CUDA
#include <nccl.h>
......@@ -23,14 +23,14 @@ limitations under the License. */
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
#include "paddle/fluid/operators/detail/proto_encoder_helper.h"
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
#include "paddle/fluid/operators/distributed/proto_encoder_helper.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
using VarMsg = sendrecv::VariableMessage;
......@@ -222,11 +222,11 @@ void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
const framework::Scope* scope,
framework::Variable** var) {
operators::detail::VariableResponse resp(scope, &ctx);
operators::distributed::VariableResponse resp(scope, &ctx);
PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
*var = resp.GetVar();
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/sendrecvop_utils.h → paddle/fluid/operators/distributed/sendrecvop_utils.h
浏览文件 @ 4e9d7ddb
......@@ -25,12 +25,12 @@ limitations under the License. */
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
typedef void (*DestroyCallback)(void*);
......@@ -61,6 +61,6 @@ inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) {
}
}
} // namespace detail
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/variable_response.cc → paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ 4e9d7ddb
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include <string>
#include <utility>
......@@ -22,12 +22,12 @@
#endif
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
enum WireType {
WIRETYPE_VARINT = 0,
......@@ -76,6 +76,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
if (total_written + size_to_write > length) {
size_to_write = length - total_written;
}
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CUDAPlace";
memory::Copy(boost::get<platform::CUDAPlace>(place),
reinterpret_cast<void*>(p), cpu, data, size_to_write,
gpu_dev_ctx.stream());
......@@ -103,6 +105,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
}
// TODO(gongwb): can we avoid copy?
platform::CPUPlace cpu;
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CPUPlace";
memory::Copy(cpu, reinterpret_cast<void*>(p), cpu, data, size_to_write);
p += size_to_write;
......@@ -158,13 +162,13 @@ bool VariableResponse::CopySelectRowsTensorData(
slr->set_height(meta_.slr_height());
auto* tensor = slr->mutable_value();
tensor->Resize(dims);
PADDLE_ENFORCE_EQ(
static_cast<size_t>(tensor->numel()),
length / framework::SizeOfType(
paddle::operators::detail::ToTypeIndex(meta_.data_type())));
PADDLE_ENFORCE_EQ(static_cast<size_t>(tensor->numel()),
length / framework::SizeOfType(
paddle::operators::distributed::ToTypeIndex(
meta_.data_type())));
void* tensor_data = tensor->mutable_data(
ctx.GetPlace(),
paddle::operators::detail::ToTypeIndex(meta_.data_type()));
paddle::operators::distributed::ToTypeIndex(meta_.data_type()));
if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
return false;
......@@ -480,6 +484,6 @@ int VariableResponse::Parse(Source* source) {
return 0;
}
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/variable_response.h → paddle/fluid/operators/distributed/variable_response.h
浏览文件 @ 4e9d7ddb
......@@ -22,17 +22,17 @@
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
namespace paddle {
namespace operators {
namespace detail {
namespace distributed {
class VariableResponse {
public:
......@@ -99,6 +99,6 @@ class VariableResponse {
sendrecv::VariableMessage meta_;
};
}; // namespace detail
}; // namespace distributed
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @ 4e9d7ddb
......@@ -42,8 +42,8 @@ class FetchBarrierOp : public framework::OperatorBase {
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
rpc_client->Wait();
......
paddle/fluid/operators/gaussian_random_mkldnn_op.cc 0 → 100644
浏览文件 @ 4e9d7ddb
/* 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 <string>
#include "paddle/fluid/operators/mean_op.h"
namespace paddle {
namespace operators {
using framework::DataLayout;
template <typename T>
class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
float mean = context.Attr<float>("mean");
float std = context.Attr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);
int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
// The format of output is set as the mkldnn's format
// TODO(@mozga-intel) The format of matrix sets inside the another layers.
tensor->set_layout(DataLayout::kMKLDNN);
tensor->set_format(mkldnn::memory::format::oihw);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(gaussian_random, MKLDNN, ::paddle::platform::CPUPlace,
ops::GaussianMKLDNNKernel<float>);
paddle/fluid/operators/gaussian_random_op.cc
浏览文件 @ 4e9d7ddb
......@@ -15,6 +15,10 @@ limitations under the License. */
#include <random>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
......@@ -62,9 +66,20 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
#ifdef PADDLE_WITH_MKLDNN
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(
static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
ctx.device_context());
ctx.device_context(), layout, library);
}
};
......@@ -95,7 +110,9 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
"(int, default 5(FP32)) "
"Output data type.")
.SetDefault(framework::proto::VarType::FP32);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddComment(R"DOC(
GaussianRandom Operator.
......
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @ 4e9d7ddb
......@@ -22,7 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/platform/nccl_helper.h"
namespace paddle {
......@@ -60,7 +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<RPCCLIENT_T>();
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
for (auto& ep : endpoint_list) {
VLOG(3) << "sending nccl id to " << ep;
......@@ -80,11 +81,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
// NOTE: Can not use unique_ptr here because the default
// deleter will call GRPC Server's base class's dtor and
// that will cause a wired crash.
detail::RequestSendHandler rpc_h(true);
std::unique_ptr<detail::RPCServer> rpc_service(
distributed::RequestSendHandler rpc_h(true);
std::unique_ptr<distributed::RPCServer> rpc_service(
new RPCSERVER_T(endpoint, 1));
rpc_service->RegisterRPC(detail::kRequestSend, &rpc_h);
rpc_service->RegisterRPC(distributed::kRequestSend, &rpc_h);
rpc_h.SetRPCServer(rpc_service.get());
framework::ProgramDesc empty_program;
......@@ -95,11 +96,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
rpc_h.SetExecutor(&executor);
std::thread server_thread(
std::bind(&detail::RPCServer::StartServer, rpc_service.get()));
std::bind(&distributed::RPCServer::StartServer, rpc_service.get()));
rpc_service->SetCond(detail::kRequestSend);
rpc_service->SetCond(distributed::kRequestSend);
VLOG(3) << "start getting nccl id from trainer 0...";
rpc_service->WaitBarrier(detail::kRequestSend);
rpc_service->WaitBarrier(distributed::kRequestSend);
VLOG(3) << "got nccl id and stop server...";
rpc_service->ShutDown();
VLOG(3) << "rpc server stopped";
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 4e9d7ddb
......@@ -21,14 +21,14 @@ limitations under the License. */
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
void RunServer(std::shared_ptr<detail::RPCServer> service) {
void RunServer(std::shared_ptr<distributed::RPCServer> service) {
service->StartServer();
VLOG(4) << "RunServer thread end";
}
......@@ -101,17 +101,16 @@ void ListenAndServOp::RunSyncLoop(
framework::Scope *recv_scope,
const std::vector<int> &prefetch_block_id_list) const {
size_t num_blocks = program->Size();
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
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);
}
std::vector<int> optimize_blocks_idx;
for (auto blk : optimize_blocks) {
optimize_blocks_idx.push_back(blk->ID());
}
auto optimize_prepared = executor->Prepare(*program, optimize_block_id_list);
auto optimize_prepared = executor->Prepare(*program, optimize_blocks_idx);
// Insert placeholder for block0 which holds current op itself.
optimize_prepared.insert(
optimize_prepared.begin(),
......@@ -121,12 +120,12 @@ void ListenAndServOp::RunSyncLoop(
while (true) {
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(detail::kRequestSend);
rpc_service_->WaitBarrier(detail::kRequestSend);
rpc_service_->SetCond(distributed::kRequestSend);
rpc_service_->WaitBarrier(distributed::kRequestSend);
if (rpc_service_->IsExit()) {
LOG(WARNING) << "get exit!rpc_processor break!";
rpc_service_->SetCond(detail::kRequestGet);
rpc_service_->SetCond(distributed::kRequestGet);
break;
}
......@@ -134,14 +133,14 @@ void ListenAndServOp::RunSyncLoop(
// and this will still work.
// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
int32_t last_parent_blkid = program->Block(1).Parent();
int32_t last_parent_blkid = optimize_blocks[0]->Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
parallel_blkids.push_back(optimize_blocks[0]->ID());
double ts = GetTimestamp();
for (size_t i = 1; i < optimize_block_id_list.size(); ++i) {
for (size_t i = 1; i < optimize_blocks.size(); ++i) {
// skip the first optimize block because it is already in the
// parallel_blkids.
int blkid = optimize_block_id_list[i];
int blkid = optimize_blocks[i]->ID();
if (program->Block(blkid).Parent() != last_parent_blkid) {
ParallelExecuteBlocks(parallel_blkids, executor, optimize_prepared,
program, recv_scope);
......@@ -154,11 +153,11 @@ void ListenAndServOp::RunSyncLoop(
recv_scope);
VLOG(2) << "run all blocks spent " << GetTimestamp() - ts << "(ms)";
rpc_service_->SetCond(detail::kRequestGet);
rpc_service_->WaitBarrier(detail::kRequestGet);
rpc_service_->SetCond(distributed::kRequestGet);
rpc_service_->WaitBarrier(distributed::kRequestGet);
rpc_service_->ResetBarrierCounter();
// reset received sparse vars to avoid reuse it in the next mini-batch
dynamic_cast<detail::RequestSendHandler *>(request_send_handler_.get())
dynamic_cast<distributed::RequestSendHandler *>(request_send_handler_.get())
->ResetSparseVarRecorder();
} // while(true)
}
......@@ -215,13 +214,13 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
}
static void FillRequestCtx(
detail::RequestHandler *h, framework::Scope *scope,
distributed::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) {
distributed::RPCServer *rpc_server) {
h->SetScope(scope);
h->SetDevCtx(dev_ctx);
h->SetExecutor(executor);
......@@ -249,18 +248,23 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
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_send_handler_.reset(new distributed::RequestSendHandler(sync_mode));
request_get_handler_.reset(new distributed::RequestGetHandler(sync_mode));
request_prefetch_handler_.reset(
new detail::RequestPrefetchHandler(sync_mode));
new distributed::RequestPrefetchHandler(sync_mode));
rpc_service_->RegisterRPC(detail::kRequestSend, request_send_handler_.get());
rpc_service_->RegisterRPC(detail::kRequestGet, request_get_handler_.get());
rpc_service_->RegisterRPC(detail::kRequestPrefetch,
rpc_service_->RegisterRPC(distributed::kRequestSend,
request_send_handler_.get());
rpc_service_->RegisterRPC(distributed::kRequestGet,
request_get_handler_.get());
rpc_service_->RegisterRPC(distributed::kRequestPrefetch,
request_prefetch_handler_.get());
auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = optimize_block->Program();
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
PADDLE_ENFORCE(optimize_blocks.size() >= 1,
"optimize blocks should be 1 at least on the pserver side.");
auto *program = optimize_blocks[0]->Program();
framework::Executor executor(dev_place);
// prepare for prefetch
......@@ -337,8 +341,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
"a map from grad name to it's optimize block id")
.SetDefault({});
AddAttr<bool>("sync_mode", "if works at sync_mode or not").SetDefault(true);
AddAttr<framework::BlockDesc *>(kOptimizeBlock,
"BlockID to run on server side.");
AddAttr<std::vector<framework::BlockDesc *>>(
kOptimizeBlocks, "Optimize blocks to run on server side.")
.SetDefault({});
AddAttr<std::vector<std::string>>(kPrefetchVarNameToBlockId,
"prefetch blocks to run on server side.")
.SetDefault({});
......
paddle/fluid/operators/listen_and_serv_op.h
浏览文件 @ 4e9d7ddb
......@@ -24,16 +24,16 @@ 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/request_handler.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
namespace paddle {
namespace operators {
constexpr char kOptimizeBlock[] = "OptimizeBlock";
constexpr char kOptimizeBlocks[] = "optimize_blocks";
constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
void RunServer(std::shared_ptr<detail::RPCServer> service);
void RunServer(std::shared_ptr<distributed::RPCServer> service);
class ListenAndServOp : public framework::OperatorBase {
public:
......@@ -62,10 +62,11 @@ class ListenAndServOp : public framework::OperatorBase {
const platform::Place& dev_place) const override;
protected:
mutable std::shared_ptr<detail::RPCServer> rpc_service_;
mutable std::shared_ptr<detail::RequestHandler> request_send_handler_;
mutable std::shared_ptr<detail::RequestHandler> request_get_handler_;
mutable std::shared_ptr<detail::RequestHandler> request_prefetch_handler_;
mutable std::shared_ptr<distributed::RPCServer> rpc_service_;
mutable std::shared_ptr<distributed::RequestHandler> request_send_handler_;
mutable std::shared_ptr<distributed::RequestHandler> request_get_handler_;
mutable std::shared_ptr<distributed::RequestHandler>
request_prefetch_handler_;
mutable std::shared_ptr<std::thread> server_thread_;
};
......
paddle/fluid/operators/logical_op.cc
浏览文件 @ 4e9d7ddb
......@@ -146,6 +146,6 @@ REGISTER_UNARY_LOGICAL_OP(logical_not, "$$Out = !X$$");
REGISTER_UNARY_LOGICAL_KERNEL(logical_not, CPU,
paddle::operators::LogicalNotFunctor);
REGISTER_BINARY_LOGICAL_OP(logical_xor,
"$$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$");
"$$Out = (X || Y) \\&\\& !(X \\&\\& Y)$$");
REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, CPU,
paddle::operators::LogicalXorFunctor);
paddle/fluid/operators/math/concat.cc
浏览文件 @ 4e9d7ddb
......@@ -93,10 +93,10 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
// computation
for (size_t k = 0; k < input_rows; ++k) {
for (int k = 0; k < input_rows; ++k) {
const T* src_ptr = input.data<T>() + k * input_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
for (size_t j = 0; j < num; ++j) {
int col_len = output_cols[j];
auto* out_tensor = outputs->at(j);
if (out_tensor != nullptr) {
......
paddle/fluid/operators/math/concat.cu
浏览文件 @ 4e9d7ddb
......@@ -22,43 +22,24 @@ namespace paddle {
namespace operators {
namespace math {
template <typename T>
__device__ T upper_bound(const T* first, T count, T val) {
const T* orig = first;
const T* it = nullptr;
T step = 0;
while (count > 0) {
it = first;
step = count / 2;
it += step;
if (!(val < *it)) {
first = ++it;
count -= step + 1;
} else {
count = step;
}
}
return first - orig;
}
template <typename T>
__global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
const int output_rows, const int output_cols,
T* output) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int segment = upper_bound<int>(input_cols, col_size, tid_x) - 1;
int curr_offset = input_cols[segment];
int curr_segment = segment;
int curr_segment = 0;
int curr_offset = input_cols[0];
for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
T curr_col_offset;
while ((curr_col_offset = input_cols[curr_segment + 1]) <= tid_x) {
int curr_col_offset = input_cols[curr_segment + 1];
while (curr_col_offset <= tid_x) {
curr_offset = curr_col_offset;
++curr_segment;
curr_col_offset = input_cols[curr_segment + 1];
}
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
T* input_ptr = inputs[curr_segment];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y)
......@@ -89,14 +70,14 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
const int in_col, const int* out_cols,
int out_cols_size, T** outputs_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int segment = upper_bound<int>(out_cols, out_cols_size, tid_x) - 1;
int curr_offset = out_cols[segment];
int curr_segment = segment;
int curr_segment = 0;
int curr_offset = out_cols[0];
for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
T curr_col_offset;
while ((curr_col_offset = out_cols[curr_segment + 1]) <= tid_x) {
int curr_col_offset = out_cols[curr_segment + 1];
while (curr_col_offset <= tid_x) {
curr_offset = curr_col_offset;
++curr_segment;
curr_col_offset = out_cols[curr_segment + 1];
}
int local_col = tid_x - curr_offset;
......@@ -228,7 +209,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
outputs_cols[0] = 0;
for (int i = 0; i < o_num; ++i) {
int t_col = outputs->at(i)->numel() / out_row;
int t_col = ref_inputs.at(i)->numel() / out_row;
if (sameShape) {
if (t_col != out0_col) sameShape = false;
}
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ 4e9d7ddb
......@@ -30,6 +30,7 @@ template struct SetConstant<platform::CPUDeviceContext, double>;
template struct SetConstant<platform::CPUDeviceContext, int>;
template struct SetConstant<platform::CPUDeviceContext, int64_t>;
template struct SetConstant<platform::CPUDeviceContext, bool>;
template struct SetConstant<platform::CPUDeviceContext, uint8_t>;
#define DEFINE_CPU_TRANS(RANK) \
template struct Transpose<platform::CPUDeviceContext, platform::float16, \
......
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ 4e9d7ddb
......@@ -295,7 +295,7 @@ class ParallelDoGradOp : public framework::OperatorBase {
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
framework::AttributeMap{});
framework::AttributeMap{{"use_mkldnn", {false}}});
VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
sum_op->Run(*sub_scopes[0], places[0]);
WaitOnPlace(places[0]);
......
paddle/fluid/operators/prefetch_op.cc
浏览文件 @ 4e9d7ddb
......@@ -41,8 +41,8 @@ class PrefetchOp : public framework::OperatorBase {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 4e9d7ddb
......@@ -429,7 +429,8 @@ class RecurrentGradOp : public RecurrentBase {
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
{{"Out", {pg_names[param_id]}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
sum_op->Run(cur_scope, place);
cur_scope.Rename(new_inside_name, inside_grad_name);
......
paddle/fluid/operators/recv_op.cc
浏览文件 @ 4e9d7ddb
......@@ -43,8 +43,8 @@ class RecvOp : public framework::OperatorBase {
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < outs.size(); i++) {
VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
......
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ 4e9d7ddb
......@@ -44,8 +44,8 @@ class SendBarrierOp : public framework::OperatorBase {
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
......
paddle/fluid/operators/send_op.cc
浏览文件 @ 4e9d7ddb
......@@ -45,8 +45,8 @@ class SendOp : public framework::OperatorBase {
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
detail::RPCClient* rpc_client =
detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
......
paddle/fluid/operators/send_recv_op_test.cc
浏览文件 @ 4e9d7ddb
......@@ -129,7 +129,10 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
// sub program run in listen_and_serv_op, for simple test we use sum
f::ProgramDesc program;
const auto &root_block = program.Block(0);
std::vector<framework::BlockDesc *> optimize_blocks;
auto *optimize_block = program.AppendBlock(root_block);
optimize_blocks.push_back(optimize_block);
auto *prefetch_block = program.AppendBlock(root_block);
// X for server side tensors, RX for received tensors, must be of same shape.
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block,
......@@ -139,7 +142,7 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
attrs.insert({"Fanin", 1});
attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
attrs.insert({"GradList", std::vector<std::string>({"x1"})});
attrs.insert({"OptimizeBlock", optimize_block});
attrs.insert({"optimize_blocks", optimize_blocks});
attrs.insert({"PrefetchBlock", prefetch_block});
attrs.insert({"grad_to_block_id", std::vector<std::string>({""})});
attrs.insert({"sync_mode", true});
......
paddle/fluid/operators/softmax_mkldnn_op.cc
浏览文件 @ 4e9d7ddb
......@@ -27,8 +27,81 @@ using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::softmax_forward;
using mkldnn::softmax_backward;
using mkldnn::prop_kind;
using mkldnn::stream;
using platform::to_void_cast;
class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
public:
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
softmax_pd_(softmax_pd) {}
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
std::shared_ptr<mkldnn::softmax_backward::primitive_desc> softmax_bwd_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
softmax_pd_(softmax_pd),
softmax_bwd_pd_(softmax_bwd_pd) {
// If we are in Grad operatgor then update a key with BWD suffix to
// distinguish from FWD memory primitives
key_ += "-BWD";
}
std::shared_ptr<mkldnn::softmax_forward> AcquireSoftmax(
std::shared_ptr<mkldnn::memory> dst_memory_p,
std::shared_ptr<mkldnn::memory> src_memory_p) {
/*Generate key*/
auto prim_key = key_ + "@softmax_p";
auto softmax_p = std::static_pointer_cast<mkldnn::softmax_forward>(
dev_ctx_.GetBlob(prim_key));
PADDLE_ENFORCE((softmax_p != nullptr) || (is_reusing_ == false),
"Fail to find softmax primitive in device context");
if (softmax_p == nullptr) {
softmax_p = std::make_shared<mkldnn::softmax_forward>(
*(softmax_pd_.get()),
*(static_cast<mkldnn::memory*>(src_memory_p.get())),
*(static_cast<mkldnn::memory*>(dst_memory_p.get())));
dev_ctx_.SetBlob(prim_key, softmax_p);
} else {
is_reusing_ = true;
}
return softmax_p;
}
std::shared_ptr<mkldnn::softmax_backward> AcquireSoftmaxBackward(
std::shared_ptr<mkldnn::memory> dst_memory_p,
std::shared_ptr<mkldnn::memory> diff_dst_memory_p,
std::shared_ptr<mkldnn::memory> diff_src_memory_p) {
auto prim_key = key_ + "@softmax_bwd_p";
auto softmax_bwd_p = std::static_pointer_cast<mkldnn::softmax_backward>(
dev_ctx_.GetBlob(prim_key));
PADDLE_ENFORCE((softmax_bwd_p != nullptr) || (is_reusing_ == false),
"Fail to find softmax backward primitive in device context");
if (softmax_bwd_p == nullptr) {
softmax_bwd_p = std::make_shared<mkldnn::softmax_backward>(
*softmax_bwd_pd_, *(dst_memory_p.get()), *(diff_dst_memory_p.get()),
*(diff_src_memory_p.get()));
dev_ctx_.SetBlob(prim_key, softmax_bwd_p);
} else {
is_reusing_ = true;
}
return softmax_bwd_p;
}
private:
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd_;
std::shared_ptr<mkldnn::softmax_backward::primitive_desc> softmax_bwd_pd_;
};
template <typename T>
class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
......@@ -54,56 +127,27 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Each MKLDNN operator may have diffrent hashing function
auto gethash = [](memory::dims& operand_dims) {
return std::string(std::to_string(operand_dims[0]) + "-" +
std::to_string(operand_dims[1]));
};
const std::string key = gethash(softmax_tz);
const std::string key_softmax_p = key + "@softmax_p";
const std::string key_softmax_src_mem_p = key + "@softmax_src_mem_p";
const std::string key_softmax_dst_mem_p = key + "@softmax_dst_mem_p";
std::shared_ptr<void> softmax_p = dev_ctx.GetBlob(key_softmax_p);
if (softmax_p == nullptr) {
// Currently only NC data format is supported
auto softmax_md =
MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
// create memory primitives
auto softmax_src_memory_p = std::make_shared<memory>(
memory::primitive_desc{softmax_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(input_data)));
dev_ctx.SetBlob(key_softmax_src_mem_p, softmax_src_memory_p);
auto softmax_dst_memory_p = std::make_shared<memory>(
memory::primitive_desc{softmax_md, mkldnn_engine},
static_cast<void*>(output_data));
dev_ctx.SetBlob(key_softmax_dst_mem_p, softmax_dst_memory_p);
auto softmax_forward_pd =
std::make_shared<softmax_forward::primitive_desc>(softmax_desc,
mkldnn_engine);
softmax_p = std::make_shared<softmax_forward>(
*(softmax_forward_pd.get()),
*(static_cast<memory*>(softmax_src_memory_p.get())),
*(static_cast<memory*>(softmax_dst_memory_p.get())));
dev_ctx.SetBlob(key_softmax_p, softmax_p);
} else {
// Primitives already exist
auto src_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_softmax_src_mem_p));
PADDLE_ENFORCE(src_memory_p != nullptr,
"Fail to find softmax src mem_p in device context");
auto dst_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_softmax_dst_mem_p));
PADDLE_ENFORCE(dst_memory_p != nullptr,
"Fail to find softmax dst mem_p in device context");
src_memory_p->set_data_handle(
reinterpret_cast<void*>(const_cast<T*>(input_data)));
dst_memory_p->set_data_handle(output_data);
}
const std::string key =
platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Output("Out"));
const std::string key_softmax_pd = key + "@softmax_pd";
// Currently only NC data format is supported
auto softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
auto softmax_pd = std::make_shared<mkldnn::softmax_forward::primitive_desc>(
softmax_desc, mkldnn_engine);
dev_ctx.SetBlob(key_softmax_pd, softmax_pd);
SoftmaxMKLDNNHandler handler(softmax_pd, dev_ctx, mkldnn_engine, key);
auto softmax_src_memory_p =
handler.AcquireSrcMemory(softmax_md, to_void_cast<T>(input_data));
auto softmax_dst_memory_p =
handler.AcquireDstMemory(softmax_md, to_void_cast<T>(output_data));
auto softmax_p =
handler.AcquireSoftmax(softmax_dst_memory_p, softmax_src_memory_p);
std::vector<primitive> pipeline{
*(static_cast<softmax_forward::primitive*>(softmax_p.get()))};
......@@ -120,6 +164,77 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
}
};
template <typename T>
class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine();
const Tensor* output = ctx.Input<Tensor>("Out");
const T* dst_data = output->data<T>();
auto* dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto* diff_dst_ptr = dout->template data<T>();
auto* dx =
ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
T* diff_src_ptr = dx->template mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
std::vector<int> src_tz(dst_tz);
PADDLE_ENFORCE(output->dims().size() == 2UL,
"The input of softmax op must be a 2D matrix.");
// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
// we will make normalization after final eg. axis: 1
PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
"Softmax input and output dimensions should match");
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Currently only supports NC data format
// retrieve eltwise primitive desc from device context
const std::string key =
platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Input("Out"));
const std::string key_softmax_pd = key + "@softmax_pd";
auto softmax_pd =
std::static_pointer_cast<mkldnn::softmax_forward::primitive_desc>(
dev_ctx.GetBlob(key_softmax_pd));
PADDLE_ENFORCE(softmax_pd != nullptr,
"Fail to find softmax_pd in device context");
// TODO(jczaja): Add layouts support when there is a need to do so
// Two dimensional softmax does support NC format
auto data_softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
auto diff_softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_bwd_desc =
softmax_backward::desc(diff_softmax_md, data_softmax_md, 1 /* dim: C*/);
auto softmax_bwd_pd =
std::make_shared<mkldnn::softmax_backward::primitive_desc>(
softmax_bwd_desc, mkldnn_engine, *softmax_pd);
SoftmaxMKLDNNHandler handler(softmax_pd, softmax_bwd_pd, dev_ctx,
mkldnn_engine, key);
auto dst_memory_p =
handler.AcquireDstMemory(data_softmax_md, to_void_cast<T>(dst_data));
auto diff_dst_memory_p = handler.AcquireDiffDstMemory(
diff_softmax_md, to_void_cast<T>(diff_dst_ptr));
auto diff_src_memory_p = handler.AcquireDiffSrcMemory(
diff_softmax_md, to_void_cast<T>(diff_src_ptr));
// Get primitve from device context
auto softmax_bwd_p = handler.AcquireSoftmaxBackward(
dst_memory_p, diff_dst_memory_p, diff_src_memory_p);
std::vector<primitive> pipeline{*softmax_bwd_p};
stream(stream::kind::eager).submit(pipeline).wait();
}
};
} // namespace operators
} // namespace paddle
......@@ -127,3 +242,5 @@ namespace ops = paddle::operators;
REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace,
ops::SoftmaxMKLDNNKernel<float>);
REGISTER_OP_KERNEL(softmax_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::SoftmaxMKLDNNGradKernel<float>);
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 4e9d7ddb
......@@ -145,16 +145,30 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
framework::StringToDataLayout(data_format), library_);
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("X")->type());
if (input_data_type == framework::proto::VarType::FP16) {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"float16 can only be used on GPU place");
}
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
library_);
}
};
......
paddle/fluid/operators/sum_mkldnn_op.cc 0 → 100644
浏览文件 @ 4e9d7ddb
// 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.
/*Licensed under the Apache License, Version 2.0(the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/operators/sum_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::CPUDeviceContext;
using framework::DataLayout;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::stream;
using mkldnn::sum;
using mkldnn::reorder;
using platform::to_void_cast;
template <typename T>
class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto in_vars = ctx.MultiInputVar("X");
const int N = in_vars.size();
auto out_var = ctx.OutputVar("Out");
bool in_place = out_var == in_vars[0];
if (out_var->IsType<framework::LoDTensor>()) {
LoDTensor* output = ctx.Output<LoDTensor>("Out");
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = framework::vectorize2int(output->dims());
auto src_tz = dst_tz;
memory::format output_format{memory::format::format_undef};
std::vector<float> scales;
std::vector<memory::primitive_desc> srcs_mpd;
std::vector<mkldnn::memory> srcs_mem;
PADDLE_ENFORCE(in_vars[0]->IsType<LoDTensor>(),
"Input[0] must be LoDTensors");
auto& input0 = in_vars[0]->Get<LoDTensor>();
PADDLE_ENFORCE(input0.layout() == DataLayout::kMKLDNN &&
input0.format() != memory::format::format_undef,
"Wrong layout/format for inputs[0]");
memory::format input_format = input0.format();
if (src_tz.size() == 1 && (input_format == memory::format::nchw ||
input_format == memory::format::nhwc)) {
input_format = memory::format::x;
}
if (src_tz.size() == 2 && (input_format == memory::format::nchw ||
input_format == memory::format::nhwc)) {
input_format = memory::format::nc;
}
for (int i = in_place ? 1 : 0; i < N; i++) {
PADDLE_ENFORCE(in_vars[i]->IsType<LoDTensor>(),
"all inputs must be all LoDTensors");
auto& input = in_vars[i]->Get<LoDTensor>();
PADDLE_ENFORCE(input.layout() == DataLayout::kMKLDNN &&
input.format() != memory::format::format_undef,
"Wrong layout/format for inputs");
if (input.numel() == 0) {
continue;
}
const T* input_data = input.data<T>();
auto src_md =
memory::desc(src_tz, memory::data_type::f32, input_format);
auto src_mpd = memory::primitive_desc(src_md, mkldnn_engine);
auto src_mem = memory(src_mpd, to_void_cast(input_data));
srcs_mpd.push_back(src_mpd);
srcs_mem.push_back(src_mem);
scales.push_back(1.0);
}
auto dst_md =
memory::desc(dst_tz, memory::data_type::f32, memory::format::any);
auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_mpd);
std::shared_ptr<memory> dst_mem;
if (in_place) {
dst_mem.reset(new memory(sum_pd.dst_primitive_desc()));
} else {
dst_mem.reset(new memory(sum_pd.dst_primitive_desc(), output_data));
}
std::vector<mkldnn::primitive::at> inputs;
for (size_t i = 0; i < srcs_mem.size(); ++i) {
inputs.push_back(srcs_mem[i]);
}
auto sum_prim = mkldnn::sum(sum_pd, inputs, *dst_mem);
output_format = (memory::format)platform::GetMKLDNNFormat(sum_pd);
primitive reorder_prim;
std::shared_ptr<memory> target_mem;
if (in_place) {
output_format = input_format;
target_mem.reset(new memory(
{{{src_tz}, memory::data_type::f32, output_format}, mkldnn_engine},
output_data));
reorder_prim = reorder(*dst_mem, *target_mem);
}
std::vector<primitive> pipeline;
pipeline.push_back(sum_prim);
if (in_place) pipeline.push_back(reorder_prim);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(output_format);
} else if (out_var->IsType<framework::SelectedRows>()) {
// TODO(@mozga-intel) Add MKLDNN SelectedRows support
std::unique_ptr<framework::SelectedRows> in0;
if (in_place) {
// If is in_place, we store the input[0] to in0
auto& in_sel0 = in_vars[0]->Get<SelectedRows>();
auto& rows = in_sel0.rows();
in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
in0->mutable_value()->ShareDataWith(in_sel0.value());
}
auto get_selected_row = [&](size_t i) -> const SelectedRows& {
if (i == 0 && in0) {
return *in0.get();
} else {
return in_vars[i]->Get<SelectedRows>();
}
};
auto* out = ctx.Output<SelectedRows>("Out");
out->mutable_rows()->clear();
auto* out_value = out->mutable_value();
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
auto& sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
auto in_dim =
framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim[0] = static_cast<int64_t>(first_dim);
out_value->Resize(framework::make_ddim(in_dim));
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
return;
}
out_value->mutable_data<T>(ctx.GetPlace());
math::SelectedRowsAddTo<CPUDeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
auto& sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
}
PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
functor(ctx.template device_context<CPUDeviceContext>(), sel_row,
offset, out);
offset += sel_row.value().numel();
}
} else if (out_var->IsType<framework::LoDTensorArray>()) {
// TODO(@mozga-intel) Add MKLDNN LoDTensorArray support
auto& out_array = *out_var->GetMutable<framework::LoDTensorArray>();
for (size_t i = in_place ? 1 : 0; i < in_vars.size(); ++i) {
PADDLE_ENFORCE(in_vars[i]->IsType<framework::LoDTensorArray>(),
"Only support all inputs are TensorArray");
auto& in_array = in_vars[i]->Get<framework::LoDTensorArray>();
for (size_t i = 0; i < in_array.size(); ++i) {
if (in_array[i].numel() != 0) {
if (i >= out_array.size()) {
out_array.resize(i + 1);
}
if (out_array[i].numel() == 0) {
framework::TensorCopy(in_array[i], in_array[i].place(),
ctx.device_context(), &out_array[i]);
out_array[i].set_lod(in_array[i].lod());
} else {
PADDLE_ENFORCE(out_array[i].lod() == in_array[i].lod());
auto in = EigenVector<T>::Flatten(in_array[i]);
auto result = EigenVector<T>::Flatten(out_array[i]);
result.device(*ctx.template device_context<MKLDNNDeviceContext>()
.eigen_device()) = result + in;
}
}
}
}
} else {
PADDLE_THROW("Unexpected branch, output variable type is %s",
out_var->Type().name());
}
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_KERNEL(sum, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::SumMKLDNNOpKernel<float>);
paddle/fluid/operators/sum_op.cc
浏览文件 @ 4e9d7ddb
......@@ -18,6 +18,10 @@ limitations under the License. */
#include "paddle/fluid/framework/var_type_inference.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
using framework::Tensor;
......@@ -63,6 +67,18 @@ class SumOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto x_vars = ctx.MultiInputVar("X");
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
#ifdef PADDLE_WITH_MKLDNN
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
if (x_vars[0]->IsType<framework::LoDTensor>()) {
int dtype = -1;
for (auto& x_var : x_vars) {
......@@ -80,26 +96,27 @@ class SumOp : public framework::OperatorWithKernel {
"Sum operator should have at least one tensor");
return framework::OpKernelType(
static_cast<framework::proto::VarType::Type>(dtype),
ctx.device_context());
static_cast<framework::proto::VarType::Type>(dtype), ctx.GetPlace(),
layout, library);
} else if (x_vars[0]->IsType<framework::SelectedRows>()) {
for (auto& var : x_vars) {
auto& value = var->Get<framework::SelectedRows>().value();
if (value.IsInitialized()) {
return framework::OpKernelType(framework::ToDataType(value.type()),
ctx.device_context());
ctx.device_context(), layout, library);
}
}
// if input sparse vars are not initialized, use an default kernel type.
return framework::OpKernelType(framework::proto::VarType::FP32,
ctx.device_context());
ctx.device_context(), layout, library);
} else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
for (auto& x_var : x_vars) {
auto& array = x_var->Get<framework::LoDTensorArray>();
for (auto& each : array) {
if (each.numel() != 0) {
return framework::OpKernelType(framework::ToDataType(each.type()),
ctx.device_context());
ctx.device_context(), layout,
library);
}
}
}
......@@ -116,6 +133,9 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddComment(R"DOC(
Sum operator.
......@@ -132,7 +152,6 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
framework::BlockDesc* block) const override {
auto& inputs = op_desc.Input("X");
auto var_type = framework::proto::VarType::SELECTED_ROWS;
for (auto& name : op_desc.Input("X")) {
VLOG(10) << name << " "
<< block->FindRecursiveOrCreateVar(name).GetType();
......@@ -206,6 +225,7 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker,
ops::SumOpVarTypeInference);
REGISTER_OP_CPU_KERNEL(
sum, ops::SumKernel<paddle::platform::CPUDeviceContext, float>,
ops::SumKernel<paddle::platform::CPUDeviceContext, double>,
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 4e9d7ddb
......@@ -14,11 +14,14 @@
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/operators/tensorrt_engine_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/operators/tensorrt_engine_op.h"
namespace paddle {
namespace operators {
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ 4e9d7ddb
......@@ -16,10 +16,12 @@
#ifdef PADDLE_WITH_CUDA
#include <string>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
namespace paddle {
namespace operators {
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ 4e9d7ddb
......@@ -179,7 +179,6 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
const std::string& z_name, bool x_created,
const shape_t& x_shape, const shape_t& y_shape,
const shape_t& z_shape) {
LOG(INFO) << "create fc op";
auto* fc = block_desc.AppendOp();
fc->SetType("mul");
......
paddle/fluid/operators/test_send_nccl_id.cc
浏览文件 @ 4e9d7ddb
......@@ -21,7 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
......@@ -37,11 +37,11 @@ USE_NO_KERNEL_OP(listen_and_serv);
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace m = paddle::operators::math;
namespace detail = paddle::operators::detail;
namespace distributed = paddle::operators::distributed;
namespace string = paddle::string;
std::unique_ptr<detail::RPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
std::unique_ptr<distributed::RPCServer> g_rpc_service;
std::unique_ptr<distributed::RequestHandler> g_req_handler;
void StartServer() {
f::Scope scope;
......@@ -57,14 +57,14 @@ void StartServer() {
g_req_handler->SetProgram(&empty_program);
g_req_handler->SetExecutor(&executor);
g_rpc_service->RegisterRPC(detail::kRequestSend, g_req_handler.get());
g_rpc_service->RegisterRPC(distributed::kRequestSend, g_req_handler.get());
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
std::bind(&distributed::RPCServer::StartServer, g_rpc_service.get()));
g_rpc_service->SetCond(detail::kRequestSend);
g_rpc_service->WaitBarrier(detail::kRequestSend);
g_rpc_service->SetCond(distributed::kRequestSend);
g_rpc_service->WaitBarrier(distributed::kRequestSend);
LOG(INFO) << "got nccl id and stop server...";
g_rpc_service->ShutDown();
......@@ -72,7 +72,7 @@ void StartServer() {
}
TEST(SendNcclId, RPCServer) {
g_req_handler.reset(new detail::RequestSendHandler(true));
g_req_handler.reset(new distributed::RequestSendHandler(true));
g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
std::thread server_thread(StartServer);
......@@ -91,7 +91,8 @@ TEST(SendNcclId, RPCServer) {
std::string ep = string::Sprintf("127.0.0.1:%d", port);
detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
LOG(INFO) << "connect to server" << ep;
client->AsyncSendVar(ep, dev_ctx, scope, NCCL_ID_VARNAME);
......
paddle/fluid/operators/while_op.cc
浏览文件 @ 4e9d7ddb
......@@ -203,11 +203,11 @@ class WhileGradOp : public framework::OperatorBase {
->set_lod(inside_tensor.lod());
}
}
auto new_inside_name = cur_scope.Rename(inside_grad_name);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
{{"Out", {pg_names[param_id]}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
sum_op->Run(cur_scope, dev_place);
cur_scope.Rename(new_inside_name, inside_grad_name);
}
......
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