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PaddleDetection
“e207fe63855d06589d312806f7edca829174ba92”上不存在“paddle/phi/kernels/cpu/squeeze_grad_kernel.cc”
提交 8855d4a7 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into dist_recordio

上级 f9556dca 0c0c5df4
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AUTHORS.md
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......@@ -4,6 +4,7 @@
| backyes | Yan-Fei Wang |
| baiyfbupt | Yi-Fan Bai |
| beckett1124 | Bin Qi |
| ChengduoZH | Cheng-Duo Zhao|
| chengxiaohua1105 | Xiao-Hua Cheng |
| cxwangyi, yiwangbaidu, wangkuiyi | Yi Wang |
| cxysteven | Xing-Yi Cheng |
......
Dockerfile
浏览文件 @ 8855d4a7
......@@ -29,7 +29,7 @@ RUN apt-get update && \
wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
curl sed grep graphviz libjpeg-dev zlib1g-dev \
python-matplotlib gcc-4.8 g++-4.8 \
automake locales clang-format swig doxygen cmake \
automake locales clang-format swig cmake \
liblapack-dev liblapacke-dev \
clang-3.8 llvm-3.8 libclang-3.8-dev \
net-tools libtool ccache && \
......
benchmark/fluid/Dockerfile 0 → 100644
浏览文件 @ 8855d4a7
FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop
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 opencv-python paddlepaddle
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
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'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
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
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl && chmod +x /usr/bin/paddle_k8s
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD fluid_benchmark.py dataset.py models/ /workspace/
benchmark/fluid/README.md
浏览文件 @ 8855d4a7
......@@ -44,11 +44,25 @@ Currently supported `--model` argument include:
## Run Distributed Benchmark on Kubernetes Cluster
You may need to build a Docker image before submitting a cluster job onto Kubernetes, or you will
have to start all those processes mannually on each node, which is not recommended.
To build the Docker image, you need to choose a paddle "whl" package to run with, you may either
download it from
http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_en.html or
build it by your own. Once you've got the "whl" package, put it under the current directory and run:
```bash
docker build -t [your docker image name]:[your docker image tag] .
```
Then push the image to a Docker registry that your Kubernetes cluster can reach.
We provide a script `kube_gen_job.py` to generate Kubernetes yaml files to submit
distributed benchmark jobs to your cluster. To generate a job yaml, just run:
```bash
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --parallel 1 --device GPU --update_method pserver " --disttype pserver
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --gpus 8 --device GPU --update_method pserver " --disttype pserver
```
Then the yaml files are generated under directory `myjob`, you can run:
......
benchmark/fluid/kube_gen_job.py
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......@@ -49,7 +49,7 @@ def parse_args():
parser.add_argument(
'--fluid', default=1, type=int, help='whether is fluid job')
parser.add_argument(
'--rdma', action='store_ture', help='whether mount rdma libs')
'--rdma', action='store_true', help='whether mount rdma libs')
parser.add_argument(
'--disttype',
default="pserver",
......
benchmark/fluid/run.sh
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......@@ -37,7 +37,8 @@ nohup stdbuf -oL nvidia-smi \
-l 1 &
# mnist
# mnist gpu mnist 128
FLAGS_benchmark=true stdbuf -oL python fluid/mnist.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=mnist \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
......@@ -46,7 +47,8 @@ FLAGS_benchmark=true stdbuf -oL python fluid/mnist.py \
# vgg16
# gpu cifar10 128
FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=vgg16 \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
......@@ -54,7 +56,8 @@ FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
2>&1 | tee -a vgg16_gpu_128.log
# flowers gpu 128
FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=vgg16 \
--device=GPU \
--batch_size=32 \
--data_set=flowers \
......@@ -64,40 +67,39 @@ FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
# resnet50
# resnet50 gpu cifar10 128
FLAGS_benchmark=true stdbuf -oL python fluid/resnet50.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=resnet50 \
--device=GPU \
--batch_size=128 \
--data_set=cifar10 \
--model=resnet_cifar10 \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a resnet50_gpu_128.log
# resnet50 gpu flowers 64
FLAGS_benchmark=true stdbuf -oL python fluid/resnet50.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=resnet50 \
--device=GPU \
--batch_size=64 \
--data_set=flowers \
--model=resnet_imagenet \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a resnet50_gpu_flowers_64.log
# lstm
# lstm gpu imdb 32 # tensorflow only support batch=32
FLAGS_benchmark=true stdbuf -oL python fluid/stacked_dynamic_lstm.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=stacked_dynamic_lstm \
--device=GPU \
--batch_size=32 \
--skip_batch_num=5 \
--iterations=30 \
--hidden_dim=512 \
--emb_dim=512 \
--crop_size=1500 \
2>&1 | tee -a lstm_gpu_32.log
# seq2seq
# seq2seq gpu wmb 128
FLAGS_benchmark=true stdbuf -oL python fluid/machine_translation.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=machine_translation \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
......
doc/fluid/api/layers.rst
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......@@ -1009,3 +1009,9 @@ ____
.. autofunction:: paddle.fluid.layers.upsampling_bilinear2d
:noindex:
gather
____
.. autofunction:: paddle.fluid.layers.gather
:noindex:
doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md
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......@@ -86,7 +86,7 @@
<br>
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/fluid_compiler.png" width=100%>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/fluid-compiler.png" width=100%>
</p>
---
......@@ -123,12 +123,12 @@
<font size=5>
- 在科学计算领域，计算图是一种描述计算的经典方式。下图展示了从前向计算图（蓝色）开始，通过添加反向（红色）和优化算法相关（绿色）操作，构建出整个计算图的过程：
-
-
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/graph_construction_example_all.png" width=60%>
</p>
- Fluid ==使用`Program`而不是计算图==来描述模型和优化过程。`Program`由`Block`、`Operator`和`Variable`构成，相关概念会在后文详细展开。
- 编译时 Fluid 接受前向计算（这里可以先简单的理解为是一段有序的计算流）`Program`，为这段前向计算按照：前向 -> 反向 -> 梯度 clip -> 正则 -> 优化 的顺序，添加相关 `Operator`和`Variable`到`Program`到完整的计算。
......@@ -328,7 +328,7 @@
</font>
---
---
### 编译时概念 ：==**[Transpiler](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/motivation/fluid_compiler.md)**==
<font size=5>
......@@ -402,7 +402,7 @@
- `Scope`
- 计算相关
- `Block`
- `Block`
- `Kernel`、`OpWithKernel`、`OpWithoutKernel`
<table>
......@@ -439,7 +439,7 @@
</tbody>
</table>
- 执行相关 ：`Executor`
- 执行相关 ：`Executor`
</font>
......@@ -798,7 +798,7 @@ class GPUAllocator : public SystemAllocator {
- step 1：添加Place类型，<span style="background-color:#DAB1D5;">由用户实现添加到框架</span>
- 可以将Place类型理解为一个整数加上一个枚举型，包括：设备号 + 设备类型
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/place.png" width=40%>
</p>
......@@ -824,7 +824,7 @@ class GPUAllocator : public SystemAllocator {
1. DataType 执行数据类型 FP32/FP64/INT32/INT64
1. Memory layout： 运行时 Tensor 在内存中的排布格式 NCHW、 NHWC
1. 使用的库
来区分Kernel，为同一个operator注册多个 Kernel。
```cpp
......@@ -876,7 +876,7 @@ step 3: 运行时的 KernelType 推断和Kernel切换，<span style="background-
namespace framework {
using LoDTensorArray = std::vector<LoDTensor>;
}
}
}
```
- 每一次循环，从原始输入中“切出”一个片段
- LoDTensorArray 在Python端暴露，是Fluid支持的基础数据结构之一，用户可以直接创建并使用
......@@ -910,7 +910,7 @@ void Run(const framework::Scope &scope,
false /*create_local_scope*/);
}
}
```
</font>
......@@ -951,7 +951,7 @@ void Run(const framework::Scope &scope,
---
#### dynamicRNN 中的 Memory
#### dynamicRNN 中的 Memory
<font size=5>
......@@ -961,7 +961,7 @@ void Run(const framework::Scope &scope,
- `memory` 在 operator A 前向计算之后，进行前向计算
- 当 `memory` 的前向计算会 "指向" A 的输出 LoDTensor
- `memory` 的输出可以是另一个 operator 的输入，于是形成了“循环”连接
</font>
---
......@@ -1107,7 +1107,7 @@ void Run(const framework::Scope &scope,
<td>
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/fluid_module_1.png" width=60%>
</p>
</p>
</td>
<td>
<p align="center">
......@@ -1127,13 +1127,13 @@ void Run(const framework::Scope &scope,
<font size=5>
- 设计概览
- 重构概览 [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/refactorization.md)
- fluid [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid.md)
- 重构概览 [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/refactorization.md)
- fluid [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid.md)
- fluid_compiler [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/motivation/fluid_compiler.md)
- 核心概念
- variable 描述 [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/var_desc.md)
- Tensor [->](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/tensor.md)
- LoDTensor [->](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
- LoDTensor [->](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
- TensorArray [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md)
- Program [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md)
- Block [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md)
......@@ -1152,7 +1152,7 @@ void Run(const framework::Scope &scope,
- 支持新设硬件设备库 [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/support_new_device.md)
- 添加新的Operator [->](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_cn.md)
- 添加新的Kernel [->](
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_kernel_en.md)
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_kernel_en.md)
</font>
......@@ -1167,10 +1167,10 @@ https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_kernel_
<font size=5>
Docker编译PaddlePaddle源码: [->](http://www.paddlepaddle.org/docs/develop/documentation/fluid/zh/build_and_install/docker_install_cn.html)
PaddlePaddle 在 Dockerhub 地址：[->](
https://hub.docker.com/r/paddlepaddle/paddle/tags/)
1. 获取PaddlePaddle的Docker镜像
```bash
docker pull paddlepaddle/paddle:latest-dev
......@@ -1183,7 +1183,7 @@ PaddlePaddle 在 Dockerhub 地址：[->](
```
1. 进入docker container后，从源码编译，请参考文档 [->]( http://www.paddlepaddle.org/docs/develop/documentation/fluid/zh/build_and_install/build_from_source_cn.html)
</font>
---
......@@ -1196,7 +1196,7 @@ PaddlePaddle 在 Dockerhub 地址：[->](
1. 开发推荐使用tag为`latest-dev`的镜像，其中打包了所有编译依赖。`latest`及`lastest-gpu`是production镜像，主要用于运行PaddlePaddle程序。
2. 在Docker中运行GPU程序，推荐使用nvidia-docker，[否则需要将CUDA库和设备挂载到Docker容器内](http://www.paddlepaddle.org/docs/develop/documentation/fluid/zh/build_and_install/docker_install_cn.html)。
<font size=4>
```bash
nvidia-docker run -it -v $PWD/Paddle:/paddle paddlepaddle/paddle:latest-dev /bin/bash
```
......@@ -1353,9 +1353,9 @@ Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实
}
};
```
</font>
---
###### 实现带Kernel的Operator <span style="background-color:#c4e1e1;">step2</span>: 定义Operator类
......@@ -1420,11 +1420,11 @@ class ClipOp : public framework::OperatorWithKernel {
2. override InferShape函数（参考 [clip_op](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/clip_op.cc#L24)）
1. 什么是`functor` ?
- 类或结构体仅重载了`()`，一般是可被多个kernel复用的计算函数。
<font size=4>
```cpp
template <typename T>
class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
......@@ -1438,9 +1438,9 @@ class ClipOp : public framework::OperatorWithKernel {
};
```
</font>
- 在 clip_op 内也会看到将一段计算函数抽象为functor的使用法： [->](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/clip_op.h#L27)。
</font>
---
......@@ -1504,7 +1504,7 @@ class ClipKernel : public framework::OpKernel<T> {
- 需要注意，<span style="background-color:#e1c4c4;">Fluid中，不区分Cost Op和中间层Op，所有Op都必须正确处理接收到的梯度</span>
2. 反向Op的输出
- 对可学习参数的求导结果
- 对所有输入的求导结果
- 对所有输入的求导结果
</font>
......@@ -1520,7 +1520,7 @@ class ClipKernel : public framework::OpKernel<T> {
1. 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
<font size=4>
```cpp
namespace ops = paddle::operators;
REGISTER_OP(clip, ops::ClipOp, ops::ClipOpMaker<float>, clip_grad,
......@@ -1530,13 +1530,13 @@ class ClipKernel : public framework::OpKernel<T> {
REGISTER_OP_CPU_KERNEL(
clip_grad, ops::ClipGradKernel<paddle::platform::CPUDeviceContext, float>);
```
- 在上面的代码片段中：
1. `REGISTER_OP` ： 注册`ops::ClipOp`类，类型名为`clip`，该类的`ProtoMaker`为`ops::ClipOpMaker`，注册`ops::ClipOpGrad`，类型名为`clip_grad`
1. `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op，例如：优化算法相关的Op
1. `REGISTER_OP_CPU_KERNEL` ：注册`ops::ClipKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::ClipGradKernel`类
</font>
1. 按照同样方法，在`.cu`文件中注册GPU Kernel
- <span style="background-color:#e1c4c4;">如果CUDA Kernel的实现基于Eigen，需在 `.cu`的开始加上宏定义 `#define EIGEN_USE_GPU` </span>
......@@ -1593,7 +1593,7 @@ class ClipKernel : public framework::OpKernel<T> {
```bash
make test ARGS="-R test_mul_op -V"
```
或者:
```
......@@ -1613,7 +1613,7 @@ class ClipKernel : public framework::OpKernel<T> {
- 如果多个Op依赖一些共用的函数，可以创建非`*_op.*`格式的文件来存放，如`gather.h`文件。
</font>
---
### ==10.== 使用相关问题
......@@ -1735,7 +1735,7 @@ class ClipKernel : public framework::OpKernel<T> {
y_data = np.random.randint(0, 8, [1]).astype("int32")
y_tensor = core.Tensor()
y_tensor.set(y_data, place)
x_data = np.random.uniform(0.1, 1, [11, 8]).astype("float32")
x_tensor = core.Tensor()
x_tensor.set(x_data, place)
......
doc/fluid/getstarted/index_cn.rst
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......@@ -17,3 +17,4 @@
:maxdepth: 1
concepts/use_concepts_cn.rst
developer's_guide_to_paddle_fluid.md
doc/fluid/getstarted/index_en.rst
浏览文件 @ 8855d4a7
......@@ -16,3 +16,4 @@ Here is an example of linear regression. It introduces workflow of PaddlePaddle,
:maxdepth: 1
concepts/index_en.rst
developer's_guide_to_paddle_fluid.md
doc/fluid/getstarted/quickstart_cn.rst
浏览文件 @ 8855d4a7
......@@ -11,7 +11,7 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
pip install paddlepaddle
如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
如果需要安装支持GPU的版本（cuda8.0_cudnn5_avx_openblas），需要执行：
.. code-block:: bash
......@@ -28,18 +28,18 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
import paddle.dataset.uci_housing as uci_housing
import paddle.fluid as fluid
with fluid.scope_guard(fluid.core.Scope()):
# initialize executor with cpu
exe = fluid.Executor(place=fluid.CPUPlace())
# load inference model
# load inference model
[inference_program, feed_target_names,fetch_targets] = \
fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
# run inference
result = exe.run(inference_program,
feed={feed_target_names[0]: uci_housing.predict_reader()},
result = exe.run(inference_program,
feed={feed_target_names[0]: uci_housing.predict_reader()},
fetch_list=fetch_targets)
# print predicted price is $12,273.97
# print predicted price is $12,273.97
print 'Predicted price: ${:,.2f}'.format(result[0][0][0] * 1000)
执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
doc/fluid/getstarted/quickstart_en.rst
浏览文件 @ 8855d4a7
......@@ -12,7 +12,7 @@ Simply run the following command to install, the version is cpu_avx_openblas:
pip install paddlepaddle
If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
If you need to install GPU version (cuda8.0_cudnn5_avx_openblas), run:
.. code-block:: bash
......@@ -31,18 +31,18 @@ code:
import paddle.dataset.uci_housing as uci_housing
import paddle.fluid as fluid
with fluid.scope_guard(fluid.core.Scope()):
# initialize executor with cpu
exe = fluid.Executor(place=fluid.CPUPlace())
# load inference model
# load inference model
[inference_program, feed_target_names,fetch_targets] = \
fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
# run inference
result = exe.run(inference_program,
feed={feed_target_names[0]: uci_housing.predict_reader()},
result = exe.run(inference_program,
feed={feed_target_names[0]: uci_housing.predict_reader()},
fetch_list=fetch_targets)
# print predicted price is $12,273.97
# print predicted price is $12,273.97
print 'Predicted price: ${:,.2f}'.format(result[0][0][0] * 1000)
Run :code:`python housing.py` and voila! It should print out a list of predictions
......
doc/fluid/howto/index_cn.rst
浏览文件 @ 8855d4a7
......@@ -4,5 +4,5 @@
.. toctree::
:maxdepth: 1
inference/index_cn.rst
optimization/index_cn.rst
inference/inference_support_in_fluid.md
doc/fluid/howto/index_en.rst
浏览文件 @ 8855d4a7
......@@ -5,4 +5,3 @@ HOW TO
:maxdepth: 1
optimization/index_en.rst
inference/inference_support_in_fluid.md
doc/fluid/howto/inference/build_and_install_lib_cn.rst 0 → 100644
浏览文件 @ 8855d4a7
安装与编译C++预测库
===========================
直接下载安装
-------------
====================== ========================================
版本说明 C++预测库
====================== ========================================
cpu_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/fluid.tgz>`_
cpu_avx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/fluid.tgz>`_
cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/fluid.tgz>`_
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>`_
====================== ========================================
从源码编译
----------
用户也可以从 PaddlePaddle 核心代码编译C++预测库，只需在编译时配制下面这些编译选项：
================= =========
选项 值
================= =========
CMAKE_BUILD_TYPE Release
FLUID_INSTALL_DIR 安装路径
WITH_FLUID_ONLY ON（推荐）
WITH_SWIG_PY OFF（推荐
WITH_PYTHON OFF（推荐）
WITH_GPU ON/OFF
WITH_MKL ON/OFF
================= =========
建议按照推荐值设置，以避免链接不必要的库。其它可选编译选项按需进行设定。
下面的代码片段从github拉取最新代码，配制编译选项（需要将PADDLE_ROOT替换为PaddlePaddle预测库的安装路径）：
.. code-block:: bash
pip install paddlepaddle-gpu
PADDLE_ROOT=/path/of/capi
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
mkdir build
cd build
cmake -DFLUID_INSTALL_DIR=$PADDLE_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_FLUID_ONLY=ON \
-DWITH_SWIG_PY=OFF \
-DWITH_PYTHON=OFF \
-DWITH_MKL=OFF \
-DWITH_GPU=OFF \
..
make
make inference_lib_dist
成功编译后，使用C++预测库所需的依赖（包括：（1）编译出的PaddlePaddle预测库和头文件；（2）第三方链接库和头文件；（3）版本信息与编译选项信息）
均会存放于PADDLE_ROOT目录中。目录结构如下：
.. code-block:: text
PaddleRoot/
├── CMakeCache.txt
├── paddle
│   └── fluid
│   ├── framework
│   ├── inference
│   ├── memory
│   ├── platform
│   ├── pybind
│   └── string
├── third_party
│   ├── boost
│   │   └── boost
│   ├── eigen3
│   │   ├── Eigen
│   │   └── unsupported
│   └── install
│   ├── gflags
│   ├── glog
│   ├── mklml
│   ├── protobuf
│   ├── snappy
│   ├── snappystream
│   └── zlib
└── version.txt
version.txt 中记录了该预测库的版本信息，包括Git Commit ID、使用OpenBlas或MKL数学库、CUDA/CUDNN版本号，如：
.. code-block:: text
GIT COMMIT ID: c95cd4742f02bb009e651a00b07b21c979637dc8
WITH_MKL: ON
WITH_GPU: ON
CUDA version: 8.0
CUDNN version: v5
doc/fluid/howto/inference/index_cn.rst 0 → 100644
浏览文件 @ 8855d4a7
预测库
------------
.. toctree::
:maxdepth: 1
build_and_install_lib_cn.rst
inference_support_in_fluid_cn.md
doc/fluid/howto/inference/inference_support_in_fluid.md → doc/fluid/howto/inference/inference_support_in_fluid_cn.md
浏览文件 @ 8855d4a7
# Fluid Inference使用指南
# 使用指南
## 目录：
- Python Inference API
- 编译Fluid Inference库
- Inference C++ API
- Inference实例
- Inference计算优化
......@@ -55,62 +54,6 @@
return [program, feed_target_names, fetch_targets]
```
## 编译Fluid Inference库
- **不需要额外的CMake选项**
- 1、 配置CMake命令，更多配置请参考[源码编译PaddlePaddle](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/build_from_source_cn.html)
```bash
$ git clone https://github.com/PaddlePaddle/Paddle.git
$ cd Paddle
$ mkdir build
$ cd build
$ cmake -DCMAKE_INSTALL_PREFIX=your/path/to/paddle_inference_lib \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_PYTHON=ON \
-DWITH_MKL=OFF \
-DWITH_GPU=OFF \
..
```
- 2、 编译PaddlePaddle
```bash
$ make
```
- 3、 部署。执行如下命令将PaddlePaddle Fluid Inference库部署到`your/path/to/paddle_inference_lib`目录。
```bash
$ make inference_lib_dist
```
- 目录结构
```bash
$ cd your/path/to/paddle_inference_lib
$ tree
.
|-- paddle
| `-- fluid
| |-- framework
| |-- inference
| | |-- io.h
| | `-- libpaddle_fluid.so
| |-- memory
| |-- platform
| `-- string
|-- third_party
| |-- eigen3
| `-- install
| |-- gflags
| |-- glog
| `-- protobuf
`-- ...
```
假设`PADDLE_ROOT=your/path/to/paddle_inference_lib`。
## 链接Fluid Inference库
- 示例项目([链接](https://github.com/luotao1/fluid_inference_example.git))
......
paddle/contrib/inference/CMakeLists.txt
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......@@ -17,46 +17,33 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
function(inference_api_test TARGET_NAME TEST_SRC)
function(inference_api_test TARGET_NAME)
set(options "")
set(oneValueArgs "")
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
set(arg_list "")
cc_test(test_paddle_inference_${TARGET_NAME}
SRCS test_paddle_inference_${TARGET_NAME}.cc
DEPS paddle_fluid_api paddle_inference_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
if(inference_test_ARGS)
foreach(arg ${inference_test_ARGS})
list(APPEND arg_list "_${arg}")
endforeach()
else()
list(APPEND arg_list "_")
set_tests_properties(test_paddle_inference_${TARGET_NAME}
PROPERTIES DEPENDS "${inference_test_ARGS}")
endif()
foreach(arg ${arg_list})
string(REGEX REPLACE "^_$" "" arg "${arg}")
cc_test(${TARGET_NAME}
SRCS ${TEST_SRC}
DEPS paddle_fluid_api paddle_inference_api paddle_inference_api_impl
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
# TODO(panyx0178): Figure out how to add word2vec and image_classification
# as deps.
# set_tests_properties(${TARGET_NAME}
# PROPERTIES DEPENDS ${DEP_TEST})
endforeach()
endfunction(inference_api_test)
cc_library(paddle_inference_api
SRCS paddle_inference_api.cc
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
cc_library(paddle_inference_api_impl
SRCS paddle_inference_api_impl.cc
DEPS paddle_inference_api paddle_fluid_api)
if(WITH_TESTING)
cc_test(test_paddle_inference_api
SRCS test_paddle_inference_api.cc
DEPS paddle_inference_api)
cc_test(test_paddle_inference_api
SRCS test_paddle_inference_api.cc
DEPS paddle_inference_api)
inference_api_test(test_paddle_inference_api_impl
test_paddle_inference_api_impl.cc)
inference_api_test(api_impl
ARGS test_word2vec test_image_classification)
endif()
paddle/contrib/inference/paddle_inference_api.h
浏览文件 @ 8855d4a7
......@@ -40,15 +40,24 @@ struct PaddleBuf {
struct PaddleTensor {
std::string name; // variable name.
std::vector<int> shape;
// TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
PaddleBuf data; // blob of data.
PaddleDType dtype;
};
enum class PaddleEngineKind {
kNative = 0, // Use the native Fluid facility.
// TODO(Superjomn) support following engines latter.
// kAnakin, // Use Anakin for inference.
// kTensorRT, // Use TensorRT for inference.
// kAutoMixedAnakin, // Automatically mix Fluid with Anakin.
// kAutoMixedTensorRT, // Automatically mix Fluid with TensorRT.
};
/*
* A simple Inference API for Paddle. Currently this API might just be used by
* non-sequence scenerios.
* TODO(Superjomn) Prepare another API for NLP-related usages.
*/
* A simple Inference API for Paddle. Currently this API can be used by
* non-sequence scenerios.
*/
class PaddlePredictor {
public:
struct Config;
......@@ -66,34 +75,35 @@ class PaddlePredictor {
// be thread-safe.
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
virtual bool InitShared() { return false; }
// Destroy the Predictor.
virtual ~PaddlePredictor() {}
friend std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
const PaddlePredictor::Config& config);
// The common configs for all the predictors.
struct Config {
enum class EngineKind;
std::string model_dir; // path to the model directory.
bool enable_engine{false}; // Enable to execute (part of) the model on
// third-party engines.
EngineKind engine_kind{Config::EngineKind::kNone};
enum class EngineKind {
kNone = -1, // Use the native Fluid facility.
kAnakin, // Use Anakin for inference.
kTensorRT, // Use TensorRT for inference.
kAutoMixedAnakin, // Automatically mix Fluid with Anakin.
kAutoMixedTensorRT, // Automatically mix Fluid with TensorRT.
};
};
};
// A factory to help create difference predictor.
template <typename ConfigT>
struct NativeConfig : public PaddlePredictor::Config {
// GPU related fields.
bool use_gpu{false};
int device{0};
float fraction_of_gpu_memory{-1.f}; // Negative to notify initialization.
std::string prog_file;
std::string param_file;
};
// A factory to help create different predictors.
//
// FOR EXTENSION DEVELOPER:
// Different predictors are designated by config type and engine kind. Similar
// configs can be merged, but there shouldn't be a huge config containing
// different fields for more than one kind of predictors.
//
// Similarly, each engine kind should map to a unique predictor implementation.
template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_impl.cc
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......@@ -54,11 +54,10 @@ std::string num2str(T a) {
}
} // namespace
bool PaddlePredictorImpl::Init() {
bool NativePaddlePredictor::Init() {
VLOG(3) << "Predictor::init()";
// TODO(panyx0718): Should CPU vs GPU device be decided by id?
if (config_.device >= 0) {
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
place_ = paddle::platform::CPUPlace();
......@@ -85,19 +84,21 @@ bool PaddlePredictorImpl::Init() {
}
ctx_ = executor_->Prepare(*inference_program_, 0);
// Create variables
// TODO(panyx0718): Why need to test share_variables here?
if (config_.share_variables) {
executor_->CreateVariables(*inference_program_, scope_.get(), 0);
}
// Create temporary variables first, so that the first batch do not need to
// create variables in the runtime. This is the logics of the old inference
// API.
// TODO(Superjomn) this should be modified when `Clone` is valid for
// multi-thread application.
executor_->CreateVariables(*inference_program_, scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
feed_target_names_ = inference_program_->GetFeedTargetNames();
fetch_target_names_ = inference_program_->GetFetchTargetNames();
return true;
}
bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data) {
bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data) {
VLOG(3) << "Predictor::predict";
Timer timer;
timer.tic();
......@@ -124,7 +125,7 @@ bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
scope_.get(),
&feed_targets,
&fetch_targets,
!config_.share_variables);
false /* don't create variable eatch time */);
if (!GetFetch(fetchs, output_data)) {
LOG(ERROR) << "fail to get fetchs";
return false;
......@@ -133,59 +134,20 @@ bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
return true;
}
std::unique_ptr<PaddlePredictor> PaddlePredictorImpl::Clone() {
std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
VLOG(3) << "Predictor::clone";
std::unique_ptr<PaddlePredictor> cls(new PaddlePredictorImpl(config_));
if (!cls->InitShared()) {
LOG(ERROR) << "fail to call InitShared";
std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init()) {
LOG(ERROR) << "fail to call Init";
return nullptr;
}
// fix manylinux compile error.
return std::move(cls);
}
// TODO(panyx0718): Consider merge with Init()?
bool PaddlePredictorImpl::InitShared() {
VLOG(3) << "Predictor::init_shared";
// 1. Define place, executor, scope
if (this->config_.device >= 0) {
place_ = platform::CUDAPlace();
} else {
place_ = platform::CPUPlace();
}
this->executor_.reset(new framework::Executor(this->place_));
this->scope_.reset(new framework::Scope());
// Initialize the inference program
if (!this->config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
this->inference_program_ = inference::Load(
this->executor_.get(), this->scope_.get(), this->config_.model_dir);
} else if (!this->config_.prog_file.empty() &&
!this->config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
this->inference_program_ = inference::Load(this->executor_.get(),
this->scope_.get(),
this->config_.prog_file,
this->config_.param_file);
}
this->ctx_ = this->executor_->Prepare(*this->inference_program_, 0);
// 3. create variables
// TODO(panyx0718): why test share_variables.
if (config_.share_variables) {
this->executor_->CreateVariables(
*this->inference_program_, this->scope_.get(), 0);
}
// 4. Get the feed_target_names and fetch_target_names
this->feed_target_names_ = this->inference_program_->GetFeedTargetNames();
this->fetch_target_names_ = this->inference_program_->GetFetchTargetNames();
return true;
}
bool PaddlePredictorImpl::SetFeed(const std::vector<PaddleTensor> &inputs,
std::vector<framework::LoDTensor> *feeds) {
bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
std::vector<framework::LoDTensor> *feeds) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feed_target_names_.size()) {
LOG(ERROR) << "wrong feed input size.";
......@@ -213,7 +175,7 @@ bool PaddlePredictorImpl::SetFeed(const std::vector<PaddleTensor> &inputs,
return true;
}
bool PaddlePredictorImpl::GetFetch(
bool NativePaddlePredictor::GetFetch(
const std::vector<framework::LoDTensor> &fetchs,
std::vector<PaddleTensor> *outputs) {
VLOG(3) << "Predictor::get_fetch";
......@@ -280,23 +242,29 @@ bool PaddlePredictorImpl::GetFetch(
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
const ConfigImpl &config) {
VLOG(3) << "create PaddlePredictorImpl";
// 1. GPU memeroy
std::vector<std::string> flags;
if (config.fraction_of_gpu_memory >= 0.0f ||
config.fraction_of_gpu_memory <= 0.95f) {
flags.push_back("dummpy");
std::string flag = "--fraction_of_gpu_memory_to_use=" +
num2str<float>(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
framework::InitGflags(flags);
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
const NativeConfig &config) {
VLOG(3) << "create NativePaddlePredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE(
config.fraction_of_gpu_memory > 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
std::vector<std::string> flags;
if (config.fraction_of_gpu_memory >= 0.0f ||
config.fraction_of_gpu_memory <= 0.95f) {
flags.push_back("dummpy");
std::string flag = "--fraction_of_gpu_memory_to_use=" +
num2str<float>(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
framework::InitGflags(flags);
}
}
std::unique_ptr<PaddlePredictor> predictor(new PaddlePredictorImpl(config));
if (!dynamic_cast<PaddlePredictorImpl *>(predictor.get())->Init()) {
std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init()) {
return nullptr;
}
return std::move(predictor);
......
paddle/contrib/inference/paddle_inference_api_impl.h
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......@@ -29,17 +29,10 @@
namespace paddle {
struct ConfigImpl : public PaddlePredictor::Config {
int device;
float fraction_of_gpu_memory;
std::string prog_file;
std::string param_file;
bool share_variables;
};
class PaddlePredictorImpl : public PaddlePredictor {
class NativePaddlePredictor : public PaddlePredictor {
public:
explicit PaddlePredictorImpl(const ConfigImpl &config) : config_(config) {}
explicit NativePaddlePredictor(const NativeConfig &config)
: config_(config) {}
bool Init();
......@@ -48,16 +41,15 @@ class PaddlePredictorImpl : public PaddlePredictor {
std::unique_ptr<PaddlePredictor> Clone() override;
~PaddlePredictorImpl() override{};
~NativePaddlePredictor() override{};
private:
bool InitShared() override;
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
std::vector<framework::LoDTensor> *feeds);
bool GetFetch(const std::vector<framework::LoDTensor> &fetchs,
std::vector<PaddleTensor> *output_data);
ConfigImpl config_;
NativeConfig config_;
platform::Place place_;
std::unique_ptr<framework::Executor> executor_;
std::unique_ptr<framework::Scope> scope_;
......
paddle/contrib/inference/test_paddle_inference_api_impl.cc
浏览文件 @ 8855d4a7
......@@ -40,19 +40,19 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
return pt;
}
ConfigImpl GetConfig() {
ConfigImpl config;
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
config.use_gpu = true;
config.device = 0;
config.share_variables = true;
return config;
}
TEST(paddle_inference_api_impl, word2vec) {
ConfigImpl config = GetConfig();
std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
NativeConfig config = GetConfig();
auto predictor = CreatePaddlePredictor<NativeConfig>(config);
framework::LoDTensor first_word, second_word, third_word, fourth_word;
framework::LoD lod{{0, 1}};
......@@ -104,7 +104,7 @@ TEST(paddle_inference_api_impl, image_classification) {
int batch_size = 2;
bool use_mkldnn = false;
bool repeat = false;
ConfigImpl config = GetConfig();
NativeConfig config = GetConfig();
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
......@@ -133,7 +133,7 @@ TEST(paddle_inference_api_impl, image_classification) {
is_combined,
use_mkldnn);
std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
auto predictor = CreatePaddlePredictor(config);
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&input));
......@@ -144,8 +144,7 @@ TEST(paddle_inference_api_impl, image_classification) {
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_LT(lod_data[j] - data[j], 1e-10);
EXPECT_GT(lod_data[j] - data[j], -1e-10);
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
}
free(data);
}
......
paddle/fluid/framework/block_desc.cc
浏览文件 @ 8855d4a7
......@@ -200,7 +200,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDesc(op_desc, prog, this));
ops_.emplace_back(new OpDesc(op_desc, this));
}
}
......@@ -209,7 +209,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
: prog_(prog), desc_(desc) {
need_update_ = true;
for (auto &op : other.ops_) {
ops_.emplace_back(new OpDesc(*op->Proto(), prog, this));
ops_.emplace_back(new OpDesc(*op, this));
}
for (auto &it : other.vars_) {
auto *var = new VarDesc(*it.second);
......
paddle/fluid/framework/block_desc.h
浏览文件 @ 8855d4a7
......@@ -105,7 +105,7 @@ class BlockDesc {
size_t OpSize() const { return ops_.size(); }
OpDesc *Op(int idx) { return ops_.at(idx).get(); }
OpDesc *Op(int idx) const { return ops_.at(idx).get(); }
void Flush();
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 8855d4a7
......@@ -11,11 +11,15 @@
// 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/multi_devices_graph_builder.h"
#include <algorithm>
#include <fstream>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/rpc_op_handle.h"
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
......@@ -26,9 +30,6 @@
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#endif
#include <string>
#include <vector>
DEFINE_string(ssa_graph_path, "/tmp/ssa_graph.dot",
"the ssa graph path only print with GLOG_v=10,"
"default /tmp/graph.dot");
......@@ -148,9 +149,9 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
const ProgramDesc &program) const {
std::unordered_map<std::string, proto::VarType::Type> var_types;
std::unordered_map<std::string, VarDesc *> all_vars;
for (auto *var : program.Block(0).AllVars()) {
var_types[var->Name()] = var->GetType();
all_vars[var->Name()] = var;
}
auto graph = new SSAGraph();
......@@ -167,12 +168,28 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
auto send_vars = FindDistTrainSendVars(program);
auto recv_vars = FindDistTrainRecvVars(program);
size_t cur_device_id = 0;
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>(
......@@ -220,13 +237,13 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
cur_device_id = get_appropriate_dev(g_name);
CreateReduceOp(&result, g_name, cur_device_id);
var_name_on_devices[cur_device_id].emplace(g_name);
bcast_var_name_set[cur_device_id].emplace(p_name);
cur_device_id = (cur_device_id + 1) % places_.size();
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
if (IsSparseGradient(var_types, g_name)) {
if (IsSparseGradient(all_vars, g_name)) {
CreateReduceOp(&result, g_name, 0);
CreateBroadcastOp(&result, g_name, 0);
} else {
......@@ -269,10 +286,10 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
bool MultiDevSSAGraphBuilder::IsSparseGradient(
const std::unordered_map<std::string, proto::VarType::Type> &var_types,
const std::unordered_map<std::string, VarDesc *> &all_vars,
const std::string &og) const {
PADDLE_ENFORCE(var_types.count(og) != 0);
if (var_types.at(og) == proto::VarType::SELECTED_ROWS) {
PADDLE_ENFORCE(all_vars.count(og) != 0);
if (all_vars.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
return true;
}
return false;
......
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ 8855d4a7
......@@ -106,7 +106,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
size_t src_dev_id) const;
bool IsSparseGradient(
const std::unordered_map<std::string, proto::VarType::Type> &var_types,
const std::unordered_map<std::string, VarDesc *> &all_vars,
const std::string &og) const;
private:
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 8855d4a7
......@@ -103,7 +103,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
need_update_ = true;
}
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
OpDesc::OpDesc(const proto::OpDesc &desc, BlockDesc *block)
: desc_(desc), need_update_(false) {
// restore inputs_
int input_size = desc_.inputs_size();
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 8855d4a7
......@@ -33,13 +33,14 @@ class OpDesc {
OpDesc(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
OpDesc(const proto::OpDesc &desc, BlockDesc *block);
explicit OpDesc(BlockDesc *block) : block_(block) {}
OpDesc(const OpDesc &other, BlockDesc *block) {
*this = other;
block_ = block;
need_update_ = true;
}
void CopyFrom(const OpDesc &op_desc);
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ 8855d4a7
......@@ -51,12 +51,15 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
auto *block = desc_.mutable_blocks(i);
blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
}
for (auto &block : blocks_) {
for (auto *op : block->AllOps()) {
for (const auto &attr : op->Proto()->attrs()) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
for (size_t block_id = 0; block_id < blocks_.size(); ++block_id) {
auto all_ops = blocks_[block_id]->AllOps();
for (size_t op_id = 0; op_id < all_ops.size(); ++op_id) {
auto &op = all_ops[op_id];
for (const std::string &attr_name : op->AttrNames()) {
if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
int sub_block_id =
o.Block(block_id).Op(op_id)->GetBlockAttr(attr_name);
op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
}
}
}
......@@ -86,6 +89,16 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
for (auto &block : blocks_) {
for (auto *op : block->AllOps()) {
for (const auto &attr : op->Proto()->attrs()) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
}
}
}
}
}
const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
......
paddle/fluid/framework/reader.cc
浏览文件 @ 8855d4a7
......@@ -25,8 +25,10 @@ void FileReader::ReadNext(std::vector<LoDTensor> *out) {
if (out->empty()) {
return;
}
PADDLE_ENFORCE_EQ(out->size(), dims_.size());
for (size_t i = 0; i < dims_.size(); ++i) {
auto &actual = out->at(i).dims();
auto &actual = (*out)[i].dims();
auto &expect = dims_[i];
PADDLE_ENFORCE_EQ(actual.size(), expect.size());
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ 8855d4a7
......@@ -39,7 +39,7 @@ template <typename T>
inline const T* Tensor::data() const {
check_memory_size();
PADDLE_ENFORCE(std::is_same<T, void>::value ||
holder_->type().hash_code() == typeid(T).hash_code(),
holder_->type() == std::type_index(typeid(T)),
"Tensor holds the wrong type, it holds %s",
this->holder_->type().name());
......@@ -53,7 +53,7 @@ template <typename T>
inline T* Tensor::data() {
check_memory_size();
PADDLE_ENFORCE(std::is_same<T, void>::value ||
holder_->type().hash_code() == typeid(T).hash_code(),
holder_->type() == std::type_index(typeid(T)),
"Tensor holds the wrong type, it holds %s",
this->holder_->type().name());
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 8855d4a7
......@@ -5,14 +5,19 @@ cc_library(paddle_fluid_api
SRCS io.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
# Create static library
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
cc_library(paddle_fluid DEPS ${fluid_modules})
if(WITH_CONTRIB)
set(fluid_modules "${fluid_modules}" paddle_inference_api)
endif()
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api)
# Create shared library
cc_library(paddle_fluid_shared SHARED
SRCS io.cc
DEPS ${fluid_modules})
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
if(NOT APPLE)
# TODO(liuyiqun): Temporarily disable the link flag because it is not support on Mac.
......
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ 8855d4a7
......@@ -21,7 +21,10 @@ limitations under the License. */
#include <deque>
#include <stack>
#include <string>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/inference/analysis/graph_traits.h"
#include "paddle/fluid/inference/analysis/node.h"
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass_tester.cc
浏览文件 @ 8855d4a7
......@@ -44,6 +44,6 @@ TEST_F(DFG_Tester, Test) {
LOG(INFO) << graph.nodes.size();
}
} // analysis
} // inference
} // paddle
}; // namespace analysis
}; // namespace inference
}; // namespace paddle
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc
浏览文件 @ 8855d4a7
......@@ -12,9 +12,11 @@ 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/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
namespace paddle {
namespace inference {
namespace analysis {
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h
浏览文件 @ 8855d4a7
......@@ -19,6 +19,8 @@
#pragma once
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/pass.h"
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc
浏览文件 @ 8855d4a7
......@@ -32,6 +32,6 @@ TEST_F(DFG_Tester, Init) {
LOG(INFO) << '\n' << graph.DotString();
}
} // analysis
} // inference
} // paddle
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/helper.h
浏览文件 @ 8855d4a7
......@@ -50,7 +50,7 @@ struct DataTypeNamer {
return dic_.at(x);
}
const std::string &repr(size_t &hash) const {
const std::string &repr(size_t &hash) const { // NOLINT
PADDLE_ENFORCE(dic_.count(hash), "unknown type for representation");
return dic_.at(hash);
}
......@@ -62,7 +62,9 @@ struct DataTypeNamer {
SET_TYPE(float);
}
std::unordered_map<decltype(typeid(int).hash_code()), std::string> dic_;
std::unordered_map<decltype(typeid(int).hash_code()), // NOLINT
std::string>
dic_;
};
#undef SET_TYPE
......
paddle/fluid/inference/analysis/pass.h
浏览文件 @ 8855d4a7
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <glog/logging.h>
#include <iosfwd>
#include <string>
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
......
paddle/fluid/inference/analysis/subgraph_splitter.h
浏览文件 @ 8855d4a7
......@@ -18,6 +18,8 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/node.h"
......
paddle/fluid/inference/analysis/ut_helper.h
浏览文件 @ 8855d4a7
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <gflags/gflags.h>
#include <gtest/gtest.h>
#include <string>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
......
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ 8855d4a7
......@@ -8,3 +8,5 @@ nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS
nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
paddle/fluid/inference/tensorrt/convert/activation_op.cc
浏览文件 @ 8855d4a7
......@@ -24,7 +24,7 @@ class ReluOpConverter : public OpConverter {
void operator()(const framework::proto::OpDesc& op) override {
// Here the two nullptr looks strange, that's because the
// framework::OpDesc's constructor is strange.
framework::OpDesc op_desc(op, nullptr, nullptr);
framework::OpDesc op_desc(op, nullptr);
LOG(INFO) << "convert a fluid relu op to tensorrt activation layer whose "
"type is Relu";
const nvinfer1::ITensor* input_tensor =
......
paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
浏览文件 @ 8855d4a7
......@@ -21,7 +21,8 @@ namespace tensorrt {
class Conv2dOpConverter : public OpConverter {
public:
Conv2dOpConverter() {}
void operator()(const framework::proto::OpDesc& op) override {
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope) override {
LOG(INFO)
<< "convert a fluid conv2d op to tensorrt conv layer without bias";
}
......
paddle/fluid/inference/tensorrt/convert/fc_op.cc 0 → 100644
浏览文件 @ 8855d4a7
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#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/platform/place.h"
namespace paddle {
namespace inference {
namespace tensorrt {
// Reorder the elements from istrides to ostrides, borrowed from TRT convert in
// tensorflow.
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorrt/convert/convert_nodes.cc#L318
template <typename T>
void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides,
T* odata, nvinfer1::DimsHW ostrides) {
for (int h = 0; h < shape.h(); ++h) {
for (int w = 0; w < shape.w(); ++w) {
odata[h * ostrides.h() + w * ostrides.w()] =
idata[h * ostrides.h() + w * ostrides.w()];
}
}
}
// Reorder the data layout from CK to KC.
void ReorderCKtoKC(TensorRTEngine::Weight& iweights,
TensorRTEngine::Weight* oweights) {
int c = iweights.dims[0];
int k = iweights.dims[1];
oweights->dims.assign({k, c});
nvinfer1::DimsHW istrides = {1, k};
nvinfer1::DimsHW ostrides = {c, 1};
Reorder2({k, c}, static_cast<float const*>(iweights.get().values), istrides,
static_cast<float*>(const_cast<void*>(oweights->get().values)),
ostrides);
}
/*
* FC converter convert a MUL op in Fluid to a FC layer in TRT.
*/
class FcOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope) override {
VLOG(4) << "convert a fluid fc op to tensorrt fc layer without bias";
framework::OpDesc op_desc(op, nullptr, nullptr);
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight
PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
// Declare inputs
auto* X = engine_->GetITensor(op_desc.Input("X").front());
// Declare weights
auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
PADDLE_ENFORCE_NOT_NULL(Y_v);
auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
// This may trigger a GPU->CPU copy, because TRT's weight can only be
// assigned from CPU memory, that can't be avoided.
auto* weight_data = Y_t->mutable_data<float>(platform::CPUPlace());
PADDLE_ENFORCE_EQ(Y_t->dims().size(), 2UL); // a matrix
size_t n_output = Y_t->dims()[1];
framework::LoDTensor tmp;
tmp.Resize(Y_t->dims());
memcpy(tmp.mutable_data<float>(platform::CPUPlace()), Y_t->data<float>(),
Y_t->dims()[0] * Y_t->dims()[1]);
TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
static_cast<void*>(weight_data),
Y_t->memory_size() / sizeof(float)};
TensorRTEngine::Weight tmp_weight(nvinfer1::DataType::kFLOAT,
static_cast<void*>(tmp.data<float>()),
Y_t->memory_size() / sizeof(float));
weight.dims.assign({Y_t->dims()[0], Y_t->dims()[1]});
tmp_weight.dims = weight.dims;
// The data layout of TRT FC layer's weight is different from fluid's FC,
// need to reorder the elements.
ReorderCKtoKC(tmp_weight, &weight);
// Currently, the framework can only handle one fluid op -> one TRT layer,
// but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just
// handle `mul`, leave `add` as another layer.
// DEBUG
TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected,
*const_cast<nvinfer1::ITensor*>(X),
n_output, weight.get(), bias.get());
auto output_name = op_desc.Output("Out").front();
engine_->DeclareOutput(layer, 0, output_name);
}
};
REGISTER_TRT_OP_CONVERTER(fc, FcOpConverter);
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(mul);
paddle/fluid/inference/tensorrt/convert/mul_op.cc
浏览文件 @ 8855d4a7
......@@ -24,10 +24,11 @@ namespace tensorrt {
class MulOpConverter : public OpConverter {
public:
MulOpConverter() {}
void operator()(const framework::proto::OpDesc& op) override {
VLOG(4) << "convert a fluid mul op to tensorrt fc layer without bias";
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope) override {
VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
framework::OpDesc op_desc(op, nullptr, nullptr);
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
auto* input2 = engine_->GetITensor(op_desc.Input("Y")[0]);
......
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ 8855d4a7
......@@ -31,27 +31,42 @@ namespace tensorrt {
class OpConverter {
public:
OpConverter() {}
virtual void operator()(const framework::proto::OpDesc& op) {}
void Run(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
std::string type = op.type();
auto* it = Registry<OpConverter>::Lookup(type);
PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]", type);
it->SetEngine(engine);
(*it)(op);
}
// Converter logic for an op.
virtual void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope) {}
// Convert a single fluid operaotr and add the corresponding layer to TRT.
void ConvertOp(const framework::proto::OpDesc& op,
const std::unordered_set<std::string>& parameters,
const framework::Scope& scope, TensorRTEngine* engine) {
framework::OpDesc op_desc(op, nullptr, nullptr);
OpConverter* it{nullptr};
// convert fluid op to tensorrt layer
void ConvertOp(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
OpConverter::Run(op, engine);
if (op_desc.Type() == "mul") {
PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL);
std::string Y = op_desc.Input("Y")[0];
if (parameters.count(Y)) {
it = Registry<OpConverter>::Lookup("fc");
}
}
if (!it) {
it = Registry<OpConverter>::Lookup(op_desc.Type());
}
PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
op_desc.Type());
it->SetEngine(engine);
(*it)(op, scope);
}
// convert fluid block to tensorrt network
void ConvertBlock(const framework::proto::BlockDesc& block,
TensorRTEngine* engine) {
const std::unordered_set<std::string>& parameters,
const framework::Scope& scope, TensorRTEngine* engine) {
for (int i = 0; i < block.ops_size(); i++) {
const auto& op = block.ops(i);
OpConverter::Run(op, engine);
ConvertOp(op, parameters, scope, engine);
}
}
......
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc 0 → 100644
浏览文件 @ 8855d4a7
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(fc_op, test) {
std::unordered_set<std::string> parameters({"mul-Y"});
framework::Scope scope;
TRTConvertValidation validator(20, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims4(8, 3, 1, 1));
validator.DeclParamVar("mul-Y", nvinfer1::Dims2(3, 2));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(8, 2));
// Prepare Op description
framework::OpDesc desc;
desc.SetType("mul");
desc.SetInput("X", {"mul-X"});
desc.SetInput("Y", {"mul-Y"});
desc.SetOutput("Out", {"mul-Out"});
validator.SetOp(*desc.Proto());
validator.Execute(10);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
浏览文件 @ 8855d4a7
......@@ -21,7 +21,9 @@ namespace inference {
namespace tensorrt {
TEST(MulOpConverter, main) {
TRTConvertValidation validator(10, 1000);
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));
......
paddle/fluid/inference/tensorrt/convert/test_op_converter.cc
浏览文件 @ 8855d4a7
......@@ -12,9 +12,10 @@ 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/inference/tensorrt/convert/op_converter.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace inference {
......@@ -27,7 +28,9 @@ TEST(OpConverter, ConvertBlock) {
conv2d_op->SetType("conv2d");
OpConverter converter;
converter.ConvertBlock(*block->Proto(), nullptr /*TensorRTEngine*/);
framework::Scope scope;
converter.ConvertBlock(*block->Proto(), {}, scope,
nullptr /*TensorRTEngine*/);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 8855d4a7
......@@ -19,6 +19,9 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/analysis/helper.h"
......@@ -58,7 +61,10 @@ class TRTConvertValidation {
public:
TRTConvertValidation() = delete;
TRTConvertValidation(int batch_size, int workspace_size = 1 << 10) {
TRTConvertValidation(int batch_size,
const std::unordered_set<std::string>& parameters,
framework::Scope& scope, int workspace_size = 1 << 10)
: parameters_(parameters), scope_(scope) {
// create engine.
engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
engine_->InitNetwork();
......@@ -73,19 +79,22 @@ class TRTConvertValidation {
engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, dims);
}
// Declare a parameter varaible in the scope.
void DeclParamVar(const std::string& name, const nvinfer1::Dims& dims) {
DeclVar(name, dims);
}
void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
DeclVar(name, dims);
}
// Declare a variable in a fluid Scope.
void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// Init Fluid tensor.
std::vector<int> dim_vec(dims.nbDims);
for (int i = 0; i < dims.nbDims; i++) {
dim_vec[i] = dims.d[i];
}
std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
auto* x = scope_.Var(name);
auto* x_tensor = x->GetMutable<framework::LoDTensor>();
x_tensor->Resize(framework::make_ddim(dim_vec));
......@@ -96,20 +105,22 @@ class TRTConvertValidation {
op_ = framework::OpRegistry::CreateOp(desc);
OpConverter op_converter;
op_converter.ConvertOp(desc, engine_.get());
op_converter.ConvertOp(desc, parameters_, scope_, engine_.get());
engine_->FreezeNetwork();
// Declare outputs.
op_desc_.reset(new framework::OpDesc(desc, nullptr, nullptr));
op_desc_.reset(new framework::OpDesc(desc, nullptr));
// Set Inputs.
for (const auto& input : op_desc_->InputArgumentNames()) {
if (parameters_.count(input)) continue;
auto* var = scope_.FindVar(input);
PADDLE_ENFORCE(var);
auto tensor = var->GetMutable<framework::LoDTensor>();
engine_->SetInputFromCPU(
input, static_cast<void*>(tensor->data<float>()),
input, static_cast<void*>(tensor->data<void>()),
sizeof(float) *
analysis::AccuDims(tensor->dims(), tensor->dims().size()));
}
......@@ -117,18 +128,21 @@ class TRTConvertValidation {
void Execute(int batch_size) {
// Execute Fluid Op
// Execute TRT
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
engine_->Execute(batch_size);
op_->Run(scope_, place);
// Execute TRT.
engine_->Execute(batch_size);
cudaStreamSynchronize(*engine_->stream());
ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
const size_t output_space_size = 200;
for (const auto& output : op_desc_->OutputArgumentNames()) {
std::vector<float> fluid_out;
std::vector<float> trt_out(200);
engine_->GetOutputInCPU(output, &trt_out[0], 200 * sizeof(float));
std::vector<float> trt_out(output_space_size);
engine_->GetOutputInCPU(output, &trt_out[0],
output_space_size * sizeof(float));
cudaStreamSynchronize(*engine_->stream());
auto* var = scope_.FindVar(output);
auto tensor = var->GetMutable<framework::LoDTensor>();
......@@ -136,7 +150,7 @@ class TRTConvertValidation {
// Compare two output
ASSERT_FALSE(fluid_out.empty());
for (size_t i = 0; i < fluid_out.size(); i++) {
EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 0.001);
EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 1e-6);
}
}
}
......@@ -146,9 +160,10 @@ class TRTConvertValidation {
private:
std::unique_ptr<TensorRTEngine> engine_;
cudaStream_t stream_;
framework::Scope scope_;
std::unique_ptr<framework::OperatorBase> op_;
std::unique_ptr<framework::OpDesc> op_desc_;
const std::unordered_set<std::string>& parameters_;
framework::Scope& scope_;
};
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @ 8855d4a7
......@@ -106,6 +106,7 @@ void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer* layer, int offset,
name);
auto* output = layer->getOutput(offset);
SetITensor(name, output);
PADDLE_ENFORCE(output != nullptr);
output->setName(name.c_str());
infer_network_->markOutput(*output);
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ 8855d4a7
......@@ -37,13 +37,15 @@ class TensorRTEngine : public EngineBase {
// Weight is model parameter.
class Weight {
public:
Weight(nvinfer1::DataType dtype, void* value, int num_elem) {
Weight(nvinfer1::DataType dtype, void* value, size_t num_elem) {
w_.type = dtype;
w_.values = value;
w_.count = num_elem;
}
const nvinfer1::Weights& get() { return w_; }
std::vector<int64_t> dims;
private:
nvinfer1::Weights w_;
};
......
paddle/fluid/operators/bilinear_interp_op.cc
浏览文件 @ 8855d4a7
......@@ -34,9 +34,22 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
int out_w = ctx->Attrs().Get<int>("out_w");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
if (ctx->HasInput("OutSize")) {
auto out_size_dim = ctx->GetInputDim("OutSize");
PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
"OutSize's dimension size must be 1");
PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
}
std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
}
};
class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -45,6 +58,10 @@ class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"(Tensor) The input tensor of bilinear interpolation, "
"This is a 4-D tensor with shape of (N x C x h x w)");
AddInput("OutSize",
"(Tensor) This is a 1-D tensor with two number. "
"The first number is height and the second number is width.")
.AsDispensable();
AddOutput("Out",
"(Tensor) The dimension of output is (N x C x out_h x out_w]");
......@@ -78,6 +95,12 @@ class BilinearInterpOpGrad : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
}
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
}
};
} // namespace operators
......
paddle/fluid/operators/bilinear_interp_op.cu
浏览文件 @ 8855d4a7
......@@ -102,10 +102,21 @@ class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
auto* input_t = ctx.Input<Tensor>("X"); // float tensor
auto* output_t = ctx.Output<Tensor>("Out"); // float tensor
auto* input = input_t->data<T>();
auto* output = output_t->mutable_data<T>(ctx.GetPlace());
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_dims = output_t->dims();
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
auto size_data = sizes.data<int>();
out_h = size_data[0];
out_w = size_data[1];
}
auto* output = output_t->mutable_data<T>(
{out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
int batch_size = input_t->dims()[0];
int channels = input_t->dims()[1];
int in_h = input_t->dims()[2];
......@@ -139,8 +150,8 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto* d_output = d_output_t->data<T>();
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
......@@ -149,6 +160,16 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
auto size_data = sizes.data<int>();
out_h = size_data[0];
out_w = size_data[1];
}
int batch_size = d_input_t->dims()[0];
int channels = d_input_t->dims()[1];
int in_h = d_input_t->dims()[2];
......
paddle/fluid/operators/bilinear_interp_op.h
浏览文件 @ 8855d4a7
......@@ -24,11 +24,18 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input_t = ctx.Input<Tensor>("X"); // float tensor
auto* output_t = ctx.Output<Tensor>("Out"); // float tensor
auto out_dims = output_t->dims();
auto* input = input_t->data<T>();
auto* output = output_t->mutable_data<T>(ctx.GetPlace());
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
auto* output = output_t->mutable_data<T>(
{out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
int batch_size = input_t->dims()[0];
int channels = input_t->dims()[1];
int in_h = input_t->dims()[2];
......@@ -83,9 +90,8 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto* d_output = d_output_t->data<T>();
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, T> zero;
......@@ -93,6 +99,14 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
int batch_size = d_input_t->dims()[0];
int channels = d_input_t->dims()[1];
int in_h = d_input_t->dims()[2];
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ 8855d4a7
if(WITH_DISTRIBUTE)
grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
request_handler_impl.cc rpc_server.cc grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor
selected_rows memory)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
......
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 8855d4a7
......@@ -205,6 +205,8 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
}
bool RPCClient::Wait() {
VLOG(3) << "RPCClient begin Wait()"
<< " req_count_:" << req_count_;
if (req_count_ <= 0) {
return true;
}
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ 8855d4a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
/*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.
......@@ -12,19 +12,12 @@ 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_server.h"
#include <limits>
#include <string>
using ::grpc::ServerAsyncResponseWriter;
#include "paddle/fluid/operators/detail/grpc_server.h"
DEFINE_int32(rpc_server_handle_send_threads, 20,
"Number of threads used to handle send at rpc server.");
DEFINE_int32(rpc_server_handle_get_threads, 20,
"Number of threads used to handle get at rpc server.");
DEFINE_int32(rpc_server_handle_prefetch_threads, 1,
"Number of threads used to handle prefetch at rpc server.");
using ::grpc::ServerAsyncResponseWriter;
namespace paddle {
namespace operators {
......@@ -36,49 +29,40 @@ enum CallStatus { PROCESS = 0, FINISH };
class RequestBase {
public:
explicit RequestBase(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq, bool sync_mode,
const platform::DeviceContext* dev_ctx)
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: service_(service),
cq_(cq),
sync_mode_(sync_mode),
status_(PROCESS),
dev_ctx_(dev_ctx) {
request_handler_(request_handler),
req_id_(req_id) {
PADDLE_ENFORCE(cq_);
}
virtual ~RequestBase() {}
virtual void Process() { assert(false); }
virtual void Process() = 0;
CallStatus Status() { return status_; }
void SetStatus(CallStatus status) { status_ = status; }
virtual std::string GetReqName() {
assert(false);
return "";
}
virtual std::string GetReqName() = 0;
protected:
::grpc::ServerContext ctx_;
GrpcService::AsyncService* service_;
::grpc::ServerCompletionQueue* cq_;
const bool sync_mode_;
CallStatus status_;
const platform::DeviceContext* dev_ctx_;
RequestHandler* request_handler_;
int req_id_;
};
class RequestSend final : public RequestBase {
public:
explicit RequestSend(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq, bool sync_mode,
framework::Scope* scope, ReceivedQueue* queue,
const platform::DeviceContext* dev_ctx, int req_id)
: RequestBase(service, cq, sync_mode, dev_ctx),
queue_(queue),
responder_(&ctx_),
req_id_(req_id) {
if (sync_mode_) {
request_.reset(new VariableResponse(scope, dev_ctx_, false));
} else {
request_.reset(new VariableResponse(scope, dev_ctx_, true));
}
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
request_.reset(new VariableResponse(request_handler->scope(),
request_handler->dev_ctx(),
!request_handler->sync_mode()));
int method_id = static_cast<int>(detail::GrpcMethod::kSendVariable);
service_->RequestAsyncUnary(
method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
......@@ -87,12 +71,17 @@ class RequestSend final : public RequestBase {
virtual ~RequestSend() {}
virtual std::string GetReqName() { return request_->Varname(); }
std::string GetReqName() override { return request_->Varname(); }
void Process() override {
std::string varname = GetReqName();
VLOG(3) << "RequestSend var_name:" << varname;
virtual void Process() {
std::string var_name = GetReqName();
VLOG(3) << "RequestSend " << var_name;
queue_->Push(std::make_pair(var_name, request_));
auto scope = request_->GetMutableLocalScope();
auto invar = request_->GetVar();
framework::Variable* outvar = nullptr;
request_handler_->Handle(varname, scope, invar, &outvar);
status_ = FINISH;
responder_.Finish(reply_, ::grpc::Status::OK,
......@@ -102,105 +91,85 @@ class RequestSend final : public RequestBase {
protected:
sendrecv::VoidMessage reply_;
std::shared_ptr<VariableResponse> request_;
ReceivedQueue* queue_;
ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
int req_id_;
};
class RequestGet final : public RequestBase {
public:
explicit RequestGet(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq, bool sync_mode,
framework::Scope* scope,
const platform::DeviceContext* dev_ctx,
framework::BlockingQueue<MessageWithName>* queue,
int req_id)
: RequestBase(service, cq, sync_mode, dev_ctx),
responder_(&ctx_),
scope_(scope),
queue_(queue),
req_id_(req_id) {
::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);
service_->RequestAsyncUnary(
method_id, &ctx_, &request_, &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
}
virtual ~RequestGet() {}
virtual std::string GetReqName() { return request_.varname(); }
std::string GetReqName() override { return request_.varname(); }
virtual void Process() {
void Process() override {
// proc request.
std::string var_name = request_.varname();
VLOG(3) << "RequestGet " << var_name;
auto* var = scope_->FindVar(var_name);
std::string varname = request_.varname();
VLOG(3) << "RequestGet " << varname;
auto scope = request_handler_->scope();
auto invar = scope->FindVar(varname);
framework::Variable* outvar = nullptr;
if (var_name != FETCH_BARRIER_MESSAGE) {
SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply_);
request_handler_->Handle(varname, scope, invar, &outvar);
if (outvar) {
SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
&reply_);
}
status_ = FINISH;
responder_.Finish(reply_, ::grpc::Status::OK,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
if (var_name == FETCH_BARRIER_MESSAGE) {
sendrecv::VariableMessage msg;
MessageWithName msg_with_name = std::make_pair(var_name, msg);
queue_->Push(msg_with_name);
}
}
protected:
sendrecv::VariableMessage request_;
::grpc::ByteBuffer reply_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
framework::Scope* scope_;
framework::BlockingQueue<MessageWithName>* queue_;
int req_id_;
};
class RequestPrefetch final : public RequestBase {
public:
explicit RequestPrefetch(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq, bool sync_mode,
framework::Scope* scope,
const platform::DeviceContext* dev_ctx,
framework::Executor* executor,
framework::ProgramDesc* program,
framework::ExecutorPrepareContext* prefetch_ctx,
int req_id)
: RequestBase(service, cq, sync_mode, dev_ctx),
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: RequestBase(service, cq, request_handler, req_id),
responder_(&ctx_),
scope_(scope),
executor_(executor),
program_(program),
prefetch_ctx_(prefetch_ctx),
req_id_(req_id) {
// prefetch always create a new sub scope
request_.reset(new VariableResponse(scope, dev_ctx_, true));
local_scope_(nullptr) {
request_.reset(new VariableResponse(request_handler->scope(),
request_handler->dev_ctx(), true));
int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
service_->RequestAsyncUnary(
method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
}
virtual ~RequestPrefetch() {}
virtual std::string GetReqName() { return request_->Varname(); }
std::string GetReqName() override { return request_->Varname(); }
virtual void Process() {
void Process() override {
// prefetch process...
std::string varname = request_->OutVarname();
VLOG(3) << "RequestPrefetch " << varname;
auto scope = request_->GetMutableLocalScope();
auto invar = scope->FindVar(varname);
framework::Variable* outvar = nullptr;
std::string var_name = request_->OutVarname();
VLOG(3) << "RequestPrefetch " << var_name;
auto var_desc = program_->Block(0).FindVar(var_name);
framework::Scope* local_scope = request_->GetMutableLocalScope();
auto* var = local_scope->FindVar(var_name);
InitializeVariable(var, var_desc->GetType());
executor_->RunPreparedContext(prefetch_ctx_, local_scope);
request_handler_->Handle(varname, scope, invar, &outvar);
SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply_);
SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
&reply_);
status_ = FINISH;
responder_.Finish(reply_, ::grpc::Status::OK,
......@@ -211,202 +180,169 @@ class RequestPrefetch final : public RequestBase {
std::shared_ptr<VariableResponse> request_;
::grpc::ByteBuffer reply_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
framework::Scope* scope_;
framework::Executor* executor_;
framework::ProgramDesc* program_;
framework::ExecutorPrepareContext* prefetch_ctx_;
int req_id_;
framework::Scope* local_scope_;
};
void AsyncGRPCServer::WaitClientGet(int count) {
int fetch_barriers = 0;
while (fetch_barriers < count) {
auto msg = var_get_queue_.Pop();
if (msg.first == FETCH_BARRIER_MESSAGE) {
fetch_barriers++;
}
}
}
void AsyncGRPCServer::WaitServerReady() {
VLOG(3) << "AsyncGRPCServer is wait server ready";
std::unique_lock<std::mutex> lock(this->mutex_ready_);
condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
VLOG(3) << "AsyncGRPCServer WaitSeverReady";
}
void AsyncGRPCServer::RunSyncUpdate() {
void AsyncGRPCServer::StartServer() {
::grpc::ServerBuilder builder;
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials(),
builder.AddListeningPort(bind_address_, ::grpc::InsecureServerCredentials(),
&selected_port_);
builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
builder.RegisterService(&service_);
cq_send_ = builder.AddCompletionQueue();
cq_get_ = builder.AddCompletionQueue();
cq_prefetch_ = builder.AddCompletionQueue();
for (auto t : rpc_call_map_) {
rpc_cq_[t.first].reset(builder.AddCompletionQueue().release());
}
server_ = builder.BuildAndStart();
LOG(INFO) << "Server listening on " << address_
LOG(INFO) << "Server listening on " << bind_address_
<< " selected port: " << selected_port_;
std::function<void(int)> send_register = std::bind(
&AsyncGRPCServer::TryToRegisterNewSendOne, this, std::placeholders::_1);
std::function<void(int)> get_register = std::bind(
&AsyncGRPCServer::TryToRegisterNewGetOne, this, std::placeholders::_1);
std::function<void(int)> prefetch_register =
std::bind(&AsyncGRPCServer::TryToRegisterNewPrefetchOne, this,
std::placeholders::_1);
std::function<void(const std::string&, int)> f =
std::bind(&AsyncGRPCServer::TryToRegisterNewOne, this,
std::placeholders::_1, std::placeholders::_2);
for (int i = 0; i < kSendReqsBufSize; ++i) {
TryToRegisterNewSendOne(i);
}
for (int i = 0; i < kGetReqsBufSize; ++i) {
TryToRegisterNewGetOne(i);
}
for (int i = 0; i < kPrefetchReqsBufSize; ++i) {
TryToRegisterNewPrefetchOne(i);
}
for (auto& t : rpc_call_map_) {
auto& rpc_name = t.first;
auto& cq = rpc_cq_[rpc_name];
auto threadnum = rpc_thread_num_[rpc_name];
auto& reqs = rpc_reqs_[rpc_name];
for (int i = 0; i < FLAGS_rpc_server_handle_send_threads; ++i) {
t_sends_.emplace_back(
new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
cq_send_.get(), "cq_send", send_register)));
}
for (int i = 0; i < FLAGS_rpc_server_handle_get_threads; ++i) {
t_gets_.emplace_back(
new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
cq_get_.get(), "cq_get", get_register)));
}
for (int i = 0; i < FLAGS_rpc_server_handle_prefetch_threads; ++i) {
t_prefetchs_.emplace_back(new std::thread(
std::bind(&AsyncGRPCServer::HandleRequest, this, cq_prefetch_.get(),
"cq_prefetch", prefetch_register)));
reqs.reserve(kRequestBufSize);
for (int i = 0; i < kRequestBufSize; i++) {
TryToRegisterNewOne(rpc_name, i);
}
for (int i = 0; i < threadnum; i++) {
rpc_threads_[rpc_name].emplace_back(new std::thread(std::bind(
&AsyncGRPCServer::HandleRequest, this, cq.get(), rpc_name, f)));
VLOG(3) << t.first << " creates threads!";
}
}
{
std::lock_guard<std::mutex> lock(this->mutex_ready_);
ready_ = 1;
}
condition_ready_.notify_all();
// wait server
server_->Wait();
for (int i = 0; i < FLAGS_rpc_server_handle_send_threads; ++i) {
t_sends_[i]->join();
}
for (int i = 0; i < FLAGS_rpc_server_handle_get_threads; ++i) {
t_gets_[i]->join();
}
for (int i = 0; i < FLAGS_rpc_server_handle_prefetch_threads; ++i) {
t_prefetchs_[i]->join();
for (auto& t : rpc_threads_) {
auto& threads = t.second;
for (size_t i = 0; i < threads.size(); ++i) {
threads[i]->join();
VLOG(3) << t.first << " threads ends!";
}
}
}
void AsyncGRPCServer::ShutdownQueue() {
std::unique_lock<std::mutex> lock(cq_mutex_);
cq_send_->Shutdown();
cq_get_->Shutdown();
cq_prefetch_->Shutdown();
for (auto& t : rpc_cq_) {
t.second->Shutdown();
VLOG(3) << t.first << " shutdown!";
}
}
// This URL explains why shutdown is complicate:
void AsyncGRPCServer::ShutDown() {
void AsyncGRPCServer::ShutDownImpl() {
std::unique_lock<std::mutex> lock(cq_mutex_);
is_shut_down_ = true;
ShutdownQueue();
VLOG(3) << "server_ shutdown!";
server_->Shutdown();
}
void AsyncGRPCServer::TryToRegisterNewSendOne(int i) {
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";
return;
}
RequestSend* send = new RequestSend(&service_, cq_send_.get(), sync_mode_,
scope_, &var_recv_queue_, dev_ctx_, i);
send_reqs_[i] = static_cast<RequestBase*>(send);
VLOG(4) << "Create RequestSend status:" << send->Status();
}
void AsyncGRPCServer::TryToRegisterNewGetOne(int req_id) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewGetOne";
return;
VLOG(4) << "register send rpc_name:" << rpc_name
<< ", handler:" << rpc_call_map_[kRequestSend];
auto& reqs = rpc_reqs_[rpc_name];
auto& handler = rpc_call_map_[rpc_name];
auto& cq = rpc_cq_[rpc_name];
RequestBase* b = nullptr;
if (rpc_name == kRequestSend) {
b = new RequestSend(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestGet) {
b = new RequestGet(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestPrefetch) {
b = new RequestPrefetch(&service_, cq.get(), handler, req_id);
} else {
PADDLE_ENFORCE(false, "not surpported rpc");
}
RequestGet* get = new RequestGet(&service_, cq_get_.get(), sync_mode_, scope_,
dev_ctx_, &var_get_queue_, req_id);
get_reqs_[req_id] = static_cast<RequestBase*>(get);
VLOG(4) << "Create RequestGet status:" << get->Status();
}
void AsyncGRPCServer::TryToRegisterNewPrefetchOne(int req_id) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewPrefetchOne";
return;
}
RequestPrefetch* prefetch = new RequestPrefetch(
&service_, cq_prefetch_.get(), sync_mode_, scope_, dev_ctx_, executor_,
program_, prefetch_ctx_.get(), req_id);
prefetch_reqs_[req_id] = static_cast<RequestBase*>(prefetch);
reqs[req_id] = b;
VLOG(4) << "Create RequestPrefetch status:" << prefetch->Status();
VLOG(4) << "Create RequestSend status:" << b->Status();
}
// FIXME(typhoonzero): change cq_name to enum.
void AsyncGRPCServer::HandleRequest(
::grpc::ServerCompletionQueue* cq, const std::string& cq_name,
std::function<void(int)> TryToRegisterNewOne) {
::grpc::ServerCompletionQueue* cq, const std::string& rpc_name,
std::function<void(const std::string&, int)> TryToRegisterNewOne) {
void* tag = NULL;
bool ok = false;
while (true) {
VLOG(3) << "HandleRequest for " << cq_name << " wait Next";
VLOG(3) << "HandleRequest " << rpc_name << " wait next";
if (!cq->Next(&tag, &ok)) {
LOG(INFO) << cq_name << " CompletionQueue shutdown!";
LOG(INFO) << "CompletionQueue " << rpc_name << " shutdown!";
break;
}
VLOG(3) << "HandleRequest for " << cq_name << " get Next";
int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
if (sync_mode_) {
// FIXME(typhoonzero): de-couple the barriers with recv_op
if (!is_shut_down_ && cq_name == "cq_get") WaitCond(1);
if (!is_shut_down_ && cq_name == "cq_send") WaitCond(0);
VLOG(3) << "HandleRequest for " << cq_name << " after WaitCond";
}
int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
VLOG(3) << "HandleRequest " << rpc_name << ", req_id:" << req_id
<< " get next";
auto& reqs = rpc_reqs_[rpc_name];
RequestBase* base = nullptr;
{
std::lock_guard<std::mutex> l(cq_mutex_);
if (cq_name == "cq_get") {
base = get_reqs_[req_id];
} else if (cq_name == "cq_send") {
base = send_reqs_[req_id];
} else if (cq_name == "cq_prefetch") {
base = prefetch_reqs_[req_id];
}
PADDLE_ENFORCE(req_id >= 0 && req_id < kRequestBufSize);
std::unique_lock<std::mutex> lock(cq_mutex_);
base = reqs[req_id];
}
// 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) << cq_name << " recv no regular event:argument name["
LOG(WARNING) << "completion queue:" << rpc_name
<< " recv no regular event:argument name["
<< base->GetReqName() << "]";
TryToRegisterNewOne(req_id);
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();
VLOG(4) << cq_name << " PROCESS status:" << base->Status();
break;
}
case FINISH: {
TryToRegisterNewOne(req_id);
VLOG(4) << cq_name << " FINISH status:" << base->Status();
TryToRegisterNewOne(rpc_name, req_id);
delete base;
break;
}
......@@ -415,20 +351,6 @@ void AsyncGRPCServer::HandleRequest(
}
}
void AsyncGRPCServer::WaitCond(int cond) {
std::unique_lock<std::mutex> lock(this->barrier_mutex_);
barrier_condition_.wait(lock,
[=] { return this->barrier_cond_step_ == cond; });
}
void AsyncGRPCServer::SetCond(int cond) {
{
std::lock_guard<std::mutex> lock(this->barrier_mutex_);
barrier_cond_step_ = cond;
}
barrier_condition_.notify_all();
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_server.h
浏览文件 @ 8855d4a7
......@@ -14,6 +14,8 @@ limitations under the License. */
#pragma once
#include <map>
#include <set>
#include <string>
#include <thread> // NOLINT
#include <utility>
......@@ -28,6 +30,8 @@ limitations under the License. */
#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"
......@@ -37,106 +41,48 @@ namespace paddle {
namespace operators {
namespace detail {
typedef std::pair<std::string, std::shared_ptr<VariableResponse>>
ReceivedMessage;
typedef framework::BlockingQueue<ReceivedMessage> ReceivedQueue;
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class RequestBase;
class AsyncGRPCServer final {
class AsyncGRPCServer final : public RPCServer {
public:
explicit AsyncGRPCServer(const std::string &address, bool sync_mode)
: address_(address), sync_mode_(sync_mode), ready_(0) {}
~AsyncGRPCServer() {}
void WaitServerReady();
void RunSyncUpdate();
// functions to sync server barrier status.
void WaitCond(int cond);
void SetCond(int cond);
void WaitClientGet(int count);
void SetScope(framework::Scope *scope) { scope_ = scope; }
void SetDevCtx(const platform::DeviceContext *dev_ctx) { dev_ctx_ = dev_ctx; }
void SetProgram(framework::ProgramDesc *program) { program_ = program; }
void SetExecutor(framework::Executor *executor) { executor_ = executor; }
void SetPrefetchPreparedCtx(
std::unique_ptr<framework::ExecutorPrepareContext> prepared) {
prefetch_ctx_.reset(prepared.release());
}
int GetSelectedPort() const { return selected_port_; }
const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); }
explicit AsyncGRPCServer(const std::string& address, int client_num)
: RPCServer(address, client_num), ready_(0) {}
void Push(const std::string &msg_name) {
this->var_recv_queue_.Push(std::make_pair(msg_name, nullptr));
}
virtual ~AsyncGRPCServer() {}
void WaitServerReady() override;
void StartServer() override;
void ShutDown();
private:
void HandleRequest(
::grpc::ServerCompletionQueue* cq, const std::string& rpc_name,
std::function<void(const std::string&, int)> TryToRegisterNewOne);
protected:
void HandleRequest(::grpc::ServerCompletionQueue *cq,
const std::string &cq_name,
std::function<void(int)> TryToRegisterNewOne);
void TryToRegisterNewSendOne(int req_id);
void TryToRegisterNewGetOne(int req_id);
void TryToRegisterNewPrefetchOne(int req_id);
void TryToRegisterNewOne(const std::string& rpc_name, int req_id);
void ShutdownQueue();
void ShutDownImpl() override;
private:
static const int kSendReqsBufSize = 100;
static const int kGetReqsBufSize = 100;
static const int kPrefetchReqsBufSize = 10;
static const int kRequestBufSize = 100;
std::mutex cq_mutex_;
volatile bool is_shut_down_ = false;
std::unique_ptr<::grpc::ServerCompletionQueue> cq_send_;
std::unique_ptr<::grpc::ServerCompletionQueue> cq_get_;
std::unique_ptr<::grpc::ServerCompletionQueue> cq_prefetch_;
RequestBase *send_reqs_[kSendReqsBufSize];
RequestBase *get_reqs_[kGetReqsBufSize];
RequestBase *prefetch_reqs_[kPrefetchReqsBufSize];
GrpcService::AsyncService service_;
std::unique_ptr<::grpc::Server> server_;
std::string address_;
const bool sync_mode_;
framework::Scope *scope_;
const platform::DeviceContext *dev_ctx_;
// received variable from RPC, operators fetch variable from this queue.
framework::BlockingQueue<MessageWithName> var_get_queue_;
// client send variable to this queue.
ReceivedQueue var_recv_queue_;
// condition of the sub program
std::mutex barrier_mutex_;
mutable int barrier_cond_step_;
std::condition_variable barrier_condition_;
std::vector<std::unique_ptr<std::thread>> t_sends_;
std::vector<std::unique_ptr<std::thread>> t_gets_;
std::vector<std::unique_ptr<std::thread>> t_prefetchs_;
std::unique_ptr<std::thread> t_prefetch_;
std::unique_ptr<framework::ExecutorPrepareContext> prefetch_ctx_;
framework::ProgramDesc *program_;
framework::Executor *executor_;
int selected_port_;
std::mutex mutex_ready_;
std::condition_variable condition_ready_;
int ready_;
std::map<std::string, std::unique_ptr<::grpc::ServerCompletionQueue>> rpc_cq_;
std::map<std::string, std::vector<std::unique_ptr<std::thread>>> rpc_threads_;
std::map<std::string, std::vector<RequestBase*>> rpc_reqs_;
};
}; // namespace detail
......
paddle/fluid/operators/detail/grpc_server_test.cc
浏览文件 @ 8855d4a7
......@@ -24,13 +24,16 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace detail = paddle::operators::detail;
USE_OP(lookup_table);
std::unique_ptr<detail::AsyncGRPCServer> rpc_service_;
std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
auto root_block = program->MutableBlock(0);
......@@ -88,8 +91,7 @@ void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
}
}
void StartServer(const std::string& endpoint) {
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, true));
void StartServer() {
framework::ProgramDesc program;
framework::Scope scope;
platform::CPUPlace place;
......@@ -99,42 +101,59 @@ void StartServer(const std::string& endpoint) {
auto prepared = exe.Prepare(program, block->ID());
InitTensorsOnServer(&scope, &place, 10);
rpc_service_->SetProgram(&program);
rpc_service_->SetPrefetchPreparedCtx(std::move(prepared));
rpc_service_->SetDevCtx(&ctx);
rpc_service_->SetScope(&scope);
rpc_service_->SetExecutor(&exe);
g_req_handler->SetProgram(&program);
g_req_handler->SetPrefetchPreparedCtx(std::move(prepared));
g_req_handler->SetDevCtx(&ctx);
g_req_handler->SetScope(&scope);
g_req_handler->SetExecutor(&exe);
g_rpc_service->RegisterRPC(detail::kRequestPrefetch, g_req_handler.get());
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
rpc_service_->RunSyncUpdate();
// FIXME(gongwb): don't use hard time.
sleep(10);
LOG(INFO) << "got nccl id and stop server...";
g_rpc_service->ShutDown();
server_thread.join();
}
TEST(PREFETCH, DISABLED_CPU) {
// start up a server instance backend
std::thread server_thread(StartServer, "127.0.0.1:8889");
sleep(2);
TEST(PREFETCH, CPU) {
g_req_handler.reset(new detail::RequestPrefetchHandler(true));
g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
std::thread server_thread(StartServer);
g_rpc_service->WaitServerReady();
detail::RPCClient client;
int port = g_rpc_service->GetSelectedPort();
std::string ep = paddle::string::Sprintf("127.0.0.1:%d", port);
framework::Scope scope;
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// create var on local scope
int64_t rows_numel = 5;
InitTensorsOnClient(&scope, &place, rows_numel);
std::string in_var_name("ids");
std::string out_var_name("out");
auto client = detail::RPCClient::GetInstance();
client->AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, in_var_name,
out_var_name);
client->Wait();
auto var = scope.Var(out_var_name);
auto value = var->GetMutable<framework::SelectedRows>()->value();
auto ptr = value.mutable_data<float>(place);
rpc_service_->ShutDown();
server_thread.join();
rpc_service_.reset(nullptr);
for (int64_t i = 0; i < rows_numel; ++i) {
EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
{
// create var on local scope
int64_t rows_numel = 5;
InitTensorsOnClient(&scope, &place, rows_numel);
std::string in_var_name("ids");
std::string out_var_name("out");
client.AsyncPrefetchVariable(ep, ctx, scope, in_var_name, out_var_name);
client.Wait();
auto var = scope.Var(out_var_name);
auto value = var->GetMutable<framework::SelectedRows>()->value();
auto ptr = value.mutable_data<float>(place);
for (int64_t i = 0; i < rows_numel; ++i) {
EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
}
}
server_thread.join();
LOG(INFO) << "begin reset";
g_rpc_service.reset(nullptr);
g_req_handler.reset(nullptr);
}
paddle/fluid/operators/detail/request_handler.h 0 → 100644
浏览文件 @ 8855d4a7
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <time.h>
#include <functional>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
constexpr char kRequestSend[] = "RequestSend";
constexpr char kRequestGet[] = "RequestGet";
constexpr char kRequestPrefetch[] = "RequestPrefetch";
class RPCServer;
class RequestHandler {
public:
explicit RequestHandler(bool sync_mode)
: sync_mode_(sync_mode),
dev_ctx_(nullptr),
executor_(nullptr),
scope_(nullptr),
program_(nullptr),
rpc_server_(nullptr) {}
virtual ~RequestHandler() {}
// Set attributes.
void SetScope(framework::Scope* scope) { scope_ = scope; }
void SetDevCtx(const platform::DeviceContext* dev_ctx) { dev_ctx_ = dev_ctx; }
void SetProgram(framework::ProgramDesc* program) { program_ = program; }
void SetExecutor(framework::Executor* executor) { executor_ = executor; }
void SetPrefetchPreparedCtx(
std::unique_ptr<framework::ExecutorPrepareContext> prepared) {
prefetch_ctx_.reset(prepared.release());
}
// Used for async.
void SetGradToPreparedCtx(
std::unordered_map<
std::string, std::shared_ptr<framework::ExecutorPrepareContext>>* g) {
grad_to_prepared_ctx_ = g;
}
void SetRPCServer(RPCServer* rpc_server) { rpc_server_ = rpc_server; }
// Get attributes.
bool sync_mode() { return sync_mode_; }
framework::Scope* scope() { return scope_; }
const platform::DeviceContext* dev_ctx() { return dev_ctx_; }
framework::ExecutorPrepareContext* prefetch_ctx() {
return prefetch_ctx_.get();
}
framework::ProgramDesc* program() { return program_; }
framework::Executor* executor() { return executor_; }
std::vector<framework::Variable*>& sparse_vars() { return sparse_vars_; }
// This function processes user's rpc request.
// The implemention is in request_handler_impl.
// example:
// std::string varname = request_.varname();
//
// auto scope = request_handler_->scope();
// auto invar = scope->FindVar(varname);
// framework::Variable* outvar = nullptr;
//
// request_handler_->Handle(varname, scope, invar, &outvar);
// if (outvar) {
// SerializeToByteBuffer(varname, outvar,
// *request_handler_->dev_ctx(), &reply_);
// }
virtual bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var,
framework::Variable** outvar) = 0;
protected:
const bool sync_mode_;
const platform::DeviceContext* dev_ctx_;
framework::Executor* executor_;
framework::Scope* scope_;
framework::ProgramDesc* program_;
std::unique_ptr<framework::ExecutorPrepareContext> prefetch_ctx_;
// Used for async.
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>*
grad_to_prepared_ctx_;
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable*> sparse_vars_;
RPCServer* rpc_server_;
std::mutex sparse_var_mutex_;
};
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/request_handler_impl.cc 0 → 100644
浏览文件 @ 8855d4a7
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <iostream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/variable_response.h"
namespace paddle {
namespace operators {
namespace detail {
bool RequestSendHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
VLOG(4) << "RequestSendHandler:" << varname;
// Async
if (!sync_mode_) {
try {
executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
scope);
} catch (std::exception& e) {
LOG(ERROR) << "async: run sub program error " << e.what();
return false;
}
return true;
}
// Sync
if (varname == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv batch barrier message";
rpc_server_->IncreaseBatchBarrier(kRequestSend);
} else {
VLOG(3) << "sync: received var_name: " << varname;
if (sync_mode_) {
rpc_server_->WaitCond(kRequestSend);
}
if (invar == nullptr) {
LOG(ERROR) << "sync: Can not find server side var: " << varname;
PADDLE_THROW("sync: Can not find server side var");
return false;
}
if (invar->IsType<framework::SelectedRows>()) {
std::unique_lock<std::mutex> lock(sparse_var_mutex_);
sparse_vars_.push_back(invar);
}
}
return true;
}
bool RequestGetHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
VLOG(4) << "RequestGetHandler:" << varname;
if (varname != FETCH_BARRIER_MESSAGE) {
if (sync_mode_) {
rpc_server_->WaitCond(kRequestGet);
}
*outvar = scope_->FindVar(varname);
return true;
}
// FETCH_BARRIER_MESSAGE
if (sync_mode_) {
VLOG(3) << "sync: recv fetch barrier message";
rpc_server_->IncreaseBatchBarrier(kRequestGet);
}
return true;
}
bool RequestPrefetchHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar) {
VLOG(4) << "RequestPrefetchHandler " << varname;
auto var_desc = program_->Block(0).FindVar(varname);
*outvar = scope->FindVar(varname);
InitializeVariable(*outvar, var_desc->GetType());
executor_->RunPreparedContext(prefetch_ctx_.get(), scope);
return true;
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/request_handler_impl.h 0 → 100644
浏览文件 @ 8855d4a7
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <time.h>
#include <functional>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
class RequestSendHandler final : public RequestHandler {
public:
explicit RequestSendHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestSendHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
};
class RequestGetHandler final : public RequestHandler {
public:
explicit RequestGetHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestGetHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
};
class RequestPrefetchHandler final : public RequestHandler {
public:
explicit RequestPrefetchHandler(bool sync_mode) : RequestHandler(sync_mode) {}
virtual ~RequestPrefetchHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar) override;
};
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_server.cc 0 → 100644
浏览文件 @ 8855d4a7
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <fstream>
#include <iostream>
#include <limits>
#include <string>
#include "paddle/fluid/operators/detail/rpc_server.h"
namespace paddle {
namespace operators {
namespace detail {
void RPCServer::ShutDown() {
LOG(INFO) << "RPCServer ShutDown ";
ShutDownImpl();
exit_flag_ = true;
barrier_cond_.notify_all();
rpc_cond_.notify_all();
}
void RPCServer::SavePort() const {
auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
std::ofstream port_file;
port_file.open(file_path);
port_file << selected_port_;
port_file.close();
VLOG(4) << "selected port written to " << file_path;
}
void RPCServer::WaitBarrier(const std::string& rpc_name) {
std::unique_lock<std::mutex> lock(this->mutex_);
barrier_cond_.wait(lock, [=] {
return (barrier_counter_[rpc_name] >= client_num_ || exit_flag_.load());
});
VLOG(3) << "batch_barrier_:" << barrier_counter_[rpc_name];
}
void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
VLOG(3) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
int b = 0;
{
std::unique_lock<std::mutex> lock(mutex_);
b = ++barrier_counter_[rpc_name];
}
VLOG(3) << "RPCServer IncreaseBatchBarrier " << rpc_name
<< ", barrier_count:" << b << ", fan_in" << client_num_;
if (b >= client_num_) {
barrier_cond_.notify_all();
}
}
void RPCServer::ResetBarrierCounter() {
VLOG(3) << "RPCServer ResetBarrierCounter ";
std::unique_lock<std::mutex> lock(mutex_);
for (auto& t : barrier_counter_) {
t.second = 0;
}
}
void RPCServer::RegisterRPC(const std::string& rpc_name,
RequestHandler* handler, int thread_num) {
rpc_call_map_[rpc_name] = handler;
rpc_thread_num_[rpc_name] = thread_num;
static int cond = -1;
rpc_cond_map_[rpc_name] = ++cond;
VLOG(4) << "RegisterRPC rpc_name:" << rpc_name << ", handler:" << handler
<< ", cond:" << rpc_cond_map_[rpc_name];
}
void RPCServer::SetCond(const std::string& rpc_name) {
VLOG(3) << "RPCServer SetCond " << rpc_name;
{
std::unique_lock<std::mutex> lock(mutex_);
cur_cond_ = rpc_cond_map_[rpc_name];
}
rpc_cond_.notify_all();
}
void RPCServer::WaitCond(const std::string& rpc_name) {
VLOG(3) << "RPCServer WaitCond " << rpc_name;
int cond = 0;
{
std::unique_lock<std::mutex> lock(mutex_);
cond = rpc_cond_map_[rpc_name];
}
std::unique_lock<std::mutex> lock(mutex_);
rpc_cond_.wait(
lock, [=] { return (cur_cond_.load() == cond || exit_flag_.load()); });
}
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/rpc_server.h 0 → 100644
浏览文件 @ 8855d4a7
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <set>
#include <string>
#include <thread> // NOLINT
#include <utility>
#include <vector>
#include "paddle/fluid/operators/detail/request_handler.h"
namespace paddle {
namespace operators {
namespace detail {
class RPCServer {
public:
explicit RPCServer(const std::string& address, int client_num)
: cur_cond_(0),
bind_address_(address),
exit_flag_(false),
selected_port_(0),
client_num_(client_num) {}
virtual ~RPCServer() {}
virtual void StartServer() = 0;
virtual void WaitServerReady() = 0;
void ShutDown();
bool IsExit() { return exit_flag_.load(); }
int GetSelectedPort() const { return selected_port_; }
void SavePort() const;
// RegisterRPC, register the rpc method name to a handler
// class, and auto generate a condition id for this call
// to be used for the barrier.
void RegisterRPC(const std::string& rpc_name, RequestHandler* handler,
int thread_num = 5);
// Wait util all the clients have reached the barrier for one
// rpc method. This function should be called in the
// RequestHandler if you want to run the server/client in a
// synchronous mode.
void WaitBarrier(const std::string& rpc_name);
void SetCond(const std::string& rpc_name);
void WaitCond(const std::string& rpc_name);
void IncreaseBatchBarrier(const std::string rpc_name);
void ResetBarrierCounter();
protected:
virtual void ShutDownImpl() = 0;
private:
std::mutex mutex_;
std::unordered_map<std::string, int> barrier_counter_;
std::condition_variable barrier_cond_;
std::unordered_map<std::string, int> rpc_cond_map_;
std::atomic<int> cur_cond_;
std::condition_variable rpc_cond_;
protected:
std::string bind_address_;
std::atomic<int> exit_flag_;
int selected_port_;
const int client_num_;
std::unordered_map<std::string, RequestHandler*> rpc_call_map_;
std::unordered_map<std::string, int> rpc_thread_num_;
friend class RequestHandler;
};
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/variable_response.h
浏览文件 @ 8855d4a7
......@@ -67,8 +67,8 @@ class VariableResponse {
framework::Scope* GetMutableLocalScope() const { return local_scope_; }
inline std::string Varname() { return meta_.varname(); }
inline std::string OutVarname() { return meta_.out_varname(); }
inline std::string Varname() const { return meta_.varname(); }
inline std::string OutVarname() const { return meta_.out_varname(); }
// should call parse first.
framework::Variable* GetVar() {
......
paddle/fluid/operators/gather_op.cc
浏览文件 @ 8855d4a7
......@@ -33,7 +33,6 @@ class GatherOp : public framework::OperatorWithKernel {
auto index_dims = ctx->GetInputDim("Index");
PADDLE_ENFORCE(index_dims.size() == 1);
int batch_size = ctx->GetInputDim("Index")[0];
PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
framework::DDim output_dims(ctx->GetInputDim("X"));
output_dims[0] = batch_size;
ctx->SetOutputDim("Out", output_dims);
......
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @ 8855d4a7
......@@ -23,6 +23,7 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/platform/nccl_helper.h"
namespace paddle {
......@@ -75,19 +76,23 @@ 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::AsyncGRPCServer rpc_service(endpoint, true);
detail::RequestSendHandler rpc_h(true);
detail::AsyncGRPCServer rpc_service(endpoint, 1);
rpc_service.RegisterRPC(detail::kRequestSend, &rpc_h);
rpc_h.SetRPCServer(&rpc_service);
framework::ProgramDesc empty_program;
framework::Executor executor(dev_ctx.GetPlace());
rpc_service.SetScope(scope);
rpc_service.SetDevCtx(&dev_ctx);
rpc_service.SetProgram(&empty_program);
rpc_service.SetExecutor(&executor);
rpc_h.SetScope(scope);
rpc_h.SetDevCtx(&dev_ctx);
rpc_h.SetProgram(&empty_program);
rpc_h.SetExecutor(&executor);
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, &rpc_service));
rpc_service.SetCond(0);
std::bind(&detail::AsyncGRPCServer::StartServer, &rpc_service));
rpc_service.SetCond(detail::kRequestSend);
VLOG(3) << "start getting nccl id from trainer 0...";
auto recv = rpc_service.Get();
rpc_service.WaitBarrier(detail::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 @ 8855d4a7
......@@ -19,14 +19,16 @@ limitations under the License. */
#include <thread> // NOLINT
#include <vector>
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/request_handler_impl.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
service->RunSyncUpdate();
void RunServer(std::shared_ptr<detail::RPCServer> service) {
service->StartServer();
VLOG(4) << "RunServer thread end";
}
static void split(const std::string &str, char sep,
......@@ -67,8 +69,6 @@ static void ParallelExecuteBlocks(
for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
}
std::atomic_int ListenAndServOp::selected_port_{0};
ListenAndServOp::ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
......@@ -78,7 +78,6 @@ ListenAndServOp::ListenAndServOp(const std::string &type,
ListenAndServOp::~ListenAndServOp() { Stop(); }
void ListenAndServOp::Stop() {
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
rpc_service_->ShutDown();
server_thread_->join();
auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
......@@ -87,26 +86,13 @@ void ListenAndServOp::Stop() {
void ListenAndServOp::SavePort() const {
// NOTE: default write file to /tmp/paddle.selected_port
selected_port_ = rpc_service_->GetSelectedPort();
auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
std::ofstream port_file;
port_file.open(file_path);
port_file << selected_port_.load();
port_file.close();
VLOG(4) << "selected port written to " << file_path;
}
void ListenAndServOp::WaitServerReady() {
while (selected_port_.load() == 0) {
}
rpc_service_->SavePort();
}
void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
framework::ProgramDesc *program,
framework::Scope *recv_scope,
framework::BlockDesc *prefetch_block) const {
auto fan_in = Attr<int>("Fanin");
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
......@@ -121,49 +107,24 @@ void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
optimize_prepared.begin(),
std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
bool exit_flag = false;
rpc_service_->ResetBarrierCounter();
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable *> sparse_vars;
while (!exit_flag && !SignalHandler::IsProgramExit()) {
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(0);
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
break;
} else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "recv batch barrier message";
batch_barrier++;
continue;
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
}
}
if (exit_flag) {
rpc_service_->SetCond(1);
rpc_service_->ShutDown();
rpc_service_->SetCond(detail::kRequestSend);
rpc_service_->WaitBarrier(detail::kRequestSend);
if (rpc_service_->IsExit()) {
LOG(WARNING) << "get exit!rpc_processor break!";
rpc_service_->SetCond(detail::kRequestGet);
break;
}
// NOTE: if is_gpu_place, CUDA kernels are launched by multiple threads
// 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();
......@@ -194,52 +155,18 @@ void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
}
rpc_service_->SetCond(1);
// FIXME(typhoonzero): use another condition to sync wait clients get.
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
rpc_service_->SetCond(detail::kRequestGet);
rpc_service_->WaitBarrier(detail::kRequestGet);
rpc_service_->ResetBarrierCounter();
} // while(true)
}
static void AsyncUpdateThread(
const std::string &var_name, const bool &exit_flag,
const std::shared_ptr<detail::ReceivedQueue> &queue,
framework::Executor *executor,
framework::ExecutorPrepareContext *prepared) {
VLOG(3) << "update thread for " << var_name << " started";
while (!exit_flag && !SignalHandler::IsProgramExit()) {
const detail::ReceivedMessage v = queue->Pop();
if (SignalHandler::IsProgramExit()) {
VLOG(3) << "update thread for " << var_name << " exit";
break;
}
auto recv_var_name = v.first;
VLOG(4) << "async update " << recv_var_name;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
auto fs = framework::Async([var_name, &executor, &v, prepared] {
try {
executor->RunPreparedContext(prepared,
v.second->GetMutableLocalScope());
} catch (const std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
});
fs.wait();
}
}
void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
framework::ProgramDesc *program) const {
VLOG(3) << "RunAsyncLoop in";
// grad name to block id
std::unordered_map<std::string, int32_t> grad_to_block_id;
std::unordered_map<int32_t, std::string> id_to_grad;
std::unordered_map<std::string, std::shared_ptr<detail::ReceivedQueue>>
grad_to_queue;
auto grad_to_block_id_str =
Attr<std::vector<std::string>>("grad_to_block_id");
......@@ -249,13 +176,9 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
VLOG(3) << "after split, grad = " << pieces[0] << ", id=" << pieces[1];
PADDLE_ENFORCE_EQ(pieces.size(), 2);
PADDLE_ENFORCE_EQ(grad_to_block_id.count(pieces[0]), 0);
int block_id = std::stoi(pieces[1]);
grad_to_block_id[pieces[0]] = block_id;
std::shared_ptr<detail::ReceivedQueue> queue =
std::make_shared<detail::ReceivedQueue>();
grad_to_queue[pieces[0]] = queue;
// record blocking queue in SignalHandler
SignalHandler::RegisterBlockingQueue(queue);
id_to_grad[block_id] = pieces[0];
}
size_t num_blocks = program->Size();
......@@ -274,39 +197,36 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
grad_to_prepared_ctx[id_to_grad[block_list[i]]] = optimize_prepared[i];
}
bool exit_flag = false;
request_send_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
request_get_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
request_prefetch_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
VLOG(3) << "start async optimize threads";
std::vector<std::future<void>> fs;
for (auto iter = grad_to_queue.begin(); iter != grad_to_queue.end(); iter++) {
std::string grad_name = iter->first;
VLOG(3) << "create async update thread for " << grad_name;
fs.push_back(framework::AsyncIO([grad_name, &exit_flag, &executor,
&grad_to_queue, &grad_to_prepared_ctx]() {
AsyncUpdateThread(grad_name, exit_flag, grad_to_queue[grad_name],
executor, grad_to_prepared_ctx[grad_name].get());
}));
}
VLOG(3) << "RunAsyncLoop into while";
while (!exit_flag && !SignalHandler::IsProgramExit()) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
while (true) {
if (rpc_service_->IsExit()) {
LOG(INFO) << "get exit!rpc_processor break!";
break;
} else {
VLOG(3) << "received grad: " << recv_var_name;
grad_to_queue[recv_var_name]->Push(v);
}
if (exit_flag) {
rpc_service_->ShutDown();
break;
}
sleep(1);
} // while(true)
}
static void FillRequestCtx(detail::RequestHandler *h, framework::Scope *scope,
platform::DeviceContext *dev_ctx,
framework::Executor *executor,
framework::ProgramDesc *program,
framework::ExecutorPrepareContext *prefetch_ctx,
detail::RPCServer *rpc_server) {
h->SetScope(scope);
h->SetDevCtx(dev_ctx);
h->SetExecutor(executor);
h->SetProgram(program);
h->SetPrefetchPreparedCtx(std::move(
std::unique_ptr<framework::ExecutorPrepareContext>(prefetch_ctx)));
h->SetRPCServer(rpc_server);
}
void ListenAndServOp::RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const {
// Mark this as PS that it should decide profiling by listening from trainer.
......@@ -316,27 +236,42 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
framework::Scope &recv_scope = scope.NewScope();
bool sync_mode = Attr<bool>("sync_mode");
auto fan_in = Attr<int>("Fanin");
PADDLE_ENFORCE(!rpc_service_);
std::string endpoint = Attr<std::string>("endpoint");
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, sync_mode));
LOG(INFO) << "sync_mode:" << sync_mode << ", fan_in:" << fan_in
<< ", end_point:" << endpoint;
// request_handler_.reset(new detail::GRPCRequestSendHandler(sync_mode));
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, fan_in));
request_send_handler_.reset(new detail::RequestSendHandler(sync_mode));
request_get_handler_.reset(new detail::RequestGetHandler(sync_mode));
request_prefetch_handler_.reset(
new detail::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,
request_prefetch_handler_.get());
auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *prefetch_block = Attr<framework::BlockDesc *>(kPrefetchBlock);
auto *program = optimize_block->Program();
framework::Executor executor(dev_place);
// prepare rpc_service
rpc_service_->SetScope(&recv_scope);
rpc_service_->SetDevCtx(&dev_ctx);
rpc_service_->SetProgram(program);
rpc_service_->SetExecutor(&executor);
// prepare for prefetch
VLOG(3) << "prefetch block id is " << prefetch_block->ID();
auto prefetch_prepared = executor.Prepare(*program, prefetch_block->ID());
rpc_service_->SetPrefetchPreparedCtx(std::move(prefetch_prepared));
auto f = std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope,
&dev_ctx, &executor, program, prefetch_prepared.release(),
rpc_service_.get());
f(request_send_handler_.get());
f(request_get_handler_.get());
f(request_prefetch_handler_.get());
// start the server listening after all member initialized.
server_thread_.reset(new std::thread(RunServer, rpc_service_));
......@@ -348,8 +283,6 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
signal(SIGTERM, SignalHandler::StopAndExit);
// Write to a file of server selected port for python use.
std::string file_path = string::Sprintf("/tmp/paddle.%d.selected_port",
static_cast<int>(::getpid()));
SavePort();
if (sync_mode) {
RunSyncLoop(&executor, program, &recv_scope, prefetch_block);
......@@ -385,27 +318,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
}
};
bool SignalHandler::program_exit_flag_ = false;
SignalHandler::BlockingQueueSet SignalHandler::blocking_queue_set_{};
void SignalHandler::StopAndExit(int signal_num) {
VLOG(3) << "Catch interrupt signal: " << signal_num << ", program will exit";
program_exit_flag_ = true;
// awake all blocking queues
for (BlockingQueueSet::iterator iter = blocking_queue_set_.begin();
iter != blocking_queue_set_.end(); iter++) {
iter->get()->Push(
std::make_pair(std::string(LISTEN_TERMINATE_MESSAGE), nullptr));
}
exit(EXIT_SUCCESS);
}
void SignalHandler::RegisterBlockingQueue(BlockingQueue &queue) {
blocking_queue_set_.insert(queue);
exit(0);
}
} // namespace operators
......
paddle/fluid/operators/listen_and_serv_op.h
浏览文件 @ 8855d4a7
......@@ -23,7 +23,8 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/request_handler.h"
#include "paddle/fluid/operators/detail/rpc_server.h"
namespace paddle {
namespace operators {
......@@ -31,7 +32,7 @@ namespace operators {
constexpr char kOptimizeBlock[] = "OptimizeBlock";
constexpr char kPrefetchBlock[] = "PrefetchBlock";
void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service);
void RunServer(std::shared_ptr<detail::RPCServer> service);
class ListenAndServOp : public framework::OperatorBase {
public:
......@@ -52,41 +53,27 @@ class ListenAndServOp : public framework::OperatorBase {
void SavePort() const;
void WaitServerReady();
int GetSelectedPort() { return selected_port_; }
int GetSelectedPort() { return rpc_service_->GetSelectedPort(); }
void Stop() override;
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override;
static void ResetPort() { selected_port_ = 0; }
protected:
mutable std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
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<std::thread> server_thread_;
// FIXME(wuyi): it's static so that the operator can be cloned.
static std::atomic_int selected_port_;
};
class SignalHandler {
public:
typedef std::shared_ptr<detail::ReceivedQueue> BlockingQueue;
typedef std::unordered_set<BlockingQueue> BlockingQueueSet;
public:
static void StopAndExit(int signal_num);
static void RegisterBlockingQueue(BlockingQueue&);
static inline bool IsProgramExit() { return program_exit_flag_; }
private:
static bool program_exit_flag_;
static BlockingQueueSet blocking_queue_set_;
DISABLE_COPY_AND_ASSIGN(SignalHandler);
};
......
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ 8855d4a7
......@@ -46,6 +46,8 @@ class SendBarrierOp : public framework::OperatorBase {
auto rpc_client = detail::RPCClient::GetInstance();
VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
// need to wait before sending send_barrier message
PADDLE_ENFORCE(rpc_client->Wait());
if (sync_mode) {
......
paddle/fluid/operators/shape_op.cc 0 → 100644
浏览文件 @ 8855d4a7
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shape_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class ShapeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input (Input) of get_shape op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output (Out) of get_shape op should not be null.");
auto in_dim = ctx->GetInputDim("Input");
ctx->SetOutputDim("Out", {in_dim.size()});
}
};
class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Input", "(Tensor), The input tensor.");
AddOutput("Out", "(Tensor), The shape of input tensor.");
AddComment(R"DOC(
Shape Operator.
Get the shape of input tensor.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(shape, ops::ShapeOp, ops::ShapeOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int64_t>,
ops::ShapeKernel<float>, ops::ShapeKernel<double>);
paddle/fluid/operators/shape_op.cu 0 → 100644
浏览文件 @ 8855d4a7
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shape_op.h"
REGISTER_OP_CUDA_KERNEL(shape, paddle::operators::ShapeKernel<int>,
paddle::operators::ShapeKernel<int64_t>,
paddle::operators::ShapeKernel<float>,
paddle::operators::ShapeKernel<double>);
paddle/fluid/operators/shape_op.h 0 → 100644
浏览文件 @ 8855d4a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class ShapeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_t = ctx.Input<Tensor>("Input");
auto* out_t = ctx.Output<Tensor>("Out");
auto out_data = out_t->mutable_data<int64_t>(platform::CPUPlace());
auto in_dims = in_t->dims();
for (int i = 0; i < in_dims.size(); ++i) {
out_data[i] = in_dims[i];
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 8855d4a7
......@@ -31,8 +31,9 @@ void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
auto max_workspace = context.Attr<int>("max_workspace");
engine_.reset(new inference::tensorrt::TensorRTEngine(
max_batch_, max_workspace, nullptr));
// TODO(Superjomn) parameters should be passed after analysised from outside.
inference::Singleton<inference::tensorrt::OpConverter>::Global().ConvertBlock(
block, engine_.get());
block, {}, context.scope(), engine_.get());
engine_->FreezeNetwork();
}
......
paddle/fluid/operators/test_send_nccl_id.cc
浏览文件 @ 8855d4a7
......@@ -21,6 +21,8 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/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"
......@@ -35,42 +37,44 @@ namespace m = paddle::operators::math;
namespace detail = paddle::operators::detail;
namespace string = paddle::string;
std::unique_ptr<detail::AsyncGRPCServer> rpc_service;
std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
std::unique_ptr<detail::RequestHandler> g_req_handler;
void StartServer(std::atomic<bool>* initialized) {
void StartServer() {
f::Scope scope;
p::CPUPlace place;
scope.Var(NCCL_ID_VARNAME);
p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(p::CPUPlace());
rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", true));
f::ProgramDesc empty_program;
f::Executor executor(dev_ctx.GetPlace());
rpc_service->SetScope(&scope);
rpc_service->SetDevCtx(&dev_ctx);
rpc_service->SetProgram(&empty_program);
rpc_service->SetExecutor(&executor);
g_req_handler->SetScope(&scope);
g_req_handler->SetDevCtx(&dev_ctx);
g_req_handler->SetProgram(&empty_program);
g_req_handler->SetExecutor(&executor);
g_rpc_service->RegisterRPC(detail::kRequestSend, g_req_handler.get());
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, rpc_service.get()));
*initialized = true;
rpc_service->SetCond(0);
auto recv = rpc_service->Get();
std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
g_rpc_service->SetCond(detail::kRequestSend);
std::cout << "before WaitFanInOfSend" << std::endl;
g_rpc_service->WaitBarrier(detail::kRequestSend);
LOG(INFO) << "got nccl id and stop server...";
rpc_service->ShutDown();
g_rpc_service->ShutDown();
server_thread.join();
}
TEST(SendNcclId, DISABLED_Normal) {
std::atomic<bool> initialized{false};
std::thread server_thread(StartServer, &initialized);
while (!initialized) {
}
// wait server to start
// sleep(2);
rpc_service->WaitServerReady();
TEST(SendNcclId, GrpcServer) {
g_req_handler.reset(new detail::RequestSendHandler(true));
g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
std::thread server_thread(StartServer);
g_rpc_service->WaitServerReady();
f::Scope scope;
p::CPUPlace place;
......@@ -78,17 +82,20 @@ TEST(SendNcclId, DISABLED_Normal) {
auto& dev_ctx = *pool.Get(p::CPUPlace());
auto var = scope.Var(NCCL_ID_VARNAME);
// var->SetType(f::proto::VarType_Type_RAW);
auto id = var->GetMutable<ncclUniqueId>();
p::dynload::ncclGetUniqueId(id);
int port = rpc_service->GetSelectedPort();
int port = g_rpc_service->GetSelectedPort();
std::string ep = string::Sprintf("127.0.0.1:%d", port);
detail::RPCClient client;
LOG(INFO) << "connect to server" << ep;
client.AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
client.Wait();
client.AsyncSendBatchBarrier(ep);
client.Wait();
server_thread.join();
auto* ptr = rpc_service.release();
delete ptr;
g_rpc_service.reset(nullptr);
g_req_handler.reset(nullptr);
}
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 8855d4a7
......@@ -15,6 +15,7 @@
#pragma once
#include <stdio.h>
#include <string>
#include <thread> // NOLINT
#include <typeindex>
#include <vector>
......
paddle/scripts/paddle_build.sh
浏览文件 @ 8855d4a7
......@@ -183,7 +183,7 @@ function build() {
============================================
EOF
make clean
make -j `nproc`
make install -j `nproc`
}
function build_android() {
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 8855d4a7
......@@ -82,6 +82,7 @@ __all__ = [
'roi_pool',
'dice_loss',
'upsampling_bilinear2d',
'gather',
'random_crop',
]
......@@ -3889,7 +3890,6 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
def dice_loss(input, label, epsilon=0.00001):
"""
**Dice loss Layer**
Dice loss for comparing the similarity of two batch of data,
usually is used for binary image segmentation i.e. labels are binary.
The dice loss can be defined as below equation:
......@@ -3944,7 +3944,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
input (Variable): The input tensor of bilinear interpolation,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_shape(list|tuple|None): Output shape of bilinear interpolation
out_shape(list|tuple|Variable|None): Output shape of bilinear interpolation
layer, the shape is (out_h, out_w).
Default: None
scale(int|None): The multiplier for the input height or width.
......@@ -3971,13 +3971,20 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
out_h = 0
out_w = 0
inputs = {"X": input}
if out_shape is not None:
if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2):
if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2) and (
out_shape is not Variable):
raise ValueError('out_shape should be a list or tuple ',
'with length 2, (out_h, out_w).')
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
if _is_list_or_turple_(out_shape):
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
else:
inputs['OutSize'] = out_shape
else:
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
......@@ -3985,13 +3992,62 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="bilinear_interp",
inputs={"X": input},
inputs=inputs,
outputs={"Out": out},
attrs={"out_h": out_h,
"out_w": out_w})
return out
def gather(input, index):
"""
Output is obtained by gathering entries of the outer-most dimension
of X indexed by `index` and concatenate them together.
.. math::
Out = X[Index]
.. code-block:: text
Given:
X = [[1, 2],
[3, 4],
[5, 6]]
Index = [1, 2]
Then:
Out = [[3, 4],
[5, 6]]
Args:
input (Variable): The source input with rank>=1.
index (Variable): The index input with rank=1.
Returns:
output (Variable): The output is a tensor with the same rank as input.
Examples:
.. code-block:: python
output = fluid.layers.gather(x, index)
"""
helper = LayerHelper('gather', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="gather",
inputs={"X": input,
"Index": index},
outputs={"Out": out})
return out
def random_crop(input, shape, seed=1):
helper = LayerHelper("random_crop", **locals())
dtype = helper.input_dtype()
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 8855d4a7
......@@ -71,6 +71,7 @@ __all__ = [
'cumsum',
'scatter',
'sum',
'shape',
] + __activations__
for _OP in set(__all__):
......
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
浏览文件 @ 8855d4a7
......@@ -17,7 +17,10 @@ import numpy as np
from op_test import OpTest
def bilinear_interp_np(input, out_h, out_w):
def bilinear_interp_np(input, out_h, out_w, out_size):
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
batch_size, channel, in_h, in_w = input.shape
if out_h > 1:
ratio_h = (in_h - 1.0) / (out_h - 1.0)
......@@ -49,12 +52,15 @@ def bilinear_interp_np(input, out_h, out_w):
class TestBilinearInterpOp(OpTest):
def setUp(self):
self.out_size = None
self.init_test_case()
self.op_type = "bilinear_interp"
input_np = np.random.random(self.input_shape).astype("float32")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
self.outputs = {'Out': output_np}
......@@ -68,6 +74,7 @@ class TestBilinearInterpOp(OpTest):
self.input_shape = [2, 3, 4, 4]
self.out_h = 2
self.out_w = 2
self.out_size = np.array([3, 3]).astype("int32")
class TestCase1(TestBilinearInterpOp):
......@@ -91,5 +98,29 @@ class TestCase3(TestBilinearInterpOp):
self.out_w = 128
class TestCase4(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.out_size = np.array([2, 2]).astype("int32")
class TestCase5(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.out_size = np.array([11, 11]).astype("int32")
class TestCase6(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.out_size = np.array([65, 129]).astype("int32")
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_gather_op.py
浏览文件 @ 8855d4a7
......@@ -20,8 +20,9 @@ from op_test import OpTest
class TestGatherOp(OpTest):
def setUp(self):
self.op_type = "gather"
xnp = np.random.random((10, 20)).astype("float32")
self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")}
self.config()
xnp = np.random.random(self.x_shape).astype("float32")
self.inputs = {'X': xnp, 'Index': np.array(self.index).astype("int32")}
self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
def test_check_output(self):
......@@ -30,6 +31,16 @@ class TestGatherOp(OpTest):
def test_check_grad(self):
self.check_grad(['X'], 'Out')
def config(self):
self.x_shape = (10, 20)
self.index = [1, 3, 5]
class TestCase1(TestGatherOp):
def config(self):
self.x_shape = (10)
self.index = [1, 3, 5]
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_shape_op.py 0 → 100644
浏览文件 @ 8855d4a7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from op_test import OpTest
class TestShapeOp(OpTest):
def setUp(self):
self.op_type = "shape"
self.config()
self.shape = [2, 3]
input = np.zeros(self.shape)
self.inputs = {'Input': input}
self.outputs = {'Out': np.array(self.shape)}
def config(self):
self.shape = [2, 3]
def test_check_output(self):
self.check_output()
class case1(TestShapeOp):
def config(self):
self.shape = [2]
class case2(TestShapeOp):
def config(self):
self.shape = [1, 2, 3]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_split_var.py
浏览文件 @ 8855d4a7
......@@ -14,7 +14,7 @@
import math
import unittest
from paddle.fluid.transpiler.distribute_transpiler import split_dense_variable
from paddle.fluid.transpiler.distribute_transpiler import split_variable
import paddle.fluid as fluid
import paddle.fluid.core as core
import random
......@@ -31,7 +31,7 @@ class TestSplitVar(unittest.TestCase):
# dtype=core.VarDesc.VarType.LOD_TENSOR,
shape=shape)
var_list.append(var)
blocks = split_dense_variable(var_list, 10, min_size)
blocks = split_variable(var_list, 10, min_size)
all_sizes = []
for s in expected_sizes:
for s2 in s:
......
python/paddle/fluid/transpiler/details/__init__.py 0 → 100644
浏览文件 @ 8855d4a7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from program_utils import *
from ufind import *
python/paddle/fluid/transpiler/details/program_utils.py 0 → 100644
浏览文件 @ 8855d4a7
# 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.
def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block.remove_op(start) for _ in xrange(end - start + 1)]
except Exception, e:
raise e
block.program.sync_with_cpp()
def find_op_by_input_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.input_arg_names:
return index
return -1
def find_op_by_output_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
return -1
python/paddle/fluid/transpiler/details/ufind.py 0 → 100644
浏览文件 @ 8855d4a7
# 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.
class UnionFind(object):
""" Union-find data structure.
Union-find is a data structure that keeps track of a set of elements partitioned
into a number of disjoint (non-overlapping) subsets.
Reference:
https://en.wikipedia.org/wiki/Disjoint-set_data_structure
Args:
elements(list): The initialize element list.
"""
def __init__(self, elementes=None):
self._parents = [] # index -> parent index
self._index = {} # element -> index
self._curr_idx = 0
if not elementes:
elementes = []
for ele in elementes:
self._parents.append(self._curr_idx)
self._index.update({ele: self._curr_idx})
self._curr_idx += 1
def find(self, x):
# Find the root index of given element x,
# execute the path compress while findind the root index
if not x in self._index:
return -1
idx = self._index[x]
while idx != self._parents[idx]:
t = self._parents[idx]
self._parents[idx] = self._parents[t]
idx = t
return idx
def union(self, x, y):
# Union two given element
x_root = self.find(x)
y_root = self.find(y)
if x_root == y_root:
return
self._parents[x_root] = y_root
def is_connected(self, x, y):
# If two given elements have the same root index,
# then they are connected.
return self.find(x) == self.find(y)
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 8855d4a7
......@@ -11,6 +11,30 @@
# 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.
"""
Transpile the program to distributed data-parallelism programs.
The main_program will be transformed to use a remote parameter server
to do parameter optimization. And the optimization graph will be put
into a parameter server program.
Use different methods to split trainable variables to different
parameter servers.
Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send splited variables to server and fetch
params(splited blocks or origin param) from server.
5. append concat_op to merge splited blocks to update local weights.
Steps to transpile pserver:
1. create new program for parameter server.
2. create params and grad variables that assigned to current server instance.
3. create a sub-block in the server side program
4. append ops that should run on current server instance.
5. add listen_and_serv op
"""
from __future__ import print_function
......@@ -21,9 +45,11 @@ from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, \
Variable, Parameter, grad_var_name
from details import *
LOOKUP_TABLE_TYPE = "lookup_table"
LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
)
RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
......@@ -40,62 +66,11 @@ class VarBlock:
return "%s:%d:%d" % (self.varname, self.offset, self.size)
class UnionFind(object):
""" Union-find data structure.
Union-find is a data structure that keeps track of a set of elements partitioned
into a number of disjoint (non-overlapping) subsets.
Reference:
https://en.wikipedia.org/wiki/Disjoint-set_data_structure
Args:
elements(list): The initialize element list.
"""
def __init__(self, elementes=None):
self._parents = [] # index -> parent index
self._index = {} # element -> index
self._curr_idx = 0
if not elementes:
elementes = []
for ele in elementes:
self._parents.append(self._curr_idx)
self._index.update({ele: self._curr_idx})
self._curr_idx += 1
def find(self, x):
# Find the root index of given element x,
# execute the path compress while findind the root index
if not x in self._index:
return -1
idx = self._index[x]
while idx != self._parents[idx]:
t = self._parents[idx]
self._parents[idx] = self._parents[t]
idx = t
return idx
def union(self, x, y):
# Union two given element
x_root = self.find(x)
y_root = self.find(y)
if x_root == y_root:
return
self._parents[x_root] = y_root
def is_connected(self, x, y):
# If two given elements have the same root index,
# then they are connected.
return self.find(x) == self.find(y)
def same_or_split_var(p_name, var_name):
return p_name == var_name or p_name.startswith(var_name + ".block")
def split_dense_variable(var_list, service_count, min_block_size=8192):
def split_variable(var_list, service_count, min_block_size=8192):
"""
We may need to split dense tensor to one or more blocks and put
them equally onto parameter server. One block is a sub-tensor
......@@ -141,99 +116,15 @@ def split_dense_variable(var_list, service_count, min_block_size=8192):
return blocks
def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block.remove_op(start) for _ in xrange(end - start + 1)]
except Exception, e:
raise e
block.program.sync_with_cpp()
def find_op_by_input_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.input_arg_names:
return index
return -1
def find_op_by_output_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
return -1
class DistributeTranspiler:
def transpile(self,
trainer_id,
program=None,
pservers="127.0.0.1:6174",
trainers=1,
split_method=RoundRobin,
sync_mode=True):
"""
Transpile the program to distributed data-parallelism programs.
The main_program will be transformed to use a remote parameter server
to do parameter optimization. And the optimization graph will be put
into a parameter server program.
Use different methods to split trainable variables to different
parameter servers.
Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send splited variables to server and fetch
params(splited blocks or origin param) from server.
5. append concat_op to merge splited blocks to update local weights.
Steps to transpile pserver:
1. create new program for parameter server.
2. create params and grad variables that assigned to current server instance.
3. create a sub-block in the server side program
4. append ops that should run on current server instance.
5. add listen_and_serv op
:param trainer_id: one unique id for each trainer in a job.
:type trainer_id: int
:param program: program to transpile, default is default_main_program
:type program: Program
:param pservers: parameter server endpoints like "m1:6174,m2:6174"
:type pservers: string
:param trainers: total number of workers/trainers in the job
:type trainers: int
:param split_method: A function to determin how to split variables
to different servers equally.
:type split_method: function
:type sync_mode: boolean default True
:param sync_mode: if sync_mode is set True, it means that dist transpiler
will transpile the program into sync_mode pserver and trainer program.
"""
assert (split_method.__bases__[0] == PSDispatcher)
if program is None:
program = default_main_program()
self.origin_program = program
self.trainer_num = trainers
self.sync_mode = sync_mode
# TODO(typhoonzero): currently trainer_id is fetched from cluster system
# like Kubernetes, we should port this to use etcd later when developing
# fluid distributed training with fault-tolerance.
self.trainer_id = trainer_id
pserver_endpoints = pservers.split(",")
self.pserver_endpoints = pserver_endpoints
self.optimize_ops, params_grads = self._get_optimize_pass()
ps_dispatcher = split_method(pserver_endpoints)
def _has_distributed_lookup_table(self):
# process lookup_table_op
# 1. check all lookup_table_op is distributed
# 2. check all lookup_table_op share the same table.
distributed_lookup_table_ops = []
# support only one distributed_lookup_table now
self.table_name = None
for op in program.global_block().ops:
for op in self.origin_program.global_block().ops:
if op.type == LOOKUP_TABLE_TYPE:
if op.attrs['is_distributed'] is True:
if self.table_name is None:
......@@ -246,20 +137,13 @@ class DistributeTranspiler:
if self.table_name is not None:
assert op.input("W")[0] != self.table_name
self.has_distributed_lookup_table = len(
distributed_lookup_table_ops) > 0
# step1: For large parameters and gradients, split them into smaller
# blocks.
param_list = []
grad_list = []
for p, g in params_grads:
# skip parameter marked not trainable
if type(p) == Parameter and p.trainable == False:
continue
param_list.append(p)
grad_list.append(g)
return len(distributed_lookup_table_ops) > 0
def _update_dist_lookup_table_vars(self, param_list, grad_list,
params_grads):
# TODO(wuyi): put find a way to put dist lookup table stuff all together.
# update self.table_param_grad and self.trainer_side_table_grad_list
program = self.origin_program
if self.has_distributed_lookup_table:
param_list = [
param for param in param_list if param.name != self.table_name
......@@ -277,7 +161,7 @@ class DistributeTranspiler:
self.trainer_side_table_grad_list = [
program.global_block().create_var(
name="%s.trainer_%d.pserver_%d" %
(table_grad_var.name, trainer_id, index),
(table_grad_var.name, self.trainer_id, index),
type=table_grad_var.type,
shape=table_grad_var.shape,
dtype=table_grad_var.dtype)
......@@ -293,23 +177,41 @@ class DistributeTranspiler:
for index in range(len(self.pserver_endpoints))
]
grad_blocks = split_dense_variable(grad_list, len(pserver_endpoints))
param_blocks = split_dense_variable(param_list, len(pserver_endpoints))
def _init_splited_vars(self, split_method):
# update these mappings for further transpile:
# 1. param_var_mapping: param var name -> [splited params vars]
# 2. grad_var_mapping: grad var name -> [splited grads vars]
# 3. grad_param_mapping: grad.blockx -> param.blockx
# 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
param_list = []
grad_list = []
for p, g in self.params_grads:
# skip parameter marked not trainable
if type(p) == Parameter and p.trainable == False:
continue
param_list.append(p)
grad_list.append(g)
self._update_dist_lookup_table_vars(param_list, grad_list,
self.params_grads)
grad_blocks = split_variable(grad_list, len(self.pserver_endpoints))
param_blocks = split_variable(param_list, len(self.pserver_endpoints))
assert (len(grad_blocks) == len(param_blocks))
# step2: Create new vars for the parameters and gradients blocks and
# add ops to do the split.
param_var_mapping = self._create_vars_from_blocklist(program,
param_blocks)
grad_var_mapping = self._create_vars_from_blocklist(
program, grad_blocks, add_trainer_suffix=self.trainer_num > 1)
grad_param_mapping = dict()
# origin_varname -> [splited_var]
self.param_var_mapping = self._create_vars_from_blocklist(
self.origin_program, param_blocks)
self.grad_var_mapping = self._create_vars_from_blocklist(
self.origin_program,
grad_blocks,
add_trainer_suffix=self.trainer_num > 1)
self.grad_param_mapping = dict()
for g, p in zip(grad_blocks, param_blocks):
g_name, g_bid, _ = g.split(":")
p_name, p_bid, _ = p.split(":")
grad_param_mapping[grad_var_mapping[g_name][int(g_bid)]] = \
param_var_mapping[p_name][int(p_bid)]
# step 3: transpile trainer side program, insert recv op and send op.
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.param_var_mapping[p_name][int(p_bid)]
# create mapping of endpoint -> split var to create pserver side program
self.param_grad_ep_mapping = dict()
......@@ -322,10 +224,50 @@ class DistributeTranspiler:
}) for ep in self.pserver_endpoints
]
def transpile(self,
trainer_id,
program=None,
pservers="127.0.0.1:6174",
trainers=1,
split_method=RoundRobin,
sync_mode=True):
"""
:param trainer_id: one unique id for each trainer in a job.
:type trainer_id: int
:param program: program to transpile, default is default_main_program
:type program: Program
:param pservers: parameter server endpoints like "m1:6174,m2:6174"
:type pservers: string
:param trainers: total number of workers/trainers in the job
:type trainers: int
:param split_method: A function to determin how to split variables
to different servers equally.
:type split_method: function
:type sync_mode: boolean default True
:param sync_mode: if sync_mode is set True, it means that dist transpiler
will transpile the program into sync_mode pserver and trainer program.
"""
assert (split_method.__bases__[0] == PSDispatcher)
if program is None:
program = default_main_program()
self.origin_program = program
self.trainer_num = trainers
self.sync_mode = sync_mode
self.trainer_id = trainer_id
pserver_endpoints = pservers.split(",")
self.pserver_endpoints = pserver_endpoints
self.optimize_ops, self.params_grads = self._get_optimize_pass()
ps_dispatcher = split_method(self.pserver_endpoints)
self.has_distributed_lookup_table = self._has_distributed_lookup_table()
# split and create vars, then put splited vars in dicts for later use.
self._init_splited_vars(split_method)
# step 3.1: insert send op to send gradient vars to parameter servers
ps_dispatcher.reset()
send_vars = []
for orig_varname, splited_vars in grad_var_mapping.items():
for orig_varname, splited_vars in self.grad_var_mapping.items():
eplist = ps_dispatcher.dispatch(splited_vars)
if len(splited_vars) == 1:
orig_varname = splited_vars[0].name
......@@ -367,7 +309,7 @@ class DistributeTranspiler:
# step 3.2: insert recv op to receive parameters from parameter server
recv_vars = []
for _, var in enumerate(send_vars):
recv_vars.append(grad_param_mapping[var])
recv_vars.append(self.grad_param_mapping[var])
ps_dispatcher.reset()
eplist = ps_dispatcher.dispatch(recv_vars)
......@@ -375,7 +317,7 @@ class DistributeTranspiler:
self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i])
self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
# step4: Concat the parameters splits together after recv.
for varname, splited_var in param_var_mapping.iteritems():
for varname, splited_var in self.param_var_mapping.iteritems():
eps = []
for var in splited_var:
index = [v.name for v in recv_vars].index(var.name)
......@@ -399,7 +341,7 @@ class DistributeTranspiler:
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
for varname, splited_var in param_var_mapping.iteritems():
for varname, splited_var in self.param_var_mapping.iteritems():
if len(splited_var) <= 1:
continue
orig_param = program.global_block().vars[varname]
......@@ -440,7 +382,6 @@ class DistributeTranspiler:
# we don't need to create them when grad arrives.
# change client side var name to origin name by
# removing ".trainer_%d" suffix
suff_idx = v.name.find(".trainer_")
if suff_idx >= 0:
orig_var_name = v.name[:suff_idx]
......@@ -477,24 +418,14 @@ class DistributeTranspiler:
# located on current pserver
opt_op_on_pserver = []
for _, op in enumerate(self.optimize_ops):
if self._is_opt_op(op) and self._is_opt_op_on_pserver(endpoint, op):
if self._is_optimizer_op(op) and self._is_opt_op_on_pserver(
endpoint, op):
opt_op_on_pserver.append(op)
# step 3.3
# Iterate through the ops, and if an op and the optimize ops
# which located on current pserver are in one set, then
# append it into the sub program.
# We try to put optimization program run parallelly, assume
# optimization program always looks like:
#
# prevop -> prevop -> opt op -> following op -> following op; ->
# prevop -> prevop -> opt op -> following op -> following op; ->
# global op -> global op
#
# we put operators that can run parallelly to many program blocks.
# in above example, we seperate ops by the ";". Global ops must run
# after all the optimize ops finished.
global_ops = []
# HACK: optimization global ops only used to scale beta1 and beta2
# replace it with dependency engine.
......@@ -502,12 +433,18 @@ class DistributeTranspiler:
if self._is_adam_connected_op(op):
global_ops.append(op)
def __append_optimize_op__(op, block, grad_to_block_id):
if self._is_opt_op(op):
def __append_optimize_op__(op, block, grad_to_block_id, merged_var):
if self._is_optimizer_op(op):
self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
self.origin_program)
self.origin_program, merged_var)
else:
self._append_pserver_non_opt_ops(block, op)
self._append_pserver_non_opt_ops(block, op, endpoint)
def __op_have_grad_input__(op):
for varname in op.input_arg_names:
if varname.find("@GRAD") >= 0:
return varname
return ""
# append lr decay ops to the child block if exists
lr_ops = self._get_lr_ops()
......@@ -515,17 +452,26 @@ class DistributeTranspiler:
lr_decay_block = pserver_program.create_block(
pserver_program.num_blocks - 1)
for _, op in enumerate(lr_ops):
self._append_pserver_non_opt_ops(lr_decay_block, op)
self._append_pserver_non_opt_ops(lr_decay_block, op, endpoint)
# append op to the current block
grad_to_block_id = []
pre_block_idx = pserver_program.num_blocks - 1
for idx, opt_op in enumerate(opt_op_on_pserver):
per_opt_block = pserver_program.create_block(pre_block_idx)
# append grad merging ops before clip and weight decay
for _, op in enumerate(self.optimize_ops):
# find the origin @GRAD var before clipping
grad_varname_for_block = __op_have_grad_input__(op)
if ufind.is_connected(op, opt_op) and grad_varname_for_block:
merged_var = self._append_pserver_grad_merge_ops(
per_opt_block, grad_varname_for_block, endpoint,
grad_to_block_id, self.origin_program)
for _, op in enumerate(self.optimize_ops):
# optimizer is connected to itself
if ufind.is_connected(op, opt_op) and op not in global_ops:
__append_optimize_op__(op, per_opt_block, grad_to_block_id)
__append_optimize_op__(op, per_opt_block, grad_to_block_id,
merged_var)
# append global ops
if global_ops:
......@@ -533,15 +479,7 @@ class DistributeTranspiler:
pserver_program.num_blocks - 1)
for glb_op in global_ops:
__append_optimize_op__(glb_op, opt_state_block,
grad_to_block_id)
# NOT USED: single block version:
#
# for _, op in enumerate(self.optimize_ops):
# for _, opt_op in enumerate(opt_op_on_pserver):
# if ufind.is_connected(op, opt_op):
# __append_optimize_op__(glb_op, optimize_block)
# break
grad_to_block_id, None)
# process distributed lookup_table
prefetch_block = None
......@@ -631,6 +569,8 @@ class DistributeTranspiler:
attrs=op.attrs)
return s_prog
# ====================== private transpiler functions =====================
# transpiler function for dis lookup_table
def _replace_lookup_table_op_with_prefetch(self, program,
pserver_endpoints):
......@@ -836,7 +776,6 @@ class DistributeTranspiler:
return table_opt_block
# ====================== private transpiler functions =====================
def _create_vars_from_blocklist(self,
program,
block_list,
......@@ -979,17 +918,74 @@ class DistributeTranspiler:
pass
return orig_shape
def _orig_varname(self, varname):
suff_idx = varname.find(".trainer_")
def _get_varname_parts(self, varname):
# returns origin, blockid, trainerid
orig_var_name = ""
if suff_idx >= 0:
orig_var_name = varname[:suff_idx]
trainer_part = ""
block_part = ""
trainer_idx = varname.find(".trainer_")
if trainer_idx >= 0:
trainer_part = varname[trainer_idx + 1:]
else:
trainer_idx = len(varname)
block_index = varname.find(".block")
if block_index >= 0:
block_part = varname[block_index + 1:trainer_idx]
else:
orig_var_name = varname
return orig_var_name
block_index = len(varname)
orig_var_name = varname[0:min(block_index, trainer_idx)]
return orig_var_name, block_part, trainer_part
def _orig_varname(self, varname):
orig, _, _ = self._get_varname_parts(varname)
return orig
def _append_pserver_grad_merge_ops(self, optimize_block,
grad_varname_for_block, endpoint,
grad_to_block_id, origin_program):
program = optimize_block.program
pserver_block = program.global_block()
grad_block = None
for g in self.param_grad_ep_mapping[endpoint]["grads"]:
if self._orig_varname(g.name) == \
self._orig_varname(grad_varname_for_block):
grad_block = g
break
if not grad_block:
# do not append this op if current endpoint
# is not dealing with this grad block
return
orig_varname, block_name, trainer_name = self._get_varname_parts(
grad_block.name)
if block_name:
merged_var_name = '.'.join([orig_varname, block_name])
else:
merged_var_name = orig_varname
merged_var = \
pserver_block.vars[merged_var_name]
grad_to_block_id.append(merged_var.name + ":" + str(optimize_block.idx))
if self.sync_mode and self.trainer_num > 1:
vars2merge = []
for i in xrange(self.trainer_num):
per_trainer_name = "%s.trainer_%d" % \
(merged_var_name, i)
vars2merge.append(pserver_block.vars[per_trainer_name])
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
outputs={"Out": merged_var})
# TODO(panyx0718): What if it's SELECTED_ROWS.
if not merged_var.type == core.VarDesc.VarType.SELECTED_ROWS:
optimize_block.append_op(
type="scale",
inputs={"X": merged_var},
outputs={"Out": merged_var},
attrs={"scale": 1.0 / float(self.trainer_num)})
return merged_var
def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
grad_to_block_id, origin_program):
grad_to_block_id, origin_program, merged_var):
program = optimize_block.program
pserver_block = program.global_block()
new_inputs = dict()
......@@ -997,40 +993,6 @@ class DistributeTranspiler:
# moment can use the updated shape
for key in opt_op.input_names:
if key == "Grad":
grad_block = None
for g in self.param_grad_ep_mapping[endpoint]["grads"]:
if same_or_split_var(
self._orig_varname(g.name),
self._orig_varname(opt_op.input(key)[0])):
grad_block = g
break
if not grad_block:
# do not append this op if current endpoint
# is not dealing with this grad block
return
merged_var = \
pserver_block.vars[self._orig_varname(grad_block.name)]
grad_to_block_id.append(merged_var.name + ":" + str(
optimize_block.idx))
if self.sync_mode and self.trainer_num > 1:
vars2merge = []
for i in xrange(self.trainer_num):
per_trainer_name = "%s.trainer_%d" % \
(self._orig_varname(grad_block.name), i)
vars2merge.append(pserver_block.vars[per_trainer_name])
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
outputs={"Out": merged_var})
# TODO(panyx0718): What if it's SELECTED_ROWS.
if not merged_var.type == core.VarDesc.VarType.SELECTED_ROWS:
optimize_block.append_op(
type="scale",
inputs={"X": merged_var},
outputs={"Out": merged_var},
attrs={"scale": 1.0 / float(self.trainer_num)})
new_inputs[key] = merged_var
elif key == "Param":
# param is already created on global program
......@@ -1089,17 +1051,31 @@ class DistributeTranspiler:
outputs=outputs,
attrs=opt_op.attrs)
def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
def _is_splited_grad_var(self, var, var_dict):
grad_block = None
for _, g in var_dict.iteritems():
if self._orig_varname(g.name) == self._orig_varname(var.name):
if g.name.find(".trainer_") == -1:
grad_block = g
break
return grad_block
def _append_pserver_non_opt_ops(self, optimize_block, opt_op, endpoint):
program = optimize_block.program
# Append the ops for parameters that do not need to be optimized/updated
inputs = self._get_input_map_from_op(
self.origin_program.global_block().vars, opt_op)
for varlist in inputs.itervalues():
for key, varlist in inputs.iteritems():
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
if not program.global_block().vars.has_key(var.name):
# for ops like clipping and weight decay, get the splited var
# for inputs/outputs
grad_block = self._is_splited_grad_var(
var, program.global_block().vars)
if grad_block:
inputs[key] = grad_block
elif not program.global_block().vars.has_key(var.name):
program.global_block().create_var(
name=var.name,
persistable=var.persistable,
......@@ -1108,13 +1084,16 @@ class DistributeTranspiler:
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, opt_op)
for varlist in outputs.itervalues():
for key, varlist in outputs.iteritems():
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
program.global_block().clone_variable(var)
grad_block = self._is_splited_grad_var(
var, program.global_block().vars)
if grad_block:
outputs[key] = grad_block
elif not program.global_block().vars.has_key(var.name):
program.global_block().clone_variable(var)
optimize_block.append_op(
type=opt_op.type,
......@@ -1160,9 +1139,17 @@ class DistributeTranspiler:
ufind.union(op1, op2)
return ufind
def _is_opt_op(self, op):
# NOTE: It's a HACK implement.
# optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
def _is_opt_role_op(self, op):
# NOTE: depend on oprole to find out whether this op is for
# optimize
op_maker = core.op_proto_and_checker_maker
optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
if op_maker.kOpRoleAttrName() in op.attrs and \
int(op.attrs[op_maker.kOpRoleAttrName()]) == int(optimize_role):
return True
return False
def _is_optimizer_op(self, op):
if "Param" in op.input_names and \
"LearningRate" in op.input_names:
return True
......@@ -1212,7 +1199,7 @@ class DistributeTranspiler:
# find learning rate variables by optimize op
lr_vars = set()
for op in self.optimize_ops:
if self._is_opt_op(op):
if self._is_optimizer_op(op):
lr_vars.add(op.input("LearningRate")[0])
find_ops = []
......@@ -1229,7 +1216,7 @@ class DistributeTranspiler:
# NOTE: we need to skip all optimize ops, since it is connected
# with forward/backward ops and lr ops, we only need the lr ops.
if op1 != op2 and self._is_op_connected(op1, op2) and \
not self._is_opt_op(op1) and not self._is_opt_op(op2):
not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
ufind.union(op1, op2)
# find all ops which is related with lr var
for op1 in block.ops:
......@@ -1250,13 +1237,21 @@ class DistributeTranspiler:
block = self.origin_program.global_block()
opt_ops = []
params_grads = []
origin_var_dict = self.origin_program.global_block().vars
for op in block.ops:
if self._is_opt_op(op):
if self._is_opt_role_op(op):
opt_ops.append(op)
params_grads.append((self.origin_program.global_block().var(
op.input("Param")[0]),
self.origin_program.global_block().var(
op.input("Grad")[0])))
# HACK(wuyi): if we find grad vars from input of optimize
# ops, we may get the output of clip op. Use syntax "@GRAD"
# and op_role_var to get the pair.
for input_name in op.input_arg_names:
if input_name.find("@GRAD") != -1 and \
op.attrs[RPC_OP_ROLE_ATTR_NAME]:
param_name = op.attrs[OP_ROLE_VAR_ATTR_NAME][0]
params_grads.append([
origin_var_dict[param_name],
origin_var_dict[input_name]
])
elif self._is_adam_connected_op(op):
opt_ops.append(op)
else:
......
python/setup.py.in
浏览文件 @ 8855d4a7
......@@ -69,7 +69,8 @@ packages=['paddle',
'paddle.fluid.proto',
'paddle.fluid.proto.profiler',
'paddle.fluid.layers',
'paddle.fluid.transpiler']
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
if '${WITH_FLUID_ONLY}'== 'OFF':
packages+=['paddle.proto',
......
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