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提交 28630dd8 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into hsigmoid_op

上级 f8395631 2a7bc64c
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Showing with 16012 addition and 5880 deletion
.clang-format
浏览文件 @ 28630dd8
......@@ -25,4 +25,3 @@ AllowAllParametersOfDeclarationOnNextLine: true
BinPackParameters: false
BinPackArguments: false
...
.gitignore
浏览文件 @ 28630dd8
......@@ -28,3 +28,4 @@ cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
paddle/pybind/pybind.h
python/paddle/version.py
.travis.yml
浏览文件 @ 28630dd8
......@@ -42,7 +42,7 @@ before_install:
script:
- |
timeout 2580 paddle/scripts/travis/${JOB}.sh # 43min timeout
RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true; else false; fi;
RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true ;else exit 1; fi;
- |
if [[ "$JOB" != "build_doc" ]]; then exit 0; fi;
if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit 0; fi;
......
CMakeLists.txt
浏览文件 @ 28630dd8
......@@ -16,10 +16,14 @@ cmake_minimum_required(VERSION 3.0)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
set(PADDLE_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
SET(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
include(system)
project(paddle CXX C Go)
message(STATUS "CXX compiler: " ${CMAKE_CXX_COMPILER} ", version: " ${CMAKE_CXX_COMPILER_VERSION})
message(STATUS "C compiler: " ${CMAKE_C_COMPILER} ", version: " ${CMAKE_C_COMPILER_VERSION})
find_package(Sphinx)
if(NOT CMAKE_CROSSCOMPILING)
......@@ -54,7 +58,9 @@ option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with grpc distributed support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
......@@ -67,9 +73,6 @@ if(ANDROID OR IOS)
if(ANDROID)
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# TODO: support glog for Android api 16 ~ 19 in the future
message(WARNING "Using the unofficial git repository <https://github.com/Xreki/glog.git> instead")
endif()
endif()
......@@ -83,6 +86,8 @@ if(ANDROID OR IOS)
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
set(WITH_GOLANG OFF CACHE STRING
"Disable golang when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
......@@ -133,6 +138,8 @@ include(external/any) # download libn::any
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/nccl)
include(external/cares)
include(external/grpc)
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
......
Dockerfile
浏览文件 @ 28630dd8
......@@ -29,7 +29,7 @@ RUN apt-get update && \
automake locales clang-format swig doxygen cmake \
liblapack-dev liblapacke-dev libboost-dev \
clang-3.8 llvm-3.8 libclang-3.8-dev \
net-tools && \
net-tools libtool && \
apt-get clean -y
# Install Go and glide
......
README.md
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......@@ -36,7 +36,7 @@ Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddl
examples:
- Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
- Highly optimized recurrent networks which can handle **variable-length**
sequence without padding.
- Optimized local and distributed training for models with high dimensional
......
RELEASE.cn.md
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# v0.11.0版本
## PaddlePaddle Fluid
- PaddlePaddle发布版本v0.11.0包含一个新的特性*PaddlePaddle Fluid*. Fluid 是设计用来让用户像Pytorch和Tensorflow Eager Execution一样执行程序。在这些系统中，不再有*模型*这个概念，应用也不再包含一个用于描述Operator图或者一系列层的符号描述，而是像通用程序那样描述训练或者预测的过程。而Fluid与PyTorch或Eager Execution的区别在于Fluid不依赖Python提供的控制流，例如 if-else-then或者for，而是提供了基于C++实现的控制流并暴露了对应的用with语法实现的Python接口。例如：
https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
- 在v0.11.0版本中，我们提供了一个C++类`Executor`用于运行一个Fluid程序。Executor类似一个解释器。在未来的版本中，我们将提升和优化Executor成为一个调试器，就像GDB。并可能提供一些编译器，这个编译器会读取一个上文所描述的应用然后编译成一个等价的
源代码，这个源代码可以被nvcc编译成可以使用CUDA的二进制，或者被icc编译成可以充分利用Intel CPU的二进制。
## 新特点
* 发布 `PaddlePaddle Fluid`。
* 增加了用于模型预测的C-API。
* 用Fluid API实现了一个简单的GAN的例子。
* 增加了关于性能调优的文档。
* 为`paddle.v2.dataset`下载数据集提供了重试机制.
* C++中使用protobuf-lite替换protobuf减少了二进制的大小。
* 发布了新特性 [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment).
* 基于Bazel API利用cmake实现了一个的新的构建系统函数库。
* 当使用编译选项`WITH_MKL=ON`时自动下载和编译Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) 函数库.
* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
- 完成了 11个 MKL-DNN 层: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN。
- 完成了 3个 MKL-DNN 网络: VGG-19, ResNet-50, GoogleNet
- 基于Intel Skylake 6148 CPU的[性能测试](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) : 相对于MKLML有2~3倍的训练加速。
* 增加 [softsign activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign)
* 增加 [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod)
* 增加 [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance)
* 增加 [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq)
* 增加 [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score)
* 增加 [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice)
* 增加 [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
* 增加移动端友好的网页
## 改进
* 使用一个Python`whl`包即可安装.
* [V2 API可以实现用户定制化评估](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标)。
* 将 `PADDLE_ONLY_CPU` 改为 `PADDLE_WITH_GPU`, 因为我们会支持多种设备。
* 删除了有一些bug的BarrierStat。
* 清理和删除了paddle::Parameter中未使用的函数。
* 删除了ProtoDataProvider。
* Huber loss同时支持回归和分类。
* 为sequence pooling 层增加`stride`参数。
* v2 API自动使用cudnn batch normalization。
* 可以使用一个固定的参数名共享BN层的参数。
* 2D convolution operation支持variable-dimension input特性。
* 重构cmake中关于CUDA的部分并实现自动检测GPU架构的功能。
* 优化网页导航。
## 错误修复
* 修复ROI pooling的Bug. cc9a761
* 修复当label是dense vector是AUC变成0的问题. #5274
* 修复WarpCTC 层的Bug.
# v0.10.0版本
我们非常高兴发布了PaddlePaddle V0.10.0版，并开发了新的[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。
......
RELEASE.md
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# Release v0.11.0
## PaddlePaddle Fluid
- Release 0.11.0 includes a new feature *PaddlePaddle Fluid*. Fluid is
designed to allow users to program like PyTorch and TensorFlow Eager Execution.
In these systems, there is no longer the concept *model* and applications
do not include a symbolic description of a graph of operators nor a sequence
of layers. Instead, applications look exactly like a usual program that
describes a process of training or inference. The difference between
Fluid and PyTorch or Eager Execution is that Fluid doesn't rely on Python's
control-flow, `if-then-else` nor `for`. Instead, Fluid provides its
C++ implementations and their Python binding using the `with` statement. For an example
https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
- In 0.11.0, we provides a C++ class `Executor` to run a Fluid program.
Executor works like an interpreter. In future version, we will improve
`Executor` into a debugger like GDB, and we might provide some compilers,
which, for example, takes an application like the above one, and outputs
an equivalent C++ source program, which can be compiled using
[`nvcc`](http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)
to generate binaries that use CUDA, or using
[`icc`](https://software.intel.com/en-us/c-compilers) to generate binaries
that make full use of Intel CPUs.
## New Features
* Release `PaddlePaddle Fluid`.
* Add C-API for model inference
* Use fluid API to create a simple GAN demo.
* Add develop guide about performance tunning.
* Add retry when download `paddle.v2.dataset`.
* Linking protobuf-lite not protobuf in C++. Reduce the binary size.
* Feature [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment) released.
* A new style cmake functions for Paddle. It is based on Bazel API.
* Automatically download and compile with Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) library as CBLAS when build `WITH_MKL=ON`.
* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
- Complete 11 MKL-DNN layers: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN.
- Complete 3 MKL-DNN networks: VGG-19, ResNet-50, GoogleNet
- [Benchmark](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) on Intel Skylake 6148 CPU: 2~3x training speedup compared with MKLML.
* Add the [`softsign` activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign).
* Add the [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod).
* Add the [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance).
* Add the [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq).
* Add the [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score).
* Add the [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice).
* Add the [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
* Add mobile friendly webpages.
## Improvements
* Build and install using a single `whl` package.
* [Custom evaluating in V2 API](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标).
* Change `PADDLE_ONLY_CPU` to `PADDLE_WITH_GPU`, since we will support many kinds of devices.
* Remove buggy BarrierStat.
* Clean and remove unused functions in paddle::Parameter.
* Remove ProtoDataProvider.
* Huber loss supports both regression and classification.
* Add the `stride` parameter for sequence pooling layers.
* Enable v2 API use cudnn batch normalization automatically.
* The BN layer's parameter can be shared by a fixed the parameter name.
* Support variable-dimension input feature for 2D convolution operation.
* Refine cmake about CUDA to automatically detect GPU architecture.
* Improved website navigation.
## Bug Fixes
* Fix bug in ROI pooling. cc9a761
* Fix AUC is zero when label is dense vector. #5274
* Fix bug in WarpCTC layer.
# Release v0.10.0
We are glad to release version 0.10.0. In this version, we are happy to release the new
......
benchmark/IntelOptimizedPaddle.md
浏览文件 @ 28630dd8
......@@ -2,27 +2,25 @@
Machine:
- Server
- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
- Laptop
- DELL XPS15-9560-R1745: i7-7700HQ 8G 256GSSD
- i5 MacBook Pro (Retina, 13-inch, Early 2015)
- Desktop
- i7-6700k
- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
- Laptop: TBD
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- OpenBLAS v0.2.20
(TODO: will rerun after 0.11.0)
PaddlePaddle: (TODO: will rerun after 0.11.0)
- paddlepaddle/paddle:latest (for MKLML and MKL-DNN)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- paddlepaddle/paddle:latest-openblas (for OpenBLAS)
- OpenBLAS v0.2.20
On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
## Benchmark Model
### Server
#### Training
Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
Input image size - 3 * 224 * 224, Time: images/second
......@@ -35,9 +33,7 @@ Input image size - 3 * 224 * 224, Time: images/second
| MKLML | 12.12 | 13.70 | 16.18 |
| MKL-DNN | 28.46 | 29.83 | 30.44 |
chart on batch size 128
TBD
<img src="figs/vgg-cpu-train.png" width="500">
- ResNet-50
......@@ -47,9 +43,7 @@ TBD
| MKLML | 32.52 | 31.89 | 33.12 |
| MKL-DNN | 81.69 | 82.35 | 84.08 |
chart on batch size 128
TBD
<img src="figs/resnet-cpu-train.png" width="500">
- GoogLeNet
......@@ -59,10 +53,35 @@ TBD
| MKLML | 128.46| 137.89| 158.63 |
| MKL-DNN     | 250.46| 264.83| 269.50 |
chart on batch size 128
TBD
<img src="figs/googlenet-cpu-train.png" width="500">
#### Inference
Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
- VGG-19
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|-------|-------|-------|-------|
| OpenBLAS | 1.07 | 1.08 | 1.06 | 0.88 | 0.65 |
| MKLML | 5.58 | 9.80 | 15.15 | 21.21 | 28.67 |
| MKL-DNN | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
- ResNet-50
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|--------|--------|--------|--------|
| OpenBLAS | 3.35 | 3.19 | 3.09 | 2.55 | 1.96 |
| MKLML | 6.33 | 12.02 | 22.88 | 40.53 | 63.09 |
| MKL-DNN | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
- GoogLeNet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | 12.04 | 11.31 | 10.00 | 9.07 | 4.34 |
| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
### Laptop
TBD
### Desktop
TBD
benchmark/paddle/image/googlenet.py
浏览文件 @ 28630dd8
......@@ -6,10 +6,21 @@ width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
is_infer = get_config_arg("is_infer", bool, False)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer
}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
......@@ -146,7 +157,6 @@ def inception(name, input, channels, \
return cat
lab = data_layer(name="label", size=1000)
data = data_layer(name="input", size=3 * height * width)
# stage 1
......@@ -224,6 +234,10 @@ pool5 = img_pool_layer(
dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
out3 = fc_layer(
name="output3", input=dropout, size=1000, act=SoftmaxActivation())
loss3 = cross_entropy(name='loss3', input=out3, label=lab)
outputs(loss3)
if is_infer:
outputs(out3)
else:
lab = data_layer(name="label", size=num_class)
loss3 = cross_entropy(name='loss3', input=out3, label=lab)
outputs(loss3)
benchmark/paddle/image/provider.py
浏览文件 @ 28630dd8
......@@ -13,14 +13,20 @@ def initHook(settings, height, width, color, num_class, **kwargs):
settings.data_size = settings.height * settings.width * 3
else:
settings.data_size = settings.height * settings.width
settings.slots = [dense_vector(settings.data_size), integer_value(1)]
settings.is_infer = kwargs.get('is_infer', False)
if settings.is_infer:
settings.slots = [dense_vector(settings.data_size)]
else:
settings.slots = [dense_vector(settings.data_size), integer_value(1)]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_list):
for i in xrange(1024):
for i in xrange(2560 if settings.is_infer else 1024):
img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
lab = random.randint(0, settings.num_class - 1)
yield img.astype('float32'), int(lab)
if settings.is_infer:
yield img.astype('float32')
else:
lab = random.randint(0, settings.num_class - 1)
yield img.astype('float32'), int(lab)
benchmark/paddle/image/resnet.py
浏览文件 @ 28630dd8
......@@ -6,11 +6,21 @@ width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg("layer_num", int, 50)
is_test = get_config_arg("is_test", bool, False)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
is_infer = get_config_arg("is_infer", bool, False)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer
}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
......@@ -45,7 +55,10 @@ def conv_bn_layer(name,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(
name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
name=name + "_bn",
input=tmp,
act=active_type,
use_global_stats=is_infer)
def bottleneck_block(name, input, num_filters1, num_filters2):
......@@ -207,7 +220,9 @@ elif layer_num == 152:
else:
print("Wrong layer number.")
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
inputs(img, lbl)
outputs(loss)
if is_infer:
outputs(resnet)
else:
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
outputs(loss)
benchmark/paddle/image/run_mkldnn_infer.sh 0 → 100755
浏览文件 @ 28630dd8
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/infer-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
if [ $thread -gt $bs ]; then
thread=$bs
fi
log="logs/infer-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $4, use True or False."
exit 0
fi
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "Training model ${topology}_${layer_num}"
paddle train --job=train \
--config="${topology}.py" \
--use_mkldnn=True \
--use_gpu=False \
--trainer_count=1 \
--num_passes=1 \
--save_dir="models/${topology}-${layer_num}" \
--config_args="batch_size=128,layer_num=${layer_num}" \
> /dev/null 2>&1
echo "Done"
fi
log_period=$((256 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_mkldnn=$use_mkldnn \
--use_gpu=False \
--trainer_count=$thread \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 1280 samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
if [ ! -d "models" ]; then
mkdir -p models
fi
# inference benchmark
for use_mkldnn in True False; do
for batchsize in 1 2 4 8 16; do
infer googlenet v1 $batchsize $use_mkldnn
infer resnet 50 $batchsize $use_mkldnn
infer vgg 19 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/run_mkldnn.sh → benchmark/paddle/image/run_mkldnn_train.sh
浏览文件 @ 28630dd8
......@@ -8,13 +8,13 @@ function train() {
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/${topology}-${layer_num}-mkldnn-${bs}.log"
log="logs/train-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/${topology}-${layer_num}-${thread}mklml-${bs}.log"
log="logs/train-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $3, use True or False."
echo "Wrong input $4, use True or False."
exit 0
fi
args="batch_size=${bs},layer_num=${layer_num}"
......@@ -30,13 +30,14 @@ function train() {
2>&1 | tee ${log}
}
if [ ! -d "train.list" ]; then
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
......
benchmark/paddle/image/vgg.py
浏览文件 @ 28630dd8
......@@ -6,10 +6,21 @@ width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg('layer_num', int, 19)
is_infer = get_config_arg("is_infer", bool, False)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer
}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
......@@ -98,6 +109,9 @@ elif layer_num == 19:
else:
print("Wrong layer number.")
lab = data_layer('label', num_class)
loss = cross_entropy(input=vgg, label=lab)
outputs(loss)
if is_infer:
outputs(vgg)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=vgg, label=lab)
outputs(loss)
cmake/cblas.cmake
浏览文件 @ 28630dd8
......@@ -3,7 +3,7 @@
# It will search MKLML, atlas, OpenBlas, reference-cblas in order.
#
# If any cblas implementation found, the following variable will be set.
# CBLAS_PROVIDER # one of MKLML, ATLAS, OPENBLAS, REFERENCE
# CBLAS_PROVIDER # one of MKLML, OPENBLAS, REFERENCE
# CBLAS_INC_DIR # the include directory for cblas.
# CBLAS_LIBS # a list of libraries should be linked by paddle.
# # Each library should be full path to object file.
......@@ -17,7 +17,7 @@ if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
set(CBLAS_INC_DIR ${MKLML_INC_DIR})
set(CBLAS_LIBRARIES ${MKLML_LIB})
add_definitions(-DPADDLE_USE_MKLML)
add_definitions(-DPADDLE_WITH_MKLML)
add_definitions(-DLAPACK_FOUND)
message(STATUS "Found cblas and lapack in MKLML "
......@@ -25,42 +25,6 @@ if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
return()
endif()
## Then find atlas.
set(ATLAS_ROOT $ENV{ATLAS_ROOT} CACHE PATH "Folder contains Atlas")
set(ATLAS_INCLUDE_SEARCH_PATHS
${ATLAS_ROOT}/include
/usr/include
/usr/include/atlas)
set(ATLAS_LIB_SEARCH_PATHS
${ATLAS_ROOT}/lib
/usr/lib
/usr/lib/blas/atlas
/usr/lib/atlas
/usr/lib/atlas-base # special for ubuntu 14.04.
)
find_path(ATLAS_INC_DIR NAMES cblas.h
PATHS ${ATLAS_INCLUDE_SEARCH_PATHS})
find_path(ATLAS_CLAPACK_INC_DIR NAMES clapack.h
PATHS ${ATLAS_INCLUDE_SEARCH_PATHS})
find_library(ATLAS_CBLAS_LIB NAMES cblas libcblas.so.3
PATHS ${ATLAS_LIB_SEARCH_PATHS})
find_library(ATLAS_CLAPACK_LIB NAMES lapack_atlas liblapack_atlas.so.3
PATHS ${ATLAS_LIB_SEARCH_PATHS})
if(ATLAS_CLAPACK_INC_DIR AND ATLAS_INC_DIR AND ATLAS_CBLAS_LIB AND ATLAS_CLAPACK_LIB)
set(CBLAS_FOUND ON)
set(CBLAS_PROVIDER ATLAS)
set(CBLAS_INC_DIR ${ATLAS_INC_DIR} ${ATLAS_CLAPACK_INC_DIR})
set(CBLAS_LIBRARIES ${ATLAS_CLAPACK_LIB} ${ATLAS_CBLAS_LIB})
add_definitions(-DPADDLE_USE_ATLAS)
add_definitions(-DLAPACK_FOUND)
message(STATUS "Found ATLAS (include: ${ATLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
message(STATUS "Found lapack in ATLAS (include: ${ATLAS_CLAPACK_INC_DIR})")
return()
endif()
## Then find openblas.
set(OPENBLAS_ROOT $ENV{OPENBLAS_ROOT} CACHE PATH "Folder contains Openblas")
set(OPENBLAS_INCLUDE_SEARCH_PATHS
......
cmake/configure.cmake
浏览文件 @ 28630dd8
......@@ -24,6 +24,11 @@ if(WITH_DOUBLE)
add_definitions(-DPADDLE_TYPE_DOUBLE)
endif(WITH_DOUBLE)
if(WITH_ARM_FP16)
add_definitions(-DPADDLE_ARM_FP16)
add_definitions("-march=armv8.2-a+fp16+simd")
endif(WITH_ARM_FP16)
if(WITH_TESTING)
add_definitions(-DPADDLE_WITH_TESTING)
endif(WITH_TESTING)
......
cmake/external/cares.cmake 0 → 100644
浏览文件 @ 28630dd8
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
IF(MOBILE_INFERENCE OR NOT WITH_DISTRIBUTE)
return()
ENDIF()
include (ExternalProject)
# NOTE: c-ares is needed when linking with grpc.
SET(CARES_SOURCES_DIR ${THIRD_PARTY_PATH}/cares)
SET(CARES_INSTALL_DIR ${THIRD_PARTY_PATH}/install/cares)
SET(CARES_INCLUDE_DIR "${CARES_INSTALL_DIR}/include/" CACHE PATH "cares include directory." FORCE)
ExternalProject_Add(
extern_cares
GIT_REPOSITORY "https://github.com/c-ares/c-ares.git"
GIT_TAG "cares-1_13_0"
PREFIX ${CARES_SOURCES_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ./buildconf && ./configure --disable-shared --prefix=${CARES_INSTALL_DIR}
BUILD_IN_SOURCE 1
BUILD_COMMAND make -j8
INSTALL_COMMAND make install
)
ADD_LIBRARY(cares STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET cares PROPERTY IMPORTED_LOCATION
"${CARES_INSTALL_DIR}/lib/libcares.a")
include_directories(${CARES_INCLUDE_DIR})
ADD_DEPENDENCIES(cares extern_cares)
cmake/external/glog.cmake
浏览文件 @ 28630dd8
......@@ -26,12 +26,21 @@ ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR})
IF(ANDROID AND ${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# Using the unofficial glog for Android API < 21
SET(GLOG_REPOSITORY "https://github.com/Xreki/glog.git")
SET(GLOG_TAG "8a547150548b284382ccb6582408e9140ff2bea8")
ELSE()
SET(GLOG_REPOSITORY "https://github.com/google/glog.git")
SET(GLOG_TAG "v0.3.5")
ENDIF()
ExternalProject_Add(
extern_glog
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS gflags
GIT_REPOSITORY "https://github.com/google/glog.git"
GIT_TAG v0.3.5
GIT_REPOSITORY ${GLOG_REPOSITORY}
GIT_TAG ${GLOG_TAG}
PREFIX ${GLOG_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......
cmake/external/grpc.cmake 0 → 100644
浏览文件 @ 28630dd8
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
IF(MOBILE_INFERENCE OR NOT WITH_DISTRIBUTE)
return()
ENDIF()
include (ExternalProject)
SET(GRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/grpc)
SET(GRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/grpc)
SET(GRPC_INCLUDE_DIR "${GRPC_INSTALL_DIR}/include/" CACHE PATH "grpc include directory." FORCE)
SET(GRPC_CPP_PLUGIN "${GRPC_INSTALL_DIR}/bin/grpc_cpp_plugin" CACHE FILEPATH "GRPC_CPP_PLUGIN" FORCE)
IF(APPLE)
SET(BUILD_CMD make -n HAS_SYSTEM_PROTOBUF=false -s -j8 static grpc_cpp_plugin | sed "s/-Werror//g" | sh)
ELSE()
SET(BUILD_CMD make HAS_SYSTEM_PROTOBUF=false -s -j8 static grpc_cpp_plugin)
ENDIF()
ExternalProject_Add(
extern_grpc
DEPENDS protobuf zlib
GIT_REPOSITORY "https://github.com/grpc/grpc.git"
GIT_TAG "v1.7.x"
PREFIX ${GRPC_SOURCES_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
# NOTE(yuyang18):
# Disable -Werror, otherwise the compile will fail in MacOS.
# It seems that we cannot configure that by make command.
# Just dry run make command and remove `-Werror`, then use a shell to run make commands
BUILD_COMMAND ${BUILD_CMD}
INSTALL_COMMAND make prefix=${GRPC_INSTALL_DIR} install
)
# FIXME(typhoonzero): hack to get static lib path, try a better way like merge them.
ADD_LIBRARY(grpc++_unsecure STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET grpc++_unsecure PROPERTY IMPORTED_LOCATION
"${GRPC_INSTALL_DIR}/lib/libgrpc++_unsecure.a")
ADD_LIBRARY(grpc++ STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET grpc++ PROPERTY IMPORTED_LOCATION
"${GRPC_INSTALL_DIR}/lib/libgrpc++.a")
ADD_LIBRARY(gpr STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET gpr PROPERTY IMPORTED_LOCATION
"${GRPC_INSTALL_DIR}/lib/libgpr.a")
ADD_LIBRARY(grpc_unsecure STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET grpc_unsecure PROPERTY IMPORTED_LOCATION
"${GRPC_INSTALL_DIR}/lib/libgrpc_unsecure.a")
include_directories(${GRPC_INCLUDE_DIR})
ADD_DEPENDENCIES(grpc++_unsecure extern_grpc)
cmake/external/mkldnn.cmake
浏览文件 @ 28630dd8
......@@ -67,5 +67,5 @@ ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
add_definitions(-DPADDLE_USE_MKLDNN)
add_definitions(-DPADDLE_WITH_MKLDNN)
LIST(APPEND external_project_dependencies mkldnn)
cmake/external/openblas.cmake
浏览文件 @ 28630dd8
......@@ -114,11 +114,7 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
IF("${CBLAS_PROVIDER}" STREQUAL "MKLML")
ADD_LIBRARY(cblas SHARED ${dummyfile})
ELSE()
ADD_LIBRARY(cblas STATIC ${dummyfile})
ENDIF()
ADD_LIBRARY(cblas STATIC ${dummyfile})
TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
IF(NOT ${CBLAS_FOUND})
......
cmake/external/protobuf.cmake
浏览文件 @ 28630dd8
......@@ -15,7 +15,18 @@
INCLUDE(ExternalProject)
# Always invoke `FIND_PACKAGE(Protobuf)` for importing function protobuf_generate_cpp
FIND_PACKAGE(Protobuf QUIET)
SET(PROTOBUF_FOUND "OFF")
macro(UNSET_VAR VAR_NAME)
UNSET(${VAR_NAME} CACHE)
UNSET(${VAR_NAME})
endmacro()
UNSET_VAR(PROTOBUF_INCLUDE_DIR)
UNSET_VAR(PROTOBUF_FOUND)
UNSET_VAR(PROTOBUF_PROTOC_EXECUTABLE)
UNSET_VAR(PROTOBUF_PROTOC_LIBRARY)
UNSET_VAR(PROTOBUF_LITE_LIBRARY)
UNSET_VAR(PROTOBUF_LIBRARY)
UNSET_VAR(PROTOBUF_INCLUDE_DIR)
UNSET_VAR(Protobuf_PROTOC_EXECUTABLE)
if(NOT COMMAND protobuf_generate_python) # before cmake 3.4, protobuf_genrerate_python is not defined.
function(protobuf_generate_python SRCS)
......@@ -110,7 +121,6 @@ macro(PROMPT_PROTOBUF_LIB)
# FIND_Protobuf.cmake uses `Protobuf_PROTOC_EXECUTABLE`.
# make `protobuf_generate_cpp` happy.
SET(Protobuf_PROTOC_EXECUTABLE ${PROTOBUF_PROTOC_EXECUTABLE})
FOREACH(dep ${protobuf_DEPS})
ADD_DEPENDENCIES(protobuf ${dep})
ADD_DEPENDENCIES(protobuf_lite ${dep})
......@@ -128,11 +138,11 @@ endmacro()
set(PROTOBUF_ROOT "" CACHE PATH "Folder contains protobuf")
if (NOT "${PROTOBUF_ROOT}" STREQUAL "")
find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include)
find_library(PROTOBUF_LIBRARY protobuf PATHS ${PROTOBUF_ROOT}/lib)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite PATHS ${PROTOBUF_ROOT}/lib)
find_library(PROTOBUF_PROTOC_LIBRARY protoc PATHS ${PROTOBUF_ROOT}/lib)
find_program(PROTOBUF_PROTOC_EXECUTABLE protoc PATHS ${PROTOBUF_ROOT}/bin)
find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_program(PROTOBUF_PROTOC_EXECUTABLE protoc PATHS ${PROTOBUF_ROOT}/bin NO_DEFAULT_PATH)
if (PROTOBUF_INCLUDE_DIR AND PROTOBUF_LIBRARY AND PROTOBUF_LITE_LIBRARY AND PROTOBUF_PROTOC_LIBRARY AND PROTOBUF_PROTOC_EXECUTABLE)
message(STATUS "Using custom protobuf library in ${PROTOBUF_ROOT}.")
SET_PROTOBUF_VERSION()
......@@ -178,14 +188,26 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
SET(OPTIONAL_CACHE_ARGS "-DZLIB_ROOT:STRING=${ZLIB_ROOT}")
ENDIF()
SET(PROTOBUF_REPO "https://github.com/google/protobuf.git")
SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546")
IF(MOBILE_INFERENCE)
# The reason why the official version is not used is described in
# https://github.com/PaddlePaddle/Paddle/issues/6114
SET(PROTOBUF_REPO "https://github.com/qingqing01/protobuf.git")
SET(PROTOBUF_TAG "v3.2.0")
IF(NOT BUILD_FOR_HOST)
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} "-Dprotobuf_BUILD_PROTOC_BINARIES=OFF")
ENDIF()
ENDIF()
ExternalProject_Add(
${TARGET_NAME}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${PROTOBUF_SOURCES_DIR}
UPDATE_COMMAND ""
DEPENDS zlib
GIT_REPOSITORY "https://github.com/google/protobuf.git"
GIT_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546"
GIT_REPOSITORY ${PROTOBUF_REPO}
GIT_TAG ${PROTOBUF_TAG}
CONFIGURE_COMMAND
${CMAKE_COMMAND} ${PROTOBUF_SOURCES_DIR}/src/${TARGET_NAME}/cmake
${OPTIONAL_ARGS}
......@@ -203,7 +225,11 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
)
ENDFUNCTION()
SET(PROTOBUF_VERSION 3.1)
IF(NOT MOBILE_INFERENCE)
SET(PROTOBUF_VERSION 3.1)
ELSE()
SET(PROTOBUF_VERSION 3.2)
ENDIF()
IF(CMAKE_CROSSCOMPILING)
build_protobuf(protobuf_host TRUE)
LIST(APPEND external_project_dependencies protobuf_host)
......
cmake/external/zlib.cmake
浏览文件 @ 28630dd8
......@@ -50,6 +50,8 @@ ExternalProject_Add(
)
LIST(APPEND external_project_dependencies zlib)
ADD_LIBRARY(zlib_target STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET zlib_target PROPERTY IMPORTED_LOCATION ${ZLIB_LIBRARIES})
IF(WITH_C_API)
INSTALL(DIRECTORY ${ZLIB_INCLUDE_DIR} DESTINATION third_party/zlib)
......
cmake/flags.cmake
浏览文件 @ 28630dd8
......@@ -111,6 +111,8 @@ set(COMMON_FLAGS
-Wno-error=sign-compare
-Wno-error=unused-local-typedefs
-Wno-error=parentheses-equality # Warnings in pybind11
-Wno-error=ignored-attributes # Warnings in Eigen, gcc 6.3
-Wno-error=terminate # Warning in PADDLE_ENFORCE
)
set(GPU_COMMON_FLAGS
......
cmake/generic.cmake
浏览文件 @ 28630dd8
......@@ -227,8 +227,8 @@ function(cc_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_executable(${TARGET_NAME} ${cc_test_SRCS})
target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} gtest gtest_main)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} gtest gtest_main)
target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME} WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
endfunction(cc_test)
......@@ -288,8 +288,8 @@ function(nv_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS})
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} gtest gtest_main)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} gtest gtest_main)
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_test(${TARGET_NAME} ${TARGET_NAME})
endif()
endfunction(nv_test)
......@@ -467,3 +467,50 @@ function(py_test TARGET_NAME)
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
endfunction()
# grpc_library generate grpc code using grpc_cpp_plugin and protoc
# then build the generated protobuf code and grpc code with your
# implementation source codes together. Use SRCS argument for your
# implementation source files and PROTO argument for your .proto
# files.
#
# Usage: grpc_library(my_target SRCS my_client.cc PROTO my_target.proto DEPS my_dep)
function(grpc_library TARGET_NAME)
set(oneValueArgs PROTO)
set(multiValueArgs SRCS DEPS)
set(options "")
cmake_parse_arguments(grpc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
message(STATUS "generating grpc ${grpc_library_PROTO}")
get_filename_component(ABS_PROTO ${grpc_library_PROTO} ABSOLUTE)
get_filename_component(PROTO_WE ${grpc_library_PROTO} NAME_WE)
get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
protobuf_generate_cpp(grpc_proto_srcs grpc_proto_hdrs "${ABS_PROTO}")
set(grpc_grpc_srcs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.cc")
set(grpc_grpc_hdrs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.h")
cc_library("${TARGET_NAME}_proto" SRCS "${grpc_proto_srcs}")
add_custom_command(
OUTPUT "${grpc_grpc_srcs}" "${grpc_grpc_hdrs}"
COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}" -I "${PROTO_PATH}"
--plugin=protoc-gen-grpc="${GRPC_CPP_PLUGIN}" "${ABS_PROTO}"
DEPENDS "${ABS_PROTO}" ${PROTOBUF_PROTOC_EXECUTABLE} extern_grpc)
# FIXME(typhoonzero): grpc generated code do not generate virtual-dtor, mark it
# as compiler warnings instead of error. Should try remove the warnings also.
set_source_files_properties(
${grpc_grpc_srcs}
PROPERTIES
COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
cc_library("${TARGET_NAME}_grpc" SRCS "${grpc_grpc_srcs}")
set_source_files_properties(
${grpc_library_SRCS}
PROPERTIES
COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
cc_library("${TARGET_NAME}" SRCS "${grpc_library_SRCS}" DEPS "${TARGET_NAME}_grpc" "${TARGET_NAME}_proto" "${grpc_library_DEPS}")
endfunction()
doc/api/index_en.rst
浏览文件 @ 28630dd8
......@@ -7,3 +7,4 @@ API
v2/model_configs.rst
v2/data.rst
v2/run_logic.rst
v2/fluid.rst
doc/api/v2/config/activation.rst
浏览文件 @ 28630dd8
......@@ -99,3 +99,10 @@ STanh
.. automodule:: paddle.v2.activation
:members: STanh
:noindex:
SoftSign
========
.. automodule:: paddle.v2.activation
:members: SoftSign
:noindex:
doc/api/v2/config/layer.rst
浏览文件 @ 28630dd8
......@@ -54,7 +54,7 @@ img_conv
.. _api_v2.layer_context_projection:
context_projection
context_projection
------------------
.. autoclass:: paddle.v2.layer.context_projection
:noindex:
......@@ -70,7 +70,7 @@ Image Pooling Layer
img_pool
--------
.. autoclass:: paddle.v2.layer.img_pool
:noindex:
:noindex:
spp
---
......@@ -104,7 +104,7 @@ sum_to_one_norm
---------------
.. autoclass:: paddle.v2.layer.sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. autoclass:: paddle.v2.layer.cross_channel_norm
......@@ -114,7 +114,7 @@ row_l2_norm
-----------
.. autoclass:: paddle.v2.layer.row_l2_norm
:noindex:
Recurrent Layers
================
......@@ -415,6 +415,13 @@ multiplex
.. autoclass:: paddle.v2.layer.multiplex
:noindex:
Factorization Machine Layer
============================
factorization_machine
---------------------
.. autoclass:: paddle.v2.layer.factorization_machine
:noindex:
Slicing and Joining Layers
==========================
......
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======================
Fluid
======================
.. toctree::
:maxdepth: 1
fluid/layers.rst
fluid/data_feeder.rst
fluid/executor.rst
fluid/initializer.rst
fluid/evaluator.rst
fluid/nets.rst
fluid/optimizer.rst
fluid/param_attr.rst
fluid/profiler.rst
fluid/regularizer.rst
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===========
DataFeeder
===========
DataFeeder
-----------
.. automodule:: paddle.v2.fluid.data_feeder
:members: DataFeeder
:noindex:
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===========
Evaluator
===========
Evaluator
-----------
.. automodule:: paddle.v2.fluid.evaluator
:members: Evaluator
:noindex:
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===========
Executor
===========
Executor
-----------
.. automodule:: paddle.v2.fluid.executor
:members: Executor
:noindex:
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===========
Initializer
===========
Initializer
-----------
.. automodule:: paddle.v2.fluid.initializer
:members: Initializer
:noindex:
ConstantInitializer
-------------------
.. automodule:: paddle.v2.fluid.initializer
:members: ConstantInitializer
:noindex:
UniformInitializer
------------------
.. automodule:: paddle.v2.fluid.initializer
:members: UniformInitializer
:noindex:
NormalInitializer
-----------------
.. automodule:: paddle.v2.fluid.initializer
:members: NormalInitializer
:noindex:
XavierInitializer
-----------------
.. automodule:: paddle.v2.fluid.initializer
:members: XavierInitializer
:noindex:
MSRAInitializer
---------------
.. automodule:: paddle.v2.fluid.initializer
:members: MSRAInitializer
:noindex:
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==========
Layers
==========
fc
---
.. autofunction:: paddle.v2.fluid.layers.fc
:noindex:
embedding
---------
.. autofunction:: paddle.v2.fluid.layers.embedding
:noindex:
dynamic_lstm
------------
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm
:noindex:
data
---------
.. autofunction:: paddle.v2.fluid.layers.data
:noindex:
mean
---------
.. autofunction:: paddle.v2.fluid.layers.mean
:noindex:
mul
---------
.. autofunction:: paddle.v2.fluid.layers.mul
:noindex:
elementwise_add
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_add
:noindex:
elementwise_div
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_div
:noindex:
dropout
---------
.. autofunction:: paddle.v2.fluid.layers.dropout
:noindex:
reshape
---------
.. autofunction:: paddle.v2.fluid.layers.reshape
:noindex:
sigmoid
---------
.. autofunction:: paddle.v2.fluid.layers.sigmoid
:noindex:
scale
---------
.. autofunction:: paddle.v2.fluid.layers.scale
:noindex:
reshape
---------
.. autofunction:: paddle.v2.fluid.layers.reshape
:noindex:
transpose
---------
.. autofunction:: paddle.v2.fluid.layers.transpose
:noindex:
sigmoid_cross_entropy_with_logits
---------
.. autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits
:noindex:
cast
---------
.. autofunction:: paddle.v2.fluid.layers.cast
:noindex:
concat
---------
.. autofunction:: paddle.v2.fluid.layers.concat
:noindex:
sums
---------
.. autofunction:: paddle.v2.fluid.layers.sums
:noindex:
linear_chain_crf
---------
.. autofunction:: paddle.v2.fluid.layers.linear_chain_crf
:noindex:
assign
---------
.. autofunction:: paddle.v2.fluid.layers.embedding
:noindex:
split_lod_tensor
---------
.. autofunction:: paddle.v2.fluid.layers.split_lod_tensor
:noindex:
merge_lod_tensor
---------
.. autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
:noindex:
cos_sim
---------
.. autofunction:: paddle.v2.fluid.layers.cos_sim
:noindex:
cross_entropy
---------
.. autofunction:: paddle.v2.fluid.layers.cross_entropy
:noindex:
square_error_cost
---------
.. autofunction:: paddle.v2.fluid.layers.square_error_cost
:noindex:
accuracy
---------
.. autofunction:: paddle.v2.fluid.layers.accuracy
:noindex:
sequence_conv
---------
.. autofunction:: paddle.v2.fluid.layers.sequence_conv
:noindex:
conv2d
---------
.. autofunction:: paddle.v2.fluid.layers.conv2d
:noindex:
sequence_pool
---------
.. autofunction:: paddle.v2.fluid.layers.sequence_pool
:noindex:
pool2d
---------
.. autofunction:: paddle.v2.fluid.layers.pool2d
:noindex:
batch_norm
---------
.. autofunction:: paddle.v2.fluid.layers.batch_norm
:noindex:
beam_search_decode
---------
.. autofunction:: paddle.v2.fluid.layers.beam_search_decode
:noindex:
lstm
---------
.. autofunction:: paddle.v2.fluid.layers.lstm
:noindex:
lod_rank_table
---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table
:noindex:
max_sequence_len
---------
.. autofunction:: paddle.v2.fluid.layers.max_sequence_len
:noindex:
topk
---------
.. autofunction:: paddle.v2.fluid.layers.topk
:noindex:
lod_tensor_to_array
---------
.. autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
:noindex:
array_to_lod_tensor
---------
.. autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
:noindex:
fill_constant
---------
.. autofunction:: paddle.v2.fluid.layers.fill_constant
:noindex:
fill_constant_batch_size_like
---------
.. autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
:noindex:
ones
---------
.. autofunction:: paddle.v2.fluid.layers.ones
:noindex:
zeros
---------
.. autofunction:: paddle.v2.fluid.layers.zeros
:noindex:
increment
---------
.. autofunction:: paddle.v2.fluid.layers.increment
:noindex:
array_write
---------
.. autofunction:: paddle.v2.fluid.layers.array_write
:noindex:
create_array
---------
.. autofunction:: paddle.v2.fluid.layers.create_array
:noindex:
less_than
---------
.. autofunction:: paddle.v2.fluid.layers.less_than
:noindex:
array_read
---------
.. autofunction:: paddle.v2.fluid.layers.array_read
:noindex:
shrink_memory
---------
.. autofunction:: paddle.v2.fluid.layers.shrink_memory
:noindex:
array_length
---------
.. autofunction:: paddle.v2.fluid.layers.array_length
:noindex:
conv2d_transpose
---------
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex:
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===========
Nets
===========
simple_img_conv_pool
-----------
.. autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
:noindex:
img_conv_group
-----------
.. autofunction:: paddle.v2.fluid.nets.img_conv_group
:noindex:
sequence_conv_pool
-----------
.. autofunction:: paddle.v2.fluid.nets.sequence_conv_pool
:noindex:
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===========
Optimizer
===========
Optimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: Optimizer
:noindex:
SGDOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: SGDOptimizer
:noindex:
MomentumOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: MomentumOptimizer
:noindex:
AdagradOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: AdagradOptimizer
:noindex:
AdamOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: AdamOptimizer
:noindex:
AdamaxOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: AdamaxOptimizer
:noindex:
DecayedAdagradOptimizer
-----------
.. automodule:: paddle.v2.fluid.optimizer
:members: DecayedAdagradOptimizer
:noindex:
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===========
ParamAttr
===========
ParamAttr
-----------
.. automodule:: paddle.v2.fluid.param_attr
:members: ParamAttr
:noindex:
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===========
Profiler
===========
Profiler
-----------
.. autofunction:: paddle.v2.fluid.profiler.cuda_profiler
:noindex:
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===========
Regularizer
===========
WeightDecayRegularizer
-----------
.. automodule:: paddle.v2.fluid.regularizer
:members: WeightDecayRegularizer
:noindex:
L2DecayRegularizer
-----------
.. automodule:: paddle.v2.fluid.regularizer
:members: L2DecayRegularizer
:noindex:
L1DecayRegularizer
-----------
.. automodule:: paddle.v2.fluid.regularizer
:members: L1DecayRegularizer
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## Evaluator Design
### The Problem
### Problem Statement
During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
During training or inference, we provide an evaluation function to measure the model performance, for example, accuracy, precision, etc. In the operator based framework design, the data passes through the network pipeline batch by batch. As a result, inside the operator, we only calculate the metrics for one minibatch. Thus, we need to provide a mechanism to calculate the metrics for each N pass/batch the user wants.
### Evaluator Design
Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
Currently, every operation is expressed in the graph. We divide the evaluator process into three steps.
1. Initialize the metric state and add it into the block.
2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
2. Calculate the concerned metrics for every mini-batch. The single evaluator operator is only responsible for calculating the necessary statistics for one mini-batch. For example, the accuracy operator only calculates the accuracy for a minibatch data if run once.
3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
### Implementation
This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
This design is shown in the Python API.
Each metric operator needs to caculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
```python
......
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......@@ -28,6 +28,51 @@ The goal of float16 is to serve as a key for the executor to find and run the co
- [Eigen](https://github.com/RLovelett/eigen) >= 3.3 supports float16 calculation on both GPU and CPU using the `Eigen::half` class. It is mostly useful for Nvidia GPUs because of the overloaded arithmetic operators using cuda intrinsics. It falls back to using software emulation on CPU for calculation and there is no special treatment to ARM processors.
- [ARM compute library](https://github.com/ARM-software/ComputeLibrary) >= 17.02.01 supports NEON FP16 kernels (requires ARMv8.2-A CPU).
### CUDA version issue
There are currently three versions of CUDA that supports `__half` data type, namely, CUDA 7.5, 8.0, and 9.0.
CUDA 7.5 and 8.0 define `__half` as a simple struct that has a `uint16_t` data (see [`cuda_fp16.h`](https://github.com/ptillet/isaac/blob/9212ab5a3ddbe48f30ef373f9c1fb546804c7a8c/include/isaac/external/CUDA/cuda_fp16.h)) as follows:
```
typedef struct __align__(2) {
unsigned short x;
} __half;
typedef __half half;
```
This struct does not define any overloaded arithmetic operators. So you have to directly use `__hadd` instead of `+` to correctly add two half types:
```
__global__ void Add() {
half a, b, c;
c = __hadd(a, b); // correct
c = a + b; // compiler error: no operator "+" matches these operands
}
```
CUDA 9.0 provides a major update to the half data type. The related code can be found in the updated [`cuda_fp16.h`](https://github.com/ptillet/isaac/blob/master/include/isaac/external/CUDA/cuda_fp16.h) and the newly added [`cuda_fp16.hpp`](https://github.com/ptillet/isaac/blob/master/include/isaac/external/CUDA/cuda_fp16.hpp).
Essentially, CUDA 9.0 renames the original `__half` type in 7.5 and 8.0 as `__half_raw`, and defines a new `__half` class type that has constructors, conversion operators, and also provides overloaded arithmetic operators such as follows:
```
typedef struct __CUDA_ALIGN__(2) {
unsigned short x;
} __half_raw;
struct __CUDA_ALIGN__(2) __half {
protected:
unsigned short __x;
public:
// constructors and conversion operators from/to
// __half_raw and other built-in data types
}
typedef __half half;
__device__ __forceinline__
__half operator+(const __half &lh, const __half &rh) {
return __hadd(lh, rh);
}
// Other overloaded operators
```
This new design makes `c = a + b` work correctly for CUDA half data type.
## Implementation
The float16 class holds a 16-bit `uint16_t` data internally.
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# Design Doc: PaddlePaddle Fluid
## Why Fluid
When Baidu developed PaddlePaddle in 2013, the only well-known open source deep learning system at the time was Caffe. However, when PaddlePaddle was open-sourced in 2016, many other choices were available. There was a challenge -- what is the need for open sourcing yet another deep learning framework?
Fluid is the answer. Fluid is similar to PyTorch and TensorFlow Eager Execution, which describes the "process" of training or inference using the concept of a model. In fact in PyTorch, TensorFlow Eager Execution and Fluid, there is no concept of a model at all. The details are covered in the sections below. Fluid is currently more extreme in the above mentioned idea than PyTorch and Eager Execution, and we are trying to push Fluid towards the directions of a compiler and a new programming language for deep learning.
## The Evolution of Deep Learning Systems
Deep learning infrastructure is one of the fastest evolving technologies. Within four years, there have already been three generations of technologies invented.
| Existed since | model as sequence of layers | model as graph of operators | No model |
|--|--|--|--|
| 2013 | Caffe, Theano, Torch, PaddlePaddle | | |
| 2015 | | TensorFlow, MxNet, Caffe2, ONNX, n-graph | |
| 2016 | | | PyTorch, TensorFlow Eager Execution, PaddlePaddle Fluid |
From the above table, we see that the deep learning technology is evolving towards getting rid of the concept of a model. To understand the reasons behind this direction, a comparison of the *programming paradigms* or the ways to program deep learning applications using these systems, would be helpful. The following section goes over these.
## Deep Learning Programming Paradigms
With the systems listed as the first or second generation, e.g., Caffe or TensorFlow, an AI application training program looks like the following:
```python
x = layer.data("image")
l = layer.data("label")
f = layer.fc(x, W)
s = layer.softmax(f)
c = layer.mse(l, s)
for i in xrange(1000): # train for 1000 iterations
m = read_minibatch()
forward({input=x, data=m}, minimize=c)
backward(...)
print W # print the trained model parameters.
```
The above program includes two parts:
1. The first part describes the model, and
2. The second part describes the training process (or inference process) for the model.
This paradigm has a well-known problem that limits the productivity of programmers. If the programmer made a mistake in configuring the model, the error messages wouldn't show up until the second part is executed and `forward` and `backward` propagations are performed. This makes it difficult for the programmer to debug and locate a mistake that is located blocks away from the actual error prompt.
This problem of being hard to debug and re-iterate fast on a program is the primary reason that programmers, in general, prefer PyTorch over the older systems. Using PyTorch, we would write the above program as following:
```python
W = tensor(...)
for i in xrange(1000): # train for 1000 iterations
m = read_minibatch()
x = m["image"]
l = m["label"]
f = layer.fc(x, W)
s = layer.softmax(f)
c = layer.mse(l, s)
backward()
print W # print the trained model parameters.
```
We can see that the main difference is the moving the model configuration part (the first step) into the training loop. This change would allow the mistakes in model configuration to be reported where they actually appear in the programming block. This change also represents the model better, or its forward pass, by keeping the configuration process in the training loop.
## Describe Arbitrary Models for the Future
Describing the process instead of the model also brings Fluid, the flexibility to define different non-standard models that haven't been invented yet.
As we write out the program for the process, we can write an RNN as a loop, instead of an RNN as a layer or as an operator. A PyTorch example would look like the following:
```python
for i in xrange(1000):
m = read_minibatch()
x = m["sentence"]
for t in xrange x.len():
h[t] = the_step(x[t])
```
With Fluid, the training loop and the RNN in the above program are not really Python loops, but just a "loop structure" provided by Fluid and implemented in C++ as the following:
```python
train_loop = layers.While(cond)
with train_loop.block():
m = read_minibatch()
x = m["sentence"]
rnn = layers.While(...)
with rnn.block():
h[t] = the_step(input[t])
```
An actual Fluid example is described [here](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44).
From the example, the Fluid programs look very similar to their PyTorch equivalent programs, except that Fluid's loop structure, wrapped with Python's `with` statement, could run much faster than just a Python loop.
We have more examples of the [`if-then-else`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/if_else_op.md) structure of Fluid.
## Turing Completeness
In computability theory, a system of data-manipulation rules, such as a programming language, is said to be Turing complete if it can be used to simulate any Turing machine. For a programming language, if it provides if-then-else and loop, it is Turing complete. From the above examples, Fluid seems to be Turing complete; however, it is noteworthy to notice that there is a slight difference between the `if-then-else` of Fluid and that of a programming language. The difference being that the former runs both of its branches and splits the input mini-batch into two -- one for the True condition and another for the False condition. This hasn't been researched in depth if this is equivalent to the `if-then-else` in programming languages that makes them Turing-complete. Based on a conversation with [Yuang Yu](https://research.google.com/pubs/104812.html), it seems to be the case but this needs to be looked into in-depth.
## The Execution of a Fluid Program
There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message [`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145) that describes the process and is conceptually like an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree).
There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program.
Fluid is moving towards the direction of a compiler, which is explain in more detail later in this article.
## Backward Compatibility of Fluid
Given all the advantages from the removal of the concept of a *model*, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference. For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as [n-graph](https://github.com/NervanaSystems/ngraph). Similarly, [Movidius](https://www.movidius.com/) is producing a mobile deep learning chip that reads and runs graphs of operators. The well-known [ONNX](https://github.com/onnx/onnx) is also a file format of graphs of operators.
For Fluid, we can write a converter that extracts the parts in the `ProgramDesc` protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.
## Towards a Deep Learning Language and the Compiler
We can change the `if-then-else` and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.
Even if we do not invent a new language, as long as we get the `ProgramDesc` message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using `nvcc`. Another transpiler could generate MKL-friendly code that should be built using `icc` from Intel. More interestingly, we can translate a Fluid program into its distributed version of two `ProgramDesc` messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the [concurrent programming design](concurrent_programming.md) document would be a good pointer. The following figure explains the proposed two-stage process:
![](fluid-compiler.png)
doc/design/mkldnn/README.MD
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# Intel® MKL-DNN on PaddlePaddle: Design Doc
我们计划将Intel深度神经网络数学库(**MKL-DNN**\[[1](#references)\])集成到PaddlePaddle，充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。
我们计划将英特尔深度神经网络数学库[Intel MKL-DNN](https://github.com/01org/mkl-dnn)
(Intel Math Kernel Library for Deep Neural Networks)集成到PaddlePaddle，
充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。
我们短期内的基本目标是：
<div align="center">
<img src="image/overview.png"><br/>
Figure 1. PaddlePaddle on IA
</div>
近期目标
- 完成常用layer的MKL-DNN实现。
- 完成常用Layer的MKL-DNN实现。
- 完成常见深度神经网络VGG，GoogLeNet 和 ResNet的MKL-DNN实现。
目前的优化，主要针对PaddlePaddle在重构之前的代码框架以及V1的API。
具体的完成状态可以参见[这里](https://github.com/PaddlePaddle/Paddle/projects/21)。
## Contents
- [Overview](#overview)
- [Actions](#actions)
- [CMake](#cmake)
- [Matrix](#matrix)
- [Layers](#layers)
- [Activations](#activations)
- [Weights](#weights)
- [Parameters](#parameters)
- [Gradients](#gradients)
- [Unit Tests](#unit-tests)
- [Protobuf Messages](#protobuf-messages)
- [Python API](#python-api)
- [Demos](#demos)
- [Benchmarking](#benchmarking)
- [Others](#others)
- [Design Concerns](#design-concerns)
## Overview
我们会把MKL-DNN作为第三方库集成进PaddlePaddle，整体框架图
我们会把MKL-DNN会作为第三方库集成进PaddlePaddle，与其他第三方库一样，会在编译PaddlePaddle的时候下载并编译MKL-DNN。
同时，为了进一步提升PaddlePaddle在基本数学运算的计算速度，我们也将MKLML即(MKL small library\[[1](#references)\])
作为另一个第三方库集成进PaddlePaddle，它只会包括生成好的动态库和头文件。
MKL，MKLML以及MKL-DNN三者关系如下表：
| Name | Open Source | License | Descriptions |
| :---------- | :--------------- | :---------- | :------------ |
| MKL | No | Proprietary | Accelerate math processing routines |
| MKLML | No | Proprietary | Small package of MKL, especially for Machine Learning |
| MKL-DNN | Yes | Apache 2.0 | Accelerate primitives processing routines especially for Deep Neural Networks |
MKLML可以与MKL-DNN共同使用，以此达到最好的性能。
<div align="center">
<img src="image/overview.png" width=350><br/>
Figure 1. PaddlePaddle on IA.
<img src="image/engine.png"><br/>
Figure 2. PaddlePaddle with MKL Engines
</div>
## Actions
我们把集成方案大致分为了如下几个方面。
添加的相关文件和目录结构如下：
```txt
PaddlePaddle/Paddle
├── ...
├── cmake/
│ ├── external/
│ │ ├── ...
│ │ ├── mkldnn.cmake
│ │ └── mklml.cmake
└── paddle/
├── ...
├── math/
│ ├── ...
│ └── MKLDNNMatrix.*
└── gserver/
├── ...
├── layers/
│ ├── ...
│ └── MKLDNN*Layer.*
├── activations/
│ ├── ...
│ └── MKLDNNActivations.*
└── tests/
├── ...
├── MKLDNNTester.*
└── test_MKLDNN.cpp
```
### CMake
我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
在`CMakeLists.txt`中提供一个与MKL有关的总开关：`WITH_MKL`，它负责决定编译时是否使用MKLML和MKL-DNN
当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
- `WITH_MKLML` 控制是否使用MKLML库。
当打开`WITH_MKL`时，会自动使用MKLML库作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。
编译时会把对应的头文件和库放在`build/third_party/install/mklml/*`目录下对应的地方。
MKLML的库目前都是动态库，主要包括`libiomp5.so`和`libmklml_intel.so`。
- `WITH_MKLDNN` 控制是否使用MKL-DNN。
当开启`WITH_MKL`时，会自动根据硬件配置[[2](#references)]选择是否编译MKL-DNN。
编译时会把对应的头文件和库放在`build/third_party/install/mkldnn/*`目录下对应的地方。
MKL-DNN的库目前只有动态库`libmkldnn.so`。
当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
### Matrix
目前在PaddlePaddle中数据都是以`NCHW`的格式存储，但是在MKL-DNN中的排列方式不止这一种。
所以我们定义了一个`MKLDNNMatrix`用于管理MKL-DNN数据的不同格式以及相互之间的转换。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
<div align="center">
<img src="image/matrix.png"><br/>
Figure 3. MKLDNNMatrix
</div>
### Layers
所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
`paddle/gserver/layers`中，并且文件名都会一以*MKLDNN*开头。
所有MKL-DNN的Layers都会继承于`MKLDNNLayer`，该类继承于PaddlePaddle的基类`Layer`。
在`MKLDNNLayer`中会提供一些必要的接口和函数，并且会写好`forward`和`backward`的基本逻辑，
子类只需要使用定义好的接口，实现具体的函数功能即可。
<div align="center">
<img src="image/layers.png"><br/>
Figure 4. MKLDNNLayer
</div>
每个MKLDNNLayer都包含用于内部存储和外部存储的一系列MKLDNNMatrix：
所有MKL-DNN的layers都会继承于一个叫做`MKLDNNLayer`的父类，该父类继承于PaddlePaddle的基类`Layer`。
- 内部存储（internel memory）：`inVal_`,`inGrad_`,`outVal_`和`outGrad_`，分别代表输入数据，输入梯度，输出数据和输出梯度。
- 外部存储（external memory）：都是以ext开头，比如`extInVal_`和`extInGrad_`，它们主要是用于，
当数据格式与PaddlePaddle默认的`NCHW`格式不匹配时，转换内存的工作。
需要注意的是，PaddlePaddle的activation会直接使用`output_.value`和`output_.grad`，
所以`extOutVal_`和`extOutGrad_`必须分别与`output_.value`和`output_.grad`共享内存，
如果不需要外部存储用于转换，那么对应的内部存储也会与它们共享内存。
- 转换函数（resetXXX）： 包括`resetInValue`，`resetInGrad`，`resetOutValue`和`resetOutGrad`，
表示对输入数据，输入梯度，输出数据和输出梯度的转换。
这些函数会根据输入参数重新设置内部和外部存储，当然这两者也可以相等，即表示不需要转换。
在`MKLDNNLayer`中会提供一些必要的接口和函数，并且会写好`forward`和`backward`的基本逻辑。部分函数定义为纯虚函数，子类只需要实现这些函数即可。
注意：每个`MKLDNNlayer`的子类只需要使用内部存储就可以了，所有外部的转换工作都会在reset系列函数中都准备好。
### Activations
由于在PaddlePaddle中，激活函数是独立于layer概念的，所以会在`paddle/gserver/activations`目录下添加`MKLDNNActivation.h`和`MKLDNNActivation.cpp`文件用于定义和使用MKL-DNN的接口。
在重构前的PaddlePaddle中，激活函数是独立于`Layer`的概念，并且输入输出都是共用一块内存，
所以添加了对应的`MKLDNNActivation`来实现，方式类似于`MKLDNNLayer`。
### Parameters
对于有参数的层，我们会保证`MKLDNNLayer`使用的参数与PaddlePaddle申请的buffer共用一块内存。
如果存在数据排列格式不一样的情况时，我们会在网络训练之前把格式转换为MKL-DNN希望的格式，
在训练结束的时候再保存为PaddlePaddle的格式，但是整个训练过程中不需要任何转换。
这样既使得最终保存的参数格式与PaddlePaddle一致，又可以避免不必要的转换。
### Gradients
由于MKL-DNN的操作都是直接覆盖的形式，也就是说输出的结果不会在原来的数据上累加，
这样带来的好处就是不需要一直清空memory，节省了不必要的操作。
但是注意的是，当网络出现分支且在`backward`的时候，需要累加不同Layer传过来的梯度。
所以在`MKLDNNlayer`中实现了一个merge的方法，此时每个小分支的`Input Gradient`
会先临时保存在`MKLDNNMatrix`中，由分支处的Layer负责求和，并把结果放到当前层的`output_.grad`中。
所以整体上，在实现每个子类的时候就不需要关心分支的事情了。
### Weights
由于有些layer是含有参数的，我们会尽量让MKL-DNN的参数与PaddlePaddle中`parameter`共享一块内存。
同时，由于MKL-DNN在训练时使用的参数layout可能与PaddlePaddle默认的`nchw`不一致，我们会在网络训练的开始和结束时分别转换这个layout，使得最终保存的参数格式与PaddlePaddle一致。
<div align="center">
<img src="image/gradients.png"><br/>
Figure 5. Merge Gradients
</div>
### Unit Tests
会在`paddle/gserver/test`目录下添加`test_MKLDNN.cpp`和`MKLDNNTester.*`用于MKL-DNN的测试。
测试分为每个layer(或activation)的单元测试和简单网络的整体测试。
我们会添加`test_MKLDNN.cpp`和`MKLDNNTester.*`用于MKL-DNN的测试。
测试分为每个Layer（或Activation）的单元测试和简单网络的整体测试。
每个测试会对比PaddlePaddle中CPU算出的结果与MKL-DNN的结果，小于某个比较小的阈值认为通过。
### Protobuf Messages
根据具体layer的需求可能会在`proto/ModelConfig.proto`里面添加必要的选项。
### Python API
目前只考虑**v1 API**。
......@@ -80,41 +172,40 @@ if use_mkldnn
self.layer_type = mkldnn_*
```
所有MKL-DNN的layer type会以*mkldnn_*开头，以示区分。
并且可能在`python/paddle/trainer_config_helper`目录下的`activations.py `和`layers.py`里面添加必要的MKL-DNN的接口。
所有MKL-DNN的`layer_type`会以*mkldnn_*开头，这些会在`MKLDNN*Layer`注册layer的时候保证，以示区分。
### Demos
会在`v1_api_demo`目录下添加一个`mkldnn`的文件夹，里面放入一些用于MKL-DNN测试的demo脚本。
同时,会在`paddle/utils.Flags`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
### Benchmarking
会添加`benchmark/paddle/image/run_mkldnn.sh`，用于测试使用MKL-DNN之后的性能。
会添加相应的脚本在[这里](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/paddle/image)，用于测试和对比在使用MKL-DNN前后的CNN网络性能。
测试的性能对比结果会在[IntelOptimizedPaddle.md](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md)
### Others
1. 如果在使用MKL-DNN的情况下，会把CPU的Buffer对齐为64。
1. 如果在使用MKL-DNN的情况下，会把CPU的Buffer对齐为4096，具体可以参考MKL-DNN中的[memory](https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp#L673)。
2. 深入PaddlePaddle，寻找有没有其他可以优化的可能，进一步优化。比如可能会用OpenMP改进SGD的更新性能。
## Design Concerns
为了更好的符合PaddlePaddle的代码风格\[[2](#references)\]，同时又尽可能少的牺牲MKL-DNN的性能\[[3](#references)\]。
为了更好的符合PaddlePaddle的代码风格\[[3](#references)\]，同时又尽可能少的牺牲MKL-DNN的性能\[[4](#references)\]。
我们总结出一些特别需要注意的点：
1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MKLDNNLayer`特有的设备ID。
2. 重写父类Layer的**init**函数，修改`deviceId_`为`-2`，代表这个layer是用于跑在MKL-DNN的环境下。
3. 创建`MKLDNNMatrix`，同时继承`CpuMatrix`和`mkldnn::memory`。用于管理MKL-DNN会用到的相关memory函数、接口以及会用的到格式信息。
4. 创建`MKLDNNBase`，定义一些除了layer和memory相关的类和函数。包括MKL-DNN会用到`MKLDNNStream`和`CPUEngine`，和未来可能还会用到`FPGAEngine`等。
5. 每个`MKLDNNlayer`都会有`inVal_`,`inGrad_`,`outVal_`和`outGrad_`，分别代表input value， input gradient，output value和output gradient。他们会存放MKL-DNN用到的internal memory。同时还会定义以*ext*开头的`MKLDNNMatrix`(表示external的memory)，主要是在格式与PaddlePaddle默认的`nchw`格式不匹配时，用于转换内存的工作。必要的转换函数也会在`MKLDNNLayer`中提前定义好，每个子类只需要调用定义好的reset buffer函数即可。
6. 每个`MKLDNNlayer`的resetbuffer相关的函数（包括reset input、output的Value和grad），他们会根据输入参数reset internal和external的memory，当然这两者也可以相等，即表示不需要转换。只需要把握一个原则，每个`MKLDNNlayer`的子类，只需要使用internal的memory就可以了，所有external的转换工作在父类的reset函数中都提前准备好了。
7. 一般来说，external的memory会尽量与PaddlePaddle中的`value`和`grad`共享内存。同时每个`MKLDNNLayer`中的external output value和gradient(也就是`extOutVal_`和`extOutGrad_`)必须分别与`output_.value`和`output_.grad`共享内存，因为PaddlePaddle的activation会直接使用`output_.value`和`output_.grad`。如果不需要external的buffer用于转换，那么internal的buffer也会与他们共享内存。
8. 如果MKL-DNN layer的后面接有cpu device，那么就会使`output_.value`与`extOutVal_`共享内存，同时数据格式就是`nchw`，这样下一个cpu device就能拿到正确的数据。在有cpu device的时候，external的memory的格式始终是`nchw`或者`nc`。
9. 由于MKL-DNN的输出操作都是覆盖data的，不是在原来的数据上累加，所以当网络出现分支时，在`backward`时会需要merge不同layer的梯度。`MKLDNNlayer`中会实现merge的方法，此时每个小分支的input gradient会先临时保存在一个`MKLDNNMatrix`中，由分支处的layer负责求和，并把结果放到这个layer的`output_.grad`中。所以整体上，每个子类并不会需要关心分支的事情，也是在父类都实现好了。
10. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，
我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MKLDNNLayer`特有的设备ID。
2. 重写父类Layer的**init**函数，修改`deviceId_`为`-2`，代表这个layer是用于跑在MKL-DNN的环境下。
3. 创建`MKLDNNBase`，定义一些除了layer和memory相关的类和函数。
包括MKL-DNN会用到`MKLDNNStream`和`CPUEngine`，和未来可能还会用到`FPGAEngine`等。
4. 如果MKL-DNN layer的后面接有cpu device，那么就会使`output_.value`与`extOutVal_`共享内存，
同时数据格式就是`NCHW`，这样下一个cpu device就能拿到正确的数据。
在有普通的CPU layer时， `extOutVal_`和`extOutGrad_`的格式始终是`NCHW`或者`NC`。
## References
1. [Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN)](https://github.com/01org/mkl-dnn "Intel MKL-DNN")
2. [原来的方案](https://github.com/PaddlePaddle/Paddle/pull/3096)会引入**nextLayer**的信息。但是在PaddlePaddle中，无论是重构前的layer还是重构后的op，都不会想要知道next layer/op的信息。
3. MKL-DNN的高性能格式与PaddlePaddle原有的`NCHW`不同(PaddlePaddle中的CUDNN部分使用的也是`NCHW`，所以不存在这个问题)，所以需要引入一个转换方法，并且只需要在必要的时候转换这种格式，才能更好的发挥MKL-DNN的性能。
1. [MKL small library](https://github.com/01org/mkl-dnn#linking-your-application)是[Intel MKL](https://software.intel.com/en-us/mkl)的一个子集。
主要包括了深度学习相关的数学原语与操作，一般由MKL-DNN在发布[新版本](https://github.com/01org/mkl-dnn/releases)时一起更新。
2. [MKL-DNN System Requirements](https://github.com/01org/mkl-dnn#system-requirements)。
目前在PaddlePaddle中，仅会在支持AVX2指令集及以上的机器才使用MKL-DNN。
3. [原来的方案](https://github.com/PaddlePaddle/Paddle/pull/3096)会引入**nextLayer**的信息。
但是在PaddlePaddle中，无论是重构前的layer还是重构后的op，都不会想要知道next layer/op的信息。
4. MKL-DNN的高性能格式与PaddlePaddle原有的`NCHW`不同(PaddlePaddle中的cuDNN部分使用的也是`NCHW`，所以不存在这个问题)。
所以需要引入一个转换方法，并且只需要在必要的时候转换这种格式，才能更好的发挥MKL-DNN的性能。
doc/design/refactor/distributed_architecture.md
浏览文件 @ 28630dd8
......@@ -2,106 +2,70 @@
## Abstract
PaddlePaddle v0.10.0 uses the "trainer-parameter server"
architecture. We run multiple replicated instances of trainers (runs
the same code written by the user) and parameter servers for
distributed training. This architecture served us well, but has some
limitations:
PaddlePaddle version 0.10.0 uses the "trainer-parameter server" architecture. We run multiple instances of trainers (where each trainer runs the same model) and parameter servers for distributed training. This architecture serves well, but has few limitations:
1. Need to write special code to handle tasks which should only be run
by a single trainer. E.g., initializing model and saving model.
1. There is a need to write special code that handles tasks which should only be run on a single trainer. E.g., initializing the model, saving the model etc.
2. Model parallelism is hard: need to write if-else branches conditioned
on the trainer ID to partition model onto each trainer, and manually
write the inter-model-shard communication code.
2. Model parallelism is hard: It would need all the if-else branches conditioned on the trainer ID to partition the model onto the trainers, and eventually manually writing out the inter-model-shard communication code to communicate between different trainers.
3. The user can not directly specify the parameter update rule: need
to modify the parameter server C++ code and compile a new
binary. This adds complication for researchers: A lot of extra
effort is required. Besides, the training job submission program
may not allow running arbitrary binaries.
3. The user can not directly specify the parameter update rule: This would need to modify the parameter server code and compile a new binary. This makes things more complicated for researchers: A lot of extra effort is required to make this work. Besides, the training job submission program may not allow running arbitrary binaries.
This design doc discusses PaddlePaddle's new distributed training
architecture that addresses the above limitations.
This design doc discusses PaddlePaddle's new distributed training architecture that addresses the above mentioned limitations.
## Analysis
We will assume the user writes the trainer program by Python, the same
analysis holds if the trainer program is written in C++.
The assumption is that the user writes the trainer program in either Python or C++.
### Limitation 1
If we look at the Python code that the user writes, there are two
kinds of functionalities:
There are two basic functionalities in the trainer program:
- The training logic such as load / save model and print log.
- The neural network definition such as the definition of the data
layer, the fully connected layer, the cost function and the
1. The training logic such as loading / saving the model and printing out the logs.
2. The neural network definition such as the definition of the data layer, the fully connected layer, the cost function and the
optimizer.
When we training with PaddlePaddle v0.10.0 distributedly, multiple
replicated Python instances are running on different nodes: both the
training logic and the neural network computation is replicated.
When we train using PaddlePaddle v0.10.0 in a distributed fashion, multiple instances of the same Python code are run on different nodes, hence both: the
training logic as well as the neural network computation logic, is replicated.
The tasks that should only run once all belong to the training logic,
if we only replicate the neural network computation, but do **not**
replicate the training logic, the limitation could be solved.
The tasks that only need to be run once belong to the training logic. Hence if we only replicate the neural network computation part, and do **not**
replicate the training logic, the limitation mentioned above can be avoided.
### Limitation 2
Model parallelism means running a single model on multiple nodes by
partitioning the model onto different nodes and managing the
inter-model-shard communications.
Model parallelism means that a single model is partitioned into different components and each node runs one of the component separately. This comes at the extra cost of managing the
inter-model-shard communication between nodes.
PaddlePaddle should be able to modify the nerual network computation
definition to support model parallelism automatically. However, the
computation is only specified in Python code, and PaddlePaddle can not
modify Python code.
PaddlePaddle should ideally be able to modify the neural network computation and figure out the support for model parallelism automatically. However, the
computation is only specified in Python code which sits outside of PaddlePaddle, hence PaddlePaddle can not support the feature in this setup.
Just like compiler uses a intermediate representation (IR) so that
programmer does not need to manually optimize their code in most of
the cases - the compiler will optimize the IR:
Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows:
<img src="src/compiler.png"/>
We can have our own IR too: PaddlePaddle can support model parallel by
converting the IR so the user no longer need to manually do it in
Python:
PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component:
<img src="src/paddle-compile.png"/>
The IR for PaddlePaddle after refactor is called `Block`, it specifies
the computation dependency graph and the variables used in the
computation.
The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation.
### Limitation 3
The user can not directly specify the parameter update rule for the
parameter server because the parameter server does not use the same
computation definition as the trainer. Instead, the update rule is
baked in the parameter server. The user can not specify the update
rule in the same way of specifying the trainer computation.
The user can not directly specify the parameter update rule for the parameter server in the Python module, since the parameter server does not use the same computation definition as the trainer. Instead, the update rule is baked inside the parameter server. The user can not specify the update rule explicitly.
This could be fixed by making the parameter server run the same
computation definition as the trainer. For a detailed explanation,
please
see
[Design Doc: Operation Graph Based Parameter Server](./dist_train.md)
This could be fixed by making the parameter server run the same computation definition as the trainer (the user's Python module). For a detailed explanation, refer to this document -
[Design Doc: Operation Graph Based Parameter Server](./parameter_server.md)
## Distributed Training Architecture
The new distributed training architecture can address the above
limitations. Below is the illustration:
The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:
<img src="src/distributed_architecture.png"/>
The architecture includes major components: *PaddlePaddle Python*,
*PaddlePaddle converter* and *PaddlePaddle runtime*:
The major components in the architecture are: *PaddlePaddle Python*, *PaddlePaddle converter* and *PaddlePaddle runtime*.
### PaddlePaddle Python
PaddlePaddle Python is the Python library that user's Python trainer
invoke to build the neural network topology, start training, etc.
PaddlePaddle Python is the Python library that user's Python code invokes, to read the data. build the neural network topology, start training, etc.
```Python
paddle.init()
......@@ -117,102 +81,60 @@ for i in range(1000):
print cost_val
```
The code above is a typical Python trainer code, the neural network
topology is built using helper functions such as
`paddle.layer.fc`. The training is done by calling `session.eval`
iteratively.
The above code is what a typical Python trainer code is, the neural network topology is built using the helper functions such as `paddle.layer.fc`. Training is done by calling `session.eval` iteratively.
#### session.eval
As shown in the graph, `session.eval` sends the IR and the evaluation
inputs/targets to the PaddlePaddle cluster for evaluation. The
targets can be any variable in the computation graph. When the target
is the `optimizer` variable, the neural network will be optimized
once. When the target is the `cost` variable, `session.eval` returns
the cost value.
As shown in the graph, `session.eval` sends the IR and the evaluation inputs or targets to the PaddlePaddle cluster for evaluation.
The targets can be any variable in the computation graph. When the target is say, the `optimizer` variable, the neural network will be optimized once. When the target is the `cost` variable, `session.eval` returns the cost value. Based on what the target is, an appropriate action is taken.
The Python `session` is a wrapper of the C++ `Session` class. For more
information about `Session`, please
see [Design Doc: Session](./session.md).
The Python `session` is a wrapper of the C++ `Session` class. For more information about `Session`, refer to this document - [Design Doc: Session](./session.md).
### PaddlePaddle Converter
PaddlePaddle converter automatically converts the IR in the request
(IR and evaluation inputs/targets) from PaddlePaddle Python to new
partitioned IRs and dispatch the new IRs and evaluation inputs/targets
to different PaddlePaddle runtimes. Below are the steps:
The PaddlePaddle converter automatically converts the IR in the request (IR and evaluation inputs/targets) from PaddlePaddle Python to partitioned IRs and dispatches the new IRs and evaluation inputs/targets to different PaddlePaddle runtimes. Below are the steps that are followed :
1. Add `feed` OP that feeds the eval inputs, and `fetch` OP that
fetches the eval targets to the IR.
1. Add a `feed` OP that feeds the eval inputs, and a `fetch` OP that fetches the eval targets to the IR.
1. Extract a new computation (sub)graph with `feed` and `fetch` OP as
the boundary. The runtime does not need to run the OP that is not
dependent by the `fetch` OP.
2. Extract a new computation (sub)graph with the `feed` and `fetch` OPs as the boundary. The runtime does not need to run the OP that is not dependent on the `fetch` OP.
1. Optimizes the computation graph.
3. Optimize the computation graph.
1. Place the OPs in the graph onto different devices on different
PaddlePaddle runtime according to a placement algorithm and device
constraint specified by the user.
4. Place the OPs in the graph onto different devices on different PaddlePaddle runtime according to a placement algorithm and the device constraints specified by the user.
1. Partition the graph according to runtime boundaries and add `send` /
`recv` OP pair on the runtime boundaries.
5. Partition the graph according to runtime boundaries and add `send` / `recv` OP pair on the runtime boundaries.
1. Dispatch the partitioned graph to different PaddlePaddle runtimes.
6. Dispatch the partitioned graph to different PaddlePaddle runtimes.
7. PaddlePaddle runtimes with the `fetch` OP reports evaluation results back to the converter, the converter reports the evaluation results back to the PaddlePaddle Python.
1. PaddlePaddle runtimes with the `fetch` OP reports evaluation
results back to the converter, the convert reports the evaluation
results back to the PaddlePaddle Python.
The output IRs will be cached to optimize the conversion latency.
#### Placement Algorithm
Our first implementation will only support "trainer-parameter server"
placement: the parameters, initializers, and optimizers are placed on
the PaddlePaddle runtimes with the parameter server role. And
everything else will be placed on the PaddlePaddle runtimes with the
trainer role. This has the same functionality of our
"trainer-parameter server" architecture of PaddlePaddle v0.10.0, but
is more general and flexible.
Our first implementation will only support "trainer-parameter server" placement: the parameters, initializers, and optimizers are all placed on the PaddlePaddle runtimes with the parameter server role. Everything else will be placed on the PaddlePaddle runtimes with the trainer role. This has the same functionality as the "trainer-parameter server" architecture of PaddlePaddle v0.10.0, but is more generic and flexible.
In the future, we will implement the general placement algorithm,
which makes placements according to the input IR, and a model of
device computation time and device communication time. Model
parallelism requires the general placement algorithm.
In the future, a more general placement algorithm should be implemented, which makes placements according to the input IR, and a model of device computation time and device communication time. Model parallelism requires the generic placement algorithm.
### PaddlePaddle Runtime
The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and
runs the IR. The runtime does not need to do OP placement since it's
already done by the converter.
The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and runs the IR. The runtime does not need to do OP placement since it is already done by the converter.
### Local Training Architecture
The local training architecture will be the same as the distributed
training architecture, the differences are everything runs locally,
and there is just one PaddlePaddle runtime:
The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:
<img src="src/local_architecture.png"/>
### Training Data
In PaddlePaddle v0.10.0, training data is typically read
with [data reader](../reader/README.md) from Python. This approach is
no longer efficient when training distributedly since the Python
process no longer runs on the same node with the trainer processes,
the Python reader will need to read from the distributed filesystem
(assuming it has the access) and send to the trainers, doubling the
network traffic.
When doing distributed training, the user can still use Python data
reader: the training data are sent with `session.eval`. However should
be used for debugging purpose only. The users are encouraged to use
the read data OPs.
In PaddlePaddle v0.10.0, training data is typically read with a [data reader](../reader/README.md) from Python. This approach is no longer efficient when training in a distributed fashion since the Python process no longer runs on the same node with the trainer processes. The Python reader will need to read from the distributed filesystem (assuming it has the required access) and send to the trainers, doubling the network traffic.
When doing distributed training, the user can still use Python data reader: the training data are sent with `session.eval`. However this should be used for debugging purpose only. The users are encouraged to use the read data OPs.
## References:
......
doc/design/releasing_process.md
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......@@ -5,8 +5,9 @@ PaddlePaddle使用git-flow branching model做分支管理，使用[Semantic Vers
PaddlePaddle每次发新的版本，遵循以下流程:
1. 从`develop`分支派生出新的分支，分支名为`release/版本号`。例如，`release/0.10.0`
2. 将新分支的版本打上tag，tag为`版本号rc.Patch号`。第一个tag为`0.10.0rc1`，第二个为`0.10.0rc2`，依次类推。
3. 对这个版本的提交，做如下几个操作:
1. 将新分支的版本打上tag，tag为`版本号rc.Patch号`。第一个tag为`0.10.0rc1`，第二个为`0.10.0rc2`，依次类推。
1. 对这个版本的提交，做如下几个操作:
* 修改`python/setup.py.in`中的版本信息,并将`istaged`字段设为`True`。
* 编译这个版本的Docker发行镜像，发布到dockerhub。如果失败，修复Docker编译镜像问题，Patch号加一，返回第二步
* 编译这个版本的Ubuntu Deb包。如果失败，修复Ubuntu Deb包编译问题，Patch号加一，返回第二步。
* 使用Regression Test List作为检查列表，测试Docker镜像/ubuntu安装包的功能正确性
......@@ -20,9 +21,9 @@ PaddlePaddle每次发新的版本，遵循以下流程:
pip install twine
twine upload dist/[package to upload]
```
4. 第三步完成后，将`release/版本号`分支合入master分支，并删除`release/版本号`分支。将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。最后删除`release/版本号`分支。
5. 编译master分支的Docker发行镜像，发布到dockerhub。编译ubuntu的deb包，发布到github release页面
6. 协同完成Release Note的书写
1. 第三步完成后，将`release/版本号`分支合入master分支，并删除`release/版本号`分支。将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。最后删除`release/版本号`分支。
1. 编译master分支的Docker发行镜像，发布到dockerhub。编译ubuntu的deb包，发布到github release页面
1. 协同完成Release Note的书写
需要注意的是:
......@@ -30,7 +31,7 @@ PaddlePaddle每次发新的版本，遵循以下流程:
* `release/版本号`分支一旦建立，一般不允许再从`develop`分支合入`release/版本号`。这样保证`release/版本号`分支功能的封闭，方便测试人员测试PaddlePaddle的行为。
* 在`release/版本号`分支存在的时候，如果有bugfix的行为，需要将bugfix的分支同时merge到`master`, `develop`和`release/版本号`这三个分支。
# PaddlePaddle 分支规范
## PaddlePaddle 分支规范
PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，并适应github的特性做了一些区别。
......@@ -47,11 +48,11 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-
* BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的`master`、`develop`与可能有的`release/版本号`分支，同时提起`Pull Request`。
# PaddlePaddle回归测试列表
## PaddlePaddle回归测试列表
本列表说明PaddlePaddle发版之前需要测试的功能点。
## PaddlePaddle Book中所有章节
### PaddlePaddle Book中所有章节
PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
......
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# Design Doc: Supporting new Device/Library
## Background
Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries flexibly and efficiently.
On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example,Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
On the other hand, users usually do not want to care about the low-level hardware and computing libraries when writing a neural network configuration. In Fluid, `Layer` is exposed in `Python`, and `Operator` is exposed in `C++`. Both `Layer` and `Operator` are hardware independent.
So, how to support a new Device/Library in Fluid becomes a challenge.
## Basic: Integrate A New Device/Library
For a general overview of fluid, please refer to the [overview doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/read_source.md).
There are mainly three parts that we have to consider while integrating a new device/library:
- Place and DeviceContext: indicates the device id and manages hardware resources
- Memory and Tensor: malloc/free data on certain device
- Math Functor and OpKernel: implement computing unit on certain devices/libraries
### Place and DeviceContext
#### Place
Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent different devices and computing libraries. There are inheritance relationships between different kinds of `Place`.
```
| CPUPlace --> MKLDNNPlace
Place --| CUDAPlace --> CUDNNPlace
| FPGAPlace
```
And `Place` is defined as follows:
```
typedef boost::variant<CUDAPlace, CPUPlace, FPGAPlace> Place;
```
#### DeviceContext
Fluid uses class [DeviceContext](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L30) to manage the resources in different hardwares, such as CUDA stream in `CDUADeviceContext`. There are also inheritance relationships between different kinds of `DeviceContext`.
```
/-> CPUDeviceContext --> MKLDeviceContext
DeviceContext ----> CUDADeviceContext --> CUDNNDeviceContext
\-> FPGADeviceContext
```
An example of Nvidia GPU is as follows:
- DeviceContext
```
class DeviceContext {
virtual Place GetPlace() const = 0;
};
```
- CUDADeviceContext
```
class CUDADeviceContext : public DeviceContext {
Place GetPlace() const override { return place_; }
private:
CUDAPlace place_;
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::unique_ptr<Eigen::GpuDevice> eigen_device_; // binds with stream_
};
```
- CUDNNDeviceContext
```
class CUDNNDeviceContext : public CUDADeviceContext {
private:
cudnnHandle_t cudnn_handle_;
};
```
### Memory and Tensor
#### memory module
Fluid provides the following [memory interfaces](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/memory/memory.h#L36):
```
template <typename Place>
void* Alloc(Place place, size_t size);
template <typename Place>
void Free(Place place, void* ptr);
template <typename Place>
size_t Used(Place place);
```
To implementing these interfaces, we have to implement MemoryAllocator for different Devices
#### Tensor
[Tensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/tensor.h#L36) holds data with some shape in a specific Place.
```cpp
class Tensor {
public:
/*! Return a pointer to mutable memory block. */
template <typename T>
inline T* data();
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place);
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place);
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
private:
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/*! points to dimensions of memory block. */
DDim dim_;
};
```
`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configure its shape, and then call `mutuable_data` to allocate the actual memory.
```cpp
paddle::framework::Tensor t;
paddle::platform::CPUPlace place;
// set size first
t.Resize({2, 3});
// allocate memory on CPU later
t.mutable_data(place);
```
### Math Functor and OpKernel
Fluid implements computing units based on different DeviceContexts. Some computing units are shared between operators. This common part will be put in operators/math directory as basic Functors.
Let's take [MaxOutFunctor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/math/maxouting.h#L27) as an example:
The interface is defined in header file.
```
template <typename DeviceContext, typename T>
class MaxOutFunctor {
public:
void operator()(const DeviceContext& context, const framework::Tensor& input,
framework::Tensor* output, int groups);
};
```
CPU implemention is in .cc file
```
template <typename T>
class MaxOutFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, framework::Tensor* output,
int groups) {
...
}
};
```
CUDA implemention is in .cu file
```
template <typename T>
class MaxOutFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, framework::Tensor* output,
int groups) {
...
}
};
```
We get computing handle from a concrete DeviceContext, and make compution on tensors.
The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
Fluid provides different register interfaces in op_registry.h
Let's take [Crop](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/crop_op.cc#L134) operator as an example:
In .cc file:
```
REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_CPU_KERNEL(
crop_grad, ops::CropGradKernel<paddle::platform::CPUDeviceContext, float>);
```
In .cu file:
```
REGISTER_OP_CUDA_KERNEL(crop, ops::CropKernel<float>);
REGISTER_OP_CUDA_KERNEL(
crop_grad, ops::CropGradKernel<paddle::platform::CUDADeviceContext, float>);
```
## Advanced topics: How to switch between different Device/Library
Generally, we will impelement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not sutibale on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run at GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
We will discuss how to implement an efficient OpKernel switch policy.
- TBD
doc/faq/build_and_install/index_cn.rst
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......@@ -14,7 +14,7 @@
$ export CUDA_SO="$(\ls usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
$ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
$ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddlepaddle:latest-gpu
$ docker run ${CUDA_SO} ${DEVICES} -it paddlepaddle/paddle:latest-gpu
更多关于Docker的安装与使用, 请参考 `PaddlePaddle Docker 文档 <http://www.paddlepaddle.org/doc_cn/build_and_install/install/docker_install.html>`_ 。
......
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从源码编译PaddlePaddle
从源码编译
======================
.. _build_step:
......@@ -7,8 +7,11 @@
----------------
PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译工具。
我们推荐您使用PaddlePaddle编译环境镜像完成编译，这样可以免去单独安装编译依赖的步骤，可选的不同编译环境
我们推荐您使用PaddlePaddle Docker编译环境镜像完成编译，这样可以免去单独安装编译依赖的步骤，可选的不同编译环境Docker镜像
可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到。
如果您选择不使用Docker镜像，则需要在本机安装下面章节列出的 `编译依赖`_ 之后才能开始编译的步骤。
编译PaddlePaddle，需要执行：
.. code-block:: bash
......@@ -16,22 +19,47 @@ PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# 如果使用Docker编译环境，执行下面的命令编译CPU-Only的二进制
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
# 如果不使用Docker编译环境，执行下面的命令
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
编译完成后会在build/python/dist目录下生成输出的whl包，可以选在在当前机器安装也可以拷贝到目标机器安装：
.. code-block:: bash
pip install python/dist/*.whl
pip install build/python/dist/*.whl
.. _build_step:
.. _run_test:
执行单元测试
----------------
如果您期望在编译完成后立即执行所有的单元测试，可以按照下面的方法：
使用Docker的情况下，设置 :code:`RUN_TEST=ON` 和 :code:`WITH_TESTING=ON` 就会在完成编译之后，立即执行单元测试。
开启 :code:`WITH_GPU=ON` 可以指定同时执行GPU上的单元测试。
.. code-block:: bash
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
如果不使用Docker，可以执行ctest命令即可：
.. code-block:: bash
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
ctest
# 指定执行其中一个单元测试 test_mul_op
ctest -R test_mul_op
.. _compile_deps:
编译依赖
----------------
......
doc/getstarted/build_and_install/build_from_source_en.rst
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Build PaddlePaddle from Sources
Build from Sources
==========================
.. _build_step:
......@@ -9,15 +9,19 @@ How To Build
PaddlePaddle mainly uses `CMake <https://cmake.org>`_ and GCC, G++ as compile
tools. We recommend you to use our pre-built Docker image to run the build
to avoid installing dependencies by yourself. We have several build environment
Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_.
Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ .
If you choose not to use Docker image for your build, you need to install the
below `Compile Dependencies`_ before run the build.
Then run:
.. code-block:: bash
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# run the following command to build CPU-Only binaries if you are using docker
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh
# run the following command to build a CPU-Only binaries if you are using docker
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
# else run these commands
mkdir build
cd build
......@@ -30,9 +34,37 @@ machine or copy it to the target machine.
.. code-block:: bash
pip install python/dist/*.whl
pip install build/python/dist/*.whl
.. _build_step:
.. _run_test:
Run Tests
----------------
If you wish to run the tests, you may follow the below steps:
When using Docker, set :code:`RUN_TEST=ON` and :code:`WITH_TESTING=ON` will run test immediately after the build.
Set :code:`WITH_GPU=ON` Can also run tests on GPU.
.. code-block:: bash
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/paddle/scripts/docker/build.sh
If you don't use Docker, just run ctest will start the tests:
.. code-block:: bash
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=ON ..
make
ctest
# run a single test like test_mul_op
ctest -R test_mul_op
.. _compile_deps:
Compile Dependencies
----------------
......@@ -85,7 +117,7 @@ You can add :code:`-D` argument to pass such options, like:
"WITH_PYTHON", "Build with integrated Python interpreter", "ON"
"WITH_STYLE_CHECK", "Check code style when building", "ON"
"WITH_TESTING", "Build unit tests", "ON"
"WITH_DOC", "Build documentaions", "OFF"
"WITH_DOC", "Build documentations", "OFF"
"WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto"
"WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON"
"WITH_MKL", "Use MKL as BLAS library, else use OpenBLAS", "ON"
......
doc/getstarted/build_and_install/docker_install_cn.rst
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使用Docker安装运行PaddlePaddle
使用Docker安装运行
================================
使用Docker安装和运行PaddlePaddle可以无需考虑依赖环境即可运行。并且也可以在Windows的docker中运行。
......@@ -114,7 +114,7 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
.. code-block:: bash
nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash
nvidia-docker run -it -v $PWD:/work paddlepaddle/paddle:latest-gpu /bin/bash
**注: 如果没有安装nvidia-docker，可以尝试以下的方法，将CUDA库和Linux设备挂载到Docker容器内：**
......@@ -122,7 +122,7 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu
docker run ${CUDA_SO} ${DEVICES} -it paddlepaddle/paddle:latest-gpu
**关于AVX：**
......
doc/getstarted/build_and_install/docker_install_en.rst
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PaddlePaddle in Docker Containers
Run in Docker Containers
=================================
Run PaddlePaddle in Docker container so that you don't need to care about
......@@ -122,7 +122,7 @@ GPU driver installed before move on.
.. code-block:: bash
nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash
nvidia-docker run -it -v $PWD:/work paddlepaddle/paddle:latest-gpu /bin/bash
**NOTE: If you don't have nvidia-docker installed, try the following method to mount CUDA libs and devices into the container.**
......@@ -130,7 +130,7 @@ GPU driver installed before move on.
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu
docker run ${CUDA_SO} ${DEVICES} -it paddlepaddle/paddle:latest-gpu
**About AVX:**
......
doc/getstarted/build_and_install/index_cn.rst
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......@@ -13,7 +13,7 @@ PaddlePaddle提供pip和Docker的安装方式：
pip_install_cn.rst
docker_install_cn.rst
../../howto/dev/build_cn.md
编译流程
++++++++
......
doc/getstarted/build_and_install/index_en.rst
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......@@ -13,6 +13,7 @@ You can choose either pip or Docker to complete your install:
pip_install_en.rst
docker_install_en.rst
../../howto/dev/build_en.md
Build from Source
......
doc/getstarted/build_and_install/pip_install_cn.rst
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使用pip安装PaddlePaddle
使用pip安装
================================
PaddlePaddle可以使用常用的Python包管理工具
......@@ -34,7 +34,7 @@ PaddlePaddle可以使用常用的Python包管理工具
:align: center
.. csv-table:: 各个版本最新的whl包
:header: "版本说明", "cp27-cp27mu", "cp27-cp27mu", "C-API"
:header: "版本说明", "cp27-cp27mu", "cp27-cp27m", "C-API"
:widths: 1, 3, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
......@@ -83,4 +83,4 @@ PaddlePaddle发布的安装包会尽量对齐 `manylinux1 <https://www.python.or
获取当前系统支持的安装包格式，并检查和需安装的包是否匹配。pypi安装包可以在 `这个 <https://pypi.python.org/pypi/paddlepaddle/0.10.5>`_ 链接中找到。
如果系统支持的是 linux_x86_64 而安装包是 manylinux1_x86_64 ，需要升级pip版本到最新； 如果系统支持 manylinux1_x86_64 而安装包（本地）是 linux_x86_64 ，可以重命名这个whl包为 manylinux1_x86_64 再安装。
\ No newline at end of file
如果系统支持的是 linux_x86_64 而安装包是 manylinux1_x86_64 ，需要升级pip版本到最新； 如果系统支持 manylinux1_x86_64 而安装包（本地）是 linux_x86_64 ，可以重命名这个whl包为 manylinux1_x86_64 再安装。
doc/getstarted/build_and_install/pip_install_en.rst
浏览文件 @ 28630dd8
Install PaddlePaddle Using pip
Install Using pip
================================
You can use current widely used Python package management
......@@ -37,7 +37,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
:align: center
.. csv-table:: whl package of each version
:header: "version", "cp27-cp27mu", "cp27-cp27mu", "C-API"
:header: "version", "cp27-cp27mu", "cp27-cp27m", "C-API"
:widths: 1, 3, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
......
doc/howto/dev/build_cn.md
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# 编译PaddlePaddle和运行单元测试
# 用Docker编译和测试PaddlePaddle
## 需要的软硬件
......
doc/howto/dev/build_en.md
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# Build PaddlePaddle from Source Code and Run Unit Test
# Build using Docker
## What Developers Need
......
doc/howto/dev/contribute_to_paddle_cn.md
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......@@ -76,18 +76,18 @@ no changes added to commit (use "git add" and/or "git commit -a")
## 构建和测试
编译 PaddlePaddle 的源码以及生成文档需要多种开发工具。为了方便大家，我们的标准开发流程是把这些工具都装进一个Docker image，称为*开发镜像*，通常名字是 `paddle:dev`。然后所有用 `cmake && make` 的地方（比如IDE配置里）都用 `docker run paddle:dev`来代替。
编译 PaddlePaddle 的源码以及生成文档需要多种开发工具。为了方便大家，我们的标准开发流程是把这些工具都装进一个Docker image，称为*开发镜像*，通常名字是 `paddle:latest-dev` 或者 `paddle:[version tag]-dev` 如 `paddle:0.11.0-dev`。然后所有用 `cmake && make` 的地方（比如IDE配置里）都用 `docker run paddle:latest-dev`来代替。
如要build这个开发镜像，在源码目录树的根目录中运行：
```bash
➜ docker build -t paddle:dev .
➜ docker build -t paddle:latest-dev .
```
随后可以用这个开发镜像开始build PaddlePaddle的源码。比如如果要build一个不依赖GPU，但是支持AVX指令集，并且包括unit tests的PaddlePaddle，可以：
```bash
➜ docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TEST=ON" paddle:dev
➜ docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "WITH_TESTING=ON" paddle:latest-dev
```
这个过程除了编译PaddlePaddle为 `./build/libpaddle.so`，并且输出一个 `./build/paddle.deb`文件之外，还会输出一个 `build/Dockerfile`。我们只需要运行下面命令把编译好的PaddlePaddle打包成一个*生产镜像*（`paddle:prod`）：
......@@ -99,7 +99,7 @@ no changes added to commit (use "git add" and/or "git commit -a")
如果要运行所有的单元测试，可以用如下命令：
```bash
➜ docker run -it -v $(pwd):/paddle paddle:dev bash -c "cd /paddle/build && ctest"
➜ docker run -it -v $(pwd):/paddle paddle:latest-dev bash -c "cd /paddle/build && ctest"
```
关于构建和测试的更多信息，请参见[这篇文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
......
doc/howto/dev/new_op_cn.md
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# 如何写新的Operator
- [概念简介](#概念简介)
- [实现C++类](#实现C++类)
- [定义ProtoMaker类](#定义ProtoMaker类)
- [定义Operator类](#定义Operator类)
- [定义OpKernel类](#定义OpKernel类)
- [注册Operator](#注册Operator)
- [实现C++类](#实现c类)
- [定义ProtoMaker类](#定义protomaker类)
- [定义Operator类](#定义operator类)
- [定义OpKernel类](#定义opkernel类)
- [注册Operator](#注册operator)
- [编译](#编译)
- [绑定Python](#绑定Python)
- [绑定Python](#绑定python)
- [实现单元测试](#实现单元测试)
- [前向Operator单测](#前向Operator单测)
- [反向Operator单测](#反向Operator单测)
- [前向Operator单测](#前向operator单测)
- [反向Operator单测](#反向operator单测)
- [编译和执行](#编译和执行)
- [注意事项](#注意事项)
## 概念简介
......@@ -30,8 +31,8 @@
-------------- | :----------------------
OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake
Op定义 | `.cc`文件
Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，GPU 实现在`.cu`文件中。
注册Op | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，GPU实现在`.cu`文件中
Kernel实现 | CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 实现在`.cu`文件中。
注册Op | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实现在`.cu`文件中
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。**系统会根据文件名自动构建op和其对应的Python扩展。**
......@@ -43,7 +44,7 @@ Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU
## 实现C++类
### 1. 定义ProtoMaker类
### 定义ProtoMaker类
矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。
......@@ -100,7 +101,7 @@ The equation is: Out = scale*X
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
### 2. 定义Operator类
### 定义Operator类
下面的点实现了MulOp的定义：
......@@ -149,11 +150,11 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和下面将要介绍的注册函数一起放在`.cc`中
### 3. 定义OpKernel类
### 定义OpKernel类
`MulKernel`继承自`framework::OpKernel`，带有下面两个模板参数:
- `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
- `typename DeviceContext`: 表示设备类型，不同设备(CPU、CUDA)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
- `typename T` : 表示数据类型，如`float`, `double`等。
......@@ -165,7 +166,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
下面是 `MulKernel` `Compute`的实现：
```cpp
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -173,33 +174,32 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
auto* Y = context.Input<Tensor>("Y");
auto* Z = context.Output<Tensor>("Out");
Z->mutable_data<T>(context.GetPlace());
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
auto& device_context = context.template device_context<DeviceContext>();
math::matmul<DeviceContext, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
};
```
需要注意：**不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
需要注意：**不同设备(CPU、CUDA)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
`MulOp`的CPU、GPU实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
`MulOp`的CPU、CUDA实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
为了使`OpKernel`的计算过程书写更加简单，并且CPU、GPU的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。
为了使`OpKernel`的计算过程书写更加简单，并且CPU、CUDA的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。
到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。
反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。
### 4. 注册Operator
### 注册Operator
- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
```cpp
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);
```
在上面的代码中：
......@@ -209,20 +209,20 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulGradKernel`类。
- 在 `.cu`文件中注册GPU Kernel。
- 请注意，如果GPU Kernel的实现基于Eigen unsupported模块，那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`，代码示例如下：
- 在 `.cu`文件中注册CUDA Kernel。
- 请注意，如果CUDA Kernel的实现基于Eigen unsupported模块，那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`，代码示例如下：
```cpp
// if use Eigen unsupported module before include head files
// #define EIGEN_USE_GPU
#define EIGEN_USE_GPU
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. 编译
### 编译
运行下面命令可以进行编译：
......@@ -236,71 +236,57 @@ make mul_op
## 实现单元测试
单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
单测包括对比前向Op不同设备(CPU、CUDA)的实现、对比反向OP不同设备(CPU、CUDA)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
### 前向Operator单元测试
### 前向Operator单测
前向Op单元测试继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`。各项更加具体的单元测试在`OpTestMeta`里完成。测试前向Operator，需要：
Op单元测试继承自`OpTest`。各项更加具体的单元测试在`TestMulOp`里完成。测试Operator，需要：
1. 在`setUp`函数定义输入、输出，以及相关的属性参数。
2. 生成随机的输入数据。
3. 在Python脚本中实现与前向operator相同的计算逻辑，得到输出值，与operator前向计算的输出进行对比。
4. 反向计算已经自动集成进测试框架，直接调用相应接口即可。
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
from op_test import OpTest
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
class TestMulOp(OpTest):
def setUp(self):
self.type = "mul"
self.op_type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
```
上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释：
- `self.type = "mul" ` : 定义类型，与operator注册时注册的类型一致。
- `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。
- `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。
def test_check_output(self):
self.check_output()
### 反向Operator单元测试
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
反向Op单元测试继承自`GradientChecker`，而`GradientChecker`继承自`unittest.TestCase`，因此，**反向单元测试函数需要以`test_`开头**。
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
```python
class TestMulGradOp(GradientChecker):
def setUp(self):
self.op = create_op("mul")
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释：
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
- `self.op_type = "mul" ` : 定义类型，与operator注册时注册的类型一致。
- `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。
- `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。
下面解释代码中一些关键的地方:
### 反向operator单测
- 调用`create_op("mul")`创建反向Op对应的前向Op。
而反向测试中：
- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。
......@@ -308,7 +294,7 @@ class TestMulGradOp(GradientChecker):
- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。
### 编译和执行单元测试
### 编译和执行
`python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译。
......@@ -328,5 +314,5 @@ ctest -R test_mul_op
- 为每个Op创建单独的`*_op.h`（如有）、`*_op.cc`和`*_op.cu`（如有）。不允许一个文件中包含多个Op，这将会导致编译出错。
- 注册Op时的类型名，需要和该Op的名字一样。即不允许在`A_op.cc`里面，注册`REGISTER_OP(B, ...)`等，这将会导致单元测试出错。
- 如果Op没有实现GPU Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 如果Op没有实现CUDA Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 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。
doc/howto/dev/new_op_en.md
浏览文件 @ 28630dd8
# How to write a new operator
- [Background](#background)
- [Implementing C++ Types](#implementing-c++-types)
- [Defining ProtoMaker](#defining-protoMaker)
- [Implementing C++ Types](#implementing-c-types)
- [Defining ProtoMaker](#defining-protomaker)
- [Defining Operator](#defining-operator)
- [Registering Operator](#registering-operator)
- [Compilation](#compilation)
......@@ -28,8 +28,8 @@ An operator can be differentiated by whether in has kernel methods. An operator
-------------- | :----------------------
OpProtoMake definition | `.cc`files, Backward Op does not need an OpProtoMake interface.
Op definition | `.cc` files
Kernel implementation | The kernel methods shared between CPU and GPU are defined in `.h` files. CPU-specific kernels live in `.cc` files, while GPU-specific kernels are implemented in `.cu`files.
Registering the Op | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the GPU implementation.
Kernel implementation | The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files.
Registering the Op | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions. **
......@@ -41,7 +41,7 @@ Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePadd
## Implementing C++ Types
### 1. Defining Class ProtoMaker
### Defining ProtoMaker
Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.
......@@ -98,7 +98,7 @@ There are two changes in this example:
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` adds `scale`constant as an attribute, and sets the default value to 1.0.
### 2. Defining Operator
### Defining Operator
The following code defines the interface for MulOp:
......@@ -147,11 +147,11 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, which also include the registration methods introduced later.
### 3. Defining OpKernel
### Defining OpKernel
`MulKernel` inherits `framework::OpKernel`, which includes the following templates:
- `typename Place` denotes device type. When different devices, namely the CPU and the GPU, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
- `typename DeviceContext` denotes device context type. When different devices, namely the CPUDeviceContext and the CUDADeviceContext, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
- `typename T` denotes data type, such as `float` or `double`.
......@@ -163,7 +163,7 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
`MulKernel`'s implementation of `Compute` is as follows:
```cpp
template <typename Place, typename T>
template <typename DeviceContext, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
......@@ -171,16 +171,15 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
auto* Y = context.Input<Tensor>("Y");
auto* Z = context.Output<Tensor>("Out");
Z->mutable_data<T>(context.GetPlace());
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
auto& device_context = context.template device_context<DeviceContext>();
math::matmul<DeviceContext, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
};
```
Note that **different devices (CPU, GPU)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**
Note that **different devices (CPU, CUDA)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**
`MulOp`'s CPU and GPU share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
`MulOp`'s CPU and CUDA share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
To ease the writing of `OpKernel` compute, and for reusing code cross-device, [`Eigen-unsupported Tensor`](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md?fileviewer=file-view-default) module is used to implement `Compute` interface. To learn about how the Eigen library is used in PaddlePaddle, please see [usage document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md).
......@@ -189,16 +188,16 @@ This concludes the forward implementation of an operator. Next its operation and
The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**.
### 4. Registering Operator
### Registering Operator
- In `.cc` files, register forward and backward operator classes and the CPU kernel.
```cpp
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);
```
In that code block,
......@@ -208,20 +207,20 @@ The definition of its corresponding backward operator, if applicable, is similar
- `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`.
- Registering GPU Kernel in `.cu` files
- Note that if GPU Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as
- Registering CUDA Kernel in `.cu` files
- Note that if CUDA Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as
```cpp
// if use Eigen unsupported module before include head files
#define EIGEN_USE_GPU
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CUDADeviceContext, float>);
```
### 5. Compilation
### Compilation
Run the following commands to compile.
......@@ -253,62 +252,51 @@ A forward operator unit test inherits `unittest.TestCase` and defines metaclass
2. Generating random input data.
3. Implementing the same computation logic in a Python script:
3. Implementing the same computation logic in a Python script.
4. Call check gradient function to check the backward operator.
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
from op_test import OpTest
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
class TestMulOp(OpTest):
def setUp(self):
self.type = "mul"
self.op_type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
Get its output, and compare it with the forward operator's own output.
The code above first loads required packages. In addition, we have
- `self.type = "mul" ` defines the type that is identical to what the operator's registered type.
- `self.op_type = "mul" ` defines the type that is identical to what the operator's registered type.
- `self.inputs` defines input, with type `numpy.array` and initializes it.
- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script.
### Testing Backward Operators
A backward operator unit test inherits `GradientChecker`, which inherits `unittest.TestCase`. As a result, **a backward operator unit test needs to be have the prefix `test_`**.
```python
class TestMulGradOp(GradientChecker):
def setUp(self):
self.op = create_op("mul")
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
Some key points in the code above include:
Some key points in checking gradient above include:
- `create_op("mul")` creates the backward operator's corresponding forward operator.
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
- The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The second variable `"Out"` points to the network's final output target `Out`.
......@@ -338,5 +326,5 @@ ctest -R test_mul_op
- Every `*_op.h` (if applicable), `*_op.cc`, and `*_op.cu` (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.
- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OP(B, ...)` in `A_op.cc` will cause unit testing failures.
- If the operator does not implement a GPU kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If the operator does not implement a CUDA kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`.
doc/howto/dev/write_docs_cn.rst
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......@@ -3,12 +3,64 @@
##################
PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
也可以利用PaddlePaddle 工具来编译文档，这个情况下所有的文件会存在整理过的的文件目录 .ppo_workspace/content 下
如何构建文档
============
PaddlePaddle的文档构建有两种方式。
PaddlePaddle的文档构建有三种方式。
使用PaddlePaddle.org工具
--------------
这个是目前推荐的使用方法。除了可以自动编译文档，也可以直接在网页预览文档。
文件工具是使用Docker，需要在系统里先安装好Docker工具包。Docker安装请参考Docker的官网。安装好Docker之后及可用以下命令启动工具
.. code-block:: bash
mkdir paddlepaddle # Create paddlepaddle working directory
cd paddlepaddle
# Clone the content repositories
git clone https://github.com/PaddlePaddle/Paddle.git
git clone https://github.com/PaddlePaddle/book.git
git clone https://github.com/PaddlePaddle/models.git
git clone https://github.com/PaddlePaddle/Mobile.git
# Please specify the working directory through -v
docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest
注意: PaddlePaddle.org 会在 -v (volume) 指定的内容存储库运行命令
之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档
编译后的文件将被存储在工作目录 <paddlepaddle working directory>/.ppo_workspace/content。
如果不想使用 Docker，你还可以通过运行Django框架直接激活工具的服务器。使用下面的命令来运行它。
.. code-block:: bash
mkdir paddlepaddle # Create paddlepaddle working directory
cd paddlepaddle
# Clone the content repositories and PaddlePaddle.org
git clone https://github.com/PaddlePaddle/Paddle.git
git clone https://github.com/PaddlePaddle/book.git
git clone https://github.com/PaddlePaddle/models.git
git clone https://github.com/PaddlePaddle/Mobile.git
git clone https://github.com/PaddlePaddle/PaddlePaddle.org.git
# Please specify the PaddlePaddle working directory. In the current setting, it should be pwd
export CONTENT_DIR=<path_to_paddlepaddle_working_directory>
export ENV=''
cd PaddlePaddle.org/portal/
pip install -r requirements.txt
python manage.py runserver
工具服务器将读取环境变量 CONTENT_DIR 搜索代码库。请指定的PaddlePaddle工作目录给环境变量 CONTENT_DIR。
之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档。
编译后的文件将被存储在工作目录 <paddlepaddle working directory>/.ppo_workspace/content。
想了解更多PaddlePaddle.org工具的详细信息，可以 `点击这里 <https://github.com/PaddlePaddle/PaddlePaddle.org/blob/develop/README.cn.md>`_ 。
使用Docker构建
--------------
......@@ -47,17 +99,12 @@ PaddlePaddle的文档构建有两种方式。
PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。
如何更新文档主题
================
PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。
如何更新doc.paddlepaddle.org
如何更新www.paddlepaddle.org
============================
更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 <http://doc.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://doc.paddlepaddle.org/develop/doc/>`_ 。
更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 <http://www.paddlepaddle.org/docs/develop/documentation/en/howto/dev/contribute_to_paddle_en.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 <http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html>`_ 和
`英文文档 <http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html>`_ 。
.. _cmake: https://cmake.org/
......
doc/howto/dev/write_docs_en.rst 0 → 100644
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##################
Contribute Documentation
##################
PaddlePaddle supports English documentation ``doc`` and Chinese documentation ``doc_cn``.
Both are compiled by `cmake`_ and `sphinx`_ , the compiled documentations will be stored under ``doc`` and ``doc_cn`` directories.
When using the PaddlePaddle.org to compile documentations, the compiled documentations will be stored under a consolidated directory: .ppo_workspace/content
How to Build Documentations
============
We recommend using PaddlePaddle.org tool to build documentation
Use PaddlePaddle.org tool
--------------
This is the recommended method to build documentation. It can compile documentation and preview the documentation in a web browser.
The tool uses Docker, please install it on your system. Please check Docker official website on how to install Docker. You may use the following commands to activate the tool
.. code-block:: bash
mkdir paddlepaddle # Create paddlepaddle working directory
cd paddlepaddle
# Clone the content repositories. You may only clone the contents you need
git clone https://github.com/PaddlePaddle/Paddle.git
git clone https://github.com/PaddlePaddle/book.git
git clone https://github.com/PaddlePaddle/models.git
git clone https://github.com/PaddlePaddle/Mobile.git
# Please specify the working directory through -v
docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest
Note: PaddlePaddle.org will read the content repos specified in the -v (volume) flag of the docker run command
Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation
The compiled documentations will be stored in <paddlepaddle working directory>/.ppo_workspace/content
If you don't wish to use Docker, you can also activate the tool through Django. Use the following the commands to set up
.. code-block:: bash
mkdir paddlepaddle # Create paddlepaddle working directory
cd paddlepaddle
# Clone the content repositories and PaddlePaddle.org
git clone https://github.com/PaddlePaddle/Paddle.git
git clone https://github.com/PaddlePaddle/book.git
git clone https://github.com/PaddlePaddle/models.git
git clone https://github.com/PaddlePaddle/Mobile.git
git clone https://github.com/PaddlePaddle/PaddlePaddle.org.git
# Please specify the PaddlePaddle working directory. In the current setting, it should be pwd
export CONTENT_DIR=<path_to_paddlepaddle_working_directory>
export ENV=''
cd PaddlePaddle.org/portal/
pip install -r requirements.txt
python manage.py runserver
Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation
The compiled documentations will be stored in <paddlepaddle working directory>/.ppo_workspace/content
If you want to learn more on the PaddlePaddle.org, please `click here <https://github.com/PaddlePaddle/PaddlePaddle.org/blob/develop/README.md>`_ 。
How to write Documentations
============
PaddlePaddle uses `sphinx`_ to compile documentations，Please check sphinx official website for more detail.
How to update www.paddlepaddle.org
============================
Please create PRs and submit them to github, please check `Contribute Code <http://www.paddlepaddle.org/docs/develop/documentation/en/howto/dev/contribute_to_paddle_en.html>`_ 。
PaddlePaddle develop branch will update the documentation once the PR is merged. User may check latest `Chinese Docs <http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html>`_ and
`English Docs <http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html>`_ 。
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
doc/howto/index_cn.rst
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......@@ -19,7 +19,7 @@
.. toctree::
:maxdepth: 1
dev/build_cn.rst
dev/contribute_to_paddle_cn.md
dev/write_docs_cn.rst
模型配置
......
doc/howto/index_en.rst
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......@@ -18,9 +18,9 @@ Development
.. toctree::
:maxdepth: 1
dev/build_en.rst
dev/new_layer_en.rst
dev/contribute_to_paddle_en.md
dev/write_docs_en.rst
Configuration
-------------
......
doc/howto/optimization/cpu_profiling.md
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此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
This tutorial introduces techniques we use to profile and tune the
CPU performance of PaddlePaddle. We will use Python packages
`cProfile` and `yep`, and Google's `perftools`.
运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
Profiling is the process that reveals performance bottlenecks,
which could be very different from what's in the developers' mind.
Performance tuning is done to fix these bottlenecks. Performance optimization
repeats the steps of profiling and tuning alternatively.
性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
PaddlePaddle users program AI applications by calling the Python API, which calls
into `libpaddle.so.` written in C++. In this tutorial, we focus on
the profiling and tuning of
Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
1. the Python code and
1. the mixture of Python and C++ code.
* Python代码的性能分析
* Python与C++混合代码的性能分析
## Profiling the Python Code
### Generate the Performance Profiling File
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
We can use Python standard
package, [`cProfile`](https://docs.python.org/2/library/profile.html),
to generate Python profiling file. For example:
```bash
python -m cProfile -o profile.out main.py
```
其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
### 查看性能分析文件
where `main.py` is the program we are going to profile, `-o` specifies
the output file. Without `-o`, `cProfile` would outputs to standard
output.
当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
### Look into the Profiling File
使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
`cProfile` generates `profile.out` after `main.py` completes. We can
use [`cprofilev`](https://github.com/ymichael/cprofilev) to look into
the details:
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
where `-a` specifies the HTTP IP, `-p` specifies the port, `-f`
specifies the profiling file, and `main.py` is the source file.
访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
Open the Web browser and points to the local IP and the specifies
port, we will see the output like the following:
```text
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
......@@ -44,23 +54,23 @@ cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
where each line corresponds to Python function, and the meaning of
each column is as follows:
| 列名 | 含义 |
| column | meaning |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
| ncalls | the number of calls into a function |
| tottime | the total execution time of the function, not including the
execution time of other functions called by the function |
| percall | tottime divided by ncalls |
| cumtime | the total execution time of the function, including the execution time of other functions being called |
| percall | cumtime divided by ncalls |
| filename:lineno(function) | where the function is defined |
### Identify Performance Bottlenecks
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
Usually, `tottime` and the related `percall` time is what we want to
focus on. We can sort above profiling file by tottime:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
......@@ -68,12 +78,15 @@ cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
We can see that the most time-consuming function is the `built-in
method run`, which is a C++ function in `libpaddle.so`. We will
explain how to profile C++ code in the next section. At this
moment, let's look into the third function `sync_with_cpp`, which is a
Python function. We can click it to understand more about it:
```text
```
Called By:
Ordered by: internal time
......@@ -92,72 +105,93 @@ Called:
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
The lists of the callers of `sync_with_cpp` might help us understand
how to improve the function definition.
## Profiling Python and C++ Code
### Generate the Profiling File
## Python与C++混合代码的性能分析
To profile a mixture of Python and C++ code, we can use a Python
package, `yep`, that can work with Google's `perftools`, which is a
commonly-used profiler for C/C++ code.
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
In Ubuntu systems, we can install `yep` and `perftools` by running the
following commands:
```bash
apt update
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
Then we can run the following command
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
to generate the profiling file. The default filename is
`main.py.prof`.
Please be aware of the `-v` command line option, which prints the
analysis results after generating the profiling file. By examining the
the print result, we'd know that if we stripped debug
information from `libpaddle.so` at build time. The following hints
help make sure that the analysis results are readable:
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. Use GCC command line option `-g` when building `libpaddle.so` so to
include the debug information. The standard building system of
PaddlePaddle is CMake, so you might want to set
`CMAKE_BUILD_TYPE=RelWithDebInfo`.
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
1. Use GCC command line option `-O2` or `-O3` to generate optimized
binary code. It doesn't make sense to profile `libpaddle.so`
without optimization, because it would anyway run slowly.
### 查看性能分析文件
1. Profiling the single-threaded binary file before the
multi-threading version, because the latter often generates tangled
profiling analysis result. You might want to set environment
variable `OMP_NUM_THREADS=1` to prevents OpenMP from automatically
starting multiple threads.
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
### Examining the Profiling File
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
The tool we used to examine the profiling file generated by
`perftools` is [`pprof`](https://github.com/google/pprof), which
provides a Web-based GUI like `cprofilev`.
We can rely on the standard Go toolchain to retrieve the source code
of `pprof` and build it:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
Then we can use it to profile `main.py.prof` generated in the previous
section:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
Where `-http` specifies the IP and port of the HTTP service.
Directing our Web browser to the service, we would see something like
the following:
![result](./pprof_1.png)
### Identifying the Performance Bottlenecks
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
Similar to how we work with `cprofilev`, we'd focus on `tottime` and
`cumtime`.
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
## 总结
We can see that the execution time of multiplication and the computing
of the gradient of multiplication takes 2% to 4% of the total running
time, and `MomentumOp` takes about 17%. Obviously, we'd want to
optimize `MomentumOp`.
至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
`pprof` would mark performance critical parts of the program in
red. It's a good idea to follow the hints.
doc/howto/optimization/cpu_profiling_cn.md 0 → 100644
浏览文件 @ 28630dd8
此教程会介绍如何使用Python的cProfile包、Python库yep、Google perftools来进行性能分析 (profiling) 与调优（performance tuning）。
Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。
PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大部分 Python API 调用用 C++ 写的 libpaddle.so。所以 PaddlePaddle 的性能分析与调优分为两个部分:
* Python 代码的性能分析
* Python 与 C++ 混合代码的性能分析
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
```bash
python -m cProfile -o profile.out main.py
```
其中 `main.py` 是我们要分析的程序，`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印到标准输出。
### 查看性能分析文件
`cProfile` 在main.py 运行完毕后输出`profile.out`。我们可以使用[`cprofilev`](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来：
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
用Web浏览器访问对应网址，即可显示性能分析的结果：
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
4696 12.040 0.003 12.040 0.003 {built-in method run}
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
| 列名 | 含义 |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python`与`C++`混合代码的性能分析来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
```text
Called By:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
Function was called by...
ncalls tottime cumtime
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
Called:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
## Python与C++混合代码的性能分析
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
```bash
apt update
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
### 查看性能分析文件
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[`pprof`](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
![result](./pprof_1.png)
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
doc/howto/read_source.md 0 → 100644
浏览文件 @ 28630dd8
# PaddlePaddle Fluid Source Code Overview
Examples: https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid/tests/book
Core: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework
Operator: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators
Optimizer: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/optimizer
Memory: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/memory
# Compile Time
The following **defines** the NN. The definition goes into this [protocol buffer](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto).
```python
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
```
- Variables: `x`, `y`, `y_predict`, `cost` and `avg_cost`. [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/framework.py#L93)
- Layers: `fluid.layers.data`, `fluid.layers.fc` and `fluid.layers.mean` are layers. [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/layers.py)
- Every Layer has one or more operators and variables/parameters
- All the operators are defined at [`paddle/operators/`](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators). Other worth-looking files:
- Base class: [`paddle/framework/operator.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h)
- Operator Registration: [`paddle/framework/op_registry.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h)
- Operator Lookup: [`paddle/framework/op_info.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_info.h)
- Optimizer: `fluid.optimizer.SGD`. It does the following
- Add backward operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/backward.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/backward.cc)]
- Add optimizer operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/optimizer.py), [C++](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/optimizer)]
# Run Time
The following **evaluates** the NN. Instantiates all the variables, operators.
```python
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
# Allocate memory. Initialize Parameter.
exe.run(fluid.default_startup_program())
# Allocate memory. Do computation.
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
```
- Place: `place`. one of CPU, GPU or FPGA. [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h)
- The device handle are at [paddle/platform/device_context.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h)
- Executor: `fluid.Executor(place)`. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/executor.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.cc)]
- Feeds the data: `feed=feeder.feed(data)`
- Evaluates all the operators
- Fetches the result: `fetch_list=[avg_cost]`
- Other worth looking files:
- Scope: [paddle/framework/scope.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/scope.h). Where all the variables live
- Variable: [paddle/framework/variable.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h). Where all the data (most likely tensors) live
- Tensor: [paddle/framework/tensor.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/tensor.h). Where we allocate memory through [`paddle/memory/`](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/memory)
paddle/api/CMakeLists.txt
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......@@ -25,8 +25,18 @@ FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
SET(SWIG_NEED_FLAGS
-ftls-model=global-dynamic
-Wno-parentheses-equality
-Wno-self-assign
-Wno-maybe-uninitialized
-Wno-missing-field-initializers)
FOREACH(flag ${SWIG_NEED_FLAGS})
safe_set_cxxflag(SWIG_CXX_FLAGS ${flag})
ENDFOREACH()
SET(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign -ftls-model=global-dynamic")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SWIG_CXX_FLAGS}")
SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
paddle_parameter
......
paddle/capi/CMakeLists.txt
浏览文件 @ 28630dd8
......@@ -4,6 +4,16 @@ else ()
set(PADDLE_FLOAT_TYPE float)
endif()
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
OUTPUT_VARIABLE PADDLE_GIT_COMMIT
RESULT_VARIABLE PADDLE_GIT_COMMIT_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT PADDLE_GIT_COMMIT)
set(PADDLE_GIT_COMMIT "no commit information")
endif()
# config.h used for C-API. It will store Paddle building configuration as a
# header. Make user just include PaddleCAPI.h then can get building
# configuration without explicitly set -DPADDLE_WITH_DOUBLE when building their
......
paddle/capi/Main.cpp
浏览文件 @ 28630dd8
......@@ -43,4 +43,11 @@ paddle_error paddle_init(int argc, char** argv) {
isInit = true;
return kPD_NO_ERROR;
}
paddle_error paddle_init_thread() {
if (FLAGS_use_gpu) {
hl_init(FLAGS_gpu_id);
}
return kPD_NO_ERROR;
}
}
paddle/capi/Matrix.cpp
浏览文件 @ 28630dd8
......@@ -40,7 +40,7 @@ paddle_error paddle_matrix_destroy(paddle_matrix mat) {
paddle_error paddle_matrix_set_row(paddle_matrix mat,
uint64_t rowID,
paddle_real* rowArray) {
if (mat == nullptr) return kPD_NULLPTR;
if (mat == nullptr || rowArray == nullptr) return kPD_NULLPTR;
auto ptr = cast(mat);
if (ptr->mat == nullptr) return kPD_NULLPTR;
if (rowID >= ptr->mat->getHeight()) return kPD_OUT_OF_RANGE;
......
paddle/capi/config.h.in
浏览文件 @ 28630dd8
......@@ -3,6 +3,9 @@
typedef @PADDLE_FLOAT_TYPE@ paddle_real;
#define __PADDLE_VERSION__ "@PADDLE_VERSION@"
#define __PADDLE_COMMIT__ "@PADDLE_GIT_COMMIT@"
// Since we only support linux and macos in compile, always use clang or
// gcc 4.8+. DLL_IMPORT/DLL_EXPORT is as simple as below.
#define PD_API __attribute__((visibility("default")))
......
paddle/capi/error.cpp 0 → 100644
浏览文件 @ 28630dd8
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "error.h"
const char* paddle_error_string(paddle_error err) {
switch (err) {
case kPD_NULLPTR:
return "nullptr error";
case kPD_OUT_OF_RANGE:
return "out of range error";
case kPD_PROTOBUF_ERROR:
return "protobuf error";
case kPD_NOT_SUPPORTED:
return "not supported error";
case kPD_UNDEFINED_ERROR:
return "undefined error";
default:
return "";
}
}
paddle/capi/error.h
浏览文件 @ 28630dd8
......@@ -15,6 +15,8 @@ limitations under the License. */
#ifndef __PADDLE_CAPI_ERROR_H__
#define __PADDLE_CAPI_ERROR_H__
#include "config.h"
/**
* Error Type for Paddle API.
*/
......@@ -27,4 +29,9 @@ typedef enum {
kPD_UNDEFINED_ERROR = -1,
} paddle_error;
/**
* Error string for Paddle API.
*/
PD_API const char* paddle_error_string(paddle_error err);
#endif
paddle/capi/examples/model_inference/multi_thread/CMakeLists.txt
浏览文件 @ 28630dd8
project(multi_thread)
cmake_minimum_required(VERSION 2.8)
aux_source_directory(. SRC_LIST)
add_executable(${PROJECT_NAME} ${SRC_LIST})
find_package (Threads)
if(NOT PADDLE_ROOT)
set(PADDLE_ROOT $ENV{PADDLE_ROOT} CACHE PATH "Paddle Path")
endif()
if(PADDLE_ROOT)
include_directories(${PADDLE_ROOT}/include)
link_directories(${PADDLE_ROOT}/lib)
endif()
set(CPU_SRCS main.c)
add_executable(${PROJECT_NAME} ${CPU_SRCS})
set_property(TARGET ${PROJECT_NAME} PROPERTY C_STANDARD 99)
target_link_libraries(${PROJECT_NAME} -lpaddle_capi_shared
${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${PROJECT_NAME}
-lpaddle_capi_shared
${CMAKE_THREAD_LIBS_INIT})
find_package(CUDA QUIET)
if(CUDA_FOUND)
set(GPU_SRCS main_gpu.c)
cuda_add_executable(${PROJECT_NAME}_gpu ${GPU_SRCS})
set_property(TARGET ${PROJECT_NAME}_gpu PROPERTY C_STANDARD 99)
target_link_libraries(${PROJECT_NAME}_gpu
-lpaddle_capi_shared
${CMAKE_THREAD_LIBS_INIT})
endif(CUDA_FOUND)
paddle/capi/examples/model_inference/multi_thread/main_gpu.c 0 → 100644
浏览文件 @ 28630dd8
#include <paddle/capi.h>
#include <pthread.h>
#include <time.h>
#include "../common/common.h"
#define CONFIG_BIN "./trainer_config.bin"
#define NUM_THREAD 4
#define NUM_ITER 1000
pthread_mutex_t mutex;
/*
* @brief It is an simple inference example that runs multi-threads on a GPU.
* Each thread holds it own local gradient_machine but shares the same
* parameters.
* If you want to run on different GPUs, you need to launch
* multi-processes or set trainer_count > 1.
*/
void* thread_main(void* gm_ptr) {
// Initialize the thread environment of Paddle.
CHECK(paddle_init_thread());
paddle_gradient_machine machine = (paddle_gradient_machine)(gm_ptr);
// Create input arguments.
paddle_arguments in_args = paddle_arguments_create_none();
// Create input matrix.
paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1,
/* size */ 784,
/* useGPU */ true);
// Create output arguments.
paddle_arguments out_args = paddle_arguments_create_none();
// Create output matrix.
paddle_matrix prob = paddle_matrix_create_none();
// CPU buffer to cache the input and output.
paddle_real* cpu_input = (paddle_real*)malloc(784 * sizeof(paddle_real));
paddle_real* cpu_output = (paddle_real*)malloc(10 * sizeof(paddle_real));
for (int iter = 0; iter < NUM_ITER; ++iter) {
// There is only one input layer of this network.
CHECK(paddle_arguments_resize(in_args, 1));
CHECK(paddle_arguments_set_value(in_args, 0, mat));
for (int i = 0; i < 784; ++i) {
cpu_input[i] = rand() / ((float)RAND_MAX);
}
CHECK(paddle_matrix_set_value(mat, cpu_input));
CHECK(paddle_gradient_machine_forward(machine,
in_args,
out_args,
/* isTrain */ false));
CHECK(paddle_arguments_get_value(out_args, 0, prob));
CHECK(paddle_matrix_get_value(prob, cpu_output));
pthread_mutex_lock(&mutex);
printf("Prob: ");
for (int i = 0; i < 10; ++i) {
printf("%.2f ", cpu_output[i]);
}
printf("\n");
pthread_mutex_unlock(&mutex);
}
CHECK(paddle_matrix_destroy(prob));
CHECK(paddle_arguments_destroy(out_args));
CHECK(paddle_matrix_destroy(mat));
CHECK(paddle_arguments_destroy(in_args));
CHECK(paddle_gradient_machine_destroy(machine));
free(cpu_input);
free(cpu_output);
return NULL;
}
int main() {
// Initalize Paddle
char* argv[] = {"--use_gpu=True"};
CHECK(paddle_init(1, (char**)argv));
// Reading config binary file. It is generated by `convert_protobin.sh`
long size;
void* buf = read_config(CONFIG_BIN, &size);
// Create a gradient machine for inference.
paddle_gradient_machine machine;
CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
CHECK(paddle_gradient_machine_randomize_param(machine));
// Loading parameter. Uncomment the following line and change the directory.
// CHECK(paddle_gradient_machine_load_parameter_from_disk(machine,
// "./some_where_to_params"));
srand(time(0));
pthread_mutex_init(&mutex, NULL);
pthread_t threads[NUM_THREAD];
for (int i = 0; i < NUM_THREAD; ++i) {
paddle_gradient_machine thread_local_machine;
CHECK(paddle_gradient_machine_create_shared_param(
machine, buf, size, &thread_local_machine));
pthread_create(&threads[i], NULL, thread_main, thread_local_machine);
}
for (int i = 0; i < NUM_THREAD; ++i) {
pthread_join(threads[i], NULL);
}
pthread_mutex_destroy(&mutex);
return 0;
}
paddle/capi/main.h
浏览文件 @ 28630dd8
......@@ -26,6 +26,13 @@ extern "C" {
*/
PD_API paddle_error paddle_init(int argc, char** argv);
/**
* Initialize the thread environment of Paddle.
* @note it is requisite for GPU runs but optional for CPU runs.
* For GPU runs, all threads will run on the same GPU devices.
*/
PD_API paddle_error paddle_init_thread();
#ifdef __cplusplus
}
#endif
......
paddle/cuda/include/hl_cnn.h
浏览文件 @ 28630dd8
......@@ -116,6 +116,7 @@ extern void hl_maxpool_backward(const int frameCnt,
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride stride between output data samples.
* @param[in] excludeMode whether to consider paddings for size.
*
*/
extern void hl_avgpool_forward(const int frameCnt,
......@@ -132,7 +133,8 @@ extern void hl_avgpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride);
const int tgtStride,
bool excludeMode);
/**
* @brief Maximum pool backward.
......@@ -154,6 +156,7 @@ extern void hl_avgpool_forward(const int frameCnt,
* @param[in] scaleB scale.
* @param[out] backGrad output grad.
* @param[in] outStride stride between output data samples.
* @param[in] excludeMode whether to consider paddings for size.
*
*/
extern void hl_avgpool_backward(const int frameCnt,
......@@ -172,7 +175,8 @@ extern void hl_avgpool_backward(const int frameCnt,
real scaleA,
real scaleB,
real* backGrad,
const int outStride);
const int outStride,
bool excludeMode);
extern void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,
......
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @ 28630dd8
......@@ -68,7 +68,8 @@ inline void hl_avgpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {}
const int tgtStride,
const bool excludeMode) {}
inline void hl_avgpool_backward(const int frameCnt,
const real* outGrad,
......@@ -86,7 +87,8 @@ inline void hl_avgpool_backward(const int frameCnt,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {}
const int outStride,
const bool excludeMode) {}
inline void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,
......
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ 28630dd8
......@@ -210,7 +210,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
const int padH,
const int padW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
const bool excludeMode) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
......@@ -224,7 +225,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
int wend = min(wstart + sizeX, width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
real aveval = 0;
inputData += (frameNum * channels + c) * height * width;
......@@ -235,7 +237,7 @@ __global__ void KeAvgPoolForward(const int nthreads,
}
int tgtIndex =
index % (pooledW * pooledH * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = aveval / pool_size;
tgtData[tgtIndex] = aveval / poolSize;
}
}
......@@ -253,7 +255,8 @@ void hl_avgpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
const bool excludeMode) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPoolForward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
......@@ -270,7 +273,8 @@ void hl_avgpool_forward(const int frameCnt,
paddingH,
paddingW,
tgtData,
tgtStride);
tgtStride,
excludeMode);
CHECK_SYNC("hl_avgpool_forward failed");
}
......@@ -290,7 +294,8 @@ __global__ void KeAvgPoolBackward(const int nthreads,
real scaleA,
real scaleB,
real* tgtGrad,
const int outStride) {
const int outStride,
const bool excludeMode) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int offsetW = index % width + padW;
......@@ -314,8 +319,9 @@ __global__ void KeAvgPoolBackward(const int nthreads,
int wstart = pw * strideW - padW;
int wend = min(wstart + sizeX, width);
wstart = max(wstart, 0);
int poolsize = (hend - hstart) * (wend - wstart);
gradient += outGrad[ph * pooledW + pw] / poolsize;
int poolSize =
excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
gradient += outGrad[ph * pooledW + pw] / poolSize;
}
}
tgtGrad[index] = scaleB * tgtGrad[index] + scaleA * gradient;
......@@ -338,7 +344,8 @@ void hl_avgpool_backward(const int frameCnt,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {
const int outStride,
const bool excludeMode) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
......@@ -358,7 +365,8 @@ void hl_avgpool_backward(const int frameCnt,
scaleA,
scaleB,
backGrad,
outStride);
outStride,
excludeMode);
CHECK_SYNC("hl_avgpool_backward failed");
}
......
paddle/framework/backward.cc
浏览文件 @ 28630dd8
......@@ -27,6 +27,18 @@
namespace paddle {
namespace framework {
static std::unordered_set<std::string>* g_ctrl_flow_ops_ = nullptr;
// Control Flow operators's backward is significantly different from
// computational operators. Hack Code here.
// We should design a better way to backward CtrlFlowOps.
static std::unordered_set<std::string>& CtrlFlowOps() {
if (g_ctrl_flow_ops_ == nullptr) {
g_ctrl_flow_ops_ = new std::unordered_set<std::string>{
"increment", "lod_rank_table", "less_than"};
}
return *g_ctrl_flow_ops_;
}
static inline std::unique_ptr<OperatorBase> CreateGradOp(
const OperatorBase& op, const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
......@@ -178,8 +190,9 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
// collect all the offset for each alias,
// insert a sum operator to add all aliases to output
insert_position.push_back(
{dup_op.back(), OpRegistry::CreateOp("sum", {{"X", dup_outputs}},
{{"Out", {name}}}, {})});
{dup_op.back(),
OpRegistry::CreateOp("sum", {{"X", dup_outputs}}, {{"Out", {name}}},
AttributeMap{})});
}
// make sure the inserted `sum` ops follow the BFS order.
......@@ -204,7 +217,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
net->AppendOp(OpRegistry::CreateOp("fill_zeros_like", {{"X", {prefix}}},
{{"Y", {grad_input}}}, {}));
{{"Y", {grad_input}}},
AttributeMap{}));
}
return false;
});
......@@ -288,12 +302,24 @@ static void CreateGradVarInBlock(
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
std::unordered_set<std::string> new_vars;
auto& ctrl_flow_ops = CtrlFlowOps();
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (block_desc->HasVar(grad_var_name)) {
if (ctrl_flow_ops.find(ops[op_index]->Type()) !=
ctrl_flow_ops.end()) {
if (block_desc->HasVarRecursive(grad_var_name)) {
return false;
}
} else {
if (block_desc->HasVar(grad_var_name)) {
return false;
}
}
if (grad_var_name == framework::kEmptyVarName) {
return false;
}
auto var = block_desc->Var(grad_var_name);
VLOG(10) << "Creating Variable " << grad_var_name;
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
if (it == param_name_map.end()) {
......@@ -333,14 +359,25 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
// All input gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
if (AllGradInSet(inputs, *no_grad_vars)) {
VLOG(10) << "Drop operator " << op_desc->Type();
return grad_op_descs; // empty vector
}
// All output gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
if (AllGradInSet(outputs, *no_grad_vars)) {
for (const std::string& name : inputs) {
no_grad_vars->insert(GradVarName(name));
VLOG(10) << "Drop operator " << op_desc->Type();
// FIXME: Hack code here
auto& ctrl_flow_ops = CtrlFlowOps();
if (ctrl_flow_ops.find(op_desc->Type()) == ctrl_flow_ops.end()) {
// Only computational op need drop input's gradient.
for (const std::string& name : inputs) {
no_grad_vars->insert(GradVarName(name));
VLOG(10) << " Also drop " << GradVarName(name);
}
}
return grad_op_descs; // empty vector
}
......@@ -357,8 +394,9 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
std::string new_name = prefix + kZeroVarSuffix;
desc->Rename(in_name, new_name);
std::unique_ptr<OpDescBind> fill_zeros_op(new OpDescBind(
"fill_zeros_like", {{"X", {prefix}}}, {{"Y", {new_name}}}, {}));
std::unique_ptr<OpDescBind> fill_zeros_op(
new OpDescBind("fill_zeros_like", {{"X", {prefix}}},
{{"Y", {new_name}}}, AttributeMap{}));
pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
}
}
......@@ -448,8 +486,9 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
sum_op_inputs.emplace_back(new_name);
next_g_name = sum_op_inputs.back();
}
std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
"sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
std::unique_ptr<OpDescBind> sum_op(
new OpDescBind("sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}},
AttributeMap{}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
......
paddle/framework/backward_test.cc
浏览文件 @ 28630dd8
......@@ -106,15 +106,15 @@ class FcOp : public operators::NetOp {
FcOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AppendOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
AppendOp(OpRegistry::CreateOp(
"mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, AttributeMap{}));
auto input_b = Inputs("b");
std::string before_act = "mul_result";
if (input_b.size() != 0) {
AppendOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
{{"Out", {Output("add_result")}}}, {}));
{{"Out", {Output("add_result")}}}, AttributeMap{}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
......@@ -124,7 +124,7 @@ class FcOp : public operators::NetOp {
}
AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
{{"Out", {Output("Out")}}}, AttributeMap{}));
CompleteAddOp(false);
}
};
......@@ -278,8 +278,9 @@ REGISTER_OPERATOR(scale, f::NoneOp);
REGISTER_OP_CPU_KERNEL(scale, f::NoneKernel<paddle::platform::CPUPlace, float>);
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto fwd =
f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
{{"Out", {"out"}}}, f::AttributeMap{});
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"x"});
ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
......@@ -296,9 +297,10 @@ TEST(Backward, net_fc_backward_normal) {
{{"mul_result", {"mul_res"}},
{"add_result", {"add_re"}},
{"Out", {"out"}}},
{});
f::AttributeMap{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
std::shared_ptr<f::OperatorBase> gop =
f::Backward(*fwd, std::unordered_set<std::string>{});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
......@@ -322,9 +324,10 @@ TEST(Backward, net_fc_backward_not_have_b) {
{{"mul_result", {"mul_res"}},
{"add_result", {"add_res"}},
{"Out", {"tmp"}}},
{});
f::AttributeMap{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
std::shared_ptr<f::OperatorBase> gop =
f::Backward(*fwd, std::unordered_set<std::string>{});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
......@@ -346,13 +349,13 @@ TEST(Backward, net_input_of_network_not_need_grad) {
{{"mul_result", {"mul_tmp_0"}},
{"add_result", {"add_tmp_0"}},
{"Out", {"hidden0"}}},
{}));
f::AttributeMap{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_tmp_1"}},
{"add_result", {"add_tmp_1"}},
{"Out", {"hidden1"}}},
{}));
f::AttributeMap{}));
net.CompleteAddOp();
auto bwd = Backward(net, {"x"}); // x@GRAD is not need.
ASSERT_TRUE(bwd->IsNetOp());
......@@ -381,12 +384,13 @@ TEST(Backward, net_input_of_network_not_need_grad) {
TEST(Backward, net_shared_weight) {
ops::NetOp net;
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
{{"Out", {"out"}}}, f::AttributeMap{}));
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
{{"Out", {"FinalOut"}}},
f::AttributeMap{}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
auto bwd = f::Backward(net, std::unordered_set<std::string>{});
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
......@@ -394,8 +398,9 @@ TEST(Backward, net_shared_weight) {
}
TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto fwd =
f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
{{"Out", {"out"}}}, f::AttributeMap{});
auto backward = f::Backward(*fwd, {"x", "b"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
......@@ -403,8 +408,9 @@ TEST(Backward, op_all_input_are_not_need) {
}
TEST(Backward, op_all_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto fwd =
f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
{{"Out", {"out"}}}, f::AttributeMap{});
auto backward = f::Backward(*fwd, {"out"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
......@@ -412,8 +418,9 @@ TEST(Backward, op_all_output_are_not_need) {
}
TEST(Backward, op_part_of_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
{{"y", {"Y"}}, {"z", {"Z"}}}, {});
auto fwd =
f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
{{"y", {"Y"}}, {"z", {"Z"}}}, f::AttributeMap{});
auto backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
......@@ -437,7 +444,7 @@ TEST(Backward, op_part_of_output_are_not_need) {
TEST(Backward, op_part_of_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
{{"Out", {"out"}}}, {});
{{"Out", {"out"}}}, f::AttributeMap{});
auto backward = f::Backward(*fwd, {"a"});
auto &grad_mul = *backward;
ASSERT_EQ(grad_mul.Type(), "mul_grad");
......@@ -458,19 +465,19 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
{{"mul_result", {"mul_out1"}},
{"add_result", {"add_out1"}},
{"Out", {"out1"}}},
{}));
f::AttributeMap{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_out2"}},
{"add_result", {"tmp_out2"}},
{"Out", {"out2"}}},
{}));
f::AttributeMap{}));
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
{{"mul_result", {"mul_out3"}},
{"add_result", {"tmp_out3"}},
{"Out", {"out3"}}},
{}));
f::AttributeMap{}));
net.CompleteAddOp();
auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
......@@ -509,7 +516,8 @@ TEST(Backward, simple_single_op) {
auto target = f::VarDescBind("out");
target.SetShape({1});
auto var_to_grad = AppendBackward(program, target, {});
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 3UL);
f::OpDescBind *fill_op = block->AllOps()[1];
......@@ -546,7 +554,7 @@ TEST(Backward, default_attribute) {
auto target = f::VarDescBind("out");
target.SetShape({1});
AppendBackward(program, target, {});
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 3UL);
EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
......@@ -585,7 +593,8 @@ TEST(Backward, simple_mult_op) {
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 6UL + 1);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
......@@ -817,7 +826,8 @@ TEST(Backward, shared_var) {
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 8UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
......
paddle/framework/block_desc.cc
浏览文件 @ 28630dd8
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/block_desc.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/program_desc.h"
namespace paddle {
......@@ -42,6 +43,8 @@ bool BlockDescBind::HasVar(const std::string &name) const {
}
VarDescBind *BlockDescBind::FindVarRecursive(const std::string &name) const {
if (name == kEmptyVarName) return nullptr;
auto it = vars_.find(name);
if (it == vars_.end()) {
return Parent() == kNoneBlockIndex ? nullptr
......
paddle/framework/executor.cc
浏览文件 @ 28630dd8
......@@ -97,6 +97,10 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
if (create_local_scope) {
local_scope = &scope->NewScope();
for (auto& var : block.AllVars()) {
if (var->Name() == framework::kEmptyVarName) {
continue;
}
if (var->Persistable()) {
auto* ptr = scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
......
paddle/framework/lod_tensor.cc
浏览文件 @ 28630dd8
......@@ -13,6 +13,8 @@
limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/data_type.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/memory/memcpy.h"
#include "paddle/memory/memory.h"
......@@ -27,11 +29,11 @@
namespace paddle {
namespace framework {
std::ostream& operator<<(std::ostream& os, const LoD& lod) {
std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto& v : lod) {
for (auto &v : lod) {
os << "{";
for (auto& i : v) {
for (auto &i : v) {
os << i << ",";
}
os << "}";
......@@ -41,7 +43,7 @@ std::ostream& operator<<(std::ostream& os, const LoD& lod) {
return os;
}
LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end) {
LoD SliceLevels(const LoD &in, size_t level_begin, size_t level_end) {
LoD new_lod;
new_lod.reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
......@@ -53,7 +55,7 @@ LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end) {
return new_lod;
}
LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin,
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
PADDLE_ENFORCE_LT(level, in.size());
PADDLE_ENFORCE_LT(elem_end, in[level].size());
......@@ -64,9 +66,9 @@ LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin,
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto& in_level = in[level + lvl];
const auto& above_level = res[lvl - 1];
auto& out_level = res[lvl];
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
......@@ -74,33 +76,33 @@ LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin,
// to make the first offset equals 0, all the elements minus the first
// element
size_t front = res[lvl].front();
for (auto& ele : res[lvl]) {
for (auto &ele : res[lvl]) {
ele -= front;
}
}
return res;
}
LoD ToAbsOffset(const LoD& in) {
LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1) return in;
LoD result = in;
for (int level = result.size() - 2; level >= 0; level--) {
for (auto& ele : result[level]) {
for (auto &ele : result[level]) {
ele = result[level + 1][ele];
}
}
return result;
}
bool operator==(const LoD& a, const LoD& b) {
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto& a_level = a[i];
const auto& b_level = b[i];
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
......@@ -151,7 +153,7 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
}
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD& lod, size_t start_idx,
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
size_t end_idx, size_t start_level) {
LoD sub_lod;
......@@ -170,7 +172,7 @@ LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD& lod, size_t start_idx,
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
}
void AppendLoD(LoD* lod, const LoD& lod_length) {
void AppendLoD(LoD *lod, const LoD &lod_length) {
PADDLE_ENFORCE(
lod->empty() || lod->size() == lod_length.size(),
"The lod_length should has the same size with the appended lod.");
......@@ -178,12 +180,139 @@ void AppendLoD(LoD* lod, const LoD& lod_length) {
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
}
for (size_t i = 0; i < lod->size(); ++i) {
auto& level = (*lod)[i];
auto &level = (*lod)[i];
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
}
}
void SerializeToStream(std::ostream &os, const LoDTensor &tensor,
const platform::DeviceContext &dev_ctx) {
// TODO(typhoonzero): serialize to ostream
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
framework::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
PADDLE_ENFORCE(size < std::numeric_limits<std::streamsize>::max(),
"Index overflow when writing tensor");
if (platform::is_gpu_place(tensor.place())) {
#ifdef PADDLE_WITH_CUDA
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
std::unique_ptr<char[]> buf(new char[kBufSize]);
auto &gpu_dev_ctx =
static_cast<const platform::CUDADeviceContext &>(dev_ctx);
platform::CPUPlace cpu;
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
while (size != 0) {
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
memory::Copy(cpu, buf.get(),
boost::get<platform::GPUPlace>(tensor.place()),
reinterpret_cast<const void *>(data), size_to_write,
gpu_dev_ctx.stream());
gpu_dev_ctx.Wait();
os.write(buf.get(), size_to_write);
data += size_to_write;
size -= size_to_write;
}
#else
PADDLE_THROW("Unexpected branch");
#endif
} else {
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
}
{ // the 4th field, lod information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
for (auto &each : lod) {
size = each.size() * sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
framework::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
"Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
platform::Place cpu = platform::CPUPlace();
switch (desc.data_type()) {
case framework::FP32:
buf = tensor->mutable_data<float>(cpu);
break;
case framework::FP64:
buf = tensor->mutable_data<double>(cpu);
break;
case framework::INT32:
buf = tensor->mutable_data<int>(cpu);
break;
case framework::INT64:
buf = tensor->mutable_data<int64_t>(cpu);
break;
default:
PADDLE_THROW("DataType %d not supported", desc.data_type());
}
is.read(static_cast<char *>(buf), tensor->memory_size());
}
{ // read lod
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
}
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @ 28630dd8
......@@ -189,5 +189,14 @@ std::pair<LoD, std::pair<size_t, size_t>> GetSubLoDAndAbsoluteOffset(
void AppendLoD(LoD* lod, const LoD& lod_length);
/*
* Serialize/Desiralize LoDTensor to std::ostream
* You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU.
*/
void SerializeToStream(std::ostream& os, const LoDTensor& tensor,
const platform::DeviceContext& dev_ctx);
void DeserializeFromStream(std::istream& is, LoDTensor* tensor);
} // namespace framework
} // namespace paddle
paddle/framework/op_desc.cc
浏览文件 @ 28630dd8
......@@ -59,13 +59,13 @@ class CompileTimeInferShapeContext : public InferShapeContext {
auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
if (in_var->GetType() != VarDesc::LOD_TENSOR) {
VLOG(3) << "input " << in << "is not LodTensor";
VLOG(3) << "input " << in << " is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), VarDesc::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
in_var->SetLoDLevel(out_var->GetLodLevel());
out_var->SetLoDLevel(in_var->GetLodLevel());
}
bool IsRuntime() const override;
......@@ -316,8 +316,8 @@ static void InitInferShapeFuncs() {
for (auto &kern_pair : OperatorWithKernel::AllOpKernels()) {
auto op_type = kern_pair.first;
auto &op_info = info_map.at(op_type);
auto op =
static_cast<OperatorWithKernel *>(op_info.Creator()("", {}, {}, {}));
auto op = static_cast<OperatorWithKernel *>(op_info.Creator()(
"", VariableNameMap{}, VariableNameMap{}, AttributeMap{}));
if (op_info.infer_shape_) { // infer_shape has been registered.
continue;
}
......@@ -466,7 +466,12 @@ DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
try {
return framework::make_ddim(var->Shape());
auto shape = var->Shape();
if (shape.empty()) {
return framework::make_ddim({0UL});
} else {
return framework::make_ddim(var->Shape());
}
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
......
paddle/framework/op_registry.h
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