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PaddleDetection
提交 4b8bcf32 编写于 作者: G guosheng
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into add-GRUOp-dev

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.travis.yml
浏览文件 @ 4b8bcf32
......@@ -30,6 +30,7 @@ addons:
- automake
- libtool
- ccache
ssh_known_hosts: 52.76.173.135
before_install:
- if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
# Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
......@@ -42,6 +43,14 @@ script:
- |
timeout 2580 paddle/scripts/travis/${JOB}.sh # 43min timeout
RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true; else false; fi;
- |
if [[ "$JOB" != "build_doc" ]]; then exit 0; fi;
if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit 0; fi;
if [[ "$TRAVIS_BRANCH" != "develop" && ! "$TRAVIS_BRANCH" =~ ^v[[:digit:]]+\.[[:digit:]]+(\.[[:digit:]]+)?(-\S*)?$ ]]; then exit 0; fi;
export DEPLOY_DOCS_SH=https://raw.githubusercontent.com/PaddlePaddle/PaddlePaddle.org/master/scripts/deploy/deploy_docs.sh
export DOCS_DIR=`pwd`
cd ..
curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH $DOCS_DIR $DOCS_DIR/build/doc
notifications:
email:
on_success: change
......
benchmark/IntelOptimizedPaddle.md 0 → 100644
浏览文件 @ 4b8bcf32
# Benchmark
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
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle: paddlepaddle/paddle:latest (TODO: will rerun after 0.11.0)
- MKL-DNN tag v0.10
- MKLML 2018.0.20170720
- 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
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
- VGG-19
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -----| --------|
| OpenBLAS | 7.82 | 8.62 | 10.34 |
| MKLML | 11.02 | 12.86 | 15.33 |
| MKL-DNN | 27.69 | 28.8 | 29.27 |
chart on batch size 128
TBD
- ResNet
- GoogLeNet
### Laptop
TBD
### Desktop
TBD
doc/design/parameter_average.md 0 → 100644
浏览文件 @ 4b8bcf32
# Averaging Parameter in PaddlePaddle
## Why Averaging
In a large scale machine learning setup where the size of the training data is huge, it could take us a large number of iterations over the training data before we can achieve the optimal values of parameters of our model. Looking at the problem setup, it is desirable if we can obtain the optimal values of parameters by going through the data in as few passes as we can.
Polyak and Juditsky (1992) showed that the test performance of simple average of parameters obtained by Stochastic Gradient Descent (SGD) is as good as that of parameter values that are obtained by training the model over and over again, over the training dataset.
Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="./images/theta_star.gif"/><br/> . The averaging is done as follows:
<img src="./images/asgd.gif" align="center"/><br/>
We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above.
### How to perform Parameter Averaging in PaddlePaddle
Parameter Averaging in PaddlePaddle works in the following way during training :
1. It will take in an instance of a normal optimizer as an input, e.g. RMSPropOptimizer
2. The optimizer itself is responsible for updating the parameters.
3. The ParameterAverageOptimizer maintains a separate copy of the parameters for itself:
1. In concept, the values of this copy are the average of the values of the parameters in the most recent N batches.
2. However, saving all the N instances of the parameters in memory is not feasible.
3. Therefore, an approximation algorithm is used.
Hence, overall we have have two copies of the parameters: one for the optimizer itself, and one for the ParameterAverageOptimizer. The former should be used in back propagation, while the latter should be used during testing and should be saved.
During the testing/ saving the model phase, we perform the following steps:
1. Perform the delayed operations.
2. Save current values of the parameters to a temporary variable.
3. Replace the values of the parameters with the averaged values.
4. Perform testing and/or save the parameters.
5. Restore the values of the parameters once done.
### How to implement Averaging of Parameter in PaddlePaddle
We can add the ParameterAverageOptimizer op to the graph through Python API. Using this approach, we manually add this op to the graph and direct the output of the optimizer op to this op during training.
**Advantages**:
- Allows for greater flexibility to the users of PaddlePaddle. Using this approach, the users can plug different optimizers into ParameterAverageOptimizer by passing in the optimizer to the op.
- Makes it easy for the users to customize and extend the framework.
**Disadvantages**:
- Implementation requires re-writing the averaging methodology in Python.
### Low-Level implementation
In the new design, we propose to create a new operation for averaging parameter updates (ParameterAverageOptimizer). For now, we can add an op that takes in the following as input:
- the optimizer
- the window_size to keep the updates
The ParameterAverageOptimizer op can be like any other operator with its own CPU/GPU implementation either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement the kernel using Eigen following the abstraction pattern implemented for [Operators](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/rmsprop_op.h). We also want to support the case when the Trainer/Optimizer runs on the GPU while ParameterAverageOptimizer runs on a CPU.
The idea of building an op for averaging is in sync with the refactored PaddlePaddle philosophy of using operators to represent any computation unit. The way the op will be added to the computation graph will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API.
### Python API implementation for ParameterAverageOptimizer
Based on Polyak and Juditsky (1992), we can generalize the averaging of updates to any optimizer. The input to the op would be the following:
- Any optimizer (RMSProp , AdaGrad etc.)
- A window size. The op keeps accumulating updated parameter values over a window of N batches and takes an average. Move the averaged value to a buffer when window is full to avoid loss of precision.
Using the ParameterAverageOptimizer op, any user can add the operation to their computation graphs. However, this will require a lot of lines of code and we should design Python APIs that support averaging. As per the PaddlePaddle [Python API design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md), the layer functions are responsible for creating operators, operator parameters and variables. Since ParameterAverageOptimizer will be an operator, it makes sense to create it in the layer functions.
We will have a wrapper written in Python that will support the functionality and implement the actual core computation in C++ core as we have done for other [Optimizers](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/rmsprop_op.cc)
#### Creation of the ParameterAverageOptimizer operator
There are two ways for creating the ParameterAverageOptimizer op:
1. We create the op immediately while building the computation graph.
2. We add the op in a lazy manner, just before the backward pass, similar to the way the optimization ops are added.
The proposal is to add the op immediately while building the computation graph.
#### High-level API
In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we also need to provide parameter average functionality in layer functions.
doc/faq/parameter/index_cn.rst
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......@@ -75,7 +75,7 @@ PaddlePaddle目前支持8种learning_rate_schedule，这8种learning_rate_schedu
optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
learning_rate_schedule="manual",
learning_rate_schedule="pass_manual",
learning_rate_args="1:1.0,2:0.9,3:0.8",)
在该示例中，当已训练pass数小于等于1时，学习率为 :code:`1e-3 * 1.0`；当已训练pass数大于1小于等于2时，学习率为 :code:`1e-3 * 0.9`；当已训练pass数大于2时，学习率为 :code:`1e-3 * 0.8`。
......
doc/howto/cross_compiling/cross_compiling_for_android.md 0 → 100644
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# Build PaddlePaddle for Android
There are two approaches to build PaddlePaddle for Android: using Docker and on Linux without Docker.
## Cross-Compiling Using Docker
Docker-based cross-compiling is the recommended approach because Docker runs on all major operating systems, including Linux, Mac OS X, and Windows.
### Build the Docker Image
The following steps pack all the tools that we need to build PaddlePaddle into a Docker image.
```bash
$ git clone https://github.com/PaddlePaddle/Paddle.git
$ cd Paddle
$ docker build -t paddle:dev-android . -f Dockerfile.android
```
### Build the Inference Library
We can run the Docker image we just created to build the inference library of PaddlePaddle for Android using the command below:
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" paddle:dev-android
```
The Docker image accepts two arguments `ANDROID_ABI` and `ANDROID_API`:
| Argument | Optional Values | Default |
|-----------------|-------------------------|---------|
|`ANDROID_ABI` |`armeabi-v7a, arm64-v8a` | `armeabi-v7a` |
|`ANDROID_API` |`>= 21` | `21` |
The ARM-64 architecture (`arm64-v8a`) requires at least level 21 of Android API.
The default entry-point of the Docker image, [`paddle/scripts/docker/build_android.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh) generates the [Android cross-compiling standalone toolchain](https://developer.android.com/ndk/guides/standalone_toolchain.html) based on the argument: `ANDROID_ABI` or `ANDROID_API`. For information about other configuration arguments, please continue reading.
The above command generates and outputs the inference library in `$PWD/install_android` and puts third-party libraries in `$PWD/install_android/third_party`.
## Cross-Compiling on Linux
The Linux-base approach to cross-compile is to run steps in `Dockerfile.android` manually on a Linux x64 computer.
### Setup the Environment
To build for Android's, we need [Android NDK](
https://developer.android.com/ndk/downloads/index.html):
```bash
wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip
unzip -q android-ndk-r14b-linux-x86_64.zip
```
Android NDK includes everything we need to build the [*standalone toolchain*](https://developer.android.com/ndk/guides/standalone_toolchain.html), which in then used to build PaddlePaddle for Android. (We plan to remove the intermediate stage of building the standalone toolchain in the near future.)
- To build the standalone toolchain for `armeabi-v7a` and Android API level 21:
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm --platform=android-21 --install-dir=your/path/to/arm_standalone_toolchain
```
The generated standalone toolchain will be in `your/path/to/arm_standalone_toolchain`.
- To build the standalone toolchain for `arm64-v8a` and Android API level 21:
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm64 --platform=android-21 --install-dir=your/path/to/arm64_standalone_toolchain
```
The generated standalone toolchain will be in `your/path/to/arm64_standalone_toolchain`.
**Please be aware that the minimum level of Android API required by PaddlePaddle is 21.**
### Cross-Compiling Arguments
CMake supports [choosing the toolchain](https://cmake.org/cmake/help/v3.0/manual/cmake-toolchains.7.html#cross-compiling). PaddlePaddle provides [`android.cmake`](https://github.com/PaddlePaddle/Paddle/blob/develop/cmake/cross_compiling/android.cmake), which configures the Android cross-compiling toolchain for CMake. `android.cmake` is not required for CMake >= 3.7, which support Android cross-compiling. PaddlePaddle detects the CMake version, for those newer than 3.7, it uses [the official version](https://cmake.org/cmake/help/v3.7/manual/cmake-toolchains.7.html#cross-compiling).
Some other CMake arguments you need to know:
- `CMAKE_SYSTEM_NAME` must be `Android`. This tells PaddlePaddle's CMake system to cross-compile third-party dependencies. This also changes some other CMake arguments like `WITH_GPU=OFF`, `WITH_AVX=OFF`, `WITH_PYTHON=OFF`, and `WITH_RDMA=OFF`.
- `WITH_C_API` must be `ON`, to build the C-based inference library for Android.
- `WITH_SWIG_PY` must be `OFF` because the Android platform doesn't support SWIG-based API.
Some Android-specific arguments:
- `ANDROID_STANDALONE_TOOLCHAIN`: the absolute path of the Android standalone toolchain, or the path relative to the CMake build directory. PaddlePaddle's CMake extensions would derive the cross-compiler, sysroot and Android API level from this argument.
- `ANDROID_TOOLCHAIN`: could be `gcc` or `clang`. The default value is `clang`.
- For CMake >= 3.7, it should anyway be `clang`. For older versions, it could be `gcc`.
- Android's official `clang` requires `glibc` >= 2.15.
- `ANDROID_ABI`: could be `armeabi-v7a` or `arm64-v8a`. The default value is `armeabi-v7a`.
- `ANDROID_NATIVE_API_LEVEL`: could be derived from the value of `ANDROID_STANDALONE_TOOLCHAIN`.
- `ANROID_ARM_MODE`:
- could be `ON` or `OFF`, and defaults to `ON`, when `ANDROID_ABI=armeabi-v7a`;
- no need to specify when `ANDROID_ABI=arm64-v8a`.
- `ANDROID_ARM_NEON`: indicates if to use NEON instructions.
- could be `ON` or `OFF`, and defaults to `ON`, when `ANDROID_ABI=armeabi-v7a`;
- no need to specify when `ANDROID_ABI=arm64-v8a`.
Other useful arguments:
- `USE_EIGEN_FOR_BLAS`: indicates if using Eigen. Could be `ON` or `OFF`, defaults to `OFF`.
- `HOST_C/CXX_COMPILER`: specifies the host compiler, which is used to build the host-specific protoc and target-specific OpenBLAS. It defaults to the value of the environment variable `CC`, or `cc`.
Some frequent configurations for your reference:
```bash
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm_standalone_toolchain \
-DANDROID_ABI=armeabi-v7a \
-DANDROID_ARM_NEON=ON \
-DANDROID_ARM_MODE=ON \
-DUSE_EIGEN_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
..
```
```
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm64_standalone_toolchain \
-DANDROID_ABI=arm64-v8a \
-DUSE_EIGEN_FOR_BLAS=OFF \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
..
```
There are some other arguments you might want to configure.
- `CMAKE_BUILD_TYPE=MinSizeRel` minimizes the size of library.
- `CMAKE_BUILD_TYPE-Release` optimizes the runtime performance.
Our own tip for performance optimization to use clang and Eigen or OpenBLAS:
- `CMAKE_BUILD_TYPE=Release`
- `ANDROID_TOOLCHAIN=clang`
- `USE_EIGEN_BLAS=ON` for `armeabi-v7a`, or `USE_EIGEN_FOR_BLAS=OFF` for `arm64-v8a`.
### Build and Install
After running `cmake`, we can run `make; make install` to build and install.
Before building, you might want to remove the `third_party` and `build` directories including pre-built libraries for other architectures.
After building，in the directory `CMAKE_INSTALL_PREFIX`, you will find three sub-directories:
- `include`: the header file of the inference library,
- `lib`: the inference library built for various Android ABIs,
- `third_party`: dependent third-party libraries built for Android.
doc/howto/cross_compiling/cross_compiling_for_android_cn.md
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# 构建Android平台上的PaddlePaddle库
用户可通过如下两种方式，交叉编译Android平台上适用的PaddlePaddle库：
- 基于Docker容器的编译方式
- 基于Docker容器的编译方式
- 基于Linux交叉编译环境的编译方式
## 基于Docker容器的编译方式
......@@ -26,14 +26,14 @@ Android的Docker开发镜像向用户提供两个可配置的参数：
|`ANDROID_API` |`>= 21` | `21` |
- 编译`armeabi-v7a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
```
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
```
- 编译`arm64-v8a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
```
- 编译`arm64-v8a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
```
执行上述`docker run`命令时，容器默认执行[paddle/scripts/docker/build_android.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh)脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置，并且会根据`ANDROID_ABI`和`ANDROID_API`自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当`ANDROID_ABI=arm64-v8a`，`ANDROID_API<21`时，Docker容器中将默认使用`Android API 21`的编译工具链。用户可以参考下文**配置交叉编译参数**章节，根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后，PaddlePaddle的C-API库将被安装到`$PWD/install_android`目录，所依赖的第三方库同时也被安装到`$PWD/install_android/third_party`目录。
......@@ -82,16 +82,16 @@ CMake系统对交叉编译提供了支持[cmake-toolchains](https://cmake.org/cm
Android平台可选配置参数：
- `ANDROID_STANDALONE_TOOLCHAIN`，独立工具链所在的绝对路径，或者相对于构建目录的相对路径。PaddlePaddle的CMake系统将根据该值自动推导和设置需要使用的交叉编译器、sysroot、以及Android API级别；否则，用户需要在cmake时手动设置这些值。无默认值。
- `ANDROID_TOOLCHAIN`，目标工具链。可设置`gcc/clang`，默认值为`clang`。
- CMake 3.7以上，将会始终使用`clang`工具链；CMake 3.7以下，可设置`ANDROID_TOOLCHAIN=gcc`以使用`gcc`工具链。
- `ANDROID_TOOLCHAIN`，目标工具链。可设置`gcc/clang`，默认值为`clang`。
- CMake 3.7以上，将会始终使用`clang`工具链；CMake 3.7以下，可设置`ANDROID_TOOLCHAIN=gcc`以使用`gcc`工具链。
- Android官方提供的`clang`编译器要求系统支持`GLIBC 2.15`以上。
- `ANDROID_ABI`，目标架构ABI。目前支持`armeabi-v7a`和`arm64-v8a`，默认值为`armeabi-v7a`。
- `ANDROID_NATIVE_API_LEVEL`，工具链的Android API级别。若没有显式设置，PaddlePaddle将根据`ANDROID_STANDALONE_TOOLCHAIN`的值自动推导得到。
- `ANROID_ARM_MODE`，是否使用ARM模式。
- `ANDROID_ABI=armeabi-v7a`时，可设置`ON/OFF`，默认值为`ON`；
- `ANROID_ARM_MODE`，是否使用ARM模式。
- `ANDROID_ABI=armeabi-v7a`时，可设置`ON/OFF`，默认值为`ON`；
- `ANDROID_ABI=arm64-v8a`时，不需要设置。
- `ANDROID_ARM_NEON`，是否使用NEON指令。
- `ANDROID_ABI=armeabi-v7a`时，可设置`ON/OFF`，默认值为`ON`；
- `ANDROID_ARM_NEON`，是否使用NEON指令。
- `ANDROID_ABI=armeabi-v7a`时，可设置`ON/OFF`，默认值为`ON`；
- `ANDROID_ABI=arm64-v8a`时，不需要设置。
其他配置参数：
......@@ -119,7 +119,7 @@ cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm64_standalone_toolchain \
-DANDROID_ABI=arm64-v8a \
-DUSE_EIGEN_FOR_BLAS=OFF \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
..
......@@ -128,8 +128,8 @@ cmake -DCMAKE_SYSTEM_NAME=Android \
用户还可根据自己的需求设置其他编译参数。比如希望最小化生成的库的大小，可以设置`CMAKE_BUILD_TYPE`为`MinSizeRel`；若希望最快的执行速度，则可设置`CMAKE_BUILD_TYPE`为`Release`。亦可以通过手动设置`CMAKE_C/CXX_FLAGS_MINSIZEREL/RELEASE`来影响PaddlePaddle的编译过程。
**性能TIPS**，为了达到最快的计算速度，在CMake参数配置上，有以下建议：
- 设置`CMAKE_BUILD_TYPE`为`Release`
- 使用`clang`编译工具链
- 设置`CMAKE_BUILD_TYPE`为`Release`
- 使用`clang`编译工具链
- `armeabi-v7a`时，设置`USE_EIGEN_BLAS=ON`，使用Eigen进行矩阵计算；`arm64-v8a`时，设置`USE_EIGEN_FOR_BLAS=OFF`，使用OpenBLAS进行矩阵计算
### 编译和安装
......
paddle/cuda/include/hl_matrix.h
浏览文件 @ 4b8bcf32
......@@ -300,4 +300,12 @@ extern void hl_matrix_col2Vol(real* dataDst,
real alpha,
real beta);
/**
* @brief Matrix col2Vol: Convert col matrix into 3D volume
* @param[out] out output int vector.
* @param[in] vec input float vector.
* @param[in] size size of the vector.
*/
extern void hl_vector_cast2int(int* out, real* vec, int size);
#endif /* HL_MATRIX_H_ */
paddle/cuda/include/stub/hl_matrix_stub.h
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......@@ -133,4 +133,6 @@ inline void hl_matrix_col2Vol(real* dataDst,
real alpha,
real beta) {}
inline void hl_vector_cast2int(int* out, real* vec, int size) {}
#endif // HL_MATRIX_STUB_H_
paddle/cuda/src/hl_cuda_matrix.cu
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......@@ -793,3 +793,14 @@ void hl_matrix_col2Vol(real* dataDst,
CHECK_SYNC("hl_matrix_col2Vol failed");
}
__global__ void keVectorCast2Int(int* out, real* vec, int size) {
for (int i = threadIdx.x; i < (size); i += blockDim.x) {
out[i] = int(vec[i]);
}
}
void hl_vector_cast2int(int* out, real* vec, int size) {
keVectorCast2Int<<<1, 512, 0, STREAM_DEFAULT>>>(out, vec, size);
CHECK_SYNC("hl_vector_cast2int failed");
}
paddle/framework/CMakeLists.txt
浏览文件 @ 4b8bcf32
......@@ -20,7 +20,8 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc
device_context)
cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
......
paddle/framework/backward.cc
浏览文件 @ 4b8bcf32
......@@ -24,7 +24,6 @@
#include "paddle/framework/op_registry.h"
#include "paddle/operators/dynamic_recurrent_op.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
namespace paddle {
namespace framework {
......@@ -38,7 +37,7 @@ static inline std::unique_ptr<OperatorBase> CreateGradOp(
op_desc.SetType(op.Type());
op_desc.SetAttrMap(op.Attrs());
auto& info = OpInfoMap::Instance().Get(op.Type());
auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var);
auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var, {});
std::vector<std::unique_ptr<OperatorBase>> grad_ops;
grad_ops.reserve(grad_descs.size());
std::transform(grad_descs.begin(), grad_descs.end(),
......@@ -220,19 +219,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
});
// process recurrent gradient op as a special operator.
if (forwardOp.Type() == "recurrent") {
// NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
// or this will result in infinite loop.
const auto& rnnop =
*static_cast<const operators::RecurrentOp*>(&forwardOp);
auto rnn_grad_op =
static_cast<operators::RecurrentGradientOp*>(grad_op.get());
const auto& stepnet_op =
*static_cast<const OperatorBase*>(&rnnop.stepnet());
// create stepnet's gradient op
rnn_grad_op->set_stepnet(
BackwardRecursive(stepnet_op, no_grad_names, grad_to_var, uniq_id));
} else if (forwardOp.Type() == "dynamic_recurrent") {
if (forwardOp.Type() == "dynamic_recurrent") {
// NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
// or this will result in infinite loop.
const auto& rnnop =
......@@ -331,7 +318,7 @@ static void CreateGradVarInBlock(
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVar(pname);
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
LOG(WARNING) << "Cannot find forward variable of " << arg
......@@ -348,7 +335,9 @@ static void CreateGradVarInBlock(
std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
const OpDescBind* op_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block =
std::vector<BlockDescBind*>()) {
std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
// All input gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
......@@ -364,9 +353,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
return grad_op_descs; // empty vector
}
grad_op_descs = OpInfoMap::Instance()
.Get(op_desc->Type())
.GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var);
grad_op_descs =
OpInfoMap::Instance()
.Get(op_desc->Type())
.GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var, grad_block);
std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
for (auto& desc : grad_op_descs) {
......@@ -400,21 +390,20 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
std::vector<std::unique_ptr<OpDescBind>> op_grads =
MakeOpGrad(*it, no_grad_vars, grad_to_var);
std::vector<std::unique_ptr<OpDescBind>> op_grads;
if ((*it)->Type() == "recurrent") {
PADDLE_ENFORCE_EQ(
op_grads.size(), static_cast<size_t>(1),
"rnn_op's gradient process should contain only one op.");
int step_block_idx = (*it)->GetBlockAttr("step_block");
auto backward_block_op_descs = MakeBlockBackward(
program_desc, step_block_idx, no_grad_vars, grad_to_var);
BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
BlockDescBind* backward_block =
program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
for (auto& ptr : backward_block_op_descs) {
backward_block->AppendAllocatedOp(std::move(ptr));
}
op_grads[0]->SetBlockAttr("step_block", *backward_block);
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else {
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
}
for (const auto& desc : op_grads) {
......
paddle/framework/block_desc.h
浏览文件 @ 4b8bcf32
......@@ -88,6 +88,8 @@ class BlockDescBind {
BlockDesc *Proto();
ProgramDescBind *Program() { return this->prog_; }
private:
void ClearPBOps();
void ClearPBVars();
......
paddle/framework/details/op_registry.h
浏览文件 @ 4b8bcf32
......@@ -108,8 +108,9 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
info->grad_op_maker_ = [](
const OpDescBind& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
T maker(fwd_op, no_grad_set, grad_to_var);
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block) {
T maker(fwd_op, no_grad_set, grad_to_var, grad_block);
return maker();
};
}
......
paddle/framework/executor.cc
浏览文件 @ 4b8bcf32
......@@ -31,7 +31,7 @@ namespace framework {
const std::string kFeedOpType = "feed";
const std::string kFetchOpType = "fetch";
Executor::Executor(const std::vector<platform::Place>& places) {
Executor::Executor(const std::vector<platform::Place>& places) : own_(true) {
PADDLE_ENFORCE_GT(places.size(), 0);
device_contexts_.resize(places.size());
for (size_t i = 0; i < places.size(); i++) {
......@@ -52,8 +52,10 @@ Executor::Executor(const std::vector<platform::Place>& places) {
}
Executor::~Executor() {
for (auto& device_context : device_contexts_) {
delete device_context;
if (own_) {
for (auto& device_context : device_contexts_) {
delete device_context;
}
}
}
......@@ -66,44 +68,61 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
} else {
PADDLE_THROW(
"Variable type must be "
"LoDTensor/SelectedRows/FEED_MINIBATCH/FETCH_LIST.");
"Variable type %d is not in "
"[LoDTensor, SelectedRows, FEED_MINIBATCH, FETCH_LIST]",
var_type);
}
}
void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id) {
void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
bool create_local_scope) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
// - will change to use multiple blocks for RNN op and Cond Op
PADDLE_ENFORCE_LT(block_id, pdesc.Size());
PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), pdesc.Size());
auto& block = pdesc.Block(block_id);
auto& device = device_contexts_[0];
Scope& local_scope = scope->NewScope();
for (auto& var : block.AllVars()) {
if (var->Persistable()) {
auto* ptr = scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope.Var(var->Name());
Scope* local_scope = scope;
if (create_local_scope) {
local_scope = &scope->NewScope();
for (auto& var : block.AllVars()) {
if (var->Persistable()) {
auto* ptr = scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto& var : block.AllVars()) {
auto* ptr = local_scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
op->Run(local_scope, *device);
op->Run(*local_scope, *device);
}
if (create_local_scope) {
scope->DeleteScope(local_scope);
}
scope->DeleteScope(&local_scope);
}
Executor::Executor(const platform::DeviceContext& device)
: device_contexts_({&device}), own_(false) {}
} // namespace framework
} // namespace paddle
paddle/framework/executor.h
浏览文件 @ 4b8bcf32
......@@ -25,6 +25,7 @@ namespace framework {
class Executor {
public:
explicit Executor(const std::vector<platform::Place>& places);
explicit Executor(const platform::DeviceContext& devices);
~Executor();
/* @Brief
......@@ -34,10 +35,11 @@ class Executor {
* ProgramDesc
* Scope
*/
void Run(const ProgramDescBind&, Scope*, int);
void Run(const ProgramDescBind&, Scope*, int, bool create_local_scope = true);
private:
std::vector<platform::DeviceContext*> device_contexts_;
std::vector<const platform::DeviceContext*> device_contexts_;
bool own_;
};
} // namespace framework
......
paddle/framework/grad_op_desc_maker.h
浏览文件 @ 4b8bcf32
......@@ -15,6 +15,7 @@
#pragma once
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/framework/op_desc.h"
#include "paddle/framework/operator.h"
......@@ -26,8 +27,13 @@ class GradOpDescMakerBase {
explicit GradOpDescMakerBase(
const OpDescBind& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var)
: fwd_op_(fwd_op), no_grad_set_(no_grad_set), grad_to_var_(grad_to_var) {}
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block =
std::vector<BlockDescBind*>())
: fwd_op_(fwd_op),
no_grad_set_(no_grad_set),
grad_to_var_(grad_to_var),
grad_block_(grad_block) {}
virtual ~GradOpDescMakerBase() = default;
virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
......@@ -102,6 +108,9 @@ class GradOpDescMakerBase {
const OpDescBind& fwd_op_;
const std::unordered_set<std::string>& no_grad_set_;
std::unordered_map<std::string, std::string>* grad_to_var_;
protected:
std::vector<BlockDescBind*> grad_block_;
};
class SingleGradOpDescMaker : public GradOpDescMakerBase {
......
paddle/framework/op_desc.cc
浏览文件 @ 4b8bcf32
......@@ -327,6 +327,19 @@ void OpDescBind::InferShape(const BlockDescBind &block) const {
PADDLE_ENFORCE(static_cast<bool>(infer_shape),
"%s's infer_shape has not been registered", this->Type());
CompileTimeInferShapeContext ctx(*this, block);
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
auto inames = this->InputArgumentNames();
sout << " From [";
std::copy(inames.begin(), inames.end(),
std::ostream_iterator<std::string>(sout, ", "));
sout << "] to [";
auto onames = this->OutputArgumentNames();
std::copy(onames.begin(), onames.end(),
std::ostream_iterator<std::string>(sout, ", "));
sout << "]";
VLOG(10) << sout.str();
}
infer_shape(&ctx);
}
......
paddle/framework/operator.cc
浏览文件 @ 4b8bcf32
......@@ -37,32 +37,32 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
"Op %s input %s should contain only one variable", type_,
name);
"Operator %s's input %s should contain only one variable.",
type_, name);
return ins.empty() ? kEmptyVarName : ins[0];
}
const std::vector<std::string>& OperatorBase::Inputs(
const std::string& name) const {
auto it = inputs_.find(name);
PADDLE_ENFORCE(it != inputs_.end(), "Op %s do not have input %s", type_,
name);
PADDLE_ENFORCE(it != inputs_.end(), "Operator %s does not have the input %s.",
type_, name);
return it->second;
}
std::string OperatorBase::Output(const std::string& name) const {
auto& outs = Outputs(name);
PADDLE_ENFORCE_LE(outs.size(), 1UL,
"Op %s output %s should contain only one variable", type_,
name);
"Operator %s's output %s should contain only one variable.",
type_, name);
return outs.empty() ? kEmptyVarName : outs[0];
}
const std::vector<std::string>& OperatorBase::Outputs(
const std::string& name) const {
auto it = outputs_.find(name);
PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output called %s",
type_, name);
PADDLE_ENFORCE(it != outputs_.end(),
"Operator %s does not have an output called %s.", type_, name);
return it->second;
}
......@@ -126,7 +126,7 @@ OperatorBase::OperatorBase(const std::string& type,
std::vector<std::string> OperatorBase::InputVars() const {
std::vector<std::string> ret_val;
for (auto& o : outputs_) {
for (auto& o : inputs_) {
ret_val.reserve(ret_val.size() + o.second.size());
ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
}
......@@ -394,7 +394,19 @@ class RuntimeInferShapeContext : public InferShapeContext {
void OperatorWithKernel::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
VLOG(3) << "Running operator " << this->Type();
if (VLOG_IS_ON(1)) {
auto inputs = this->InputVars();
auto outputs = this->OutputVars(true);
std::ostringstream sout;
sout << "Run operator " << this->Type() << " From [";
std::ostream_iterator<std::string> out_it(sout, ",");
std::copy(inputs.begin(), inputs.end(), out_it);
sout << "] to [";
std::copy(outputs.begin(), outputs.end(), out_it);
sout << "]";
VLOG(1) << sout.str();
}
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
......
paddle/framework/operator.h
浏览文件 @ 4b8bcf32
......@@ -427,7 +427,8 @@ class OperatorWithKernel : public OperatorBase {
int tmp = static_cast<int>(ToDataType(t->type()));
VLOG(3) << "Input " << ipt_name << " with data_type " << tmp;
PADDLE_ENFORCE(tmp == data_type || data_type == -1,
"DataType of Paddle Op %s must be same.", Type());
"DataType of Paddle Op %s must be the same.",
Type());
data_type = tmp;
}
}
......
paddle/framework/scope.cc
浏览文件 @ 4b8bcf32
......@@ -47,8 +47,12 @@ Variable* Scope::Var(const std::string& name) {
return v;
}
Variable* Scope::Var() {
return Var(string::Sprintf("%p.%d", this, vars_.size()));
Variable* Scope::Var(std::string* name) {
auto var_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = var_name;
}
return Var(var_name);
}
Variable* Scope::FindVar(const std::string& name) const {
......
paddle/framework/scope.h
浏览文件 @ 4b8bcf32
......@@ -49,7 +49,7 @@ class Scope {
Variable* Var(const std::string& name);
/// Create a variable with a scope-unique name.
Variable* Var();
Variable* Var(std::string* name = nullptr);
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
......
paddle/framework/tensor.h
浏览文件 @ 4b8bcf32
......@@ -118,12 +118,14 @@ class Tensor {
const platform::DeviceContext& ctx);
/**
* @brief Return the slice of the tensor.
* @brief Return a sub-tensor of the given tensor.
*
* @param[in] begin_idx The begin index of the slice.
* @param[in] end_idx The end index of the slice.
* @param[in] begin_idx The index of the start row(inclusive) to slice.
* The index number begins from 0.
* @param[in] end_idx The index of the end row(exclusive) to slice.
* The index number begins from 0.
*/
inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
inline Tensor Slice(int begin_idx, int end_idx) const;
platform::Place place() const {
PADDLE_ENFORCE_NOT_NULL(
......
paddle/framework/tensor_impl.h
浏览文件 @ 4b8bcf32
......@@ -112,9 +112,10 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
PADDLE_ENFORCE_GT(numel(), 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
PADDLE_ENFORCE_GT(
numel(), 0,
"When calling this method, the Tensor's numel must be larger than zero. "
"Please check Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
......@@ -227,12 +228,14 @@ inline void Tensor::CopyFromVector(const std::vector<T>& src,
#endif
}
inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
inline Tensor Tensor::Slice(int begin_idx, int end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx, 0, "Slice begin index is less than zero.");
PADDLE_ENFORCE_LE(end_idx, dims_[0], "Slice end index is out of bound.");
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_GE(begin_idx, 0,
"The start row index must be greater than 0.");
PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is out of bound.");
PADDLE_ENFORCE_LT(
begin_idx, end_idx,
"The start row index must be lesser than the end row index.");
if (dims_[0] == 1) {
return *this;
......
paddle/framework/type_defs.h
浏览文件 @ 4b8bcf32
......@@ -29,6 +29,7 @@ class OpDescBind;
class BlockDescBind;
class BlockDesc;
class InferShapeContext;
class BlockDescBind;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
......@@ -46,7 +47,8 @@ using OpCreator = std::function<OperatorBase*(
using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
std::unordered_map<std::string, std::string>* /*grad_to_var*/)>;
std::unordered_map<std::string, std::string>* /*grad_to_var*/,
const std::vector<BlockDescBind*>& grad_block)>;
using InferVarTypeFN = std::function<void(const OpDescBind& /*op_desc*/,
BlockDescBind* /*block*/)>;
......
paddle/gserver/evaluators/Evaluator.cpp
浏览文件 @ 4b8bcf32
......@@ -395,14 +395,24 @@ real AucEvaluator::evalImp(std::vector<Argument>& arguments) {
CHECK_LE(arguments.size(), (size_t)3);
MatrixPtr output = arguments[0].value;
IVectorPtr label = arguments[1].ids;
MatrixPtr labelval = arguments[1].value;
bool supportWeight = (3 == arguments.size()) ? true : false;
MatrixPtr weight = supportWeight ? arguments[2].value : nullptr;
if (nullptr == output || nullptr == label ||
(supportWeight && nullptr == weight)) {
if (nullptr == output || (supportWeight && nullptr == weight)) {
return 0;
}
size_t insNum = output->getHeight();
size_t outputDim = output->getWidth();
// Copy label from value to a vector.
if (nullptr == label && nullptr != labelval) {
// label width is 1
CHECK_EQ(1U, labelval->getWidth());
VectorPtr vec =
Vector::create(labelval->getData(), insNum, output->useGpu());
label = vec->castToInt();
}
CHECK_EQ(insNum, label->getSize());
if (supportWeight) {
CHECK_EQ(insNum, weight->getHeight());
......@@ -443,6 +453,7 @@ real AucEvaluator::evalImp(std::vector<Argument>& arguments) {
int* labelD = label->getData();
real* weightD = supportWeight ? weight->getData() : nullptr;
size_t pos = realColumnIdx_;
for (size_t i = 0; i < insNum; ++i) {
real value = outputD[pos];
uint32_t binIdx = static_cast<uint32_t>(value * kBinNum_);
......
paddle/gserver/layers/CRFLayer.cpp
浏览文件 @ 4b8bcf32
......@@ -101,8 +101,10 @@ void CRFLayer::backward(const UpdateCallback& callback) {
: real(1.0f);
instanceWeight *= coeff_;
MatrixPtr grad = output.grad->subRowMatrix(starts[i], starts[i + 1]);
grad->add(*crfs_[i].getXGrad(), real(1.0f), instanceWeight);
if (output.grad) {
MatrixPtr grad = output.grad->subRowMatrix(starts[i], starts[i + 1]);
grad->add(*crfs_[i].getXGrad(), real(1.0f), instanceWeight);
}
if (needWGrad) {
weight_->getWGrad()->add(
*crfs_[i].getWGrad(), real(1.0f), instanceWeight);
......
paddle/gserver/layers/LinearChainCRF.cpp
浏览文件 @ 4b8bcf32
......@@ -102,7 +102,6 @@ real LinearChainCRF::forward(real* x, int* s, int length) {
}
void LinearChainCRF::backward(real* x, int* s, int length, bool needWGrad) {
MatrixPtr matX = Matrix::create(x, length, numClasses_);
Matrix::resizeOrCreate(matGrad_, length, numClasses_);
Matrix::resizeOrCreate(beta_, length, numClasses_);
real* b = b_->getData();
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 4b8bcf32
......@@ -273,31 +273,37 @@ void MKLDNNTester::printVector(const VectorPtr& v) {
VLOG(MKLDNN_ALL) << std::endl << ostr.str();
}
double MKLDNNTester::getDelta(const real* d1,
const real* d2,
double MKLDNNTester::getDelta(const real* refer,
const real* value,
size_t len,
const float failRate,
const float thres) {
double delta = 0, sum = 0;
int failCnt = 0;
const double eps = 1e-5;
double maxOut = 0;
double maxRatio = 0;
for (size_t i = 0; i < len; ++i) {
double ref = fabs(d2[i]);
double diff = fabs(d1[i] - d2[i]);
double ref = fabs(refer[i]);
double val = fabs(value[i]);
double diff = fabs(refer[i] - value[i]);
delta += diff;
sum += ref;
if (ref > eps && fabs(d1[i]) > eps && diff / ref > thres) {
maxOut = std::max(maxOut, diff / ref);
if (ref < eps && val < eps) { // both values are very small
continue;
}
double ratio = diff / ref;
if (ratio > thres) {
maxRatio = std::max(maxRatio, ratio);
failCnt++;
}
}
EXPECT_TRUE(std::isnormal(sum));
EXPECT_FALSE(std::isinf(sum));
EXPECT_FALSE(std::isnan(sum));
EXPECT_FALSE(std::isnan(delta));
VLOG(MKLDNN_ALL) << "reference avg data: " << sum / len
<< ", delta: " << delta / sum << ", failCnt:" << failCnt;
return (failCnt / (float)len) > failRate ? maxOut : delta / sum;
double res = sum > eps ? delta / sum : eps;
return (failCnt / (float)len) > failRate ? maxRatio : res;
}
double MKLDNNTester::compareMatrix(const MatrixPtr& m1, const MatrixPtr& m2) {
......@@ -515,12 +521,16 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
gradientMachine->forward(in.inArgs[i], &outArgs, PASS_TRAIN);
// save forward result
for (size_t k = 0; k < outArgs.size(); k++) {
MatrixPtr value = Matrix::create(outArgs[k].value->getHeight(),
outArgs[k].value->getWidth(),
false,
false);
value->copyFrom(*outArgs[k].value);
out.outValues.push_back(value);
const MatrixPtr& src = outArgs[k].value;
MatrixPtr dst =
Matrix::create(src->getHeight(), src->getWidth(), false, false);
if (typeid(*src) == typeid(MKLDNNMatrix)) {
MKLDNNMatrixPtr dnnSrc = std::dynamic_pointer_cast<MKLDNNMatrix>(src);
dnnSrc->copyTo(*dst);
} else {
dst->copyFrom(*src);
}
out.outValues.push_back(dst);
}
// random backward input
......@@ -543,19 +553,19 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
void MKLDNNTester::compareResult(DataOut& ref, DataOut& dnn, float eps) {
CHECK_EQ(ref.outValues.size(), dnn.outValues.size());
CHECK_EQ(ref.paraValues.size(), dnn.paraValues.size());
VLOG(MKLDNN_TESTS) << "compare value size: " << ref.outValues.size();
for (size_t i = 0; i < ref.outValues.size(); i++) {
VLOG(MKLDNN_TESTS) << "compare value index: " << i;
EXPECT_LE(fabs(compareMatrix(ref.outValues[i], dnn.outValues[i])), eps);
}
VLOG(MKLDNN_TESTS) << "compare param size: " << ref.outValues.size();
for (size_t i = 0; i < ref.paraValues.size(); i++) {
VLOG(MKLDNN_TESTS) << "compare param index: " << i;
EXPECT_LE(fabs(compareVector(ref.paraValues[i], dnn.paraValues[i])), eps);
}
}
void MKLDNNTester::runBranchesTest(const std::string& configPath,
size_t iter,
float eps) {
void MKLDNNTester::runNetTest(const std::string& configPath,
size_t iter,
float eps) {
DataIn in;
initArgument(in, configPath, iter);
DataOut outCpu, outDnn;
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 4b8bcf32
......@@ -85,17 +85,17 @@ public:
bool printDetails = false,
size_t iter = 3,
float epsilon = 1e-4);
static void runBranchesTest(const std::string& configPath,
size_t iter = 3,
float eps = 1e-4);
static void runNetTest(const std::string& configPath,
size_t iter = 2,
float eps = 1e-4);
static void initArgument(DataIn& data,
const std::string& configPath,
size_t iter = 3);
size_t iter = 2);
static void getOutResult(const std::string& configPath,
DataIn& in,
DataOut& out,
bool use_mkldnn,
size_t iter = 3);
size_t iter = 2);
private:
void reset(const TestConfig& dnn, const TestConfig& ref, size_t batchSize);
......@@ -128,13 +128,13 @@ private:
/**
* Get delta percent
* if many(>failRate) wrong(abs(dnn-ref)/abs(ref)>thres) points return the
* max(diff/ref)
* else return sum(abs(a-b)) / sum(abs(b))
* if many(>failRate) wrong(abs(val-ref)/abs(ref) > thres) points
* return the max(diff/ref)
* else return sum(abs(diff)) / sum(abs(ref))
* The return value should be smaller than eps when passing.
*/
static double getDelta(const real* d1,
const real* d2,
static double getDelta(const real* refer,
const real* value,
size_t len,
const float failRate = 1e-3,
const float thres = 0.1);
......
paddle/trainer/tests/sample_trainer_config_branch_net.conf → paddle/gserver/tests/mkldnn_branch_net.conf
浏览文件 @ 4b8bcf32
......@@ -14,36 +14,82 @@
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 128,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
settings(batch_size=16)
channels = get_config_arg("channels", int, 2)
def two_conv(input, group_name):
out1 = img_conv_layer(input=input,
name=group_name+'_conv1_',
filter_size=1,
num_filters=channels,
padding=0,
shared_biases=True,
act=ReluActivation())
out2 = img_conv_layer(input=input,
name=group_name+'_conv2_',
filter_size=3,
num_filters=channels,
padding=1,
shared_biases=True,
act=ReluActivation())
return out1, out2
def two_conv_bn(input, group_name):
out1, out2 = two_conv(input, group_name)
out1 = batch_norm_layer(input=out1,
name=group_name+'_bn1_',
use_global_stats=False,
act=ReluActivation())
out2 = batch_norm_layer(input=out2,
name=group_name+'_bn2_',
use_global_stats=False,
act=ReluActivation())
return out1, out2
def two_conv_pool(input, group_name):
out1, out2 = two_conv(input, group_name)
out1 = img_pool_layer(input=out1,
name=group_name+'_pool1_',
pool_size=3,
stride=2,
padding=0,
pool_type=MaxPooling())
out2 = img_pool_layer(input=out2,
name=group_name+'_pool2_',
pool_size=5,
stride=2,
padding=1,
pool_type=MaxPooling())
return out1, out2
def two_fc(input, group_name):
out1 = fc_layer(input=input,
name=group_name+'_fc1_',
size=channels,
bias_attr=False,
act=LinearActivation())
tmp = img_conv_layer(input=data,
num_channels=1,
filter_size=3,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
out2 = fc_layer(input=input,
name=group_name+'_fc2_',
size=channels,
bias_attr=False,
act=LinearActivation())
return out1, out2
a1 = img_conv_layer(input=tmp,
filter_size=1,
num_filters=32,
padding=0,
shared_biases=True,
act=ReluActivation())
data = data_layer(name ="input", size=channels*16*16)
a2 = img_conv_layer(input=tmp,
tmp = img_conv_layer(input=data,
num_channels=channels,
filter_size=3,
num_filters=32,
num_filters=channels,
padding=1,
shared_biases=True,
act=ReluActivation())
a1, a2 = two_conv(tmp, 'conv_branch')
tmp = addto_layer(input=[a1, a2],
act=ReluActivation(),
bias_attr=False)
......@@ -54,36 +100,11 @@ tmp = img_pool_layer(input=tmp,
padding=1,
pool_type=AvgPooling())
b1 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
b1 = img_pool_layer(input=b1,
pool_size=3,
stride=2,
padding=0,
pool_type=MaxPooling())
b2 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=64,
padding=1,
shared_biases=True,
act=ReluActivation())
b2 = img_pool_layer(input=b2,
pool_size=5,
stride=2,
padding=1,
pool_type=MaxPooling())
b1, b2 = two_conv_pool(tmp, 'pool_branch')
tmp = concat_layer(input=[b1, b2])
tmp = img_pool_layer(input=tmp,
num_channels=96,
num_channels=channels*2,
pool_size=3,
stride=2,
padding=1,
......@@ -91,8 +112,9 @@ tmp = img_pool_layer(input=tmp,
tmp = img_conv_layer(input=tmp,
filter_size=3,
num_filters=32,
num_filters=channels,
padding=1,
stride=2,
shared_biases=True,
act=LinearActivation(),
bias_attr=False)
......@@ -101,33 +123,20 @@ tmp = batch_norm_layer(input=tmp,
use_global_stats=False,
act=ReluActivation())
c1 = img_conv_layer(input=tmp,
filter_size=1,
num_filters=32,
padding=0,
shared_biases=True,
act=ReluActivation())
c2 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
c1, c2 = two_conv_bn(tmp, 'bn_branch')
tmp = addto_layer(input=[c1, c2],
act=ReluActivation(),
bias_attr=False)
tmp = fc_layer(input=tmp, size=64,
bias_attr=False,
act=TanhActivation())
tmp = fc_layer(input=tmp, size=channels,
bias_attr=True,
act=ReluActivation())
output = fc_layer(input=tmp, size=10,
d1, d2 = two_fc(tmp, 'fc_branch')
tmp = addto_layer(input=[d1, d2])
out = fc_layer(input=tmp, size=10,
bias_attr=True,
act=SoftmaxActivation())
lbl = data_layer(name ="label", size=10)
cost = classification_cost(input=output, label=lbl)
outputs(cost)
outputs(out)
paddle/gserver/tests/mkldnn_branches_fc.conf 已删除 100644 → 0
浏览文件 @ faad8351
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=16)
channels = get_config_arg("channels", int, 2)
def two_fc(input, group_name):
out1 = fc_layer(input=input,
name=group_name+'_fc1',
size=channels,
bias_attr=False,
act=LinearActivation())
out2 = fc_layer(input=input,
name=group_name+'_fc2',
size=channels,
bias_attr=False,
act=LinearActivation())
return out1, out2
data = data_layer(name ="input", size=channels*16*16)
conv = img_conv_layer(input=data,
num_channels=channels,
filter_size=3,
num_filters=channels,
padding=1,
shared_biases=True,
act=LinearActivation())
pool = img_pool_layer(input=conv,
pool_size=3,
stride=2,
padding=1,
pool_type=AvgPooling())
a1, a2 = two_fc(input=pool, group_name='a')
concat = concat_layer(input=[a1, a2])
b1, b2 = two_fc(input=pool, group_name='b')
addto = addto_layer(input=[b1, b2])
outputs([concat, addto])
paddle/gserver/tests/mkldnn_branches_pool.conf 已删除 100644 → 0
浏览文件 @ faad8351
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=16)
channels = get_config_arg("channels", int, 2)
def two_pool(input, group_name):
out1 = img_pool_layer(input=input,
name=group_name+'_pool1',
pool_size=3,
stride=2,
padding=0,
pool_type=MaxPooling())
out2 = img_pool_layer(input=input,
name=group_name+'_pool2',
pool_size=5,
stride=2,
padding=1,
pool_type=MaxPooling())
return out1, out2
data = data_layer(name ="input", size=channels*16*16)
conv = img_conv_layer(input=data,
num_channels=channels,
filter_size=3,
num_filters=channels,
padding=1,
shared_biases=True,
act=LinearActivation())
pool = img_pool_layer(input=conv,
pool_size=3,
stride=1,
padding=1,
pool_type=AvgPooling())
a1, a2 = two_pool(input=pool, group_name='a')
concat = concat_layer(input=[a1, a2])
b1, b2 = two_pool(input=pool, group_name='b')
addto = addto_layer(input=[b1, b2])
outputs([concat, addto])
paddle/gserver/tests/mkldnn_branches_conv.conf → paddle/gserver/tests/mkldnn_simple_net.conf
浏览文件 @ 4b8bcf32
......@@ -17,40 +17,48 @@ from paddle.trainer_config_helpers import *
settings(batch_size=16)
channels = get_config_arg("channels", int, 2)
def two_conv(input, group_name):
out1 = img_conv_layer(input=input,
name=group_name+'_conv1',
filter_size=1,
num_filters=channels,
padding=0,
shared_biases=True,
act=ReluActivation())
data = data_layer(name ="input", size=channels*16*16)
out2 = img_conv_layer(input=input,
name=group_name+'_conv2',
tmp = img_conv_layer(input=data,
num_channels=channels,
filter_size=3,
num_filters=channels,
padding=1,
shared_biases=True,
act=ReluActivation())
return out1, out2
data = data_layer(name ="input", size=channels*16*16)
tmp = img_pool_layer(input=tmp,
pool_size=3,
stride=1,
padding=0,
pool_type=AvgPooling())
conv = img_conv_layer(input=data,
num_channels=channels,
tmp = img_conv_layer(input=tmp,
filter_size=3,
num_filters=channels,
padding=1,
shared_biases=True,
act=ReluActivation())
act=LinearActivation(),
bias_attr=False)
a1, a2 = two_conv(input=conv, group_name='a')
tmp = batch_norm_layer(input=tmp,
use_global_stats=False,
act=ReluActivation())
concat = concat_layer(input=[a1, a2])
tmp = img_pool_layer(input=tmp,
pool_size=3,
stride=2,
padding=1,
pool_type=MaxPooling())
b1, b2 = two_conv(input=conv, group_name='b')
tmp = fc_layer(input=tmp,
size=channels,
bias_attr=False,
act=ReluActivation())
addto = addto_layer(input=[b1, b2])
out = fc_layer(input=tmp,
size=10,
bias_attr=True,
act=SoftmaxActivation())
outputs([concat, addto])
outputs(out)
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 4b8bcf32
......@@ -234,8 +234,7 @@ static void getMKLDNNBatchNormConfig(TestConfig& cfg,
cfg.inputDefs.push_back({INPUT_DATA, "layer_2_moving_var", 1, size_t(pm.ic)});
cfg.inputDefs.back().isStatic = true;
LayerInputConfig* input = cfg.layerConfig.add_inputs();
// TODO(TJ): uncomment me when refine and support comparing all zeroes vector
// cfg.layerConfig.set_active_type("relu");
cfg.layerConfig.set_active_type("relu");
cfg.layerConfig.add_inputs();
cfg.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
......@@ -309,15 +308,15 @@ TEST(MKLDNNActivation, Activations) {
}
DECLARE_string(config_args);
TEST(MKLDNNLayer, branches) {
std::vector<std::string> cases = {"conv", "pool", "fc"};
TEST(MKLDNNNet, net) {
std::vector<std::string> cases = {"simple", "branch"};
for (auto name : cases) {
std::string config = "./gserver/tests/mkldnn_branches_" + name + ".conf";
std::string config = "./gserver/tests/mkldnn_" + name + "_net.conf";
for (auto channels : {2, 32}) {
std::ostringstream oss;
oss << "channels=" << channels;
FLAGS_config_args = oss.str();
MKLDNNTester::runBranchesTest(config);
MKLDNNTester::runNetTest(config);
}
}
}
......
paddle/math/MKLDNNMatrix.h
浏览文件 @ 4b8bcf32
......@@ -102,6 +102,11 @@ public:
m_->copyFrom(src);
}
void copyTo(Matrix& dst) {
// TODO(TJ): reorder data if this format is not nchw or x
dst.copyFrom(*m_);
}
public:
/**
* Reorder this MKLDNNMatrix from other format.
......
paddle/math/Vector.cpp
浏览文件 @ 4b8bcf32
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <memory>
#include "Matrix.h"
#include "hl_gpu.h"
#include "hl_matrix.h"
#include "hl_table_apply.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/Logging.h"
......@@ -99,6 +100,19 @@ MatrixPtr VectorT<int>::toOneHotSparseMatrix(size_t idRange, bool useGpu) {
return mat;
}
template <>
std::shared_ptr<VectorT<int>> VectorT<real>::castToInt() {
std::shared_ptr<VectorT<int>> ret = IVector::create(this->getSize(), useGpu_);
if (useGpu_) {
hl_vector_cast2int(ret->getData(), this->getData(), this->getSize());
} else {
for (size_t i = 0; i < getSize(); ++i) {
ret->getData()[i] = int(this->getData()[i]);
}
}
return ret;
}
template <class T>
GpuVectorT<T>::GpuVectorT(size_t size)
: VectorT<T>(size,
......
paddle/math/Vector.h
浏览文件 @ 4b8bcf32
......@@ -162,6 +162,13 @@ public:
*/
std::shared_ptr<Matrix> toOneHotSparseMatrix(size_t idRange, bool useGpu);
/**
* @brief cast vector of "real" elements to "int" elements.
*
* @note: float -> int must be casted, or you'll get wrong data.
*/
std::shared_ptr<VectorT<int>> castToInt();
/**
* This function will crash if the size of src and dest is different.
*/
......
paddle/operators/CMakeLists.txt
浏览文件 @ 4b8bcf32
......@@ -131,9 +131,10 @@ add_subdirectory(math)
add_subdirectory(nccl)
set(DEPS_OPS
recurrent_op
cond_op
cross_entropy_op
recurrent_op
dynamic_recurrent_op
softmax_with_cross_entropy_op
sum_op
pool_op
......@@ -143,9 +144,6 @@ set(DEPS_OPS
lstm_op
gru_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor net_op)
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
......@@ -158,7 +156,9 @@ endif()
op_library(sequence_conv_op DEPS context_project)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array)
op_library(recurrent_op SRCS recurrent_op.cc DEPS executor)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
op_library(${src})
......@@ -170,8 +170,9 @@ cc_test(gather_test SRCS gather_test.cc DEPS tensor)
cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc DEPS dynamic_recurrent_op recurrent_op tensor_array)
cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc
rnn/recurrent_op_utils.cc
DEPS dynamic_recurrent_op)
if(WITH_GPU)
nv_test(nccl_op_test SRCS nccl_op_test.cu DEPS nccl_op gpu_info device_context)
endif()
......
paddle/operators/activation_op.cc
浏览文件 @ 4b8bcf32
......@@ -43,7 +43,12 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sigmoid operator");
AddOutput("Y", "Output of Sigmoid operator");
AddComment("Sigmoid activation operator, sigmoid = 1 / (1 + exp(-x))");
AddComment(R"DOC(
Sigmoid activation operator.
$y = 1 / (1 + e^{-x})$
)DOC");
}
};
......@@ -54,8 +59,12 @@ class LogSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LogSigmoid operator");
AddOutput("Y", "Output of LogSigmoid operator");
AddComment(
"Logsigmoid activation operator, logsigmoid = log (1 / (1 + exp(-x)))");
AddComment(R"DOC(
Logsigmoid activation operator.
$y = \log(1 / (1 + e^{-x}))$
)DOC");
}
};
......@@ -65,7 +74,12 @@ class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Exp operator");
AddOutput("Y", "Output of Exp operator");
AddComment("Exp activation operator, exp(x) = e^x");
AddComment(R"DOC(
Exp activation operator.
$y = e^x$
)DOC");
}
};
......@@ -75,7 +89,12 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu operator");
AddOutput("Y", "Output of Relu operator");
AddComment("Relu activation operator, relu(x) = max(x, 0)");
AddComment(R"DOC(
Relu activation operator.
$y = \max(x, 0)$
)DOC");
}
};
......@@ -87,11 +106,14 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Y", "Output of LeakyRelu operator");
AddComment(
"LeakyRelu activation operator, "
"leaky_relu = max(x, alpha * x)");
AddAttr<AttrType>("alpha", "The small negative slope")
.SetDefault(static_cast<AttrType>(0.02f));
AddComment(R"DOC(
LeakyRelu activation operator.
$y = \max(x, \alpha * x)$
)DOC");
}
};
......@@ -103,12 +125,20 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softshrink operator");
AddOutput("Y", "Output of Softshrink operator");
AddComment(
"Softshrink activation operator, "
"softshrink = x - lambda, if x > lambda;"
" x + lambda, if x < lambda; 0 otherwise");
AddAttr<AttrType>("lambda", "non-negative offset")
.SetDefault(static_cast<AttrType>(0.5f));
AddComment(R"DOC(
Softshrink activation operator.
$$
y = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
)DOC");
}
};
......@@ -118,9 +148,12 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Tanh operator");
AddOutput("Y", "Output of Tanh operator");
AddComment(
"Tanh activation operator, tanh = (exp(x) - exp(-x)) / (exp(x) + "
"exp(-x))");
AddComment(R"DOC(
Tanh activation operator.
$$y = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC");
}
};
......@@ -131,7 +164,12 @@ class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of TanhShrink operator");
AddOutput("Y", "Output of TanhShrink operator");
AddComment("TanhShrink activation operator, tanhshrink(x) = x - tanh(x)");
AddComment(R"DOC(
TanhShrink activation operator.
$$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC");
}
};
......@@ -143,13 +181,20 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardShrink operator");
AddOutput("Y", "Output of HardShrink operator");
AddComment(
"HardShrink activation operator, "
"hard_shrink(x) = x if x > lambda"
"hard_shrink(x) = x if x < -lambda"
"hard_shrink(x) = 0 otherwise");
AddAttr<AttrType>("threshold", "The value of threshold for HardShrink")
.SetDefault(static_cast<AttrType>(0.5));
AddComment(R"DOC(
HardShrink activation operator.
$$
y = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
)DOC");
}
};
......@@ -159,7 +204,12 @@ class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sqrt operator");
AddOutput("Y", "Output of Sqrt operator");
AddComment("Sqrt activation operator, sqrt(x) = x^(1/2)");
AddComment(R"DOC(
Sqrt activation operator.
$y = \sqrt{x}$
)DOC");
}
};
......@@ -169,7 +219,12 @@ class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Abs operator");
AddOutput("Y", "Output of Abs operator");
AddComment("Abs activation operator, abs(x) = |x|");
AddComment(R"DOC(
Abs activation operator.
$y = |x|$
)DOC");
}
};
......@@ -180,7 +235,12 @@ class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Reciprocal operator");
AddOutput("Y", "Output of Reciprocal operator");
AddComment("Reciprocal activation operator, reciprocal(x) = 1 / x");
AddComment(R"DOC(
Reciprocal activation operator.
$$y = \frac{1}{x}$$
)DOC");
}
};
......@@ -190,7 +250,14 @@ class LogOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Log operator");
AddOutput("Y", "Output of Log operator");
AddComment("Log activation operator, log(x) = natural logarithm of x");
AddComment(R"DOC(
Log activation operator.
$y = \ln(x)$
Natural logarithm of x.
)DOC");
}
};
......@@ -200,7 +267,12 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Square operator");
AddOutput("Y", "Output of Square operator");
AddComment("Square activation operator, square(x) = x^2");
AddComment(R"DOC(
Square activation operator.
$y = x^2$
)DOC");
}
};
......@@ -211,7 +283,12 @@ class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softplus operator");
AddOutput("Y", "Output of Softplus operator");
AddComment("Softplus activation operator, softplus(x) = log(1 + exp(x))");
AddComment(R"DOC(
Softplus activation operator.
$y = \ln(1 + e^{x})$
)DOC");
}
};
......@@ -222,7 +299,12 @@ class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softsign operator");
AddOutput("Y", "Output of Softsign operator");
AddComment("Softsign activation operator, softsign(x) = x / (1 + |x|)");
AddComment(R"DOC(
Softsign activation operator.
$$y = \frac{x}{1 + |x|}$$
)DOC");
}
};
......@@ -233,11 +315,16 @@ class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of BRelu operator");
AddOutput("Y", "Output of BRelu operator");
AddComment("BRelu activation operator, brelu = max(min(x, t_min), t_max)");
AddAttr<AttrType>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<AttrType>(0));
AddAttr<AttrType>("t_max", "The max marginal value of BRelu")
.SetDefault(static_cast<AttrType>(24));
AddComment(R"DOC(
BRelu activation operator.
$y = \max(\min(x, t_{min}), t_{max})$
)DOC");
}
};
......@@ -249,11 +336,14 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SoftRelu operator");
AddOutput("Y", "Output of SoftRelu operator");
AddComment(
"SoftRelu activation operator, soft_relu = log(1 + exp(max(min(x, "
"threshold), threshold)))");
AddAttr<AttrType>("threshold", "The threshold value of SoftRelu")
.SetDefault(static_cast<AttrType>(40));
AddComment(R"DOC(
SoftRelu activation operator.
$y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
)DOC");
}
};
......@@ -262,19 +352,19 @@ class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input of ELU operator, it shouldn't be empty. Input "
"is flattened and treated as a 1D array.");
AddOutput("Y",
"(Tensor) The output of ELU operator. It has the same shape as "
"the input.");
AddAttr<AttrType>(
"alpha", "(float, default 1.0) Alpha value in the elu formulation.")
.SetDefault(static_cast<AttrType>(1.));
AddInput("X", "Input of ELU operator");
AddOutput("Y", "Output of ELU operator");
AddAttr<AttrType>("alpha", "The alpha value of ELU")
.SetDefault(static_cast<AttrType>(1.0f));
AddComment(R"DOC(
ELU activation operator. It applies this element-wise computation on
the input: f(x) = max(0, x) + min(0, alpha * (exp(x) - 1)).
Check .. _Link: https://arxiv.org/abs/1511.07289 for more details.)DOC");
ELU activation operator.
Applies the following element-wise computation on the input according to
https://arxiv.org/abs/1511.07289.
$y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
)DOC");
}
};
......@@ -285,9 +375,14 @@ class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu6 operator");
AddOutput("Y", "Output of Relu6 operator");
AddComment("Relu6 activation operator, relu6 = min(max(0, x), 6)");
AddAttr<AttrType>("threshold", "The threshold value of Relu6")
.SetDefault(static_cast<AttrType>(6));
AddComment(R"DOC(
Relu6 activation operator.
$y = \min(\max(0, x), 6)$
)DOC");
}
};
......@@ -298,9 +393,14 @@ class PowOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Pow operator");
AddOutput("Y", "Output of Pow operator");
AddComment("Pow activation operator, pow(x, factor) = x^factor");
AddAttr<AttrType>("factor", "The exponential factor of Pow")
.SetDefault(static_cast<AttrType>(1));
AddComment(R"DOC(
Pow activation operator.
$y = x^{factor}$
)DOC");
}
};
......@@ -311,11 +411,16 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of STanh operator");
AddOutput("Y", "Output of STanh operator");
AddComment("STanh activation operator, stanh = b * tanh(a * x)");
AddAttr<AttrType>("scale_a", "The scale parameter of a for the input")
.SetDefault(static_cast<AttrType>(2 / 3));
AddAttr<AttrType>("scale_b", "The scale parameter of b for the input")
.SetDefault(static_cast<AttrType>(1.7159));
AddComment(R"DOC(
STanh activation operator.
$$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC");
}
};
......@@ -327,12 +432,19 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Y", "Output of ThresholdedRelu operator");
AddComment(
"ThresholdedRelu activation operator, "
"thresholded_relu = x for x > threshold, "
"thresholded_relu = 0 otherwise.");
AddAttr<AttrType>("threshold", "The threshold location of activation")
.SetDefault(static_cast<AttrType>(1.0));
AddComment(R"DOC(
ThresholdedRelu activation operator.
$$
y = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
$$
)DOC");
}
};
......@@ -344,27 +456,23 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.2));
AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.5));
AddComment(R"DOC(
Hard Sigmoid activation operator.
HardSigmoid activation operator.
Segment-wise linear approximation of sigmoid[1].
This is much faster than sigmoid.
Segment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
which is much faster than sigmoid.
hard_sigmoid = max(0, min(1, slope * x + shift))
$y = \max(0, \min(1, slope * x + shift))$
The slope should be positive. The offset can be either positive or negative.
The default slope and shift are set from [1].
The default slope and shift are set according to the above reference.
It is recommended to use the defaults for this activation.
References:
[1] Noisy Activation Functions
(https://arxiv.org/abs/1603.00391)
)DOC");
AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.2));
AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.5));
)DOC");
}
};
......
paddle/operators/activation_op.h
浏览文件 @ 4b8bcf32
......@@ -232,7 +232,7 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
}
};
// softshrink(x) = x - lambda, if x > lambda; x + lambda, if x < lambda; 0
// softshrink(x) = x - lambda, if x > lambda; x + lambda, if x < -lambda; 0
// otherwise
template <typename T>
struct SoftShrinkFunctor : public BaseActivationFunctor<T> {
......
paddle/operators/conv2d_transpose_cudnn_op.cc 0 → 100644
浏览文件 @ 4b8bcf32
/* 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 "paddle/operators/conv2d_transpose_op.h"
namespace paddle {
namespace operators {
class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
public:
CudnnConv2DTransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DTransposeOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault(std::vector<int>{1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
"allocated/freed each time the operator runs, larger "
"workspace size can increase performance but also requires "
"better hardward. This size should be carefully setted.")
.SetDefault(4096);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv2d_transpose_cudnn, ops::Conv2DTransposeOp,
ops::CudnnConv2DTransposeOpMaker, conv2d_transpose_cudnn_grad,
ops::Conv2DTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn,
ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn_grad,
ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/conv2d_transpose_cudnn_op.cu 0 → 100644
浏览文件 @ 4b8bcf32
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/memory/memory.h"
#include "paddle/operators/conv2d_transpose_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
using DataLayout = platform::DataLayout;
using CUDADeviceContext = platform::CUDADeviceContext;
static constexpr size_t kConvCudnnWorkspaceLimitBytes = 1024 * 1024 * 1024;
template <typename T>
class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
// cudnn v5 does not support dilations
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
ScopedFilterDescriptor filter_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
// N, M, H, W
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()));
// N, C, O_h, O_w
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
layout, framework::vectorize2int(output->dims()));
// M, C, K_h, K_w
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()));
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
// ------------------- cudnn conv workspace ---------------------
void* cudnn_workspace = nullptr;
size_t workspace_size_in_bytes; // final workspace to allocate.
size_t workspace_size_limit = kConvCudnnWorkspaceLimitBytes;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t algo;
auto handle = ctx.cuda_device_context().cudnn_handle();
// Get the algorithm
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_output_desc, CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
// get workspace size able to allocate
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
// Allocate on GPU memory
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv transpose forward ---------------------
T alpha = 1.0f, beta = 0.0f;
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data, cudnn_input_desc,
input_data, cudnn_conv_desc, algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_output_desc, output_data));
// Release the cudnn workspace
paddle::memory::Free(gpu, cudnn_workspace);
}
};
template <typename T>
class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto input = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
const T* input_data = input->data<T>();
const T* output_grad_data = output_grad->data<T>();
const T* filter_data = filter->data<T>();
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
// cudnn v5 does not support dilations
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
ScopedFilterDescriptor filter_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
// Input: (N, M, H, W)
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()));
// Output: (N, C, O_H, O_W)
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
layout, framework::vectorize2int(output_grad->dims()));
// Filter (M, C, K_H, K_W)
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()));
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
// ------------------- cudnn backward algorithm ---------------------
cudnnConvolutionFwdAlgo_t data_algo;
cudnnConvolutionBwdFilterAlgo_t filter_algo;
size_t bwd_filter_ws_size, fwd_ws_size;
size_t workspace_size_in_bytes = 0;
size_t workspace_size_limit = kConvCudnnWorkspaceLimitBytes;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
}
auto handle = ctx.cuda_device_context().cudnn_handle();
if (input_grad) {
// choose backward algorithm for data
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, data_algo, &fwd_ws_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size);
}
if (filter_grad) {
// choose backward algorithm for filter
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &filter_algo));
// get workspace for backwards filter algorithm
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &bwd_filter_ws_size));
workspace_size_in_bytes =
std::max(workspace_size_in_bytes, bwd_filter_ws_size);
}
// ------------------- cudnn conv workspace ---------------------
// Already on GPU
void* cudnn_workspace = nullptr;
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
T alpha = 1.0f, beta = 0.0f;
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*input_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc, output_grad_data,
cudnn_filter_desc, filter_data, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data));
}
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*filter_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
// Gradient with respect to the filter
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc, output_grad_data, cudnn_input_desc,
input_data, cudnn_conv_desc, filter_algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_filter_desc, filter_grad_data));
}
// Release the cudnn workspace
paddle::memory::Free(gpu, cudnn_workspace);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>);
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>);
paddle/operators/conv2dtranspose_op.cc → paddle/operators/conv2d_transpose_op.cc
浏览文件 @ 4b8bcf32
......@@ -12,7 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
#include "paddle/operators/conv2d_transpose_op.h"
namespace paddle {
namespace operators {
......@@ -95,13 +95,13 @@ void Conv2DTransposeOpGrad::InferShape(
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv2dtranspose, ops::Conv2DTransposeOp,
ops::Conv2DTransposeOpMaker, conv2dtranspose_grad,
REGISTER_OP(conv2d_transpose, ops::Conv2DTransposeOp,
ops::Conv2DTransposeOpMaker, conv2d_transpose_grad,
ops::Conv2DTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
conv2dtranspose,
conv2d_transpose,
ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv2dtranspose_grad,
conv2d_transpose_grad,
ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/conv2dtranspose_op.cu → paddle/operators/conv2d_transpose_op.cu
浏览文件 @ 4b8bcf32
......@@ -12,13 +12,13 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
#include "paddle/operators/conv2d_transpose_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
conv2dtranspose,
conv2d_transpose,
ops::GemmConv2DTransposeKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv2dtranspose_grad,
conv2d_transpose_grad,
ops::GemmConv2DTransposeGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/conv2dtranspose_op.h → paddle/operators/conv2d_transpose_op.h
浏览文件 @ 4b8bcf32
......@@ -62,7 +62,7 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
// groups will alway be disabled in conv2d_transpose.
const int batch_size = input->dims()[0];
const int m = input->dims()[1];
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ 4b8bcf32
......@@ -28,8 +28,9 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
......@@ -38,8 +39,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
"If Attr(soft_label) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"If Attr(soft_label) == false, the 2nd dimension of "
PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
"If Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
......@@ -48,7 +49,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
}
protected:
// CrossEntropy's data type just determined by "X"
// Explicitly set that data type of the output of the cross_entropy operator
// is determined by its input "X".
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("X")->type());
......
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ 4b8bcf32
......@@ -36,7 +36,12 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
[](int a) { return static_cast<int64_t>(a); });
auto dims = framework::make_ddim(shape_int64);
dims[0] = ctx->GetInputDim("Input")[0];
int dim_idx = ctx->Attrs().Get<int>("dim_idx");
PADDLE_ENFORCE_GE(dim_idx, 0);
PADDLE_ENFORCE_GT(static_cast<int>(shape.size()), dim_idx);
PADDLE_ENFORCE_GT(ctx->GetInputDim("Input").size(), dim_idx);
dims[dim_idx] = ctx->GetInputDim("Input")[dim_idx];
ctx->SetOutputDim("Out", dims);
}
......@@ -57,15 +62,18 @@ class FillConstantBatchSizeLikeOpMaker
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
AddInput("Input",
"(Tensor) Tensor "
"whose first dimension is used to specify the batch_size");
"whose dim_idx th dimension is used to specify the batch_size");
AddOutput("Out",
"(Tensor) Tensor of specified shape will be filled "
"with the specified value");
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<int>("dim_idx",
"(int, default 0) the index of batch size dimension")
.SetDefault(0);
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
AddComment(R"DOC(Fill up a variable with specified constant value.)DOC");
}
};
......
paddle/operators/linear_chain_crf_op.cc 0 → 100644
浏览文件 @ 4b8bcf32
/* 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 "paddle/operators/linear_chain_crf_op.h"
namespace paddle {
namespace operators {
class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LinearChainCRFOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Emission",
"(LoDTensor, default: LoDTensor<float>). "
"The unscaled emission weight matrix for the linear chain CRF. "
"This input is a LoDTensor with shape [N x D] where N is the size of "
"the mini-batch and D is the total tag number.");
AddInput(
"Transition",
"(Tensor, default: Tensor<float>). A Tensor with shape [(D + 2) x D]. "
"The learnable parameter for the linear_chain_crf operator. "
"See more details in the operator's comments.");
AddInput(
"Label",
"(LoDTensor, default: LoDTensor<int>). The ground truth which is a 2-D "
"LoDTensor with shape [N x 1], where N is the total element number in "
"a mini-batch.");
AddOutput(
"Alpha",
"Tensor, default: Tensor<float>. The forward vectors for the entire "
"batch. A two dimensional tensor with shape [N x D], "
"denoted as \f$\alpha\f$. \f$\alpha$\f is a memo table used to "
"calculate the normalization factor in CRF. \f$\alpha[k, v]$\f stores "
"the unnormalized probabilites of all possible unfinished sequences of "
"tags that end at position \f$k$\f with tag \f$v$\f. For each \f$k$\f, "
"\f$\alpha[k, v]$\f is a vector of length \f$D$\f with a component for "
"each tag value \f$v$\f. This vector is called a forward vecotr and "
"will also be used in backward computations.")
.AsIntermediate();
AddOutput("EmissionExps",
"The exponentials of Input(Emission). This is an intermediate "
"computational result in forward computation, and will be reused "
"in backward computation.")
.AsIntermediate();
AddOutput("TransitionExps",
"The exponentials of Input(Transition). This is an intermediate "
"computational result in forward computation, and will be reused "
"in backward computation.")
.AsIntermediate();
AddOutput(
"LogLikelihood",
"(Tensor, default: Tensor<float>). The logarithm of the conditional "
"likelihood of each training sample in a mini-batch. This is a 2-D "
"tensor with shape [S x 1], where S is the sequence number in a "
"mini-batch. Note: S is equal to the sequence number in a mini-batch. "
"The output is no longer a LoDTensor.");
AddComment(R"DOC(
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability \f$P(Y|X)\f$, where
\f$X = (x_1, x_2, ... , x_n)\f$ are structured inputs and
\f$Y = (y_1, y_2, ... , y_n)\f$ are labels for the inputs.
Linear chain CRF is a special case of CRF that is useful for sequence labeling
task. Sequence labeling tasks do not assume a lot of conditional
independences among inputs. The only constraint they impose is that the input
and output must be linear sequences. Thus, the graph of such a CRF is a simple
chain or a line, which results in the linear chain CRF.
This operator implements the Forward-Backward algorithm for the linear chain
CRF. Please see http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for reference.
Equation:
- Denote Input(Emission) to this operator as \f$x\f$ here.
- The first D values of Input(Transition) to this operator are for starting
weights, denoted as \f$a\f$ here.
- The next D values of Input(Transition) of this operator are for ending
weights, denoted as \f$b\f$ here.
- The remaning values of Input(Transition) are for transition weights,
denoted as \f$w\f$ here.
- Denote Input(Label) as \f$s\f$ here.
The probability of a sequence \f$s\f$ of length \f$L\f$ is defined as:
\f$P(s) = (1/Z) exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})\f$
where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over
all possible sequences is \f$1\f$, and \f$x\f$ is the emission feature weight
to the linear chain CRF.
Finaly, the linear chain CRF operator outputs the logarithm of the conditional
likelihood of each training sample in a mini-batch.
NOTE:
1. The feature function for a CRF is made up of the emission features and the
transition features. The emission feature weights are NOT computed in
this operator. They MUST be computed first before this operator is called.
2. Because this operator performs global normalization over all possible
sequences internally, it expects UNSCALED emission feature weights.
Please do not call this op with the emission feature being output of any
nonlinear activation.
3. The 2nd dimension of Input(Emission) MUST be equal to the tag number.
)DOC");
}
};
class LinearChainCRFOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Emission"),
"Input(Emission) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Transition"),
"Input(Transition) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Alpha"),
"Output(Alpha) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("EmissionExps"),
"Output(EmissionExps) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("TransitionExps"),
"Output(TransitionExps) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("LogLikelihood"),
"Output(LogLikelihood) should be not null.");
auto emission_dims = ctx->GetInputDim("Emission");
PADDLE_ENFORCE_EQ(emission_dims.size(), 2UL,
"The Input(Emission) should be a 2-D tensor.");
PADDLE_ENFORCE(emission_dims[0], "An empty mini-batch is not allowed.");
auto transition_dims = ctx->GetInputDim("Transition");
PADDLE_ENFORCE_EQ(transition_dims.size(), 2UL,
"The Input(Transition) should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(
transition_dims[0] - 2, transition_dims[1],
"An invalid dimension for the Input(Transition), which should "
"be a 2-D tensor with shape [(D + 2) x D].");
PADDLE_ENFORCE_EQ(
emission_dims[1], transition_dims[1],
"The 2nd dimension of the Input(Emission) and the Input(Transition) "
"should be equal to the tag number.");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
"The Input(Label) should be a 2-D tensor with the 2nd "
"dimensions fixed to 1.");
PADDLE_ENFORCE_EQ(
emission_dims[0], label_dims[0],
"The height of Input(Emission) and the height of Input(Label) "
"should be the same.");
ctx->SetOutputDim("Alpha", emission_dims);
ctx->SetOutputDim("EmissionExps", emission_dims);
ctx->SetOutputDim("TransitionExps", transition_dims);
// TODO(caoying) This is tricky. The 1st dimension of Output(LogLikelihood)
// is the sequence number in a mini-batch. The dimension set here should be
// resized to its correct size in the function Compute. Fix this once we can
// get LoD information in the InferShape interface.
ctx->SetOutputDim("LogLikelihood", {emission_dims[0], 1});
}
protected:
// Explicitly set that the data type of output of the linear_chain_crf
// operator is determined by its input "Emission".
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type());
}
};
class LinearChainCRFGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("EmissionExps"),
"Input(EmissionExps) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("TransitionExps"),
"Input(TransitionExps) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("LogLikelihood")),
"Input(LogLikelihood@GRAD) shoudl be not null.");
auto emission_exps_dims = ctx->GetInputDim("EmissionExps");
PADDLE_ENFORCE_EQ(emission_exps_dims.size(), 2UL,
"The Input(EmissionExps) should be a 2-D tensor.");
PADDLE_ENFORCE(emission_exps_dims[0],
"An empty mini-batch is not allowed.");
auto transition_exps_dims = ctx->GetInputDim("TransitionExps");
PADDLE_ENFORCE_EQ(transition_exps_dims.size(), 2UL,
"The Input(TransitionExps) should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(
transition_exps_dims[0] - 2, transition_exps_dims[1],
"An invalid dimension for the Input(TransitionExps), which should "
"be a 2-D tensor with shape [(D + 2) x D].");
PADDLE_ENFORCE_EQ(
emission_exps_dims[1], transition_exps_dims[1],
"The 2nd dimension of the Input(EmissionExps) and the "
"Input(TransitionExps) should be equal to the tag number.");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
"The Input(Label) should be a 2-D tensor with the 2nd "
"dimensions fixed to 1.");
PADDLE_ENFORCE_EQ(
emission_exps_dims[0], label_dims[0],
"The height of Input(EmissionExps) and the height of Input(Label) "
"should be the same.");
if (ctx->HasOutput(framework::GradVarName("Emission"))) {
ctx->SetOutputDim(framework::GradVarName("Emission"), emission_exps_dims);
}
if (ctx->HasOutput(framework::GradVarName("Transition"))) {
ctx->SetOutputDim(framework::GradVarName("Transition"),
transition_exps_dims);
}
}
protected:
// Explicitly set that the data type of output of the linear_chain_crf_grad
// operator is determined by its input: gradients of LogLikelihood.
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(
ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))->type());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(linear_chain_crf, ops::LinearChainCRFOp, ops::LinearChainCRFOpMaker,
linear_chain_crf_grad, ops::LinearChainCRFGradOp);
REGISTER_OP_CPU_KERNEL(
linear_chain_crf,
ops::LinearChainCRFOpKernel<paddle::platform::CPUPlace, float>,
ops::LinearChainCRFOpKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
linear_chain_crf_grad,
ops::LinearChainCRFGradOpKernel<paddle::platform::CPUPlace, float>,
ops::LinearChainCRFGradOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/math/detail/hl_avx_functions.h → paddle/operators/linear_chain_crf_op.cu
浏览文件 @ 4b8bcf32
/* 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
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
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,
......@@ -12,21 +12,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_AVX_FUNCTIONS_H_
#define HL_AVX_FUNCTIONS_H_
#include "paddle/operators/linear_chain_crf_op.h"
#include <immintrin.h>
namespace ops = paddle::operators;
namespace hppl {
__m256 relu(const __m256 a);
__m256 sigmoid(const __m256 a);
__m256 tanh(const __m256 a);
__m256 linear(const __m256 a);
__m256 relu(const __m256 a, const __m256 b);
__m256 sigmoid(const __m256 a, const __m256 b);
__m256 tanh(const __m256 a, const __m256 b);
__m256 linear(const __m256 a, const __m256 b);
} // namespace hppl
#endif // HL_AVX_FUNCTIONS_H_
REGISTER_OP_GPU_KERNEL(
linear_chain_crf,
ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, float>,
ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
linear_chain_crf_grad,
ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, float>,
ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/linear_chain_crf_op.h 0 → 100644
浏览文件 @ 4b8bcf32
此差异已折叠。
paddle/operators/lookup_table_op.h
浏览文件 @ 4b8bcf32
......@@ -90,11 +90,13 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto* d_output_data = d_output->data<T>();
auto* d_table_data = d_table->mutable_data<T>(context.GetPlace());
memset(d_table_data, 0, d_table->numel() * sizeof(T));
for (int64_t i = 0; i < ids->numel(); ++i) {
PADDLE_ENFORCE_LT(ids_data[i], N);
PADDLE_ENFORCE_GE(ids_data[i], 0);
for (int j = 0; j < D; ++j) {
d_table_data[ids_data[i] * D + j] = d_output_data[i * D + j];
d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
}
}
}
......
paddle/operators/lstm_unit_op.cu
浏览文件 @ 4b8bcf32
......@@ -12,6 +12,10 @@
See the License for the specific language governing permissions and
limitations under the License. */
/* Acknowledgement: the following code is strongly inspired by
https://github.com/caffe2/caffe2/blob/master/caffe2/operators/lstm_unit_op_gpu.cu
*/
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
......
paddle/operators/lstm_unit_op.h
浏览文件 @ 4b8bcf32
......@@ -12,6 +12,10 @@
See the License for the specific language governing permissions and
limitations under the License. */
/* Acknowledgement: the following code is strongly inspired by
https://github.com/caffe2/caffe2/blob/master/caffe2/operators/lstm_unit_op.h
*/
#pragma once
#include "glog/logging.h"
#include "paddle/framework/op_registry.h"
......
paddle/operators/math/detail/CMakeLists.txt
浏览文件 @ 4b8bcf32
if(WITH_AVX)
cc_library(activation_functions SRCS hl_cpu_functions.cc hl_avx_functions.cc)
else()
cc_library(activation_functions SRCS hl_cpu_functions.cc)
cc_library(activation_functions SRCS avx_functions.cc)
endif()
paddle/operators/math/detail/activation_functions.h 0 → 100644
浏览文件 @ 4b8bcf32
/* 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. */
#pragma once
#include <math.h>
#include "paddle/platform/hostdevice.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace paddle {
namespace operators {
namespace math {
namespace detail {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
namespace forward {
template <typename T>
DEVICE T Identity(const T a) {
return a;
}
template <typename T>
DEVICE T Relu(const T a) {
return a > static_cast<T>(0.0) ? a : static_cast<T>(0.0);
}
template <typename T>
DEVICE T Sigmoid(const T a) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<T>(1.0) / (static_cast<T>(1.0) + exp(-tmp));
}
template <typename T>
DEVICE T Tanh(const T a) {
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
} // namespace forward
namespace backward {
template <typename T>
DEVICE T Identity(const T a, const T b) {
return a;
}
template <typename T>
DEVICE T Relu(const T a, const T b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
template <typename T>
DEVICE T Sigmoid(const T a, const T b) {
return a * b * (1.0 - b);
}
template <typename T>
DEVICE T Tanh(const T a, const T b) {
return a * (1.0 - b * b);
}
} // namespace backward
template <typename T>
struct Active {
typedef T (*Act)(T);
typedef T (*ActGrad)(T, T);
};
static DEVICE Active<float>::Act kActFloat[] = {
&forward::Sigmoid<float>, &forward::Relu<float>, &forward::Tanh<float>,
&forward::Identity<float>};
static DEVICE Active<float>::ActGrad kActGradFloat[] = {
&backward::Sigmoid<float>, &backward::Relu<float>, &backward::Tanh<float>,
&backward::Identity<float>};
static DEVICE Active<double>::Act kActDouble[] = {
&forward::Sigmoid<double>, &forward::Relu<double>, &forward::Tanh<double>,
&forward::Identity<double>};
static DEVICE Active<double>::ActGrad kActGradDouble[] = {
&backward::Sigmoid<double>, &backward::Relu<double>,
&backward::Tanh<double>, &backward::Identity<double>};
namespace forward {
inline DEVICE float activation(float a, int index) {
return kActFloat[index](a);
}
inline DEVICE double activation(double a, int index) {
return kActDouble[index](a);
}
} // namespace forward
namespace backward {
inline DEVICE float activation(float a, float b, int index) {
return kActGradFloat[index](a, b);
}
inline DEVICE double activation(double a, double b, int index) {
return kActGradDouble[index](a, b);
}
} // namespace backward
#ifdef __AVX__
namespace forward {
namespace avx {
__m256 Relu(const __m256 a);
__m256 Sigmoid(const __m256 a);
__m256 Tanh(const __m256 a);
__m256 Identity(const __m256 a);
} // namespace avx
} // namespace forward
namespace backward {
namespace avx {
__m256 Relu(const __m256 a, const __m256 b);
__m256 Sigmoid(const __m256 a, const __m256 b);
__m256 Tanh(const __m256 a, const __m256 b);
__m256 Identity(const __m256 a, const __m256 b);
} // namespace avx
} // namespace backward
static Active<__m256>::Act kActAvx[] = {
&forward::avx::Sigmoid, &forward::avx::Relu, &forward::avx::Tanh,
&forward::avx::Identity};
static Active<__m256>::ActGrad kActGradAvx[] = {
&backward::avx::Sigmoid, &backward::avx::Relu, &backward::avx::Tanh,
&backward::avx::Identity};
namespace forward {
inline __m256 activation(__m256 a, int index) { return kActAvx[index](a); }
} // namespace forward
namespace backward {
inline __m256 activation(__m256 a, __m256 b, int index) {
return kActGradAvx[index](a, b);
}
} // namespace backward
#endif
} // namespace detail
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detail/hl_avx_functions.cc → paddle/operators/math/detail/avx_functions.cc
浏览文件 @ 4b8bcf32
......@@ -13,58 +13,74 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <immintrin.h>
#include "hl_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
// TODO(qingqing) refine this dependence
#include "paddle/cuda/src/avx_mathfun.h"
namespace hppl {
namespace paddle {
namespace operators {
namespace math {
namespace detail {
__m256 exp(__m256 a) { return exp256_ps(a); }
__m256 Exp(__m256 a) { return exp256_ps(a); }
__m256 relu(const __m256 a) {
namespace forward {
namespace avx {
__m256 Relu(const __m256 a) {
__m256 tmp = _mm256_set1_ps(0.0f);
return _mm256_max_ps(a, tmp);
}
__m256 sigmoid(const __m256 a) {
__m256 Sigmoid(const __m256 a) {
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);
__m256 tmp = _mm256_max_ps(a, min);
tmp = _mm256_min_ps(tmp, max);
tmp = _mm256_sub_ps(_mm256_set1_ps(0.0f), tmp);
tmp = exp(tmp);
tmp = Exp(tmp);
tmp = _mm256_add_ps(_mm256_set1_ps(1.0f), tmp);
tmp = _mm256_div_ps(_mm256_set1_ps(1.0f), tmp);
return tmp;
}
__m256 tanh(const __m256 a) {
__m256 Tanh(const __m256 a) {
__m256 max = _mm256_set1_ps(EXP_MAX_INPUT);
__m256 tmp = _mm256_mul_ps(_mm256_set1_ps(-2.0f), a);
tmp = _mm256_min_ps(tmp, max);
tmp = exp(tmp);
tmp = Exp(tmp);
return _mm256_sub_ps(_mm256_div_ps(_mm256_set1_ps(2.0f),
_mm256_add_ps(_mm256_set1_ps(1.0f), tmp)),
_mm256_set1_ps(1.0f));
}
__m256 linear(const __m256 a) { return a; }
__m256 Identity(const __m256 a) { return a; }
__m256 relu(const __m256 a, const __m256 b) {
} // namespace avx
} // namespace forward
namespace backward {
namespace avx {
__m256 Relu(const __m256 a, const __m256 b) {
return _mm256_mul_ps(
a, _mm256_and_ps(_mm256_cmp_ps(b, _mm256_set1_ps(0.0f), _CMP_GT_OS),
_mm256_set1_ps(1.0f)));
}
__m256 sigmoid(const __m256 a, const __m256 b) {
__m256 Sigmoid(const __m256 a, const __m256 b) {
return _mm256_mul_ps(_mm256_mul_ps(a, b),
_mm256_sub_ps(_mm256_set1_ps(1.0f), b));
}
__m256 tanh(const __m256 a, const __m256 b) {
__m256 Tanh(const __m256 a, const __m256 b) {
return _mm256_mul_ps(
a, _mm256_sub_ps(_mm256_set1_ps(1.0f), _mm256_mul_ps(b, b)));
}
__m256 linear(const __m256 a, const __m256 b) { return a; }
} // namespace hppl
__m256 Identity(const __m256 a, const __m256 b) { return a; }
} // namespace avx
} // namespace backward
} // namespace detail
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detail/hl_activation_functions.h 已删除 100644 → 0
浏览文件 @ faad8351
/* 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. */
#ifndef HL_ACTIVATION_FUNCTIONS_H_
#define HL_ACTIVATION_FUNCTIONS_H_
#include "hl_functions.h"
#include "paddle/operators/math/lstm_compute.h"
/**
* Active functions: sigmoid, relu, tanh and linear.
*/
#define FLOAT_ACTIVE_FUNCTION \
{ \
hppl::typef::sigmoid, hppl::typef::relu, hppl::typef::tanh, \
hppl::typef::linear \
}
#define DOUBLE_ACTIVE_FUNCTION \
{ \
hppl::typed::sigmoid, hppl::typed::relu, hppl::typed::tanh, \
hppl::typed::linear \
}
#define AVX_ACTIVE_FUNCTION \
{ hppl::sigmoid, hppl::relu, hppl::tanh, hppl::linear }
namespace hppl {
using activation_mode_t = paddle::operators::math::activation_mode_t;
/**
* Hppl supports sigmoid, relu, tanh, linear active functions
* for neural networks' forward and backward activation.
*/
template <class T>
class Active {
public:
typedef T (*forward)(T);
typedef T (*backward)(T, T);
};
template <typename T>
struct ForwardActType;
template <>
struct ForwardActType<float> {
using type = Active<float>::forward;
};
template <>
struct ForwardActType<double> {
using type = Active<double>::forward;
};
template <typename T>
struct BackwardActType;
template <>
struct BackwardActType<float> {
using type = Active<float>::backward;
};
template <>
struct BackwardActType<double> {
using type = Active<double>::backward;
};
#ifdef __NVCC__
namespace gpu {
static __device__ Active<float>::forward forward[] = FLOAT_ACTIVE_FUNCTION;
static __device__ Active<float>::backward backward[] = FLOAT_ACTIVE_FUNCTION;
static __device__ Active<double>::forward forward_d[] = DOUBLE_ACTIVE_FUNCTION;
static __device__ Active<double>::backward backward_d[] =
DOUBLE_ACTIVE_FUNCTION;
template <typename T>
struct ForwardAct {
__device__ typename ForwardActType<T>::type operator()(
activation_mode_t type);
};
template <>
struct ForwardAct<float> {
__device__ ForwardActType<float>::type operator()(activation_mode_t type) {
return forward[type];
}
};
template <>
struct ForwardAct<double> {
__device__ ForwardActType<double>::type operator()(activation_mode_t type) {
return forward_d[type];
}
};
template <typename T>
struct BackwardAct {
__device__ typename BackwardActType<T>::type operator()(
activation_mode_t type);
};
template <>
struct BackwardAct<float> {
__device__ BackwardActType<float>::type operator()(activation_mode_t type) {
return backward[type];
}
};
template <>
struct BackwardAct<double> {
__device__ BackwardActType<double>::type operator()(activation_mode_t type) {
return backward_d[type];
}
};
} // namespace gpu
#else
namespace cpu {
static Active<float>::forward forward[] = FLOAT_ACTIVE_FUNCTION;
static Active<float>::backward backward[] = FLOAT_ACTIVE_FUNCTION;
static Active<double>::forward forward_d[] = DOUBLE_ACTIVE_FUNCTION;
static Active<double>::backward backward_d[] = DOUBLE_ACTIVE_FUNCTION;
template <typename T>
struct ForwardAct {
typename ForwardActType<T>::type operator()(activation_mode_t type);
};
template <>
struct ForwardAct<float> {
ForwardActType<float>::type operator()(activation_mode_t type) {
return forward[type];
}
};
template <>
struct ForwardAct<double> {
ForwardActType<double>::type operator()(activation_mode_t type) {
return forward_d[type];
}
};
template <typename T>
struct BackwardAct {
typename BackwardActType<T>::type operator()(activation_mode_t type);
};
template <>
struct BackwardAct<float> {
BackwardActType<float>::type operator()(activation_mode_t type) {
return backward[type];
}
};
template <>
struct BackwardAct<double> {
BackwardActType<double>::type operator()(activation_mode_t type) {
return backward_d[type];
}
};
} // namespace cpu
#ifdef __AVX__
namespace avx {
static Active<__m256>::forward forward[] = AVX_ACTIVE_FUNCTION;
static Active<__m256>::backward backward[] = AVX_ACTIVE_FUNCTION;
} // namespace avx
#endif
#endif
} // namespace hppl
#endif // HL_ACTIVATION_FUNCTIONS_H_
paddle/operators/math/detail/hl_cpu_functions.cc 已删除 100644 → 0
浏览文件 @ faad8351
/* 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 <math.h>
#include "hl_functions.h"
namespace hppl {
namespace typef {
float relu(const float a) {
return a > static_cast<float>(0.0) ? a : static_cast<float>(0.0);
}
float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<float>(1.0) / (static_cast<float>(1.0) + exp(-tmp));
}
float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
float linear(const float a) { return a; }
float relu(const float a, const float b) { return a * (b > 0.0 ? 1.0 : 0.0); }
float sigmoid(const float a, const float b) {
return a * b * (static_cast<float>(1) - b);
}
float tanh(const float a, const float b) {
return a * (static_cast<float>(1) - b * b);
}
float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
double relu(const double a) {
return a > static_cast<double>(0.0) ? a : static_cast<double>(0.0);
}
double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<double>(1.0) / (static_cast<double>(1.0) + exp(-tmp));
}
double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
double linear(const double a) { return a; }
double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
double sigmoid(const double a, const double b) {
return a * b * (static_cast<double>(1) - b);
}
double tanh(const double a, const double b) {
return a * (static_cast<double>(1) - b * b);
}
double linear(const double a, const double b) { return a; }
} // namespace typed
} // namespace hppl
paddle/operators/math/detail/hl_functions.h 已删除 100644 → 0
浏览文件 @ faad8351
/* 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. */
#ifndef HL_FUNCTIONS_H_
#define HL_FUNCTIONS_H_
/**
* sigmoid threshold maximum
*/
#define SIGMOID_THRESHOLD_MIN -40.0
/**
* sigmoid threshold minimum
*/
#define SIGMOID_THRESHOLD_MAX 13.0
/**
* The maximum input value for exp, used to avoid overflow problem.
* currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
#ifndef __NVCC__
namespace hppl {
namespace typef {
float relu(const float a);
float sigmoid(const float a);
float tanh(const float a);
float linear(const float a);
float relu(const float a, const float b);
float sigmoid(const float a, const float b);
float tanh(const float a, const float b);
float linear(const float a, const float b);
} // namespace typef
namespace typed {
double relu(const double a);
double sigmoid(const double a);
double tanh(const double a);
double linear(const double a);
double relu(const double a, const double b);
double sigmoid(const double a, const double b);
double tanh(const double a, const double b);
double linear(const double a, const double b);
} // namespace typed
} // namespace hppl
#ifdef __AVX__
#include "hl_avx_functions.h"
#endif
#else
#include "hl_gpu_functions.h"
#endif
#endif // HL_FUNCTIONS_H_
paddle/operators/math/detail/hl_gpu_functions.h 已删除 100644 → 0
浏览文件 @ faad8351
/* 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. */
#ifndef HL_GPU_FUNCTIONS_CUH_
#define HL_GPU_FUNCTIONS_CUH_
#include "hl_base.h"
namespace hppl {
namespace typef {
__device__ static float relu(const float a) { return a > 0.0f ? a : 0.0f; }
__device__ static float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return __fdividef(1.0f, 1.0f + __expf(-tmp));
}
__device__ static float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return __fdividef(2.0f, (1.0f + __expf(-2.0f * tmp))) - 1.0f;
}
__device__ static float linear(const float a) { return a; }
__device__ static float relu(const float a, const float b) {
return a * (b > 0.0f ? 1.0f : 0.0f);
}
__device__ static float sigmoid(const float a, const float b) {
return a * b * (1.0f - b);
}
__device__ static float tanh(const float a, const float b) {
return a * (1.0f - b * b);
}
__device__ static float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
__device__ static double relu(const double a) { return a > 0.0 ? a : 0.0; }
__device__ static double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return 1.0 / (1.0 + exp(-tmp));
}
__device__ static double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(-2.0 * a))) - 1.0;
}
__device__ static double linear(const double a) { return a; }
__device__ static double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
__device__ static double sigmoid(const double a, const double b) {
return a * b * (1 - b);
}
__device__ static double tanh(const double a, const double b) {
return a * (1.0 - b * b);
}
__device__ static double linear(const double a, const double b) { return a; }
} // namespace typef
} // namespace hppl
#endif // HL_GPU_FUNCTIONS_CUH_
paddle/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ 4b8bcf32
......@@ -14,7 +14,7 @@ limitations under the License. */
#pragma once
#include <type_traits>
#include "paddle/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/operators/math/lstm_compute.h"
namespace paddle {
......@@ -26,7 +26,10 @@ namespace detail {
template <class T, class Op>
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frameSize) {
int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -58,7 +61,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
valueIn[i] = rValueIn;
valueIg[i] = rValueIg;
......@@ -72,7 +75,10 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize) {
LstmMetaGrad<T> grad, int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -122,7 +128,7 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg,
rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv,
rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad,
rCheckOGrad);
rCheckOGrad, active_node, active_gate, active_state);
gradIn[i] = rGradIn;
gradIg[i] = rGradIg;
......@@ -176,8 +182,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO, hppl::avx::forward[active_node],
hppl::avx::forward[active_gate], hppl::avx::forward[active_state]);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
valueIn[i] = rValueIn;
valueIg[i] = rValueIg;
......@@ -246,8 +251,7 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg,
rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv,
rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad,
rCheckOGrad, hppl::avx::backward[active_node],
hppl::avx::backward[active_gate], hppl::avx::backward[active_state]);
rCheckOGrad, active_node, active_gate, active_state);
gradIn[i] = rGradIn;
gradIg[i] = rGradIg;
......@@ -274,7 +278,8 @@ void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frameSize,
avx_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_forward_one_sequence<T>(op, value, frameSize);
naive_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
}
}
......@@ -287,7 +292,8 @@ void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
avx_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize);
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
}
}
......
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ 4b8bcf32
......@@ -13,13 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <type_traits>
#include "paddle/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/operators/math/lstm_compute.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/device_context.h"
#include <glog/logging.h>
#include <type_traits>
namespace paddle {
namespace operators {
......@@ -32,7 +31,9 @@ namespace detail {
*/
template <class T, class Op, bool isBatch>
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
int batchSize) {
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -69,7 +70,7 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
value.gateValue[frameIdx] = rValueIn;
value.gateValue[frameIdx + frameSize] = rValueIg;
......@@ -88,7 +89,9 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
template <class T, class Op, bool isBatch>
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize,
int batchSize) {
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -141,7 +144,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg, rGradOg,
rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv, rOutputGrad,
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad);
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad,
active_node, active_gate, active_state);
grad.gateGrad[frameIdx] = rGradIn;
grad.gateGrad[frameIdx + frameSize] = rGradIg;
......@@ -197,11 +201,13 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmForward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize);
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
} else {
KeLstmForward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize);
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
}
}
......@@ -220,9 +226,9 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
threads = dim3(framePerBlock, 1);
grid = dim3(frameBlocks, 1);
} else {
/* framePerBlock = 32 batchPerBlock = 32 */
threads = dim3(32, 32);
grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32);
/* framePerBlock = 32 batchPerBlock = 16 */
threads = dim3(32, 16);
grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 16 - 1) / 16);
}
auto stream =
......@@ -230,12 +236,19 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmBackward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
} else {
KeLstmBackward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
}
cudaStreamSynchronize(stream);
// TODO(qingqing): Add cuda error check for each kernel.
cudaError_t err = cudaGetLastError();
PADDLE_ENFORCE(err, cudaGetErrorString(err));
}
} // namespace detail
......
paddle/operators/math/detail/lstm_kernel.h
浏览文件 @ 4b8bcf32
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/platform/hostdevice.h"
#include <type_traits>
......@@ -24,45 +24,22 @@ namespace detail {
namespace forward {
template <typename T>
DEVICE inline T sigmoid(const T a) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<T>(1.0) / (static_cast<T>(1.0) + exp(-tmp));
}
template <typename T>
DEVICE inline T tanh(const T a) {
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
template <class T>
class lstm {
public:
HOSTDEVICE void operator()(T &valueIn, T &valueIg, T &valueFg, T &valueOg,
T &prevState, T &state, T &stateAtv, T &output,
T &checkI, T &checkF, T &checkO) {
#if 0
// TODO(qingqing) support to activation speficed by users
valueIn = actInput(valueIn);
valueIg = actGate(valueIg + prevState * checkI);
valueFg = actGate(valueFg + prevState * checkF);
state = valueIn * valueIg + prevState * valueFg;
valueOg = actGate(valueOg + state * checkO);
stateAtv = actState(state);
output = valueOg * stateAtv;
#else
valueIn = tanh<T>(valueIn);
valueIg = sigmoid<T>(valueIg + prevState * checkI);
valueFg = sigmoid<T>(valueFg + prevState * checkF);
T &checkI, T &checkF, T &checkO,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
valueIn = activation(valueIn, active_node);
valueIg = activation(valueIg + prevState * checkI, active_gate);
valueFg = activation(valueFg + prevState * checkF, active_gate);
state = valueIn * valueIg + prevState * valueFg;
valueOg = sigmoid<T>(valueOg + state * checkO);
stateAtv = tanh<T>(state);
valueOg = activation(valueOg + state * checkO, active_gate);
stateAtv = activation(state, active_state);
output = valueOg * stateAtv;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -75,16 +52,19 @@ class lstm {
__m256 &valueOg, __m256 &prevState, __m256 &state,
__m256 &stateAtv, __m256 &output, __m256 &checkI,
__m256 &checkF, __m256 &checkO,
hppl::Active<__m256>::forward actInput,
hppl::Active<__m256>::forward actGate,
hppl::Active<__m256>::forward actState) {
valueIn = actInput(valueIn);
valueIg = actGate(_mm256_add_ps(valueIg, _mm256_mul_ps(prevState, checkI)));
valueFg = actGate(_mm256_add_ps(valueFg, _mm256_mul_ps(prevState, checkF)));
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
valueIn = activation(valueIn, active_node);
valueIg = activation(
_mm256_add_ps(valueIg, _mm256_mul_ps(prevState, checkI)), active_gate);
valueFg = activation(
_mm256_add_ps(valueFg, _mm256_mul_ps(prevState, checkF)), active_gate);
state = _mm256_add_ps(_mm256_mul_ps(valueIn, valueIg),
_mm256_mul_ps(prevState, valueFg));
valueOg = actGate(_mm256_add_ps(valueOg, _mm256_mul_ps(state, checkO)));
stateAtv = actState(state);
valueOg = activation(_mm256_add_ps(valueOg, _mm256_mul_ps(state, checkO)),
active_gate);
stateAtv = activation(state, active_state);
output = _mm256_mul_ps(valueOg, stateAtv);
}
#endif
......@@ -95,16 +75,6 @@ class lstm {
namespace backward {
template <typename T>
DEVICE inline T sigmoid(const T a, const T b) {
return a * b * (1.0 - b);
}
template <typename T>
DEVICE inline T tanh(const T a, const T b) {
return a * (1.0 - b * b);
}
template <class T>
class lstm {
public:
......@@ -113,29 +83,20 @@ class lstm {
T &prevState, T &prevStateGrad, T &state,
T &stateGrad, T &stateAtv, T &outputGrad,
T &checkI, T &checkF, T &checkO, T &checkIGrad,
T &checkFGrad, T &checkOGrad) {
#if 0
// TODO(qingqing) support to activation speficed by users
gradOg = actGate(outputGrad * stateAtv, valueOg);
stateGrad += actState(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = actInput(stateGrad * valueIg, valueIn);
gradIg = actGate(stateGrad * valueIn, valueIg);
gradFg = actGate(stateGrad * prevState, valueFg);
T &checkFGrad, T &checkOGrad,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
gradOg = activation(outputGrad * stateAtv, valueOg, active_gate);
stateGrad += activation(outputGrad * valueOg, stateAtv, active_state) +
gradOg * checkO;
gradIn = activation(stateGrad * valueIg, valueIn, active_node);
gradIg = activation(stateGrad * valueIn, valueIg, active_gate);
gradFg = activation(stateGrad * prevState, valueFg, active_gate);
prevStateGrad = gradIg * checkI + gradFg * checkF + stateGrad * valueFg;
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#else
gradOg = sigmoid<T>(outputGrad * stateAtv, valueOg);
stateGrad += tanh<T>(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = tanh<T>(stateGrad * valueIg, valueIn);
gradIg = sigmoid<T>(stateGrad * valueIn, valueIg);
gradFg = sigmoid<T>(stateGrad * prevState, valueFg);
prevStateGrad = gradIg * checkI + gradFg * checkF + stateGrad * valueFg;
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -143,24 +104,26 @@ class lstm {
#else
// Only float support AVX optimization
static const bool avx = std::is_same<T, float>::value;
HOSTDEVICE void operator()(__m256 &valueIn, __m256 &valueIg, __m256 &valueFg,
__m256 &valueOg, __m256 &gradIn, __m256 &gradIg,
__m256 &gradFg, __m256 &gradOg, __m256 &prevState,
__m256 &prevStateGrad, __m256 &state,
__m256 &stateGrad, __m256 &stateAtv,
__m256 &outputGrad, __m256 &checkI, __m256 &checkF,
__m256 &checkO, __m256 &checkIGrad,
__m256 &checkFGrad, __m256 &checkOGrad,
hppl::Active<__m256>::backward actInput,
hppl::Active<__m256>::backward actGate,
hppl::Active<__m256>::backward actState) {
gradOg = actGate(_mm256_mul_ps(outputGrad, stateAtv), valueOg);
HOSTDEVICE void operator()(
__m256 &valueIn, __m256 &valueIg, __m256 &valueFg, __m256 &valueOg,
__m256 &gradIn, __m256 &gradIg, __m256 &gradFg, __m256 &gradOg,
__m256 &prevState, __m256 &prevStateGrad, __m256 &state,
__m256 &stateGrad, __m256 &stateAtv, __m256 &outputGrad, __m256 &checkI,
__m256 &checkF, __m256 &checkO, __m256 &checkIGrad, __m256 &checkFGrad,
__m256 &checkOGrad, activation_mode_t active_node,
activation_mode_t active_gate, activation_mode_t active_state) {
gradOg =
activation(_mm256_mul_ps(outputGrad, stateAtv), valueOg, active_gate);
stateGrad = _mm256_add_ps(
actState(_mm256_mul_ps(outputGrad, valueOg), stateAtv), stateGrad);
activation(_mm256_mul_ps(outputGrad, valueOg), stateAtv, active_state),
stateGrad);
stateGrad = _mm256_add_ps(_mm256_mul_ps(gradOg, checkO), stateGrad);
gradIn = actInput(_mm256_mul_ps(stateGrad, valueIg), valueIn);
gradIg = actGate(_mm256_mul_ps(stateGrad, valueIn), valueIg);
gradFg = actGate(_mm256_mul_ps(stateGrad, prevState), valueFg);
gradIn =
activation(_mm256_mul_ps(stateGrad, valueIg), valueIn, active_node);
gradIg =
activation(_mm256_mul_ps(stateGrad, valueIn), valueIg, active_gate);
gradFg =
activation(_mm256_mul_ps(stateGrad, prevState), valueFg, active_gate);
prevStateGrad = _mm256_add_ps(_mm256_mul_ps(gradIg, checkI),
_mm256_mul_ps(gradFg, checkF));
prevStateGrad =
......
paddle/operators/mul_op.cc
浏览文件 @ 4b8bcf32
......@@ -29,9 +29,14 @@ class MulOpShapeInference : public framework::InferShapeBase {
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
VLOG(3) << "mul operator x.shape=" << x_dims << " y.shape=" << y_dims
<< " x_num_col_dims=" << x_num_col_dims
<< " y_num_col_dims=" << y_num_col_dims;
PADDLE_ENFORCE_GT(
x_dims.size(), x_num_col_dims,
"The input tensor X's rank of MulOp should be larger than "
......
paddle/operators/precision_recall_op.cc 0 → 100644
浏览文件 @ 4b8bcf32
/* 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 "paddle/operators/precision_recall_op.h"
namespace paddle {
namespace operators {
class PrecisionRecallOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("MaxProbs"),
"Input(MaxProbs) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Indices"),
"Input(Indices) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchMetrics"),
"Output(BatchMetrics) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("AccumMetrics"),
"Output(AccumMetrics) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("AccumStatesInfo"),
"Output(AccumStatesInfo) should not be null.");
int64_t cls_num =
static_cast<int64_t>(ctx->Attrs().Get<int>("class_number"));
auto max_probs_dims = ctx->GetInputDim("MaxProbs");
auto labels_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(max_probs_dims[1], 1,
"Each instance contains one max probability, so the "
"shape of Input(MaxProbs) should be [batch_size, 1].");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Indices"), max_probs_dims,
"The shape of Input(Indices) should be [batch_size, 1].");
PADDLE_ENFORCE_EQ(max_probs_dims[0], labels_dims[0],
"The 1st dimension of Input(MaxProbs) and "
"Input(Labels) both are batch_size and the shape should "
"be the same.");
PADDLE_ENFORCE_EQ(labels_dims[1], 1,
"The 2nd dimension of Input(Labels) contains instance "
"label and the shape should be equal to 1.");
if (ctx->HasInput("Weights")) {
auto weights_dims = ctx->GetInputDim("Weights");
PADDLE_ENFORCE_EQ(weights_dims,
framework::make_ddim({max_probs_dims[0], 1}),
"The shape of Input(Weights) should be "
"[batch_size, 1].");
}
if (ctx->HasInput("StatesInfo")) {
auto states_dims = ctx->GetInputDim("StatesInfo");
PADDLE_ENFORCE_EQ(states_dims, framework::make_ddim({cls_num, 4}),
"The shape of Input(StatesInfo) should be "
"[class_number, 4].");
}
// Layouts of BatchMetrics and AccumMetrics both are:
// [
// macro average precision, macro average recall, macro average F1 score,
// micro average precision, micro average recall, micro average F1 score
// ]
ctx->SetOutputDim("BatchMetrics", {6});
ctx->SetOutputDim("AccumMetrics", {6});
// Shape of AccumStatesInfo is [class_number, 4]
// The layout of each row is:
// [ TP, FP, TN, FN ]
ctx->SetOutputDim("AccumStatesInfo", {cls_num, 4});
}
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext &ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("MaxProbs")->type());
}
};
class PrecisionRecallOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PrecisionRecallOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("MaxProbs",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x 1, "
"where N is the batch size. Each row contains the max probability "
"of an instance which computed by the previous top_k (k=1) "
"operator.");
AddInput("Indices",
"(Tensor, default Tensor<int>), a 2-D tensor with shape N x 1, "
"where N is the batch size. Each row contains the corresponding "
"index which computed by the previous top_k (k=1) operator.");
AddInput("Labels",
"(Tensor, default Tensor<int>), a 2-D tensor with shape N x 1, "
"where N is the batch size. Each element is a label and the "
"value should be in [0, class_number - 1].");
AddInput("Weights",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x 1, "
"where N is the batch size. This input is optional. If provided, "
"weight of instance would be considered when computing metrics.")
.AsDispensable();
AddInput("StatesInfo",
"(Tensor, default Tensor<int>), a 2-D tensor with shape D x 4, "
"where D is the number of classes. This input is optional. If "
"provided, current state will be accumulated to this state and "
"the accumulation state will be as the output state.")
.AsDispensable();
AddOutput("BatchMetrics",
"(Tensor, default Tensor<float>), a 1-D tensor with shape {6}."
"This output tensor contains metrics for current batch data."
"The layout is [macro average precision, macro average recall, "
"macro f1 score, micro average precision, micro average recall, "
"micro f1 score]");
AddOutput("AccumMetrics",
"(Tensor, default Tensor<float>), a 1-D tensor with shape {6}."
"This output tensor contains metrics for accumulated data."
"The layout is [macro average precision, macro average recall, "
"macro f1 score, micro average precision, micro average recall, "
"micro f1 score]");
AddOutput("AccumStatesInfo",
"(Tensor, default Tensor<float>), a 2-D tensor with shape D x 4, "
"where D is equal to class number. This output tensor contains "
"accumulated state variables used to compute metrics. The layout "
"for each class is [true positives, false positives, "
"true negatives, false negatives].");
AddAttr<int>("class_number", "Number of classes to be evaluated.");
AddComment(R"DOC(
When given 'Input(Indices)' and 'Input(Labels)', this operator can be used
to compute various metrics including:
- macro average precision
- macro average recall
- macro f1 score
- micro average precision
- micro average recall
- micro f1 score
To compute the above metrics, we need to do statistics for true positives,
false positives and false negatives. Here count of true negatives is not
necessary, but counting it may provide potential usage and the cost is
trivial, so the operator also provides count of true negatives.
We define state as a 2-D tensor with shape [class_number, 4]. Each row of a
state contains statistic variables for corresponding class. Layout of each row
is: TP(true positives), FP(false positives), TN(true negatives),
FN(false negatives). If 'Input(Weights)' provided, TP, FP, TN, FN will be
calculated by given weight instead of instance count.
This operator also supports metrics computing for cross-batch situation. To
achieve this, 'Input(StatesInfo)' should be provided. State of current batch
data will be accumulated to 'Input(StatesInfo)' and 'Output(AccumStatesInfo)'
is the accumulation state.
'Output(BatchMetrics)' is metrics of current batch data while
'Output(AccumStatesInfo)' is metrics of accumulation data.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(precision_recall, ops::PrecisionRecallOp,
ops::PrecisionRecallOpMaker);
REGISTER_OP_CPU_KERNEL(
precision_recall,
ops::PrecisionRecallKernel<paddle::platform::CPUPlace, float>,
ops::PrecisionRecallKernel<paddle::platform::CPUPlace, double>);
paddle/operators/precision_recall_op.h 0 → 100644
浏览文件 @ 4b8bcf32
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
enum StateVariable { TP = 0, FP, TN, FN };
template <typename Place, typename T>
class PrecisionRecallKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in0 = ctx.Input<Tensor>("Indices");
auto* in1 = ctx.Input<Tensor>("Labels");
auto* in2 = ctx.Input<Tensor>("Weights");
auto* in3 = ctx.Input<Tensor>("StatesInfo");
auto* out0 = ctx.Output<Tensor>("BatchMetrics");
auto* out1 = ctx.Output<Tensor>("AccumMetrics");
auto* out2 = ctx.Output<Tensor>("AccumStatesInfo");
const int* ids_data = in0->data<int>();
const int* labels_data = in1->data<int>();
size_t cls_num = static_cast<size_t>(ctx.Attr<int>("class_number"));
const T* weights_data = in2 ? in2->data<T>() : nullptr;
const T* states_data = in3 ? in3->data<T>() : nullptr;
double* batch_metrics_data = out0->mutable_data<double>(ctx.GetPlace());
double* accum_metrics_data = out1->mutable_data<double>(ctx.GetPlace());
out2->mutable_data<T>(ctx.GetPlace());
auto accum_states = EigenMatrix<T>::From(*out2);
accum_states.setZero();
T* accum_states_data = out2->data<T>();
size_t sample_num = in0->dims()[0];
size_t state_var_num = 4; // TP FP TN FN
// get states info for current batch
for (size_t i = 0; i < sample_num; ++i) {
size_t idx = ids_data[i];
size_t label = labels_data[i];
PADDLE_ENFORCE(idx >= 0 && idx < cls_num,
"Class index of each instance should be in "
"[0, class_number).");
PADDLE_ENFORCE(label >= 0 && label < cls_num,
"Label of each instance should be in [0, class_number).");
T w = weights_data ? weights_data[i] : 1.0;
if (idx == label) {
accum_states_data[idx * state_var_num + TP] += w;
for (size_t j = 0; j < cls_num; ++j) {
accum_states_data[j * state_var_num + TN] += w;
}
accum_states_data[idx * state_var_num + TN] -= w;
} else {
accum_states_data[label * state_var_num + FN] += w;
accum_states_data[idx * state_var_num + FP] += w;
for (size_t j = 0; j < cls_num; ++j) {
accum_states_data[j * state_var_num + TN] += w;
}
accum_states_data[idx * state_var_num + TN] -= w;
accum_states_data[label * state_var_num + TN] -= w;
}
}
ComputeMetrics(accum_states_data, batch_metrics_data, state_var_num,
cls_num);
if (states_data) {
for (size_t i = 0; i < cls_num; ++i) {
for (size_t j = 0; j < state_var_num; ++j) {
size_t idx = i * state_var_num + j;
accum_states_data[idx] += states_data[idx];
}
}
}
ComputeMetrics(accum_states_data, accum_metrics_data, state_var_num,
cls_num);
}
// expose to be reused
static inline T CalcPrecision(T tp_count, T fp_count) {
if (tp_count > 0.0 || fp_count > 0.0) {
return tp_count / (tp_count + fp_count);
}
return 1.0;
}
static inline T CalcRecall(T tp_count, T fn_count) {
if (tp_count > 0.0 || fn_count > 0.0) {
return tp_count / (tp_count + fn_count);
}
return 1.0;
}
static inline T CalcF1Score(T precision, T recall) {
if (precision > 0.0 || recall > 0.0) {
return 2 * precision * recall / (precision + recall);
}
return 0.0;
}
protected:
void ComputeMetrics(const T* states_data, double* metrics_data,
size_t state_var_num, size_t cls_num) const {
T total_tp_count = 0;
T total_fp_count = 0;
T total_fn_count = 0;
T macro_avg_precision = 0.0;
T macro_avg_recall = 0.0;
for (size_t i = 0; i < cls_num; ++i) {
T tp_count = states_data[i * state_var_num + TP];
T fp_count = states_data[i * state_var_num + FP];
T fn_count = states_data[i * state_var_num + FN];
total_tp_count += tp_count;
total_fp_count += fp_count;
total_fn_count += fn_count;
macro_avg_precision += CalcPrecision(tp_count, fp_count);
macro_avg_recall += CalcRecall(tp_count, fn_count);
}
macro_avg_precision /= cls_num;
macro_avg_recall /= cls_num;
T macro_f1_score = CalcF1Score(macro_avg_precision, macro_avg_recall);
T micro_avg_precision = CalcPrecision(total_tp_count, total_fp_count);
T micro_avg_recall = CalcRecall(total_tp_count, total_fn_count);
T micro_f1_score = CalcF1Score(micro_avg_precision, micro_avg_recall);
// fill metrics data
metrics_data[0] = macro_avg_precision;
metrics_data[1] = macro_avg_recall;
metrics_data[2] = macro_f1_score;
metrics_data[3] = micro_avg_precision;
metrics_data[4] = micro_avg_recall;
metrics_data[5] = micro_f1_score;
}
};
} // namespace operators
} // namespace paddle
paddle/operators/recurrent_op.cc
浏览文件 @ 4b8bcf32
此差异已折叠。
paddle/operators/recurrent_op.h 已删除 100644 → 0
浏览文件 @ faad8351
/* 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. */
#pragma once
#include "paddle/framework/operator.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/rnn/recurrent_op_utils.h"
namespace paddle {
namespace operators {
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO(Superjom)
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
// 4. More Complex RNN architecture, such as Gated Feedback RNN.
// Refer to: https://arxiv.org/pdf/1502.02367.pdf
class RecurrentAlgorithm {
public:
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void Init(rnn::Argument* arg,
std::unique_ptr<framework::OperatorBase>* stepnet) {
PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
arg_ = arg;
stepnet_ = stepnet;
}
protected:
/*
* The step scopes will be stored in the father scope as a variable.
*
* NOTE the scopes are reused in both the forward and backward, so just
* create once and expand its size if more steps need.
*/
void CreateScopes(const framework::Scope& scope, size_t seq_len) const;
const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)
->GetMutable<std::vector<framework::Scope*>>();
}
void InitMemories(framework::Scope* step_scopes) const;
private:
std::unique_ptr<framework::OperatorBase>* stepnet_;
rnn::Argument* arg_;
};
class RecurrentGradientAlgorithm {
/**
* RNN's backward alogorithm.
*
* To accelerate the development of RecurrentGradientOp, we decouple RNN's
* algorithm and `OperatorBase`'s implementation, the former contains the core
* implementation of a RNN, and will keep stable even if the framework changes
* a
* lot, and the latter is a wrapper acts like an dapter for it to make RNN an
* operator.
*/
public:
void Init(rnn::Argument* arg,
std::unique_ptr<framework::OperatorBase>* stepnet) {
PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
arg_ = std::move(arg);
stepnet_ = stepnet;
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(framework::Scope* step_scopes) const;
protected:
inline const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)
->GetMutable<std::vector<framework::Scope*>>();
}
private:
rnn::Argument* arg_;
std::unique_ptr<framework::OperatorBase>* stepnet_;
};
class RecurrentOp : public framework::OperatorBase {
public:
RecurrentOp(const std::string& type, const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs);
RecurrentOp(const RecurrentOp& o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
// TODO(yuyang18): Implement copy ctor well.
PADDLE_THROW("Not implemented");
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
void set_stepnet(std::unique_ptr<OperatorBase> net) {
stepnet_ = std::move(net);
}
const OperatorBase& stepnet() const { return *stepnet_; }
static const rnn::ArgumentName kArgName;
private:
RecurrentAlgorithm alg_;
rnn::Argument arg_;
std::unique_ptr<OperatorBase> stepnet_;
};
class RecurrentGradientOp : public framework::OperatorBase {
public:
RecurrentGradientOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs);
RecurrentGradientOp(const RecurrentGradientOp& o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
// TODO(yuyang18): Implement Copy ctor.
PADDLE_THROW("Not Implemented");
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
static const rnn::ArgumentName kArgName;
/*
* set a stepnet that is created according to a RecurrentOp's stepnet.
*/
void set_stepnet(std::unique_ptr<OperatorBase> net) {
stepnet_ = std::move(net);
}
const OperatorBase& stepnet() const { return *stepnet_; }
private:
RecurrentGradientAlgorithm alg_;
std::unique_ptr<OperatorBase> stepnet_;
rnn::Argument arg_;
};
} // namespace operators
} // namespace paddle
paddle/operators/rnn_memory_helper_op.cc 0 → 100644
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此差异已折叠。
paddle/operators/seq_expand_op.h
浏览文件 @ 4b8bcf32
......@@ -32,7 +32,8 @@ class SeqExpandKernel : public framework::OpKernel<T> {
const T* x_data = x->data<T>();
auto x_dims = x->dims();
auto* y = context.Input<LoDTensor>("Y");
PADDLE_ENFORCE_EQ(x_dims[0], y->lod().back().size() - 1,
PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims[0]),
y->lod().back().size() - 1,
"The size of last lod level in Input(Y)"
"must be equal to dims[0] of Input(X).");
out->set_lod(y->lod());
......
paddle/operators/sequence_pool_op.cc
浏览文件 @ 4b8bcf32
......@@ -42,7 +42,8 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::string>(
"pooltype",
"(int, default AVERAGE) the pooling pooltype of SequencePoolOp.")
.SetDefault("AVERAGE");
.SetDefault("AVERAGE")
.InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
AddComment(R"DOC(
SequencePoolOp pools features of all time-steps of each instance.
......
paddle/operators/softmax_with_cross_entropy_op.cc
浏览文件 @ 4b8bcf32
......@@ -32,9 +32,9 @@ class SoftmaxWithCrossEntropyOpMaker
AddInput("Label",
"(Tensor, default: Tensor<int>), The ground truth which is a 2-D "
"tensor. "
"If softLable is set to 0, Label is a Tensor<int> with shape [N x "
"1]. "
"If softLable is set to 1, Label is a Tensor<float/double> "
"If softLabel is set to false, Label is a Tensor<int> with shape "
"[N x 1]."
"If softLabel is set to true, Label is a Tensor<float/double> "
"with shape [N x K].");
AddOutput(
"Softmax",
......@@ -60,19 +60,23 @@ Because this operators performs a softmax on logits internally, it expects
unscaled logits. Please do not call this op with the output of softmax operator,
which will produce incorrect results.
This operators expects mutually exclusive hard labels, each sample in a batch
is in exactly one class with probabilities 1. Each sample in the batch with one
and only one label.
When the attribute softLabel is set false, this operators expects mutually
exclusive hard labels, each sample in a batch is in exactly one class with
probabilities 1. Each sample in the batch with one and only one label.
Equation:
1) hard label (one-hot label)
Loss_j = -\text{Logit}_{Label_j} + \log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right), j = 1, ..., K
Loss_j = \f$ -\text{Logit}_{Label_j} +
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right),
j = 1, ..., K $\f
2) soft label (a distribution over all classes)
Loss_j = -\sum_{i=0}^{K}\text{Label}_i\left(\text{Logit}_i-\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right), j = 1,...,K
Loss_j = \f$ -\sum_{i=0}^{K}\text{Label}_i\left(\text{Logit}_i -
\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right),
j = 1,...,K $\f
)DOC");
}
......
paddle/operators/sum_op.h
浏览文件 @ 4b8bcf32
......@@ -29,22 +29,27 @@ template <typename Place, typename T>
class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& in_vars = context.MultiInputVar("X");
auto in_vars = context.MultiInputVar("X");
int N = in_vars.size();
auto out_var = context.OutputVar("Out");
bool in_place = out_var == in_vars[0];
if (out_var->IsType<framework::LoDTensor>()) {
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto result = EigenVector<T>::Flatten(*out);
math::SetConstant<Place, T> constant_functor;
constant_functor(context.device_context(), out, 0.0);
if (!in_place) {
math::SetConstant<Place, T> constant_functor;
constant_functor(context.device_context(), out, 0.0);
}
math::SelectedRowsAddToTensor<Place, T> functor;
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < N; i++) {
// If in_place, just skip the first tensor
for (int i = in_place ? 1 : 0; i < N; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto& in_t = in_vars[i]->Get<framework::LoDTensor>();
auto in = EigenVector<T>::Flatten(in_t);
......@@ -57,6 +62,7 @@ class SumKernel : public framework::OpKernel<T> {
}
}
} else if (out_var->IsType<framework::SelectedRows>()) {
PADDLE_ENFORCE(!in_place, "SelectedRows not support inplace sum now");
auto* out = context.Output<SelectedRows>("Out");
auto* out_value = out->mutable_value();
......
paddle/optimizer/parameter_optimizer_test.cpp
浏览文件 @ 4b8bcf32
......@@ -85,7 +85,7 @@ public:
for (size_t i = 0; i < opts_.size(); ++i) {
int s = 0;
float* newp = (float*)opts_[i]->get_weight(&s);
EXPECT_EQ(s, kSize);
EXPECT_EQ(static_cast<size_t>(s), kSize);
for (size_t j = 0; j < kSize; ++j) {
EXPECT_EQ(newp[j], (*p)[j]);
}
......
paddle/pybind/pybind.cc
浏览文件 @ 4b8bcf32
......@@ -28,7 +28,6 @@ limitations under the License. */
#include "paddle/operators/cond_op.h"
#include "paddle/operators/dynamic_recurrent_op.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/exception.h"
......@@ -428,25 +427,6 @@ All parameter, weight, gradient are variables in Paddle.
return self.UnstackShared(source);
});
// recurrent_op
py::class_<operators::RecurrentOp, OperatorBase>(m, "RecurrentOp")
.def_static(
"create",
[](py::bytes protobin) -> operators::RecurrentOp * {
OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto rnn_op = OpRegistry::CreateOp(desc);
return static_cast<operators::RecurrentOp *>(rnn_op.release());
})
.def("set_stepnet", [](operators::RecurrentOp &self,
const operators::NetOp &net) -> void {
self.set_stepnet(net.Clone());
});
py::class_<operators::DynamicRecurrentOp, OperatorBase>(m,
"DynamicRecurrentOp")
.def_static("create",
......
paddle/scripts/travis/build_doc.sh
浏览文件 @ 4b8bcf32
......@@ -53,8 +53,8 @@ function deploy_docs() {
set +e
rm -rf ${DIR}/doc ${DIR}/doc_cn
set -e
mv ../doc/cn/html ${DIR}/doc_cn
mv ../doc/en/html ${DIR}/doc
cp -r ../doc/cn/html ${DIR}/doc_cn
cp -r ../doc/en/html ${DIR}/doc
git add .
}
......
paddle/trainer/tests/CMakeLists.txt
浏览文件 @ 4b8bcf32
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python/paddle/v2/framework/evaluator.py 0 → 100644
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此差异已折叠。
python/paddle/v2/framework/net_drawer.py 0 → 100644
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此差异已折叠。
python/paddle/v2/framework/nets.py
浏览文件 @ 4b8bcf32
此差异已折叠。
python/paddle/v2/framework/tests/test_evaluator.py 0 → 100644
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此差异已折叠。
python/paddle/v2/plot/plot.py
浏览文件 @ 4b8bcf32
此差异已折叠。
python/requirements.txt
浏览文件 @ 4b8bcf32
此差异已折叠。
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