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提交 448d4db5 编写于 作者: D dongzhihong
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_op_gradient

上级 12ee5014 940b4a9c
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Showing with 1569 addition and 1159 deletion
.clang_format.hook 0 → 100755
浏览文件 @ 448d4db5
#!/bin/bash
set -e
readonly VERSION="3.8"
version=$(clang-format -version)
if ! [[ $version == *"$VERSION"* ]]; then
echo "clang-format version check failed."
echo "a version contains '$VERSION' is needed, but get '$version'"
echo "you can install the right version, and make an soft-link to '\$PATH' env"
exit -1
fi
clang-format $@
.pre-commit-config.yaml
浏览文件 @ 448d4db5
......@@ -19,10 +19,10 @@
- id: end-of-file-fixer
- repo: local
hooks:
- id: clang-format
- id: clang-format-with-version-check
name: clang-format
description: Format files with ClangFormat.
entry: clang-format -i
entry: ./.clang_format.hook -i
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
- repo: https://github.com/PaddlePaddle/pre-commit-golang
......
CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -36,8 +36,8 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." OFF)
option(WITH_MKLML "Compile PaddlePaddle with mklml package." OFF)
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......
Dockerfile
浏览文件 @ 448d4db5
......@@ -34,9 +34,6 @@ RUN apt-get update && \
net-tools && \
apt-get clean -y
# paddle is using numpy.flip, which is introduced since 1.12.0
RUN pip --no-cache-dir install 'numpy>=1.12.0'
# Install Go and glide
RUN wget -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
......@@ -58,33 +55,22 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# FIXME: due to temporary ipykernel dependency issue, specify ipykernel jupyter
# version util jupyter fixes this issue.
RUN pip install --upgrade pip && \
pip install -U 'protobuf==3.1.0' && \
pip install -U wheel pillow BeautifulSoup && \
pip install -U wheel && \
pip install -U docopt PyYAML sphinx && \
pip install -U sphinx-rtd-theme==0.1.9 recommonmark && \
pip install pre-commit 'requests==2.9.2' 'ipython==5.3.0' && \
pip install -U sphinx-rtd-theme==0.1.9 recommonmark
RUN pip install pre-commit 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python rarfile 'scipy>=0.19.0' 'nltk>=3.2.2'
pip install opencv-python
COPY ./python/requirements.txt /root/
RUN pip install -r /root/requirements.txt
# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
RUN apt-get install -y libssl-dev libffi-dev
RUN pip install certifi urllib3[secure]
# TODO(qijun) The template library Eigen doesn't work well with GCC 5
# coming with the default Docker image, so we switch to use GCC 4.8
# by default. And I will check Eigen library later.
RUN ln -sf gcc-4.8 /usr/bin/gcc && \
ln -sf gcc-ar-4.8 /usr/bin/gcc-ar && \
ln -sf gcc-nm-4.8 /usr/bin/gcc-nm && \
ln -sf gcc-ranlib-4.8 /usr/bin/gcc-ranlib && \
ln -sf gcc-4.8 /usr/bin/x86_64-linux-gnu-gcc && \
ln -sf gcc-ar-4.8 /usr/bin/x86_64-linux-gnu-gcc-ar && \
ln -sf gcc-nm-4.8 /usr/bin/x86_64-linux-gnu-gcc-nm && \
ln -sf gcc-ranlib-4.8 /usr/bin/x86_64-linux-gnu-gcc-ranlib && \
ln -sf g++-4.8 /usr/bin/g++ && \
ln -sf g++-4.8 /usr/bin/x86_64-linux-gnu-g++
# Install woboq_codebrowser to /woboq
RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \
......
cmake/external/openblas.cmake
浏览文件 @ 448d4db5
......@@ -73,10 +73,18 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
ADD_LIBRARY(cblas STATIC ${dummyfile})
IF(${CBLAS_PROVIDER} MATCHES MKL)
ADD_LIBRARY(cblas SHARED ${dummyfile})
ELSE()
ADD_LIBRARY(cblas STATIC ${dummyfile})
ENDIF()
TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
IF(NOT ${CBLAS_FOUND})
ADD_DEPENDENCIES(cblas extern_openblas)
LIST(APPEND external_project_dependencies cblas)
ELSE()
IF("${CBLAS_PROVIDER}" STREQUAL "MKLML")
ADD_DEPENDENCIES(cblas mklml)
ENDIF()
ENDIF(NOT ${CBLAS_FOUND})
cmake/flags.cmake
浏览文件 @ 448d4db5
......@@ -9,13 +9,6 @@ function(CheckCompilerCXX11Flag)
if(${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.8)
message(FATAL_ERROR "Unsupported GCC version. GCC >= 4.8 required.")
endif()
if(NOT ANDROID)
# TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
# Use Debug mode instead for now.
if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9)
set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE)
endif()
endif()
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
# cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang"
# Apple Clang is a different compiler than upstream Clang which havs different version numbers.
......@@ -160,7 +153,7 @@ set(CUDA_PROPAGATE_HOST_FLAGS OFF)
# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
# So, don't set these flags here.
LIST(APPEND CUDA_NVCC_FLAGS -std=c++11 --default-stream per-thread)
LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
......
doc/design/cluster_train/large_model_dist_train.md 0 → 100644
浏览文件 @ 448d4db5
# Alalysis of large model distributed training in Paddle
***NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.***
## What is it
We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer's memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters.
## How to use
Specify command-line argument like `--loadsave_parameters_in_pserver=true --ports_num_for_sparse=1 --use_old_updater=1` when starting the paddle trainer. And also add something like `--ports_num_for_sparse=1 --pserver_num_threads=5` when starting pserver processes.
Accrodingly, configure your embedding layers like:
```python
SPARSE_REMOTE=True
w1 = data_layer(name="w1", size=dict_size)
emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
w2 = data_layer(name="w2", size=dict_size)
emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
...
```
## Implementation details
```c++
enum MatType {
MAT_NORMAL,
MAT_NORMAL_SHARED,
MAT_VALUE_SHARED,
MAT_SPARSE_ROW_IDS,
MAT_SPARSE_ROW_AUTO_GROW,
MAT_CACHE_ROW,
MAT_SPARSE_ROW,
MAT_SPARSE_ROW_PREFETCH,
MAT_SPARSE_ROW_PREFETCH_FULL_SIZE,
};
```
`MAT_SPARSE_ROW_PREFETCH` is what we use when configured to fetch only row of matrix when training.
In `trainer_internal.cpp:L93 trainOneBatch`:
```c++
if (config_->getOptConfig().use_sparse_remote_updater()) {
REGISTER_TIMER("prefetch");
gradientMachine_->prefetch(inArgs);
parameterUpdater_->getParametersRemote();
}
```
When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver.
In `trainer/RemoteParameterUpdater.cpp`: `parameterUpdater_->getParametersRemote();`:
```c++
if (fullSize) {
...
} else {
getParams = [&] {
parameterClient_->getParameterSparse(
/* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType);
};
applyL1 = [](Parameter& para, real decayRate) {
para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate);
};
}
```
Calling `parameterClient_->getParameterSparse` will do remote call to pserver's `getParameterSparse`:
```c++
void ParameterServer2::getParameterSparse(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
(void)inputBuffers;
auto& buffer = *readWriteBuffer_;
size_t numReals = 0;
for (const auto& block : request.blocks()) {
numReals += getParameterConfig(block).dims(1);
}
buffer.resize(numReals);
VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals;
ReadLockGuard guard(parameterMutex_);
size_t offset = 0;
for (const auto& block : request.blocks()) {
size_t width = getParameterConfig(block).dims(1);
Buffer buf = {buffer.data() + offset, width};
int type = request.send_back_parameter_type();
sendBackParameterSparse(block, type, response, &buf, width, outputBuffers);
offset += width;
}
}
```
`getParameterConfig(block).dims(1)` returns the width of the current "parameter block"(a shard of parameter object),
then `getParameterSparse` remote call returns only one row of data to the client.
doc/design/mkldnn/README.MD
浏览文件 @ 448d4db5
......@@ -101,6 +101,7 @@ if use_mkldnn
5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。
6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。
7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展[Header](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/parameter/Parameter.h#L247)里面的`int32_t version`。这个值不管是在v1还是在v2里面，一直保存的是0，所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，从而`MKLDNNLayer`就可以从输入的参数中获取需要的格式信息。
## References
......
doc/getstarted/build_and_install/build_from_source_en.md
浏览文件 @ 448d4db5
......@@ -68,7 +68,7 @@ As a simple example, consider the following:
1. **BLAS Dependencies(optional)**
CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically.
CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically.
To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`.
```bash
......@@ -131,9 +131,9 @@ As a simple example, consider the following:
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. A supported version of Linux with a gcc compiler and toolchain
2. A supported version of Linux with a GCC compiler and toolchain
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
......@@ -172,6 +172,7 @@ export PATH=<path to install>/bin:$PATH
# install PaddlePaddle Python modules.
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
```
## <span id="centos">Build on Centos 7</span>
### Install Dependencies
......@@ -192,9 +193,9 @@ sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. A supported version of Linux with a gcc compiler and toolchain
2. A supported version of Linux with a GCC compiler and toolchain
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
......@@ -222,7 +223,7 @@ mkdir build && cd build
```
Finally, you can build and install PaddlePaddle:
```bash
# you can add build option here, such as:
cmake3 .. -DCMAKE_INSTALL_PREFIX=<path to install>
......
paddle/capi/gradient_machine.cpp
浏览文件 @ 448d4db5
......@@ -146,3 +146,19 @@ paddle_error paddle_gradient_machine_randomize_param(
m->machine->randParameters();
return kPD_NO_ERROR;
}
paddle_error paddle_gradient_machine_get_layer_output(
paddle_gradient_machine machine,
const char* layerName,
paddle_arguments args) {
auto m = cast(machine);
auto out = paddle::capi::cast<paddle::capi::CArguments>(args);
if (m == nullptr || layerName == nullptr || out == nullptr ||
m->machine == nullptr) {
return kPD_NULLPTR;
}
auto layerOutput = m->machine->getLayerOutput(layerName);
out->args.push_back(layerOutput);
return kPD_NO_ERROR;
}
paddle/capi/gradient_machine.h
浏览文件 @ 448d4db5
......@@ -39,7 +39,11 @@ PD_API paddle_error paddle_gradient_machine_create_for_inference(
/**
* @brief Create a gradient machine used for model inference, using config with
* parameters which is generated by `paddle merge_model`.
* @param [out] machine that used for model inference.
* Example:
* paddle merge_model \
* --model_dir="pass-00000" \
* --model_file="merged_model.paddle"
* @param [out] machine that used for model inference
* @param [in] mergedModel
* @param [in] size
* @return paddle_error
......@@ -97,6 +101,18 @@ paddle_gradient_machine_randomize_param(paddle_gradient_machine machine);
PD_API paddle_error
paddle_gradient_machine_destroy(paddle_gradient_machine machine);
/**
* @brief Get the output of the layer named `layerName`.
* @param [in] gradient machine that have run a inference
* @param [in] layerName name of specified layer
* @param [out] args output of the specified layer
* @return paddle_error
*/
PD_API paddle_error
paddle_gradient_machine_get_layer_output(paddle_gradient_machine machine,
const char* layerName,
paddle_arguments args);
#ifdef __cplusplus
}
#endif
......
paddle/framework/CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -38,7 +38,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
......
paddle/framework/backward.cc
浏览文件 @ 448d4db5
......@@ -15,14 +15,17 @@
#include "paddle/framework/backward.h"
#include <list>
#include <memory>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
namespace paddle {
namespace framework {
template <typename Map, typename T>
static void ForEachVarName(Map& names, T callback) {
static void ForEachVarName(const Map& names, T callback) {
for (auto& name : names) {
for (auto& n : name.second) {
if (callback(n)) return;
......@@ -30,6 +33,7 @@ static void ForEachVarName(Map& names, T callback) {
}
}
// return whether all the names + suffixes in the set
static bool AllInSet(
const std::map<std::string, std::vector<std::string>>& names,
const std::string& suffix, const std::unordered_set<std::string>& set) {
......@@ -41,14 +45,14 @@ static bool AllInSet(
return all_in_set;
}
static std::shared_ptr<OperatorBase> NOP() {
auto net_op = std::make_shared<operators::NetOp>();
net_op->type_ = "@NOP@";
static std::unique_ptr<OperatorBase> NOP() {
auto net_op = new operators::NetOp();
net_op->SetType("@NOP@");
net_op->CompleteAddOp();
return net_op;
return std::unique_ptr<OperatorBase>(net_op);
}
// Get backward operator from a forward operator, recursively implementation.
// Get backward operator from a forward operator, a recursive implementation.
//
// no_grad_names the gradient variable names without gradient calculating.
//
......@@ -56,28 +60,27 @@ static std::shared_ptr<OperatorBase> NOP() {
// BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
// pass `uniq_id` through recursive calling.
//
// returns The backward operator. For simple situation, it is a simple
// operator. For complex situation, it is a NetOp.
// returns The backward operator. In a simple situation, it may be a simple
// operator, in a complex situation, it maybe a NetOp.
//
// See Backward.h for details
static std::shared_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names, size_t& uniq_id);
std::shared_ptr<OperatorBase> BackwardRecursive(
static std::unique_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
// If all input gradients of forwarding operator do not need to calculate,
// just return an NOP. Not return null ptr because NOP does not take
// too much time for calculation, but it is useful for simplifying logic.
if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
if (AllInSet(forwardOp.Inputs() /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
return NOP();
}
// All output gradients of forwarding operator do not need to calculate.
// Then all input gradients cannot be computed at all, and we put them into
// `no_grad_names` set. Return an NOP.
if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
ForEachVarName(forwardOp.inputs_,
if (AllInSet(forwardOp.Outputs() /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
ForEachVarName(forwardOp.Inputs(),
[&no_grad_names](const std::string& name) -> bool {
no_grad_names.insert(GradVarName(name));
return false;
......@@ -86,71 +89,77 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
}
// Returned gradient network
auto net = std::make_shared<operators::NetOp>();
auto net = std::unique_ptr<operators::NetOp>(new operators::NetOp());
if (forwardOp.IsNetOp()) {
// Because forwardOp is a net op, it can static_cast.
auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
// Map from output gradient variable name to operator's indices in
// backward net. That operator generates that variable.
// backward net's ops_. That operator generates that variable.
std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;
size_t local_op_id = 0;
// reversely travel forwardNet
// reversely travel forwardNet and collect all duplicate outputs.
for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
++it, ++local_op_id) {
auto fwd = *it;
auto& fwd = *it;
auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
net->AddOp(bwd);
ForEachVarName(bwd->outputs_,
ForEachVarName(bwd->Outputs(),
[&dup_output_ops, local_op_id](const std::string& out) {
dup_output_ops[out].emplace_back(local_op_id);
return false;
});
net->AddOp(std::move(bwd));
}
// Get unique ID for this method.
auto uid = uniq_id++;
// TODO(dzh): more comment
using Pos = std::pair<size_t, std::shared_ptr<OperatorBase>>;
// multiple operators which have the same output (y for example) may
// overwrite the same y variable when backward, special operations are token
// to handle this case. For each duplicate output, rename it to an alias
// (original name with a offset), append an `add` op for its operator,
// and finally sum all the alias variable to the final output variable y.
using Pos = std::pair<size_t, std::unique_ptr<OperatorBase>>;
std::list<Pos> insert_position;
for (auto& dup_output_op : dup_output_ops) {
const std::string& name = dup_output_op.first;
auto& dup_op = dup_output_op.second;
// no duplicate output
if (dup_op.size() == 1) continue;
std::vector<std::string> dup_outputs;
// process the duplicate outputs
std::vector<std::string> dup_outputs;
for (size_t i = 0; i < dup_op.size(); ++i) {
// rename each duplicate output to an alias
auto op_offset = dup_op[i];
dup_outputs.push_back(name + "@RENAME@" + std::to_string(uid) + "@" +
std::to_string(i));
net->ops_[op_offset]->Rename(name, dup_outputs.back());
}
// collect all the offset to append `add` op for each alias
insert_position.push_back(
{dup_op.back(),
OpRegistry::CreateOp(
"add", {{"X", {dup_outputs}}}, {{"Out", {name}}},
{{"input_format",
std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
{dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}},
{{"Out", {name}}}, {})});
}
// make sure the inserted `add` ops follow the BFS order.
insert_position.sort(
[](const Pos& l, const Pos& r) { return l.first > r.first; });
for (auto& pos : insert_position) {
net->InsertOp(pos.first + 1, pos.second);
net->InsertOp(pos.first + 1, std::move(pos.second));
}
} else {
std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
std::unique_ptr<OperatorBase> grad_op(OpRegistry::CreateGradOp(forwardOp));
ForEachVarName(grad_op->inputs_, [&no_grad_names,
&net](std::string& grad_input) {
ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
const std::string& grad_input) {
if (no_grad_names.count(grad_input)) {
// +1 for \0
std::string prefix = grad_input.substr(
0, grad_input.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
grad_input = prefix + kZeroVarSuffix;
grad_op->Rename(grad_input, prefix + kZeroVarSuffix);
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
......@@ -160,26 +169,42 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
return false;
});
ForEachVarName(grad_op->outputs_,
[&no_grad_names](std::string& grad_output) {
ForEachVarName(grad_op->Outputs(),
[&no_grad_names, &grad_op](const std::string& grad_output) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
grad_op->Rename(grad_output, kEmptyVarName);
}
return false;
});
// process recurrent gradient op as a special operator.
if (forwardOp.Type() == "recurrent_op") {
// 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, uniq_id));
}
if (net->ops_.empty()) { // Current no aux op is added to network
return grad_op;
}
net->AddOp(grad_op);
net->AddOp(std::move(grad_op));
}
net->type_ = "@GENERATED_BACKWARD@";
net->SetType("@GENERATED_BACKWARD@");
net->CompleteAddOp();
return net;
return std::unique_ptr<OperatorBase>(
static_cast<OperatorBase*>(net.release()));
}
// See header for comments
std::shared_ptr<OperatorBase> Backward(
std::unique_ptr<OperatorBase> Backward(
const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_names;
......
paddle/framework/backward.h
浏览文件 @ 448d4db5
......@@ -20,7 +20,7 @@ namespace framework {
// Create the backward operator from a forward operator.
// TODO(yuyang18): Add more API reference comment.
extern std::shared_ptr<OperatorBase> Backward(
extern std::unique_ptr<OperatorBase> Backward(
const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars);
} // namespace framework
......
paddle/framework/backward_test.cc
浏览文件 @ 448d4db5
......@@ -28,20 +28,13 @@ using OpAttrChecker = framework::OpAttrChecker;
using Scope = framework::Scope;
using DeviceContext = platform::DeviceContext;
class EmptyOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
};
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input X of Add").AsNoGradient();
AddInput("b", "Bias of Add").AsNoGradient();
AddOutput("Out", "Out of Add").AsNoGradient();
AddInput("X", "Input X of Add").NotInGradient();
AddInput("b", "Bias of Add").NotInGradient();
AddOutput("Out", "Out of Add").NotInGradient();
AddComment("Add Op");
}
};
......@@ -155,27 +148,24 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
REGISTER_OP(rowwise_add, f::EmptyOp, f::RowWiseAddOpMaker);
REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, f::EmptyOp);
REGISTER_OP(mul, f::EmptyOp, f::MulOpMaker);
REGISTER_GRADIENT_OP(mul, mul_grad, f::EmptyOp);
REGISTER_OP(sigmoid, f::EmptyOp, f::SigmoidOpMaker);
REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, f::EmptyOp);
REGISTER_OP(nograd, f::EmptyOp, f::NoGradOpMaker);
REGISTER_OP(fill_zeros_like, f::EmptyOp, f::FillZeroOpMaker);
REGISTER_OP(add, f::EmptyOp, f::AddOpMaker);
REGISTER_GRADIENT_OP(add, add_grad, f::EmptyOp);
REGISTER_OP(fc, f::FcOp, f::FcOpMaker);
REGISTER_OP(many_output_op, f::EmptyOp, f::ManyOutputOpMaker);
REGISTER_GRADIENT_OP(many_output_op, many_output_op_grad, f::EmptyOp);
REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
f::NOP);
REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
f::NOP);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(1UL, gop->inputs_.size());
ASSERT_EQ("rowwise_add_grad", gop->type_);
ASSERT_EQ(1UL, gop->Inputs().size());
ASSERT_EQ("rowwise_add_grad", gop->Type());
ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
}
......@@ -190,8 +180,7 @@ TEST(Backward, simple_op_not_need_grad) {
auto no_input_gop = f::Backward(*fwd, {"x", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL,
std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
ASSERT_EQ(0UL, static_cast<ops::NetOp *>(no_input_gop.get())->ops_.size());
}
TEST(Backward, net_fc_backward_normal) {
......@@ -211,13 +200,13 @@ TEST(Backward, net_fc_backward_normal) {
ASSERT_EQ(3UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
f::OperatorBase &d_add = *net->ops_[1];
ASSERT_EQ("rowwise_add_grad", d_add.type_);
ASSERT_EQ("rowwise_add_grad", d_add.Type());
f::OperatorBase &d_mul = *net->ops_[2];
ASSERT_EQ("mul_grad", d_mul.type_);
ASSERT_EQ("mul_grad", d_mul.Type());
}
TEST(Backward, net_fc_backward_not_have_b) {
......@@ -237,10 +226,10 @@ TEST(Backward, net_fc_backward_not_have_b) {
ASSERT_EQ(2UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
f::OperatorBase &d_mul = *net->ops_[1];
ASSERT_EQ("mul_grad", d_mul.type_);
ASSERT_EQ("mul_grad", d_mul.Type());
}
TEST(Backward, net_input_of_network_not_need_grad) {
......@@ -294,7 +283,7 @@ TEST(Backward, net_shared_weight) {
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
ASSERT_EQ("add", bwd_net->ops_[2]->type_);
ASSERT_EQ("add", bwd_net->ops_[2]->Type());
}
TEST(Backward, op_register_grad_not_for_network) {
......@@ -335,15 +324,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
ASSERT_EQ(net->ops_.size(), 2UL);
auto &fill_zero = *net->ops_[0];
ASSERT_EQ("fill_zeros_like", fill_zero.type_);
ASSERT_EQ("fill_zeros_like", fill_zero.Type());
ASSERT_EQ(1UL, fill_zero.Inputs("Src").size());
ASSERT_EQ("Z", fill_zero.Input("Src"));
ASSERT_EQ(1UL, fill_zero.Outputs("Dst").size());
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Dst"));
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
ASSERT_EQ("many_output_op_grad", d_many_out.Type());
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.Inputs().size()); // I/O/OG
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix,
d_many_out.Input(f::GradVarName("z")));
ASSERT_EQ(f::GradVarName("Y"), d_many_out.Input(f::GradVarName("y")));
......@@ -355,9 +344,9 @@ TEST(Backward, op_part_of_input_are_not_need) {
{{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"a"});
auto &grad_mul = *backward;
ASSERT_EQ(grad_mul.type_, "mul_grad");
ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
ASSERT_EQ(grad_mul.Type(), "mul_grad");
ASSERT_EQ(grad_mul.Inputs().size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.Outputs().size(), 2UL);
ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
......@@ -395,18 +384,18 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
auto &grad_fc = *bwd_net->ops_[0];
const char *all = paddle::operators::NetOp::kAll;
EXPECT_EQ(grad_fc.inputs_[all].size(),
EXPECT_EQ(grad_fc.Inputs(all).size(),
2UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
+ 2U /* internal variable number*/);
EXPECT_EQ(grad_fc.outputs_[all].size(),
EXPECT_EQ(grad_fc.Outputs(all).size(),
2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add
*/
+ 1UL /* input number of sigmod */);
EXPECT_EQ(bwd_net->ops_[1]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->outputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
}
paddle/framework/framework.proto
浏览文件 @ 448d4db5
......@@ -60,7 +60,7 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool no_gradient = 5 [ default = false ];
optional bool not_in_gradient = 5 [ default = false ];
}
// AttrProto describes the C++ type Attribute.
......
paddle/framework/grad_op_builder.cc
浏览文件 @ 448d4db5
......@@ -13,25 +13,22 @@ express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
enum class OpArgType { IN, OUT };
static void TransOpArg(const OperatorBase* src_op,
OperatorBase::VarNameMap* vars,
const OpArgType& src_type, bool is_grad) {
static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
bool is_grad, OperatorBase::VarNameMap* vars) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
auto& dst_inout = *vars;
const OpProto& proto = OpProtos().at(src_op->type_);
const OpProto* proto = OpRegistry::op_info_map().at(src_op->Type()).proto_;
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
src_type == OpArgType::IN ? proto->inputs() : proto->outputs();
for (const auto& arg : src_arg_list) {
if (arg.no_gradient() && !is_grad) continue;
if (arg.not_in_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_inout[dst_name].reserve(src_inout.at(src_name).size());
......@@ -43,22 +40,26 @@ static void TransOpArg(const OperatorBase* src_op,
}
OperatorBase* BuildGradOp(const OperatorBase* op) {
auto gop_type_it = OpRegistry::grad_ops().find(op->type_);
PADDLE_ENFORCE(gop_type_it != OpRegistry::grad_ops().end(),
"Operator %s do not register gradient type", op->type_);
auto& grad_op_type = gop_type_it->second;
auto it = OpRegistry::op_info_map().find(op->Type());
PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
"'%s' has not been registered.", op->Type());
PADDLE_ENFORCE(it->second.proto_ != nullptr, "'%s' has no OpProto.",
op->Type());
std::string grad_op_type = it->second.grad_op_type_;
PADDLE_ENFORCE(!grad_op_type.empty(), "'%s' has no gradient operator.",
op->Type());
OperatorBase::VarNameMap inputs;
OperatorBase::VarNameMap outputs;
TransOpArg(op, &inputs, OpArgType::IN, false); // I
TransOpArg(op, &inputs, OpArgType::OUT, false); // O
TransOpArg(op, &inputs, OpArgType::OUT, true); // OG
TransOpArg(op, &outputs, OpArgType::IN, true); // IG
auto gop_it = OpRegistry::op_creators().find(grad_op_type);
PADDLE_ENFORCE(gop_it != OpRegistry::op_creators().end(),
"Operator %s 's Gradient %s's creator cannot be found",
op->type_, grad_op_type);
TransOpArg(op, OpArgType::IN, false, &inputs); // I
TransOpArg(op, OpArgType::OUT, false, &inputs); // O
TransOpArg(op, OpArgType::OUT, true, &inputs); // OG
TransOpArg(op, OpArgType::IN, true, &outputs); // IG
return gop_it->second(grad_op_type, inputs, outputs, op->attrs_);
it = OpRegistry::op_info_map().find(grad_op_type);
PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
"'%s' has not been registered.", grad_op_type);
return it->second.creator_(grad_op_type, inputs, outputs, op->Attrs());
}
} // namespace framework
......
paddle/framework/grad_op_builder_test.cc
浏览文件 @ 448d4db5
......@@ -8,14 +8,6 @@ USE_OP(add_two);
namespace paddle {
namespace framework {
class NOP : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
};
class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
......@@ -34,10 +26,10 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").AsDuplicable().AsNoGradient();
AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
AddInput("In3_mult", "another multiple input").AsDuplicable();
AddOutput("Out1_mult", "a multiple output").AsDuplicable();
AddOutput("Out2", "a single output").AsNoGradient();
AddOutput("Out2", "a single output").NotInGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
......@@ -52,8 +44,8 @@ TEST(GradOpBuilder, AddTwo) {
"add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->inputs_.size(), 4UL);
EXPECT_EQ(grad_add_op->outputs_.size(), 2UL);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
......@@ -62,10 +54,8 @@ TEST(GradOpBuilder, AddTwo) {
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
REGISTER_GRADIENT_OP(mult_io, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
......@@ -76,7 +66,7 @@ TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->inputs_.size(), 3UL + 2UL + 2UL);
ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
......@@ -90,7 +80,7 @@ TEST(GradOpBuilder, MutiInOut) {
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
......@@ -109,7 +99,7 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->inputs_.size(), 2UL + 1UL + 2UL);
ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
......@@ -121,7 +111,7 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>(
......
paddle/framework/op_registry.cc
浏览文件 @ 448d4db5
......@@ -17,5 +17,48 @@ limitations under the License. */
#include <vector>
namespace paddle {
namespace framework {} // namespace framework
namespace framework {
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const std::string& type,
const VarNameMap& inputs,
const VarNameMap& outputs,
AttributeMap attrs) {
auto it = op_info_map().find(type);
PADDLE_ENFORCE(it != op_info_map().end(),
"Operator '%s' has not been registered.", type);
it->second.checker_->Check(attrs);
auto op = it->second.creator_(type, inputs, outputs, attrs);
return std::unique_ptr<OperatorBase>(op);
}
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = GetAttrValue(attr);
}
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap(
const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars) {
VarNameMap ret_val;
for (auto& var : op_desc_vars) {
auto& var_names = ret_val[var.parameter()];
auto& var_names_in_proto = var.arguments();
var_names.reserve(static_cast<size_t>(var_names_in_proto.size()));
std::copy(var_names_in_proto.begin(), var_names_in_proto.end(),
std::back_inserter(var_names));
}
return ret_val;
}
std::unique_ptr<OperatorBase> OpRegistry::CreateGradOp(const OperatorBase& op) {
PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops");
return std::unique_ptr<OperatorBase>(BuildGradOp(&op));
}
} // namespace framework
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ 448d4db5
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <algorithm>
#include <atomic>
#include <type_traits>
#include <typeinfo>
#include <unordered_map>
#include <unordered_set>
#include "paddle/framework/attribute.h"
......@@ -28,97 +29,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: proto_(proto), op_checker_(op_checker) {}
~OpProtoAndCheckerMaker() {
PADDLE_ENFORCE(validated_, "should call Validate after build");
}
void Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
}
protected:
struct VariableBuilder {
OpProto::Var* var_;
VariableBuilder& AsDuplicable() {
var_->set_duplicable(true);
return *this;
}
VariableBuilder& AsIntermediate() {
var_->set_intermediate(true);
return *this;
}
// TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it
// means that input/output is not needed when calculate gradient. It does
// not mean no gradient when backward. It should be changed soon.
VariableBuilder& AsNoGradient() {
var_->set_no_gradient(true);
return *this;
}
};
VariableBuilder AddInput(const std::string& name,
const std::string& comment) {
auto* input = proto_->add_inputs();
input->set_name(name);
input->set_comment(comment);
return VariableBuilder{input};
}
VariableBuilder AddOutput(const std::string& name,
const std::string& comment) {
auto* output = proto_->add_outputs();
output->set_name(name);
output->set_comment(comment);
return VariableBuilder{output};
}
template <typename T>
TypedAttrChecker<T>& AddAttr(const std::string& name,
const std::string& comment,
bool generated = false) {
auto* attr = proto_->add_attrs();
attr->set_name(name);
attr->set_comment(comment);
attr->set_generated(generated);
attr->set_type(AttrTypeID<T>());
return op_checker_->AddAttrChecker<T>(name);
}
void AddComment(const std::string& comment) { proto_->set_comment(comment); }
private:
void CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
names.insert(name);
};
for (auto& attr : proto_->attrs()) {
checker(attr.name());
}
for (auto& input : proto_->inputs()) {
checker(input.name());
}
for (auto& output : proto_->outputs()) {
checker(output.name());
}
}
OpProto* proto_;
OpAttrChecker* op_checker_;
bool validated_{false};
};
class OpRegistry {
using VarNameMap = OperatorBase::VarNameMap;
using OpCreator = std::function<OperatorBase*(
......@@ -126,123 +36,85 @@ class OpRegistry {
const VarNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
public:
template <typename OpType, typename ProtoMakerType>
static void RegisterOp(const std::string& op_type) {
op_creators()[op_type] = [](
const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs) {
return new OpType(type, inputs, outputs, attrs);
};
OpAttrChecker& op_checker = op_checkers()[op_type];
OpProto& op_proto = OpProtos()[op_type];
auto maker = ProtoMakerType(&op_proto, &op_checker);
maker.Validate();
op_proto.set_type(op_type);
PADDLE_ENFORCE(
op_proto.IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_proto.InitializationErrorString());
}
struct OpInfo {
OpCreator creator_;
std::string grad_op_type_;
OpProto* proto_;
OpAttrChecker* checker_;
};
template <typename GradOpType>
static void RegisterGradOp(const std::string& op_type,
const std::string& grad_op_type) {
op_creators()[grad_op_type] = [](
const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs) {
return new GradOpType(type, inputs, outputs, attrs);
template <typename OpType, typename ProtoMakerType, typename GradOpType>
static void RegisterOp(const std::string& op_type,
const std::string& grad_op_type) {
PADDLE_ENFORCE(op_info_map().count(op_type) == 0,
"'%s' is registered more than once.", op_type);
OpInfo op_info;
op_info.creator_ = [](const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs,
const AttributeMap& attrs) {
return new OpType(type, inputs, outputs, attrs);
};
grad_ops()[op_type] = grad_op_type;
op_info.grad_op_type_ = grad_op_type;
if (std::type_index(typeid(ProtoMakerType)) !=
std::type_index(typeid(NOPMaker))) {
op_info.proto_ = new OpProto;
op_info.checker_ = new OpAttrChecker;
auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
maker.Validate();
op_info.proto_->set_type(op_type);
PADDLE_ENFORCE(
op_info.proto_->IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_info.proto_->InitializationErrorString());
} else {
op_info.proto_ = nullptr;
op_info.checker_ = nullptr;
}
op_info_map().insert(std::make_pair(op_type, op_info));
// register gradient op
if (!grad_op_type.empty()) {
RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
}
}
static std::shared_ptr<OperatorBase> CreateOp(const std::string& type,
static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
const VarNameMap& inputs,
const VarNameMap& outputs,
AttributeMap attrs) {
auto op_create_it = op_creators().find(type);
PADDLE_ENFORCE(op_create_it != op_creators().end(),
"Operator %s cannot be found.", type);
op_checkers().at(type).Check(attrs);
AttributeMap attrs);
auto op = op_create_it->second(type, inputs, outputs, attrs);
return std::shared_ptr<OperatorBase>(op);
}
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static VarNameMap ConvertOpDescVarsToVarNameMap(
const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars) {
VarNameMap ret_val;
for (auto& var : op_desc_vars) {
auto& var_names = ret_val[var.parameter()];
auto& var_names_in_proto = var.arguments();
var_names.reserve(static_cast<size_t>(var_names_in_proto.size()));
std::copy(var_names_in_proto.begin(), var_names_in_proto.end(),
std::back_inserter(var_names));
}
return ret_val;
}
const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars);
static std::shared_ptr<OperatorBase> CreateOp(const OpDesc& op_desc) {
VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = GetAttrValue(attr);
}
static std::unique_ptr<OperatorBase> CreateGradOp(const OperatorBase& op);
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
static std::shared_ptr<OperatorBase> CreateGradOp(const OperatorBase& op) {
PADDLE_ENFORCE(!op.IsNetOp(),
"Use framework::Backward to get backward ops");
std::shared_ptr<OperatorBase> grad_op(BuildGradOp(&op));
return grad_op;
}
static std::unordered_map<std::string, std::string>& grad_ops() {
static std::unordered_map<std::string, std::string> grad_ops_;
return grad_ops_;
}
static std::unordered_map<std::string, OpCreator>& op_creators() {
static std::unordered_map<std::string, OpCreator> op_creators_;
return op_creators_;
}
private:
static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
static std::unordered_map<std::string, const OpInfo>& op_info_map() {
static std::unordered_map<std::string, const OpInfo> op_info_map_;
return op_info_map_;
}
};
class Registrar {
public:
// In our design, various kinds of classes, e.g., operators and kernels, have
// their corresponding registry and registrar. The action of registration is
// in the constructor of a global registrar variable, which, however, are not
// used in the code that calls package framework, and would be removed from
// the generated binary file by the linker. To avoid such removal, we add
// Touch to all registrar classes and make USE_OP macros to call this
// method. So, as long as the callee code calls USE_OP, the global
// In our design, various kinds of classes, e.g., operators and kernels,
// have their corresponding registry and registrar. The action of
// registration is in the constructor of a global registrar variable, which,
// however, are not used in the code that calls package framework, and would
// be removed from the generated binary file by the linker. To avoid such
// removal, we add Touch to all registrar classes and make USE_OP macros to
// call this method. So, as long as the callee code calls USE_OP, the global
// registrar variable won't be removed by the linker.
void Touch() {}
};
template <typename OpType, typename ProtoMakerType>
template <typename OpType, typename ProtoMakerType, typename GradOpType>
class OpRegistrar : public Registrar {
public:
explicit OpRegistrar(const char* op_type) {
OpRegistry::RegisterOp<OpType, ProtoMakerType>(op_type);
}
};
template <typename GradOpType>
class GradOpRegistrar : public Registrar {
public:
GradOpRegistrar(const char* op_type, const char* grad_op_type) {
OpRegistry::RegisterGradOp<GradOpType>(op_type, grad_op_type);
explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
OpRegistrar(const char* op_type, const char* grad_op_type) {
OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
grad_op_type);
}
};
......@@ -268,30 +140,30 @@ class OpKernelRegistrar : public Registrar {
/**
* Macro to register Operator.
*/
#define REGISTER_OP(op_type, op_class, op_maker_class) \
#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
grad_op_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
static ::paddle::framework::OpRegistrar<op_class, op_maker_class> \
__op_registrar_##op_type##__(#op_type); \
class _OpClass_##op_type##_ : public op_class { \
public: \
DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \
DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \
}; \
class _OpGradClass_##op_type##_ : public grad_op_class { \
public: \
DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_); \
DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class); \
}; \
static ::paddle::framework::OpRegistrar< \
_OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_> \
__op_registrar_##op_type##__(#op_type, #grad_op_type); \
int TouchOpRegistrar_##op_type() { \
__op_registrar_##op_type##__.Touch(); \
return 0; \
}
/**
* Macro to register Gradient Operator.
*/
#define REGISTER_GRADIENT_OP(op_type, grad_op_type, grad_op_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_gradient_op__##op_type##_##grad_op_type, \
"REGISTER_GRADIENT_OP must be called in global namespace"); \
static ::paddle::framework::GradOpRegistrar<grad_op_class> \
__op_gradient_registrar_##op_type##_##grad_op_type##__(#op_type, \
#grad_op_type); \
int TouchOpGradientRegistrar_##op_type() { \
__op_gradient_registrar_##op_type##_##grad_op_type##__.Touch(); \
return 0; \
}
#define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
/**
* Macro to register OperatorKernel.
......@@ -307,14 +179,6 @@ class OpKernelRegistrar : public Registrar {
return 0; \
}
/**
* Macro to Forbid user register Gradient Operator.
*/
#define NO_GRADIENT(op_type) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_gradient_op__##op_type##_##op_type##_grad, \
"NO_GRADIENT must be called in global namespace")
#define REGISTER_OP_GPU_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
......@@ -322,7 +186,8 @@ class OpKernelRegistrar : public Registrar {
REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
/**
* Macro to mark what Operator and Kernel we will use and tell the compiler to
* Macro to mark what Operator and Kernel
* we will use and tell the compiler to
* link them into target.
*/
#define USE_OP_ITSELF(op_type) \
......@@ -333,23 +198,6 @@ class OpKernelRegistrar : public Registrar {
static int use_op_itself_##op_type##_ __attribute__((unused)) = \
TouchOpRegistrar_##op_type()
// TODO(fengjiayi): Most ops' gradient op have not been compeleted. So we use
// `NO_GRAD` to disable micro USE_OP_GRADIENT(op_type). Otherwise the code can't
// be compiled. `NO_GRAD` should be removed after all gradient ops are
// compeleted.
#define NO_GRAD
#ifndef NO_GRAD
#define USE_OP_GRADIENT(op_type) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__use_op_gradient_##op_type, \
"USE_OP_GRADIENT must be called in global namespace"); \
extern int TouchOpGradientRegistrar_##op_type(); \
static int use_op_gradient_##op_type##_ __attribute__((unused)) = \
TouchOpGradientRegistrar_##op_type()
#else
#define USE_OP_GRADIENT(op_type)
#endif
#define USE_OP_DEVICE_KERNEL(op_type, DEVICE_TYPE) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__use_op_kernel_##op_type##_##DEVICE_TYPE##__, \
......@@ -359,7 +207,8 @@ class OpKernelRegistrar : public Registrar {
__attribute__((unused)) = \
TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE()
// TODO(fengjiayi): The following macros seems ugly, do we have better method?
// TODO(fengjiayi): The following macros
// seems ugly, do we have better method?
#ifdef PADDLE_ONLY_CPU
#define USE_OP_KERNEL(op_type) USE_OP_DEVICE_KERNEL(op_type, CPU)
......@@ -369,18 +218,13 @@ class OpKernelRegistrar : public Registrar {
USE_OP_DEVICE_KERNEL(op_type, GPU)
#endif
#define USE_NO_GRAD_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_KERNEL(op_type)
#define USE_CPU_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, CPU); \
USE_OP_GRADIENT(op_type)
#define USE_CPU_ONLY_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, CPU);
#define USE_OP(op_type) \
USE_NO_GRAD_OP(op_type); \
USE_OP_GRADIENT(op_type)
#define USE_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_KERNEL(op_type)
} // namespace framework
} // namespace paddle
paddle/framework/op_registry_test.cc
浏览文件 @ 448d4db5
......@@ -59,11 +59,10 @@ static void BuildVar(const std::string& param_name,
var->add_arguments(arg_name);
}
}
REGISTER_OP(cos_sim, paddle::framework::CosineOp,
paddle::framework::CosineOpProtoAndCheckerMaker);
REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
paddle::framework::MyTestOpProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(cos_sim, paddle::framework::CosineOp,
paddle::framework::CosineOpProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(my_test_op, paddle::framework::MyTestOp,
paddle::framework::MyTestOpProtoAndCheckerMaker);
TEST(OpRegistry, CreateOp) {
paddle::framework::OpDesc op_desc;
......@@ -77,8 +76,7 @@ TEST(OpRegistry, CreateOp) {
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(scale);
std::shared_ptr<paddle::framework::OperatorBase> op =
paddle::framework::OpRegistry::CreateOp(op_desc);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
......@@ -119,8 +117,7 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_TRUE(op_desc.IsInitialized());
std::shared_ptr<paddle::framework::OperatorBase> op =
paddle::framework::OpRegistry::CreateOp(op_desc);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
......
paddle/framework/operator.cc
浏览文件 @ 448d4db5
......@@ -33,14 +33,6 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
static std::unordered_map<std::string, OpProto>* g_op_protos = nullptr;
std::unordered_map<std::string, OpProto>& OpProtos() {
if (g_op_protos == nullptr) {
g_op_protos = new std::unordered_map<std::string, OpProto>();
}
return *g_op_protos;
}
const std::string& OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_EQ(ins.size(), 1UL,
......@@ -149,14 +141,18 @@ std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
}
return ret_val;
}
auto it = OpProtos().find(type_);
auto it = OpRegistry::op_info_map().find(type_);
PADDLE_ENFORCE(
it != OpProtos().end(),
it != OpRegistry::op_info_map().end(),
"Operator %s not registered, cannot figure out intermediate outputs",
type_);
PADDLE_ENFORCE(
it->second.proto_ != nullptr,
"Operator %s has no OpProto, cannot figure out intermediate outputs",
type_);
// get all OpProto::Var for outputs
for (auto& o : it->second.outputs()) {
for (auto& o : it->second.proto_->outputs()) {
// ignore all intermediate output
if (o.intermediate()) continue;
auto out = outputs_.find(o.name());
......@@ -168,5 +164,43 @@ std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
return ret_val;
}
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
const std::string& name, const std::string& comment) {
auto* input = proto_->add_inputs();
input->set_name(name);
input->set_comment(comment);
return OpProtoAndCheckerMaker::VariableBuilder{input};
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
const std::string& name, const std::string& comment) {
auto* output = proto_->add_outputs();
output->set_name(name);
output->set_comment(comment);
return OpProtoAndCheckerMaker::VariableBuilder{output};
}
void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
names.insert(name);
};
for (auto& attr : proto_->attrs()) {
checker(attr.name());
}
for (auto& input : proto_->inputs()) {
checker(input.name());
}
for (auto& output : proto_->outputs()) {
checker(output.name());
}
}
} // namespace framework
} // namespace paddle
paddle/framework/operator.h
浏览文件 @ 448d4db5
......@@ -50,8 +50,6 @@ inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
extern std::unordered_map<std::string, OpProto>& OpProtos();
class OperatorBase;
class InferShapeContext;
class ExecutionContext;
......@@ -69,10 +67,6 @@ class OperatorBase {
OperatorBase(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs);
OperatorBase(const OperatorBase& o) = delete;
OperatorBase& operator=(const OperatorBase& o) = delete;
OperatorBase(OperatorBase&& o) = delete;
virtual ~OperatorBase() {}
template <typename T>
......@@ -99,6 +93,8 @@ class OperatorBase {
/// rename inputs outputs name
void Rename(const std::string& old_name, const std::string& new_name);
const VarNameMap& Inputs() const { return inputs_; }
const VarNameMap& Outputs() const { return outputs_; }
//! Get a input with argument's name described in `op_proto`
const std::string& Input(const std::string& name) const;
//! Get a input which has multiple variables.
......@@ -112,13 +108,18 @@ class OperatorBase {
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
std::string Type() const { return type_; }
const std::string& Type() const { return type_; }
void SetType(const std::string& type) { type_ = type; }
const AttributeMap& Attrs() const { return attrs_; }
public:
// Return a new operator instance, which is as same as this.
// Use unique_ptr to prevent caller forget to delete this pointer.
virtual std::unique_ptr<OperatorBase> Clone() const = 0;
protected:
std::string type_;
// NOTE: in case of OpGrad, inputs_ contains:
// I (Inputs)
// I (Inputs)opear
// O (Outputs)
// OG (Output Gradients)
VarNameMap inputs_;
......@@ -129,6 +130,99 @@ class OperatorBase {
AttributeMap attrs_;
};
// Macro for define a clone method.
// If you are writing an kernel operator, `Clone` will be defined when you
// register it. i.e. `Clone` method is not needed to define by yourself.
#define DEFINE_OP_CLONE_METHOD(CLS) \
std::unique_ptr<OperatorBase> Clone() const final { \
return std::unique_ptr<OperatorBase>(new CLS(*this)); \
}
// Macro for define a default constructor for Operator.
// You can also use
// using PARENT_CLASS::PARENT_CLASS;
// to use parent's constructor.
#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS) \
CLS(const std::string& type, const VarNameMap& inputs, \
const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \
: PARENT_CLS(type, inputs, outputs, attrs) {}
class NOP : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
std::unique_ptr<OperatorBase> Clone() const override {
return std::unique_ptr<OperatorBase>(new NOP(*this));
}
};
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: proto_(proto), op_checker_(op_checker) {}
~OpProtoAndCheckerMaker() {
PADDLE_ENFORCE(validated_, "should call Validate after build");
}
void Validate();
protected:
struct VariableBuilder {
OpProto::Var* var_;
VariableBuilder& AsDuplicable() {
var_->set_duplicable(true);
return *this;
}
VariableBuilder& AsIntermediate() {
var_->set_intermediate(true);
return *this;
}
VariableBuilder& NotInGradient() {
var_->set_not_in_gradient(true);
return *this;
}
};
VariableBuilder AddInput(const std::string& name, const std::string& comment);
VariableBuilder AddOutput(const std::string& name,
const std::string& comment);
template <typename T>
TypedAttrChecker<T>& AddAttr(const std::string& name,
const std::string& comment,
bool generated = false) {
auto* attr = proto_->add_attrs();
attr->set_name(name);
attr->set_comment(comment);
attr->set_generated(generated);
attr->set_type(AttrTypeID<T>());
return op_checker_->AddAttrChecker<T>(name);
}
void AddComment(const std::string& comment) { proto_->set_comment(comment); }
private:
void CheckNoDuplicatedInOutAttrs();
OpProto* proto_;
OpAttrChecker* op_checker_;
bool validated_{false};
};
class NOPMaker : public OpProtoAndCheckerMaker {
public:
NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {}
};
class InferShapeContext {
public:
InferShapeContext(const OperatorBase& op, const Scope& scope)
......@@ -210,7 +304,7 @@ class InferShapeContext {
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
PADDLE_ENFORCE_NOT_NULL(
var, "MultiOutput(%s:%s) should not be nullptr", name,
var, "MultiOutput(%s:%s) should not be nullptr.", name,
sub_name);
return var->GetMutable<T>();
});
......
paddle/framework/operator_test.cc
浏览文件 @ 448d4db5
......@@ -65,8 +65,9 @@ static void BuildVar(const std::string& param_name,
}
}
REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(
test_operator, paddle::framework::OpWithoutKernelTest,
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
TEST(OperatorBase, all) {
paddle::framework::OpDesc op_desc;
......@@ -184,8 +185,9 @@ class CPUKernalMultiInputsTest : public OpKernel {
} // namespace framework
} // namespace paddle
REGISTER_OP(op_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(
op_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestProtoAndCheckerMaker);
REGISTER_OP_CPU_KERNEL(op_with_kernel,
paddle::framework::CPUKernelTest<float, float>);
......@@ -210,8 +212,9 @@ TEST(OpKernel, all) {
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
}
REGISTER_OP(op_multi_inputs_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestMultiInputsProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(
op_multi_inputs_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestMultiInputsProtoAndCheckerMaker);
REGISTER_OP_CPU_KERNEL(op_multi_inputs_with_kernel,
paddle::framework::CPUKernalMultiInputsTest);
......@@ -242,3 +245,21 @@ TEST(OpKernel, multi_inputs) {
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
op->Run(scope, cpu_device_context);
}
class OperatorClone : public paddle::framework::OperatorBase {
public:
DEFINE_OP_CLONE_METHOD(OperatorClone);
OperatorClone(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs,
const paddle::framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(const paddle::framework::Scope& scope) const override {}
void Run(const paddle::framework::Scope& scope,
const paddle::platform::DeviceContext& dev_ctx) const override {}
};
TEST(Operator, Clone) {
OperatorClone a("ABC", {}, {}, {});
auto b = a.Clone();
ASSERT_EQ(a.Type(), b->Type());
}
\ No newline at end of file
paddle/framework/pybind.cc
浏览文件 @ 448d4db5
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor_py.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/string/to_string.h"
......@@ -30,8 +31,8 @@ limitations under the License. */
namespace py = pybind11;
USE_OP(add_two);
USE_CPU_OP(onehot_cross_entropy);
USE_NO_GRAD_OP(sgd);
USE_CPU_ONLY_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
USE_OP(mean);
USE_OP(sigmoid);
......@@ -47,29 +48,6 @@ namespace framework {
using Tensor = framework::Tensor;
template <typename ClassType>
void ExposeOperator(ClassType &m) {
m.def("infer_shape", &ClassType::type::InferShape)
.def("run", &ClassType::type::Run)
.def("type",
[](const typename ClassType::type &op) -> std::string {
return op.type_;
})
.def("outputs",
[](const typename ClassType::type &op)
-> std::map<std::string, std::vector<std::string>> {
return op.outputs_;
})
.def("inputs",
[](const typename ClassType::type &op) { return op.inputs_; })
.def("__str__", &ClassType::type::DebugString)
.def("no_intermediate_outputs",
[](const typename ClassType::type &op) {
return op.OutputVars(false);
})
.def("support_gpu", &ClassType::type::SupportGPU);
}
static size_t UniqueIntegerGenerator() {
static std::atomic<size_t> generator;
return generator.fetch_add(1);
......@@ -160,13 +138,16 @@ All parameter, weight, gradient are variables in Paddle.
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
auto &protos = OpProtos();
auto &op_info_map = OpRegistry::op_info_map();
std::vector<py::bytes> ret_values;
for (auto it = protos.begin(); it != protos.end(); ++it) {
PADDLE_ENFORCE(it->second.IsInitialized(),
"OpProto must all be initialized");
for (auto it = op_info_map.begin(); it != op_info_map.end(); ++it) {
const OpProto *proto = it->second.proto_;
if (proto == nullptr) {
continue;
}
PADDLE_ENFORCE(proto->IsInitialized(), "OpProto must all be initialized");
std::string str;
PADDLE_ENFORCE(it->second.SerializeToString(&str),
PADDLE_ENFORCE(proto->SerializeToString(&str),
"Serialize OpProto Error. This could be a bug of Paddle.");
ret_values.push_back(py::bytes(str));
}
......@@ -203,47 +184,69 @@ All parameter, weight, gradient are variables in Paddle.
.def(py::init<>())
.def("__str__", string::to_string<const platform::CPUPlace &>);
py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
m, "Operator");
operator_base.def_static("create", [](py::bytes protobin) {
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());
return OpRegistry::CreateOp(desc);
});
operator_base.def("backward",
[](const OperatorBase &forwardOp,
const std::unordered_set<std::string> &no_grad_vars) {
return Backward(forwardOp, no_grad_vars);
});
ExposeOperator(operator_base);
py::class_<operators::NetOp, std::shared_ptr<operators::NetOp>> net(m, "Net");
net.def_static("create",
[]() -> std::shared_ptr<operators::NetOp> {
auto retv = std::make_shared<operators::NetOp>();
retv->type_ = "plain_net";
return retv;
})
.def("add_op", &operators::NetOp::AddOp)
.def("add_op",
[](operators::NetOp &self,
const std::shared_ptr<operators::NetOp> &net) -> void {
self.AddOp(std::static_pointer_cast<OperatorBase>(net));
py::class_<OperatorBase>(m, "Operator")
.def_static("create",
[](py::bytes protobin) {
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());
return OpRegistry::CreateOp(desc);
})
.def("backward",
[](const OperatorBase &forwardOp,
const std::unordered_set<std::string> &no_grad_vars) {
return Backward(forwardOp, no_grad_vars).release();
})
.def("infer_shape", &OperatorBase::InferShape)
.def("run", &OperatorBase::Run)
.def("type",
[](const OperatorBase &op) -> std::string { return op.Type(); })
.def("outputs",
[](const OperatorBase &op)
-> std::map<std::string, std::vector<std::string>> {
return op.Outputs();
})
.def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
.def("__str__", &OperatorBase::DebugString)
.def("no_intermediate_outputs",
[](const OperatorBase &op) { return op.OutputVars(false); })
.def("support_gpu", &OperatorBase::SupportGPU);
py::class_<operators::NetOp, OperatorBase>(m, "Net")
.def_static("create",
[]() -> operators::NetOp * {
auto *retv = new operators::NetOp;
retv->SetType("plain_net");
return retv;
})
.def("add_op", [](operators::NetOp &self,
const OperatorBase &op) { self.AddOp(op); })
.def("complete_add_op", &operators::NetOp::CompleteAddOp)
.def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
self->CompleteAddOp();
});
ExposeOperator(net);
// 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());
});
m.def("unique_integer", UniqueIntegerGenerator);
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 448d4db5
......@@ -57,11 +57,14 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
}
void MKLDNNFcLayer::convertWeightsFromPaddle() {
if (FLAGS_use_mkldnn_wgt) {
if (hasInitedWgt_) {
return;
}
if (hasInitedWgt_) {
// TODO(TJ): dst format should get from wgtVal_
int dstFmt = PARAM_FORMAT_MKLDNN_OI;
int srcFmt = weight_->getParameterPtr()->getHeaderFormat();
if (srcFmt == dstFmt) {
return;
}
......@@ -78,6 +81,7 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
MatrixPtr paddleWgtT;
paddleWgt->transpose(paddleWgtT, true);
weight_->getW()->copyFrom(*paddleWgtT);
weight_->getParameterPtr()->setHeaderFormat(dstFmt);
hasInitedWgt_ = true;
}
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 448d4db5
......@@ -330,9 +330,7 @@ void MKLDNNTester::run(const TestConfig& dnn,
log_ = log;
lvl_ = level;
// Firstly test FLAGS_use_mkldnn_wgt = false
FLAGS_use_mkldnn_wgt = false;
// reset and run once
// Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight
reset(dnn, ref, batchSize);
randomWgtDatas();
clearWgtDiffs();
......@@ -342,17 +340,32 @@ void MKLDNNTester::run(const TestConfig& dnn,
runOnce();
}
// Then test FLAGS_use_mkldnn_wgt = true
FLAGS_use_mkldnn_wgt = true;
// after run once the mkldnn weight has been stored in dnnlayer
if (parameters_[DNN].empty()) {
// has no paramters
return;
}
// After run some iterations, the mkldnn weight has been stored in dnnLayer
// and we can also get the mkldnn weight parameter header format.
// Weight parameter should always be index 0 (and bias index 1).
// TODO(TJ): should also consider mean and var format when batchnorm ready
int dnnWgtFmt = parameters_[DNN][0]->getHeaderFormat();
int refWgtFmt = parameters_[REF][0]->getHeaderFormat();
if (dnnWgtFmt == refWgtFmt) {
// weight format are equal, so no need check more
return;
}
// then save the weights and restart again
vector<VectorPtr> dnnWgts, refWgts;
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
saveWgt(parameters_[DNN], dnnWgts);
saveWgt(parameters_[REF], refWgts);
// restart again with flag true
// restart again with dnn weight format
reset(dnn, ref, batchSize);
// TODO(TJ): should also considerate mean and var format when batchnorm ready
parameters_[DNN][0]->setHeaderFormat(dnnWgtFmt);
// restore wgt
restoreWgt(dnnWgts, parameters_[DNN]);
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 448d4db5
......@@ -108,7 +108,7 @@ private:
* 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))
* The return value should smaller than eps when passing.
* The return value should be smaller than eps when passing.
*/
double getDelta(const real* d1,
const real* d2,
......
paddle/memory/CMakeLists.txt
浏览文件 @ 448d4db5
add_subdirectory(detail)
cc_library(memory SRCS memory.cc)
cc_library(memcpy SRCS memcpy.cc DEPS device_context)
cc_library(memcpy SRCS memcpy.cc)
cc_library(paddle_memory
DEPS
......
paddle/memory/detail/system_allocator.cc
浏览文件 @ 448d4db5
......@@ -27,7 +27,7 @@ limitations under the License. */
// between host and device. Allocates too much would reduce the amount
// of memory available to the system for paging. So, by default, we
// should set false to use_pinned_memory.
DEFINE_bool(use_pinned_memory, false, "If set, allocate cpu pinned memory.");
DEFINE_bool(use_pinned_memory, true, "If set, allocate cpu pinned memory.");
namespace paddle {
namespace memory {
......
paddle/memory/memcpy.cc
浏览文件 @ 448d4db5
......@@ -16,8 +16,6 @@ limitations under the License. */
#include <cstring> // for memcpy
#include "paddle/platform/device_context.h"
namespace paddle {
namespace memory {
......
paddle/memory/memory.cc
浏览文件 @ 448d4db5
......@@ -13,22 +13,33 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/memory/memory.h"
#include <algorithm> // for transform
#include <cstring> // for memcpy
#include <memory> // for unique_ptr
#include <mutex> // for call_once
#include "paddle/memory/detail/buddy_allocator.h"
#include "paddle/memory/detail/system_allocator.h"
#include <cstring> // for memcpy
namespace paddle {
namespace memory {
detail::BuddyAllocator* GetCPUBuddyAllocator() {
static detail::BuddyAllocator* a = nullptr;
if (a == nullptr) {
a = new detail::BuddyAllocator(new detail::CPUAllocator,
platform::CpuMinChunkSize(),
platform::CpuMaxChunkSize());
}
return a;
using BuddyAllocator = detail::BuddyAllocator;
std::once_flag cpu_allocator_flag;
std::once_flag gpu_allocator_flag;
BuddyAllocator* GetCPUBuddyAllocator() {
static std::unique_ptr<BuddyAllocator> a{nullptr};
std::call_once(cpu_allocator_flag, [&]() {
a.reset(new BuddyAllocator(new detail::CPUAllocator,
platform::CpuMinChunkSize(),
platform::CpuMaxChunkSize()));
});
return a.get();
}
template <>
......@@ -48,20 +59,31 @@ size_t Used<platform::CPUPlace>(platform::CPUPlace place) {
#ifndef PADDLE_ONLY_CPU
detail::BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
static detail::BuddyAllocator** as = NULL;
if (as == NULL) {
BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
using BuddyAllocVec = std::vector<BuddyAllocator*>;
static std::unique_ptr<BuddyAllocVec, void (*)(BuddyAllocVec * p)> as{
new BuddyAllocVec, [](BuddyAllocVec* p) {
std::for_each(p->begin(), p->end(),
[](BuddyAllocator* p) { delete p; });
}};
// GPU buddy allocators
auto& allocators = *as.get();
// GPU buddy allocator initialization
std::call_once(gpu_allocator_flag, [&]() {
int gpu_num = platform::GetDeviceCount();
as = new detail::BuddyAllocator*[gpu_num];
allocators.reserve(gpu_num);
for (int gpu = 0; gpu < gpu_num; gpu++) {
platform::SetDeviceId(gpu);
as[gpu] = new detail::BuddyAllocator(new detail::GPUAllocator,
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
allocators.emplace_back(new BuddyAllocator(new detail::GPUAllocator,
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize()));
}
}
});
platform::SetDeviceId(gpu_id);
return as[gpu_id];
return allocators[gpu_id];
}
template <>
......
paddle/operators/CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -44,6 +44,8 @@ endfunction()
add_subdirectory(math)
cc_test(gather_test SRCS gather_test.cc DEPS tensor)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
cc_library(net_op SRCS net_op.cc DEPS op_registry)
cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
......@@ -64,6 +66,5 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor op_registry operator net_op)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
op_library(uniform_random_op
SRCS uniform_random_op.cc uniform_random_op.cu)
paddle/operators/add_op.cc
浏览文件 @ 448d4db5
......@@ -57,8 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker);
REGISTER_GRADIENT_OP(add_two, add_two_grad, ops::AddOpGrad);
REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::CPUPlace, float>);
paddle/operators/cross_entropy_op.cc
浏览文件 @ 448d4db5
......@@ -68,12 +68,11 @@ OnehotCrossEntropy Operator.
namespace ops = paddle::operators;
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
ops::OnehotCrossEntropyOpMaker);
ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(
onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(onehot_cross_entropy, onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(
onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 448d4db5
......@@ -46,7 +46,8 @@ The output will have the same size with input.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(fill_zeros_like, ops::FillZerosLikeOp, ops::FillZerosLikeOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
ops::FillZerosLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_zeros_like,
ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
paddle/operators/gather.h
浏览文件 @ 448d4db5
......@@ -29,7 +29,7 @@ void CPUGather(const T* params, const int* indices, const int slice_size,
const int index_size, T* output) {
const size_t slice_bytes = slice_size * sizeof(T);
for (size_t i = 0; i < index_size; ++i) {
for (int i = 0; i < index_size; ++i) {
int index_ = indices[i];
memcpy(output + i * slice_size, params + index_ * slice_size, slice_bytes);
}
......@@ -60,7 +60,7 @@ void Gather(const platform::Place& place, const paddle::framework::Tensor* src,
// slice size
int slice_size = 1;
for (size_t i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
// Gathering
if (platform::is_cpu_place(place)) {
......
paddle/operators/gather_test.cc
浏览文件 @ 448d4db5
......@@ -35,7 +35,7 @@ TEST(Gather, GatherData) {
p_src = src->mutable_data<int>(make_ddim({3, 4}), CPUPlace());
p_index = index->mutable_data<int>(make_ddim({2}), CPUPlace());
for (size_t i = 0; i < 12; ++i) p_src[i] = i;
for (int i = 0; i < 12; ++i) p_src[i] = i;
p_index[0] = 1;
p_index[1] = 0;
......@@ -43,6 +43,10 @@ TEST(Gather, GatherData) {
Gather<int>(CPUPlace(), src, index, output);
for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4);
for (size_t i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4);
for (int i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4);
for (int i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4);
delete src;
delete index;
delete output;
}
paddle/operators/gaussian_random_op.cc
浏览文件 @ 448d4db5
......@@ -81,5 +81,6 @@ Use to initialize tensor with gaussian random generator.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
ops::GaussianRandomOpMaker);
REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
paddle/operators/math/CMakeLists.txt
浏览文件 @ 448d4db5
if(WITH_MKLML)
set(BLAS_LIB mklml)
else()
set(BLAS_LIB cblas)
endif()
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu DEPS ${BLAS_LIB} device_context)
nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context)
else()
cc_library(math_function SRCS math_function.cc DEPS ${BLAS_LIB} device_context)
cc_library(math_function SRCS math_function.cc DEPS cblas device_context)
endif()
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
paddle/operators/mean_op.cc
浏览文件 @ 448d4db5
......@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").AsNoGradient();
AddOutput("Out", "The output of mean op").NotInGradient();
AddComment("Mean Operator");
}
};
......@@ -54,9 +54,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean_grad,
ops::MeanGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/mean_op.h
浏览文件 @ 448d4db5
......@@ -55,9 +55,10 @@ class MeanGradKernel : public framework::OpKernel {
IG->mutable_data<T>(context.GetPlace());
T ig_size = (T)framework::product(IG->dims());
Eigen::DSizes<int, 1> bcast(ig_size);
EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
EigenScalar<T>::From(*OG) / ig_size;
(EigenVector<T>::From(*OG) / ig_size).broadcast(bcast);
}
};
......
paddle/operators/mul_op.cc
浏览文件 @ 448d4db5
......@@ -70,7 +70,5 @@ class MulOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker);
REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad);
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
paddle/operators/net_op.cc
浏览文件 @ 448d4db5
......@@ -29,7 +29,7 @@ void NetOp::CompleteAddOp(bool calc) {
std::set<std::string> input_set;
std::set<std::string> output_set;
for (auto& op : ops_) {
for (auto& ipt : op->inputs_) {
for (auto& ipt : op->Inputs()) {
for (auto& var_name : ipt.second) {
if (!Contains(output_set, var_name)) { // Not other op's output
input_set.insert(var_name);
......@@ -39,7 +39,7 @@ void NetOp::CompleteAddOp(bool calc) {
}
}
for (auto& opt : op->outputs_) {
for (auto& opt : op->Outputs()) {
for (auto& var_name : opt.second) {
output_set.insert(var_name);
}
......@@ -85,7 +85,14 @@ NetOp::NetOp(const std::string& type,
const framework::OperatorBase::VarNameMap& inputs,
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
: framework::OperatorBase(type, inputs, outputs, attrs) {}
std::unique_ptr<framework::OperatorBase> NetOp::Clone() const {
PADDLE_ENFORCE(
add_op_done_,
"Must clone a sealed NetOp, invoke Net::CompleteAddOp before clone");
return std::unique_ptr<OperatorBase>(new NetOp(*this));
}
} // namespace operators
} // namespace paddle
paddle/operators/net_op.h
浏览文件 @ 448d4db5
......@@ -41,6 +41,16 @@ class NetOp : public framework::OperatorBase {
NetOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const framework::AttributeMap& attrs);
NetOp(const NetOp& o) : framework::OperatorBase(o.type_, {}, {}, o.attrs_) {
this->ops_.reserve(o.ops_.size());
std::transform(
o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_),
[](const std::unique_ptr<framework::OperatorBase>& op) {
return std::unique_ptr<framework::OperatorBase>(op->Clone());
});
this->CompleteAddOp();
}
/**
* Infer all the operators' input and output variables' shapes, will be called
* before every mini-batch
......@@ -74,21 +84,27 @@ class NetOp : public framework::OperatorBase {
return true;
}
void AddOp(const framework::OperatorBase& op) { AddOp(op.Clone()); }
/**
* @brief Add an operator by ptr
*/
void AddOp(const std::shared_ptr<OperatorBase>& op) {
void AddOp(std::unique_ptr<framework::OperatorBase> op) {
PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed");
PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
ops_.push_back(op);
ops_.push_back(std::move(op));
}
void InsertOp(size_t pos, const std::shared_ptr<OperatorBase>& op) {
void InsertOp(size_t pos, std::unique_ptr<framework::OperatorBase> op) {
PADDLE_ENFORCE(!add_op_done_,
"Cannot InsertOp when this network is sealed");
PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
PADDLE_ENFORCE_LE(pos, ops_.size(), "Out of range");
ops_.insert(ops_.begin() + pos, op);
ops_.insert(ops_.begin() + pos, std::move(op));
}
void InsertOp(size_t pos, const framework::OperatorBase& op) {
InsertOp(pos, op.Clone());
}
void CompleteAddOp(bool calculate = true);
......@@ -98,7 +114,9 @@ class NetOp : public framework::OperatorBase {
bool IsNetOp() const override;
std::vector<std::string> OutputVars(bool has_intermediate) const override;
std::vector<std::shared_ptr<OperatorBase>> ops_;
std::unique_ptr<framework::OperatorBase> Clone() const override;
std::vector<std::unique_ptr<framework::OperatorBase>> ops_;
private:
bool add_op_done_{false};
......
paddle/operators/net_op_test.cc
浏览文件 @ 448d4db5
......@@ -13,6 +13,7 @@ static int run_cnt = 0;
class TestOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
DEFINE_OP_CLONE_METHOD(TestOp);
void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
......@@ -20,13 +21,6 @@ class TestOp : public framework::OperatorBase {
}
};
class EmptyOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
};
template <typename T>
void AssertSameVectorWithoutOrder(const std::vector<T>& expected,
const std::vector<T>& actual) {
......@@ -44,20 +38,17 @@ TEST(OpKernel, all) {
auto net = std::make_shared<NetOp>();
ASSERT_NE(net, nullptr);
auto op1 = std::shared_ptr<TestOp>(
net->AddOp(std::unique_ptr<TestOp>(
new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net->AddOp(op1);
auto op2 = std::shared_ptr<TestOp>(
{{"Out", {"y"}}}, {})));
net->AddOp(std::unique_ptr<TestOp>(
new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"Out", {"z"}}}, {}));
net->AddOp(op2);
{{"Out", {"z"}}}, {})));
net->CompleteAddOp();
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
net->inputs_.at(NetOp::kAll));
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_.at(NetOp::kAll));
net->Inputs(NetOp::kAll));
AssertSameVectorWithoutOrder({"y", "z"}, net->Outputs(NetOp::kAll));
auto final_outs = net->OutputVars(false);
......@@ -67,15 +58,31 @@ TEST(OpKernel, all) {
TEST(NetOp, insert_op) {
NetOp net;
auto op1 = std::shared_ptr<EmptyOp>(
new EmptyOp("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net.AddOp(op1);
net.InsertOp(0, op1);
auto op1 = std::unique_ptr<framework::NOP>(
new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net.AddOp(*op1);
net.InsertOp(0, *op1);
ASSERT_EQ(2UL, net.ops_.size());
net.InsertOp(2, op1);
net.InsertOp(2, std::move(op1));
ASSERT_EQ(3UL, net.ops_.size());
}
TEST(NetOp, Clone) {
NetOp net;
net.AddOp(
std::unique_ptr<framework::NOP>(new framework::NOP{"empty", {}, {}, {}}));
net.AddOp(std::unique_ptr<framework::NOP>(
new framework::NOP{"empty2", {}, {}, {}}));
net.CompleteAddOp(true);
auto new_net_op = net.Clone();
ASSERT_NE(new_net_op, nullptr);
ASSERT_TRUE(new_net_op->IsNetOp());
auto* new_net = static_cast<NetOp*>(new_net_op.get());
ASSERT_EQ(2, new_net->ops_.size());
ASSERT_EQ(new_net->ops_[0]->Type(), "empty");
ASSERT_EQ(new_net->ops_[1]->Type(), "empty2");
}
} // namespace operators
} // namespace paddle
paddle/operators/recurrent_op.cc
浏览文件 @ 448d4db5
......@@ -36,15 +36,13 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
true /*infer_shape_mode*/);
InitMemories(step_scopes[0], true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (size_t i = 0; i < seq_len_; i++) {
if (i > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
(*stepnet_)->InferShape(*step_scopes[i]);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
true /*infer_shape_mode*/);
......@@ -56,7 +54,6 @@ void RecurrentAlgorithm::Run(const Scope& scope,
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
InitMemories(step_scopes[0], false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
for (size_t step_id = 0; step_id < seq_len_; step_id++) {
// create output alias variables
......@@ -64,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
(*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
false /*infer_shape_mode*/);
......@@ -78,18 +75,16 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
// Now all variables in scope must be created outside of op.
auto net_var = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
arg_->step_net);
auto net_op = net_var->GetMutable<NetOp>();
PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs");
PADDLE_ENFORCE_NOT_NULL(stepnet_);
PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs");
if (seq_len_ > step_scopes->size()) {
for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
auto& step_scope = scope.NewScope();
// create step net's temp inputs
for (auto& input : net_op->inputs_) {
for (auto& input : (*stepnet_)->Inputs()) {
// the weight are located in parent scope
for (auto& var_name : input.second) {
if (!step_scope.FindVar(var_name)) {
......@@ -98,7 +93,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
}
}
// create stepnet's outputs
for (const auto& output : net_op->outputs_) {
for (const auto& output : (*stepnet_)->Outputs()) {
for (auto& var_name : output.second) {
step_scope.NewVar(var_name);
}
......@@ -140,9 +135,8 @@ RecurrentOp::RecurrentOp(const std::string& type,
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
rnn::InitArgument(kArgName, &arg_, *this);
alg_.Init(&arg_, &stepnet_);
}
class RecurrentAlgorithmProtoAndCheckerMaker
......@@ -158,7 +152,6 @@ class RecurrentAlgorithmProtoAndCheckerMaker
.AsDuplicable();
AddInput(name.boot_memories, "variables to initialize memories.")
.AsDuplicable();
AddInput(name.step_net, "network shared by all steps.");
AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
.AsDuplicable();
......@@ -180,14 +173,12 @@ void RecurrentGradientAlgorithm::Run(
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
(*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
}
LinkBootMemoryGradients(step_scopes[0], false);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
......@@ -219,14 +210,12 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
(*stepnet_)->InferShape(*step_scopes[step_id]);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
true /*infer_shape_mode*/);
......@@ -238,13 +227,13 @@ RecurrentGradientOp::RecurrentGradientOp(
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
rnn::InitArgument(kArgName, &arg_, *this);
alg_.Init(&arg_, &stepnet_);
}
} // namespace operators
} // namespace paddle
REGISTER_OP(recurrent_op, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
REGISTER_OP_WITHOUT_GRADIENT(
recurrent_op, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
paddle/operators/recurrent_op.h
浏览文件 @ 448d4db5
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/framework/operator.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/rnn/recurrent_op_utils.h"
namespace paddle {
......@@ -33,7 +34,12 @@ class RecurrentAlgorithm {
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
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;
}
/**
* InferShape must be called before Run.
......@@ -58,7 +64,8 @@ class RecurrentAlgorithm {
void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
private:
std::unique_ptr<rnn::Argument> arg_;
std::unique_ptr<framework::OperatorBase>* stepnet_;
rnn::Argument* arg_;
mutable size_t seq_len_;
};
......@@ -74,7 +81,12 @@ class RecurrentGradientAlgorithm {
* operator.
*/
public:
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
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;
......@@ -95,17 +107,25 @@ class RecurrentGradientAlgorithm {
}
private:
std::unique_ptr<rnn::Argument> arg_;
rnn::Argument* arg_;
mutable size_t seq_len_;
std::unique_ptr<framework::OperatorBase>* stepnet_;
};
class RecurrentOp final : public framework::OperatorBase {
class RecurrentOp : public framework::OperatorBase {
public:
RecurrentOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& 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");
}
/**
* InferShape must be called before Run.
*/
* InferShape must be called before Run.
*/
void InferShape(const framework::Scope& scope) const override {
alg_.InferShape(scope);
}
......@@ -115,18 +135,32 @@ class RecurrentOp final : public framework::OperatorBase {
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 final : public framework::OperatorBase {
class RecurrentGradientOp : public framework::OperatorBase {
public:
RecurrentGradientOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& 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");
}
/**
* InferShape must be called before Run.
*/
......@@ -141,8 +175,15 @@ class RecurrentGradientOp final : public framework::OperatorBase {
static const rnn::ArgumentName kArgName;
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
......
paddle/operators/recurrent_op_test.cc 已删除 100644 → 0
浏览文件 @ 12ee5014
/*
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/recurrent_op.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using namespace paddle::framework;
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepScopes();
CreateStepNet();
CreateRNNGradientAlgorithm();
// segment inputs
SegmentInputs();
// link forward memories
LinkeMemories();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
// inputs: x
LOG(INFO) << "create global variable x";
Variable* x = scope_.NewVar("x");
DDim dims =
make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// inputs: h_boot
LOG(INFO) << "create global variable h_boot";
Variable* h_boot = scope_.NewVar("h_boot");
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
// inputs: w
LOG(INFO) << "create global variable w";
Variable* w = scope_.NewVar("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
platform::CPUPlace());
// inputs: h_grad
LOG(INFO) << "create variable h_grad";
Variable* dh = scope_.NewVar("h_grad");
dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
platform::CPUPlace());
// inputs: step_scopes
LOG(INFO) << "create variable step_scopes";
scope_.NewVar("step_scopes");
// inputs: step_net
LOG(INFO) << "create variable step_net";
scope_.NewVar("step_net");
// outputs: w_grad
LOG(INFO) << "create global variable w_grad";
scope_.NewVar("rnn/w_grad");
// outputs: x_grad
LOG(INFO) << "create global variable x_grad";
scope_.NewVar("x_grad");
// outputs: h_boot_grad
LOG(INFO) << "create global variable h_boot_grad";
scope_.NewVar("h_boot_grad");
}
void CreateStepScopes() {
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 0; i < 10; ++i) {
auto& scope = scope_.NewScope();
auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
// for unit test of ConcatOutputs
auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
xg->mutable_data<float>({20, 30}, platform::CPUPlace());
step_scopes->emplace_back(&scope);
}
// last time step
auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
g->mutable_data<float>({20, 30}, platform::CPUPlace());
}
void CreateRNNGradientAlgorithm() {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
arg->step_net = "step_net";
arg->step_scopes = "step_scopes";
rnn::Link inlink;
inlink.external = "h_grad";
inlink.internal = "rnn/h_grad";
arg->inlinks = std::vector<rnn::Link>{inlink};
rnn::Link outlink;
outlink.external = "x_grad";
outlink.internal = "rnn/x_grad";
arg->outlinks = std::vector<rnn::Link>{outlink};
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre_grad";
mem_attr.var = "rnn/h_grad";
mem_attr.boot_var = "h_boot_grad";
arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
rnn_grad_algo_.Init(std::move(arg));
}
void CreateStepNet() {
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
// TODO(qingqing) modify backward op create for RNNOp unit test
// and the unit test will be removed to Python.
// net->AddOp(OpRegistry::CreateOp("mul", {"X", {"rnn/h_pre", "rnn/w",
// "rnn/s_grad"}}, {"Y", {"rnn/h_pre_grad", "rnn/w_grad"}}, {}));
// net->AddOp(OpRegistry::CreateOp("add_two", {"X", {"rnn/h_grad"}},
// {"Y", {"rnn/x_grad"}}, {"Out", "rnn/s_grad"}}, {}));
net->CompleteAddOp();
}
void SegmentInputs() {
LOG(INFO) << "segment inputs";
std::vector<std::string> inlinks = {"x"};
std::vector<std::string> inlinks_alias = {"rnn/x"};
rnn::Link inlink;
inlink.external = "x";
inlink.internal = "rnn/x";
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
true /*infer_shape_mode*/);
}
void LinkeMemories() {
LOG(INFO) << "link memories";
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre";
mem_attr.var = "rnn/h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(*step_scopes, memories, i, -1,
true /*infer_shape_mode*/);
}
}
Scope scope_;
RecurrentGradientAlgorithm rnn_grad_algo_;
};
// TEST_F(RecurrentGradientAlgorithmTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_grad_algo_.Run(scope_, ctx);
// }
} // namespace operators
} // namespace paddle
TEST(RecurrentOp, LinkMemories) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators;
// create and init step scopes
size_t len = 10;
std::vector<Scope*> step_scopes;
for (size_t i = 0; i < len; ++i) {
auto scope = new Scope();
scope->NewVar("pre_h");
auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
for (size_t j = 0; j < 15 * 20; ++j) {
data[j] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
}
// create MemoryAttr
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "pre_h";
mem_attr.var = "h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false
/*infer_shape_mode*/);
}
// check
for (size_t i = 0; i < len - 1; ++i) {
const float* a =
step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
const float* b = step_scopes[i + 1]
->FindVar("pre_h")
->GetMutable<Tensor>()
->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1, false
/*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
const float* a =
step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
const float* b =
step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (auto s : step_scopes) {
delete s;
}
}
USE_OP(add_two);
USE_OP(mul);
USE_OP_ITSELF(recurrent_op);
paddle/operators/rnn/recurrent_op_utils.cc
浏览文件 @ 448d4db5
......@@ -106,7 +106,6 @@ void LinkMemories(const std::vector<Scope*>& scopes,
void InitArgument(const ArgumentName& name, Argument* arg,
const framework::OperatorBase& op) {
arg->step_net = op.Input(name.step_net);
arg->step_scopes = op.Output(name.step_scopes);
auto inlinks = op.Inputs(name.inlinks);
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 448d4db5
......@@ -57,10 +57,6 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// PADDLE_ENFORCE(ctx.InputSize() == 4UL,
// "RowwiseAddGrad inputs is I, O, OG, size must be 4");
// PADDLE_ENFORCE(ctx.OutputSize() == 2,
// "RowwiseAddGrad output is IG, size must be 2");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
......@@ -76,11 +72,10 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker);
REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
rowwise_add_grad);
REGISTER_OP_CPU_KERNEL(
rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, ops::RowwiseAddGradOp);
REGISTER_OP_CPU_KERNEL(
rowwise_add_grad,
ops::RowwiseAddGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/rowwise_add_op.h
浏览文件 @ 448d4db5
......@@ -51,9 +51,9 @@ template <typename Place, typename T>
class RowwiseAddGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
auto* db = context.Output<Tensor>(framework::GradVarName("b"));
auto* dOut = context.Output<Tensor>(framework::GradVarName("Out"));
dX->mutable_data<T>(context.GetPlace());
db->mutable_data<T>(context.GetPlace());
......
paddle/operators/scatter.h 0 → 100644
浏览文件 @ 448d4db5
/* 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 <cstring>
#include "paddle/framework/ddim.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/place.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
// Implementation of CPU copy
template <typename T>
void CPUScatterUpdate(const paddle::framework::Tensor* src, const int* index,
const size_t index_size,
paddle::framework::Tensor* output) {
paddle::framework::DDim output_dims = output->dims();
for (size_t i = 0; i < index_size; ++i) {
int index_ = index[i];
paddle::framework::Tensor src_ = *src;
paddle::framework::Tensor output_ = *output;
if (index_size > 1) src_ = src->Slice<T>(i, i + 1);
if (output_dims[0] > 1) output_ = output->Slice<T>(index_, index_ + 1);
auto X = EigenVector<T>::Flatten(src_);
auto Y = EigenVector<T>::Flatten(output_);
Y = X + Y;
}
}
// Implementation of GPU scatter:
template <typename T>
void GPUScatterUpdate(const T* src, const int* index, const int slice_size,
const int index_size, T* output);
/**
* Return a updated tensor from source tensor, scattered according to index:
* dst[i] += src[index[i]]
* input[src]: type-T source Tensor
* input[index]: type-int index Tensor (1-D)
* return: output tensor
*/
template <typename T>
void ScatterUpdate(const platform::Place& place,
const paddle::framework::Tensor* src,
const paddle::framework::Tensor* index,
paddle::framework::Tensor* output) {
// check index of shape 1-D
PADDLE_ENFORCE(index->dims().size() == 1);
int index_size = index->dims()[0];
auto src_dims = src->dims();
auto dst_dims = output->dims();
// check src shape and dst shape should match
for (int i = 1; i < src_dims.size(); i++)
PADDLE_ENFORCE(src_dims[i] == dst_dims[i]);
// slice size
size_t slice_size = 1;
for (int i = 0; i < src_dims.size(); ++i) slice_size *= src_dims[i];
if (platform::is_cpu_place(place)) {
CPUScatterUpdate<T>(src, index->data<int>(), index_size, output);
} else {
}
}
} // namespace operators
} // namespace paddle
paddle/operators/scatter_test.cc 0 → 100644
浏览文件 @ 448d4db5
/* 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/scatter.h"
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/place.h"
#include <gtest/gtest.h>
#include <iostream>
#include <string>
TEST(scatter, ScatterUpdate) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators;
Tensor* src = new Tensor();
Tensor* index = new Tensor();
Tensor* output = new Tensor();
float* p_src = nullptr;
int* p_index = nullptr;
p_src = src->mutable_data<float>(make_ddim({1, 4}), CPUPlace());
p_index = index->mutable_data<int>(make_ddim({1}), CPUPlace());
for (size_t i = 0; i < 4; ++i) p_src[i] = float(i);
p_index[0] = 1;
float* p_output = output->mutable_data<float>(make_ddim({4, 4}), CPUPlace());
ScatterUpdate<float>(CPUPlace(), src, index, output);
for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], float(0));
for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
for (size_t i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], float(i - 4));
for (size_t i = 4; i < 8; ++i)
EXPECT_EQ(output->data<float>()[i], float(i - 4));
for (size_t i = 8; i < 16; ++i) EXPECT_EQ(p_output[i], float(0));
for (size_t i = 8; i < 16; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
delete src;
delete index;
delete output;
}
paddle/operators/sgd_op.cc
浏览文件 @ 448d4db5
......@@ -51,6 +51,6 @@ param_out = param - learning_rate * grad;
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sgd, ops::SGDOp, ops::SGDOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(sgd, ops::SGDOp, ops::SGDOpMaker);
REGISTER_OP_CPU_KERNEL(sgd,
ops::SGDOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sigmoid_op.cc
浏览文件 @ 448d4db5
......@@ -44,7 +44,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
ctx.Output<Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
......@@ -52,9 +53,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker);
REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad);
REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
......
paddle/operators/sigmoid_op.h
浏览文件 @ 448d4db5
......@@ -37,7 +37,7 @@ class SigmoidKernel : public framework::OpKernel {
auto Y = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp());
Y.device(place) = 1. / (1. + (-X).exp());
}
};
......
paddle/operators/softmax_op.cc
浏览文件 @ 448d4db5
......@@ -62,9 +62,9 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(
softmax_grad, ops::SoftmaxGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/uniform_random_op.cc
浏览文件 @ 448d4db5
......@@ -81,7 +81,7 @@ Used to initialize tensor with uniform random generator.
} // namespace operators
} // namespace paddle
REGISTER_OP(uniform_random, paddle::operators::UniformRandomOp,
paddle::operators::UniformRandomOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp,
paddle::operators::UniformRandomOpMaker);
REGISTER_OP_CPU_KERNEL(uniform_random,
paddle::operators::CPUUniformRandomKernel<float>);
paddle/parameter/Parameter.cpp
浏览文件 @ 448d4db5
......@@ -48,7 +48,8 @@ Parameter::Parameter(const ParameterConfig& config, bool useGpu, bool doInit)
deviceId_(-1),
sharedCount_(0),
updateCounter_(0),
updated_(false) {
updated_(false),
headerFormat_(PARAM_FORMAT_ORIGINAL) {
setID(-1); /* capture uninitialized id */
if (useGpu_ && FLAGS_parallel_nn) {
/* gpu environment is specified by device property */
......@@ -285,7 +286,7 @@ bool Parameter::save(const std::string& filename) const {
bool Parameter::save(std::ostream& s) const {
CpuVector vec(*bufs_[PARAMETER_VALUE].get());
Header header;
header.version = kFormatVersion;
header.format = headerFormat_;
header.valueSize = sizeof(real);
header.size = getSize();
......@@ -344,8 +345,9 @@ bool Parameter::load(std::istream& s) {
Header header;
CHECK(s.read(reinterpret_cast<char*>(&header), sizeof(header)))
<< "Fail to read parameter " << getName();
CHECK_EQ(header.version, kFormatVersion) << "Incorrect format version: "
<< header.version;
CHECK(isHeaderFormatSupported(header.format)) << "Incorrect format version: "
<< header.format;
headerFormat_ = header.format;
CHECK_EQ(header.size, getSize())
<< "The size (" << header.size << ") in the file does not match the size "
<< "(" << getSize() << ") of the parameter: " << getName();
......
paddle/parameter/Parameter.h
浏览文件 @ 448d4db5
......@@ -34,6 +34,20 @@ limitations under the License. */
namespace paddle {
typedef enum {
/// The paddle original basic format
PARAM_FORMAT_ORIGINAL = 0,
/// See mkldnn_memory_format_t in
/// https://github.com/01org/mkl-dnn/blob/master/include/mkldnn_types.h
/// for a detailed description.
/// 2D weights tensor in the format (output channels, input channels).
PARAM_FORMAT_MKLDNN_OI,
/// The total format items numbers
PARAM_FORMAT_ITEMS,
} PARAM_FORMAT;
class SparsePrefetchRowCpuMatrix;
class Parameter;
......@@ -242,14 +256,30 @@ public:
/// Initialize the value to 0
void zeroMem();
static const int kFormatVersion = 0;
/// file header structure
struct Header {
int32_t version; // = 0, file format version
int32_t format; // = PARAM_FORMAT
uint32_t valueSize; // = sizeof(real)
uint64_t size; // = getSize()
};
/**
* @brief Is the header format supported.
*/
static bool isHeaderFormatSupported(int32_t fmt) {
return fmt < PARAM_FORMAT_ITEMS;
}
/**
* @brief Get the format in header.
*/
int getHeaderFormat() { return headerFormat_; }
/**
* @brief Set the format in header.
*/
void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; }
/**
* @brief Parameter Update Hook.
*
......@@ -321,6 +351,9 @@ protected:
bool updated_;
SparseFormat format_;
/// The header format for saving or loading param
int32_t headerFormat_;
std::vector<std::shared_ptr<IParameterUpdaterHook>> updaterHooks_;
public:
......
paddle/platform/CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -16,5 +16,8 @@ ELSE()
set(GPU_CTX_DEPS)
ENDIF()
cc_library(device_context SRCS device_context.cc DEPS place eigen3 ${GPU_CTX_DEPS})
# memcpy deoends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
paddle/platform/device_context.cc
浏览文件 @ 448d4db5
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/platform/device_context.h"
#include "paddle/memory/memory.h"
namespace paddle {
namespace platform {
......@@ -36,6 +37,59 @@ Place CPUDeviceContext::GetPlace() const { return CPUPlace(); }
#ifndef PADDLE_ONLY_CPU
class EigenCudaStreamDevice : public Eigen::StreamInterface {
public:
EigenCudaStreamDevice() : scratch_(nullptr), semaphore_(nullptr) {
Eigen::initializeDeviceProp();
}
~EigenCudaStreamDevice() override {}
void Reinitialize(const cudaStream_t* cuda_stream, GPUPlace place) {
stream_ = cuda_stream;
place_ = place;
device_prop_ = &Eigen::m_deviceProperties[place.device];
}
const cudaStream_t& stream() const override { return *stream_; }
const cudaDeviceProp& deviceProperties() const override {
return *device_prop_;
}
void* allocate(size_t num_bytes) const override {
return paddle::memory::Alloc(place_, num_bytes);
}
void deallocate(void* buffer) const override {
paddle::memory::Free(place_, buffer);
}
void* scratchpad() const override {
if (scratch_ == NULL) {
scratch_ = allocate(Eigen::kCudaScratchSize + sizeof(unsigned int));
}
return scratch_;
}
unsigned int* semaphore() const override {
if (semaphore_ == NULL) {
char* scratch =
static_cast<char*>(scratchpad()) + Eigen::kCudaScratchSize;
semaphore_ = reinterpret_cast<unsigned int*>(scratch);
PADDLE_ENFORCE(
cudaMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_));
}
return semaphore_;
}
private:
GPUPlace place_;
const cudaStream_t* stream_; // not owned;
const cudaDeviceProp* device_prop_; // not owned;
mutable void* scratch_;
mutable unsigned int* semaphore_;
};
template <>
Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
return reinterpret_cast<const CUDADeviceContext*>(this)->eigen_device();
......@@ -43,19 +97,9 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) {
SetDeviceId(place_.device);
// TODO(qijun) Pass a created cuda stream to Eigen::CudaStreamDevice directly
// here will cause segment fault. We must implement a class derived from
// Eigen::StreamInterface, and reinitialize it with a cuda stream and a gpu id
// later. Please refer to the implementation of class EigenCudaStreamDevice
// in TensorFlow.
//
// We find that CUDA 7 introduces a new option, the per-thread default stream,
// that has two effects. Please refer to https://devblogs.nvidia.com/
// parallelforall/gpu-pro-tip-cuda-7-streams-simplify-concurrency/
//
// So, we decide to use default stream and add –default-stream per-thread nvcc
// flag. Than, two threads with two CUDADeviceContexts will run parallelly.
eigen_stream_.reset(new Eigen::CudaStreamDevice());
PADDLE_ENFORCE(cudaStreamCreate(&stream_));
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
}
......@@ -75,12 +119,13 @@ CUDADeviceContext::~CUDADeviceContext() {
}
eigen_stream_.reset();
eigen_device_.reset();
PADDLE_ENFORCE(cudaStreamDestroy(stream_));
}
Place CUDADeviceContext::GetPlace() const { return place_; }
void CUDADeviceContext::Wait() const {
PADDLE_ENFORCE(cudaStreamSynchronize(0));
PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
}
Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
......@@ -91,6 +136,7 @@ cublasHandle_t CUDADeviceContext::cublas_handle() {
if (!cublas_handle_) {
SetDeviceId(place_.device);
PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
}
return cublas_handle_;
}
......@@ -99,10 +145,13 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() {
if (!cudnn_handle_) {
SetDeviceId(place_.device);
PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_));
}
return cudnn_handle_;
}
cudaStream_t CUDADeviceContext::stream() { return stream_; }
curandGenerator_t CUDADeviceContext::curand_generator() {
if (!curand_generator_) {
SetDeviceId(place_.device);
......@@ -110,6 +159,8 @@ curandGenerator_t CUDADeviceContext::curand_generator() {
CURAND_RNG_PSEUDO_DEFAULT));
PADDLE_ENFORCE(
dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_));
PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_));
}
return curand_generator_;
}
......
paddle/platform/device_context.h
浏览文件 @ 448d4db5
......@@ -52,6 +52,7 @@ class CPUDeviceContext : public DeviceContext {
};
#ifndef PADDLE_ONLY_CPU
class EigenCudaStreamDevice;
class CUDADeviceContext : public DeviceContext {
public:
......@@ -76,6 +77,9 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return curand handle in the device context. */
curandGenerator_t curand_generator();
/*! \brief Return cuda stream in the device context. */
cudaStream_t stream();
// clang-format on
private:
......@@ -83,15 +87,16 @@ class CUDADeviceContext : public DeviceContext {
private:
std::unique_ptr<Eigen::GpuDevice> eigen_device_;
std::unique_ptr<Eigen::CudaStreamDevice> eigen_stream_;
std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
private:
uint64_t seed_;
// clang-format off
cudnnHandle_t cudnn_handle_ = nullptr;
cublasHandle_t cublas_handle_ = nullptr;
curandGenerator_t curand_generator_ = nullptr;
cudaStream_t stream_{nullptr};
cudnnHandle_t cudnn_handle_{nullptr};
cublasHandle_t cublas_handle_{nullptr};
curandGenerator_t curand_generator_{nullptr};
// clang-format on
};
......
paddle/platform/device_context_test.cc
浏览文件 @ 448d4db5
......@@ -45,6 +45,7 @@ TEST(Device, CUDADeviceContext) {
ASSERT_NE(nullptr, cublas_handle);
curandGenerator_t curand_handle = device_context->curand_generator();
ASSERT_NE(nullptr, curand_handle);
ASSERT_NE(nullptr, device_context->stream());
delete device_context;
}
}
paddle/platform/enforce.h
浏览文件 @ 448d4db5
......@@ -14,14 +14,21 @@ limitations under the License. */
#pragma once
#include <execinfo.h>
#include <dlfcn.h> // for dladdr
#include <execinfo.h> // for backtrace
#include <iomanip>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include "paddle/string/printf.h"
#include "paddle/string/to_string.h"
#ifdef __GNUC__
#include <cxxabi.h> // for __cxa_demangle
#endif
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/dynload/cublas.h"
......@@ -39,6 +46,19 @@ limitations under the License. */
namespace paddle {
namespace platform {
namespace {
#ifdef __GNUC__
inline std::string demangle(std::string name) {
int status = -4; // some arbitrary value to eliminate the compiler warning
std::unique_ptr<char, void (*)(void*)> res{
abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
return (status == 0) ? res.get() : name;
}
#else
inline std::string demangle(std::string name) { return name; }
#endif
}
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
std::string err_str_;
......@@ -48,15 +68,29 @@ struct EnforceNotMet : public std::exception {
std::rethrow_exception(exp_);
} catch (const std::exception& exp) {
std::ostringstream sout;
sout << string::Sprintf("%s at [%s:%d]", exp.what(), f, l) << std::endl;
sout << "Call Stacks: " << std::endl;
sout << "PaddlePaddle Call Stacks: " << std::endl;
void* call_stack[TRACE_STACK_LIMIT];
int sz = backtrace(call_stack, TRACE_STACK_LIMIT);
auto line = backtrace_symbols(call_stack, sz);
for (int i = 0; i < sz; ++i) {
sout << line[i] << std::endl;
auto size = backtrace(call_stack, TRACE_STACK_LIMIT);
auto symbols = backtrace_symbols(call_stack, size);
Dl_info info;
for (int i = 0; i < size; ++i) {
if (dladdr(call_stack[i], &info)) {
auto demangled = demangle(info.dli_sname);
auto addr_offset = static_cast<char*>(call_stack[i]) -
static_cast<char*>(info.dli_saddr);
sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
2 + sizeof(void*) * 2, call_stack[i],
demangled, addr_offset);
} else {
sout << string::Sprintf("%-3d %*0p\n", i, 2 + sizeof(void*) * 2,
call_stack[i]);
}
}
free(line);
free(symbols);
err_str_ = sout.str();
}
}
......@@ -170,7 +204,7 @@ inline void throw_on_error(T e) {
* PADDLE_ENFORCE_EQ(a, b);
*
* will raise an expression described as follows:
* "enforce a == b failed, 1 != 2" with detailed stack infomation.
* "enforce a == b failed, 1 != 2" with detailed stack information.
*
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
......
paddle/pserver/ParameterServer2.cpp
浏览文件 @ 448d4db5
......@@ -1032,8 +1032,8 @@ void ParameterServer2::loadValueVector(const LoadValueRequest& request,
Parameter::Header header;
CHECK(fs.read(reinterpret_cast<char*>(&header), sizeof(header)))
<< "Fail to read parameters in pserver";
CHECK_EQ(header.version, Parameter::kFormatVersion)
<< "Incorrect format version: " << header.version;
CHECK(Parameter::isHeaderFormatSupported(header.format))
<< "Incorrect format version: " << header.format;
CHECK_EQ(header.size, (size_t)size_)
<< "The size (" << header.size << ") in the file does not match the size "
<< "(" << size_ << ") of the pserver: " << serverId_;
......@@ -1063,7 +1063,8 @@ void ParameterServer2::saveValueVector(const SaveValueRequest& request,
CpuVector& vec = vectors_[PARAMETER_APPLY] ? *vectors_[PARAMETER_APPLY]
: *vectors_[PARAMETER_VALUE];
Parameter::Header header;
header.version = Parameter::kFormatVersion;
// TODO(TJ): save param headerFormat_
header.format = PARAM_FORMAT_ORIGINAL;
header.valueSize = sizeof(real);
header.size = size_;
......
paddle/scripts/docker/build.sh
浏览文件 @ 448d4db5
......@@ -82,10 +82,6 @@ EOF
fi
# To build documentation, we need to run cmake again after installing
# PaddlePaddle. This awkwardness is due to
# https://github.com/PaddlePaddle/Paddle/issues/1854. It also
# describes a solution.
if [[ ${WITH_DOC:-OFF} == "ON" ]]; then
cat <<EOF
========================================
......@@ -93,11 +89,6 @@ Building documentation ...
In /paddle/build_doc
========================================
EOF
# build documentation need install Paddle before
make install -j `nproc`
pip install /usr/local/opt/paddle/share/wheels/*.whl
paddle version
mkdir -p /paddle/build_doc
pushd /paddle/build_doc
cmake .. \
......@@ -106,7 +97,8 @@ EOF
-DWITH_AVX=${WITH_AVX:-ON} \
-DWITH_SWIG_PY=ON \
-DWITH_STYLE_CHECK=OFF
make paddle_docs paddle_docs_cn
make -j `nproc` gen_proto_py
make -j `nproc` paddle_docs paddle_docs_cn
popd
fi
......@@ -128,25 +120,6 @@ EOF
/woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
fi
# generate deb package for current build
# FIXME(typhoonzero): should we remove paddle/scripts/deb ?
if [[ ${WITH_DEB:-ON} == "ON" ]]; then
cat <<EOF
========================================
Generating .deb package ...
========================================
EOF
set +e
cpack -D CPACK_GENERATOR='DEB' -j `nproc` ..
err_code=$?
if [ ${err_code} -ne 0 ]; then
# cat error logs if cpack failed.
cat /paddle/build/_CPack_Packages/Linux/DEB/PreinstallOutput.log
exit ${err_code}
fi
set -e
fi
cat <<EOF
========================================
Generate /paddle/build/Dockerfile ...
......@@ -166,15 +139,15 @@ EOF
fi
cat >> /paddle/build/Dockerfile <<EOF
# Use different deb file when building different type of images
ADD *.deb /
ADD python/dist/*.whl /
# run paddle version to install python packages first
RUN apt-get update &&\
apt-get install -y wget python-pip && pip install -U pip && \
dpkg -i /*.deb ; apt-get install -f -y && \
pip install /*.whl; apt-get install -f -y && \
apt-get clean -y && \
rm -f /*.deb && \
paddle version
rm -f /*.whl && \
paddle version && \
ldconfig
${DOCKERFILE_CUDNN_DSO}
${DOCKERFILE_GPU_ENV}
ADD go/cmd/pserver/pserver /usr/bin/
......@@ -182,3 +155,7 @@ ADD go/cmd/master/master /usr/bin/
# default command shows the paddle version and exit
CMD ["paddle", "version"]
EOF
set +xe
printf "If you need to install PaddlePaddle in develop docker image,"
printf "please make install or pip install build/python/dist/*.whl.\n"
paddle/scripts/submit_local.sh.in
浏览文件 @ 448d4db5
......@@ -18,6 +18,8 @@ function version(){
echo "PaddlePaddle @PADDLE_VERSION@, compiled with"
echo " with_avx: @WITH_AVX@"
echo " with_gpu: @WITH_GPU@"
echo " with_mkldnn: @WITH_MKLDNN"
echo " with_mklml: @WITH_MKLML@"
echo " with_double: @WITH_DOUBLE@"
echo " with_python: @WITH_PYTHON@"
echo " with_rdma: @WITH_RDMA@"
......@@ -54,8 +56,7 @@ if [ -z "${PADDLE_NO_STAT+x}" ]; then
fi
fi
MYDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
PADDLE_BIN_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
if [ ! -z "${DEBUGGER}" ]; then
echo "Using debug command ${DEBUGGER}"
......@@ -91,34 +92,16 @@ else:
sys.exit(0)
EOF
if [ $? -eq 1 ]; then # Older version installed, or not installed at all
echo "First time run paddle, need to install some python dependencies."
# setuptools normalizes package version, so we need to use normalized
# package version for paddle python package
PYTHON_PADDLE_VERSION=$(python -c 'import packaging.version
import setuptools
print str(packaging.version.Version("@PADDLE_VERSION@"))
' 2>/dev/null)
BASEDIR=$(dirname "$0")
pip install ${BASEDIR}/../opt/paddle/share/wheels/*-${PYTHON_PADDLE_VERSION}-*.whl
if [ $? -ne 0 ]; then
echo "pip install wheels failed. "
echo "Please use 'sudo paddle' at the first time you use PaddlePaddle"
echo "PaddlePaddle will install some python dependencies automatically."
exit 1
fi
echo "Python dependencies are installed."
fi
case "$1" in
"train")
${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_trainer ${@:2}
${DEBUGGER} $PADDLE_BIN_PATH/paddle_trainer ${@:2}
;;
"merge_model")
${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_merge_model ${@:2}
${DEBUGGER} $PADDLE_BIN_PATH/paddle_merge_model ${@:2}
;;
"pserver")
${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_pserver_main ${@:2}
${DEBUGGER} $PADDLE_BIN_PATH/paddle_pserver_main ${@:2}
;;
"dump_config")
python -m paddle.utils.dump_config ${@:2}
......@@ -127,7 +110,7 @@ case "$1" in
python -m paddle.utils.make_model_diagram ${@:2}
;;
"usage")
$MYDIR/../opt/paddle/bin/paddle_usage ${@:2}
$PADDLE_BIN_PATH/paddle_usage ${@:2}
;;
"version")
version
......
paddle/trainer/TrainerConfigHelper.cpp
浏览文件 @ 448d4db5
......@@ -29,7 +29,6 @@ DECLARE_bool(with_gpu);
DECLARE_bool(parallel_nn);
DECLARE_string(config_args);
DECLARE_bool(use_mkldnn);
DECLARE_bool(use_mkldnn_wgt);
const char *kConfigParserModuleName = "paddle.trainer.config_parser";
const char *kConfigParserFuncName = "parse_config_and_serialize";
......@@ -47,7 +46,6 @@ TrainerConfigHelper::TrainerConfigHelper(const std::string &configFilePath)
<< ",with_cost=" << FLAGS_with_cost << ",use_gpu=" << FLAGS_use_gpu
<< ",parallel_nn=" << FLAGS_parallel_nn
<< ",use_mkldnn=" << FLAGS_use_mkldnn
<< ",use_mkldnn_wgt=" << FLAGS_use_mkldnn_wgt
<< ",cudnn_version=" << hl_get_cudnn_lib_version();
if (!FLAGS_config_args.empty()) {
configArgs << "," << FLAGS_config_args;
......
paddle/utils/Flags.cpp
浏览文件 @ 448d4db5
......@@ -27,7 +27,6 @@ DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training");
DEFINE_bool(use_mkldnn, false, "Only support CPU training");
#endif
DEFINE_bool(use_mkldnn_wgt, false, "Init weight from CPU weight");
DEFINE_bool(parallel_nn,
false,
"Whether to use multi-threads to calculate one neural network."
......
paddle/utils/Flags.h
浏览文件 @ 448d4db5
......@@ -41,4 +41,3 @@ DECLARE_string(predict_file);
DECLARE_bool(prev_batch_state);
DECLARE_string(init_model_path);
DECLARE_bool(use_mkldnn);
DECLARE_bool(use_mkldnn_wgt);
python/CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -21,6 +21,18 @@ if(WITH_GOLANG)
add_dependencies(copy_paddle_master paddle_master)
endif(WITH_GOLANG)
set(MKL_SHARED_LIBS "")
set(MKL_DEPENDS "")
if(WITH_MKLML)
list(APPEND MKL_SHARED_LIBS ${MKLML_LIB} ${MKLML_IOMP_LIB})
list(APPEND MKL_DEPENDS mklml)
endif()
if(WITH_MKLDNN)
list(APPEND MKL_SHARED_LIBS "${MKLDNN_LIB}" "${MKLDNN_LIB}.0")
list(APPEND MKL_DEPENDS mkldnn)
endif()
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in
${CMAKE_CURRENT_BINARY_DIR}/setup.py)
......@@ -38,8 +50,11 @@ add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_PYTHON_BUILD_DIR}/lib* ${PADDLE_PYTHON_BUILD_DIR}/lib-python
DEPENDS gen_proto_py copy_paddle_pybind framework_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
add_custom_target(paddle_python ALL DEPENDS
${PADDLE_PYTHON_BUILD_DIR}/.timestamp paddle_pserver_main paddle_trainer paddle_merge_model python_api_wheel)
set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp paddle_pserver_main paddle_trainer paddle_merge_model ${MKL_DEPENDS})
if(WITH_SWIG_PY)
list(APPEND paddle_python_deps python_api_wheel)
endif()
add_custom_target(paddle_python ALL DEPENDS ${paddle_python_deps})
set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
......
python/paddle/trainer_config_helpers/evaluators.py
浏览文件 @ 448d4db5
......@@ -298,8 +298,8 @@ def pnpair_evaluator(
input,
label,
info,
name=None,
weight=None, ):
weight=None,
name=None, ):
"""
Positive-negative pair rate Evaluator which adapts to rank task like
learning to rank. This evaluator must contain at least three layers.
......@@ -308,27 +308,31 @@ def pnpair_evaluator(
.. code-block:: python
eval = pnpair_evaluator(input, info, label)
eval = pnpair_evaluator(input, label, info)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: LayerOutput
:param info: Label layer name. (TODO, explaination)
:param info: Info layer name. (TODO, explaination)
:type info: LayerOutput
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. (TODO, explaination)
:type weight: LayerOutput
:param name: Evaluator name.
:type name: None|basestring
"""
if not isinstance(input, list):
input = [input]
if label:
input.append(label)
if info:
input.append(info)
evaluator_base(
name=name,
type="pnpair",
input=input,
label=label,
info=info,
weight=weight)
type="pnpair",
weight=weight,
name=name, )
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
......@@ -429,12 +433,12 @@ def chunk_evaluator(
.. code-block:: text
Scheme Description
Scheme Description
plain Use the same label for the whole chunk.
IOB Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
IOB Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
IOE Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
IOBES Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
IOBES Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
To make it clear, let's illustrate by an NER example.
Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
if 'IOB' scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
......@@ -451,7 +455,7 @@ def chunk_evaluator(
tagType = label % numTagType
chunkType = label / numTagType
otherChunkType = numChunkTypes
The following table shows the mapping rule between tagType and tag type in each scheme.
.. code-block:: text
......@@ -475,7 +479,7 @@ def chunk_evaluator(
O 6
In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
"IOB" so tagType has two values: 0 for B and 1 for I.
"IOB" so tagType has two values: 0 for B and 1 for I.
Here we will use I-LOC to explain the above mapping rules in detail.
For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
and the tag is I.
......@@ -486,7 +490,7 @@ def chunk_evaluator(
eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
:param input: The input layers.
:type input: LayerOutput
:param label: An input layer containing the ground truth label.
......
python/paddle/v2/framework/op.py
浏览文件 @ 448d4db5
......@@ -23,7 +23,7 @@ class OpDescCreationMethod(object):
"""
A Functor object to convert user input(use key word args) to OpDesc based on
OpProto.
:param op_proto: The OpProto object.
:type op_proto: op_proto_pb2.OpProto
"""
......@@ -177,4 +177,26 @@ class OperatorFactory(object):
return self.get_op_info(type).attrs
class __RecurrentOp__(object):
__proto__ = None
type = 'recurrent_op'
def __init__(self):
# cache recurrent_op's proto
if self.__proto__ is None:
for op_proto in get_all_op_protos():
if op_proto.type == self.type:
self.__proto__ = op_proto
def __call__(self, *args, **kwargs):
if self.type not in args and 'type' not in kwargs:
kwargs['type'] = self.type
# create proto
create_method = OpDescCreationMethod(self.__proto__)
proto = create_method(*args, **kwargs)
# create rnnop
return core.RecurrentOp.create(proto.SerializeToString())
Operator = OperatorFactory() # Default global factory
RecurrentOp = __RecurrentOp__()
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ 448d4db5
......@@ -25,3 +25,4 @@ py_test(test_operator SRCS test_operator.py)
# py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
py_test(test_recurrent_op SRCS test_recurrent_op.py)
py_test(test_gradient_checker SRCS test_gradient_checker.py)
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @ 448d4db5
import unittest
import numpy
import itertools
import paddle.v2.framework.core as core
from paddle.v2.framework.op import Operator
......@@ -8,6 +9,7 @@ __all__ = ['get_numeric_gradient']
def create_op(op_type):
# TODO need to set attrs
kwargs = dict()
for in_name in Operator.get_op_input_names(op_type):
kwargs[in_name] = in_name
......@@ -66,7 +68,6 @@ def get_numeric_gradient(op,
local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
))
# TODO(yuyang18): Only CPU is support now.
cpu_ctx = core.DeviceContext.create(core.CPUPlace())
def get_output():
......@@ -109,12 +110,110 @@ def get_numeric_gradient(op,
class GradientChecker(unittest.TestCase):
def assert_is_close(self, numeric_grads, scope, max_relative_error,
msg_prefix):
for name in numeric_grads:
b = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
a = numeric_grads[name]
def __get_gradient(self, forward_op, backward_op, input_value, grad_names,
place):
"""Get the input gradients after running forward and backward operators
on the given places.
:param forward_op: forward operator
:type forward_op: Operator
:param backward_op: backward operator
:type backward_op: Operator
:param input_value: input values.
:type input_value: dict{string:numpy.array}
:param grad_names: the names of returned input gradients.
:type input_value: a list of string
:param place: the device type.
:type place: CPUPlace or GPUPlace
:return: the input grdients of given grad_names.
:rtype: a list of numpy.array
"""
scope = core.Scope()
ctx = core.DeviceContext.create(place)
inputs = forward_op.inputs()
in_names = [item for k in inputs for item in inputs[k]]
outputs = forward_op.outputs()
out_names = [item for k in outputs for item in outputs[k]]
# create input var and set value
for name, value in input_value.iteritems():
if name not in in_names:
raise ValueError(name + "does not exist in Op's inputs.")
var = scope.new_var(name).get_tensor()
var.set_dims(value.shape)
var.set(value, place)
# run forward op
for out_name in out_names:
scope.new_var(out_name)
forward_op.infer_shape(scope)
forward_op.run(scope, ctx)
# set output var's shape
# set output grad to ones
for name in out_names:
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
grad_tensor.set_dims(out_tensor.shape())
data = numpy.ones(out_tensor.shape(), dtype=numpy.float32)
grad_tensor.set(data, place)
# run backward op
for name in backward_op.outputs():
scope.new_var(name)
backward_op.infer_shape(scope)
backward_op.run(scope, ctx)
outs = [
numpy.array(scope.find_var(name).get_tensor())
for name in grad_names
]
return outs
def compare_grad(self, forward_op, input_value):
""" Compare the input gradients between CPU and GPU for the given forward
operator.
:param forward_op: forward operator
:type forward_op: Operator
:param input_value: input values.
:type input_value: dict{string:numpy.array}
:raises: AssertionError, there is different gradient value.
"""
backward_op = core.Operator.backward(forward_op, set())
# return if not compile with GPU or not implementing GPU kernel
if not (core.is_compile_gpu() and backward_op.support_gpu()):
return
outputs = backward_op.outputs()
out_names = [item for k in outputs for item in outputs[k]]
cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
out_names, core.CPUPlace())
gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
out_names, core.GPUPlace(0))
for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads,
out_names):
self.assertTrue(
numpy.allclose(
c_grad, g_grad, atol=1e-4),
"output name: " + name + " has diff")
def __assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
"""Use relative error for the comparison.
:param numeric_grads: the numerical graidents.
:type numeric_grads: a list of numpy.array
:param analytic_grads: the analytical graidents.
:type analytic_grads: a list of numpy.array
:param name: the names of gradients, used to print for debug.
:type names: a list of string
:param msg_prefix: string info, used to print for debug.
:type msf_prefix: string
"""
for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
abs_a = numpy.abs(a)
# if abs_a is nearly zero, then use abs error for a, not relative
# error.
......@@ -159,106 +258,26 @@ class GradientChecker(unittest.TestCase):
inputs = forward_op.inputs()
in_names = [item for k in inputs for item in inputs[k]]
outputs = forward_op.outputs()
out_names = [item for k in outputs for item in outputs[k]]
for no_grad in no_grad_set:
if no_grad not in in_names:
raise ValueError("no_grad should be in in_names")
backward_op = core.Operator.backward(forward_op, no_grad_set)
bwd_outputs = backward_op.outputs()
bwd_out_names = [item for k in bwd_outputs for item in bwd_outputs[k]]
places = [core.CPUPlace()]
if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
places.append(core.GPUPlace(0))
numeric_grad = dict()
# get numeric gradient
for check_name in inputs_to_check:
numeric_grad[check_name] = \
get_numeric_gradient(forward_op, input_vars, output_name,
check_name)
# get numerical gradients
numeric_grads = [
get_numeric_gradient(forward_op, input_vars, output_name, name)
for name in inputs_to_check
]
# get operator gradient according to different device
check_names = [grad_var_name(name) for name in inputs_to_check]
for place in places:
scope = core.Scope()
ctx = core.DeviceContext.create(place)
# create input var and set value
for name, value in input_vars.iteritems():
if name not in in_names:
raise ValueError(name + " not in op.inputs_")
var = scope.new_var(name).get_tensor()
var.set_dims(value.shape)
var.set(value, place)
# create output var
for out_name in out_names:
scope.new_var(out_name).get_tensor()
# infer the shape of output var and compute/set value of output var
forward_op.infer_shape(scope)
forward_op.run(scope, ctx)
# create output grad var
# set shape as the output var
# set value of this grad to ones
for name in out_names:
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
grad_tensor.set_dims(out_tensor.shape())
data = 1.0 * numpy.ones(out_tensor.shape())
grad_tensor.set(data, place)
# create input grad var
for name in bwd_out_names:
scope.new_var(name).get_tensor()
# infer the shape of input gradient var and compute/set it's value
# with backward op
backward_op.infer_shape(scope)
backward_op.run(scope, ctx)
self.assert_is_close(numeric_grad, scope, max_relative_error,
"Gradient Check On %s" % str(place))
if __name__ == '__main__':
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
add_op = Operator('add_two', X="X", Y="Y", Out="Z")
x = numpy.random.random((10, 1)).astype("float32")
y = numpy.random.random((10, 1)).astype("float32")
arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2)
def test_softmax_op(self):
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - numpy.max(x)
exps = numpy.exp(shiftx)
return exps / numpy.sum(exps)
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(Y.shape[0]):
d = numpy.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
softmax_op = Operator("softmax", X="X", Y="Y")
X = numpy.random.random((2, 2)).astype("float32")
Y = numpy.apply_along_axis(stable_softmax, 1, X)
dY = numpy.ones(Y.shape)
dX = label_softmax_grad(Y, dY)
arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
unittest.main()
# get analytical gradients according to different device
analytic_grads = self.__get_gradient(forward_op, backward_op,
input_vars, check_names, place)
self.__assert_is_close(numeric_grads, analytic_grads, check_names,
max_relative_error,
"Gradient Check On %s" % str(place))
python/paddle/v2/framework/tests/test_gradient_checker.py 0 → 100644
浏览文件 @ 448d4db5
import unittest
import numpy
from paddle.v2.framework.op import Operator
from gradient_checker import GradientChecker
from gradient_checker import get_numeric_gradient
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
add_op = Operator('add_two', X="X", Y="Y", Out="Z")
x = numpy.random.random((10, 1)).astype("float32")
y = numpy.random.random((10, 1)).astype("float32")
arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - numpy.max(x)
exps = numpy.exp(shiftx)
return exps / numpy.sum(exps)
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(Y.shape[0]):
d = numpy.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
softmax_op = Operator("softmax", X="X", Y="Y")
X = numpy.random.random((2, 2)).astype("float32")
Y = numpy.apply_along_axis(stable_softmax, 1, X)
dY = numpy.ones(Y.shape)
dX = label_softmax_grad(Y, dY)
arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_mean_op.py
浏览文件 @ 448d4db5
import unittest
from op_test_util import OpTestMeta
from gradient_checker import GradientChecker, create_op
import numpy as np
......@@ -12,5 +13,12 @@ class TestMeanOp(unittest.TestCase):
self.outputs = {'Out': np.mean(self.inputs['X'])}
class MeanGradOpTest(GradientChecker):
def test_normal(self):
op = create_op("mean")
inputs = {"X": np.random.random((10, 10)).astype("float32")}
self.check_grad(op, inputs, set("X"), "Out")
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @ 448d4db5
......@@ -2,7 +2,7 @@ import logging
import paddle.v2.framework.core as core
import unittest
import numpy as np
from paddle.v2.framework.op import Operator
from paddle.v2.framework.op import Operator, RecurrentOp
def py_sigmoid(x):
......@@ -98,11 +98,11 @@ class TestRecurrentOp(unittest.TestCase):
def forward(self):
self.scope = core.Scope()
self.create_global_variables()
self.create_rnn_op()
self.create_step_net()
rnn_op = self.create_rnn_op()
ctx = core.DeviceContext.create(core.CPUPlace())
rnn_op.infer_shape(self.scope)
rnn_op.run(self.scope, ctx)
self.rnnop.infer_shape(self.scope)
self.rnnop.run(self.scope, ctx)
return np.array(self.scope.find_var("h").get_tensor())
def create_global_variables(self):
......@@ -128,8 +128,7 @@ class TestRecurrentOp(unittest.TestCase):
def create_rnn_op(self):
# create RNNOp
rnnop = Operator(
"recurrent_op",
self.rnnop = RecurrentOp(
# inputs
inlinks=["x"],
boot_memories=["h_boot"],
......@@ -142,14 +141,9 @@ class TestRecurrentOp(unittest.TestCase):
outlink_alias=["h@alias"],
pre_memories=["h@pre"],
memories=["h@alias"])
return rnnop
def create_step_net(self):
var = self.scope.new_var("stepnet")
stepnet = var.get_net()
# x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx")
# h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh")
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
......@@ -158,6 +152,7 @@ class TestRecurrentOp(unittest.TestCase):
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.add_op(op)
stepnet.complete_add_op(True)
self.rnnop.set_stepnet(stepnet)
def test_forward(self):
print 'test recurrent op forward'
......
python/paddle/v2/framework/tests/test_sigmoid_op.py
浏览文件 @ 448d4db5
import unittest
from op_test_util import OpTestMeta
import numpy as np
from op_test_util import OpTestMeta
from gradient_checker import GradientChecker, create_op
class TestSigmoidOp(unittest.TestCase):
......@@ -8,12 +9,20 @@ class TestSigmoidOp(unittest.TestCase):
def setUp(self):
self.type = "sigmoid"
self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
self.inputs = {'X': np.random.random((15, 31)).astype("float32")}
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
#class TestSigmoidGradOp(unittest.TestCase):
#TODO(qingqing) add unit test
class TestSigmoidGradOp(GradientChecker):
def test_grad(self):
op = create_op("sigmoid")
inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")}
# compare gpu and cpu results for backward op.
# this test will be skiped if only compiling CPU version.
self.compare_grad(op, inputs)
# check gradients
self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/reader/creator.py
浏览文件 @ 448d4db5
......@@ -57,7 +57,7 @@ def text_file(path):
return reader
def recordio_local(paths, buf_size=100):
def recordio(paths, buf_size=100):
"""
Creates a data reader from given RecordIO file paths separated by ",",
glob pattern is supported.
......@@ -67,15 +67,19 @@ def recordio_local(paths, buf_size=100):
import recordio as rec
import paddle.v2.reader.decorator as dec
import cPickle as pickle
def reader():
a = ','.join(paths)
f = rec.reader(a)
if isinstance(paths, basestring):
path = paths
else:
path = ",".join(paths)
f = rec.reader(path)
while True:
r = f.read()
if r is None:
break
yield r
yield pickle.loads(r)
f.close()
return dec.buffered(reader, buf_size)
......
python/paddle/v2/reader/tests/creator_test.py
浏览文件 @ 448d4db5
......@@ -34,5 +34,27 @@ class TestTextFile(unittest.TestCase):
self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
class TestRecordIO(unittest.TestCase):
def do_test(self, path):
reader = paddle.v2.reader.creator.recordio(path)
idx = 0
for e in reader():
if idx == 0:
self.assertEqual(e, (1, 2, 3))
elif idx == 1:
self.assertEqual(e, (4, 5, 6))
idx += 1
self.assertEqual(idx, 2)
def test_recordIO(self):
self.do_test(
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat"))
self.do_test([
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat")
])
if __name__ == '__main__':
unittest.main()
python/paddle/v2/trainer.py
浏览文件 @ 448d4db5
......@@ -27,16 +27,24 @@ class SGD(object):
SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.
:param update_equation: The optimizer object.
:type update_equation: paddle.v2.optimizer.Optimizer
:param cost: Target cost that neural network should be optimized.
:type cost: paddle.v2.config_base.Layer
:param parameters: The parameters dictionary.
:type parameters: paddle.v2.parameters.Parameters
:param update_equation: The optimizer object.
:type update_equation: paddle.v2.optimizer.Optimizer
:param extra_layers: Some layers in the neural network graph are not
in the path of cost layer.
:param pserver_spec: pserver location, eg: localhost:3000
:type extra_layers: paddle.v2.config_base.Layer
:param is_local: Whether trainning locally
:type is_local: bool
:param pserver_spec: comma string for pserver location,
eg:127.10.0.10:3000,127.10.0.11:3000,
and this parameter is only used for fault
tolerant mode cluster training.
:type pserver_spec: string
:param use_etcd: Whether using etcd pserver.
:param use_etcd: bool
"""
def __init__(self,
......
python/requirements.txt
浏览文件 @ 448d4db5
requests==2.9.2
numpy>=1.12
protobuf==3.1
recordio
recordio>=0.1.0
matplotlib
rarfile
scipy>=0.19.0
......
python/setup.py.in
浏览文件 @ 448d4db5
......@@ -23,6 +23,19 @@ with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f:
if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']:
setup_requires+=["opencv-python"]
# the prefix is sys.prefix which should always be usr
paddle_bin_dir = 'opt/paddle/bin'
paddle_bins = ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage',
'${PADDLE_BINARY_DIR}/paddle/trainer/paddle_trainer',
'${PADDLE_BINARY_DIR}/paddle/trainer/paddle_merge_model',
'${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main',
'${PADDLE_BINARY_DIR}/paddle/scripts/paddle']
paddle_rt_lib_dir = 'lib'
paddle_rt_libs = ['${WARPCTC_LIBRARIES}']
if '${MKL_SHARED_LIBS}'!= '':
paddle_rt_libs += '${MKL_SHARED_LIBS}'.split(';')
setup(name='paddlepaddle',
version='${PADDLE_VERSION}',
description='Parallel Distributed Deep Learning',
......@@ -40,11 +53,7 @@ setup(name='paddlepaddle',
'paddle.v2.framework.proto': '${PADDLE_BINARY_DIR}/paddle/framework',
'py_paddle': '${PADDLE_SOURCE_DIR}/paddle/py_paddle'
},
scripts=['${PADDLE_BINARY_DIR}/paddle/scripts/paddle'],
scripts=paddle_bins,
distclass=BinaryDistribution,
data_files=[('/usr/local/opt/paddle/bin',
['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage',
'${PADDLE_BINARY_DIR}/paddle/trainer/paddle_trainer',
'${PADDLE_BINARY_DIR}/paddle/trainer/paddle_merge_model',
'${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main'])]
data_files=[(paddle_rt_lib_dir, paddle_rt_libs)]
)
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