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提交 feb05c3a 编写于 作者: F fengjiayi
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into rename_fill_zero_op_output

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README.md
浏览文件 @ feb05c3a
......@@ -61,32 +61,32 @@ Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddl
## Installation
It is recommended to check out the
[Docker installation guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html)
[Docker installation guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html)
before looking into the
[build from source guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html).
[build from source guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/build_from_source_en.html).
## Documentation
We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
[Chinese](http://doc.paddlepaddle.org/doc_cn/) documentation.
We provide [English](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html) and
[Chinese](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html) documentation.
- [Deep Learning 101](http://book.paddlepaddle.org/index.html)
- [Deep Learning 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
- [Distributed Training](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/usage/cluster/cluster_train_en.html)
You can run distributed training jobs on MPI clusters.
- [Distributed Training on Kubernetes](http://doc.paddlepaddle.org/develop/doc/howto/usage/k8s/k8s_en.html)
- [Distributed Training on Kubernetes](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/usage/cluster/k8s_en.html)
You can also run distributed training jobs on Kubernetes clusters.
- [Python API](http://doc.paddlepaddle.org/develop/doc/api/index_en.html)
- [Python API](http://www.paddlepaddle.org/docs/develop/documentation/en/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://doc.paddlepaddle.org/develop/doc/howto/dev/contribute_to_paddle_en.html)
- [How to Contribute](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/dev/contribute_to_paddle_en.html)
We appreciate your contributions!
......
benchmark/paddle/image/run_mkldnn_train.sh → benchmark/paddle/image/run_mkl_train.sh
浏览文件 @ feb05c3a
......@@ -28,6 +28,10 @@ function train() {
--test_period=100 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
......
benchmark/paddle/image/run_openblas_infer.sh 0 → 100755
浏览文件 @ feb05c3a
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
thread=`nproc`
if [ $thread -gt $bs ]; then
thread=$bs
fi
log="logs/infer-${topology}-${layer_num}-${thread}openblas-${bs}.log"
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "./run_mkl_infer.sh to save the model first"
exit 0
fi
log_period=$((256 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_gpu=False \
--trainer_count=$thread \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 1280 samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# inference benchmark
for batchsize in 1 2 4 8 16; do
infer googlenet v1 $batchsize
infer resnet 50 $batchsize
infer vgg 19 $batchsize
done
benchmark/paddle/image/run_openblas_train.sh 0 → 100755
浏览文件 @ feb05c3a
set -e
function train() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log"
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
--use_gpu=False \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for batchsize in 64 128 256; do
train vgg 19 $batchsize
train resnet 50 $batchsize
train googlenet v1 $batchsize
done
cmake/external/protobuf.cmake
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......@@ -253,9 +253,9 @@ IF(NOT PROTOBUF_FOUND)
IF(WITH_C_API)
INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
IF(ANDROID)
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib)
INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib)
ENDIF()
ENDIF()
......
doc/api/v2/fluid/layers.rst
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......@@ -188,12 +188,6 @@ beam_search_decode
:noindex:
lstm
---------
.. autofunction:: paddle.v2.fluid.layers.lstm
:noindex:
lod_rank_table
---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table
......@@ -300,3 +294,27 @@ conv2d_transpose
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex:
sequence_expand
---------
.. autofunction:: paddle.v2.fluid.layers.sequence_expand
:noindex:
lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
:noindex:
sequence_softmax
---------
.. autofunction:: paddle.v2.fluid.layers.sequence_softmax
:noindex:
reduce_sum
---------
.. autofunction:: paddle.v2.fluid.layers.reduce_sum
:noindex:
doc/design/executor.md
浏览文件 @ feb05c3a
# Executor Design Doc
## Motivation
In [fluid](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid.md), we encourage the user to use deep learning programming paradigms to describe the training process. When the user-written Python program is executed, it will first create a protobuf message
[`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145) that describes the process and is conceptually like an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree).
We use executor to do the runtime evaluation of a `ProgramDesc`.
The executor runs the `ProgramDesc` like an interpreter. `ProgramDesc` contains the intrinsics (operators in this case) and variables which will be used, executor explicitly executes the stored precompiled code.
## Overview
An executor takes a `ProgramDesc`, a `block_id` and a `Scope`. The `ProgramDesc` is a list of blocks and each block contains the protobuf definition of all the parameters and operators. The `block_id` specifies the entrance block. And the `Scope` is the container of all the variable instance, which is persistent throughout different runs.
An executor takes a `ProgramDesc`, a `block_id` and a `Scope`. The `ProgramDesc` is a list of blocks and each block contains the protobuf definition of all the parameters and operators in the block. The `block_id` specifies the entrance block. And the `Scope` is the container of all the variable instances, which is persistent throughout different runs.
### What does executor do?
## Executor
It evaluates all the operators in the `block_id`th block of a `ProgramDesc`.
The `Executor` explicitly executes all the intrinsics (operators here) in the `block_id`th block of a `ProgramDesc`. Essentially, it instantiates Variables and Operators, then runs all the operators in sequence one-by-one.
It is very similar to how a push stack frame works when entering a block, following which it cleans up all the temporary variables when a mini-batch is finished. It does not however, have the stack frame pop process.
### What does executor NOT do?
### The interface
```c++
Executor(places);
```
A executor does not own any computing resources, a user can only construct an executor using the specified places.
It does not do runtime optimization, meaning intelligently parse the dependency of each op a choose which one to be run and in which order they should be run.
### Running an Executor
It does not do graph partitioning, meaning dividing the `ProgramDesc` into several small pieces and executing them on different devices.
## Implementation
`Executor` evaluates a `ProgramDesc`. Essentially, it instantiates Variables and Operators, then run all the operators in sequence. [[code]](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.cc)
```
void Run(ProgramDesc, Scope, block_id, create_local_scope);
```
An `Executor` only provides a unified way to execute `ProgramDesc`. `ProgramDesc` is the target that will be executed, the `Scope` specifies the variable container, the `block_id` indicates the entrance block and `create_local_scope` is a boolean that states whether it will destroy the temporary variables after the execution is finished.
doc/design/kernel_hint_design.md 0 → 100644
浏览文件 @ feb05c3a
## Problem
In PaddlePaddle's [Design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md), one Operator may have multiple kernels. Users may have some personal preference to choose a certain type of kernel for an operator, such as `force_cpu` to choose a CPU kernel, `use_cudnn` to choose a CUDNN kernel, we need to provide a way for users to do this.
In the current design, we use KernelType to describe one kernel.
```cpp
struct KernelType {
Place place_;
DataType data_type_;
LayoutType layout_;
};
```
`place_` `data_type_` and `layout_` can be got from the input tensors of the operator, `GetActualKernelType(inputs)` use inputs to infer the proper kernel key that fit the incoming data, but users can not directly configure it.
The [design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md) also provides a virtual method `GetExpectedKernelType` that user can overload and use to choose the KernelType they want to use.
So we should send the information user defined in proto to `GetExpectedKernelType` for choosing a kernel.
The problem is, how should we define and send the information for `GetExpectedKernelType` to use?
## Solution
### Potential choice
1. Do nothing, let the user add the information they want to operator‘s attribute and get them inside `GetExpectedKernelType`, this can work properly. But there is a little problem that users may define many kinds of hints for the same purpose, such as `force_cpu`, `use_cpu`, `cpu_kernel` to choose CPU kernel, and `use_cudnn`, `force_cudnn`, `cudnn_kernel` to choose CUDNN kernel.
2. Pre-define all the needed option and use a single attr key such as `kernel_hint` for the user, this is not so flexible if the user wants to define some more kind of hint.
### Final choice
To provide enough flexibility while avoiding confusion definition, we can define some global constants for these attribute names, such as `force_cpu`, `use_cudnn`, `use_mkldnn` for a user to choose.
In C++
```cpp
const std::string kForceCPU = "force_cpu";
const std::string kUseCUDNN = "use_cudnn";
const std::string kUseMKLDNN = "use_mkldnn";
KernelType GetExpectedKernelType() {
if (Attr<bool>(kForceCPU)) {
return KernelType(CPUPlace, ...)
} else {
...
}
}
```
In Python code
```python
FORCE_CPU = core.kForceCPU()
def xx_layer(..., force_cpu=false):
layer_helper = LayerHelper(...)
layer_helper.append_op(
type="xx",
attr={FORCE_CPU: force_cpu})
```
doc/design/mkl/mkl_packed.md
浏览文件 @ feb05c3a
......@@ -30,10 +30,10 @@
由于在现有的某些情况下（例如RNN），多次调用 cblas_?gemm 会使用相同的原数据，因此，每次调用时对原数据的重复Packing便成为了冗余。
为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API:
* cblas_?gemm_alloc
* cblas_?gemm_pack
* cblas_?gemm_compute
* cblas_?gemm_free
* [cblas_?gemm_alloc](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-alloc)
* [cblas_?gemm_pack](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-pack)
* [cblas_?gemm_compute](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-compute)
* [cblas_?gemm_free](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-free)
通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。
......@@ -84,7 +84,20 @@ PaddlePaddle/Paddle
2. 对比优化后layer与相对应的PaddlePaddle原有layer, 在batch mode下的结果。
### Python API
TBD
计划在`paddle/utils.Flags`中添加`use_mkl_packed`的flag，用于选择是否使用相关功能，并且当编译时`WITH_MKL=ON`的情况下，默认设置为`true`。
同时，在`python/paddle/trainer/config_parser.py`中对应的layer处，添加`use_mkl_packed`这个选择，方便用户在Python端选择是否启用这个功能。
具体实现方式比如：
```python
use_mkl_packed = bool(int(g_command_config_args.get("use_mkl_packed", 0)))
if use_mkl_packed:
self.layer_type = mkl_packed_*
```
所有相关的`layer_type`会以*mkl_packed_*开头，这些会在`MKLPacked*Layer`注册layer的时候保证，以示区分。
### Benchmarking
会添加相应的脚本用于测试和对比在使用MKL Packed recurrent layers 前后的网络性能。
......
doc/design/refactor/multi_cpu.md 0 → 100644
浏览文件 @ feb05c3a
# Design Doc: Execute the Program with Multi CPU
## Abstract
This Design Doc propose an approach to make the user-defined Op graph
running with multi-CPU, we will use an auto transpiler to convert the user-defined
Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
## Transpiler
<img src="src/multi-threads/single-thread@3x.png" width="300">
After converted:
<img src="src/multi-threads/multi-threads@3x.png" width="1000">
## Implement
- `Multi-CPU Transpiler` will convert the graph to a multi-CPU graph
which would be executed with multi-threads.
- `BlockingCounter` will `Init/Decrement` an atomic counter, and Blocking `Wait`
for the atomic counter become `0`:
```cpp
BlockingCounter bc(thread_count);
for (int i = 0; i < thread_count; ++i) {
thread_pool->Start([&bc] {bc.DecrementCount(); })
}
bc.Wait();
```
- `ParallelDo` Operator
- Initialize a thread pool which is a Singleton.
- Use a block id as the input, and create run the specify Block on independent scope
with multi-threads.
- Initialize a `BlockingCounter` instance and wait until all threads are done.
- `Split` Operator will split the Input Tensor into a TensorArray.
- `Merge` merge all the gradients which calculated in different threads
with `mean/sum/max/min...` method, and then run the Optimizer Op to optimize `W`.
## TODO
- Improve the optimizer stage with multi-threads, since we could
assign the parameters to the different threads and execute
optimizer with multi-threads.
doc/design/switch_kernel.md 0 → 100644
浏览文件 @ feb05c3a
## Background
Every operator has many kernels because there are multiple data types, places, data layout that Fluid supports. We use the `KernelType` to describe kernel types that operators can hold.
The `KernelType` is as follows.
```
struct KernelType {
Place place_;
DataType data_type_;
LayoutType layout_;
};
```
The `place_` is a descriptor of the device and the computational library, e.g., `MKLDNNPlace`, `CUDAPlace`.
The `data_type_` is the data type that this kernel performs on, e.g., `FP32`, `INT64`. Note that one kernel may have inputs with different data types. However, it will be a major `data_type`. For example, the `cross_entropy` takes `int64` as it label, and `double`/`float` as its input logit and output cost. The major `data_type` of `cross_entropy` is `float`/`double`.
The `layout` is useful for some computational library. One example is that MKLDNN uses many kinds of layout, such as `nChw8c`. Each kind of layout will invoke the different kernel.
## Problem
We register a kernel for every operator and every kernel type ideally. However, it is impracticable for the following situations.
1. Some operators, like CRF, are complicated and inefficient to be implemented on GPU. The CRF operator will only have a CPU kernel.
2. Some operators will take too many memory. It is better to force them into CPU. However, the rest of operators in this neural network will be performed on GPU, i.e., model parallel problem.
3. Some layout and place are particular. One example is that MKLDNN uses `nChw8` and there is no other library uses `nChw8c`.
Problems under these situations are similar. We can formalise this problem as follow.
We register kernels with types $KT = \{kt_1, kt_2, kt_3, ...\}$ for one operator. The inputs of this operator should be run on kernel type $kt_{?}$, which the $kt_{?} \notin KT$. How to cast the input of this operator from $kt_{?}$ to any of kernel type in $KT$.
## Solution
It is clearly that transforming inputs of an operator toadapt another kernel type is not related to the particular operator. So we should register these transformation methods as global methods.
We can infer a kernel type from the inputs of an operators. We let this kernel type as `actual kernel type`, which means this kernel type is the actually kernel type that operator should be performed.
We can get a kernel type by 1) The configuration of operator description. (Users may want to force use `MKL` for `conv` operator). 2) The place of the current executor. (Executor is running on GPU). This kernel type is what we expect the operator will be performed on. We let this kernel type as `expect kernel type`.
We transform the input data from `actual` to `expect` if the expect kernel type is not as same as actual kernel type.
The algorithm is described as follow
```cpp
using DataTransformationFN = std::function<void(const Tensor& in, Tensor* out)>;
using KernelTypePair = std::pair<KernelType, KernelType>;
map<KernelTypePair, DataTransformationFN> g_data_transformation_;
void OpWithKernel::Run() {
vec<Tensor> inputs = ...
auto actual_kernel_type = GetActualKernelType(inputs);
// The expected kernel type is related to actual kernel type.
// For the most operators, the expected kernel type is as same as
// actual kernel type.
//
// So we pass `actual_kernel_type` as a parameter of
// GetExpectedKernelType
auto expect_kernel_type = GetExpectedKernelType(actual_kernel_type);
auto trans = g_data_transformation_[{actual_kernel_type, expect_kernel_type}];
kernel.run(trans(inputs));
}
```
doc/getstarted/build_and_install/docker_install_cn.rst
浏览文件 @ feb05c3a
......@@ -128,7 +128,7 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
AVX是一种CPU指令集，可以加速PaddlePaddle的计算。最新的PaddlePaddle Docker镜像默认
是开启AVX编译的，所以，如果您的电脑不支持AVX，需要单独
`编译 <./build_from_source_cn.rst>`_ PaddlePaddle为no-avx版本。
`编译 <./build_from_source_cn.html>`_ PaddlePaddle为no-avx版本。
以下指令能检查Linux电脑是否支持AVX：
......
doc/getstarted/build_and_install/docker_install_en.rst
浏览文件 @ feb05c3a
......@@ -137,7 +137,7 @@ GPU driver installed before move on.
AVX is a kind of CPU instruction can accelerate PaddlePaddle's calculations.
The latest PaddlePaddle Docker image turns AVX on by default, so, if your
computer doesn't support AVX, you'll probably need to
`build <./build_from_source_en.rst>`_ with :code:`WITH_AVX=OFF`.
`build <./build_from_source_en.html>`_ with :code:`WITH_AVX=OFF`.
The following command will tell you whether your computer supports AVX.
......
doc/howto/dev/new_op_cn.md
浏览文件 @ feb05c3a
......@@ -53,7 +53,7 @@ Kernel实现 | CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU
```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor), 2D tensor of size (M x K)");
AddInput("Y", "(Tensor), 2D tensor of size (K x N)");
......@@ -82,7 +82,7 @@ The equation is: Out = X * Y
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ScaleOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of scale operator.").NotInGradient();
AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
......
doc/howto/dev/new_op_en.md
浏览文件 @ feb05c3a
......@@ -50,7 +50,7 @@ First, define `ProtoMaker` to describe the Operator's input, output, and additio
```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor), 2D tensor of size (M x K)");
AddInput("Y", "(Tensor), 2D tensor of size (K x N)");
......@@ -79,7 +79,7 @@ An additional example [`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/de
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ScaleOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of scale operator.").NotInGradient();
AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
......
doc/howto/index_cn.rst
浏览文件 @ feb05c3a
......@@ -9,9 +9,6 @@
usage/cmd_parameter/index_cn.rst
usage/cluster/cluster_train_cn.md
usage/k8s/k8s_basis_cn.md
usage/k8s/k8s_cn.md
usage/k8s/k8s_distributed_cn.md
开发标准
--------
......
doc/howto/index_en.rst
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......@@ -9,8 +9,6 @@ Usage
usage/cmd_parameter/index_en.rst
usage/cluster/cluster_train_en.md
usage/k8s/k8s_en.md
usage/k8s/k8s_aws_en.md
Development
------------
......
doc/howto/usage/cluster/cluster_train_cn.md
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# PaddlePaddle分布式训练
* [概述](#概述)
* [环境准备](#环境准备)
* [启动参数说明](#启动参数说明)
* [启动参数服务器](#启动参数服务器)
* [启动计算节点](#启动计算节点)
* [准备数据集](#准备数据集)
* [准备训练程序](#准备训练程序)
* [使用分布式计算平台或工具](#使用分布式计算平台或工具)
* [使用Fabric启动集群作业](#使用fabric启动集群作业)
* [准备一个Linux集群](#准备一个linux集群)
* [启动集群作业](#启动集群作业)
* [终止集群作业](#终止集群作业)
* [检查集群训练结果](#检查集群训练结果)
* [检查模型输出](#检查模型输出)
* [在OpenMPI集群中提交训练作业](#在openmpi集群中提交训练作业)
* [准备OpenMPI集群](#准备OpenMPI集群)
* [启动集群作业](#启动集群作业-1)
* [在Kubernetes集群中提交训练作业](#在kubernetes集群中提交训练作业)
# 分布式训练
## 概述
本文将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
......@@ -32,10 +15,11 @@
在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
## 环境准备
1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/getstarted/build_and_install)的多种安装方式。我们推荐使用[Docker](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)安装方式来快速安装PaddlePaddle。
1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/index_cn.html)的多种安装方式。我们推荐使用[Docker](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html)安装方式来快速安装PaddlePaddle。
安装完成之后，执行下面的命令可以查看已经安装的版本（docker安装方式可以进入docker容器执行：`docker run -it paddlepaddle/paddle:[tag] /bin/bash`）：
```bash
......@@ -63,12 +47,12 @@ $ paddle pserver --port=7164 --ports_num=1 --ports_num_for_sparse=1 --num_gradie
$ stdbuf -oL /usr/bin/nohup paddle pserver --port=7164 --ports_num=1 --ports_num_for_sparse=1 --num_gradient_servers=1 &> pserver.log
```
| 参数 | 是否必选 | 默认值 | 说明 |
| ------------- | ------------- | ------------- | ------------- |
| port | 必选 | 7164 | pserver监听的起始端口，根据ports_num决定<br>总端口个数，从起始端口监听多个端口用于通信 |
| ports_num | 必选 | 1 | 监听的端口个数 |
| ports_num_for_sparse | 必选 | 1 | 用于稀疏类型参数通信的端口个数 |
| num_gradient_servers | 必选 | 1 | 当前训练任务pserver总数 |
参数说明
- port：**必选，默认7164**，pserver监听的起始端口，根据ports_num决定总端口个数，从起始端口监听多个端口用于通信
- ports_num：**必选，默认1**，监听的端口个数
- ports_num_for_sparse：**必选，默认1**，用于稀疏类型参数通信的端口个数
- num_gradient_servers：**必选，默认1**，当前训练任务pserver总数
### 启动计算节点
执行以下命令启动使用python编写的trainer程序（文件名为任意文件名，如train.py）
......@@ -105,16 +89,16 @@ paddle.init(
pservers="127.0.0.1")
```
| 参数 | 是否必选 | 默认 | 说明 |
| ------------- | ------------- | ------------- | ------------- |
| use_gpu | 可选 | False | 是否启用GPU训练 |
| trainer_count | 必选 | 1 | 当前训练任务trainer总个数 |
| port | 必选 | 7164 | 连接到pserver的端口 |
| ports_num | 必选 | 1 | 连接到pserver的端口个数 |
| ports_num_for_sparse | 必选 | 1 | 和pserver之间用于稀疏类型参数通信的端口个数 |
| num_gradient_servers | 必选 | 1 | 当前训练任务pserver总数 |
| trainer_id | 必选 | 0 | 每个trainer的唯一ID，从0开始的整数 |
| pservers | 必选 | 127.0.0.1 | 当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开 |
参数说明
- use_gpu： **可选，默认False**，是否启用GPU训练
- trainer_count：**必选，默认1**，当前训练任务trainer总个数
- port：**必选，默认7164**，连接到pserver的端口
- ports_num：**必选，默认1**，连接到pserver的端口个数
- ports_num_for_sparse：**必选，默认1**，和pserver之间用于稀疏类型参数通信的端口个数
- num_gradient_servers：**必选，默认1**，当前训练任务pserver总数
- trainer_id：**必选，默认0**，每个trainer的唯一ID，从0开始的整数
- pservers：**必选，默认127.0.0.1**，当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开
### 准备数据集
......@@ -171,7 +155,7 @@ test.txt-00002
- `my_lib.py`：会被`train.py`调用的一些用户定义的库函数，比如PIL库等。
- `word_dict.pickle`：在`train.py`中会使用到的字典数据文件。
- `train.py`：训练程序，代码参考[api_train_v2_cluster.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py)。***注意：*** 对于本样例代码，在使用不同的分布式计算平台时，您可能需要修改`train.py`开头的部分（如下），以便获得训练数据的位置和获取环境变量配置：
- `train.py`：训练程序，代码参考[api_train_v2_cluster.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py)。***注意：*** 对于本样例代码，在使用不同的分布式计算平台时，您可能需要修改`train.py`开头的部分（如下），以便获得训练数据的位置和获取环境变量配置：
```python
cluster_train_file = "./train_data_dir/train/train.txt"
......@@ -195,91 +179,10 @@ PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务
在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
### 使用Fabric启动集群作业
#### 准备一个Linux集群
可以在`paddle/scripts/cluster_train_v2/fabric/docker_cluster`目录下，执行`kubectl -f ssh_servers.yaml`启动一个测试集群，并使用`kubectl get po -o wide`获得这些节点的IP地址。
#### 启动集群作业
`paddle.py` 提供了自动化脚本来启动不同节点中的所有 PaddlePaddle 集群进程。默认情况下，所有命令行选项可以设置为 `paddle.py` 命令选项并且 `paddle.py` 将透明、自动地将这些选项应用到 PaddlePaddle 底层进程。
`paddle.py` 为方便作业启动提供了两个独特的命令选项。
- `job_dispatch_package` 设为本地 `workspace` 目录，它将被分发到 `conf.py` 中设置的所有节点。它有助于帮助频繁修改和访问工作区文件的用户减少负担，否则频繁的多节点工作空间部署可能会很麻烦。
- `job_workspace` 设为已部署的工作空间目录，`paddle.py` 将跳过分发阶段直接启动所有节点的集群作业。它可以帮助减少分发延迟。
`cluster_train/run.sh` 提供了命令样例来运行 `doc/howto/usage/cluster/src/word2vec` 集群任务，只需用您定义的目录修改 `job_dispatch_package` 和 `job_workspace`，然后：
```
sh run.sh
```
集群作业将会在几秒后启动。
#### 终止集群作业
`paddle.py`能获取`Ctrl + C` SIGINT 信号来自动终止它启动的所有进程。只需中断 `paddle.py` 任务来终止集群作业。如果程序崩溃你也可以手动终止。
#### 检查集群训练结果
详细信息请检查 $workspace/log 里的日志，每一个节点都有相同的日志结构。
`paddle_trainer.INFO`
提供几乎所有训练的内部输出日志，与本地训练相同。这里检验运行时间模型的收敛。
`paddle_pserver2.INFO`
提供 pserver 运行日志，有助于诊断分布式错误。
`server.log`
提供 parameter server 进程的 stderr 和 stdout。训练失败时可以检查错误日志。
`train.log`
提供训练过程的 stderr 和 stdout。训练失败时可以检查错误日志。
#### 检查模型输出
运行完成后，模型文件将被写入节点 0 的 `output` 目录中。
工作空间中的 `nodefile` 表示当前集群作业的节点 ID。
### 在OpenMPI集群中提交训练作业
#### 准备OpenMPI集群
执行下面的命令以启动3个节点的OpenMPI集群和一个"head"节点：
```bash
paddle/scripts/cluster_train_v2/openmpi/docker_cluster
kubectl create -f head.yaml
kubectl create -f mpi-nodes.yaml
```
然后可以从head节点ssh无密码登录到OpenMPI的每个节点上。
#### 启动集群作业
您可以按照下面的步骤在OpenMPI集群中提交paddle训练任务：
```bash
# 获得head和node节点的IP地址
kubectl get po -o wide
# 将node节点的IP地址保存到machines文件中
kubectl get po -o wide | grep nodes | awk '{print $6}' > machines
# 拷贝必要的文件到head节点
scp -i ssh/id_rsa.mpi.pub machines prepare.py train.py start_mpi_train.sh tutorial@[headIP]:~
# ssh 登录到head节点
ssh -i ssh/id_rsa.mpi.pub tutorial@[headIP]
# --------------- 以下操作均在head节点中执行 ---------------
# 准备训练数据
python prepare.py
# 拷贝训练程序和字典文件到每台MPI节点
cat machines | xargs -i scp word_dict.pickle train.py start_mpi_train.sh machines {}:/home/tutorial
# 创建日志目录
mpirun -hostfile machines -n 3 mkdir /home/tutorial/logs
# 拷贝训练数据到各自的节点
scp train.txt-00000 test.txt-00000 [node1IP]:/home/tutorial
scp train.txt-00001 test.txt-00001 [node2IP]:/home/tutorial
scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
# 启动训练任务
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
### 在Kubernetes集群中提交训练作业
## 在不同集群中运行
此部分的使用方法可以参考[here](../k8s/k8s_distributed_cn.md)。
- [fabric集群](fabric_cn.md)
- [openmpi集群](openmpi_cn.md)
- [kubernetes单机](k8s_cn.md)
- [kubernetes distributed分布式](k8s_distributed_cn.md)
- [AWS上运行kubernetes集群训练](k8s_aws_cn.md)
doc/howto/usage/cluster/cluster_train_en.md
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# PaddlePaddle Distributed Training
* [Introduction](#introduction)
* [Preparations](#preparations)
* [Command-line arguments](#command-line-arguments)
* [Starting parameter server](#starting-parameter-server)
* [Starting trainer](#starting-trainer)
* [Prepare Training Dataset](#prepare-training-dataset)
* [Prepare Training program](#prepare-training-program)
* [Use cluster platforms or cluster management tools](#use-cluster-platforms-or-cluster-management-tools)
* [Cluster Training Using Fabric](#cluster-training-using-fabric)
* [Prepare a Linux cluster](#prepare-a-linux-cluster)
* [Launching Cluster Job](#launching-cluster-job)
* [Kill Cluster Job](#kill-cluster-job)
* [Check Cluster Training Result](#check-cluster-training-result)
* [Check Model Output](#check-model-output)
* [Cluster Training Using OpenMPI](#cluster-training-using-openmpi)
* [Prepare an OpenMPI cluster](#prepare-an-openmpi-cluster)
* [Launching Cluster Job](#launching-cluster-job-1)
* [Cluster Training Using Kubernetes](#cluster-training-using-kubernetes)
# Distributed Training
## Introduction
......@@ -35,7 +16,7 @@ When training with synchronize SGD, PaddlePaddle uses an internal "synchronize b
## Preparations
1. Prepare your computer cluster. It's normally a bunch of Linux servers connected by LAN. Each server will be assigned a unique IP address. The computers in the cluster can be called "nodes".
2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/getstarted/build_and_install) document. We strongly recommend using [Docker installation](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html) document. We strongly recommend using [Docker installation](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html).
After installation, you can check the version by typing the below command (run a docker container if using docker: `docker run -it paddlepaddle/paddle:[tag] /bin/bash`):
......@@ -67,12 +48,12 @@ If you wish to run parameter servers in background, and save a log file, you can
$ stdbuf -oL /usr/bin/nohup paddle pserver --port=7164 --ports_num=1 --ports_num_for_sparse=1 --num_gradient_servers=1 &> pserver.log
```
| param | required | default | description |
| ------------- | ------------- | ------------- | ------------- |
| port | required | 7164 | port which parameter server will listen on. If ports_num greater than 1, parameter server will listen on multiple ports for more network throughput |
| ports_num | required | 1 | total number of ports will listen on |
| ports_num_for_sparse | required | 1 | number of ports which serves sparse parameter update |
| num_gradient_servers | required | 1 | total number of gradient servers |
Parameter Description
- port: **required, default 7164**, port which parameter server will listen on. If ports_num greater than 1, parameter server will listen on multiple ports for more network throughput.
- ports_num: **required, default 1**, total number of ports will listen on.
- ports_num_for_sparse: **required, default 1**, number of ports which serves sparse parameter update.
- num_gradient_servers: **required, default 1**, total number of gradient servers.
### Starting trainer
Type the command below to start the trainer(name the file whatever you want, like "train.py")
......@@ -111,16 +92,16 @@ paddle.init(
pservers="127.0.0.1")
```
| param | required | default | description |
| ------------- | ------------- | ------------- | ------------- |
| use_gpu | optional | False | set to "True" to enable GPU training |
| trainer_count | required | 1 | total count of trainers in the training job |
| port | required | 7164 | port to connect to parameter server |
| ports_num | required | 1 | number of ports for communication |
| ports_num_for_sparse | required | 1 | number of ports for sparse type caculation |
| num_gradient_servers | required | 1 | total number of gradient server |
| trainer_id | required | 0 | ID for every trainer, start from 0 |
| pservers | required | 127.0.0.1 | list of IPs of parameter servers, separated by "," |
Parameter Description
- use_gpu: **optional, default False**, set to "True" to enable GPU training.
- trainer_count: **required, default 1**, total count of trainers in the training job.
- port: **required, default 7164**, port to connect to parameter server.
- ports_num: **required, default 1**, number of ports for communication.
- ports_num_for_sparse: **required, default 1**, number of ports for sparse type caculation.
- num_gradient_servers: **required, default 1**, total number of gradient server.
- trainer_id: **required, default 0**, ID for every trainer, start from 0.
- pservers: **required, default 127.0.0.1**, list of IPs of parameter servers, separated by ",".
### Prepare Training Dataset
......@@ -178,7 +159,7 @@ Your workspace may looks like:
- `my_lib.py`: user defined libraries, like PIL libs. This is optional.
- `word_dict.pickle`: dict file for training word embeding.
- `train.py`: training program. Sample code: [api_train_v2_cluster.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py). ***NOTE:*** You may need to modify the head part of `train.py` when using different cluster platform to retrive configuration environment variables:
- `train.py`: training program. Sample code: [api_train_v2_cluster.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py). ***NOTE:*** You may need to modify the head part of `train.py` when using different cluster platform to retrive configuration environment variables:
```python
cluster_train_file = "./train_data_dir/train/train.txt"
......@@ -202,92 +183,9 @@ We'll introduce cluster job management on these platforms. The examples can be f
These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
### Cluster Training Using Fabric
#### Prepare a Linux cluster
Run `kubectl -f ssh_servers.yaml` under the directory: `paddle/scripts/cluster_train_v2/fabric/docker_cluster` will launch a demo cluster. Run `kubectl get po -o wide` to get IP addresses of these nodes.
#### Launching Cluster Job
`paddle.py` provides automatical scripts to start all PaddlePaddle cluster processes in different nodes. By default, all command line options can be set as `paddle.py` command options and `paddle.py` will transparently and automatically set these options to PaddlePaddle lower level processes.
`paddle.py`provides two distinguished command option for easy job launching.
- `job_dispatch_package` set it with local `workspace` directory, it will be dispatched to all nodes which is set in `conf.py`. It could be helpful for frequently manipulating workspace files. otherwise, frequent multi-nodes workspace deployment is very annoying.
- `job_workspace` set it with already deployed workspace directory, `paddle.py` will skip dispatch stage to directly launch cluster job with all nodes. It could help to reduce heavy
dispatch latency.
`cluster_train/run.sh` provides command line sample to run `demo/recommendation` cluster job, just modify `job_dispatch_package` and `job_workspace` with your defined directory, then:
```
sh run.sh
```
The cluster Job will start in several seconds.
#### Kill Cluster Job
`paddle.py` can capture `Ctrl + C` SIGINT signal to automatically kill all processes launched by it. So just stop `paddle.py` to kill cluster job. You should manually kill the job if the program crashed.
#### Check Cluster Training Result
Check log in $workspace/log for details, each node owns same log structure.
`paddle_trainer.INFO`
It provides almost all internal output log for training, same as local training. Check runtime model convergence here.
`paddle_pserver2.INFO`
It provides parameter server running log, which could help to diagnose distributed error.
`server.log`
It provides stderr and stdout of parameter server process. Check error log if training crashes.
`train.log`
It provides stderr and stdout of trainer process. Check error log if training crashes.
#### Check Model Output
After one pass finished, model files will be written in `output` directory in node 0.
`nodefile` in workspace indicates the node id of current cluster job.
### Cluster Training Using OpenMPI
#### Prepare an OpenMPI cluster
Run the following command to start a 3-node MPI cluster and one "head" node.
```bash
cd paddle/scripts/cluster_train_v2/openmpi/docker_cluster
kubectl create -f head.yaml
kubectl create -f mpi-nodes.yaml
```
Then you can log in to every OpenMPI node using ssh without input any passwords.
#### Launching Cluster Job
Follow the steps to launch a PaddlePaddle training job in OpenMPI cluster:\
```bash
# find out node IP addresses
kubectl get po -o wide
# generate a "machines" file containing node IP addresses
kubectl get po -o wide | grep nodes | awk '{print $6}' > machines
# copy necessary files onto "head" node
scp -i ssh/id_rsa.mpi.pub machines prepare.py train.py start_mpi_train.sh tutorial@[headIP]:~
# login to head node using ssh
ssh -i ssh/id_rsa.mpi.pub tutorial@[headIP]
# --------------- in head node ---------------
# prepare training data
python prepare.py
# copy training data and dict file to MPI nodes
cat machines | xargs -i scp word_dict.pickle train.py start_mpi_train.sh machines {}:/home/tutorial
# creat a directory for storing log files
mpirun -hostfile machines -n 3 mkdir /home/tutorial/logs
# copy training data to every node
scp train.txt-00000 test.txt-00000 [node1IP]:/home/tutorial
scp train.txt-00001 test.txt-00001 [node2IP]:/home/tutorial
scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
# start the job
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
### Cluster Training Using Kubernetes
## Use different clusters
The details can be found [here](../k8s/k8s_cn.md)
- [fabric](fabric_en.md)
- [openmpi](openmpi_en.md)
- [kubernetes](k8s_en.md)
- [kubernetes on AWS](k8s_aws_en.md)
doc/howto/usage/cluster/fabric_cn.md 0 → 100644
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# 使用fabric启动集群训练
## 准备一个Linux集群
可以在`paddle/scripts/cluster_train_v2/fabric/docker_cluster`目录下，执行`kubectl -f ssh_servers.yaml`启动一个测试集群，并使用`kubectl get po -o wide`获得这些节点的IP地址。
## 启动集群作业
`paddle.py` 提供了自动化脚本来启动不同节点中的所有 PaddlePaddle 集群进程。默认情况下，所有命令行选项可以设置为 `paddle.py` 命令选项并且 `paddle.py` 将透明、自动地将这些选项应用到 PaddlePaddle 底层进程。
`paddle.py` 为方便作业启动提供了两个独特的命令选项。
- `job_dispatch_package` 设为本地 `workspace` 目录，它将被分发到 `conf.py` 中设置的所有节点。它有助于帮助频繁修改和访问工作区文件的用户减少负担，否则频繁的多节点工作空间部署可能会很麻烦。
- `job_workspace` 设为已部署的工作空间目录，`paddle.py` 将跳过分发阶段直接启动所有节点的集群作业。它可以帮助减少分发延迟。
`cluster_train/run.sh` 提供了命令样例来运行 `doc/howto/usage/cluster/src/word2vec` 集群任务，只需用您定义的目录修改 `job_dispatch_package` 和 `job_workspace`，然后：
```
sh run.sh
```
集群作业将会在几秒后启动。
## 终止集群作业
`paddle.py`能获取`Ctrl + C` SIGINT 信号来自动终止它启动的所有进程。只需中断 `paddle.py` 任务来终止集群作业。如果程序崩溃你也可以手动终止。
## 检查集群训练结果
详细信息请检查 $workspace/log 里的日志，每一个节点都有相同的日志结构。
`paddle_trainer.INFO`
提供几乎所有训练的内部输出日志，与本地训练相同。这里检验运行时间模型的收敛。
`paddle_pserver2.INFO`
提供 pserver 运行日志，有助于诊断分布式错误。
`server.log`
提供 parameter server 进程的 stderr 和 stdout。训练失败时可以检查错误日志。
`train.log`
提供训练过程的 stderr 和 stdout。训练失败时可以检查错误日志。
## 检查模型输出
运行完成后，模型文件将被写入节点 0 的 `output` 目录中。
工作空间中的 `nodefile` 表示当前集群作业的节点 ID。
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# Cluster Training Using Fabric
## Prepare a Linux cluster
Run `kubectl -f ssh_servers.yaml` under the directory: `paddle/scripts/cluster_train_v2/fabric/docker_cluster` will launch a demo cluster. Run `kubectl get po -o wide` to get IP addresses of these nodes.
## Launching Cluster Job
`paddle.py` provides automatical scripts to start all PaddlePaddle cluster processes in different nodes. By default, all command line options can be set as `paddle.py` command options and `paddle.py` will transparently and automatically set these options to PaddlePaddle lower level processes.
`paddle.py`provides two distinguished command option for easy job launching.
- `job_dispatch_package` set it with local `workspace` directory, it will be dispatched to all nodes which is set in `conf.py`. It could be helpful for frequently manipulating workspace files. otherwise, frequent multi-nodes workspace deployment is very annoying.
- `job_workspace` set it with already deployed workspace directory, `paddle.py` will skip dispatch stage to directly launch cluster job with all nodes. It could help to reduce heavy
dispatch latency.
`cluster_train/run.sh` provides command line sample to run `demo/recommendation` cluster job, just modify `job_dispatch_package` and `job_workspace` with your defined directory, then:
```
sh run.sh
```
The cluster Job will start in several seconds.
## Kill Cluster Job
`paddle.py` can capture `Ctrl + C` SIGINT signal to automatically kill all processes launched by it. So just stop `paddle.py` to kill cluster job. You should manually kill the job if the program crashed.
## Check Cluster Training Result
Check log in $workspace/log for details, each node owns same log structure.
`paddle_trainer.INFO`
It provides almost all internal output log for training, same as local training. Check runtime model convergence here.
`paddle_pserver2.INFO`
It provides parameter server running log, which could help to diagnose distributed error.
`server.log`
It provides stderr and stdout of parameter server process. Check error log if training crashes.
`train.log`
It provides stderr and stdout of trainer process. Check error log if training crashes.
## Check Model Output
After one pass finished, model files will be written in `output` directory in node 0.
`nodefile` in workspace indicates the node id of current cluster job.
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k8s_aws_en.md
\ No newline at end of file
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......@@ -493,7 +493,7 @@ spec:
spec:
containers:
- name: paddle-data
image: paddledev/paddle-tutorial:k8s_data
image: paddlepaddle/paddle-tutorial:k8s_data
imagePullPolicy: Always
volumeMounts:
- mountPath: "/efs"
......@@ -522,7 +522,7 @@ NAME DESIRED SUCCESSFUL AGE
paddle-data 1 1 6m
```
Data preparation is done by docker image `paddledev/paddle-tutorial:k8s_data`, see [here](src/k8s_data/README.md) for how to build this docker image and source code.
Data preparation is done by docker image `paddlepaddle/paddle-tutorial:k8s_data`, see [here](src/k8s_data/README.md) for how to build this docker image and source code.
#### Start Training
......@@ -545,7 +545,7 @@ spec:
claimName: efsvol
containers:
- name: trainer
image: paddledev/paddle-tutorial:k8s_train
image: paddlepaddle/paddle-tutorial:k8s_train
command: ["bin/bash", "-c", "/root/start.sh"]
env:
- name: JOB_NAME
......@@ -617,7 +617,7 @@ kubectl --kubeconfig=kubeconfig log -f POD_NAME
Run `kubectl --kubeconfig=kubeconfig describe job paddle-cluster-job` to check training job status. It will complete in around 20 minutes.
The details for start `pserver` and `trainer` are hidden inside docker image `paddledev/paddle-tutorial:k8s_train`, see [here](src/k8s_train/README.md) for how to build the docker image and source code.
The details for start `pserver` and `trainer` are hidden inside docker image `paddlepaddle/paddle-tutorial:k8s_train`, see [here](src/k8s_train/README.md) for how to build the docker image and source code.
#### Inspect Training Output
......
doc/howto/usage/k8s/k8s_cn.md → doc/howto/usage/cluster/k8s_cn.md
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# Kubernetes单机训练
在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。
在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的PaddlePaddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。
## 制作Docker镜像
在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在
Paddle的Docker image里。为此，我们需要制作一个包含训练数据的Paddle镜像。
在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式PaddlePaddle训练任务中
的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在
PaddlePaddle的Docker Image里。为此，我们需要制作一个包含训练数据的PaddlePaddle镜像。
PaddlePaddle的 `paddlepaddle/paddle:cpu-demo-latest` 镜像里有PaddlePaddle的源码与demo，
（请注意，默认的PaddlePaddle生产环境镜像 `paddlepaddle/paddle:latest` 是不包括源码的，PaddlePaddle的各版本镜像可以参考
[Docker Installation Guide](http://paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html)），
下面我们使用这个镜像来下载数据到Docker Container中，并把这个包含了训练数据的Container保存为一个新的镜像。
Paddle 的 [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html)
里介绍了用Paddle源码中的脚本下载训练数据的过程。
而 `paddledev/paddle:cpu-demo-latest` 镜像里有 Paddle 源码与demo，（ 请注意，默认的
Paddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, Paddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。
### 运行容器
```
$ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest
$ docker run --name quick_start_data -it paddlepaddle/paddle:cpu-demo-latest
```
### 下载数据
......@@ -103,7 +104,7 @@ spec:
restartPolicy: Never
```
### 创建Paddle Job
### 创建PaddlePaddle Job
使用上文创建的yaml文件创建Kubernetes Job，命令为：
......
doc/howto/usage/k8s/k8s_distributed_cn.md → doc/howto/usage/cluster/k8s_distributed_cn.md
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# Kubernetes分布式训练
前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。
有关Kubernetes相关概念以及如何搭建和配置Kubernetes集群，可以参考[k8s_basis](./k8s_basis_cn.md)。
前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](http://www.paddlepaddle.org/docs/develop/documentation/zh/howto/usage/cluster/cluster_train_cn.html)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。
## 整体方案
......@@ -28,7 +26,7 @@ PaddlePaddle镜像需要提供`paddle pserver`与`paddle train`进程的运行
- 拷贝训练文件到容器内
- 生成`paddle pserver`与`paddle train`进程的启动参数，并且启动训练
因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/src/k8s_train/Dockerfile)。
因为官方镜像 `paddlepaddle/paddle:latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/src/k8s_train/Dockerfile)。
```bash
$ cd doc/howto/usage/k8s/src/k8s_train
......@@ -62,7 +60,7 @@ spec:
hostNetwork: true
containers:
- name: paddle-data
image: paddledev/paddle-tutorial:k8s_data
image: paddlepaddle/paddle-tutorial:k8s_data
imagePullPolicy: Always
volumeMounts:
- mountPath: "/mnt"
......@@ -149,20 +147,19 @@ spec:
文件中，`metadata`下的`name`表示这个job的名字。`parallelism，completions`字段表示这个job会同时开启3个PaddlePaddle节点，成功训练且退出的pod数目为3时，这个job才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了MFS上。
`env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。
`env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内：
环境变量 | 说明
--- | ---
JOB_PATH | 共享存储挂在的路径
JOB_NAME | Job的名字
TRAIN_CONFIG_DIR | 本次训练文件所在目录，与JOB_PATH,JOB_NAME组合可以找到本次训练需要的文件路径
CONF_PADDLE_NIC | `paddle pserver`进程需要的`--nics`参数，即网卡名
CONF_PADDLE_PORT | `paddle paserver`的`--port`参数
CONF_PADDLE_PORTS_NUM | 稠密更新的端口数量，即`--ports_num`参数
CONF_PADDLE_PORTS_NUM_SPARSE | 稀疏更新的端口数量，即`--ports_num_for_sparse`参数
CONF_PADDLE_GRADIENT_NUM | 训练节点数量，即`--num_gradient_servers参数`
- JOB_PATH：共享存储挂在的路径
- JOB_NAME：Job的名字
- TRAIN_CONFIG_DIR：本次训练文件所在目录，与JOB_PATH,JOB_NAME组合可以找到本次训练需要的文件路径
- CONF_PADDLE_NIC：`paddle pserver`进程需要的`--nics`参数，即网卡名
- CONF_PADDLE_PORT：`paddle paserver`的`--port`参数
- CONF_PADDLE_PORTS_NUM：稠密更新的端口数量，即`--ports_num`参数
- CONF_PADDLE_PORTS_NUM_SPARSE：稀疏更新的端口数量，即`--ports_num_for_sparse`参数
- CONF_PADDLE_GRADIENT_NUM：训练节点数量，即`--num_gradient_servers参数`
这些参数的具体描述，读者可以查看[这里](http://www.paddlepaddle.org/doc/ui/cmd_argument/detail_introduction.html#parameter-server-and-distributed-communication)。
这些参数的具体描述，读者可以查看[这里](http://www.paddlepaddle.org/docs/develop/documentation/zh/howto/usage/cmd_parameter/detail_introduction_cn.html)。
编写完YAML文件后，可以使用Kubernetes的命令行工具创建job。
......
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# Paddle On Kubernetes
# PaddlePaddle On Kubernetes
>In this article, we will introduce how to run Paddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run Paddle training job on distributed cluster.
In this article, we will introduce how to run PaddlePaddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run PaddlePaddle training job on distributed cluster.
## Build Docker Image
In distributed Kubernetes cluster, we will use Ceph or other shared storage system for storing training related data so that all processes in Paddle training can retrieve data from Ceph. In this example, we will only demo training job on single machine. In order to simplify the requirement of the environment, we will directly put training data into Paddle's Docker Image, so we need to create a Paddle Docker image that already includes the training data.
In distributed Kubernetes cluster, we will use Ceph or other distributed
storage system for storing training related data so that all processes in
PaddlePaddle training can retrieve data from Ceph. In this example, we will
only demo training job on single machine. In order to simplify the requirement
of the environment, we will directly put training data into the PaddlePaddle Docker Image,
so we need to create a PaddlePaddle Docker image that includes the training data.
The production Docker Image `paddlepaddle/paddle:cpu-demo-latest` has the PaddlePaddle
source code and demo. (Caution: Default PaddlePaddle Docker Image `paddlepaddle/paddle:latest` doesn't include
the source code, PaddlePaddle's different versions of Docker Image can be referred here:
[Docker Installation Guide](http://paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_en.html)),
so we run this Docker Image and download the training data, and then commit the whole
Container to be a new Docker Image.
Paddle's [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) introduces how to download and train data by using script from Paddle's source code.
And `paddledev/paddle:cpu-demo-latest` image has the Paddle source code and demo. (Caution: Default Paddle image `paddledev/paddle:cpu-latest` doesn't include the source code, Paddle's different versions of image can be referred here: [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html)), so we run this container and download the training data, and then commit the whole container to be a new Docker image.
### Run Docker Container
```
$ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest
$ docker run --name quick_start_data -it paddlepaddle/paddle:cpu-demo-latest
```
### Download Training Data
......@@ -67,7 +76,7 @@ $ docker commit quick_start_data mypaddle/paddle:quickstart
## Use Kubernetes For Training
>We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
### Create Yaml Files
......@@ -99,7 +108,7 @@ spec:
restartPolicy: Never
```
### Start Paddle Job
### Start PaddlePaddle Job
Using the above yaml file to start the Kubernetes job.
......
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# 在OpenMPI集群中提交训练作业
## 准备OpenMPI集群
执行下面的命令以启动3个节点的OpenMPI集群和一个"head"节点：
```bash
paddle/scripts/cluster_train_v2/openmpi/docker_cluster
kubectl create -f head.yaml
kubectl create -f mpi-nodes.yaml
```
然后可以从head节点ssh无密码登录到OpenMPI的每个节点上。
## 启动集群作业
您可以按照下面的步骤在OpenMPI集群中提交paddle训练任务：
```bash
# 获得head和node节点的IP地址
kubectl get po -o wide
# 将node节点的IP地址保存到machines文件中
kubectl get po -o wide | grep nodes | awk '{print $6}' > machines
# 拷贝必要的文件到head节点
scp -i ssh/id_rsa.mpi.pub machines prepare.py train.py start_mpi_train.sh tutorial@[headIP]:~
# ssh 登录到head节点
ssh -i ssh/id_rsa.mpi.pub tutorial@[headIP]
# --------------- 以下操作均在head节点中执行 ---------------
# 准备训练数据
python prepare.py
# 拷贝训练程序和字典文件到每台MPI节点
cat machines | xargs -i scp word_dict.pickle train.py start_mpi_train.sh machines {}:/home/tutorial
# 创建日志目录
mpirun -hostfile machines -n 3 mkdir /home/tutorial/logs
# 拷贝训练数据到各自的节点
scp train.txt-00000 test.txt-00000 [node1IP]:/home/tutorial
scp train.txt-00001 test.txt-00001 [node2IP]:/home/tutorial
scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
# 启动训练任务
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
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# Cluster Training Using OpenMPI
## Prepare an OpenMPI cluster
Run the following command to start a 3-node MPI cluster and one "head" node.
```bash
cd paddle/scripts/cluster_train_v2/openmpi/docker_cluster
kubectl create -f head.yaml
kubectl create -f mpi-nodes.yaml
```
Then you can log in to every OpenMPI node using ssh without input any passwords.
## Launching Cluster Job
Follow the steps to launch a PaddlePaddle training job in OpenMPI cluster:\
```bash
# find out node IP addresses
kubectl get po -o wide
# generate a "machines" file containing node IP addresses
kubectl get po -o wide | grep nodes | awk '{print $6}' > machines
# copy necessary files onto "head" node
scp -i ssh/id_rsa.mpi.pub machines prepare.py train.py start_mpi_train.sh tutorial@[headIP]:~
# login to head node using ssh
ssh -i ssh/id_rsa.mpi.pub tutorial@[headIP]
# --------------- in head node ---------------
# prepare training data
python prepare.py
# copy training data and dict file to MPI nodes
cat machines | xargs -i scp word_dict.pickle train.py start_mpi_train.sh machines {}:/home/tutorial
# creat a directory for storing log files
mpirun -hostfile machines -n 3 mkdir /home/tutorial/logs
# copy training data to every node
scp train.txt-00000 test.txt-00000 [node1IP]:/home/tutorial
scp train.txt-00001 test.txt-00001 [node2IP]:/home/tutorial
scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
# start the job
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
doc/howto/usage/k8s/src/Dockerfile → doc/howto/usage/cluster/src/Dockerfile
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FROM paddledev/paddle:cpu-latest
FROM paddlepaddle/paddle:latest
MAINTAINER zjsxzong89@gmail.com
......
doc/howto/usage/k8s/src/k8s_train/Dockerfile → doc/howto/usage/cluster/src/k8s_train/Dockerfile
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FROM paddledev/paddle:cpu-latest
FROM paddlepaddle/paddle:latest
COPY start.sh /root/
COPY start_paddle.py /root/
......
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浏览文件 @ f56f1492
# Kubernetes 简介
[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。
- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。
- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是Kubernetes的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET，PID，IPC，UTS等Linux namespace。由于容器之间共享NET namespace，所以它们使用同一个IP地址，可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。
- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 描述Kubernetes上运行的作业，一次作业称为一个job，通常每个job包括一个或者多个pods，job启动后会创建这些pod并开始执行一个程序，等待这个程序执行成功并返回0则成功退出，如果执行失败，也可以配置不同的重试机制。
- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume，就可以将这些文件持久化存储。Kubernetes支持多种volume，例如hostPath(宿主机目录)，gcePersistentDisk，awsElasticBlockStore等。
- [*Namespaces*](https://kubernetes.io/docs/user-guide/namespaces/) 命名空间，在kubernetes中创建的所有资源对象(例如上文的pod，job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。
- [*PersistentVolume*](https://kubernetes.io/docs/user-guide/persistent-volumes/): 和[*PersistentVolumeClaim*](https://kubernetes.io/docs/user-guide/persistent-volumes/#persistentvolumeclaims)结合，将外部的存储服务在Kubernetes中描述成为统一的资源形式，便于存储资源管理和Pod引用。
## 部署Kubernetes集群
Kubernetes提供了多种集群部署的方案，本文档内不重复介绍。这里给出集中常见的部署方法：
- [*minikube*](https://kubernetes.io/docs/getting-started-guides/minikube/): 快速在本地启动一个单机的kubernetes服务器，便于本地验证和测试。
- [*kubeadm*](http://kubernetes.io/docs/getting-started-guides/kubeadm/): 在不同操作系统，不同主机(Bare-Metal, AWS, GCE)条件下，快速部署集群。
- [*AWS EC2*](https://kubernetes.io/docs/getting-started-guides/aws/): 在aws上快速部署集群。
- [*Bare-Metal*](https://kubernetes.io/docs/getting-started-guides/centos/centos_manual_config/): 在物理机上手动部署。
可以参考[这个表格](https://kubernetes.io/docs/getting-started-guides/#table-of-solutions)选择适合您的场景的合适方案。
## 选择存储方案
容器不会保留在运行时生成的数据，job或者应用程序在容器中运行时生成的数据会在容器销毁时消失。为了完成分布式机器学习训练任务，需要有一个外部的存储服务来保存训练所需数据和训练输出。
常见的可选存储服务包括：
- [*NFS*](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/nfs): 可以将磁盘上某个目录共享给网络中其他机器访问。部署和配置比较简单，可以用于小量数据的验证。不提供分布式存储，高可用，冗余等功能。NFS的部署方法可以参考[这里](http://www.tecmint.com/how-to-setup-nfs-server-in-linux/)。
- [*GlusterFS*](http://gluster.readthedocs.io/en/latest/Quick-Start-Guide/Quickstart/): 网络分布式文件系统，可以在Kubernetes中按照[这个](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/glusterfs)例子使用。
- [*Ceph*](http://docs.ceph.com/docs/master/): 分布式文件系统，支持rbd，POSIX API接口(ceph fs)和对象存储API，参考[这里](https://kubernetes.io/docs/user-guide/volumes/#rbd)。
- [*MooseFS*](https://moosefs.com/documentation.html): 一个分布式的存储系统。需要先挂载到服务器Node上再通过kubernetes hostPath Volume挂载到容器中。
## 配置kubectl
### 安装kubectl
```
# OS X
curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/darwin/amd64/kubectl
# Linux
curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl
# Windows
curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/windows/amd64/kubectl.exe
```
### 配置kubectl访问你的kubernetes集群
编辑`~/.kube/config`这个配置文件，修改`Master-IP`的地址。如果使用SSL认证，则需要配置`certificate-authority`和`users`中的用户证书。如果是使用非SSL方式访问（比如通过8080端口），也可以去掉这些证书的配置。
```
apiVersion: v1
clusters:
- cluster:
certificate-authority: /path/to/ca.crt
server: https://[Master-IP]:443
name: minikube
contexts:
- context:
cluster: minikube
user: minikube
name: minikube
current-context: minikube
kind: Config
preferences: {}
users:
- name: minikube
user:
client-certificate: /path/to/apiserver.crt
client-key: /Users/wuyi/.minikube/apiserver.key
```
paddle/framework/CMakeLists.txt
浏览文件 @ feb05c3a
......@@ -58,3 +58,6 @@ cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
proto_desc)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
cc_library(init SRCS init.cc DEPS gflags executor place stringpiece)
cc_test(init_test SRCS init_test.cc DEPS init)
paddle/framework/attribute.cc
浏览文件 @ feb05c3a
......@@ -19,42 +19,42 @@ limitations under the License. */
namespace paddle {
namespace framework {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
Attribute GetAttrValue(const proto::OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
case framework::AttrType::BOOLEAN: {
case proto::AttrType::BOOLEAN: {
return attr_desc.b();
}
case framework::AttrType::INT: {
case proto::AttrType::INT: {
return attr_desc.i();
}
case framework::AttrType::FLOAT: {
case proto::AttrType::FLOAT: {
return attr_desc.f();
}
case framework::AttrType::STRING: {
case proto::AttrType::STRING: {
return attr_desc.s();
}
case framework::AttrType::BOOLEANS: {
case proto::AttrType::BOOLEANS: {
std::vector<bool> val(attr_desc.bools_size());
for (int i = 0; i < attr_desc.bools_size(); ++i) {
val[i] = attr_desc.bools(i);
}
return val;
}
case framework::AttrType::INTS: {
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
return val;
}
case framework::AttrType::FLOATS: {
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
return val;
}
case framework::AttrType::STRINGS: {
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
......
paddle/framework/attribute.h
浏览文件 @ feb05c3a
......@@ -27,12 +27,12 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <typename T>
inline AttrType AttrTypeID() {
inline proto::AttrType AttrTypeID() {
Attribute tmp = T();
return static_cast<AttrType>(tmp.which() - 1);
return static_cast<proto::AttrType>(tmp.which() - 1);
}
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
Attribute GetAttrValue(const proto::OpDesc::Attr& attr_desc);
class AttrReader {
public:
......
paddle/framework/backward.cc
浏览文件 @ feb05c3a
......@@ -42,7 +42,7 @@ static std::unordered_set<std::string>& CtrlFlowOps() {
static inline std::unique_ptr<OperatorBase> CreateGradOp(
const OperatorBase& op, const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var) {
OpDescBind op_desc;
OpDesc op_desc;
op_desc.SetInputMap(op.Inputs());
op_desc.SetOutputMap(op.Outputs());
op_desc.SetType(op.Type());
......@@ -53,7 +53,7 @@ static inline std::unique_ptr<OperatorBase> CreateGradOp(
grad_ops.reserve(grad_descs.size());
std::transform(grad_descs.begin(), grad_descs.end(),
std::back_inserter(grad_ops),
[](const std::unique_ptr<OpDescBind>& grad_desc) {
[](const std::unique_ptr<OpDesc>& grad_desc) {
return OpRegistry::CreateOp(*grad_desc);
});
PADDLE_ENFORCE(!grad_ops.empty());
......@@ -296,7 +296,7 @@ static std::string FwdName(const std::string& grad_name) {
static void CreateGradVarInBlock(
size_t grad_op_start_index,
const std::unordered_map<std::string, std::string>& param_name_map,
BlockDescBind* block_desc,
BlockDesc* block_desc,
std::unordered_map<std::string, GradVarInfo>* grad_var_record) {
auto ops = block_desc->AllOps();
for (size_t op_index = grad_op_start_index; op_index < ops.size();
......@@ -341,7 +341,7 @@ static void CreateGradVarInBlock(
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
grad->SetDataType(DataType::FP32);
grad->SetDataType(proto::DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
......@@ -350,12 +350,11 @@ static void CreateGradVarInBlock(
}
}
std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
const OpDescBind* op_desc, std::unordered_set<std::string>* no_grad_vars,
std::vector<std::unique_ptr<OpDesc>> MakeOpGrad(
const OpDesc* op_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block =
std::vector<BlockDescBind*>()) {
std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
const std::vector<BlockDesc*>& grad_block = std::vector<BlockDesc*>()) {
std::vector<std::unique_ptr<OpDesc>> grad_op_descs;
// All input gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
if (AllGradInSet(inputs, *no_grad_vars)) {
......@@ -386,7 +385,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
.Get(op_desc->Type())
.GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var, grad_block);
std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
std::list<std::unique_ptr<OpDesc>> pending_fill_zeros_ops;
for (auto& desc : grad_op_descs) {
for (const std::string& in_name : desc->InputArgumentNames()) {
if (no_grad_vars->count(in_name)) {
......@@ -394,7 +393,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
std::string new_name = prefix + kZeroVarSuffix;
desc->Rename(in_name, new_name);
std::unique_ptr<OpDescBind> fill_zeros_op(
std::unique_ptr<OpDesc> fill_zeros_op(
new OpDescBind("fill_zeros_like", {{"X", {prefix}}},
{{"Out", {new_name}}}, AttributeMap{}));
pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
......@@ -408,34 +407,33 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
return grad_op_descs;
}
static BlockDescBind* CreateStepBlock(
ProgramDescBind& program_desc,
std::unordered_set<std::string>* no_grad_vars,
static BlockDesc* CreateStepBlock(
ProgramDesc& program_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx);
std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::vector<std::unique_ptr<OpDesc>> MakeBlockBackward(
ProgramDesc& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
VLOG(5) << "MakeBlockBackward";
BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDescBind*> op_descs = cur_block->AllOps();
BlockDesc* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDesc*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
std::vector<std::unique_ptr<OpDesc>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
VLOG(5) << "Making backward " << (*it)->Type() << " op";
std::vector<std::unique_ptr<OpDescBind>> op_grads;
std::vector<std::unique_ptr<OpDesc>> op_grads;
if ((*it)->Type() == "recurrent" || (*it)->Type() == "while") {
int step_block_idx = (*it)->GetBlockAttr("sub_block");
BlockDescBind* backward_block = CreateStepBlock(
program_desc, no_grad_vars, grad_to_var, step_block_idx);
BlockDesc* backward_block = CreateStepBlock(program_desc, no_grad_vars,
grad_to_var, step_block_idx);
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else if ((*it)->Type() == "conditional_block") {
BlockDescBind* backward_block =
BlockDesc* backward_block =
CreateStepBlock(program_desc, no_grad_vars, grad_to_var,
(*it)->GetBlockAttr("sub_block"));
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
......@@ -463,14 +461,14 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
}
++grad_desc_idx;
}
std::transform(
op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
[](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
std::transform(op_grads.begin(), op_grads.end(),
std::back_inserter(backward_descs),
[](std::unique_ptr<OpDesc>& ptr) { return std::move(ptr); });
}
VLOG(5) << "Appending Sums";
// Check whether some variables are written more than once
std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
std::list<std::pair<size_t, std::unique_ptr<OpDesc>>> pending_sum_ops;
for (const auto& dup : dup_out_ops) {
const std::string& out_name = dup.first;
const std::vector<size_t> dup_op = dup.second;
......@@ -486,18 +484,17 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
sum_op_inputs.emplace_back(new_name);
next_g_name = sum_op_inputs.back();
}
std::unique_ptr<OpDescBind> sum_op(
new OpDescBind("sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}},
AttributeMap{}));
std::unique_ptr<OpDesc> sum_op(new OpDesc("sum", {{"X", sum_op_inputs}},
{{"Out", {out_name}}},
AttributeMap{}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
pending_sum_ops.sort(
[](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
return a.first > b.first;
});
pending_sum_ops.sort([](const std::pair<size_t, std::unique_ptr<OpDesc>>& a,
const std::pair<size_t, std::unique_ptr<OpDesc>>& b) {
return a.first > b.first;
});
for (auto& p : pending_sum_ops) {
backward_descs.insert(backward_descs.begin() + p.first + 1,
std::move(p.second));
......@@ -508,14 +505,13 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
return backward_descs;
}
static BlockDescBind* CreateStepBlock(
ProgramDescBind& program_desc,
std::unordered_set<std::string>* no_grad_vars,
static BlockDesc* CreateStepBlock(
ProgramDesc& program_desc, std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx) {
auto backward_block_op_descs = MakeBlockBackward(program_desc, step_block_idx,
no_grad_vars, grad_to_var);
BlockDescBind* backward_block =
BlockDesc* backward_block =
program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
for (auto& ptr : backward_block_op_descs) {
backward_block->AppendAllocatedOp(move(ptr));
......@@ -524,7 +520,7 @@ static BlockDescBind* CreateStepBlock(
}
ParamGradInfoMap AppendBackward(
ProgramDescBind& program_desc, const VarDescBind& target,
ProgramDesc& program_desc, const VarDesc& target,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_var_names;
no_grad_var_names.reserve(no_grad_vars.size() + 1);
......@@ -541,11 +537,11 @@ ParamGradInfoMap AppendBackward(
PADDLE_ENFORCE(is_scalar, "target should be scalar");
VLOG(3) << "backward from loss=" << target.Name()
<< " data_type=" << target.GetDataType();
std::unique_ptr<OpDescBind> fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"dtype", target.GetDataType()}}));
std::unique_ptr<OpDesc> fill_one_op(
new OpDesc("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"dtype", target.GetDataType()}}));
// infer var type of fill_one_op
fill_one_op->InferVarType(root_block);
......
paddle/framework/backward.h
浏览文件 @ feb05c3a
......@@ -49,7 +49,7 @@ using ParamGradInfoMap = std::unordered_map<std::string /*fwd_var_name*/,
GradVarInfo /*grad_var_info*/>;
ParamGradInfoMap AppendBackward(
ProgramDescBind& program_desc, const VarDescBind& target,
ProgramDesc& program_desc, const VarDesc& target,
const std::unordered_set<std::string>& no_grad_vars);
} // namespace framework
......
paddle/framework/backward_test.cc
浏览文件 @ feb05c3a
......@@ -58,13 +58,13 @@ class RowWiseAddGradMaker : public SingleGradOpDescMaker {
using SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<OpDescBind> Apply() const override {
auto grad_op = new OpDescBind();
std::unique_ptr<OpDesc> Apply() const override {
auto grad_op = new OpDesc();
grad_op->SetInput(GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(GradVarName("X"), InputGrad("X"));
grad_op->SetOutput(GradVarName("b"), InputGrad("b"));
grad_op->SetType("rowwise_add_grad");
return std::unique_ptr<OpDescBind>(grad_op);
return std::unique_ptr<OpDesc>(grad_op);
}
};
......@@ -166,7 +166,7 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker {
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
SumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input tensors of sum operator.").AsDuplicable();
AddOutput("Out", "the output tensor of sum operator.");
......@@ -190,11 +190,11 @@ class MinusGradOpDescMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
std::vector<std::unique_ptr<OpDescBind>> retv;
std::vector<std::unique_ptr<OpDesc>> operator()() const override {
std::vector<std::unique_ptr<OpDesc>> retv;
auto x_g = InputGrad("X");
if (!x_g.empty()) {
auto *op_desc = new OpDescBind();
auto *op_desc = new OpDesc();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", x_g);
......@@ -204,7 +204,7 @@ class MinusGradOpDescMaker : public GradOpDescMakerBase {
auto y_g = InputGrad("Y");
if (!y_g.empty()) {
auto *op_desc = new OpDescBind();
auto *op_desc = new OpDesc();
op_desc->SetType("scale");
op_desc->SetInput("X", OutputGrad("Out"));
op_desc->SetOutput("Out", y_g);
......@@ -505,25 +505,25 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
}
TEST(Backward, simple_single_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
f::OpDesc *op = block->AppendOp();
op->SetType("rowwise_add");
op->SetInput("X", {"x"});
op->SetInput("b", {"b"});
op->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
auto target = f::VarDesc("out");
target.SetShape({1});
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 3UL);
f::OpDescBind *fill_op = block->AllOps()[1];
f::OpDesc *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
f::OpDesc *grad_op = block->AllOps()[2];
EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op->InputNames().size(), 1UL);
ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
......@@ -543,16 +543,16 @@ TEST(Backward, simple_single_op) {
}
TEST(Backward, default_attribute) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDesc *op = block->AppendOp();
op->SetType("mul");
op->SetInput("X", {"x"});
op->SetInput("Y", {"y"});
op->SetOutput("Out", {"out"});
op->CheckAttrs();
auto target = f::VarDescBind("out");
auto target = f::VarDesc("out");
target.SetShape({1});
AppendBackward(program, target, std::unordered_set<std::string>{});
......@@ -560,47 +560,47 @@ TEST(Backward, default_attribute) {
EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
f::OpDescBind *fill_op = block->AllOps()[1];
f::OpDesc *fill_op = block->AllOps()[1];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op = block->AllOps()[2];
f::OpDesc *grad_op = block->AllOps()[2];
ASSERT_EQ(grad_op->Type(), "mul_grad");
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
}
TEST(Backward, simple_mult_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDesc *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
f::OpDesc *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
f::OpDesc *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
auto target = f::VarDesc("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 6UL + 1);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
f::OpDesc *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
f::OpDesc *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -611,7 +611,7 @@ TEST(Backward, simple_mult_op) {
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op2 = block->AllOps()[5];
f::OpDesc *grad_op2 = block->AllOps()[5];
EXPECT_EQ(grad_op2->Type(), "mul_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
......@@ -625,7 +625,7 @@ TEST(Backward, simple_mult_op) {
EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *grad_op3 = block->AllOps()[4];
f::OpDesc *grad_op3 = block->AllOps()[4];
EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
......@@ -655,42 +655,42 @@ TEST(Backward, simple_mult_op) {
}
TEST(Backward, intermedia_var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDesc *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
f::OpDesc *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"x2"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
f::OpDesc *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
f::OpDescBind *op4 = block->AppendOp();
f::OpDesc *op4 = block->AppendOp();
op4->SetType("mul");
op4->SetInput("X", {"out1"});
op4->SetInput("Y", {"out3"});
op4->SetOutput("Out", {"out4"});
auto target = f::VarDescBind("out4");
auto target = f::VarDesc("out4");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"out3"});
ASSERT_EQ(block->AllOps().size(), 7UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
f::OpDesc *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op1 = block->AllOps()[6];
f::OpDesc *grad_op1 = block->AllOps()[6];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -701,7 +701,7 @@ TEST(Backward, intermedia_var_no_grad) {
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op4 = block->AllOps()[5];
f::OpDesc *grad_op4 = block->AllOps()[5];
EXPECT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
......@@ -726,32 +726,32 @@ TEST(Backward, intermedia_var_no_grad) {
}
TEST(Backward, var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDesc *op1 = block->AppendOp();
op1->SetType("mult_in_out");
op1->SetInput("X", {"x1"});
op1->SetInput("H", {"h1"});
op1->SetOutput("Y", {"y1"});
op1->SetOutput("Z", {"z1"});
f::OpDescBind *op2 = block->AppendOp();
f::OpDesc *op2 = block->AppendOp();
op2->SetType("mult_in_out");
op2->SetInput("X", {"y1"});
op2->SetInput("H", {"z1"});
op2->SetOutput("Y", {"y2"});
op2->SetOutput("Z", {"z2"});
auto target = f::VarDescBind("z2");
auto target = f::VarDesc("z2");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"z1"});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
f::OpDesc *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op2 = block->AllOps()[3];
f::OpDesc *grad_op2 = block->AllOps()[3];
ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
......@@ -767,7 +767,7 @@ TEST(Backward, var_no_grad) {
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), std::vector<std::string>());
f::OpDescBind *fill_zero_op = block->AllOps()[4];
f::OpDesc *fill_zero_op = block->AllOps()[4];
ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
......@@ -775,7 +775,7 @@ TEST(Backward, var_no_grad) {
EXPECT_EQ(fill_zero_op->Output("Out"),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
f::OpDescBind *grad_op1 = block->AllOps()[5];
f::OpDesc *grad_op1 = block->AllOps()[5];
ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -803,37 +803,37 @@ TEST(Backward, var_no_grad) {
}
TEST(Backward, shared_var) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::OpDesc *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
f::OpDesc *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
f::OpDesc *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out1"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
auto target = f::VarDesc("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad =
AppendBackward(program, target, std::unordered_set<std::string>{});
ASSERT_EQ(block->AllOps().size(), 8UL);
f::OpDescBind *fill_op = block->AllOps()[forward_len];
f::OpDesc *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
f::OpDescBind *grad_op3 = block->AllOps()[4];
f::OpDesc *grad_op3 = block->AllOps()[4];
ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
......@@ -844,7 +844,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
f::OpDescBind *grad_op4 = block->AllOps()[5];
f::OpDesc *grad_op4 = block->AllOps()[5];
ASSERT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
......@@ -858,7 +858,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *sum_op = block->AllOps()[6];
f::OpDesc *sum_op = block->AllOps()[6];
ASSERT_EQ(sum_op->Type(), "sum");
ASSERT_EQ(sum_op->InputNames().size(), 1UL);
ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
......@@ -868,7 +868,7 @@ TEST(Backward, shared_var) {
EXPECT_EQ(sum_op->Output("Out"),
std::vector<std::string>({f::GradVarName("out1")}));
f::OpDescBind *grad_op1 = block->AllOps()[7];
f::OpDesc *grad_op1 = block->AllOps()[7];
ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
......@@ -895,19 +895,19 @@ TEST(Backward, shared_var) {
}
TEST(Backward, half_backward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
auto *op1 = block->AppendOp();
op1->SetType("minus");
op1->SetInput("X", {"a"});
op1->SetInput("Y", {"b"});
op1->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
auto target = f::VarDesc("out");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"b"});
f::OpDescBind *fill_op = block->AllOps()[forward_len];
f::OpDesc *fill_op = block->AllOps()[forward_len];
EXPECT_EQ(fill_op->Type(), "fill_constant");
auto ops = block->AllOps();
ASSERT_EQ(3UL, ops.size());
......
paddle/framework/block_desc.cc
浏览文件 @ feb05c3a
......@@ -19,18 +19,18 @@ limitations under the License. */
namespace paddle {
namespace framework {
VarDescBind *BlockDescBind::Var(const std::string &name) {
VarDesc *BlockDesc::Var(const std::string &name) {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second.get();
}
need_update_ = true;
auto *var = new VarDescBind(name);
auto *var = new VarDesc(name);
vars_[name].reset(var);
return var;
}
VarDescBind *BlockDescBind::FindVar(const std::string &name) const {
VarDesc *BlockDesc::FindVar(const std::string &name) const {
auto it = vars_.find(name);
if (it == vars_.end()) {
return nullptr;
......@@ -38,11 +38,11 @@ VarDescBind *BlockDescBind::FindVar(const std::string &name) const {
return it->second.get();
}
bool BlockDescBind::HasVar(const std::string &name) const {
bool BlockDesc::HasVar(const std::string &name) const {
return vars_.find(name) != vars_.end();
}
VarDescBind *BlockDescBind::FindVarRecursive(const std::string &name) const {
VarDesc *BlockDesc::FindVarRecursive(const std::string &name) const {
if (name == kEmptyVarName) return nullptr;
auto it = vars_.find(name);
......@@ -53,53 +53,52 @@ VarDescBind *BlockDescBind::FindVarRecursive(const std::string &name) const {
return it->second.get();
}
VarDescBind *BlockDescBind::FindRecursiveOrCreateVar(
const std::string &name_bytes) {
VarDescBind *res = FindVarRecursive(name_bytes);
VarDesc *BlockDesc::FindRecursiveOrCreateVar(const std::string &name_bytes) {
VarDesc *res = FindVarRecursive(name_bytes);
if (res == nullptr) {
res = Var(name_bytes);
}
return res;
}
bool BlockDescBind::HasVarRecursive(const std::string &name) const {
bool BlockDesc::HasVarRecursive(const std::string &name) const {
return FindVarRecursive(name) != nullptr;
}
std::vector<VarDescBind *> BlockDescBind::AllVars() const {
std::vector<VarDescBind *> res;
std::vector<VarDesc *> BlockDesc::AllVars() const {
std::vector<VarDesc *> res;
for (const auto &p : vars_) {
res.push_back(p.second.get());
}
return res;
}
OpDescBind *BlockDescBind::AppendOp() {
OpDesc *BlockDesc::AppendOp() {
need_update_ = true;
ops_.emplace_back(new OpDescBind());
ops_.emplace_back(new OpDesc());
return ops_.back().get();
}
void BlockDescBind::AppendAllocatedOp(std::unique_ptr<OpDescBind> &&op_desc) {
void BlockDesc::AppendAllocatedOp(std::unique_ptr<OpDesc> &&op_desc) {
need_update_ = true;
ops_.emplace_back(std::move(op_desc));
}
OpDescBind *BlockDescBind::PrependOp() {
OpDesc *BlockDesc::PrependOp() {
need_update_ = true;
ops_.emplace_front(new OpDescBind());
ops_.emplace_front(new OpDesc());
return ops_.front().get();
}
std::vector<OpDescBind *> BlockDescBind::AllOps() const {
std::vector<OpDescBind *> res;
std::vector<OpDesc *> BlockDesc::AllOps() const {
std::vector<OpDesc *> res;
for (const auto &op : ops_) {
res.push_back(op.get());
}
return res;
}
void BlockDescBind::Flush() {
void BlockDesc::Flush() {
for (auto &op_desc : ops_) {
op_desc->Flush();
}
......@@ -121,43 +120,43 @@ void BlockDescBind::Flush() {
}
}
BlockDescBind *BlockDescBind::ParentBlock() const {
BlockDesc *BlockDesc::ParentBlock() const {
if (this->desc_->parent_idx() == kNoneBlockIndex) {
return nullptr;
}
return prog_->MutableBlock(static_cast<size_t>(this->desc_->parent_idx()));
}
BlockDesc *BlockDescBind::Proto() {
proto::BlockDesc *BlockDesc::Proto() {
Flush();
return desc_;
}
BlockDescBind::BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
: prog_(prog), desc_(desc), need_update_(false) {
for (const VarDesc &var_desc : desc_->vars()) {
vars_[var_desc.name()].reset(new VarDescBind(var_desc));
for (const proto::VarDesc &var_desc : desc_->vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDescBind(op_desc, prog));
for (const proto::OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDesc(op_desc, prog));
}
}
BlockDescBind::BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
ProgramDescBind *prog)
BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
ProgramDesc *prog)
: prog_(prog), desc_(desc) {
need_update_ = true;
for (auto &op : other.ops_) {
ops_.emplace_back(new OpDescBind(*op));
ops_.emplace_back(new OpDesc(*op));
}
for (auto &it : other.vars_) {
auto *var = new VarDescBind(*it.second);
auto *var = new VarDesc(*it.second);
vars_[it.first].reset(var);
}
}
void BlockDescBind::ClearPBOps() {
void BlockDesc::ClearPBOps() {
auto ops = this->desc_->mutable_ops();
while (!ops->empty()) {
// we do not own the OpDesc, so release the ownership.
......@@ -165,7 +164,7 @@ void BlockDescBind::ClearPBOps() {
}
}
void BlockDescBind::ClearPBVars() {
void BlockDesc::ClearPBVars() {
auto vars = this->desc_->mutable_vars();
while (!vars->empty()) {
// we do not own the VarDesc, so release the ownership.
......
paddle/framework/block_desc.h
浏览文件 @ feb05c3a
......@@ -28,20 +28,19 @@ limitations under the License. */
namespace paddle {
namespace framework {
class ProgramDescBind;
class ProgramDesc;
// Each Protobuf Message, we provide a XXXBind class. In that class, we optimize
// read/write speed. Only when we want the protobuf message, the local changes
// will be synchronized (by `Sync` method).
class BlockDescBind {
class BlockDesc {
public:
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc);
BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc);
BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
ProgramDescBind *prog);
BlockDesc(const BlockDesc &other, proto::BlockDesc *desc, ProgramDesc *prog);
~BlockDescBind() {
~BlockDesc() {
this->ClearPBVars();
this->ClearPBOps();
}
......@@ -50,15 +49,15 @@ class BlockDescBind {
int32_t Parent() const { return desc_->parent_idx(); }
VarDescBind *Var(const std::string &name_bytes);
VarDesc *Var(const std::string &name_bytes);
VarDescBind *FindVar(const std::string &name_bytes) const;
VarDesc *FindVar(const std::string &name_bytes) const;
bool HasVar(const std::string &var_name) const;
VarDescBind *FindVarRecursive(const std::string &name_bytes) const;
VarDesc *FindVarRecursive(const std::string &name_bytes) const;
VarDescBind *FindRecursiveOrCreateVar(const std::string &name_bytes);
VarDesc *FindRecursiveOrCreateVar(const std::string &name_bytes);
bool HasVarRecursive(const std::string &var_name) const;
......@@ -70,41 +69,41 @@ class BlockDescBind {
return var_names;
}
std::vector<VarDescBind *> AllVars() const;
std::vector<VarDesc *> AllVars() const;
BlockDescBind *ParentBlock() const;
BlockDesc *ParentBlock() const;
OpDescBind *AppendOp();
OpDesc *AppendOp();
void AppendAllocatedOp(std::unique_ptr<OpDescBind> &&op_desc);
void AppendAllocatedOp(std::unique_ptr<OpDesc> &&op_desc);
OpDescBind *PrependOp();
OpDesc *PrependOp();
std::vector<OpDescBind *> AllOps() const;
std::vector<OpDesc *> AllOps() const;
size_t OpSize() const { return ops_.size(); }
OpDescBind *Op(int idx) { return ops_.at(idx).get(); }
OpDesc *Op(int idx) { return ops_.at(idx).get(); }
void Flush();
BlockDesc *Proto();
proto::BlockDesc *Proto();
ProgramDescBind *Program() { return this->prog_; }
ProgramDesc *Program() { return this->prog_; }
private:
void ClearPBOps();
void ClearPBVars();
private:
ProgramDescBind *prog_; // not_own
BlockDesc *desc_; // not_own
ProgramDesc *prog_; // not_own
proto::BlockDesc *desc_; // not_own
bool need_update_;
std::deque<std::unique_ptr<OpDescBind>> ops_;
std::unordered_map<std::string, std::unique_ptr<VarDescBind>> vars_;
std::deque<std::unique_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::unique_ptr<VarDesc>> vars_;
DISABLE_COPY_AND_ASSIGN(BlockDescBind);
DISABLE_COPY_AND_ASSIGN(BlockDesc);
};
} // namespace framework
} // namespace paddle
paddle/framework/data_type.h
浏览文件 @ feb05c3a
......@@ -20,7 +20,8 @@
namespace paddle {
namespace framework {
inline DataType ToDataType(std::type_index type) {
inline proto::DataType ToDataType(std::type_index type) {
using namespace paddle::framework::proto;
if (typeid(float).hash_code() == type.hash_code()) {
return DataType::FP32;
} else if (typeid(double).hash_code() == type.hash_code()) {
......@@ -36,7 +37,8 @@ inline DataType ToDataType(std::type_index type) {
}
}
inline std::type_index ToTypeIndex(DataType type) {
inline std::type_index ToTypeIndex(proto::DataType type) {
using namespace paddle::framework::proto;
switch (type) {
case DataType::FP32:
return typeid(float);
......@@ -54,7 +56,8 @@ inline std::type_index ToTypeIndex(DataType type) {
}
template <typename Visitor>
inline void VisitDataType(DataType type, Visitor visitor) {
inline void VisitDataType(proto::DataType type, Visitor visitor) {
using namespace paddle::framework::proto;
switch (type) {
case DataType::FP32:
visitor.template operator()<float>();
......
paddle/framework/ddim_test.cc
浏览文件 @ feb05c3a
/* 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 <sstream>
#include <vector>
......
paddle/framework/details/op_registry.h
浏览文件 @ feb05c3a
......@@ -90,7 +90,7 @@ struct OpInfoFiller<T, kOperator> {
template <typename T>
struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
void operator()(const char* op_type, OpInfo* info) const {
info->proto_ = new OpProto;
info->proto_ = new proto::OpProto;
info->checker_ = new OpAttrChecker();
auto maker = T(info->proto_, info->checker_);
maker.Validate();
......@@ -106,10 +106,10 @@ template <typename T>
struct OpInfoFiller<T, kGradOpDescMaker> {
void operator()(const char* op_type, OpInfo* info) const {
info->grad_op_maker_ = [](
const OpDescBind& fwd_op,
const OpDesc& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block) {
const std::vector<BlockDesc*>& grad_block) {
T maker(fwd_op, no_grad_set, grad_to_var, grad_block);
return maker();
};
......@@ -119,7 +119,7 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
template <typename T>
struct OpInfoFiller<T, kVarTypeInference> {
void operator()(const char* op_type, OpInfo* info) const {
info->infer_var_type_ = [](const OpDescBind& fwd_op, BlockDescBind* block) {
info->infer_var_type_ = [](const OpDesc& fwd_op, BlockDesc* block) {
T inference;
inference(fwd_op, block);
};
......
paddle/framework/executor.cc
浏览文件 @ feb05c3a
......@@ -33,48 +33,28 @@ namespace framework {
const std::string kFeedOpType = "feed";
const std::string kFetchOpType = "fetch";
Executor::Executor(const std::vector<platform::Place>& places) : own_(true) {
PADDLE_ENFORCE_GT(places.size(), 0);
device_contexts_.resize(places.size());
for (size_t i = 0; i < places.size(); i++) {
if (platform::is_cpu_place(places[i])) {
device_contexts_[i] = new platform::CPUDeviceContext(
boost::get<platform::CPUPlace>(places[i]));
} else if (platform::is_gpu_place(places[i])) {
#ifdef PADDLE_WITH_CUDA
device_contexts_[i] = new platform::CUDADeviceContext(
boost::get<platform::GPUPlace>(places[i]));
#else
PADDLE_THROW(
"'GPUPlace' is not supported, Please re-compile with WITH_GPU "
"option");
#endif
}
}
}
DeviceContextPool* DeviceContextPool::pool = nullptr;
Executor::~Executor() {
if (own_) {
for (auto& device_context : device_contexts_) {
delete device_context;
}
}
Executor::Executor(const std::vector<platform::Place>& places) {
DeviceContextPool& pool = DeviceContextPool::Get();
auto borrowed_contexts = pool.Borrow(places);
device_contexts_.swap(borrowed_contexts);
}
static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
if (var_type == VarDesc::LOD_TENSOR) {
static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
if (var_type == proto::VarDesc::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == VarDesc::SELECTED_ROWS) {
} else if (var_type == proto::VarDesc::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == VarDesc::FEED_MINIBATCH) {
} else if (var_type == proto::VarDesc::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::FETCH_LIST) {
} else if (var_type == proto::VarDesc::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::STEP_SCOPES) {
} else if (var_type == proto::VarDesc::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
} else if (var_type == VarDesc::LOD_RANK_TABLE) {
} else if (var_type == proto::VarDesc::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == VarDesc::LOD_TENSOR_ARRAY) {
} else if (var_type == proto::VarDesc::LOD_TENSOR_ARRAY) {
var->GetMutable<LoDTensorArray>();
} else {
PADDLE_THROW(
......@@ -84,7 +64,7 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
}
}
void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
......@@ -132,8 +112,5 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
}
}
Executor::Executor(const platform::DeviceContext& device)
: device_contexts_({&device}), own_(false) {}
} // namespace framework
} // namespace paddle
paddle/framework/executor.h
浏览文件 @ feb05c3a
......@@ -14,19 +14,98 @@ limitations under the License. */
#pragma once
#include <map>
#include <unordered_map>
#include "paddle/framework/op_info.h"
#include "paddle/framework/program_desc.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
class DeviceContextPool {
public:
static DeviceContextPool& Get() {
PADDLE_ENFORCE_NOT_NULL(pool, "Need to Create DeviceContextPool first!");
return *pool;
}
static DeviceContextPool& Create(const std::vector<platform::Place>& places) {
if (pool == nullptr) {
pool = new DeviceContextPool(places);
}
return *pool;
}
std::vector<const platform::DeviceContext*> Borrow(
const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
PADDLE_ENFORCE_LE(places.size(), device_contexts_.size());
std::vector<const platform::DeviceContext*> borrowed_contexts;
for (auto& place : places) {
auto range = device_contexts_.equal_range(place);
if (range.first == range.second) {
PADDLE_THROW(
"'Place' is not supported, Please re-compile with WITH_GPU "
"option");
}
// TODO(dzhwinter) : assign the first found device. Will enhanced later.
// device load balancer maybe useful here.
borrowed_contexts.emplace_back(range.first->second);
}
return borrowed_contexts;
}
explicit DeviceContextPool(const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
for (size_t i = 0; i < places.size(); i++) {
if (platform::is_cpu_place(places[i])) {
device_contexts_.emplace(
places[i], new platform::CPUDeviceContext(
boost::get<platform::CPUPlace>(places[i])));
} else if (platform::is_gpu_place(places[i])) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(
places[i], new platform::CUDADeviceContext(
boost::get<platform::GPUPlace>(places[i])));
#else
PADDLE_THROW(
"'GPUPlace' is not supported, Please re-compile with WITH_GPU "
"option");
#endif
}
}
}
~DeviceContextPool() {}
private:
static DeviceContextPool* pool;
struct Hash {
std::hash<int> hash_;
size_t operator()(const platform::Place& place) const {
return hash_(place.which());
}
};
std::unordered_multimap<const platform::Place, const platform::DeviceContext*,
Hash>
device_contexts_;
DISABLE_COPY_AND_ASSIGN(DeviceContextPool);
};
class Executor {
public:
// TODO(dzhwinter) : Do not rely on this function, it will be removed
explicit Executor(const platform::DeviceContext& device)
: Executor(std::vector<platform::Place>({device.GetPlace()})) {}
explicit Executor(const platform::Place& place)
: Executor(std::vector<platform::Place>({place})) {}
explicit Executor(const std::vector<platform::Place>& places);
explicit Executor(const platform::DeviceContext& devices);
~Executor();
/* @Brief
* Runtime evaluation of the given ProgramDesc under certain Scope
......@@ -35,11 +114,10 @@ class Executor {
* ProgramDesc
* Scope
*/
void Run(const ProgramDescBind&, Scope*, int, bool create_local_scope = true);
void Run(const ProgramDesc&, Scope*, int, bool create_local_scope = true);
private:
std::vector<const platform::DeviceContext*> device_contexts_;
bool own_;
};
} // namespace framework
......
paddle/framework/framework.proto
浏览文件 @ feb05c3a
......@@ -14,7 +14,7 @@ limitations under the License. */
syntax = "proto2";
option optimize_for = LITE_RUNTIME;
package paddle.framework;
package paddle.framework.proto;
enum AttrType {
INT = 0;
......
paddle/framework/grad_op_desc_maker.h
浏览文件 @ feb05c3a
......@@ -25,18 +25,16 @@ namespace framework {
class GradOpDescMakerBase {
public:
explicit GradOpDescMakerBase(
const OpDescBind& fwd_op,
const std::unordered_set<std::string>& no_grad_set,
const OpDesc& fwd_op, const std::unordered_set<std::string>& no_grad_set,
std::unordered_map<std::string, std::string>* grad_to_var,
const std::vector<BlockDescBind*>& grad_block =
std::vector<BlockDescBind*>())
const std::vector<BlockDesc*>& grad_block = std::vector<BlockDesc*>())
: fwd_op_(fwd_op),
no_grad_set_(no_grad_set),
grad_to_var_(grad_to_var),
grad_block_(grad_block) {}
virtual ~GradOpDescMakerBase() = default;
virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
virtual std::vector<std::unique_ptr<OpDesc>> operator()() const = 0;
protected:
std::vector<std::string> InputGrad(const std::string& name,
......@@ -105,26 +103,26 @@ class GradOpDescMakerBase {
std::string ForwardOpType() const { return this->fwd_op_.Type(); }
private:
const OpDescBind& fwd_op_;
const OpDesc& fwd_op_;
const std::unordered_set<std::string>& no_grad_set_;
std::unordered_map<std::string, std::string>* grad_to_var_;
protected:
std::vector<BlockDescBind*> grad_block_;
std::vector<BlockDesc*> grad_block_;
};
class SingleGradOpDescMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const {
std::vector<std::unique_ptr<OpDescBind>> retv;
std::vector<std::unique_ptr<OpDesc>> operator()() const {
std::vector<std::unique_ptr<OpDesc>> retv;
retv.emplace_back(this->Apply());
return retv;
}
protected:
virtual std::unique_ptr<OpDescBind> Apply() const = 0;
virtual std::unique_ptr<OpDesc> Apply() const = 0;
};
template <bool DropEmptyIG = true>
......@@ -133,8 +131,8 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
using SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
virtual std::unique_ptr<OpDescBind> Apply() const {
auto* grad = new OpDescBind();
virtual std::unique_ptr<OpDesc> Apply() const {
auto* grad = new OpDesc();
grad->SetType(this->GradOpType());
for (auto& input_param : this->InputNames()) {
......@@ -150,7 +148,7 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
grad->SetAttrMap(this->Attrs());
return std::unique_ptr<OpDescBind>(grad);
return std::unique_ptr<OpDesc>(grad);
}
virtual std::string GradOpType() const {
......@@ -161,7 +159,7 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
class EmptyGradOpMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
std::vector<std::unique_ptr<OpDesc>> operator()() const override {
return {};
}
};
......
paddle/framework/init.cc 0 → 100644
浏览文件 @ feb05c3a
/* 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 <algorithm>
#include <string>
#include "paddle/framework/executor.h"
#include "paddle/framework/init.h"
#include "paddle/platform/place.h"
#include "paddle/string/piece.h"
namespace paddle {
namespace framework {
std::once_flag gflags_init_flag;
// TODO(qijun) move init gflags to init.cc
void InitGflags(std::vector<std::string> &argv) {
std::call_once(gflags_init_flag, [&]() {
int argc = argv.size();
char **arr = new char *[argv.size()];
std::string line;
for (size_t i = 0; i < argv.size(); i++) {
arr[i] = &argv[i][0];
line += argv[i];
line += ' ';
}
google::ParseCommandLineFlags(&argc, &arr, true);
VLOG(1) << "Init commandline: " << line;
});
}
bool InitDevices(const std::vector<std::string> &devices) {
// device format
// CPU
// GPU:1
// TODO(dzhwinter) : add device format annotation for users.
std::vector<platform::Place> places;
for (auto &device : devices) {
auto p = string::Piece(device);
if (string::Find(p, ':', 0) == string::Piece::npos) {
places.emplace_back(platform::CPUPlace());
} else if (string::HasPrefix(p, "GPU")) {
#ifdef PADDLE_WITH_CUDA
auto pos = string::RFind(p, ':', string::Piece::npos);
auto number = device.substr(pos + 1);
places.emplace_back(platform::GPUPlace(std::stoi(number)));
#else
LOG(WARNING)
<< "'GPU' is not supported, Please re-compile with WITH_GPU option";
#endif
} else {
return false;
}
}
if (std::find_if(places.begin(), places.end(),
[&](const platform::Place &place) {
return platform::is_cpu_place(place);
}) == places.end()) {
places.emplace_back(platform::CPUPlace());
LOG(WARNING) << "Not specified any device, use CPU by Default.";
}
DeviceContextPool::Create(places);
return true;
return true;
}
} // namespace framework
} // namespace paddle
paddle/framework/init.h 0 → 100644
浏览文件 @ feb05c3a
/* 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 <mutex>
#include "gflags/gflags.h"
#include "glog/logging.h"
namespace paddle {
namespace framework {
void InitGflags(std::vector<std::string> &argv);
bool InitDevices(const std::vector<std::string> &devices);
} // namespace framework
} // namespace paddle
paddle/framework/init_test.cc 0 → 100644
浏览文件 @ feb05c3a
/* 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 "gtest/gtest.h"
#include "paddle/framework/init.h"
TEST(Init, InitDevices) {
using paddle::framework::InitDevices;
std::vector<std::string> ds1 = {"CPU"};
ASSERT_EQ(InitDevices(ds1), true);
#ifdef PADDLE_WITH_CUDA
std::vector<std::string> ds2 = {"CPU", "GPU:0", "GPU:1"};
ASSERT_EQ(InitDevices(ds2), true);
#endif
}
paddle/framework/lod_rank_table.cc
浏览文件 @ feb05c3a
......@@ -46,4 +46,13 @@ void LoDRankTable::Reset(const LoD& lod, size_t level) {
}
} // namespace framework
std::ostream& operator<<(std::ostream& out,
const framework::LoDRankTable& table) {
out << "NumOfSequence " << table.items().size() << "\n";
for (auto& each_item : table.items()) {
out << "\tSeq #" << each_item.index << ", Len=" << each_item.length << "\n";
}
return out;
}
} // namespace paddle
paddle/framework/lod_rank_table.h
浏览文件 @ feb05c3a
......@@ -13,6 +13,7 @@
limitations under the License. */
#pragma once
#include <iosfwd>
#include "paddle/framework/lod_tensor.h"
namespace paddle {
......@@ -52,4 +53,8 @@ class LoDRankTable {
};
} // namespace framework
std::ostream& operator<<(std::ostream& out,
const framework::LoDRankTable& table);
} // namespace paddle
paddle/framework/lod_tensor.cc
浏览文件 @ feb05c3a
......@@ -197,7 +197,7 @@ void SerializeToStream(std::ostream &os, const LoDTensor &tensor,
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
framework::TensorDesc desc;
proto::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
......@@ -262,7 +262,7 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
framework::TensorDesc desc;
proto::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
......@@ -281,16 +281,16 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
void *buf;
platform::Place cpu = platform::CPUPlace();
switch (desc.data_type()) {
case framework::FP32:
case proto::FP32:
buf = tensor->mutable_data<float>(cpu);
break;
case framework::FP64:
case proto::FP64:
buf = tensor->mutable_data<double>(cpu);
break;
case framework::INT32:
case proto::INT32:
buf = tensor->mutable_data<int>(cpu);
break;
case framework::INT64:
case proto::INT64:
buf = tensor->mutable_data<int64_t>(cpu);
break;
default:
......
paddle/framework/lod_tensor.h
浏览文件 @ feb05c3a
......@@ -184,6 +184,18 @@ LoDTensor LodExpand(const LoDTensor& source, const LoD& lod, size_t level,
return tensor;
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
// relative length of details for every levels(i.e., [start_level: ]).
//
// For example,
// lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
// start_level = 0
// start_idx = 1
// end_idx = 3
//
// Returns:
// LoD = [[1, 4], [2, 4, 2, 3, 2]]
// pair<size_t, size_t> = {11, 24}
std::pair<LoD, std::pair<size_t, size_t>> GetSubLoDAndAbsoluteOffset(
const LoD& lod, size_t start_idx, size_t end_idx, size_t start_level);
......
paddle/framework/op_desc.cc
浏览文件 @ feb05c3a
......@@ -25,12 +25,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
class OpDescBind;
class BlockDescBind;
class OpDesc;
class BlockDesc;
class CompileTimeInferShapeContext : public InferShapeContext {
public:
CompileTimeInferShapeContext(const OpDescBind &op,
const BlockDescBind &block);
CompileTimeInferShapeContext(const OpDesc &op, const BlockDesc &block);
bool HasInput(const std::string &name) const override;
......@@ -58,11 +57,11 @@ class CompileTimeInferShapeContext : public InferShapeContext {
PADDLE_ENFORCE_LT(j, Outputs(out).size());
auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
if (in_var->GetType() != VarDesc::LOD_TENSOR) {
if (in_var->GetType() != proto::VarDesc::LOD_TENSOR) {
VLOG(3) << "input " << in << " is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), VarDesc::LOD_TENSOR,
PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarDesc::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
out_var->SetLoDLevel(in_var->GetLodLevel());
......@@ -70,19 +69,18 @@ class CompileTimeInferShapeContext : public InferShapeContext {
bool IsRuntime() const override;
protected:
VarDesc::VarType GetVarType(const std::string &name) const override;
proto::VarDesc::VarType GetVarType(const std::string &name) const override;
DDim GetDim(const std::string &name) const override;
void SetDim(const std::string &name, const DDim &dim) override;
const OpDescBind &op_;
const BlockDescBind &block_;
const OpDesc &op_;
const BlockDesc &block_;
};
OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs) {
OpDesc::OpDesc(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs) {
desc_.set_type(type);
inputs_ = inputs;
outputs_ = outputs;
......@@ -90,12 +88,12 @@ OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
need_update_ = true;
}
OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
: desc_(desc), need_update_(false) {
// restore inputs_
int input_size = desc_.inputs_size();
for (int i = 0; i < input_size; ++i) {
const OpDesc::Var &var = desc_.inputs(i);
const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int argu_size = var.arguments_size();
args.reserve(argu_size);
......@@ -106,7 +104,7 @@ OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
// restore outputs_
int output_size = desc_.outputs_size();
for (int i = 0; i < output_size; ++i) {
const OpDesc::Var &var = desc_.outputs(i);
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int argu_size = var.arguments_size();
args.reserve(argu_size);
......@@ -115,9 +113,9 @@ OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
}
}
// restore attrs_
for (const OpDesc::Attr &attr : desc_.attrs()) {
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != AttrType::BLOCK) {
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = GetAttrValue(attr);
} else {
auto bid = attr.block_idx();
......@@ -126,20 +124,19 @@ OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
}
}
OpDesc *OpDescBind::Proto() {
proto::OpDesc *OpDesc::Proto() {
Flush();
return &desc_;
}
const std::vector<std::string> &OpDescBind::Input(
const std::string &name) const {
const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
auto it = inputs_.find(name);
PADDLE_ENFORCE(it != inputs_.end(), "Input %s cannot be found in Op %s", name,
Type());
return it->second;
}
std::vector<std::string> OpDescBind::InputArgumentNames() const {
std::vector<std::string> OpDesc::InputArgumentNames() const {
std::vector<std::string> retv;
for (auto &ipt : this->inputs_) {
retv.insert(retv.end(), ipt.second.begin(), ipt.second.end());
......@@ -147,21 +144,20 @@ std::vector<std::string> OpDescBind::InputArgumentNames() const {
return retv;
}
void OpDescBind::SetInput(const std::string &param_name,
const std::vector<std::string> &args) {
void OpDesc::SetInput(const std::string &param_name,
const std::vector<std::string> &args) {
need_update_ = true;
inputs_[param_name] = args;
}
const std::vector<std::string> &OpDescBind::Output(
const std::string &name) const {
const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
auto it = outputs_.find(name);
PADDLE_ENFORCE(it != outputs_.end(), "Output %s cannot be found in Op %s",
name, Type());
return it->second;
}
std::vector<std::string> OpDescBind::OutputArgumentNames() const {
std::vector<std::string> OpDesc::OutputArgumentNames() const {
std::vector<std::string> retv;
for (auto &ipt : this->outputs_) {
retv.insert(retv.end(), ipt.second.begin(), ipt.second.end());
......@@ -169,19 +165,19 @@ std::vector<std::string> OpDescBind::OutputArgumentNames() const {
return retv;
}
void OpDescBind::SetOutput(const std::string &param_name,
const std::vector<std::string> &args) {
void OpDesc::SetOutput(const std::string &param_name,
const std::vector<std::string> &args) {
need_update_ = true;
this->outputs_[param_name] = args;
}
AttrType OpDescBind::GetAttrType(const std::string &name) const {
proto::AttrType OpDesc::GetAttrType(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return static_cast<AttrType>(it->second.which() - 1);
return static_cast<proto::AttrType>(it->second.which() - 1);
}
std::vector<std::string> OpDescBind::AttrNames() const {
std::vector<std::string> OpDesc::AttrNames() const {
std::vector<std::string> retv;
retv.reserve(attrs_.size());
for (auto &attr : attrs_) {
......@@ -190,41 +186,39 @@ std::vector<std::string> OpDescBind::AttrNames() const {
return retv;
}
void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
this->attrs_[name] = v;
need_update_ = true;
}
void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
void OpDesc::SetBlockAttr(const std::string &name, BlockDesc &block) {
this->attrs_[name] = &block;
need_update_ = true;
}
void OpDescBind::SetAttrMap(
void OpDesc::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
need_update_ = true;
}
Attribute OpDescBind::GetAttr(const std::string &name) const {
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return it->second;
}
int OpDescBind::GetBlockAttr(const std::string &name) const {
int OpDesc::GetBlockAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return boost::get<BlockDescBind *>(it->second)->ID();
return boost::get<BlockDesc *>(it->second)->ID();
}
const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()
const {
const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
return attrs_;
}
void OpDescBind::Rename(const std::string &old_name,
const std::string &new_name) {
void OpDesc::Rename(const std::string &old_name, const std::string &new_name) {
for (auto &input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
......@@ -235,8 +229,8 @@ void OpDescBind::Rename(const std::string &old_name,
need_update_ = true;
}
void OpDescBind::RenameOutput(const std::string &old_name,
const std::string &new_name) {
void OpDesc::RenameOutput(const std::string &old_name,
const std::string &new_name) {
for (auto &output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
......@@ -244,8 +238,8 @@ void OpDescBind::RenameOutput(const std::string &old_name,
need_update_ = true;
}
void OpDescBind::RenameInput(const std::string &old_name,
const std::string &new_name) {
void OpDesc::RenameInput(const std::string &old_name,
const std::string &new_name) {
for (auto &input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
......@@ -253,8 +247,8 @@ void OpDescBind::RenameInput(const std::string &old_name,
}
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
mutable OpDesc::Attr *attr_;
explicit SetAttrDescVisitor(proto::OpDesc::Attr *attr) : attr_(attr) {}
mutable proto::OpDesc::Attr *attr_;
void operator()(int v) const { attr_->set_i(v); }
void operator()(float v) const { attr_->set_f(v); }
void operator()(const std::string &v) const { attr_->set_s(v); }
......@@ -272,11 +266,13 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->idx()); }
void operator()(proto::BlockDesc *desc) const {
attr_->set_block_idx(desc->idx());
}
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
void OpDescBind::Flush() {
void OpDesc::Flush() {
if (need_update_) {
this->desc_.mutable_inputs()->Clear();
for (auto &ipt : inputs_) {
......@@ -297,7 +293,7 @@ void OpDescBind::Flush() {
auto *attr_desc = desc_.add_attrs();
attr_desc->set_name(attr.first);
attr_desc->set_type(
static_cast<framework::AttrType>(attr.second.which() - 1));
static_cast<proto::AttrType>(attr.second.which() - 1));
SetAttrDescVisitor visitor(attr_desc);
boost::apply_visitor(visitor, attr.second);
}
......@@ -328,7 +324,7 @@ static void InitInferShapeFuncs() {
});
}
void OpDescBind::CheckAttrs() {
void OpDesc::CheckAttrs() {
PADDLE_ENFORCE(!Type().empty(),
"CheckAttr() can not be called before type is setted.");
auto *checker = OpInfoMap::Instance().Get(Type()).Checker();
......@@ -340,7 +336,7 @@ void OpDescBind::CheckAttrs() {
checker->Check(attrs_);
}
void OpDescBind::InferShape(const BlockDescBind &block) const {
void OpDesc::InferShape(const BlockDesc &block) const {
VLOG(3) << "CompileTime infer shape on " << Type();
InitInferShapeFuncs();
auto &infer_shape = OpInfoMap::Instance().Get(this->Type()).infer_shape_;
......@@ -363,7 +359,7 @@ void OpDescBind::InferShape(const BlockDescBind &block) const {
infer_shape(&ctx);
}
void OpDescBind::InferVarType(BlockDescBind *block) const {
void OpDesc::InferVarType(BlockDesc *block) const {
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
......@@ -375,14 +371,14 @@ void OpDescBind::InferVarType(BlockDescBind *block) const {
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->FindRecursiveOrCreateVar(out_var_name)
->SetType(VarDesc::LOD_TENSOR);
->SetType(proto::VarDesc::LOD_TENSOR);
}
}
}
}
CompileTimeInferShapeContext::CompileTimeInferShapeContext(
const OpDescBind &op, const BlockDescBind &block)
const OpDesc &op, const BlockDesc &block)
: op_(op), block_(block) {}
bool CompileTimeInferShapeContext::HasInput(const std::string &name) const {
......@@ -484,7 +480,7 @@ void CompileTimeInferShapeContext::SetDim(const std::string &name,
}
bool CompileTimeInferShapeContext::IsRuntime() const { return false; }
VarDesc::VarType CompileTimeInferShapeContext::GetVarType(
proto::VarDesc::VarType CompileTimeInferShapeContext::GetVarType(
const std::string &name) const {
return block_.FindVarRecursive(name)->GetType();
}
......
paddle/framework/op_desc.h
浏览文件 @ feb05c3a
......@@ -23,19 +23,19 @@ limitations under the License. */
namespace paddle {
namespace framework {
class BlockDescBind;
class ProgramDescBind;
class BlockDesc;
class ProgramDesc;
class OpDescBind {
class OpDesc {
public:
OpDescBind() {}
OpDesc() {}
OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDesc(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDescBind(const OpDesc &desc, ProgramDescBind *prog);
OpDesc(const proto::OpDesc &desc, ProgramDesc *prog);
OpDesc *Proto();
proto::OpDesc *Proto();
std::string Type() const { return desc_.type(); }
......@@ -59,13 +59,13 @@ class OpDescBind {
return attrs_.find(name) != attrs_.end();
}
AttrType GetAttrType(const std::string &name) const;
proto::AttrType GetAttrType(const std::string &name) const;
std::vector<std::string> AttrNames() const;
void SetAttr(const std::string &name, const Attribute &v);
void SetBlockAttr(const std::string &name, BlockDescBind &block);
void SetBlockAttr(const std::string &name, BlockDesc &block);
Attribute GetAttr(const std::string &name) const;
......@@ -107,9 +107,9 @@ class OpDescBind {
void CheckAttrs();
void InferShape(const BlockDescBind &block) const;
void InferShape(const BlockDesc &block) const;
void InferVarType(BlockDescBind *block) const;
void InferVarType(BlockDesc *block) const;
void MarkAsTarget() { desc_.set_is_target(true); }
......@@ -126,7 +126,7 @@ class OpDescBind {
return ret_val;
}
OpDesc desc_;
proto::OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
......
paddle/framework/op_info.h
浏览文件 @ feb05c3a
......@@ -34,7 +34,7 @@ class InferShapeBase {
struct OpInfo {
OpCreator creator_;
GradOpMakerFN grad_op_maker_;
OpProto* proto_{nullptr};
proto::OpProto* proto_{nullptr};
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
......@@ -43,7 +43,7 @@ struct OpInfo {
return proto_ != nullptr && checker_ != nullptr;
}
const OpProto& Proto() const {
const proto::OpProto& Proto() const {
PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered");
PADDLE_ENFORCE(proto_->IsInitialized(),
"Operator Proto must be initialized in op info");
......
paddle/framework/op_proto_maker.h
浏览文件 @ feb05c3a
......@@ -22,6 +22,8 @@ namespace framework {
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
using OpProto = proto::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: proto_(proto), op_checker_(op_checker) {}
......@@ -80,7 +82,7 @@ class OpProtoAndCheckerMaker {
class NOPMaker : public OpProtoAndCheckerMaker {
public:
NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
NOPMaker(OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {}
};
......
paddle/framework/op_proto_maker_test.cc
浏览文件 @ feb05c3a
......@@ -18,7 +18,7 @@ limitations under the License. */
class TestAttrProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
TestAttrProtoMaker(paddle::framework::OpProto* proto,
TestAttrProtoMaker(paddle::framework::proto::OpProto* proto,
paddle::framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<float>("scale", "scale of test op");
......@@ -27,7 +27,7 @@ class TestAttrProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
};
TEST(ProtoMaker, DuplicatedAttr) {
paddle::framework::OpProto op_proto;
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
......@@ -35,7 +35,7 @@ TEST(ProtoMaker, DuplicatedAttr) {
class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
TestInOutProtoMaker(paddle::framework::OpProto* proto,
TestInOutProtoMaker(paddle::framework::proto::OpProto* proto,
paddle::framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
......@@ -44,7 +44,7 @@ class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
};
TEST(ProtoMaker, DuplicatedInOut) {
paddle::framework::OpProto op_proto;
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
......
paddle/framework/op_registry.cc
浏览文件 @ feb05c3a
......@@ -31,7 +31,8 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
}
static VariableNameMap ConvertOpDescVarsToVarNameMap(
const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars) {
const google::protobuf::RepeatedPtrField<proto::OpDesc::Var>&
op_desc_vars) {
VariableNameMap ret_val;
for (auto& var : op_desc_vars) {
auto& var_names = ret_val[var.parameter()];
......@@ -43,9 +44,10 @@ static VariableNameMap ConvertOpDescVarsToVarNameMap(
return ret_val;
}
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
const proto::OpDesc& op_desc) {
VLOG(1) << "CreateOp directly from OpDesc is deprecated. It should only be"
"used in unit tests. Use CreateOp(const OpDescBind& op_desc) "
"used in unit tests. Use CreateOp(const OpDesc& op_desc) "
"instead.";
VariableNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VariableNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
......@@ -57,7 +59,7 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDescBind& op_desc) {
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
return CreateOp(op_desc.Type(), op_desc.Inputs(), op_desc.Outputs(),
op_desc.GetAttrMap());
}
......
paddle/framework/op_registry.h
浏览文件 @ feb05c3a
......@@ -77,9 +77,9 @@ class OpRegistry {
const VariableNameMap& outputs,
AttributeMap attrs);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const proto::OpDesc& op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
};
template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
......
paddle/framework/op_registry_test.cc
浏览文件 @ feb05c3a
......@@ -51,7 +51,7 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
paddle::framework::proto::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
var->add_arguments(arg_name);
......@@ -63,7 +63,7 @@ REGISTER_OP_WITHOUT_GRADIENT(my_test_op, paddle::framework::MyTestOp,
paddle::framework::MyTestOpProtoAndCheckerMaker);
TEST(OpRegistry, CreateOp) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("cos_sim");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
......@@ -71,7 +71,7 @@ TEST(OpRegistry, CreateOp) {
float scale = 3.3;
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_type(paddle::framework::proto::AttrType::FLOAT);
attr->set_f(scale);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
......@@ -83,14 +83,14 @@ TEST(OpRegistry, CreateOp) {
}
TEST(OpRegistry, IllegalAttr) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("cos_sim");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_type(paddle::framework::proto::AttrType::FLOAT);
attr->set_f(-2.0);
bool caught = false;
......@@ -108,7 +108,7 @@ TEST(OpRegistry, IllegalAttr) {
}
TEST(OpRegistry, DefaultValue) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("cos_sim");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
......@@ -123,7 +123,7 @@ TEST(OpRegistry, DefaultValue) {
}
TEST(OpRegistry, CustomChecker) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("my_test_op");
BuildVar("input", {"ii"}, op_desc.add_inputs());
BuildVar("output", {"oo"}, op_desc.add_outputs());
......@@ -145,7 +145,7 @@ TEST(OpRegistry, CustomChecker) {
// set 'test_attr' set to an illegal value
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_type(paddle::framework::proto::AttrType::INT);
attr->set_i(3);
caught = false;
try {
......@@ -164,7 +164,7 @@ TEST(OpRegistry, CustomChecker) {
op_desc.mutable_attrs()->Clear();
attr = op_desc.mutable_attrs()->Add();
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_type(paddle::framework::proto::AttrType::INT);
attr->set_i(4);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
......
paddle/framework/operator.cc
浏览文件 @ feb05c3a
......@@ -377,7 +377,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
}
VarDesc::VarType GetVarType(const std::string& name) const override {
proto::VarDesc::VarType GetVarType(const std::string& name) const override {
auto* var = scope_.FindVar(name);
return ToVarType(var->Type());
}
......@@ -417,7 +417,7 @@ OpKernelType OperatorWithKernel::GetKernelType(
const ExecutionContext& ctx) const {
return OpKernelType(IndicateDataType(ctx), ctx.GetPlace());
}
DataType OperatorWithKernel::IndicateDataType(
proto::DataType OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
auto& scope = ctx.scope();
int data_type = -1;
......@@ -443,7 +443,7 @@ DataType OperatorWithKernel::IndicateDataType(
}
}
PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
return static_cast<DataType>(data_type);
return static_cast<proto::DataType>(data_type);
}
} // namespace framework
......
paddle/framework/operator.h
浏览文件 @ feb05c3a
......@@ -358,12 +358,13 @@ struct OpKernelType {
};
platform::Place place_;
DataType data_type_;
proto::DataType data_type_;
OpKernelType(DataType data_type, platform::Place place)
OpKernelType(proto::DataType data_type, platform::Place place)
: place_(place), data_type_(data_type) {}
OpKernelType(DataType data_type, const platform::DeviceContext& dev_ctx)
OpKernelType(proto::DataType data_type,
const platform::DeviceContext& dev_ctx)
: place_(dev_ctx.GetPlace()), data_type_(data_type) {}
bool operator==(const OpKernelType& o) const {
......@@ -409,7 +410,7 @@ class OperatorWithKernel : public OperatorBase {
private:
// indicate kernel DataType by input data. Defaultly all input data must be
// same.
DataType IndicateDataType(const ExecutionContext& ctx) const;
proto::DataType IndicateDataType(const ExecutionContext& ctx) const;
};
std::ostream& operator<<(std::ostream& os, const OpKernelType& kernel_key);
......
paddle/framework/operator_test.cc
浏览文件 @ feb05c3a
......@@ -58,7 +58,7 @@ class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
paddle::framework::proto::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
*var->mutable_arguments()->Add() = arg_name;
......@@ -70,14 +70,14 @@ REGISTER_OP_WITHOUT_GRADIENT(
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
TEST(OperatorBase, all) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("test_operator");
BuildVar("input", {"IN1"}, op_desc.add_inputs());
BuildVar("output", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_type(paddle::framework::proto::AttrType::FLOAT);
attr->set_f(3.14);
paddle::platform::CPUDeviceContext device_context;
......@@ -115,7 +115,7 @@ class OpWithKernelTest : public OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {}
OpKernelType GetKernelType(const ExecutionContext& ctx) const override {
return OpKernelType(DataType::FP32, ctx.GetPlace());
return OpKernelType(proto::DataType::FP32, ctx.GetPlace());
}
};
......@@ -195,14 +195,14 @@ REGISTER_OP_CPU_KERNEL(op_with_kernel,
// test with single input
TEST(OpKernel, all) {
paddle::framework::OpDesc op_desc;
paddle::framework::proto::OpDesc op_desc;
op_desc.set_type("op_with_kernel");
BuildVar("x", {"IN1"}, op_desc.add_inputs());
BuildVar("y", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_type(paddle::framework::proto::AttrType::FLOAT);
attr->set_f(3.14);
paddle::platform::CPUDeviceContext cpu_device_context;
......@@ -224,7 +224,7 @@ REGISTER_OP_CPU_KERNEL(op_multi_inputs_with_kernel,
TEST(OpKernel, multi_inputs) {
using namespace paddle::framework;
OpDesc op_desc;
proto::OpDesc op_desc;
op_desc.set_type("op_multi_inputs_with_kernel");
BuildVar("xs", {"x0", "x1", "x2"}, op_desc.add_inputs());
BuildVar("k", {"k0"}, op_desc.add_inputs());
......@@ -232,7 +232,7 @@ TEST(OpKernel, multi_inputs) {
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_type(paddle::framework::proto::AttrType::FLOAT);
attr->set_f(3.14);
paddle::platform::CPUDeviceContext cpu_device_context;
......
paddle/framework/program_desc.cc
浏览文件 @ feb05c3a
......@@ -18,49 +18,49 @@ limitations under the License. */
namespace paddle {
namespace framework {
BlockDescBind *ProgramDescBind::AppendBlock(const BlockDescBind &parent) {
BlockDesc *ProgramDesc::AppendBlock(const BlockDesc &parent) {
auto *b = desc_.add_blocks();
b->set_parent_idx(parent.ID());
b->set_idx(desc_.blocks_size() - 1);
blocks_.emplace_back(new BlockDescBind(this, b));
blocks_.emplace_back(new BlockDesc(this, b));
return blocks_.back().get();
}
ProgramDesc *ProgramDescBind::Proto() {
proto::ProgramDesc *ProgramDesc::Proto() {
for (auto &block : blocks_) {
block->Flush();
}
return &desc_;
}
ProgramDescBind::ProgramDescBind() {
ProgramDesc::ProgramDesc() {
auto *block = desc_.mutable_blocks()->Add();
block->set_idx(kRootBlockIndex);
block->set_parent_idx(kNoneBlockIndex);
blocks_.emplace_back(new BlockDescBind(this, block));
blocks_.emplace_back(new BlockDesc(this, block));
}
ProgramDescBind::ProgramDescBind(const ProgramDescBind &o) {
ProgramDesc::ProgramDesc(const ProgramDesc &o) {
desc_ = o.desc_;
for (int i = 0; i < desc_.blocks_size(); ++i) {
auto *block = desc_.mutable_blocks(i);
blocks_.emplace_back(new BlockDescBind(*o.blocks_[i], block, this));
blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
}
}
ProgramDescBind::ProgramDescBind(const ProgramDesc &desc) {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
desc_ = desc;
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDescBind(this, &block_desc));
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
}
ProgramDescBind::ProgramDescBind(const std::string &binary_str) {
ProgramDesc::ProgramDesc(const std::string &binary_str) {
PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
"Fail to parse program_desc from binary string.");
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDescBind(this, &block_desc));
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
}
......
paddle/framework/program_desc.h
浏览文件 @ feb05c3a
......@@ -23,32 +23,32 @@ limitations under the License. */
namespace paddle {
namespace framework {
class BlockDescBind;
class BlockDesc;
class ProgramDescBind {
class ProgramDesc {
public:
ProgramDescBind();
ProgramDesc();
explicit ProgramDescBind(const ProgramDesc &desc);
explicit ProgramDesc(const proto::ProgramDesc &desc);
ProgramDescBind(const ProgramDescBind &o);
ProgramDesc(const ProgramDesc &o);
explicit ProgramDescBind(const std::string &binary_str);
explicit ProgramDesc(const std::string &binary_str);
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDesc *AppendBlock(const BlockDesc &parent);
BlockDescBind *MutableBlock(size_t idx) { return blocks_[idx].get(); }
BlockDesc *MutableBlock(size_t idx) { return blocks_[idx].get(); }
const BlockDescBind &Block(size_t idx) const { return *blocks_[idx]; }
const BlockDesc &Block(size_t idx) const { return *blocks_[idx]; }
size_t Size() const { return blocks_.size(); }
ProgramDesc *Proto();
proto::ProgramDesc *Proto();
private:
ProgramDesc desc_;
proto::ProgramDesc desc_;
std::vector<std::unique_ptr<BlockDescBind>> blocks_;
std::vector<std::unique_ptr<BlockDesc>> blocks_;
};
} // namespace framework
} // namespace paddle
paddle/framework/program_desc_test.cc
浏览文件 @ feb05c3a
......@@ -19,18 +19,18 @@
namespace paddle {
namespace framework {
TEST(ProgramDesc, copy_ctor) {
ProgramDescBind program;
ProgramDesc program;
auto* global_block = program.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetType(proto::VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
x->SetDataType(FP32);
x->SetDataType(proto::FP32);
x->SetShape({1000, 784});
auto* y = global_block->Var("Y");
y->SetType(VarDesc_VarType_LOD_TENSOR);
y->SetType(proto::VarDesc_VarType_LOD_TENSOR);
y->SetLoDLevel(0);
y->SetDataType(FP32);
y->SetDataType(proto::FP32);
y->SetShape({784, 100});
auto* op = global_block->AppendOp();
......@@ -39,15 +39,15 @@ TEST(ProgramDesc, copy_ctor) {
op->SetInput("Y", {y->Name()});
auto* out = global_block->Var("Out");
out->SetType(VarDesc_VarType_LOD_TENSOR);
out->SetType(proto::VarDesc_VarType_LOD_TENSOR);
op->SetOutput("Y", {out->Name()});
ProgramDescBind program_copy(program);
ProgramDesc program_copy(program);
auto* global_block_copy = program_copy.MutableBlock(0);
ASSERT_NE(global_block, global_block_copy);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
auto assert_same_var = [&](const std::string& name, VarDesc* var_before) {
ASSERT_TRUE(global_block_copy->HasVar(name));
auto* copy = global_block_copy->Var(name);
ASSERT_NE(copy, var_before);
......@@ -81,18 +81,18 @@ TEST(ProgramDesc, copy_ctor) {
}
TEST(ProgramDescBind, serialize_and_deserialize) {
ProgramDescBind program_origin;
ProgramDesc program_origin;
auto* global_block = program_origin.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetType(proto::VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
x->SetDataType(FP32);
x->SetDataType(proto::FP32);
x->SetShape({1000, 784});
auto* y = global_block->Var("Y");
y->SetType(VarDesc_VarType_LOD_TENSOR);
y->SetType(proto::VarDesc_VarType_LOD_TENSOR);
y->SetLoDLevel(0);
y->SetDataType(FP32);
y->SetDataType(proto::FP32);
y->SetShape({784, 100});
auto* op = global_block->AppendOp();
......@@ -101,17 +101,17 @@ TEST(ProgramDescBind, serialize_and_deserialize) {
op->SetInput("Y", {y->Name()});
auto* out = global_block->Var("Out");
out->SetType(VarDesc_VarType_LOD_TENSOR);
out->SetType(proto::VarDesc_VarType_LOD_TENSOR);
op->SetOutput("Y", {out->Name()});
std::string binary_str;
program_origin.Proto()->SerializeToString(&binary_str);
ProgramDescBind program_restored(binary_str);
ProgramDesc program_restored(binary_str);
auto* global_block_restored = program_restored.MutableBlock(0);
ASSERT_NE(global_block, global_block_restored);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
auto assert_same_var = [&](const std::string& name, VarDesc* var_before) {
ASSERT_TRUE(global_block_restored->HasVar(name));
auto* restored = global_block_restored->Var(name);
ASSERT_NE(restored, var_before);
......
paddle/framework/prune.cc
浏览文件 @ feb05c3a
......@@ -29,7 +29,7 @@ const std::string kFetchOpType = "fetch";
const std::string kDropOutOpType = "dropout";
const std::string kBatchNormOpType = "batch_norm";
bool HasDependentVar(const OpDesc& op_desc,
bool HasDependentVar(const proto::OpDesc& op_desc,
const std::set<std::string>& dependent_vars) {
for (auto& var : op_desc.outputs()) {
for (auto& argu : var.arguments()) {
......@@ -41,14 +41,15 @@ bool HasDependentVar(const OpDesc& op_desc,
return false;
}
bool IsTarget(const OpDesc& op_desc) {
bool IsTarget(const proto::OpDesc& op_desc) {
if (op_desc.has_is_target()) {
return op_desc.is_target();
}
return false;
}
void prune_impl(const ProgramDesc& input, ProgramDesc* output, int block_id) {
void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
int block_id) {
// TODO(tonyyang-svail):
// - will change to use multiple blocks for RNN op and Cond Op
......@@ -104,12 +105,12 @@ void prune_impl(const ProgramDesc& input, ProgramDesc* output, int block_id) {
}
// TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
void Prune(const ProgramDesc& input, ProgramDesc* output) {
void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
prune_impl(input, output, 0);
}
void inference_optimize_impl(const ProgramDesc& input, ProgramDesc* output,
int block_id) {
void inference_optimize_impl(const proto::ProgramDesc& input,
proto::ProgramDesc* output, int block_id) {
*output = input;
auto* op_field = output->mutable_blocks(block_id)->mutable_ops();
for (auto& op_desc : *op_field) {
......@@ -125,7 +126,8 @@ void inference_optimize_impl(const ProgramDesc& input, ProgramDesc* output,
}
}
void InferenceOptimize(const ProgramDesc& input, ProgramDesc* output) {
void InferenceOptimize(const proto::ProgramDesc& input,
proto::ProgramDesc* output) {
inference_optimize_impl(input, output, 0);
}
......
paddle/framework/prune.h
浏览文件 @ feb05c3a
......@@ -20,9 +20,10 @@ limitations under the License. */
namespace paddle {
namespace framework {
void Prune(const ProgramDesc& input, ProgramDesc* output);
void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output);
void InferenceOptimize(const ProgramDesc& input, ProgramDesc* output);
void InferenceOptimize(const proto::ProgramDesc& input,
proto::ProgramDesc* output);
} // namespace framework
} // namespace paddle
paddle/framework/prune_test.cc
浏览文件 @ feb05c3a
......@@ -29,12 +29,12 @@ namespace ops = paddle::operators;
void AddOp(const std::string &type, const f::VariableNameMap &inputs,
const f::VariableNameMap &outputs, f::AttributeMap attrs,
paddle::framework::BlockDescBind *block) {
paddle::framework::BlockDesc *block) {
// insert output
for (auto kv : outputs) {
for (auto v : kv.second) {
auto var = block->Var(v);
var->SetDataType(paddle::framework::DataType::FP32);
var->SetDataType(paddle::framework::proto::DataType::FP32);
}
}
......@@ -51,26 +51,26 @@ void AddOp(const std::string &type, const f::VariableNameMap &inputs,
}
TEST(Prune, one_operator) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, f::AttributeMap{},
block);
f::ProgramDesc *pdesc = program.Proto();
f::ProgramDesc pruned;
f::proto::ProgramDesc *pdesc = program.Proto();
f::proto::ProgramDesc pruned;
Prune(*pdesc, &pruned);
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 0);
pdesc->mutable_blocks(0)->mutable_ops(0)->set_is_target(true);
Prune(*pdesc, &pruned);
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 1);
}
TEST(Prune, forward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, f::AttributeMap{},
block);
......@@ -81,19 +81,19 @@ TEST(Prune, forward) {
AddOp("one_one", {{"input", {"d"}}}, {{"output", {"e"}}}, f::AttributeMap{},
block);
f::ProgramDesc *pdesc = program.Proto();
f::proto::ProgramDesc *pdesc = program.Proto();
for (int i = 0; i < pdesc->blocks(0).ops_size(); ++i) {
f::ProgramDesc pruned;
f::proto::ProgramDesc pruned;
pdesc->mutable_blocks(0)->mutable_ops(i)->set_is_target(true);
Prune(*pdesc, &pruned);
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), i + 1);
}
}
TEST(Prune, multi_input_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, f::AttributeMap{},
block);
......@@ -104,17 +104,17 @@ TEST(Prune, multi_input_op) {
AddOp("three_one", {{"input", {"b0", "b1", "b2"}}}, {{"output", {"c"}}},
f::AttributeMap{}, block);
f::ProgramDesc *pdesc = program.Proto();
f::proto::ProgramDesc *pdesc = program.Proto();
pdesc->mutable_blocks(0)->mutable_ops(3)->set_is_target(true);
f::ProgramDesc pruned;
Prune(*pdesc, &pruned);
f::proto::ProgramDesc pruned;
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 4);
}
TEST(Prune, multi_output_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}},
f::AttributeMap{}, block);
......@@ -123,17 +123,17 @@ TEST(Prune, multi_output_op) {
AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, f::AttributeMap{},
block);
f::ProgramDesc *pdesc = program.Proto();
f::proto::ProgramDesc *pdesc = program.Proto();
pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
f::ProgramDesc pruned;
Prune(*pdesc, &pruned);
f::proto::ProgramDesc pruned;
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 2);
}
TEST(Prune, multi_target) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.MutableBlock(0);
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}},
f::AttributeMap{}, block);
......@@ -142,11 +142,11 @@ TEST(Prune, multi_target) {
AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, f::AttributeMap{},
block);
f::ProgramDesc *pdesc = program.Proto();
f::proto::ProgramDesc *pdesc = program.Proto();
pdesc->mutable_blocks(0)->mutable_ops(1)->set_is_target(true);
pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
f::ProgramDesc pruned;
Prune(*pdesc, &pruned);
f::proto::ProgramDesc pruned;
f::Prune(*pdesc, &pruned);
PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 3);
}
paddle/framework/shape_inference.cc
浏览文件 @ feb05c3a
......@@ -57,17 +57,17 @@ void InferShapeContext::SetDims(const std::vector<std::string> &names,
SetDim(names[i], dims[i]);
}
}
std::vector<VarDesc::VarType> InferShapeContext::GetInputsVarType(
std::vector<proto::VarDesc::VarType> InferShapeContext::GetInputsVarType(
const std::string &name) const {
return GetVarTypes(Inputs(name));
}
std::vector<VarDesc::VarType> InferShapeContext::GetOutputsVarType(
std::vector<proto::VarDesc::VarType> InferShapeContext::GetOutputsVarType(
const std::string &name) const {
return GetVarTypes(Outputs(name));
}
std::vector<VarDesc::VarType> InferShapeContext::GetVarTypes(
std::vector<proto::VarDesc::VarType> InferShapeContext::GetVarTypes(
const std::vector<std::string> &names) const {
std::vector<VarDesc::VarType> retv;
std::vector<proto::VarDesc::VarType> retv;
retv.resize(names.size());
std::transform(names.begin(), names.end(), retv.begin(),
std::bind(std::mem_fn(&InferShapeContext::GetVarType), this,
......
paddle/framework/shape_inference.h
浏览文件 @ feb05c3a
......@@ -27,8 +27,9 @@ class InferShapeContext {
virtual bool HasInput(const std::string &name) const = 0;
virtual bool HasOutput(const std::string &name) const = 0;
std::vector<VarDesc::VarType> GetInputsVarType(const std::string &name) const;
std::vector<VarDesc::VarType> GetOutputsVarType(
std::vector<proto::VarDesc::VarType> GetInputsVarType(
const std::string &name) const;
std::vector<proto::VarDesc::VarType> GetOutputsVarType(
const std::string &name) const;
virtual bool HasInputs(const std::string &name) const = 0;
......@@ -65,10 +66,10 @@ class InferShapeContext {
std::vector<framework::DDim> GetDims(
const std::vector<std::string> &names) const;
std::vector<VarDesc::VarType> GetVarTypes(
std::vector<proto::VarDesc::VarType> GetVarTypes(
const std::vector<std::string> &names) const;
virtual VarDesc::VarType GetVarType(const std::string &name) const = 0;
virtual proto::VarDesc::VarType GetVarType(const std::string &name) const = 0;
};
} // namespace framework
......
paddle/framework/type_defs.h
浏览文件 @ feb05c3a
......@@ -25,11 +25,9 @@
namespace paddle {
namespace framework {
class OperatorBase;
class OpDescBind;
class BlockDescBind;
class BlockDesc;
class OpDesc;
class InferShapeContext;
class BlockDescBind;
class BlockDesc;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
......@@ -37,7 +35,7 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using Attribute =
boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDescBind*>;
std::vector<bool>, BlockDesc*>;
using AttributeMap = std::unordered_map<std::string, Attribute>;
......@@ -45,13 +43,13 @@ using OpCreator = std::function<OperatorBase*(
const std::string& /*type*/, const VariableNameMap& /*inputs*/,
const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDesc>>(
const OpDesc&, const std::unordered_set<std::string>& /*no_grad_set*/,
std::unordered_map<std::string, std::string>* /*grad_to_var*/,
const std::vector<BlockDescBind*>& grad_block)>;
const std::vector<BlockDesc*>& grad_block)>;
using InferVarTypeFN = std::function<void(const OpDescBind& /*op_desc*/,
BlockDescBind* /*block*/)>;
using InferVarTypeFN =
std::function<void(const OpDesc& /*op_desc*/, BlockDesc* /*block*/)>;
using InferShapeFN = std::function<void(InferShapeContext*)>;
......
paddle/framework/var_desc.cc
浏览文件 @ feb05c3a
......@@ -18,30 +18,32 @@ limitations under the License. */
namespace paddle {
namespace framework {
VarDesc::VarType VarDescBind::GetType() const { return desc_.type(); }
proto::VarDesc::VarType VarDesc::GetType() const { return desc_.type(); }
void VarDescBind::SetType(VarDesc::VarType type) { desc_.set_type(type); }
void VarDesc::SetType(proto::VarDesc::VarType type) { desc_.set_type(type); }
void VarDescBind::SetShape(const std::vector<int64_t> &dims) {
void VarDesc::SetShape(const std::vector<int64_t> &dims) {
VectorToRepeated(dims, mutable_tensor_desc()->mutable_dims());
}
void VarDescBind::SetDataType(DataType data_type) {
void VarDesc::SetDataType(proto::DataType data_type) {
mutable_tensor_desc()->set_data_type(data_type);
}
std::vector<int64_t> VarDescBind::Shape() const {
std::vector<int64_t> VarDesc::Shape() const {
return RepeatedToVector(tensor_desc().dims());
}
DataType VarDescBind::GetDataType() const { return tensor_desc().data_type(); }
proto::DataType VarDesc::GetDataType() const {
return tensor_desc().data_type();
}
void VarDescBind::SetLoDLevel(int32_t lod_level) {
void VarDesc::SetLoDLevel(int32_t lod_level) {
switch (desc_.type()) {
case VarDesc::LOD_TENSOR:
case proto::VarDesc::LOD_TENSOR:
desc_.mutable_lod_tensor()->set_lod_level(lod_level);
break;
case VarDesc::LOD_TENSOR_ARRAY:
case proto::VarDesc::LOD_TENSOR_ARRAY:
desc_.mutable_tensor_array()->set_lod_level(lod_level);
break;
default:
......@@ -50,11 +52,11 @@ void VarDescBind::SetLoDLevel(int32_t lod_level) {
}
}
int32_t VarDescBind::GetLodLevel() const {
int32_t VarDesc::GetLodLevel() const {
switch (desc_.type()) {
case VarDesc::LOD_TENSOR:
case proto::VarDesc::LOD_TENSOR:
return desc_.lod_tensor().lod_level();
case VarDesc::LOD_TENSOR_ARRAY:
case proto::VarDesc::LOD_TENSOR_ARRAY:
return desc_.tensor_array().lod_level();
default:
PADDLE_THROW("Tensor type=%d does not support LoDLevel",
......@@ -62,29 +64,29 @@ int32_t VarDescBind::GetLodLevel() const {
}
}
const TensorDesc &VarDescBind::tensor_desc() const {
const proto::TensorDesc &VarDesc::tensor_desc() const {
PADDLE_ENFORCE(desc_.has_type(), "invoke TensorDesc must after set type");
switch (desc_.type()) {
case VarDesc::SELECTED_ROWS:
case proto::VarDesc::SELECTED_ROWS:
return desc_.selected_rows();
case VarDesc::LOD_TENSOR:
case proto::VarDesc::LOD_TENSOR:
return desc_.lod_tensor().tensor();
case VarDesc::LOD_TENSOR_ARRAY:
case proto::VarDesc::LOD_TENSOR_ARRAY:
return desc_.tensor_array().tensor();
default:
PADDLE_THROW("Unexpected branch.");
}
}
TensorDesc *VarDescBind::mutable_tensor_desc() {
proto::TensorDesc *VarDesc::mutable_tensor_desc() {
PADDLE_ENFORCE(desc_.has_type(),
"invoke MutableTensorDesc must after set type");
switch (desc_.type()) {
case VarDesc::SELECTED_ROWS:
case proto::VarDesc::SELECTED_ROWS:
return desc_.mutable_selected_rows();
case VarDesc::LOD_TENSOR:
case proto::VarDesc::LOD_TENSOR:
return desc_.mutable_lod_tensor()->mutable_tensor();
case VarDesc::LOD_TENSOR_ARRAY:
case proto::VarDesc::LOD_TENSOR_ARRAY:
return desc_.mutable_tensor_array()->mutable_tensor();
default:
PADDLE_THROW("Unexpected branch.");
......
paddle/framework/var_desc.h
浏览文件 @ feb05c3a
......@@ -53,44 +53,44 @@ inline void VectorToRepeated(const std::vector<bool> &vec,
}
}
class VarDescBind {
class VarDesc {
public:
explicit VarDescBind(const std::string &name) {
explicit VarDesc(const std::string &name) {
desc_.set_name(name);
desc_.set_type(VarDesc::LOD_TENSOR);
desc_.set_type(proto::VarDesc::LOD_TENSOR);
}
explicit VarDescBind(const VarDesc &desc) : desc_(desc) {}
explicit VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
VarDesc *Proto() { return &desc_; }
proto::VarDesc *Proto() { return &desc_; }
std::string Name() const { return desc_.name(); }
void SetShape(const std::vector<int64_t> &dims);
void SetDataType(DataType data_type);
void SetDataType(proto::DataType data_type);
std::vector<int64_t> Shape() const;
DataType GetDataType() const;
proto::DataType GetDataType() const;
void SetLoDLevel(int32_t lod_level);
int32_t GetLodLevel() const;
VarDesc::VarType GetType() const;
proto::VarDesc::VarType GetType() const;
void SetType(VarDesc::VarType type);
void SetType(proto::VarDesc::VarType type);
bool Persistable() const { return desc_.persistable(); }
void SetPersistable(bool persistable) { desc_.set_persistable(persistable); }
private:
const TensorDesc &tensor_desc() const;
TensorDesc *mutable_tensor_desc();
const proto::TensorDesc &tensor_desc() const;
proto::TensorDesc *mutable_tensor_desc();
VarDesc desc_;
proto::VarDesc desc_;
};
} // namespace framework
} // namespace paddle
paddle/framework/var_type.h
浏览文件 @ feb05c3a
......@@ -20,15 +20,15 @@
namespace paddle {
namespace framework {
inline VarDesc::VarType ToVarType(std::type_index type) {
inline proto::VarDesc::VarType ToVarType(std::type_index type) {
if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return VarDesc_VarType_LOD_TENSOR;
return proto::VarDesc_VarType_LOD_TENSOR;
} else if (type.hash_code() == typeid(LoDRankTable).hash_code()) {
return VarDesc_VarType_LOD_RANK_TABLE;
return proto::VarDesc_VarType_LOD_RANK_TABLE;
} else if (type.hash_code() == typeid(LoDTensorArray).hash_code()) {
return VarDesc_VarType_LOD_TENSOR_ARRAY;
return proto::VarDesc_VarType_LOD_TENSOR_ARRAY;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return VarDesc_VarType_SELECTED_ROWS;
return proto::VarDesc_VarType_SELECTED_ROWS;
} else {
PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
}
......@@ -37,16 +37,16 @@ inline VarDesc::VarType ToVarType(std::type_index type) {
template <typename Visitor>
inline void VisitVarType(const Variable& var, Visitor visitor) {
switch (ToVarType(var.Type())) {
case VarDesc_VarType_LOD_TENSOR:
case proto::VarDesc_VarType_LOD_TENSOR:
visitor(var.Get<framework::LoDTensor>());
return;
case VarDesc_VarType_LOD_RANK_TABLE:
case proto::VarDesc_VarType_LOD_RANK_TABLE:
visitor(var.Get<LoDRankTable>());
return;
case VarDesc_VarType_LOD_TENSOR_ARRAY:
case proto::VarDesc_VarType_LOD_TENSOR_ARRAY:
visitor(var.Get<LoDTensorArray>());
return;
case VarDesc_VarType_SELECTED_ROWS:
case proto::VarDesc_VarType_SELECTED_ROWS:
visitor(var.Get<SelectedRows>());
return;
default:
......
paddle/framework/var_type_inference.h
浏览文件 @ feb05c3a
......@@ -21,8 +21,7 @@ namespace framework {
class VarTypeInference {
public:
virtual ~VarTypeInference() {}
virtual void operator()(const OpDescBind& op_desc,
BlockDescBind* block) const = 0;
virtual void operator()(const OpDesc& op_desc, BlockDesc* block) const = 0;
};
} // namespace framework
......
paddle/framework/var_type_inference_test.cc
浏览文件 @ feb05c3a
......@@ -33,17 +33,16 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
class SumOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDescBind &op_desc,
BlockDescBind *block) const override {
void operator()(const OpDesc &op_desc, BlockDesc *block) const override {
auto &inputs = op_desc.Input("X");
auto default_var_type = VarDesc::SELECTED_ROWS;
auto default_var_type = proto::VarDesc::SELECTED_ROWS;
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string &name) {
return block->Var(name)->GetType() == VarDesc::LOD_TENSOR;
return block->Var(name)->GetType() == proto::VarDesc::LOD_TENSOR;
});
if (any_input_is_lod_tensor) {
default_var_type = VarDesc::LOD_TENSOR;
default_var_type = proto::VarDesc::LOD_TENSOR;
}
auto out_var_name = op_desc.Output("Out").front();
......@@ -62,43 +61,43 @@ namespace paddle {
namespace framework {
TEST(InferVarType, sum_op) {
ProgramDescBind prog;
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
prog.MutableBlock(0)->Var("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_a")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_c")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_out");
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::SELECTED_ROWS,
ASSERT_EQ(proto::VarDesc::SELECTED_ROWS,
prog.MutableBlock(0)->Var("test_out")->GetType());
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarDesc::LOD_TENSOR);
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::LOD_TENSOR,
ASSERT_EQ(proto::VarDesc::LOD_TENSOR,
prog.MutableBlock(0)->Var("test_out")->GetType());
}
TEST(InferVarType, sum_op_without_infer_var_type) {
ProgramDescBind prog;
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum_without_infer_var_type");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
prog.MutableBlock(0)->Var("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_out");
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc_VarType_LOD_TENSOR,
ASSERT_EQ(proto::VarDesc_VarType_LOD_TENSOR,
prog.MutableBlock(0)->Var("test2_out")->GetType());
}
......
paddle/function/GemmConvOp.cpp
浏览文件 @ feb05c3a
......@@ -128,7 +128,7 @@ public:
}
#ifdef PADDLE_MOBILE_INFERENCE
if (Device == DEVICE_TYPE_CPU) {
delete memory_;
memory_.reset();
}
#endif
}
......
paddle/operators/accuracy_op.cc
浏览文件 @ feb05c3a
......@@ -63,8 +63,7 @@ class AccuracyOp : public framework::OperatorWithKernel {
class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AccuracyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
AccuracyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// TODO(typhoonzero): support both inference value and indices.
AddInput("Out", "The network output of topk (inferences)");
......
paddle/operators/accuracy_op.cu
浏览文件 @ feb05c3a
......@@ -26,7 +26,7 @@ template <int BlockSize>
__global__ void AccuracyCudaKernel(const int N, const int D,
const int64_t* Xdata,
const int64_t* labeldata, int* correct_data,
float* accuracy) {
float* accuracy, int* total_data) {
int count = 0;
__shared__ int total[BlockSize];
......@@ -47,6 +47,7 @@ __global__ void AccuracyCudaKernel(const int N, const int D,
if (threadIdx.x == 0) {
*correct_data = result;
*accuracy = static_cast<float>(result) / static_cast<float>(N);
*total_data = N;
}
}
......@@ -80,22 +81,11 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
if (num_samples == 0) {
return;
}
platform::GpuMemcpyAsync(total_data, &num_samples, sizeof(int),
cudaMemcpyHostToDevice, stream);
AccuracyCudaKernel<
PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
num_samples, infer_width, indices_data, label_data, correct_data,
accuracy_data);
int d_num_samples, d_num_correct;
float d_accuracy;
platform::GpuMemcpyAsync(&d_num_correct, correct_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_num_samples, total_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_accuracy, accuracy_data, sizeof(float),
cudaMemcpyDeviceToHost, stream);
accuracy_data, total_data);
}
};
......
paddle/operators/activation_op.cc
浏览文件 @ feb05c3a
......@@ -38,9 +38,8 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sigmoid operator");
AddOutput("Y", "Output of Sigmoid operator");
AddComment(R"DOC(
......@@ -54,9 +53,8 @@ $$y = \frac{1}{1 + e^{-x}}$$
class LogSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LogSigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
LogSigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LogSigmoid operator");
AddOutput("Y", "Output of LogSigmoid operator");
AddComment(R"DOC(
......@@ -70,8 +68,8 @@ $$y = \log \frac{1}{1 + e^{-x}}$$
class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ExpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
ExpOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Exp operator");
AddOutput("Y", "Output of Exp operator");
AddComment(R"DOC(
......@@ -85,8 +83,8 @@ $y = e^x$
class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
ReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu operator");
AddOutput("Y", "Output of Relu operator");
AddComment(R"DOC(
......@@ -100,9 +98,8 @@ $y = \max(x, 0)$
class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LeakyReluOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
LeakyReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Y", "Output of LeakyRelu operator");
AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
......@@ -117,9 +114,8 @@ $y = \max(x, \alpha * x)$
class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftShrinkOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SoftShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softshrink operator");
AddOutput("Y", "Output of Softshrink operator");
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
......@@ -140,8 +136,8 @@ $$
class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
TanhOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Tanh operator");
AddOutput("Y", "Output of Tanh operator");
AddComment(R"DOC(
......@@ -155,9 +151,8 @@ $$y = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TanhShrinkOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
TanhShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of TanhShrink operator");
AddOutput("Y", "Output of TanhShrink operator");
AddComment(R"DOC(
......@@ -171,9 +166,8 @@ $$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardShrinkOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
HardShrinkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardShrink operator");
AddOutput("Y", "Output of HardShrink operator");
AddAttr<float>("threshold", "The value of threshold for HardShrink")
......@@ -195,8 +189,8 @@ $$
class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SqrtOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SqrtOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Sqrt operator");
AddOutput("Y", "Output of Sqrt operator");
AddComment(R"DOC(
......@@ -210,8 +204,8 @@ $y = \sqrt{x}$
class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AbsOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AbsOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Abs operator");
AddOutput("Y", "Output of Abs operator");
AddComment(R"DOC(
......@@ -225,8 +219,8 @@ $y = |x|$
class CeilOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CeilOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
CeilOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Ceil operator");
AddOutput("Y", "Output of Ceil operator");
AddComment(R"DOC(
......@@ -240,8 +234,8 @@ $y = ceil(x)$
class FloorOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FloorOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
FloorOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Floor operator");
AddOutput("Y", "Output of Floor operator");
AddComment(R"DOC(
......@@ -255,8 +249,8 @@ $y = floor(x)$
class RoundOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RoundOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
RoundOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Round operator");
AddOutput("Y", "Output of Round operator");
AddComment(R"DOC(
......@@ -270,9 +264,8 @@ $y = [x]$
class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReciprocalOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
ReciprocalOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Reciprocal operator");
AddOutput("Y", "Output of Reciprocal operator");
AddComment(R"DOC(
......@@ -286,8 +279,8 @@ $$y = \frac{1}{x}$$
class LogOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LogOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
LogOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Log operator");
AddOutput("Y", "Output of Log operator");
AddComment(R"DOC(
......@@ -303,8 +296,8 @@ Natural logarithm of x.
class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SquareOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SquareOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Square operator");
AddOutput("Y", "Output of Square operator");
AddComment(R"DOC(
......@@ -318,9 +311,8 @@ $y = x^2$
class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftplusOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SoftplusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softplus operator");
AddOutput("Y", "Output of Softplus operator");
AddComment(R"DOC(
......@@ -334,9 +326,8 @@ $y = \ln(1 + e^{x})$
class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftsignOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SoftsignOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softsign operator");
AddOutput("Y", "Output of Softsign operator");
AddComment(R"DOC(
......@@ -350,8 +341,8 @@ $$y = \frac{x}{1 + |x|}$$
class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
BReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of BRelu operator");
AddOutput("Y", "Output of BRelu operator");
AddAttr<float>("t_min", "The min marginal value of BRelu")
......@@ -369,9 +360,8 @@ $y = \max(\min(x, t_{min}), t_{max})$
class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftReluOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SoftReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SoftRelu operator");
AddOutput("Y", "Output of SoftRelu operator");
AddAttr<float>("threshold", "The threshold value of SoftRelu")
......@@ -387,8 +377,8 @@ $y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
ELUOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ELU operator");
AddOutput("Y", "Output of ELU operator");
AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
......@@ -406,8 +396,8 @@ $y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
public:
Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
Relu6OpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu6 operator");
AddOutput("Y", "Output of Relu6 operator");
AddAttr<float>("threshold", "The threshold value of Relu6")
......@@ -423,8 +413,8 @@ $y = \min(\max(0, x), 6)$
class PowOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PowOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
PowOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Pow operator");
AddOutput("Y", "Output of Pow operator");
AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
......@@ -439,8 +429,8 @@ $y = x^{factor}$
class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
public:
STanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
STanhOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of STanh operator");
AddOutput("Y", "Output of STanh operator");
AddAttr<float>("scale_a", "The scale parameter of a for the input")
......@@ -458,9 +448,8 @@ $$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ThresholdedReluOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
ThresholdedReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Y", "Output of ThresholdedRelu operator");
AddAttr<float>("threshold", "The threshold location of activation")
......@@ -481,9 +470,8 @@ $$
class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardSigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
HardSigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddAttr<float>("slope", "Slope for linear approximation of sigmoid")
......@@ -508,8 +496,8 @@ It is recommended to use the defaults for this activation.
class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SwishOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
SwishOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Swish operator");
AddOutput("Y", "Output of Swish operator");
AddAttr<float>("beta", "Constant beta of swish operator").SetDefault(1.0f);
......
paddle/operators/adadelta_op.cc
浏览文件 @ feb05c3a
......@@ -59,8 +59,7 @@ class AdadeltaOp : public framework::OperatorWithKernel {
class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdadeltaOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
AdadeltaOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......
paddle/operators/adagrad_op.cc
浏览文件 @ feb05c3a
......@@ -59,8 +59,7 @@ class AdagradOp : public framework::OperatorWithKernel {
class AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdagradOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
AdagradOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......
paddle/operators/adam_op.cc
浏览文件 @ feb05c3a
......@@ -73,7 +73,7 @@ class AdamOp : public framework::OperatorWithKernel {
class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdamOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
AdamOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......
paddle/operators/adamax_op.cc
浏览文件 @ feb05c3a
......@@ -67,7 +67,7 @@ class AdamaxOp : public framework::OperatorWithKernel {
class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdamaxOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
AdamaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......
paddle/operators/array_to_lod_tensor_op.cc
浏览文件 @ feb05c3a
......@@ -114,8 +114,7 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ArrayToLoDTensorOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ArrayToLoDTensorOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(std::vector<LodTensor>) A vector of tensors that is going to "
......@@ -150,14 +149,14 @@ class ArrayToLoDTensorGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("lod_tensor_to_array");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetInput("RankTable", Input("RankTable"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/assign_op.cc
浏览文件 @ feb05c3a
......@@ -86,8 +86,7 @@ class AssignOp : public framework::OperatorBase {
class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
AssignOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
AssignOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
......@@ -109,8 +108,8 @@ class AssignInferShape : public framework::InferShapeBase {
void operator()(framework::InferShapeContext *context) const override {
if (context->HasInput("X")) {
auto type = context->GetInputsVarType("X")[0];
if (type == framework::VarDesc_VarType_SELECTED_ROWS ||
type == framework::VarDesc_VarType_LOD_TENSOR) {
if (type == framework::proto::VarDesc_VarType_SELECTED_ROWS ||
type == framework::proto::VarDesc_VarType_LOD_TENSOR) {
context->SetOutputDim("Out", context->GetInputDim("X"));
}
}
......@@ -122,12 +121,12 @@ class AssignGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *op = new framework::OpDesc();
op->SetType("assign");
op->SetInput("X", OutputGrad("Out"));
op->SetOutput("Out", InputGrad("X"));
return std::unique_ptr<framework::OpDescBind>(op);
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/operators/auc_op.cc
浏览文件 @ feb05c3a
......@@ -49,7 +49,7 @@ class AucOp : public framework::OperatorWithKernel {
class AucOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
AucOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Out",
"A floating point 2D tensor, values are in the range [0, 1]."
......
paddle/operators/batch_norm_op.cc
浏览文件 @ feb05c3a
......@@ -85,8 +85,7 @@ class BatchNormOp : public framework::OperatorWithKernel {
class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BatchNormOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
BatchNormOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<bool>("is_test", "").SetDefault(false);
AddAttr<float>("momentum", "").SetDefault(0.9);
......
paddle/operators/beam_search_decode_op.cc
浏览文件 @ feb05c3a
......@@ -83,9 +83,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
class BeamSearchDecodeOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
BeamSearchDecodeOpProtoMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
BeamSearchDecodeOpProtoMaker(OpProto* proto, OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ids",
"(LodTensorArray)"
"score of the candidate words in each step");
......@@ -120,13 +119,13 @@ class BeamSearchDecodeInferShape : public framework::InferShapeBase {
class BeamSearchDecodeInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind& op_desc,
framework::BlockDescBind* block) const override {
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
for (auto& o : op_desc.Output("SentenceIds")) {
block->Var(o)->SetType(framework::VarDesc::LOD_TENSOR);
block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
}
for (auto& o : op_desc.Output("SentenceScores")) {
block->Var(o)->SetType(framework::VarDesc::LOD_TENSOR);
block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
}
}
};
......
paddle/operators/beam_search_op.cc
浏览文件 @ feb05c3a
......@@ -153,8 +153,7 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
class BeamSearchProtoAndCheckerMaker
: public framework::OpProtoAndCheckerMaker {
public:
BeamSearchProtoAndCheckerMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
BeamSearchProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// inputs and outputs stored in proto
AddInput("pre_ids", "ids in previous step");
......
paddle/operators/bilinear_tensor_product_op.cc
浏览文件 @ feb05c3a
......@@ -65,8 +65,7 @@ class BilinearTensorProductOp : public framework::OperatorWithKernel {
class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BilinearTensorProductOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
BilinearTensorProductOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of bilinear_tensor_product operator.");
AddInput("Y", "The second input of bilinear_tensor_product operator.");
......
paddle/operators/cast_op.cc
浏览文件 @ feb05c3a
......@@ -20,8 +20,7 @@ namespace operators {
class CastOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
CastOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
CastOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of cast op");
AddOutput("Out", "The output tensor of cast op");
......@@ -53,14 +52,14 @@ class CastOpGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto grad = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto grad = new framework::OpDesc();
grad->SetType("cast");
grad->SetInput("X", OutputGrad("Out"));
grad->SetOutput("Out", InputGrad("X"));
grad->SetAttr("out_dtype", GetAttr("in_dtype"));
grad->SetAttr("in_dtype", GetAttr("out_dtype"));
return std::unique_ptr<framework::OpDescBind>(grad);
return std::unique_ptr<framework::OpDesc>(grad);
}
};
......
paddle/operators/cast_op.h
浏览文件 @ feb05c3a
......@@ -55,7 +55,7 @@ class CastOpKernel : public framework::OpKernel<InT> {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
framework::VisitDataType(
static_cast<framework::DataType>(context.Attr<int>("out_dtype")),
static_cast<framework::proto::DataType>(context.Attr<int>("out_dtype")),
CastOpFunctor<DeviceContext, InT>(
in, out, context.template device_context<DeviceContext>()));
}
......
paddle/operators/chunk_eval_op.cc
浏览文件 @ feb05c3a
......@@ -57,15 +57,14 @@ class ChunkEvalOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(framework::DataType::FP32,
return framework::OpKernelType(framework::proto::DataType::FP32,
ctx.device_context());
}
};
class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ChunkEvalOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ChunkEvalOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Inference",
"(Tensor, default: Tensor<int64_t>). "
......
paddle/operators/clip_by_norm_op.cc
浏览文件 @ feb05c3a
......@@ -37,8 +37,7 @@ class ClipByNormOp : public framework::OperatorWithKernel {
class ClipByNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ClipByNormOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ClipByNormOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input of clip_by_norm op."
......
paddle/operators/clip_op.cc
浏览文件 @ feb05c3a
......@@ -38,7 +38,7 @@ class ClipOp : public framework::OperatorWithKernel {
template <typename AttrType>
class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ClipOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
ClipOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor)The input of clip op."
......
paddle/operators/compare_op.cc
浏览文件 @ feb05c3a
......@@ -20,8 +20,7 @@ namespace operators {
template <typename OpComment>
class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
CompareOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
CompareOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
OpComment comment;
AddInput("X",
......
paddle/operators/concat_op.cc
浏览文件 @ feb05c3a
......@@ -58,7 +58,7 @@ class ConcatOp : public framework::OperatorWithKernel {
class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ConcatOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ConcatOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input tensors of concat operator.").AsDuplicable();
AddOutput("Out", "Output tensor of concat operator.");
......
paddle/operators/cond_op.cc
浏览文件 @ feb05c3a
......@@ -205,8 +205,7 @@ void CondOp::Run(const Scope& scope,
class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
public:
CondOpProtoAndCheckerMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CondOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Cond", "The condition, which is a bool vector");
AddInput("Xs", "Inputs of Subnets").AsDuplicable();
......
paddle/operators/conditional_block_op.cc
浏览文件 @ feb05c3a
......@@ -65,7 +65,7 @@ class ConditionalBlockOp : public ConditionalOp {
scopes->front() = &scope.NewScope();
auto &cur_scope = *scopes->front();
auto *block = Attr<framework::BlockDescBind *>("sub_block");
auto *block = Attr<framework::BlockDesc *>("sub_block");
framework::Executor exec(dev_ctx);
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
}
......@@ -74,8 +74,7 @@ class ConditionalBlockOp : public ConditionalOp {
class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ConditionalBlockOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ConditionalBlockOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The conditional variable of this operator. If X is empty, the "
......@@ -87,7 +86,7 @@ class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"(std::vector<Scope*>) The step scope of conditional block. To "
"unify the conditional block, rnn and while op, the type of "
"scope is std::vector<Scope*>");
AddAttr<framework::BlockDescBind *>(
AddAttr<framework::BlockDesc *>(
"sub_block", "The step block of conditional block operator");
AddComment(R"DOC(Conditional block operator
......@@ -117,7 +116,7 @@ class ConditionalBlockGradOp : public ConditionalOp {
auto &scopes = scope_var->Get<std::vector<framework::Scope *>>();
framework::Scope &cur_scope = *scopes[0];
auto *block = Attr<framework::BlockDescBind *>("sub_block");
auto *block = Attr<framework::BlockDesc *>("sub_block");
framework::Executor exec(dev_ctx);
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
......@@ -171,8 +170,8 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto grad_op = new framework::OpDesc();
grad_op->SetType("conditional_block_grad");
grad_op->SetInput("X", Input("X"));
grad_op->SetInput("Params", Input("Params"));
......@@ -182,7 +181,7 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetOutput(framework::GradVarName("Params"), InputGrad("Params"));
grad_op->SetBlockAttr("sub_block", *this->grad_block_[0]);
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ feb05c3a
......@@ -19,8 +19,7 @@ namespace operators {
class CudnnConv2DOpMaker : public Conv2DOpMaker {
public:
CudnnConv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
......@@ -34,8 +33,7 @@ class CudnnConv2DOpMaker : public Conv2DOpMaker {
class CudnnConv3DOpMaker : public Conv3DOpMaker {
public:
CudnnConv3DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: Conv3DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
......
paddle/operators/conv_op.cc
浏览文件 @ feb05c3a
......@@ -66,8 +66,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}
Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
......@@ -138,8 +137,7 @@ $$
)DOC");
}
Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
Conv3DOpMaker::Conv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
......
paddle/operators/conv_op.h
浏览文件 @ feb05c3a
......@@ -50,14 +50,12 @@ inline bool IsExpand(std::vector<int64_t>& filter_dim,
// operator implementations can reuse the code.
class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
class Conv3DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv3DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Conv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
class ConvOp : public framework::OperatorWithKernel {
......
paddle/operators/conv_shift_op.cc
浏览文件 @ feb05c3a
......@@ -75,8 +75,7 @@ class ConvShiftGradOp : public framework::OperatorWithKernel {
class ConvShiftOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ConvShiftOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ConvShiftOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape B x M, "
......
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @ feb05c3a
......@@ -19,11 +19,8 @@ namespace operators {
class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
public:
CudnnConv2DTransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv2DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: Conv2DTransposeOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault({1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......@@ -36,11 +33,8 @@ class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
class CudnnConv3DTransposeOpMaker : public Conv3DTransposeOpMaker {
public:
CudnnConv3DTransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv3DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: Conv3DTransposeOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault({1, 1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......
paddle/operators/conv_transpose_op.cc
浏览文件 @ feb05c3a
......@@ -29,6 +29,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
......@@ -41,20 +42,24 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
"ConvTransposeOp paddings dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(paddings.size(), dilations.size(),
"ConvTransposeOp paddings dimension and dilations "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"In ConvTransposeOp, The input channel should be the same "
"as the number of filters.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
for (size_t i = 0; i < strides.size(); ++i) {
auto filter_extent = dilations[i] * (filter_dims[i + 2] - 1) + 1;
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
filter_dims[i + 2]);
filter_extent);
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}
Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
framework::OpProto* proto, framework::OpAttrChecker* op_checker)
Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
......@@ -73,6 +78,12 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of convolution "
"transpose operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>(
"strides",
"(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of "
......@@ -87,7 +98,7 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
Convolution2D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and dilations, strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
......@@ -112,8 +123,8 @@ Example:
)DOC");
}
Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
framework::OpProto* proto, framework::OpAttrChecker* op_checker)
Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor) The input tensor of convolution transpose operator."
......@@ -136,6 +147,13 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"Where N is batch size, C is "
"the number of channels, D is the depth of the feature, H is the "
"height of the feature, and W is the width of the feature.");
AddAttr<std::vector<int>>(
"dilations",
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation,h_dilation, w_dilation) of convolution "
"transpose operator.")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1, 1}), the "
"strides{d_stride, h_stride, w_stride} of "
......@@ -149,7 +167,7 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
Convolution3D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and dilations, strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the
number of channels, D is the depth of the feature, H is the height of the feature,
......
paddle/operators/conv_transpose_op.h
浏览文件 @ feb05c3a
......@@ -30,14 +30,12 @@ using DDim = framework::DDim;
// operator implementations can reuse the code.
class Conv2DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DTransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Conv2DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
class Conv3DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv3DTransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Conv3DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
class ConvTransposeOp : public framework::OperatorWithKernel {
......@@ -63,6 +61,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
// groups will alway be disabled in conv2dtranspose.
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -115,7 +114,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
math::Col2VolFunctor<DeviceContext, T> col2vol;
std::vector<int> dilations({1, 1, 1});
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on input)
......@@ -167,6 +165,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -221,7 +220,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
math::Vol2ColFunctor<DeviceContext, T> vol2col;
std::vector<int> dilations({1, 1, 1});
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
......
paddle/operators/cos_sim_op.cc
浏览文件 @ feb05c3a
......@@ -62,7 +62,7 @@ class CosSimOp : public framework::OperatorWithKernel {
class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CosSimOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
CosSimOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The 1st input of cos_sim op.");
AddInput("Y", "The 2nd input of cos_sim op.");
......
paddle/operators/crf_decoding_op.cc
浏览文件 @ feb05c3a
......@@ -18,8 +18,7 @@ namespace paddle {
namespace operators {
class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CRFDecodingOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CRFDecodingOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Emission",
"(LoDTensor, default: LoDTensor<float>). A LoDTensor with shape "
......
paddle/operators/crop_op.cc
浏览文件 @ feb05c3a
......@@ -52,7 +52,7 @@ class CropOp : public framework::OperatorWithKernel {
class CropOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CropOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
CropOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -88,7 +88,8 @@ There are two ways to set shape:
The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
Case 1:
Given
X = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
......@@ -107,6 +108,27 @@ we get:
Out = [[1, 2],
[3, 4]].
Case 2:
Given
X = [[0, 1, 2, 5, 0]
[0, 3, 4, 6, 0]
[0, 0, 0, 0, 0]],
and
offsets = [0, 1],
and
Y = [[0, 0, 0]
[0, 0, 0]],
we get:
Out = [[1, 2, 5],
[3, 4, 6]].
)DOC");
}
};
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ feb05c3a
......@@ -111,8 +111,7 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CrossEntropyOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CrossEntropyOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
......
paddle/operators/decayed_adagrad_op.cc
浏览文件 @ feb05c3a
......@@ -55,8 +55,7 @@ class DecayedAdagradOp : public framework::OperatorWithKernel {
class DecayedAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DecayedAdagradOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
DecayedAdagradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......
paddle/operators/dropout_op.cc
浏览文件 @ feb05c3a
......@@ -40,8 +40,7 @@ class DropoutOp : public framework::OperatorWithKernel {
template <typename AttrType>
class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DropoutOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
DropoutOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of dropout op.");
AddOutput("Out", "The output of dropout op.");
......
paddle/operators/dropout_op.cu
浏览文件 @ feb05c3a
......@@ -71,7 +71,7 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
auto M = EigenMatrix<T>::Reshape(*mask, 1);
Y.device(place) = X * M;
} else {
Y.device(place) = X * dropout_prob;
Y.device(place) = X * (1.0f - dropout_prob);
}
}
};
......
paddle/operators/dropout_op.h
浏览文件 @ feb05c3a
......@@ -57,7 +57,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
Y.device(place) = X * dropout_prob;
Y.device(place) = X * (1.0f - dropout_prob);
}
}
};
......
paddle/operators/elementwise_add_op.cc
浏览文件 @ feb05c3a
......@@ -19,8 +19,7 @@ namespace paddle {
namespace operators {
class ElementwiseAddOpMaker : public ElementwiseOpMaker {
public:
ElementwiseAddOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ElementwiseAddOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: ElementwiseOpMaker(proto, op_checker) {
SetComment("Add", "$Out = X + Y$");
AddComment(comment_);
......
paddle/operators/elementwise_div_op.cc
浏览文件 @ feb05c3a
......@@ -19,8 +19,7 @@ namespace paddle {
namespace operators {
class ElementwiseDivOpMaker : public ElementwiseOpMaker {
public:
ElementwiseDivOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ElementwiseDivOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: ElementwiseOpMaker(proto, op_checker) {
SetComment("Div", "$Out = X / Y$");
AddComment(comment_);
......
paddle/operators/elementwise_mul_op.cc
浏览文件 @ feb05c3a
......@@ -20,8 +20,7 @@ namespace operators {
class ElementwiseMulOpMaker : public ElementwiseOpMaker {
public:
ElementwiseMulOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ElementwiseMulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: ElementwiseOpMaker(proto, op_checker) {
SetComment("Mul", "$Out = X \\odot\\ Y$");
AddComment(comment_);
......
paddle/operators/elementwise_op.h
浏览文件 @ feb05c3a
......@@ -43,8 +43,7 @@ class ElementwiseOp : public framework::OperatorWithKernel {
class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ElementwiseOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ElementwiseOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The first input tensor of elementwise op");
AddInput("Y", "(Tensor) The second input tensor of elementwise op");
......
paddle/operators/elementwise_op_function.h
浏览文件 @ feb05c3a
......@@ -103,10 +103,12 @@ class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
MidWiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
++j_;
i_ = j_ / post_;
if (UNLIKELY(i_ == n_)) {
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
i_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
}
return *this;
}
......@@ -125,10 +127,10 @@ class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
private:
const T* ptr_;
int i_;
int64_t i_;
int64_t j_;
int64_t n_;
int post_;
int64_t post_;
};
#ifdef __NVCC__
......
paddle/operators/elementwise_sub_op.cc
浏览文件 @ feb05c3a
......@@ -19,8 +19,7 @@ namespace paddle {
namespace operators {
class ElementwiseSubOpMaker : public ElementwiseOpMaker {
public:
ElementwiseSubOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ElementwiseSubOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: ElementwiseOpMaker(proto, op_checker) {
SetComment("Sub", "$Out = X - Y$");
AddComment(comment_);
......
paddle/operators/expand_op.cc
浏览文件 @ feb05c3a
......@@ -55,7 +55,7 @@ class ExpandOp : public framework::OperatorWithKernel {
class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ExpandOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
ExpandOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>) A tensor with rank in [1, 6]."
......
paddle/operators/feed_op.cc
浏览文件 @ feb05c3a
......@@ -54,8 +54,7 @@ class FeedOp : public framework::OperatorBase {
class FeedOpInfoMaker : public framework::OpProtoAndCheckerMaker {
public:
FeedOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
FeedOpInfoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of feed op");
AddOutput("Out", "The output of feed op");
......
paddle/operators/fetch_op.cc
浏览文件 @ feb05c3a
......@@ -61,8 +61,7 @@ class FetchOp : public framework::OperatorBase {
class FetchOpInfoMaker : public framework::OpProtoAndCheckerMaker {
public:
FetchOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
FetchOpInfoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of fetch op");
AddOutput("Out", "The output of fetch op");
......
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ feb05c3a
......@@ -52,7 +52,7 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
static_cast<framework::proto::DataType>(ctx.Attr<int>("dtype")),
ctx.device_context());
}
};
......@@ -60,13 +60,12 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
class FillConstantBatchSizeLikeOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
FillConstantBatchSizeLikeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
FillConstantBatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddInput("Input",
"(Tensor) Tensor "
"whose dim_idx th dimension is used to specify the batch_size");
......
paddle/operators/fill_constant_op.cc
浏览文件 @ feb05c3a
......@@ -34,7 +34,8 @@ class FillConstantOp : public framework::OperatorBase {
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto data_type = static_cast<framework::DataType>(Attr<int>("dtype"));
auto data_type =
static_cast<framework::proto::DataType>(Attr<int>("dtype"));
auto value = Attr<float>("value");
auto force_cpu = Attr<bool>("force_cpu");
auto &out =
......@@ -52,13 +53,12 @@ class FillConstantOp : public framework::OperatorBase {
class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FillConstantOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
FillConstantOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
......
paddle/operators/fill_op.cc
浏览文件 @ feb05c3a
......@@ -48,7 +48,7 @@ class FillOp : public framework::OperatorBase {
"Cannot find variable %s", Output("Out"))
.GetMutable<framework::LoDTensor>());
out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
auto dtype = static_cast<framework::DataType>(Attr<int>("dtype"));
auto dtype = static_cast<framework::proto::DataType>(Attr<int>("dtype"));
platform::CPUPlace cpu;
auto force_cpu = Attr<bool>("force_cpu");
out.mutable_data(force_cpu ? cpu : dev_ctx.GetPlace(),
......@@ -76,7 +76,7 @@ class FillOp : public framework::OperatorBase {
class FillOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FillOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
FillOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddComment(R"DOC(Fill operator
......@@ -88,7 +88,7 @@ Fill an tensor with `value` and `shape`. The type of the tensor is specify by
"value", "The float values of tensor, which are flatten in row major");
AddAttr<std::vector<int>>("shape", "The shape of output tensor");
AddAttr<int>("dtype", "The data type of output tensor, Default is float")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddAttr<bool>("force_cpu",
"Whether the output tensor must be at CPU memory or not. "
"Default is false.")
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ feb05c3a
......@@ -33,8 +33,7 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
class FillZerosLikeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FillZerosLikeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
FillZerosLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of fill-zeros-like op.");
AddOutput("Out", "The variable will be filled up with zeros.");
......
paddle/operators/ftrl_op.cc
浏览文件 @ feb05c3a
......@@ -57,7 +57,7 @@ class FTRLOp : public framework::OperatorWithKernel {
class FTRLOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FTRLOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
FTRLOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param",
"(Tensor, default Tensor<float>) "
......
paddle/operators/gather_op.cc
浏览文件 @ feb05c3a
......@@ -67,7 +67,7 @@ class GatherGradOp : public framework::OperatorWithKernel {
class GatherOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GatherOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
GatherOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The source input of gather op");
AddInput("Index", "The index input of gather op");
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ feb05c3a
......@@ -60,15 +60,14 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
static_cast<framework::proto::DataType>(ctx.Attr<int>("dtype")),
ctx.device_context());
}
};
class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GaussianRandomOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
GaussianRandomOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Out", "Output matrix of gaussian random op");
......@@ -91,7 +90,7 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("dtype",
"(int, default 5(FP32)) "
"Output data type.")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddComment(R"DOC(
GaussianRandom Operator.
......
paddle/operators/gru_op.cc
浏览文件 @ feb05c3a
......@@ -67,7 +67,7 @@ class GRUOp : public framework::OperatorWithKernel {
class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GRUOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
GRUOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(LoDTensor) The first input is a LodTensor, which supports "
......
paddle/operators/gru_unit_op.cc
浏览文件 @ feb05c3a
......@@ -71,8 +71,7 @@ class GRUUnitOp : public framework::OperatorWithKernel {
class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GRUUnitOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
GRUUnitOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
......
paddle/operators/hinge_loss_op.cc
浏览文件 @ feb05c3a
......@@ -46,8 +46,7 @@ class HingeLossOp : public framework::OperatorWithKernel {
template <typename AttrType>
class HingeLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HingeLossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
HingeLossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Logits",
"The input value (Logits) of Hinge loss op."
......
paddle/operators/huber_loss_op.cc
浏览文件 @ feb05c3a
......@@ -45,8 +45,7 @@ class HuberLossOp : public framework::OperatorWithKernel {
template <typename AttrType>
class HuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HuberLossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
HuberLossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input value of huber loss op."
......
paddle/operators/increment_op.cc
浏览文件 @ feb05c3a
......@@ -70,8 +70,7 @@ class IncrementOp : public framework::OperatorBase {
class IncrementOpMaker : public framework::OpProtoAndCheckerMaker {
public:
IncrementOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
IncrementOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input tensor of increment operator");
AddOutput("Out", "(Tensor) The output tensor of increment operator.");
......@@ -94,13 +93,13 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("increment");
grad_op->SetInput("X", Output("Out"));
grad_op->SetOutput("Out", Input("X"));
grad_op->SetAttr("step", -boost::get<float>(GetAttr("step")));
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/is_empty_op.cc
浏览文件 @ feb05c3a
......@@ -47,8 +47,7 @@ class IsEmptyOp : public framework::OperatorBase {
class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
IsEmptyOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
IsEmptyOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(kInput, "(Tensor) Tensor which is to be checked.");
AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
......
paddle/operators/l1_norm_op.cc
浏览文件 @ feb05c3a
......@@ -48,7 +48,7 @@ class L1NormGradOp : public framework::OperatorWithKernel {
class L1NormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
L1NormOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
L1NormOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input of l1_norm op.");
AddOutput("Out", "(Scalar) The output of l1_norm op.");
......
paddle/operators/linear_chain_crf_op.cc
浏览文件 @ feb05c3a
......@@ -19,8 +19,7 @@ namespace operators {
class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LinearChainCRFOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
LinearChainCRFOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Emission",
"(LoDTensor, default LoDTensor<float>) "
......
paddle/operators/load_op.cc
浏览文件 @ feb05c3a
......@@ -58,8 +58,7 @@ class LoadOp : public framework::OperatorBase {
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoadOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LoadOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Out", "(Tensor) The tensor need to be loaded");
AddAttr<std::string>("file_path",
......
paddle/operators/lod_array_length_op.cc
浏览文件 @ feb05c3a
......@@ -38,8 +38,7 @@ class LoDArrayLengthOp : public framework::OperatorBase {
class LoDArrayLengthProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDArrayLengthProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LoDArrayLengthProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensorArray) The input tensor array.");
AddOutput("Out", "(Tensor) 1x1 CPU Tensor of length, int64_t");
......
paddle/operators/lod_rank_table_op.cc
浏览文件 @ feb05c3a
......@@ -30,13 +30,13 @@ class LoDRankTableOp : public framework::OperatorBase {
scope.FindVar(Output("Out"))->GetMutable<framework::LoDRankTable>();
VLOG(10) << "Level = " << static_cast<size_t>(Attr<int>("level"));
out->Reset(x.lod(), static_cast<size_t>(Attr<int>("level")));
VLOG(10) << Input("X") << "'s lod information is " << *out;
}
};
class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDRankTableOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LoDRankTableOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor) input lod tensor, must contain lod information.");
......@@ -63,11 +63,11 @@ class LoDRankTableInferShape : public framework::InferShapeBase {
class LoDRankTableInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
for (auto &o : op_desc.Output("Out")) {
block->FindRecursiveOrCreateVar(o)->SetType(
framework::VarDesc::LOD_RANK_TABLE);
framework::proto::VarDesc::LOD_RANK_TABLE);
}
}
};
......
paddle/operators/lod_reset_op.cc
浏览文件 @ feb05c3a
......@@ -48,8 +48,7 @@ class LoDResetOp : public framework::OperatorWithKernel {
class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDResetOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LoDResetOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor) The input tensor of lod_reset operator.");
AddInput("TargetLoD",
......
paddle/operators/lod_tensor_to_array_op.cc
浏览文件 @ feb05c3a
......@@ -97,8 +97,7 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
class LoDTensorToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDTensorToArrayOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LoDTensorToArrayOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddInput("RankTable", "");
......@@ -128,10 +127,10 @@ class LoDTensorToArrayInferShape : public framework::InferShapeBase {
class LoDTensorToArrayInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
for (auto &out_var : op_desc.Output("Out")) {
block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
block->Var(out_var)->SetType(framework::proto::VarDesc::LOD_TENSOR_ARRAY);
}
}
};
......@@ -141,14 +140,14 @@ class LoDTensorToArrayGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("array_to_lod_tensor");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetInput("RankTable", Input("RankTable"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/log_loss_op.cc
浏览文件 @ feb05c3a
......@@ -46,8 +46,7 @@ class LogLossOp : public framework::OperatorWithKernel {
template <typename AttrType>
class LogLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LogLossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
LogLossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Predicted",
"The input value (Predicted) of Log loss op."
......
paddle/operators/logical_op.cc
浏览文件 @ feb05c3a
......@@ -20,8 +20,7 @@ namespace operators {
template <typename OpComment>
class BinaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
BinaryLogicalOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
BinaryLogicalOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
OpComment comment;
AddInput("X",
......@@ -45,8 +44,7 @@ Each element of Out is calculated by %s
template <typename OpComment>
class UnaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
UnaryLogicalOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
UnaryLogicalOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
OpComment comment;
AddInput("X", string::Sprintf("(LoDTensor) Operand of %s operator",
......
paddle/operators/lookup_table_op.cc
浏览文件 @ feb05c3a
......@@ -51,8 +51,7 @@ class LookupTableOp : public framework::OperatorWithKernel {
class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LookupTableOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
LookupTableOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("W",
"An input represents embedding tensors, "
......@@ -109,19 +108,20 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind& op_desc,
framework::BlockDescBind* block) const override {
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto out_var_name = op_desc.Output(framework::GradVarName("W")).front();
auto attr = op_desc.GetAttr("is_sparse");
bool is_sparse = boost::get<bool>(attr);
if (is_sparse) {
VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
<< " is set to SelectedRows";
block->Var(out_var_name)->SetType(framework::VarDesc::SELECTED_ROWS);
block->Var(out_var_name)
->SetType(framework::proto::VarDesc::SELECTED_ROWS);
} else {
VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
block->Var(out_var_name)->SetType(framework::VarDesc::LOD_TENSOR);
block->Var(out_var_name)->SetType(framework::proto::VarDesc::LOD_TENSOR);
}
}
};
......
paddle/operators/lrn_op.cc
浏览文件 @ feb05c3a
......@@ -140,7 +140,7 @@ class LRNOp : public framework::OperatorWithKernel {
template <typename T>
class LRNOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LRNOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
LRNOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input of LRN operator. "
......
paddle/operators/lstm_op.cc
浏览文件 @ feb05c3a
......@@ -102,7 +102,7 @@ class LSTMOp : public framework::OperatorWithKernel {
class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LSTMOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
LSTMOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(LoDTensor) the first input is a LodTensor, which support "
......
paddle/operators/lstm_unit_op.cc
浏览文件 @ feb05c3a
......@@ -48,10 +48,12 @@ class LstmUnitOp : public framework::OperatorWithKernel {
class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LstmUnitOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
LstmUnitOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "FC input before the non-linear activation.");
AddInput("X",
"Lstm unit only applies non-linear activations, please make sure"
"that linear tranformation has already been applied to `X`. "
"Linear tranformation can be applied by adding a `fc` layer");
AddInput(
"C_prev",
"The cell state tensor of last time-step in the Lstm Unit operator.");
......
paddle/operators/margin_rank_loss_op.cc
浏览文件 @ feb05c3a
......@@ -42,8 +42,7 @@ class MarginRankLossOp : public framework::OperatorWithKernel {
template <typename T>
class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MarginRankLossOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MarginRankLossOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X1",
"(2-D tensor with shape [batch_size x 1]) The score for "
......
paddle/operators/math/im2col.cc
浏览文件 @ feb05c3a
......@@ -61,14 +61,13 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / filter_width / filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
......@@ -130,16 +129,14 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / filter_width / filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_row_idx += c_im * im_height;
im_data[im_row_idx * im_width + im_col_idx] +=
im_data[(im_row_idx + c_im * im_height) * im_width + im_col_idx] +=
col_data[(c * col_height + h) * col_width + w];
}
}
......@@ -199,12 +196,13 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
int im_col_offset =
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
((((col_row_idx)*col_width + col_col_idx) * im_channels +
channel) *
......@@ -271,12 +269,13 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
int im_col_offset =
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
(((col_row_idx * col_width + col_col_idx) * im_channels +
channel) *
......@@ -284,6 +283,7 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
filter_row_idx) *
filter_width +
filter_col_idx;
if (im_row_offset >= 0 && im_row_offset < im_height &&
im_col_offset >= 0 && im_col_offset < im_width) {
int im_offset =
......
paddle/operators/math/math_function.cu
浏览文件 @ feb05c3a
......@@ -274,7 +274,7 @@ void set_constant_with_place<platform::GPUPlace>(
}
template <>
void set_constant_with_place<platform::CudnnPlace>(
void set_constant_with_place<platform::CUDNNPlace>(
const platform::DeviceContext& context, framework::Tensor* tensor,
float value) {
set_constant_with_place<platform::GPUPlace>(context, tensor, value);
......
paddle/operators/matmul_op.cc
浏览文件 @ feb05c3a
......@@ -130,7 +130,7 @@ class MatMulOp : public framework::OperatorWithKernel {
class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MatMulOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
MatMulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of MatMul op");
AddInput("Y", "The second input of MatMul op");
......
paddle/operators/max_sequence_len_op.cc
浏览文件 @ feb05c3a
......@@ -40,8 +40,7 @@ class MaxSeqenceLenOp : public framework::OperatorBase {
class MaxSeqenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxSeqenceLenOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MaxSeqenceLenOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("RankTable", "The lod_rank_table.");
AddOutput("Out", "The max sequence length.");
......
paddle/operators/maxout_op.cc
浏览文件 @ feb05c3a
......@@ -20,7 +20,7 @@ using framework::Tensor;
class MaxOutOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxOutOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
MaxOutOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......
paddle/operators/mean_op.cc
浏览文件 @ feb05c3a
......@@ -32,7 +32,7 @@ class MeanOp : public framework::OperatorWithKernel {
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
MeanOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op");
......@@ -60,13 +60,13 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto* grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("mean_grad");
grad_op->SetInput("X", Input("X"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/merge_lod_tensor_op.cc
浏览文件 @ feb05c3a
......@@ -114,8 +114,7 @@ class MergeLoDTensorOp : public framework::OperatorBase {
class MergeLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
MergeLoDTensorOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MergeLoDTensorOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input LoDTensor, contains complete lod information to "
......@@ -162,15 +161,15 @@ class MergeLoDTensorGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("split_lod_tensor");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetInput("Mask", Input("Mask"));
grad_op->SetOutput("OutTrue", InputGrad("InTrue"));
grad_op->SetOutput("OutFalse", InputGrad("InFalse"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/minus_op.cc
浏览文件 @ feb05c3a
......@@ -46,7 +46,7 @@ class MinusOp : public framework::OperatorWithKernel {
class MinusOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MinusOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left tensor of minus operator.");
AddInput("Y", "The right tensor of minus operator.");
......@@ -70,12 +70,11 @@ class MinusGradMaker : public framework::GradOpDescMakerBase {
public:
using framework::GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<framework::OpDescBind>> operator()()
const override {
std::vector<std::unique_ptr<framework::OpDescBind>> ops;
std::vector<std::unique_ptr<framework::OpDesc>> operator()() const override {
std::vector<std::unique_ptr<framework::OpDesc>> ops;
auto x_g = InputGrad("X");
if (!x_g.empty()) {
auto *x_g_op = new framework::OpDescBind();
auto *x_g_op = new framework::OpDesc();
x_g_op->SetType("scale");
x_g_op->SetInput("X", OutputGrad("Out"));
x_g_op->SetOutput("Out", x_g);
......@@ -85,7 +84,7 @@ class MinusGradMaker : public framework::GradOpDescMakerBase {
auto y_g = InputGrad("Y");
if (!y_g.empty()) {
auto *y_g_op = new framework::OpDescBind();
auto *y_g_op = new framework::OpDesc();
y_g_op->SetType("scale");
y_g_op->SetInput("X", OutputGrad("Out"));
y_g_op->SetOutput("Out", y_g);
......
paddle/operators/modified_huber_loss_op.cc
浏览文件 @ feb05c3a
......@@ -39,8 +39,7 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel {
class ModifiedHuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ModifiedHuberLossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ModifiedHuberLossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input tensor of modified huber loss op. "
......
paddle/operators/momentum_op.cc
浏览文件 @ feb05c3a
......@@ -54,8 +54,7 @@ class MomentumOp : public framework::OperatorWithKernel {
class MomentumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MomentumOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MomentumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param",
"(Tensor, default Tensor<float>) "
......
paddle/operators/mul_op.cc
浏览文件 @ feb05c3a
......@@ -71,7 +71,7 @@ class MulOpShapeInference : public framework::InferShapeBase {
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
MulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
AddInput("Y", "The second input of mul op");
......
paddle/operators/multiplex_op.cc
浏览文件 @ feb05c3a
......@@ -61,8 +61,7 @@ class MultiplexOp : public framework::OperatorWithKernel {
class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MultiplexOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
MultiplexOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ids", "The index tensor of multiplex operator.");
AddInput("X", "The candidate tensors of multiplex operator.")
......
paddle/operators/nccl_op.cc
浏览文件 @ feb05c3a
......@@ -43,8 +43,7 @@ class NCCLInitOp : public framework::OperatorBase {
class NCCLInitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
NCCLInitOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
NCCLInitOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Communicator",
"Create Communicator for communicating between gpus");
......@@ -52,7 +51,7 @@ class NCCLInitOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddComment(R"DOC(
NCCLInit Operator.
......@@ -141,8 +140,7 @@ class NCCLBcastOp : public framework::OperatorWithKernel {
// AllreduceOp
class NCCLAllReduceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
NCCLAllReduceOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
NCCLAllReduceOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of AllReduce op");
AddInput("Communicator", "Communicator for communicating between gpus");
......@@ -163,8 +161,7 @@ AllReduce the input tensors.
// ReduceOp
class NCCLReduceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
NCCLReduceOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
NCCLReduceOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of Reduce op");
AddInput("Communicator", "Communicator for communicating between gpus");
......@@ -190,8 +187,7 @@ Reduce the tensors.
// BcastOp
class NCCLBcastOpMaker : public framework::OpProtoAndCheckerMaker {
public:
NCCLBcastOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
NCCLBcastOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of BcastSend op");
AddInput("Communicator", "Communicator for communicating between gpus");
......
paddle/operators/nccl_op_test.cu.cc
浏览文件 @ feb05c3a
......@@ -65,7 +65,7 @@ class NCCLTester : public ::testing::Test {
}
void NCCLInitOp() {
std::unique_ptr<f::OpDescBind> op1(new f::OpDescBind);
std::unique_ptr<f::OpDesc> op1(new f::OpDesc);
op1->SetType("ncclInit");
op1->SetOutput("Communicator", {"comm"});
......@@ -81,10 +81,9 @@ class NCCLTester : public ::testing::Test {
}
template <class T>
void PerThreadProgram(int gpu_id, const f::OpDescBind &op_desc,
f::Scope *scope) {
void PerThreadProgram(int gpu_id, const f::OpDesc &op_desc, f::Scope *scope) {
std::unique_lock<std::mutex> lk(mu);
const f::OpDescBind *op1 = &op_desc;
const f::OpDesc *op1 = &op_desc;
p::GPUPlace place(gpu_id);
auto &ctx = dev_ctxs.at(gpu_id);
......@@ -125,7 +124,7 @@ class NCCLTester : public ::testing::Test {
// ncclInitOp with desc
TEST(NCCL, ncclInitOp) {
std::unique_ptr<f::OpDescBind> op_desc(new f::OpDescBind);
std::unique_ptr<f::OpDesc> op_desc(new f::OpDesc);
op_desc->SetType("ncclInit");
op_desc->SetOutput("Communicator", {"x1"});
......@@ -145,7 +144,7 @@ TEST(NCCL, ncclInitOp) {
// ncclAllReduceOp with desc
TEST_F(NCCLTester, ncclAllReduceOp) {
std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
std::unique_ptr<f::OpDesc> op2(new f::OpDesc);
op2->SetType("ncclAllReduce");
op2->SetInput("X", {"st"});
op2->SetInput("Communicator", {"comm"});
......@@ -192,7 +191,7 @@ TEST_F(NCCLTester, ncclAllReduceOp) {
// ncclReduceOp with desc
TEST_F(NCCLTester, ncclReduceOp) {
std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
std::unique_ptr<f::OpDesc> op2(new f::OpDesc);
const int kRoot = 0;
op2->SetType("ncclReduce");
op2->SetInput("X", {"st"});
......@@ -240,7 +239,7 @@ TEST_F(NCCLTester, ncclReduceOp) {
// ncclBcastOp with desc
TEST_F(NCCLTester, ncclBcastOp) {
std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
std::unique_ptr<f::OpDesc> op2(new f::OpDesc);
const int kRoot = 5;
op2->SetType("ncclBcast");
op2->SetInput("X", {"st"});
......
paddle/operators/nce_op.cc
浏览文件 @ feb05c3a
......@@ -73,7 +73,7 @@ class NCEOp : public framework::OperatorWithKernel {
class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
public:
NCEOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
NCEOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input", "(Tensor) A tensor of shape [batch_size, dim].");
AddInput(
......
paddle/operators/name_convention.md → paddle/operators/op_documentation/name_convention.md
浏览文件 @ feb05c3a
......@@ -35,8 +35,8 @@ Here we give some examples to show how these rules will be used.
```c++
class AccumulateOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AccumulateOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
AccumulateOpMaker(OpProto *proto,
OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor.
If the output size is not the same as input size,
......
paddle/operators/op_documentation/op_markdown_format.md 0 → 100644
浏览文件 @ feb05c3a
# Standard Markdown Format for Operators
The following should be the standard format for documentation for all the operators that will get rendered in the `html`:
```
Operator Name (In PaddlePaddle)
Operator Name (Standard)
Operator description.
LaTeX equation of how the operator performs an update.
The signature of the operator.
```
Each section mentioned above has been covered in further detail in the rest of the document.
# PaddlePaddle Operator Name
This should be in all small letters, in case of multiple words, we separate them with an underscore. For example:
`array to lod tensor` should be written as `array_to_lod_tensor`.
This naming convention should be standard across all PaddlePaddle operators.
# Standard Operator Name
This is the standard name of the operator as used in the community. The general standard is usually:
- Standard abbreviations like `SGD` are written in all capital letters.
- Operator names that have multiple words inside a single word use `camelCase` (capitalize word boundaries inside of a word).
- Keep numbers inside a word as is, with no boundary delimiters.
- Follow the name of the operator with the keyword: `Activation Operator.`
# Operator description
This section should contain the description of what the operator does, including the operation performed, the literature from where it comes and was introduced first, and other important details. The relevant paper/article including the hyperlink should be cited in this section.
# LaTeX equation
This section should contain an overall equation of the update or operation that the operator performs. The variables used in the equation should follow the naming convention of operators as described [here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/name_convention.md). Two words in the same word should be separated by an underscore (`_`).
# The signature
This section describes the signature of the operator. A list of Inputs and Outputs, each of which have a small description of what the variable represents and the type of variable. The variable names follow the `CamelCase` naming convention. The proposed format for this is:
`Section :
VariableName : (VariableType) VariableDescription
...
...
`
The following example for an `sgd` operator covers the above mentioned sections as they would ideally look like in the `html`:
```
sgd
SGD operator
This operator implements one step of the stochastic gradient descent algorithm.
param_out = param_learning_rate * grad
Inputs:
Param : (Tensor) Input parameter
LearningRate : (Tensor) Learning rate of SGD
Grad : (Tensor) Input gradient
Outputs:
ParamOut : (Tensor) Output parameter
```
paddle/operators/pad_op.cc
浏览文件 @ feb05c3a
......@@ -48,7 +48,7 @@ class PadOp : public framework::OperatorWithKernel {
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
PadOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -116,14 +116,14 @@ class PadOpGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto* bind = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* bind = new framework::OpDesc();
bind->SetInput("X", Input("X"));
bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
bind->SetOutput(framework::GradVarName("X"), InputGrad("X"));
bind->SetAttrMap(Attrs());
bind->SetType("pad_grad");
return std::unique_ptr<framework::OpDescBind>(bind);
return std::unique_ptr<framework::OpDesc>(bind);
}
};
......
paddle/operators/pool_op.cc
浏览文件 @ feb05c3a
......@@ -67,8 +67,7 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......@@ -136,8 +135,7 @@ Example:
)DOC");
}
Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input tensor of pooling operator. "
......
paddle/operators/pool_op.h
浏览文件 @ feb05c3a
......@@ -40,14 +40,12 @@ class PoolOpGrad : public framework::OperatorWithKernel {
class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Pool2dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Pool2dOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Pool3dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
Pool3dOpMaker(OpProto* proto, OpAttrChecker* op_checker);
};
template <typename DeviceContext, typename T>
......
paddle/operators/pool_with_index_op.cc
浏览文件 @ feb05c3a
......@@ -100,8 +100,7 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxPool2dWithIndexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MaxPool2dWithIndexOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......@@ -178,8 +177,7 @@ Example:
class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxPool3dWithIndexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MaxPool3dWithIndexOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input tensor of pooling operator. "
......
paddle/operators/positive_negative_pair_op.cc
浏览文件 @ feb05c3a
......@@ -95,8 +95,7 @@ class PositiveNegativePairOp : public framework::OperatorWithKernel {
class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PositiveNegativePairOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
PositiveNegativePairOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Score",
"(Tensor, float) Model Score on an item (with "
......
paddle/operators/precision_recall_op.cc
浏览文件 @ feb05c3a
......@@ -90,8 +90,7 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
class PrecisionRecallOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PrecisionRecallOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
PrecisionRecallOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("MaxProbs",
"(Tensor, default Tensor<float>) A 2-D tensor with shape N x 1, "
......
paddle/operators/prelu_op.cc
浏览文件 @ feb05c3a
......@@ -38,7 +38,7 @@ class PReluOp : public framework::OperatorWithKernel {
class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
PReluOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of prelu operator.");
AddInput("Alpha", "The alpha weight of prelu operator.");
......
paddle/operators/proximal_adagrad_op.cc
浏览文件 @ feb05c3a
......@@ -59,8 +59,7 @@ class ProximalAdagradOp : public framework::OperatorWithKernel {
class ProximalAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ProximalAdagradOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ProximalAdagradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param",
"(Tensor, default Tensor<float>) "
......
paddle/operators/proximal_gd_op.cc
浏览文件 @ feb05c3a
......@@ -47,8 +47,7 @@ class ProximalGDOp : public framework::OperatorWithKernel {
class ProximalGDOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ProximalGDOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ProximalGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param",
"(Tensor, default Tensor<float>) "
......
paddle/operators/rank_loss_op.cc
浏览文件 @ feb05c3a
......@@ -45,8 +45,7 @@ class RankLossOp : public framework::OperatorWithKernel {
class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RankLossOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RankLossOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Label",
"(2-D Tensor with shape [batch_size x 1]) "
......
paddle/operators/recurrent_op.cc
浏览文件 @ feb05c3a
......@@ -234,7 +234,7 @@ class RecurrentOp : public RecurrentBase {
auto reverse = Attr<bool>(kReverse);
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
for (size_t i = 0; i < seq_len; ++i) {
......@@ -317,7 +317,7 @@ class RecurrentGradOp : public RecurrentBase {
auto reverse = Attr<bool>(kReverse);
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
for (size_t step_id = 0; step_id < seq_len; ++step_id) {
......@@ -497,8 +497,7 @@ class RecurrentGradOp : public RecurrentBase {
class RecurrentOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
RecurrentOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RecurrentOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(kInputs, "rnn inputs").AsDuplicable();
AddInput(kInitialStates, "rnn initial states").AsDuplicable();
......@@ -523,8 +522,7 @@ The ex-state means the state value in the ex-timestep or the previous time step
string::Sprintf(
"The state variable names. [%s, %s, %s] must be the same order",
kExStates, kStates, kInitStateGrads));
AddAttr<framework::BlockDescBind *>(kStepBlock,
"The step block inside RNN");
AddAttr<framework::BlockDesc *>(kStepBlock, "The step block inside RNN");
AddAttr<bool>(kReverse, R"DOC(Calculate RNN reversely or not.
By default reverse=False
......@@ -566,8 +564,8 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
virtual std::unique_ptr<framework::OpDescBind> Apply() const {
auto *grad = new framework::OpDescBind();
virtual std::unique_ptr<framework::OpDesc> Apply() const {
auto *grad = new framework::OpDesc();
grad->SetType("recurrent_grad");
for (auto &input_param : this->InputNames()) {
grad->SetInput(input_param, this->Input(input_param));
......@@ -589,7 +587,7 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
grad->SetAttrMap(this->Attrs());
grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
return std::unique_ptr<framework::OpDescBind>(grad);
return std::unique_ptr<framework::OpDesc>(grad);
}
};
......
paddle/operators/recv_op.cc
浏览文件 @ feb05c3a
......@@ -97,7 +97,7 @@ class RecvOp : public framework::OperatorBase {
class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RecvOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
RecvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("RX", "(Tensor) Input tensor to be saved");
AddComment(R"DOC(
......
paddle/operators/reduce_op.cc
浏览文件 @ feb05c3a
......@@ -83,7 +83,7 @@ class ReduceGradOp : public framework::OperatorWithKernel {
class ReduceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReduceOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ReduceOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input tensor. Tensors with rank at most 6 are "
......@@ -135,8 +135,7 @@ If reduce_all is true, just reduce along all dimensions and output a scalar.
class ReduceSumOpMaker : public ReduceOpMaker {
public:
ReduceSumOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReduceSumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: ReduceOpMaker(proto, op_checker) {
SetComment("ReduceSum", "sum");
AddComment(comment_);
......@@ -145,8 +144,7 @@ class ReduceSumOpMaker : public ReduceOpMaker {
class ReduceMeanOpMaker : public ReduceOpMaker {
public:
ReduceMeanOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReduceMeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: ReduceOpMaker(proto, op_checker) {
SetComment("ReduceMean", "mean");
AddComment(comment_);
......@@ -155,8 +153,7 @@ class ReduceMeanOpMaker : public ReduceOpMaker {
class ReduceMaxOpMaker : public ReduceOpMaker {
public:
ReduceMaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReduceMaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: ReduceOpMaker(proto, op_checker) {
SetComment("ReduceMax", "max");
AddComment(comment_);
......@@ -165,8 +162,7 @@ class ReduceMaxOpMaker : public ReduceOpMaker {
class ReduceMinOpMaker : public ReduceOpMaker {
public:
ReduceMinOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReduceMinOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: ReduceOpMaker(proto, op_checker) {
SetComment("ReduceMin", "min");
AddComment(comment_);
......
paddle/operators/reshape_op.cc
浏览文件 @ feb05c3a
......@@ -77,8 +77,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReshapeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReshapeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of reshape operator.");
AddOutput("Out", "The output tensor of reshape operator.");
......
paddle/operators/rmsprop_op.cc
浏览文件 @ feb05c3a
......@@ -63,8 +63,7 @@ class RmspropOp : public framework::OperatorWithKernel {
class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RmspropOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RmspropOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param",
"(Tensor, default Tensor<float>) "
......
paddle/operators/rnn_memory_helper_op.cc
浏览文件 @ feb05c3a
......@@ -57,15 +57,14 @@ class RNNMemoryHelperOpShapeInference : public framework::InferShapeBase {
class RNNMemoryHelperOpInfoMaker : public framework::OpProtoAndCheckerMaker {
public:
RNNMemoryHelperOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RNNMemoryHelperOpInfoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddOutput("Out", "");
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddComment("");
}
};
......@@ -114,8 +113,7 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
class RNNMemoryHelperGradOpInfoMaker
: public framework::OpProtoAndCheckerMaker {
public:
RNNMemoryHelperGradOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RNNMemoryHelperGradOpInfoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(framework::GradVarName("Out"), "");
AddInput("X", "");
......@@ -124,7 +122,7 @@ class RNNMemoryHelperGradOpInfoMaker
AddAttr<int>("dtype",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
AddComment("");
}
};
......
paddle/operators/roi_pool_op.cc
浏览文件 @ feb05c3a
......@@ -99,8 +99,7 @@ class ROIPoolGradOp : public framework::OperatorWithKernel {
class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ROIPoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ROIPoolOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor), "
......
paddle/operators/row_conv_op.cc
浏览文件 @ feb05c3a
......@@ -76,8 +76,7 @@ class RowConvGradOp : public framework::OperatorWithKernel {
class RowConvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RowConvOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RowConvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor), the input(X) is a LodTensor, which supports "
......
paddle/operators/save_op.cc
浏览文件 @ feb05c3a
......@@ -94,8 +94,7 @@ class SaveOp : public framework::OperatorBase {
class SaveOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
SaveOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
SaveOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor ) Input tensor to be saved");
AddComment(R"DOC(
......
paddle/operators/scale_op.cc
浏览文件 @ feb05c3a
......@@ -38,7 +38,7 @@ class ScaleOp : public framework::OperatorWithKernel {
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ScaleOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor of scale operator.");
AddOutput("Out", "(Tensor) Output tensor of scale operator.");
......@@ -58,13 +58,13 @@ class ScaleGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", GetAttr("scale"));
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/scatter_op.cc
浏览文件 @ feb05c3a
......@@ -78,8 +78,7 @@ class ScatterGradOp : public framework::OperatorWithKernel {
class ScatterOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScatterOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
ScatterOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ref", "The source input of scatter op");
AddInput("Index",
......
paddle/operators/send_op.cc
浏览文件 @ feb05c3a
......@@ -59,7 +59,7 @@ class SendOp : public framework::OperatorBase {
class SendOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SendOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
SendOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor to be saved");
AddOutput("Out", "(Tensor) Output fetched from server");
......
paddle/operators/sequence_concat_op.cc
浏览文件 @ feb05c3a
......@@ -43,8 +43,7 @@ class SequenceConcatOp : public framework::OperatorWithKernel {
class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceConcatOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequenceConcatOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LodTensorArray) Input is a vector of LoDTensor, "
......
paddle/operators/sequence_conv_op.cc
浏览文件 @ feb05c3a
......@@ -100,8 +100,7 @@ class SequenceConvGradOp : public framework::OperatorWithKernel {
class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequenceConvOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......
paddle/operators/seq_expand_op.cc → paddle/operators/sequence_expand_op.cc
浏览文件 @ feb05c3a
......@@ -12,14 +12,14 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/seq_expand_op.h"
#include "paddle/operators/sequence_expand_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class SeqExpandOp : public framework::OperatorWithKernel {
class SequenceExpandOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -35,25 +35,24 @@ class SeqExpandOp : public framework::OperatorWithKernel {
}
};
class SeqExpandOpMaker : public framework::OpProtoAndCheckerMaker {
class SequenceExpandOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SeqExpandOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequenceExpandOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor or LoDTensor) The input(X) of this operator can be a "
"LoDTensor or a base Tensor.");
AddInput("Y",
"(LoDTensor)The reference input(Y) of seq_expand op."
"(LoDTensor)The reference input(Y) of sequence_expand op."
"It must be a LoDTensor with k-level(k>0)."
"The input(X) will be expanded according to LOD of input(Y)."
"The element numbers of last level in input(Y) "
"must be equal to dims[0] of input(X).");
AddOutput("Out",
"(LodTensor)The output of seq_expand op."
"(LodTensor)The output of sequence_expand op."
"The lod of output will be as same as input(Y)'s lod.");
AddComment(R"DOC(
Seq Expand Operator.
Sequence Expand Operator.
This operator expands input(X) according to LOD of input(Y).
Following are cases to better explain how this works:
......@@ -124,7 +123,7 @@ then we get 2-level LoDTensor
}
};
class SeqExpandOpGrad : public framework::OperatorWithKernel {
class SequenceExpandOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -146,11 +145,11 @@ class SeqExpandOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(seq_expand, ops::SeqExpandOp, ops::SeqExpandOpMaker,
seq_expand_grad, ops::SeqExpandOpGrad);
REGISTER_OP(sequence_expand, ops::SequenceExpandOp, ops::SequenceExpandOpMaker,
sequence_expand_grad, ops::SequenceExpandOpGrad);
REGISTER_OP_CPU_KERNEL(
seq_expand,
ops::SeqExpandKernel<paddle::platform::CPUDeviceContext, float>);
sequence_expand,
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
seq_expand_grad,
ops::SeqExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
sequence_expand_grad,
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/operators/seq_expand_op.cu → paddle/operators/sequence_expand_op.cu
浏览文件 @ feb05c3a
......@@ -13,12 +13,12 @@
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/seq_expand_op.h"
#include "paddle/operators/sequence_expand_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
seq_expand,
ops::SeqExpandKernel<paddle::platform::CUDADeviceContext, float>);
sequence_expand,
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
seq_expand_grad,
ops::SeqExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
sequence_expand_grad,
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/operators/seq_expand_op.h → paddle/operators/sequence_expand_op.h
浏览文件 @ feb05c3a
......@@ -24,7 +24,7 @@ namespace operators {
using LoDTensor = framework::LoDTensor;
template <typename DeviceContext, typename T>
class SeqExpandKernel : public framework::OpKernel<T> {
class SequenceExpandKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<LoDTensor>("X");
......@@ -71,7 +71,7 @@ class SeqExpandKernel : public framework::OpKernel<T> {
*
* */
template <typename DeviceContext, typename T>
class SeqExpandGradKernel : public framework::OpKernel<T> {
class SequenceExpandGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
......
paddle/operators/sequence_pool_op.cc
浏览文件 @ feb05c3a
......@@ -37,8 +37,7 @@ class SequencePoolOp : public framework::OperatorWithKernel {
class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequencePoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequencePoolOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor) The variable-length input of SequencePoolOp");
AddOutput("Out",
......
paddle/operators/sequence_slice_op.cc
浏览文件 @ feb05c3a
......@@ -79,8 +79,7 @@ class SequenceSliceGradOp : public framework::OperatorWithKernel {
class SequenceSliceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceSliceOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequenceSliceOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor), "
......
paddle/operators/sequence_softmax_op.cc
浏览文件 @ feb05c3a
......@@ -33,8 +33,7 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel {
class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceSoftmaxOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequenceSoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension "
......@@ -51,10 +50,14 @@ input Tensor can be either [N, 1] or [N], where N is the sum of the length
of all sequences.
The algorithm works as follows:
for i-th sequence in a mini-batch:
$$Out(X[lod[i]:lod[i+1]], :) =
\frac{\exp(X[lod[i]:lod[i+1], :])}
{\sum(\exp(X[lod[i]:lod[i+1], :]))}$$
$$
Out(X[lod[i]:lod[i+1]], :) = \
\frac{\exp(X[lod[i]:lod[i+1], :])} \
{\sum(\exp(X[lod[i]:lod[i+1], :]))}
$$
For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
......
paddle/operators/sgd_op.cc
浏览文件 @ feb05c3a
......@@ -43,7 +43,7 @@ class SGDOp : public framework::OperatorWithKernel {
class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SGDOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
SGDOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("LearningRate", "(Tensor) Learning rate of SGD");
......
paddle/operators/shrink_rnn_memory_op.cc
浏览文件 @ feb05c3a
......@@ -54,8 +54,7 @@ class ShrinkRNNMemoryOp : public ArrayOp {
class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ShrinkRNNMemoryOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ShrinkRNNMemoryOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor) The RNN step memory to be shrinked.");
AddInput("RankTable", "(LoDRankTable) The lod_rank_table of dynamic RNN.");
......@@ -137,14 +136,14 @@ class ShrinkRNNGradOpMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *op = new framework::OpDesc();
op->SetType("shrink_rnn_memory_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(op);
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @ feb05c3a
......@@ -86,8 +86,8 @@ class SigmoidCrossEntropyWithLogitsGradOp
class SigmoidCrossEntropyWithLogitsOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
SigmoidCrossEntropyWithLogitsOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SigmoidCrossEntropyWithLogitsOpMaker(OpProto* proto,
OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
......
paddle/operators/sign_op.cc
浏览文件 @ feb05c3a
......@@ -34,7 +34,7 @@ class SignOp : public framework::OperatorWithKernel {
template <typename AttrType>
class SignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SignOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
SignOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor of sign operator.");
AddOutput("Out", "(Tensor) Output tensor of sign operator.");
......@@ -50,13 +50,13 @@ class SignGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", 0.0f);
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @ feb05c3a
......@@ -47,8 +47,7 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
template <typename AttrType>
class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SmoothL1LossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SmoothL1LossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
......
paddle/operators/softmax_op.cc
浏览文件 @ feb05c3a
......@@ -36,8 +36,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input tensor of softmax. "
......
paddle/operators/softmax_with_cross_entropy_op.cc
浏览文件 @ feb05c3a
......@@ -20,8 +20,7 @@ namespace operators {
class SoftmaxWithCrossEntropyOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
SoftmaxWithCrossEntropyOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SoftmaxWithCrossEntropyOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Logits",
"(Tensor, default: Tensor<float>), The unscaled log probabilities "
......@@ -174,8 +173,8 @@ class SoftmaxGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto* grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("softmax_with_cross_entropy_grad");
grad_op->SetInput("Label", Input("Label"));
grad_op->SetInput("Softmax", Output("Softmax"));
......@@ -184,7 +183,7 @@ class SoftmaxGradMaker : public framework::SingleGradOpDescMaker {
grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/split_lod_tensor_op.cc
浏览文件 @ feb05c3a
......@@ -118,8 +118,7 @@ class SplitLoDTensorOp : public framework::OperatorBase {
class SplitLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
SplitLoDTensorOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
SplitLoDTensorOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input LoDTensor");
AddInput("Mask", "A bool column vector which mask the input");
......@@ -164,8 +163,8 @@ class SplitLoDTensorArrayGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("merge_lod_tensor");
grad_op->SetInput("InTrue", OutputGrad("OutTrue"));
grad_op->SetInput("InFalse", OutputGrad("OutFalse"));
......@@ -173,7 +172,7 @@ class SplitLoDTensorArrayGradMaker : public framework::SingleGradOpDescMaker {
grad_op->SetInput("X", Input("X"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/split_op.cc
浏览文件 @ feb05c3a
......@@ -65,7 +65,7 @@ class SplitOp : public framework::OperatorWithKernel {
class SplitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SplitOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
SplitOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor of the split operator.");
AddOutput("Out", "(Tensor) Output tensors of the split operator.")
......@@ -108,13 +108,13 @@ class SplitGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto op = new framework::OpDesc();
op->SetType("concat");
op->SetInput("X", OutputGrad("Out"));
op->SetOutput("Out", InputGrad("X"));
op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(op);
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/operators/spp_op.cc 0 → 100644
浏览文件 @ feb05c3a
/* 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.
Indicesou 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/spp_op.h"
namespace paddle {
namespace operators {
class SppOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SppOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor) The input tensor of spp operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature.");
AddOutput("Out",
"(Tensor) The output tensor of spp operator."
"N * M."
"M = C * H * W");
AddAttr<int>("pyramid_height", "(int), multi level pooling");
AddAttr<std::string>(
"pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddComment(R"DOC(
"With spatial pyramid pooling, the input image can
be of any sizes. This not only allows arbitrary aspect
ratios, but also allows arbitrary scales. We can resize
the input image to any scale (e.g., min(w, h)=180, 224,
...) and apply the same deep network. When the
input image is at different scales, the network (with
the same filter sizes) will extract features at different
scales. The scales play important roles in traditional
methods.
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(H_{out}, W_{out})$
Where
$$
H_{out} = N \\
W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in}
$$
paper https://arxiv.org/pdf/1406.4729v4.pdf
)DOC");
}
};
class SppOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SppOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SppOp should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
int pyramid_height = ctx->Attrs().Get<int>("pyramid_height");
PADDLE_ENFORCE(in_x_dims.size() == 4,
"Spping intput must be of 4-dimensional.");
int outlen = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * in_x_dims[1];
std::vector<int64_t> output_shape({in_x_dims[0], outlen});
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
};
class SppOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad);
REGISTER_OP_CPU_KERNEL(
spp, ops::SppKernel<paddle::platform::CPUDeviceContext, float>,
ops::SppKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
spp_grad, ops::SppGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::SppGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/spp_op.cu.cc 0 → 100644
浏览文件 @ feb05c3a
/* 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.
Indicesou 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/spp_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
spp, ops::SppKernel<paddle::platform::CUDADeviceContext, float>,
ops::SppKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
spp_grad, ops::SppGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SppGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/spp_op.h 0 → 100644
浏览文件 @ feb05c3a
/* 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.
Indicesou may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/pooling.h"
#include "paddle/operators/strided_memcpy.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SppKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
int pyramid_height = context.template Attr<int>("pyramid_height");
std::string pooling_type =
context.template Attr<std::string>("pooling_type");
out->mutable_data<T>(context.GetPlace());
auto out_stride = framework::stride(out->dims());
int input_h = in_x->dims()[2];
int input_w = in_x->dims()[3];
size_t output_offset = 0;
for (int p = 0; p < pyramid_height; ++p) {
int bins = std::pow(2, p);
int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
std::vector<int> strides({kernel_size_h, kernel_size_w});
std::vector<int> paddings({padding_h, padding_w});
// pooling output shape
framework::Tensor out_level;
std::vector<int64_t> output_shape_vec(
{in_x->dims()[0], in_x->dims()[1], bins, bins});
framework::DDim output_shape(framework::make_ddim(output_shape_vec));
out_level.mutable_data<T>(output_shape, context.GetPlace());
// pooling
if (pooling_type == "max") {
math::Pool2dFunctor<DeviceContext, math::MaxPool<T>, T> pool_forward;
math::MaxPool<T> max_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, max_process, &out_level);
} else if (pooling_type == "avg") {
math::Pool2dFunctor<DeviceContext, math::AvgPool<T>, T> pool_forward;
math::AvgPool<T> avg_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, avg_process, &out_level);
}
// flatten pooling output shape
int output_flatten_w = in_x->dims()[1] * bins * bins;
std::vector<int64_t> output_flatten_shape_vec(
{in_x->dims()[0], output_flatten_w});
framework::DDim output_flatten_shape(
framework::make_ddim(output_flatten_shape_vec));
out_level.Resize(output_flatten_shape);
// concat
auto out_level_stride = framework::stride(out_level.dims());
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out_level.data<T>(), out_level_stride, out_level.dims(),
out_stride, out->data<T>() + output_offset);
output_offset += out_level.dims()[1] * out_level_stride[1];
}
}
};
template <typename DeviceContext, typename T>
class SppGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* out = context.Input<framework::Tensor>("Out");
const framework::Tensor* out_grad =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor* in_x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
int pyramid_height = context.template Attr<int>("pyramid_height");
std::string pooling_type =
context.template Attr<std::string>("pooling_type");
auto& device_ctx = context.template device_context<DeviceContext>();
math::SetConstant<DeviceContext, T> zero;
in_x_grad->mutable_data<T>(context.GetPlace());
zero(device_ctx, in_x_grad, static_cast<T>(0));
auto out_stride = framework::stride(out->dims());
int input_h = in_x->dims()[2];
int input_w = in_x->dims()[3];
size_t out_offset = 0;
for (int p = 0; p < pyramid_height; ++p) {
int bins = std::pow(2, p);
int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
std::vector<int> strides({kernel_size_h, kernel_size_w});
std::vector<int> paddings({padding_h, padding_w});
// split out and outgrad ... to flatten
framework::Tensor out_level;
framework::Tensor outgrad_level;
int out_flatten_w = in_x->dims()[1] * bins * bins;
std::vector<int64_t> out_flatten_shape_vec(
{in_x->dims()[0], out_flatten_w});
framework::DDim out_flatten_shape(
framework::make_ddim(out_flatten_shape_vec));
out_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
outgrad_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
auto flatten_stride = framework::stride(out_level.dims());
// memcpy
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out->data<T>() + out_offset, out_stride,
out_level.dims(), flatten_stride, out_level.data<T>());
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out_grad->data<T>() + out_offset, out_stride,
outgrad_level.dims(), flatten_stride,
outgrad_level.data<T>());
out_offset += out_level.dims()[1] * out_stride[1];
// flatten backward to nchw
std::vector<int64_t> out_shape_vec({in_x->dims()[0], in_x->dims()[1]});
out_shape_vec.push_back(
(input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1);
out_shape_vec.push_back(
(input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1);
framework::DDim out_shape(framework::make_ddim(out_shape_vec));
out_level.ShareDataWith(out_level);
out_level.Resize(out_shape);
outgrad_level.ShareDataWith(outgrad_level);
outgrad_level.Resize(out_shape);
// pooling backward
if (pooling_type == "max") {
math::MaxPool2dGradFunctor<DeviceContext, T> pool2d_backward;
pool2d_backward(context.template device_context<DeviceContext>(), *in_x,
*&out_level, *&outgrad_level, kernel_size, strides,
paddings, in_x_grad);
} else if (pooling_type == "avg") {
math::Pool2dGradFunctor<DeviceContext, math::AvgPoolGrad<T>, T>
pool_backward;
math::AvgPoolGrad<T> avg_process;
pool_backward(context.template device_context<DeviceContext>(), *in_x,
*&out_level, *&outgrad_level, kernel_size, strides,
paddings, avg_process, in_x_grad);
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/squared_l2_distance_op.cc
浏览文件 @ feb05c3a
......@@ -56,8 +56,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SquaredL2DistanceOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SquaredL2DistanceOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input of SquaredL2DistanceOp.");
AddInput("Y", "(Tensor) Target of SquaredL2DistanceOp.");
......
paddle/operators/squared_l2_norm_op.cc
浏览文件 @ feb05c3a
......@@ -48,8 +48,7 @@ class SquaredL2NormGradOp : public framework::OperatorWithKernel {
class SquaredL2NormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SquaredL2NormOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SquaredL2NormOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input of squared_l2_norm op.");
AddOutput("Out", "(Scalar) The output of squared_l2_norm op.");
......
paddle/operators/sum_op.cc
浏览文件 @ feb05c3a
......@@ -29,7 +29,7 @@ class SumOp : public framework::OperatorWithKernel {
"Output(Out) of SumOp should not be null.");
if (ctx->IsRuntime() &&
ctx->GetOutputsVarType("Out")[0] ==
framework::VarDesc::LOD_TENSOR_ARRAY) {
framework::proto::VarDesc::LOD_TENSOR_ARRAY) {
return; // skip runtime infershape when is tensor array;
}
......@@ -72,8 +72,8 @@ class SumOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NE(dtype, -1,
"Sum operator should have at least one tensor");
return framework::OpKernelType(static_cast<framework::DataType>(dtype),
ctx.device_context());
return framework::OpKernelType(
static_cast<framework::proto::DataType>(dtype), ctx.device_context());
} else if (x_vars[0]->IsType<framework::SelectedRows>()) {
return framework::OpKernelType(
framework::ToDataType(
......@@ -98,7 +98,7 @@ class SumOp : public framework::OperatorWithKernel {
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
SumOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
......@@ -115,10 +115,10 @@ the LoD information with the first input.
class SumOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind& op_desc,
framework::BlockDescBind* block) const override {
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto& inputs = op_desc.Input("X");
auto var_type = framework::VarDesc::SELECTED_ROWS;
auto var_type = framework::proto::VarDesc::SELECTED_ROWS;
for (auto& name : op_desc.Input("X")) {
VLOG(10) << name << " "
......@@ -128,12 +128,12 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string& name) {
return block->FindRecursiveOrCreateVar(name)->GetType() ==
framework::VarDesc::LOD_TENSOR;
framework::proto::VarDesc::LOD_TENSOR;
});
auto is_tensor_array = [block](const std::string& name) {
return detail::Ref(block->FindRecursiveOrCreateVar(name)).GetType() ==
framework::VarDesc::LOD_TENSOR_ARRAY;
framework::proto::VarDesc::LOD_TENSOR_ARRAY;
};
bool any_input_is_tensor_array =
......@@ -152,9 +152,9 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
PADDLE_ENFORCE(all_inputs_are_tensor_array,
"Not all inputs are tensor array:\n%s", os.str());
}
var_type = framework::VarDesc::LOD_TENSOR_ARRAY;
var_type = framework::proto::VarDesc::LOD_TENSOR_ARRAY;
} else if (any_input_is_lod_tensor) {
var_type = framework::VarDesc::LOD_TENSOR;
var_type = framework::proto::VarDesc::LOD_TENSOR;
}
auto out_var_name = op_desc.Output("Out").front();
......@@ -169,20 +169,19 @@ class SumGradMaker : public framework::GradOpDescMakerBase {
public:
using framework::GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<framework::OpDescBind>> operator()()
const override {
std::vector<std::unique_ptr<framework::OpDesc>> operator()() const override {
auto x_grads = InputGrad("X");
std::vector<std::unique_ptr<framework::OpDescBind>> grad_ops;
std::vector<std::unique_ptr<framework::OpDesc>> grad_ops;
grad_ops.reserve(x_grads.size());
auto og = OutputGrad("Out");
std::transform(x_grads.begin(), x_grads.end(), std::back_inserter(grad_ops),
[&og](const std::string& x_grad) {
auto* grad_op = new framework::OpDescBind();
auto* grad_op = new framework::OpDesc();
grad_op->SetType("scale");
grad_op->SetInput("X", og);
grad_op->SetOutput("Out", {x_grad});
grad_op->SetAttr("scale", 1.0f);
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
});
return grad_ops;
}
......
paddle/operators/tensor_array_read_write_op.cc
浏览文件 @ feb05c3a
......@@ -51,8 +51,7 @@ class WriteToArrayOp : public ArrayOp {
class WriteToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
WriteToArrayOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
WriteToArrayOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor) the tensor will be written to tensor array");
AddInput(
......@@ -97,14 +96,14 @@ class WriteToArrayInferShape : public framework::InferShapeBase {
class WriteToArrayInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto x_name = op_desc.Input("X")[0];
auto out_name = op_desc.Output("Out")[0];
VLOG(10) << "Set Variable " << out_name << " as LOD_TENSOR_ARRAY";
auto &out = detail::Ref(block->FindRecursiveOrCreateVar(out_name),
"Cannot found %s", out_name);
out.SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
out.SetType(framework::proto::VarDesc::LOD_TENSOR_ARRAY);
auto *x = block->FindVarRecursive(x_name);
if (x != nullptr) {
out.SetDataType(x->GetDataType());
......@@ -140,8 +139,7 @@ class ReadFromArrayOp : public ArrayOp {
class ReadFromArrayProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ReadFromArrayProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ReadFromArrayProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(TensorArray) the array will be read from.");
AddInput("I",
......@@ -177,14 +175,14 @@ class WriteToArrayGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("read_from_array");
grad_op->SetInput("I", Input("I"));
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......@@ -193,14 +191,14 @@ class ReadFromArrayGradMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("write_to_array");
grad_op->SetInput("I", Input("I"));
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
......
paddle/operators/top_k_op.cc
浏览文件 @ feb05c3a
......@@ -46,7 +46,7 @@ class TopkOp : public framework::OperatorWithKernel {
class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TopkOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
TopkOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op");
......
paddle/operators/transpose_op.cc
浏览文件 @ feb05c3a
......@@ -55,8 +55,7 @@ class TransposeOp : public framework::OperatorWithKernel {
class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
TransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......
paddle/operators/uniform_random_op.cc
浏览文件 @ feb05c3a
......@@ -66,15 +66,14 @@ class UniformRandomOp : public framework::OperatorWithKernel {
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
static_cast<framework::DataType>(ctx.Attr<int>("dtype")),
static_cast<framework::proto::DataType>(ctx.Attr<int>("dtype")),
ctx.GetPlace());
}
};
class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker {
public:
UniformRandomOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
UniformRandomOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Out", "(Tensor) The output tensor of uniform random op");
AddComment(R"DOC(
......@@ -100,7 +99,7 @@ uniform distribution.
"0 means use a seed generated by the system.")
.SetDefault(0);
AddAttr<int>("dtype", "(int, default 5(FP32)) Output tensor data type")
.SetDefault(framework::DataType::FP32);
.SetDefault(framework::proto::DataType::FP32);
}
};
} // namespace operators
......
paddle/operators/unpool_op.cc
浏览文件 @ feb05c3a
......@@ -18,8 +18,7 @@ namespace operators {
class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Unpool2dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
Unpool2dOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......
paddle/operators/while_op.cc
浏览文件 @ feb05c3a
......@@ -46,7 +46,7 @@ class WhileOp : public framework::OperatorBase {
PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1}));
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
auto step_scopes =
......@@ -64,7 +64,7 @@ class WhileOp : public framework::OperatorBase {
class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
public:
WhileOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
WhileOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(kParameters,
"A set of variables, which are required by operators inside the "
......@@ -82,8 +82,8 @@ class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
"(StepScopeVar) A vector of local scope, which size equals the "
"step number of While Op. The i'th scope storages temporary "
"variables generated in the i'th step.");
AddAttr<framework::BlockDescBind *>(kStepBlock,
"The step block inside WhileOp");
AddAttr<framework::BlockDesc *>(kStepBlock,
"The step block inside WhileOp");
AddComment(R"DOC(
)DOC");
}
......@@ -99,7 +99,7 @@ class WhileGradOp : public framework::OperatorBase {
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
auto *step_scopes =
......@@ -209,8 +209,8 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad = new framework::OpDescBind();
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad = new framework::OpDesc();
grad->SetType("while_grad");
grad->SetInput(kParameters, Input(kParameters));
......@@ -279,14 +279,14 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
// while operator could be renamed.
grad->SetAttr("original_output_grad", extra_inputs_list);
return std::unique_ptr<framework::OpDescBind>(grad);
return std::unique_ptr<framework::OpDesc>(grad);
}
};
class WhileGradOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto p_names = op_desc.Input(kParameters);
auto pg_names = op_desc.Output(framework::GradVarName(kParameters));
......@@ -321,10 +321,10 @@ class WhileGradOpShapeInference : public framework::InferShapeBase {
continue;
}
auto dims = ctx->GetInputsElementDim(kParameters, i);
if (var_types[i] == framework::VarDesc::LOD_TENSOR) {
if (var_types[i] == framework::proto::VarDesc::LOD_TENSOR) {
names_to_set.push_back(pg_names[i]);
dims_to_set.push_back(dims);
} else if (var_types[i] == framework::VarDesc::LOD_TENSOR_ARRAY) {
} else if (var_types[i] == framework::proto::VarDesc::LOD_TENSOR_ARRAY) {
// not sure how to set the dim of LOD_TENSOR_ARRAY
names_to_set.push_back(pg_names[i]);
dims_to_set.push_back(dims);
......
paddle/platform/device_context.cc
浏览文件 @ feb05c3a
......@@ -19,7 +19,7 @@ CPUDeviceContext::CPUDeviceContext() {
eigen_device_.reset(new Eigen::DefaultDevice());
}
CPUDeviceContext::CPUDeviceContext(CPUPlace place) {
CPUDeviceContext::CPUDeviceContext(CPUPlace place) : place_(place) {
eigen_device_.reset(new Eigen::DefaultDevice());
}
......@@ -27,7 +27,7 @@ Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const {
return eigen_device_.get();
}
Place CPUDeviceContext::GetPlace() const { return CPUPlace(); }
Place CPUDeviceContext::GetPlace() const { return place_; }
#ifdef PADDLE_WITH_CUDA
......@@ -125,21 +125,21 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
cudaStream_t CUDADeviceContext::stream() const { return stream_; }
CudnnDeviceContext::CudnnDeviceContext(CudnnPlace place)
CUDNNDeviceContext::CUDNNDeviceContext(CUDNNPlace place)
: CUDADeviceContext(place), place_(place) {
PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream()));
}
CudnnDeviceContext::~CudnnDeviceContext() {
CUDNNDeviceContext::~CUDNNDeviceContext() {
SetDeviceId(place_.device);
Wait();
PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
}
Place CudnnDeviceContext::GetPlace() const { return CudnnPlace(); }
Place CUDNNDeviceContext::GetPlace() const { return CUDNNPlace(); }
cudnnHandle_t CudnnDeviceContext::cudnn_handle() const { return cudnn_handle_; }
cudnnHandle_t CUDNNDeviceContext::cudnn_handle() const { return cudnn_handle_; }
#endif
......
paddle/platform/device_context.h
浏览文件 @ feb05c3a
......@@ -45,6 +45,7 @@ class CPUDeviceContext : public DeviceContext {
Place GetPlace() const override;
private:
CPUPlace place_;
std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};
......@@ -86,10 +87,10 @@ class CUDADeviceContext : public DeviceContext {
cublasHandle_t cublas_handle_;
};
class CudnnDeviceContext : public CUDADeviceContext {
class CUDNNDeviceContext : public CUDADeviceContext {
public:
explicit CudnnDeviceContext(CudnnPlace place);
virtual ~CudnnDeviceContext();
explicit CUDNNDeviceContext(CUDNNPlace place);
virtual ~CUDNNDeviceContext();
/*! \brief Return place in the device context. */
Place GetPlace() const final;
......@@ -99,7 +100,7 @@ class CudnnDeviceContext : public CUDADeviceContext {
private:
cudnnHandle_t cudnn_handle_;
CudnnPlace place_;
CUDNNPlace place_;
};
#endif
......
paddle/platform/device_context_test.cc
浏览文件 @ feb05c3a
......@@ -47,14 +47,14 @@ TEST(Device, CUDADeviceContext) {
}
}
TEST(Device, CudnnDeviceContext) {
using paddle::platform::CudnnDeviceContext;
using paddle::platform::CudnnPlace;
TEST(Device, CUDNNDeviceContext) {
using paddle::platform::CUDNNDeviceContext;
using paddle::platform::CUDNNPlace;
if (paddle::platform::dynload::HasCUDNN()) {
int count = paddle::platform::GetCUDADeviceCount();
for (int i = 0; i < count; ++i) {
CudnnDeviceContext* device_context =
new CudnnDeviceContext(CudnnPlace(i));
CUDNNDeviceContext* device_context =
new CUDNNDeviceContext(CUDNNPlace(i));
cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
ASSERT_NE(nullptr, cudnn_handle);
ASSERT_NE(nullptr, device_context->stream());
......
paddle/platform/place.h
浏览文件 @ feb05c3a
......@@ -51,9 +51,9 @@ struct GPUPlace {
int device;
};
struct CudnnPlace : public GPUPlace {
CudnnPlace() : GPUPlace() {}
explicit CudnnPlace(int d) : GPUPlace(d) {}
struct CUDNNPlace : public GPUPlace {
CUDNNPlace() : GPUPlace() {}
explicit CUDNNPlace(int d) : GPUPlace(d) {}
};
struct IsGPUPlace : public boost::static_visitor<bool> {
......@@ -72,7 +72,7 @@ struct IsMKLDNNPlace : public boost::static_visitor<bool> {
// should be less equal than 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT)
#define NUM_PLACE_TYPE_LIMIT_IN_BIT 4
typedef boost::variant<CudnnPlace, GPUPlace, CPUPlace, MKLDNNPlace> Place;
typedef boost::variant<CUDNNPlace, GPUPlace, CPUPlace, MKLDNNPlace> Place;
// static check number of place types is less equal than
// 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT)
......
paddle/platform/place_test.cc
浏览文件 @ feb05c3a
......@@ -5,16 +5,22 @@
TEST(Place, Equality) {
paddle::platform::CPUPlace cpu;
paddle::platform::GPUPlace g0(0), g1(1), gg0(0);
paddle::platform::CUDNNPlace d0(0), d1(1), dd0(0);
EXPECT_EQ(cpu, cpu);
EXPECT_EQ(g0, g0);
EXPECT_EQ(g1, g1);
EXPECT_EQ(g0, gg0);
EXPECT_EQ(d0, dd0);
EXPECT_NE(g0, g1);
EXPECT_NE(d0, d1);
EXPECT_TRUE(paddle::platform::places_are_same_class(g0, gg0));
EXPECT_FALSE(paddle::platform::places_are_same_class(g0, cpu));
EXPECT_TRUE(paddle::platform::is_gpu_place(d0));
EXPECT_FALSE(paddle::platform::places_are_same_class(g0, d0));
}
TEST(Place, Default) {
......
paddle/pybind/CMakeLists.txt
浏览文件 @ feb05c3a
if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc
DEPS pybind python backward proto_desc paddle_memory executor prune
SRCS pybind.cc exception.cc protobuf.cc const_value.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init
${GLOB_OP_LIB})
endif(WITH_PYTHON)
......
paddle/pybind/const_value.cc 0 → 100644
浏览文件 @ feb05c3a
/* 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 "const_value.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace pybind {
void BindConstValue(pybind11::module& m) {
m.def("kEmptyVarName", [] { return framework::kEmptyVarName; });
m.def("kTempVarName", [] { return framework::kTempVarName; });
m.def("kGradVarSuffix", [] { return framework::kGradVarSuffix; });
m.def("kZeroVarSuffix", [] { return framework::kZeroVarSuffix; });
}
} // namespace pybind
} // namespace paddle
paddle/pybind/const_value.h 0 → 100644
浏览文件 @ feb05c3a
/* 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 <Python.h>
#include "paddle/platform/enforce.h"
#include "pybind11/pybind11.h"
namespace py = pybind11;
namespace paddle {
namespace pybind {
extern void BindConstValue(pybind11::module& m);
} // namespace pybind
} // namespace paddle
paddle/pybind/print_operators_doc.cc
浏览文件 @ feb05c3a
......@@ -31,31 +31,32 @@ std::string Escape(const std::string& s) {
return r;
}
std::string AttrType(paddle::framework::AttrType at) {
std::string AttrType(paddle::framework::proto::AttrType at) {
switch (at) {
case paddle::framework::INT:
case paddle::framework::proto::INT:
return "int";
case paddle::framework::FLOAT:
case paddle::framework::proto::FLOAT:
return "float";
case paddle::framework::STRING:
case paddle::framework::proto::STRING:
return "string";
case paddle::framework::BOOLEAN:
case paddle::framework::proto::BOOLEAN:
return "bool";
case paddle::framework::INTS:
case paddle::framework::proto::INTS:
return "int array";
case paddle::framework::FLOATS:
case paddle::framework::proto::FLOATS:
return "float array";
case paddle::framework::STRINGS:
case paddle::framework::proto::STRINGS:
return "string array";
case paddle::framework::BOOLEANS:
case paddle::framework::proto::BOOLEANS:
return "bool array";
case paddle::framework::BLOCK:
case paddle::framework::proto::BLOCK:
return "block id";
}
return "UNKNOWN"; // not possible
}
void PrintVar(const paddle::framework::OpProto::Var& v, std::stringstream& ss) {
void PrintVar(const paddle::framework::proto::OpProto::Var& v,
std::stringstream& ss) {
ss << " { "
<< "\n"
<< " \"name\" : \"" << Escape(v.name()) << "\",\n"
......@@ -65,7 +66,7 @@ void PrintVar(const paddle::framework::OpProto::Var& v, std::stringstream& ss) {
<< " },";
}
void PrintAttr(const paddle::framework::OpProto::Attr& a,
void PrintAttr(const paddle::framework::proto::OpProto::Attr& a,
std::stringstream& ss) {
ss << " { "
<< "\n"
......@@ -81,7 +82,7 @@ void PrintOpProto(const std::string& type,
std::stringstream& ss) {
std::cerr << "Processing " << type << "\n";
const paddle::framework::OpProto* p = opinfo.proto_;
const paddle::framework::proto::OpProto* p = opinfo.proto_;
if (p == nullptr) {
return; // It is possible that an operator doesn't have OpProto.
}
......
paddle/pybind/protobuf.cc
浏览文件 @ feb05c3a
......@@ -108,21 +108,21 @@ static py::bytes SerializeMessage(T &self) {
// Bind Methods
void BindProgramDesc(py::module &m) {
py::class_<ProgramDescBind>(m, "ProgramDesc", "")
py::class_<ProgramDesc>(m, "ProgramDesc", "")
.def(py::init<>())
.def("__init__",
[](ProgramDescBind &self, const ProgramDescBind &other) {
new (&self) ProgramDescBind(other);
[](ProgramDesc &self, const ProgramDesc &other) {
new (&self) ProgramDesc(other);
})
.def("__init__",
[](ProgramDescBind &self, const py::bytes &binary_str) {
[](ProgramDesc &self, const py::bytes &binary_str) {
std::string str(binary_str);
new (&self) ProgramDescBind(str);
new (&self) ProgramDesc(str);
})
.def("append_block", &ProgramDescBind::AppendBlock,
.def("append_block", &ProgramDesc::AppendBlock,
py::return_value_policy::reference)
.def("append_backward",
[](ProgramDescBind &program_desc, const VarDescBind &target,
[](ProgramDesc &program_desc, const VarDesc &target,
const std::unordered_set<std::string> &no_grad_vars) {
ParamGradInfoMap param_grad_map =
AppendBackward(program_desc, target, no_grad_vars);
......@@ -138,13 +138,13 @@ void BindProgramDesc(py::module &m) {
}
return retv;
})
.def("block", &ProgramDescBind::MutableBlock,
.def("block", &ProgramDesc::MutableBlock,
py::return_value_policy::reference)
.def("num_blocks", &ProgramDescBind::Size)
.def("serialize_to_string", SerializeMessage<ProgramDescBind>)
.def("num_blocks", &ProgramDesc::Size)
.def("serialize_to_string", SerializeMessage<ProgramDesc>)
.def("parse_from_string",
[](ProgramDescBind &program_desc, const std::string &data) {
ProgramDesc *desc = program_desc.Proto();
[](ProgramDesc &program_desc, const std::string &data) {
proto::ProgramDesc *desc = program_desc.Proto();
PADDLE_ENFORCE(desc->ParseFromString(data),
"Fail to parse ProgramDesc from string. This could "
"be a bug of Paddle.");
......@@ -152,109 +152,108 @@ void BindProgramDesc(py::module &m) {
}
void BindBlockDesc(py::module &m) {
py::class_<BlockDescBind>(m, "BlockDesc", "")
.def_property_readonly("id", &BlockDescBind::ID)
.def_property_readonly("parent", &BlockDescBind::Parent)
.def("append_op", &BlockDescBind::AppendOp,
py::class_<BlockDesc>(m, "BlockDesc", "")
.def_property_readonly("id", &BlockDesc::ID)
.def_property_readonly("parent", &BlockDesc::Parent)
.def("append_op", &BlockDesc::AppendOp,
py::return_value_policy::reference)
.def("prepend_op", &BlockDescBind::PrependOp,
.def("prepend_op", &BlockDesc::PrependOp,
py::return_value_policy::reference)
.def("var",
[](BlockDescBind &self, py::bytes byte_name) {
[](BlockDesc &self, py::bytes byte_name) {
std::string name = byte_name;
return self.Var(name);
},
py::return_value_policy::reference)
.def("has_var",
[](BlockDescBind &self, py::bytes byte_name) {
[](BlockDesc &self, py::bytes byte_name) {
std::string name = byte_name;
return self.HasVar(name);
})
.def("find_var",
[](BlockDescBind &self, py::bytes byte_name) {
[](BlockDesc &self, py::bytes byte_name) {
std::string name = byte_name;
return self.FindVar(name);
},
py::return_value_policy::reference)
.def("all_vars", &BlockDescBind::AllVars,
py::return_value_policy::reference)
.def("op_size", &BlockDescBind::OpSize)
.def("op", &BlockDescBind::Op, py::return_value_policy::reference)
.def("serialize_to_string", SerializeMessage<BlockDescBind>);
.def("all_vars", &BlockDesc::AllVars, py::return_value_policy::reference)
.def("op_size", &BlockDesc::OpSize)
.def("op", &BlockDesc::Op, py::return_value_policy::reference)
.def("serialize_to_string", SerializeMessage<BlockDesc>);
}
void BindVarDsec(py::module &m) {
py::enum_<DataType>(m, "DataType", "")
.value("BOOL", DataType::BOOL)
.value("INT16", DataType::INT16)
.value("INT32", DataType::INT32)
.value("INT64", DataType::INT64)
.value("FP16", DataType::FP16)
.value("FP32", DataType::FP32)
.value("FP64", DataType::FP64);
py::enum_<proto::DataType>(m, "DataType", "")
.value("BOOL", proto::DataType::BOOL)
.value("INT16", proto::DataType::INT16)
.value("INT32", proto::DataType::INT32)
.value("INT64", proto::DataType::INT64)
.value("FP16", proto::DataType::FP16)
.value("FP32", proto::DataType::FP32)
.value("FP64", proto::DataType::FP64);
py::class_<VarDescBind> var_desc(m, "VarDesc", "");
py::class_<VarDesc> var_desc(m, "VarDesc", "");
var_desc
.def("name",
[](const VarDescBind &self) {
[](const VarDesc &self) {
py::bytes name = self.Name();
return name;
},
py::return_value_policy::reference)
.def("set_shape", &VarDescBind::SetShape)
.def("set_dtype", &VarDescBind::SetDataType)
.def("shape", &VarDescBind::Shape, py::return_value_policy::reference)
.def("dtype", &VarDescBind::GetDataType)
.def("lod_level", &VarDescBind::GetLodLevel)
.def("set_lod_level", &VarDescBind::SetLoDLevel)
.def("type", &VarDescBind::GetType)
.def("set_type", &VarDescBind::SetType)
.def("serialize_to_string", SerializeMessage<VarDescBind>)
.def("persistable", &VarDescBind::Persistable)
.def("set_persistable", &VarDescBind::SetPersistable);
.def("set_shape", &VarDesc::SetShape)
.def("set_dtype", &VarDesc::SetDataType)
.def("shape", &VarDesc::Shape, py::return_value_policy::reference)
.def("dtype", &VarDesc::GetDataType)
.def("lod_level", &VarDesc::GetLodLevel)
.def("set_lod_level", &VarDesc::SetLoDLevel)
.def("type", &VarDesc::GetType)
.def("set_type", &VarDesc::SetType)
.def("serialize_to_string", SerializeMessage<VarDesc>)
.def("persistable", &VarDesc::Persistable)
.def("set_persistable", &VarDesc::SetPersistable);
py::enum_<VarDesc::VarType>(var_desc, "VarType", "")
.value("LOD_TENSOR", VarDesc::LOD_TENSOR)
.value("SELECTED_ROWS", VarDesc::SELECTED_ROWS)
.value("FEED_MINIBATCH", VarDesc::FEED_MINIBATCH)
.value("FETCH_LIST", VarDesc::FETCH_LIST)
.value("STEP_SCOPES", VarDesc::STEP_SCOPES)
.value("LOD_RANK_TABLE", VarDesc::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", VarDesc::LOD_TENSOR_ARRAY);
py::enum_<proto::VarDesc::VarType>(var_desc, "VarType", "")
.value("LOD_TENSOR", proto::VarDesc::LOD_TENSOR)
.value("SELECTED_ROWS", proto::VarDesc::SELECTED_ROWS)
.value("FEED_MINIBATCH", proto::VarDesc::FEED_MINIBATCH)
.value("FETCH_LIST", proto::VarDesc::FETCH_LIST)
.value("STEP_SCOPES", proto::VarDesc::STEP_SCOPES)
.value("LOD_RANK_TABLE", proto::VarDesc::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", proto::VarDesc::LOD_TENSOR_ARRAY);
}
void BindOpDesc(py::module &m) {
py::enum_<AttrType>(m, "AttrType", "")
.value("INT", AttrType::INT)
.value("INTS", AttrType::INTS)
.value("FLOAT", AttrType::FLOAT)
.value("FLOATS", AttrType::FLOATS)
.value("STRING", AttrType::STRING)
.value("STRINGS", AttrType::STRINGS)
.value("BOOL", AttrType::BOOLEAN)
.value("BOOLS", AttrType::BOOLEANS)
.value("BLOCK", AttrType::BLOCK);
py::enum_<proto::AttrType>(m, "AttrType", "")
.value("INT", proto::AttrType::INT)
.value("INTS", proto::AttrType::INTS)
.value("FLOAT", proto::AttrType::FLOAT)
.value("FLOATS", proto::AttrType::FLOATS)
.value("STRING", proto::AttrType::STRING)
.value("STRINGS", proto::AttrType::STRINGS)
.value("BOOL", proto::AttrType::BOOLEAN)
.value("BOOLS", proto::AttrType::BOOLEANS)
.value("BLOCK", proto::AttrType::BLOCK);
py::class_<OpDescBind> op_desc(m, "OpDesc", "");
op_desc.def("type", &OpDescBind::Type)
.def("set_type", &OpDescBind::SetType)
.def("input", &OpDescBind::Input)
.def("input_names", &OpDescBind::InputNames)
.def("set_input", &OpDescBind::SetInput)
.def("output", &OpDescBind::Output)
.def("output_names", &OpDescBind::OutputNames)
.def("set_output", &OpDescBind::SetOutput)
.def("has_attr", &OpDescBind::HasAttr)
.def("attr_type", &OpDescBind::GetAttrType)
.def("attr_names", &OpDescBind::AttrNames)
.def("set_attr", &OpDescBind::SetAttr)
.def("attr", &OpDescBind::GetAttr)
.def("set_block_attr", &OpDescBind::SetBlockAttr)
.def("block_attr", &OpDescBind::GetBlockAttr)
.def("check_attrs", &OpDescBind::CheckAttrs)
.def("infer_shape", &OpDescBind::InferShape)
.def("infer_var_type", &OpDescBind::InferVarType)
.def("serialize_to_string", SerializeMessage<OpDescBind>);
py::class_<OpDesc> op_desc(m, "OpDesc", "");
op_desc.def("type", &OpDesc::Type)
.def("set_type", &OpDesc::SetType)
.def("input", &OpDesc::Input)
.def("input_names", &OpDesc::InputNames)
.def("set_input", &OpDesc::SetInput)
.def("output", &OpDesc::Output)
.def("output_names", &OpDesc::OutputNames)
.def("set_output", &OpDesc::SetOutput)
.def("has_attr", &OpDesc::HasAttr)
.def("attr_type", &OpDesc::GetAttrType)
.def("attr_names", &OpDesc::AttrNames)
.def("set_attr", &OpDesc::SetAttr)
.def("attr", &OpDesc::GetAttr)
.def("set_block_attr", &OpDesc::SetBlockAttr)
.def("block_attr", &OpDesc::GetBlockAttr)
.def("check_attrs", &OpDesc::CheckAttrs)
.def("infer_shape", &OpDesc::InferShape)
.def("infer_var_type", &OpDesc::InferVarType)
.def("serialize_to_string", SerializeMessage<OpDesc>);
}
} // namespace pybind
......
paddle/pybind/pybind.cc
浏览文件 @ feb05c3a
......@@ -16,11 +16,11 @@ limitations under the License. */
#include <mutex> // for call_once
#include <unordered_map>
#include "gflags/gflags.h"
#include "paddle/framework/backward.h"
#include "paddle/framework/executor.h"
#include "paddle/framework/feed_fetch_method.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/init.h"
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/lod_tensor_array.h"
......@@ -30,6 +30,7 @@ limitations under the License. */
#include "paddle/operators/net_op.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/const_value.h"
#include "paddle/pybind/exception.h"
#include "paddle/pybind/pybind.h"
#include "paddle/pybind/tensor_py.h"
......@@ -51,24 +52,6 @@ static size_t UniqueIntegerGenerator(const std::string &prefix) {
return generators[prefix].fetch_add(1);
}
std::once_flag gflags_init_flag;
// TODO(qijun) move init gflags to init.cc
void InitGflags(std::vector<std::string> &argv) {
std::call_once(gflags_init_flag, [&]() {
int argc = argv.size();
char **arr = new char *[argv.size()];
std::string line;
for (size_t i = 0; i < argv.size(); i++) {
arr[i] = &argv[i][0];
line += argv[i];
line += ' ';
}
google::ParseCommandLineFlags(&argc, &arr, true);
VLOG(1) << "Init commandline: " << line;
});
}
bool IsCompileGPU() {
#ifndef PADDLE_WITH_CUDA
return false;
......@@ -283,36 +266,36 @@ All parameter, weight, gradient are variables in Paddle.
return ret_values;
});
m.def("get_grad_op_descs",
[](const OpDescBind &op_desc,
[](const OpDesc &op_desc,
const std::unordered_set<std::string> &no_grad_set,
std::unordered_map<std::string, std::string> &grad_to_var,
const std::vector<BlockDescBind *> &grad_sub_block) {
std::vector<std::unique_ptr<OpDescBind>> grad_op_descs =
const std::vector<BlockDesc *> &grad_sub_block) {
std::vector<std::unique_ptr<OpDesc>> grad_op_descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, no_grad_set, &grad_to_var,
grad_sub_block);
std::vector<OpDescBind *> grad_op_desc_ptrs(grad_op_descs.size());
std::vector<OpDesc *> grad_op_desc_ptrs(grad_op_descs.size());
std::transform(
grad_op_descs.begin(), grad_op_descs.end(),
grad_op_desc_ptrs.begin(),
[](std::unique_ptr<OpDescBind> &p) { return p.release(); });
[](std::unique_ptr<OpDesc> &p) { return p.release(); });
return grad_op_desc_ptrs;
});
m.def("prune", [](const ProgramDescBind &origin,
m.def("prune", [](const ProgramDesc &origin,
const std::vector<std::array<size_t, 2>> &targets) {
ProgramDescBind prog_with_targets(origin);
ProgramDesc prog_with_targets(origin);
for (const auto &t : targets) {
prog_with_targets.MutableBlock(t[0])->Op(t[1])->MarkAsTarget();
}
ProgramDesc pruned_desc;
proto::ProgramDesc pruned_desc;
Prune(*prog_with_targets.Proto(), &pruned_desc);
return new ProgramDescBind(pruned_desc);
return new ProgramDesc(pruned_desc);
});
m.def("inference_optimize", [](ProgramDescBind &origin) {
ProgramDesc pruned_desc;
m.def("inference_optimize", [](ProgramDesc &origin) {
proto::ProgramDesc pruned_desc;
InferenceOptimize(*(origin.Proto()), &pruned_desc);
return new ProgramDescBind(pruned_desc);
return new ProgramDesc(pruned_desc);
});
m.def_submodule(
"var_names",
......@@ -362,7 +345,7 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<OperatorBase>(m, "Operator")
.def_static("create",
[](py::bytes protobin) {
OpDesc desc;
proto::OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
......@@ -415,7 +398,7 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
.def_static("create",
[](py::bytes protobin) -> operators::CondOp * {
OpDesc desc;
proto::OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
......@@ -438,7 +421,8 @@ All parameter, weight, gradient are variables in Paddle.
.def("run", &Executor::Run);
m.def("unique_integer", UniqueIntegerGenerator);
m.def("init_gflags", InitGflags);
m.def("init_gflags", framework::InitGflags);
m.def("init_devices", &framework::InitDevices);
m.def("is_compile_gpu", IsCompileGPU);
m.def("set_feed_variable", framework::SetFeedVariable);
......@@ -448,6 +432,7 @@ All parameter, weight, gradient are variables in Paddle.
BindBlockDesc(m);
BindVarDsec(m);
BindOpDesc(m);
BindConstValue(m);
py::class_<framework::LoDRankTable>(m, "LodRankTable")
.def("items", [](framework::LoDRankTable &table) {
......
paddle/scripts/travis/build_doc.sh
浏览文件 @ feb05c3a
......@@ -14,9 +14,8 @@ make -j `nproc` print_operators_doc
paddle/pybind/print_operators_doc > doc/en/html/operators.json
# check websites for broken links
# It will be failed now!
#linkchecker doc/en/html/index.html
#linkchecker doc/cn/html/index.html
linkchecker doc/en/html/index.html
linkchecker doc/cn/html/index.html
# Parse Github URL
REPO=`git config remote.origin.url`
......
python/paddle/trainer_config_helpers/networks.py
浏览文件 @ feb05c3a
......@@ -25,10 +25,10 @@ from paddle.trainer.config_parser import *
__all__ = [
'sequence_conv_pool', 'simple_lstm', "simple_img_conv_pool",
"img_conv_bn_pool", 'lstmemory_group', 'lstmemory_unit', 'small_vgg',
'img_conv_group', 'vgg_16_network', 'gru_unit', 'gru_group', 'simple_gru',
'simple_attention', 'dot_product_attention', 'multi_head_attention',
'simple_gru2', 'bidirectional_gru', 'text_conv_pool', 'bidirectional_lstm',
'inputs', 'outputs'
'img_conv_group', 'img_separable_conv', 'vgg_16_network', 'gru_unit',
'gru_group', 'simple_gru', 'simple_attention', 'dot_product_attention',
'multi_head_attention', 'simple_gru2', 'bidirectional_gru',
'text_conv_pool', 'bidirectional_lstm', 'inputs', 'outputs'
]
######################################################
......@@ -251,13 +251,13 @@ def img_conv_bn_pool(input,
pool_layer_attr=None):
"""
Convolution, batch normalization, pooling group.
Img input => Conv => BN => Pooling => Output.
:param name: group name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:type input: LayerOutput
:param filter_size: see img_conv_layer for details.
:type filter_size: int
:param num_filters: see img_conv_layer for details.
......@@ -435,6 +435,85 @@ def img_conv_group(input,
input=tmp, stride=pool_stride, pool_size=pool_size, pool_type=pool_type)
@wrap_name_default("separable_conv")
def img_separable_conv(input,
num_channels,
num_out_channels,
filter_size,
stride=1,
padding=0,
depth_multiplier=1,
act=None,
bias_attr=None,
param_attr=None,
shared_bias=True,
layer_type='exconv',
name=None):
"""
Separable Convolution.
The separable convolution module is consisted of a depthwise convolution
that acts separately on input channels, followed by a pointwise convolution
with 1*1 kernels that mixes channels. It is used for Xception:
https://arxiv.org/pdf/1610.02357.pdf
:param input: input layer.
:type input: LayerOutput
:param num_channels: the number of input channels.
:type num_channels: int
:param num_out_channels: the number of output channels.
:type num_out_channels: int
:param filter_size: the filter size for the depthwise convolution.
:type filter_size: int|tuple
:param stride: the stride size for the depthwise convolution.
:type stride: int|tuple
:param padding: the padding size for the depthwise convolution.
:type padding: int|tuple
:param depth_multiplier: the number of filter for one channel in the
depthwize convolution.
:type depth_multiplier: int
:param act: the activation function for the output.
:type act: BaseActivation
:param bias_attr: see img_conv_layer for details.
:type bias_attr: ParameterAttribute
:param param_attr: see img_conv_layer for details.
:type param_attr: ParameterAttribute
:param shared_bias: see img_conv_layer for details.
:type shared_bias: bool
:param layer_type: see img_conv_layer for details.
:type layer_type: bool
:return: layer's output
:rtype: LayerOutput
"""
__depthwise_conv__ = img_conv_layer(
name="%s_depthwise_conv" % name,
input=input,
num_channels=num_channels,
num_filters=num_channels * depth_multiplier,
groups=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=shared_bias,
layer_type=layer_type)
__pointwise_conv__ = img_conv_layer(
name="%s_pointwise_conv" % name,
input=__depthwise_conv__,
num_channels=num_channels * depth_multiplier,
num_filters=num_out_channels,
filter_size=1,
stride=1,
padding=0,
act=act,
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=shared_bias)
return __pointwise_conv__
def small_vgg(input_image, num_channels, num_classes):
def __vgg__(ipt, num_filter, times, dropouts, num_channels_=None):
return img_conv_group(
......@@ -648,7 +727,7 @@ def lstmemory_unit(input,
lstm_bias_attr=None,
lstm_layer_attr=None):
"""
lstmemory_unit defines the caculation process of a LSTM unit during a
lstmemory_unit defines the caculation process of a LSTM unit during a
single time step. This function is not a recurrent layer, so it can not be
directly used to process sequence input. This function is always used in
recurrent_group (see layers.py for more details) to implement attention
......@@ -869,7 +948,7 @@ def gru_unit(input,
gru_layer_attr=None,
naive=False):
"""
gru_unit defines the calculation process of a gated recurrent unit during a single
gru_unit defines the calculation process of a gated recurrent unit during a single
time step. This function is not a recurrent layer, so it can not be
directly used to process sequence input. This function is always used in
the recurrent_group (see layers.py for more details) to implement attention
......@@ -1012,7 +1091,7 @@ def simple_gru(input,
simple_gru in network.py. The reason why there are so many interfaces is
that we have two ways to implement recurrent neural network. One way is to
use one complete layer to implement rnn (including simple rnn, gru and lstm)
with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But
with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But
the multiplication operation :math:`W x_t` is not computed in these layers.
See details in their interfaces in layers.py.
The other implementation is to use an recurrent group which can ensemble a
......@@ -1116,11 +1195,12 @@ def simple_gru2(input,
:type act: BaseActivation
:param gate_act: gate activiation type of gru
:type gate_act: BaseActivation
:param gru_bias_attr: bias parameter attribute of gru layer,
:param gru_bias_attr: bias parameter attribute of gru layer,
False means no bias, None means default bias.
:type gru_bias_attr: ParameterAttribute|False|None
:param gru_layer_attr: Extra attribute of the gru layer.
:type gru_layer_attr: ExtraLayerAttribute
:param gru_param_attr: param parameter attribute of gru layer,
None means default param.
:type gru_param_attr: ParameterAttribute|None
:return: the gru group.
:rtype: LayerOutput
"""
......@@ -1188,7 +1268,7 @@ def bidirectional_gru(input,
:type size: int
:param return_seq: If set False, the last time step of output are
concatenated and returned.
If set True, the entire output sequences in forward
If set True, the entire output sequences in forward
and backward directions are concatenated and returned.
:type return_seq: bool
:return: LayerOutput object.
......@@ -1277,7 +1357,7 @@ def bidirectional_lstm(input,
:type size: int
:param return_seq: If set False, the last time step of output are
concatenated and returned.
If set True, the entire output sequences in forward
If set True, the entire output sequences in forward
and backward directions are concatenated and returned.
:type return_seq: bool
:return: LayerOutput object.
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ feb05c3a
......@@ -16,12 +16,13 @@ import regularizer
from param_attr import ParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, CPUPlace, GPUPlace
import clip
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + [
'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
'regularizer', 'LoDTensor', 'CPUPlace', 'GPUPlace', 'Tensor', 'ParamAttr'
'DataFeeder'
'DataFeeder', 'clip'
]
......
python/paddle/v2/fluid/clip.py 0 → 100644
浏览文件 @ feb05c3a
import functools
import layers
__all__ = ['GradientClipByValue', 'append_gradient_clip_ops']
class BaseGradientClipAttr(object):
def process_context(self, context, p_g):
raise NotImplementedError()
def create_operators(self, param, grad):
raise NotImplementedError()
class NullGradientClipAttr(BaseGradientClipAttr):
def process_context(self, context, p_g):
pass
def create_operators(self, param, grad):
return param, grad
class GradientClipByValue(BaseGradientClipAttr):
def __init__(self, max, min=None):
max = float(max)
if min is None:
min = -max
else:
min = float(min)
self.max = max
self.min = min
def process_context(self, context, p_g):
pass
def create_operators(self, param, grad):
new_grad = layers.clip(x=grad, min=self.min, max=self.max)
return param, new_grad
def append_gradient_clip_ops(param_grad):
context = dict()
create_op_callbacks = []
for p, g in param_grad:
clip_attr = getattr(p, 'clip_attr', NullGradientClipAttr())
if clip_attr is None:
clip_attr = NullGradientClipAttr()
if not isinstance(clip_attr, BaseGradientClipAttr):
raise TypeError(
"clip attribute should be an instance of BaseGradientClippingAttr"
)
clip_attr.process_context(context=context, p_g=param_grad)
create_op_callbacks.append(
functools.partial(
clip_attr.create_operators, param=p, grad=g))
return [each_callback() for each_callback in create_op_callbacks]
ClipByValue = GradientClipByValue
python/paddle/v2/fluid/evaluator.py
浏览文件 @ feb05c3a
import numpy as np
import layers
from framework import Program, unique_name, Variable
from framework import Program, unique_name, Variable, program_guard
from layer_helper import LayerHelper
__all__ = ['Accuracy', 'ChunkEvaluator']
......@@ -49,15 +49,12 @@ class Evaluator(object):
if reset_program is None:
reset_program = Program()
for var in self.states:
assert isinstance(var, Variable)
g_var = _clone_var_(reset_program.current_block(), var)
layers.fill_constant(
shape=g_var.shape,
value=0.0,
dtype=g_var.dtype,
out=g_var,
main_program=reset_program)
with program_guard(main_program=reset_program):
for var in self.states:
assert isinstance(var, Variable)
g_var = _clone_var_(reset_program.current_block(), var)
layers.fill_constant(
shape=g_var.shape, value=0.0, dtype=g_var.dtype, out=g_var)
executor.run(reset_program)
......@@ -104,20 +101,14 @@ class Accuracy(Evaluator):
self.total = self.create_state(dtype='int64', shape=[1], suffix='total')
self.correct = self.create_state(
dtype='int64', shape=[1], suffix='correct')
kwargs = {'main_program': main_program}
total = self.helper.create_tmp_variable(dtype='int')
correct = self.helper.create_tmp_variable(dtype='int')
acc = layers.accuracy(
input=input,
label=label,
k=k,
total=total,
correct=correct,
**kwargs)
total = layers.cast(x=total, dtype='int64', **kwargs)
correct = layers.cast(x=correct, dtype='int64', **kwargs)
layers.sums(input=[self.total, total], out=self.total, **kwargs)
layers.sums(input=[self.correct, correct], out=self.correct, **kwargs)
input=input, label=label, k=k, total=total, correct=correct)
total = layers.cast(x=total, dtype='int64')
correct = layers.cast(x=correct, dtype='int64')
layers.sums(input=[self.total, total], out=self.total)
layers.sums(input=[self.correct, correct], out=self.correct)
self.metrics.append(acc)
......@@ -125,12 +116,12 @@ class Accuracy(Evaluator):
if eval_program is None:
eval_program = Program()
block = eval_program.current_block()
kwargs = {'main_program': eval_program}
total = _clone_var_(block, self.total)
correct = _clone_var_(block, self.correct)
total = layers.cast(total, dtype='float32', **kwargs)
correct = layers.cast(correct, dtype='float32', **kwargs)
out = layers.elementwise_div(x=correct, y=total, **kwargs)
with program_guard(main_program=eval_program):
total = _clone_var_(block, self.total)
correct = _clone_var_(block, self.correct)
total = layers.cast(total, dtype='float32')
correct = layers.cast(correct, dtype='float32')
out = layers.elementwise_div(x=correct, y=total)
return np.array(executor.run(eval_program, fetch_list=[out])[0])
......@@ -141,14 +132,14 @@ class ChunkEvaluator(Evaluator):
numbers.
"""
def __init__(self,
input,
label,
chunk_scheme,
num_chunk_types,
excluded_chunk_types=None,
**kwargs):
super(ChunkEvaluator, self).__init__("chunk_eval", **kwargs)
def __init__(
self,
input,
label,
chunk_scheme,
num_chunk_types,
excluded_chunk_types=None, ):
super(ChunkEvaluator, self).__init__("chunk_eval")
main_program = self.helper.main_program
if main_program.current_block().idx != 0:
raise ValueError("You can only invoke Evaluator in root block")
......@@ -159,26 +150,21 @@ class ChunkEvaluator(Evaluator):
dtype='int64', shape=[1], suffix='num_label_chunks')
self.num_correct_chunks = self.create_state(
dtype='int64', shape=[1], suffix='num_correct_chunks')
kwargs = {'main_program': main_program}
precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval(
input=input,
label=label,
chunk_scheme=chunk_scheme,
num_chunk_types=num_chunk_types,
excluded_chunk_types=excluded_chunk_types,
**kwargs)
excluded_chunk_types=excluded_chunk_types, )
layers.sums(
input=[self.num_infer_chunks, num_infer_chunks],
out=self.num_infer_chunks,
**kwargs)
out=self.num_infer_chunks)
layers.sums(
input=[self.num_label_chunks, num_label_chunks],
out=self.num_label_chunks,
**kwargs)
out=self.num_label_chunks)
layers.sums(
input=[self.num_correct_chunks, num_correct_chunks],
out=self.num_correct_chunks,
**kwargs)
out=self.num_correct_chunks)
self.metrics.extend([precision, recall, f1_score])
......@@ -186,7 +172,6 @@ class ChunkEvaluator(Evaluator):
if eval_program is None:
eval_program = Program()
block = eval_program.current_block()
kwargs = {'main_program': eval_program}
num_infer_chunks, num_label_chunks, num_correct_chunks = executor.run(
eval_program,
fetch_list=[_clone_var_(block, state) for state in self.states])
......
python/paddle/v2/fluid/executor.py
浏览文件 @ feb05c3a
......@@ -46,6 +46,13 @@ class Executor(object):
p.set_place(each)
act_places.append(p)
# TODO(dzhwinter) : consider that our fluid tests all written in
# GPUPlace(gpu_id), this will be changed in next PR.
if core.is_compile_gpu():
core.init_devices(["CPU", "GPU:0"])
else:
core.init_devices(["CPU"])
self.executor = core.Executor(act_places)
self.places = places
......
python/paddle/v2/fluid/framework.py
浏览文件 @ feb05c3a
import collections
import contextlib
import numpy as np
from . import core
import proto.framework_pb2 as framework_pb2
import google.protobuf.message
import contextlib
from . import core
__all__ = [
'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
......@@ -12,6 +12,18 @@ __all__ = [
'switch_main_program'
]
EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
def grad_var_name(var_name):
"""
return gradient name for a certain var name
"""
return var_name + GRAD_VAR_SUFFIX
def unique_name(prefix):
"""
......@@ -704,6 +716,7 @@ class Block(object):
trainable=p.trainable,
optimize_attr=p.optimize_attr,
regularizer=p.regularizer,
clip_attr=p.clip_attr,
name=v.name)
self.vars[new_p.name] = new_p
......@@ -866,6 +879,8 @@ class Parameter(Variable):
self.regularizer = kwargs.get('regularizer', None)
self.clip_attr = kwargs.get('clip_attr', None)
# program is a global instance.
_main_program_ = Program()
......
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ feb05c3a
......@@ -21,19 +21,11 @@ class LayerHelper(object):
@property
def main_program(self):
prog = self.kwargs.get('main_program', None)
if prog is None:
return default_main_program()
else:
return prog
return default_main_program()
@property
def startup_program(self):
prog = self.kwargs.get('startup_program', None)
if prog is None:
return default_startup_program()
else:
return prog
return default_startup_program()
def append_op(self, *args, **kwargs):
return self.main_program.current_block().append_op(*args, **kwargs)
......@@ -151,13 +143,6 @@ class LayerHelper(object):
persistable=True,
initializer=initializer)
@property
def to_kwargs(self):
return {
'main_program': self.main_program,
'startup_program': self.startup_program
}
def append_bias_op(self, input_var, dim_start=1, dim_end=None):
"""
Append bias operator and return its output. If the user does not set
......
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @ feb05c3a
......@@ -14,11 +14,7 @@ __all__ = [
]
def split_lod_tensor(input,
mask,
level=0,
main_program=None,
startup_program=None):
def split_lod_tensor(input, mask, level=0):
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.dtype)
out_false = helper.create_tmp_variable(dtype=input.dtype)
......@@ -34,13 +30,7 @@ def split_lod_tensor(input,
return out_true, out_false
def merge_lod_tensor(in_true,
in_false,
x,
mask,
level=0,
main_program=None,
startup_program=None):
def merge_lod_tensor(in_true, in_false, x, mask, level=0):
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=in_true.dtype)
helper.append_op(
......@@ -135,9 +125,8 @@ class StaticRNN(object):
IN_RNN_BLOCK = 1
AFTER_RNN_BLOCK = 2
def __init__(self, name=None, main_program=None):
self.helper = LayerHelper(
"static_rnn", name=name, main_program=main_program)
def __init__(self, name=None):
self.helper = LayerHelper("static_rnn", name=name)
self.memories = {} # memory map, from pre_mem.name --> MemoryLink
self.inputs = [] # input variable list in current block
self.outputs = [] # output variable list in parent block
......@@ -354,8 +343,8 @@ class While(object):
IN_WHILE_BLOCK = 1
AFTER_WHILE_BLOCK = 2
def __init__(self, cond, name=None, main_program=None):
self.helper = LayerHelper("while", name=name, main_program=main_program)
def __init__(self, cond, name=None):
self.helper = LayerHelper("while", name=name)
self.status = While.BEFORE_WHILE_BLOCK
if not isinstance(cond, Variable):
raise TypeError("condition should be a variable")
......@@ -406,7 +395,7 @@ class While(object):
attrs={'sub_block': while_block})
def lod_rank_table(x, level=0, main_program=None):
def lod_rank_table(x, level=0):
"""
This function creates an operator for creating a LOD_RANK_TABLE
using the input x.
......@@ -423,7 +412,7 @@ def lod_rank_table(x, level=0, main_program=None):
return table
def max_sequence_len(rank_table, main_program=None):
def max_sequence_len(rank_table):
"""
This function creates an operator to calculate the length of
max seqence through input rank_table(should be a lod_rank_table)
......@@ -437,7 +426,7 @@ def max_sequence_len(rank_table, main_program=None):
return res
def topk(input, k, main_program=None, startup_program=None):
def topk(input, k):
helper = LayerHelper('topk', **locals())
topk_out = helper.create_tmp_variable(dtype=input.data_type)
topk_indices = helper.create_tmp_variable(dtype='int64')
......@@ -450,7 +439,7 @@ def topk(input, k, main_program=None, startup_program=None):
return topk_out, topk_indices
def lod_tensor_to_array(x, table, main_program=None):
def lod_tensor_to_array(x, table):
"""
This function creates an operator to convert an LOD_Tensor to
an array.
......@@ -468,7 +457,7 @@ def lod_tensor_to_array(x, table, main_program=None):
return array
def array_to_lod_tensor(x, table, main_program=None, startup_program=None):
def array_to_lod_tensor(x, table):
"""
This function creates an operator to convert an array to a
LOD_Tensor.
......@@ -483,11 +472,7 @@ def array_to_lod_tensor(x, table, main_program=None, startup_program=None):
return tmp
def increment(x,
value=1.0,
in_place=True,
main_program=None,
startup_program=None):
def increment(x, value=1.0, in_place=True):
"""
This function creates an operator to increment each value in the input
`x` by an amount: `value` as mentioned in the input parameter. This
......@@ -506,7 +491,7 @@ def increment(x,
return out
def array_write(x, i, array=None, main_program=None, startup_program=None):
def array_write(x, i, array=None):
"""
This function creates an operator to write the data out as a
LOD_TENSOR_ARRAY.
......@@ -525,7 +510,7 @@ def array_write(x, i, array=None, main_program=None, startup_program=None):
return array
def create_array(dtype, main_program=None):
def create_array(dtype):
helper = LayerHelper("array", **locals())
return helper.create_variable(
name="{0}.out".format(helper.name),
......@@ -533,7 +518,25 @@ def create_array(dtype, main_program=None):
dtype=dtype)
def less_than(x, y, cond=None, main_program=None, **ignored):
def less_than(x, y, cond=None, **ignored):
"""
**Less than**
This layer returns the truth value of :math:`x < y` elementwise.
Args:
x(Variable): First operand of *less_than*
y(Variable): Second operand of *less_than*
cond(Variable|None): Optional output variable to store the result of *less_than*
Returns:
Variable: The tensor variable storing the output of *less_than*.
Examples:
.. code-block:: python
less = fluid.layers.less_than(x=label, y=limit)
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
......@@ -545,7 +548,7 @@ def less_than(x, y, cond=None, main_program=None, **ignored):
return cond
def array_read(array, i, main_program=None, startup_program=None):
def array_read(array, i):
"""
This function creates an operator to read the data in as a
LOD_TENSOR_ARRAY.
......@@ -564,7 +567,7 @@ def array_read(array, i, main_program=None, startup_program=None):
return out
def shrink_memory(x, i, table, main_program=None, startup_program=None):
def shrink_memory(x, i, table):
"""
This function creates an operator to shrink_rnn_memory using the RankTable
as mentioned in the input parameter.
......@@ -581,7 +584,7 @@ def shrink_memory(x, i, table, main_program=None, startup_program=None):
return out
def array_length(array, main_program=None):
def array_length(array):
"""
This function creates an operator to find the length of the
LOD_TENSOR_ARRAY.
......@@ -611,20 +614,12 @@ class ConditionalBlockGuard(BlockGuard):
class ConditionalBlock(object):
def __init__(self,
inputs,
name=None,
main_program=None,
startup_program=None):
def __init__(self, inputs, name=None):
for each_input in inputs:
if not isinstance(each_input, Variable):
raise TypeError("Each input should be variable")
self.inputs = inputs
self.helper = LayerHelper(
'conditional_block',
name=name,
main_program=main_program,
startup_program=startup_program)
self.helper = LayerHelper('conditional_block', name=name)
def block(self):
return ConditionalBlockGuard(self)
......@@ -709,15 +704,10 @@ class IfElse(object):
IN_IF_ELSE_TRUE_BLOCKS = 1
IN_IF_ELSE_FALSE_BLOCKS = 2
def __init__(self, cond, name=None, main_program=None,
startup_program=None):
def __init__(self, cond, name=None):
if not isinstance(cond, Variable):
raise TypeError("cond must be a Variable")
self.helper = LayerHelper(
'ifelse',
name=name,
main_program=main_program,
startup_program=startup_program)
self.helper = LayerHelper('ifelse', name=name)
self.cond = cond
self.input_table = {}
self.status = IfElse.OUT_IF_ELSE_BLOCKS
......@@ -782,11 +772,7 @@ class IfElse(object):
out_table.append(outside_out)
# assign local var to outside
assign(
input=each_out,
output=outside_out,
main_program=self.helper.main_program,
startup_program=self.helper.startup_program)
assign(input=each_out, output=outside_out)
def __call__(self):
if self.status != self.OUT_IF_ELSE_BLOCKS:
......@@ -810,9 +796,7 @@ class IfElse(object):
in_false=false_var,
mask=self.cond,
x=self.cond,
level=0,
main_program=self.helper.main_program,
startup_program=self.helper.startup_program))
level=0))
return rlist
......@@ -821,12 +805,8 @@ class DynamicRNN(object):
IN_RNN = 1
AFTER_RNN = 2
def __init__(self, name=None, main_program=None, startup_program=None):
self.helper = LayerHelper(
'dynamic_rnn',
name=name,
main_program=main_program,
startup_program=startup_program)
def __init__(self, name=None):
self.helper = LayerHelper('dynamic_rnn', name=name)
self.status = DynamicRNN.BEFORE_RNN
self.lod_rank_table = None
self.max_seq_len = None
......@@ -880,8 +860,7 @@ class DynamicRNN(object):
inputs={'X': x,
'RankTable': self.lod_rank_table},
outputs={'Out': input_array})
return array_read(
array=input_array, i=self.step_idx, **self.helper.to_kwargs)
return array_read(array=input_array, i=self.step_idx)
@contextlib.contextmanager
def block(self):
......@@ -892,32 +871,18 @@ class DynamicRNN(object):
self.status = DynamicRNN.IN_RNN
with self.while_op.block():
yield
increment(
x=self.step_idx,
value=1.0,
in_place=True,
**self.helper.to_kwargs)
increment(x=self.step_idx, value=1.0, in_place=True)
for new_mem, mem_array in self.mem_link:
array_write(
x=new_mem,
i=self.step_idx,
array=mem_array,
**self.helper.to_kwargs)
less_than(
x=self.step_idx,
y=self.max_seq_len,
cond=self.cond,
**self.helper.to_kwargs)
array_write(x=new_mem, i=self.step_idx, array=mem_array)
less_than(x=self.step_idx, y=self.max_seq_len, cond=self.cond)
self.status = DynamicRNN.AFTER_RNN
for each_array in self.output_array:
self.outputs.append(
array_to_lod_tensor(
x=each_array,
table=self.lod_rank_table,
**self.helper.to_kwargs))
x=each_array, table=self.lod_rank_table))
def __call__(self, *args, **kwargs):
if self.status != DynamicRNN.AFTER_RNN:
......@@ -944,13 +909,9 @@ class DynamicRNN(object):
inputs={'X': init,
'I': self.zero_idx},
outputs={'Out': mem_array})
retv = array_read(
array=mem_array, i=self.step_idx, **self.helper.to_kwargs)
retv = array_read(array=mem_array, i=self.step_idx)
retv = shrink_memory(
x=retv,
i=self.step_idx,
table=self.lod_rank_table,
**self.helper.to_kwargs)
x=retv, i=self.step_idx, table=self.lod_rank_table)
self.mem_dict[retv.name] = mem_array
return retv
else:
......
python/paddle/v2/fluid/layers/io.py
浏览文件 @ feb05c3a
......@@ -10,8 +10,6 @@ def data(name,
dtype='float32',
lod_level=0,
type=core.VarDesc.VarType.LOD_TENSOR,
main_program=None,
startup_program=None,
stop_gradient=True):
"""
Data Layer.
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ feb05c3a
......@@ -5,12 +5,15 @@ All layers just related to the neural network.
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from tensor import concat
__all__ = [
'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose'
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
'lstm_unit', 'reduce_sum'
]
......@@ -20,36 +23,50 @@ def fc(input,
param_attr=None,
bias_attr=None,
act=None,
name=None,
main_program=None,
startup_program=None):
name=None):
"""
Fully Connected Layer.
**Fully Connected Layer**
This layer accepts multiple inputs and applies a linear transformation to each input.
If activation type is provided, the corresponding activation function is applied to the
output of the linear transformation. For each input :math:`X`, the equation is:
.. math::
Out = Act(WX + b)
In the above equation:
* :math:`X`: Input value, a tensor with rank at least 2.
* :math:`W`: Weight, a 2-D tensor with shape [M, N].
* :math:`b`: Bias, a 2-D tensor with shape [M, 1].
* :math:`Act`: Activation function.
* :math:`Out`: Output value, same shape with :math:`X`.
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
input: The input tensor to the function
size: The size of the layer
num_flatten_dims: Number of columns in input
param_attr: The parameters/weights to the FC Layer
param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used
bias_attr: The bias parameter for the FC layer
bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used
act: Activation to be applied to the output of FC layer
name: Name/alias of the function
main_program: Name of the main program that calls this
startup_program: Name of the startup program
This function can take in multiple inputs and performs the Fully Connected
function (linear transformation) on top of each of them.
So for input x, the output will be : Wx + b. Where W is the parameter,
b the bias and x is the input.
The function also applies an activation (non-linearity) on top of the
output, if activation is passed in the input.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
input(Variable|list): Input tensors. Each tensor has a rank of atleast 2
size(int): Output size
num_flatten_dims(int): Number of columns in input
param_attr(ParamAttr|list): The parameters/weights to the FC Layer
bias_attr(ParamAttr|list): Bias parameter for the FC layer
act(str): Activation type
name(str): Name/alias of the function
Returns:
Variable: The tensor variable storing the transformation and \
non-linearity activation result.
Raises:
ValueError: If rank of input tensor is less than 2.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
"""
helper = LayerHelper('fc', **locals())
......@@ -88,33 +105,32 @@ def fc(input,
return helper.append_activation(pre_activation)
def embedding(input,
size,
is_sparse=False,
param_attr=None,
dtype='float32',
main_program=None,
startup_program=None):
def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
"""
Embedding Layer.
**Embedding Layer**
This layer is used to lookup a vector of IDs, provided by *input*, in a lookup table.
The result of this lookup is the embedding of each ID in the *input*.
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
param_initializer:
input: The input to the function
size: The size of the layer
is_sparse: A flag that decleares whether the input is sparse
param_attr: Parameters for this layer
dtype: The type of data : float32, float_16, int etc
main_program: Name of the main program that calls this
startup_program: Name of the startup program
input(Variable): Input to the function
size(int): Output size
is_sparse(bool): Boolean flag that specifying whether the input is sparse
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
This function can take in the input (which is a vector of IDs) and
performs a lookup in the lookup_table using these IDs, to result into
the embedding of each ID in the input.
Returns:
Variable: The tensor variable storing the embeddings of the \
supplied inputs.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
Examples:
.. code-block:: python
data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
fc = fluid.layers.embedding(input=data, size=16)
"""
helper = LayerHelper('embedding', **locals())
......@@ -140,9 +156,7 @@ def dynamic_lstm(input,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
dtype='float32',
main_program=None,
startup_program=None):
dtype='float32'):
helper = LayerHelper('lstm', **locals())
size = size / 4
weight = helper.create_parameter(
......@@ -185,9 +199,7 @@ def gru_unit(input,
weight=None,
bias=None,
activation='tanh',
gate_activation='sigmoid',
main_program=None,
startup_program=None):
gate_activation='sigmoid'):
"""
GRUUnit Operator implements partial calculations of the GRU unit as following:
......@@ -250,11 +262,7 @@ def gru_unit(input,
return updated_hidden, reset_hidden_pre, gate
def linear_chain_crf(input,
label,
param_attr=None,
main_program=None,
startup_program=None):
def linear_chain_crf(input, label, param_attr=None):
helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[1]
transition = helper.create_parameter(
......@@ -280,11 +288,7 @@ def linear_chain_crf(input,
return log_likelihood
def crf_decoding(input,
param_attr,
label=None,
main_program=None,
startup_program=None):
def crf_decoding(input, param_attr, label=None):
helper = LayerHelper('crf_decoding', **locals())
transition = helper.get_parameter(param_attr.name)
viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
......@@ -392,7 +396,7 @@ def chunk_eval(input,
excluded_chunk_types=None,
**kwargs):
"""
This function computes and outputs the precision, recall and
This function computes and outputs the precision, recall and
F1-score of chunk detection.
"""
helper = LayerHelper("chunk_eval", **kwargs)
......@@ -419,8 +423,8 @@ def chunk_eval(input,
},
attrs={
"num_chunk_types": num_chunk_types,
'chunk_scheme': chunk_scheme,
'excluded_chunk_types': excluded_chunk_types or []
"chunk_scheme": chunk_scheme,
"excluded_chunk_types": excluded_chunk_types or []
})
return precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks
......@@ -432,9 +436,7 @@ def sequence_conv(input,
padding=None,
bias_attr=None,
param_attr=None,
act=None,
main_program=None,
startup_program=None):
act=None):
"""
This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given
......@@ -477,9 +479,7 @@ def conv2d(input,
param_attr=None,
bias_attr=None,
act=None,
name=None,
main_program=None,
startup_program=None):
name=None):
"""
This function creates the op for a 2-dimensional Convolution.
This is performed using the parameters of filters(size, dimensionality etc)
......@@ -565,9 +565,7 @@ def pool2d(input,
pool_type,
pool_stride=None,
pool_padding=None,
global_pooling=False,
main_program=None,
startup_program=None):
global_pooling=False):
"""
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
......@@ -613,9 +611,7 @@ def batch_norm(input,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
data_layout='NCHW',
main_program=None,
startup_program=None):
data_layout='NCHW'):
"""
This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters.
......@@ -685,7 +681,7 @@ def batch_norm(input,
return helper.append_activation(batch_norm_out)
def beam_search_decode(ids, scores, main_program=None, startup_program=None):
def beam_search_decode(ids, scores):
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
......@@ -708,9 +704,8 @@ def conv2d_transpose(input,
filter_size=None,
padding=None,
stride=None,
param_attr=None,
main_program=None,
startup_program=None):
dilation=None,
param_attr=None):
"""
The transpose of conv2d layer.
......@@ -733,6 +728,9 @@ def conv2d_transpose(input,
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation.
param_attr: Parameter Attribute.
main_program(Program): the main program
startup_program(Program): the startup program
......@@ -753,10 +751,15 @@ def conv2d_transpose(input,
op_attr['paddings'] = padding
if isinstance(stride, int):
op_attr['strides'] = stride
op_attr['strides'] = [stride, stride]
elif stride is not None:
op_attr['strides'] = stride
if isinstance(dilation, int):
op_attr['dilations'] = [dilation, dilation]
elif dilation is not None:
op_attr['dilations'] = dilation
if filter_size is None:
if output_size is None:
raise ValueError("output_size must be set when filter_size is None")
......@@ -765,14 +768,17 @@ def conv2d_transpose(input,
padding = op_attr.get('paddings', [0, 0])
stride = op_attr.get('strides', [1, 1])
dilation = op_attr.get('dilations', [1, 1])
h_in = input.shape[2]
w_in = input.shape[3]
filter_size_h = output_size[0] - \
(h_in - 1) * stride[0] + 2 * padding[0]
filter_size_w = output_size[1] - \
(w_in - 1) * stride[1] + 2 * padding[1]
filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 *
padding[0] - 1) / dilation[0] + 1
filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
padding[1] - 1) / dilation[1] + 1
filter_size = [filter_size_h, filter_size_w]
elif isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
......@@ -789,3 +795,220 @@ def conv2d_transpose(input,
attrs=op_attr)
return out
def sequence_expand(x, y):
"""Sequence Expand Layer. This layer will expand the input variable **x**
according to LoD information of **y**. And the following examples will
explain how sequence_expand works:
.. code-block:: text
* Case 1
x is a LoDTensor:
x.lod = [[0, 2, 3],
[0, 1, 3, 4]]
x.data = [a, b, c, d]
x.dims = [4, 1]
y is a LoDTensor:
y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
with condition len(y.lod[-1]) - 1 == x.dims[0]
then output is a 2-level LoDTensor:
out.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
out.data = [a, a, a, b, b, b, c, d]
out.dims = [8, 1]
* Case 2
x is a Tensor:
x.data = [a, b, c]
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[0, 2, 3, 6]]
with condition len(y.lod[-1]) - 1 == x.dims[0]
then output is a 1-level LoDTensor:
out.lod = [[0, 2, 3, 6]]
out.data = [a, a, b, c, c, c]
out.dims = [6, 1]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a LoDTensor.
Returns:
Variable: The expanded variable which is a LoDTensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 20],
dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y)
"""
helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type='sequence_expand', inputs={'X': x,
'Y': y}, outputs={'Out': tmp})
return tmp
def lstm_unit(x_t,
hidden_t_prev,
cell_t_prev,
forget_bias=0.0,
param_attr=None,
bias_attr=None):
"""Lstm unit layer. The equation of a lstm step is:
.. math::
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
h_t & = o_t tanh(c_t)
The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take :math:`i_t` as an
example. The linear transformation is applied by calling a `fc` layer and
the equation is:
.. math::
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i
The non-linear transformation is applied by calling `lstm_unit_op` and the
equation is:
.. math::
i_t = \sigma(L_{i_t})
This layer has two outputs including :math:`h_t` and :math:`o_t`.
Args:
x_t (Variable): The input value of current step.
hidden_t_prev (Variable): The hidden value of lstm unit.
cell_t_prev (Variable): The cell value of lstm unit.
forget_bias (float): The forget bias of lstm unit.
param_attr (ParamAttr): The attributes of parameter weights, used to set
initializer, name etc.
bias_attr (ParamAttr): The attributes of bias weights, if not False,
bias weights will be created and be set to default value.
Returns:
tuple: The hidden value and cell value of lstm unit.
Raises:
ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\
not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \
and **cell_t_prev** not be the same.
Examples:
.. code-block:: python
x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
cell_t_prev=prev_cell)
"""
helper = LayerHelper('lstm_unit', **locals())
if len(x_t.shape) != 2:
raise ValueError("Rank of x_t must be 2.")
if len(hidden_t_prev.shape) != 2:
raise ValueError("Rank of hidden_t_prev must be 2.")
if len(cell_t_prev.shape) != 2:
raise ValueError("Rank of cell_t_prev must be 2.")
if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
0] != cell_t_prev.shape[0]:
raise ValueError("The 1s dimension of x_t, hidden_t_prev and "
"cell_t_prev must be the same.")
if bias_attr is None:
bias_attr = ParamAttr()
size = cell_t_prev.shape[1]
concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
fc_out = fc(input=concat_out,
size=4 * size,
param_attr=param_attr,
bias_attr=bias_attr)
dtype = x_t.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm_unit',
inputs={"X": fc_out,
"C_prev": cell_t_prev},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
return h, c
def reduce_sum(input, dim=None, keep_dim=False):
"""
Computes the sum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_sum(x) # [3.5]
fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6]
fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6]
fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]]
"""
helper = LayerHelper('reduce_sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_sum',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
python/paddle/v2/fluid/layers/ops.py
浏览文件 @ feb05c3a
......@@ -2,7 +2,7 @@ from ..registry import register_layer
__all__ = [
'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
'elementwise_sub', 'elementwise_mul', 'clip', 'abs', 'sequence_softmax'
]
for _OP in set(__all__):
......
python/paddle/v2/fluid/layers/tensor.py
浏览文件 @ feb05c3a
......@@ -6,12 +6,12 @@ __all__ = [
]
def create_tensor(dtype, name=None, main_program=None, startup_program=None):
def create_tensor(dtype, name=None):
helper = LayerHelper("create_tensor", **locals())
return helper.create_variable(name=helper.name, dtype=dtype)
def cast(x, dtype, main_program=None):
def cast(x, dtype):
"""
This function takes in the input with input_dtype
and casts it to the output_dtype as the output.
......@@ -27,7 +27,7 @@ def cast(x, dtype, main_program=None):
return out
def concat(input, axis, main_program=None, startup_program=None):
def concat(input, axis):
"""
This function concats the input along the axis mentioned
and returns that as the output.
......@@ -42,7 +42,7 @@ def concat(input, axis, main_program=None, startup_program=None):
return out
def sums(input, out=None, main_program=None, startup_program=None):
def sums(input, out=None):
"""
This function takes in the input and performs the sum operation on it
and returns that as the output.
......@@ -54,7 +54,7 @@ def sums(input, out=None, main_program=None, startup_program=None):
return out
def assign(input, output, main_program=None, startup_program=None):
def assign(input, output):
helper = LayerHelper('assign', **locals())
helper.append_op(
type='scale',
......@@ -64,16 +64,28 @@ def assign(input, output, main_program=None, startup_program=None):
return output
def fill_constant(shape,
dtype,
value,
out=None,
main_program=None,
startup_program=None):
def fill_constant(shape, dtype, value, out=None):
"""
This function creates a tensor , with shape as mentioned in the input and
specified dtype and fills this up with a constant value that
comes in the input. It also sets the stop_gradient to be True.
**fill_constant**
This function creates a tensor of specified *shape* and
*dtype*, and initializes this with a constant supplied in *value*.
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.DataType|str): Data type of output tensor
value(float): Constant value to initialize the output tensor
out(Variable): Output Variable to initialize
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
"""
helper = LayerHelper("fill_constant", **locals())
if out is None:
......@@ -94,9 +106,32 @@ def fill_constant_batch_size_like(input,
dtype,
value,
input_dim_idx=0,
output_dim_idx=0,
main_program=None,
startup_program=None):
output_dim_idx=0):
"""
**fill_constant_batch_size_like**
This function creates a tensor of specified *shape*, *dtype* and batch size,
and initializes this with a constant supplied in *value*. The batch size is
obtained from the `input` tensor.
It also sets *stop_gradient* to True.
Args:
input(Variable): Tensor whose dimensions will be used to get batch size
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.DataType|str): Data type of output tensor
value(float): Constant value to initialize the output tensor
input_dim_idx(int): Index of input's batch size dimension
output_dim_idx(int): Index of output's batch size dimension
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_tmp_variable(dtype=dtype)
helper.append_op(
......@@ -114,7 +149,7 @@ def fill_constant_batch_size_like(input,
return out
def ones(shape, dtype, main_program=None):
def ones(shape, dtype):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 1.0.
......@@ -122,7 +157,7 @@ def ones(shape, dtype, main_program=None):
return fill_constant(value=1.0, **locals())
def zeros(shape, dtype, main_program=None):
def zeros(shape, dtype):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 0.0.
......
python/paddle/v2/fluid/nets.py
浏览文件 @ feb05c3a
......@@ -10,25 +10,19 @@ def simple_img_conv_pool(input,
pool_stride,
act,
param_attr=None,
pool_type='max',
main_program=None,
startup_program=None):
pool_type='max'):
conv_out = layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
param_attr=param_attr,
act=act,
main_program=main_program,
startup_program=startup_program)
act=act)
pool_out = layers.pool2d(
input=conv_out,
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
main_program=main_program,
startup_program=startup_program)
pool_stride=pool_stride)
return pool_out
......@@ -42,9 +36,7 @@ def img_conv_group(input,
conv_with_batchnorm=False,
conv_batchnorm_drop_rate=None,
pool_stride=1,
pool_type=None,
main_program=None,
startup_program=None):
pool_type=None):
"""
Image Convolution Group, Used for vgg net.
"""
......@@ -75,31 +67,19 @@ def img_conv_group(input,
filter_size=conv_filter_size[i],
padding=conv_padding[i],
param_attr=param_attr[i],
act=local_conv_act,
main_program=main_program,
startup_program=startup_program)
act=local_conv_act)
if conv_with_batchnorm[i]:
tmp = layers.batch_norm(
input=tmp,
act=conv_act,
main_program=main_program,
startup_program=startup_program)
tmp = layers.batch_norm(input=tmp, act=conv_act)
drop_rate = conv_batchnorm_drop_rate[i]
if abs(drop_rate) > 1e-5:
tmp = layers.dropout(
x=tmp,
dropout_prob=drop_rate,
main_program=main_program,
startup_program=startup_program)
tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)
pool_out = layers.pool2d(
input=tmp,
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
main_program=main_program,
startup_program=startup_program)
pool_stride=pool_stride)
return pool_out
......@@ -108,21 +88,13 @@ def sequence_conv_pool(input,
filter_size,
param_attr=None,
act="sigmoid",
pool_type="max",
main_program=None,
startup_program=None):
pool_type="max"):
conv_out = layers.sequence_conv(
input=input,
num_filters=num_filters,
filter_size=filter_size,
param_attr=param_attr,
act=act,
main_program=main_program,
startup_program=startup_program)
act=act)
pool_out = layers.sequence_pool(
input=conv_out,
pool_type=pool_type,
main_program=main_program,
startup_program=startup_program)
pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type)
return pool_out
python/paddle/v2/fluid/optimizer.py
浏览文件 @ feb05c3a
......@@ -2,10 +2,11 @@ from collections import defaultdict
import framework
from backward import append_backward_ops
from framework import unique_name
from framework import unique_name, program_guard
from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
from clip import append_gradient_clip_ops
__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
......@@ -159,34 +160,32 @@ class Optimizer(object):
# Create any accumulators
program = loss.block.program
self.helper = LayerHelper(
self.__class__.__name__,
main_program=program,
startup_program=startup_program)
self._create_accumulators(loss.block,
[p[0] for p in parameters_and_grads])
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[0].trainable is True and param_and_grad[
1] is not None:
optimize_op = self._append_optimize_op(loss.block,
param_and_grad)
optimize_ops.append(optimize_op)
# Returned list of ops can include more ops in addition
# to optimization ops
return_ops = optimize_ops
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
finish_ops = self._finish_update(loss.block)
if finish_ops is not None:
return_ops += finish_ops
if self._global_step is not None:
return_ops.append(self._increment_global_step(loss.block))
return return_ops
with program_guard(program, startup_program):
self.helper = LayerHelper(self.__class__.__name__)
self._create_accumulators(loss.block,
[p[0] for p in parameters_and_grads])
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[0].trainable is True and param_and_grad[
1] is not None:
optimize_op = self._append_optimize_op(loss.block,
param_and_grad)
optimize_ops.append(optimize_op)
# Returned list of ops can include more ops in addition
# to optimization ops
return_ops = optimize_ops
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
finish_ops = self._finish_update(loss.block)
if finish_ops is not None:
return_ops += finish_ops
if self._global_step is not None:
return_ops.append(self._increment_global_step(loss.block))
return return_ops
def minimize(self,
loss,
......@@ -199,9 +198,13 @@ class Optimizer(object):
`create_optimization_pass()` into one.
"""
params_grads = append_backward_ops(loss, parameter_list, no_grad_set)
params_grads = append_gradient_clip_ops(params_grads)
# Add regularization if any
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self.create_optimization_pass(params_grads, loss,
startup_program)
return optimize_ops
......
python/paddle/v2/fluid/param_attr.py
浏览文件 @ feb05c3a
from initializer import Initializer, Xavier, Constant
from regularizer import WeightDecayRegularizer
__all__ = ['ParamAttr']
class ParamAttr(object):
def __init__(self,
......@@ -8,12 +10,14 @@ class ParamAttr(object):
initializer=None,
learning_rate=1.0,
regularizer=None,
trainable=True):
trainable=True,
clip=None):
self.name = name
self.initializer = initializer
self.learning_rate = learning_rate
self.regularizer = regularizer
self.trainable = trainable
self.clip = clip
def set_default_initializer(self, initializer):
if initializer is None:
......@@ -56,7 +60,8 @@ class ParamAttr(object):
'name': self.name,
'learning_rate': self.learning_rate,
'regularizer': self.regularizer,
'trainable': self.trainable
'trainable': self.trainable,
'clip_attr': self.clip
}
if with_initializer:
kwargs['initializer'] = self.initializer
......
python/paddle/v2/fluid/tests/.gitignore
浏览文件 @ feb05c3a
image/
fit_a_line.model/
tmp
cuda_profiler.txt
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ feb05c3a
......@@ -11,7 +11,9 @@ regularizer = fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
hidden1 = fluid.layers.fc(input=image,
size=128,
act='relu',
param_attr=regularizer)
param_attr=fluid.ParamAttr(
regularizer=regularizer,
clip=fluid.clip.ClipByValue(10)))
hidden2 = fluid.layers.fc(input=hidden1,
size=64,
act='relu',
......@@ -33,11 +35,10 @@ opts = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
inference_program = fluid.default_main_program().clone()
test_accuracy = fluid.evaluator.Accuracy(
input=predict, label=label, main_program=inference_program)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = fluid.io.get_inference_program(
test_target, main_program=inference_program)
with fluid.program_guard(inference_program):
test_accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = fluid.io.get_inference_program(test_target)
train_reader = paddle.batch(
paddle.reader.shuffle(
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
浏览文件 @ feb05c3a
......@@ -4,12 +4,7 @@ import paddle.v2.fluid as fluid
from paddle.v2.fluid.layer_helper import LayerHelper
def lstm(x,
c_pre_init,
hidden_dim,
forget_bias=None,
main_program=None,
startup_program=None):
def lstm(x, c_pre_init, hidden_dim, forget_bias=None):
"""
This function helps create an operator for the LSTM (Long Short Term
Memory) cell that can be used inside an RNN.
......@@ -20,15 +15,8 @@ def lstm(x,
c_pre = rnn.memory(init=c_pre_init)
x_t = rnn.step_input(x)
before_fc = fluid.layers.concat(
input=[x_t, c_pre],
axis=1,
main_program=main_program,
startup_program=startup_program)
after_fc = fluid.layers.fc(input=before_fc,
size=hidden_dim * 4,
main_program=main_program,
startup_program=startup_program)
before_fc = fluid.layers.concat(input=[x_t, c_pre], axis=1)
after_fc = fluid.layers.fc(input=before_fc, size=hidden_dim * 4)
dtype = x.dtype
c = helper.create_tmp_variable(dtype)
......
python/paddle/v2/fluid/tests/test_batch_norm_op.py
浏览文件 @ feb05c3a
......@@ -3,10 +3,7 @@ import numpy as np
from op_test import OpTest
import paddle.v2.fluid.core as core
from paddle.v2.fluid.op import Operator
def grad_var_name(var_name):
return var_name + "@GRAD"
from paddle.v2.fluid.framework import grad_var_name
def get_backward_op(scope, op, no_grad_set):
......
python/paddle/v2/fluid/tests/test_const_value.py 0 → 100644
浏览文件 @ feb05c3a
import unittest
import paddle.v2.fluid.framework as framework
class ConditionalBlock(unittest.TestCase):
def test_const_value(self):
self.assertEqual(framework.GRAD_VAR_SUFFIX, "@GRAD")
self.assertEqual(framework.TEMP_VAR_NAME, "@TEMP@")
self.assertEqual(framework.GRAD_VAR_SUFFIX, "@GRAD")
self.assertEqual(framework.ZERO_VAR_SUFFIX, "@ZERO")
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
浏览文件 @ feb05c3a
......@@ -3,14 +3,17 @@ import numpy as np
from op_test import OpTest
def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
def conv2dtranspose_forward_naive(input_, filter_, attrs):
in_n, in_c, in_h, in_w = input_.shape
f_c, out_c, f_h, f_w = filter_.shape
assert in_c == f_c
stride, pad = conv2dtranspose_param['stride'], conv2dtranspose_param['pad']
out_h = (in_h - 1) * stride[0] + f_h
out_w = (in_w - 1) * stride[1] + f_w
stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
'dilations']
d_bolck_h = dilations[0] * (f_h - 1) + 1
d_bolck_w = dilations[1] * (f_w - 1) + 1
out_h = (in_h - 1) * stride[0] + d_bolck_h
out_w = (in_w - 1) * stride[1] + d_bolck_w
out = np.zeros((in_n, out_c, out_h, out_w))
......@@ -23,9 +26,9 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
for k in range(out_c):
tmp_out = np.sum(input_masked * filter_[:, k, :, :], axis=0)
i1, i2 = i * stride[0], i * stride[0] + f_h
j1, j2 = j * stride[0], j * stride[0] + f_w
out[n, k, i1:i2, j1:j2] += tmp_out
i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
j1, j2 = j * stride[0], j * stride[0] + d_bolck_h
out[n, k, i1:i2:dilations[0], j1:j2:dilations[1]] += tmp_out
out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
return out
......@@ -37,11 +40,8 @@ class TestConv2dTransposeOp(OpTest):
self.init_op_type()
self.init_test_case()
conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
input_ = np.random.random(self.input_size).astype("float32")
filter_ = np.random.random(self.filter_size).astype("float32")
output = conv2dtranspose_forward_naive(
input_, filter_, conv2dtranspose_param).astype('float32')
self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = {
......@@ -49,6 +49,10 @@ class TestConv2dTransposeOp(OpTest):
'paddings': self.pad,
'dilations': self.dilations
}
output = conv2dtranspose_forward_naive(input_, filter_,
self.attrs).astype('float32')
self.outputs = {'Output': output}
def test_check_output(self):
......@@ -104,11 +108,60 @@ class TestWithStride(TestConv2dTransposeOp):
self.filter_size = [f_c, 6, 3, 3]
class TestWithDilation(TestConv2dTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [2, 2]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
# ------------ test_cudnn ------------
class TestCudnn(TestConv2dTransposeOp):
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1]
# self.stride = [2, 2]
# self.dilations = [2, 2]
# self.input_size = [2, 3, 5, 5] # NCHW
# f_c = self.input_size[1]
# self.filter_size = [f_c, 6, 3, 3]
#
# def init_op_type(self):
# self.op_type = "conv2d_transpose_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
浏览文件 @ feb05c3a
......@@ -3,15 +3,20 @@ import numpy as np
from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
def conv3dtranspose_forward_naive(input_, filter_, attrs):
in_n, in_c, in_d, in_h, in_w = input_.shape
f_c, out_c, f_d, f_h, f_w = filter_.shape
assert in_c == f_c
stride, pad = conv3dtranspose_param['stride'], conv3dtranspose_param['pad']
out_d = (in_d - 1) * stride[0] + f_d
out_h = (in_h - 1) * stride[1] + f_h
out_w = (in_w - 1) * stride[2] + f_w
stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
'dilations']
d_bolck_d = dilations[0] * (f_d - 1) + 1
d_bolck_h = dilations[1] * (f_h - 1) + 1
d_bolck_w = dilations[2] * (f_w - 1) + 1
out_d = (in_d - 1) * stride[0] + d_bolck_d
out_h = (in_h - 1) * stride[1] + d_bolck_h
out_w = (in_w - 1) * stride[2] + d_bolck_w
out = np.zeros((in_n, out_c, out_d, out_h, out_w))
for n in range(in_n):
......@@ -25,10 +30,11 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
for k in range(out_c):
tmp_out = np.sum(input_masked * filter_[:, k, :, :, :],
axis=0)
d1, d2 = d * stride[0], d * stride[0] + f_d
i1, i2 = i * stride[1], i * stride[1] + f_h
j1, j2 = j * stride[2], j * stride[2] + f_w
out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
d1, d2 = d * stride[0], d * stride[0] + d_bolck_d
i1, i2 = i * stride[1], i * stride[1] + d_bolck_h
j1, j2 = j * stride[2], j * stride[2] + d_bolck_w
out[n, k, d1:d2:dilations[0], i1:i2:dilations[1], j1:j2:
dilations[2]] += tmp_out
out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
pad[2]]
......@@ -41,18 +47,19 @@ class TestConv3dTransposeOp(OpTest):
self.init_op_type()
self.init_test_case()
conv3dtranspose_param = {'stride': self.stride, 'pad': self.pad}
input_ = np.random.random(self.input_size).astype("float32")
filter_ = np.random.random(self.filter_size).astype("float32")
output = conv3dtranspose_forward_naive(
input_, filter_, conv3dtranspose_param).astype("float32")
self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
# 'dilations': self.dilations
'dilations': self.dilations
}
output = conv3dtranspose_forward_naive(input_, filter_,
self.attrs).astype("float32")
self.outputs = {'Output': output}
def test_check_output(self):
......@@ -108,11 +115,60 @@ class TestWithStride(TestConv3dTransposeOp):
self.filter_size = [f_c, 6, 3, 3, 3]
class TestWithDilation(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [2, 2, 2]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
# ------------ test_cudnn ------------
class TestCudnn(TestConv3dTransposeOp):
def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1, 1]
# self.stride = [2, 2, 2]
# self.dilations = [2, 2, 2]
# self.input_size = [2, 3, 5, 5, 5] # NCDHW
# f_c = self.input_size[1]
# self.filter_size = [f_c, 6, 3, 3, 3]
#
# def init_op_type(self):
# self.op_type = "conv3d_transpose_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_dropout_op.py
浏览文件 @ feb05c3a
......@@ -47,7 +47,9 @@ class TestDropoutOp4(OpTest):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 0.35, 'is_test': True}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
self.outputs = {
'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
}
def test_check_output(self):
self.check_output()
......@@ -58,7 +60,9 @@ class TestDropoutOp5(OpTest):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
self.attrs = {'dropout_prob': 0.75, 'is_test': True}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
self.outputs = {
'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
}
def test_check_output(self):
self.check_output()
......
python/paddle/v2/fluid/tests/test_image_classification_layer.py
浏览文件 @ feb05c3a
......@@ -5,12 +5,7 @@ import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.framework import Program
def conv_block(input,
num_filter,
groups,
dropouts,
main_program=None,
startup_program=None):
def conv_block(input, num_filter, groups, dropouts):
return nets.img_conv_group(
input=input,
pool_size=2,
......@@ -20,90 +15,54 @@ def conv_block(input,
conv_act='relu',
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max',
main_program=main_program,
startup_program=startup_program)
pool_type='max')
class TestLayer(unittest.TestCase):
def test_batch_norm_layer(self):
main_program = Program()
startup_program = Program()
images = fluid.layers.data(
name='pixel',
shape=[3, 48, 48],
dtype='float32',
main_program=main_program)
hidden1 = fluid.layers.batch_norm(
input=images,
main_program=main_program,
startup_program=startup_program)
hidden2 = fluid.layers.fc(input=hidden1,
size=128,
act='relu',
main_program=main_program)
hidden3 = fluid.layers.batch_norm(
input=hidden2,
main_program=main_program,
startup_program=startup_program)
with fluid.program_guard(main_program, startup_program):
images = fluid.layers.data(
name='pixel', shape=[3, 48, 48], dtype='float32')
hidden1 = fluid.layers.batch_norm(input=images)
hidden2 = fluid.layers.fc(input=hidden1, size=128, act='relu')
fluid.layers.batch_norm(input=hidden2)
print str(main_program)
def test_dropout_layer(self):
main_program = Program()
startup_program = Program()
images = fluid.layers.data(
name='pixel',
shape=[3, 48, 48],
dtype='float32',
main_program=main_program)
fluid.layers.dropout(
x=images,
dropout_prob=0.5,
main_program=main_program,
startup_program=startup_program)
with fluid.program_guard(main_program, startup_program):
images = fluid.layers.data(
name='pixel', shape=[3, 48, 48], dtype='float32')
fluid.layers.dropout(x=images, dropout_prob=0.5)
# print str(main_program)
print str(main_program)
def test_img_conv_group(self):
main_program = Program()
startup_program = Program()
images = fluid.layers.data(
name='pixel',
shape=[3, 48, 48],
dtype='float32',
main_program=main_program,
startup_program=startup_program)
conv1 = conv_block(images, 64, 2, [0.3, 0], main_program,
startup_program)
conv2 = conv_block(conv1, 256, 3, [0.4, 0.4, 0], main_program,
startup_program)
with fluid.program_guard(main_program, startup_program):
images = fluid.layers.data(
name='pixel', shape=[3, 48, 48], dtype='float32')
conv1 = conv_block(images, 64, 2, [0.3, 0])
conv_block(conv1, 256, 3, [0.4, 0.4, 0])
# print str(main_program)
print str(main_program)
def test_elementwise_add_with_act(self):
main_program = Program()
startup_program = Program()
image1 = fluid.layers.data(
name='pixel1',
shape=[3, 48, 48],
dtype='float32',
main_program=main_program,
startup_program=startup_program)
image2 = fluid.layers.data(
name='pixel2',
shape=[3, 48, 48],
dtype='float32',
main_program=main_program,
startup_program=startup_program)
out = fluid.layers.elementwise_add(
x=image1,
y=image2,
act='relu',
main_program=main_program,
startup_program=startup_program)
# print(main_program)
with fluid.program_guard(main_program, startup_program):
image1 = fluid.layers.data(
name='pixel1', shape=[3, 48, 48], dtype='float32')
image2 = fluid.layers.data(
name='pixel2', shape=[3, 48, 48], dtype='float32')
fluid.layers.elementwise_add(x=image1, y=image2, act='relu')
print(main_program)
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_inference_model_io.py
浏览文件 @ feb05c3a
......@@ -6,7 +6,7 @@ import paddle.v2.fluid.core as core
import paddle.v2.fluid.executor as executor
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.optimizer as optimizer
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, program_guard
from paddle.v2.fluid.io import save_inference_model, load_inference_model
......@@ -16,35 +16,18 @@ class TestBook(unittest.TestCase):
init_program = Program()
program = Program()
x = layers.data(
name='x',
shape=[2],
dtype='float32',
main_program=program,
startup_program=init_program)
y = layers.data(
name='y',
shape=[1],
dtype='float32',
main_program=program,
startup_program=init_program)
y_predict = layers.fc(input=x,
size=1,
act=None,
main_program=program,
startup_program=init_program)
cost = layers.square_error_cost(
input=y_predict,
label=y,
main_program=program,
startup_program=init_program)
avg_cost = layers.mean(
x=cost, main_program=program, startup_program=init_program)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost, init_program)
with program_guard(program, init_program):
x = layers.data(name='x', shape=[2], dtype='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
......
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ feb05c3a
......@@ -161,6 +161,41 @@ class TestBook(unittest.TestCase):
x=dat, label=lbl))
print(str(program))
def test_sequence_expand(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[10], dtype='float32')
y = layers.data(
name='y', shape=[10, 20], dtype='float32', lod_level=1)
self.assertIsNotNone(layers.sequence_expand(x=x, y=y))
print(str(program))
def test_lstm_unit(self):
program = Program()
with program_guard(program):
x_t_data = layers.data(
name='x_t_data', shape=[10, 10], dtype='float32')
x_t = layers.fc(input=x_t_data, size=10)
prev_hidden_data = layers.data(
name='prev_hidden_data', shape=[10, 20], dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=20)
prev_cell_data = layers.data(
name='prev_cell', shape=[10, 30], dtype='float32')
prev_cell = layers.fc(input=prev_cell_data, size=30)
self.assertIsNotNone(
layers.lstm_unit(
x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
print(str(program))
def test_sequence_softmax(self):
program = Program()
with program_guard(program):
seq_data = layers.data(
name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
seq = layers.fc(input=seq_data, size=20)
self.assertIsNotNone(layers.sequence_softmax(x=seq))
print(str(program))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
浏览文件 @ feb05c3a
......@@ -2,7 +2,7 @@ import unittest
import paddle.v2.fluid.core as core
import numpy
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, program_guard
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.backward import append_backward_ops
......@@ -118,16 +118,17 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
def main(self, tensor, expect_array, expect_lod, expect_max_len, level=0):
place = self.place()
program = Program()
x = layers.data(name='x', shape=[10], main_program=program)
x.persistable = True
table = layers.lod_rank_table(x, level=level, main_program=program)
max_len = layers.max_sequence_len(table, main_program=program)
max_len.persistable = True
array = layers.lod_tensor_to_array(x, table, main_program=program)
array.persistable = True
result = layers.array_to_lod_tensor(array, table, main_program=program)
result.persistable = True
with program_guard(program):
x = layers.data(name='x', shape=[10])
x.persistable = True
table = layers.lod_rank_table(x, level=level)
max_len = layers.max_sequence_len(table)
max_len.persistable = True
array = layers.lod_tensor_to_array(x, table)
array.persistable = True
result = layers.array_to_lod_tensor(array, table)
result.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(program, feed={'x': tensor}, scope=scope)
......@@ -160,19 +161,16 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
place = core.CPUPlace()
program = Program()
x = layers.data(
name='x',
shape=[1],
dtype='float32',
main_program=program,
stop_gradient=False)
table = layers.lod_rank_table(x, level=0, main_program=program)
array = layers.lod_tensor_to_array(x, table, main_program=program)
result = layers.array_to_lod_tensor(array, table, main_program=program)
with program_guard(program):
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False)
table = layers.lod_rank_table(x, level=0)
array = layers.lod_tensor_to_array(x, table)
result = layers.array_to_lod_tensor(array, table)
mean = layers.mean(x=result, main_program=program)
mean = layers.mean(x=result)
append_backward_ops(mean)
append_backward_ops(mean)
tensor = core.LoDTensor()
tensor.set(numpy.arange(10).reshape(10, 1).astype('float32'), place)
......
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
浏览文件 @ feb05c3a
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, program_guard, default_main_program, default_startup_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import MomentumOptimizer
import paddle.v2.fluid.core as core
......@@ -10,44 +10,42 @@ import numpy as np
class TestMNISTIfElseOp(unittest.TestCase):
def test_raw_api(self):
kwargs = {'startup_program': Program(), 'main_program': Program()}
image = layers.data(name='x', shape=[784], dtype='float32', **kwargs)
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64', **kwargs)
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
cond = layers.less_than(x=label, y=limit, **kwargs)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond, **kwargs)
true_out = layers.create_tensor(dtype='float32')
true_cond = layers.ConditionalBlock([true_image])
true_out = layers.create_tensor(dtype='float32', **kwargs)
true_cond = layers.ConditionalBlock([true_image], **kwargs)
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=true_out)
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
layers.assign(input=prob, output=true_out, **kwargs)
false_out = layers.create_tensor(dtype='float32')
false_cond = layers.ConditionalBlock([false_image])
false_out = layers.create_tensor(dtype='float32', **kwargs)
false_cond = layers.ConditionalBlock([false_image], **kwargs)
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
with false_cond.block():
hidden = layers.fc(input=false_image,
size=200,
act='tanh',
**kwargs)
prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
layers.assign(input=prob, output=false_out, **kwargs)
prob = layers.merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(x=loss)
prob = layers.merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image, **kwargs)
loss = layers.cross_entropy(input=prob, label=label, **kwargs)
avg_loss = layers.mean(x=loss, **kwargs)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, kwargs['startup_program'])
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -57,7 +55,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
place = core.CPUPlace()
exe = Executor(place)
exe.run(kwargs['startup_program'])
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
......@@ -65,7 +63,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(kwargs['main_program'],
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
......@@ -75,39 +73,36 @@ class TestMNISTIfElseOp(unittest.TestCase):
self.assertFalse(True)
def test_ifelse(self):
kwargs = {'startup_program': Program(), 'main_program': Program()}
image = layers.data(name='x', shape=[784], dtype='float32', **kwargs)
label = layers.data(name='y', shape=[1], dtype='int64', **kwargs)
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
cond = layers.less_than(x=label, y=limit, **kwargs)
ie = layers.IfElse(cond, **kwargs)
with ie.true_block():
true_image = ie.input(image)
hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = layers.fc(input=false_image,
size=200,
act='tanh',
**kwargs)
prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
ie.output(prob)
prob = ie()
loss = layers.cross_entropy(input=prob[0], label=label, **kwargs)
avg_loss = layers.mean(x=loss, **kwargs)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, kwargs['startup_program'])
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = layers.less_than(x=label, y=limit)
ie = layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
loss = layers.cross_entropy(input=prob[0], label=label)
avg_loss = layers.mean(x=loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
......@@ -135,4 +130,5 @@ class TestMNISTIfElseOp(unittest.TestCase):
if __name__ == '__main__':
unittest.main()
# temp disable if else unittest since it could be buggy.
exit(0)
python/paddle/v2/fluid/tests/test_operator.py
浏览文件 @ feb05c3a
import unittest
import paddle.v2.fluid.op as op
import paddle.v2.fluid.core as core
import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
......
python/paddle/v2/fluid/tests/test_program.py
浏览文件 @ feb05c3a
from __future__ import print_function
import unittest
from paddle.v2.fluid.framework import Program, default_main_program
from paddle.v2.fluid.framework import Program, default_main_program, program_guard, grad_var_name
import paddle.v2.fluid.layers as layers
main_program = default_main_program()
......@@ -109,12 +109,10 @@ class TestProgram(unittest.TestCase):
self.assertEqual(add_op.idx, 1)
param_to_grad = prog.append_backward(mean_out, set())
def grad_name(name):
return name + "@GRAD"
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out",
"mean.out"):
self.assertEqual(param_to_grad[var_name][0], grad_name(var_name))
self.assertEqual(param_to_grad[var_name][0],
grad_var_name(var_name))
self.assertEqual(param_to_grad[var_name][1], 0)
expect_ops = [
......@@ -129,13 +127,10 @@ class TestProgram(unittest.TestCase):
def test_program_clone_with_parameter(self):
main_program = Program()
startup_program = Program()
kwargs = {
'main_program': main_program,
'startup_program': startup_program
}
d = layers.data(name='x', shape=[784], dtype='float32', **kwargs)
hidden = layers.fc(input=d, size=100, **kwargs)
layers.fc(input=hidden, size=100, **kwargs)
with program_guard(main_program, startup_program):
d = layers.data(name='x', shape=[784], dtype='float32')
hidden = layers.fc(input=d, size=100)
layers.fc(input=hidden, size=100)
new_program = main_program.clone()
self.assertNotEqual(0, len(new_program.blocks[0].all_parameters()))
......
python/paddle/v2/fluid/tests/test_recurrent_op.py
浏览文件 @ feb05c3a
import unittest
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, grad_var_name
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.backward import append_backward_ops
import numpy as np
......@@ -164,7 +164,7 @@ class RecurrentOpTest1(unittest.TestCase):
for x in self.data_field
}
fetch_list = [
self.main_program.global_block().var(x + "@GRAD")
self.main_program.global_block().var(grad_var_name(x))
for x in self.data_field
]
......
python/paddle/v2/fluid/tests/test_seq_expand.py → python/paddle/v2/fluid/tests/test_sequence_expand.py
浏览文件 @ feb05c3a
......@@ -3,7 +3,7 @@ import numpy as np
from op_test import OpTest
class TestSeqExpand(OpTest):
class TestSequenceExpand(OpTest):
def set_data(self):
x_data = np.random.uniform(0.1, 1, [3, 1]).astype('float32')
y_data = np.random.uniform(0.1, 1, [8, 1]).astype('float32')
......@@ -21,7 +21,7 @@ class TestSeqExpand(OpTest):
self.outputs = {'Out': out}
def setUp(self):
self.op_type = 'seq_expand'
self.op_type = 'sequence_expand'
self.set_data()
self.compute()
......@@ -32,7 +32,7 @@ class TestSeqExpand(OpTest):
self.check_grad(["X"], "Out")
class TestSeqExpandCase1(TestSeqExpand):
class TestSequenceExpandCase1(TestSequenceExpand):
def set_data(self):
x_data = np.random.uniform(0.1, 1, [5, 1]).astype('float32')
x_lod = [[0, 2, 5]]
......@@ -41,7 +41,7 @@ class TestSeqExpandCase1(TestSeqExpand):
self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
class TestSeqExpandCase2(TestSeqExpand):
class TestSequenceExpandCase2(TestSequenceExpand):
def set_data(self):
x_data = np.random.uniform(0.1, 1, [1, 2, 2]).astype('float32')
x_lod = [[0, 1]]
......@@ -50,7 +50,7 @@ class TestSeqExpandCase2(TestSeqExpand):
self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
class TestSeqExpandCase3(TestSeqExpand):
class TestSequenceExpandCase3(TestSequenceExpand):
def set_data(self):
x_data = np.random.uniform(0.1, 1, [4, 1]).astype('float32')
x_lod = [[0, 1, 2, 3, 4]]
......
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
浏览文件 @ feb05c3a
......@@ -2,7 +2,7 @@ import unittest
import paddle.v2.fluid.core as core
import numpy as np
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, program_guard
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.backward import append_backward_ops
......@@ -75,26 +75,22 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
level=0):
place = self.place()
program = Program()
x = layers.data(name='x', shape=[1], main_program=program)
x.persistable = True
with program_guard(program):
x = layers.data(name='x', shape=[1])
x.persistable = True
y = layers.data(name='y', shape=[1], main_program=program)
y.persistable = True
y = layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level, main_program=program)
out_true.persistable = True
out_false.persistable = True
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out_true.persistable = True
out_false.persistable = True
out = layers.merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level,
main_program=program)
out = layers.merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
out.persistable = True
out.persistable = True
exe = Executor(place)
scope = core.Scope()
......@@ -123,34 +119,21 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
def test_grad(self):
place = core.CPUPlace()
program = Program()
with program_guard(program):
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False)
x = layers.data(
name='x',
shape=[1],
dtype='float32',
main_program=program,
stop_gradient=False)
y = layers.data(
name='y',
shape=[1],
dtype='bool',
main_program=program,
stop_gradient=False)
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level, main_program=program)
out = layers.merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level,
main_program=program)
mean = layers.mean(x=out, main_program=program)
append_backward_ops(mean)
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out = layers.merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
mean = layers.mean(x=out)
append_backward_ops(mean)
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('float32'), place)
......
python/paddle/v2/fluid/tests/test_spp_op.py 0 → 100644
浏览文件 @ feb05c3a
import unittest
import numpy as np
from op_test import OpTest
from test_pool2d_op import max_pool2D_forward_naive
from test_pool2d_op import avg_pool2D_forward_naive
class TestSppOp(OpTest):
def setUp(self):
self.op_type = "spp"
self.init_test_case()
input = np.random.random(self.shape).astype("float32")
nsize, csize, hsize, wsize = input.shape
out_level_flatten = []
for i in xrange(self.pyramid_height):
bins = np.power(2, i)
kernel_size = [0, 0]
padding = [0, 0]
kernel_size[0] = np.ceil(hsize /
bins.astype("double")).astype("int32")
padding[0] = (
(kernel_size[0] * bins - hsize + 1) / 2).astype("int32")
kernel_size[1] = np.ceil(wsize /
bins.astype("double")).astype("int32")
padding[1] = (
(kernel_size[1] * bins - wsize + 1) / 2).astype("int32")
out_level = self.pool2D_forward_naive(input, kernel_size,
kernel_size, padding)
out_level_flatten.append(
out_level.reshape(nsize, bins * bins * csize))
if i == 0:
output = out_level_flatten[i]
else:
output = np.concatenate((output, out_level_flatten[i]), 1)
# output = np.concatenate(out_level_flatten.tolist(), 0);
self.inputs = {'X': input.astype('float32'), }
self.attrs = {
'pyramid_height': self.pyramid_height,
'pooling_type': self.pool_type
}
self.outputs = {'Out': output.astype('float32')}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
if self.pool_type != "avg":
self.check_grad(['X'], 'Out', max_relative_error=0.05)
def init_test_case(self):
self.shape = [3, 2, 4, 4]
self.pyramid_height = 3
self.pool2D_forward_naive = max_pool2D_forward_naive
self.pool_type = "max"
class TestCase2(TestSppOp):
def init_test_case(self):
self.shape = [3, 2, 4, 4]
self.pyramid_height = 3
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.pool_type = "avg"
if __name__ == '__main__':
unittest.main()
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