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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into fix-addtolayer-check

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doc/howto/optimization/cpu_profiling.md 0 → 100644
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此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
* Python代码的性能分析
* Python与C++混合代码的性能分析
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
```bash
python -m cProfile -o profile.out main.py
```
其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
### 查看性能分析文件
当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
```text
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
4696 12.040 0.003 12.040 0.003 {built-in method run}
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
| 列名 | 含义 |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
```text
Called By:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
Function was called by...
ncalls tottime cumtime
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
Called:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
## Python与C++混合代码的性能分析
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
```bash
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
### 查看性能分析文件
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
![result](./pprof_1.png)
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
## 总结
至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
paddle/gserver/layers/ROIPoolLayer.cpp
浏览文件 @ f6e82bcf
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "ROIPoolLayer.h"
#include <cfloat>
namespace paddle {
......@@ -126,10 +127,8 @@ void ROIPoolLayer::forward(PassType passType) {
bool isEmpty = (hend <= hstart) || (wend <= wstart);
size_t poolIndex = ph * pooledWidth_ + pw;
if (isEmpty) {
outputData[poolIndex] = 0;
argmaxData[poolIndex] = -1;
}
outputData[poolIndex] = isEmpty ? 0 : -FLT_MAX;
argmaxData[poolIndex] = -1;
for (size_t h = hstart; h < hend; ++h) {
for (size_t w = wstart; w < wend; ++w) {
......
paddle/operators/CMakeLists.txt
浏览文件 @ f6e82bcf
......@@ -73,6 +73,13 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
endif()
# conv_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "conv_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_cudnn);\n")
endif()
# pool_op contains several operators
if ("${TARGET}" STREQUAL "pool_op")
set(pybind_flag 1)
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ f6e82bcf
......@@ -17,10 +17,10 @@
namespace paddle {
namespace operators {
class CudnnConvOpMaker : public Conv2DOpMaker {
class CudnnConv2DOpMaker : public Conv2DOpMaker {
public:
CudnnConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
......@@ -32,16 +32,43 @@ class CudnnConvOpMaker : public Conv2DOpMaker {
}
};
class CudnnConv3DOpMaker : public Conv3DOpMaker {
public:
CudnnConv3DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv3DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
"allocated/freed each time the operator runs, larger "
"workspace size can increase performance but also requires "
"better hardware. This size should be chosen carefully.")
.SetDefault(4096);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::ConvOpGrad);
REGISTER_OP(conv2d_cudnn, ops::ConvOp, ops::CudnnConv2DOpMaker,
conv2d_cudnn_grad, ops::ConvOpGrad);
REGISTER_OP(conv3d_cudnn, ops::ConvOp, ops::CudnnConv3DOpMaker,
conv3d_cudnn_grad, ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(conv_cudnn,
REGISTER_OP_CPU_KERNEL(conv3d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
conv3d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @ f6e82bcf
......@@ -56,6 +56,21 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
ScopedFilterDescriptor filter_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
if (input->dims().size() == 5) {
layout = DataLayout::kNCDHW;
}
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 0)
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
......@@ -63,19 +78,34 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
layout, framework::vectorize2int(output->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int output_channels = output->dims()[1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
int input_height, input_width, input_depth;
if (input->dims().size() == 5) {
input_depth = input->dims()[2];
input_height = input->dims()[3];
input_width = input->dims()[4];
} else { // dim size is enforced in InferShape
input_depth = 1;
input_height = input->dims()[2];
input_width = input->dims()[3];
}
int output_channels = filter->dims()[0];
int output_height, output_width, output_depth;
if (output->dims().size() == 5) {
output_depth = output->dims()[2];
output_height = output->dims()[3];
output_width = output->dims()[4];
} else {
output_depth = 1;
output_height = output->dims()[2];
output_width = output->dims()[3];
}
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_in =
input_channels / groups * input_height * input_width * input_depth;
int group_offset_out =
output_channels / groups * output_height * output_width;
output_channels / groups * output_height * output_width * output_depth;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn conv workspace ---------------------
void* cudnn_workspace = nullptr;
......@@ -138,12 +168,26 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_grad_desc;
ScopedTensorDescriptor input_grad_desc;
ScopedFilterDescriptor filter_desc;
ScopedFilterDescriptor filter_grad_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
if (input->dims().size() == 5) {
layout = DataLayout::kNCDHW;
}
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 0)
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
......@@ -152,22 +196,35 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
layout, framework::vectorize2int(output_grad->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int input_height, input_width, input_depth;
if (input->dims().size() == 5) {
input_depth = input->dims()[2];
input_height = input->dims()[3];
input_width = input->dims()[4];
} else { // dim size is enforced in InferShape
input_depth = 1;
input_height = input->dims()[2];
input_width = input->dims()[3];
}
int output_grad_channels = filter->dims()[0];
int output_grad_height = output_grad->dims()[2];
int output_grad_width = output_grad->dims()[3];
int output_grad_height, output_grad_width, output_grad_depth;
if (input->dims().size() == 5) {
output_grad_depth = output_grad->dims()[2];
output_grad_height = output_grad->dims()[3];
output_grad_width = output_grad->dims()[4];
} else {
output_grad_depth = 1;
output_grad_height = output_grad->dims()[2];
output_grad_width = output_grad->dims()[3];
}
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_out =
output_grad_channels / groups * output_grad_height * output_grad_width;
int group_offset_in =
input_channels / groups * input_height * input_width * input_depth;
int group_offset_out = output_grad_channels / groups * output_grad_height *
output_grad_width * output_grad_depth;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn backward algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t data_algo;
......@@ -180,8 +237,6 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
auto handle = ctx.cuda_device_context().cudnn_handle();
if (input_grad) {
cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
layout, framework::vectorize2int(input_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
......@@ -190,19 +245,17 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
cudnn_output_grad_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_input_grad_desc,
cudnn_input_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_input_grad_desc, data_algo, &tmp_size));
cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
if (filter_grad) {
cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
layout, framework::vectorize2int(filter_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
......@@ -222,7 +275,6 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
T alpha = 1.0f, beta = 0.0f;
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
......@@ -233,21 +285,20 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_input_grad_desc, input_grad_data + i * group_offset_in));
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data + i * group_offset_in));
}
}
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_filter_grad_desc,
workspace_size_in_bytes, &beta, cudnn_filter_desc,
filter_grad_data + i * group_offset_filter));
}
}
......@@ -259,8 +310,16 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>,
REGISTER_OP_GPU_KERNEL(conv2d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
REGISTER_OP_GPU_KERNEL(conv3d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
paddle/platform/cudnn_helper.h
浏览文件 @ f6e82bcf
......@@ -116,7 +116,7 @@ inline cudnnTensorFormat_t GetCudnnTensorFormat(
case DataLayout::kNCHW:
return CUDNN_TENSOR_NCHW;
case DataLayout::kNCDHW:
return CUDNN_TENSOR_NCHW; // TODO(chengduoZH) : add CUDNN_TENSOR_NCDHW
return CUDNN_TENSOR_NCHW; // NOTE: cudnn treat NdTensor as the same
default:
PADDLE_THROW("Unknown cudnn equivalent for order");
}
......@@ -143,7 +143,7 @@ class ScopedTensorDescriptor {
strides[i] = dims[i + 1] * strides[i + 1];
}
// Update tensor descriptor dims setting if groups > 1
// FIXME(typhoonzero): Assume using NCHW or NCDHW order
// NOTE: Assume using NCHW or NCDHW order
std::vector<int> dims_with_group(dims.begin(), dims.end()); // copy
if (groups > 1) {
dims_with_group[1] = dims_with_group[1] / groups;
......@@ -186,7 +186,6 @@ class ScopedFilterDescriptor {
// width of the filter.
std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
if (groups > 1) {
// M /= groups
kernel_with_group[0] /= groups;
// NOTE: input filter(C) of the filter is already asserted to be C/groups.
}
......
python/paddle/v2/fluid/executor.py
浏览文件 @ f6e82bcf
import numpy as np
import paddle.v2.fluid.core as core
from paddle.v2.fluid.framework import Block, Program, g_main_program
g_scope = core.Scope()
def as_numpy(tensor):
if isinstance(tensor, list):
return [as_numpy(t) for t in tensor]
assert isinstance(tensor, core.LoDTensor)
lod = tensor.lod()
tensor_data = np.array(tensor)
if len(lod) == 0:
ans = tensor_data
else:
raise RuntimeError("LoD Calculate lacks unit tests and buggy")
# elif len(lod) == 1:
# ans = []
# idx = 0
# while idx < len(lod) - 1:
# ans.append(tensor_data[lod[idx]:lod[idx + 1]])
# idx += 1
# else:
# for l in reversed(lod):
# ans = []
# idx = 0
# while idx < len(l) - 1:
# ans.append(tensor_data[l[idx]:l[idx + 1]])
# idx += 1
# tensor_data = ans
# ans = tensor_data
return ans
class Executor(object):
def __init__(self, places):
if not isinstance(places, list) and not isinstance(places, tuple):
......@@ -16,6 +45,47 @@ class Executor(object):
act_places.append(p)
self.executor = core.Executor(act_places)
self.places = places
def aslodtensor(self, data):
def accumulate(data):
if not isinstance(data, list):
return 1
return sum([accumulate(sub) for sub in data])
def parselod(data):
seq_lens = [accumulate(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
return lod
assert len(self.places) != 0
if not isinstance(data, list):
# pure tensor case
tensor = core.LoDTensor()
tensor.set(data, self.places[0])
return tensor
else:
raise RuntimeError("Current implementation lacks unittests")
# lodtensor case
lod = []
if not isinstance(data[0], list):
lod.append(parselod(data))
flattened_data = np.concatenate(data, axis=0).astype("int64")
else:
while isinstance(data[0], list):
lod.append(parselod(seq))
flattened_data = [item for seq in data for item in seq]
data = flattened_data
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
tensor = core.LoDTensor()
tensor.set(flattened_data, self.places[0])
tensor.set_lod(lod)
return tensor
def run(self,
program=None,
......@@ -23,7 +93,8 @@ class Executor(object):
fetch_list=None,
feed_var_name='feed',
fetch_var_name='fetch',
scope=None):
scope=None,
return_numpy=True):
if feed is None:
feed = {}
if fetch_list is None:
......@@ -52,7 +123,10 @@ class Executor(object):
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i})
core.set_feed_variable(scope, feed[name], feed_var.name, i)
cur_feed = feed[name]
if not isinstance(cur_feed, core.LoDTensor):
cur_feed = self.aslodtensor(cur_feed)
core.set_feed_variable(scope, cur_feed, feed_var.name, i)
fetch_var = global_block.create_var(
name=fetch_var_name,
......@@ -66,7 +140,11 @@ class Executor(object):
attrs={'col': i})
self.executor.run(program.desc, scope, 0, True)
return [
outs = [
core.get_fetch_variable(scope, fetch_var_name, i)
for i in xrange(len(fetch_list))
]
if return_numpy:
outs = as_numpy(outs)
return outs
python/paddle/v2/fluid/tests/op_test.py
浏览文件 @ f6e82bcf
......@@ -261,7 +261,10 @@ class OpTest(unittest.TestCase):
feed_map = self.feed_var(inputs, place)
exe = Executor(place)
outs = exe.run(program, feed=feed_map, fetch_list=fetch_list)
outs = exe.run(program,
feed=feed_map,
fetch_list=fetch_list,
return_numpy=False)
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
if out_name not in self.outputs:
......@@ -500,5 +503,6 @@ class OpTest(unittest.TestCase):
fetch_list = [g for p, g in param_grad_list]
executor = Executor(place)
result = executor.run(prog, feed_dict, fetch_list)
return map(np.array, result)
return map(
np.array,
executor.run(prog, feed_dict, fetch_list, return_numpy=False))
python/paddle/v2/fluid/tests/test_array_read_write_op.py
浏览文件 @ f6e82bcf
......@@ -52,15 +52,13 @@ class TestArrayReadWrite(unittest.TestCase):
exe = Executor(cpu)
tensor = core.LoDTensor()
tensor.set(numpy.random.random(size=(100, 100)).astype('float32'), cpu)
outs = map(numpy.array,
exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=[a_sum, x_sum],
scope=scope))
tensor = numpy.random.random(size=(100, 100)).astype('float32')
outs = exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=[a_sum, x_sum],
scope=scope)
self.assertEqual(outs[0], outs[1])
total_sum = layers.sums(input=[a_sum, x_sum])
......@@ -72,12 +70,11 @@ class TestArrayReadWrite(unittest.TestCase):
[each_x.name + "@GRAD" for each_x in x])
g_out = [
item.sum()
for item in map(
numpy.array,
exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=g_vars))
for item in exe.run(
feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=g_vars)
]
g_out_sum = numpy.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_conditional_block.py
浏览文件 @ f6e82bcf
......@@ -21,18 +21,15 @@ class ConditionalBlock(unittest.TestCase):
exe = Executor(cpu)
exe.run(g_startup_program)
x = core.LoDTensor()
x.set(numpy.random.random(size=(10, 1)).astype('float32'), cpu)
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = map(numpy.array, exe.run(feed={'X': x}, fetch_list=[out]))[0]
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
print outs
loss = layers.mean(x=out)
append_backward_ops(loss=loss)
outs = map(numpy.array,
exe.run(feed={'X': x},
fetch_list=[
g_main_program.block(0).var(data.name + "@GRAD")
]))[0]
outs = exe.run(
feed={'X': x},
fetch_list=[g_main_program.block(0).var(data.name + "@GRAD")])[0]
print outs
......
python/paddle/v2/fluid/tests/test_conv2d_op.py
浏览文件 @ f6e82bcf
......@@ -16,8 +16,8 @@ def conv2d_forward_naive(input, filter, group, conv_param):
out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) / stride[1]
out = np.zeros((in_n, out_c, out_h, out_w))
d_bolck_w = (dilation[0] * (f_h - 1) + 1)
d_bolck_h = (dilation[1] * (f_w - 1) + 1)
d_bolck_h = (dilation[0] * (f_h - 1) + 1)
d_bolck_w = (dilation[1] * (f_w - 1) + 1)
input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
mode='constant',
......@@ -167,27 +167,27 @@ class TestWithDilation(TestConv2dOp):
#----------------Conv2dCudnn----------------
class TestCudnn(TestConv2dOp):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithGroup(TestWithGroup):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWith1x1(TestWith1x1):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
# cudnn v5 does not support dilation conv.
......
python/paddle/v2/fluid/tests/test_conv3d_op.py
浏览文件 @ f6e82bcf
......@@ -169,5 +169,31 @@ class TestWithDilation(TestConv3dOp):
self.groups = 3
class TestCudnn(TestConv3dOp):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWithGroup1Cudnn(TestWithGroup1):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWithGroup2Cudnn(TestWithGroup2):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWith1x1Cudnn(TestWith1x1):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
# FIXME(typhoonzero): find a way to determine if
# using cudnn > 6 in python
# class TestWithDilationCudnn(TestWithDilation):
# def init_op_type(self):
# self.op_type = "conv3d_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_executor_and_mul.py
浏览文件 @ f6e82bcf
import unittest
from paddle.v2.fluid.layers import mul, data
from paddle.v2.fluid.layers import mul, data, sequence_pool
import paddle.v2.fluid.core as core
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.framework import g_main_program
......@@ -17,17 +17,13 @@ class TestExecutor(unittest.TestCase):
out = mul(x=a, y=b)
place = core.CPUPlace()
a_np = numpy.random.random((100, 784)).astype('float32')
tensor_a = core.LoDTensor()
tensor_a.set(a_np, place)
b_np = numpy.random.random((784, 100)).astype('float32')
tensor_b = core.LoDTensor()
tensor_b.set(b_np, place)
exe = Executor(place)
outs = exe.run(g_main_program,
feed={'a': tensor_a,
'b': tensor_b},
feed={'a': a_np,
'b': b_np},
fetch_list=[out])
out = numpy.array(outs[0])
out = outs[0]
self.assertEqual((100, 100), out.shape)
self.assertTrue(numpy.allclose(out, numpy.dot(a_np, b_np)))
......
python/paddle/v2/fluid/tests/test_inference_model_io.py
浏览文件 @ f6e82bcf
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import unittest
import numpy as np
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
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.io import save_inference_model, load_inference_model
import paddle.v2.fluid.executor as executor
import unittest
import numpy as np
class TestBook(unittest.TestCase):
......@@ -44,7 +44,7 @@ class TestBook(unittest.TestCase):
x=cost, main_program=program, startup_program=init_program)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost, init_program)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
......@@ -52,25 +52,20 @@ class TestBook(unittest.TestCase):
exe.run(init_program, feed={}, fetch_list=[])
for i in xrange(100):
x_data = np.array(
tensor_x = np.array(
[[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
y_data = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
outs = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
expected = np.array(outs[0])
expected = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])[0]
reload(executor) # reload to build a new scope
exe = executor.Executor(place)
......@@ -83,7 +78,7 @@ class TestBook(unittest.TestCase):
feed={feed_var_names[0]: tensor_x,
feed_var_names[1]: tensor_y},
fetch_list=fetch_vars)
actual = np.array(outs[0])
actual = outs[0]
self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1)
......
python/paddle/v2/fluid/tests/test_lod_array_length_op.py
浏览文件 @ f6e82bcf
......@@ -13,7 +13,7 @@ class TestLoDArrayLength(unittest.TestCase):
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = numpy.array(exe.run(fetch_list=[arr_len])[0])
result = exe.run(fetch_list=[arr_len])[0]
self.assertEqual(11, result[0])
......
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
浏览文件 @ f6e82bcf
......@@ -151,10 +151,11 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
exe = Executor(place)
g_out = [
item.sum()
for item in map(
numpy.array,
exe.run(program, feed={'x': tensor}, fetch_list=[g_vars]))
numpy.array(item).sum()
for item in exe.run(program,
feed={'x': tensor},
fetch_list=[g_vars],
return_numpy=False)
]
g_out_sum = numpy.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
浏览文件 @ f6e82bcf
......@@ -65,17 +65,10 @@ 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)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = map(np.array,
exe.run(kwargs['main_program'],
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_loss]))
outs = exe.run(kwargs['main_program'],
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
......@@ -129,19 +122,12 @@ class TestMNISTIfElseOp(unittest.TestCase):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
y_data = y_data.reshape((y_data.shape[0], 1))
outs = map(np.array,
exe.run(kwargs['main_program'],
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_loss]))
outs = exe.run(kwargs['main_program'],
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
......
python/paddle/v2/fluid/tests/test_parameter.py
浏览文件 @ f6e82bcf
......@@ -24,7 +24,7 @@ class TestParameter(unittest.TestCase):
self.assertEqual(0, param.block.idx)
exe = Executor(core.CPUPlace())
p = exe.run(g_main_program, fetch_list=[param])[0]
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
self.assertTrue(np.allclose(p, np.ones(shape) * val))
p = io.get_parameter_value_by_name('fc.w', exe, g_main_program)
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
......
python/paddle/v2/fluid/tests/test_recurrent_op.py
浏览文件 @ f6e82bcf
......@@ -156,7 +156,7 @@ class RecurrentOpTest1(unittest.TestCase):
feed=self.feed_map,
fetch_list=[self.output])
return np.array(out[0])
return out[0]
def backward(self):
self.feed_map = {
......@@ -171,7 +171,8 @@ class RecurrentOpTest1(unittest.TestCase):
exe = Executor(self.place)
return exe.run(self.main_program,
feed=self.feed_map,
fetch_list=fetch_list)
fetch_list=fetch_list,
return_numpy=False)
def test_backward(self):
self.check_forward()
......
python/paddle/v2/fluid/tests/test_rnn_memory_helper_op.py
浏览文件 @ f6e82bcf
......@@ -7,12 +7,6 @@ import numpy as np
import paddle.v2.fluid.core as core
def create_tensor(np_data, place):
tensor = core.LoDTensor()
tensor.set(np_data, place)
return tensor
class RNNMemoryHelperOpTest(unittest.TestCase):
def setUp(self):
self.program = Program()
......@@ -30,13 +24,13 @@ class RNNMemoryHelperOpTest(unittest.TestCase):
def test_forward(self):
x_np = np.random.normal(size=(2, 3)).astype("float32")
self.feed_map = {'X': create_tensor(x_np, self.place)}
self.feed_map = {'X': x_np}
self.fetch_list = [self.Out]
exe = Executor(self.place)
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), x_np, rtol=1e-5)
self.assertTrue(np.allclose(out[0], x_np, rtol=1e-5))
class RNNMemoryHelperGradOpTest(unittest.TestCase):
......@@ -66,8 +60,7 @@ class RNNMemoryHelperGradOpTest(unittest.TestCase):
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
name: np.random.normal(size=(2, 3)).astype("float32")
for name in self.input_names
}
self.fetch_list = [self.output_vars['X@GRAD']]
......@@ -76,7 +69,7 @@ class RNNMemoryHelperGradOpTest(unittest.TestCase):
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), self.feed_map['Out@GRAD'], rtol=1e-5)
np.isclose(out[0], self.feed_map['Out@GRAD'], rtol=1e-5)
class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
......@@ -110,8 +103,7 @@ class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
name: np.random.normal(size=(2, 3)).astype("float32")
for name in ['X', 'Out']
}
self.fetch_list = [self.output_vars['X@GRAD']]
......@@ -120,10 +112,9 @@ class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(
np.array(out[0]),
np.zeros(shape=(2, 3)).astype("float32"),
rtol=1e-5)
self.assertTrue(
np.allclose(
out[0], np.zeros(shape=(2, 3)).astype("float32"), rtol=1e-5))
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @ f6e82bcf
......@@ -27,19 +27,16 @@ class TestShrinkRNNMemory(unittest.TestCase):
tensor_np = numpy.random.random(size=(3, 100)).astype('float32')
tensor.set(tensor_np, cpu)
exe = Executor(cpu)
outs = map(numpy.array,
exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3]))
outs = exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3])
self.assertTrue(numpy.allclose(tensor_np[0:3], outs[0]))
self.assertTrue(numpy.allclose(tensor_np[0:2], outs[1]))
self.assertTrue(numpy.allclose(tensor_np[0:1], outs[2]))
mem3_mean = layers.mean(x=mem3)
append_backward_ops(loss=mem3_mean)
x_grad = map(numpy.array,
exe.run(feed={'x': tensor},
fetch_list=[
g_main_program.global_block().var('x@GRAD')
]))[0]
x_grad = exe.run(
feed={'x': tensor},
fetch_list=[g_main_program.global_block().var('x@GRAD')])[0]
self.assertAlmostEqual(1.0, x_grad.sum(), delta=0.1)
......
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
浏览文件 @ f6e82bcf
......@@ -98,7 +98,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
exe = Executor(place)
scope = core.Scope()
exe.run(program, feed={'x': tensor, 'y': mask}, scope=scope)
exe.run(program,
feed={'x': tensor,
'y': mask},
scope=scope,
return_numpy=False)
var_true = scope.find_var(out_true.name).get_tensor()
......@@ -169,7 +173,8 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
feed={'x': tensor,
'y': mask},
fetch_list=[g_vars],
scope=scope))
scope=scope,
return_numpy=False))
]
g_out_sum = np.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_while_op.py
浏览文件 @ f6e82bcf
......@@ -55,19 +55,10 @@ class TestWhileOp(unittest.TestCase):
for i in xrange(3):
d.append(numpy.random.random(size=[10]).astype('float32'))
d_tensor = []
for item in d:
t = core.LoDTensor()
t.set(item, cpu)
d_tensor.append(t)
outs = map(numpy.array,
exe.run(feed={
'd0': d_tensor[0],
'd1': d_tensor[1],
'd2': d_tensor[2]
},
fetch_list=[sum_result]))
outs = exe.run(feed={'d0': d[0],
'd1': d[1],
'd2': d[2]},
fetch_list=[sum_result])
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
......
python/paddle/v2/framework/tests/test_elementwise_mod_op.py 已删除 100644 → 0
浏览文件 @ 5981918c
import unittest
import numpy as np
from op_test import OpTest
class ElementwiseModOp(OpTest):
def setUp(self):
self.op_type = "elementwise_mod"
""" Warning
CPU gradient check error!
'X': np.random.randint((32,84)).astype("int32"),
'Y': np.random.randint((32,84)).astype("int32")
"""
self.inputs = {
'X': np.random.randint(1, 10, [13, 17]).astype("int32"),
'Y': np.random.randint(1, 10, [13, 17]).astype("int32")
}
self.outputs = {'Out': np.mod(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
if __name__ == '__main__':
unittest.main()
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