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提交 a8890110 编写于 作者: Z zchen0211
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

上级 fabfe17a 3663bd88
隐藏空白更改
内联 并排
Showing with 812 addition and 93 deletion
go/master/client.go
浏览文件 @ a8890110
......@@ -63,13 +63,24 @@ func WithAddr(addr string) func(c *Client) error {
// WithEtcd sets the client to use etcd for master discovery.
func WithEtcd(endpoints []string, timeout time.Duration) func(*Client) error {
return func(c *Client) error {
cli, err := clientv3.New(clientv3.Config{
Endpoints: endpoints,
DialTimeout: timeout,
})
if err != nil {
var cli *clientv3.Client
f := func() error {
var err error
cli, err = clientv3.New(clientv3.Config{
Endpoints: endpoints,
DialTimeout: timeout,
})
return err
}
for {
err := f()
if err != nil {
log.Warningln(err)
} else {
break
}
time.Sleep(time.Second)
}
ch := make(chan string, 1)
a, err := GetKey(cli, DefaultAddrPath, timeout)
......@@ -101,9 +112,6 @@ func NewClient(opts ...func(*Client) error) (*Client, error) {
}
}
c.ch = make(chan record, c.bufSize)
// FIXME: connection is created asyncrosly in monitorMaster go routine,
// ensure the connection is ready for use before calling c.addClient.
time.Sleep(time.Second)
return c, nil
}
......
paddle/cuda/include/hl_cuda_cudnn.h
浏览文件 @ a8890110
......@@ -214,7 +214,8 @@ extern void hl_conv_workspace(hl_tensor_descriptor input,
int* convBwdDataAlgo,
size_t* bwdDataLimitBytes,
int* convBwdFilterAlgo,
size_t* bwdFilterLimitBytes);
size_t* bwdFilterLimitBytes,
bool useDilation);
/**
* @brief destroy filter descriptor.
......@@ -242,7 +243,9 @@ extern void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width);
int stride_width,
int dilation_h = 1,
int dilation_w = 1);
/**
* @brief reset convolution descriptor.
......@@ -262,7 +265,9 @@ extern void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width);
int stride_width,
int dilation_h = 1,
int dilation_w = 1);
/**
* @brief destroy convolution descriptor.
......
paddle/cuda/include/stub/hl_cuda_cudnn_stub.h
浏览文件 @ a8890110
......@@ -78,7 +78,9 @@ inline void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width) {}
int stride_width,
int dilation_h,
int dilation_w) {}
inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
hl_tensor_descriptor image,
......@@ -86,7 +88,9 @@ inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width) {}
int stride_width,
int dilation_h,
int dilation_w) {}
inline void hl_destroy_convolution_descriptor(hl_convolution_descriptor conv) {}
......@@ -99,7 +103,8 @@ inline void hl_conv_workspace(hl_tensor_descriptor input,
int* convBwdDataAlgo,
size_t* bwdDataLimitBytes,
int* convBwdFilterAlgo,
size_t* bwdFilterLimitBytes) {}
size_t* bwdFilterLimitBytes,
bool useDilation) {}
inline void hl_convolution_forward(hl_tensor_descriptor input,
real* input_data,
......
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @ a8890110
......@@ -201,7 +201,8 @@ void hl_conv_workspace(hl_tensor_descriptor input,
int* convBwdDataAlgo,
size_t* bwdDataLimitBytes,
int* convBwdFilterAlgo,
size_t* bwdFilterLimitBytes) {
size_t* bwdFilterLimitBytes,
bool useDilation) {
#if CUDNN_VERSION >= 4000
CHECK_NOTNULL(input);
......@@ -213,21 +214,60 @@ void hl_conv_workspace(hl_tensor_descriptor input,
size_t memoryLimitBytes =
(1LL << 20) * FLAGS_cudnn_conv_workspace_limit_in_mb;
// For dilation
int algo = 0;
// cudnn convolution forward configuration
cudnnTensorDescriptor_t fwd_src_desc = GET_TENSOR_DESCRIPTOR(input);
cudnnTensorDescriptor_t fwd_dest_desc = GET_TENSOR_DESCRIPTOR(output);
cudnnFilterDescriptor_t fwd_filter_desc = GET_FILTER_DESCRIPTOR(filter);
cudnnConvolutionDescriptor_t fwd_conv_desc = GET_CONVOLUTION_DESCRIPTOR(conv);
// cudnn convolution backward data configuration
cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter);
cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output);
cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input);
cudnnConvolutionDescriptor_t bwd_data_conv_desc =
GET_CONVOLUTION_DESCRIPTOR(conv);
// cudnn convolution backward filter configuration
cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input);
cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output);
cudnnConvolutionDescriptor_t bwd_filter_conv_desc =
GET_CONVOLUTION_DESCRIPTOR(conv);
cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter);
CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm(
t_resource.cudnn_handle,
fwd_src_desc,
fwd_filter_desc,
fwd_conv_desc,
fwd_dest_desc,
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionFwdAlgo_t*>(convFwdAlgo)));
if (useDilation) {
convFwdAlgo = &algo;
convBwdDataAlgo = &algo;
convBwdFilterAlgo = &algo;
} else {
CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm(
t_resource.cudnn_handle,
fwd_src_desc,
fwd_filter_desc,
fwd_conv_desc,
fwd_dest_desc,
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionFwdAlgo_t*>(convFwdAlgo)));
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm(
t_resource.cudnn_handle,
bwd_data_filter_desc,
bwd_data_diff_desc,
bwd_data_conv_desc,
bwd_data_grad_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionBwdDataAlgo_t*>(convBwdDataAlgo)));
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
t_resource.cudnn_handle,
bwd_filter_src_desc,
bwd_filter_diff_desc,
bwd_filter_conv_desc,
bwd_filter_grad_desc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionBwdFilterAlgo_t*>(convBwdFilterAlgo)));
}
CHECK_CUDNN(dynload::cudnnGetConvolutionForwardWorkspaceSize(
t_resource.cudnn_handle,
......@@ -238,23 +278,6 @@ void hl_conv_workspace(hl_tensor_descriptor input,
static_cast<cudnnConvolutionFwdAlgo_t>(*convFwdAlgo),
fwdLimitBytes));
// cudnn convolution backward data configuration
cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter);
cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output);
cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input);
cudnnConvolutionDescriptor_t bwd_data_conv_desc =
GET_CONVOLUTION_DESCRIPTOR(conv);
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm(
t_resource.cudnn_handle,
bwd_data_filter_desc,
bwd_data_diff_desc,
bwd_data_conv_desc,
bwd_data_grad_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionBwdDataAlgo_t*>(convBwdDataAlgo)));
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
t_resource.cudnn_handle,
bwd_data_filter_desc,
......@@ -264,23 +287,6 @@ void hl_conv_workspace(hl_tensor_descriptor input,
static_cast<cudnnConvolutionBwdDataAlgo_t>(*convBwdDataAlgo),
bwdDataLimitBytes));
// cudnn convolution backward filter configuration
cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input);
cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output);
cudnnConvolutionDescriptor_t bwd_filter_conv_desc =
GET_CONVOLUTION_DESCRIPTOR(conv);
cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter);
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
t_resource.cudnn_handle,
bwd_filter_src_desc,
bwd_filter_diff_desc,
bwd_filter_conv_desc,
bwd_filter_grad_desc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
memoryLimitBytes,
reinterpret_cast<cudnnConvolutionBwdFilterAlgo_t*>(convBwdFilterAlgo)));
CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
t_resource.cudnn_handle,
bwd_filter_src_desc,
......@@ -603,7 +609,9 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width) {
int stride_width,
int dilation_h,
int dilation_w) {
CHECK_NOTNULL(conv);
cudnn_convolution_descriptor hl_conv = (cudnn_convolution_descriptor)malloc(
......@@ -625,18 +633,24 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv,
padding_width,
stride_height,
stride_width,
1,
1,
dilation_h,
dilation_w,
mode,
data_type));
#else
if (dilation_h > 1 || dilation_w > 1) {
LOG(FATAL)
<< "Current cuDNN version does't support for dilation convolution. "
<< "The dilation convolution requires cuDNN >= v6.0.";
}
CHECK_CUDNN(dynload::cudnnSetConvolution2dDescriptor(hl_conv->desc,
padding_height,
padding_width,
stride_height,
stride_width,
1,
1,
dilation_h,
dilation_w,
mode));
#endif
......@@ -659,7 +673,9 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
int padding_height,
int padding_width,
int stride_height,
int stride_width) {
int stride_width,
int dilation_h,
int dilation_w) {
CHECK_NOTNULL(conv);
CHECK_NOTNULL(image);
CHECK_NOTNULL(filter);
......@@ -678,8 +694,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
padding_width,
stride_height,
stride_width,
1,
1,
dilation_h,
dilation_w,
mode,
data_type));
#else
......@@ -688,8 +704,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv,
padding_width,
stride_height,
stride_width,
1,
1,
dilation_h,
dilation_w,
mode));
#endif
......
paddle/gserver/layers/ConvBaseLayer.cpp
浏览文件 @ a8890110
......@@ -32,9 +32,11 @@ bool ConvBaseLayer::init(const LayerMap& layerMap,
const ConvConfig& conf = inputConfig.conv_conf();
padding_.push_back(conf.padding());
stride_.push_back(conf.stride());
dilation_.push_back(conf.dilation());
filterSize_.push_back(conf.filter_size());
paddingY_.push_back(conf.padding_y());
strideY_.push_back(conf.stride_y());
dilationY_.push_back(conf.dilation_y());
filterSizeY_.push_back(conf.filter_size_y());
filterPixels_.push_back(filterSize_.back() * filterSizeY_.back());
channels_.push_back(conf.channels());
......@@ -89,7 +91,11 @@ size_t ConvBaseLayer::calOutputSize() {
size_t layerSize = 0;
auto setLayerSize = [&](IntV& inH, IntV& inW, IntV& outH, IntV& outW) {
size_t filterSizeY;
size_t filterSize;
for (size_t i = 0; i < inputLayers_.size(); i++) {
filterSizeY = (filterSizeY_[i] - 1) * dilationY_[i] + 1;
filterSize = (filterSize_[i] - 1) * dilation_[i] + 1;
inH.push_back(inputLayers_[i]->getOutput().getFrameHeight());
inW.push_back(inputLayers_[i]->getOutput().getFrameWidth());
const ConvConfig& conf = config_.inputs(i).conv_conf();
......@@ -98,17 +104,17 @@ size_t ConvBaseLayer::calOutputSize() {
inH[i] = conf.has_output_y() ? conf.output_y() : conf.output_x();
if (inW[i] == 0) inW[i] = conf.output_x();
outH.push_back(imageSize(
inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_));
outW.push_back(imageSize(
inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_));
inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
outW.push_back(
imageSize(inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
} else {
if (inH[i] == 0)
inH[i] = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
if (inW[i] == 0) inW[i] = conf.img_size();
outH.push_back(outputSize(
inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_));
inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
outW.push_back(outputSize(
inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_));
inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
}
CHECK_EQ(outH[i], outH[0]);
CHECK_EQ(outW[i], outW[0]);
......
paddle/gserver/layers/ConvBaseLayer.h
浏览文件 @ a8890110
......@@ -40,6 +40,10 @@ protected:
IntV stride_;
/// The y dimension of the stride.
IntV strideY_;
/// The x dimension of the dilation.
IntV dilation_;
/// The y dimension of the dilation.
IntV dilationY_;
/// The x dimension of a filter kernel.
IntV filterSize_;
/// The y dimension of a filter kernel.
......
paddle/gserver/layers/ConvBaseOperator.cpp
浏览文件 @ a8890110
......@@ -59,7 +59,8 @@ void ConvBaseOperator::allocConvWorkSpace() {
&bwdDataAlgo_,
&bwdDataLimitBytes_,
&bwdFilterAlgo_,
&bwdFilterLimitBytes_);
&bwdFilterLimitBytes_,
/*useDilation*/ false);
size_t maxWorkSpace = 0;
maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);
......
paddle/gserver/layers/ConvBaseProjection.cpp
浏览文件 @ a8890110
......@@ -41,6 +41,11 @@ void ConvBaseProjection::getConvParams() {
strideH_ = conf.stride_y();
strideW_ = conf.stride();
dilationH_ = conf.dilation_y();
dilationW_ = conf.dilation();
CHECK_GT(dilationH_, 0);
CHECK_GT(dilationW_, 0);
filterH_ = conf.filter_size_y();
filterW_ = conf.filter_size();
......@@ -77,7 +82,9 @@ void ConvBaseProjection::initCudnn() {
paddingH_,
paddingW_,
strideH_,
strideW_);
strideW_,
dilationH_,
dilationW_);
// initialize all to default algorithms
fwdAlgo_ = 0;
......@@ -131,7 +138,9 @@ void ConvBaseProjection::reshapeTensorDesc(int batchSize) {
paddingH_,
paddingW_,
strideH_,
strideW_);
strideW_,
dilationH_,
dilationW_);
}
void ConvBaseProjection::reshape(int batchSize) {
......@@ -140,6 +149,10 @@ void ConvBaseProjection::reshape(int batchSize) {
CHECK_EQ(calInputSize(), in_->value->getWidth());
reshapeTensorDesc(batchSize);
bool useDilation = false;
if (dilationH_ > 1 || dilationW_ > 1) {
useDilation = true;
}
hl_conv_workspace(imageDesc_,
outputDesc_,
filterDesc_,
......@@ -149,7 +162,8 @@ void ConvBaseProjection::reshape(int batchSize) {
&bwdDataAlgo_,
&bwdDataLimitBytes_,
&bwdFilterAlgo_,
&bwdFilterLimitBytes_);
&bwdFilterLimitBytes_,
useDilation);
size_t maxWorkSpace = 0;
maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);
......
paddle/gserver/layers/ConvBaseProjection.h
浏览文件 @ a8890110
......@@ -63,6 +63,7 @@ protected:
int configChannels_, configNumFilters_;
int paddingH_, paddingW_;
int strideH_, strideW_;
int dilationH_, dilationW_;
int filterH_, filterW_;
/// One group offset of input data.
int inputOffset_;
......
paddle/gserver/layers/ConvProjection.cpp
浏览文件 @ a8890110
......@@ -25,12 +25,12 @@ size_t ConvProjection::calOutputSize() {
if (imageH_ == 0) imageH_ = configImgH_;
if (imageW_ == 0) imageW_ = configImgW_;
outputH_ = outputSize(imageH_,
filterH_,
(filterH_ - 1) * dilationH_ + 1,
paddingH_,
strideH_,
/* caffeMode */ true);
outputW_ = outputSize(imageW_,
filterW_,
(filterW_ - 1) * dilationW_ + 1,
paddingW_,
strideW_,
/* caffeMode */ true);
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ a8890110
......@@ -12,6 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef PADDLE_ONLY_CPU
#include <cudnn.h>
#endif
#include <gtest/gtest.h>
#include <string>
#include <vector>
......@@ -189,10 +192,16 @@ TEST(Projection, scaling) {
void testProjectionConv(size_t groups, bool isDeconv) {
const int NUM_FILTERS = 18;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 4;
const int FILTER_SIZE_Y = 2;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
#if CUDNN_VERSION >= 6000
const int DILATION = 2;
#else
const int DILATION = 1;
#endif
ProjectionConfig conf;
if (isDeconv) {
conf.set_type("convt");
......@@ -209,6 +218,8 @@ void testProjectionConv(size_t groups, bool isDeconv) {
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_dilation(DILATION);
conv->set_dilation_y(DILATION);
conv->set_groups(groups);
if (isDeconv) {
conv->set_filter_channels(NUM_FILTERS / conv->groups());
......@@ -217,12 +228,12 @@ void testProjectionConv(size_t groups, bool isDeconv) {
}
conv->set_img_size(IMAGE_SIZE);
int output_x = outputSize(conv->img_size(),
conv->filter_size(),
(conv->filter_size() - 1) * DILATION + 1,
conv->padding(),
conv->stride(),
/* caffeMode */ true);
int output_y = outputSize(conv->img_size(),
conv->filter_size_y(),
(conv->filter_size_y() - 1) * DILATION + 1,
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true);
......@@ -424,27 +435,38 @@ void testConvLayer(const string& type, bool trans, bool useGpu) {
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
int dilation = 1;
if (type == "cudnn_conv") {
#if CUDNN_VERSION >= 6000
dilation = 2;
#else
dilation = 1;
#endif
}
config.inputDefs.push_back({INPUT_DATA, "layer_0", 768, 192});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_filter_size_y(2);
conv->set_channels(3);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_dilation(dilation);
conv->set_dilation_y(dilation);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_img_size_y(8);
conv->set_img_size_y(16);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
(conv->filter_size() - 1) * dilation + 1,
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
(conv->filter_size_y() - 1) * dilation + 1,
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
......
paddle/operators/CMakeLists.txt
浏览文件 @ a8890110
......@@ -42,6 +42,7 @@ function(op_library TARGET)
endfunction()
add_subdirectory(math)
cc_test(gather_test SRCS gather_test.cc DEPS tensor)
op_library(gather_op SRCS gather_op.cc gather_op.cu)
......@@ -67,7 +68,7 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor op_registry operator net_op)
op_library(uniform_random_op
SRCS uniform_random_op.cc uniform_random_op.cu)
op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu)
op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu)
op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
paddle/operators/fill_zeros_like_op.h
浏览文件 @ a8890110
......@@ -26,7 +26,7 @@ class FillZerosLikeKernel : public framework::OpKernel {
auto* output = context.Output<framework::Tensor>("Dst");
output->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*output);
t.device(context.GetEigenDevice<Place>()) = t.constant(T(0));
t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
}
};
......
paddle/operators/lookup_table_op.cc 0 → 100644
浏览文件 @ a8890110
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/lookup_table_op.h"
namespace paddle {
namespace operators {
class LookupTableOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<Tensor>("Out");
output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
}
};
class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LookupTableOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("W",
"An input represents embedding tensors,"
" which is a learnable parameter.");
AddInput("Ids",
"An input with type int32 or int64"
"contains the ids to be looked up in W.");
AddOutput("Out", "The lookup results, which have the same type with W.");
AddComment(
"This operator is used to perform lookups on the parameter W,"
"then concatenated into a dense tensor.");
}
};
class LookupTableOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table = context.Input<Tensor>("W");
auto d_table = context.Output<Tensor>(framework::GradVarName("W"));
d_table->Resize(table->dims());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
lookup_table_grad, ops::LookupTableOpGrad);
REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);
paddle/operators/lookup_table_op.cu 0 → 100644
浏览文件 @ a8890110
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
__global__ void LookupTable(T* output, const T* table, const int32_t* ids,
const int N, const int K, const int D) {
int idx = threadIdx.x;
int idy = blockIdx.x + threadIdx.y * GridDimX;
while (idy < K) {
int id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
T* out = output + idy * D;
const T* tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
out[i] = tab[i];
}
idy += BlockDimY * GridDimX;
}
}
template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
__global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids,
const int N, const int K, const int D) {
int idx = threadIdx.x;
int idy = blockIdx.x + threadIdx.y * GridDimX;
while (idy < K) {
int id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
const T* out = output + idy * D;
T* tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
paddle::platform::CudaAtomicAdd(&tab[i], out[i]);
}
idy += BlockDimY * GridDimX;
}
}
template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<Tensor>("Out");
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
size_t K = product(ids_t->dims());
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
dim3 threads(128, 8);
dim3 grids(8, 1);
LookupTable<T, 128, 8, 8><<<grids, threads>>>(output, table, ids, N, K, D);
}
};
template <typename T>
class LookupTableGradCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto ids_t = context.Input<Tensor>("Ids");
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
int K = product(ids_t->dims());
const int32_t* ids = ids_t->data<int32_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_table_t);
t.device(context.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
dim3 threads(128, 8);
dim3 grids(8, 1);
LookupTableGrad<T, 128, 8, 8><<<grids, threads>>>(d_table, d_output, ids, N,
K, D);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(lookup_table, ops::LookupTableCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(lookup_table_grad,
ops::LookupTableGradCUDAKernel<float>);
paddle/operators/lookup_table_op.h 0 → 100644
浏览文件 @ a8890110
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class LookupTableKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W"); // float tensor
auto ids_t = context.Input<Tensor>("Ids"); // int tensor
auto output_t = context.Output<Tensor>("Out"); // float tensor
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
for (size_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
}
}
};
template <typename T>
class LookupTableGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto ids_t = context.Input<Tensor>("Ids");
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
size_t N = d_table_t->dims()[0];
size_t D = d_table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_table_t);
t.device(context.GetEigenDevice<platform::CPUPlace>()) =
t.constant(static_cast<T>(0));
for (size_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
for (size_t j = 0; j < D; ++j) {
d_table[ids[i] * D + j] += d_output[i * D + j];
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/rowwise_add_op.cu
浏览文件 @ a8890110
......@@ -18,3 +18,6 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
rowwise_add, ops::RowwiseAddKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
rowwise_add_grad,
ops::RowwiseAddGradKernel<paddle::platform::GPUPlace, float>);
paddle/platform/cuda_helper.h 0 → 100644
浏览文件 @ a8890110
/* 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 <cuda.h>
namespace paddle {
namespace platform {
#define CUDA_ATOMIC_WRAPPER(op, T) \
__device__ __forceinline__ T CudaAtomic##op(T* address, const T val)
#define USE_CUDA_ATOMIC(op, T) \
CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); }
// For atomicAdd.
USE_CUDA_ATOMIC(Add, float);
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600
USE_CUDA_ATOMIC(Add, double);
#else
CUDA_ATOMIC_WRAPPER(Add, double) {
unsigned long long int* address_as_ull =
reinterpret_cast<unsigned long long int*>(address);
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed,
__double_as_longlong(val + __longlong_as_double(assumed)));
// Note: uses integer comparison to avoid hang in case of NaN
} while (assumed != old);
return __longlong_as_double(old);
}
#endif
} // namespace platform
} // namespace paddle
paddle/pybind/CMakeLists.txt
浏览文件 @ a8890110
......@@ -15,6 +15,7 @@ cc_library(paddle_pybind SHARED
uniform_random_op
gaussian_random_op
fill_zeros_like_op
lookup_table_op
scale_op
minus_op)
endif(WITH_PYTHON)
paddle/pybind/pybind.cc
浏览文件 @ a8890110
......@@ -42,6 +42,7 @@ USE_OP(fill_zeros_like);
USE_OP_ITSELF(recurrent_op);
USE_OP(gaussian_random);
USE_OP(uniform_random);
USE_OP(lookup_table);
USE_OP(scale);
USE_OP_ITSELF(identity);
USE_OP(minus);
......
proto/ModelConfig.proto
浏览文件 @ a8890110
......@@ -82,6 +82,9 @@ message ConvConfig {
// if not set, use img_size
optional uint32 img_size_y = 14;
optional uint32 dilation = 15 [ default = 1 ];
optional uint32 dilation_y = 16 [ default = 1 ];
}
message PoolConfig {
......
python/paddle/trainer/config_parser.py
浏览文件 @ a8890110
......@@ -870,12 +870,16 @@ class Conv(Cfg):
caffe_mode=True,
filter_size_y=None,
padding_y=None,
stride_y=None):
stride_y=None,
dilation=None,
dilation_y=None):
self.add_keys(locals())
if filter_size_y is None:
self.filter_size_y = filter_size
if padding_y is None:
self.padding_y = padding
if dilation_y is None:
self.dilation_y = dilation
if stride_y is None:
self.stride_y = stride
if output_x is not None:
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ a8890110
......@@ -2342,6 +2342,7 @@ def img_conv_layer(input,
groups=1,
stride=1,
padding=0,
dilation=1,
bias_attr=None,
param_attr=None,
shared_biases=True,
......@@ -2349,6 +2350,7 @@ def img_conv_layer(input,
filter_size_y=None,
stride_y=None,
padding_y=None,
dilation_y=None,
trans=False,
layer_type=None):
"""
......@@ -2413,6 +2415,11 @@ def img_conv_layer(input,
:type padding: int|tuple|list
:param padding_y: The y dimension of the padding.
:type padding_y: int
:param dilation: The x dimension of the dilation. Or input a tuple for two
image dimension
:type dilation: int|tuple|list
:param dilation_y: The y dimension of the dilation.
:type dilation_y: int
:param bias_attr: Convolution bias attribute. None means default bias.
False means no bias.
:type bias_attr: ParameterAttribute|False
......@@ -2460,6 +2467,13 @@ def img_conv_layer(input,
else:
padding_y = padding
if dilation_y is None:
if isinstance(dilation, collections.Sequence):
assert len(dilation) == 2
dilation, dilation_y = dilation
else:
dilation_y = dilation
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size**2 * num_channels))**0.5
......@@ -2469,6 +2483,8 @@ def img_conv_layer(input,
param_attr.attr["initial_smart"] = False
if layer_type:
if dilation > 1 or dilation_y > 1:
assert layer_type in ["cudnn_conv", "cudnn_convt"]
if trans:
assert layer_type in ["exconvt", "cudnn_convt"]
else:
......@@ -2484,11 +2500,13 @@ def img_conv_layer(input,
conv=Conv(
filter_size=filter_size,
padding=padding,
dilation=dilation,
stride=stride,
channels=num_channels,
groups=groups,
filter_size_y=filter_size_y,
padding_y=padding_y,
dilation_y=dilation_y,
stride_y=stride_y),
**param_attr.attr),
active_type=act.name,
......
python/paddle/trainer_config_helpers/tests/configs/img_layers.py
浏览文件 @ a8890110
......@@ -12,6 +12,7 @@ img_conv = img_conv_layer(
num_filters=64,
filter_size=(32, 32),
padding=(1, 1),
dilation=(1, 1),
stride=(1, 1),
act=LinearActivation())
img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())
......
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ a8890110
......@@ -28,4 +28,6 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
py_test(test_recurrent_op SRCS test_recurrent_op.py)
py_test(test_sgd_op SRCS test_sgd_op.py)
py_test(test_gradient_checker SRCS test_gradient_checker.py)
py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
py_test(mnist SRCS mnist.py)
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @ a8890110
......@@ -23,6 +23,10 @@ def grad_var_name(var_name):
return var_name + "@GRAD"
def empty_var_name():
return "@EMPTY@"
def get_numeric_gradient(op,
input_values,
output_name,
......@@ -182,7 +186,7 @@ class GradientChecker(unittest.TestCase):
]
return outs
def compare_grad(self, forward_op, input_value):
def compare_grad(self, forward_op, input_value, no_grad_set=None):
""" Compare the input gradients between CPU and GPU for the given forward
operator.
......@@ -190,15 +194,20 @@ class GradientChecker(unittest.TestCase):
:type forward_op: Operator
:param input_value: input values.
:type input_value: dict{string:numpy.array}
:param no_grad_set: the set of variables names without gradients.
:type no_grad_set: a set of string
:raises: AssertionError, there is different gradient value.
"""
backward_op = core.Operator.backward(forward_op, set())
if no_grad_set is None:
no_grad_set = set()
backward_op = core.Operator.backward(forward_op, no_grad_set)
# return if not compile with GPU or not implementing GPU kernel
if not (core.is_compile_gpu() and backward_op.support_gpu()):
return
outputs = backward_op.outputs()
out_names = [item for k in outputs for item in outputs[k]]
out_names = filter(lambda x: x != empty_var_name(), out_names)
cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
out_names, core.CPUPlace())
gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
......
python/paddle/v2/framework/tests/mnist.py 0 → 100644
浏览文件 @ a8890110
import paddle.v2.framework.core as core
from paddle.v2.framework.op import Operator
import numpy
import paddle.v2 as paddle
BATCH_SIZE = 100
scope = core.Scope()
place = core.CPUPlace()
# if you want to test GPU training, you can use gpu place
# place = core.GPUPlace(0)
dev_ctx = core.DeviceContext.create(place)
init_net = core.Net.create()
forward_net = core.Net.create()
backward_net = None
optimize_net = core.Net.create()
def atomic_id():
id = 0
while True:
yield id
id += 1
uniq_id = atomic_id().next
def data_layer(name, dims):
var = scope.new_var(name)
tensor = var.get_tensor()
tensor.set_dims(dims) # 1 is batch size holder.
return name
def feed_data(name, data):
assert isinstance(data, numpy.ndarray)
tensor = scope.find_var(name).get_tensor()
tensor.set_dims(data.shape)
if data.dtype == numpy.dtype('int32'):
tensor.alloc_int(place)
elif data.dtype == numpy.dtype('float32'):
tensor.alloc_float(place)
else:
raise ValueError("data type not supported")
tensor.set(data, place)
def grad_var_name(var_name):
return var_name + "@GRAD"
def sgd_optimizer(net, param_name, learning_rate=0.005):
grad_name = grad_var_name(param_name)
optimize_op = Operator(
"sgd",
param=param_name,
grad=grad_name,
param_out=param_name,
learning_rate=learning_rate)
net.append_op(optimize_op)
# should use operator and add these to the init_network
def init_param(net, param_name, dims):
scope.new_var(param_name)
op = Operator(
"uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10)
op.infer_shape(scope)
net.append_op(op)
# fc_layer
def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
"""
Add a fc layer to net
:param input: input variable name.
:type input: str
:param size: fully connected layer size.
:param act: activation name
:param param: parameter attribute, used for initialize parameters.
:param bias: bias attribute. False will not have a bias.
:param name: the name of fc layer. If not set, model will generate a
readable name
:return: output variable name.
"""
if name is None:
name = 'fc_%d' % uniq_id()
if not isinstance(name, str):
raise ValueError("name should be string")
input_dims = scope.find_var(input).get_tensor().get_dims()
w_name = param or name + ".w"
init_param(net=init_net, param_name=w_name, dims=[input_dims[1], size])
sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)
pre_activation = name + ".mul.out"
scope.new_var(pre_activation)
mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
net.append_op(mul_op)
# create bias variable if needed
if bias:
bias_name = name + ".b"
init_param(net=init_net, param_name=bias_name, dims=[size])
sgd_optimizer(
net=optimize_net, param_name=bias_name, learning_rate=0.001)
bias_out = name + ".rowwise_add.out"
scope.new_var(bias_out)
rowwise_append_op = Operator(
"rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
net.append_op(rowwise_append_op)
pre_activation = bias_out
activation_op = Operator(act, X=pre_activation, Y=name)
net.append_op(activation_op)
scope.new_var(name)
net.infer_shape(scope)
return name
def cross_entropy_layer(net, input, label):
cost_name = 'cross_entropy_%d' % uniq_id()
cross_entropy_op = Operator(
"onehot_cross_entropy", X=input, label=label, Y=cost_name)
net.append_op(cross_entropy_op)
scope.new_var(cost_name)
net.infer_shape(scope)
return cost_name
def create_backward_net(forward_net):
net = core.Operator.backward(forward_net, set())
for input in net.inputs()["all"]:
var = scope.new_var(input)
var.get_tensor()
for output in net.outputs()["all"]:
var = scope.new_var(output)
var.get_tensor()
return net
def debug_print_op(op):
print("===============" + op.type() + "==============")
print("***inputs:***")
for input in op.inputs()["all"]:
print input, scope.find_var(input).get_tensor().get_dims()
print("\n***outputs:***")
for output in op.outputs()["all"]:
print output, scope.find_var(output).get_tensor().get_dims()
print("")
print("")
def set_cost(cost):
cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape
cost_grad = \
scope.find_var(grad_var_name(cost)).get_tensor()
cost_grad.set_dims(cost_shape)
cost_grad.alloc_float(place)
cost_grad.set(numpy.ones(cost_shape).astype("float32"), place)
def get_cost_mean(cost):
cost_data = numpy.array(scope.find_var(cost).get_tensor())
return cost_data.sum() / len(cost_data)
def error_rate(predict, label):
predict_var = numpy.array(scope.find_var(predict).get_tensor()).argmax(
axis=1)
label = numpy.array(scope.find_var(label).get_tensor())
error_num = numpy.sum(predict_var != label)
return error_num / float(len(label))
images = data_layer(name='pixel', dims=[BATCH_SIZE, 784])
labels = data_layer(name='label', dims=[BATCH_SIZE])
fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
predict = fc_layer(net=forward_net, input=fc2, size=100, act="softmax")
cost = cross_entropy_layer(net=forward_net, input=predict, label=labels)
init_net.complete_add_op(True)
forward_net.complete_add_op(True)
backward_net = create_backward_net(forward_net)
optimize_net.complete_add_op(True)
print(init_net)
print(forward_net)
print(backward_net)
print(optimize_net)
debug_print_op(forward_net)
debug_print_op(backward_net)
debug_print_op(optimize_net)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
def test(cost_name):
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
cost = []
error = []
for data in test_reader():
image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
feed_data(images, image_data)
feed_data(labels, label_data)
forward_net.infer_shape(scope)
forward_net.run(scope, dev_ctx)
cost.append(get_cost_mean(cost_name))
error.append(error_rate(predict, "label"))
print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str(
sum(error) / float(len(error))))
PASS_NUM = 1
init_net.run(scope, dev_ctx)
for pass_id in range(PASS_NUM):
batch_id = 0
for data in train_reader():
image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
feed_data(images, image_data)
feed_data(labels, label_data)
forward_net.infer_shape(scope)
forward_net.run(scope, dev_ctx)
set_cost(cost)
backward_net.infer_shape(scope)
backward_net.run(scope, dev_ctx)
optimize_net.run(scope, dev_ctx)
if batch_id % 100 == 0:
print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]")
test(cost)
batch_id = batch_id + 1
python/paddle/v2/framework/tests/test_lookup_table.py 0 → 100644
浏览文件 @ a8890110
import unittest
import numpy as np
from op_test_util import OpTestMeta
from gradient_checker import GradientChecker, create_op
class TestSigmoidOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = 'lookup_table'
table = np.random.random((17, 31)).astype('float32')
ids = np.random.randint(0, 17, 4).astype('int32')
self.inputs = {'W': table, 'Ids': ids}
self.outputs = {'Out': table[ids]}
class TestSigmoidGradOp(GradientChecker):
def test_grad(self):
op = create_op('lookup_table')
table = np.random.random((17, 31)).astype('float32')
ids = np.random.randint(0, 17, 4).astype('int32')
inputs = {'W': table, 'Ids': ids}
# comapre gradients
self.compare_grad(op, inputs, set(['Ids']))
# check gradients
self.check_grad(op, inputs, set('W'), 'Out')
if __name__ == '__main__':
unittest.main()
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