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未验证 提交 a3f7ebd6 编写于 作者: L lujun 提交者： GitHub
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Merge pull request #10 from PaddlePaddle/develop 

merge to local
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README.md
浏览文件 @ a3f7ebd6
......@@ -3,8 +3,8 @@
English | [简体中文](./README_cn.md)
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -18,7 +18,7 @@ learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### Latest PaddlePaddle Release: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -26,9 +26,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.2.0.post87
pip install paddlepaddle-gpu==1.3.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.2.0.post85
pip install paddlepaddle-gpu==1.3.0.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......@@ -75,26 +75,26 @@ pip install paddlepaddle-gpu==1.2.0.post85
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html) on our website.
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) documentation.
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
- [Distributed Training](http://paddlepaddle.org/documentation/docs/en/1.3/user_guides/howto/training/multi_node_en.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
- [Python API](http://paddlepaddle.org/documentation/docs/en/1.3/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
- [How to Contribute](http://paddlepaddle.org/documentation/docs/en/1.3/advanced_usage/development/contribute_to_paddle/index_en.html)
We appreciate your contributions!
......
README_cn.md
浏览文件 @ a3f7ebd6
......@@ -3,8 +3,8 @@
[English](./README.md) | 简体中文
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -16,7 +16,7 @@ PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效
跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### PaddlePaddle最新版本: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### 安装最新稳定版本:
```
# Linux CPU
......@@ -24,9 +24,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.2.0.post87
pip install paddlepaddle-gpu==1.3.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.2.0.post85
pip install paddlepaddle-gpu==1.3.0.post85
# 其他平台上的安装指引请参考 http://paddlepaddle.org/
```
......@@ -57,26 +57,26 @@ pip install paddlepaddle-gpu==1.2.0.post85
## 安装
推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html)
推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/install/index_cn.html)
## 文档
我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)和
[中文](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) 文档
我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)和
[中文](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) 文档
- [深度学习101](https://github.com/PaddlePaddle/book)
或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.3/user_guides/howto/training/multi_node.html)
可以在MPI集群上运行分布式训练任务
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.3/api_cn/index_cn.html)
新的API支持代码更少更简洁的程序
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.3/advanced_usage/development/contribute_to_paddle/index_cn.html)
欢迎您的贡献!
......
cmake/external/mklml.cmake
浏览文件 @ a3f7ebd6
......@@ -40,9 +40,7 @@ IF(WIN32)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll)
ELSE()
#TODO(intel-huying):
# Now enable Erf function in mklml library temporarily, it will be updated as offical version later.
SET(MKLML_VER "VsErf_mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_VER "mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
......
paddle/fluid/API.spec
浏览文件 @ a3f7ebd6
......@@ -71,7 +71,7 @@ paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'v
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False))
paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
......@@ -121,6 +121,7 @@ paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=
paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
paddle.fluid.layers.sampled_softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ a3f7ebd6
......@@ -135,12 +135,15 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
void AppendMultiDevPass(const BuildStrategy &strategy) {
ir::Pass *multi_devices_pass;
if (strategy_.is_distribution_) {
VLOG(3) << "multi device parameter server mode";
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
VLOG(3) << "multi devices collective mode with allreduce";
multi_devices_pass =
AppendPass("allreduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
VLOG(3) << "multi deivces collective mode with reduce";
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ a3f7ebd6
......@@ -937,9 +937,21 @@ void DistSSAGraphBuilder::InsertCollectiveOp(ir::Graph *result,
}
void DistSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
if (need_broadcast_var_ ||
(UseGPU() &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce)) {
// broad cast received parameters when training in parameter server mode.
if (need_broadcast_var_) {
// There are 4 conditions:
// 1. GPU && Reduce: Reduce gradient then broadcast gradient to other GPUS.
// Need to broadcast received parameters to other GPU.
// 2. GPU && AllReduce: AllReduce all graident to each GPU. Need to
// broadcast received parameters to other GPU.
// 3. CPU && AllReduce: AllReduce all gradient to each thread. Need to
// broadcast received parameters to other scope.
// 4. CPU && Reduce: because all parameters share the same memory, did not
// broadcast received parameters.
if (!UseGPU() &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
return;
}
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(result, bcast_var_name_set_);
} else {
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ a3f7ebd6
......@@ -66,7 +66,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler sample_prob tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search)
if (WITH_GPU)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)
......
paddle/fluid/operators/activation_op.h
浏览文件 @ a3f7ebd6
......@@ -11,7 +11,6 @@ limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <algorithm>
#include <string>
#include <unordered_set>
#include <utility>
......@@ -25,7 +24,6 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/float16.h"
#ifdef PADDLE_WITH_MKLDNN
......@@ -303,28 +301,8 @@ template <typename T>
struct GeluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
// Because the execute or device context can not be deliver here, it keep the
// marco for NVCC.
#if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \
!defined(__OSX__) && !defined(PADDLE_WITH_CUDA)
auto x_data = x.data();
auto out_data = out.data();
int n = std::min(x.size(), out.size());
std::memset(out_data, 0, n * sizeof(T));
math::CBlas<T>::AXPY(n, static_cast<T>(M_SQRT1_2), x_data, 1, out_data, 1);
math::CBlas<T>::VMERF(n, out_data, out_data, VML_LA);
for (int i = 0; i < n; i++) {
out_data[i] += static_cast<T>(1);
}
math::CBlas<T>::VMUL(n, x_data, out_data, out_data);
for (int i = 0; i < n; i++) {
out_data[i] *= static_cast<T>(0.5);
}
#else
auto temp = (x * static_cast<T>(M_SQRT1_2)).erf();
out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp);
#endif
}
};
......
paddle/fluid/operators/beam_search_decode_op.h
浏览文件 @ a3f7ebd6
......@@ -122,7 +122,7 @@ void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
auto cpu_place = std::unique_ptr<paddle::platform::CPUPlace>(
new paddle::platform::CPUPlace());
paddle::platform::CPUDeviceContext cpu_ctx(*cpu_place.get());
paddle::platform::CPUDeviceContext cpu_ctx(*cpu_place);
framework::LoD lod;
lod.push_back(source_level_lod);
......
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ a3f7ebd6
......@@ -144,34 +144,40 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
This operator generates yolov3 loss based on given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimention, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors, In the second dimension(the channel
dimension), C should be equal to S * (class_num + 5), class_num is the object
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
correspnd to:
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box predictions
should be as follows:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
$$
$$
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
In the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger then ignore
the max IoU should be 1, and if the anchor box has IoU bigger than ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
......@@ -186,13 +192,13 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follow.
calculated as follows.
$$
weight_{box} = 2.0 - t_w * t_h
$$
Final loss will be represented as follow.
Final loss will be represented as follows.
$$
loss = (loss_{xy} + loss_{wh}) * weight_{box}
......
paddle/fluid/operators/lstm_op.h
浏览文件 @ a3f7ebd6
......@@ -151,9 +151,10 @@ class LSTMKernel : public framework::OpKernel<T> {
lstm_value.output_value = out_t.data<T>();
lstm_value.state_value = cell_t.data<T>();
lstm_value.state_active_value = cell_pre_act_t.data<T>();
T cell_clip = 0.0;
math::LstmUnitFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, frame_size, cur_batch_size, gate_act,
cell_act, cand_act);
device_ctx, lstm_value, frame_size, cur_batch_size, cell_clip,
gate_act, cell_act, cand_act);
lstm_value.prev_state_value = lstm_value.state_value;
}
......@@ -316,9 +317,10 @@ class LSTMGradKernel : public framework::OpKernel<T> {
lstm_value.output_value = nullptr;
lstm_grad.state_active_grad = nullptr;
int cur_batch_size = bend - bstart;
T cell_clip = 0.0;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
cell_clip, gate_act, cell_act, cand_act);
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
......
paddle/fluid/operators/lstmp_op.cc
浏览文件 @ a3f7ebd6
......@@ -73,12 +73,6 @@ class LSTMPOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(C0) of LSTMP operator should not be null after "
"Input(H0) provided.");
auto h_dims = ctx->GetInputDim("H0");
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
ctx->SetOutputDim("OrderedP0", {h_dims[0], proj_dims[1]});
}
auto b_dims = ctx->GetInputDim("Bias");
......@@ -180,11 +174,6 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"This LoDTensor is obtained in the forward and used in the "
"backward.")
.AsIntermediate();
AddOutput("OrderedP0",
"(Tensor) the projection of the initial hidden state "
"H0. This is a tensor with shape (N x P), where N is the "
"batch size and P is the hidden size.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
......@@ -193,6 +182,16 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, defalut: False) "
"whether to compute reversed LSTMP.")
.SetDefault(false);
AddAttr<float>("cell_clip",
"(float, defalut: 0.0) "
"Clip for Tensor for cell state tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
AddAttr<float>("proj_clip",
"(float, defalut: 0.0) "
"Clip for Tensor for projection tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
AddAttr<std::string>(
"gate_activation",
"(string, default: sigmoid)"
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ a3f7ebd6
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/activation_op.h"
......@@ -21,17 +22,50 @@ limitations under the License. */
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
using platform::Transform;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
class _ClipFunctor {
public:
explicit _ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
HOSTDEVICE T operator()(const T& x) const {
if (x < min_)
return min_;
else if (x > max_)
return max_;
else
return x;
}
private:
T min_;
T max_;
};
template <typename T>
class _ClipGradFunctor {
public:
explicit _ClipGradFunctor(const T min, const T max) : min_(min), max_(max) {}
HOSTDEVICE T operator()(const T& x, const T& y) const {
return (y > min_ && y < max_) ? x : 0;
}
private:
T min_;
T max_;
};
template <typename DeviceContext, typename T>
inline void ReorderInitState(const DeviceContext& ctx,
const framework::Tensor& src,
......@@ -67,9 +101,11 @@ class LSTMPKernel : public framework::OpKernel<T> {
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_t0 = ctx.Input<Tensor>("H0");
auto* ordered_proj0 = ctx.Output<Tensor>("OrderedP0");
auto* cell_t0 = ctx.Input<Tensor>("C0");
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* proj_out = ctx.Output<LoDTensor>("Projection");
......@@ -110,6 +146,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
}
lstmp_value.prev_state_value = nullptr;
Tensor ordered_c0;
Tensor ordered_h0;
framework::Vector<size_t> order(batch_gate->lod()[2]);
......@@ -169,18 +206,9 @@ class LSTMPKernel : public framework::OpKernel<T> {
// Since the batch computing for LSTMP reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor ordered_h0;
ordered_proj0->mutable_data<T>(ctx.GetPlace());
ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
&ordered_h0, true);
blas.MatMul(ordered_h0, false, *proj_weight, false, static_cast<T>(1.0),
ordered_proj0, static_cast<T>(0.0));
if (proj_act != math::detail::ActivationType::kIdentity) {
auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
ActCompute(cell_act, place, proj0_dev, proj0_dev);
}
blas.MatMul(*ordered_proj0, false, *weight, false, static_cast<T>(1.0),
blas.MatMul(ordered_h0, false, *weight, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
}
......@@ -189,8 +217,8 @@ class LSTMPKernel : public framework::OpKernel<T> {
lstmp_value.state_value = cell_t.data<T>();
lstmp_value.state_active_value = cell_pre_act_t.data<T>();
math::LstmUnitFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, frame_size, cur_batch_size, gate_act,
cell_act, cand_act);
device_ctx, lstmp_value, frame_size, cur_batch_size, cell_clip,
gate_act, cell_act, cand_act);
lstmp_value.prev_state_value = lstmp_value.state_value;
blas.MatMul(hidden_t, false, *proj_weight, false, static_cast<T>(1.0),
&proj_t, static_cast<T>(0.0));
......@@ -198,6 +226,14 @@ class LSTMPKernel : public framework::OpKernel<T> {
auto proj_t_dev = EigenMatrix<T>::From(proj_t);
ActCompute(cell_act, place, proj_t_dev, proj_t_dev);
}
if (proj_clip && proj_clip > 0.0) {
T* x_data = proj_t.data<T>();
int64_t numel = proj_t.numel();
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), x_data,
x_data + numel, x_data,
_ClipFunctor<T>(-1.0 * proj_clip, proj_clip));
}
}
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
......@@ -239,6 +275,9 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
auto* proj_out = ctx.Input<LoDTensor>("Projection");
auto* cell_out = ctx.Input<LoDTensor>("Cell");
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
auto* batch_hidden = ctx.Input<LoDTensor>("BatchHidden");
......@@ -253,7 +292,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto* h0 = ctx.Input<Tensor>("H0");
auto* ordered_proj0 = ctx.Input<Tensor>("OrderedP0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* h0_g = ctx.Output<Tensor>(framework::GradVarName("H0"));
......@@ -363,6 +401,17 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
Tensor cur_proj = batch_proj.Slice(bstart, bend);
Tensor proj_g = batch_proj_g.Slice(bstart, bend);
if (proj_clip && proj_clip > 0.0) {
T* dx_data = proj_g.data<T>();
T* x_data = cur_proj.data<T>();
int64_t numel = proj_g.numel();
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), dx_data,
dx_data + numel, x_data, dx_data,
_ClipGradFunctor<T>(-1.0 * proj_clip, proj_clip));
}
if (proj_act != math::detail::ActivationType::kIdentity) {
auto cur_proj_dev = EigenMatrix<T>::From(cur_proj);
auto proj_g_dev = EigenMatrix<T>::From(proj_g);
......@@ -412,7 +461,7 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
cell_clip, gate_act, cell_act, cand_act);
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
......@@ -431,32 +480,15 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
&ordered_h0, true);
if (weight_g) {
blas.MatMul(*ordered_proj0, true, gate_g, false,
static_cast<T>(1.0), weight_g, static_cast<T>(1.0));
blas.MatMul(ordered_h0, true, gate_g, false, static_cast<T>(1.0),
weight_g, static_cast<T>(1.0));
}
}
if (h0 && (h0_g || proj_weight_g)) {
ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
Tensor proj0_g;
proj0_g.Resize({in_dims[0], proj_weight->dims()[1]});
proj0_g.mutable_data<T>(ctx.GetPlace());
blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
&proj0_g, static_cast<T>(0.0));
if (proj_act != math::detail::ActivationType::kIdentity) {
auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
auto proj0_g_dev = EigenMatrix<T>::From(proj0_g);
ActGradCompute(cell_act, place, proj0_dev, proj0_dev, proj0_g_dev,
proj0_g_dev);
}
if (h0_g) {
blas.MatMul(proj0_g, false, *proj_weight, true, static_cast<T>(1.0),
&ordered_h0_g, static_cast<T>(0.0));
}
if (proj_weight_g) {
blas.MatMul(ordered_h0, true, proj0_g, false, static_cast<T>(1.0),
proj_weight_g, static_cast<T>(1.0));
}
}
}
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ a3f7ebd6
......@@ -39,6 +39,7 @@ math_library(cross_entropy)
math_library(cos_sim_functor)
math_library(depthwise_conv DEPS cub)
math_library(im2col)
math_library(sample_prob)
math_library(sampler)
math_library(gru_compute DEPS activation_functions math_function)
......
paddle/fluid/operators/math/blas.h
浏览文件 @ a3f7ebd6
......@@ -184,9 +184,6 @@ class Blas {
template <typename T>
void VINV(int n, const T* a, T* y) const;
template <typename T>
void VMERF(int n, const T* a, T* y, int64_t mode) const;
private:
const DeviceContext& context_;
};
......@@ -293,11 +290,6 @@ class BlasT : private Blas<DeviceContext> {
Base()->template VINV<T>(args...);
}
template <typename... ARGS>
void VMERF(ARGS... args) const {
Base()->template VMERF<T>(args...);
}
private:
const Blas<DeviceContext>* Base() const {
return static_cast<const Blas<DeviceContext>*>(this);
......
paddle/fluid/operators/math/blas_impl.h
浏览文件 @ a3f7ebd6
......@@ -123,11 +123,6 @@ struct CBlas<float> {
static void VINV(ARGS... args) {
platform::dynload::vsInv(args...);
}
template <typename... ARGS>
static void VMERF(ARGS... args) {
platform::dynload::vmsErf(args...);
}
};
template <>
......@@ -228,11 +223,6 @@ struct CBlas<double> {
static void VINV(ARGS... args) {
platform::dynload::vdInv(args...);
}
template <typename... ARGS>
static void VMERF(ARGS... args) {
platform::dynload::vmdErf(args...);
}
};
#else
......@@ -635,19 +625,6 @@ void Blas<DeviceContext>::VINV(int n, const T *a, T *y) const {
#endif
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::VMERF(int n, const T *a, T *y,
int64_t mode) const {
#ifdef PADDLE_WITH_MKLML
CBlas<T>::VMERF(n, a, y, mode);
#else
for (int i = 0; i < n; ++i) {
y[i] = std::erf(a[i]);
}
#endif
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ a3f7ebd6
......@@ -32,7 +32,8 @@ namespace detail {
template <class T, class Op>
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
T r_value_in;
......@@ -67,7 +68,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -82,7 +83,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
ActivationType active_node,
T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
T r_value_in;
......@@ -135,7 +136,7 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......@@ -154,7 +155,8 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
#ifdef __AVX__
......@@ -194,7 +196,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -210,7 +212,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
ActivationType active_node,
T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
#ifdef __AVX__
......@@ -268,7 +270,7 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......@@ -292,27 +294,27 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frame_size,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
T cell_clip, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
avx_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
active_gate, active_state);
avx_lstm_forward_one_sequence<T>(op, value, frame_size, cell_clip,
active_node, active_gate, active_state);
} else {
naive_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
active_gate, active_state);
naive_lstm_forward_one_sequence<T>(op, value, frame_size, cell_clip,
active_node, active_gate, active_state);
}
}
template <class T, class Op>
void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
avx_lstm_backward_one_sequence<T>(op, value, grad, frame_size, active_node,
active_gate, active_state);
avx_lstm_backward_one_sequence<T>(op, value, grad, frame_size, cell_clip,
active_node, active_gate, active_state);
} else {
naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size,
naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size, cell_clip,
active_node, active_gate, active_state);
}
}
......
paddle/fluid/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ a3f7ebd6
......@@ -31,7 +31,8 @@ namespace detail {
*/
template <class T, class Op, bool is_batch>
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
int batch_size, ActivationType active_node,
int batch_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -72,7 +73,7 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value.gate_value[frame_idx] = r_value_in;
value.gate_value[frame_idx + frame_size] = r_value_ig;
......@@ -91,7 +92,8 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
template <class T, class Op, bool is_batch>
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
int batch_size, ActivationType active_node,
int batch_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -148,8 +150,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_grad_in, &r_grad_ig,
&r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad, &r_state,
&r_state_grad, &r_state_atv, &r_output_grad, &r_checkI, &r_checkF,
&r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad, active_node,
active_gate, active_state);
&r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad, &cell_clip,
active_node, active_gate, active_state);
grad.gate_grad[frame_idx] = r_grad_in;
grad.gate_grad[frame_idx + frame_size] = r_grad_ig;
......@@ -185,8 +187,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
LstmMetaValue<T> value, int frame_size, int batch_size,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
T cell_clip, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
dim3 threads;
dim3 grid;
if (batch_size == 1) {
......@@ -205,12 +207,12 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if (batch_size == 1) {
KeLstmForward<T, Op,
/* is_batch= */ false><<<grid, threads, 0, stream>>>(
op, value, frame_size, batch_size, active_node, active_gate,
op, value, frame_size, batch_size, cell_clip, active_node, active_gate,
active_state);
} else {
KeLstmForward<T, Op,
/* is_batch= */ true><<<grid, threads, 0, stream>>>(
op, value, frame_size, batch_size, active_node, active_gate,
op, value, frame_size, batch_size, cell_clip, active_node, active_gate,
active_state);
}
}
......@@ -218,7 +220,7 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
template <class T, class Op>
void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
dim3 threads;
......@@ -239,13 +241,13 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
if (batch_size == 1) {
KeLstmBackward<T, Op,
/* is_batch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frame_size, batch_size, active_node, active_gate,
active_state);
op, value, grad, frame_size, batch_size, cell_clip, active_node,
active_gate, active_state);
} else {
KeLstmBackward<T, Op,
/* is_batch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frame_size, batch_size, active_node, active_gate,
active_state);
op, value, grad, frame_size, batch_size, cell_clip, active_node,
active_gate, active_state);
}
}
......
paddle/fluid/operators/math/detail/lstm_kernel.h
浏览文件 @ a3f7ebd6
......@@ -29,7 +29,7 @@ class lstm {
public:
HOSTDEVICE void operator()(T *value_in, T *value_ig, T *value_fg, T *value_og,
T *prev_state, T *state, T *state_atv, T *output,
T *checkI, T *checkF, T *checkO,
T *checkI, T *checkF, T *checkO, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
......@@ -37,6 +37,15 @@ class lstm {
*value_ig = activation(*value_ig + (*prev_state) * (*checkI), active_gate);
*value_fg = activation(*value_fg + (*prev_state) * (*checkF), active_gate);
*state = (*value_in) * (*value_ig) + (*prev_state) * (*value_fg);
if (*cell_clip > 0.0) {
if (*state < -1.0 * (*cell_clip)) {
*state = -1.0 * (*cell_clip);
}
if (*state > *cell_clip) {
*state = *cell_clip;
}
}
*value_og = activation(*value_og + (*state) * (*checkO), active_gate);
*state_atv = activation(*state, active_state);
*output = (*value_og) * (*state_atv);
......@@ -52,7 +61,7 @@ class lstm {
__m256 *value_fg, __m256 *value_og,
__m256 *prev_state, __m256 *state,
__m256 *state_atv, __m256 *output, __m256 *checkI,
__m256 *checkF, __m256 *checkO,
__m256 *checkF, __m256 *checkO, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
......@@ -65,6 +74,13 @@ class lstm {
active_gate);
*state = _mm256_add_ps(_mm256_mul_ps(*value_in, *value_ig),
_mm256_mul_ps(*prev_state, *value_fg));
if (*cell_clip > 0.0f) {
__m256 min = _mm256_set1_ps(0.0f - *cell_clip);
__m256 max = _mm256_set1_ps(*cell_clip);
*state = _mm256_min_ps(max, *state);
*state = _mm256_max_ps(min, *state);
}
*value_og = activation(
_mm256_add_ps(*value_og, _mm256_mul_ps(*state, *checkO)), active_gate);
*state_atv = activation(*state, active_state);
......@@ -86,15 +102,26 @@ class lstm {
T *prev_state, T *prev_state_grad, T *state,
T *state_grad, T *state_atv, T *output_grad,
T *checkI, T *checkF, T *checkO, T *checkIGrad,
T *checkFGrad, T *checkOGrad,
T *checkFGrad, T *checkOGrad, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
*grad_og =
activation((*output_grad) * (*state_atv), *value_og, active_gate);
if (*cell_clip > 0.0f) {
if (*state >= (*cell_clip) || *state <= (0.0f - (*cell_clip))) {
*state_grad = 0.0f;
} else {
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
}
} else {
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
}
*grad_in = activation((*state_grad) * (*value_ig), *value_in, active_node);
*grad_ig = activation((*state_grad) * (*value_in), *value_ig, active_gate);
*grad_fg =
......@@ -117,15 +144,24 @@ class lstm {
__m256 *prev_state, __m256 *prev_state_grad, __m256 *state,
__m256 *state_grad, __m256 *state_atv, __m256 *output_grad,
__m256 *checkI, __m256 *checkF, __m256 *checkO, __m256 *checkIGrad,
__m256 *checkFGrad, __m256 *checkOGrad, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
__m256 *checkFGrad, __m256 *checkOGrad, T *cell_clip,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
*grad_og = activation(_mm256_mul_ps(*output_grad, *state_atv), *value_og,
active_gate);
if (*cell_clip > 0.0f) {
T *state_ = reinterpret_cast<T *>(state);
if (*state_ >= (*cell_clip) || *state_ <= (0.0f - (*cell_clip))) {
*state_grad = _mm256_set1_ps(0.0f);
} else {
*state_grad =
_mm256_add_ps(activation(_mm256_mul_ps(*output_grad, *value_og),
*state_atv, active_state),
*state_grad);
*state_grad = _mm256_add_ps(_mm256_mul_ps(*grad_og, *checkO), *state_grad);
*state_grad =
_mm256_add_ps(_mm256_mul_ps(*grad_og, *checkO), *state_grad);
}
}
*grad_in = activation(_mm256_mul_ps(*state_grad, *value_ig), *value_in,
active_node);
*grad_ig = activation(_mm256_mul_ps(*state_grad, *value_in), *value_ig,
......
paddle/fluid/operators/math/lstm_compute.cc
浏览文件 @ a3f7ebd6
......@@ -24,12 +24,12 @@ template <class T>
struct LstmUnitFunctor<platform::CPUDeviceContext, T> {
static void compute(const platform::CPUDeviceContext& context,
LstmMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType& gate_act,
T cell_clip, const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::cpu_lstm_forward(detail::forward::lstm<T>(), value, frame_size,
cand_act, gate_act, cell_act);
cell_clip, cand_act, gate_act, cell_act);
value.gate_value += frame_size * 4;
value.state_value += frame_size;
value.state_active_value += frame_size;
......@@ -45,13 +45,14 @@ template <class T>
struct LstmUnitGradFunctor<platform::CPUDeviceContext, T> {
static void compute(const platform::CPUDeviceContext& context,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::cpu_lstm_backward(detail::backward::lstm<T>(), value, grad,
frame_size, cand_act, gate_act, cell_act);
frame_size, cell_clip, cand_act, gate_act,
cell_act);
value.gate_value += frame_size * 4;
value.state_value += frame_size;
......
paddle/fluid/operators/math/lstm_compute.cu
浏览文件 @ a3f7ebd6
......@@ -24,12 +24,12 @@ template <class T>
struct LstmUnitFunctor<platform::CUDADeviceContext, T> {
static void compute(const platform::CUDADeviceContext& context,
LstmMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType& gate_act,
T cell_clip, const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
detail::gpu_lstm_forward<T>(context, detail::forward::lstm<T>(), value,
frame_size, batch_size, cand_act, gate_act,
cell_act);
frame_size, batch_size, cell_clip, cand_act,
gate_act, cell_act);
}
};
......@@ -37,13 +37,13 @@ template <class T>
struct LstmUnitGradFunctor<platform::CUDADeviceContext, T> {
static void compute(const platform::CUDADeviceContext& context,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
detail::gpu_lstm_backward(context, detail::backward::lstm<T>(), value, grad,
frame_size, batch_size, cand_act, gate_act,
cell_act);
frame_size, batch_size, cell_clip, cand_act,
gate_act, cell_act);
}
};
......
paddle/fluid/operators/math/lstm_compute.h
浏览文件 @ a3f7ebd6
......@@ -50,7 +50,7 @@ template <typename DeviceContext, typename T>
class LstmUnitFunctor {
public:
static void compute(const DeviceContext &context, LstmMetaValue<T> value,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType &gate_act,
const detail::ActivationType &cell_act,
const detail::ActivationType &cand_act);
......@@ -61,7 +61,7 @@ class LstmUnitGradFunctor {
public:
static void compute(const DeviceContext &context, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size, int batch_size,
const detail::ActivationType &gate_act,
T cell_clip, const detail::ActivationType &gate_act,
const detail::ActivationType &cell_act,
const detail::ActivationType &cand_act);
};
......
paddle/fluid/operators/math/sample_prob.cc 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/sample_prob.h"
namespace paddle {
namespace operators {
namespace math {
template class SampleWithProb<platform::CPUDeviceContext, float>;
template class SampleWithProb<platform::CPUDeviceContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sample_prob.cu 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <thrust/random.h>
#include <thrust/sort.h>
#include <iostream>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/sampler.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
template <typename T>
__device__ T gpu_adjust_prob(const T prob, const int num_samples,
const int num_tries) {
if (num_samples == num_tries) {
return prob * num_samples;
} else {
return -expm1(num_tries * log1p(-prob));
}
}
class GPULogUniformSampler {
public:
__device__ int64_t Sample(float random, const int range,
const float log_range) const;
__device__ float Probability(int64_t value, const float log_range) const;
};
__device__ int64_t GPULogUniformSampler::Sample(float random, const int range,
const float log_range) const {
// Got Log Uniform distribution from uniform distribution by
// inverse_transform_sampling method
const int64_t value = static_cast<int64_t>(exp(random * log_range)) - 1;
// Mathematically, value should be <= range_, but might not be due to some
// floating point roundoff, so we mod by range_.
return value % range;
}
__device__ float GPULogUniformSampler::Probability(
int64_t value, const float log_range) const {
// Given f(x) = 1/[(x+1) * log_range_]
// The value's probability is integral of f(x) from value to (value + 1)
return (log((value + 2.0) / (value + 1.0))) / log_range;
}
template <typename T>
__global__ void SamplingCondidate(
const size_t n, const int num_tries, const int range, const float log_range,
const int num_true, const std::size_t num_samples,
const int64_t* label_data, int64_t* samples_data, T* probabilities_data) {
const int num_sampled_classes = num_true + num_samples;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = 0;
GPULogUniformSampler sampler;
for (; idx < n; idx += blockDim.x * gridDim.x) {
int col_idx = idx % num_sampled_classes;
int row_idx = idx / num_sampled_classes;
if (col_idx < num_true) {
samples_data[idx] = label_data[row_idx * num_true + col_idx];
} else {
samples_data[idx] = samples_data[col_idx];
}
probabilities_data[idx] = sampler.Probability(samples_data[idx], log_range);
probabilities_data[idx] =
gpu_adjust_prob(probabilities_data[idx], num_samples, num_tries);
}
}
template <typename T>
int UniqSampler(const Sampler& sampler, const std::size_t num_samples,
int64_t* samples_data) {
// sample num_samles unique samples for an example, note that they are not
// all negative samples
std::unordered_set<int64_t> tmp_samples;
tmp_samples.clear();
int num_tries = 0;
int j = 0;
while (j < num_samples) {
++num_tries;
auto v = sampler.Sample();
auto insert_ok = tmp_samples.insert(v).second;
if (!insert_ok) {
continue;
}
samples_data[j] = v;
++j;
}
return num_tries;
}
template <typename T>
void GPUSampleWithProb<T>::operator()(
const platform::CUDADeviceContext& context, const int seed,
const int dict_size, const bool uniq, const std::size_t num_samples,
const Tensor* L, Tensor* S, Tensor* P) {
// UNDERSTAND: dimension issues
const auto lbl_dim = L->dims();
const int batch_size = lbl_dim[0];
const int num_true = lbl_dim[1];
const int num_sampled_classes = num_true + num_samples;
framework::DDim ret_dim{batch_size, num_sampled_classes};
// UNDERSTAND: raw data view
const int64_t* label_data = L->data<int64_t>();
int64_t* samples_data = S->data<int64_t>();
T* probabilities_data = P->data<T>();
int s_size = num_samples;
framework::DDim s_dim{s_size};
Tensor s;
int64_t* s_data = s.mutable_data<int64_t>(s_dim, platform::CPUPlace());
math::LogUniformSampler sampler(dict_size, seed);
int range = dict_size;
float log_range = log(range + 1);
int num_tries = UniqSampler<T>(sampler, num_samples, s_data);
VLOG(1) << "num_tries: " << num_tries;
PADDLE_ENFORCE(cudaMemcpy(samples_data + num_true, s_data,
sizeof(int64_t) * num_samples,
cudaMemcpyHostToDevice));
int threads = 512;
const size_t size = batch_size * num_sampled_classes;
int grid = (batch_size * num_sampled_classes + threads - 1) / threads;
SamplingCondidate<T><<<grid, threads, 0, context.stream()>>>(
size, num_tries, range, log_range, num_true, num_samples, label_data,
samples_data, probabilities_data);
}
template class GPUSampleWithProb<float>;
template class GPUSampleWithProb<double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sample_prob.h 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <iostream>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/sampler.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
/* UNDERSTAND: utility function to adjust probability for unique sampling,
return whatever as it is if not using unique samping */
template <typename T>
static T adjust_prob(const T prob, const int num_samples, const int num_tries) {
if (num_samples == num_tries) {
return prob * num_samples;
} else {
return -expm1(num_tries * log1p(-prob));
}
}
template <typename DeviceContext, typename T>
class SampleWithProb {
public:
void operator()(const DeviceContext& context, const Sampler& sampler,
const std::size_t num_samples, const Tensor* L, Tensor* S,
Tensor* P) {
// UNDERSTAND: dimension issues
const auto lbl_dim = L->dims();
const int batch_size = lbl_dim[0];
const int num_true = lbl_dim[1];
const int num_sampled_classes = num_true + num_samples;
framework::DDim ret_dim{batch_size, num_sampled_classes};
// UNDERSTAND: raw data view
const int64_t* label_data = L->data<int64_t>();
int64_t* samples_data =
S->mutable_data<int64_t>(ret_dim, context.GetPlace());
T* probabilities_data = P->mutable_data<T>(ret_dim, context.GetPlace());
// temp sets for unique sampling
std::unordered_set<int64_t> tmp_samples;
int j = 0; // column index
// add true labels, not that efficient
while (j < num_true) {
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + j;
auto v = label_data[i * num_true + j];
samples_data[samples_index] = v;
probabilities_data[samples_index] = sampler.Probability(v);
}
++j;
}
// sample num_samles unique samples for an example, note that they are not
// all negative samples
tmp_samples.clear();
int num_tries = 0;
while (j < num_sampled_classes) {
++num_tries;
auto v = sampler.Sample();
auto insert_ok = tmp_samples.insert(v).second;
if (!insert_ok) {
continue;
}
auto p = sampler.Probability(v);
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + j;
samples_data[samples_index] = v;
probabilities_data[samples_index] = p;
}
++j;
}
// compute Q(y|x), because of unique sampling, probabilities need to be
// adjusted
for (int k = 0; k < num_sampled_classes; ++k) {
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + k;
probabilities_data[samples_index] = adjust_prob(
probabilities_data[samples_index], num_samples, num_tries);
}
}
}
};
#ifdef PADDLE_WITH_CUDA
template <typename T>
class GPUSampleWithProb {
public:
void operator()(const platform::CUDADeviceContext& context, const int seed,
const int dict_size, const bool uniq,
const std::size_t num_samples, const Tensor* L, Tensor* S,
Tensor* P);
};
#endif
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
浏览文件 @ a3f7ebd6
......@@ -225,7 +225,7 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find src_memory in device context");
src_memory->set_data_handle(*p_src_data.get());
src_memory->set_data_handle(*p_src_data);
std::shared_ptr<memory> diff_src_memory;
......
paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc
浏览文件 @ a3f7ebd6
......@@ -198,7 +198,7 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
std::vector<mkldnn::primitive> pipeline{*pool_p};
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
......@@ -367,8 +367,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
dev_ctx.SetBlob(key_pool_diff_dst_mem_p, diff_dst_memory);
pool_bwd_p = std::make_shared<pooling_backward>(
pool_bwd_pd, *(diff_dst_memory.get()), *workspace_memory,
*(diff_src_memory));
pool_bwd_pd, *diff_dst_memory, *workspace_memory, *diff_src_memory);
dev_ctx.SetBlob(key_pool_bwd_p, pool_bwd_p);
} else {
......@@ -404,7 +403,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
if (is_diff_dst_reordered) {
pipeline.push_back(reorder_diff_dst);
}
pipeline.push_back(*(pool_bwd_p.get()));
pipeline.push_back(*pool_bwd_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
in_x_grad->set_layout(DataLayout::kMKLDNN);
......
paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
浏览文件 @ a3f7ebd6
......@@ -66,8 +66,7 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
"Fail to find softmax primitive in device context");
if (softmax_p == nullptr) {
softmax_p = std::make_shared<mkldnn::softmax_forward>(
*(softmax_pd_.get()),
*(static_cast<mkldnn::memory*>(src_memory_p.get())),
*softmax_pd_, *(static_cast<mkldnn::memory*>(src_memory_p.get())),
*(static_cast<mkldnn::memory*>(dst_memory_p.get())));
dev_ctx_.SetBlob(prim_key, softmax_p);
} else {
......@@ -88,8 +87,8 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
"Fail to find softmax backward primitive in device context");
if (softmax_bwd_p == nullptr) {
softmax_bwd_p = std::make_shared<mkldnn::softmax_backward>(
*softmax_bwd_pd_, *(dst_memory_p.get()), *(diff_dst_memory_p.get()),
*(diff_src_memory_p.get()));
*softmax_bwd_pd_, *dst_memory_p, *diff_dst_memory_p,
*diff_src_memory_p);
dev_ctx_.SetBlob(prim_key, softmax_bwd_p);
} else {
is_reusing_ = true;
......
paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc
浏览文件 @ a3f7ebd6
......@@ -160,7 +160,7 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto get_selected_row = [&](size_t i) -> const SelectedRows& {
if (i == 0 && in0) {
return *in0.get();
return *in0;
} else {
return in_vars[i]->Get<SelectedRows>();
}
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ a3f7ebd6
......@@ -262,28 +262,37 @@ Example:
For exclusive = false:
$$
hstart = i * strides[0] - paddings[0]
$$
$$
hend = hstart + ksize[0]
$$
$$
wstart = j * strides[1] - paddings[1]
$$
$$
wend = wstart + ksize[1]
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = true:
$$
hstart = max(0, i * strides[0] - paddings[0])
$$
$$
hend = min(H, hstart + ksize[0])
$$
$$
wstart = max(0, j * strides[1] - paddings[1])
$$
$$
wend = min(W, wstart + ksize[1])
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
hstart = floor(i * H_{in} / H_{out})
hend = ceil((i + 1) * H_{in} / H_{out})
wstart = floor(j * W_{in} / W_{out})
wend = ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC");
}
......@@ -393,45 +402,65 @@ Example:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
For ceil_mode = false:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[2]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For ceil_mode = true:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = false:
$$
dstart = i * strides[0] - paddings[0]
$$
$$
dend = dstart + ksize[0]
$$
$$
hstart = j * strides[1] - paddings[1]
$$
$$
hend = hstart + ksize[1]
$$
$$
wstart = k * strides[2] - paddings[2]
$$
$$
wend = wstart + ksize[2]
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = true:
$$
dstart = max(0, i * strides[0] - paddings[0])
$$
$$
dend = min(D, dstart + ksize[0])
hstart = max(0, j * strides[1] - paddings[1])
$$
$$
hend = min(H, hstart + ksize[1])
$$
$$
wstart = max(0, k * strides[2] - paddings[2])
$$
$$
wend = min(W, wstart + ksize[2])
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
dstart = floor(i * D_{in} / D_{out})
dend = ceil((i + 1) * D_{in} / D_{out})
hstart = floor(j * H_{in} / H_{out})
hend = ceil((j + 1) * H_{in} / H_{out})
wstart = floor(k * W_{in} / W_{out})
wend = ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
......
paddle/fluid/operators/sample_logits_op.cc 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sample_logits_op.h"
#include "paddle/fluid/operators/math/sample_prob.h"
namespace paddle {
namespace operators {
class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Logits",
"(Tensor, default: Tensor<float>), The unscaled log probabilities "
"which is a 2-D tensor with shape [N x K]. N is the batch_size, "
"and K is the class number.");
AddInput("Labels",
"(Tensor) The ground truth which is a 2-D tensor. Labels is a "
"Tensor<int64> with shape [N x NT], where NT is the number of"
"true labels for each example.");
AddInput("CustomizedSamples",
"(Tensor, default: Tensor<int64_t>), A 2-D tensor with shape [N, "
"NT + S],"
" where N is the batch size, NT is the number of true labels "
"and S is the number of negtive sample for each example."
"The first NT elements of each row should be the same with true "
"labels, "
"followed by S custom negtive samples. This tensor"
"is only used when use_customized_samples is true.")
.AsDispensable();
AddInput(
"CustomizedProbabilities",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The tensor has the same shape with CustomSamples,"
"and each element represents probability of element in CustomSamples. "
"This "
"tensor is only used when use_customized_samples is true.")
.AsDispensable();
AddOutput("Samples",
"(Tensor, default: Tensor<int64_t>), A 2-D tensor with shape [N, "
"NT + S]."
"The outputs value of sampler, including NT true lables and S "
"negetive samples "
"for each example. This will be used in"
"backward calculation.")
.AsIntermediate();
AddOutput(
"Probabilities",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The probabilites of sampled positive and negtive labels.")
.AsIntermediate();
AddOutput("SampledLogits",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape"
"[N, NT + S]. The outputs value of sampled logits, which will be"
"used in backward propagation.")
.AsIntermediate();
AddOutput(
"SampledLabels",
"(Tensor, default: Tensor<int64>), A 2-D tensor. The sampled labels"
"with shape [N, NT]. The tonsor contains hard labels as input to "
" softmax op, that is 0, 1, ..., NT-1 because of the first NT elements"
" of Sampels are positive lables.");
AddAttr<bool>(
"use_customized_samples",
"An indicator whether to use customized samples with probabilities, if "
"True"
"the operator will use customized samples and customized probabilities"
"otherwise, the operator will generate them by itself.")
.SetDefault(false);
AddAttr<bool>(
"uniq",
"An indicator whether to sample non-repetitive negtive labels, if True"
"the operator will sample negtive labels without replacement."
"Otherwise, the operator will sample negtive labels with replacement.")
.SetDefault(true);
AddAttr<bool>(
"remove_accidental_hits",
"An indicator whether to remove accidental hits when samples hits true"
"labels, the removal is implemented by subtracting the corresponding"
"logits by float_max to subpress their softmax to be zero.")
.SetDefault(true);
AddAttr<int>("num_samples", "The number of negative samples.");
AddAttr<int>("seed", "Random seed for generating samples").SetDefault(0);
AddComment(R"DOC(
"""
Computes sampled output training logits and labels suitable for implementing
sampled softmax.
"""
)DOC");
}
};
class SampleLogitsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Samples"),
"Output(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Probabilities"),
"Output(Probabilities) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLogits"),
"Output(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
"Output(SampledLabels) should be not null.");
auto logits_dims = ctx->GetInputDim("Logits");
auto labels_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(
logits_dims.size(), 2UL,
"The logits of softmax_with_cross_entropy should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
"The labels should be a 2-D tensor.");
const int num_samples = ctx->Attrs().Get<int>("num_samples");
const int num_sampled_classes = labels_dims[1] + num_samples;
ctx->SetOutputDim("Samples", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLabels", {logits_dims[0], labels_dims[1]});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Logits"));
framework::OpKernelType kt =
framework::OpKernelType(data_type, ctx.device_context());
return kt;
}
};
// UNDERSTAND: InferShape for Grad
class SampleLogitsOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Samples"),
"Input(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
"Input(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
"Input(SampledLogits@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
"Output(Logits@Grad) should be not null.");
auto logit_dims = ctx->GetInputDim("Logits");
auto label_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"The label should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
"The logits should be a 2-D tensor.");
ctx->SetOutputDim(framework::GradVarName("Logits"),
ctx->GetInputDim("Logits"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(
ctx.InputVar(framework::GradVarName("SampledLogits")));
framework::OpKernelType kt =
framework::OpKernelType(data_type, ctx.device_context());
return kt;
}
};
// UNDERSTAND: what's the rule for making a GradMaker TODO
class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("sample_logits_grad");
grad_op->SetInput("Logits", Input("Logits"));
grad_op->SetInput("Labels", Input("Labels"));
grad_op->SetInput("Samples", Output("Samples"));
grad_op->SetInput("SampledLogits", Output("SampledLogits"));
grad_op->SetInput(framework::GradVarName("SampledLogits"),
OutputGrad("SampledLogits"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sample_logits, ops::SampleLogitsOp, ops::SampleLogitsOpMaker,
ops::SampleLogitsGradMaker);
REGISTER_OPERATOR(sample_logits_grad, ops::SampleLogitsOpGrad);
REGISTER_OP_CPU_KERNEL(sample_logits, ops::SampleLogitsKernel<float>,
ops::SampleLogitsKernel<double>);
REGISTER_OP_CPU_KERNEL(sample_logits_grad, ops::SampleLogitsGradKernel<float>,
ops::SampleLogitsGradKernel<double>);
paddle/fluid/operators/sample_logits_op.cu 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/softmax.h"
#include "paddle/fluid/operators/sample_logits_op.h"
namespace paddle {
namespace operators {
// UNDERSTAND: something like take_along_axis in numpy.
template <typename T>
__global__ void GPUTakeAlongD1(size_t size, const int batch_size,
const int array_slice_size,
const int idx_slice_size, const T* p_array,
const int64_t* p_index, T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
int i = idx / idx_slice_size;
auto array_index = p_index[idx];
p_value[idx] = p_array[i * array_slice_size + array_index];
}
}
// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
// indices, scatter is done in += way.
template <typename T>
__global__ void GPUPutAlongD1(size_t size, const int batch_size,
const int array_slice_size,
const int idx_slice_size, T* p_array,
const int64_t* p_index, const T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
// size == batch_size
for (; idx < size; idx += step_size) {
int i = idx;
for (int j = 0; j < idx_slice_size; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_array[i * array_slice_size + array_index] +=
p_value[i * idx_slice_size + j];
}
}
}
// UNDERSTAND: set label as 0,1,...,num_true-1
template <typename T>
__global__ void GPUSetLabel(size_t size, const int num_true, int64_t* p_array) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
p_array[idx] = idx % num_true;
}
}
// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
// logits by a float max, here 1e20
template <typename T>
__global__ void gpu_compute_remove_accidental_hits(const int size,
const int num_true,
const int idx_slice_size,
const int64_t* p_index,
T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
int i = idx / idx_slice_size;
if (idx % idx_slice_size < num_true) continue;
for (int j = 0; j < num_true; ++j) {
const auto true_idx = i * idx_slice_size + j;
if (p_index[true_idx] == p_index[idx]) {
p_value[idx] -= 1e20;
break;
}
}
}
}
template <typename T>
class SampleLogitsCUDAKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
// get necessary inputs
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Labels");
VLOG(3) << "Enter SampleLogitsCUDAKernel";
// get necessary outputs
Tensor* samples = context.Output<Tensor>("Samples");
Tensor* probabilities = context.Output<Tensor>("Probabilities");
Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
Tensor* sampled_labels = context.Output<Tensor>("SampledLabels");
// shapes
const auto batch_size = logits->dims()[0];
const auto num_classes = logits->dims()[1];
const auto labels_dim = labels->dims();
const auto num_true = labels_dim[1];
const auto samples_dim = samples->dims();
// attrs
const auto num_samples = context.Attr<int>("num_samples");
const bool use_customized_samples =
context.Attr<bool>("use_customized_samples");
const bool uniq = context.Attr<bool>("uniq");
const bool remove_accidental_hits =
context.Attr<bool>("remove_accidental_hits");
// device contexts
auto& dev_ctx = context.cuda_device_context();
// UNDERSTAND: allocate memories for temporaries
sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, sampled_logits, static_cast<T>(0));
auto sampled_labels_data =
sampled_labels->mutable_data<int64_t>(labels_dim, context.GetPlace());
int threads = 512;
size_t size = batch_size * num_true;
int grid = (size + threads - 1) / threads;
GPUSetLabel<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, num_true, sampled_labels_data);
if (use_customized_samples) {
const Tensor* customized_samples =
context.Input<Tensor>("CustomizedSamples");
const Tensor* customized_probabilities =
context.Input<Tensor>("CustomizedProbabilities");
samples->ShareDataWith(*customized_samples);
probabilities->ShareDataWith(*customized_probabilities);
} else {
samples->mutable_data<int64_t>(context.GetPlace());
probabilities->mutable_data<T>(samples_dim, context.GetPlace());
// UNDERSTAND: sampling
const auto seed = context.Attr<int>("seed");
auto sampler_with_prob = math::GPUSampleWithProb<T>();
sampler_with_prob(context.cuda_device_context(), seed, num_classes, uniq,
num_samples, labels, samples, probabilities);
}
// UNDERSTAND: gather sampled logits and remove accidental hits if needed
const auto num_take = samples->dims()[1];
const auto array_dims = logits->dims();
const auto idx_dims = samples->dims();
const T* p_array = logits->data<T>();
const int64_t* p_index = samples->data<int64_t>();
T* p_value = sampled_logits->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
size = batch_size * num_take;
grid = (size + threads - 1) / threads;
GPUTakeAlongD1<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
p_value);
if (remove_accidental_hits) {
const size_t size = batch_size * (num_true + num_samples);
int grid = (size + threads - 1) / threads;
gpu_compute_remove_accidental_hits<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, num_true, idx_slice_size, p_index, p_value);
}
// subtracted sampled logits with logQ(y|x)
auto probs = EigenMatrix<T>::From(*probabilities);
auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
smp_logits.device(*dev_ctx.eigen_device()) =
(smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
.unaryExpr(TolerableValue<T>());
}
};
template <typename T>
class SampleLogitsGradCUDAKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* samples = context.Input<Tensor>("Samples");
const Tensor* sampled_logits_grad =
context.Input<Tensor>(framework::GradVarName("SampledLogits"));
logits_grad->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.cuda_device_context();
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, logits_grad, static_cast<T>(0));
// UNDERSTAND: scatter it back to logit_grad
const auto batch_size = samples->dims()[0];
const auto num_put = samples->dims()[1];
const auto array_dims = logits_grad->dims();
const auto idx_dims = samples->dims();
T* p_array = logits_grad->data<T>();
const int64_t* p_index = samples->data<int64_t>();
const T* p_value = sampled_logits_grad->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
int threads = 128;
const size_t size = batch_size;
int grid = (size + threads - 1) / threads;
GPUPutAlongD1<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
p_value);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(sample_logits, ops::SampleLogitsCUDAKernel<float>,
ops::SampleLogitsCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(sample_logits_grad,
ops::SampleLogitsGradCUDAKernel<float>,
ops::SampleLogitsGradCUDAKernel<double>);
paddle/fluid/operators/sample_logits_op.h 0 → 100644
浏览文件 @ a3f7ebd6
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/softmax.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct TolerableValue {
HOSTDEVICE T operator()(const T& x) const {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) return kApproInf;
if (x == -INFINITY) return -kApproInf;
return x;
}
};
// UNDERSTAND: something like take_along_axis in numpy.
template <typename T>
static void CPUTakeAlongD1(const platform::DeviceContext& ctx,
const framework::Tensor& array,
const framework::Tensor& index,
framework::Tensor* value) {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
// UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
PADDLE_ENFORCE(index.dims().size() == 2 && array.dims().size() == 2 &&
index.dims()[0] == array.dims()[0] &&
index.dims() == value->dims());
const auto batch_size = index.dims()[0];
const auto num_take = index.dims()[1];
const auto array_dims = array.dims();
const auto idx_dims = index.dims();
// UNDERSTAND: no allocations here
const T* p_array = array.data<T>();
const int64_t* p_index = index.data<int64_t>();
T* p_value = value->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
const auto value_slice_size = idx_slice_size;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_take; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_value[i * value_slice_size + j] =
p_array[i * array_slice_size + array_index];
}
}
}
// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
// indices, scatter is done in += way.
template <typename T>
static void CPUPutAlongD1(const platform::DeviceContext& ctx,
framework::Tensor* array,
const framework::Tensor& index,
const framework::Tensor& value) {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
// UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
PADDLE_ENFORCE(index.dims().size() == 2 && array->dims().size() == 2 &&
index.dims()[0] == array->dims()[0] &&
index.dims() == value.dims());
const auto batch_size = index.dims()[0];
const auto num_put = index.dims()[1];
auto array_dims = array->dims();
auto idx_dims = index.dims();
// UNDERSTAND: no allocations here
T* p_array = array->data<T>();
const int64_t* p_index = index.data<int64_t>();
const T* p_value = value.data<T>();
// slice sizes
const auto array_slice_size = array_dims[1];
const auto idx_slice_size = idx_dims[1];
const auto value_slice_size = idx_slice_size;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_put; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_array[i * array_slice_size + array_index] +=
p_value[i * value_slice_size + j];
}
}
}
// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
// logits by a float max, here 1e20
template <typename T>
static void compute_remove_accidental_hits(const platform::DeviceContext& ctx,
framework::Tensor* sampled_logits,
const framework::Tensor& samples,
const int num_true) {
const auto batch_size = sampled_logits->dims()[0];
const auto num_sampled_classes = sampled_logits->dims()[1];
T* sampled_logits_data = sampled_logits->data<T>();
const auto samples_data = samples.data<int64_t>();
std::unordered_set<int64_t> tmp_true_labels;
for (int i = 0; i < batch_size; ++i) {
tmp_true_labels.clear();
tmp_true_labels.insert(samples_data + i * num_sampled_classes,
samples_data + i * num_sampled_classes + num_true);
for (int j = num_true; j < num_sampled_classes; ++j) {
const auto idx = i * num_sampled_classes + j;
if (tmp_true_labels.find(samples_data[idx]) != tmp_true_labels.end())
sampled_logits_data[idx] -= 1e20;
}
}
}
template <typename T>
class SampleLogitsKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
"This kernel only runs on CPU.");
VLOG(3) << "Enter SampleLogitsKernel";
// get necessary inputs
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Labels");
// get necessary outputs
Tensor* samples = context.Output<Tensor>("Samples");
Tensor* probabilities = context.Output<Tensor>("Probabilities");
Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
Tensor* sampled_labels = context.Output<Tensor>("SampledLabels");
// shapes
const auto batch_size = logits->dims()[0];
const auto num_classes = logits->dims()[1];
const auto labels_dim = labels->dims();
const auto num_true = labels_dim[1];
const auto samples_dim = samples->dims();
// attrs
const auto num_samples = context.Attr<int>("num_samples");
const bool use_customized_samples =
context.Attr<bool>("use_customized_samples");
const bool remove_accidental_hits =
context.Attr<bool>("remove_accidental_hits");
// device contexts
auto& dev_ctx =
context.template device_context<platform::CPUDeviceContext>();
// UNDERSTAND: allocate memories for temporaries
sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
auto sampled_labels_data =
sampled_labels->mutable_data<int64_t>(labels_dim, context.GetPlace());
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_true; ++j) {
sampled_labels_data[i * num_true + j] = j;
}
}
if (use_customized_samples) {
const Tensor* customized_samples =
context.Input<Tensor>("CustomizedSamples");
const Tensor* customized_probabilities =
context.Input<Tensor>("CustomizedProbabilities");
samples->ShareDataWith(*customized_samples);
probabilities->ShareDataWith(*customized_probabilities);
} else {
samples->mutable_data<int64_t>(context.GetPlace());
probabilities->mutable_data<T>(samples_dim, context.GetPlace());
// UNDERSTAND: sampling
const auto seed = context.Attr<int>("seed");
auto sampler_with_prob =
math::SampleWithProb<platform::CPUDeviceContext, T>();
sampler_with_prob(dev_ctx, math::LogUniformSampler(num_classes, seed),
num_samples, labels, samples, probabilities);
}
// UNDERSTAND: gather sampled logits and remove accidental hits if needed
CPUTakeAlongD1<T>(dev_ctx, *logits, *samples, sampled_logits);
if (remove_accidental_hits) {
compute_remove_accidental_hits<T>(dev_ctx, sampled_logits, *samples,
num_true);
}
// subtracted sampled logits with logQ(y|x)
auto probs = EigenMatrix<T>::From(*probabilities);
auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
smp_logits.device(*dev_ctx.eigen_device()) =
(smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
.unaryExpr(TolerableValue<T>());
}
};
template <typename T>
class SampleLogitsGradKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* samples = context.Input<Tensor>("Samples");
const Tensor* sampled_logits_grad =
context.Input<Tensor>(framework::GradVarName("SampledLogits"));
logits_grad->mutable_data<T>(context.GetPlace());
auto& dev_ctx =
context.template device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, T> set_zero;
set_zero(dev_ctx, logits_grad, static_cast<T>(0));
// UNDERSTAND: scatter it back to logit_grad
CPUPutAlongD1<T>(dev_ctx, logits_grad, *samples, *sampled_logits_grad);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/platform/device_context.cc
浏览文件 @ a3f7ebd6
......@@ -394,7 +394,7 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
int tid = platform::get_cur_thread_id();
std::lock_guard<std::mutex> lock(*p_mutex_.get());
std::lock_guard<std::mutex> lock(*p_mutex_);
// Find KeyBlob for current thread
auto map_it = pMap->find(tid);
......@@ -427,7 +427,7 @@ std::shared_ptr<void> MKLDNNDeviceContext::GetBlob(
int tid = platform::get_cur_thread_id();
std::lock_guard<std::mutex> lock(*p_mutex_.get());
std::lock_guard<std::mutex> lock(*p_mutex_);
// Find KeyBlob for current thread firstly
auto map_it = pMap->find(tid);
......
paddle/fluid/platform/device_tracer.cc
浏览文件 @ a3f7ebd6
......@@ -136,7 +136,7 @@ void EnableActivity() {
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DRIVER));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_RUNTIME));
// We don't track these activities for now.
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_OVERHEAD));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DEVICE));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_CONTEXT));
......@@ -155,7 +155,7 @@ void DisableActivity() {
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_CONTEXT));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_DRIVER));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_RUNTIME));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_NAME));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MARKER));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_OVERHEAD));
......@@ -212,6 +212,14 @@ void CUPTIAPI bufferCompleted(CUcontext ctx, uint32_t streamId, uint8_t *buffer,
memcpy->correlationId, memcpy->bytes);
break;
}
case CUPTI_ACTIVITY_KIND_MEMSET: {
auto *memset =
reinterpret_cast<const CUpti_ActivityMemset *>(record);
tracer->AddKernelRecords("MEMSET", memset->start, memset->end,
memset->deviceId, memset->streamId,
memset->correlationId);
break;
}
case CUPTI_ACTIVITY_KIND_DRIVER: {
auto *api = reinterpret_cast<const CUpti_ActivityAPI *>(record);
if (api->start != 0 && api->end != 0)
......@@ -348,6 +356,8 @@ class DeviceTracerImpl : public DeviceTracer {
const std::vector<int> cbids {
CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyAsync_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemset_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemsetAsync_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaLaunch_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000
#if CUDA_VERSION >= 9000
......
paddle/fluid/platform/dynload/mklml.h
浏览文件 @ a3f7ebd6
......@@ -86,8 +86,6 @@ extern void* mklml_dso_handle;
__macro(vdPowx); \
__macro(vsInv); \
__macro(vdInv); \
__macro(vmsErf); \
__macro(vmdErf); \
__macro(MKL_Set_Num_Threads)
MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ a3f7ebd6
......@@ -548,9 +548,8 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
"Fail to find convolution primitive in device context");
if (conv_p == nullptr) {
conv_p = std::make_shared<forward_t>(*conv_pd_, *(src_memory_p),
*(weights_memory_p.get()),
*(dst_memory_p.get()));
conv_p = std::make_shared<forward_t>(*conv_pd_, *src_memory_p,
*weights_memory_p, *dst_memory_p);
dev_ctx_.SetBlob(prim_key, conv_p);
} else {
......@@ -570,9 +569,9 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
"Fail to find convolution primitive in device context");
if (conv_p == nullptr) {
conv_p = std::make_shared<forward_t>(
*conv_pd_, *(src_memory_p), *(weights_memory_p.get()),
*(bias_memory_p.get()), *(dst_memory_p.get()));
conv_p = std::make_shared<forward_t>(*conv_pd_, *src_memory_p,
*weights_memory_p, *bias_memory_p,
*dst_memory_p);
dev_ctx_.SetBlob(prim_key, conv_p);
} else {
......
paddle/fluid/train/demo/demo_trainer.cc
浏览文件 @ a3f7ebd6
......@@ -73,7 +73,7 @@ int main() {
PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
// init all parameters
executor.Run(*startup_program.get(), &scope, 0);
executor.Run(*startup_program, &scope, 0);
// prepare data
auto x_var = scope.Var("x");
......@@ -101,7 +101,7 @@ int main() {
clock_t t1 = clock();
for (int i = 0; i < 10; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
executor.Run(*train_program, &scope, 0, false, true);
std::cout << "step: " << i << " loss: "
<< loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
<< std::endl;
......
paddle/fluid/train/test_train_recognize_digits.cc
浏览文件 @ a3f7ebd6
......@@ -74,7 +74,7 @@ void Train() {
float first_loss = 0.0;
float last_loss = 0.0;
for (int i = 0; i < 100; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
executor.Run(*train_program, &scope, 0, false, true);
if (i == 0) {
first_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
} else if (i == 99) {
......
python/paddle/fluid/compiler.py
浏览文件 @ a3f7ebd6
......@@ -19,6 +19,7 @@ import sys
from .. import compat as cpt
from . import core
from . import framework
__all__ = ['CompiledProgram', 'ExecutionStrategy', 'BuildStrategy']
......@@ -110,6 +111,8 @@ class CompiledProgram(object):
self._exec_strategy = ExecutionStrategy()
if self._build_strategy is None:
self._build_strategy = BuildStrategy()
self._build_strategy.is_distribution = framework.is_pserver_mode(
self._program)
return self
def with_inference_optimize(self, config):
......
python/paddle/fluid/framework.py
浏览文件 @ a3f7ebd6
......@@ -87,6 +87,15 @@ def _current_expected_place():
return _imperative_current_expected_place_
def is_pserver_mode(main_program):
main = main_program if main_program \
else default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
class NameScope(object):
def __init__(self, name="", parent=None):
self._children = dict()
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ a3f7ebd6
......@@ -17,7 +17,7 @@ import contextlib
import sys
import numpy as np
import collections
from .. import unique_name
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid.imperative import base
......@@ -26,14 +26,33 @@ __all__ = ['Layer', 'PyLayer']
class Layer(core.Layer):
"""Layers composed of operators."""
"""Layers composed of operators.
Args:
name_scope: prefix name used by the layer to name parameters.
If prefix is "my_model/layer_1", parameter name in MyLayer
can be "my_model/layer_1/MyLayer/w_n", where w is the parameter
base name and n is an unique suffix auto-generated.
dtype: data type for the variables in the layer.
"""
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
def __init__(self, name_scope, dtype=core.VarDesc.VarType.FP32):
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def full_name(self):
"""Full name for this layers.
Full name is composed by name_scope + "/" + MyLayer.__class__.__name__
Returns full name of this name.
"""
return self._full_name
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
......
python/paddle/fluid/imperative/nn.py
浏览文件 @ a3f7ebd6
......@@ -27,6 +27,7 @@ __all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding']
class Conv2D(layers.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -38,19 +39,17 @@ class Conv2D(layers.Layer):
act=None,
param_attr=None,
bias_attr=None,
name=None,
dtype=core.VarDesc.VarType.FP32):
assert param_attr is not False, "param_attr should not be False here."
super(Conv2D, self).__init__(name=name, dtype=dtype)
super(Conv2D, self).__init__(name_scope, dtype=dtype)
# TODO(minqiyang): Move this to the top.
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
type(self).__name__,
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
dtype=dtype,
name=name,
act=act)
self._groups = groups
......@@ -143,6 +142,7 @@ class Conv2D(layers.Layer):
class Pool2D(layers.Layer):
def __init__(self,
name_scope,
pool_size=-1,
pool_type="max",
pool_stride=1,
......@@ -151,7 +151,6 @@ class Pool2D(layers.Layer):
use_cudnn=True,
ceil_mode=False,
exclusive=True,
name=None,
dtype=core.VarDesc.VarType.FP32):
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -166,10 +165,10 @@ class Pool2D(layers.Layer):
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
super(Pool2D, self).__init__(name=name, dtype=dtype)
super(Pool2D, self).__init__(name_scope, dtype=dtype)
from ..layer_helper import LayerHelper
self._helper = LayerHelper(type(self).__name__, dtype=dtype, name=name)
self._helper = LayerHelper(self.full_name(), dtype=dtype)
self._pool_type = pool_type
self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
......@@ -205,25 +204,24 @@ class Pool2D(layers.Layer):
class FC(layers.Layer):
def __init__(self,
name_scope,
size,
param_attr=None,
bias_attr=None,
num_flatten_dims=1,
dtype=core.VarDesc.VarType.FP32,
act=None,
name=None):
super(FC, self).__init__()
act=None):
super(FC, self).__init__(name_scope)
self._size = size
self._num_flatten_dims = num_flatten_dims
self._dtype = dtype
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'FC',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
act=act,
name=name)
act=act)
def _build_once(self, input):
input_shape = input.shape
......@@ -282,6 +280,7 @@ class FC(layers.Layer):
class BatchNorm(layers.Layer):
def __init__(self,
name_scope,
num_channels,
act=None,
is_test=False,
......@@ -292,22 +291,20 @@ class BatchNorm(layers.Layer):
dtype=core.VarDesc.VarType.FP32,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=False,
fuse_with_relu=False,
use_global_stats=False):
super(BatchNorm, self).__init__()
super(BatchNorm, self).__init__(name_scope)
assert bias_attr is not False, "bias_attr should not be False in batch_norm."
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'batch_norm',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
name=name,
act=act)
if dtype == core.VarDesc.VarType.FP16:
......@@ -419,6 +416,7 @@ class Embedding(layers.Layer):
constructor.
Args:
name_scope: See base class.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
......@@ -446,6 +444,7 @@ class Embedding(layers.Layer):
"""
def __init__(self,
name_scope,
size,
is_sparse=False,
is_distributed=False,
......@@ -453,7 +452,7 @@ class Embedding(layers.Layer):
param_attr=None,
dtype='float32'):
super(Embedding, self).__init__()
super(Embedding, self).__init__(name_scope)
self._size = size
self._is_sparse = is_sparse
self._is_distributed = is_distributed
......@@ -468,7 +467,7 @@ class Embedding(layers.Layer):
assert self._is_sparse is True and self._is_distributed is False
from ..layer_helper import LayerHelper
self._helper = LayerHelper('embedding', param_attr=param_attr)
self._helper = LayerHelper(self.full_name(), param_attr=param_attr)
self._w = self._helper.create_parameter(
attr=self._param_attr,
shape=self._size,
......
python/paddle/fluid/layer_helper.py
浏览文件 @ a3f7ebd6
......@@ -34,6 +34,9 @@ class LayerHelper(object):
self.kwargs = kwargs
self.layer_type = layer_type
name = self.kwargs.get('name', None)
# TODO(panyx0718, minqiyang): imperative mode
# can not use both `layer_type` and `name`. Deprecate LayerHelper
# and write a Helper for imperative mode.
if name is None:
self.kwargs['name'] = unique_name.generate(self.layer_type)
......
python/paddle/fluid/layers/detection.py
浏览文件 @ a3f7ebd6
......@@ -551,9 +551,10 @@ def yolov3_loss(x,
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
helper = LayerHelper('yolov3_loss', **locals())
......
python/paddle/fluid/layers/nn.py
浏览文件 @ a3f7ebd6
......@@ -87,6 +87,7 @@ __all__ = [
'transpose',
'im2sequence',
'nce',
'sampled_softmax_with_cross_entropy',
'hsigmoid',
'beam_search',
'row_conv',
......@@ -668,7 +669,11 @@ def dynamic_lstmp(input,
candidate_activation='tanh',
proj_activation='tanh',
dtype='float32',
name=None):
name=None,
h_0=None,
c_0=None,
cell_clip=None,
proj_clip=None):
"""
**Dynamic LSTMP Layer**
......@@ -785,6 +790,17 @@ def dynamic_lstmp(input,
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the projection size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
cell_clip(float): If provided the cell state is clipped
by this value prior to the cell output activation.
proj_clip(float): If `num_proj > 0` and `proj_clip` is
provided, then the projected values are clipped elementwise to within
`[-proj_clip, proj_clip]`.
Returns:
tuple: A tuple of two output variable: the projection of hidden state, \
......@@ -831,25 +847,41 @@ def dynamic_lstmp(input,
batch_hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstmp',
inputs={
inputs = {
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
},
}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, proj_size), \
'The shape of h0 should be (batch_size, %d)' % proj_size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
if cell_clip:
assert cell_clip >= 0, "cell_clip should not be negtive."
if proj_clip:
assert proj_clip >= 0, "proj_clip should not be negtive."
helper.append_op(
type='lstmp',
inputs=inputs,
outputs={
'Projection': projection,
'Cell': cell,
'OrderedP0': ordered_proj0,
'BatchHidden': batch_hidden,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'cell_clip': cell_clip,
'proj_clip': proj_clip,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
......@@ -2569,7 +2601,27 @@ def adaptive_pool2d(input,
require_index=False,
name=None):
"""
${comment}
**Adaptive Pool2d Operator**
The adaptive_pool2d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(pool_size) should contain two elements which
represent height and width, respectively. Also the H and W dimensions of output(Out)
is same as Parameter(pool_size).
For average adaptive pool2d:
.. math::
hstart &= floor(i * H_{in} / H_{out})
hend &= ceil((i + 1) * H_{in} / H_{out})
wstart &= floor(j * W_{in} / W_{out})
wend &= ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
......@@ -2579,8 +2631,8 @@ def adaptive_pool2d(input,
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
require_index (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
......@@ -2661,18 +2713,42 @@ def adaptive_pool3d(input,
require_index=False,
name=None):
"""
${comment}
**Adaptive Pool3d Operator**
The adaptive_pool3d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) 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, and W is the width of the feature. Parameters(pool_size) should contain
three elements which represent height and width, respectively. Also the D, H and W
dimensions of output(Out) is same as Parameter(pool_size).
For average adaptive pool3d:
.. math::
dstart &= floor(i * D_{in} / D_{out})
dend &= ceil((i + 1) * D_{in} / D_{out})
hstart &= floor(j * H_{in} / H_{out})
hend &= ceil((j + 1) * H_{in} / H_{out})
wstart &= floor(k * W_{in} / W_{out})
wend &= ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
input tensor is NCDHW, 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.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (Depth, Height, Width).
it must contain three integers, (Depth, Height, Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
require_index (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
......@@ -2709,7 +2785,7 @@ def adaptive_pool3d(input,
name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d(
input=data,
pool_size=[3, 3],
pool_size=[3, 3, 3],
pool_type='avg')
"""
if pool_type not in ["max", "avg"]:
......@@ -5765,6 +5841,132 @@ def softmax_with_cross_entropy(logits,
return loss
def sampled_softmax_with_cross_entropy(logits,
label,
num_samples,
num_true=1,
remove_accidental_hits=True,
use_customized_samples=False,
customized_samples=None,
customized_probabilities=None,
seed=0):
"""
**Sampled Softmax With Cross Entropy Operator.**
Cross entropy loss with sampled softmax is used as the output layer for
larger output classes extensively. This operator samples a number of samples
for all examples, and computes the softmax normalized values for each
row of the sampled tensor, after which cross-entropy loss is computed.
Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of
softmax operator since that would produce incorrect results.
For examples with T true labels (T >= 1), we assume that each true label has
a probability of 1/T. For each sample, S samples are generated using a
log uniform distribution. True labels are concatenated with these samples to
form T + S samples for each example. So, assume the shape of logits is
[N x K], the shape for samples is [N x (T+S)]. For each sampled label, a
probability is calculated, which corresponds to the Q(y|x) in
[Jean et al., 2014](http://arxiv.org/abs/1412.2007).
Logits are sampled according to the sampled labels. Then if
remove_accidental_hits is True, if a sample[i, j] accidentally hits true
labels, then the corresponding sampled_logits[i, j] is minus by 1e20 to
make its softmax result close to zero. Then sampled logits are subtracted by
logQ(y|x), these sampled logits and re-indexed labels are used to compute
a softmax with cross entropy.
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
label (Variable): The ground truth which is a 2-D tensor. Label is a
Tensor<int64> with shape [N x T], where T is the number of true
labels per example.
num_samples (int): The number for each example, num_samples should be
less than the number of class.
num_true(int): The number of target classes per training example.
remove_accidental_hits (bool): A flag indicating whether to remove
accidental hits when sampling. If True and if a sample[i, j]
accidentally hits true labels, then the corresponding
sampled_logits[i, j] is minus by 1e20 to make its softmax result
close to zero. Default is True.
use_customized_samples (bool): Whether to use custom samples and probabities to sample
logits.
customized_samples (Variable): User defined samples, which is a 2-D tensor
with shape [N, T + S]. S is the num_samples, and T is the number of true
labels per example.
customized_probabilities (Variable): User defined probabilities of samples,
a 2-D tensor which has the same shape with customized_samples.
seed (int): The random seed for generating random number, which is used
in the process of sampling. Default is 0.
Returns:
Variable: Return the cross entropy loss which is a 2-D tensor with shape
[N x 1].
Examples:
.. code-block:: python
logits = fluid.layers.data(name='data', shape=[256], dtype='float32')
label = fluid.layers.data(name='label', shape=[5], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.sampled_softmax_with_cross_entropy(
logits=fc, label=label, num_samples=25)
"""
helper = LayerHelper('sample_logits', **locals())
samples = helper.create_variable_for_type_inference(dtype='int64')
probabilities = helper.create_variable_for_type_inference(
dtype=logits.dtype)
sampled_logits \
= helper.create_variable_for_type_inference(dtype=logits.dtype)
sampled_label = helper.create_variable_for_type_inference(dtype='int64')
sampled_softlabel = helper.create_variable_for_type_inference(
dtype=logits.dtype)
helper.append_op(
type='sample_logits',
inputs={
'Logits': logits,
'Labels': label,
'CustomizedSamples': customized_samples,
'CustomizedProbabilities': customized_probabilities
},
outputs={
'Samples': samples,
'Probabilities': probabilities,
'SampledLabels': sampled_label,
'SampledLogits': sampled_logits
},
attrs={
'use_customized_samples': use_customized_samples,
'uniq': True,
'remove_accidental_hits': remove_accidental_hits,
'num_samples': num_samples,
'seed': seed
})
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(
type='one_hot',
inputs={'X': sampled_label},
attrs={'depth': num_samples + 1},
outputs={'Out': sampled_softlabel})
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': sampled_logits,
'Label': sampled_softlabel},
outputs={'Softmax': softmax,
'Loss': loss},
attrs={
'soft_label': True,
'ignore_index': False,
'numeric_stable_mode': False
})
return loss / num_true
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`.
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ a3f7ebd6
......@@ -29,15 +29,6 @@ ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
def _is_pserver_mode(main_program):
main = main_program if main_program \
else framework.default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
class ParallelExecutor(object):
"""
ParallelExecutor is designed for data parallelism, which focuses on distributing
......@@ -140,7 +131,7 @@ class ParallelExecutor(object):
# FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
# num_trainers is 1, so the current fields of build_strategy doesn't tell if
# it's distributed model.
build_strategy.is_distribution = _is_pserver_mode(
build_strategy.is_distribution = framework.is_pserver_mode(
main_program) or num_trainers > 1
# step4: get main_program, scope, local_scopes
......
python/paddle/fluid/tests/unittests/test_base_layer.py
浏览文件 @ a3f7ebd6
......@@ -20,10 +20,10 @@ from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
def __init__(self, prefix):
super(L1, self).__init__(prefix)
self._helper = LayerHelper(
'MyLayer',
self.full_name(),
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -43,20 +43,20 @@ class L1(fluid.imperative.Layer):
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def __init__(self, prefix):
super(L2, self).__init__(prefix)
self.layer1 = L1(self.full_name())
self.layer2 = L1(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def __init__(self, prefix):
super(L3, self).__init__(prefix)
self.layer1 = L2(self.full_name())
self.layer2 = L2(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
......@@ -65,16 +65,23 @@ class L3(fluid.imperative.Layer):
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
l = L1('test_one_level')
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertEqual(l.w1.name, "test_one_level/L1_0_0.w_0")
self.assertEqual(l.w2.name, "test_one_level/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
l = L3('test_three_level')
names = [p.name for p in l.parameters()]
ret = l()
self.assertEqual(names[0], "test_three_level/L3_0/L2_0/L1_0_0.w_0")
self.assertEqual(names[1], "test_three_level/L3_0/L2_0/L1_0_0.w_1")
self.assertEqual(names[2], "test_three_level/L3_0/L2_0/L1_1_0.w_0")
self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1_0.w_1")
self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0_0.w_0")
self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
......
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ a3f7ebd6
......@@ -15,7 +15,6 @@
import contextlib
import unittest
import numpy as np
import sys
import paddle.fluid as fluid
from paddle.fluid import core
......@@ -24,8 +23,8 @@ from test_imperative_base import new_program_scope
class MyLayer(fluid.imperative.Layer):
def __init__(self):
super(MyLayer, self).__init__()
def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope)
def forward(self, inputs):
x = fluid.layers.relu(inputs)
......@@ -50,12 +49,14 @@ class MyPyLayer(fluid.imperative.PyLayer):
class MLP(fluid.imperative.Layer):
def __init__(self):
super(MLP, self).__init__()
self._fc1 = FC(3,
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(),
3,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self._fc2 = FC(4,
self._fc2 = FC(self.full_name(),
4,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -67,8 +68,9 @@ class MLP(fluid.imperative.Layer):
class SimpleRNNCell(fluid.imperative.Layer):
def __init__(self, step_input_size, hidden_size, output_size, param_attr):
super(SimpleRNNCell, self).__init__()
def __init__(self, name_scope, step_input_size, hidden_size, output_size,
param_attr):
super(SimpleRNNCell, self).__init__(name_scope)
self.step_input_size = step_input_size
self.hidden_size = hidden_size
self.output_size = output_size
......@@ -158,10 +160,11 @@ class SimpleRNNCell(fluid.imperative.Layer):
class SimpleRNN(fluid.imperative.Layer):
def __init__(self):
super(SimpleRNN, self).__init__()
def __init__(self, name_scope):
super(SimpleRNN, self).__init__(name_scope)
self.seq_len = 4
self._cell = SimpleRNNCell(
self.full_name(),
3,
3,
3,
......@@ -205,7 +208,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
cl = core.Layer()
cl.forward([])
l = fluid.imperative.Layer()
l = fluid.imperative.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
......@@ -281,7 +284,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
l = MyLayer()
l = MyLayer("my_layer")
x = l(var_inp)[0]
self.assertIsNotNone(x)
dy_out = x._numpy()
......@@ -291,7 +294,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer()
l = MyLayer("my_layer")
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
......@@ -309,7 +312,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
......@@ -318,7 +321,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
......@@ -334,10 +337,10 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
......@@ -353,7 +356,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy()
outs[3]._backward()
......@@ -364,7 +367,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[1, 4, 3], append_batch_size=False)
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn(inp)
param_grads = fluid.backward.append_backward(outs[3])
exe = fluid.Executor(fluid.CPUPlace())
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ a3f7ebd6
......@@ -28,10 +28,10 @@ from paddle.fluid.imperative.base import to_variable
class Discriminator(fluid.imperative.Layer):
def __init__(self):
super(Discriminator, self).__init__()
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def __init__(self, name_scope):
super(Discriminator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=32, act='elu')
self._fc2 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -39,11 +39,11 @@ class Discriminator(fluid.imperative.Layer):
class Generator(fluid.imperative.Layer):
def __init__(self):
super(Generator, self).__init__()
self._fc1 = FC(size=64, act='elu', name="g_fc1")
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def __init__(self, name_scope):
super(Generator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=64, act='elu')
self._fc2 = FC(self.full_name(), size=64, act='elu')
self._fc3 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -65,8 +65,8 @@ class TestImperativeMnist(unittest.TestCase):
scope = fluid.core.Scope()
with new_program_scope(
main=discriminate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
img = fluid.layers.data(
name="img", shape=[2, 1], append_batch_size=False)
......@@ -93,8 +93,8 @@ class TestImperativeMnist(unittest.TestCase):
sgd.minimize(d_loss)
with new_program_scope(main=generate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
noise = fluid.layers.data(
name="noise", shape=[2, 2], append_batch_size=False)
......@@ -134,8 +134,8 @@ class TestImperativeMnist(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
sgd = SGDOptimizer(learning_rate=1e-3)
d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ a3f7ebd6
......@@ -28,6 +28,7 @@ from test_imperative_base import new_program_scope
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -44,9 +45,10 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__()
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -59,6 +61,7 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -73,19 +76,20 @@ class SimpleImgConvPool(fluid.imperative.Layer):
class MNIST(fluid.imperative.Layer):
def __init__(self, param_attr=None, bias_attr=None):
super(MNIST, self).__init__()
def __init__(self, name_scope, param_attr=None, bias_attr=None):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
1, 20, 5, 2, 2, act="relu")
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
20, 50, 5, 2, 2, act="relu")
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(10,
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
......@@ -106,7 +110,7 @@ class TestImperativeMnist(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
......@@ -150,7 +154,7 @@ class TestImperativeMnist(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ a3f7ebd6
......@@ -28,12 +28,13 @@ from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
num_steps,
num_layers=2,
init_scale=0.1,
dropout=None):
super(SimpleLSTMRNN, self).__init__()
super(SimpleLSTMRNN, self).__init__(name_scope)
self._hidden_size = hidden_size
self._num_layers = num_layers
self._init_scale = init_scale
......@@ -130,13 +131,14 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
class PtbModel(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
vocab_size,
num_layers=2,
num_steps=20,
init_scale=0.1,
dropout=None):
super(PtbModel, self).__init__()
super(PtbModel, self).__init__(name_scope)
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.init_scale = init_scale
......@@ -146,12 +148,14 @@ class PtbModel(fluid.imperative.Layer):
from paddle.fluid.layer_helper import LayerHelper
self._helper = LayerHelper('PtbModel', act="tanh")
self.simple_lstm_rnn = SimpleLSTMRNN(
self.full_name(),
hidden_size,
num_steps,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
......@@ -226,6 +230,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......@@ -265,6 +270,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ a3f7ebd6
......@@ -70,15 +70,17 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__()
super(ConvBNLayer, self).__init__(name_scope)
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -88,7 +90,7 @@ class ConvBNLayer(fluid.imperative.Layer):
act=None,
bias_attr=None)
self._batch_norm = BatchNorm(num_filters, act=act)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
......@@ -98,21 +100,29 @@ class ConvBNLayer(fluid.imperative.Layer):
class BottleneckBlock(fluid.imperative.Layer):
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__()
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
......@@ -120,6 +130,7 @@ class BottleneckBlock(fluid.imperative.Layer):
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
......@@ -141,13 +152,13 @@ class BottleneckBlock(fluid.imperative.Layer):
y = fluid.layers.elementwise_add(x=short, y=conv2)
layer_helper = LayerHelper('elementwise_add_activation', act='relu')
layer_helper = LayerHelper(self.full_name(), act='relu')
return layer_helper.append_activation(y)
class ResNet(fluid.imperative.Layer):
def __init__(self, layers=50, class_dim=102):
super(ResNet, self).__init__()
def __init__(self, name_scope, layers=50, class_dim=102):
super(ResNet, self).__init__(name_scope)
self.layers = layers
supported_layers = [50, 101, 152]
......@@ -163,9 +174,18 @@ class ResNet(fluid.imperative.Layer):
num_filters = [64, 128, 256, 512]
self.conv = ConvBNLayer(
num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
......@@ -175,6 +195,7 @@ class ResNet(fluid.imperative.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
......@@ -184,12 +205,13 @@ class ResNet(fluid.imperative.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = FC(size=class_dim,
self.out = FC(self.full_name(),
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
......@@ -214,7 +236,7 @@ class TestImperativeResnet(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
import random
......@@ -275,7 +297,7 @@ class TestImperativeResnet(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ a3f7ebd6
......@@ -374,6 +374,17 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(output)
print(str(program))
def test_sampled_softmax_with_cross_entropy(self):
program = Program()
with program_guard(program):
logits = layers.data(name='Logits', shape=[256], dtype='float64')
label = layers.data(name='Label', shape=[1], dtype='int64')
num_samples = 25
output = layers.sampled_softmax_with_cross_entropy(logits, label,
num_samples)
self.assertIsNotNone(output)
print(str(program))
@decorators.prog_scope()
def test_nce(self):
window_size = 5
......
python/paddle/fluid/tests/unittests/test_lstmp_op.py
浏览文件 @ a3f7ebd6
......@@ -36,12 +36,14 @@ def lstmp(
w_b=None, # 1 x 4D
w_c=None, # 1 x 3D
is_reverse=False,
proj_clip=0.0,
cell_clip=0.0,
act_gate=None,
act_cell=None,
act_cand=None,
act_proj=None):
def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand,
act_proj):
def _step(x, w_r, w_rh, w_c, r_pre, c_pre, proj_clip, cell_clip, act_gate,
act_cell, act_cand, act_proj):
g = np.dot(r_pre, w_r) # 1 x 4D
g = g + x
g = np.reshape(g, (1, g.size))
......@@ -55,6 +57,17 @@ def lstmp(
g_f = act_gate(g_f + w_fc * c_pre) # 1 x D
c = g_f * c_pre + g_i * act_cand(c) # 1 x D
def array_clip(a, clip):
size = np.prod(a.shape)
new_a = np.reshape(a, (size))
for i in range(size):
new_a[i] = max(new_a[i], -1.0 * clip)
new_a[i] = min(new_a[i], clip)
new_a = np.reshape(new_a, a.shape)
return new_a
if cell_clip > 0.0:
c = array_clip(c, cell_clip)
if w_c is None:
g_o = act_gate(g_o) # 1 x D
else:
......@@ -64,6 +77,8 @@ def lstmp(
# projection
r = np.dot(h, w_rh)
r = act_proj(r)
if proj_clip > 0.0:
r = array_clip(r, proj_clip)
return r, c
def _reverse(x, offset):
......@@ -87,13 +102,13 @@ def lstmp(
# compute one sequence
seq_len = lod[0][i]
x = input[offset[i]:offset[i + 1], :]
r_pre = np.dot(h0[i], w_rh) # 1 x P
r_pre = act_proj(r_pre)
r_pre = h0[i]
c_pre = c0[i] # 1 x D
for j in range(seq_len):
# compute one step
r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, act_gate,
act_cell, act_cand, act_proj)
r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, proj_clip,
cell_clip, act_gate, act_cell, act_cand,
act_proj)
projection.append(r_pre.flatten())
cell.append(c_pre.flatten())
......@@ -123,13 +138,12 @@ class TestLstmpOp(LstmTest.TestLstmOp):
T = sum(self.lod[0])
N = len(self.lod[0])
x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(N, self.D)).astype('float64')
h0 = np.random.normal(size=(N, self.P)).astype('float64')
c0 = np.random.normal(size=(N, self.D)).astype('float64')
else:
h0 = np.zeros((N, self.D)).astype('float64')
h0 = np.zeros((N, self.P)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
w = np.random.normal(size=(self.P, 4 * self.D)).astype('float64')
if self.use_peepholes:
......@@ -140,9 +154,12 @@ class TestLstmpOp(LstmTest.TestLstmOp):
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
w_rh = np.random.normal(size=(self.D, self.P)).astype('float64')
proj_clip = 0.1
cell_clip = 0.1
r, c = lstmp(x, self.lod, h0, c0, w, w_rh, w_b, w_c, self.is_reverse,
ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
ACTIVATION[self.act_cand], ACTIVATION[self.act_proj])
proj_clip, cell_clip, ACTIVATION[self.act_gate],
ACTIVATION[self.act_cell], ACTIVATION[self.act_cand],
ACTIVATION[self.act_proj])
self.inputs = {'Input': (x, self.lod), 'Weight': w, 'ProjWeight': w_rh}
......@@ -159,6 +176,8 @@ class TestLstmpOp(LstmTest.TestLstmOp):
self.attrs = {
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'proj_clip': proj_clip,
'cell_clip': cell_clip,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand,
......@@ -171,14 +190,14 @@ class TestLstmpOp(LstmTest.TestLstmOp):
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2)
max_relative_error=1e-2,
numeric_grad_delta=0.0000005)
class TestLstmpOpHasInitial(TestLstmpOp):
......@@ -188,7 +207,6 @@ class TestLstmpOpHasInitial(TestLstmpOp):
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -196,11 +214,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'H0', 'C0'],
['Projection'],
numeric_grad_delta=0.0000005,
max_relative_error=1e-2)
def test_check_grad_ingore_bias(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -208,11 +226,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'ProjWeight', 'Weight'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Bias'))
def test_check_grad_ingore_weight(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -220,11 +238,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Weight'))
def test_check_grad_ingore_proj_weight(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -232,11 +250,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('ProjWeight'))
def test_check_grad_ingore_input(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -244,11 +262,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Weight', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Input'))
def test_check_grad_ingore_h0(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -256,11 +274,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'C0'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('H0'))
def test_check_grad_ingore_c0(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -268,6 +286,7 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'H0'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('C0'))
......
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