Merge pull request #10 from PaddlePaddle/develop

merge to local

README.md

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

+/* 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

+/* 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

+/* 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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

+/* 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

+/* 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

+/* 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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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'))