Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into clip_op

.gitignore

@@ -27,3 +27,4 @@ CMakeFiles
 cmake_install.cmake
 paddle/.timestamp
 python/paddlepaddle.egg-info/
+paddle/pybind/pybind.h

doc/design/refactorization.md

+# Design Doc: Refactorization Overview
+
+The goal of refactorizaiton include:
+
+1. Make it easy for external contributors to write new elementory computaiton operations.
+1. Make the codebase clean and readable.
+1. Introduce a new design of computation representation -- a computation graph of operators and variables.
+1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing.
+
+## Computation Graphs
+
+1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.
+
+  1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example.
+
+1. Users write Python programs to describe the graphs and run it (locally or remotely).
+
+1. A graph is composed of *variabels* and *operators*.
+
+1. The description of graphs must be able to be serialized/deserialized, so it
+
+   1. could to be sent to the cloud for distributed execution, and
+   1. be sent to clients for mobile or enterprise deployment.
+
+1. The Python program do
+
+   1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to
+      1. the C++ library `libpaddle.so` for local execution,
+      1. the master process of a distributed training job for training, or
+      1. the server process of a Kubernetes serving job for distributed serving.
+   1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them.
+
+## Description and Realization
+
+At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph.
+
+At runtime, the C++ program realizes the graph and run it.
+
+| | Representation (protobuf messages) | Realization (C++ class objects) |
+|---|---|---|
+|Data|[VarDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L107)|[Variable](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24)|
+|Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)|
+|Block|BlockDesc|Block|
+
+The word *graph* is exchangable with *block* in this document.  A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }.
+
+## Compilation and Execution
+
+1. Run an applicaton Python program to describe the graph.  In particular,
+
+   1. create VarDesc to represent local/intermediate variables,
+   1. create operators and set attributes,
+   1. validate attribute values,
+   1. inference the type and the shape of variables,
+   1. plan for memory-reuse for variables,
+   1. generate backward and optimization part of the Graph.
+   1. possiblly split the graph for distributed training.
+
+1. The invocation of `train` or `infer` in the application Python program:
+
+   1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block,
+      1. realize local variables defined in the BlockDesc message in the new scope,
+      1. a scope is similar to the stack frame in programming languages,
+
+   1. create an instance of class `Block`, in which,
+      1. realize operators in the BlockDesc message,
+
+   1. run the Block by calling
+      1. `Block::Eval(vector<Variable>* targets)` for forward and backward computations, or
+      1. `Block::Eval(vector<Operator>* targets)` for optimization.
+
+
+## Intermediate Representation (IR)
+
+```text
+Compile Time -> IR -> Runtime
+```
+
+### Benefit
+
+- Optimization
+  ```text
+  Compile Time -> IR -> Optimized IR -> Runtime
+  ```
+- Send automatically partitioned IR to different nodes.
+  - Automatic data parallel
+    ```text
+    Compile Time
+    |-> Single GPU IR
+        |-> [trainer-IR-0, trainer-IR-1, pserver-IR]
+            |-> Node-0 (runs trainer-IR-0)
+            |-> Node-1 (runs trainer-IR-1)
+            |-> Node-2 (runs pserver-IR)
+    ```
+  - Automatic model parallel (planned for future)
+
+---
+
+# Operator/OpWithKernel/OpKernel
+
+![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot)
+
+---
+
+# Operator
+![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
+
+* `Operator` is the fundamental building block as the user interface.
+    * Operator stores input/output variable name, and attributes.
+    * The `InferShape` interface is used to infer output variable shapes by its input shapes.
+    * Use `Run` to compute `input variables` to `output variables`.
+
+---
+
+# OpWithKernel/Kernel
+
+![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot)
+
+* `OpWithKernel` inherits `Operator`.
+* `OpWithKernel` contains a Kernel map.
+    * `OpWithKernel::Run` get device's kernel, and invoke `OpKernel::Compute`.
+    * `OpKernelKey` is the map key. Only device place now, but may be data type later.
+
+---
+
+# Why separate Kernel and Operator
+
+* Separate GPU and CPU code.
+    * Make Paddle can run without GPU.
+* Make one operator (which is user interface) can contain many implementations.
+    * Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel.
+---
+
+# Libraries for Kernel development
+
+* `Eigen::Tensor` contains basic math and element-wise functions.
+    * Note that `Eigen::Tensor` has broadcast implementation.
+    * Limit number of `tensor.device(dev) = ` in your code.
+* `thrust::tranform` and `std::transform`.
+    * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel.
+    * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`.
+* Hand-writing `GPUKernel` and `CPU` code
+    * Do not write `.h`. CPU Kernel should be in `.cc`. CPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
+---
+# Operator Register
+
+## Why register is necessary?
+We need a method to build mappings between Op type names and Op classes.
+
+## How to do the register?
+
+Maintain a map, whose key is the type name and value is corresponding Op constructor.
+
+---
+# The Registry Map
+
+### `OpInfoMap`
+
+`op_type(string)` -> `OpInfo`
+
+`OpInfo`:
+
+- **`creator`**: The Op constructor.
+- **`grad_op_type`**: The type of the gradient Op.
+- **`proto`**: The Op's Protobuf, including inputs, outputs and required attributes.
+- **`checker`**: Used to check attributes.
+
+---
+# Related Concepts
+
+### Op_Maker
+It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
+
+### Register Macros
+```cpp
+REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class)
+REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
+```
+
+### `USE` Macros
+make sure the registration process is executed and linked.
+
+---
+# Register Process
+1. Write Op class, as well as its gradient Op class if there is.
+2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes.
+3. Invoke macro `REGISTER_OP`. The macro will
+	1. call maker class to complete `proto` and `checker`
+	2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap`
+
+4. Invoke `USE` macro in where the Op is used to make sure it is linked.
+
+---
+# Backward Module (1/2)
+### Create Backward Operator
+- Mapping from forwarding Op to backward Op
+![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png)
+
+---
+# Backward Module (2/2)
+### Build Backward Network
+- **Input** graph of forwarding operators
+- **Output** graph of backward operators
+- **corner case in construction**
+	- shared variable => insert `Add` operator
+	- no gradient => insert `fill_zero_grad` operator
+	- recursive netOp => call `Backward` recursively
+	- RNN Op => recursively call `Backward` on stepnet
+
+
+---
+# Scope, Variable, Tensor
+
+* `Tensor` is an n-dimension array with type.
+	* Only dims and data pointers are stored in `Tensor`.
+	* All operators on `Tensor` is written in `Operator` or global functions.
+	* variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
+* `Variable` is the inputs and outputs of an operator. Not just `Tensor`.
+	* step_scopes in RNN is a variable and not a tensor.
+* `Scope` is where variables store at.
+	* map<string/*var name */, Variable>
+	* `Scope` has a hierarchical structure. The local scope can get variable from its parent scope.
+
+---
+# Block (in design)
+## the difference with original RNNOp
+- as an operator is more intuitive than `RNNOp`,
+- offers new interface `Eval(targets)` to deduce the minimal block to `Run`,
+- fits the compile-time/ runtime separation design.
+  - during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc`
+  - when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run`
+
+---
+# Milestone
+- take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
+- model migration
+  - framework development gives **priority support** to model migration, for example,
+    - the MNIST demo needs a Python interface,
+    - the RNN models require the framework to support `LoDTensor`.
+  - determine some timelines,
+  - heavily-relied Ops need to be migrated first,
+  - different models can be migrated parallelly.
+- improve the framework at the same time
+- accept imperfection, concentrated on solving the specific problem at the right price.
+
+---
+# Control the migration quality
+- compare the performance of migrated models with old ones.
+- follow google C style
+- build the automatic workflow of generating Python/C++ documentations
+  - the documentation of layers and ops should be written inside the code
+  - take the documentation quality into account when doing PR
+  - preview the documentations, read and improve them from users' perspective

paddle/cuda/include/hl_cuda_cudnn.h

@@ -22,10 +22,10 @@ limitations under the License. */
  */
 typedef enum {
   HL_POOLING_MAX = 0,
-  // average includes padded values
-  HL_POOLING_AVERAGE = 1,
   // average does not include padded values
-  HL_POOLING_AVERAGE_EXCLUDE_PADDING = 2,
+  HL_POOLING_AVERAGE = 1,
+  // average includes padded values
+  HL_POOLING_AVERAGE_INCLUDE_PADDING = 2,
   HL_POOLING_END
 } hl_pooling_mode_t;
 

paddle/cuda/include/hl_tensor_ops.h

@@ -461,7 +461,7 @@ class add<float32x4_t> {
 public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
-    return vmulq_f32(a, b);
+    return vaddq_f32(a, b);
   }
 };
 

paddle/cuda/src/hl_cuda_cnn.cu

@@ -211,13 +211,11 @@ __global__ void KeAvgPoolForward(const int nthreads,
 
     int hstart = ph * strideH - padH;
     int wstart = pw * strideW - padW;
-    int hend = min(hstart + sizeY, height + padH);
-    int wend = min(wstart + sizeX, width + padW);
-    int pool_size = (hend - hstart) * (wend - wstart);
+    int hend = min(hstart + sizeY, height);
+    int wend = min(wstart + sizeX, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
-    hend = min(hend, height);
-    wend = min(wend, width);
+    int pool_size = (hend - hstart) * (wend - wstart);
 
     real aveval = 0;
     inputData += (frameNum * channels + c) * height * width;
@@ -299,12 +297,14 @@ __global__ void KeAvgPoolBackward(const int nthreads,
     outGrad += (frameNum * outStride + offsetC * pooledH * pooledW);
 
     for (int ph = phstart; ph < phend; ++ph) {
+      int hstart = ph * strideH - padH;
+      int hend = min(hstart + sizeY, height);
+      hstart = max(hstart, 0);
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
-        int hstart = ph * strideH - padH;
         int wstart = pw * strideW - padW;
-        int hend = min(hstart + sizeY, height + padH);
-        int wend = min(wstart + sizeX, width + padW);
+        int wend = min(wstart + sizeX, width);
+        wstart = max(wstart, 0);
         int poolsize = (hend - hstart) * (wend - wstart);
         gradient += outGrad[ph * pooledW + pw] / poolsize;
       }
@@ -600,16 +600,13 @@ __global__ void KeAvgPool3DForward(const int nthreads,
     int dstart = pd * strideD - padD;
     int hstart = ph * strideH - padH;
     int wstart = pw * strideW - padW;
-    int dend = min(dstart + sizeZ, depth + padD);
-    int hend = min(hstart + sizeY, height + padH);
-    int wend = min(wstart + sizeX, width + padW);
-    int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
+    int dend = min(dstart + sizeZ, depth);
+    int hend = min(hstart + sizeY, height);
+    int wend = min(wstart + sizeX, width);
     dstart = max(dstart, 0);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
-    dend = min(dend, depth);
-    hend = min(hend, height);
-    wend = min(wend, width);
+    int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
 
     real aveval = 0;
     inputData += (frameNum * channels + c) * depth * height * width;
@@ -712,15 +709,18 @@ __global__ void KeAvgPool3DBackward(const int nthreads,
     outGrad += (frameNum * channels + offsetC) * pooledD * pooledH * pooledW;
 
     for (int pd = pdstart; pd < pdend; ++pd) {
+      int dstart = pd * strideD - padD;
+      int dend = min(dstart + sizeZ, depth);
+      dstart = max(dstart, 0);
       for (int ph = phstart; ph < phend; ++ph) {
+        int hstart = ph * strideH - padH;
+        int hend = min(hstart + sizeY, height);
+        hstart = max(hstart, 0);
         for (int pw = pwstart; pw < pwend; ++pw) {
           // figure out the pooling size
-          int dstart = pd * strideD - padD;
-          int hstart = ph * strideH - padH;
           int wstart = pw * strideW - padW;
-          int dend = min(dstart + sizeZ, depth + padD);
-          int hend = min(hstart + sizeY, height + padH);
-          int wend = min(wstart + sizeX, width + padW);
+          int wend = min(wstart + sizeX, width);
+          wstart = max(wstart, 0);
           int poolsize = (dend - dstart) * (hend - hstart) * (wend - wstart);
           gradient += outGrad[(pd * pooledH + ph) * pooledW + pw] / poolsize;
         }

paddle/cuda/src/hl_cuda_cudnn.cc

@@ -432,11 +432,11 @@ void hl_create_pooling_descriptor(hl_pooling_descriptor* pooling_desc,
       cudnn_mode = CUDNN_POOLING_MAX;
       break;
     case HL_POOLING_AVERAGE:
-      cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
-      break;
-    case HL_POOLING_AVERAGE_EXCLUDE_PADDING:
       cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
       break;
+    case HL_POOLING_AVERAGE_INCLUDE_PADDING:
+      cudnn_mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
+      break;
     default:
       LOG(FATAL) << "parameter mode error";
   }

paddle/framework/operator.cc

@@ -22,14 +22,14 @@ namespace framework {
 template <>
 Eigen::DefaultDevice& ExecutionContext::GetEigenDevice<
     platform::CPUPlace, Eigen::DefaultDevice>() const {
-  return *device_context_->get_eigen_device<Eigen::DefaultDevice>();
+  return *device_context_.get_eigen_device<Eigen::DefaultDevice>();
 }
 
 #ifndef PADDLE_ONLY_CPU
 template <>
 Eigen::GpuDevice&
 ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
-  return *device_context_->get_eigen_device<Eigen::GpuDevice>();
+  return *device_context_.get_eigen_device<Eigen::GpuDevice>();
 }
 #endif
 

paddle/framework/operator.h

@@ -366,7 +366,7 @@ struct EigenDeviceConverter<platform::GPUPlace> {
 class ExecutionContext : public InferShapeContext {
  public:
   ExecutionContext(const OperatorBase& op, const Scope& scope,
-                   const platform::DeviceContext* device_context)
+                   const platform::DeviceContext& device_context)
       : InferShapeContext(op, scope), device_context_(device_context) {}
 
   template <typename PlaceType,
@@ -374,9 +374,9 @@ class ExecutionContext : public InferShapeContext {
                 typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
   DeviceType& GetEigenDevice() const;
 
-  platform::Place GetPlace() const { return device_context_->GetPlace(); }
+  platform::Place GetPlace() const { return device_context_.GetPlace(); }
 
-  const platform::DeviceContext* device_context() const {
+  const platform::DeviceContext& device_context() const {
     return device_context_;
   }
 
@@ -401,7 +401,8 @@ class ExecutionContext : public InferShapeContext {
     return res;
   }
 
-  const platform::DeviceContext* device_context_;
+ private:
+  const platform::DeviceContext& device_context_;
 };
 
 template <>
@@ -461,7 +462,7 @@ class OperatorWithKernel : public OperatorBase {
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const final {
     auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
-    opKernel->Compute(ExecutionContext(*this, scope, &dev_ctx));
+    opKernel->Compute(ExecutionContext(*this, scope, dev_ctx));
   }
 
   static std::unordered_map<std::string /* op_type */, OpKernelMap>&

paddle/gserver/layers/CudnnPoolLayer.cpp

@@ -29,9 +29,9 @@ bool CudnnPoolLayer::typeCheck(const std::string &poolType,
     if (mode) {
       *mode = HL_POOLING_AVERAGE;
     }
-  } else if (poolType == "cudnn-avg-excl-pad-pool") {
+  } else if (poolType == "cudnn-avg-incl-pad-pool") {
     if (mode) {
-      *mode = HL_POOLING_AVERAGE_EXCLUDE_PADDING;
+      *mode = HL_POOLING_AVERAGE_INCLUDE_PADDING;
     }
   } else {
     return false;

paddle/gserver/layers/DetectionOutputLayer.cpp

@@ -143,7 +143,7 @@ void DetectionOutputLayer::forward(PassType passType) {
     resetOutput(numKept, 7);
   } else {
     MatrixPtr outV = getOutputValue();
-    outV = NULL;
+    if (outV) outV->resize(0, 0);
     return;
   }
   MatrixPtr outV = getOutputValue();

paddle/gserver/layers/MKLDNNPoolLayer.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "MKLDNNPoolLayer.h"
+#include "paddle/math/MathUtils.h"
+#include "paddle/utils/Logging.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_pool, MKLDNNPoolLayer);
+
+bool MKLDNNPoolLayer::init(const LayerMap& layerMap,
+                           const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+
+  /* the size of inputs for pool-layer is 1 */
+  CHECK_EQ(config_.inputs_size(), 1);
+  const PoolConfig& conf = config_.inputs(0).pool_conf();
+  ic_ = conf.channels();
+  ih_ = conf.img_size_y();
+  iw_ = conf.img_size();
+  oc_ = ic_;
+  oh_ = conf.output_y();
+  ow_ = conf.output_x();
+  fh_ = conf.size_y();
+  fw_ = conf.size_x();
+  ph_ = conf.padding_y();
+  pw_ = conf.padding();
+  sh_ = conf.stride_y();
+  sw_ = conf.stride();
+
+  const std::string& type = conf.pool_type();
+  if (type == "max-projection") {
+    poolAlgo_ = algorithm::pooling_max;
+  } else if (type == "avg-projection") {
+    // paddle only use exclude_padding
+    poolAlgo_ = algorithm::pooling_avg_exclude_padding;
+  } else {
+    LOG(FATAL) << "unknow pooling type!";
+  }
+  return true;
+}
+
+void MKLDNNPoolLayer::reshape(
+    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+  reshapeInput(bs, ih, iw);
+  // ic_ and oc can not be changed
+  CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
+      << "Input channel can not be changed";
+
+  // cal output sizes
+  // paddle used false caffeMode for pooling
+  oh = outputSize(ih, fh_, ph_, sh_, false);
+  ow = outputSize(iw, fw_, pw_, sw_, false);
+  reshapeOutput(oh, ow);
+
+  resizeOutput(bs, oc * oh * ow);
+
+  printSizeInfo();
+}
+
+void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
+                               MKLDNNMatrixPtr& in,
+                               MKLDNNMatrixPtr& wgt,
+                               MKLDNNMatrixPtr& bias,
+                               MKLDNNMatrixPtr& out) {
+  resetFwdBuffers(in, out);
+
+  resetFwdPD(fwdPD_, in, out);
+
+  resetFwdPipeline(pipeline, fwdPD_, in, out);
+
+  printValueFormatFlow();
+}
+
+void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
+                               MKLDNNMatrixPtr& in,
+                               MKLDNNMatrixPtr& wgt,
+                               MKLDNNMatrixPtr& bias,
+                               MKLDNNMatrixPtr& out) {
+  std::shared_ptr<pool_bwd::primitive_desc> pd;
+
+  resetBwdBuffers(in, out);
+
+  resetBwdPD(pd, in, out);
+
+  resetBwdPipeline(pipeline, pd, in, out);
+
+  printGradFormatFlow();
+}
+
+void MKLDNNPoolLayer::updateInputData() {
+  inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
+}
+
+void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
+                                      MKLDNNMatrixPtr& out) {
+  resetInValue(in);
+
+  resetOutValue(out);
+}
+
+void MKLDNNPoolLayer::resetInValue(MKLDNNMatrixPtr& in) {
+  if (inputIsOnlyMKLDNN()) {
+    const MatrixPtr& dnnIn = getInputValue(0);
+    in = std::dynamic_pointer_cast<MKLDNNMatrix>(dnnIn);
+    CHECK(in) << "Input should be MKLDNNMatrix";
+  } else {
+    CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
+    const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
+    in = MKLDNNMatrix::create(
+        cpuIn, {bs_, ic_, ih_, iw_}, format::nchw, engine_);
+  }
+}
+
+void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
+  CHECK(inVal_) << "Should reset input value first";
+  memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
+  out = MKLDNNMatrix::create(
+      output_.value, outDims, inVal_->getFormat(), engine_);
+  output_.value = std::dynamic_pointer_cast<Matrix>(out);
+
+  // create reorder if output value has cpu device and pd do not match
+  cpuOutVal_ = nullptr;
+  cvtOutVal_ = nullptr;
+  if (!outputIsOnlyMKLDNN()) {
+    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
+    cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
+    if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
+      cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
+      CHECK(cvtOutVal_) << "should not be emptry";
+    } else {
+      // CPU output share the same data of MKLDNN output
+      cpuOut->setData(out->getData());
+      cpuOutVal_ = out;
+    }
+  }
+}
+
+void MKLDNNPoolLayer::resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
+                                 MKLDNNMatrixPtr in,
+                                 MKLDNNMatrixPtr out) {
+  memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
+  memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
+  memory::dims kernels = memory::dims{fh_, fw_};
+  memory::dims strides = memory::dims{sh_, sw_};
+  memory::dims padL = memory::dims{ph_, pw_};
+  memory::dims padR = getPaddingR();
+  padding_kind padKind = padding_kind::zero;
+  prop_kind pk = passType_ == PASS_TEST ? prop_kind::forward_scoring
+                                        : prop_kind::forward_training;
+  auto fwdDesc = pool_fwd::desc(pk,
+                                poolAlgo_,
+                                in->getMemoryDesc(),
+                                out->getMemoryDesc(),
+                                strides,
+                                kernels,
+                                padL,
+                                padR,
+                                padKind);
+  pd.reset(new pool_fwd::primitive_desc(fwdDesc, engine_));
+
+  // prepare workspace if necessary
+  workspace_ =
+      (passType_ != PASS_TEST && poolAlgo_ == algorithm::pooling_max)
+          ? std::make_shared<memory>(memory(pd->workspace_primitive_desc()))
+          : nullptr;
+}
+
+void MKLDNNPoolLayer::resetFwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<pool_fwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr& in,
+    MKLDNNMatrixPtr& out) {
+  pipeline.clear();
+  fwd_ = workspace_
+             ? std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out, *workspace_))
+             : std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out));
+  pipeline.push_back(*fwd_);
+
+  if (cvtOutVal_) {
+    pipeline.push_back(*cvtOutVal_);
+  }
+}
+
+void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
+                                      MKLDNNMatrixPtr& out) {
+  resetOutGrad(out);
+
+  resetInGrad(in);
+}
+void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
+  CHECK(outVal_) << "Should have output value";
+  out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
+
+  // create reorder if output value has cpu device and pd do not match
+  cpuOutGrad_ = nullptr;
+  cvtOutGrad_ = nullptr;
+  if (!outputIsOnlyMKLDNN()) {
+    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
+    cpuOutGrad_ = MKLDNNMatrix::create(
+        cpuOut, memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_);
+    if (cpuOutGrad_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
+      cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
+      CHECK(cvtOutGrad_) << "should not be emptry";
+    } else {
+      // share the same data of CPU output
+      output_.grad->setData(cpuOut->getData());
+      out = cpuOutGrad_;
+    }
+  }
+}
+
+void MKLDNNPoolLayer::resetInGrad(MKLDNNMatrixPtr& in) {
+  in = nullptr;
+  const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
+  if (inGrad == nullptr) {
+    return;
+  }
+  CHECK(inVal_);
+  in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
+}
+
+void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
+                                 MKLDNNMatrixPtr& in,
+                                 MKLDNNMatrixPtr& out) {
+  memory::dims kernels = memory::dims{fh_, fw_};
+  memory::dims strides = memory::dims{sh_, sw_};
+  memory::dims padL = memory::dims{ph_, pw_};
+  memory::dims padR = getPaddingR();
+  CHECK(in);
+  CHECK(out);
+  auto bwdDesc = pool_bwd::desc(poolAlgo_,
+                                in->getMemoryDesc(),
+                                out->getMemoryDesc(),
+                                strides,
+                                kernels,
+                                padL,
+                                padR,
+                                padding_kind::zero);
+  pd.reset(new pool_bwd::primitive_desc(bwdDesc, engine_, *fwdPD_));
+}
+
+void MKLDNNPoolLayer::resetBwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<pool_bwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr& in,
+    MKLDNNMatrixPtr& out) {
+  pipeline.clear();
+  if (cvtOutGrad_) {
+    pipeline.push_back(*cvtOutGrad_);
+  }
+
+  bwdData_ =
+      workspace_
+          ? std::make_shared<pool_bwd>(pool_bwd(*pd, *out, *workspace_, *in))
+          : std::make_shared<pool_bwd>(pool_bwd(*pd, *out, *in));
+  pipeline.push_back(*bwdData_);
+}
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNPoolLayer.h

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "MKLDNNLayer.h"
+#include "mkldnn.hpp"
+
+namespace paddle {
+typedef mkldnn::pooling_forward pool_fwd;
+typedef mkldnn::pooling_backward pool_bwd;
+
+/**
+ * @brief A subclass of MKLDNNLayer pool layer.
+ *
+ * The config file api is mkldnn_pool
+ */
+class MKLDNNPoolLayer : public MKLDNNLayer {
+protected:
+  // padding height and width
+  int ph_, pw_;
+  // stride height and width
+  int sh_, sw_;
+  // filter(kenerl) height and width
+  int fh_, fw_;
+
+  // pooling_avg or pooling_max
+  mkldnn::algorithm poolAlgo_;
+
+  // MKLDNNMatrixPtr which should be created from CPU Device
+  MKLDNNMatrixPtr cpuOutVal_;
+  MKLDNNMatrixPtr cpuOutGrad_;
+  // convert handle between CPU device and MKLDNN device
+  std::shared_ptr<mkldnn::reorder> cvtOutVal_;
+  std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
+
+  // save forward primitive_desc, which can be used backward
+  std::shared_ptr<pool_fwd::primitive_desc> fwdPD_;
+  // according to https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
+  // test_pooling_forward.cpp, pool need workspace for backward
+  std::shared_ptr<mkldnn::memory> workspace_;
+
+public:
+  explicit MKLDNNPoolLayer(const LayerConfig& config) : MKLDNNLayer(config) {}
+
+  ~MKLDNNPoolLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void reshape(
+      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+
+  void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void updateInputData() override;
+
+  void printSizeInfo() override {
+    MKLDNNLayer::printSizeInfo();
+    VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
+                       << ": ph: " << ph_ << ", pw: " << pw_ << ", sh: " << sh_
+                       << ", sw: " << sw_;
+  }
+
+protected:
+  /**
+   * Forward functions: reset buffers(input, output),
+   *                    reset primitive descriptor,
+   *                    reset pipeline.
+   */
+  void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
+  void resetInValue(MKLDNNMatrixPtr& in);
+  void resetOutValue(MKLDNNMatrixPtr& out);
+  void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
+                  MKLDNNMatrixPtr in,
+                  MKLDNNMatrixPtr out);
+  void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::shared_ptr<pool_fwd::primitive_desc>& pd,
+                        MKLDNNMatrixPtr& in,
+                        MKLDNNMatrixPtr& out);
+
+  /**
+   * Backward functions: reset buffers(input, output),
+   *                     reset primitive descriptor,
+   *                     reset pipeline.
+   */
+  void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
+  void resetOutGrad(MKLDNNMatrixPtr& out);
+  void resetInGrad(MKLDNNMatrixPtr& in);
+  void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
+                  MKLDNNMatrixPtr& in,
+                  MKLDNNMatrixPtr& out);
+  void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::shared_ptr<pool_bwd::primitive_desc>& pd,
+                        MKLDNNMatrixPtr& in,
+                        MKLDNNMatrixPtr& out);
+
+  /**
+   * get padding_r according to
+   * https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
+   * test_pooling_forward.cpp
+   */
+  mkldnn::memory::dims getPaddingR() const {
+    mkldnn::memory::dims padR = {ph_, pw_};
+    for (int i = 0; i < 2; ++i) {
+      if ((ih_ + ph_ + padR[0] - fh_) / sh_ + 1 < oh_) {
+        ++padR[0];
+      }
+      if ((iw_ + pw_ + padR[1] - fw_) / sw_ + 1 < ow_) {
+        ++padR[1];
+      }
+    }
+    return padR;
+  }
+};
+
+}  // namespace paddle

paddle/gserver/tests/test_MKLDNN.cpp

@@ -141,6 +141,68 @@ TEST(MKLDNNLayer, ConvLayer) {
   testConvLayer({4, 4, 16, 3, 3, 16, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1});
 }
 
+struct testPoolDesc {
+  int bs, ch;  // input channel and output channel are the same
+  int ih, iw;
+  int oh, ow;
+  int fh, fw;
+  int ph, pw;
+  int sh, sw;
+};
+
+void testPoolLayer(const testPoolDesc& pm) {
+  const std::string compareTypes[] = {"mkldnn_pool", "pool"};
+  TestConfig cfg;
+  cfg.layerConfig.set_type(compareTypes[0]);
+  cfg.layerConfig.set_size(pm.ch * pm.oh * pm.ow);
+  cfg.inputDefs.push_back(
+      {INPUT_DATA,
+       "layer_0",
+       /* size of input layer= */ size_t(pm.ch * pm.ih * pm.iw),
+       0});
+  LayerInputConfig* input = cfg.layerConfig.add_inputs();
+  PoolConfig* pool = input->mutable_pool_conf();
+  // pool->set_pool_type(poolType);
+  pool->set_channels(pm.ch);
+  pool->set_img_size(pm.iw);
+  pool->set_img_size_y(pm.ih);
+  pool->set_output_x(pm.ow);
+  pool->set_output_y(pm.oh);
+  pool->set_size_x(pm.fw);
+  pool->set_size_y(pm.fh);
+  pool->set_padding(pm.pw);
+  pool->set_padding_y(pm.ph);
+  pool->set_stride(pm.sw);
+  pool->set_stride_y(pm.sh);
+
+  int oh = outputSize(pm.ih, pm.fh, pm.ph, pm.sh, false);
+  int ow = outputSize(pm.iw, pm.fw, pm.pw, pm.sw, false);
+  CHECK_EQ(ow, pm.ow) << "output size check failed";
+  CHECK_EQ(oh, pm.oh) << "output size check failed";
+
+  MKLDNNTester tester;
+  for (auto type : {"max-projection", "avg-projection"}) {
+    pool->set_pool_type(type);
+    TestConfig ref = cfg;
+    ref.layerConfig.set_type(compareTypes[1]);
+    for (auto bs : {pm.bs, 1}) {
+      tester.run(cfg, ref, bs, pm.ih, pm.iw);
+    }
+  }
+}
+
+TEST(MkldnnLayer, PoolLayer) {
+  /* bs, ch, ih, iw, oh, ow, fh, fw, ph, pw, sh, sw*/
+  testPoolLayer({2, 1, 4, 4, 2, 2, 3, 3, 0, 0, 2, 2});
+  testPoolLayer({10, 8, 16, 16, 8, 8, 2, 2, 0, 0, 2, 2});
+  testPoolLayer({4, 2, 5, 5, 3, 3, 3, 3, 1, 1, 2, 2});
+  testPoolLayer({8, 16, 56, 56, 28, 28, 3, 3, 0, 0, 2, 2});
+  testPoolLayer({8, 16, 14, 14, 7, 7, 3, 3, 0, 0, 2, 2});
+  testPoolLayer({4, 16, 7, 7, 1, 1, 7, 7, 0, 0, 1, 1});
+  testPoolLayer({4, 2, 5, 5, 3, 3, 5, 5, 1, 1, 1, 1});
+  testPoolLayer({2, 8, 56, 56, 29, 29, 3, 3, 1, 1, 2, 2});
+}
+
 // TODO(TJ): add branch test
 
 int main(int argc, char** argv) {

paddle/math/BaseMatrix.cu

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <cmath>
 #include "BaseMatrix.h"
 #include "MathFunctions.h"
+#include "NEONFunctions.h"
 #include "SIMDFunctions.h"
 #include "hl_matrix_apply.cuh"
 #include "hl_matrix_base.cuh"
@@ -666,6 +667,13 @@ void BaseMatrixT<T>::relu(BaseMatrixT& b) {
   applyBinary(binary::Relu<T>(), b);
 }
 
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+template <>
+void BaseMatrixT<float>::relu(BaseMatrixT& b) {
+  neon::relu(data_, b.data_, height_ * width_);
+}
+#endif
+
 DEFINE_MATRIX_BINARY_OP(ReluDerivative, a *= (b > 0.0f ? 1.0f : 0.0f));
 template <class T>
 void BaseMatrixT<T>::reluDerivative(BaseMatrixT& b) {

paddle/math/Matrix.cpp

@@ -1033,17 +1033,15 @@ void GpuMatrix::maxPoolForward(Matrix& inputMat,
 
   real* inputData = inputMat.getData();
   size_t frameNum = inputMat.getHeight();
-  size_t width = imgSizeW;
-  size_t height = imgSizeH;
-  CHECK(height * width * channels == inputMat.getWidth());
+  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
   CHECK(height_ == inputMat.getHeight());
   CHECK(width_ == outputH * outputW * channels);
 
   hl_maxpool_forward(frameNum,
                      inputData,
                      channels,
-                     height,
-                     width,
+                     imgSizeH,
+                     imgSizeW,
                      outputH,
                      outputW,
                      sizeX,
@@ -1080,11 +1078,8 @@ void GpuMatrix::maxPoolBackward(Matrix& inputMat,
   real* outDiff = outGrad.getData();
   size_t frameNum = inputMat.getHeight();
   size_t channels = outV.getWidth() / outputH / outputW;
-  size_t width = imgSizeW;
-  size_t height = imgSizeH;
-  CHECK(height * width * channels == inputMat.getWidth());
+  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
   CHECK(height_ == inputMat.getHeight());
-  CHECK(width_ == width * height * channels);
   CHECK(outGrad.getHeight() == outV.getHeight() &&
         outGrad.getWidth() == outV.getWidth());
 
@@ -1093,8 +1088,8 @@ void GpuMatrix::maxPoolBackward(Matrix& inputMat,
                       outData,
                       outDiff,
                       channels,
-                      height,
-                      width,
+                      imgSizeH,
+                      imgSizeW,
                       outputH,
                       outputW,
                       sizeX,
@@ -1125,17 +1120,15 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
 
   real* inputData = inputMat.getData();
   size_t frameNum = inputMat.getHeight();
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-  CHECK(height * width * channels == inputMat.getWidth());
+  CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
   CHECK(height_ == inputMat.getHeight());
   CHECK(width_ == outputH * outputW * channels);
 
   hl_avgpool_forward(frameNum,
                      inputData,
                      channels,
-                     height,
-                     width,
+                     imgSizeH,
+                     imgSizeW,
                      outputH,
                      outputW,
                      sizeX,
@@ -1166,17 +1159,15 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
   real* outDiff = outGrad.getData();
   size_t frameNum = outGrad.getHeight();
   size_t channels = outGrad.getWidth() / outputH / outputW;
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-  CHECK(height * width * channels == width_);
+  CHECK(imgSizeH * imgSizeW * channels == width_);
   CHECK(height_ == outGrad.getHeight());
   CHECK(outGrad.getWidth() == outputH * outputW * channels);
 
   hl_avgpool_backward(frameNum,
                       outDiff,
                       channels,
-                      height,
-                      width,
+                      imgSizeH,
+                      imgSizeW,
                       outputH,
                       outputW,
                       sizeX,
@@ -1214,19 +1205,16 @@ void GpuMatrix::maxPool3DForward(Matrix& inputMat,
   real* inputData = inputMat.getData();
   real* maxPoolIdxData = maxPoolIdx.getData();
   size_t num = inputMat.getHeight();
-  size_t width = imgSizeW;
-  size_t height = imgSizeH;
-  size_t depth = imgSizeD;
-  CHECK(depth * height * width * channels == inputMat.getWidth());
+  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
   CHECK(height_ == inputMat.getHeight());
   CHECK(width_ == outputD * outputH * outputW * channels);
 
   hl_maxpool3D_forward(num,
                        inputData,
                        channels,
-                       depth,
-                       height,
-                       width,
+                       imgSizeD,
+                       imgSizeH,
+                       imgSizeW,
                        outputD,
                        outputH,
                        outputW,
@@ -1269,20 +1257,16 @@ void GpuMatrix::maxPool3DBackward(Matrix& outGrad,
   real* maxPoolIdxData = maxPoolIdx.getData();
   size_t frameNum = getHeight();
   size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
-  size_t width = imgSizeW;
-  size_t height = imgSizeH;
-  size_t depth = imgSizeD;
-  CHECK(depth * height * width * channels == getWidth());
-  CHECK(width_ == depth * width * height * channels);
+  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
   CHECK(outGrad.getHeight() == maxPoolIdx.getHeight() &&
         outGrad.getWidth() == maxPoolIdx.getWidth());
 
   hl_maxpool3D_backward(frameNum,
                         outDiff,
                         channels,
-                        depth,
-                        height,
-                        width,
+                        imgSizeD,
+                        imgSizeH,
+                        imgSizeW,
                         outputD,
                         outputH,
                         outputW,
@@ -1323,19 +1307,16 @@ void GpuMatrix::avgPool3DForward(Matrix& inputMat,
 
   real* inputData = inputMat.getData();
   size_t frameNum = inputMat.getHeight();
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-  size_t depth = imgSizeD;
-  CHECK(depth * height * width * channels == inputMat.getWidth());
+  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == inputMat.getWidth());
   CHECK(height_ == inputMat.getHeight());
   CHECK(width_ == outputD * outputH * outputW * channels);
 
   hl_avgpool3D_forward(frameNum,
                        inputData,
                        channels,
-                       depth,
-                       height,
-                       width,
+                       imgSizeD,
+                       imgSizeH,
+                       imgSizeW,
                        outputD,
                        outputH,
                        outputW,
@@ -1375,19 +1356,16 @@ void GpuMatrix::avgPool3DBackward(Matrix& outGrad,
   real* outDiff = outGrad.getData();
   size_t frameNum = outGrad.getHeight();
   size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-  size_t depth = imgSizeD;
-  CHECK(depth * height * width * channels == width_);
+  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == width_);
   CHECK(height_ == outGrad.getHeight());
   CHECK(outGrad.getWidth() == outputD * outputH * outputW * channels);
 
   hl_avgpool3D_backward(frameNum,
                         outDiff,
                         channels,
-                        depth,
-                        height,
-                        width,
+                        imgSizeD,
+                        imgSizeH,
+                        imgSizeW,
                         outputD,
                         outputH,
                         outputW,
@@ -1999,11 +1977,11 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
   real* inputData = inputMat.getData();
   real* outData = data_;
   size_t num = inputMat.getHeight();
-  size_t inWidth = imgSizeW;
-  size_t inHeight = imgSizeH;
-  CHECK(inHeight * inWidth == inputMat.getWidth() / channels);
+  size_t inLength = imgSizeH * imgSizeW;
+  size_t outLength = outputH * outputW;
+  CHECK(inLength == inputMat.getWidth() / channels);
   CHECK_EQ(num, this->getHeight());
-  CHECK_EQ(channels * outputH * outputW, this->getWidth());
+  CHECK_EQ(channels * outLength, this->getWidth());
   size_t outStride = getStride();
 
   /* initialize the data_ */
@@ -2020,24 +1998,24 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
     }
     for (size_t c = 0; c < channels; ++c) {  // channel by channel
       for (size_t ph = 0; ph < outputH; ++ph) {
+        int hstart = ph * strideH - paddingH;
+        int hend = std::min(hstart + sizeY, imgSizeH);
+        hstart = std::max(hstart, 0);
         for (size_t pw = 0; pw < outputW; ++pw) {
-          int hstart = ph * strideH - paddingH;
           int wstart = pw * strideW - paddingW;
-          int hend = std::min(hstart + sizeY, inHeight);
-          int wend = std::min(wstart + sizeX, inWidth);
-          hstart = std::max(hstart, 0);
+          int wend = std::min(wstart + sizeX, imgSizeW);
           wstart = std::max(wstart, 0);
           for (int h = hstart; h < hend; ++h) {
             for (int w = wstart; w < wend; ++w) {
-              outData[ph * outputW + pw] = std::max(outData[ph * outputW + pw],
-                                                    inputData[h * inWidth + w]);
+              outData[ph * outputW + pw] = std::max(
+                  outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
             }
           }
         }
       }
       // compute offset
-      inputData += inHeight * inWidth;
-      outData += outputH * outputW;
+      inputData += inLength;
+      outData += outLength;
     }
   }
 }
@@ -2058,8 +2036,10 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
                                 size_t paddingH,
                                 size_t paddingW) {
   size_t num = image.getHeight();
-  size_t channels = size_t(width_ / imgSizeH / imgSizeW);
-  CHECK(image.getWidth() == imgSizeH * imgSizeW * channels);
+  size_t inLength = imgSizeH * imgSizeW;
+  size_t outLength = outputH * outputW;
+  size_t channels = size_t(width_ / inLength);
+  CHECK(image.getWidth() == inLength * channels);
   CHECK(image.getHeight() == height_ && image.getWidth() == width_);
   CHECK(outV.getHeight() == outGrad.getHeight() &&
         outV.getWidth() == outGrad.getWidth());
@@ -2080,12 +2060,12 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t ph = 0; ph < outputH; ++ph) {
+        int hstart = ph * strideH - paddingH;
+        int hend = std::min(hstart + sizeY, imgSizeH);
+        hstart = std::max(hstart, 0);
         for (size_t pw = 0; pw < outputW; ++pw) {
-          int hstart = ph * strideH - paddingH;
           int wstart = pw * strideW - paddingW;
-          int hend = std::min(hstart + sizeY, imgSizeH);
           int wend = std::min(wstart + sizeX, imgSizeW);
-          hstart = std::max(hstart, 0);
           wstart = std::max(wstart, 0);
           for (int h = hstart; h < hend; ++h) {
             for (int w = wstart; w < wend; ++w) {
@@ -2098,10 +2078,10 @@ void CpuMatrix::maxPoolBackward(Matrix& image,
         }
       }
       // offset
-      inData += imgSizeH * imgSizeW;
-      tgtGrad += imgSizeH * imgSizeW;
-      otData += outputH * outputW;
-      otGrad += outputH * outputW;
+      inData += inLength;
+      tgtGrad += inLength;
+      otData += outLength;
+      otGrad += outLength;
     }
   }
 }
@@ -2120,10 +2100,10 @@ void CpuMatrix::avgPoolForward(Matrix& input,
                                size_t paddingW) {
   // The main loop
   size_t num = input.getHeight();
-  size_t inHeight = imgSizeH;
-  size_t inWidth = imgSizeW;
-  CHECK(inHeight * inWidth * channels == input.getWidth());
-  CHECK(outputH * outputW * channels * num == height_ * width_);
+  size_t inLength = imgSizeH * imgSizeW;
+  size_t outLength = outputH * outputW;
+  CHECK(inLength * channels == input.getWidth());
+  CHECK(outLength * channels * num == height_ * width_);
   real* tgtData = data_;
   real* inData = input.getData();
 
@@ -2133,30 +2113,27 @@ void CpuMatrix::avgPoolForward(Matrix& input,
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t ph = 0; ph < outputH; ++ph) {
+        int hstart = ph * strideH - paddingH;
+        int hend = std::min(hstart + sizeY, imgSizeH);
+        hstart = std::max(hstart, 0);
         for (size_t pw = 0; pw < outputW; ++pw) {
-          int hstart = ph * strideH - paddingH;
           int wstart = pw * strideW - paddingW;
-          int hend = std::min(hstart + sizeY, inHeight + paddingH);
-          int wend = std::min(wstart + sizeX, inWidth + paddingW);
-          int poolSize = (hend - hstart) * (wend - wstart);
-          hstart = std::max(hstart, 0);
+          int wend = std::min(wstart + sizeX, imgSizeW);
           wstart = std::max(wstart, 0);
-          hend = std::min(hend, static_cast<int>(inHeight));
-          wend = std::min(wend, static_cast<int>(inWidth));
-
-          CHECK(poolSize);
           tgtData[ph * outputW + pw] = 0;  // clear
           for (int h = hstart; h < hend; ++h) {
             for (int w = wstart; w < wend; ++w) {
-              tgtData[ph * outputW + pw] += inData[h * inWidth + w];
+              tgtData[ph * outputW + pw] += inData[h * imgSizeW + w];
             }
           }
+          int poolSize = (hend - hstart) * (wend - wstart);
+          CHECK(poolSize);
           tgtData[ph * outputW + pw] /= poolSize;
         }
       }
       // compute offset
-      inData += inHeight * inWidth;
-      tgtData += outputH * outputW;
+      inData += inLength;
+      tgtData += outLength;
     }
   }
 }
@@ -2176,7 +2153,9 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
                                 size_t paddingW) {
   size_t num = input.getHeight();
   size_t channels = input.getWidth() / outputH / outputW;
-  CHECK(imgSizeH * imgSizeW * channels == getWidth());
+  size_t inLength = imgSizeH * imgSizeW;
+  size_t outLength = outputH * outputW;
+  CHECK(inLength * channels == getWidth());
   real* inData = input.getData();
   real* outData = getData();
 
@@ -2186,16 +2165,14 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t ph = 0; ph < outputH; ++ph) {
+        int hstart = ph * strideH - paddingH;
+        int hend = std::min(hstart + sizeY, imgSizeH);
+        hstart = std::max(hstart, 0);
         for (size_t pw = 0; pw < outputW; ++pw) {
-          int hstart = ph * strideH - paddingH;
           int wstart = pw * strideW - paddingW;
-          int hend = std::min(hstart + sizeY, imgSizeH + paddingH);
-          int wend = std::min(wstart + sizeX, imgSizeW + paddingW);
-          int poolSize = (hend - hstart) * (wend - wstart);
-          hstart = std::max(hstart, 0);
+          int wend = std::min(wstart + sizeX, imgSizeW);
           wstart = std::max(wstart, 0);
-          hend = std::min(hend, static_cast<int>(imgSizeH));
-          wend = std::min(wend, static_cast<int>(imgSizeW));
+          int poolSize = (hend - hstart) * (wend - wstart);
           CHECK(poolSize);
 
           for (int h = hstart; h < hend; ++h) {
@@ -2206,8 +2183,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
         }
       }
       // offset
-      outData += imgSizeH * imgSizeW;
-      inData += outputH * outputW;
+      outData += inLength;
+      inData += outLength;
     }
   }
 }
@@ -2234,12 +2211,11 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
   real* outData = getData();
   real* maxPoolIdxData = maxPoolIdx.getData();
   size_t num = inputMat.getHeight();
-  size_t inWidth = imgSizeW;
-  size_t inHeight = imgSizeH;
-  size_t inDepth = imgSizeD;
-  CHECK(inHeight * inWidth * inDepth == inputMat.getWidth() / channels);
+  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
+  size_t outLength = outputH * outputW * outputD;
+  CHECK(inLength == inputMat.getWidth() / channels);
   CHECK_EQ(num, this->getHeight());
-  CHECK_EQ(channels * outputH * outputW * outputD, this->getWidth());
+  CHECK_EQ(channels * outLength, this->getWidth());
   size_t outStride = getStride();
 
   /* initialize the data_ */
@@ -2258,16 +2234,16 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
     }
     for (size_t c = 0; c < channels; ++c) {  // channel by channel
       for (size_t pd = 0; pd < outputD; ++pd) {
+        int dstart = pd * strideD - paddingD;
+        int dend = std::min(dstart + sizeZ, imgSizeD);
+        dstart = std::max(dstart, 0);
         for (size_t ph = 0; ph < outputH; ++ph) {
+          int hstart = ph * strideH - paddingH;
+          int hend = std::min(hstart + sizeY, imgSizeH);
+          hstart = std::max(hstart, 0);
           for (size_t pw = 0; pw < outputW; ++pw) {
-            int dstart = pd * strideD - paddingD;
-            int hstart = ph * strideH - paddingH;
             int wstart = pw * strideW - paddingW;
-            int dend = std::min(dstart + sizeZ, inDepth);
-            int hend = std::min(hstart + sizeY, inHeight);
-            int wend = std::min(wstart + sizeX, inWidth);
-            dstart = std::max(dstart, 0);
-            hstart = std::max(hstart, 0);
+            int wend = std::min(wstart + sizeX, imgSizeW);
             wstart = std::max(wstart, 0);
             int maxIdx = -1;
             real maxOutData = outData[(pd * outputH + ph) * outputW + pw];
@@ -2275,9 +2251,9 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
               for (int h = hstart; h < hend; ++h) {
                 for (int w = wstart; w < wend; ++w) {
                   if (maxOutData <
-                      inputData[(d * inHeight + h) * inWidth + w]) {
-                    maxOutData = inputData[(d * inHeight + h) * inWidth + w];
-                    maxIdx = (d * inHeight + h) * inWidth + w;
+                      inputData[(d * imgSizeH + h) * imgSizeW + w]) {
+                    maxOutData = inputData[(d * imgSizeH + h) * imgSizeW + w];
+                    maxIdx = (d * imgSizeH + h) * imgSizeW + w;
                   }
                 }
               }
@@ -2288,9 +2264,9 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat,
         }
       }
       // compute offset
-      inputData += inDepth * inHeight * inWidth;
-      outData += outputD * outputH * outputW;
-      maxPoolIdxData += outputD * outputH * outputW;
+      inputData += inLength;
+      outData += outLength;
+      maxPoolIdxData += outLength;
     }
   }
 }
@@ -2315,7 +2291,9 @@ void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
                                   real scaleTargets,
                                   real scaleOutput) {
   size_t num = getHeight();
-  size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW);
+  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
+  size_t outLength = outputH * outputW * outputD;
+  size_t channels = size_t(width_ / inLength);
   CHECK(maxPoolIdx.getHeight() == outGrad.getHeight() &&
         maxPoolIdx.getWidth() == outGrad.getWidth());
 
@@ -2341,9 +2319,9 @@ void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
         }
       }
       // offset
-      tgtGrad += imgSizeD * imgSizeH * imgSizeW;
-      otGrad += outputD * outputH * outputW;
-      maxPoolIdxData += outputD * outputH * outputW;
+      tgtGrad += inLength;
+      otGrad += outLength;
+      maxPoolIdxData += outLength;
     }
   }
 }
@@ -2367,11 +2345,10 @@ void CpuMatrix::avgPool3DForward(Matrix& input,
                                  size_t paddingW) {
   // The main loop
   size_t num = input.getHeight();
-  size_t inDepth = imgSizeD;
-  size_t inHeight = imgSizeH;
-  size_t inWidth = imgSizeW;
-  CHECK(inDepth * inHeight * inWidth * channels == input.getWidth());
-  CHECK(outputD * outputH * outputW * channels * num == height_ * width_);
+  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
+  size_t outLength = outputH * outputW * outputD;
+  CHECK(inLength * channels == input.getWidth());
+  CHECK(outLength * channels * num == height_ * width_);
   real* tgtData = getData();
   real* inData = input.getData();
 
@@ -2381,39 +2358,36 @@ void CpuMatrix::avgPool3DForward(Matrix& input,
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t pd = 0; pd < outputD; ++pd) {
+        int dstart = pd * strideD - paddingD;
+        int dend = std::min(dstart + sizeZ, imgSizeD);
+        dstart = std::max(dstart, 0);
         for (size_t ph = 0; ph < outputH; ++ph) {
+          int hstart = ph * strideH - paddingH;
+          int hend = std::min(hstart + sizeY, imgSizeH);
+          hstart = std::max(hstart, 0);
           for (size_t pw = 0; pw < outputW; ++pw) {
-            int dstart = pd * strideD - paddingD;
-            int hstart = ph * strideH - paddingH;
             int wstart = pw * strideW - paddingW;
-            int dend = std::min(dstart + sizeZ, inDepth + paddingD);
-            int hend = std::min(hstart + sizeY, inHeight + paddingH);
-            int wend = std::min(wstart + sizeX, inWidth + paddingW);
-            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
-            dstart = std::max(dstart, 0);
-            hstart = std::max(hstart, 0);
+            int wend = std::min(wstart + sizeX, imgSizeW);
             wstart = std::max(wstart, 0);
-            dend = std::min(dend, static_cast<int>(inDepth));
-            hend = std::min(hend, static_cast<int>(inHeight));
-            wend = std::min(wend, static_cast<int>(inWidth));
 
-            CHECK(poolSize);
             tgtData[(pd * outputH + ph) * outputW + pw] = 0;  // clear
             for (int d = dstart; d < dend; ++d) {
               for (int h = hstart; h < hend; ++h) {
                 for (int w = wstart; w < wend; ++w) {
                   tgtData[(pd * outputH + ph) * outputW + pw] +=
-                      inData[(d * inHeight + h) * inWidth + w];
+                      inData[(d * imgSizeH + h) * imgSizeW + w];
                 }
               }
             }
+            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
+            CHECK(poolSize);
             tgtData[(pd * outputH + ph) * outputW + pw] /= poolSize;
           }
         }
       }
       // compute offset
-      inData += inDepth * inHeight * inWidth;
-      tgtData += outputD * outputH * outputW;
+      inData += inLength;
+      tgtData += outLength;
     }
   }
 }
@@ -2437,8 +2411,10 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
                                   real scaleTargets,
                                   real scaleOutput) {
   size_t num = input.getHeight();
-  size_t channels = input.getWidth() / outputD / outputH / outputW;
-  CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
+  size_t inLength = imgSizeH * imgSizeW * imgSizeD;
+  size_t outLength = outputH * outputW * outputD;
+  size_t channels = input.getWidth() / outLength;
+  CHECK(inLength * channels == getWidth());
   real* inData = input.getData();
   real* outData = getData();
 
@@ -2448,21 +2424,18 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t pd = 0; pd < outputD; ++pd) {
+        int dstart = pd * strideD - paddingD;
+        int dend = std::min(dstart + sizeZ, imgSizeD);
+        dstart = std::max(dstart, 0);
         for (size_t ph = 0; ph < outputH; ++ph) {
+          int hstart = ph * strideH - paddingH;
+          int hend = std::min(hstart + sizeY, imgSizeH);
+          hstart = std::max(hstart, 0);
           for (size_t pw = 0; pw < outputW; ++pw) {
-            int dstart = pd * strideD - paddingD;
-            int hstart = ph * strideH - paddingH;
             int wstart = pw * strideW - paddingW;
-            int dend = std::min(dstart + sizeZ, imgSizeD + paddingD);
-            int hend = std::min(hstart + sizeY, imgSizeH + paddingH);
-            int wend = std::min(wstart + sizeX, imgSizeW + paddingW);
-            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
-            dstart = std::max(dstart, 0);
-            hstart = std::max(hstart, 0);
+            int wend = std::min(wstart + sizeX, imgSizeW);
             wstart = std::max(wstart, 0);
-            dend = std::min(dend, static_cast<int>(imgSizeD));
-            hend = std::min(hend, static_cast<int>(imgSizeH));
-            wend = std::min(wend, static_cast<int>(imgSizeW));
+            int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
             CHECK(poolSize);
             for (int d = dstart; d < dend; ++d) {
               for (int h = hstart; h < hend; ++h) {
@@ -2476,8 +2449,8 @@ void CpuMatrix::avgPool3DBackward(Matrix& input,
         }
       }
       // offset
-      outData += imgSizeD * imgSizeH * imgSizeW;
-      inData += outputD * outputH * outputW;
+      outData += inLength;
+      inData += outLength;
     }
   }
 }

paddle/math/NEONFunctions.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+
+#include "NEONFunctions.h"
+#include <arm_neon.h>
+
+namespace paddle {
+namespace neon {
+
+// b[i] = a[i] > 0.0f ? a[i] : 0.0f
+void relu(const float* a, float* b, int len) {
+  int offset = len % 16;
+  float32x4_t ma0, ma1, ma2, ma3;
+  float32x4_t mb0, mb1, mb2, mb3;
+
+  float32x4_t zero = vdupq_n_f32(0.f);
+  for (int k = 0; k < len / 16; k++, a += 16, b += 16) {
+    ma0 = vld1q_f32(a);
+    ma1 = vld1q_f32(a + 4);
+    ma2 = vld1q_f32(a + 8);
+    ma3 = vld1q_f32(a + 12);
+
+    mb0 = vmaxq_f32(ma0, zero);
+    mb1 = vmaxq_f32(ma1, zero);
+    mb2 = vmaxq_f32(ma2, zero);
+    mb3 = vmaxq_f32(ma3, zero);
+
+    vst1q_f32(b, mb0);
+    vst1q_f32(b + 4, mb1);
+    vst1q_f32(b + 8, mb2);
+    vst1q_f32(b + 12, mb3);
+  }
+
+  for (int i = 0; i < offset; i++) {
+    b[i] = a[i] > 0.0f ? a[i] : 0.0f;
+  }
+}
+
+}  // namespace neon
+}  // namespace paddle
+
+#endif

paddle/math/NEONFunctions.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+namespace paddle {
+namespace neon {
+
+void relu(const float* a, float* b, int len);
+
+}  // namespace neon
+}  // namespace paddle

paddle/math/tests/test_matrixCompare.cpp

@@ -825,9 +825,8 @@ void testMaxPoolFwdBwd(int numSamples,
                        int strideW,
                        int padH,
                        int padW) {
-  int outH = 0, outW = 0;
-  outH = (imgSizeH - ksizeH + 2 * padH + strideH - 1) / strideH + 1;
-  outW = (imgSizeW - ksizeW + 2 * padW + strideW - 1) / strideW + 1;
+  int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
+  int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
 
   int inWidth = imgSizeH * imgSizeW * channels;
   MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
@@ -927,9 +926,8 @@ void testAvgPoolFwdBwd(int numSamples,
                        int strideW,
                        int padH,
                        int padW) {
-  int outH = 0, outW = 0;
-  outH = (imgSizeH - ksizeH + 2 * padH + strideH - 1) / strideH + 1;
-  outW = (imgSizeW - ksizeW + 2 * padW + strideW - 1) / strideW + 1;
+  int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
+  int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
 
   int inWidth = imgSizeH * imgSizeW * channels;
   MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);

paddle/operators/cross_entropy_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/cross_entropy_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::LoDTensor;
+
+class CrossEntropyOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"), "Output(Y) must not be null.");
+
+    auto x = ctx.Input<Tensor>("X");
+    auto label = ctx.Input<Tensor>("Label");
+    PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
+    PADDLE_ENFORCE_EQ(label->dims().size(), 2,
+                      "Input(Label)'s rank must be 2.");
+    // TODO(xinghai-sun): remove this check after swtiching to bool
+    PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
+                   ctx.Attr<int>("soft_label") == 1);
+    PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
+                      "The 1st dimension of Input(X) and Input(Label) must "
+                      "be equal.");
+    if (ctx.Attr<int>("soft_label") == 1) {
+      PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
+                        "If Attr(soft_label) == 1, The 2nd dimension of "
+                        "Input(X) and Input(Label) must be equal.");
+    } else {
+      PADDLE_ENFORCE_EQ(label->dims()[1], 1,
+                        "If Attr(soft_label) == 0, The 2nd dimension of "
+                        "Input(Label) must be 1.");
+    }
+
+    ctx.Output<LoDTensor>("Y")->Resize({x->dims()[0], 1});
+  }
+};
+
+class CrossEntropyGradientOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
+                            "Input(Y@GRAD) must not be null.");
+
+    auto x = ctx.Input<Tensor>("X");
+    auto label = ctx.Input<Tensor>("Label");
+    auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
+    PADDLE_ENFORCE_EQ(dy->dims().size(), 2, "Input(Y@Grad)'s rank must be 2.");
+    PADDLE_ENFORCE_EQ(label->dims().size(), 2,
+                      "Input(Label)'s rank must be 2.");
+    // TODO(xinghai-sun): remove this check after swtiching to bool
+    PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
+                   ctx.Attr<int>("soft_label") == 1);
+    PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
+                      "The 1st dimension of Input(X) and Input(Label) must "
+                      "be equal.");
+    PADDLE_ENFORCE_EQ(x->dims()[0], dy->dims()[0],
+                      "The 1st dimension of Input(X) and Input(Y@Grad) must "
+                      "be equal.");
+    PADDLE_ENFORCE_EQ(dy->dims()[1], 1,
+                      "The 2nd dimension of Input(Y@Grad) must be 1.");
+    if (ctx.Attr<int>("soft_label") == 1) {
+      PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
+                        "If Attr(soft_label) == 1, The 2nd dimension of "
+                        "Input(X) and Input(Label) must be equal.");
+    } else {
+      PADDLE_ENFORCE_EQ(label->dims()[1], 1,
+                        "If Attr(soft_label) == 0, The 2nd dimension of "
+                        "Input(Label) must be 1.");
+    }
+
+    auto dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    dx->Resize(x->dims());
+  }
+};
+
+class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CrossEntropyOpMaker(framework::OpProto *proto,
+                      framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The first input of CrossEntropyOp");
+    AddInput("Label", "The second input of CrossEntropyOp");
+    AddOutput("Y", "The output of CrossEntropyOp");
+    AddAttr<int>("soft_label", "Is soft label. Default zero.").SetDefault(0);
+
+    AddComment(R"DOC(
+CrossEntropy Operator.
+
+It supports both standard cross-entropy and soft-label cross-entropy loss
+computation.
+1) One-hot cross-entropy:
+    soft_label = 0, Label[i, 0] indicates the class index for sample i:
+
+                Y[i] = -log(X[i, Label[i]])
+
+2) Soft-label cross-entropy:
+    soft_label = 1, Label[i, j] indicates the soft label of class j
+    for sample i:
+
+                Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
+
+   Please make sure that in this case the summuation of each row of Label
+   equals one.
+
+3) One-hot cross-entropy with vecterized Input(Label):
+     As a special case of 2), when each row of Input(Label) has only one
+     non-zero element (equals 1), soft-label cross-entropy degenerates to a
+     one-hot cross-entropy with one-hot label representation.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
+            cross_entropy_grad, ops::CrossEntropyGradientOp);
+REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<float>);
+REGISTER_OP_CPU_KERNEL(cross_entropy_grad,
+                       ops::CrossEntropyGradientOpKernel<float>);

paddle/operators/onehot_cross_entropy_op.cu → paddle/operators/cross_entropy_op.cu

@@ -13,27 +13,13 @@
    limitations under the License. */
 
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/cross_entropy_op.h"
 #include "paddle/platform/assert.h"
+#include "paddle/platform/hostdevice.h"
 
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-
-template <typename T>
-__host__ __device__ T clipping_log(const T x) {
-  PADDLE_ASSERT(std::is_floating_point<T>::value);
-  const T kApproInf = 1e20;
-  T v = log(x);
-  if (v == INFINITY) {
-    return kApproInf;
-  }
-  if (v == -INFINITY) {
-    return -kApproInf;
-  }
-  return v;
-}
-
 template <typename T>
 __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
                                    const int N, const int D) {
@@ -42,7 +28,20 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
        i += blockDim.x * gridDim.x) {
     PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
-    Y[i] = -clipping_log(X[i * D + label[i]]);
+    Y[i] = -tolerable_value(log(X[i * D + label[i]]));
+  }
+}
+
+template <typename T>
+__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
+                                       const int N, const int D) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    T sum = static_cast<T>(0);
+    for (int j = 0; j < D; j++) {
+      sum += label[i * D + j] * tolerable_value(log(X[i * D + j]));
+    }
+    Y[i] = -sum;
   }
 }
 
@@ -69,57 +68,84 @@ __global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
 }
 
 template <typename T>
-class OnehotCrossEntropyOpCUDAKernel : public framework::OpKernel {
+__global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
+                                               const T* label, const int N,
+                                               const int D) {
+  // TOOD(qingqing): optimize for this kernel
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    for (int j = 0; j < D; ++j) {
+      int idx = i * D + j;
+      dX[idx] = -label[idx] * dY[i] / X[idx];
+    }
+  }
+}
+
+template <typename T>
+class CrossEntropyOpCUDAKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "It must use GPUPlace.");
 
-    auto X = ctx.Input<Tensor>("X");
-    const T* Xdata = X->data<T>();
-    const int* label_data = ctx.Input<Tensor>("label")->data<int>();
-    auto Y = ctx.Output<Tensor>("Y");
-    Y->mutable_data<T>(ctx.GetPlace());
-    T* Ydata = Y->data<T>();
+    auto x = ctx.Input<Tensor>("X");
+    auto y = ctx.Output<Tensor>("Y");
+    auto label = ctx.Input<Tensor>("Label");
 
-    int N = X->dims()[0];
-    int D = X->dims()[1];
+    auto* x_data = x->data<T>();
+    y->mutable_data<T>(ctx.GetPlace());
+    auto* y_data = y->data<T>();
+
+    int n = x->dims()[0];
+    int d = x->dims()[1];
     int block = 512;
-    int grid = (N + block - 1) / block;
+    int grid = (n + block - 1) / block;
     // TODO(qingqing) launch kernel on specified stream
     // base on ExecutionContext.
-    CrossEntropyKernel<T><<<grid, block>>>(Ydata, Xdata, label_data, N, D);
+    if (ctx.Attr<int>("soft_label") == 1) {
+      auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
+      SoftCrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n,
+                                                 d);
+    } else {
+      auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
+      CrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n, d);
+    }
   }
 };
 
 template <typename T>
-class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
+class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "It must use GPUPlace.");
 
-    auto X = ctx.Input<Tensor>("X");
-    auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
-    auto label = ctx.Input<Tensor>("label");
+    auto x = ctx.Input<Tensor>("X");
+    auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    auto label = ctx.Input<Tensor>("Label");
 
-    auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
-    auto* dYdata = dY->template data<T>();
-    auto* Xdata = X->template data<T>();
-    auto* label_data = label->data<int>();
+    auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    auto* dy_data = dy->data<T>();
+    auto* x_data = x->data<T>();
 
-    int N = X->dims()[0];
-    int D = X->dims()[1];
+    int n = x->dims()[0];
+    int d = x->dims()[1];
     int block = 512;
-    int grid = (N * D + block - 1) / block;
-    zero<T><<<grid, block>>>(dXdata, N * D);
-
-    grid = (N + block - 1) / block;
+    int grid = (n * d + block - 1) / block;
+    zero<T><<<grid, block>>>(dx_data, n * d);
+    grid = (n + block - 1) / block;
     // TODO(qingqing): launch kernel on specified stream
     // base on ExecutionContext.
-    CrossEntropyGradientKernel<T><<<grid, block>>>(dXdata, dYdata, Xdata,
-                                                   label_data, N, D);
+    if (ctx.Attr<int>("soft_label") == 1) {
+      auto* label_data = label->data<T>();
+      SoftCrossEntropyGradientKernel<T><<<grid, block>>>(
+          dx_data, dy_data, x_data, label_data, n, d);
+    } else {
+      auto* label_data = label->data<int>();
+      CrossEntropyGradientKernel<T><<<grid, block>>>(dx_data, dy_data, x_data,
+                                                     label_data, n, d);
+    }
   }
 };
 
@@ -127,7 +153,6 @@ class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
-                       ops::OnehotCrossEntropyOpCUDAKernel<float>);
-REGISTER_OP_GPU_KERNEL(onehot_cross_entropy_grad,
-                       ops::OnehotCrossEntropyGradientOpCUDAKernel<float>);
+REGISTER_OP_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>);
+REGISTER_OP_GPU_KERNEL(cross_entropy_grad,
+                       ops::CrossEntropyGradientOpCUDAKernel<float>);

paddle/operators/onehot_cross_entropy_op.h → paddle/operators/cross_entropy_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 #include "paddle/framework/op_registry.h"
+#include "paddle/platform/hostdevice.h"
 
 namespace paddle {
 namespace operators {
@@ -21,75 +22,93 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename T>
-inline T tolerable_value(const T x) {
-  static_assert(std::is_floating_point<T>::value,
-                "tolerable_value works only on float, "
-                "double and double double.");
-
+HOSTDEVICE T tolerable_value(const T x) {
+  PADDLE_ASSERT(std::is_floating_point<T>::value);
   const T kApproInf = 1e20;
-
   if (x == INFINITY) {
     return kApproInf;
   }
-
   if (x == -INFINITY) {
     return -kApproInf;
   }
-
   return x;
 }
 
 template <typename T>
-class OnehotCrossEntropyOpKernel : public framework::OpKernel {
+class CrossEntropyOpKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
                    "It must use CPUPlace.");
 
-    auto X = ctx.Input<Tensor>("X");
-    const T* Xdata = X->data<T>();
-    const int* label_data = ctx.Input<Tensor>("label")->data<int>();
-    auto Y = ctx.Output<Tensor>("Y");
-
-    Y->mutable_data<T>(ctx.GetPlace());
-
-    T* Ydata = Y->data<T>();
-
-    int batch_size = X->dims()[0];
-    int class_num = X->dims()[1];
-
-    for (int i = 0; i < batch_size; ++i) {
-      int index = i * class_num + label_data[i];
-      Ydata[i] = -tolerable_value(std::log(Xdata[index]));
+    auto x = ctx.Input<Tensor>("X");
+    auto y = ctx.Output<Tensor>("Y");
+
+    auto* x_data = x->data<T>();
+    y->mutable_data<T>(ctx.GetPlace());
+    auto* y_data = y->data<T>();
+
+    int batch_size = x->dims()[0];
+    int class_num = x->dims()[1];
+
+    if (ctx.Attr<int>("soft_label") == 1) {
+      auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
+      int index = 0;
+      for (int i = 0; i < batch_size; ++i) {
+        T sum = static_cast<T>(0);
+        for (int j = 0; j < class_num; ++j) {
+          sum += label_data[index] * tolerable_value(std::log(x_data[index]));
+          y_data[i] = -sum;
+          index++;
+        }
+      }
+    } else {
+      auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
+      for (int i = 0; i < batch_size; ++i) {
+        int index = i * class_num + label_data[i];
+        y_data[i] = -tolerable_value(std::log(x_data[index]));
+      }
     }
   }
 };
 
 template <typename T>
-class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
+class CrossEntropyGradientOpKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
                    "It must use CPUPlace.");
 
-    auto X = ctx.Input<Tensor>("X");
-    auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
-    auto label = ctx.Input<Tensor>("label");
+    auto x = ctx.Input<Tensor>("X");
+    auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    auto label = ctx.Input<Tensor>("Label");
 
-    auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
-    auto* dYdata = dY->template data<T>();
-    auto* Xdata = X->template data<T>();
-    auto* label_data = label->data<int>();
+    auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    auto* dy_data = dy->data<T>();
+    auto* x_data = x->data<T>();
 
-    const int batch_size = X->dims()[0];
-    const int class_num = X->dims()[1];
+    int batch_size = x->dims()[0];
+    int class_num = x->dims()[1];
 
     // TODO(qingqing): make zero setting an common function.
-    memset(dXdata, 0, sizeof(T) * batch_size * class_num);
-    for (int i = 0; i < batch_size; ++i) {
-      int index = i * class_num + label_data[i];
-      dXdata[index] = -tolerable_value(dYdata[i] / Xdata[index]);
+    if (ctx.Attr<int>("soft_label") == 1) {
+      auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
+      int index = 0;
+      for (int i = 0; i < batch_size; ++i) {
+        for (int j = 0; j < class_num; ++j) {
+          dx_data[index] = -label_data[index] * dy_data[i] / x_data[index];
+          index++;
+        }
+      }
+    } else {
+      auto* label_data = label->data<int>();
+      memset(dx_data, 0, sizeof(T) * batch_size * class_num);
+      for (int i = 0; i < batch_size; ++i) {
+        PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
+        int index = i * class_num + label_data[i];
+        dx_data[index] = -dy_data[i] / x_data[index];
+      }
     }
   }
 };

paddle/operators/dropout_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/dropout_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using framework::LoDTensor;
+
+class DropoutOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_GE(ctx.Attr<float>("dropout_prob"), 0);
+    PADDLE_ENFORCE_LE(ctx.Attr<float>("dropout_prob"), 1);
+    // TODO(xinghai-sun): remove this check after swtiching to bool
+    PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
+                   ctx.Attr<int>("is_training") == 1);
+
+    auto dims = ctx.Input<Tensor>("X")->dims();
+    ctx.Output<LoDTensor>("Out")->Resize(dims);
+    if (ctx.Attr<int>("is_training") == 1) {
+      ctx.Output<LoDTensor>("Mask")->Resize(dims);
+    }
+  }
+};
+
+template <typename AttrType>
+class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  DropoutOpMaker(framework::OpProto *proto,
+                 framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddAttr<AttrType>("dropout_prob", "Probability of setting units to zero.")
+        .SetDefault(.5f);
+    // TODO(xinghai-sun): use bool for is_training after bool is supported.
+    AddAttr<int>("is_training", "Whether in training phase.").SetDefault(1);
+    AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
+    AddInput("X", "The input of dropout op.");
+    AddOutput("Out", "The output of dropout op.");
+    AddOutput("Mask", "The random sampled dropout mask.").AsIntermediate();
+
+    AddComment(R"DOC(
+Dropout Operator.
+
+"Dropout" refers to randomly dropping out units in a nerual network. It is a
+regularization technique for reducing overfitting by preventing neuron
+co-adaption during training. The dropout operator randomly set (according to
+the given dropout probability) the outputs of some units to zero, while others
+being set to their inputs.
+)DOC");
+  }
+};
+
+template <typename AttrType>
+class DropoutOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx.Attr<int>("is_training"), 1,
+                      "GradOp is only callable when is_training is true");
+
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Mask"), "Mask must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) must not be null.");
+
+    PADDLE_ENFORCE_GE(ctx.Attr<AttrType>("dropout_prob"), 0);
+    PADDLE_ENFORCE_LE(ctx.Attr<AttrType>("dropout_prob"), 1);
+    // TODO(xinghai-sun): remove this check after swtiching to bool
+    PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
+                   ctx.Attr<int>("is_training") == 1);
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    PADDLE_ENFORCE_EQ(x_dims, out_dims,
+                      "Dimensions of Input(X) and Out@Grad must be the same.");
+    auto mask_dims = ctx.Input<Tensor>("Mask")->dims();
+    PADDLE_ENFORCE_EQ(x_dims, mask_dims,
+                      "Dimensions of Input(X) and Mask must be the same.");
+
+    auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    x_grad->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(dropout, ops::DropoutOp, ops::DropoutOpMaker<float>, dropout_grad,
+            ops::DropoutOpGrad<float>);
+REGISTER_OP_CPU_KERNEL(
+    dropout, ops::CPUDropoutKernel<paddle::platform::CPUPlace, float, float>);
+REGISTER_OP_CPU_KERNEL(
+    dropout_grad, ops::DropoutGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/dropout_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#define EIGEN_USE_GPU
+#include <thrust/device_ptr.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/random.h>
+#include <thrust/transform.h>
+#include "paddle/operators/dropout_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T, typename AttrType>
+struct MaskGenerator {
+  AttrType dropout_prob;
+  int seed;
+
+  __host__ __device__ MaskGenerator(AttrType dropout_prob, int seed)
+      : dropout_prob(dropout_prob), seed(seed) {}
+
+  __host__ __device__ T operator()(const unsigned int n) const {
+    thrust::minstd_rand rng;
+    rng.seed(seed);
+    thrust::uniform_real_distribution<AttrType> dist(0, 1);
+    rng.discard(n);
+    if (dist(rng) < dropout_prob) {
+      return static_cast<T>(0);
+    } else {
+      return static_cast<T>(1);
+    }
+  }
+};
+
+// It seems that Eigen::Tensor::setRandom in GPU will SEGFAULT.
+// Use std::random and thrust::random(thrust is a std library in CUDA) to
+// implement uniform random.
+template <typename Place, typename T, typename AttrType>
+class GPUDropoutKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto* y = context.Output<Tensor>("Out");
+    y->mutable_data<T>(context.GetPlace());
+    AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
+
+    auto X = EigenMatrix<T>::Reshape(*x, 1);
+    auto Y = EigenMatrix<T>::Reshape(*y, 1);
+
+    auto place = context.GetEigenDevice<Place>();
+    if (context.Attr<int>("is_training") == 1) {
+      auto* mask = context.Output<Tensor>("Mask");
+      auto* mask_data = mask->mutable_data<T>(context.GetPlace());
+      int size = framework::product(mask->dims());
+      int seed = context.Attr<int>("seed");
+      thrust::counting_iterator<unsigned int> index_sequence_begin(0);
+      thrust::transform(index_sequence_begin, index_sequence_begin + size,
+                        thrust::device_ptr<T>(mask_data),
+                        MaskGenerator<T, AttrType>(dropout_prob, seed));
+      auto M = EigenMatrix<T>::Reshape(*mask, 1);
+      Y.device(place) = X * M;
+    } else {
+      Y.device(place) = X * dropout_prob;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    dropout, ops::GPUDropoutKernel<paddle::platform::GPUPlace, float, float>);
+REGISTER_OP_GPU_KERNEL(
+    dropout_grad, ops::DropoutGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/dropout_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+#include <random>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.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 Place, typename T, typename AttrType>
+class CPUDropoutKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto* y = context.Output<Tensor>("Out");
+    const auto* x_data = x->data<T>();
+    auto* y_data = y->mutable_data<T>(context.GetPlace());
+    AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
+
+    if (context.Attr<int>("is_training") == 1) {
+      auto* mask = context.Output<Tensor>("Mask");
+      auto* mask_data = mask->mutable_data<T>(context.GetPlace());
+      int seed = context.Attr<int>("seed");
+      std::minstd_rand engine;
+      engine.seed(seed);
+      std::uniform_real_distribution<AttrType> dist(0, 1);
+      size_t size = framework::product(mask->dims());
+      for (size_t i = 0; i < size; ++i) {
+        if (dist(engine) < dropout_prob) {
+          mask_data[i] = 0;
+          y_data[i] = 0;
+        } else {
+          mask_data[i] = 1;
+          y_data[i] = x_data[i];
+        }
+      }
+    } else {
+      auto X = EigenMatrix<T>::Reshape(*x, 1);
+      auto Y = EigenMatrix<T>::Reshape(*y, 1);
+      auto place = context.GetEigenDevice<Place>();
+      Y.device(place) = X * dropout_prob;
+    }
+  }
+};
+
+template <typename Place, typename T>
+class DropoutGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(context.Attr<int>("is_training"), 1,
+                      "GradOp is only callable when is_training is true");
+
+    auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* mask = context.Input<Tensor>("Mask");
+    grad_x->mutable_data<T>(context.GetPlace());
+
+    auto M = EigenMatrix<T>::Reshape(*mask, 1);
+    auto dX = EigenMatrix<T>::Reshape(*grad_x, 1);
+    auto dY = EigenMatrix<T>::Reshape(*grad_y, 1);
+
+    auto place = context.GetEigenDevice<Place>();
+    dX.device(place) = dY * M;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/math_function.cc

@@ -19,12 +19,13 @@ namespace operators {
 namespace math {
 
 template <>
-void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
+void gemm<platform::CPUPlace, float>(const platform::DeviceContext& context,
+                                     const CBLAS_TRANSPOSE transA,
                                      const CBLAS_TRANSPOSE transB, const int M,
                                      const int N, const int K,
                                      const float alpha, const float* A,
-                                     const float* B, const float beta, float* C,
-                                     platform::DeviceContext* context) {
+                                     const float* B, const float beta,
+                                     float* C) {
   int lda = (transA == CblasNoTrans) ? K : M;
   int ldb = (transB == CblasNoTrans) ? N : K;
   int ldc = N;
@@ -33,13 +34,13 @@ void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
 }
 
 template <>
-void gemm<platform::CPUPlace, double>(const CBLAS_TRANSPOSE transA,
+void gemm<platform::CPUPlace, double>(const platform::DeviceContext& context,
+                                      const CBLAS_TRANSPOSE transA,
                                       const CBLAS_TRANSPOSE transB, const int M,
                                       const int N, const int K,
                                       const double alpha, const double* A,
                                       const double* B, const double beta,
-                                      double* C,
-                                      platform::DeviceContext* context) {
+                                      double* C) {
   int lda = (transA == CblasNoTrans) ? K : M;
   int ldb = (transB == CblasNoTrans) ? N : K;
   int ldc = N;
@@ -48,13 +49,10 @@ void gemm<platform::CPUPlace, double>(const CBLAS_TRANSPOSE transA,
 }
 
 template <>
-void matmul<platform::CPUPlace, float>(const framework::Tensor& matrix_a,
-                                       bool trans_a,
-                                       const framework::Tensor& matrix_b,
-                                       bool trans_b, float alpha,
-                                       framework::Tensor* matrix_out,
-                                       float beta,
-                                       platform::DeviceContext* context) {
+void matmul<platform::CPUPlace, float>(
+    const platform::DeviceContext& context, const framework::Tensor& matrix_a,
+    bool trans_a, const framework::Tensor& matrix_b, bool trans_b, float alpha,
+    framework::Tensor* matrix_out, float beta) {
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -74,18 +72,15 @@ void matmul<platform::CPUPlace, float>(const framework::Tensor& matrix_a,
   CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
 
   gemm<platform::CPUPlace, float>(
-      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
-      matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
+      context, transA, transB, M, N, K, alpha, matrix_a.data<float>(),
+      matrix_b.data<float>(), beta, matrix_out->data<float>());
 }
 
 template <>
-void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
-                                        bool trans_a,
-                                        const framework::Tensor& matrix_b,
-                                        bool trans_b, double alpha,
-                                        framework::Tensor* matrix_out,
-                                        double beta,
-                                        platform::DeviceContext* context) {
+void matmul<platform::CPUPlace, double>(
+    const platform::DeviceContext& context, const framework::Tensor& matrix_a,
+    bool trans_a, const framework::Tensor& matrix_b, bool trans_b, double alpha,
+    framework::Tensor* matrix_out, double beta) {
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -105,8 +100,8 @@ void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
   CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
 
   gemm<platform::CPUPlace, double>(
-      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
-      matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
+      context, transA, transB, M, N, K, alpha, matrix_a.data<double>(),
+      matrix_b.data<double>(), beta, matrix_out->data<double>());
 }
 
 }  // namespace math

paddle/operators/math/math_function.cu

@@ -19,12 +19,13 @@ namespace operators {
 namespace math {
 
 template <>
-void gemm<platform::GPUPlace, float>(const CBLAS_TRANSPOSE transA,
+void gemm<platform::GPUPlace, float>(const platform::DeviceContext& context,
+                                     const CBLAS_TRANSPOSE transA,
                                      const CBLAS_TRANSPOSE transB, const int M,
                                      const int N, const int K,
                                      const float alpha, const float* A,
-                                     const float* B, const float beta, float* C,
-                                     platform::DeviceContext* context) {
+                                     const float* B, const float beta,
+                                     float* C) {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -35,18 +36,19 @@ void gemm<platform::GPUPlace, float>(const CBLAS_TRANSPOSE transA,
       (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
 
   PADDLE_ENFORCE(platform::dynload::cublasSgemm(
-      reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
+      reinterpret_cast<const platform::CUDADeviceContext&>(context)
+          .cublas_handle(),
       cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
 }
 
 template <>
-void gemm<platform::GPUPlace, double>(const CBLAS_TRANSPOSE transA,
+void gemm<platform::GPUPlace, double>(const platform::DeviceContext& context,
+                                      const CBLAS_TRANSPOSE transA,
                                       const CBLAS_TRANSPOSE transB, const int M,
                                       const int N, const int K,
                                       const double alpha, const double* A,
                                       const double* B, const double beta,
-                                      double* C,
-                                      platform::DeviceContext* context) {
+                                      double* C) {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -56,18 +58,16 @@ void gemm<platform::GPUPlace, double>(const CBLAS_TRANSPOSE transA,
   cublasOperation_t cuTransB =
       (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
   PADDLE_ENFORCE(platform::dynload::cublasDgemm(
-      reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
+      reinterpret_cast<const platform::CUDADeviceContext&>(context)
+          .cublas_handle(),
       cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
 }
 
 template <>
-void matmul<platform::GPUPlace, float>(const framework::Tensor& matrix_a,
-                                       bool trans_a,
-                                       const framework::Tensor& matrix_b,
-                                       bool trans_b, float alpha,
-                                       framework::Tensor* matrix_out,
-                                       float beta,
-                                       platform::DeviceContext* context) {
+void matmul<platform::GPUPlace, float>(
+    const platform::DeviceContext& context, const framework::Tensor& matrix_a,
+    bool trans_a, const framework::Tensor& matrix_b, bool trans_b, float alpha,
+    framework::Tensor* matrix_out, float beta) {
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -87,18 +87,15 @@ void matmul<platform::GPUPlace, float>(const framework::Tensor& matrix_a,
   CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
 
   gemm<platform::GPUPlace, float>(
-      transA, transB, M, N, K, alpha, matrix_a.data<float>(),
-      matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
+      context, transA, transB, M, N, K, alpha, matrix_a.data<float>(),
+      matrix_b.data<float>(), beta, matrix_out->data<float>());
 }
 
 template <>
-void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
-                                        bool trans_a,
-                                        const framework::Tensor& matrix_b,
-                                        bool trans_b, double alpha,
-                                        framework::Tensor* matrix_out,
-                                        double beta,
-                                        platform::DeviceContext* context) {
+void matmul<platform::GPUPlace, double>(
+    const platform::DeviceContext& context, const framework::Tensor& matrix_a,
+    bool trans_a, const framework::Tensor& matrix_b, bool trans_b, double alpha,
+    framework::Tensor* matrix_out, double beta) {
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -118,8 +115,8 @@ void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
   CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
 
   gemm<platform::GPUPlace, double>(
-      transA, transB, M, N, K, alpha, matrix_a.data<double>(),
-      matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
+      context, transA, transB, M, N, K, alpha, matrix_a.data<double>(),
+      matrix_b.data<double>(), beta, matrix_out->data<double>());
 }
 
 }  // namespace math

paddle/operators/math/math_function.h

@@ -66,16 +66,16 @@ namespace math {
 // For more detailed info, please refer to
 // http://www.netlib.org/lapack/explore-html/d4/de2/sgemm_8f.html
 template <typename Place, typename T>
-void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
-          const int M, const int N, const int K, const T alpha, const T* A,
-          const T* B, const T beta, T* C, platform::DeviceContext* context);
+void gemm(const platform::DeviceContext& context, const CBLAS_TRANSPOSE transA,
+          const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
+          const T alpha, const T* A, const T* B, const T beta, T* C);
 
 // matrix multiply with continuous memory
 template <typename Place, typename T>
-void matmul(const framework::Tensor& matrix_a, bool trans_a,
+void matmul(const platform::DeviceContext& context,
+            const framework::Tensor& matrix_a, bool trans_a,
             const framework::Tensor& matrix_b, bool trans_b, T alpha,
-            framework::Tensor* matrix_out, T beta,
-            platform::DeviceContext* context);
+            framework::Tensor* matrix_out, T beta);
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/math_function_test.cc

@@ -15,8 +15,7 @@ TEST(math_function, notrans_mul_trans) {
   memcpy(input1_ptr, arr, 6 * sizeof(float));
 
   auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::DeviceContext* context =
-      new paddle::platform::CUDADeviceContext(*gpu_place);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
 
   input1_gpu.CopyFrom<float>(input1, *gpu_place);
   input2_gpu.CopyFrom<float>(input1, *gpu_place);
@@ -24,7 +23,7 @@ TEST(math_function, notrans_mul_trans) {
   out_gpu.mutable_data<float>({2, 2}, *gpu_place);
 
   paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
-      input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0, context);
+      context, input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0);
 
   out.CopyFrom<float>(out_gpu, *cpu_place);
 
@@ -33,6 +32,7 @@ TEST(math_function, notrans_mul_trans) {
   EXPECT_EQ(out_ptr[1], 14);
   EXPECT_EQ(out_ptr[2], 14);
   EXPECT_EQ(out_ptr[3], 50);
+  delete gpu_place;
 }
 
 TEST(math_function, trans_mul_notrans) {
@@ -48,8 +48,7 @@ TEST(math_function, trans_mul_notrans) {
   memcpy(input1_ptr, arr, 6 * sizeof(float));
 
   auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::DeviceContext* context =
-      new paddle::platform::CUDADeviceContext(*gpu_place);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
 
   input1_gpu.CopyFrom<float>(input1, *gpu_place);
   input2_gpu.CopyFrom<float>(input1, *gpu_place);
@@ -57,7 +56,7 @@ TEST(math_function, trans_mul_notrans) {
   out_gpu.mutable_data<float>({3, 3}, *gpu_place);
 
   paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
-      input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0, context);
+      context, input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0);
 
   out.CopyFrom<float>(out_gpu, *cpu_place);
 
@@ -71,5 +70,6 @@ TEST(math_function, trans_mul_notrans) {
   EXPECT_EQ(out_ptr[6], 15);
   EXPECT_EQ(out_ptr[7], 22);
   EXPECT_EQ(out_ptr[8], 29);
+  delete gpu_place;
 }
 #endif

paddle/operators/mul_op.h

@@ -46,10 +46,8 @@ class MulKernel : public framework::OpKernel {
             : *y;
 
     z->mutable_data<T>(context.GetPlace());
-    auto* device_context =
-        const_cast<platform::DeviceContext*>(context.device_context_);
-    math::matmul<Place, T>(x_matrix, false, y_matrix, false, 1, z, 0,
-                           device_context);
+    math::matmul<Place, T>(context.device_context(), x_matrix, false, y_matrix,
+                           false, 1, z, 0);
   }
 };
 
@@ -71,16 +69,14 @@ class MulGradKernel : public framework::OpKernel {
 
     Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     Tensor* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-    auto* device_context =
-        const_cast<platform::DeviceContext*>(ctx.device_context_);
     if (dx) {
       dx->mutable_data<T>(ctx.GetPlace());
       Tensor dx_matrix = dx->dims().size() > 2 ? framework::ReshapeToMatrix<T>(
                                                      *dx, x_num_col_dims)
                                                : *dx;
       // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
-      math::matmul<Place, T>(*dout, false, y_matrix, true, 1, &dx_matrix, 0,
-                             device_context);
+      math::matmul<Place, T>(ctx.device_context(), *dout, false, y_matrix, true,
+                             1, &dx_matrix, 0);
     }
     if (dy) {
       dy->mutable_data<T>(ctx.GetPlace());
@@ -88,8 +84,8 @@ class MulGradKernel : public framework::OpKernel {
                                                      *dy, y_num_col_dims)
                                                : *dy;
       // dy = x' * dout. dy K x N, dout : M x N, x : M x K
-      math::matmul<Place, T>(x_matrix, true, *dout, false, 1, &dy_matrix, 0,
-                             device_context);
+      math::matmul<Place, T>(ctx.device_context(), x_matrix, true, *dout, false,
+                             1, &dy_matrix, 0);
     }
   }
 };

paddle/operators/onehot_cross_entropy_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/operators/onehot_cross_entropy_op.h"
-
-namespace paddle {
-namespace operators {
-
-class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_NOT_NULL(
-        ctx.InputVar("X"),
-        "Input(X) of OnehotCrossEntropyOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(
-        ctx.InputVar("label"),
-        "Input(label) of OnehotCrossEntropyOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(
-        ctx.OutputVar("Y"),
-        "Output(Y) of OnehotCrossEntropyOp should not be null.");
-
-    auto *X = ctx.Input<Tensor>("X");
-    auto *label = ctx.Input<Tensor>("label");
-
-    PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
-    PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
-    PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
-    ctx.Output<framework::LoDTensor>("Y")->Resize({X->dims()[0], 1});
-  }
-};
-
-class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto dX = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
-    auto X = ctx.Input<Tensor>("X");
-
-    dX->Resize(X->dims());
-  }
-};
-
-class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  OnehotCrossEntropyOpMaker(framework::OpProto *proto,
-                            framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The first input of OnehotCrossEntropyOp");
-    AddInput("label", "The second input of OnehotCrossEntropyOp");
-    AddOutput("Y", "The output of OnehotCrossEntropyOp");
-    AddComment(R"DOC(
-OnehotCrossEntropy Operator.
-
-                Y[i] = -log(X[i][j])
-
-)DOC");
-  }
-};
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
-            ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
-            ops::OnehotCrossEntropyGradientOp);
-REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
-                       ops::OnehotCrossEntropyOpKernel<float>);
-REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,
-                       ops::OnehotCrossEntropyGradientOpKernel<float>);

paddle/operators/prelu_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/prelu_op.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace operators {
+
+class PReluOp : public framework::OperatorWithKernel {
+ public:
+  PReluOp(const std::string &type, const framework::VariableNameMap &inputs,
+          const framework::VariableNameMap &outputs,
+          const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    auto *in = ctx.Input<framework::Tensor>("X");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Alpha"),
+                            "Input(Alpha) should not be null");
+    auto *alpha = ctx.Input<framework::Tensor>("Alpha");
+    PADDLE_ENFORCE(alpha->numel() == 1, "Size of weight Alpha must be one.");
+
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) should not be null");
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
+    out->Resize(in->dims());
+  }
+};
+
+class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input tensor of prelu operator.");
+    AddInput("Alpha", "The alpha weight of PRelu operator.");
+    AddOutput("Out", "The output tensor of PRelu operator.");
+    AddComment(R"DOC(PRelu operator
+
+The equation is:
+
+  f(x) = alpha * x , for x < 0
+  f(x) = x         , for x >= 0
+
+)DOC");
+  }
+};
+
+// The operator to calculate gradients of a prelu operator.
+class PReluGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto *dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *x = ctx.Input<framework::Tensor>("X");
+
+    auto *dalpha =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Alpha"));
+    auto *alpha = ctx.Input<framework::Tensor>("Alpha");
+
+    dx->Resize(x->dims());
+    dalpha->Resize(alpha->dims());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP(prelu, ops::PReluOp, ops::PReluOpMaker, prelu_grad,
+            ops::PReluGradOp);
+REGISTER_OP_CPU_KERNEL(prelu,
+                       ops::PReluKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(prelu_grad,
+                       ops::PReluGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/prelu_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/prelu_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    prelu, paddle::operators::PReluKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    prelu_grad,
+    paddle::operators::PReluGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/prelu_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using platform::Transform;
+
+template <typename T>
+class PReluFunctor {
+ public:
+  explicit PReluFunctor(const T* alpha) : alpha_(alpha) {}
+
+  HOSTDEVICE T operator()(const T& x) const {
+    if (x > 0)
+      return x;
+    else
+      return x * (*alpha_);
+  }
+
+ private:
+  const T* alpha_;
+};
+
+template <typename Place, typename T>
+class PReluKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto* alpha = context.Input<Tensor>("Alpha");
+    auto* out = context.Output<Tensor>("Out");
+
+    const T* x_ptr = x->data<T>();
+    T* o_ptr = out->mutable_data<T>(context.GetPlace());
+
+    auto* alpha_ptr = alpha->data<T>();
+
+    int numel = x->numel();
+
+    Transform(context.device_context(), x_ptr, x_ptr + numel, o_ptr,
+              PReluFunctor<T>(alpha_ptr));
+  }
+};
+
+template <typename T>
+class PReluGradFunctor {
+ public:
+  explicit PReluGradFunctor(const T* alpha) : alpha_(alpha) {}
+
+  HOSTDEVICE T operator()(const T& out, const T& dout) const {
+    if (out > 0)
+      return dout;
+    else
+      return dout * (*alpha_);
+  }
+
+ private:
+  const T* alpha_;
+};
+
+template <typename Place, typename T>
+class PReluGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto* out = context.Input<Tensor>("Out");
+    auto* alpha = context.Input<Tensor>("Alpha");
+    auto* alpha_ptr = alpha->data<T>();
+
+    T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
+    const T* dout_ptr = dout->data<T>();
+    const T* out_ptr = out->data<T>();
+    int numel = dx->numel();
+
+    Transform(context.device_context(), out_ptr, out_ptr + numel, dout_ptr,
+              dx_ptr, PReluGradFunctor<T>(alpha_ptr));
+
+    // TODO (Zhuoyuan): add dalpha upgrade when GPU kernels ready
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/split_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/split_op.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class SplitOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    // infershape
+    auto *in = ctx.Input<framework::Tensor>("X");
+    auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
+    size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
+    size_t num = static_cast<size_t>(ctx.Attr<int>("num"));
+    std::vector<int> sections =
+        static_cast<std::vector<int>>(ctx.Attr<std::vector<int>>("sections"));
+    const size_t n = outs.size();
+
+    if (num > 0) {
+      int64_t in_axis_dim = in->dims()[axis];
+      PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
+                        "tensor split does not result"
+                        " in an equal division");
+      size_t out_axis_dim = in_axis_dim / num;
+      for (size_t i = 0; i < n; ++i) {
+        auto dim = in->dims();
+        dim[axis] = out_axis_dim;
+        outs[i]->Resize(dim);
+      }
+    } else if (sections.size() > 0) {
+      PADDLE_ENFORCE_EQ(sections.size(), n,
+                        "tensor split sections size"
+                        "should be equal to output size.");
+      for (size_t i = 0; i < n; ++i) {
+        auto dim = in->dims();
+        dim[axis] = sections[i];
+        outs[i]->Resize(dim);
+      }
+    } else {
+      PADDLE_ENFORCE_NOT_NULL(nullptr, "split operator should",
+                              " specify indices or sections.");
+    }
+  }
+};
+
+class SplitOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SplitOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "the input tensor of split operator.");
+    AddOutput("Out", "the output tensors of split operator.").AsDuplicable();
+    AddComment(R"DOC(
+      Split the input tensor into multiple sub-tensors.
+      Example:
+        Input = [[1,2],
+                 [3,4],
+                 [5,6]]
+        sections = [2,1]
+        axis = 0
+        Output[0] = [[1,2],
+                     [3,4]]
+        Output[1] = [[5,6]]
+
+    )DOC");
+    AddAttr<std::vector<int>>("sections",
+                              "the length for each"
+                              "output along with the specify axis.")
+        .SetDefault(std::vector<int>{});
+    AddAttr<int>("num",
+                 "number of the sub-tensors, it must evenly divide "
+                 "Input.dims()[axis]")
+        .SetDefault(0);
+    AddAttr<int>("axis", "The axis which the input will be splited on.")
+        .SetDefault(0);
+  }
+};
+
+class SplitOpGrad : public NetOp {
+ public:
+  SplitOpGrad(const std::string &type, const framework::VariableNameMap &inputs,
+              const framework::VariableNameMap &outputs,
+              const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    auto out_grad = Inputs(framework::GradVarName("Out"));
+    auto x_grad = Output(framework::GradVarName("X"));
+    AppendOp(framework::OpRegistry::CreateOp("concat", {{"X", out_grad}},
+                                             {{"Out", {x_grad}}}, attrs));
+    CompleteAddOp(false);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+USE_CPU_ONLY_OP(concat);
+REGISTER_OP(split, ops::SplitOp, ops::SplitOpMaker, split_grad,
+            ops::SplitOpGrad);
+REGISTER_OP_CPU_KERNEL(split,
+                       ops::SplitKernel<paddle::platform::CPUPlace, float>);

paddle/operators/split_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <vector>
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class SplitKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<framework::Tensor>("X");
+    auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
+    size_t before = 1, after = 1;
+    const size_t n = outs.size();
+    size_t input_axis_dim = in->dims()[axis];
+
+    for (int64_t i = 0; i < in->dims().size(); ++i) {
+      if (i == axis) {
+        continue;
+      }
+      if (i < axis) {
+        before *= in->dims()[i];
+      } else {
+        after *= in->dims()[i];
+      }
+    }
+    size_t input_offset = 0;
+    for (size_t i = 0; i < n; i++) {
+      auto& out = outs[i];
+      size_t axis_dim = out->dims()[axis];
+      for (size_t j = 0; j < before; j++) {
+        size_t len = axis_dim * after * sizeof(T);
+        T* dest =
+            out->mutable_data<T>(platform::CPUPlace()) + axis_dim * after * j;
+        const T* src =
+            in->data<T>() + input_offset + input_axis_dim * after * j;
+        memcpy(dest, src, len);
+      }
+      input_offset += axis_dim * after;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/platform/CMakeLists.txt

@@ -24,4 +24,4 @@ cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
 nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
 
 nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
-nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place)
+nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context)

paddle/platform/device_context.cc

@@ -101,19 +101,17 @@ CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) {
   eigen_stream_.reset(new EigenCudaStreamDevice());
   eigen_stream_->Reinitialize(&stream_, place);
   eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
+  PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
+  PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
+  PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
+  PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_));
 }
 
 CUDADeviceContext::~CUDADeviceContext() {
   SetDeviceId(place_.device);
   Wait();
-  if (cublas_handle_) {
-    PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
-  }
-
-  if (cudnn_handle_) {
-    PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
-  }
-
+  PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
+  PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
   eigen_stream_.reset();
   eigen_device_.reset();
   PADDLE_ENFORCE(cudaStreamDestroy(stream_));
@@ -129,25 +127,13 @@ Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
   return eigen_device_.get();
 }
 
-cublasHandle_t CUDADeviceContext::cublas_handle() {
-  if (!cublas_handle_) {
-    SetDeviceId(place_.device);
-    PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
-    PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
-  }
+cublasHandle_t CUDADeviceContext::cublas_handle() const {
   return cublas_handle_;
 }
 
-cudnnHandle_t CUDADeviceContext::cudnn_handle() {
-  if (!cudnn_handle_) {
-    SetDeviceId(place_.device);
-    PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_));
-    PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_));
-  }
-  return cudnn_handle_;
-}
+cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
 
-cudaStream_t CUDADeviceContext::stream() { return stream_; }
+cudaStream_t CUDADeviceContext::stream() const { return stream_; }
 
 #endif  // PADDLE_ONLY_CPU
 

paddle/platform/device_context.h

@@ -67,16 +67,14 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return eigen device in the device context. */
   Eigen::GpuDevice* eigen_device() const;
 
-  // clang-format off
   /*! \brief  Return cublas handle in the device context. */
-  cublasHandle_t    cublas_handle();
+  cublasHandle_t cublas_handle() const;
 
   /*! \brief  Return cudnn  handle in the device context. */
-  cudnnHandle_t     cudnn_handle();
+  cudnnHandle_t cudnn_handle() const;
 
   /*! \brief  Return cuda stream in the device context. */
-  cudaStream_t      stream();
-  // clang-format on
+  cudaStream_t stream() const;
 
  private:
   GPUPlace place_;
@@ -84,11 +82,9 @@ class CUDADeviceContext : public DeviceContext {
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
 
-  // clang-format off
-  cudaStream_t       stream_{nullptr};
-  cudnnHandle_t      cudnn_handle_{nullptr};
-  cublasHandle_t     cublas_handle_{nullptr};
-  // clang-format on
+  cudaStream_t stream_;
+  cudnnHandle_t cudnn_handle_;
+  cublasHandle_t cublas_handle_;
 };
 
 #endif

paddle/platform/transform.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include "paddle/platform/device_context.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/hostdevice.h"
 #include "paddle/platform/place.h"
@@ -21,6 +22,7 @@
 #include <algorithm>
 #include <type_traits>
 #ifdef __NVCC__
+#include <thrust/execution_policy.h>
 #include <thrust/transform.h>
 #include "paddle/platform/details/device_ptr_cast.h"
 #endif
@@ -28,34 +30,39 @@
 namespace paddle {
 namespace platform {
 // Transform on host or device. It provides the same API in std library.
-template <typename Place, typename InputIter, typename OutputIter,
-          typename UnaryOperation>
-void Transform(Place place, InputIter first, InputIter last, OutputIter result,
-               UnaryOperation op) {
+template <typename InputIter, typename OutputIter, typename UnaryOperation>
+void Transform(const DeviceContext& context, InputIter first, InputIter last,
+               OutputIter result, UnaryOperation op) {
+  auto place = context.GetPlace();
   if (is_cpu_place(place)) {
     std::transform(first, last, result, op);
   } else {
 #ifdef __NVCC__
+    auto& ctx = reinterpret_cast<const CUDADeviceContext&>(context);
     using namespace details;
-    thrust::transform(DevPtrCast(first), DevPtrCast(last), DevPtrCast(result),
-                      op);
+    thrust::transform(thrust::cuda::par.on(ctx.stream()), DevPtrCast(first),
+                      DevPtrCast(last), DevPtrCast(result), op);
 #else
     PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
 #endif
   }
 }
 
-template <typename Place, typename InputIter1, typename InputIter2,
-          typename OutputIter, typename BinaryOperation>
-void Transform(Place place, InputIter1 first1, InputIter1 last1,
-               InputIter2 first2, OutputIter result, BinaryOperation op) {
+template <typename InputIter1, typename InputIter2, typename OutputIter,
+          typename BinaryOperation>
+void Transform(const DeviceContext& context, InputIter1 first1,
+               InputIter1 last1, InputIter2 first2, OutputIter result,
+               BinaryOperation op) {
+  auto place = context.GetPlace();
   if (is_cpu_place(place)) {
     std::transform(first1, last1, first2, result, op);
   } else {
 #ifdef __NVCC__
+    auto& ctx = reinterpret_cast<const CUDADeviceContext&>(context);
     using namespace details;
-    thrust::transform(DevPtrCast(first1), DevPtrCast(last1), DevPtrCast(first2),
-                      DevPtrCast(result), op);
+    thrust::transform(thrust::cuda::par.on(ctx.stream()), DevPtrCast(first1),
+                      DevPtrCast(last1), DevPtrCast(first2), DevPtrCast(result),
+                      op);
 #else
     PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
 #endif

paddle/platform/transform_test.cu

@@ -36,8 +36,9 @@ class Multiply {
 
 TEST(Transform, CPUUnary) {
   using namespace paddle::platform;
+  CPUDeviceContext ctx;
   float buf[4] = {0.1, 0.2, 0.3, 0.4};
-  Transform(CPUPlace(), buf, buf + 4, buf, Scale<float>(10));
+  Transform(ctx, buf, buf + 4, buf, Scale<float>(10));
   for (int i = 0; i < 4; ++i) {
     ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
   }
@@ -47,10 +48,12 @@ TEST(Transform, GPUUnary) {
   using namespace paddle::platform;
   using namespace paddle::memory;
   GPUPlace gpu0(0);
+  CUDADeviceContext ctx(gpu0);
   float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
   float* gpu_buf = static_cast<float*>(Alloc(gpu0, sizeof(float) * 4));
   Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf));
-  Transform(gpu0, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
+  Transform(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
+  ctx.Wait();
   Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf));
   Free(gpu0, gpu_buf);
   for (int i = 0; i < 4; ++i) {
@@ -62,7 +65,7 @@ TEST(Transform, CPUBinary) {
   using namespace paddle::platform;
   using namespace paddle::memory;
   int buf[4] = {1, 2, 3, 4};
-  Transform(CPUPlace(), buf, buf + 4, buf, buf, Multiply<int>());
+  Transform(CPUDeviceContext(), buf, buf + 4, buf, buf, Multiply<int>());
   for (int i = 0; i < 4; ++i) {
     ASSERT_EQ((i + 1) * (i + 1), buf[i]);
   }
@@ -73,9 +76,11 @@ TEST(Transform, GPUBinary) {
   using namespace paddle::memory;
   int buf[4] = {1, 2, 3, 4};
   GPUPlace gpu0(0);
+  CUDADeviceContext ctx(gpu0);
   int* gpu_buf = static_cast<int*>(Alloc(gpu0, sizeof(buf)));
   Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf));
-  Transform(gpu0, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
+  Transform(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
+  ctx.Wait();
   Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf));
   Free(gpu0, gpu_buf);
   for (int i = 0; i < 4; ++i) {

python/paddle/trainer/config_parser.py

@@ -2286,8 +2286,15 @@ class NormLayer(LayerBase):
 
 @config_layer('pool')
 class PoolLayer(LayerBase):
+    layer_type = 'pool'
+
     def __init__(self, name, inputs, ceil_mode=True, **xargs):
-        super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs)
+        use_mkldnn = int(g_command_config_args.get("use_mkldnn", 0))
+        if self.layer_type == "mkldnn_pool":
+            config_assert(use_mkldnn, "mkldnn_pool only support MKLDNN")
+        self.layer_type = 'mkldnn_pool' if use_mkldnn else 'pool'
+        super(PoolLayer, self).__init__(
+            name, self.layer_type, 0, inputs=inputs, **xargs)
         for input_index in xrange(len(self.inputs)):
             input_layer = self.get_input_layer(input_index)
             pool_conf = self.config.inputs[input_index].pool_conf
@@ -2297,6 +2304,11 @@ class PoolLayer(LayerBase):
                                pool_conf.channels)
 
 
+@config_layer('mkldnn_pool')
+class MKLDNNPoolLayer(PoolLayer):
+    layer_type = 'mkldnn_pool'
+
+
 @config_layer('pool3d')
 class Pool3DLayer(LayerBase):
     def __init__(self, name, inputs, ceil_mode=True, **xargs):

python/paddle/trainer_config_helpers/layers.py

@@ -1224,8 +1224,8 @@ def detection_output_layer(input_loc,
                            name=None):
     """
     Apply the NMS to the output of network and compute the predict bounding
-    box location. The output of this layer could be None if there is no valid
-    bounding box.
+    box location. The output's shape of this layer could be zero if there is
+    no valid bounding box.
 
     :param name: The Layer Name.
     :type name: basestring

python/paddle/v2/framework/tests/op_test.py

@@ -192,25 +192,29 @@ class OpTest(unittest.TestCase):
         self.op.run(self.scope, ctx)
 
         for out_name, out_dup in Operator.get_op_outputs(self.op.type()):
+            if out_name not in self.outputs:
+                continue
+
             if out_dup:
                 sub_out = self.outputs[out_name]
-                for sub_out_name, sub_out_array in sub_out:
+                if not isinstance(sub_out, list):
+                    raise AssertionError("sub_out type %s is not list",
+                                         type(sub_out))
+
+                for sub_out_name, expect in sub_out:
                     actual = np.array(
                         self.scope.find_var(sub_out_name).get_tensor())
-                    expect = sub_out_array
                     self.assertTrue(
                         np.allclose(
                             actual, expect, atol=1e-05),
                         "output name: " + out_name + " has diff")
             else:
-                var = self.scope.find_var(out_name)
-                if var is not None:
-                    actual = np.array(var.get_tensor())
-                    expect = self.outputs[out_name]
-                    self.assertTrue(
-                        np.allclose(
-                            actual, expect, atol=1e-05),
-                        "output name: " + out_name + " has diff")
+                actual = np.array(self.scope.find_var(out_name).get_tensor())
+                expect = self.outputs[out_name]
+                self.assertTrue(
+                    np.allclose(
+                        actual, expect, atol=1e-05),
+                    "output name: " + out_name + " has diff")
 
     def check_output(self):
         places = [core.CPUPlace()]

python/paddle/v2/framework/tests/test_cross_entropy_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestCrossEntropyOp1(OpTest):
+    """Test standard cross-entropy, with index representation of labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        batch_size = 30
+        class_num = 10
+        X = np.random.uniform(0.1, 1.0,
+                              [batch_size, class_num]).astype("float32")
+        label = np.random.randint(0, class_num, (batch_size, 1), dtype="int32")
+        cross_entropy = np.asmatrix(
+            [[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])],
+            dtype="float32")
+        self.inputs = {"X": X, "Label": label}
+        self.outputs = {"Y": cross_entropy}
+        self.attrs = {'soft_label': 0}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Y")
+
+
+class TestCrossEntropyOp2(OpTest):
+    """Test soft-label cross-entropy, with vecterized soft labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        batch_size = 10
+        class_num = 5
+        X = np.random.uniform(0.1, 1.0,
+                              [batch_size, class_num]).astype("float32")
+        label = np.random.uniform(0.1, 1.0,
+                                  [batch_size, class_num]).astype("float32")
+        label /= label.sum(axis=1, keepdims=True)
+        cross_entropy = (-label * np.log(X)).sum(
+            axis=1, keepdims=True).astype("float32")
+        self.inputs = {'X': X, 'Label': label}
+        self.outputs = {'Y': cross_entropy}
+        self.attrs = {'soft_label': 1}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y')
+
+
+class TestCrossEntropyOp3(OpTest):
+    """Test one-hot cross-entropy, with vecterized one-hot representation of
+    labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        batch_size = 30
+        class_num = 10
+        X = np.random.uniform(0.1, 1.0,
+                              [batch_size, class_num]).astype("float32")
+        label_index = np.random.randint(
+            0, class_num, (batch_size), dtype="int32")
+        label = np.zeros(X.shape)
+        label[np.arange(batch_size), label_index] = 1
+        cross_entropy = np.asmatrix(
+            [[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])],
+            dtype="float32")
+        cross_entropy2 = (-label * np.log(X)).sum(
+            axis=1, keepdims=True).astype("float32")
+        self.inputs = {'X': X, 'Label': label}
+        self.outputs = {'Y': cross_entropy}
+        self.attrs = {'soft_label': 1}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y')
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/v2/framework/tests/test_dropout_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestDropoutOp(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {'dropout_prob': 0.0, 'is_training': 1}
+        self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64))}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.05)
+
+
+class TestDropoutOp2(TestDropoutOp):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {'dropout_prob': 1.0, 'is_training': 1}
+        self.outputs = {'Out': np.zeros((32, 64)), 'Mask': np.zeros((32, 64))}
+
+
+class TestDropoutOp3(TestDropoutOp):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
+        self.attrs = {'dropout_prob': 0.0, 'is_training': 1}
+        self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64, 2))}
+
+
+class TestDropoutOp4(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {'dropout_prob': 0.35, 'is_training': 0}
+        self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestDropoutOp5(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
+        self.attrs = {'dropout_prob': 0.75, 'is_training': 0}
+        self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_mnist.py

@@ -128,7 +128,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
 def cross_entropy_layer(net, input, label):
     cost_name = "cross_entropy_%d" % uniq_id()
     cross_entropy_op = Operator(
-        "onehot_cross_entropy", X=input, label=label, Y=cost_name)
+        "cross_entropy", X=input, Label=label, Y=cost_name)
     net.append_op(cross_entropy_op)
     scope.new_var(cost_name)
     net.infer_shape(scope)
@@ -181,7 +181,7 @@ def error_rate(predict, label):
 
 
 images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
-labels = data_layer(name="label", dims=[BATCH_SIZE])
+labels = data_layer(name="label", dims=[BATCH_SIZE, 1])
 fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
 fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
 predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")
@@ -215,6 +215,7 @@ def test(cost_name):
     for data in test_reader():
         image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
         label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        label_data = numpy.expand_dims(label_data, axis=1)
         feed_data(images, image_data)
         feed_data(labels, label_data)
 
@@ -235,6 +236,7 @@ for pass_id in range(PASS_NUM):
     for data in train_reader():
         image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
         label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        label_data = numpy.expand_dims(label_data, axis=1)
         feed_data(images, image_data)
         feed_data(labels, label_data)
 

python/paddle/v2/framework/tests/test_onehot_cross_entropy_op.py

-import unittest
-import numpy
-from op_test import OpTest
-
-
-class TestOnehotCrossEntropyOp(OpTest):
-    def setUp(self):
-        self.op_type = "onehot_cross_entropy"
-        batch_size = 30
-        class_num = 10
-
-        X = numpy.random.uniform(0.1, 1.0,
-                                 [batch_size, class_num]).astype("float32")
-        labels = numpy.random.randint(0, class_num, batch_size, dtype="int32")
-
-        cross_entropy = numpy.asmatrix(
-            [[-numpy.log(X[i][labels[i]])] for i in range(X.shape[0])],
-            dtype="float32")
-        self.inputs = {"X": X, "label": labels}
-        self.outputs = {"Y": cross_entropy}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Y")
-
-
-if __name__ == "__main__":
-    unittest.main()

python/paddle/v2/framework/tests/test_prelu_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class PReluTest(OpTest):
+    def setUp(self):
+        self.op_type = "prelu"
+        x_np = np.random.normal(size=(10, 10)).astype("float32")
+        x_np_sign = np.sign(x_np)
+        x_np = x_np_sign * np.maximum(x_np, .005)
+        alpha_np = np.array([.1])
+        self.inputs = {'X': x_np, 'Alpha': alpha_np}
+        out_np = np.maximum(self.inputs['X'], 0.)
+        out_np = out_np + np.minimum(self.inputs['X'],
+                                     0.) * self.inputs['Alpha']
+        assert out_np is not self.inputs['X']
+        self.outputs = {'Out': out_np}
+
+    def not_test_check_output(self):
+        self.check_output()
+
+    def not_test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/v2/framework/tests/test_split_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestSplitOp(OpTest):
+    def setUp(self):
+        self.op_type = "split"
+        axis = 0
+        num = 2
+        x = np.random.random((4, 2)).astype('float32')
+        out = np.split(x, num, axis)
+        self.inputs = {'X': x}
+        self.attrs = {'axis': axis, 'num': num}
+        self.outputs = {'Out': [('out%d' % i, out[i]) \
+            for i in xrange(len(out))]}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], ['out0', 'out1'])
+
+
+if __name__ == '__main__':
+    unittest.main()