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

cmake/external/openblas.cmake

@@ -25,7 +25,12 @@ IF(NOT ${CBLAS_FOUND})
         "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
         CACHE FILEPATH "openblas library." FORCE)
 
+    IF(APPLE)
+        SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}")
+        SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
+    ELSE()
         SET(COMMON_ARGS CC=${CMAKE_C_COMPILER} NO_SHARED=1 NO_LAPACK=1 libs)
+    ENDIF()
 
     IF(CMAKE_CROSSCOMPILING)
         IF(ANDROID)
@@ -40,11 +45,11 @@ IF(NOT ${CBLAS_FOUND})
             SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=${TARGET} ARM_SOFTFP_ABI=1 USE_THREAD=0)
         ELSEIF(RPI)
             # use hardfp
-            SET(OPENBLAS_COMMIT "v0.2.19")
+            SET(OPENBLAS_COMMIT "v0.2.20")
             SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=ARMV7 USE_THREAD=0)
         ENDIF()
     ELSE()
-        SET(OPENBLAS_COMMIT "v0.2.19")
+        SET(OPENBLAS_COMMIT "v0.2.20")
         SET(OPTIONAL_ARGS "")
         IF(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86(_64)?$")
             SET(OPTIONAL_ARGS DYNAMIC_ARCH=1 NUM_THREADS=64)

doc/design/functions_operators_layers.md

+# Design Doc: Functions, Operators, and Layers
+
+In a DL system, we can compose one or more fine grained operators into a coarse grained one.  For example, the FC layer can be composed of a multiplication operator and an add operator.
+
+Historically, some fine grained operations are known as operators, and some coarse level ones are known as layers.  But we need a well-defined separation.
+
+In general, operators are those very fine grained operations, e.g., mul and add. In the implementation, we can write them as C++ functions:
+
+```c++
+template <typename T> T add(T x, T y) { return x + y; }
+template <typename T> T mul(T x, T y) { return x * y; }
+```
+
+Then we can wrap them into operators which are C++ classes and can be created from Python bindings by name.  A C macro can do this. For example, the following macro invocation
+
+```c++
+#define MAKE_FUNCTION_OPERATOR(mul);
+```
+
+generates
+
+```c++
+template <typename T> class mulOp : public OperatorBase {...};
+REGISTER_OP(mulOp<float32>, "mul");
+```
+
+so that in Python we can create operator mul by:
+
+```python
+X1 = Var()
+X2 = Var()
+Y = Var()
+paddle.cpp.create_operator("mul", input=[X1, X2], output=Y)
+```
+
+Also, at the same time, we can compose a coarse level C++ operator class by composing functions `mul` and `add`:
+
+```c++
+template <typename T>
+class FCOp : public OperatorBase {
+ public:
+  void Run(...) {
+    add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b");
+  }
+};
+REGISTER_OP(FCOp, "fc");
+```
+
+We need to support such composition in Python as well.  To do so, we need a higher level Python wrapping of operator creation than `paddle.cpp.create_operator`.  This higher level operator API should be compatible with the layer API.
+
+Let's explain using an example.  Suppose that we are going to compose the FC using mul and add in Python, we'd like to have Python functions `mul` and `add` defined in module `operator`:
+
+```python
+def operator.mul(X1, X2):
+    O = Var()
+    paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
+    return O
+
+def operator.add(X1, X2):
+    O = Var()
+    paddle.cpp.create_operator("add", input={X1, X2], output=O)
+    return O
+```
+
+Above code snippets are automatically generated.  Given them, users can define
+
+```python
+def layer.fc(X):
+    W = Var()
+    b = Var()
+    return operator.add(operator.mul(X, W), b)
+```
+
+If we don't have `operator.mul` and `operator.add`, the definiton of `layer.fc` would be complicated:
+
+```python
+def layer.fc(X):
+    W = Var()
+    b = Var()
+    O1 = Var()
+    paddle.cpp.create_operator("mul", input=[X, W], output=O1)
+    O2 = Var()
+    paddle.cpp.create_operator("add", input=[O1, b], output=O2)
+    return O2
+```
+
+We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`.  So we have the following concepts in above illustrative example:
+
+```
+| C++ functions/functors | mul          | add          |             |          |
+| C++ operator class     | mulOp        | addOp        | FCOp        |          |
+| Python binding         | operator.mul | operator.add | operator.fc |          |
+| Python function        |              |              |             | layer.fc |
+```
+
+This is how we differentiate layer and operators in PaddlePaddle:
+
+- those defined in C++ and have a lightweighted Python wrapper in module `operators` are operators; whereas
+- those who don't have C++ implementations but a Python implementation that compose C++ operators are known as layers.

doc/design/if_else_op.md

+IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has M (M<=N) instances, each corresponds to a true element in `cond`.
+
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+
+b = pd.create_ifop(inputs=[x], output_num=1)
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+
+If we want the output still has N instances, we can use IfElseOp with a default value, whose minibatch size must be N:
+
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop(inputs=[x], output_num=1)
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+with b.false_block():
+    x = b.inputs(0)
+    z = layer.fc(x)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+
+If only true_block is set in an IfElseOp, we can have a default value for false as:
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop(inputs=[x], output_num=1, default_value)
+
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+where default_value is a list of vars for `cond` == False.

doc/howto/dev/new_op_cn.md

@@ -178,13 +178,13 @@ class MulKernel : public framework::OpKernel {
 
 ```c++
 namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mul_grad,
               ops::MulGradKernel<paddle::platform::CPUPlace, float>);
 ```
     
-  - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
+  - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，并且注册`ops::MulOpGrad`为其反向Op。
   - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
   - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
 

paddle/cuda/include/hl_cnn.h

@@ -173,6 +173,96 @@ extern void hl_avgpool_backward(const int frameCnt,
                                 real* backGrad,
                                 const int outStride);
 
+extern void hl_maxpool3D_forward(const int frameCnt,
+                                 const real* inputData,
+                                 const int channels,
+                                 const int depth,
+                                 const int height,
+                                 const int width,
+                                 const int pooledD,
+                                 const int pooledH,
+                                 const int pooledW,
+                                 const int sizeZ,
+                                 const int sizeY,
+                                 const int sizeX,
+                                 const int strideD,
+                                 const int strideH,
+                                 const int strideW,
+                                 const int paddingD,
+                                 const int paddingH,
+                                 const int paddingW,
+                                 real* tgtData,
+                                 real* maxPoolIdxData,
+                                 const int tgtStride);
+
+extern void hl_maxpool3D_backward(const int frameCnt,
+                                  const real* outGrad,
+                                  const int channels,
+                                  const int depth,
+                                  const int height,
+                                  const int width,
+                                  const int pooledD,
+                                  const int pooledH,
+                                  const int pooledW,
+                                  const int sizeZ,
+                                  const int sizeY,
+                                  const int sizeX,
+                                  const int strideD,
+                                  const int strideH,
+                                  const int strideW,
+                                  const int paddingD,
+                                  const int paddingH,
+                                  const int paddingW,
+                                  real scaleA,
+                                  real scaleB,
+                                  real* targetGrad,
+                                  real* maxPoolIdxData,
+                                  const int outStride);
+
+extern void hl_avgpool3D_forward(const int frameCnt,
+                                 const real* inputData,
+                                 const int channels,
+                                 const int depth,
+                                 const int height,
+                                 const int width,
+                                 const int pooledD,
+                                 const int pooledH,
+                                 const int pooledW,
+                                 const int sizeZ,
+                                 const int sizeY,
+                                 const int sizeX,
+                                 const int strideD,
+                                 const int strideH,
+                                 const int strideW,
+                                 const int paddingD,
+                                 const int paddingH,
+                                 const int paddingW,
+                                 real* tgtData,
+                                 const int tgtStride);
+
+extern void hl_avgpool3D_backward(const int frameCnt,
+                                  const real* outGrad,
+                                  const int channels,
+                                  const int depth,
+                                  const int height,
+                                  const int width,
+                                  const int pooledD,
+                                  const int pooledH,
+                                  const int pooledW,
+                                  const int sizeZ,
+                                  const int sizeY,
+                                  const int sizeX,
+                                  const int strideD,
+                                  const int strideH,
+                                  const int strideW,
+                                  int paddingD,
+                                  int paddingH,
+                                  int paddingW,
+                                  real scaleA,
+                                  real scaleB,
+                                  real* backGrad,
+                                  const int outStride);
+
 /**
  * @brief   Bilinear interpolation forward.
  *
@@ -275,4 +365,4 @@ extern void hl_maxout_backward(real* inGrad,
                                size_t featLen,
                                size_t groups);
 
-#endif /* HL_CNN_H_ */
+#endif  // HL_CNN_H_

paddle/cuda/include/stub/hl_cnn_stub.h

@@ -87,6 +87,96 @@ inline void hl_avgpool_backward(const int frameCnt,
                                 real* backGrad,
                                 const int outStride) {}
 
+inline void hl_maxpool3D_forward(const int frameCnt,
+                                 const real* inputData,
+                                 const int channels,
+                                 const int depth,
+                                 const int height,
+                                 const int width,
+                                 const int pooledD,
+                                 const int pooledH,
+                                 const int pooledW,
+                                 const int sizeZ,
+                                 const int sizeY,
+                                 const int sizeX,
+                                 const int strideD,
+                                 const int strideH,
+                                 const int strideW,
+                                 const int paddingD,
+                                 const int paddingH,
+                                 const int paddingW,
+                                 real* tgtData,
+                                 real* maxPoolIdxData,
+                                 const int tgtStride) {}
+
+inline void hl_maxpool3D_backward(const int frameCnt,
+                                  const real* outGrad,
+                                  const int channels,
+                                  const int depth,
+                                  const int height,
+                                  const int width,
+                                  const int pooledD,
+                                  const int pooledH,
+                                  const int pooledW,
+                                  const int sizeZ,
+                                  const int sizeY,
+                                  const int sizeX,
+                                  const int strideD,
+                                  const int strideH,
+                                  const int strideW,
+                                  const int paddingD,
+                                  const int paddingH,
+                                  const int paddingW,
+                                  real scaleA,
+                                  real scaleB,
+                                  real* targetGrad,
+                                  real* maxPoolIdxData,
+                                  const int outStride) {}
+
+inline void hl_avgpool3D_forward(const int frameCnt,
+                                 const real* inputData,
+                                 const int channels,
+                                 const int depth,
+                                 const int height,
+                                 const int width,
+                                 const int pooledD,
+                                 const int pooledH,
+                                 const int pooledW,
+                                 const int sizeZ,
+                                 const int sizeY,
+                                 const int sizeX,
+                                 const int strideD,
+                                 const int strideH,
+                                 const int strideW,
+                                 const int paddingD,
+                                 const int paddingH,
+                                 const int paddingW,
+                                 real* tgtData,
+                                 const int tgtStride) {}
+
+inline void hl_avgpool3D_backward(const int frameCnt,
+                                  const real* outGrad,
+                                  const int channels,
+                                  const int depth,
+                                  const int height,
+                                  const int width,
+                                  const int pooledD,
+                                  const int pooledH,
+                                  const int pooledW,
+                                  const int sizeZ,
+                                  const int sizeY,
+                                  const int sizeX,
+                                  const int strideD,
+                                  const int strideH,
+                                  const int strideW,
+                                  const int paddingD,
+                                  const int paddingH,
+                                  const int paddingW,
+                                  real scaleA,
+                                  real scaleB,
+                                  real* backGrad,
+                                  const int outStride) {}
+
 inline void hl_bilinear_forward(const real* inData,
                                 const size_t inImgH,
                                 const size_t inImgW,

paddle/framework/backward.md

@@ -18,7 +18,7 @@ A backward network is built up with several backward operators. Backward operato
 For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro:
 
 ```cpp
-REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
+REGISTER_OP(mul, MulOp, MulOpMaker, MulOpGrad);
 ```
 
 `mul` is the operator's type. `MulOp` and `MulOpMaker` are the operator class and the operator maker class respectively.

paddle/framework/backward_test.cc

@@ -127,8 +127,8 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker {
  public:
   FillZeroOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("x", "x");
-    AddOutput("out", "out");
+    AddInput("Src", "x");
+    AddOutput("Dst", "out");
     AddComment("");
   }
 };
@@ -138,7 +138,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
   AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "x").AsDuplicable();
-    AddOutput("Y", "y");
+    AddOutput("Out", "out");
     AddComment("");
   }
 };
@@ -148,16 +148,14 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
 namespace f = paddle::framework;
 namespace ops = paddle::operators;
 using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
-            f::NOP);
-REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
-REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
+REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, f::NOP);
 REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
 REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
-REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
+REGISTER_OP(add, f::NOP, f::AddOpMaker, f::NOP);
 REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
-REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
-            f::NOP);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, f::NOP);
 
 TEST(Backward, simple_op_grad) {
   auto fwd = f::OpRegistry::CreateOp(

paddle/framework/grad_op_builder_test.cc

@@ -54,8 +54,8 @@ TEST(GradOpBuilder, AddTwo) {
   EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
 }
 
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
+REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, f::NOP);
+REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, f::NOP);
 
 TEST(GradOpBuilder, MutiInOut) {
   std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(

paddle/framework/lod_tensor.cc

@@ -19,25 +19,24 @@
 namespace paddle {
 namespace framework {
 
-LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin,
-                                           size_t level_end) const {
+LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end) {
   LOD new_lod;
   new_lod.reserve(level_end - level_begin);
   for (size_t i = level_begin; i < level_end; i++) {
-    new_lod.emplace_back(at(i));
+    new_lod.emplace_back(in.at(i));
   }
   return new_lod;
 }
 
-LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
-                                            size_t elem_end) const {
+LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin,
+                 size_t elem_end) {
   // slice the lod.
   LOD new_lod;
-  new_lod.reserve(size() - level);
-  auto start = this->at(level)[elem_begin];
-  auto end = this->at(level)[elem_end];
+  new_lod.reserve(in.size() - level);
+  auto start = in.at(level)[elem_begin];
+  auto end = in.at(level)[elem_end];
 
-  for (auto it = this->begin() + level; it != this->end(); it++) {
+  for (auto it = in.begin() + level; it != in.end(); it++) {
     auto it_begin = std::find(it->begin(), it->end(), start);
     auto it_end = std::find(it_begin, it->end(), end);
     PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
@@ -49,11 +48,11 @@ LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
                    [start](int v) { return v - start; });
     PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD");
   }
-  PADDLE_ENFORCE_LE(new_lod.size(), this->size());
+  PADDLE_ENFORCE_LE(new_lod.size(), in.size());
   return new_lod;
 }
 
-bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
+bool operator==(const LOD& a, const LOD& b) {
   if (a.size() != b.size()) {
     return false;
   }
@@ -70,9 +69,27 @@ bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
       }
     }
   }
-
   return true;
 }
 
+void LODTensor::SliceLevels(size_t level_begin, size_t level_end) {
+  auto new_lod = framework::SliceLevels(lod_, level_begin, level_end);
+  lod_ = new_lod;
+}
+
+void LODTensor::SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) {
+  PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
+                 NumLevels());
+  PADDLE_ENFORCE(elem_begin < NumElements(level),
+                 "element begin [%d] out of range [%d]", elem_begin,
+                 NumElements(level));
+  PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
+                 "element end [%d] out of range [%d]", elem_end,
+                 NumElements(level));
+
+  auto new_lod = framework::SliceInLevel(lod_, level, elem_begin, elem_end);
+  lod_ = new_lod;
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/lod_tensor.h

@@ -15,7 +15,7 @@
 #pragma once
 
 #include <memory>
-#if !defined(PADDLE_ONLY_CPU)
+#ifndef PADDLE_ONLY_CPU
 #include <thrust/device_vector.h>
 #include <thrust/host_vector.h>
 #endif
@@ -27,33 +27,39 @@
 namespace paddle {
 namespace framework {
 
+#ifdef PADDLE_ONLY_CPU
+template <typename T>
+using Vector = std::vector<T>;
+#else
+template <typename T>
+using Vector = thrust::host_vector<T>;
+#endif
+
+using LOD = std::vector<Vector<size_t>>;
+
+LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end);
+
+LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin,
+                 size_t elem_end);
+
+bool operator==(const LOD& a, const LOD& b);
+
 /*
  * LODTensor (Level of details Tensor)
  * see https://en.wikipedia.org/wiki/Level_of_details for reference.
  */
-class LODTensor : public Tensor {
- public:
-// Level save offsets of each unit.
-#ifdef PADDLE_ONLY_CPU
-  template <typename T>
-  using Vector = std::vector<T>;
-#else
-  template <typename T>
-  using Vector = thrust::host_vector<T>;
-#endif
-  // LoD stores offsets of each level of units, the largest units level first,
-  // then the smaller units level. Each Level stores the offsets of units in
-  // Tesor.
-  class LOD : public std::vector<Vector<size_t>> {
+class LODTensor {
  public:
-    LOD SliceLevels(size_t level_begin, size_t level_end) const;
-    LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const;
-  };
-
   LODTensor() {}
-  explicit LODTensor(const LOD &lod) : lod_(lod) {}
+  LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
+
+  void set_lod(const LOD& lod) { lod_ = lod; }
+
+  void set_tensor(Tensor* tensor) { tensor_ = tensor; }
+
+  Tensor& tensor() { return *tensor_; }
 
-  virtual Tensor *Clone() const { return new LODTensor(lod_); }
+  LOD lod() { return lod_; }
 
   /*
    * Get a element from LOD.
@@ -79,71 +85,23 @@ class LODTensor : public Tensor {
     PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
                    NumLevels());
     // the last offset is the end of last element
-    return lod_[level].size() - 1;
+    return (lod_)[level].size() - 1;
   }
 
   /*
-   * Slice of levels[level_begin:level_end], with tensor shared.
+   * Slice of levels[level_begin:level_end]
    */
-  template <typename T>
-  LODTensor SliceLevels(size_t level_begin, size_t level_end) const;
+  void SliceLevels(size_t level_begin, size_t level_end);
 
   /*
-   * Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
+   * Slice of elements of a level, [elem_begin: elem_end]
    * @note: low performance in slice lod_.
    */
-  template <typename T>
-  LODTensor SliceInLevel(size_t level, size_t elem_begin,
-                         size_t elem_end) const;
-
-  /*
-   * Copy other's lod_'s content, free to mutate.
-   */
-  void CopyLOD(const LODTensor &other) { lod_ = other.lod_; }
-  /*
-   * Determine whether LODTensor has a valid LOD info.
-   */
-  const LOD &lod() const { return lod_; }
-  LOD *mutable_lod() { return &lod_; }
-
-  virtual ~LODTensor() {}
+  void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end);
 
  private:
   LOD lod_;
+  Tensor* tensor_;  // not owned
 };
-
-bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b);
-
-template <typename T>
-LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const {
-  auto new_lod = lod_.SliceLevels(level_begin, level_end);
-  // slice levels just need to update LOD info, each level will contains the
-  // whole tensor_, so no need to modify tensor_.
-  LODTensor new_tensor(new_lod);
-  new_tensor.ShareDataWith<T>(*this);
-  return new_tensor;
-}
-
-template <typename T>
-LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin,
-                                  size_t elem_end) const {
-  PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
-                 NumLevels());
-  PADDLE_ENFORCE(elem_begin < NumElements(level),
-                 "element begin [%d] out of range [%d]", elem_begin,
-                 NumElements(level));
-  PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
-                 "element end [%d] out of range [%d]", elem_end,
-                 NumElements(level));
-
-  auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end);
-
-  // slice elements just need to update LOD info, because offsets are not
-  // changed, so the original tensor_ can be reused.
-  LODTensor new_tensor(new_lod);
-  new_tensor.ShareDataWith<T>(*this);
-  return new_tensor;
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/framework/lod_tensor.md

+# Design Doc: LoD (Level-of-Detail) Tensor
+
+PaddlePaddle's RNN doesn't require that all instances have the same length.  To do so, we introduce an extension to Tensor, namely, LoD Tensor.
+
+## Challenge of Variable-length Inputs
+
+People usually represent a mini-batch by a Tensor.  For example, a mini-batch of 32 images, each of size 32x32, is a 10x32x32 Tensor.  So a transformation, T, of all images can be a matrix multiplication of the 32x32xO-dimensional tensor T and the 10x32x32 Tensor.
+
+Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector.  If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor.  However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths.
+
+## LoD as a Solution
+
+### Mini-Batch of variable-length sentenses
+
+Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively.  We can represent it by a (3+1+2)xD tensor plus some index information:
+
+```
+   3
+3   1 2
+||| | ||
+```
+
+Each `|` represents a D-dimensional word vectors.  The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence.
+
+### Mini-Batch of variable-length videos
+
+This approach generalizes to the case where elements are not words, but higher dimensional objects, like images.  Suppose that a mini-batch contains videos of the same frame size 640x480.  If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively.  The underlying tensor is of size (3+1+2)x640x480.  The index information illustrates as:
+
+```
+     3
+3     1  2
+口口口 口 口口
+```
+
+where each `口` represents an image.
+
+### Mini-Batch of fixed-size images
+
+Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images.  The LoD Tensor representation is
+
+```
+     M
+1 1 1 1     1
+口口口口 ... 口
+```
+
+The many 1's on the second level seem duplicated.  For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index.
+
+### Design and summarization
+
+In summary, as long as that the essential elements (words  or images) have the same size, we can represent mini-batches by a LoD Tensor:
+
+- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and
+- the first dimension size L has an additon property -- a LoD index as a nested vector:
+
+  ```c++
+  typedef std::vector<std::vector> > LoD;
+  ```
+
+- The LoD index can is not necessary when there are only two levels and all elements of the second level have length 1.
+
+## Slicing of LoD Tensor
+
+Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words.  This network requires that mini-batches represented by 4 level LoD Tensor, for example,
+
+```
+         3
+3           1  2
+3   2  4    1  2  3
+||| || |||| |  || |||
+```
+
+To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the <i,j>-slice**
+
+For example, the <2,1>-slice of above slice is
+
+```
+2
+||
+```
+
+and the <1,2>-slice of above example is
+
+```
+2
+2  3
+|| |||
+```
+
+Let's go on slicing this slice.  Its <1,1>-slice is
+
+```
+3
+|||
+```
+
+### The General Slicing Algorithm
+
+The algorithm, with over-simplified data structure, is defined as
+
+```c++
+typedef vector<vector<int> > LoD;
+
+struct LoDTensor {
+  LoD lod_;
+  float* tensor_;
+};
+
+LoDTensor Slice(const LoDTensor& lodt, int level, int sequence) {
+
+}
+```
+
+### Slicing the Top Level
+
+Please be aware that an RNN operator only slices the top level of a LoD Tensor to get the step inputs.
+
+```c++
+LoDTensor Slice(const LoDTensor& lodt, int sequence) {
+
+}
+```

paddle/framework/lod_tensor_test.cc

@@ -24,13 +24,12 @@ namespace framework {
 class LODTensorTester : public ::testing::Test {
  public:
   virtual void SetUp() override {
-    lod_tensor.reset(new LODTensor);
     // tensor's batch_size: 30
     // 3 levels
     // 0 10 20
     // 0 5 10 15 20
     // 0 2 5 7 10 12 15 20
-    LODTensor::LOD lod;
+    LOD lod;
     lod.push_back(std::vector<size_t>{0, 10, 20});
     lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
     lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
@@ -41,75 +40,65 @@ class LODTensorTester : public ::testing::Test {
     // malloc memory
     tensor.mutable_data<float>(place);
 
-    lod_tensor.reset(new LODTensor(lod));
-    lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/});
-
-    lod_tensor->ShareDataWith<float>(tensor);
-    // lod_tensor->ShareDataWith<Tensor>(tensor);
+    lod_tensor.set_lod(lod);
+    lod_tensor.set_tensor(&tensor);
   }
 
  protected:
-  std::unique_ptr<LODTensor> lod_tensor;
   platform::CPUPlace place;
   Tensor tensor;
+  LODTensor lod_tensor;
 };
 
-TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
+TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); }
 
 TEST_F(LODTensorTester, NumElements) {
-  ASSERT_EQ(lod_tensor->NumElements(0), 2UL);
-  ASSERT_EQ(lod_tensor->NumElements(1), 4UL);
-  ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
+  ASSERT_EQ(lod_tensor.NumElements(0), 2UL);
+  ASSERT_EQ(lod_tensor.NumElements(1), 4UL);
+  ASSERT_EQ(lod_tensor.NumElements(2), 8UL);
 }
 
 TEST_F(LODTensorTester, SliceLevels) {
   // slice 1 level
   for (size_t level = 0; level < 3UL; ++level) {
-    auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 1);
+    LODTensor new_lod_tensor = lod_tensor;
+    new_lod_tensor.SliceLevels(level, level + 1);
     ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
-    ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
-    // ASSERT_EQ(new_lod_tensor, *lod_tensor);
+    ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
+    ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+              lod_tensor.tensor().data<float>());
   }
   // slice 2 level
   for (size_t level = 0; level < 2UL; ++level) {
-    auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 2);
+    LODTensor new_lod_tensor = lod_tensor;
+    new_lod_tensor.SliceLevels(level, level + 2);
     ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
-    ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
-    ASSERT_EQ(new_lod_tensor.NumElements(1),
-              lod_tensor->NumElements(level + 1));
-    ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
+    ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
+    ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1));
+    ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+              lod_tensor.tensor().data<float>());
   }
 }
 
 TEST_F(LODTensorTester, SliceInLevel) {
   size_t level = 0;
-  auto new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
+  LODTensor new_lod_tensor = lod_tensor;
+  new_lod_tensor.SliceInLevel(level, 0, 2);
   EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
   EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
   EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
   EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
-  ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
+  ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+            lod_tensor.tensor().data<float>());
 
   level = 1;
-  new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
+  new_lod_tensor = lod_tensor;
+  new_lod_tensor.SliceInLevel(level, 0, 2);
   ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
   ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
   ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
-  ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
-}
-
-TEST_F(LODTensorTester, ShareLOD) {
-  LODTensor new_lod_tensor;
-  new_lod_tensor.CopyLOD(*lod_tensor);
-  ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
-}
-
-TEST_F(LODTensorTester, CopyLOD) {
-  LODTensor new_lod_tensor;
-  new_lod_tensor.CopyLOD(*lod_tensor);
-  bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(),
-                           new_lod_tensor.lod().begin());
-  ASSERT_TRUE(equals);
+  ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+            lod_tensor.tensor().data<float>());
 }
 
 }  // namespace framework

paddle/framework/op_info.h

@@ -80,9 +80,19 @@ class OpInfoMap {
   }
 
   const OpInfo& Get(const std::string& type) const {
+    auto op_info_ptr = GetNullable(type);
+    PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not been registered",
+                            type);
+    return *op_info_ptr;
+  }
+
+  const OpInfo* GetNullable(const std::string& type) const {
     auto it = map_.find(type);
-    PADDLE_ENFORCE(it != map_.end(), "Operator %s are not found", type);
-    return it->second;
+    if (it == map_.end()) {
+      return nullptr;
+    } else {
+      return &it->second;
+    }
   }
 
   template <typename Callback>

paddle/framework/op_registry.h

@@ -33,8 +33,7 @@ namespace framework {
 class OpRegistry {
  public:
   template <typename OpType, typename ProtoMakerType, typename GradOpType>
-  static void RegisterOp(const std::string& op_type,
-                         const std::string& grad_op_type) {
+  static void RegisterOp(const std::string& op_type) {
     PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
                    "'%s' is registered more than once.", op_type);
     OpInfo op_info;
@@ -43,9 +42,9 @@ class OpRegistry {
         const VariableNameMap& outputs, const AttributeMap& attrs) {
       return new OpType(type, inputs, outputs, attrs);
     };
-    op_info.grad_op_type_ = grad_op_type;
     if (std::type_index(typeid(ProtoMakerType)) !=
         std::type_index(typeid(NOPMaker))) {
+      op_info.grad_op_type_ = op_type + "_grad";
       op_info.proto_ = new OpProto;
       op_info.checker_ = new OpAttrChecker;
       auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
@@ -55,15 +54,14 @@ class OpRegistry {
           op_info.proto_->IsInitialized(),
           "Fail to initialize %s's OpProto, because %s is not initialized",
           op_type, op_info.proto_->InitializationErrorString());
+      // register gradient op
+      RegisterOp<GradOpType, NOPMaker, NOP>(op_info.grad_op_type_);
     } else {
+      op_info.grad_op_type_ = "";
       op_info.proto_ = nullptr;
       op_info.checker_ = nullptr;
     }
     OpInfoMap::Instance().Insert(op_type, op_info);
-    // register gradient op
-    if (!grad_op_type.empty()) {
-      RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
-    }
   }
 
   static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
@@ -92,10 +90,8 @@ class Registrar {
 template <typename OpType, typename ProtoMakerType, typename GradOpType>
 class OpRegistrar : public Registrar {
  public:
-  explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
-  OpRegistrar(const char* op_type, const char* grad_op_type) {
-    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
-                                                               grad_op_type);
+  explicit OpRegistrar(const char* op_type) {
+    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type);
   }
 };
 
@@ -121,8 +117,7 @@ class OpKernelRegistrar : public Registrar {
 /**
  * Macro to register Operator.
  */
-#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type,          \
-                    grad_op_class)                                            \
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_class)         \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                             \
       __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
   class _OpClass_##op_type##_ : public op_class {                             \
@@ -137,14 +132,14 @@ class OpKernelRegistrar : public Registrar {
   };                                                                          \
   static ::paddle::framework::OpRegistrar<                                    \
       _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_>       \
-      __op_registrar_##op_type##__(#op_type, #grad_op_type);                  \
+      __op_registrar_##op_type##__(#op_type);                                 \
   int TouchOpRegistrar_##op_type() {                                          \
     __op_registrar_##op_type##__.Touch();                                     \
     return 0;                                                                 \
   }
 
 #define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
-  REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
+  REGISTER_OP(op_type, op_class, op_maker_class, ::paddle::framework::NOP)
 
 /**
  * Macro to register OperatorKernel.

paddle/framework/operator.cc

@@ -33,12 +33,12 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
 }
 #endif
 
-const std::string& OperatorBase::Input(const std::string& name) const {
+std::string OperatorBase::Input(const std::string& name) const {
   auto& ins = Inputs(name);
-  PADDLE_ENFORCE_EQ(ins.size(), 1UL,
+  PADDLE_ENFORCE_LE(ins.size(), 1UL,
                     "Op %s input %s should contain only one variable", type_,
                     name);
-  return ins[0];
+  return ins.empty() ? kEmptyVarName : ins[0];
 }
 
 const std::vector<std::string>& OperatorBase::Inputs(
@@ -49,12 +49,12 @@ const std::vector<std::string>& OperatorBase::Inputs(
   return it->second;
 }
 
-const std::string& OperatorBase::Output(const std::string& name) const {
+std::string OperatorBase::Output(const std::string& name) const {
   auto& outs = Outputs(name);
-  PADDLE_ENFORCE_EQ(outs.size(), 1UL,
+  PADDLE_ENFORCE_LE(outs.size(), 1UL,
                     "Op %s output %s should contain only one variable", type_,
                     name);
-  return outs[0];
+  return outs.empty() ? kEmptyVarName : outs[0];
 }
 
 const std::vector<std::string>& OperatorBase::Outputs(
@@ -119,16 +119,8 @@ OperatorBase::OperatorBase(const std::string& type,
                            const VariableNameMap& outputs,
                            const AttributeMap& attrs)
     : type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) {
-  static std::atomic<size_t> gUniqId(0UL);
-  for (auto& output : outputs_) {
-    for (auto& output_name : output.second) {
-      if (output_name == kTempVarName) {
-        output_name += type_;
-        output_name += "@";
-        output_name += std::to_string(gUniqId.fetch_add(1));
-      }
-    }
-  }
+  GenerateTemporaryNames();
+  CheckAllInputOutputSet();
 }
 
 std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
@@ -156,6 +148,35 @@ std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
   return ret_val;
 }
 
+void OperatorBase::CheckAllInputOutputSet() const {
+  auto& info_map = OpInfoMap::Instance();
+  auto* op_info = info_map.GetNullable(Type());
+  if (op_info == nullptr || op_info->proto_ == nullptr) return;
+
+  for (auto& in : op_info->Proto().inputs()) {
+    PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
+                   "Type %s's input %s is not set", Type(), in.name());
+  }
+
+  for (auto& out : op_info->Proto().outputs()) {
+    PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
+                   "Type %s's output %s is not set", Type(), out.name());
+  }
+}
+
+void OperatorBase::GenerateTemporaryNames() {
+  static std::atomic<size_t> gUniqId(0UL);
+  for (auto& output : outputs_) {
+    for (auto& output_name : output.second) {
+      if (output_name == kTempVarName) {
+        output_name += type_;
+        output_name += "@";
+        output_name += std::to_string(gUniqId.fetch_add(1));
+      }
+    }
+  }
+}
+
 void OpProtoAndCheckerMaker::Validate() {
   validated_ = true;
   CheckNoDuplicatedInOutAttrs();

paddle/framework/operator.h

@@ -95,12 +95,12 @@ class OperatorBase {
   const VariableNameMap& Inputs() const { return inputs_; }
   const VariableNameMap& Outputs() const { return outputs_; }
   //! Get a input with argument's name described in `op_proto`
-  const std::string& Input(const std::string& name) const;
+  std::string Input(const std::string& name) const;
   //! Get a input which has multiple variables.
   const std::vector<std::string>& Inputs(const std::string& name) const;
 
   //! Get a output with argument's name described in `op_proto`
-  const std::string& Output(const std::string& name) const;
+  std::string Output(const std::string& name) const;
   //! Get an output which has multiple variables.
   //! TODO add a vector_view to prevent memory copy.
   const std::vector<std::string>& Outputs(const std::string& name) const;
@@ -127,6 +127,10 @@ class OperatorBase {
   // IG (Inputs Gradients)
   VariableNameMap outputs_;
   AttributeMap attrs_;
+
+ private:
+  void GenerateTemporaryNames();
+  void CheckAllInputOutputSet() const;
 };
 
 // Macro for define a clone method.
@@ -238,11 +242,13 @@ class InferShapeContext {
   }
 
   const Variable* InputVar(const std::string& name) const {
-    return scope_.FindVar(op_.Input(name));
+    auto ipt = op_.Input(name);
+    return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
   }
 
   Variable* OutputVar(const std::string& name) const {
-    return scope_.FindVar(op_.Output(name));
+    auto opt = op_.Output(name);
+    return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
   }
 
   const std::vector<const Variable*> MultiInputVar(
@@ -250,9 +256,11 @@ class InferShapeContext {
     auto names = op_.Inputs(name);
     std::vector<const Variable*> res;
     res.reserve(names.size());
-    std::transform(
-        names.begin(), names.end(), std::back_inserter(res),
-        [this](const std::string& name) { return scope_.FindVar(name); });
+    std::transform(names.begin(), names.end(), std::back_inserter(res),
+                   [this](const std::string& name) {
+                     return name == kEmptyVarName ? nullptr
+                                                  : scope_.FindVar(name);
+                   });
     return res;
   }
 
@@ -260,24 +268,24 @@ class InferShapeContext {
     auto names = op_.Outputs(name);
     std::vector<const Variable*> res;
     res.reserve(names.size());
-    std::transform(
-        names.begin(), names.end(), std::back_inserter(res),
-        [this](const std::string& name) { return scope_.FindVar(name); });
+    std::transform(names.begin(), names.end(), std::back_inserter(res),
+                   [this](const std::string& name) {
+                     return name == kEmptyVarName ? nullptr
+                                                  : scope_.FindVar(name);
+                   });
     return res;
   }
 
   template <typename T>
   const T* Input(const std::string& name) const {
     auto* var = InputVar(name);
-    PADDLE_ENFORCE_NOT_NULL(var, "Input(%s) should not be nullptr", name);
-    return &var->Get<T>();
+    return var == nullptr ? nullptr : &var->Get<T>();
   }
 
   template <typename T>
   T* Output(const std::string& name) const {
     auto var = OutputVar(name);
-    PADDLE_ENFORCE_NOT_NULL(var, "Output(%s) should not be nullptr", name);
-    return var->GetMutable<T>();
+    return var == nullptr ? nullptr : var->GetMutable<T>();
   }
 
   template <typename T>
@@ -288,10 +296,7 @@ class InferShapeContext {
     std::transform(names.begin(), names.end(), std::back_inserter(res),
                    [&](const std::string& sub_name) {
                      auto var = scope_.FindVar(sub_name);
-                     PADDLE_ENFORCE_NOT_NULL(
-                         var, "MultiInput(%s:%s) should not be nullptr", name,
-                         sub_name);
-                     return &var->Get<T>();
+                     return var == nullptr ? nullptr : &var->Get<T>();
                    });
     return res;
   }
@@ -304,10 +309,7 @@ class InferShapeContext {
     std::transform(names.begin(), names.end(), std::back_inserter(res),
                    [&](const std::string& sub_name) {
                      auto var = scope_.FindVar(sub_name);
-                     PADDLE_ENFORCE_NOT_NULL(
-                         var, "MultiOutput(%s:%s) should not be nullptr.", name,
-                         sub_name);
-                     return var->GetMutable<T>();
+                     return var == nullptr ? nullptr : var->GetMutable<T>();
                    });
     return res;
   }

paddle/framework/tensor_impl.h

@@ -117,6 +117,8 @@ inline void Tensor::CopyFrom(const Tensor& src,
     memory::Copy(boost::get<platform::GPUPlace>(dst_place), dst_ptr,
                  boost::get<platform::GPUPlace>(src_place), src_ptr, size, 0);
   }
+  PADDLE_ENFORCE(cudaStreamSynchronize(0),
+                 "cudaStreamSynchronize failed in Tensor CopyFrom");
 
 #endif
 }

paddle/gserver/layers/CrossEntropyOverBeam.cpp

@@ -223,7 +223,7 @@ void CrossEntropyOverBeam::checkInputs() {
                                 << inputLayers_[i * 3]->getName()
                                 << " should be a nested sequence";
       CHECK_EQ(getInputValue(i * 3 + 1)->getWidth(), beamSize_);
-      CHECK_EQ(scores.getNumSequences(), batchSize_);
+      CHECK_EQ(batchSize_, static_cast<size_t>(scores.getNumSequences()));
       CHECK_EQ(scores.getNumSubSequences(), selCandidates.getBatchSize());
     } else {
       CHECK(scores.hasSeq()) << "input " << i << " "
@@ -231,10 +231,10 @@ void CrossEntropyOverBeam::checkInputs() {
                              << " should be a sequence";
       batchSize_ = scores.getNumSequences();
       beamSize_ = getInputValue(i * 3 + 1)->getWidth();
-      CHECK_EQ(batchSize_, selCandidates.getBatchSize());
+      CHECK_EQ(batchSize_, static_cast<size_t>(selCandidates.getBatchSize()));
     }
     CHECK_EQ(1U, scores.value->getWidth());
-    CHECK_EQ(batchSize_, goldSeq.getBatchSize());
+    CHECK_EQ(batchSize_, static_cast<size_t>(goldSeq.getBatchSize()));
   }
 }
 
@@ -377,8 +377,8 @@ void CrossEntropyOverBeam::forward(PassType passType) {
 
   MatrixPtr outputValue = getOutputValue();
   for (size_t i = 0; i < batchSize_; ++i) {
-    beamCosts_[i].setData(
-        std::move(std::make_shared<BeamExpansion>(beamPerSeq_[i])), beamSize_);
+    BeamExpansionPtr ptr = std::make_shared<BeamExpansion>(beamPerSeq_[i]);
+    beamCosts_[i].setData(std::move(ptr), beamSize_);
     outputValue->getData()[i] = beamCosts_[i].forward();
   }
 }

paddle/gserver/layers/Pool3DLayer.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. */
+
+#include "Pool3DLayer.h"
+#include "PoolProjectionLayer.h"
+#include "paddle/utils/Logging.h"
+
+namespace paddle {
+
+REGISTER_LAYER(pool3d, Pool3DLayer);
+
+bool Pool3DLayer::init(const LayerMap& layerMap,
+                       const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  /* the size of inputs for pool-layer is 1 */
+  CHECK_EQ(config_.inputs_size(), 1);
+
+  const PoolConfig& conf = config_.inputs(0).pool_conf();
+  poolType_ = conf.pool_type();
+  channels_ = conf.channels();
+
+  sizeX_ = conf.size_x();
+  sizeY_ = conf.size_y();
+  sizeZ_ = conf.size_z();
+
+  strideW_ = conf.stride();
+  strideH_ = conf.stride_y();
+  strideD_ = conf.stride_z();
+
+  imgSizeW_ = conf.img_size();
+  imgSizeH_ = conf.img_size_y();
+  imgSizeD_ = conf.img_size_z();
+
+  paddingW_ = conf.padding();
+  paddingH_ = conf.padding_y();
+  paddingD_ = conf.padding_z();
+
+  outputW_ = conf.output_x();
+  outputH_ = conf.output_y();
+  outputD_ = conf.output_z();
+
+  return true;
+}
+
+size_t Pool3DLayer::getSize() {
+  CHECK_EQ(inputLayers_.size(), 1UL);
+
+  size_t layerSize = 0;
+  outputD_ = outputSize(imgSizeD_, sizeZ_, paddingD_, strideD_, false);
+  outputH_ = outputSize(imgSizeH_, sizeY_, paddingH_, strideH_, false);
+  outputW_ = outputSize(imgSizeW_, sizeX_, paddingW_, strideW_, false);
+
+  layerSize = outputD_ * outputH_ * outputW_ * channels_;
+  getOutput().setFrameHeight(outputH_);
+  getOutput().setFrameWidth(outputW_);
+  getOutput().setFrameDepth(outputD_);
+  return layerSize;
+}
+
+void Pool3DLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
+  size_t batchSize = inMat->getHeight();
+  size_t outWidth = getSize();
+  resetOutput(batchSize, outWidth);
+  Matrix::resizeOrCreate(maxPoolIdx_, batchSize, outWidth, false, useGpu_);
+  const MatrixPtr outMat = getOutputValue();
+
+  if (poolType_ == "avg") {
+    outMat->avgPool3DForward(*inMat,
+                             channels_,
+                             imgSizeD_,
+                             imgSizeH_,
+                             imgSizeW_,
+                             outputD_,
+                             outputH_,
+                             outputW_,
+                             sizeZ_,
+                             sizeY_,
+                             sizeX_,
+                             strideD_,
+                             strideH_,
+                             strideW_,
+                             paddingD_,
+                             paddingH_,
+                             paddingW_);
+  } else if (poolType_ == "max") {
+    outMat->maxPool3DForward(*inMat,
+                             *maxPoolIdx_,
+                             channels_,
+                             imgSizeD_,
+                             imgSizeH_,
+                             imgSizeW_,
+                             outputD_,
+                             outputH_,
+                             outputW_,
+                             sizeZ_,
+                             sizeY_,
+                             sizeX_,
+                             strideD_,
+                             strideH_,
+                             strideW_,
+                             paddingD_,
+                             paddingH_,
+                             paddingW_);
+  } else {
+    LOG(FATAL) << "Unknown pool type: " << poolType_;
+  }
+  forwardActivation();
+}
+
+void Pool3DLayer::backward(const UpdateCallback& callback) {
+  backwardActivation();
+
+  (void)callback;
+  if (NULL == getInputGrad(0)) return;
+  MatrixPtr inMat = inputLayers_[0]->getOutputValue();
+  MatrixPtr inGradMat = inputLayers_[0]->getOutputGrad();
+  MatrixPtr outMat = getOutputValue();
+  MatrixPtr outGradMat = getOutputGrad();
+
+  if (poolType_ == "avg") {
+    inGradMat->avgPool3DBackward(*outGradMat,
+                                 imgSizeD_,
+                                 imgSizeH_,
+                                 imgSizeW_,
+                                 outputD_,
+                                 outputH_,
+                                 outputW_,
+                                 sizeZ_,
+                                 sizeY_,
+                                 sizeZ_,
+                                 strideD_,
+                                 strideH_,
+                                 strideW_,
+                                 paddingD_,
+                                 paddingH_,
+                                 paddingW_,
+                                 1.0,
+                                 1.0);
+  } else if (poolType_ == "max") {
+    inGradMat->maxPool3DBackward(*outGradMat,
+                                 *maxPoolIdx_,
+                                 imgSizeD_,
+                                 imgSizeH_,
+                                 imgSizeW_,
+                                 outputD_,
+                                 outputH_,
+                                 outputW_,
+                                 sizeZ_,
+                                 sizeY_,
+                                 sizeZ_,
+                                 strideD_,
+                                 strideH_,
+                                 strideW_,
+                                 paddingD_,
+                                 paddingH_,
+                                 paddingW_,
+                                 1.0,
+                                 1.0);
+  } else {
+    LOG(FATAL) << "Unknown pool type: " << poolType_;
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/Pool3DLayer.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 "Layer.h"
+#include "paddle/math/MathUtils.h"
+#include "paddle/math/Matrix.h"
+
+namespace paddle {
+
+/**
+ * @brief Basic parent layer of pooling
+ * Pools the input within regions
+ */
+class Pool3DLayer : public Layer {
+public:
+  explicit Pool3DLayer(const LayerConfig& config) : Layer(config) {}
+  ~Pool3DLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback) override;
+  size_t getSize();
+
+protected:
+  int channels_;
+  int sizeX_, sizeY_, sizeZ_;
+  int strideW_, strideH_, strideD_;
+  int paddingW_, paddingH_, paddingD_;
+  int imgSizeW_, imgSizeH_, imgSizeD_;
+  int outputW_, outputH_, outputD_;
+  std::string poolType_;
+  MatrixPtr maxPoolIdx_;
+};
+}  // namespace paddle

paddle/gserver/layers/PrintLayer.cpp

@@ -48,7 +48,16 @@ public:
                  << inputLayers_.size() << ") at " << getName();
     }
     s << format.substr(pos);
-    LOG(INFO) << s.str();
+
+    const std::string delimiter("\n");
+    std::string content = s.str();
+    std::string::size_type foundPos = 0;
+    std::string::size_type prevPos = 0;
+    while ((foundPos = content.find(delimiter, prevPos)) != std::string::npos) {
+      LOG(INFO) << content.substr(prevPos, foundPos - prevPos);
+      prevPos = foundPos + delimiter.size();
+    }
+    LOG(INFO) << content.substr(prevPos);
   }
 
   void backward(const UpdateCallback& callback) override {}

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1246,6 +1246,75 @@ TEST(Layer, PoolLayer) {
 #endif
 }
 
+void setPool3DConfig(TestConfig* config,
+                     PoolConfig* pool,
+                     const string& poolType) {
+  // filter size
+  const int NUM_FILTERS = 16;
+  const int FILTER_SIZE = 3;
+  const int FILTER_SIZE_Y = 3;
+  const int FILTER_SIZE_Z = 3;
+  const int CHANNELS = 16;
+
+  (*config).biasSize = 0;
+  (*config).layerConfig.set_type("pool3d");
+  (*config).layerConfig.set_num_filters(NUM_FILTERS);
+
+  int kw = FILTER_SIZE, kh = FILTER_SIZE_Y, kd = FILTER_SIZE_Z;
+  int pw = 0, ph = 0, pd = 0;
+  int sw = 2, sh = 2, sd = 2;
+
+  pool->set_pool_type(poolType);
+  pool->set_pool_type("avg");
+  pool->set_channels(CHANNELS);
+  pool->set_size_x(kw);
+  pool->set_size_y(kh);
+  pool->set_size_z(kd);
+  pool->set_padding(0);
+  pool->set_padding_y(0);
+  pool->set_padding_z(0);
+  pool->set_stride(sw);
+  pool->set_stride_y(sh);
+  pool->set_stride_z(sd);
+  pool->set_start(0);
+  int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
+  int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
+  int od = outputSize(pool->img_size_z(), kd, pd, sd, /* caffeMode */ false);
+  pool->set_output_x(ow);
+  pool->set_output_y(oh);
+  pool->set_output_z(od);
+}
+
+void testPool3DLayer(const string& poolType, bool trans, bool useGpu) {
+  TestConfig config;
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 11664, 0});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  PoolConfig* pool = input->mutable_pool_conf();
+
+  const int IMAGE_SIZE = 9;
+  const int IMAGE_SIZE_Y = 9;
+  const int IMAGE_SIZE_Z = 9;
+
+  pool->set_img_size(IMAGE_SIZE);
+  pool->set_img_size_y(IMAGE_SIZE_Y);
+  pool->set_img_size_z(IMAGE_SIZE_Z);
+
+  setPool3DConfig(&config, pool, poolType);
+  config.layerConfig.set_size(pool->output_x() * pool->output_y() *
+                              pool->channels());
+
+  testLayerGrad(config, "pool3d", 100, trans, useGpu);
+}
+
+TEST(Layer, Pool3DLayer) {
+  testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ false);
+  testPool3DLayer("max", /* trans= */ false, /* useGpu= */ false);
+#ifndef PADDLE_ONLY_CPU
+  testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ true);
+  testPool3DLayer("max", /* trans= */ false, /* useGpu= */ true);
+#endif
+}
+
 void testSppLayer(const string& poolType,
                   const int pyramidHeight,
                   bool trans,

paddle/math/Matrix.h

@@ -928,6 +928,93 @@ public:
                                size_t paddingW) {
     LOG(FATAL) << "Not implemeted";
   }
+  /**
+   * Pooling 3D forward operation, pick out the largest element
+   * in the sizeX of value
+   */
+  virtual void maxPool3DForward(Matrix& inputMat,
+                                Matrix& maxPoolIdx,
+                                size_t channels,
+                                size_t imgSizeD,
+                                size_t imgSizeH,
+                                size_t imgSizeW,
+                                size_t outputD,
+                                size_t outputH,
+                                size_t outputW,
+                                size_t sizeZ,
+                                size_t sizeY,
+                                size_t sizeX,
+                                size_t strideD,
+                                size_t strideH,
+                                size_t strideW,
+                                size_t paddingD,
+                                size_t paddingH,
+                                size_t paddingW) {
+    LOG(FATAL) << "Not implemeted";
+  }
+
+  virtual void maxPool3DBackward(Matrix& outGrad,
+                                 Matrix& maxPoolIdx,
+                                 size_t imgSizeD,
+                                 size_t imgSizeH,
+                                 size_t imgSizeW,
+                                 size_t outputD,
+                                 size_t outputH,
+                                 size_t outputW,
+                                 size_t sizeZ,
+                                 size_t sizeY,
+                                 size_t sizeX,
+                                 size_t strideD,
+                                 size_t strideH,
+                                 size_t strideW,
+                                 size_t paddingD,
+                                 size_t paddingH,
+                                 size_t paddingW,
+                                 real scaleTargets,
+                                 real scaleOutput) {
+    LOG(FATAL) << "Not implemeted";
+  }
+
+  virtual void avgPool3DForward(Matrix& input,
+                                size_t channels,
+                                size_t imgSizeD,
+                                size_t imgSizeH,
+                                size_t imgSizeW,
+                                size_t outputD,
+                                size_t outputH,
+                                size_t outputW,
+                                size_t sizeZ,
+                                size_t sizeY,
+                                size_t sizeX,
+                                size_t strideD,
+                                size_t strideH,
+                                size_t strideW,
+                                size_t paddingD,
+                                size_t paddingH,
+                                size_t paddingW) {
+    LOG(FATAL) << "Not implemeted";
+  }
+
+  virtual void avgPool3DBackward(Matrix& input,
+                                 size_t imgSizeD,
+                                 size_t imgSizeH,
+                                 size_t imgSizeW,
+                                 size_t outputD,
+                                 size_t outputH,
+                                 size_t outputW,
+                                 size_t sizeZ,
+                                 size_t sizeY,
+                                 size_t sizeX,
+                                 size_t strideD,
+                                 size_t strideH,
+                                 size_t strideW,
+                                 size_t paddingD,
+                                 size_t paddingH,
+                                 size_t paddingW,
+                                 real scaleTargets,
+                                 real scaleOutput) {
+    LOG(FATAL) << "Not implemeted";
+  }
 
   /**
  * Input: one or more sequences. Each sequence contains some instances.
@@ -1384,6 +1471,82 @@ public:
                        size_t paddingH,
                        size_t paddingW);
 
+  void maxPool3DForward(Matrix& inputMat,
+                        Matrix& maxPoolIdx,
+                        size_t channels,
+                        size_t imgSizeD,
+                        size_t imgSizeH,
+                        size_t imgSizeW,
+                        size_t outputD,
+                        size_t outputH,
+                        size_t outputW,
+                        size_t sizeZ,
+                        size_t sizeY,
+                        size_t sizeX,
+                        size_t strideD,
+                        size_t strideH,
+                        size_t strideW,
+                        size_t paddingD,
+                        size_t paddingH,
+                        size_t paddingW);
+
+  void maxPool3DBackward(Matrix& outGrad,
+                         Matrix& maxPoolIdx,
+                         size_t imgSizeD,
+                         size_t imgSizeH,
+                         size_t imgSizeW,
+                         size_t outputD,
+                         size_t outputH,
+                         size_t outputW,
+                         size_t sizeZ,
+                         size_t sizeY,
+                         size_t sizeX,
+                         size_t strideD,
+                         size_t strideH,
+                         size_t strideW,
+                         size_t paddingD,
+                         size_t paddingH,
+                         size_t paddingW,
+                         real scaleTargets,
+                         real scaleOutput);
+
+  void avgPool3DForward(Matrix& input,
+                        size_t channels,
+                        size_t imgSizeD,
+                        size_t imgSizeH,
+                        size_t imgSizeW,
+                        size_t outputD,
+                        size_t outputH,
+                        size_t outputW,
+                        size_t sizeZ,
+                        size_t sizeY,
+                        size_t sizeX,
+                        size_t strideD,
+                        size_t strideH,
+                        size_t strideW,
+                        size_t paddingD,
+                        size_t paddingH,
+                        size_t paddingW);
+
+  void avgPool3DBackward(Matrix& input,
+                         size_t imgSizeD,
+                         size_t imgSizeH,
+                         size_t imgSizeW,
+                         size_t outputD,
+                         size_t outputH,
+                         size_t outputW,
+                         size_t sizeZ,
+                         size_t sizeY,
+                         size_t sizeX,
+                         size_t strideD,
+                         size_t strideH,
+                         size_t strideW,
+                         size_t paddingD,
+                         size_t paddingH,
+                         size_t paddingW,
+                         real scaleTargets,
+                         real scaleOutput);
+
   void maxSequenceForward(Matrix& input,
                           const IVector& sequence,
                           IVector& index);
@@ -1575,6 +1738,82 @@ public:
                        size_t paddingH,
                        size_t paddingW);
 
+  void maxPool3DForward(Matrix& inputMat,
+                        Matrix& maxPoolIdx,
+                        size_t channels,
+                        size_t imgSizeD,
+                        size_t imgSizeH,
+                        size_t imgSizeW,
+                        size_t outputD,
+                        size_t outputH,
+                        size_t outputW,
+                        size_t sizeZ,
+                        size_t sizeY,
+                        size_t sizeX,
+                        size_t strideD,
+                        size_t strideH,
+                        size_t strideW,
+                        size_t paddingD,
+                        size_t paddingH,
+                        size_t paddingW);
+
+  void maxPool3DBackward(Matrix& outGrad,
+                         Matrix& maxPoolIdx,
+                         size_t imgSizeD,
+                         size_t imgSizeH,
+                         size_t imgSizeW,
+                         size_t outputD,
+                         size_t outputH,
+                         size_t outputW,
+                         size_t sizeZ,
+                         size_t sizeY,
+                         size_t sizeX,
+                         size_t strideD,
+                         size_t strideH,
+                         size_t strideW,
+                         size_t paddingD,
+                         size_t paddingH,
+                         size_t paddingW,
+                         real scaleTargets,
+                         real scaleOutput);
+
+  void avgPool3DForward(Matrix& input,
+                        size_t channels,
+                        size_t imgSizeD,
+                        size_t imgSizeH,
+                        size_t imgSizeW,
+                        size_t outputD,
+                        size_t outputH,
+                        size_t outputW,
+                        size_t sizeZ,
+                        size_t sizeY,
+                        size_t sizeX,
+                        size_t strideD,
+                        size_t strideH,
+                        size_t strideW,
+                        size_t paddingD,
+                        size_t paddingH,
+                        size_t paddingW);
+
+  void avgPool3DBackward(Matrix& input,
+                         size_t imgSizeD,
+                         size_t imgSizeH,
+                         size_t imgSizeW,
+                         size_t outputD,
+                         size_t outputH,
+                         size_t outputW,
+                         size_t sizeZ,
+                         size_t sizeY,
+                         size_t sizeX,
+                         size_t strideD,
+                         size_t strideH,
+                         size_t strideW,
+                         size_t paddingD,
+                         size_t paddingH,
+                         size_t paddingW,
+                         real scaleTargets,
+                         real scaleOutput);
+
   void maxSequenceForward(Matrix& input,
                           const IVector& sequence,
                           IVector& index);

paddle/math/tests/test_matrixCompare.cpp

@@ -1204,6 +1204,399 @@ TEST(Matrix, warpCTC) {
   }
 }
 
+void testMaxPool3DFwdBwd(int numSamples,
+                         int channels,
+                         int imgSizeD,
+                         int imgSizeH,
+                         int imgSizeW,
+                         int ksizeD,
+                         int ksizeH,
+                         int ksizeW,
+                         int strideD,
+                         int strideH,
+                         int strideW,
+                         int padD,
+                         int padH,
+                         int padW) {
+  int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true);
+  int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
+  int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
+
+  int inWidth = channels * imgSizeD * imgSizeH * imgSizeW;
+  MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
+
+  int outWidth = channels * outD * outH * outW;
+  MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
+  MatrixPtr maxIdx = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr maxIdxGpu = GpuMatrix::create(numSamples, outWidth, false, true);
+
+  input->randomizeUniform();
+  target->randomizeUniform();
+  inputGpu->copyFrom(*input);
+  targetGpu->copyFrom(*target);
+
+  target->maxPool3DForward(*input,
+                           *maxIdx,
+                           channels,
+                           imgSizeD,
+                           imgSizeH,
+                           imgSizeW,
+                           outD,
+                           outH,
+                           outW,
+                           ksizeD,
+                           ksizeH,
+                           ksizeW,
+                           strideD,
+                           strideH,
+                           strideW,
+                           padD,
+                           padH,
+                           padW);
+  targetGpu->maxPool3DForward(*inputGpu,
+                              *maxIdxGpu,
+                              channels,
+                              imgSizeD,
+                              imgSizeH,
+                              imgSizeW,
+                              outD,
+                              outH,
+                              outW,
+                              ksizeD,
+                              ksizeH,
+                              ksizeW,
+                              strideD,
+                              strideH,
+                              strideW,
+                              padD,
+                              padH,
+                              padW);
+  MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false);
+  targetCheck->copyFrom(*targetGpu);
+  checkMatrixEqual(target, targetCheck);
+
+  MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
+  MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpuGrad =
+      GpuMatrix::create(numSamples, outWidth, false, true);
+
+  inputGrad->randomizeUniform();
+  targetGrad->randomizeUniform();
+  inputGpuGrad->copyFrom(*inputGrad);
+  targetGpuGrad->copyFrom(*targetGrad);
+
+  inputGrad->maxPool3DBackward(*targetGrad,
+                               *maxIdx,
+                               imgSizeD,
+                               imgSizeH,
+                               imgSizeW,
+                               outD,
+                               outH,
+                               outW,
+                               ksizeD,
+                               ksizeH,
+                               ksizeW,
+                               strideD,
+                               strideH,
+                               strideW,
+                               padD,
+                               padH,
+                               padW,
+                               1.0,
+                               1.0);
+  inputGpuGrad->maxPool3DBackward(*targetGpuGrad,
+                                  *maxIdxGpu,
+                                  imgSizeD,
+                                  imgSizeH,
+                                  imgSizeW,
+                                  outD,
+                                  outH,
+                                  outW,
+                                  ksizeD,
+                                  ksizeH,
+                                  ksizeW,
+                                  strideD,
+                                  strideH,
+                                  strideW,
+                                  padD,
+                                  padH,
+                                  padW,
+                                  1.0,
+                                  1.0);
+  MatrixPtr targetBwdCheck =
+      CpuMatrix::create(numSamples, inWidth, false, false);
+  targetBwdCheck->copyFrom(*inputGpuGrad);
+  checkMatrixEqual(inputGrad, targetBwdCheck);
+}
+
+void testAvgPool3DFwdBwd(int numSamples,
+                         int channels,
+                         int imgSizeD,
+                         int imgSizeH,
+                         int imgSizeW,
+                         int ksizeD,
+                         int ksizeH,
+                         int ksizeW,
+                         int strideD,
+                         int strideH,
+                         int strideW,
+                         int padD,
+                         int padH,
+                         int padW) {
+  int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true);
+  int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
+  int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
+
+  int inWidth = imgSizeD * imgSizeH * imgSizeW * channels;
+  MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
+
+  int outWidth = channels * outD * outH * outW;
+  MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
+
+  input->randomizeUniform();
+  target->randomizeUniform();
+  inputGpu->copyFrom(*input);
+  targetGpu->copyFrom(*target);
+
+  target->avgPool3DForward(*input,
+                           channels,
+                           imgSizeD,
+                           imgSizeH,
+                           imgSizeW,
+                           outD,
+                           outH,
+                           outW,
+                           ksizeD,
+                           ksizeH,
+                           ksizeW,
+                           strideD,
+                           strideH,
+                           strideW,
+                           padD,
+                           padH,
+                           padW);
+
+  targetGpu->avgPool3DForward(*inputGpu,
+                              channels,
+                              imgSizeD,
+                              imgSizeH,
+                              imgSizeW,
+                              outD,
+                              outH,
+                              outW,
+                              ksizeD,
+                              ksizeH,
+                              ksizeW,
+                              strideD,
+                              strideH,
+                              strideW,
+                              padD,
+                              padH,
+                              padW);
+
+  TensorCheckErr(*target, *targetGpu);
+
+  MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
+  MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpuGrad =
+      GpuMatrix::create(numSamples, outWidth, false, true);
+
+  inputGrad->randomizeUniform();
+  targetGrad->randomizeUniform();
+  inputGpuGrad->copyFrom(*inputGrad);
+  targetGpuGrad->copyFrom(*targetGrad);
+
+  inputGrad->avgPool3DBackward(*targetGrad,
+                               imgSizeD,
+                               imgSizeH,
+                               imgSizeW,
+                               outD,
+                               outH,
+                               outW,
+                               ksizeD,
+                               ksizeH,
+                               ksizeW,
+                               strideD,
+                               strideH,
+                               strideW,
+                               padD,
+                               padH,
+                               padW,
+                               1.0,
+                               1.0);
+
+  inputGpuGrad->avgPool3DBackward(*targetGpuGrad,
+                                  imgSizeD,
+                                  imgSizeH,
+                                  imgSizeW,
+                                  outD,
+                                  outH,
+                                  outW,
+                                  ksizeD,
+                                  ksizeH,
+                                  ksizeW,
+                                  strideD,
+                                  strideH,
+                                  strideW,
+                                  padD,
+                                  padH,
+                                  padW,
+                                  1.0,
+                                  1.0);
+  TensorCheckErr(*inputGrad, *inputGpuGrad);
+}
+
+// TODO(yi): I noticed many such blindly combinatorial tests in this
+// file.  They are no help to locate defects at all.
+TEST(Matrix, Pool3DFwdBwd) {
+  for (auto numSamples : {1, 3}) {
+    for (auto channels : {3}) {
+      for (auto imgSizeD : {9, 16}) {
+        for (auto imgSizeH : {9, 32}) {
+          for (auto imgSizeW : {9, 32}) {
+            for (auto sizeX : {3}) {
+              for (auto sizeY : {3}) {
+                for (auto sizeZ : {3}) {
+                  for (auto sD : {2}) {
+                    for (auto sH : {2}) {
+                      for (auto sW : {2}) {
+                        for (auto pD : {0, (sizeZ - 1) / 2}) {
+                          for (auto pH : {0, (sizeY - 1) / 2}) {
+                            for (auto pW : {0, (sizeX - 1) / 2}) {
+                              VLOG(3) << " numSamples=" << numSamples
+                                      << " channels=" << channels
+                                      << " imgSizeD=" << imgSizeD
+                                      << " imgSizeH=" << imgSizeH
+                                      << " imgSizeW=" << imgSizeW
+                                      << " sizeX=" << sizeX
+                                      << " sizeY=" << sizeY
+                                      << " sizeZ=" << sizeZ << " strideD=" << sD
+                                      << " strideH=" << sH << " strideW=" << sW
+                                      << " padingD=" << pD << " padingH=" << pH
+                                      << " padingW=" << pW;
+
+                              testMaxPool3DFwdBwd(numSamples,
+                                                  channels,
+                                                  imgSizeD,
+                                                  imgSizeH,
+                                                  imgSizeW,
+                                                  sizeX,
+                                                  sizeY,
+                                                  sizeZ,
+                                                  sD,
+                                                  sH,
+                                                  sW,
+                                                  pD,
+                                                  pH,
+                                                  pW);
+                              testAvgPool3DFwdBwd(numSamples,
+                                                  channels,
+                                                  imgSizeD,
+                                                  imgSizeH,
+                                                  imgSizeW,
+                                                  sizeX,
+                                                  sizeY,
+                                                  sizeZ,
+                                                  sD,
+                                                  sH,
+                                                  sW,
+                                                  pD,
+                                                  pH,
+                                                  pW);
+                            }
+                          }
+                        }
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  //  for (auto numSamples : {1, 3}) {
+  //    for (auto channels : {1, 3}) {
+  //      for (auto imgSizeD : {9,16}) {
+  //      for (auto imgSizeH : {9, 32}) {
+  //        for (auto imgSizeW : {9, 32}) {
+  //          for (auto sizeX : {2, 3}) {
+  //            for (auto sizeY : {2, 3}) {
+  //            for (auto sizeZ : {2,3}){
+  //              for (auto sD : {1, 2}) {
+  //              for (auto sH : {1, 2}) {
+  //                for (auto sW : {1, 2}) {
+  //                  for (auto pD : {0, (sizeZ - 1) / 2}){
+  //                  for (auto pH : {0, (sizeY - 1) / 2}) {
+  //                    for (auto pW : {0, (sizeX - 1) / 2}) {
+  //                      VLOG(3) << " numSamples=" << numSamples
+  //                              << " channels=" << channels
+  //                              << " imgSizeD=" << imgSizeD
+  //                              << " imgSizeH=" << imgSizeH
+  //                              << " imgSizeW=" << imgSizeW
+  //                              << " sizeX=" << sizeX
+  //                              << " sizeY=" << sizeY
+  //                              << " sizeZ=" << sizeZ
+  //                              << " strideD=" << sD
+  //                              << " strideH=" << sH
+  //                              << " strideW=" << sW
+  //                              << " padingD=" << pD
+  //                              << " padingH=" << pH
+  //                              << " padingW=" << pW;
+  //
+  //                      testMaxPool3DFwdBwd(numSamples,
+  //                                        channels,
+  //                                        imgSizeD,
+  //                                        imgSizeH,
+  //                                        imgSizeW,
+  //                                        sizeX,
+  //                                        sizeY,
+  //                                        sizeZ,
+  //                                        sD,
+  //                                        sH,
+  //                                        sW,
+  //                                        pD,
+  //                                        pH,
+  //                                        pW);
+  //                      testAvgPool3DFwdBwd(numSamples,
+  //                                        channels,
+  //                                        imgSizeD,
+  //                                        imgSizeH,
+  //                                        imgSizeW,
+  //                                        sizeX,
+  //                                        sizeY,
+  //                                        sizeZ,
+  //                                        sD,
+  //                                        sH,
+  //                                        sW,
+  //                                        pD,
+  //                                        pH,
+  //                                        pW);
+  //                    }
+  //                  }
+  //                }
+  //              }
+  //            }
+  //            }
+  //          }
+  //        }
+  //      }
+  //      }
+  //    }
+  //    }
+  //  }
+  //  }
+}
+
 void testMatrixCol2Vol(int depth, int height, int width) {
   int channel = 3;
   int filterX = 3, filterY = 4, filterZ = 5;
@@ -1303,6 +1696,5 @@ TEST(Matrix, col2Vol) {
     }
   }
 }
-///////
 
 #endif

paddle/operators/CMakeLists.txt

+file(GLOB GENERAL_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*_op.cc")
+string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}")
 function(op_library TARGET)
     # op_library is a function to create op library. The interface is same as
     # cc_library. But it handle split GPU/CPU code and link some common library
     # for ops.
+    set(OP_LIBRARY ${TARGET} ${OP_LIBRARY} PARENT_SCOPE)
     set(cc_srcs)
     set(cu_srcs)
     set(op_common_deps operator op_registry)
@@ -43,33 +46,26 @@ endfunction()
 
 add_subdirectory(math)
 
-cc_test(gather_test SRCS gather_test.cc DEPS tensor)
-op_library(gather_op SRCS gather_op.cc gather_op.cu)
-
-cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
-op_library(scatter_op SRCS scatter_op.cc scatter_op.cu)
-
-cc_library(net_op SRCS net_op.cc DEPS op_registry)
-cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
-
-op_library(add_op SRCS add_op.cc add_op.cu)
-
-op_library(mean_op SRCS mean_op.cc mean_op.cu)
+list(REMOVE_ITEM GENERAL_OPS
+     net_op
+     minus_op
+     mul_op
+     recurrent_op
+     scale_op)
 
+op_library(net_op SRCS net_op.cc)
+op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
 op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
-op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
+op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc 
+  DEPS framework_proto tensor operator net_op)
+op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
 
-op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
-op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
-op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu)
-op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
-op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
+foreach(src ${GENERAL_OPS})
+    op_library(${src} SRCS ${src}.cc ${src}.cu)
+endforeach()
 
-op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
+set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
 
-op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
-    DEPS framework_proto tensor op_registry operator net_op)
-op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu)
-op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu)
-op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
-op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
+cc_test(gather_test SRCS gather_test.cc DEPS tensor)
+cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
+cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)

paddle/operators/add_op.cc

@@ -57,7 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, ops::AddOpGrad);
 
 REGISTER_OP_CPU_KERNEL(add_two,
                        ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/cross_entropy_op.cc

@@ -67,8 +67,7 @@ OnehotCrossEntropy Operator.
 
 namespace ops = paddle::operators;
 REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
-            ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
-            ops::OnehotCrossEntropyGradientOp);
+            ops::OnehotCrossEntropyOpMaker, ops::OnehotCrossEntropyGradientOp);
 REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
                        ops::OnehotCrossEntropyOpKernel<float>);
 REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,

paddle/operators/gather_op.cc

@@ -63,8 +63,7 @@ Out = X[Index]
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad,
-            ops::GatherGradOp);
+REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, ops::GatherGradOp);
 REGISTER_OP_CPU_KERNEL(gather,
                        ops::GatherOpKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/lookup_table_op.cc

@@ -66,7 +66,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
-            lookup_table_grad, ops::LookupTableOpGrad);
+            ops::LookupTableOpGrad);
 
 REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
 REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);

paddle/operators/mean_op.cc

@@ -54,7 +54,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(mean,
                        ops::MeanKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mean_grad,

paddle/operators/minus_op.cc

@@ -81,7 +81,6 @@ class MinusGradOp : public NetOp {
 USE_OP(scale);
 USE_OP_ITSELF(identity);
 namespace ops = paddle::operators;
-REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
-            ops::MinusGradOp<float>);
+REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradOp<float>);
 REGISTER_OP_CPU_KERNEL(minus,
                        ops::MinusKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mul_op.cc

@@ -84,7 +84,7 @@ class MulOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(mul_grad,
                        ops::MulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rowwise_add_op.cc

@@ -74,7 +74,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
-            rowwise_add_grad, ops::RowwiseAddGradOp);
+            ops::RowwiseAddGradOp);
 REGISTER_OP_CPU_KERNEL(
     rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/scale_op.cc

@@ -97,7 +97,7 @@ class IdentityOp : public NetOp {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
+REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
             ops::ScaleGradOp<float>);
 REGISTER_OP_CPU_KERNEL(scale,
                        ops::ScaleKernel<paddle::platform::CPUPlace, float>);

paddle/operators/scatter_op.cc

@@ -77,8 +77,7 @@ Out[Index] = Ref[Index] + Updates
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad,
-            ops::ScatterGradOp);
+REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, ops::ScatterGradOp);
 REGISTER_OP_CPU_KERNEL(scatter,
                        ops::ScatterOpKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/sigmoid_op.cc

@@ -53,8 +53,7 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
-            ops::SigmoidOpGrad);
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, ops::SigmoidOpGrad);
 REGISTER_OP_CPU_KERNEL(sigmoid,
                        ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/operators/softmax_op.cc

@@ -62,8 +62,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
-            ops::SoftmaxOpGrad);
+REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(softmax,
                        ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(

paddle/pybind/CMakeLists.txt

@@ -2,21 +2,5 @@ if(WITH_PYTHON)
 cc_library(paddle_pybind SHARED
     SRCS pybind.cc
     DEPS pybind python backward
-    sgd_op
-    gather_op
-    scatter_op
-    add_op
-    mul_op
-    rowwise_add_op
-    sigmoid_op
-    softmax_op
-    mean_op
-    cross_entropy_op
-    recurrent_op
-    uniform_random_op
-    gaussian_random_op
-    fill_zeros_like_op
-    lookup_table_op
-    scale_op
-    minus_op)
+    ${GLOB_OP_LIB})
 endif(WITH_PYTHON)

proto/ModelConfig.proto

@@ -133,6 +133,12 @@ message PoolConfig {
 
   // if not set, use padding
   optional uint32 padding_y = 13;
+
+  optional uint32 size_z = 14 [ default = 1 ];
+  optional uint32 stride_z = 15 [ default = 1 ];
+  optional uint32 output_z = 16 [ default = 1 ];
+  optional uint32 img_size_z = 17 [ default = 1 ];
+  optional uint32 padding_z = 18 [ default = 1 ];
 }
 
 message SppConfig {

python/paddle/trainer/config_parser.py

@@ -938,6 +938,31 @@ class Pool(Cfg):
         self.add_keys(locals())
 
 
+@config_class
+class Pool3d(Cfg):
+    def __init__(
+            self,
+            pool_type,
+            channels,
+            size_x,
+            size_y=None,
+            size_z=None,
+            start=None,
+            stride=None,  # 1 by defalut in protobuf
+            stride_y=None,
+            stride_z=None,
+            padding=None,  # 0 by defalut in protobuf
+            padding_y=None,
+            padding_z=None):
+        self.add_keys(locals())
+        self.filter_size_y = size_y if size_y else size_x
+        self.filter_size_z = size_z if size_z else size_x
+        self.padding_y = padding_y if padding_y else padding
+        self.padding_z = padding_z if padding_z else padding
+        self.stride_y = stride_y if stride_y else stride
+        self.stride_z = stride_z if stride_z else stride
+
+
 @config_class
 class SpatialPyramidPool(Cfg):
     def __init__(self, pool_type, pyramid_height, channels):
@@ -1253,6 +1278,45 @@ def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
                                          pool_conf.stride_y, not ceil_mode)
 
 
+def parse_pool3d(pool, input_layer_name, pool_conf, ceil_mode):
+    pool_conf.pool_type = pool.pool_type
+    config_assert(pool.pool_type in ['max-projection', 'avg-projection'],
+                  "pool-type %s is not in "
+                  "['max-projection', 'avg-projection']" % pool.pool_type)
+
+    pool_conf.channels = pool.channels
+
+    pool_conf.size_x = pool.size_x
+    pool_conf.stride = pool.stride
+    pool_conf.padding = pool.padding
+
+    pool_conf.size_y = default(pool.size_y, pool_conf.size_x)
+    pool_conf.size_z = default(pool.size_z, pool_conf.size_x)
+    pool_conf.stride_y = default(pool.stride_y, pool_conf.stride)
+    pool_conf.stride_z = default(pool.stride_z, pool_conf.stride)
+    pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
+    pool_conf.padding_z = default(pool.padding_z, pool_conf.padding)
+
+    pool_conf.img_size, pool_conf.img_size_y, pool_conf.img_size_z = \
+        get_img3d_size(input_layer_name, pool.channels)
+
+    config_assert(not pool.start, "start is deprecated in pooling.")
+
+    if pool.padding is not None:
+        pool_conf.padding = pool.padding
+    pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
+    pool_conf.padding_z = default(pool.padding_z, pool_conf.padding)
+    pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
+                                         pool_conf.padding, pool_conf.stride,
+                                         not ceil_mode)
+    pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
+                                         pool_conf.padding_y,
+                                         pool_conf.stride_y, not ceil_mode)
+    pool_conf.output_z = cnn_output_size(pool_conf.img_size_z, pool_conf.size_z,
+                                         pool_conf.padding_z,
+                                         pool_conf.stride_z, not ceil_mode)
+
+
 def parse_spp(spp, input_layer_name, spp_conf):
     parse_image(spp, input_layer_name, spp_conf.image_conf)
     spp_conf.pool_type = spp.pool_type
@@ -1897,9 +1961,9 @@ class DataLayer(LayerBase):
     def __init__(self,
                  name,
                  size,
+                 depth=None,
                  height=None,
                  width=None,
-                 depth=None,
                  device=None):
         super(DataLayer, self).__init__(
             name, 'data', size, inputs=[], device=device)
@@ -2215,6 +2279,35 @@ class PoolLayer(LayerBase):
                                pool_conf.channels)
 
 
+@config_layer('pool3d')
+class Pool3DLayer(LayerBase):
+    def __init__(self, name, inputs, ceil_mode=True, **xargs):
+        super(Pool3DLayer, self).__init__(
+            name, 'pool3d', 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
+            parse_pool3d(self.inputs[input_index].pool, input_layer.name,
+                         pool_conf, ceil_mode)
+            self.set_cnn_layer(name, pool_conf.output_z, pool_conf.output_y,
+                               pool_conf.output_x, pool_conf.channels)
+
+    def set_cnn_layer(self,
+                      input_layer_name,
+                      depth,
+                      height,
+                      width,
+                      channels,
+                      is_print=True):
+        size = depth * height * width * channels
+        self.set_layer_size(size)
+        self.set_layer_height_width(height, width)
+        self.set_layer_depth(depth)
+        if is_print:
+            print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
+                  (input_layer_name, channels, depth, height, width, size))
+
+
 @config_layer('spp')
 class SpatialPyramidPoolLayer(LayerBase):
     def __init__(self, name, inputs, **xargs):

python/paddle/trainer_config_helpers/layers.py

@@ -137,7 +137,8 @@ __all__ = [
     'clip_layer',
     'slice_projection',
     'seq_slice_layer',
-    'kmax_sequence_score_layer',
+    'kmax_seq_score_layer',
+    'img_pool3d_layer',
     'scale_shift_layer',
     'img_conv3d_layer',
 ]
@@ -168,6 +169,7 @@ class LayerType(object):
     EXCONVTRANS_LAYER = 'exconvt'
     CUDNNCONV_LAYER = 'cudnn_conv'
     POOL_LAYER = 'pool'
+    POOL3D_LAYER = 'pool3d'
     BATCH_NORM_LAYER = 'batch_norm'
     NORM_LAYER = 'norm'
     SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
@@ -900,7 +902,7 @@ def mixed_layer(size=0,
 
 
 @layer_support()
-def data_layer(name, size, height=None, width=None, depth=None,
+def data_layer(name, size, depth=None, height=None, width=None,
                layer_attr=None):
     """
     Define DataLayer For NeuralNetwork.
@@ -938,7 +940,7 @@ def data_layer(name, size, height=None, width=None, depth=None,
     num_filters = None
     if height is not None and width is not None:
         num_filters = size / (width * height * depth)
-        assert num_filters * width * height*depth == size, \
+        assert num_filters * width * height * depth == size, \
                 "size=%s width=%s height=%s depth=%s"  % (size, width, height, depth)
 
     return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
@@ -2663,6 +2665,146 @@ def img_pool_layer(input,
         size=l.config.size)
 
 
+@wrap_name_default("pool3d")
+@layer_support()
+def img_pool3d_layer(input,
+                     pool_size,
+                     name=None,
+                     num_channels=None,
+                     pool_type=None,
+                     stride=1,
+                     padding=0,
+                     layer_attr=None,
+                     pool_size_y=None,
+                     stride_y=None,
+                     padding_y=None,
+                     pool_size_z=None,
+                     stride_z=None,
+                     padding_z=None,
+                     ceil_mode=True):
+    """
+    Image pooling Layer.
+
+    The details of pooling layer, please refer ufldl's pooling_ .
+
+    .. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
+
+    - ceil_mode=True:
+
+    ..  math::
+
+        w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
+        h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
+        d = 1 + int(ceil(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z))
+
+    - ceil_mode=False:
+
+    ..  math::
+
+        w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
+        h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
+        d = 1 + int(floor(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z))
+
+    The example usage is:
+
+    ..  code-block:: python
+
+        maxpool = img_pool3d_layer(input=conv,
+                                 pool_size=3,
+                                 num_channels=8,
+                                 stride=1,
+                                 padding=1,
+                                 pool_type=MaxPooling())
+
+    :param padding: pooling padding width.
+    :type padding: int|tuple|list
+    :param name: name of pooling layer
+    :type name: basestring.
+    :param input: layer's input
+    :type input: LayerOutput
+    :param pool_size: pooling window width
+    :type pool_size: int|tuple|list
+    :param num_channels: number of input channel.
+    :type num_channels: int
+    :param pool_type: pooling type. MaxPooling or AvgPooling. Default is
+                      MaxPooling.
+    :type pool_type: BasePoolingType
+    :param stride: stride width of pooling.
+    :type stride: int|tuple|list
+    :param layer_attr: Extra Layer attribute.
+    :type layer_attr: ExtraLayerAttribute
+    :param ceil_mode: Wether to use ceil mode to calculate output height and with.
+                      Defalut is True. If set false, Otherwise use floor.
+
+    :type ceil_mode: bool
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    if num_channels is None:
+        assert input.num_filters is not None
+        num_channels = input.num_filters
+
+    if pool_type is None:
+        pool_type = MaxPooling()
+    elif isinstance(pool_type, AvgPooling):
+        pool_type.name = 'avg'
+
+    type_name = pool_type.name + '-projection' \
+        if (
+        isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
+        else pool_type.name
+
+    if isinstance(pool_size, collections.Sequence):
+        assert len(pool_size) == 3
+        pool_size, pool_size_y, pool_size_z = pool_size
+    else:
+        pool_size_y = pool_size
+        pool_size_z = pool_size
+
+    if isinstance(stride, collections.Sequence):
+        assert len(stride) == 3
+        stride, stride_y, stride_z = stride
+    else:
+        stride_y = stride
+        stride_z = stride
+
+    if isinstance(padding, collections.Sequence):
+        assert len(padding) == 3
+        padding, padding_y, padding_y = padding
+    else:
+        padding_y = padding
+        padding_z = padding
+
+    l = Layer(
+        name=name,
+        type=LayerType.POOL3D_LAYER,
+        inputs=[
+            Input(
+                input.name,
+                pool=Pool3d(
+                    pool_type=type_name,
+                    channels=num_channels,
+                    size_x=pool_size,
+                    start=None,
+                    stride=stride,
+                    padding=padding,
+                    size_y=pool_size_y,
+                    stride_y=stride_y,
+                    padding_y=padding_y,
+                    size_z=pool_size_z,
+                    stride_z=stride_z,
+                    padding_z=padding_z))
+        ],
+        ceil_mode=ceil_mode,
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(
+        name,
+        LayerType.POOL_LAYER,
+        parents=[input],
+        num_filters=num_channels,
+        size=l.config.size)
+
+
 @wrap_name_default("spp")
 @layer_support()
 def spp_layer(input,
@@ -5852,7 +5994,7 @@ def cross_entropy_over_beam(input, name=None):
     Note that, if gold falls off the beam at search step t, then the cost is
     calculated over the beam at step t.
 
-    This cost layer always works together with kmax_sequence_score_layer,
+    This cost layer always works together with kmax_seq_score_layer,
     sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a
     sub-search space.
 
@@ -6455,14 +6597,14 @@ def seq_slice_layer(input, starts, ends, name=None):
 
 @wrap_name_default()
 @layer_support()
-def kmax_sequence_score_layer(input, name=None, beam_size=1):
+def kmax_seq_score_layer(input, name=None, beam_size=1):
     """
     This layer accepts one input which are scores over a sequence or a nested
     sequence, and returns indices of beam_size sequences with highest scores.
 
     .. code-block:: python
 
-        kmax_indices = kmax_sequence_score_layer(input=input_layer, beam_size)
+        kmax_indices = kmax_seq_score_layer(input=input_layer, beam_size)
 
 
     :param name: The Layer Name.
@@ -6475,10 +6617,10 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
-    assert isinstance(input, LayerOutput), ("kmax_sequence_score_layer "
+    assert isinstance(input, LayerOutput), ("kmax_seq_score_layer "
                                             "accepts only one input.")
     assert input.size == 1, (
-        "input of kmax_sequence_score_layer is a score"
+        "input of kmax_seq_score_layer is a score "
         "over a sequence or a nested sequence, so its width must be 1.")
 
     Layer(

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -8,7 +8,8 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
 test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
 test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
-test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
-test_seq_slice_layer test_cross_entropy_over_beam  test_conv3d_layer test_deconv3d_layer)
+test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
+test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
+test_conv3d_layer test_deconv3d_layer)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr

@@ -12,7 +12,7 @@ layers {
   active_type: ""
 }
 layers {
-  name: "__kmax_sequence_score_layer_0__"
+  name: "__kmax_seq_score_layer_0__"
   type: "kmax_seq_score"
   active_type: ""
   inputs {
@@ -29,7 +29,7 @@ layers {
     input_layer_name: "sentence_states"
   }
   inputs {
-    input_layer_name: "__kmax_sequence_score_layer_0__"
+    input_layer_name: "__kmax_seq_score_layer_0__"
   }
 }
 layers {
@@ -44,7 +44,7 @@ layers {
   bias_parameter_name: "___fc_layer_0__.wbias"
 }
 layers {
-  name: "__kmax_sequence_score_layer_1__"
+  name: "__kmax_seq_score_layer_1__"
   type: "kmax_seq_score"
   active_type: ""
   inputs {
@@ -61,7 +61,7 @@ layers {
     input_layer_name: "__sub_nested_seq_layer_0__"
   }
   inputs {
-    input_layer_name: "__kmax_sequence_score_layer_1__"
+    input_layer_name: "__kmax_seq_score_layer_1__"
   }
   select_first: true
 }
@@ -77,7 +77,7 @@ layers {
   bias_parameter_name: "___fc_layer_1__.wbias"
 }
 layers {
-  name: "__kmax_sequence_score_layer_2__"
+  name: "__kmax_seq_score_layer_2__"
   type: "kmax_seq_score"
   active_type: ""
   inputs {
@@ -111,7 +111,7 @@ layers {
     input_layer_name: "sentence_scores"
   }
   inputs {
-    input_layer_name: "__kmax_sequence_score_layer_0__"
+    input_layer_name: "__kmax_seq_score_layer_0__"
   }
   inputs {
     input_layer_name: "sentences_ids"
@@ -120,7 +120,7 @@ layers {
     input_layer_name: "__fc_layer_0__"
   }
   inputs {
-    input_layer_name: "__kmax_sequence_score_layer_1__"
+    input_layer_name: "__kmax_seq_score_layer_1__"
   }
   inputs {
     input_layer_name: "start_ids"
@@ -129,7 +129,7 @@ layers {
     input_layer_name: "__fc_layer_1__"
   }
   inputs {
-    input_layer_name: "__kmax_sequence_score_layer_2__"
+    input_layer_name: "__kmax_seq_score_layer_2__"
   }
   inputs {
     input_layer_name: "end_ids"
@@ -185,13 +185,13 @@ sub_models {
   name: "root"
   layer_names: "sentence_states"
   layer_names: "sentence_scores"
-  layer_names: "__kmax_sequence_score_layer_0__"
+  layer_names: "__kmax_seq_score_layer_0__"
   layer_names: "__sub_nested_seq_layer_0__"
   layer_names: "__fc_layer_0__"
-  layer_names: "__kmax_sequence_score_layer_1__"
+  layer_names: "__kmax_seq_score_layer_1__"
   layer_names: "__seq_slice_layer_0__"
   layer_names: "__fc_layer_1__"
-  layer_names: "__kmax_sequence_score_layer_2__"
+  layer_names: "__kmax_seq_score_layer_2__"
   layer_names: "sentences_ids"
   layer_names: "start_ids"
   layer_names: "end_ids"

python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr

@@ -17,7 +17,7 @@ layers {
   bias_parameter_name: "___fc_layer_0__.wbias"
 }
 layers {
-  name: "__kmax_sequence_score_layer_0__"
+  name: "__kmax_seq_score_layer_0__"
   type: "kmax_seq_score"
   active_type: ""
   inputs {
@@ -46,14 +46,14 @@ parameters {
   initial_smart: false
 }
 input_layer_names: "input_seq"
-output_layer_names: "__kmax_sequence_score_layer_0__"
+output_layer_names: "__kmax_seq_score_layer_0__"
 sub_models {
   name: "root"
   layer_names: "input_seq"
   layer_names: "__fc_layer_0__"
-  layer_names: "__kmax_sequence_score_layer_0__"
+  layer_names: "__kmax_seq_score_layer_0__"
   input_layer_names: "input_seq"
-  output_layer_names: "__kmax_sequence_score_layer_0__"
+  output_layer_names: "__kmax_seq_score_layer_0__"
   is_recurrent_layer_group: false
 }
 

python/paddle/trainer_config_helpers/tests/configs/protostr/test_pooling3D_layer.protostr

+type: "nn"
+layers {
+  name: "data_2d"
+  type: "data"
+  size: 6000
+  active_type: ""
+  height: 20
+  width: 10
+}
+layers {
+  name: "pool___2d"
+  type: "pool"
+  size: 840
+  active_type: ""
+  inputs {
+    input_layer_name: "data_2d"
+    pool_conf {
+      pool_type: "avg-projection"
+      channels: 30
+      size_x: 5
+      stride: 3
+      output_x: 4
+      img_size: 10
+      padding: 1
+      size_y: 5
+      stride_y: 3
+      output_y: 7
+      img_size_y: 20
+      padding_y: 1
+    }
+  }
+  height: 7
+  width: 4
+}
+layers {
+  name: "data_3d_1"
+  type: "data"
+  size: 60000
+  active_type: ""
+  height: 20
+  width: 10
+  depth: 10
+}
+layers {
+  name: "pool_3d_1"
+  type: "pool3d"
+  size: 3360
+  active_type: ""
+  inputs {
+    input_layer_name: "data_3d_1"
+    pool_conf {
+      pool_type: "avg-projection"
+      channels: 30
+      size_x: 5
+      stride: 3
+      output_x: 4
+      img_size: 10
+      padding: 1
+      size_y: 5
+      stride_y: 3
+      output_y: 7
+      img_size_y: 20
+      padding_y: 1
+      size_z: 5
+      stride_z: 3
+      output_z: 4
+      img_size_z: 10
+      padding_z: 1
+    }
+  }
+  height: 7
+  width: 4
+  depth: 4
+}
+layers {
+  name: "pool_3d_2"
+  type: "pool3d"
+  size: 3360
+  active_type: ""
+  inputs {
+    input_layer_name: "data_3d_1"
+    pool_conf {
+      pool_type: "max-projection"
+      channels: 30
+      size_x: 5
+      stride: 3
+      output_x: 4
+      img_size: 10
+      padding: 1
+      size_y: 5
+      stride_y: 3
+      output_y: 7
+      img_size_y: 20
+      padding_y: 1
+      size_z: 5
+      stride_z: 3
+      output_z: 4
+      img_size_z: 10
+      padding_z: 1
+    }
+  }
+  height: 7
+  width: 4
+  depth: 4
+}
+input_layer_names: "data_2d"
+output_layer_names: "pool___2d"
+output_layer_names: "pool_3d_1"
+output_layer_names: "pool_3d_2"
+sub_models {
+  name: "root"
+  layer_names: "data_2d"
+  layer_names: "pool___2d"
+  layer_names: "data_3d_1"
+  layer_names: "pool_3d_1"
+  layer_names: "pool_3d_2"
+  input_layer_names: "data_2d"
+  output_layer_names: "pool___2d"
+  output_layer_names: "pool_3d_1"
+  output_layer_names: "pool_3d_2"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py

@@ -7,14 +7,14 @@ beam_size = 5
 # the first beam expansion.
 sentence_states = data_layer(name="sentence_states", size=32)
 sentence_scores = data_layer(name="sentence_scores", size=1)
-topk_sentence_ids = kmax_sequence_score_layer(
+topk_sentence_ids = kmax_seq_score_layer(
     input=sentence_scores, beam_size=beam_size)
 
 # the second beam expansion.
 topk_sen = sub_nested_seq_layer(
     input=sentence_states, selected_indices=topk_sentence_ids)
 start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation())
-topk_start_pos_ids = kmax_sequence_score_layer(
+topk_start_pos_ids = kmax_seq_score_layer(
     input=sentence_scores, beam_size=beam_size)
 
 # the final beam expansion.
@@ -22,7 +22,7 @@ topk_start_spans = seq_slice_layer(
     input=topk_sen, starts=topk_start_pos_ids, ends=None)
 end_pos_scores = fc_layer(
     input=topk_start_spans, size=1, act=LinearActivation())
-topk_end_pos_ids = kmax_sequence_score_layer(
+topk_end_pos_ids = kmax_seq_score_layer(
     input=end_pos_scores, beam_size=beam_size)
 
 # define the cost

python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py

@@ -4,6 +4,6 @@ from paddle.trainer_config_helpers import *
 
 data = data_layer(name="input_seq", size=128)
 scores = fc_layer(input=data, size=1, act=ExpActivation())
-kmax_seq_id = kmax_sequence_score_layer(input=scores, beam_size=5)
+kmax_seq_id = kmax_seq_score_layer(input=scores, beam_size=5)
 
 outputs(kmax_seq_id)

python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py

+from paddle.trainer_config_helpers import *
+
+settings(batch_size=100, learning_rate=1e-5)
+
+data_2d = data_layer(name='data_2d', size=6000, height=20, width=10)
+
+pool_2d = img_pool_layer(
+    name="pool___2d",
+    input=data_2d,
+    num_channels=30,
+    pool_size=5,
+    stride=3,
+    padding=1,
+    pool_type=AvgPooling())
+outputs(pool_2d)
+
+data_3d = data_layer(
+    name='data_3d_1', size=60000, depth=10, height=20, width=10)
+
+pool_3d_1 = img_pool3d_layer(
+    name="pool_3d_1",
+    input=data_3d,
+    num_channels=30,
+    pool_size=5,
+    stride=3,
+    padding=1,
+    pool_type=AvgPooling())
+outputs(pool_3d_1)
+
+pool_3d_2 = img_pool3d_layer(
+    name="pool_3d_2",
+    input=data_3d,
+    num_channels=30,
+    pool_size=[5, 5, 5],
+    stride=[3, 3, 3],
+    padding=[1, 1, 1],
+    pool_type=MaxPooling())
+outputs(pool_3d_2)

python/paddle/v2/__init__.py

@@ -78,6 +78,8 @@ def init(**kwargs):
 
     if 'use_gpu' in kwargs:
         cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
+    if 'use_mkldnn' in kwargs:
+        cp.g_command_config_args['use_mkldnn'] = kwargs['use_mkldnn']
     assert 'parallel_nn' not in kwargs, ("currently 'parallel_nn' is not "
                                          "supported in v2 APIs.")