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

benchmark/paddle/image/resnet.py

+#!/usr/bin/env python
+from paddle.trainer_config_helpers import *
+
+height = 224
+width = 224
+num_class = 1000
+batch_size = get_config_arg('batch_size', int, 64)
+layer_num = get_config_arg("layer_num", int, 50)
+is_test = get_config_arg("is_test", bool, False)
+
+args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
+define_py_data_sources2(
+    "train.list", None, module="provider", obj="process", args=args)
+
+settings(
+    batch_size=batch_size,
+    learning_rate=0.01 / batch_size,
+    learning_method=MomentumOptimizer(0.9),
+    regularization=L2Regularization(0.0005 * batch_size))
+
+
+#######################Network Configuration #############
+def conv_bn_layer(name,
+                  input,
+                  filter_size,
+                  num_filters,
+                  stride,
+                  padding,
+                  channels=None,
+                  active_type=ReluActivation()):
+    """
+    A wrapper for conv layer with batch normalization layers.
+    Note:
+    conv layer has no activation.
+    """
+
+    tmp = img_conv_layer(
+        name=name + "_conv",
+        input=input,
+        filter_size=filter_size,
+        num_channels=channels,
+        num_filters=num_filters,
+        stride=stride,
+        padding=padding,
+        act=LinearActivation(),
+        bias_attr=False)
+    return batch_norm_layer(
+        name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
+
+
+def bottleneck_block(name, input, num_filters1, num_filters2):
+    """
+    A wrapper for bottlenect building block in ResNet.
+    Last conv_bn_layer has no activation.
+    Addto layer has activation of relu.
+    """
+    last_name = conv_bn_layer(
+        name=name + '_branch2a',
+        input=input,
+        filter_size=1,
+        num_filters=num_filters1,
+        stride=1,
+        padding=0)
+    last_name = conv_bn_layer(
+        name=name + '_branch2b',
+        input=last_name,
+        filter_size=3,
+        num_filters=num_filters1,
+        stride=1,
+        padding=1)
+    last_name = conv_bn_layer(
+        name=name + '_branch2c',
+        input=last_name,
+        filter_size=1,
+        num_filters=num_filters2,
+        stride=1,
+        padding=0,
+        active_type=LinearActivation())
+
+    return addto_layer(
+        name=name + "_addto", input=[input, last_name], act=ReluActivation())
+
+
+def mid_projection(name, input, num_filters1, num_filters2, stride=2):
+    """
+    A wrapper for middile projection in ResNet.
+    projection shortcuts are used for increasing dimensions,
+    and other shortcuts are identity
+    branch1: projection shortcuts are used for increasing
+    dimensions, has no activation.
+    branch2x: bottleneck building block, shortcuts are identity.
+    """
+    # stride = 2
+    branch1 = conv_bn_layer(
+        name=name + '_branch1',
+        input=input,
+        filter_size=1,
+        num_filters=num_filters2,
+        stride=stride,
+        padding=0,
+        active_type=LinearActivation())
+
+    last_name = conv_bn_layer(
+        name=name + '_branch2a',
+        input=input,
+        filter_size=1,
+        num_filters=num_filters1,
+        stride=stride,
+        padding=0)
+    last_name = conv_bn_layer(
+        name=name + '_branch2b',
+        input=last_name,
+        filter_size=3,
+        num_filters=num_filters1,
+        stride=1,
+        padding=1)
+
+    last_name = conv_bn_layer(
+        name=name + '_branch2c',
+        input=last_name,
+        filter_size=1,
+        num_filters=num_filters2,
+        stride=1,
+        padding=0,
+        active_type=LinearActivation())
+
+    return addto_layer(
+        name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
+
+
+img = data_layer(name='image', size=height * width * 3)
+
+
+def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
+    """
+    A wrapper for 50,101,152 layers of ResNet.
+    res2_num: number of blocks stacked in conv2_x
+    res3_num: number of blocks stacked in conv3_x
+    res4_num: number of blocks stacked in conv4_x
+    res5_num: number of blocks stacked in conv5_x
+    """
+    # For ImageNet
+    # conv1: 112x112
+    tmp = conv_bn_layer(
+        "conv1",
+        input=img,
+        filter_size=7,
+        channels=3,
+        num_filters=64,
+        stride=2,
+        padding=3)
+    tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
+
+    # conv2_x: 56x56
+    tmp = mid_projection(
+        name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
+    for i in xrange(2, res2_num + 1, 1):
+        tmp = bottleneck_block(
+            name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
+
+    # conv3_x: 28x28
+    tmp = mid_projection(
+        name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
+    for i in xrange(2, res3_num + 1, 1):
+        tmp = bottleneck_block(
+            name="res3_" + str(i),
+            input=tmp,
+            num_filters1=128,
+            num_filters2=512)
+
+    # conv4_x: 14x14
+    tmp = mid_projection(
+        name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
+    for i in xrange(2, res4_num + 1, 1):
+        tmp = bottleneck_block(
+            name="res4_" + str(i),
+            input=tmp,
+            num_filters1=256,
+            num_filters2=1024)
+
+    # conv5_x: 7x7
+    tmp = mid_projection(
+        name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
+    for i in xrange(2, res5_num + 1, 1):
+        tmp = bottleneck_block(
+            name="res5_" + str(i),
+            input=tmp,
+            num_filters1=512,
+            num_filters2=2048)
+
+    tmp = img_pool_layer(
+        name='avgpool',
+        input=tmp,
+        pool_size=7,
+        stride=1,
+        pool_type=AvgPooling())
+
+    return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
+
+
+if layer_num == 50:
+    resnet = deep_res_net(3, 4, 6, 3)
+elif layer_num == 101:
+    resnet = deep_res_net(3, 4, 23, 3)
+elif layer_num == 152:
+    resnet = deep_res_net(3, 8, 36, 3)
+else:
+    print("Wrong layer number.")
+
+lbl = data_layer(name="label", size=num_class)
+loss = cross_entropy(name='loss', input=resnet, label=lbl)
+inputs(img, lbl)
+outputs(loss)

benchmark/paddle/image/run_mkldnn.sh

@@ -5,22 +5,23 @@ function train() {
   export OMP_DYNAMIC="FALSE"
   export KMP_AFFINITY="granularity=fine,compact,0,0"
   topology=$1
-  bs=$2
-  use_mkldnn=$3
-  if [ $3 == "True" ]; then
+  layer_num=$2
+  bs=$3
+  use_mkldnn=$4
+  if [ $4 == "True" ]; then
     thread=1
-    log="logs/${topology}-mkldnn-${bs}.log"
-  elif [ $3 == "False" ]; then
+    log="logs/${topology}-${layer_num}-mkldnn-${bs}.log"
+  elif [ $4 == "False" ]; then
     thread=`nproc`
     # each trainer_count use only 1 core to avoid conflict
     export OMP_NUM_THREADS=1
     export MKL_NUM_THREADS=1
-    log="logs/${topology}-${thread}mklml-${bs}.log"
+    log="logs/${topology}-${layer_num}-${thread}mklml-${bs}.log"
   else
     echo "Wrong input $3, use True or False."
     exit 0
   fi
-  args="batch_size=${bs}"
+  args="batch_size=${bs},layer_num=${layer_num}"
   config="${topology}.py"
   paddle train --job=time \
     --config=$config \
@@ -40,12 +41,9 @@ if [ ! -d "logs" ]; then
   mkdir logs
 fi
 
-#========== mkldnn ==========#
-train vgg 64 True
-train vgg 128 True
-train vgg 256 True
-
-#========== mklml ===========#
-train vgg 64 False
-train vgg 128 False
-train vgg 256 False
+for use_mkldnn in True False; do
+  for batchsize in 64 128 256; do
+    train vgg 19 $batchsize $use_mkldnn
+    train resnet 50  $batchsize $use_mkldnn
+  done
+done

benchmark/paddle/image/vgg.py

@@ -13,7 +13,7 @@ define_py_data_sources2(
 
 settings(
     batch_size=batch_size,
-    learning_rate=0.01 / batch_size,
+    learning_rate=0.001 / batch_size,
     learning_method=MomentumOptimizer(0.9),
     regularization=L2Regularization(0.0005 * batch_size))
 

cmake/external/mkldnn.cmake

@@ -46,16 +46,20 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
     MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
 ENDIF()
 
+SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
+SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} -Wno-error=strict-overflow")
 ExternalProject_Add(
     ${MKLDNN_PROJECT}
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS             ${MKLDNN_DEPENDS}
     GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
-    GIT_TAG             "v0.10"
+    GIT_TAG             "v0.11"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
     CMAKE_ARGS          -DMKLROOT=${MKLDNN_MKLROOT}
+    CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
+    CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
                         -DMKLROOT:PATH=${MKLDNN_MKLROOT}
 )

cmake/external/mklml.cmake

@@ -27,8 +27,8 @@ ENDIF()
 INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
-SET(MKLML_VER           "mklml_lnx_2018.0.20170720")
-SET(MKLML_URL           "https://github.com/01org/mkl-dnn/releases/download/v0.10/${MKLML_VER}.tgz")
+SET(MKLML_VER           "mklml_lnx_2018.0.1.20171007")
+SET(MKLML_URL           "https://github.com/01org/mkl-dnn/releases/download/v0.11/${MKLML_VER}.tgz")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")

doc/api/v2/data.rst

@@ -2,112 +2,9 @@
 Data Reader Interface and DataSets
 ==================================
 
+..  toctree::
+    :maxdepth: 1
 
-DataTypes
-=========
-
-..  automodule:: paddle.v2.data_type
-    :members:
-    :noindex:
-
-DataFeeder
-==========
-
-..  automodule:: paddle.v2.data_feeder
-    :members:
-    :noindex:
-
-Reader
-======
-
-..  automodule:: paddle.v2.reader
-    :members:
-    :noindex:
-
-..  automodule:: paddle.v2.reader.creator
-    :members:
-    :noindex:
-
-minibatch
-=========
-
-..  automodule:: paddle.v2.minibatch
-    :members:
-    :noindex:
-
-Dataset
-=======
-
-..  automodule:: paddle.v2.dataset
-    :members:
-    :noindex:
-
-mnist
-+++++
-
-..  automodule:: paddle.v2.dataset.mnist
-    :members:
-    :noindex:
-
-cifar
-+++++
-
-..  automodule:: paddle.v2.dataset.cifar
-    :members:
-    :noindex:
-
-conll05
-+++++++
-
-..  automodule:: paddle.v2.dataset.conll05
-    :members: get_dict,get_embedding,test
-    :noindex:
-
-imdb
-++++
-
-..  automodule:: paddle.v2.dataset.imdb
-    :members:
-    :noindex:
-
-imikolov
-++++++++
-
-..  automodule:: paddle.v2.dataset.imikolov
-    :members:
-    :noindex:
-
-movielens
-+++++++++
-
-..  automodule:: paddle.v2.dataset.movielens
-    :members:
-    :noindex:
-
-..  autoclass:: paddle.v2.dataset.movielens.MovieInfo
-    :noindex:
-    
-..  autoclass:: paddle.v2.dataset.movielens.UserInfo
-    :noindex:
-
-sentiment
-+++++++++
-
-..  automodule:: paddle.v2.dataset.sentiment
-    :members:
-    :noindex:
-
-uci_housing
-+++++++++++
-
-..  automodule:: paddle.v2.dataset.uci_housing
-    :members:
-    :noindex:
-
-wmt14
-+++++
-
-..  automodule:: paddle.v2.dataset.wmt14
-    :members:
-    :noindex:
-
+    data/data_reader.rst
+    data/image.rst
+    data/dataset.rst

doc/api/v2/data/data_reader.rst

+=====================
+Data Reader Interface
+=====================
+
+
+DataTypes
+=========
+
+..  automodule:: paddle.v2.data_type
+    :members:
+    :noindex:
+
+DataFeeder
+==========
+
+..  automodule:: paddle.v2.data_feeder
+    :members:
+    :noindex:
+
+Reader
+======
+
+..  automodule:: paddle.v2.reader
+    :members:
+    :noindex:
+
+..  automodule:: paddle.v2.reader.creator
+    :members:
+    :noindex:
+
+minibatch
+=========
+
+..  automodule:: paddle.v2.minibatch
+    :members:
+    :noindex:

doc/api/v2/data/dataset.rst

+Dataset
+=======
+
+..  automodule:: paddle.v2.dataset
+    :members:
+    :noindex:
+
+mnist
++++++
+
+..  automodule:: paddle.v2.dataset.mnist
+    :members:
+    :noindex:
+
+cifar
++++++
+
+..  automodule:: paddle.v2.dataset.cifar
+    :members:
+    :noindex:
+
+conll05
++++++++
+
+..  automodule:: paddle.v2.dataset.conll05
+    :members: get_dict,get_embedding,test
+    :noindex:
+
+imdb
+++++
+
+..  automodule:: paddle.v2.dataset.imdb
+    :members:
+    :noindex:
+
+imikolov
+++++++++
+
+..  automodule:: paddle.v2.dataset.imikolov
+    :members:
+    :noindex:
+
+movielens
++++++++++
+
+..  automodule:: paddle.v2.dataset.movielens
+    :members:
+    :noindex:
+
+..  autoclass:: paddle.v2.dataset.movielens.MovieInfo
+    :noindex:
+    
+..  autoclass:: paddle.v2.dataset.movielens.UserInfo
+    :noindex:
+
+sentiment
++++++++++
+
+..  automodule:: paddle.v2.dataset.sentiment
+    :members:
+    :noindex:
+
+uci_housing
++++++++++++
+
+..  automodule:: paddle.v2.dataset.uci_housing
+    :members:
+    :noindex:
+
+wmt14
++++++
+
+..  automodule:: paddle.v2.dataset.wmt14
+    :members:
+    :noindex:

doc/api/v2/data/image.rst

+Image Interface
+===============
+
+..  automodule:: paddle.v2.image
+    :members:

doc/design/float16.md

+# Design Doc: float16
+
+## Why float16
+Half precision (float16) is a binary floating-point format that occupies 16 bits in memory. float16 is half the size of traditional 32-bit single precision format (float) and has lower precision and smaller range. 
+
+When high precision computation is not required, using float16 data type could potentially 
+
+- reduce storage space, memory bandwidth, and power usages; 
+- increase the chance of data fitting into a smaller cache of lower latency; 
+- provide arithmetic speed up if supported by hardware. 
+
+## Survey of current float16 support
+A brief survey of float16 support on different compilers, hardwares, and libraries can be found below. Interested readers can refer to [link1](https://github.com/PaddlePaddle/Paddle/issues/4853) and [link2](https://github.com/Xreki/Xreki.github.io/blob/master/multi_data_types_in_dl_framework/ppt/float16_and_quantized_type.md) for more info.
+
+The goal of float16 is to serve as a key for the executor to find and run the correct version of compute method specialized for float16 in operator kernel. It should be compatible with various natively supported float16 implementations including `__half` for cuda, `float16_t` for ARM, and `Eigen::half` for Eigen to make writing customized float16 kernels easier. 
+
+### Compiler
+- nvcc supports `__half` data type after CUDA 7.5.
+- `__fp16` or `float16_t` is supported as storage type for gcc >= 6.1 and clang >= 3.4.
+- `__fp16` or `float16_t` is supported as arithmetic type for gcc >= 7.1 and clang >= 3.9.
+
+### Hardware
+- `__half` is supported on GPU with compute capability >= 5.3.
+- `__fp16` is supported as storage type for ARMv7-A, ARMv8-A, and above.
+- `__fp16` is supported as arithmetic type after ARMv8.2-A (currently, the only microarchitecture implementing ARMv8.2-A is ARM Cortex-A75, which is announced in May 2017. There seems to be no application processors currently available on market that adopts this architecture. It is reported that Qualcomm Snapdragon 845 uses Cortex-A75 design and will be available in mobile devices in early 2018).
+
+### Libraries
+- [Eigen](https://github.com/RLovelett/eigen) >= 3.3 supports float16 calculation on both GPU and CPU using the `Eigen::half` class. It is mostly useful for Nvidia GPUs because of the overloaded arithmetic operators using cuda intrinsics. It falls back to using software emulation on CPU for calculation and there is no special treatment to ARM processors.
+- [ARM compute library](https://github.com/ARM-software/ComputeLibrary) >= 17.02.01 supports NEON FP16 kernels (requires ARMv8.2-A CPU).
+
+
+## Implementation
+The float16 class holds a 16-bit `uint16_t` data internally.
+```
+struct float16 {
+  uint16_t x;
+};
+``` 
+
+float16 supports the following features:
+  - constructors / assignment operators that take input from primitive data types including bool, integers of various length, float, and double. 
+  - constructors / assignment operators that take input from `__half` on cuda, `float16_t` on ARM, and `Eigen::half` on Eigen.
+  - conversion operators to primitive data types and half precision data types on cuda, ARM and Eigen. 
+  - overloaded arithmetic operators for cuda, arm, and non-arm cpu, respectively. These operators will take advantage of the cuda and ARM intrinsics on the corresponding hardware. 
+  
+To support the above features, two fundamental conversion functions are provided:
+```
+float16 float_to_half_rn(float f);  // convert to half precision in round-to-nearest-even mode
+float half_to_float(float16 h);
+```
+which provides one-to-one conversion between float32 and float16. These twos functions will do different conversion routines based on the current hardware. CUDA/ARM instrinsics will be used when the corresonding hardware is available. If the hardware or compiler level does not support float32 to float16 conversion, software emulation will be performed to do the conversion.
+
+## To do
+After float16 class is available, some of the future items are below:
+
+- Update pybind/tensor_py.h to bind c++ float16 with numpy float16. 
+
+- Modify `GetKernelType()` method in `framework/operator.h` to make it compatible with float16.
+
+- Create a type-casting operator that can convert the data type in tensor between float16 and other types.

paddle/framework/backward_test.cc

@@ -21,7 +21,7 @@
 #include "paddle/framework/var_desc.h"
 #include "paddle/operators/net_op.h"
 
-USE_OP(fill_constant);
+USE_NO_KERNEL_OP(fill_constant);
 
 namespace paddle {
 namespace framework {

paddle/framework/data_type.h

@@ -34,6 +34,21 @@ inline DataType ToDataType(std::type_index type) {
   }
 }
 
+inline std::type_index ToTypeIndex(DataType type) {
+  switch (type) {
+    case DataType::FP32:
+      return typeid(float);
+    case DataType::FP64:
+      return typeid(double);
+    case DataType::INT32:
+      return typeid(int);
+    case DataType::INT64:
+      return typeid(int64_t);
+    default:
+      PADDLE_THROW("Not support type %d", type);
+  }
+}
+
 template <typename Visitor>
 inline void VisitDataType(DataType type, Visitor visitor) {
   switch (type) {

paddle/framework/ddim.cc

@@ -79,6 +79,13 @@ DDim make_ddim(const std::vector<int64_t>& dims) {
   return result;
 }
 
+DDim make_ddim(const std::vector<int>& dims) {
+  std::vector<int64_t> res(dims.size());
+  std::transform(dims.begin(), dims.end(), res.begin(),
+                 [](int d) { return static_cast<int64_t>(d); });
+  return make_ddim(res);
+}
+
 /// @cond HIDDEN
 // XXX For some reason, putting this in an anonymous namespace causes errors
 class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
@@ -117,7 +124,7 @@ int64_t DDim::operator[](int idx) const {
   return boost::apply_visitor(DynamicConstIndexer(idx), var);
 }
 
-int64_t DDim::size() const { return arity(*this); }
+int DDim::size() const { return arity(*this); }
 
 bool DDim::operator==(DDim d) const {
   if (var.which() != d.getVar().which()) {

paddle/framework/ddim.h

@@ -71,7 +71,7 @@ struct DDim {
 
   DDim operator*(DDim d) const;
 
-  int64_t size() const;
+  int size() const;
 };
 
 /**
@@ -81,6 +81,8 @@ struct DDim {
  */
 DDim make_ddim(const std::vector<int64_t>& dims);
 
+DDim make_ddim(const std::vector<int>& dims);
+
 /**
  * \brief Make a DDim from an initializer list
  *

paddle/framework/lod_rank_table.cc

@@ -31,6 +31,7 @@ void LoDRankTable::Reset(const LoD& lod, size_t level) {
     TableItem item;
     item.index = i;
     item.length = vec[i + 1] - vec[i];
+    VLOG(10) << "Add item to rank table " << item.index << " " << item.length;
     items_.emplace_back(item);
   }
   // NOTE(yuyang18):

paddle/framework/lod_tensor.cc

@@ -27,6 +27,20 @@
 namespace paddle {
 namespace framework {
 
+std::ostream& operator<<(std::ostream& os, const LoD& lod) {
+  os << "{";
+  for (auto& v : lod) {
+    os << "{";
+    for (auto& i : v) {
+      os << i << ",";
+    }
+    os << "}";
+  }
+  os << "}";
+
+  return os;
+}
+
 LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end) {
   LoD new_lod;
   new_lod.reserve(level_end - level_begin);
@@ -136,37 +150,35 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
   ShareDataWith(Slice(begin, end));
 }
 
-void GetFineGrainedLoDLength(const LoD& lod, size_t start_idx, size_t end_idx,
-                             std::vector<std::vector<size_t>>* lod_length,
-                             size_t* start_offset) {
-  lod_length->clear();
-  PADDLE_ENFORCE(start_idx < lod.size() - 1,
-                 "start_idx should be >= 0 and < lod.size() - 1.");
-  PADDLE_ENFORCE(end_idx < lod.size(),
-                 "end_idx should be >= 0 and < lod.size().");
-  PADDLE_ENFORCE_LE(start_idx, end_idx,
-                    "start_idx should be less than end_idx.");
-  for (size_t level_idx = 0; level_idx < lod.size(); ++level_idx) {
+using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
+LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD& lod, size_t start_idx,
+                                        size_t end_idx, size_t start_level) {
+  LoD sub_lod;
+
+  for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
+    PADDLE_ENFORCE_LE(start_idx, end_idx);
+    PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
     std::vector<size_t> level_lens;
     for (size_t i = start_idx; i < end_idx; ++i) {
       level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
     }
-    lod_length->emplace_back(level_lens);
+    sub_lod.emplace_back(level_lens);
     start_idx = lod[level_idx][start_idx];
     end_idx = lod[level_idx][end_idx];
   }
-  *start_offset = start_idx;
+
+  return LoDAndOffset{sub_lod, {start_idx, end_idx}};
 }
 
-void AppendLoD(LoD* lod, const std::vector<std::vector<size_t>>& lod_length) {
-  PADDLE_ENFORCE_EQ(
-      lod->size(), lod_length.size(),
+void AppendLoD(LoD* lod, const LoD& lod_length) {
+  PADDLE_ENFORCE(
+      lod->empty() || lod->size() == lod_length.size(),
       "The lod_length should has the same size with the appended lod.");
+  if (lod->empty()) {
+    *lod = LoD(lod_length.size(), std::vector<size_t>({0}));
+  }
   for (size_t i = 0; i < lod->size(); ++i) {
     auto& level = (*lod)[i];
-    if (level.empty()) {
-      level.push_back(0);
-    }
     for (size_t len : lod_length[i]) {
       level.push_back(level.back() + len);
     }

paddle/framework/lod_tensor.h

@@ -56,6 +56,8 @@ using Vector = thrust::host_vector<
  */
 using LoD = std::vector<Vector<size_t>>;
 
+std::ostream& operator<<(std::ostream& os, const LoD& lod);
+
 /*
  * Slice levels from a LoD.
  * NOTE the lowest level should always be the absolute offsets of the underlying
@@ -181,11 +183,10 @@ LoDTensor LodExpand(const LoDTensor& source, const LoD& lod, size_t level,
   return tensor;
 }
 
-void GetFineGrainedLoDLength(const LoD& lod, size_t start_idx, size_t end_idx,
-                             std::vector<std::vector<size_t>>* lod_length,
-                             size_t* start_offset);
+std::pair<LoD, std::pair<size_t, size_t>> GetSubLoDAndAbsoluteOffset(
+    const LoD& lod, size_t start_idx, size_t end_idx, size_t start_level);
 
-void AppendLoD(LoD* lod, const std::vector<std::vector<size_t>>& lod_length);
+void AppendLoD(LoD* lod, const LoD& lod_length);
 
 }  // namespace framework
 }  // namespace paddle

paddle/framework/lod_tensor_test.cc

@@ -146,43 +146,44 @@ TEST(LodExpand, test) {
 
 TEST(LoD, GetFineGrainedLoDLength) {
   LoD lod;
-  lod.push_back(std::vector<size_t>{0, 2, 4, 5});
-  lod.push_back(std::vector<size_t>{0, 1, 6, 8, 10, 11});
+  lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
+  lod.push_back(std::vector<size_t>({0, 1, 6, 8, 10, 11}));
   lod.push_back(
-      std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26, 29});
+      std::vector<size_t>({0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26, 29}));
 
-  std::vector<std::vector<size_t>> lod_length;
-  size_t start_offset;
-  paddle::framework::GetFineGrainedLoDLength(lod, 1, 2, &lod_length,
-                                             &start_offset);
+  auto lod_and_offset =
+      paddle::framework::GetSubLoDAndAbsoluteOffset(lod, 1, 2, 0);
+  LoD lod_length = lod_and_offset.first;
+  size_t start_offset = lod_and_offset.second.first;
+  size_t end_offset = lod_and_offset.second.second;
 
-  std::vector<std::vector<size_t>> expected;
+  LoD expected;
   expected.push_back(std::vector<size_t>{2});
   expected.push_back(std::vector<size_t>{2, 2});
   expected.push_back(std::vector<size_t>{2, 3, 4, 2});
   EXPECT_EQ(lod_length, expected);
   EXPECT_EQ(start_offset, 15UL);
+  EXPECT_EQ(end_offset, 26UL);
 }
 
 TEST(LoD, AppendLoD) {
-  std::vector<std::vector<size_t>> lod_lens;
-  lod_lens.push_back(std::vector<size_t>{2});
-  lod_lens.push_back(std::vector<size_t>{2, 2});
-  lod_lens.push_back(std::vector<size_t>{2, 3, 4, 2});
+  LoD lod_lens;
+  lod_lens.push_back(std::vector<size_t>({2}));
+  lod_lens.push_back(std::vector<size_t>({2, 2}));
+  lod_lens.push_back(std::vector<size_t>({2, 3, 4, 2}));
 
   LoD origin;
-  origin.push_back(std::vector<size_t>{0, 2});
-  origin.push_back(std::vector<size_t>{0, 1, 6});
-  origin.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15});
+  origin.push_back(std::vector<size_t>({0, 2}));
+  origin.push_back(std::vector<size_t>({0, 1, 6}));
+  origin.push_back(std::vector<size_t>({0, 2, 5, 7, 10, 12, 15}));
 
   paddle::framework::AppendLoD(&origin, lod_lens);
 
   LoD expected;
-  expected.push_back(std::vector<size_t>{0, 2, 4});
-  expected.push_back(std::vector<size_t>{0, 1, 6, 8, 10});
+  expected.push_back(std::vector<size_t>({0, 2, 4}));
+  expected.push_back(std::vector<size_t>({0, 1, 6, 8, 10}));
   expected.push_back(
-      std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26});
-
+      std::vector<size_t>({0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26}));
   EXPECT_EQ(origin, expected);
 }
 

paddle/framework/op_registry.h

@@ -92,8 +92,7 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
 
   void operator()(const char* op_type) const {
     using T = typename KERNEL_TYPE::ELEMENT_TYPE;
-    OperatorWithKernel::OpKernelKey key(ToDataType(std::type_index(typeid(T))),
-                                        PlaceType());
+    OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType());
     OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
 
     constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;

paddle/framework/operator.cc

@@ -254,8 +254,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
   return res;
 }
 
-std::ostream& operator<<(std::ostream& os,
-                         const OperatorWithKernel::OpKernelKey& kernel_key) {
+std::ostream& operator<<(std::ostream& os, const OpKernelType& kernel_key) {
   os << "place[" << kernel_key.place_ << "]:data_type[" << kernel_key.data_type_
      << "]";
   return os;
@@ -432,7 +431,7 @@ void OperatorWithKernel::Run(const Scope& scope,
 
   // check if op[type] have kernel for kernel_key
   OpKernelMap& kernels = kernels_iter->second;
-  auto kernel_key = OpKernelKey(IndicateDataType(ctx), dev_ctx);
+  auto kernel_key = GetKernelType(ctx);
   auto kernel_iter = kernels.find(kernel_key);
 
   if (kernel_iter == kernels.end()) {
@@ -440,6 +439,41 @@ void OperatorWithKernel::Run(const Scope& scope,
   }
 
   kernel_iter->second->Compute(ctx);
+
+  // throws errors if have.
+  dev_ctx.Finish();
+}
+OpKernelType OperatorWithKernel::GetKernelType(
+    const ExecutionContext& ctx) const {
+  return OpKernelType(IndicateDataType(ctx), ctx.device_context());
+}
+DataType OperatorWithKernel::IndicateDataType(
+    const ExecutionContext& ctx) const {
+  auto& scope = ctx.scope();
+  int data_type = -1;
+  for (auto& input : this->inputs_) {
+    for (auto& ipt_name : input.second) {
+      auto* var = scope.FindVar(ipt_name);
+      if (var != nullptr) {
+        const Tensor* t = nullptr;
+        if (var->IsType<Tensor>()) {
+          t = &var->Get<Tensor>();
+        } else if (var->IsType<LoDTensor>()) {
+          t = &var->Get<LoDTensor>();
+        } else if (var->IsType<SelectedRows>()) {
+          t = &(var->Get<SelectedRows>().value());
+        }
+        if (t != nullptr) {
+          int tmp = static_cast<int>(ToDataType(t->type()));
+          PADDLE_ENFORCE(tmp == data_type || data_type == -1,
+                         "DataType of Paddle Op %s must be the same.", Type());
+          data_type = tmp;
+        }
+      }
+    }
+  }
+  PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
+  return static_cast<DataType>(data_type);
 }
 
 }  // namespace framework

paddle/framework/operator.h

@@ -345,27 +345,10 @@ class OpKernel : public OpKernelBase {
   using ELEMENT_TYPE = T;
 };
 
-class OperatorWithKernel : public OperatorBase {
- public:
-  struct OpKernelKey {
-    platform::Place place_;
-    DataType data_type_;
-
-    OpKernelKey(DataType data_type, platform::Place place)
-        : place_(place), data_type_(data_type) {}
-
-    OpKernelKey(DataType data_type, const platform::DeviceContext& dev_ctx)
-        : place_(dev_ctx.GetPlace()), data_type_(data_type) {}
-
-    bool operator==(const OpKernelKey& o) const {
-      return platform::places_are_same_class(place_, o.place_) &&
-             data_type_ == o.data_type_;
-    }
-  };
-
-  struct OpKernelHash {
+struct OpKernelType {
+  struct Hash {
     std::hash<int> hash_;
-    size_t operator()(const OpKernelKey& key) const {
+    size_t operator()(const OpKernelType& key) const {
       int place = key.place_.which();
       int data_type = static_cast<int>(key.data_type_);
       int pre_hash = data_type << NUM_PLACE_TYPE_LIMIT_IN_BIT |
@@ -374,9 +357,26 @@ class OperatorWithKernel : public OperatorBase {
     }
   };
 
+  platform::Place place_;
+  DataType data_type_;
+
+  OpKernelType(DataType data_type, platform::Place place)
+      : place_(place), data_type_(data_type) {}
+
+  OpKernelType(DataType data_type, const platform::DeviceContext& dev_ctx)
+      : place_(dev_ctx.GetPlace()), data_type_(data_type) {}
+
+  bool operator==(const OpKernelType& o) const {
+    return platform::places_are_same_class(place_, o.place_) &&
+           data_type_ == o.data_type_;
+  }
+};
+
+class OperatorWithKernel : public OperatorBase {
+ public:
   using OpKernelMap =
-      std::unordered_map<OpKernelKey, std::unique_ptr<OpKernelBase>,
-                         OpKernelHash>;
+      std::unordered_map<OpKernelType, std::unique_ptr<OpKernelBase>,
+                         OpKernelType::Hash>;
 
   OperatorWithKernel(const std::string& type, const VariableNameMap& inputs,
                      const VariableNameMap& outputs, const AttributeMap& attrs)
@@ -404,40 +404,15 @@ class OperatorWithKernel : public OperatorBase {
   }
 
  protected:
+  virtual OpKernelType GetKernelType(const ExecutionContext& ctx) const;
+
+ private:
   // indicate kernel DataType by input data. Defaultly all input data must be
   // same.
-  virtual DataType IndicateDataType(const ExecutionContext& ctx) const {
-    auto& scope = ctx.scope();
-    int data_type = -1;
-    for (auto& input : this->inputs_) {
-      for (auto& ipt_name : input.second) {
-        auto* var = scope.FindVar(ipt_name);
-        if (var != nullptr) {
-          const Tensor* t = nullptr;
-          if (var->IsType<Tensor>()) {
-            t = &var->Get<Tensor>();
-          } else if (var->IsType<LoDTensor>()) {
-            t = &var->Get<LoDTensor>();
-          } else if (var->IsType<SelectedRows>()) {
-            t = &(var->Get<SelectedRows>().value());
-          }
-          if (t != nullptr) {
-            int tmp = static_cast<int>(ToDataType(t->type()));
-            PADDLE_ENFORCE(tmp == data_type || data_type == -1,
-                           "DataType of Paddle Op %s must be the same.",
-                           Type());
-            data_type = tmp;
-          }
-        }
-      }
-    }
-    PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
-    return static_cast<DataType>(data_type);
-  }
+  DataType IndicateDataType(const ExecutionContext& ctx) const;
 };
 
-std::ostream& operator<<(std::ostream& os,
-                         const OperatorWithKernel::OpKernelKey& kernel_key);
+std::ostream& operator<<(std::ostream& os, const OpKernelType& kernel_key);
 
 extern bool OpSupportGPU(const std::string& op_type);
 

paddle/framework/operator_test.cc

@@ -114,8 +114,8 @@ class OpWithKernelTest : public OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {}
-  DataType IndicateDataType(const ExecutionContext& ctx) const override {
-    return DataType::FP32;
+  OpKernelType GetKernelType(const ExecutionContext& ctx) const override {
+    return OpKernelType(DataType::FP32, ctx.device_context());
   }
 };
 

paddle/framework/tensor_impl.h

@@ -52,7 +52,7 @@ struct SizeOfTypeFunctor<HEAD, TAIL...> {
 };
 
 static inline size_t SizeOfType(std::type_index type) {
-  SizeOfTypeFunctor<int, float, double, int16_t, int64_t> functor;
+  SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool> functor;
   size_t size = functor(type);
   PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s", type.name());
   return size;

paddle/framework/var_desc.cc

@@ -45,7 +45,8 @@ void VarDescBind::SetLoDLevel(int32_t lod_level) {
       desc_.mutable_tensor_array()->set_lod_level(lod_level);
       break;
     default:
-      PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
+      PADDLE_THROW("Tensor type=%d does not support LoDLevel",
+                   desc_.tensor_array().lod_level());
   }
 }
 
@@ -56,7 +57,8 @@ int32_t VarDescBind::GetLodLevel() const {
     case VarDesc::LOD_TENSOR_ARRAY:
       return desc_.tensor_array().lod_level();
     default:
-      PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
+      PADDLE_THROW("Tensor type=%d does not support LoDLevel",
+                   desc_.tensor_array().lod_level());
   }
 }
 

paddle/function/CMakeLists.txt

@@ -45,6 +45,7 @@ if(WITH_GPU)
     add_simple_unittest(BlockExpandOpTest)
     add_simple_unittest(CropOpTest)
     add_simple_unittest(SwitchOpTest)
+    add_simple_unittest(ScaleSubRegionOpTest)
 endif()
 
 add_simple_unittest(Im2ColTest)

paddle/function/FunctionTest.h

@@ -110,6 +110,7 @@ public:
         function2_(FunctionBase::funcRegistrar_.createByType(name2)) {
     function1_->init(config);
     function2_->init(config);
+    initArgsCallback_ = nullptr;
   }
 
   ~Compare2Function() {}
@@ -170,6 +171,10 @@ public:
                                       *seq2_));
   }
 
+  void registerInitCallback(std::function<void(BufferArg&, size_t)> callback) {
+    initArgsCallback_ = callback;
+  }
+
   // output need only contains shape, do not contains data.
   void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) {
     size_t size =
@@ -340,6 +345,10 @@ protected:
         initArg(*func1Inputs_[i]);
       }
 
+      if (initArgsCallback_ != nullptr) {
+        initArgsCallback_(*func1Inputs_[i], i);
+      }
+
       copyArg_(*func1Inputs_[i], *func2Inputs_[i]);
     }
   }
@@ -386,6 +395,7 @@ protected:
   std::shared_ptr<SequenceIdArg> seq1_;
   std::shared_ptr<SequenceIdArg> seq2_;
   test::CopyArgument<DType1, DType2> copyArg_;
+  std::function<void(BufferArg&, size_t)> initArgsCallback_;
 };
 
 class CpuGpuFuncCompare

paddle/function/ScaleSubRegionOp.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 "ScaleSubRegionOp.h"
+#include "paddle/function/TensorShape.h"
+
+namespace paddle {
+
+template <>
+void ScaleSubRegion<DEVICE_TYPE_CPU>(real* outputs,
+                                     const real* inputs,
+                                     const real* indices,
+                                     const TensorShape shape,
+                                     const FuncConfig& conf) {
+  real value = conf.get<real>("value");
+
+  int number = shape[0];
+  int channel = shape[1];
+  int height = shape[2];
+  int width = shape[3];
+
+  memcpy(outputs, inputs, number * channel * height * width * sizeof(real));
+
+  for (int n = 0; n < number; ++n) {
+    // indices start from 1
+    int offset = n * 6;
+    for (int c = indices[offset] - 1; c < indices[offset + 1]; ++c) {
+      for (int h = indices[offset + 2] - 1; h < indices[offset + 3]; ++h) {
+        for (int w = indices[offset + 4] - 1; w < indices[offset + 5]; ++w) {
+          int idx = ((n * channel + c) * height + h) * width + w;
+          outputs[idx] *= value;
+        }
+      }
+    }
+  }
+}
+
+template <>
+void ScaleSubRegionGrad<DEVICE_TYPE_CPU>(const real* inGrad,
+                                         real* outGrad,
+                                         const real* indices,
+                                         const TensorShape shape,
+                                         const FuncConfig& conf) {
+  real value = conf.get<real>("value");
+
+  int number = shape[0];
+  int channel = shape[1];
+  int height = shape[2];
+  int width = shape[3];
+
+  for (int n = 0; n < number; ++n) {
+    for (int c = 0; c < channel; ++c) {
+      for (int h = 0; h < height; ++h) {
+        for (int w = 0; w < width; ++w) {
+          int idx = ((n * channel + c) * height + h) * width + w;
+          int offset = n * 6;
+          if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
+              h >= (indices[offset + 2] - 1) &&
+              h <= (indices[offset + 3] - 1) &&
+              w >= (indices[offset + 4] - 1) &&
+              w <= (indices[offset + 5] - 1)) {
+            outGrad[idx] += inGrad[idx] * value;
+          } else {
+            outGrad[idx] += inGrad[idx];
+          }
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \brief For each instance, ScaleSubRegion can be used to multiply a value to
+ *        a specified sub continuous region. By providing start index and end
+ *        index for C/H/W, you can specify the location and shape of the region.
+ *
+ * Argument in this Function:
+ * \param inputs    A 4-D tensor with shape [N, C, H, W], only one input.
+ * \param indices   A 2-D tensor with shape [N, 6], indicates the sub region.
+ * \param outputs   A 4-D tensor with same shape as inputs, output value.
+ */
+template <DeviceType Device>
+class ScaleSubRegionFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override { conf_ = config; }
+
+  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
+    CHECK_EQ(2UL, inputs.size());
+    CHECK_EQ(1UL, outputs.size());
+    CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
+
+    TensorShape shape = inputs[0].shape();
+
+    ScaleSubRegion<Device>(outputs[0].data<real>(),
+                           inputs[0].data<real>(),
+                           inputs[1].data<real>(),
+                           shape,
+                           conf_);
+  }
+
+private:
+  FuncConfig conf_;
+};
+
+/**
+ * \brief The backward propagation of ScaleSubRegion Function.
+ *
+ * Argument in this Function:
+ * \param inputs  A 4-D tensor with shape [N, C, H, W], output gradient.
+ * \param indices A 2-D tensor with shape [N, 6], indicates the sub region.
+ * \param outputs A 4-D tensor with shape [N, C, H, W], gradient of input value.
+ */
+
+template <DeviceType Device>
+class ScaleSubRegionGradFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override { conf_ = config; }
+
+  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
+    CHECK_EQ(2UL, inputs.size());
+    CHECK_EQ(1UL, outputs.size());
+    CHECK_EQ(outputs[0].getArgType(), ADD_TO);
+
+    TensorShape shape = inputs[0].shape();
+
+    ScaleSubRegionGrad<Device>(inputs[0].data<real>(),
+                               outputs[0].data<real>(),
+                               inputs[1].data<real>(),
+                               shape,
+                               conf_);
+  }
+
+private:
+  FuncConfig conf_;
+};
+
+REGISTER_TYPED_FUNC(ScaleSubRegion, CPU, ScaleSubRegionFunc);
+REGISTER_TYPED_FUNC(ScaleSubRegionGrad, CPU, ScaleSubRegionGradFunc);
+#ifdef PADDLE_WITH_CUDA
+REGISTER_TYPED_FUNC(ScaleSubRegion, GPU, ScaleSubRegionFunc);
+REGISTER_TYPED_FUNC(ScaleSubRegionGrad, GPU, ScaleSubRegionGradFunc);
+#endif
+
+}  // namespace paddle

paddle/function/ScaleSubRegionOp.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 "Function.h"
+
+namespace paddle {
+
+/**
+ * \brief Function to multiply a value to values in specified sub continuous
+ *        region. Indices must be provided to indcate the location and shape of
+ *        the region and the multiplied value is passed by configure variable.
+ *
+ *
+ * \param[out] outputs  Output value.
+ * \param[in]  inputs   Input data which contains NCHW information.
+ * \param[in]  indices  Indices data to indcate the sub region.
+ * \param[in]  shape    Tensor shape of input value.
+ * \param[in]  conf     Configure variable which contains the multiplied value.
+ */
+template <DeviceType Device>
+void ScaleSubRegion(real* outputs,
+                    const real* inputs,
+                    const real* indices,
+                    const TensorShape shape,
+                    const FuncConfig& conf);
+
+/**
+ * \brief Backward propagation function of ScaleSubRegion.
+ *
+ * \param[out] inGrad   Gradients of previous layer.
+ * \param[in]  outGrad  Output gradient.
+ * \param[in]  indices  Indices data.
+ * \param[in]  shape    The Shape of input tensor.
+ * \param[in]  conf     Configure variable.
+ */
+template <DeviceType Device>
+void ScaleSubRegionGrad(const real* inGrad,
+                        real* outGrad,
+                        const real* indices,
+                        const TensorShape shape,
+                        const FuncConfig& conf);
+}  // namespace paddle

paddle/function/ScaleSubRegionOpGpu.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "ScaleSubRegionOp.h"
+#include "hl_base.h"
+
+namespace paddle {
+
+__global__ void KeScaleSubRegion(real* outputs,
+                                 const real* inputs,
+                                 const real* indices,
+                                 real value,
+                                 int channel,
+                                 int height,
+                                 int width,
+                                 int nthreads) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < nthreads) {
+    const int w = idx % width;
+    const int h = (idx / width) % height;
+    const int c = (idx / width / height) % channel;
+    const int n = idx / width / height / channel;
+
+    const int offset = n * 6;
+    if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
+        h >= (indices[offset + 2] - 1) && h <= (indices[offset + 3] - 1) &&
+        w >= (indices[offset + 4] - 1) && w <= (indices[offset + 5] - 1)) {
+      outputs[idx] = inputs[idx] * value;
+    } else {
+      outputs[idx] = inputs[idx];
+    }
+  }
+}
+
+template <>
+void ScaleSubRegion<DEVICE_TYPE_GPU>(real* outputs,
+                                     const real* inputs,
+                                     const real* indices,
+                                     const TensorShape shape,
+                                     const FuncConfig& conf) {
+  real value = conf.get<real>("value");
+
+  int number = shape[0];
+  int channel = shape[1];
+  int height = shape[2];
+  int width = shape[3];
+
+  size_t nth = number * channel * height * width;
+  int blockSize = 1024;
+  int gridSize = (nth + blockSize - 1) / blockSize;
+
+  KeScaleSubRegion<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
+      outputs, inputs, indices, value, channel, height, width, nth);
+  CHECK_SYNC("ScaleSubRegion");
+}
+
+__global__ void KeScaleSubRegionDiff(const real* inGrad,
+                                     real* outGrad,
+                                     const real* indices,
+                                     real value,
+                                     int channel,
+                                     int height,
+                                     int width,
+                                     int nthreads) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < nthreads) {
+    const int w = idx % width;
+    const int h = (idx / width) % height;
+    const int c = (idx / width / height) % channel;
+    const int n = idx / width / height / channel;
+
+    const int offset = n * 6;
+    if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
+        h >= (indices[offset + 2] - 1) && h <= (indices[offset + 3] - 1) &&
+        w >= (indices[offset + 4] - 1) && w <= (indices[offset + 5] - 1)) {
+      outGrad[idx] += inGrad[idx] * value;
+    } else {
+      outGrad[idx] += inGrad[idx];
+    }
+  }
+}
+
+template <>
+void ScaleSubRegionGrad<DEVICE_TYPE_GPU>(const real* inGrad,
+                                         real* outGrad,
+                                         const real* indices,
+                                         const TensorShape shape,
+                                         const FuncConfig& conf) {
+  real value = conf.get<real>("value");
+
+  int number = shape[0];
+  int channel = shape[1];
+  int height = shape[2];
+  int width = shape[3];
+
+  size_t nth = number * channel * height * width;
+  int blockSize = 1024;
+  int gridSize = (nth + blockSize - 1) / blockSize;
+
+  KeScaleSubRegionDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
+      inGrad, outGrad, indices, value, channel, height, width, nth);
+  CHECK_SYNC("ScaleSubRegionGrad");
+}
+
+}  // namespace paddle

paddle/function/ScaleSubRegionOpTest.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 <gtest/gtest.h>
+#include "FunctionTest.h"
+
+namespace paddle {
+
+TEST(ScaleSubRegion, real) {
+  for (size_t numSamples : {5, 32}) {
+    for (size_t channels : {5, 32}) {
+      for (size_t imgSizeH : {5, 33}) {
+        for (size_t imgSizeW : {5, 32}) {
+          for (real value : {-0.5, 0.0, 0.5}) {
+            for (bool firstHalf : {false, true}) {
+              VLOG(3) << " numSamples=" << numSamples
+                      << " channels=" << channels << " imgSizeH=" << imgSizeH
+                      << " imgSizeW=" << imgSizeW;
+
+              for (bool testGrad : {false, true}) {
+                CpuGpuFuncCompare compare(
+                    testGrad ? "ScaleSubRegionGrad" : "ScaleSubRegion",
+                    FuncConfig().set<real>("value", value));
+
+                TensorShape shape{numSamples, channels, imgSizeH, imgSizeW};
+                TensorShape indicesShape{numSamples, 6};
+
+                compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
+                compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, indicesShape));
+
+                compare.registerInitCallback([=](BufferArg& arg, size_t index) {
+                  if (index == 1) {
+                    real* data = (real*)arg.data();
+
+                    for (size_t i = 0; i < numSamples; ++i) {
+                      size_t offset = i * 6;
+                      data[offset] = firstHalf ? 1 : channels / 2;
+                      data[offset + 1] = firstHalf ? channels / 2 : channels;
+                      data[offset + 2] = firstHalf ? 1 : imgSizeH / 2;
+                      data[offset + 3] = firstHalf ? imgSizeH / 2 : imgSizeH;
+                      data[offset + 4] = firstHalf ? 1 : imgSizeW / 2;
+                      data[offset + 5] = firstHalf ? imgSizeW / 2 : imgSizeW;
+                    }
+                  }
+                });
+
+                compare.addOutputs(
+                    BufferArg(
+                        VALUE_TYPE_FLOAT, shape, testGrad ? ADD_TO : ASSIGN_TO),
+                    testGrad ? ADD_TO : ASSIGN_TO);
+                compare.run();
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNAddtoLayer.cpp

@@ -62,16 +62,14 @@ void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
                                 MKLDNNMatrixPtr& wgt,
                                 MKLDNNMatrixPtr& bias,
                                 MKLDNNMatrixPtr& out) {
-  if (biases_) {
-    LOG(FATAL) << "not implemented yet";
-  }
-  resetFwdBuffers(inVals_, out);
+  resetFwdBuffers(inVals_, bias, out);
   in = inVals_[0];
 
   std::shared_ptr<sum::primitive_desc> fwdPD;
-  resetFwdPD(fwdPD, inVals_, out);
+  std::shared_ptr<sum::primitive_desc> biasPD;
+  resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
 
-  resetFwdPipeline(pipeline, fwdPD, inVals_, out);
+  resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
 }
 
 void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
@@ -79,7 +77,7 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
                                 MKLDNNMatrixPtr& wgt,
                                 MKLDNNMatrixPtr& bias,
                                 MKLDNNMatrixPtr& out) {
-  resetBwdBuffers(inGrads_, out);
+  resetBwdBuffers(inGrads_, bias, out);
   in = inGrads_[0];
 
   // backward only need share output grad to input grad
@@ -89,6 +87,20 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
       inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
     }
   }
+
+  // backward bias
+  bwdBias_ = nullptr;
+  if (bias) {
+    std::vector<float> scales(bs_, 1.0);
+    std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
+    auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
+    std::vector<primitive::at> srcs;
+    for (size_t i = 0; i < grads_.size(); ++i) {
+      srcs.push_back(*(grads_[i]));
+    }
+    bwdBias_.reset(new sum(biasPD, srcs, *bias));
+    pipeline.push_back(*bwdBias_);
+  }
 }
 
 void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
@@ -97,7 +109,25 @@ void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
   }
 }
 
+void MKLDNNAddtoLayer::prepareBias(MKLDNNMatrixPtr& bias,
+                                   const MatrixPtr& biasMat,
+                                   const MKLDNNMatrixPtr& out,
+                                   std::vector<MKLDNNMatrixPtr>& outs) {
+  auto pd = MKLDNNMatrix::createPrimitiveDesc(
+      {(int)layerSize_}, memory::format::x, engine_);
+  bias = MKLDNNMatrix::create(pd, biasMat);
+  outs.clear();
+  real* data = out->getData();
+  CHECK_EQ(bs_ * layerSize_, out->getElementCnt());
+  for (int i = 0; i < bs_; ++i) {
+    MatrixPtr tmp =
+        Matrix::create(data + i * layerSize_, 1, layerSize_, false, false);
+    outs.push_back(MKLDNNMatrix::create(bias->getPrimitiveDesc(), tmp));
+  }
+}
+
 void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                       MKLDNNMatrixPtr& bias,
                                        MKLDNNMatrixPtr& out) {
   inputs.resize(inputLayers_.size());
   for (size_t i = 0; i < inputs.size(); i++) {
@@ -110,12 +140,20 @@ void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
   }
 
   resetOutValue(out, inputs[0]->getPrimitiveDesc());
+
+  if (biases_ && biases_->getW()) {
+    prepareBias(bias, biases_->getW(), out, vals_);
+  } else {
+    bias = nullptr;
+  }
 }
 
 void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
+                                  std::shared_ptr<sum::primitive_desc>& biasPD,
                                   std::vector<MKLDNNMatrixPtr>& inputs,
+                                  MKLDNNMatrixPtr bias,
                                   MKLDNNMatrixPtr out) {
-  std::vector<double> scales(inputs.size(), 1.0);
+  std::vector<float> scales(inputs.size(), 1.0);
   std::vector<memory::primitive_desc> srcPDs;
   for (size_t i = 0; i < inputs.size(); i++) {
     srcPDs.push_back(inputs[i]->getPrimitiveDesc());
@@ -123,12 +161,23 @@ void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
   CHECK(out);
   pd.reset(new sum::primitive_desc(out->getMemoryDesc(), scales, srcPDs));
   CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
+
+  biasPD = nullptr;
+  if (bias) {
+    std::vector<float> scales(2, 1.0);
+    std::vector<memory::primitive_desc> srcPDs(2, bias->getPrimitiveDesc());
+    biasPD.reset(
+        new sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs));
+    CHECK_PRIMITIVE_DESC_EQ(bias, biasPD->dst_primitive_desc());
+  }
 }
 
 void MKLDNNAddtoLayer::resetFwdPipeline(
     std::vector<primitive>& pipeline,
     std::shared_ptr<sum::primitive_desc>& pd,
+    std::shared_ptr<sum::primitive_desc>& biasPD,
     std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
   std::vector<primitive::at> srcs;
   for (size_t i = 0; i < inputs.size(); i++) {
@@ -136,9 +185,23 @@ void MKLDNNAddtoLayer::resetFwdPipeline(
   }
   fwd_.reset(new sum(*pd, srcs, *out));
   pipeline.push_back(*fwd_);
+
+  fwdBias_.clear();
+  if (biasPD == nullptr || bias == nullptr) {
+    return;
+  }
+  fwdBias_.resize(vals_.size());
+  for (size_t i = 0; i < vals_.size(); ++i) {
+    std::vector<primitive::at> srcs;
+    srcs.push_back(*(vals_[i]));
+    srcs.push_back(*bias);
+    fwdBias_[i].reset(new sum(*biasPD, srcs, *vals_[i]));
+    pipeline.push_back(*fwdBias_[i]);
+  }
 }
 
 void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                       MKLDNNMatrixPtr& bias,
                                        MKLDNNMatrixPtr& out) {
   CHECK(outVal_);
   resetOutGrad(out, outVal_->getPrimitiveDesc());
@@ -149,6 +212,12 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
     resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
     CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
   }
+
+  if (biases_ && biases_->getWGrad()) {
+    prepareBias(bias, biases_->getWGrad(), out, grads_);
+  } else {
+    bias = nullptr;
+  }
 }
 
 }  // namespace paddle

paddle/gserver/layers/MKLDNNAddtoLayer.h

@@ -32,9 +32,15 @@ protected:
   // layer size == ic * ih * iw == oc * oh *ow, and can not be changed
   size_t layerSize_;
 
-  // TODO(TJ): this part has not been optimized by MKL-DNN
   std::unique_ptr<Weight> biases_;
 
+  // buffers for adding bias
+  std::vector<MKLDNNMatrixPtr> vals_;
+  std::vector<MKLDNNMatrixPtr> grads_;
+  // primitives for adding bias
+  std::vector<std::shared_ptr<mkldnn::primitive>> fwdBias_;
+  std::shared_ptr<mkldnn::primitive> bwdBias_;
+
 public:
   explicit MKLDNNAddtoLayer(const LayerConfig& config) : MKLDNNLayer(config) {}
 
@@ -91,20 +97,34 @@ protected:
    *                    reset pipeline.
    */
   void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
   void resetFwdPD(std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
+                  std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
                   std::vector<MKLDNNMatrixPtr>& inputs,
+                  MKLDNNMatrixPtr bias,
                   MKLDNNMatrixPtr out);
   void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
                         std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
+                        std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
                         std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
 
   /**
    * Backward functions: reset buffers(inputs, output, bias)
    */
   void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
+
+  /**
+   * prepare for bias
+   */
+  void prepareBias(MKLDNNMatrixPtr& bias,
+                   const MatrixPtr& biasMat,
+                   const MKLDNNMatrixPtr& out,
+                   std::vector<MKLDNNMatrixPtr>& outs);
 };
 
 }  // namespace paddle

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -60,18 +60,16 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
   }
 
   CHECK(wgtVal_) << "should have been initialized";
-  bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
   auto targetDim = wgtVal_->getDims();
-  auto srcFmt = hasNoSpatial_ ? format::io : format::ihwo;
+  auto srcFmt = targetDim.size() == 2 ? format::io : format::ihwo;
   wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
   hasInitedWgt_ = true;
 }
 
 void MKLDNNFcLayer::convertWeightsToPaddle() {
   CHECK(wgtVal_) << "should have been initialized";
-  bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
   auto targetDim = wgtVal_->getDims();
-  auto dstFmt = hasNoSpatial_ ? format::io : format::ihwo;
+  auto dstFmt = targetDim.size() == 2 ? format::io : format::ihwo;
   wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
 }
 

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -181,21 +181,17 @@ void MKLDNNLayer::resetInValue(
   auto extPD = MKLDNNMatrix::createPrimitiveDesc(
       {bs_, ic_, ih_, iw_}, format::nchw, engine_);
   const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue();
-  in = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
-  CHECK_EQ(inputIsOnlyMKLDNN(), in != nullptr);
-  if (in == nullptr || in->getFormat() == format::nc) {
-    in = MKLDNNMatrix::create(extPD, inMat);
-  }
-  extInVal_ = isPaddleFormat(in->getFormat()) ? in : nullptr;
-  if (in->getFormat() == format::nc) {
-    CHECK(ih_ == 1 && iw_ == 1);
+  extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
+  CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr);
+  if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) {
+    extInVal_ = MKLDNNMatrix::create(extPD, inMat);
   }
+  in = extInVal_;
   if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
     return;
   }
   // need create reorder
   in = MKLDNNMatrix::create(*intPD);
-  extInVal_ = extInVal_ ? extInVal_ : MKLDNNMatrix::create(extPD, inMat);
   cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
   CHECK(cvtInVal_) << "should not be emptry";
 }
@@ -291,7 +287,7 @@ void MKLDNNLayer::resetMergeGrad(MKLDNNMatrixPtr& out) {
     return;
   }
   CHECK(out) << "should have reset internal ouput grad";
-  std::vector<double> scales(outputMap_.size(), 1.0);
+  std::vector<float> scales(outputMap_.size(), 1.0);
   std::vector<memory::primitive_desc> srcPDs;
   std::vector<primitive::at> srcs;
   for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {

paddle/gserver/layers/ScaleSubRegionLayer.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 "ScaleSubRegionLayer.h"
+#include "paddle/utils/Stat.h"
+namespace paddle {
+
+REGISTER_LAYER(scale_sub_region, ScaleSubRegionLayer);
+
+bool ScaleSubRegionLayer::init(const LayerMap& layerMap,
+                               const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+  CHECK_EQ(static_cast<int>(inputLayers_.size()), 2);
+  auto& conf = config_.inputs(0).scale_sub_region_conf();
+  value_ = conf.value();
+
+  createFunction(forward_, "ScaleSubRegion", FuncConfig().set("value", value_));
+  createFunction(
+      backward_, "ScaleSubRegionGrad", FuncConfig().set("value", value_));
+
+  return true;
+}
+
+void ScaleSubRegionLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  auto in0 = getInput(0);
+  imgH_ = in0.getFrameHeight();
+  imgW_ = in0.getFrameWidth();
+  if (imgH_ == 0 || imgW_ == 0) {
+    auto& conf = config_.inputs(0).scale_sub_region_conf();
+    imgH_ = conf.image_conf().img_size_y();
+    imgW_ = conf.image_conf().img_size();
+  }
+  MatrixPtr imgV = in0.value;
+  size_t batchSize = imgV->getHeight();
+  size_t spatialSize = imgH_ * imgW_;
+  channelsNum_ = imgV->getWidth() / spatialSize;
+  shape_ = TensorShape({batchSize, channelsNum_, imgH_, imgW_});
+
+  resetOutput(batchSize, imgV->getWidth());
+  auto out = getOutput();
+  out.setFrameHeight(imgH_);
+  out.setFrameWidth(imgW_);
+
+  MatrixPtr indicesV = getInputValue(1);
+  indicesShape_ = TensorShape({batchSize, 6});
+
+  REGISTER_TIMER_INFO("ScaleSubRegionForward", getName().c_str());
+  BufferArgs inArgs;
+  BufferArgs outArgs;
+  inArgs.addArg(*imgV, shape_);
+  inArgs.addArg(*indicesV, indicesShape_);
+  outArgs.addArg(*out.value, shape_, ASSIGN_TO);
+  forward_[0]->calc(inArgs, outArgs);
+}
+
+void ScaleSubRegionLayer::backward(const UpdateCallback& callback) {
+  REGISTER_TIMER_INFO("ScaleSubRegionBackward", getName().c_str());
+  BufferArgs inArgs;
+  BufferArgs outArgs;
+  inArgs.addArg(*getOutputGrad(), shape_);
+  inArgs.addArg(*getInputValue(1), indicesShape_);
+  outArgs.addArg(*getInputGrad(0), shape_, ADD_TO);
+  backward_[0]->calc(inArgs, outArgs);
+}
+
+}  // namespace paddle

paddle/operators/fill_constant_op.h → paddle/gserver/layers/ScaleSubRegionLayer.h

@@ -13,25 +13,40 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/op_registry.h"
+
+#include "Layer.h"
 
 namespace paddle {
-namespace operators {
-
-template <typename Place, typename T>
-class FillConstantOpKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* out = ctx.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(ctx.GetPlace());
-    auto value = ctx.Attr<float>("value");
-
-    auto out_eigen = framework::EigenVector<T>::Flatten(*out);
-    auto place = ctx.GetEigenDevice<Place>();
-    out_eigen.device(place) = out_eigen.constant(static_cast<T>(value));
-  }
+
+/**
+ * \brief  For each instance, this layer can be used to multiply a value to a
+ *         specified sub continuous region. By providing start index and end
+ *         index for C/H/W, you can specify the location and shape of the
+ *         region.
+ *
+ *         input_0: Input value.
+ *         input_1: Indices value to specify the location an shape of the
+ *                  region.
+ */
+class ScaleSubRegionLayer : public Layer {
+public:
+  explicit ScaleSubRegionLayer(const LayerConfig& config) : Layer(config) {}
+
+  ~ScaleSubRegionLayer() {}
+
+  bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
+
+  void forward(PassType passType);
+
+  void backward(const UpdateCallback& callback = nullptr);
+
+protected:
+  TensorShape shape_;
+  TensorShape indicesShape_;
+  size_t imgH_;
+  size_t imgW_;
+  size_t channelsNum_;
+  real value_;
 };
 
-}  // namespace operators
 }  // namespace paddle

paddle/gserver/tests/test_LayerGrad.cpp

@@ -2358,6 +2358,38 @@ TEST(Layer, ScaleShiftLayer) {
   }
 }
 
+TEST(Layer, ScaleSubRegionLayer) {
+  const size_t batchSize = 64;
+  const size_t size = 4096;
+  TestConfig config;
+  config.layerConfig.set_type("scale_sub_region");
+  config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
+  MatrixPtr indicesV = Matrix::create(batchSize, 6, false, false);
+  auto* data = indicesV->getData();
+  for (size_t i = 0; i < batchSize; ++i) {
+    data[i * 2] = 2;
+    data[i * 2 + 1] = 4;
+    data[i * 2 + 2] = 16;
+    data[i * 2 + 3] = 32;
+    data[i * 2 + 4] = 16;
+    data[i * 2 + 5] = 32;
+  }
+  config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "indices", indicesV, {}});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  ScaleSubRegionConfig* scaleSubRegionConf =
+      input->mutable_scale_sub_region_conf();
+  ImageConfig* imgConf = scaleSubRegionConf->mutable_image_conf();
+  imgConf->set_img_size(32);
+  imgConf->set_img_size_y(32);
+  imgConf->set_channels(4);
+  scaleSubRegionConf->set_value(2.0);
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "scale_sub_region", batchSize, false, useGpu, false);
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

paddle/gserver/tests/test_MKLDNN.cpp

@@ -300,13 +300,8 @@ void testAddtoLayer(const testImageDesc& pm, const size_t nInputs) {
   TestConfig dnnConfig;
   getAddtoConfig(dnnConfig, pm, nInputs);
   dnnConfig.layerConfig.set_type("mkldnn_addto");
-  // TODO(TJ): test with bias
-  for (auto withBias : {false}) {
-    if (withBias) {
-      dnnConfig.biasSize = pm.ic * pm.ih * pm.iw;
-    } else {
-      dnnConfig.biasSize = 0;
-    }
+  for (auto withBias : {false, true}) {
+    dnnConfig.biasSize = withBias ? pm.ic * pm.ih * pm.iw : 0;
     RUN_MKLDNN_TEST_LAYER(dnnConfig, "addto", pm)
   }
 }

paddle/math/tests/TensorCheck.h

@@ -169,7 +169,7 @@ void TensorCheck(AssertEq compare,
       count++;
     }
   }
-  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
+  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
 }
 
 template <typename AssertEq, typename Tensor1, typename Tensor2>

paddle/operators/CMakeLists.txt

@@ -62,6 +62,11 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
     endif()
 
+    if ("${TARGET}" STREQUAL "compare_op")
+        set(pybind_flag 1)
+        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+    endif()
+
     # pool_with_index_op contains several operators
     if ("${TARGET}" STREQUAL "pool_with_index_op")
         set(pybind_flag 1)
@@ -165,6 +170,8 @@ set(DEPS_OPS
     sequence_conv_op
     sequence_pool_op
     lod_rank_table_op
+    lod_tensor_to_array_op
+    array_to_lod_tensor_op
     lstm_op
     tensor_array_read_write_op
     gru_op)
@@ -177,6 +184,8 @@ op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
+op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
+op_library(array_to_lod_tensor_op SRCS array_to_lod_tensor_op.cc DEPS lod_rank_table_op)
 op_library(tensor_array_read_write_op SRCS tensor_array_read_write_op.cc)
 if(WITH_GPU)
 op_library(nccl_op DEPS nccl_common)
@@ -186,8 +195,13 @@ op_library(sequence_pool_op DEPS sequence_pooling)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
 op_library(conv_transpose_op DEPS vol2col)
 op_library(gru_op DEPS sequence2batch gru_compute)
-op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
-        DEPS net_op tensor_array)
+if(WITH_TESTING)
+    op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
+        DEPS net_op tensor_array gtest)
+else()
+    op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
+            DEPS net_op tensor_array)
+endif()
 op_library(recurrent_op SRCS recurrent_op.cc DEPS executor)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})

paddle/operators/accuracy_op.cc

@@ -47,10 +47,11 @@ class AccuracyOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  // IndicateDataType
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Out")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Out")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/accuracy_op.cu

@@ -65,7 +65,7 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
 
     size_t num_samples = inference->dims()[0];
     size_t infer_width = inference->dims()[1];
-    cudaMemset((void**)&accuracy_data, 0, sizeof(float));
+    PADDLE_ENFORCE(cudaMemset(accuracy_data, 0, sizeof(float)));
 
     if (num_samples == 0) {
       return;

paddle/operators/array_operator.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+#include "paddle/framework/lod_tensor_array.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+class ArrayOp : public framework::OperatorBase {
+ public:
+  ArrayOp(const std::string &type, const framework::VariableNameMap &inputs,
+          const framework::VariableNameMap &outputs,
+          const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+ protected:
+  size_t GetOffset(const framework::Scope &scope,
+                   const platform::DeviceContext &dev_ctx) const {
+    auto *i = scope.FindVar(Input("I"));
+    PADDLE_ENFORCE(i != nullptr, "I must be set");
+    auto &i_tensor = i->Get<framework::LoDTensor>();
+    PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
+    size_t offset;
+    if (platform::is_gpu_place(i_tensor.place())) {
+      // FIXME: Avoid copy from GPU to CPU
+      framework::Tensor t;
+      t.CopyFrom(i_tensor, platform::CPUPlace(), dev_ctx);
+      dev_ctx.Wait();
+      offset = static_cast<size_t>(*t.data<int64_t>());
+    } else {
+      offset = static_cast<size_t>(*i_tensor.data<int64_t>());
+    }
+    return offset;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/array_to_lod_tensor_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+#include <numeric>
+#include "paddle/framework/lod_rank_table.h"
+#include "paddle/framework/lod_tensor_array.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/memory/memcpy.h"
+
+namespace paddle {
+namespace operators {
+
+using LoD = framework::LoD;
+
+class ArrayToLoDTensorOp : public framework::OperatorBase {
+ public:
+  ArrayToLoDTensorOp(const std::string &type,
+                     const framework::VariableNameMap &inputs,
+                     const framework::VariableNameMap &outputs,
+                     const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
+    auto &rank_table =
+        scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
+    auto *out =
+        scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
+
+    // Check dims, place and data type of input's elements and infer output's
+    // dim
+    PADDLE_ENFORCE(!x.empty(), "There's no element in the input array.");
+    int rank = x[0].dims().size();
+    platform::Place place = x[0].place();
+    std::type_index data_type = x[0].type();
+    framework::DDim ins_dims = framework::slice_ddim(x[0].dims(), 1, rank);
+    int64_t batch_size = x[0].dims()[0];
+    for (size_t i = 1; i < x.size(); ++i) {
+      PADDLE_ENFORCE_EQ(framework::slice_ddim(x[i].dims(), 1, rank), ins_dims,
+                        "The dimension of the %zu'th element in LoDTensorArray "
+                        "differs from previous ones.",
+                        i);
+      PADDLE_ENFORCE(platform::places_are_same_class(x[i].place(), place),
+                     "The place class of the %zu'th element in LoDTensorArray "
+                     "differs from previous ones.",
+                     i);
+      PADDLE_ENFORCE(x[i].type() == data_type,
+                     "The date type of the %zu'th element in LoDTensorArray "
+                     "differs from previous ones.",
+                     i);
+      batch_size += x[i].dims()[0];
+    }
+    auto ins_dim_vec = framework::vectorize(ins_dims);
+    ins_dim_vec.insert(ins_dim_vec.begin(), batch_size);
+    framework::DDim out_dims = framework::make_ddim(ins_dim_vec);
+    out->Resize(out_dims);
+    out->mutable_data(place, data_type);
+
+    auto &table_items = rank_table.items();
+    std::vector<size_t> table_item_idx(table_items.size());
+    // table_item_idx = range(table_items_idx.size())
+    std::iota(table_item_idx.begin(), table_item_idx.end(), 0);
+    std::sort(table_item_idx.begin(), table_item_idx.end(),
+              [&](size_t a, size_t b) {
+                return table_items[a].index < table_items[b].index;
+              });
+
+    // Build LoDTensor `out`
+    framework::LoD *out_lod = out->mutable_lod();
+    out_lod->clear();
+    size_t out_offset = 0;
+    auto prefix_lod = rank_table.coarse_lod();
+    prefix_lod.emplace_back();
+    auto &cur_level_lod = prefix_lod.back();
+    cur_level_lod.push_back(0);
+    for (size_t idx : table_item_idx) {
+      cur_level_lod.push_back(cur_level_lod.back() + table_items[idx].length);
+      for (size_t x_idx = 0; x_idx < table_items[idx].length; ++x_idx) {
+        auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
+            x[x_idx].lod(), idx, idx + 1, 0);
+
+        auto &lod_length = lod_and_offset.first;
+        framework::AppendLoD(out_lod, lod_length);
+
+        size_t start_offset = lod_and_offset.second.first;
+        size_t end_offset = lod_and_offset.second.second;
+        VLOG(10) << "idx=" << idx << " x_idx=" << x_idx << " ["
+                 << ", " << end_offset << "]";
+        // Copy data
+        PADDLE_ENFORCE_GE(end_offset, start_offset);
+        size_t len = end_offset - start_offset;
+        if (len == 0) {
+          continue;
+        }
+        out->Slice(out_offset, out_offset + len)
+            .CopyFrom(x[x_idx].Slice(start_offset, end_offset), place, dev_ctx);
+        out_offset += len;
+      }
+    }
+    out_lod->insert(out_lod->begin(), prefix_lod.begin(), prefix_lod.end());
+  }
+};
+
+class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ArrayToLoDTensorOpProtoMaker(framework::OpProto *proto,
+                               framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(std::vector<LodTensor>) A vector of tensors that is going to "
+             "be casted to a big LoDTensor.");
+    AddInput("RankTable",
+             "(LoDRankTable) RankTable provides the coarse lod infomation to "
+             "build the output LoDTensor. See "
+             "'paddle/framework/lod_rank_table.h' for more details.");
+    AddOutput("Out", "(LoDTensor) The LoDTensor formed by input tensor array.");
+    AddComment(
+        R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor> 
+          and a LoDRankTable. It is supposed to be used in getting dynamic RNN's
+          outputs back to a normal LoDTensor. The std::vector<LoDTensor> 
+          would be the output of RNN Op and the LoDRankTable would be build 
+          with RNN's input.)DOC");
+  }
+};
+
+class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("X"),
+                   "ArrayToLoDTensorOp must has input X.");
+    PADDLE_ENFORCE(context->HasInput("RankTable"),
+                   "ArrayToLoDTensorOp must has input RankTable.");
+    context->SetOutputDim("Out", context->GetInputDim("X"));
+  }
+};
+
+class ArrayToLoDTensorGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("lod_tensor_to_array");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetInput("RankTable", Input("RankTable"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(array_to_lod_tensor, ops::ArrayToLoDTensorOp,
+                  ops::ArrayToLoDTensorOpProtoMaker,
+                  ops::ArrayToLoDTensorInferShape,
+                  ops::ArrayToLoDTensorGradMaker);

paddle/operators/auc_op.cc

@@ -39,10 +39,11 @@ class AucOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  // IndicateDataType
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Out")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Out")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/batch_norm_op.cc

@@ -303,7 +303,8 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim(framework::GradVarName("Bias"), {C});
   }
 
-  framework::DataType IndicateDataType(
+ protected:
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
     const auto *var = ctx.InputVar(framework::GradVarName("Y"));
     if (var == nullptr) {
@@ -318,7 +319,8 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
     if (t == nullptr) {
       PADDLE_THROW("can't find Y@GRAD");
     }
-    return framework::ToDataType(t->type());
+    return framework::OpKernelType(framework::ToDataType(t->type()),
+                                   ctx.device_context());
   }
 };
 

paddle/operators/clip_by_norm_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/clip_by_norm_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ClipByNormOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ClipByNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of ClipByNormOp should not be null.");
+    auto max_norm = ctx->Attrs().Get<float>("max_norm");
+    PADDLE_ENFORCE_GT(max_norm, 0, "max_norm should be greater than 0.");
+    auto x_dims = ctx->GetInputDim("X");
+    ctx->SetOutputDim("Out", x_dims);
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class ClipByNormOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ClipByNormOpMaker(framework::OpProto* proto,
+                    framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(Tensor) The input of clip_by_norm op."
+             "The number of dimensions must be between [1, 9].");
+    AddOutput("Out",
+              "(Tensor) The output of clip_by_norm op with shape as input(X)");
+    AddAttr<float>("max_norm", "(float) The maximum norm value.");
+    AddComment(R"DOC(
+ClipByNorm operator limits the L2 norm of the input 'X' within 'max_norm'. 
+If the L2 norm of 'X' is less than or equal to 'max_norm', 'Out' will be 
+the same as 'X'. If the L2 norm of 'X' is greater than 'max_norm', 'X' will 
+be linearly scaled to make the L2 norm of 'Out' equal to 'max_norm', as 
+shown in the following formula：
+
+'Out' = 'max_norm' * 'X' / norm('X'),
+
+where norm('X') represents the L2 norm of 'X'.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(clip_by_norm, ops::ClipByNormOp,
+                             ops::ClipByNormOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    clip_by_norm, ops::ClipByNormKernel<paddle::platform::CPUPlace, float>);

paddle/operators/fill_constant_op.cu → paddle/operators/clip_by_norm_op.cu

@@ -12,13 +12,8 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#define EIGEN_USE_GPU
-#include "paddle/framework/op_registry.h"
-#include "paddle/operators/fill_constant_op.h"
+#include "paddle/operators/clip_by_norm_op.h"
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    fill_constant, ops::FillConstantOpKernel<paddle::platform::GPUPlace, float>,
-    ops::FillConstantOpKernel<paddle::platform::GPUPlace, double>,
-    ops::FillConstantOpKernel<paddle::platform::GPUPlace, int>,
-    ops::FillConstantOpKernel<paddle::platform::GPUPlace, int64_t>);
+    clip_by_norm, ops::ClipByNormKernel<paddle::platform::GPUPlace, float>);

paddle/operators/clip_by_norm_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class ClipByNormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto max_norm = context.Attr<T>("max_norm");
+    auto* input = context.Input<Tensor>("X");
+    auto* output = context.Output<Tensor>("Out");
+    output->mutable_data<T>(context.GetPlace());
+
+    auto x = EigenVector<T>::Flatten(*input);
+    auto out = EigenVector<T>::Flatten(*output);
+    auto x_norm = x.square().sum().sqrt();
+    auto place = context.GetEigenDevice<Place>();
+
+    auto temp = (x_norm <= max_norm).template cast<T>().eval();
+    auto scaling = temp + (static_cast<T>(1) - temp) * max_norm / x_norm;
+    Eigen::array<int, 1> one_dim{{1}};
+    Eigen::DSizes<int, 1> m_dsize(input->numel());
+    out.device(place) = x * scaling.reshape(one_dim).broadcast(m_dsize);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/compare_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/compare_op.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+template <typename OpComment>
+class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CompareOpProtoMaker(framework::OpProto *proto,
+                      framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    OpComment comment;
+    AddInput("X",
+             string::Sprintf("(LoDTensor) the left hand operand of %s operator",
+                             comment.type));
+    AddInput("Y", string::Sprintf(
+                      "(LoDTensor) the right hand operand of %s operator",
+                      comment.type));
+    AddOutput("Out", string::Sprintf(
+                         "(LoDTensor) n-dim bool tensor. Each element is %s",
+                         comment.equation));
+    AddComment(string::Sprintf(R"DOC(%s Operator
+
+It operates element-wise on X and Y, and returns the Out. Each of them is a
+N-dim tensor. X and Y could be any type.  The each element of the Out tensor is
+calculated by %s
+)DOC",
+                               comment.type, comment.equation));
+  }
+};
+
+template <typename OpComment>
+class CompareOpInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    OpComment comment;
+    PADDLE_ENFORCE(context->HasInput("X"), "%s operator must has input X",
+                   comment.type);
+    PADDLE_ENFORCE(context->HasInput("Y"), "%s operator must has input Y",
+                   comment.type);
+    auto dim_x = context->GetInputDim("X");
+    auto dim_y = context->GetInputDim("Y");
+    PADDLE_ENFORCE_EQ(framework::product(dim_x), framework::product(dim_y),
+                      "The number of elements in X and Y should be same");
+
+    context->SetOutputDim("Out", context->GetInputDim("X"));
+    context->ShareLoD("X", "Out");
+  }
+};
+
+class CompareOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  framework::OpKernelType GetKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    framework::OpKernelType kt = OperatorWithKernel::GetKernelType(ctx);
+    // CompareOp kernel's device type is decided by input tensor place
+    kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
+    return kt;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+#define REGISTER_LOGICAL_OP(op_type, _equation)                      \
+  struct _##op_type##Comment {                                       \
+    static char type[];                                              \
+    static char equation[];                                          \
+  };                                                                 \
+  char _##op_type##Comment::type[]{#op_type};                        \
+  char _##op_type##Comment::equation[]{_equation};                   \
+  REGISTER_OPERATOR(                                                 \
+      op_type, ::paddle::operators::CompareOp,                       \
+      ::paddle::operators::CompareOpProtoMaker<_##op_type##Comment>, \
+      ::paddle::operators::CompareOpInferShape<_##op_type##Comment>, \
+      ::paddle::framework::EmptyGradOpMaker);
+
+REGISTER_LOGICAL_OP(less_than, "Out = X < Y");
+REGISTER_LOGICAL_KERNEL(less_than, CPU, paddle::operators::LessThanFunctor);
+REGISTER_LOGICAL_OP(equal, "Out = X == Y");
+REGISTER_LOGICAL_KERNEL(equal, CPU, paddle::operators::EqualFunctor);

paddle/operators/compare_op.cu

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

paddle/operators/compare_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+#include <math.h>
+#include <type_traits>
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct LessThanFunctor {
+  using ELEM_TYPE = T;
+  HOSTDEVICE bool operator()(const T& a, const T& b) const { return a < b; }
+};
+
+template <typename T>
+struct EqualFunctor {
+  using ELEM_TYPE = T;
+  HOSTDEVICE bool operator()(const T& a, const T& b) const {
+    if (std::is_floating_point<T>::value) {
+      // This branch will be optimized while compiling if T is integer. It is
+      // safe to cast a and b to double.
+      return fabs(static_cast<double>(a - b)) < 1e-8;
+    } else {
+      return (a == b);
+    }
+  }
+};
+
+template <typename Place, typename Functor>
+class CompareOpKernel
+    : public framework::OpKernel<typename Functor::ELEM_TYPE> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    using T = typename Functor::ELEM_TYPE;
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* y = context.Input<framework::Tensor>("Y");
+    auto* out = context.Output<framework::Tensor>("Out");
+    Functor binary_func;
+    platform::Transform<Place> trans;
+    trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
+          y->data<T>(), out->mutable_data<bool>(context.GetPlace()),
+          binary_func);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+#define REGISTER_LOGICAL_KERNEL(op_type, dev, functor)                     \
+  REGISTER_OP_##dev##_KERNEL(                                              \
+      op_type,                                                             \
+      ::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
+                                           functor<int>>,                  \
+      ::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
+                                           functor<int64_t>>,              \
+      ::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
+                                           functor<float>>,                \
+      ::paddle::operators::CompareOpKernel<::paddle::platform::dev##Place, \
+                                           functor<double>>);

paddle/operators/crf_decoding_op.cc

@@ -120,9 +120,11 @@ class CRFDecodingOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type()),
+        ctx.device_context());
   }
 };
 }  // namespace operators

paddle/operators/cross_entropy_op.cc

@@ -51,9 +51,11 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
  protected:
   // Explicitly set that the data type of computation kernel of cross_entropy
   // is determined by its input "X".
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        ctx.device_context());
   }
 };
 
@@ -98,9 +100,11 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
  protected:
   // Explicitly set that the data type of computation kernel of cross_entropy
   // is determined by its input "X".
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/fill_constant_batch_size_like_op.cc

@@ -49,9 +49,11 @@ class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
+    return framework::OpKernelType(
+        static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
+        ctx.device_context());
   }
 };
 
@@ -73,10 +75,10 @@ class FillConstantBatchSizeLikeOpMaker
               "with the specified value");
     AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
     AddAttr<int>("input_dim_idx",
-                 "(int, default 0) the index of input's batch size dimension")
+                 "(int, default 0) The index of input's batch size dimension")
         .SetDefault(0);
     AddAttr<int>("output_dim_idx",
-                 "(int, default 0) the index of output's batch size dimension")
+                 "(int, default 0) The index of output's batch size dimension")
         .SetDefault(0);
     AddAttr<float>("value", "(float, default 0) The value to be filled")
         .SetDefault(0.0f);

paddle/operators/fill_constant_op.cc

@@ -12,32 +12,41 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/operators/fill_constant_op.h"
+#include "paddle/framework/data_type.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
 
-class FillConstantOp : public framework::OperatorWithKernel {
+class FillConstantInferShape : public framework::InferShapeBase {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void operator()(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of FillConstantOp should not be null.");
     auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
-    std::vector<int64_t> shape_int64(shape.size(), 0);
-    std::transform(shape.begin(), shape.end(), shape_int64.begin(),
-                   [](int a) { return static_cast<int64_t>(a); });
-    auto dims = framework::make_ddim(shape_int64);
-    ctx->SetOutputDim("Out", dims);
+    ctx->SetOutputDim("Out", framework::make_ddim(shape));
   }
+};
 
- protected:
-  framework::DataType IndicateDataType(
-      const framework::ExecutionContext &ctx) const override {
-    int data_type = ctx.Attr<int>("data_type");
-    VLOG(10) << " FillConstant data_type = " << data_type;
-    return static_cast<framework::DataType>(data_type);
+class FillConstantOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto data_type = static_cast<framework::DataType>(Attr<int>("data_type"));
+    auto value = Attr<float>("value");
+    auto force_cpu = Attr<bool>("force_cpu");
+    auto &out =
+        *scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
+    out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
+    if (force_cpu) {
+      auto cpu = platform::CPUPlace();
+      out.mutable_data(cpu, framework::ToTypeIndex(data_type));
+    } else {
+      out.mutable_data(dev_ctx.GetPlace(), framework::ToTypeIndex(data_type));
+    }
+    math::set_constant(dev_ctx, &out, value);
   }
 };
 
@@ -53,6 +62,11 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
     AddAttr<float>("value", "(float, default 0) The value to be filled")
         .SetDefault(0.0f);
+    AddAttr<bool>("force_cpu",
+                  "(bool, default false) Force fill output variable to cpu "
+                  "memory. Otherwise, fill output variable to the running "
+                  "device")
+        .SetDefault(false);
     AddOutput("Out",
               "(Tensor) Tensor of specified shape will be filled "
               "with the specified value");
@@ -68,10 +82,6 @@ Fill up a variable with specified constant value.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp,
-                             ops::FillConstantOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    fill_constant, ops::FillConstantOpKernel<paddle::platform::CPUPlace, float>,
-    ops::FillConstantOpKernel<paddle::platform::CPUPlace, double>,
-    ops::FillConstantOpKernel<paddle::platform::CPUPlace, int>,
-    ops::FillConstantOpKernel<paddle::platform::CPUPlace, int64_t>);
+REGISTER_OPERATOR(fill_constant, ops::FillConstantOp,
+                  ops::FillConstantInferShape, ops::FillConstantOpMaker,
+                  paddle::framework::EmptyGradOpMaker);

paddle/operators/gather_op.cc

@@ -40,9 +40,11 @@ class GatherOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        ctx.device_context());
   }
 };
 
@@ -55,9 +57,11 @@ class GatherGradOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/gaussian_random_op.cc

@@ -57,9 +57,11 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
+    return framework::OpKernelType(
+        static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
+        ctx.device_context());
   }
 };
 

paddle/operators/linear_chain_crf_op.cc

@@ -183,9 +183,11 @@ class LinearChainCRFOp : public framework::OperatorWithKernel {
  protected:
   // Explicitly set that the data type of computation kernel of linear_chain_crf
   // is determined by its input "Emission".
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type()),
+        ctx.device_context());
   }
 };
 
@@ -240,10 +242,13 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
  protected:
   // Explicitly set that the data type of output of the linear_chain_crf_grad
   // operator is determined by its input: gradients of LogLikelihood.
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(
-        ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))->type());
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))
+                ->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/lod_rank_table_op.cc

@@ -28,6 +28,7 @@ class LoDRankTableOp : public framework::OperatorBase {
     auto x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
     auto *out =
         scope.FindVar(Output("Out"))->GetMutable<framework::LoDRankTable>();
+    VLOG(10) << "Level = " << static_cast<size_t>(Attr<int>("level"));
     out->Reset(x.lod(), static_cast<size_t>(Attr<int>("level")));
   }
 };

paddle/operators/lod_tensor_to_array_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+#include "paddle/framework/lod_rank_table.h"
+#include "paddle/framework/lod_tensor_array.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+struct CopyRange {
+  size_t begin;
+  size_t end;
+};
+
+class LoDTensorToArrayOp : public framework::OperatorBase {
+ public:
+  LoDTensorToArrayOp(const std::string &type,
+                     const framework::VariableNameMap &inputs,
+                     const framework::VariableNameMap &outputs,
+                     const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
+    auto &rank_table =
+        scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
+    auto &out =
+        *scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
+
+    auto &items = rank_table.items();
+    auto max_seq_len = items[0].length;
+    auto rank_level = rank_table.level();
+    out.resize(max_seq_len);
+    std::vector<std::vector<CopyRange>> copy_ranges(max_seq_len);
+
+    // set out[i] lod
+    for (size_t t = 0; t < max_seq_len; t++) {
+      auto &lod = *out[t].mutable_lod();
+      lod.clear();
+      for (auto &item : items) {
+        if (t >= item.length) {
+          break;
+        }
+        size_t start_idx = x.lod()[rank_level][item.index] + t;
+        auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
+            x.lod(), start_idx, start_idx + 1, rank_level + 1);
+
+        auto &lod_length = lod_and_offset.first;
+        framework::AppendLoD(&lod, lod_length);
+
+        size_t start_offset = lod_and_offset.second.first;
+        size_t end_offset = lod_and_offset.second.second;
+        copy_ranges[t].emplace_back(CopyRange{start_offset, end_offset});
+      }
+    }
+
+    for (size_t i = 0; i < max_seq_len; ++i) {
+      auto &ranges = copy_ranges[i];
+      size_t height = std::accumulate(
+          ranges.begin(), ranges.end(), 0UL,
+          [](size_t a, const CopyRange &b) { return a + b.end - b.begin; });
+      auto x_dim = x.dims();
+      x_dim[0] = static_cast<int64_t>(height);
+      out[i].Resize(x_dim);
+      out[i].mutable_data(x.place(), x.type());
+      size_t offset = 0;
+      for (auto &each_range : ranges) {
+        size_t len = each_range.end - each_range.begin;
+        if (len == 0) {
+          continue;
+        }
+        // out[i][offset: offset+len] = x[each_range.begin: each_range.end]
+        out[i]
+            .Slice(static_cast<int>(offset), static_cast<int>(offset + len))
+            .CopyFrom(x.Slice(static_cast<int>(each_range.begin),
+                              static_cast<int>(each_range.end)),
+                      x.place(), dev_ctx);
+        offset += len;
+      }
+    }
+  }
+};
+
+class LoDTensorToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LoDTensorToArrayOpProtoMaker(framework::OpProto *proto,
+                               framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "");
+    AddInput("RankTable", "");
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class LoDTensorToArrayInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("X"),
+                   "Input(X) of LoDTensorToArrayOp should not be null.");
+    PADDLE_ENFORCE(
+        context->HasInput("RankTable"),
+        "Input(RankTable) of LoDTensorToArrayOp should not be null.");
+
+    PADDLE_ENFORCE(context->HasOutput("Out"),
+                   "Output(Out) of LoDTensorToArrayOp should not be null.");
+
+    auto x_dim = context->GetInputDim("X");
+    // The first dim of each LoDTensor in Output can only be set at run-time.;
+    // We still have to Resize each LoDTensor in Output.
+    context->SetOutputDim("Out", x_dim);
+  }
+};
+
+class LoDTensorToArrayInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDescBind &op_desc,
+                  framework::BlockDescBind *block) const override {
+    for (auto &out_var : op_desc.Output("Out")) {
+      block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
+    }
+  }
+};
+
+class LoDTensorToArrayGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("array_to_lod_tensor");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetInput("RankTable", Input("RankTable"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(lod_tensor_to_array, ops::LoDTensorToArrayOp,
+                  ops::LoDTensorToArrayOpProtoMaker,
+                  ops::LoDTensorToArrayInferShape,
+                  ops::LoDTensorToArrayInferVarType,
+                  ops::LoDTensorToArrayGradMaker);

paddle/operators/lookup_table_op.cc

@@ -41,9 +41,11 @@ class LookupTableOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<LoDTensor>("W")->type()),
+        ctx.device_context());
   }
 };
 
@@ -97,9 +99,11 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<LoDTensor>("W")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/lookup_table_op.cu

@@ -74,8 +74,9 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
 
     dim3 threads(128, 8);
     dim3 grids(8, 1);
-    LookupTable<T, 128, 8,
-                8><<<grids, threads, 0, context.device_context().stream()>>>(
+    LookupTable<
+        T, 128, 8,
+        8><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
         output, table, ids, N, K, D);
   }
 };
@@ -135,7 +136,7 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
       dim3 grids(8, 1);
       LookupTableGrad<
           T, 128, 8,
-          8><<<grids, threads, 0, context.device_context().stream()>>>(
+          8><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
           d_table, d_output, ids, N, K, D);
     }
   }

paddle/operators/lstm_op.cc

@@ -92,10 +92,11 @@ class LSTMOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(
-        ctx.Input<framework::LoDTensor>("Input")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
+        ctx.device_context());
   }
 };
 
@@ -267,10 +268,11 @@ class LSTMGradOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(
-        ctx.Input<framework::LoDTensor>("Input")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/lstm_unit_op.cc

@@ -34,10 +34,10 @@ class LstmUnitOp : public framework::OperatorWithKernel {
     auto c_prev_dims = ctx->GetInputDim("C_prev");
 
     PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
-    PADDLE_ENFORCE(x_dims[0] == c_prev_dims[0],
-                   "Batch size of inputs and states must be equal");
-    PADDLE_ENFORCE(x_dims[1] == c_prev_dims[1] * 4,
-                   "Dimension of FC should equal to prev state * 4");
+    PADDLE_ENFORCE_EQ(x_dims[0], c_prev_dims[0],
+                      "Batch size of inputs and states must be equal");
+    PADDLE_ENFORCE_EQ(x_dims[1], c_prev_dims[1] * 4,
+                      "Dimension of FC should equal to prev state * 4");
 
     int b_size = c_prev_dims[0];  // batch size
     int s_dim = c_prev_dims[1];   // state dim

paddle/operators/math/detail/lstm_gpu_kernel.h

@@ -246,11 +246,6 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
         op, value, grad, frameSize, batchSize, active_node, active_gate,
         active_state);
   }
-
-  cudaStreamSynchronize(stream);
-  // TODO(qingqing): Add cuda error check for each kernel.
-  cudaError_t err = cudaGetLastError();
-  PADDLE_ENFORCE(err, cudaGetErrorString(err));
 }
 
 }  // namespace detail

paddle/operators/math/math_function.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/math/math_function.h"
+#include "paddle/framework/data_type.h"
 
 namespace paddle {
 namespace operators {
@@ -233,6 +234,52 @@ void gemv<platform::CPUPlace, double>(const platform::DeviceContext& context,
 
 template struct SetConstant<platform::CPUPlace, float>;
 
+struct TensorSetConstant {
+  TensorSetConstant(framework::Tensor* tensor, float value)
+      : tensor_(tensor), value_(value) {}
+  template <typename T>
+  void operator()() const {
+    auto cpu = platform::CPUPlace();
+    auto* begin = tensor_->mutable_data<T>(cpu);
+    std::fill(begin, begin + tensor_->numel(), static_cast<T>(value_));
+  }
+  framework::Tensor* tensor_;
+  float value_;
+};
+
+template <>
+void set_constant_with_place<platform::CPUPlace>(
+    const platform::DeviceContext& context, framework::Tensor* tensor,
+    float value) {
+  framework::VisitDataType(framework::ToDataType(tensor->type()),
+                           TensorSetConstant(tensor, value));
+}
+
+struct TensorSetConstantWithPlace : public boost::static_visitor<void> {
+  TensorSetConstantWithPlace(const platform::DeviceContext& context,
+                             framework::Tensor* tensor, float value)
+      : context_(context), tensor_(tensor), value_(value) {}
+
+  template <typename Place>
+  void operator()(Place place) const {
+    set_constant_with_place<Place>(context_, tensor_, value_);
+  }
+
+  const platform::DeviceContext& context_;
+  framework::Tensor* tensor_;
+  float value_;
+};
+
+void set_constant(const platform::DeviceContext& context,
+                  framework::Tensor* tensor, float value) {
+  TensorSetConstantWithPlace func(context, tensor, value);
+#ifdef PADDLE_WITH_CUDA
+  tensor->place().apply_visitor(func);
+#else
+  func(platform::CPUPlace());
+#endif
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.cu

@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include "paddle/framework/data_type.h"
 #include "paddle/operators/math/math_function.h"
 
 namespace paddle {
@@ -232,6 +233,30 @@ void gemv<platform::GPUPlace, double>(const platform::DeviceContext& context,
 
 template struct SetConstant<platform::GPUPlace, float>;
 
+struct TensorSetConstant {
+  TensorSetConstant(const platform::DeviceContext& context,
+                    framework::Tensor* tensor, float value)
+      : context_(context), tensor_(tensor), value_(value) {}
+
+  template <typename T>
+  void operator()() const {
+    SetConstant<platform::GPUPlace, T> functor;
+    functor(context_, tensor_, static_cast<T>(value_));
+  }
+
+  const platform::DeviceContext& context_;
+  framework::Tensor* tensor_;
+  float value_;
+};
+
+template <>
+void set_constant_with_place<platform::GPUPlace>(
+    const platform::DeviceContext& context, framework::Tensor* tensor,
+    float value) {
+  framework::VisitDataType(framework::ToDataType(tensor->type()),
+                           TensorSetConstant(context, tensor, value));
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.h

@@ -108,6 +108,13 @@ struct SetConstant {
   }
 };
 
+template <typename Place>
+void set_constant_with_place(const platform::DeviceContext& context,
+                             framework::Tensor* tensor, float value);
+
+void set_constant(const platform::DeviceContext& context,
+                  framework::Tensor* tensor, float value);
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function_test.cc

@@ -139,3 +139,15 @@ TEST(math_function, gemv) {
   GemvTest<float>(12, 7, true);
   GemvTest<double>(7, 9, true);
 }
+
+TEST(math_funciton, set_constant) {
+  paddle::framework::Tensor t;
+  t.Resize({10, 10});
+  t.mutable_data<int>(paddle::platform::CPUPlace());
+  auto* ctx = new paddle::platform::CPUDeviceContext();
+  paddle::operators::math::set_constant(*ctx, &t, 10);
+  for (int64_t i = 0; i < t.numel(); ++i) {
+    PADDLE_ENFORCE_EQ(10, t.data<int>()[i]);
+  }
+  delete ctx;
+}

paddle/operators/mean_op.cc

@@ -51,6 +51,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", framework::GradVarName("X"));
   }
 };
 

paddle/operators/multiplex_op.cc

@@ -51,9 +51,11 @@ class MultiplexOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type()),
+        ctx.device_context());
   }
 };
 
@@ -107,9 +109,11 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.MultiInput<Tensor>("X")[0]->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/multiplex_op.cu

@@ -71,7 +71,7 @@ class MultiplexGradGPUKernel : public framework::OpKernel<T> {
     index_t_cpu.CopyFrom(*ids, platform::CPUPlace(), ctx.device_context());
     auto* index = index_t_cpu.data<int32_t>();
 
-    auto stream = ctx.device_context().stream();
+    auto stream = ctx.cuda_device_context().stream();
     Place place = boost::get<Place>(ctx.GetPlace());
     for (auto i = 0; i < rows; i++) {
       size_t k = static_cast<size_t>(index[i]);

paddle/operators/pool_cudnn_op.cu

@@ -37,11 +37,11 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
     const T *input_data = input->data<T>();
     T *output_data = output->mutable_data<T>(ctx.GetPlace());
 
-    std::string pooling_type = ctx.Attr<std::string>("poolingType");
+    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
-    if (ctx.Attr<bool>("globalPooling")) {
+    if (ctx.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(input->dims()[i + 2]);
@@ -92,12 +92,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
         ctx.Input<Tensor>(framework::GradVarName("Out"));
     Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
 
-    std::string pooling_type = ctx.Attr<std::string>("poolingType");
+    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
 
-    if (ctx.Attr<bool>("globalPooling")) {
+    if (ctx.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(input->dims()[i + 2]);

paddle/operators/pool_op.cc

@@ -29,7 +29,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
 
   auto in_x_dims = ctx->GetInputDim("X");
 
-  std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
+  std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
   std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
   std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
   std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
@@ -37,7 +37,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
   PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
                  "Pooling intput should be 4-D or 5-D tensor.");
 
-  if (ctx->Attrs().Get<bool>("globalPooling")) {
+  if (ctx->Attrs().Get<bool>("global_pooling")) {
     ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
     for (size_t i = 0; i < ksize.size(); ++i) {
       paddings[i] = 0;
@@ -83,20 +83,20 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
             "H is the height of the feature, "
             "and W is the width of the feature.");
 
-  AddAttr<std::string>("poolingType",
+  AddAttr<std::string>("pooling_type",
                        "(string), pooling type, can be \"max\" for max-pooling "
                        "and \"avg\" for average-pooling.")
       .InEnum({"max", "avg"});
   AddAttr<std::vector<int>>("ksize",
                             "(vector<int>) The pooling window "
                             "size(height, width) of the pooling operator. "
-                            "If globalPooling = true, ksize and paddings will "
+                            "If global_pooling = true, ksize and paddings will "
                             "be ignored.");  // TODO(Chengduo): Add checker.
                                              // (Currently,
   // TypedAttrChecker don't support vector type.)
-  AddAttr<bool>("globalPooling",
+  AddAttr<bool>("global_pooling",
                 "(bool, default false) Whether to use the global pooling. "
-                "If globalPooling = true, ksize and paddings will be ignored.")
+                "If global_pooling = true, ksize and paddings will be ignored.")
       .SetDefault(false);
   AddAttr<std::vector<int>>("strides",
                             "(vector<int>, default {1, 1}), strides(height, "
@@ -107,7 +107,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
       "paddings",
       "(vector<int>, defalut {0,0}), paddings(height, width) of pooling "
       "operator."
-      "If globalPooling = true, paddings and ksize will be ignored.")
+      "If global_pooling = true, paddings and ksize will be ignored.")
       .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
   // TypedAttrChecker don't support vector type.)
 
@@ -115,7 +115,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
 Pool2d Operator.
 
 The pooling2d operation calculates the output based on
-the input, poolingType and ksize, strides, paddings parameters.
+the input, pooling_type and ksize, strides, paddings parameters.
 Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
 number of channels, H is the height of the feature, and W is the width of the feature.
 Parameters(ksize, strides, paddings) are two elements.
@@ -152,7 +152,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
             "the number of channels, and D, H and W is the depth, height and "
             "width of the feature, respectively.");
 
-  AddAttr<std::string>("poolingType",
+  AddAttr<std::string>("pooling_type",
                        "(string) Pooling type, can be \"max\" for max-pooling "
                        "and \"avg\" for average-pooling.")
       .InEnum({"max", "avg"});
@@ -160,13 +160,14 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
       "ksize",
       "(vector<int>) The pooling window size(depth, height, "
       "width) of pooling operator. "
-      "If globalPooling = true, ksize and paddings will "
+      "If global_pooling = true, ksize and paddings will "
       "be ignored.");  // TODO(Chengduo): Add checker.
                        // (Currently,
   // TypedAttrChecker don't support vector type.)
-  AddAttr<bool>("globalPooling",
-                "(bool, default false) Whether to use the global pooling. "
-                "If globalPooling = true, ksize and paddings wille be ignored.")
+  AddAttr<bool>(
+      "global_pooling",
+      "(bool, default false) Whether to use the global pooling. "
+      "If global_pooling = true, ksize and paddings wille be ignored.")
       .SetDefault(false);
   AddAttr<std::vector<int>>(
       "strides",
@@ -178,7 +179,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
       "paddings",
       "(vector<int>, defalut {0,0,0}), paddings(depth, height, "
       "width) of pooling operator. "
-      "If globalPooling = true, ksize and paddings will be ignored.")
+      "If global_pooling = true, ksize and paddings will be ignored.")
       .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
                                // TypedAttrChecker don't support vector type.)
 
@@ -186,7 +187,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
 Pool3d Operator.
 
 The pooling3d operation calculates the output based on
-the input, poolingType, ksize, strides, and paddings parameters.
+the input, pooling_type, ksize, strides, and paddings parameters.
 Input(X) and output(Out) are in NCDHW format, where N is batch
 size, C is the number of channels, and D, H and W are the depth, height and
 width of the feature, respectively. Parameters(ksize, strides, paddings) 

paddle/operators/pool_op.h

@@ -57,11 +57,11 @@ class PoolKernel : public framework::OpKernel<T> {
     const Tensor* in_x = context.Input<Tensor>("X");
     Tensor* out = context.Output<Tensor>("Out");
 
-    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::string pooling_type = context.Attr<std::string>("pooling_type");
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
@@ -119,12 +119,12 @@ class PoolGradKernel : public framework::OpKernel<T> {
         context.Input<Tensor>(framework::GradVarName("Out"));
     Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
 
-    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::string pooling_type = context.Attr<std::string>("pooling_type");
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);

paddle/operators/pool_with_index_op.cc

@@ -44,7 +44,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
                    "Pooling intput should be 4-D or 5-D tensor.");
 
-    if (ctx->Attrs().Get<bool>("globalPooling")) {
+    if (ctx->Attrs().Get<bool>("global_pooling")) {
       ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
@@ -110,14 +110,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>("ksize",
                               "(vector<int>) The pooling window size(height, "
                               "width) of pooling operator. "
-                              "If globalPooling = true, ksize and paddings "
+                              "If global_pooling = true, ksize and paddings "
                               "will be ignored.");  // TODO(Chengduo): Add
                                                     // checker. (Currently,
     // TypedAttrChecker don't support vector type.)
     AddAttr<bool>(
-        "globalPooling",
+        "global_pooling",
         "(bool, default false) Whether to use the global pooling. "
-        "If globalPooling = true, ksize and paddings will be ignored.")
+        "If global_pooling = true, ksize and paddings will be ignored.")
         .SetDefault(false);
     AddAttr<std::vector<int>>("strides",
                               "(vector<int>, default {1, 1}), strides(height, "
@@ -128,7 +128,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
         "paddings",
         "(vector<int>, defalut {0, 0}), paddings(height, width) of pooling "
         "operator. "
-        "If globalPooling = true, paddings and will be ignored.")
+        "If global_pooling = true, paddings and will be ignored.")
         .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
     // TypedAttrChecker don't support vector type.)
 
@@ -188,14 +188,14 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>("ksize",
                               "(vector<int>) The pooling window size(depth, "
                               "height, width) of pooling operator. "
-                              "If globalPooling = true, ksize and paddings "
+                              "If global_pooling = true, ksize and paddings "
                               "will be ignored.");  // TODO(Chengduo): Add
                                                     // checker. (Currently,
     // TypedAttrChecker don't support vector type.)
     AddAttr<bool>(
-        "globalPooling",
+        "global_pooling",
         "(bool, default false) Whether to use the global pooling. "
-        "If globalPooling = true, ksize and paddings will be ignored.")
+        "If global_pooling = true, ksize and paddings will be ignored.")
         .SetDefault(false);
     AddAttr<std::vector<int>>("strides",
                               "(vector<int>, default {1,1,1}), strides(depth, "
@@ -206,7 +206,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
         "paddings",
         "(vector, defalut {0,0,0}), paddings(depth, "
         "height, width) of pooling operator. "
-        "If globalPooling = true, paddings and ksize will be ignored.")
+        "If global_pooling = true, paddings and ksize will be ignored.")
         .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
     // TypedAttrChecker don't support vector type.)
 

paddle/operators/pool_with_index_op.h

@@ -35,7 +35,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
@@ -72,7 +72,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);

paddle/operators/positive_negative_pair_op.cc

@@ -85,9 +85,11 @@ class PositiveNegativePairOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Score")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Score")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/precision_recall_op.cc

@@ -80,9 +80,11 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("MaxProbs")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("MaxProbs")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/scatter_op.cc

@@ -49,9 +49,11 @@ class ScatterOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Ref")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Ref")->type()),
+        ctx.device_context());
   }
 };
 
@@ -66,9 +68,11 @@ class ScatterGradOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Ref")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Ref")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/sequence_pool_op.cc

@@ -107,9 +107,11 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/shrink_rnn_memory_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+#include "paddle/framework/lod_rank_table.h"
+#include "paddle/operators/array_operator.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+class ShrinkRNNMemoryOp : public ArrayOp {
+ public:
+  ShrinkRNNMemoryOp(const std::string &type,
+                    const framework::VariableNameMap &inputs,
+                    const framework::VariableNameMap &outputs,
+                    const framework::AttributeMap &attrs)
+      : ArrayOp(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto *x_var = scope.FindVar(Input("X"));
+    PADDLE_ENFORCE(x_var != nullptr, "Input X must be set");
+    auto &x_tensor = x_var->Get<framework::LoDTensor>();
+    size_t offset = this->GetOffset(scope, dev_ctx);
+    auto *rank_table_var = scope.FindVar(Input("RankTable"));
+    PADDLE_ENFORCE(rank_table_var != nullptr, "RankTable must be set");
+    auto &rank_table = rank_table_var->Get<framework::LoDRankTable>();
+
+    auto &rank_items = rank_table.items();
+    int dst_num_rows =
+        std::lower_bound(rank_items.begin(), rank_items.end(), offset,
+                         [](const framework::LoDRankTable::TableItem &a,
+                            size_t b) { return a.length > b; }) -
+        rank_items.begin();
+
+    auto *out_var = scope.FindVar(Output("Out"));
+    PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
+    auto &out_tensor = *out_var->GetMutable<framework::LoDTensor>();
+    if (dst_num_rows != 0) {
+      out_tensor.ShareDataWith(x_tensor.Slice(0, dst_num_rows));
+    }
+  }
+};
+
+class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ShrinkRNNMemoryOpProtoMaker(framework::OpProto *proto,
+                              framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "");
+    AddInput("RankTable", "");
+    AddInput("I", "");
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class ShrinkRNNMemoryInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("X"));
+    PADDLE_ENFORCE(context->HasInput("I"));
+    PADDLE_ENFORCE(context->HasInput("RankTable"));
+    context->SetOutputDim("Out", context->GetInputDim("X"));
+  }
+};
+
+class ShrinkRNNMemoryGradOp : public ArrayOp {
+ public:
+  ShrinkRNNMemoryGradOp(const std::string &type,
+                        const framework::VariableNameMap &inputs,
+                        const framework::VariableNameMap &outputs,
+                        const framework::AttributeMap &attrs)
+      : ArrayOp(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
+    auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
+    PADDLE_ENFORCE(dx_var != nullptr, "Input Gradient should not be nullptr");
+    auto *x_var = scope.FindVar(Input("X"));
+    PADDLE_ENFORCE(x_var != nullptr);
+
+    auto &x_tensor = x_var->Get<framework::LoDTensor>();
+    auto &dx_tensor = *dx_var->GetMutable<framework::LoDTensor>();
+    dx_tensor.Resize(x_tensor.dims());
+    dx_tensor.mutable_data(x_tensor.place(), x_tensor.type());
+
+    if (dout_var == nullptr) {  // dx_tensor fill zero
+      math::set_constant(dev_ctx, &dx_tensor, 0.0f);
+    } else {
+      auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
+      auto height = dout_tensor.dims()[0];
+      dx_tensor.Slice(0, static_cast<int>(height))
+          .CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx);
+      if (dx_tensor.dims()[0] < height) {
+        auto rest_tensor = dx_tensor.Slice(
+            static_cast<int>(height), static_cast<int>(dout_tensor.dims()[0]));
+        math::set_constant(dev_ctx, &rest_tensor, 0.0f);
+      }
+    }
+  }
+};
+
+class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("X"));
+    PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
+    context->SetOutputDim(framework::GradVarName("X"),
+                          context->GetInputDim("X"));
+  }
+};
+
+class ShrinkRNNGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *op = new framework::OpDescBind();
+    op->SetType("shrink_rnn_memory_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(shrink_rnn_memory, ops::ShrinkRNNMemoryOp,
+                  ops::ShrinkRNNMemoryInferShape,
+                  ops::ShrinkRNNMemoryOpProtoMaker, ops::ShrinkRNNGradOpMaker);
+REGISTER_OPERATOR(shrink_rnn_memory_grad, ops::ShrinkRNNMemoryGradOp,
+                  ops::ShrinkRNNMemoryGradInferShape);

paddle/operators/softmax_with_cross_entropy_op.cc

@@ -121,9 +121,11 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<Tensor>("Logits")->type());
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Logits")->type()),
+        ctx.device_context());
   }
 };
 
@@ -160,10 +162,12 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(
-        ctx.Input<Tensor>(framework::GradVarName("Loss"))->type());
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<Tensor>(framework::GradVarName("Loss"))->type()),
+        ctx.device_context());
   }
 };
 

paddle/operators/sum_op.cc

@@ -47,20 +47,24 @@ class SumOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
     auto x_vars = ctx.MultiInputVar("X");
     if (x_vars[0]->IsType<framework::LoDTensor>()) {
-      return framework::ToDataType(
-          x_vars[0]->Get<framework::LoDTensor>().type());
+      return framework::OpKernelType(
+          framework::ToDataType(x_vars[0]->Get<framework::LoDTensor>().type()),
+          ctx.device_context());
     } else if (x_vars[0]->IsType<framework::SelectedRows>()) {
-      return framework::ToDataType(
-          x_vars[0]->Get<framework::SelectedRows>().value().type());
+      return framework::OpKernelType(
+          framework::ToDataType(
+              x_vars[0]->Get<framework::SelectedRows>().value().type()),
+          ctx.device_context());
     } else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
       auto& array = x_vars[0]->Get<framework::LoDTensorArray>();
       for (auto& each : array) {
         if (each.numel() != 0) {
-          return framework::ToDataType(each.type());
+          return framework::OpKernelType(framework::ToDataType(each.type()),
+                                         ctx.device_context());
         }
       }
     }

paddle/operators/tensor_array_read_write_op.cc

@@ -11,48 +11,18 @@
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License. */
-#include "paddle/framework/lod_tensor_array.h"
-#include "paddle/framework/op_registry.h"
+#include "paddle/operators/array_operator.h"
 
 namespace paddle {
 namespace operators {
-class ArrayOpBase : public framework::OperatorBase {
- public:
-  ArrayOpBase(const std::string &type, const framework::VariableNameMap &inputs,
-              const framework::VariableNameMap &outputs,
-              const framework::AttributeMap &attrs)
-      : OperatorBase(type, inputs, outputs, attrs) {}
-  void Run(const framework::Scope &scope,
-           const platform::DeviceContext &dev_ctx) const override {}
-
- protected:
-  size_t GetOffset(const framework::Scope &scope,
-                   const platform::DeviceContext &dev_ctx) const {
-    auto *i = scope.FindVar(Input("I"));
-    PADDLE_ENFORCE(i != nullptr, "I must be set");
-    auto &i_tensor = i->Get<framework::LoDTensor>();
-    PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
-    size_t offset;
-    if (platform::is_gpu_place(i_tensor.place())) {
-      // FIXME: Avoid copy from GPU to CPU
-      framework::Tensor t;
-      t.CopyFrom(i_tensor, platform::CPUPlace(), dev_ctx);
-      dev_ctx.Wait();
-      offset = static_cast<size_t>(*t.data<int64_t>());
-    } else {
-      offset = static_cast<size_t>(*i_tensor.data<int64_t>());
-    }
-    return offset;
-  }
-};
 
-class WriteToArrayOp : public ArrayOpBase {
+class WriteToArrayOp : public ArrayOp {
  public:
   WriteToArrayOp(const std::string &type,
                  const framework::VariableNameMap &inputs,
                  const framework::VariableNameMap &outputs,
                  const framework::AttributeMap &attrs)
-      : ArrayOpBase(type, inputs, outputs, attrs) {}
+      : ArrayOp(type, inputs, outputs, attrs) {}
 
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
@@ -122,13 +92,13 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
   }
 };
 
-class ReadFromArrayOp : public ArrayOpBase {
+class ReadFromArrayOp : public ArrayOp {
  public:
   ReadFromArrayOp(const std::string &type,
                   const framework::VariableNameMap &inputs,
                   const framework::VariableNameMap &outputs,
                   const framework::AttributeMap &attrs)
-      : ArrayOpBase(type, inputs, outputs, attrs) {}
+      : ArrayOp(type, inputs, outputs, attrs) {}
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
     auto *x = scope.FindVar(Input("X"));

paddle/operators/uniform_random_op.cc

@@ -63,9 +63,11 @@ class UniformRandomOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::DataType IndicateDataType(
+  framework::OpKernelType GetKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
+    return framework::OpKernelType(
+        static_cast<framework::DataType>(ctx.Attr<int>("data_type")),
+        ctx.device_context());
   }
 };
 

paddle/platform/device_context.cc

@@ -124,6 +124,11 @@ void CUDADeviceContext::Wait() const {
   PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
 }
 
+void CUDADeviceContext::Finish() const {
+  Wait();
+  PADDLE_ENFORCE(cudaGetLastError());
+}
+
 Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
   return eigen_device_.get();
 }

paddle/platform/device_context.h

@@ -46,6 +46,8 @@ class DeviceContext {
   DeviceType* GetEigenDevice() const;
 
   virtual void Wait() const {}
+
+  virtual void Finish() const {}
 };
 
 class CPUDeviceContext : public DeviceContext {
@@ -77,6 +79,9 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Wait for all operations completion in the stream. */
   void Wait() const override;
 
+  /*! \brief  Check potential errors for the cuda kernel calls. */
+  void Finish() const override;
+
   /*! \brief  Return place in the device context. */
   Place GetPlace() const override;
 

paddle/platform/transform.h

@@ -49,8 +49,6 @@ struct Transform<platform::CPUPlace> {
   template <typename InputIter, typename OutputIter, typename UnaryOperation>
   void operator()(const DeviceContext& context, InputIter first, InputIter last,
                   OutputIter result, UnaryOperation op) {
-    auto place = context.GetPlace();
-    PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
     std::transform(first, last, result, op);
   }
 
@@ -59,8 +57,6 @@ struct Transform<platform::CPUPlace> {
   void operator()(const DeviceContext& context, InputIter1 first1,
                   InputIter1 last1, InputIter2 first2, OutputIter result,
                   BinaryOperation op) {
-    auto place = context.GetPlace();
-    PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
     std::transform(first1, last1, first2, result, op);
   }
 };

paddle/pybind/pybind.cc

@@ -113,11 +113,13 @@ PYBIND11_PLUGIN(core) {
       .def("set", PyCPUTensorSetFromArray<int>)
       .def("set", PyCPUTensorSetFromArray<double>)
       .def("set", PyCPUTensorSetFromArray<int64_t>)
+      .def("set", PyCPUTensorSetFromArray<bool>)
 #ifdef PADDLE_WITH_CUDA
       .def("set", PyCUDATensorSetFromArray<float>)
       .def("set", PyCUDATensorSetFromArray<int>)
       .def("set", PyCUDATensorSetFromArray<double>)
       .def("set", PyCUDATensorSetFromArray<int64_t>)
+      .def("set", PyCUDATensorSetFromArray<bool>)
 #endif
       .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
       .def("set_float_element", TensorSetElement<float>)

paddle/pybind/tensor_py.h

@@ -85,7 +85,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
 }  // namespace details
 inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
   auto buffer_info =
-      details::CastToPyBufferImpl<true, 0, float, int, double, int64_t>()(
+      details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool>()(
           tensor);
   return buffer_info;
 }

paddle/scripts/docker/build.sh

@@ -174,8 +174,6 @@ EOF
 EOF
 }
 
-set +xe
-
 cmake_gen
 run_build
 run_test

proto/ModelConfig.proto

@@ -321,6 +321,11 @@ message ClipConfig {
   required double max = 2;
 }
 
+message ScaleSubRegionConfig {
+  required ImageConfig image_conf = 1;
+  required float value = 2;
+}
+
 message LayerInputConfig {
   required string input_layer_name = 1;
   optional string input_parameter_name = 2;
@@ -342,6 +347,7 @@ message LayerInputConfig {
   optional MultiBoxLossConfig multibox_loss_conf = 16;
   optional DetectionOutputConfig detection_output_conf = 17;
   optional ClipConfig clip_conf = 18;
+  optional ScaleSubRegionConfig scale_sub_region_conf = 19;
 }
 
 message LayerConfig {

python/paddle/trainer/config_parser.py

@@ -3801,6 +3801,25 @@ class SwitchOrderLayer(LayerBase):
         self.config.reshape_conf.width_axis.extend(reshape['width'])
 
 
+@config_layer('scale_sub_region')
+class ScaleSubRegionLayer(LayerBase):
+    def __init__(self, name, inputs, value, **xargs):
+        super(ScaleSubRegionLayer, self).__init__(
+            name, 'scale_sub_region', 0, inputs=inputs, **xargs)
+        scale_sub_region_conf = self.config.inputs[0].scale_sub_region_conf
+        scale_sub_region_conf.value = value
+
+        # get channel, width and height from input_0 layer
+        input_layer = self.get_input_layer(0)
+        image_conf = scale_sub_region_conf.image_conf
+        image_conf.img_size = input_layer.width
+        image_conf.img_size_y = input_layer.height
+        image_conf.channels = input_layer.size / (input_layer.width *
+                                                  input_layer.height)
+        self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
+                           image_conf.channels)
+
+
 # Deprecated, use a new layer specific class instead
 @config_func
 def Layer(name, type, **xargs):

python/paddle/trainer_config_helpers/layers.py

@@ -144,6 +144,7 @@ __all__ = [
     'img_conv3d_layer',
     'resize_layer',
     'sub_seq_layer',
+    'scale_sub_region_layer',
 ]
 
 
@@ -255,6 +256,8 @@ class LayerType(object):
     RESIZE = 'resize'
     SUB_SEQ_LAYER = 'subseq'
 
+    SCALE_SUB_REGION_LAYER = 'scale_sub_region'
+
     @staticmethod
     def is_layer_type(type_name):
         """
@@ -6548,26 +6551,27 @@ def switch_order_layer(input,
 @layer_support()
 def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None):
     """
-    This layer crops images by offset and shape. User can set crop shape by
-    args 'shape' explicitly or by reference input layer.
+    This layer crops images according to the offset and shape. Users can set
+    the crop shape through the argument 'shape' explicitly or by specifying a
+    reference input layer.
 
     The example usage is:
 
     .. code-block:: python
     crop = crop_layer(input=[image_input, reference_input], axis=2, offset=[2, 3])
 
-    :param input: The input of this layer. If two inputs are given, the second input
-                  will be regarded as reference input.
+    :param input: The input of this layer. If two inputs are given, the second one
+                  will be regarded as the reference.
     :type input: LayerOutput | Sequence
     :param offset: The crop offset.
     :type offset: Sequence
-    :param axis: start axis to be cropped. To image input layer:
+    :param axis: The start axis to be cropped. For image input layer:
         - 0: batch size
         - 1: channels
         - 2: height
         - 3: width
-    :type partial_sum: int
-    :param shape: The shape to be cropped. Default is None.
+    :type axis: int
+    :param shape: The shape to be cropped to. Default is None.
     :type shape: Sequence | None
     :param name: The name of this layer. It is optional.
     :type name: basestring
@@ -6702,9 +6706,9 @@ def seq_slice_layer(input, starts, ends, name=None):
     :type name: basestring
     :param input: The input of this layer, which should be a sequence.
     :type input: LayerOutput
-    :param starts: start indices to slice the input sequence.
+    :param starts: The start indices to slice the input sequence.
     :type starts: LayerOutput | None
-    :param ends: end indices to slice the input sequence.
+    :param ends: The end indices to slice the input sequence.
     :type ends: LayerOutput | None
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -6744,7 +6748,7 @@ def seq_slice_layer(input, starts, ends, name=None):
 @layer_support()
 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
+    This layer accepts one input which is scores over a sequence or a nested
     sequence, and returns indices of beam_size sequences with highest scores.
 
     .. code-block:: python
@@ -6754,11 +6758,11 @@ def kmax_seq_score_layer(input, name=None, beam_size=1):
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input: The input of this layer. It stores scores over a sequence or a nested
-        sequence and its size must be 1.
+    :param input: The input of this layer. It stores scores over a sequence or
+                  a nested sequence and its size must be 1.
     :type input: LayerOutput
-    :param beam_size: sequence indices with top beam_size scores are returned.
-    :type beam_size: double
+    :param beam_size: The indices of the sequences with top beam_size scores are returned.
+    :type beam_size: int
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -6814,38 +6818,42 @@ def img_conv3d_layer(input,
     :type name: basestring
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param filter_size: The x dimension of a filter kernel. Or input a list.
+    :param filter_size: The dimensions of the filter kernel along three axises. If the parameter
+                        is set to one integer, the three dimensions will be same.
     :type filter_size: int | tuple | list
-    :param num_filters: Each filter group's number of filter
+    :param num_filters: The number of filters in each group.
+    :type num_filters: int
     :param act: Activation type. ReluActivation is the default.
     :type act: BaseActivation
-    :param groups: Group size of filters.
+    :param groups: The number of the filter groups.
     :type groups: int
-    :param stride: The x dimension of the stride. Or input a tuple for two image
-                   dimension.
+    :param stride: The strides of the convolution along three axises. If the parameter
+                   is set to one integer, the three strides will be same.
     :type stride: int | tuple | list
-    :param padding: The x dimension of the padding. Or input a tuple for two
-                    image dimension
+    :param padding: The numbers of padding along three axises. If the parameter is set to
+                    one integer, they will be same.
     :type padding: int | tuple | list
-    :param bias_attr: Convolution bias attribute. None means default bias.
-                      False means no bias.
+    :param bias_attr: The Bias Attribute. If the parameter is set to
+                      False or something not type of ParameterAttribute,
+                      no bias is defined. If the parameter is set to
+                      True, the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param num_channels: number of input channels. If None will be set
-                        automatically from previous output.
+    :param num_channels: The number of input channels. If the parameter is not set or
+                         set to None,  its actual value will be automatically set to
+                         the channels number of the input .
     :type num_channels: int
-    :param param_attr: Convolution param attribute. None means default attribute
+    :param param_attr: The parameter attribute of the convolution.
     :type param_attr: ParameterAttribute
-    :param shared_biases: Is biases will be shared between filters or not.
+    :param shared_biases: Whether biases will be shared between filters or not.
     :type shared_biases: bool
-    :param layer_attr: Layer Extra Attribute.
+    :param layer_attr: Extra layer attributes.
     :type layer_attr: ExtraLayerAttribute
-    :param trans: true if it is a convTransLayer, false if it is a convLayer
+    :param trans: True if it is a convTransLayer, False if it is a convLayer
     :type trans: bool
-    :param layer_type: specify the layer_type, default is None. If trans=True,
-                       layer_type has to be "exconvt" or "cudnn_convt",
-                       otherwise layer_type has to be either "exconv" or
-                       "cudnn_conv"
-    :type layer_type: String
+    :param layer_type: Specify the layer_type. If the parameter is set, it must be "deconv3d"
+                       when trans=True. If not set, it will be automatically set to "deconv3d"
+                       when trans=True and "conv3d" when trans=False.
+    :type layer_type: basestring
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -6927,7 +6935,7 @@ def img_conv3d_layer(input,
 def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None):
     """
     A layer applies a linear transformation to each element in each row of
-    the input matrix. For each element, the layer first re-scale it and then
+    the input matrix. For each element, the layer first re-scales it and then
     adds a bias to it.
 
     This layer is very like the SlopeInterceptLayer, except the scale and
@@ -7001,12 +7009,12 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
     :type name: basestring
     :param input: The input of this layer, which should be sequence.
     :type input: LayerOutput
-    :param offsets: offset indices to slice the input sequence, which should be
-                    sequence type.
+    :param offsets: The offset indices to slice the input sequence, which should
+                    be sequence type.
     :type offsets: LayerOutput
-    :param sizes: sizes of the sub-sequences, which should be sequence type.
+    :param sizes: The sizes of the sub-sequences, which should be sequence type.
     :type sizes: LayerOutput
-    :param act: Layer activation, default is LinearActivation
+    :param act: Activation type, LinearActivation is the default.
     :type act: BaseActivation.
     :param bias_attr: The Bias Attribute. If the parameter is set to
                       False or something not type of ParameterAttribute,
@@ -7037,3 +7045,54 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
         LayerType.SUB_SEQ_LAYER,
         parents=[input, offsets, sizes],
         size=input.size)
+
+
+@wrap_name_default('scale_sub_region')
+def scale_sub_region_layer(input, indices, value, name=None):
+    """
+    Given an image or feature map with CHW information, scale_sub_region_layer
+    can be used to multiply a real value to values of a sub continuous region.
+    You can provide start and end indices of CHW for each instance.
+    Please notice that all start indices are counting from 1.
+    The shape of indices should be [batch_size, 6] and the layout for each row
+    is [C_Start, C_End, H_Start, H_End, W_Start, W_End].
+
+    .. code-block:: python
+
+        scale_sub_region = scale_sub_region_layer(input=input,
+                                                  indices=indices,
+                                                  value=value)
+
+    :param name: The name of this layer. It is optional.
+    :type name: basestring
+    :param input: The input of this layer which should contains CHW information.
+    :type input: LayerOutput
+    :param indices: Start index and end index for C H W, the input value should
+                    be a 2-D matrix with shape [batch_size, 6].
+    :type indices: LayerOutput.
+    :param value: value to multiply.
+    :type value: float
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+
+    assert isinstance(input, LayerOutput), (
+        'The first input of scale_sub_region_layer, '
+        'must be a PaddlePaddle layer.')
+    assert isinstance(indices, LayerOutput), (
+        'The start and end indices for CHW, must be a PaddlePaddle layer.')
+    assert isinstance(value, float), (
+        'The value to multiply, must be a real value.')
+
+    Layer(
+        name=name,
+        type=LayerType.SCALE_SUB_REGION_LAYER,
+        inputs=[input.name, indices.name],
+        value=value)
+
+    return LayerOutput(
+        name,
+        LayerType.SCALE_SUB_REGION_LAYER,
+        parents=[input, indices],
+        num_filters=input.num_filters,
+        size=input.size)

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

@@ -10,6 +10,6 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_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 test_BatchNorm3D test_resize_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer)
 
 export whole_configs=(test_split_datasource)

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

+type: "nn"
+layers {
+  name: "data"
+  type: "data"
+  size: 2016
+  active_type: ""
+  height: 48
+  width: 42
+}
+layers {
+  name: "indices"
+  type: "data"
+  size: 6
+  active_type: ""
+}
+layers {
+  name: "__scale_sub_region_0__"
+  type: "scale_sub_region"
+  size: 2016
+  active_type: ""
+  inputs {
+    input_layer_name: "data"
+    scale_sub_region_conf {
+      image_conf {
+        channels: 1
+        img_size: 42
+        img_size_y: 48
+      }
+      value: 0.0
+    }
+  }
+  inputs {
+    input_layer_name: "indices"
+  }
+  height: 48
+  width: 42
+}
+input_layer_names: "data"
+input_layer_names: "indices"
+output_layer_names: "__scale_sub_region_0__"
+sub_models {
+  name: "root"
+  layer_names: "data"
+  layer_names: "indices"
+  layer_names: "__scale_sub_region_0__"
+  input_layer_names: "data"
+  input_layer_names: "indices"
+  output_layer_names: "__scale_sub_region_0__"
+  is_recurrent_layer_group: false
+}
+

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

+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-5)
+
+data = data_layer(name='data', size=2016, height=48, width=42)
+indices = data_layer(name='indices', size=6)
+
+scale_sub_region = scale_sub_region_layer(
+    input=data, indices=indices, value=0.0)
+
+outputs(scale_sub_region)

python/paddle/v2/dataset/uci_housing.py

@@ -22,6 +22,7 @@ parse training set and test set into paddle reader creators.
 import numpy as np
 import os
 import paddle.v2.dataset.common
+from paddle.v2.parameters import Parameters
 
 __all__ = ['train', 'test']
 
@@ -34,7 +35,8 @@ feature_names = [
 
 UCI_TRAIN_DATA = None
 UCI_TEST_DATA = None
-
+URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fit_a_line.tar'
+MD5_MODEL = '52fc3da8ef3937822fcdd87ee05c0c9b'
 
 def feature_range(maximums, minimums):
     import matplotlib
@@ -111,6 +113,13 @@ def test():
     return reader
 
 
+def model():
+    tar_file = paddle.v2.dataset.common.download(URL_MODEL, 'fit_a_line.tar', MD5_MODEL)
+    with open(tar_file, 'r') as f:
+        parameters = Parameters.from_tar(f)
+    return parameters
+
+
 def fetch():
     paddle.v2.dataset.common.download(URL, 'uci_housing', MD5)
 

python/paddle/v2/framework/layers.py

@@ -87,7 +87,8 @@ def data(name,
          type=core.VarDesc.VarType.LOD_TENSOR,
          append_batch_size=True,
          main_program=None,
-         startup_program=None):
+         startup_program=None,
+         stop_gradient=True):
     helper = LayerHelper('data', **locals())
     shape = list(shape)
     for i in xrange(len(shape)):
@@ -101,7 +102,11 @@ def data(name,
         shape = [-1] + shape  # append batch size as -1
 
     return helper.create_global_variable(
-        name=name, shape=shape, dtype=data_type, type=type, stop_gradient=True)
+        name=name,
+        shape=shape,
+        dtype=data_type,
+        type=type,
+        stop_gradient=stop_gradient)
 
 
 def _convert_(name):
@@ -134,9 +139,7 @@ def _create_op_func_(op_type):
     o_name = not_intermediate_outputs[0].name
     intermediate_output_names = [output.name for output in intermediate_outputs]
 
-    def func(**kwargs):
-        helper = LayerHelper(op_type, **kwargs)
-        inputs = dict()
+    def infer_and_check_data_type(op_proto, **kwargs):
         dtype = None
         for ipt in op_proto.inputs:
             name = _convert_(ipt.name)
@@ -153,6 +156,20 @@ def _create_op_func_(op_type):
                 elif dtype != each.data_type:
                     raise ValueError(
                         "operator {0} must input same dtype".format(op_type))
+
+        return dtype
+
+    def func(**kwargs):
+        helper = LayerHelper(op_type, **kwargs)
+
+        dtype = infer_and_check_data_type(op_proto, **kwargs)
+
+        inputs = dict()
+        for ipt in op_proto.inputs:
+            name = _convert_(ipt.name)
+            val = kwargs.pop(name, [])
+            if not isinstance(val, list) and not isinstance(val, tuple):
+                val = [val]
             inputs[ipt.name] = val
 
         outputs = dict()
@@ -178,6 +195,20 @@ _create_op_func_('reshape')
 _create_op_func_('elementwise_add')
 _create_op_func_('sigmoid')
 _create_op_func_('scale')
+_create_op_func_('reshape')
+_create_op_func_('transpose')
+
+
+def fill_constant(data_type, shape, value=None, program=None):
+    helper = LayerHelper('fill_constant', **locals())
+    out = helper.create_tmp_variable(dtype=data_type)
+    helper.append_op(
+        type='fill_constant',
+        outputs={'Out': [out]},
+        attrs={'data_type': data_type,
+               'shape': shape,
+               'value': value})
+    return out
 
 
 def cast(x, data_type, main_program=None):
@@ -414,9 +445,9 @@ def pool2d(input,
         inputs={"X": input},
         outputs={"Out": pool_out},
         attrs={
-            "poolingType": pool_type,
+            "pooling_type": pool_type,
             "ksize": pool_size,
-            "globalPooling": global_pooling,
+            "global_pooling": global_pooling,
             "strides": pool_stride,
             "paddings": pool_padding
         })
@@ -762,6 +793,46 @@ class StaticRNN(object):
             })
 
 
+def lstm(x,
+         c_pre_init,
+         hidden_dim,
+         forget_bias=None,
+         main_program=None,
+         startup_program=None):
+    helper = LayerHelper('lstm_unit', **locals())
+    rnn = StaticRNN()
+    with rnn.step():
+        c_pre = rnn.memory(init=c_pre_init)
+        x_t = rnn.step_input(x)
+
+        before_fc = concat(
+            input=[x_t, c_pre],
+            axis=1,
+            main_program=main_program,
+            startup_program=startup_program)
+        after_fc = fc(input=before_fc,
+                      size=hidden_dim * 4,
+                      main_program=main_program,
+                      startup_program=startup_program)
+
+        data_type = x.data_type
+        c = helper.create_tmp_variable(data_type)
+        h = helper.create_tmp_variable(data_type)
+
+        helper.append_op(
+            type='lstm_unit',
+            inputs={"X": after_fc,
+                    "C_prev": c_pre},
+            outputs={"C": c,
+                     "H": h},
+            attrs={"forget_bias": forget_bias})
+
+        rnn.update_memory(c_pre, c)
+        rnn.output(h)
+
+    return rnn()
+
+
 def lod_rank_table(x, level=0, main_program=None):
     helper = LayerHelper("lod_rank_table", **locals())
     table = helper.create_variable(
@@ -775,6 +846,31 @@ def lod_rank_table(x, level=0, main_program=None):
     return table
 
 
+def lod_tensor_to_array(x, table, main_program=None):
+    helper = LayerHelper("lod_tensor_to_array", **locals())
+    array = helper.create_variable(
+        name=unique_name("lod_tensor_to_array"),
+        type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
+        dtype=x.data_type)
+    helper.append_op(
+        type='lod_tensor_to_array',
+        inputs={'X': x,
+                'RankTable': table},
+        outputs={'Out': array})
+    return array
+
+
+def array_to_lod_tensor(x, table, main_program=None):
+    helper = LayerHelper("array_to_lod_tensor", **locals())
+    tmp = helper.create_tmp_variable(dtype=x.data_type)
+    helper.append_op(
+        type="array_to_lod_tensor",
+        inputs={'X': x,
+                'RankTable': table},
+        outputs={'Out': tmp})
+    return tmp
+
+
 def fill_constant(shape, dtype, value, main_program=None):
     helper = LayerHelper("ones", **locals())
     out = helper.create_tmp_variable(dtype=dtype)
@@ -801,13 +897,13 @@ def zeros(shape, dtype, main_program=None):
 
 def increment(x, value=1.0, main_program=None):
     helper = LayerHelper("increment", **locals())
-    tmp = helper.create_tmp_variable(dtype=x.data_type)
+    out = helper.create_tmp_variable(dtype=x.data_type)
     helper.append_op(
         type='increment',
         inputs={'X': [x]},
-        outputs={'Out': [tmp]},
+        outputs={'Out': [out]},
         attrs={'step': value})
-    return tmp
+    return out
 
 
 def array_write(x, i, array=None, main_program=None):
@@ -838,3 +934,16 @@ def array_read(array, i, main_program=None):
                 'I': [i]},
         outputs={'Out': [out]})
     return out
+
+
+def shrink_memory(x, i, table, main_program=None):
+    helper = LayerHelper('shrink_memory', **locals())
+    out = helper.create_tmp_variable(dtype=x.data_type)
+    helper.append_op(
+        type='shrink_rnn_memory',
+        inputs={'X': [x],
+                'I': [i],
+                'RankTable': [table]},
+        outputs={'Out': [out]},
+        attrs={})
+    return out

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestClipByNormOp(OpTest):
+    def setUp(self):
+        self.max_relative_error = 0.006
+        self.initTestCase()
+        input = np.random.random(self.shape).astype("float32")
+        input[np.abs(input) < self.max_relative_error] = 0.5
+        self.op_type = "clip_by_norm"
+        self.inputs = {'X': input, }
+        self.attrs = {}
+        self.attrs['max_norm'] = self.max_norm
+        norm = np.sqrt(np.sum(np.square(input)))
+        if norm > self.max_norm:
+            output = self.max_norm * input / norm
+        else:
+            output = input
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def initTestCase(self):
+        self.shape = (100, )
+        self.max_norm = 1.0
+
+
+class TestCase1(TestClipByNormOp):
+    def initTestCase(self):
+        self.shape = (100, )
+        self.max_norm = 1e20
+
+
+class TestCase2(TestClipByNormOp):
+    def initTestCase(self):
+        self.shape = (16, 16)
+        self.max_norm = 0.1
+
+
+class TestCase3(TestClipByNormOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 16)
+        self.max_norm = 1.0
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+import op_test
+import unittest
+import numpy
+
+
+def create_test_class(op_type, typename, callback):
+    class Cls(op_test.OpTest):
+        def setUp(self):
+            a = numpy.random.random(size=(10, 7)).astype(typename)
+            b = numpy.random.random(size=(10, 7)).astype(typename)
+            c = callback(a, b)
+            self.inputs = {'X': a, 'Y': b}
+            self.outputs = {'Out': c}
+            self.op_type = op_type
+
+        def test_output(self):
+            self.check_output()
+
+    cls_name = "{0}_{1}".format(op_type, typename)
+    Cls.__name__ = cls_name
+    globals()[cls_name] = Cls
+
+
+for _type_name in {'float32', 'float64', 'int32', 'int64'}:
+    create_test_class('less_than', _type_name, lambda _a, _b: _a < _b)
+    create_test_class('equal', _type_name, lambda _a, _b: _a == _b)
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -18,7 +18,6 @@ class TestLoDRankTable(unittest.TestCase):
         tensor = core.LoDTensor()
         tensor.set(numpy.random.random(size=(17, 100)), cpu)
         tensor.set_lod([[0, 1, 3], [0, 5, 6, 7], [0, 3, 4, 9, 10, 13, 16, 17]])
-
         exe.run(g_main_program, scope=scope, feed={'x': tensor})
         var = scope.find_var(rank_table.name)
         table = var.get_lod_rank_table()

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

+import unittest
+import paddle.v2.framework.core as core
+import numpy
+import paddle.v2.framework.layers as layers
+from paddle.v2.framework.framework import Program
+from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.backward import append_backward_ops
+
+
+class TestCPULoDTensorArrayOps(unittest.TestCase):
+    def place(self):
+        return core.CPUPlace()
+
+    def test_lod_tensor_to_array_level_0(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(10).reshape(10, 1).astype('int32'), self.place())
+        tensor.set_lod([[0, 3, 9, 10]])
+        expect = map(lambda x: numpy.array(x).astype('int32'),
+                     [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
+        self.main(tensor=tensor, expect_array=expect, expect_lod=[] * 6)
+
+    def test_lod_tensor_to_array_level_0_empty_seq(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(10).reshape(10, 1).astype('int32'), self.place())
+        tensor.set_lod([[0, 3, 9, 9, 10]])
+        expect = map(lambda x: numpy.array(x).astype('int32'),
+                     [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
+        self.main(tensor=tensor, expect_array=expect, expect_lod=[] * 6)
+
+    def test_lod_tensor_to_array_level_1(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(20).reshape(20, 1).astype('int32'), self.place())
+        tensor.set_lod([[0, 2, 5], [0, 3, 9, 11, 17, 20]])
+
+        expect = [
+            numpy.array(
+                [9, 10, 0, 1, 2], dtype='int32'), numpy.array(
+                    [11, 12, 13, 14, 15, 16, 3, 4, 5, 6, 7, 8], dtype='int32'),
+            numpy.array(
+                [17, 18, 19], dtype='int32')
+        ]
+
+        lod = [[[0, 2, 5]], [[0, 6, 12]], [[0, 3]]]
+        self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
+
+    def test_lod_tensor_to_array_level_1_empty_seq(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(31).reshape(31, 1).astype('int32'), self.place())
+
+        tensor.set_lod([[0, 3, 5, 9, 11],
+                        [0, 3, 7, 11, 11, 12, 17, 19, 21, 23, 30, 31]])
+
+        expect = [
+            numpy.array(
+                item, dtype='int32')
+            for item in [[
+                12, 13, 14, 15, 16, 0, 1, 2, 23, 24, 25, 26, 27, 28, 29
+            ], [17, 18, 3, 4, 5, 6, 11, 30], [19, 20, 7, 8, 9, 10], [21, 22]]
+        ]
+
+        lod = [[[0, 5, 8, 8, 15]], [[0, 2, 6, 7, 8]], [[0, 2, 6]], [[0, 2]]]
+        self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
+
+    def test_lod_tensor_to_array_level_2(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(50).reshape(50, 1).astype('int32'), self.place())
+        tensor.set_lod([[0, 2, 5, 6], [0, 2, 5, 6, 10, 12, 13],
+                        [0, 3, 7, 11, 17, 21, 22, 23, 27, 31, 39, 45, 46, 50]])
+
+        expect = [
+            numpy.array(
+                item, dtype='int32')
+            for item in [[21, 0, 1, 2, 3, 4, 5, 6, 46, 47, 48, 49], range(
+                22, 39) + range(7, 21), range(39, 46)]
+        ]
+        lod = [[[0, 1, 3, 4], [0, 1, 4, 8, 12]],
+               [[0, 4, 7], [0, 1, 5, 9, 17, 21, 27, 31]], [[0, 2], [0, 6, 7]]]
+        self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
+
+    def test_lod_tensor_to_array_level_2_skip_level(self):
+        tensor = core.LoDTensor()
+        tensor.set(
+            numpy.arange(50).reshape(50, 1).astype('int32'), self.place())
+        tensor.set_lod([[0, 2, 5, 6], [0, 2, 5, 6, 10, 12, 13],
+                        [0, 3, 7, 11, 17, 21, 22, 23, 27, 31, 39, 45, 46, 50]])
+        self.main(tensor=tensor, expect_array=None, expect_lod=None, level=1)
+
+    def main(self, tensor, expect_array, expect_lod, level=0):
+        place = self.place()
+        program = Program()
+        x = layers.data(name='x', shape=[10], main_program=program)
+        x.persistable = True
+        table = layers.lod_rank_table(x, level=level, main_program=program)
+        array = layers.lod_tensor_to_array(x, table, main_program=program)
+        array.persistable = True
+
+        result = layers.array_to_lod_tensor(array, table, main_program=program)
+        result.persistable = True
+        exe = Executor(place)
+        scope = core.Scope()
+        exe.run(program, feed={'x': tensor}, scope=scope)
+        var = scope.find_var(array.name)
+        array = var.get_lod_tensor_array()
+        if expect_array is not None and expect_lod is not None:
+            self.check_array_same(array, expect_array, expect_lod)
+        self.check_tensor_same(scope.find_var(result.name).get_tensor(), tensor)
+
+    def check_array_same(self, array, expect_tensor, expect_lod):
+        self.assertEqual(len(expect_tensor), len(array))
+        for i, exp in enumerate(zip(expect_tensor, expect_lod)):
+            exp_tensor, exp_lod = exp
+            exp_tensor = numpy.expand_dims(exp_tensor, axis=1)
+            self.assertTrue(numpy.allclose(exp_tensor, numpy.array(array[i])))
+            self.assertEqual(exp_lod, array[i].lod())
+
+    def check_tensor_same(self, actual, expect):
+        self.assertTrue(
+            numpy.allclose(numpy.array(actual), numpy.array(expect)))
+        self.assertEqual(actual.lod(), expect.lod())
+
+
+class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
+    def test_grad(self):
+        place = core.CPUPlace()
+        program = Program()
+
+        x = layers.data(
+            name='x',
+            shape=[1],
+            data_type='float32',
+            main_program=program,
+            stop_gradient=False)
+        table = layers.lod_rank_table(x, level=0, main_program=program)
+        array = layers.lod_tensor_to_array(x, table, main_program=program)
+        result = layers.array_to_lod_tensor(array, table, main_program=program)
+
+        mean = layers.mean(x=result, main_program=program)
+
+        append_backward_ops(mean)
+
+        tensor = core.LoDTensor()
+        tensor.set(numpy.arange(10).reshape(10, 1).astype('float32'), place)
+        tensor.set_lod([[0, 3, 9, 10]])
+
+        g_vars = program.global_block().var(x.name + "@GRAD")
+
+        exe = Executor(place)
+        g_out = [
+            item.sum()
+            for item in map(
+                numpy.array,
+                exe.run(program, feed={'x': tensor}, fetch_list=[g_vars]))
+        ]
+        g_out_sum = numpy.array(g_out).sum()
+
+        self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_pool2d_op.py

@@ -61,8 +61,8 @@ class TestPool2d_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'poolingType': self.pool_type,
-            'globalPooling': self.global_pool,
+            'pooling_type': self.pool_type,
+            'global_pooling': self.global_pool,
         }
 
         self.outputs = {'Out': output.astype('float32')}

python/paddle/v2/framework/tests/test_pool3d_op.py

@@ -67,8 +67,8 @@ class TestPool3d_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'poolingType': self.pool_type,
-            'globalPooling': self.global_pool,
+            'pooling_type': self.pool_type,
+            'global_pooling': self.global_pool,
         }
 
         self.outputs = {'Out': output.astype('float32')}

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

@@ -86,7 +86,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'globalPooling': self.global_pool,
+            'global_pooling': self.global_pool,
         }
 
         self.inputs = {'X': input}

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

+import unittest
+import paddle.v2.framework.core as core
+from paddle.v2.framework.executor import Executor
+import paddle.v2.framework.layers as layers
+from paddle.v2.framework.backward import append_backward_ops
+from paddle.v2.framework.framework import g_main_program
+import numpy
+
+
+class TestShrinkRNNMemory(unittest.TestCase):
+    def test_shrink_rnn_memory(self):
+        x = layers.data('x', shape=[100], data_type='float32')
+        x.stop_gradient = False
+        table = layers.lod_rank_table(x=x)
+        i = layers.zeros(dtype='int64', shape=[1])
+        mem1 = layers.shrink_memory(x=x, i=i, table=table)
+        i = layers.increment(x=i)
+        i.stop_gradient = True
+        mem2 = layers.shrink_memory(x=mem1, i=i, table=table)
+        i = layers.increment(x=i)
+        i.stop_gradient = True
+        mem3 = layers.shrink_memory(x=mem2, i=i, table=table)
+
+        cpu = core.CPUPlace()
+        tensor = core.LoDTensor()
+        tensor.set_lod([[0, 2, 5, 6]])
+        tensor_np = numpy.random.random(size=(3, 100)).astype('float32')
+        tensor.set(tensor_np, cpu)
+        exe = Executor(cpu)
+        outs = map(numpy.array,
+                   exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3]))
+        self.assertTrue(numpy.allclose(tensor_np[0:3], outs[0]))
+        self.assertTrue(numpy.allclose(tensor_np[0:2], outs[1]))
+        self.assertTrue(numpy.allclose(tensor_np[0:1], outs[2]))
+
+        mem3_mean = layers.mean(x=mem3)
+        append_backward_ops(loss=mem3_mean)
+        x_grad = map(numpy.array,
+                     exe.run(feed={'x': tensor},
+                             fetch_list=[
+                                 g_main_program.global_block().var('x@GRAD')
+                             ]))[0]
+        self.assertAlmostEqual(1.0, x_grad.sum(), delta=0.1)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+import paddle.v2 as paddle
+import paddle.v2.framework.layers as layers
+import paddle.v2.framework.core as core
+import paddle.v2.framework.optimizer as optimizer
+
+from paddle.v2.framework.framework import g_main_program, g_startup_program
+from paddle.v2.framework.executor import Executor
+
+import numpy as np
+
+
+def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
+    data = layers.data(
+        name="words",
+        shape=[seq_len * batch_size, 1],
+        append_batch_size=False,
+        data_type="int64")
+    label = layers.data(
+        name="label",
+        shape=[batch_size, 1],
+        append_batch_size=False,
+        data_type="int64")
+
+    emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
+    emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
+    emb = layers.transpose(x=emb, axis=[1, 0, 2])
+
+    c_pre_init = layers.fill_constant(
+        dtype=emb.data_type, shape=[batch_size, emb_dim], value=0.0)
+    layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
+    layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
+
+    prediction = layers.fc(input=layer_1_out, size=class_dim, act="softmax")
+    cost = layers.cross_entropy(input=prediction, label=label)
+
+    avg_cost = layers.mean(x=cost)
+    adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
+    opts = adam_optimizer.minimize(avg_cost)
+    acc = layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = core.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+def chop_data(data, chop_len=80, batch_len=50):
+    data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
+
+    return data[:batch_len]
+
+
+def prepare_feed_data(data, place):
+    tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
+
+    label = np.array(map(lambda x: x[1], data)).astype("int64")
+    label = label.reshape([50, 1])
+    tensor_label = core.LoDTensor()
+    tensor_label.set(label, place)
+
+    return tensor_words, tensor_label
+
+
+def main():
+    word_dict = paddle.dataset.imdb.word_dict()
+    cost, acc = lstm_net(dict_dim=len(word_dict), class_dim=2)
+
+    batch_size = 100
+    train_data = paddle.batch(
+        paddle.reader.buffered(
+            paddle.dataset.imdb.train(word_dict), size=batch_size * 10),
+        batch_size=batch_size)
+
+    data = chop_data(next(train_data()))
+
+    place = core.CPUPlace()
+    tensor_words, tensor_label = prepare_feed_data(data, place)
+    exe = Executor(place)
+    exe.run(g_startup_program)
+
+    while True:
+        outs = exe.run(g_main_program,
+                       feed={"words": tensor_words,
+                             "label": tensor_label},
+                       fetch_list=[cost, acc])
+        cost_val = np.array(outs[0])
+        acc_val = np.array(outs[1])
+
+        print("cost=" + str(cost_val) + " acc=" + str(acc_val))
+        if acc_val > 0.9:
+            break
+
+
+if __name__ == '__main__':
+    main()

python/paddle/v2/framework/tests/test_word2vec.py

@@ -118,6 +118,10 @@ train_reader = paddle.batch(
 place = core.CPUPlace()
 exe = Executor(place)
 
+# fix https://github.com/PaddlePaddle/Paddle/issues/5434 then remove
+# below exit line.
+exit(0)
+
 exe.run(startup_program, feed={}, fetch_list=[])
 PASS_NUM = 100
 for pass_id in range(PASS_NUM):

python/paddle/v2/image.py

-import numpy as np
-try:
-    import cv2
-except ImportError:
-    cv2 = None
-import os
-import tarfile
-import cPickle
-
-__all__ = [
-    "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
-    "random_crop", "left_right_flip", "simple_transform", "load_and_transform",
-    "batch_images_from_tar"
-]
 """
 This file contains some common interfaces for image preprocess.
 Many users are confused about the image layout. We introduce
 the image layout as follows.
 
 - CHW Layout
+
   - The abbreviations: C=channel, H=Height, W=Width
   - The default layout of image opened by cv2 or PIL is HWC.
     PaddlePaddle only supports the CHW layout. And CHW is simply
     a transpose of HWC. It must transpose the input image.
 
 - Color format: RGB or BGR
+
   OpenCV use BGR color format. PIL use RGB color format. Both
   formats can be used for training. Noted that, the format should
   be keep consistent between the training and inference peroid.
 """
+import numpy as np
+try:
+    import cv2
+except ImportError:
+    cv2 = None
+import os
+import tarfile
+import cPickle
+
+__all__ = [
+    "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
+    "random_crop", "left_right_flip", "simple_transform", "load_and_transform",
+    "batch_images_from_tar"
+]
 
 
 def batch_images_from_tar(data_file,
@@ -36,17 +38,18 @@ def batch_images_from_tar(data_file,
                           num_per_batch=1024):
     """
     Read images from tar file and batch them into batch file.
-    param data_file: path of image tar file
-    type data_file: string
-    param dataset_name: 'train','test' or 'valid'
-    type dataset_name: string
-    param img2label: a dic with image file name as key 
+
+    :param data_file: path of image tar file
+    :type data_file: string
+    :param dataset_name: 'train','test' or 'valid'
+    :type dataset_name: string
+    :param img2label: a dic with image file name as key 
                     and image's label as value
-    type img2label: dic
-    param num_per_batch: image number per batch file
-    type num_per_batch: int
-    return: path of list file containing paths of batch file
-    rtype: string
+    :type img2label: dic
+    :param num_per_batch: image number per batch file
+    :type num_per_batch: int
+    :return: path of list file containing paths of batch file
+    :rtype: string
     """
     batch_dir = data_file + "_batch"
     out_path = "%s/%s" % (batch_dir, dataset_name)
@@ -99,14 +102,16 @@ def load_image_bytes(bytes, is_color=True):
     Example usage:
     
     .. code-block:: python
+
         with open('cat.jpg') as f:
             im = load_image_bytes(f.read())
 
     :param bytes: the input image bytes array.
-    :type file: str
+    :type bytes: str
     :param is_color: If set is_color True, it will load and
                      return a color image. Otherwise, it will
                      load and return a gray image.
+    :type is_color: bool
     """
     flag = 1 if is_color else 0
     file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
@@ -121,6 +126,7 @@ def load_image(file, is_color=True):
     Example usage:
     
     .. code-block:: python
+
         im = load_image('cat.jpg')
 
     :param file: the input image path.
@@ -128,6 +134,7 @@ def load_image(file, is_color=True):
     :param is_color: If set is_color True, it will load and
                      return a color image. Otherwise, it will
                      load and return a gray image.
+    :type is_color: bool
     """
     # cv2.IMAGE_COLOR for OpenCV3
     # cv2.CV_LOAD_IMAGE_COLOR for older OpenCV Version
@@ -147,6 +154,7 @@ def resize_short(im, size):
     Example usage:
     
     .. code-block:: python
+
         im = load_image('cat.jpg')
         im = resize_short(im, 256)
     
@@ -175,6 +183,7 @@ def to_chw(im, order=(2, 0, 1)):
     Example usage:
     
     .. code-block:: python
+
         im = load_image('cat.jpg')
         im = resize_short(im, 256)
         im = to_chw(im)
@@ -196,6 +205,7 @@ def center_crop(im, size, is_color=True):
     Example usage:
     
     .. code-block:: python
+
         im = center_crop(im, 224)
     
     :param im: the input image with HWC layout.
@@ -223,6 +233,7 @@ def random_crop(im, size, is_color=True):
     Example usage:
     
     .. code-block:: python
+
         im = random_crop(im, 224)
     
     :param im: the input image with HWC layout.
@@ -251,6 +262,7 @@ def left_right_flip(im):
     Example usage:
     
     .. code-block:: python
+
         im = left_right_flip(im)
     
     :paam im: input image with HWC layout
@@ -275,6 +287,7 @@ def simple_transform(im,
     Example usage:
     
     .. code-block:: python
+
         im = simple_transform(im, 256, 224, True)
 
     :param im: The input image with HWC layout.
@@ -285,6 +298,11 @@ def simple_transform(im,
     :type crop_size: int
     :param is_train: Whether it is training or not.
     :type is_train: bool
+    :param is_color: whether the image is color or not.
+    :type is_color: bool
+    :param mean: the mean values, which can be element-wise mean values or 
+                 mean values per channel.
+    :type mean: numpy array | list
     """
     im = resize_short(im, resize_size)
     if is_train:
@@ -324,6 +342,7 @@ def load_and_transform(filename,
     Example usage:
     
     .. code-block:: python
+
         im = load_and_transform('cat.jpg', 256, 224, True)
 
     :param filename: The file name of input image.
@@ -334,6 +353,11 @@ def load_and_transform(filename,
     :type crop_size: int
     :param is_train: Whether it is training or not.
     :type is_train: bool
+    :param is_color: whether the image is color or not.
+    :type is_color: bool
+    :param mean: the mean values, which can be element-wise mean values or 
+                 mean values per channel.
+    :type mean: numpy array | list
     """
     im = load_image(filename)
     im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)