Resolve conflicts.

doc/api/v2/fluid/layers.rst

@@ -18,6 +18,11 @@ dynamic_lstm
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
     :noindex:
 
+dynamic_gru
+-----------
+..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
+    :noindex:
+
 data
 ----
 ..  autofunction:: paddle.v2.fluid.layers.data
@@ -500,6 +505,11 @@ swish
 ..  autofunction:: paddle.v2.fluid.layers.swish
     :noindex:
 
+im2sequence
+------
+..  autofunction:: paddle.v2.fluid.layers.im2sequence
+    :noindex:
+
 edit_distance
 ---------------
 ..  autofunction:: paddle.v2.fluid.layers.edit_distance_error

doc/design/csp.md

+# Design Doc: CSP in PaddlePaddle Fluid
+
+## Motivation
+
+Concurrent programming is important for deep learning.  Few example applications are:
+
+1.  The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.
+2.  The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.
+
+Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn't have the concept of a graph at all, as the design goal of Fluid is that of a programming language.
+
+## Concurrent Programming Models
+
+There were many concurrent programming models, implemented in various forms:
+
+| concurrent programming model | implementation |
+|-----|-----|
+| mutex | types and functions in standard libraries |
+| semaphore | types and functions in standard libraries |
+| communicating sequential processes (CSP) | Go programming language |
+| actor model | Erlang programming language |
+| message passing | MPI |
+| bulk synchronous parallel (BSP) | Pregel distributed programming framework |
+
+Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.
+
+### CSP v.s. Actor Model
+
+A well-known implementation of Actor Model is the Erlang programming language.  In Actor Model, *processes* could send messages to another process and receive messages from another process given the process IDs.  We can find the three ingredients, process with ID, send, and recv, in MPI too.  Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code.  Our concern with Actor Model is that it doesn't seem reasonable to implement process management in a programming language's runtime library; instead, it should be the operating systems' responsibility to manage processes and libraries like MPI for send/recv.
+
+## CSP in Fluid
+
+Fluid has two fundamental control-flows: *if-else* and *while*.  If we are to implement CSP, we need the following:
+
+1. a new data type: *channel* and operators *send* and *recv*,
+1. *goroutine* or thread, and
+1. a new control-flow: select.
+
+We also need Python wrappers for the above components.
+
+The type *channel* is conceptually the blocking queue.  In Go, its implemented is a [blocking circular queue](https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50), which supports send and recv.
+
+The `select` operation has been in OS kernels long before Go language.  All Unix kernels implement system calls *poll* and *select*.  They monitor multiple file descriptors to see if I/O is possible on any of them.  This takes O(N) time.  Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time.  In BSD systems, there is a similar system call *kqueue*.  Go's Linux implementation uses epoll.
+
+It might be a good idea to implement Fluid's select using epoll too.  In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
+
+### Type Channel
+
+Fluid supports many data types:
+
+1. Tensor,
+1. Row-sparse Tensor
+1. LoD Tensor,
+1. Tensor array, etc
+
+Each data type is registered in the [`framework.proto`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127) as an enum value.  To add a new type channel, we need to add a new type enum.
+
+To expose a C++ type to Python, we need to edit the [`pybind.cc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) file.  [Here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164) is an example how we expose C++ class LoDTensor.
+
+## Syntax Design
+
+### Create Channel
+
+In Go, we create a channel by specifying the element type and buffer size:
+
+```go
+ch  := make(chan int)       // a channel without buffer
+ch1 := make(chan int, 100)  // a channel that can buffer 100 ints.
+```
+
+In Fluid, we should be able to do the same:
+
+```python
+ch  = fluid.make_chan(dtype=INT)
+ch1 = fluid.make_chan(dtype=INT, 100)
+```
+
+In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
+
+```python
+ch = fluid.make_chan(dtype=Tensor, etype=float16)
+```
+
+or Tensors of Tensors of float16 etc.
+
+The point here is that we need a consistent way to compose types, like in C++ we can have `Tensor<Tensor<...<float16>...> >`.
+
+### Send and Recv
+
+### Select
+
+## Example Programs
+
+### 1. RPC between Trainers and Parameter Servers
+
+### 2. Concurrent Minibatch Loading

doc/design/dist_refactor/parameter_server.md

@@ -9,16 +9,16 @@ different purposes.
 
 ## Background
 
-The previous implementations of the parameter server does not run a
+The previous implementations of the parameter server do not run a
 fluid sub-program. Parameter initialization, optimizer computation, network
 communication and checkpointing are implemented twice on both the
-trainer and the parameter server.
+trainer as well as the parameter server.
 
-It would be great if we can write code once and use them on both the
-trainer and the parameter server: reduces code duplication and
-improves extensibility. Given that after the current refactor, we are
-representing everything as a computing graph on the
-trainer. Representing everything as a computing graph on the parameter
+It would be great if we can write code once and use them on both: the
+trainer and the parameter server, since this reduces code duplication and
+improves extensibility. Given that after the current refactoring, we are
+representing everything as a computation graph on the
+trainer. Representing everything as a computation graph on the parameter
 server becomes a natural extension.
 
 ## Design
@@ -30,9 +30,9 @@ into sub-programs to be scheduled on different nodes with the following
 steps:
 
 1. OP placement: the OPs will be placed on different nodes according
-   to heuristic that minimizes estimated total computation
+   to a heuristic that minimizes the estimated total computation
    time. Currently we will use a simple heuristic that puts parameter
-   varable on parameter server workers and everything else on trainer
+   variable on parameter server workers and everything else on trainer
    workers.
 1. Add communication OPs to enable the communication between nodes.
 
@@ -47,22 +47,22 @@ After converting:
 
 <img src="src/dist-graph.png" width="700"/>
 
-1. The parameter variable W and it's optimizer program are placed on the parameter server.
+1. The parameter variable W and its optimizer program are placed on the parameter server.
 1. Operators are added to the program.
    - *Send* sends data to the connected *Recv* operator.  The
 	 scheduler on the receive node will only schedule *Recv* operator
 	 to run when the *Send* operator has ran (the *Send* OP will mark
 	 the *Recv* OP runnable automatically).
-   - *Enueue* enqueues the input variable, it can block until space
+   - *Enqueue* enqueues the input variable, it can block until space
      become available in the queue.
    - *Dequeue* outputs configurable numbers of tensors from the
-     queue. It will block until the queue have the required number of
+     queue. It will block until the queue has the required number of
      tensors.
 
 
 ### Benefits
 
-- Model parallelism become easier to implement: it's an extension to
+- Model parallelism becomes easier to implement: it is an extension to
   the trainer - parameter server approach. We can have several "Transpilers"
   to achieve different goals.
 - User-defined optimizer is easier to add - user can now express it as
@@ -72,22 +72,22 @@ After converting:
 
 ### Challenges
 
-- It's important to balance the parameter shards of on multiple
-  parameter server. If a single parameter is very big (some
+- It is important to balance the parameter shards on multiple
+  parameter servers. If a single parameter is very big (for example: some
   word-embedding, fully connected, softmax layer), we need to
   automatically partition the single parameter onto different
   parameter servers when possible (only element-wise optimizer depends
   on the parameter variable).
-- In the "Aync SGD" figure, the "W" variable on the parameter server
-  could be read and wrote concurrently. See
+- In the "Async SGD" figure, the "W" variable on the parameter server
+  could be read and written concurrently. See
   [here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more
-  details about concurrent program in fluid.
+  details about concurrent program in Fluid.
 
 ### Discussion
 
 - Can the Enqueue OP be implemented under our current tensor design
-  (puts the input tensor into the queue tensor)?
-- *Dequeue* OP will have variable numbers of output (depends on the
+  (put the input tensor into the queue tensor)?
+- *Dequeue* OP will have variable numbers of output (depending on the
   `min_count` attribute), does our current design support it? (similar
   question for the *Add* OP)
 

doc/design/ops/sequence_decoder.md

@@ -22,7 +22,7 @@ The current `LoDTensor` is designed to store levels of variable-length sequences
 The integers in each level represent the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
 let's call this format the **absolute-offset LoD** for clarity.
 
-The relative-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
+The absolute-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
 ```python
 [[0, 3, 9]
  [0, 2, 3, 3, 3, 9]]
@@ -119,7 +119,7 @@ def generate():
         encoder_ctx_expanded = pd.lod_expand(encoder_ctx, target_word)
         decoder_input = pd.fc(
             act=pd.activation.Linear(),
-            input=[target_word, encoder_ctx],
+            input=[target_word, encoder_ctx_expanded],
             size=3 * decoder_dim)
         gru_out, cur_mem = pd.gru_step(
             decoder_input, mem=decoder_mem, size=decoder_dim)

doc/getstarted/build_and_install/docker_install_cn.rst

@@ -25,14 +25,14 @@
 
   .. code-block:: bash
 
-     docker pull docker.paddlepaddle.org/paddle
+     docker pull docker.paddlepaddlehub.com/paddle
 
 下载GPU版本（cuda8.0_cudnn5_avx_mkl）的Docker镜像：
 
   .. code-block:: bash
 
      docker pull paddlepaddle/paddle:latest-gpu
-     docker pull docker.paddlepaddle.org/paddle:latest-gpu
+     docker pull docker.paddlepaddlehub.com/paddle:latest-gpu
 
 选择下载使用不同的BLAS库的Docker镜像：
 
@@ -49,7 +49,7 @@
 
      docker pull paddlepaddle/paddle:[tag]
      # 比如：
-     docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
+     docker pull docker.paddlepaddlehub.com/paddle:0.11.0-gpu
 
 .. _docker_run:
 

doc/getstarted/build_and_install/docker_install_en.rst

@@ -26,14 +26,14 @@ For users in China, we provide a faster mirror:
 
   .. code-block:: bash
 
-     docker pull docker.paddlepaddle.org/paddle
+     docker pull docker.paddlepaddlehub.com/paddle
 
 Download GPU version (cuda8.0_cudnn5_avx_mkl) images:
 
   .. code-block:: bash
 
      docker pull paddlepaddle/paddle:latest-gpu
-     docker pull docker.paddlepaddle.org/paddle:latest-gpu
+     docker pull docker.paddlepaddlehub.com/paddle:latest-gpu
 
 Choose between different BLAS version:
 
@@ -53,7 +53,7 @@ and run:
 
      docker pull paddlepaddle/paddle:[tag]
      # i.e.
-     docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
+     docker pull docker.paddlepaddlehub.com/paddle:0.11.0-gpu
 
 .. _docker_run:
 

doc/howto/optimization/cpu_profiling.md

@@ -60,8 +60,7 @@ each column is as follows:
 | column | meaning |
 | --- | --- |
 | ncalls | the number of calls into a function |
-| tottime | the total execution time of the function, not including the
- execution time of other functions called by the function |
+| tottime | the total execution time of the function, not including the execution time of other functions called by the function |
 | percall | tottime divided by ncalls |
 | cumtime | the total execution time of the function, including the execution time of other functions being called |
 | percall | cumtime divided by ncalls |

paddle/framework/attribute.cc

@@ -61,6 +61,9 @@ Attribute GetAttrValue(const proto::OpDesc::Attr& attr_desc) {
       }
       return val;
     }
+    case proto::AttrType::LONG: {
+      return attr_desc.l();
+    }
     default:
       PADDLE_THROW("Unsupport attr type %d", attr_desc.type());
   }

paddle/framework/attribute.h

@@ -168,6 +168,32 @@ struct ExtractAttribute<bool> {
   const std::string& attr_name_;
 };
 
+template <>
+struct ExtractAttribute<int64_t> {
+  explicit ExtractAttribute(const std::string& attr_name)
+      : attr_name_(attr_name) {}
+
+  int64_t* operator()(Attribute& attr) const {
+    if (attr.type() == typeid(int)) {  // NOLINT
+      int val = boost::get<int>(attr);
+      attr = static_cast<int64_t>(val);
+    } else if (attr.type() == typeid(float)) {  // NOLINT
+      int val = boost::get<float>(attr);
+      attr = static_cast<int64_t>(val);
+    }
+    int64_t* attr_value = nullptr;
+    try {
+      attr_value = &boost::get<int64_t>(attr);
+    } catch (boost::bad_get& bad_get) {
+      PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
+                   attr_name_, attr.type().name());
+    }
+    return attr_value;
+  }
+
+  const std::string& attr_name_;
+};
+
 // check whether a certain attribute fit its limits
 // an attribute can have more than one limits
 template <typename T>

paddle/framework/block_desc.cc

@@ -75,7 +75,7 @@ std::vector<VarDesc *> BlockDesc::AllVars() const {
 
 OpDesc *BlockDesc::AppendOp() {
   need_update_ = true;
-  ops_.emplace_back(new OpDesc());
+  ops_.emplace_back(new OpDesc(this));
   return ops_.back().get();
 }
 
@@ -86,7 +86,7 @@ void BlockDesc::AppendAllocatedOp(std::unique_ptr<OpDesc> &&op_desc) {
 
 OpDesc *BlockDesc::PrependOp() {
   need_update_ = true;
-  ops_.emplace_front(new OpDesc());
+  ops_.emplace_front(new OpDesc(this));
   return ops_.front().get();
 }
 
@@ -153,7 +153,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
     vars_[var_desc.name()].reset(new VarDesc(var_desc));
   }
   for (const proto::OpDesc &op_desc : desc_->ops()) {
-    ops_.emplace_back(new OpDesc(op_desc, prog));
+    ops_.emplace_back(new OpDesc(op_desc, prog, this));
   }
 }
 
@@ -162,7 +162,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
     : prog_(prog), desc_(desc) {
   need_update_ = true;
   for (auto &op : other.ops_) {
-    ops_.emplace_back(new OpDesc(*op));
+    ops_.emplace_back(new OpDesc(*op, this));
   }
 
   for (auto &it : other.vars_) {

paddle/framework/executor.cc

@@ -117,12 +117,13 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
 
   for (auto& op_desc : block.AllOps()) {
     auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
-    VLOG(3) << op->DebugStringEx(local_scope);
+    VLOG(4) << op->DebugStringEx(local_scope);
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     platform::RecordEvent record_event(op->Type(), pool.Get(place_));
 
     op->Run(*local_scope, place_);
+    VLOG(3) << op->DebugStringEx(local_scope);
     if (FLAGS_do_memory_benchmark) {
       VLOG(2) << "Memory used after operator " + op->Type() + " running: "
               << memory::memory_usage(place_);

paddle/framework/framework.proto

@@ -26,6 +26,7 @@ enum AttrType {
   BOOLEAN = 6;
   BOOLEANS = 7;
   BLOCK = 8;
+  LONG = 9;
 }
 
 // OpDesc describes an instance of a C++ framework::OperatorBase
@@ -44,6 +45,7 @@ message OpDesc {
     optional bool b = 10;
     repeated bool bools = 11;
     optional int32 block_idx = 12;
+    optional int64 l = 13;
   };
 
   message Var {

paddle/framework/lod_tensor.cc

@@ -107,9 +107,10 @@ LoD ToAbsOffset(const LoD &in) {
   // the lowest level stores relative offsets
   if (in.empty() || in.size() == 1) return in;
   LoD result = in;
-  for (int level = result.size() - 2; level >= 0; level--) {
-    for (auto &ele : result[level]) {
-      ele = result[level + 1][ele];
+  for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
+    for (size_t i = 0; i < in[level].size(); ++i) {
+      size_t index = in[level][i];
+      result[level][i] = result[level + 1][index];
     }
   }
   return result;

paddle/framework/op_desc.cc

@@ -97,7 +97,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
   need_update_ = true;
 }
 
-OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
+OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
     : desc_(desc), need_update_(false) {
   // restore inputs_
   int input_size = desc_.inputs_size();
@@ -131,6 +131,7 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog)
       attrs_[attr_name] = prog->MutableBlock(bid);
     }
   }
+  this->block_ = block;
 }
 
 proto::OpDesc *OpDesc::Proto() {
@@ -282,6 +283,7 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
     VectorToRepeated(v, attr_->mutable_bools());
   }
   void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
+  void operator()(int64_t v) const { attr_->set_l(v); }
   void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
 };
 

paddle/framework/op_desc.h

@@ -25,7 +25,6 @@ namespace framework {
 
 class BlockDesc;
 class ProgramDesc;
-
 class OpDesc {
  public:
   OpDesc() {}
@@ -33,7 +32,14 @@ class OpDesc {
   OpDesc(const std::string &type, const VariableNameMap &inputs,
          const VariableNameMap &outputs, const AttributeMap &attrs);
 
-  OpDesc(const proto::OpDesc &desc, ProgramDesc *prog);
+  OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
+
+  explicit OpDesc(BlockDesc *block) : block_(block) {}
+
+  OpDesc(const OpDesc &other, BlockDesc *block) {
+    *this = other;
+    block_ = block;
+  }
 
   void CopyFrom(const OpDesc &op_desc);
 
@@ -117,6 +123,10 @@ class OpDesc {
 
   void Flush();
 
+  BlockDesc *Block() { return this->block_; }
+
+  void SetBlock(BlockDesc *block) { this->block_ = block; }
+
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
@@ -129,6 +139,7 @@ class OpDesc {
   }
 
   proto::OpDesc desc_;
+  BlockDesc *block_;  // not_own
   // input arg name => input variable names
   VariableNameMap inputs_;
   // output arg name => output variable names

paddle/framework/type_defs.h

@@ -35,7 +35,7 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
 using Attribute =
     boost::variant<boost::blank, int, float, std::string, std::vector<int>,
                    std::vector<float>, std::vector<std::string>, bool,
-                   std::vector<bool>, BlockDesc*>;
+                   std::vector<bool>, BlockDesc*, int64_t>;
 
 using AttributeMap = std::unordered_map<std::string, Attribute>;
 

paddle/framework/var_desc.h

@@ -66,6 +66,8 @@ class VarDesc {
 
   std::string Name() const { return desc_.name(); }
 
+  void SetName(std::string name) { desc_.set_name(name); }
+
   void SetShape(const std::vector<int64_t> &dims);
 
   void SetDataType(proto::DataType data_type);

paddle/framework/variable_test.cc

@@ -12,19 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-/*
-  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 <memory>
 #include <string>
 

paddle/operators/beam_search_op.cc

@@ -24,8 +24,18 @@ namespace operators {
 void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
                             framework::LoDTensor *selected_ids,
                             framework::LoDTensor *selected_scores) {
+  auto abs_lod = framework::ToAbsOffset(ids_->lod());
+  auto &high_level = abs_lod[lod_level_];
+
   auto items = SelectTopBeamSizeItems();
-  auto selected_items = ToMap(items);
+  auto selected_items = ToMap(items, high_level.back());
+  VLOG(3) << "selected_items:";
+  for (size_t i = 0; i < selected_items.size(); ++i) {
+    VLOG(3) << "offset:" << i;
+    for (auto &item : selected_items[i]) {
+      VLOG(3) << ItemToString(item);
+    }
+  }
   PruneEndidCandidates(pre_ids, &selected_items);
   // calculate the output tensor's height
   size_t num_instances = std::accumulate(
@@ -63,11 +73,12 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
   low_level.push_back(low_offset);
 
   // fill lod
-  auto abs_lod = framework::ToAbsOffset(ids_->lod());
-  auto &high_level = abs_lod[lod_level_];
   framework::LoD lod(2);
   lod[0].assign(high_level.begin(), high_level.end());
   lod[1].assign(low_level.begin(), low_level.end());
+  if (!framework::CheckLoD(lod)) {
+    PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
+  }
   selected_ids->set_lod(lod);
   selected_scores->set_lod(lod);
 }
@@ -90,13 +101,11 @@ int BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
 }
 
 std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
-    const std::vector<std::vector<Item>> &items) {
+    const std::vector<std::vector<Item>> &items, size_t element_num) {
   std::vector<std::vector<Item>> result;
+  result.resize(element_num);
   for (auto &entries : items) {
     for (const auto &item : entries) {
-      if (item.offset >= result.size()) {
-        result.resize(item.offset + 1);
-      }
       result[item.offset].push_back(item);
     }
   }
@@ -122,6 +131,14 @@ BeamSearch::SelectTopBeamSizeItems() {
     }
     result.emplace_back(items);
   }
+  VLOG(3) << "SelectTopBeamSizeItems result size " << result.size();
+  for (auto &items : result) {
+    VLOG(3) << "item set:";
+    for (auto &item : items) {
+      VLOG(3) << ItemToString(item);
+    }
+  }
+
   return result;
 }
 
@@ -159,6 +176,22 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
   return true;
 }
 
+std::ostream &operator<<(std::ostream &os, const BeamSearch::Item &item) {
+  os << "{";
+  os << "offset: " << item.offset << ", ";
+  os << "id: " << item.id << ", ";
+  os << "score: " << item.score << "";
+  os << "}";
+
+  return os;
+}
+
+std::string ItemToString(const BeamSearch::Item &item) {
+  std::ostringstream stream;
+  stream << item;
+  return stream.str();
+}
+
 class BeamSearchProtoAndCheckerMaker
     : public framework::OpProtoAndCheckerMaker {
  public:
@@ -186,8 +219,40 @@ class BeamSearchProtoAndCheckerMaker
   }
 };
 
+class BeamSearchInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    for (const std::string &arg :
+         std::vector<std::string>({"pre_ids", "ids", "scores"})) {
+      PADDLE_ENFORCE(context->HasInput(arg),
+                     "BeamSearch need input argument '%s'", arg);
+    }
+    for (const std::string &arg :
+         std::vector<std::string>({"selected_ids", "selected_scores"})) {
+      PADDLE_ENFORCE(context->HasOutput(arg),
+                     "BeamSearch need output argument '%s'", arg);
+    }
+  }
+};
+
+class BeamSearchInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    for (auto &o : op_desc.Output("selected_ids")) {
+      block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
+    }
+    for (auto &o : op_desc.Output("selected_scores")) {
+      block->Var(o)->SetType(framework::proto::VarDesc::LOD_TENSOR);
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_WITHOUT_GRADIENT(beam_search, paddle::operators::BeamSearchOp,
-                             paddle::operators::BeamSearchProtoAndCheckerMaker);
+REGISTER_OPERATOR(beam_search, paddle::operators::BeamSearchOp,
+                  paddle::operators::BeamSearchProtoAndCheckerMaker,
+                  paddle::operators::BeamSearchInferShape,
+                  paddle::operators::BeamSearchInferVarType,
+                  paddle::framework::EmptyGradOpMaker);

paddle/operators/beam_search_op.h

@@ -136,8 +136,6 @@ class BeamSearch {
   void operator()(const framework::LoDTensor& pre_ids,
                   framework::LoDTensor* selected_ids,
                   framework::LoDTensor* selected_scores);
-
- protected:
   /*
    * The basic items help to sort.
    */
@@ -155,6 +153,7 @@ class BeamSearch {
     score_t score;
   };
 
+ protected:
   /*
    * Delete all the records that follows the end token.
    */
@@ -166,7 +165,7 @@ class BeamSearch {
    * NOTE low performance
    */
   std::vector<std::vector<Item>> ToMap(
-      const std::vector<std::vector<Item>>& inputs);
+      const std::vector<std::vector<Item>>& inputs, size_t element_num);
 
   /*
    * For each source, select top beam_size records.
@@ -187,6 +186,10 @@ class BeamSearch {
   int end_id_{0};
 };
 
+std::ostream& operator<<(std::ostream& os, const BeamSearch::Item& item);
+
+std::string ItemToString(const BeamSearch::Item& item);
+
 class BeamSearchOp : public framework::OperatorBase {
  public:
   BeamSearchOp(const std::string& type,
@@ -203,7 +206,6 @@ class BeamSearchOp : public framework::OperatorBase {
 
   void Run(const framework::Scope& scope,
            const platform::Place& dev_place) const override {
-    LOG(INFO) << "run beam search op";
     auto ids_var = scope.FindVar(Input("ids"));
     auto scores_var = scope.FindVar(Input("scores"));
     auto pre_ids_var = scope.FindVar(Input("pre_ids"));
@@ -217,10 +219,8 @@ class BeamSearchOp : public framework::OperatorBase {
     size_t level = Attr<int>("level");
     size_t beam_size = Attr<int>("beam_size");
     int end_id = Attr<int>("end_id");
-    LOG(INFO) << "init beam search";
     BeamSearch alg(ids, scores, level, beam_size, end_id);
 
-    LOG(INFO) << "after beam search";
     auto selected_ids_var = scope.FindVar(Output("selected_ids"));
     auto selected_scores_var = scope.FindVar(Output("selected_scores"));
     PADDLE_ENFORCE_NOT_NULL(selected_ids_var);
@@ -229,9 +229,7 @@ class BeamSearchOp : public framework::OperatorBase {
         *selected_ids_var->GetMutable<framework::LoDTensor>();
     auto& selected_scores_tensor =
         *selected_scores_var->GetMutable<framework::LoDTensor>();
-    LOG(INFO) << "run beam search";
     alg(pre_ids, &selected_ids_tensor, &selected_scores_tensor);
-    LOG(INFO) << "finish beam search";
   }
 };
 

paddle/operators/bipartite_match_op.cc

@@ -21,8 +21,6 @@ namespace operators {
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
-constexpr char kEPS = 1e-6;
-
 class BipartiteMatchOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -46,6 +44,7 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
   // The match_dist must be initialized to 0 at first.
   void BipartiteMatch(const Tensor& dist, int* match_indices,
                       T* match_dist) const {
+    constexpr T kEPS = static_cast<T>(1e-6);
     PADDLE_ENFORCE_EQ(dist.dims().size(), 2, "The rank of dist must be 2.");
     int64_t row = dist.dims()[0];
     int64_t col = dist.dims()[1];

paddle/operators/iou_similarity_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/iou_similarity_op.h"
+
+namespace paddle {
+namespace operators {
+
+class IOUSimilarityOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of IOUSimilarityOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Y"),
+                   "Input(Y) of IOUSimilarityOp should not be null.");
+    auto x_dims = ctx->GetInputDim("X");
+    auto y_dims = ctx->GetInputDim("Y");
+
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "The rank of Input(X) must be 2.");
+    PADDLE_ENFORCE_EQ(x_dims[1], 4UL, "The shape of X is [N, 4]");
+    PADDLE_ENFORCE_EQ(y_dims.size(), 2UL, "The rank of Input(Y) must be 2.");
+    PADDLE_ENFORCE_EQ(y_dims[1], 4UL, "The shape of Y is [M, 4]");
+
+    ctx->ShareLoD("X", /*->*/ "Out");
+    ctx->SetOutputDim("Out", framework::make_ddim({x_dims[0], y_dims[0]}));
+  }
+};
+
+class IOUSimilarityOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  IOUSimilarityOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, "
+             "each box is represented as [xmin, ymin, xmax, ymax], "
+             "the shape of X is [N, 4]. [xmin, ymin] is the left top "
+             "coordinate of the box if the input is image feature map, they "
+             "are close to the origin of the coordinate system. "
+             "[xmax, ymax] is the right bottom coordinate of the box. "
+             "This tensor can contain LoD information to represent a batch "
+             "of inputs. One instance of this batch can contain different "
+             "numbers of entities.");
+    AddInput("Y",
+             "(Tensor, default Tensor<float>) "
+             "Box list Y holds M boxes, each box is represented as "
+             "[xmin, ymin, xmax, ymax], the shape of X is [N, 4]. "
+             "[xmin, ymin] is the left top coordinate of the box if the "
+             "input is image feature map, and [xmax, ymax] is the right "
+             "bottom coordinate of the box.");
+
+    AddOutput("Out",
+              "(LoDTensor, the lod is same as input X) The output of "
+              "iou_similarity op, a tensor with shape [N, M] "
+              "representing pairwise iou scores.");
+
+    AddComment(R"DOC(
+IOU Similarity Operator.
+Computes intersection-over-union (IOU) between two box lists.
+ Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
+ boxes in 'Y' are shared by all instance of the batched inputs of X.
+ Given two boxes A and B, the calculation of IOU is as follows:
+
+$$
+IOU(A, B) = 
+\frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)}
+$$
+
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(iou_similarity, ops::IOUSimilarityOp,
+                             ops::IOUSimilarityOpMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    iou_similarity,
+    ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::IOUSimilarityKernel<paddle::platform::CPUDeviceContext, double>);

paddle/operators/iou_similarity_op.cu

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

paddle/operators/iou_similarity_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/op_registry.h"
+#include "paddle/platform/for_range.h"
+
+template <typename T>
+inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
+                                  T ymin2, T xmax2, T ymax2) {
+  constexpr T zero = static_cast<T>(0);
+  T area1 = (ymax1 - ymin1) * (xmax1 - xmin1);
+  T area2 = (ymax2 - ymin2) * (xmax2 - xmin2);
+  T inter_xmax = xmax1 > xmax2 ? xmax2 : xmax1;
+  T inter_ymax = ymax1 > ymax2 ? ymax2 : ymax1;
+  T inter_xmin = xmin1 > xmin2 ? xmin1 : xmin2;
+  T inter_ymin = ymin1 > ymin2 ? ymin1 : ymin2;
+  T inter_height = inter_ymax - inter_ymin;
+  T inter_width = inter_xmax - inter_xmin;
+  inter_height = inter_height > zero ? inter_height : zero;
+  inter_width = inter_width > zero ? inter_width : zero;
+  T inter_area = inter_width * inter_height;
+  T union_area = area1 + area2 - inter_area;
+  T sim_score = inter_area / union_area;
+  return sim_score;
+}
+
+template <typename T>
+struct IOUSimilarityFunctor {
+  IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols)
+      : x_(x), y_(y), z_(z), cols_(static_cast<size_t>(cols)) {}
+
+  inline HOSTDEVICE void operator()(size_t row_id) const {
+    T x_min1 = x_[row_id * 4];
+    T y_min1 = x_[row_id * 4 + 1];
+    T x_max1 = x_[row_id * 4 + 2];
+    T y_max1 = x_[row_id * 4 + 3];
+    for (size_t i = 0; i < cols_; ++i) {
+      T x_min2 = y_[i * 4];
+      T y_min2 = y_[i * 4 + 1];
+      T x_max2 = y_[i * 4 + 2];
+      T y_max2 = y_[i * 4 + 3];
+
+      T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2,
+                            x_max2, y_max2);
+
+      z_[row_id * cols_ + i] = sim;
+    }
+  }
+  const T* x_;
+  const T* y_;
+  T* z_;
+  const size_t cols_;
+};
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class IOUSimilarityKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const framework::LoDTensor* in_x = ctx.Input<framework::LoDTensor>("X");
+    const framework::Tensor* in_y = ctx.Input<framework::Tensor>("Y");
+    framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
+
+    int x_n = in_x->dims()[0];
+    int y_n = in_y->dims()[0];
+    IOUSimilarityFunctor<T> functor(in_x->data<T>(), in_y->data<T>(),
+                                    out->mutable_data<T>(ctx.GetPlace()), y_n);
+
+    platform::ForRange<DeviceContext> for_range(
+        static_cast<const DeviceContext&>(ctx.device_context()), x_n);
+    for_range(functor);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/lookup_table_op.cc

@@ -66,6 +66,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(boolean, default false) "
                   "Sparse update")
         .SetDefault(false);
+    AddAttr<int64_t>("padding_idx",
+                     "(int64, default -1) "
+                     "If the value is -1, it makes no effect to lookup. "
+                     "Otherwise the given value indicates padding the output "
+                     "with zeros whenever lookup encounters it in Ids.")
+        .SetDefault(-1);
     AddComment(R"DOC(
 Lookup Table Operator.
 

paddle/operators/lookup_table_op.cu

@@ -21,9 +21,11 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
+template <typename T, int BlockDimX, int BlockDimY, int GridDimX,
+          bool PaddingFlag>
 __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
-                            const int64_t N, const int64_t K, const int64_t D) {
+                            const int64_t N, const int64_t K, const int64_t D,
+                            const int64_t padding_idx) {
   int idx = threadIdx.x;
   int idy = blockIdx.x + threadIdx.y * GridDimX;
 
@@ -34,7 +36,14 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
     T* out = output + idy * D;
     const T* tab = table + id * D;
     for (int i = idx; i < D; i += BlockDimX) {
-      out[i] = tab[i];
+      if (PaddingFlag) {
+        if (id == padding_idx)
+          out[i] = static_cast<T>(0);
+        else
+          out[i] = tab[i];
+      } else {
+        out[i] = tab[i];
+      }
     }
     idy += BlockDimY * GridDimX;
   }
@@ -67,6 +76,7 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
     auto* table_t = context.Input<LoDTensor>("W");
     auto* ids_t = context.Input<LoDTensor>("Ids");
     auto* output_t = context.Output<LoDTensor>("Out");
+    int64_t padding_idx = context.Attr<int64_t>("padding_idx");
 
     size_t N = table_t->dims()[0];
     size_t D = table_t->dims()[1];
@@ -77,10 +87,17 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
 
     dim3 threads(128, 8);
     dim3 grids(8, 1);
-    LookupTable<
-        T, 128, 8,
-        8><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
-        output, table, ids, N, K, D);
+
+    if (padding_idx == -1)
+      LookupTable<
+          T, 128, 8, 8,
+          false><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
+          output, table, ids, N, K, D, padding_idx);
+    else
+      LookupTable<
+          T, 128, 8, 8,
+          true><<<grids, threads, 0, context.cuda_device_context().stream()>>>(
+          output, table, ids, N, K, D, padding_idx);
   }
 };
 
@@ -91,6 +108,8 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
     auto& dev_ctx =
         context.template device_context<platform::CUDADeviceContext>();
     bool is_sparse = context.Attr<bool>("is_sparse");
+    // Since paddings are not trainable and fixed in forward, the gradient of
+    // paddings makes no sense and we don't deal with it in backward.
     if (is_sparse) {
       auto* ids = context.Input<LoDTensor>("Ids");
       auto* table = context.Input<LoDTensor>("W");

paddle/operators/lookup_table_op.h

@@ -32,16 +32,30 @@ class LookupTableKernel : public framework::OpKernel<T> {
     auto* table_t = context.Input<LoDTensor>("W");      // float tensor
     auto* ids_t = context.Input<LoDTensor>("Ids");      // int tensor
     auto* output_t = context.Output<LoDTensor>("Out");  // float tensor
+    int64_t padding_idx = context.Attr<int64_t>("padding_idx");
 
     int N = table_t->dims()[0];
     int D = table_t->dims()[1];
     auto* ids = ids_t->data<int64_t>();
     auto* table = table_t->data<T>();
     auto* output = output_t->mutable_data<T>(context.GetPlace());
-    for (int64_t i = 0; i < ids_t->numel(); ++i) {
-      PADDLE_ENFORCE_LT(ids[i], N);
-      PADDLE_ENFORCE_GE(ids[i], 0);
-      memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
+
+    if (padding_idx == -1) {
+      for (int64_t i = 0; i < ids_t->numel(); ++i) {
+        PADDLE_ENFORCE_LT(ids[i], N);
+        PADDLE_ENFORCE_GE(ids[i], 0);
+        memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
+      }
+    } else {
+      for (int64_t i = 0; i < ids_t->numel(); ++i) {
+        if (ids[i] == padding_idx) {
+          memset(output + i * D, 0, D * sizeof(T));
+        } else {
+          PADDLE_ENFORCE_LT(ids[i], N);
+          PADDLE_ENFORCE_GE(ids[i], 0);
+          memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
+        }
+      }
     }
   }
 };
@@ -51,6 +65,8 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     bool is_sparse = context.Attr<bool>("is_sparse");
+    // Since paddings are not trainable and fixed in forward, the gradient of
+    // paddings makes no sense and we don't deal with it in backward.
     if (is_sparse) {
       auto* ids = context.Input<LoDTensor>("Ids");
       auto* table = context.Input<LoDTensor>("W");

paddle/operators/nce_op.cc

@@ -124,7 +124,8 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
                               "This attribute only be used in unitest. Classes "
                               "in this list wiil be used as negative classes "
                               "for every samples. Under normal conditions, "
-                              "user should avoid setting this attribute.");
+                              "user should avoid setting this attribute.")
+        .SetDefault({});
     AddComment(R"DOC(
 Compute and return the noise-contrastive estimation training loss.
 See [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).

paddle/operators/nce_op.h

@@ -197,7 +197,8 @@ class NCEGradKernel : public framework::OpKernel<T> {
     // get d_x
     auto d_x = context.Output<Tensor>(framework::GradVarName("Input"));
     if (d_x != nullptr) {
-      d_x->mutable_data<T>(context.GetPlace());
+      auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      std::fill(d_x_data, d_x_data + d_x->numel(), 0.0);
       auto d_x_matrix = EigenMatrix<T>::From(*d_x);
       auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {

paddle/operators/prior_box_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/prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+class PriorBoxOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of PriorBoxOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Image"),
+                   "Input(Image) of PriorBoxOp should not be null.");
+
+    auto image_dims = ctx->GetInputDim("Image");
+    auto input_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW.");
+    PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
+
+    PADDLE_ENFORCE_LT(input_dims[2], image_dims[2],
+                      "The height of input must smaller than image.");
+
+    PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
+                      "The width of input must smaller than image.");
+
+    auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
+    auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
+    auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
+    auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
+    bool flip = ctx->Attrs().Get<bool>("flip");
+
+    PADDLE_ENFORCE_GT(min_sizes.size(), 0,
+                      "Size of min_sizes must be at least 1.");
+    for (size_t i = 0; i < min_sizes.size(); ++i) {
+      PADDLE_ENFORCE_GT(min_sizes[i], 0, "min_sizes[%d] must be positive.", i);
+    }
+
+    std::vector<float> aspect_ratios_vec;
+    ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
+
+    int num_priors = aspect_ratios_vec.size() * min_sizes.size();
+    if (max_sizes.size() > 0) {
+      PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
+                        "The number of min_size and max_size must be equal.");
+      for (size_t i = 0; i < min_sizes.size(); ++i) {
+        PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
+                          "max_size[%d] must be greater than min_size[%d].", i,
+                          i);
+        num_priors += 1;
+      }
+    }
+
+    PADDLE_ENFORCE_EQ(variances.size(), 4, "Must and only provide 4 variance.");
+    for (size_t i = 0; i < variances.size(); ++i) {
+      PADDLE_ENFORCE_GT(variances[i], 0.0,
+                        "variance[%d] must be greater than 0.", i);
+    }
+
+    const float step_h = ctx->Attrs().Get<float>("step_h");
+    PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
+    const float step_w = ctx->Attrs().Get<float>("step_w");
+    PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
+
+    std::vector<int64_t> dim_vec(4);
+    dim_vec[0] = input_dims[2];
+    dim_vec[1] = input_dims[3];
+    dim_vec[2] = num_priors;
+    dim_vec[3] = 4;
+    ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
+    ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
+  }
+};
+
+class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PriorBoxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input",
+             "(Tensor, default Tensor<float>), "
+             "the input feature data of PriorBoxOp, The layout is NCHW.");
+    AddInput("Image",
+             "(Tensor, default Tensor<float>), "
+             "the input image data of PriorBoxOp, The layout is NCHW.");
+    AddOutput("Boxes",
+              "(Tensor, default Tensor<float>), the output prior boxes of "
+              "PriorBoxOp. The layout is [H, W, num_priors, 4]. "
+              "H is the height of input, W is the width of input, num_priors "
+              "is the box count of each position.");
+    AddOutput("Variances",
+              "(Tensor, default Tensor<float>), the expanded variances of "
+              "PriorBoxOp. The layout is [H, W, num_priors, 4]. "
+              "H is the height of input, W is the width of input, num_priors "
+              "is the box count of each position.");
+    AddAttr<std::vector<int>>("min_sizes", "(vector<int>) ",
+                              "List of min sizes of generated prior boxes.");
+    AddAttr<std::vector<int>>("max_sizes", "(vector<int>) ",
+                              "List of max sizes of generated prior boxes.");
+    AddAttr<std::vector<float>>(
+        "aspect_ratios", "(vector<float>) ",
+        "List of aspect ratios of generated prior boxes.");
+    AddAttr<std::vector<float>>(
+        "variances", "(vector<float>) ",
+        "List of variances to be encoded in prior boxes.");
+    AddAttr<bool>("flip", "(bool) ", "Whether to flip aspect ratios.")
+        .SetDefault(true);
+    AddAttr<bool>("clip", "(bool) ", "Whether to clip out-of-boundary boxes.")
+        .SetDefault(true);
+    AddAttr<float>("step_w",
+                   "Prior boxes step across width, 0 for auto calculation.")
+        .SetDefault(0.0);
+    AddAttr<float>("step_h",
+                   "Prior boxes step across height, 0 for auto calculation.")
+        .SetDefault(0.0);
+    AddAttr<float>("offset",
+                   "(float) "
+                   "Prior boxes center offset.")
+        .SetDefault(0.5);
+    AddComment(R"DOC(
+Prior box operator
+Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
+Each position of the input produce N prior boxes, N is determined by
+ the count of min_sizes, max_sizes and aspect_ratios, The size of the
+ box is in range(min_size, max_size) interval, which is generated in
+ sequence according to the aspect_ratios.
+
+Please get more information from the following papers:
+https://arxiv.org/abs/1512.02325.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
+    ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);

paddle/operators/prior_box_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/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
+                               bool flip,
+                               std::vector<float>& output_aspect_ratior) {
+  constexpr float epsilon = 1e-6;
+  output_aspect_ratior.clear();
+  output_aspect_ratior.push_back(1.);
+  for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
+    float ar = input_aspect_ratior[i];
+    bool already_exist = false;
+    for (size_t j = 0; j < output_aspect_ratior.size(); ++j) {
+      if (fabs(ar - output_aspect_ratior[j]) < epsilon) {
+        already_exist = true;
+        break;
+      }
+    }
+    if (!already_exist) {
+      output_aspect_ratior.push_back(ar);
+      if (flip) {
+        output_aspect_ratior.push_back(1. / ar);
+      }
+    }
+  }
+}
+
+template <typename T>
+struct ClipFunctor {
+  HOSTDEVICE T operator()(T in) const {
+    return std::min<T>(std::max<T>(in, 0.), 1.);
+  }
+};
+
+template <typename Place, typename T>
+class PriorBoxOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* image = ctx.Input<paddle::framework::Tensor>("Image");
+    auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
+    auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
+    auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto flip = ctx.Attr<bool>("flip");
+    auto clip = ctx.Attr<bool>("clip");
+
+    std::vector<float> aspect_ratios;
+    ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
+
+    T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
+    T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto img_width = image->dims()[3];
+    auto img_height = image->dims()[2];
+
+    auto feature_width = input->dims()[3];
+    auto feature_height = input->dims()[2];
+
+    T step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<T>(img_width) / feature_width;
+      step_height = static_cast<T>(img_height) / feature_height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+
+    int num_priors = aspect_ratios.size() * min_sizes.size();
+    if (max_sizes.size() > 0) {
+      num_priors += max_sizes.size();
+    }
+
+    boxes->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
+    for (int h = 0; h < feature_height; ++h) {
+      for (int w = 0; w < feature_width; ++w) {
+        T center_x = (w + offset) * step_width;
+        T center_y = (h + offset) * step_height;
+        T box_width, box_height;
+        int idx = 0;
+        for (size_t s = 0; s < min_sizes.size(); ++s) {
+          int min_size = min_sizes[s];
+          // first prior: aspect_ratio = 1, size = min_size
+          box_width = box_height = min_size;
+          // xmin
+          e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+          // ymin
+          e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+          // xmax
+          e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+          // ymax
+          e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+
+          idx++;
+          if (max_sizes.size() > 0) {
+            int max_size = max_sizes[s];
+            // second prior: aspect_ratio = 1,
+            // size = sqrt(min_size * max_size)
+            box_width = box_height = sqrt(min_size * max_size);
+            // xmin
+            e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+            // ymin
+            e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+            // xmax
+            e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+            // ymax
+            e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+            idx++;
+          }
+
+          // rest of priors
+          for (size_t r = 0; r < aspect_ratios.size(); ++r) {
+            float ar = aspect_ratios[r];
+            if (fabs(ar - 1.) < 1e-6) {
+              continue;
+            }
+            box_width = min_size * sqrt(ar);
+            box_height = min_size / sqrt(ar);
+            // xmin
+            e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+            // ymin
+            e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+            // xmax
+            e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+            // ymax
+            e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+            idx++;
+          }
+        }
+      }
+    }
+
+    if (clip) {
+      platform::Transform<platform::CPUDeviceContext> trans;
+      ClipFunctor<T> clip_func;
+      trans(ctx.template device_context<platform::CPUDeviceContext>(),
+            boxes->data<T>(), boxes->data<T>() + boxes->numel(),
+            boxes->data<T>(), clip_func);
+    }
+
+    framework::Tensor var_t;
+    var_t.mutable_data<T>(
+        framework::make_ddim({1, static_cast<int>(variances.size())}),
+        ctx.GetPlace());
+    auto var_et = framework::EigenTensor<T, 2>::From(var_t);
+    for (size_t i = 0; i < variances.size(); ++i) {
+      var_et(0, i) = variances[i];
+    }
+
+    int box_num = feature_height * feature_width * num_priors;
+    auto var_dim = vars->dims();
+    vars->Resize({box_num, static_cast<int>(variances.size())});
+
+    auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
+    e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
+
+    vars->Resize(var_dim);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/sequence_expand_op.h

@@ -32,6 +32,7 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
     const T* x_data = x->data<T>();
     auto x_dims = x->dims();
     auto* y = context.Input<LoDTensor>("Y");
+    PADDLE_ENFORCE(!y->lod().empty(), "y should have lod");
     PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims[0]),
                       y->lod().back().size() - 1,
                       "The size of last lod level in Input(Y)"

paddle/operators/top_k_op.h

@@ -22,6 +22,7 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
 
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
@@ -33,9 +34,9 @@ class TopkKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     // Get the top k elements of each row of input tensor
     // FIXME: only deal with matrix(2d tensor).
-    auto* input = ctx.Input<Tensor>("X");
-    auto* output = ctx.Output<Tensor>("Out");
-    auto* indices = ctx.Output<Tensor>("Indices");
+    auto* input = ctx.Input<LoDTensor>("X");
+    auto* output = ctx.Output<LoDTensor>("Out");
+    auto* indices = ctx.Output<LoDTensor>("Indices");
     // k is determined by Attr
     const size_t k = static_cast<int>(ctx.Attr<int>("k"));
 

paddle/pybind/print_operators_doc.cc

@@ -64,6 +64,8 @@ std::string AttrType(paddle::framework::proto::AttrType at) {
       return "bool array";
     case paddle::framework::proto::BLOCK:
       return "block id";
+    case paddle::framework::proto::LONG:
+      return "long";
   }
   return "UNKNOWN";  // not possible
 }

paddle/pybind/protobuf.cc

@@ -212,6 +212,7 @@ void BindVarDsec(py::module &m) {
              return name;
            },
            py::return_value_policy::reference)
+      .def("set_name", &VarDesc::SetName)
       .def("set_shape", &VarDesc::SetShape)
       .def("set_dtype", &VarDesc::SetDataType)
       .def("shape", &VarDesc::Shape, py::return_value_policy::reference)
@@ -280,7 +281,8 @@ void BindOpDesc(py::module &m) {
       .def("check_attrs", &OpDesc::CheckAttrs)
       .def("infer_shape", &OpDesc::InferShape)
       .def("infer_var_type", &OpDesc::InferVarType)
-      .def("serialize_to_string", SerializeMessage<OpDesc>);
+      .def("serialize_to_string", SerializeMessage<OpDesc>)
+      .def("block", &OpDesc::Block, py::return_value_policy::reference);
 }
 
 }  // namespace pybind

python/paddle/v2/dataset/wmt16.py

@@ -305,9 +305,9 @@ def get_dict(lang, dict_size, reverse=False):
 
     dict_path = os.path.join(paddle.v2.dataset.common.DATA_HOME,
                              "wmt16/%s_%d.dict" % (lang, dict_size))
-    assert (os.path.exists(dict_path), "Word dictionary does not exist. "
-            "Please invoke paddle.dataset.wmt16.train/test/validation "
-            "first to build the dictionary.")
+    assert os.path.exists(dict_path), "Word dictionary does not exist. "
+    "Please invoke paddle.dataset.wmt16.train/test/validation first "
+    "to build the dictionary."
     tar_file = os.path.join(paddle.v2.dataset.common.DATA_HOME, "wmt16.tar.gz")
     return __load_dict(tar_file, dict_size, lang, reverse)
 

python/paddle/v2/fluid/layer_helper.py

@@ -100,7 +100,8 @@ class LayerHelper(object):
             if dtype is None:
                 dtype = each.dtype
             elif dtype != each.dtype:
-                raise ValueError("Data Type mismatch")
+                raise ValueError("Data Type mismatch: %d to %d" %
+                                 (dtype, each.dtype))
         return dtype
 
     def create_parameter(self,

python/paddle/v2/fluid/layers/control_flow.py

@@ -769,7 +769,7 @@ def topk(input, k):
           array = fluid.layers.topk(x, k)
     """
     helper = LayerHelper('topk', **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.data_type)
+    topk_out = helper.create_tmp_variable(dtype=input.dtype)
     topk_indices = helper.create_tmp_variable(dtype='int64')
     helper.append_op(
         type='top_k',

python/paddle/v2/fluid/layers/nn.py

@@ -19,12 +19,14 @@ from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
 from ..param_attr import ParamAttr
+from layer_function_generator import autodoc
 from tensor import concat
 
 __all__ = [
     'fc',
     'embedding',
     'dynamic_lstm',
+    'dynamic_gru',
     'gru_unit',
     'linear_chain_crf',
     'crf_decoding',
@@ -57,6 +59,9 @@ __all__ = [
     'warpctc',
     'sequence_reshape',
     'transpose',
+    'im2sequence',
+    'nce',
+    'beam_search',
 ]
 
 
@@ -159,10 +164,8 @@ def fc(input,
         tmp = helper.create_tmp_variable(dtype)
         helper.append_op(
             type="mul",
-            inputs={
-                "X": input_var,
-                "Y": w,
-            },
+            inputs={"X": input_var,
+                    "Y": w},
             outputs={"Out": tmp},
             attrs={"x_num_col_dims": num_flatten_dims,
                    "y_num_col_dims": 1})
@@ -181,22 +184,35 @@ def fc(input,
     return helper.append_activation(pre_activation)
 
 
-def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
+def embedding(input,
+              size,
+              is_sparse=False,
+              padding_idx=None,
+              param_attr=None,
+              dtype='float32'):
     """
     **Embedding Layer**
 
-    This layer is used to lookup a vector of IDs, provided by *input*, in a lookup table.
-    The result of this lookup is the embedding of each ID in the *input*.
+    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in 
+    a lookup table. The result of this lookup is the embedding of each ID in the
+    :attr:`input`.
 
     All the input variables are passed in as local variables to the LayerHelper
     constructor.
 
     Args:
-       input(Variable): Input to the function
-       size(tuple|list|None): Shape of the look up table parameter
-       is_sparse(bool): Boolean flag that specifying whether the input is sparse
-       param_attr(ParamAttr): Parameters for this layer
-       dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
+        input(Variable): The tensor variable containing the IDs.
+        size(tuple|list): The shape of the look up table parameter. It should
+            have two elements which indicate the size of the dictionary of
+            embeddings and the size of each embedding vector respectively.
+        is_sparse(bool): The flag indicating whether to use sparse update.
+        padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
+            Otherwise the given :attr:`padding_idx` indicates padding the output
+            with zeros whenever lookup encounters it in :attr:`input`. If 
+            :math:`padding_idx < 0`, the padding_idx to use in lookup is 
+            :math:`size[0] + dim`.
+        param_attr(ParamAttr): Parameters for this layer
+        dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
 
     Returns:
         Variable: The tensor variable storing the embeddings of the \
@@ -214,12 +230,15 @@ def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
     w = helper.create_parameter(
         attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
     tmp = helper.create_tmp_variable(dtype)
+    padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
+        size[0] + padding_idx)
     helper.append_op(
         type='lookup_table',
         inputs={'Ids': input,
                 'W': w},
         outputs={'Out': tmp},
-        attrs={'is_sparse': is_sparse})
+        attrs={'is_sparse': is_sparse,
+               'padding_idx': padding_idx})
     return tmp
 
 
@@ -366,6 +385,113 @@ def dynamic_lstm(input,
     return hidden, cell
 
 
+def dynamic_gru(input,
+                size,
+                param_attr=None,
+                bias_attr=None,
+                is_reverse=False,
+                gate_activation='sigmoid',
+                candidate_activation='tanh',
+                h_0=None):
+    """
+    **Dynamic GRU Layer**
+
+    Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on 
+    Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
+    
+    The formula is as follows:
+
+    .. math::
+
+        u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
+
+        r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
+
+        \\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
+        
+        h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
+    
+    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
+    is the update gate and reset gate activation function and :math:`sigmoid` 
+    is usually used for it. :math:`act_c` is the activation function for 
+    candidate hidden state and :math:`tanh` is usually used for it.
+
+    Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
+    the input :math:`x_{t}` are NOT included in this operator. Users can choose
+    to use fully-connect layer before GRU layer. 
+
+    Args:
+        input(Variable): The input of dynamic_gru layer, which supports 
+            variable-time length input sequence. The underlying tensor in this 
+            Variable is a matrix with shape :math:`(T \\times 3D)`, where
+            :math:`T` is the total time steps in this mini-batch, :math:`D` 
+            is the hidden size.
+        size(int): The dimension of the gru cell.
+        param_attr(ParamAttr|None): The parameter attribute for the learnable 
+            hidden-hidden weight matrix. Note:
+
+            - The shape of the weight matrix is :math:`(T \\times 3D)`, where 
+              :math:`D` is the hidden size.
+            - All elements in the weight matrix can be divided into two parts. 
+              The first part are weights of the update gate and reset gate with
+              shape :math:`(D \\times 2D)`, and the second part are weights for 
+              candidate hidden state with shape :math:`(D \\times D)`.
+        bias_attr(ParamAttr): The parameter attribute for learnable the 
+            hidden-hidden bias.
+        is_reverse(bool): Whether to compute reversed GRU, default 
+            :attr:`False`.
+        gate_activation(str): The activation for update gate and reset gate.
+            Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
+        activation(str): The activation for candidate hidden state. 
+            Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
+
+    Returns:
+        Variable: The hidden state of GRU. The shape is (T \\times D), and lod \
+            is the same with the input.
+    
+    Examples:
+        .. code-block:: python
+
+            hidden_dim = 512
+            x = fluid.layers.fc(input=data, size=hidden_dim * 3)
+            hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
+    """
+
+    helper = LayerHelper('gru', **locals())
+    dtype = helper.input_dtype()
+
+    weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
+    bias = helper.create_parameter(
+        attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
+    inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
+    if h_0 != None:
+        assert h_0.shape == (
+            size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
+        inputs['h0'] = h_0
+
+    hidden = helper.create_tmp_variable(dtype)
+    batch_gate = helper.create_tmp_variable(dtype)
+    batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
+    batch_hidden = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='gru',
+        inputs=inputs,
+        outputs={
+            'Hidden': hidden,
+            'BatchGate': batch_gate,
+            'BatchResetHiddenPrev': batch_reset_hidden_prev,
+            'BatchHidden': batch_hidden
+        },
+        attrs={
+            'is_reverse': is_reverse,
+            'gate_activation': gate_activation,
+            'activation': candidate_activation
+        })
+    return hidden
+
+
 def gru_unit(input,
              hidden,
              size,
@@ -1424,6 +1550,38 @@ def sequence_expand(x, y, name=None):
     return tmp
 
 
+def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
+    '''
+    This function implements the beam search algorithm.
+    '''
+    helper = LayerHelper('beam_search', **locals())
+    score_type = scores.dtype
+    id_type = ids.dtype
+
+    selected_scores = helper.create_tmp_variable(dtype=score_type)
+    selected_ids = helper.create_tmp_variable(dtype=id_type)
+
+    helper.append_op(
+        type='beam_search',
+        inputs={
+            'pre_ids': pre_ids,
+            'ids': ids,
+            'scores': scores,
+        },
+        outputs={
+            'selected_ids': selected_ids,
+            'selected_scores': selected_scores,
+        },
+        attrs={
+            # TODO(ChunweiYan) to assure other value support
+            'level': level,
+            'beam_size': beam_size,
+            'end_id': end_id,
+        })
+
+    return selected_ids, selected_scores
+
+
 def lstm_unit(x_t,
               hidden_t_prev,
               cell_t_prev,
@@ -2190,6 +2348,61 @@ def sequence_reshape(input, new_dim):
     return out
 
 
+@autodoc()
+def nce(input,
+        label,
+        num_total_classes,
+        sample_weight=None,
+        param_attr=None,
+        bias_attr=None,
+        num_neg_samples=None):
+    helper = LayerHelper('nce', **locals())
+    assert isinstance(input, Variable)
+    dim = input.shape[1]
+    assert isinstance(label, Variable)
+    num_true_class = label.shape[1]
+    w = helper.create_parameter(
+        attr=helper.param_attr,
+        shape=[num_total_classes, dim],
+        is_bias=False,
+        dtype=input.dtype)
+    b = helper.create_parameter(
+        attr=helper.bias_attr,
+        shape=[num_total_classes, 1],
+        is_bias=True,
+        dtype=input.dtype)
+    cost = helper.create_tmp_variable(dtype=input.dtype)
+    sample_logits = helper.create_tmp_variable(dtype=input.dtype)
+    sample_labels = helper.create_tmp_variable(dtype=label.dtype)
+
+    if num_neg_samples is None:
+        num_neg_samples = 10
+    else:
+        num_neg_samples = int(num_neg_samples)
+
+    attrs = {
+        'num_total_classes': int(num_total_classes),
+        'num_neg_samples': num_neg_samples
+    }
+
+    helper.append_op(
+        type='nce',
+        inputs={
+            'Input': input,
+            'Label': label,
+            'Weight': w,
+            'Bias': b,
+            'SampleWeight': sample_weight if sample_weight is not None else []
+        },
+        outputs={
+            'Cost': cost,
+            'SampleLogits': sample_logits,
+            'SampleLabels': sample_labels
+        },
+        attrs=attrs)
+    return cost / (num_neg_samples + 1)
+
+
 def transpose(x, perm, name=None):
     """
     **transpose Layer**
@@ -2226,3 +2439,128 @@ def transpose(x, perm, name=None):
         outputs={'Out': [out]},
         attrs={'axis': perm})
     return out
+
+
+def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
+    """
+    Extracts image patches from the input tensor to form a tensor of shape
+    {input.batch_size * output_height * output_width, filter_size_H *
+    filter_size_W * input.channels} which is similar with im2col.
+    This op use filter / kernel to scan images and convert these images to
+    sequences. After expanding, the number of time step are
+    output_height * output_width for an image, in which output_height and
+    output_width are calculated by below equation:
+
+    .. math::
+
+        output\_size = 1 + \
+            (2 * padding + img\_size - block\_size + stride - 1) / stride
+
+    And the dimension of each time step is block_y * block_x * input.channels.
+
+    Args:
+        input (Variable): The input should be a tensor in NCHW format.
+
+        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
+            it must contain two integers, (filter_size_H, filter_size_W).
+            Otherwise, the filter will be a square.
+
+        stride(int|tuple): The stride size. If stride is a tuple, it must
+            contain two integers, (stride_H, stride_W). Otherwise, the
+            stride_H = stride_W = stride. Default: stride = 1.
+
+        padding(int|tuple): The padding size. If padding is a tuple, it can
+            contain two integers like (padding_H, padding_W) which means
+            padding_up = padding_down = padding_H and
+            padding_left = padding_right = padding_W. Or it can use
+            (padding_up, padding_left, padding_down, padding_right) to indicate
+            paddings of four direction. Otherwise, a scalar padding means
+            padding_up = padding_down = padding_left = padding_right = padding
+            Default: padding = 0.
+
+        name (int): The name of this layer. It is optional.
+
+    Returns:
+        output: The output is a LoDTensor with shape
+        {input.batch_size * output_height * output_width,
+        filter_size_H * filter_size_W * input.channels}.
+        If we regard output as a matrix, each row of this matrix is
+        a step of a sequence.
+
+    Examples:
+
+    As an example:
+
+        .. code-block:: text
+
+            Given:
+
+            x = [[[[ 6.  2.  1.]
+                   [ 8.  3.  5.]
+                   [ 0.  2.  6.]]
+
+                  [[ 2.  4.  4.]
+                   [ 6.  3.  0.]
+                   [ 6.  4.  7.]]]
+
+                 [[[ 6.  7.  1.]
+                   [ 5.  7.  9.]
+                   [ 2.  4.  8.]]
+
+                  [[ 1.  2.  1.]
+                   [ 1.  3.  5.]
+                   [ 9.  0.  8.]]]]
+
+            x.dims = {2, 2, 3, 3}
+
+            And:
+
+            filter = [2, 2]
+            stride = [1, 1]
+            padding = [0, 0]
+
+            Then:
+
+            output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
+                           [ 2.  1.  3.  5.  4.  4.  3.  0.]
+                           [ 8.  3.  0.  2.  6.  3.  6.  4.]
+                           [ 3.  5.  2.  6.  3.  0.  4.  7.]
+                           [ 6.  7.  5.  7.  1.  2.  1.  3.]
+                           [ 7.  1.  7.  9.  2.  1.  3.  5.]
+                           [ 5.  7.  2.  4.  1.  3.  9.  0.]
+                           [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
+            output.dims = {8, 9}
+
+            output.lod = [[0, 4, 8]]
+
+        The simple usage is:
+
+        .. code-block:: python
+
+            output = fluid.layers.im2sequence(input=layer, stride=[1, 1], filter_size=[2, 2])
+
+    """
+
+    if isinstance(filter_size, int):
+        filter_size = [filter_size, filter_size]
+    if isinstance(stride, int):
+        stride = [stride, stride]
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    if len(padding) == 2:
+        padding.append(padding[0])
+        padding.append(padding[1])
+
+    helper = LayerHelper('im2sequence', **locals())
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='im2sequence',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={
+            'kernels': filter_size,
+            'strides': stride,
+            'paddings': padding,
+        })
+    return out

python/paddle/v2/fluid/memory_optimization_transpiler.py

@@ -31,10 +31,12 @@ dtype_to_size = {
 
 
 class ControlFlowGraph(object):
-    def __init__(self, Program):
+    def __init__(self, Program, ops, forward_num):
         self._program = Program
-        self._succesors = defaultdict(set)
-        self._presucessors = defaultdict(set)
+        self._ops = ops
+        self._forward_num = forward_num
+        self._successors = defaultdict(set)
+        self._presuccessors = defaultdict(set)
         self._uses = defaultdict(set)
         self._defs = defaultdict(set)
         self._live_in = defaultdict(set)
@@ -45,25 +47,16 @@ class ControlFlowGraph(object):
             self._add(node1, node2)
 
     def _add(self, node1, node2):
-        self._succesors[node1].add(node2)
-        self._presucessors[node2].add(node1)
+        self._successors[node1].add(node2)
+        self._presuccessors[node2].add(node1)
 
     def _build_graph(self):
-        program_desc = self._program.get_desc()
-        block_size = program_desc.num_blocks()
-
-        # TODO(qijun) handle Program with if/while operators
-        self.global_block_desc = program_desc.block(0)
-        self.op_size = self.global_block_desc.op_size()
-
+        self.op_size = len(self._ops)
         op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
         self._add_connections(op_node_connections)
-
-        self.ops = [self.global_block_desc.op(i) for i in range(self.op_size)]
-
         for i in range(self.op_size):
-            self._uses[i].update(self.ops[i].input_arg_names())
-            self._defs[i].update(self.ops[i].output_arg_names())
+            self._uses[i].update(self._ops[i].input_arg_names())
+            self._defs[i].update(self._ops[i].output_arg_names())
 
     def _update_graph(self, old_name, new_name, begin_idx=0):
         for i in range(begin_idx, self.op_size):
@@ -103,7 +96,7 @@ class ControlFlowGraph(object):
                 live_out[i] = set(self._live_out[i])
                 self._live_in[i] = self._uses[i] | (
                     self._live_out[i] - self._defs[i])
-                for s in self._succesors[i]:
+                for s in self._successors[i]:
                     self._live_out[i] |= self._live_in[s]
 
             if self._reach_fixed_point(live_in, live_out):
@@ -113,39 +106,76 @@ class ControlFlowGraph(object):
         u = a & b
         return a - u, b - u
 
+    def _has_var(self, block_desc, var_name, is_forward):
+        if is_forward:
+            return block_desc.has_var(str(var_name))
+        else:
+            return block_desc.has_var_recursive(str(var_name))
+
+    def _find_var(self, block_desc, var_name, is_forward):
+        if is_forward:
+            return block_desc.find_var(str(var_name))
+        else:
+            return block_desc.find_var_recursive(str(var_name))
+
     def memory_optimize(self):
+        def check_var_validity(block_desc, x, is_forward):
+            if str(x) == "@EMPTY@":
+                return False
+            if not self._has_var(block_desc, x, is_forward):
+                return False
+            if self._find_var(block_desc, x, is_forward).persistable():
+                return False
+            if self._find_var(
+                    block_desc, x,
+                    is_forward).type() != core.VarDesc.VarType.LOD_TENSOR:
+                return False
+            return True
+
         self._build_graph()
         self._dataflow_analyze()
         self.pool = []
         for i in range(self.op_size):
+            op = self._ops[i]
+            if op.type() == "while" or op.type() == "while_grad":
+                continue
+            block_desc = op.block()
+            is_forward = i < self._forward_num
             if self.pool:
-                out_pair = [(x, self.global_block_desc.var(str(x)).shape())
-                            for x in self._defs[i]]
+                defs_can_optimize = filter(
+                    lambda x: check_var_validity(block_desc, x, is_forward),
+                    self._defs[i])
+                out_pair = [
+                    (x, self._find_var(block_desc, x, is_forward).shape())
+                    for x in defs_can_optimize
+                ]
                 for x, x_shape in out_pair:
-                    if not self.global_block_desc.var(str(x)).persistable():
-                        for index, cache_pair in enumerate(self.pool):
-                            cache_var = cache_pair[0]
-                            cache_shape = cache_pair[1]
-                            if x_shape == cache_shape:
-                                x_dtype = self.global_block_desc.var(str(
-                                    x)).dtype()
-                                cache_dtype = self.global_block_desc.var(
-                                    str(cache_var)).dtype()
+                    for index, cache_pair in enumerate(self.pool):
+                        cache_var = cache_pair[0]
+                        cache_shape = cache_pair[1]
+                        if x_shape == cache_shape:
+                            if self._has_var(block_desc, cache_var, is_forward):
+                                x_dtype = self._find_var(block_desc, x,
+                                                         is_forward).dtype()
+                                cache_dtype = self._find_var(
+                                    block_desc, cache_var, is_forward).dtype()
                                 # TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
                                 # and dtype_to_size[cache_dtype]
                                 if x_dtype == cache_dtype:
-                                    print(
-                                        ("Hit Cache !!!! cache pool index "
-                                         "is %d, var name is %s, "
-                                         "cached var name is %s, "
-                                         "var shape is %s ") %
-                                        (index, x, cache_var, str(cache_shape)))
+                                    print(("Hit Cache !!!! cache pool index "
+                                           "is %d, var name is %s, "
+                                           "cached var name is %s, "
+                                           "var shape is %s ") %
+                                          (index, x, cache_var,
+                                           str(cache_shape)))
                                     self.pool.pop(index)
+                                    if x == cache_var:
+                                        break
                                     _rename_arg_(
-                                        self.ops, x, cache_var, begin_idx=i)
-                                    self._program.current_block().var(str(
-                                        x)).desc = self.global_block_desc.var(
-                                            str(cache_var))
+                                        self._ops, x, cache_var, begin_idx=i)
+                                    self._program.block(block_desc.id).var(
+                                        str(x)).desc = self._find_var(
+                                            block_desc, cache_var, is_forward)
                                     self._update_graph(
                                         x, cache_var, begin_idx=i)
                                     break
@@ -153,20 +183,70 @@ class ControlFlowGraph(object):
             in_diff, out_diff = self._get_diff(self._live_in[i],
                                                self._live_out[i])
             can_optimize = filter(
-                lambda x: not self.global_block_desc.var(str(x)).persistable(),
+                lambda x: check_var_validity(block_desc, x, is_forward),
                 in_diff)
             if can_optimize:
                 for var_name in can_optimize:
-                    self.pool.append(
-                        (var_name,
-                         self.global_block_desc.var(str(var_name)).shape()))
-
-    def get_program(self):
-        return self._program
+                    self.pool.append((var_name, self._find_var(
+                        block_desc, var_name, is_forward).shape()))
+
+
+def get_cfgs(input_program):
+    ops_list = []
+    pdesc = input_program.get_desc()
+    block_desc = pdesc.block(0)
+    op_size = block_desc.op_size()
+    # Get global block ops
+    ops_list.append(([block_desc.op(i) for i in range(op_size)], op_size))
+
+    while_sub_block_ids = []
+    while_grad_sub_block_ids = []
+    while_pair = []
+
+    for i in range(op_size):
+        op = block_desc.op(i)
+        if op.type() == "while":
+            while_sub_block_ids.append(op.attr("sub_block").id)
+        elif op.type() == "while_grad":
+            while_grad_sub_block_ids.append(op.attr("sub_block").id)
+
+    # Find while/while_grad block pair
+    for grad_id in while_grad_sub_block_ids:
+        parent_id = pdesc.block(grad_id).parent
+        if parent_id in while_sub_block_ids:
+            while_pair.append((parent_id, grad_id))
+            while_sub_block_ids.remove(parent_id)
+
+    # Get while/while_grad block ops
+    for parent_id, grad_id in while_pair:
+        while_block_ops = []
+        while_block = pdesc.block(parent_id)
+        while_block_op_size = while_block.op_size()
+        for i in range(while_block_op_size):
+            while_block_ops.append(while_block.op(i))
+
+        while_grad_block = pdesc.block(grad_id)
+        while_grad_block_op_size = while_grad_block.op_size()
+        for i in range(while_grad_block_op_size):
+            while_block_ops.append(while_grad_block.op(i))
+
+        ops_list.append((while_block_ops, while_block_op_size))
+
+    # Process rest while block ops
+    for parent_id in while_sub_block_ids:
+        while_block_ops = []
+        while_block = pdesc.block(parent_id)
+        while_block_op_size = while_block.op_size()
+        for i in range(while_block_op_size):
+            while_block_ops.append(while_block.op(i))
+
+        ops_list.append((while_block_ops, while_block_op_size))
+
+    cfgs = [ControlFlowGraph(input_program, i, j) for i, j in ops_list]
+    return cfgs
 
 
 def memory_optimize(input_program):
-    graph = ControlFlowGraph(input_program)
-    graph.memory_optimize()
-    result_program = graph.get_program()
-    return result_program
+    cfgs = get_cfgs(input_program)
+    for cfg in cfgs:
+        cfg.memory_optimize()

python/paddle/v2/fluid/nets.py

@@ -56,7 +56,7 @@ def img_conv_group(input,
                    conv_act=None,
                    param_attr=None,
                    conv_with_batchnorm=False,
-                   conv_batchnorm_drop_rate=None,
+                   conv_batchnorm_drop_rate=0.0,
                    pool_stride=1,
                    pool_type=None,
                    use_cudnn=True):
@@ -127,21 +127,21 @@ def sequence_conv_pool(input,
 
 def glu(input, dim=-1):
     """
-    The gated linear unit composed by split, sigmoid activation and elementwise 
-    multiplication. Specifically, Split the input into two equal sized parts 
-    :math:`a` and :math:`b` along the given dimension and then compute as 
+    The gated linear unit composed by split, sigmoid activation and elementwise
+    multiplication. Specifically, Split the input into two equal sized parts
+    :math:`a` and :math:`b` along the given dimension and then compute as
     following:
 
         .. math::
 
             {GLU}(a, b)= a \otimes \sigma(b)
 
-    Refer to `Language Modeling with Gated Convolutional Networks 
+    Refer to `Language Modeling with Gated Convolutional Networks
     <https://arxiv.org/pdf/1612.08083.pdf>`_.
-    
+
     Args:
         input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (int): The dimension along which to split. If :math:`dim < 0`, the 
+        dim (int): The dimension along which to split. If :math:`dim < 0`, the
             dimension to split along is :math:`rank(input) + dim`.
 
     Returns:
@@ -164,24 +164,24 @@ def dot_product_attention(querys, keys, values):
     """
     The dot-product attention.
 
-    Attention mechanism can be seen as mapping a query and a set of key-value 
-    pairs to an output. The output is computed as a weighted sum of the values, 
-    where the weight assigned to each value is computed by a compatibility 
+    Attention mechanism can be seen as mapping a query and a set of key-value
+    pairs to an output. The output is computed as a weighted sum of the values,
+    where the weight assigned to each value is computed by a compatibility
     function (dot-product here) of the query with the corresponding key.
-    
-    The dot-product attention can be implemented through (batch) matrix 
+
+    The dot-product attention can be implemented through (batch) matrix
     multipication as follows:
 
         .. math::
 
             Attention(Q, K, V)= softmax(QK^\mathrm{T})V
 
-    Refer to `Attention Is All You Need 
+    Refer to `Attention Is All You Need
     <https://arxiv.org/pdf/1706.03762.pdf>`_.
 
-    Note that batch data containing sequences with different lengths is not 
+    Note that batch data containing sequences with different lengths is not
     supported by this because of the (batch) matrix multipication.
-    
+
     Args:
         query (Variable): The input variable which is a Tensor or LoDTensor.
         key (Variable): The input variable which is a Tensor or LoDTensor.

python/paddle/v2/fluid/tests/book/test_fit_a_line.py

@@ -49,7 +49,7 @@ for pass_id in range(PASS_NUM):
         avg_loss_value, = exe.run(fluid.default_main_program(),
                                   feed=feeder.feed(data),
                                   fetch_list=[avg_cost])
-
+        print(avg_loss_value)
         if avg_loss_value[0] < 10.0:
             exit(0)  # if avg cost less than 10.0, we think our code is good.
 exit(1)

python/paddle/v2/fluid/tests/book/test_machine_translation.py

@@ -17,7 +17,7 @@ import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.core as core
 import paddle.v2.fluid.framework as framework
-import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.layers as pd
 from paddle.v2.fluid.executor import Executor
 
 dict_size = 30000
@@ -26,53 +26,136 @@ src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
 hidden_dim = 32
 word_dim = 16
 IS_SPARSE = True
-batch_size = 10
-max_length = 50
+batch_size = 2
+max_length = 8
 topk_size = 50
 trg_dic_size = 10000
+beam_size = 2
 
 decoder_size = hidden_dim
 
+place = core.CPUPlace()
 
-def encoder_decoder():
+
+def encoder():
     # encoder
-    src_word_id = layers.data(
+    src_word_id = pd.data(
         name="src_word_id", shape=[1], dtype='int64', lod_level=1)
-    src_embedding = layers.embedding(
+    src_embedding = pd.embedding(
         input=src_word_id,
         size=[dict_size, word_dim],
         dtype='float32',
         is_sparse=IS_SPARSE,
         param_attr=fluid.ParamAttr(name='vemb'))
 
-    fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
-    lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
-    encoder_out = layers.sequence_last_step(input=lstm_hidden0)
+    fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
+    lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4)
+    encoder_out = pd.sequence_last_step(input=lstm_hidden0)
+    return encoder_out
+
 
+def decoder_train(context):
     # decoder
-    trg_language_word = layers.data(
+    trg_language_word = pd.data(
         name="target_language_word", shape=[1], dtype='int64', lod_level=1)
-    trg_embedding = layers.embedding(
+    trg_embedding = pd.embedding(
         input=trg_language_word,
         size=[dict_size, word_dim],
         dtype='float32',
         is_sparse=IS_SPARSE,
         param_attr=fluid.ParamAttr(name='vemb'))
 
-    rnn = fluid.layers.DynamicRNN()
+    rnn = pd.DynamicRNN()
     with rnn.block():
         current_word = rnn.step_input(trg_embedding)
-        mem = rnn.memory(init=encoder_out)
-        fc1 = fluid.layers.fc(input=[current_word, mem],
+        pre_state = rnn.memory(init=context)
+        current_state = pd.fc(input=[current_word, pre_state],
                               size=decoder_size,
                               act='tanh')
-        out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
-        rnn.update_memory(mem, fc1)
-        rnn.output(out)
+
+        current_score = pd.fc(input=current_state,
+                              size=target_dict_dim,
+                              act='softmax')
+        rnn.update_memory(pre_state, current_state)
+        rnn.output(current_score)
 
     return rnn()
 
 
+def decoder_decode(context):
+    init_state = context
+    array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
+    counter = pd.zeros(shape=[1], dtype='int64')
+
+    # fill the first element with init_state
+    state_array = pd.create_array('float32')
+    pd.array_write(init_state, array=state_array, i=counter)
+
+    # ids, scores as memory
+    ids_array = pd.create_array('int64')
+    scores_array = pd.create_array('float32')
+
+    init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2)
+    init_scores = pd.data(
+        name="init_scores", shape=[1], dtype="float32", lod_level=2)
+
+    pd.array_write(init_ids, array=ids_array, i=counter)
+    pd.array_write(init_scores, array=scores_array, i=counter)
+
+    cond = pd.less_than(x=counter, y=array_len)
+
+    while_op = pd.While(cond=cond)
+    with while_op.block():
+        pre_ids = pd.array_read(array=ids_array, i=counter)
+        pre_state = pd.array_read(array=state_array, i=counter)
+        pre_score = pd.array_read(array=scores_array, i=counter)
+
+        # expand the lod of pre_state to be the same with pre_score
+        pre_state_expanded = pd.sequence_expand(pre_state, pre_score)
+
+        pre_ids_emb = pd.embedding(
+            input=pre_ids,
+            size=[dict_size, word_dim],
+            dtype='float32',
+            is_sparse=IS_SPARSE)
+
+        # use rnn unit to update rnn
+        current_state = pd.fc(input=[pre_ids_emb, pre_state_expanded],
+                              size=decoder_size,
+                              act='tanh')
+
+        # use score to do beam search
+        current_score = pd.fc(input=current_state,
+                              size=target_dict_dim,
+                              act='softmax')
+        topk_scores, topk_indices = pd.topk(current_score, k=50)
+        selected_ids, selected_scores = pd.beam_search(
+            pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0)
+
+        pd.increment(x=counter, value=1, in_place=True)
+
+        # update the memories
+        pd.array_write(current_state, array=state_array, i=counter)
+        pd.array_write(selected_ids, array=ids_array, i=counter)
+        pd.array_write(selected_scores, array=scores_array, i=counter)
+
+        pd.less_than(x=counter, y=array_len, cond=cond)
+
+    translation_ids, translation_scores = pd.beam_search_decode(
+        ids=ids_array, scores=scores_array)
+
+    # return init_ids, init_scores
+
+    return translation_ids, translation_scores
+
+
+def set_init_lod(data, lod, place):
+    res = core.LoDTensor()
+    res.set(data, place)
+    res.set_lod(lod)
+    return res
+
+
 def to_lodtensor(data, place):
     seq_lens = [len(seq) for seq in data]
     cur_len = 0
@@ -88,12 +171,13 @@ def to_lodtensor(data, place):
     return res
 
 
-def main():
-    rnn_out = encoder_decoder()
-    label = layers.data(
+def train_main():
+    context = encoder()
+    rnn_out = decoder_train(context)
+    label = pd.data(
         name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
-    cost = layers.cross_entropy(input=rnn_out, label=label)
-    avg_cost = fluid.layers.mean(x=cost)
+    cost = pd.cross_entropy(input=rnn_out, label=label)
+    avg_cost = pd.mean(x=cost)
 
     optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
     optimizer.minimize(avg_cost)
@@ -103,13 +187,12 @@ def main():
             paddle.dataset.wmt14.train(dict_size), buf_size=1000),
         batch_size=batch_size)
 
-    place = core.CPUPlace()
     exe = Executor(place)
 
     exe.run(framework.default_startup_program())
 
     batch_id = 0
-    for pass_id in xrange(2):
+    for pass_id in xrange(1):
         for data in train_data():
             word_data = to_lodtensor(map(lambda x: x[0], data), place)
             trg_word = to_lodtensor(map(lambda x: x[1], data), place)
@@ -125,9 +208,48 @@ def main():
             print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
                   " avg_cost=" + str(avg_cost_val))
             if batch_id > 3:
-                exit(0)
+                break
             batch_id += 1
 
 
+def decode_main():
+    context = encoder()
+    translation_ids, translation_scores = decoder_decode(context)
+
+    exe = Executor(place)
+    exe.run(framework.default_startup_program())
+
+    init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64')
+    init_scores_data = np.array(
+        [1. for _ in range(batch_size)], dtype='float32')
+    init_ids_data = init_ids_data.reshape((batch_size, 1))
+    init_scores_data = init_scores_data.reshape((batch_size, 1))
+    init_lod = [i for i in range(batch_size)] + [batch_size]
+    init_lod = [init_lod, init_lod]
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
+        batch_size=batch_size)
+    for _, data in enumerate(train_data()):
+        init_ids = set_init_lod(init_ids_data, init_lod, place)
+        init_scores = set_init_lod(init_scores_data, init_lod, place)
+
+        src_word_data = to_lodtensor(map(lambda x: x[0], data), place)
+
+        result_ids, result_scores = exe.run(
+            framework.default_main_program(),
+            feed={
+                'src_word_id': src_word_data,
+                'init_ids': init_ids,
+                'init_scores': init_scores
+            },
+            fetch_list=[translation_ids, translation_scores],
+            return_numpy=False)
+        print result_ids.lod()
+        break
+
+
 if __name__ == '__main__':
-    main()
+    # train_main()
+    decode_main()

python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py

@@ -68,10 +68,10 @@ else:
         fluid.io.save_persistables(exe, "./fit_a_line.model/")
         fluid.io.load_persistables(exe, "./fit_a_line.model/")
         for data in train_reader():
-            avg_loss_value, = exe.run(trainer_prog,
-                                      feed=feeder.feed(data),
-                                      fetch_list=[avg_cost])
-
+            avg_loss_value = exe.run(trainer_prog,
+                                     feed=feeder.feed(data),
+                                     fetch_list=[avg_cost])
+            print("loss:" + str(avg_loss_value))
             if avg_loss_value[0] < 10.0:
                 exit(0)
 exit(1)

python/paddle/v2/fluid/tests/book_distribute/notest_machine_translation.py

+#  Copyright (c) 2018 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.
+
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.executor import Executor
+import os
+
+dict_size = 30000
+source_dict_dim = target_dict_dim = dict_size
+src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
+hidden_dim = 32
+word_dim = 16
+IS_SPARSE = True
+batch_size = 10
+max_length = 50
+topk_size = 50
+trg_dic_size = 10000
+
+decoder_size = hidden_dim
+
+
+def encoder_decoder():
+    # encoder
+    src_word_id = layers.data(
+        name="src_word_id", shape=[1], dtype='int64', lod_level=1)
+    src_embedding = layers.embedding(
+        input=src_word_id,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
+    lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
+    encoder_out = layers.sequence_last_step(input=lstm_hidden0)
+
+    # decoder
+    trg_language_word = layers.data(
+        name="target_language_word", shape=[1], dtype='int64', lod_level=1)
+    trg_embedding = layers.embedding(
+        input=trg_language_word,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    rnn = fluid.layers.DynamicRNN()
+    with rnn.block():
+        current_word = rnn.step_input(trg_embedding)
+        mem = rnn.memory(init=encoder_out)
+        fc1 = fluid.layers.fc(input=[current_word, mem],
+                              size=decoder_size,
+                              act='tanh')
+        out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
+        rnn.update_memory(mem, fc1)
+        rnn.output(out)
+
+    return rnn()
+
+
+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 main():
+    rnn_out = encoder_decoder()
+    label = layers.data(
+        name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
+    cost = layers.cross_entropy(input=rnn_out, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
+    optimize_ops, params_grads = optimizer.minimize(avg_cost)
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
+        batch_size=batch_size)
+
+    place = core.CPUPlace()
+    exe = Executor(place)
+
+    t = fluid.DistributeTranspiler()
+    # all parameter server endpoints list for spliting parameters
+    pserver_endpoints = os.getenv("PSERVERS")
+    # server endpoint for current node
+    current_endpoint = os.getenv("SERVER_ENDPOINT")
+    # run as trainer or parameter server
+    training_role = os.getenv(
+        "TRAINING_ROLE", "TRAINER")  # get the training role: trainer/pserver
+    t.transpile(
+        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+    if training_role == "PSERVER":
+        if not current_endpoint:
+            print("need env SERVER_ENDPOINT")
+            exit(1)
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+        exe.run(pserver_startup)
+        exe.run(pserver_prog)
+    elif training_role == "TRAINER":
+        trainer_prog = t.get_trainer_program()
+        exe.run(framework.default_startup_program())
+
+        batch_id = 0
+        for pass_id in xrange(2):
+            for data in train_data():
+                word_data = to_lodtensor(map(lambda x: x[0], data), place)
+                trg_word = to_lodtensor(map(lambda x: x[1], data), place)
+                trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
+                outs = exe.run(trainer_prog,
+                               feed={
+                                   'src_word_id': word_data,
+                                   'target_language_word': trg_word,
+                                   'target_language_next_word': trg_word_next
+                               },
+                               fetch_list=[avg_cost])
+                avg_cost_val = np.array(outs[0])
+                print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
+                      " avg_cost=" + str(avg_cost_val))
+                if batch_id > 3:
+                    exit(0)
+                batch_id += 1
+    else:
+        print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+if __name__ == '__main__':
+    main()

python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py

@@ -16,6 +16,11 @@ import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 
+# need to fix random seed and training data to compare the loss
+# value accurately calculated by the default and the memory optimization
+# version.
+fluid.default_startup_program().random_seed = 111
+
 x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
 y_predict = fluid.layers.fc(input=x, size=1, act=None)
@@ -28,15 +33,18 @@ avg_cost = fluid.layers.mean(x=cost)
 sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
 sgd_optimizer.minimize(avg_cost)
 
-# memopt_program = fluid.default_main_program()
-memopt_program = fluid.memory_optimize(fluid.default_main_program())
+fluid.memory_optimize(fluid.default_main_program())
 
 BATCH_SIZE = 200
 
+# fix the order of training data
 train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
+    paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE)
+
+# train_reader = paddle.batch(
+#     paddle.reader.shuffle(
+#         paddle.dataset.uci_housing.train(), buf_size=500),
+#     batch_size=BATCH_SIZE)
 
 place = fluid.CPUPlace()
 feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
@@ -49,7 +57,7 @@ for pass_id in range(PASS_NUM):
     fluid.io.save_persistables(exe, "./fit_a_line.model/")
     fluid.io.load_persistables(exe, "./fit_a_line.model/")
     for data in train_reader():
-        avg_loss_value, = exe.run(memopt_program,
+        avg_loss_value, = exe.run(fluid.default_main_program(),
                                   feed=feeder.feed(data),
                                   fetch_list=[avg_cost])
 

python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py

@@ -19,6 +19,11 @@ import sys
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 
+# need to fix random seed and training data to compare the loss
+# value accurately calculated by the default and the memory optimization
+# version.
+fluid.default_startup_program().random_seed = 111
+
 
 def resnet_cifar10(input, depth=32):
     def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
@@ -117,31 +122,37 @@ opts = optimizer.minimize(avg_cost)
 
 accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
 
-# memopt_program = fluid.default_main_program()
-memopt_program = fluid.memory_optimize(fluid.default_main_program())
+fluid.memory_optimize(fluid.default_main_program())
 
 BATCH_SIZE = 128
 PASS_NUM = 1
 
+# fix the order of training data
 train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.cifar.train10(), buf_size=128 * 10),
-    batch_size=BATCH_SIZE)
+    paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE)
+
+# train_reader = paddle.batch(
+#     paddle.reader.shuffle(
+#         paddle.dataset.cifar.train10(), buf_size=128 * 10),
+#     batch_size=BATCH_SIZE)
 
 place = fluid.CPUPlace()
 exe = fluid.Executor(place)
 feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
 exe.run(fluid.default_startup_program())
 
+i = 0
 for pass_id in range(PASS_NUM):
     accuracy.reset(exe)
     for data in train_reader():
-        loss, acc = exe.run(memopt_program,
+        loss, acc = exe.run(fluid.default_main_program(),
                             feed=feeder.feed(data),
                             fetch_list=[avg_cost] + accuracy.metrics)
         pass_acc = accuracy.eval(exe)
         print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
             pass_acc))
         # this model is slow, so if we can train two mini batch, we think it works properly.
-        exit(0)
+        if i > 2:
+            exit(0)
+        i += 1
 exit(1)

python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py

+#   Copyright (c) 2018 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.
+
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.executor import Executor
+
+dict_size = 30000
+source_dict_dim = target_dict_dim = dict_size
+src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
+hidden_dim = 32
+word_dim = 16
+IS_SPARSE = True
+batch_size = 10
+max_length = 50
+topk_size = 50
+trg_dic_size = 10000
+
+decoder_size = hidden_dim
+
+# need to fix random seed and training data to compare the loss
+# value accurately calculated by the default and the memory optimization
+# version.
+fluid.default_startup_program().random_seed = 111
+
+
+def encoder_decoder():
+    # encoder
+    src_word_id = layers.data(
+        name="src_word_id", shape=[1], dtype='int64', lod_level=1)
+    src_embedding = layers.embedding(
+        input=src_word_id,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
+    lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
+    encoder_out = layers.sequence_last_step(input=lstm_hidden0)
+
+    # decoder
+    trg_language_word = layers.data(
+        name="target_language_word", shape=[1], dtype='int64', lod_level=1)
+    trg_embedding = layers.embedding(
+        input=trg_language_word,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    rnn = fluid.layers.DynamicRNN()
+    with rnn.block():
+        current_word = rnn.step_input(trg_embedding)
+        mem = rnn.memory(init=encoder_out)
+        fc1 = fluid.layers.fc(input=[current_word, mem],
+                              size=decoder_size,
+                              act='tanh')
+        out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
+        rnn.update_memory(mem, fc1)
+        rnn.output(out)
+
+    return rnn()
+
+
+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 main():
+    rnn_out = encoder_decoder()
+    label = layers.data(
+        name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
+    cost = layers.cross_entropy(input=rnn_out, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
+    optimizer.minimize(avg_cost)
+
+    fluid.memory_optimize(fluid.default_main_program())
+
+    # fix the order of training data
+    train_data = paddle.batch(
+        paddle.dataset.wmt14.train(dict_size), batch_size=batch_size)
+
+    # train_data = paddle.batch(
+    #     paddle.reader.shuffle(
+    #         paddle.dataset.wmt14.train(dict_size), buf_size=1000),
+    #     batch_size=batch_size)
+
+    place = core.CPUPlace()
+    exe = Executor(place)
+
+    exe.run(framework.default_startup_program())
+
+    batch_id = 0
+    for pass_id in xrange(10):
+        for data in train_data():
+            word_data = to_lodtensor(map(lambda x: x[0], data), place)
+            trg_word = to_lodtensor(map(lambda x: x[1], data), place)
+            trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
+            outs = exe.run(fluid.default_main_program(),
+                           feed={
+                               'src_word_id': word_data,
+                               'target_language_word': trg_word,
+                               'target_language_next_word': trg_word_next
+                           },
+                           fetch_list=[avg_cost])
+            avg_cost_val = np.array(outs[0])
+            print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
+                  " avg_cost=" + str(avg_cost_val))
+            if batch_id > 2:
+                exit(0)
+            batch_id += 1
+
+
+if __name__ == '__main__':
+    main()

python/paddle/v2/fluid/tests/test_bipartite_match_op.py

@@ -16,13 +16,13 @@ import numpy as np
 from op_test import OpTest
 
 
-def bipartite_match(distance, match_indices, match_dis):
+def bipartite_match(distance, match_indices, match_dist):
     """Bipartite Matching algorithm.
     Arg:
         distance (numpy.array) : The distance of two entries with shape [M, N].
         match_indices (numpy.array): the matched indices from column to row
             with shape [1, N], it must be initialized to -1.
-        match_dis (numpy.array): The matched distance from column to row
+        match_dist (numpy.array): The matched distance from column to row
             with shape [1, N], it must be initialized to 0.
     """
     match_pair = []
@@ -36,13 +36,13 @@ def bipartite_match(distance, match_indices, match_dis):
     row_indices = -1 * np.ones((row, ), dtype=np.int)
 
     idx = 0
-    for i, j, dis in match_sorted:
+    for i, j, dist in match_sorted:
         if idx >= row:
             break
-        if match_indices[j] == -1 and row_indices[i] == -1 and dis > 0:
+        if match_indices[j] == -1 and row_indices[i] == -1 and dist > 0:
             match_indices[j] = i
             row_indices[i] = j
-            match_dis[j] = dis
+            match_dist[j] = dist
             idx += 1
 
 
@@ -55,24 +55,24 @@ def batch_bipartite_match(distance, lod):
     n = len(lod) - 1
     m = distance.shape[1]
     match_indices = -1 * np.ones((n, m), dtype=np.int)
-    match_dis = np.zeros((n, m), dtype=np.float32)
+    match_dist = np.zeros((n, m), dtype=np.float32)
     for i in range(len(lod) - 1):
         bipartite_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
-                        match_dis[i, :])
-    return match_indices, match_dis
+                        match_dist[i, :])
+    return match_indices, match_dist
 
 
 class TestBipartiteMatchOpForWithLoD(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
         lod = [[0, 5, 11, 23]]
-        dis = np.random.random((23, 217)).astype('float32')
-        match_indices, match_dis = batch_bipartite_match(dis, lod[0])
+        dist = np.random.random((23, 217)).astype('float32')
+        match_indices, match_dist = batch_bipartite_match(dist, lod[0])
 
-        self.inputs = {'DistMat': (dis, lod)}
+        self.inputs = {'DistMat': (dist, lod)}
         self.outputs = {
             'ColToRowMatchIndices': (match_indices),
-            'ColToRowMatchDis': (match_dis),
+            'ColToRowMatchDis': (match_dist),
         }
 
     def test_check_output(self):
@@ -83,13 +83,13 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
         lod = [[0, 8]]
-        dis = np.random.random((8, 17)).astype('float32')
-        match_indices, match_dis = batch_bipartite_match(dis, lod[0])
+        dist = np.random.random((8, 17)).astype('float32')
+        match_indices, match_dist = batch_bipartite_match(dist, lod[0])
 
-        self.inputs = {'DistMat': dis}
+        self.inputs = {'DistMat': dist}
         self.outputs = {
-            'ColToRowMatchIndices': (match_indices),
-            'ColToRowMatchDis': (match_dis),
+            'ColToRowMatchIndices': match_indices,
+            'ColToRowMatchDis': match_dist,
         }
 
     def test_check_output(self):

python/paddle/v2/fluid/tests/test_detection_output_op.py

@@ -68,4 +68,6 @@ class TestUnpoolOp(OpTest):
 
 
 if __name__ == '__main__':
-    unittest.main()
+    # FIXME: detection_output_op will be rewritten. This unittest should be
+    # enabled after rewriting.
+    exit(0)  # temporary disable this unittest

python/paddle/v2/fluid/tests/test_iou_similarity_op.py

+#   Copyright (c) 2018 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.
+
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+class TestIOUSimilarityOp(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "iou_similarity"
+        self.boxes1 = np.array(
+            [[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]).astype('float32')
+        self.boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
+                                [0.0, 0.0, 20.0, 20.0]]).astype('float32')
+        self.output = np.array(
+            [[2.0 / 16.0, 0, 6.0 / 400.0],
+             [1.0 / 16.0, 0.0, 5.0 / 400.0]]).astype('float32')
+
+        self.inputs = {'X': self.boxes1, 'Y': self.boxes2}
+
+        self.outputs = {'Out': self.output}
+
+
+class TestIOUSimilarityOpWithLoD(TestIOUSimilarityOp):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        super(TestIOUSimilarityOpWithLoD, self).setUp()
+        self.boxes1_lod = [[0, 1, 2]]
+        self.output_lod = [[0, 1, 2]]
+
+        self.inputs = {'X': (self.boxes1, self.boxes1_lod), 'Y': self.boxes2}
+        self.outputs = {'Out': (self.output, self.output_lod)}
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/fluid/tests/test_layers.py

@@ -17,8 +17,9 @@ import unittest
 
 import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.nets as nets
-from paddle.v2.fluid.framework import Program, program_guard
+from paddle.v2.fluid.framework import Program, program_guard, default_main_program
 from paddle.v2.fluid.param_attr import ParamAttr
+import decorators
 
 
 class TestBook(unittest.TestCase):
@@ -225,6 +226,51 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_im2sequence(self):
+        print("test_im2sequence")
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
+            output = layers.im2sequence(
+                input=x, stride=[1, 1], filter_size=[2, 2])
+            self.assertIsNotNone(output)
+        print(str(program))
+
+    @decorators.prog_scope()
+    def test_nce(self):
+        window_size = 5
+        words = []
+        for i in xrange(window_size):
+            words.append(
+                layers.data(
+                    name='word_{0}'.format(i), shape=[1], dtype='int64'))
+
+        dict_size = 10000
+        label_word = int(window_size / 2) + 1
+
+        embs = []
+        for i in xrange(window_size):
+            if i == label_word:
+                continue
+
+            emb = layers.embedding(
+                input=words[i],
+                size=[dict_size, 32],
+                param_attr='emb.w',
+                is_sparse=True)
+
+            embs.append(emb)
+
+        embs = layers.concat(input=embs, axis=1)
+        loss = layers.nce(input=embs,
+                          label=words[label_word],
+                          num_total_classes=dict_size,
+                          param_attr='nce.w',
+                          bias_attr='nce.b')
+        avg_loss = layers.mean(x=loss)
+        self.assertIsNotNone(avg_loss)
+        print(str(default_main_program()))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/fluid/tests/test_lookup_table_op.py

@@ -33,5 +33,19 @@ class TestLookupTableOp(OpTest):
         self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
 
 
+class TestLookupTableOpWithPadding(TestLookupTableOp):
+    def test_check_output(self):
+        ids = np.squeeze(self.inputs['Ids'])
+        padding_idx = np.random.choice(ids, 1)[0]
+        self.outputs['Out'][ids == padding_idx] = np.zeros(31)
+        self.attrs = {'padding_idx': long(padding_idx)}
+        self.check_output()
+
+    def test_check_grad(self):
+        # Since paddings are not trainable and fixed in forward, the gradient of 
+        # paddings makes no sense and we don't test the gradient here.
+        pass
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/v2/fluid/tests/test_nce.py

@@ -109,4 +109,6 @@ class TestNCECase1(TestNCE):
 
 
 if __name__ == '__main__':
+    # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/7778
+    exit(0)
     unittest.main()

python/paddle/v2/fluid/tests/test_prior_box_op.py

+#   Copyright (c) 2018 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.
+
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+class TestPriorBoxOp(OpTest):
+    def set_data(self):
+        self.init_test_params()
+        self.init_test_input()
+        self.init_test_output()
+        self.inputs = {'Input': self.input, 'Image': self.image}
+
+        self.attrs = {
+            'min_sizes': self.min_sizes,
+            'max_sizes': self.max_sizes,
+            'aspect_ratios': self.aspect_ratios,
+            'variances': self.variances,
+            'flip': self.flip,
+            'clip': self.clip,
+            'step_w': self.step_w,
+            'step_h': self.step_h,
+            'offset': self.offset
+        }
+
+        self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        return
+
+    def setUp(self):
+        self.op_type = "prior_box"
+        self.set_data()
+
+    def init_test_params(self):
+        self.layer_w = 4
+        self.layer_h = 4
+
+        self.image_w = 20
+        self.image_h = 20
+
+        self.step_w = float(self.image_w) / float(self.layer_w)
+        self.step_h = float(self.image_h) / float(self.layer_h)
+
+        self.input_channels = 2
+        self.image_channels = 3
+        self.batch_size = 10
+
+        self.min_sizes = [2, 4]
+        self.min_sizes = np.array(self.min_sizes).astype('int64')
+        self.max_sizes = [5, 10]
+        self.max_sizes = np.array(self.max_sizes).astype('int64')
+        self.aspect_ratios = [2.0, 3.0]
+        self.flip = True
+        self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
+        self.aspect_ratios = np.array(
+            self.aspect_ratios, dtype=np.float).flatten()
+        self.variances = [0.1, 0.1, 0.2, 0.2]
+        self.variances = np.array(self.variances, dtype=np.float).flatten()
+
+        self.clip = True
+
+        self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
+        if len(self.max_sizes) > 1:
+            self.num_priors += len(self.max_sizes)
+        self.offset = 0.5
+
+    def init_test_input(self):
+        self.image = np.random.random(
+            (self.batch_size, self.image_channels, self.image_w,
+             self.image_h)).astype('float32')
+
+        self.input = np.random.random(
+            (self.batch_size, self.input_channels, self.layer_w,
+             self.layer_h)).astype('float32')
+
+    def init_test_output(self):
+        out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
+        out_boxes = np.zeros(out_dim).astype('float32')
+        out_var = np.zeros(out_dim).astype('float32')
+
+        idx = 0
+        for h in range(self.layer_h):
+            for w in range(self.layer_w):
+                c_x = (w + self.offset) * self.step_w
+                c_y = (h + self.offset) * self.step_h
+                idx = 0
+                for s in range(len(self.min_sizes)):
+                    min_size = self.min_sizes[s]
+                    c_w = c_h = min_size / 2.
+                    out_boxes[h, w, idx, :] = [
+                        (c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h,
+                        (c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h
+                    ]
+                    idx += 1
+
+                    if len(self.max_sizes) > 0:
+                        max_size = self.max_sizes[s]
+                        # second prior: aspect_ratio = 1,
+                        c_w = c_h = math.sqrt(min_size * max_size) / 2
+                        out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
+                                                   (c_y - c_h) / self.image_h,
+                                                   (c_x + c_w) / self.image_w,
+                                                   (c_y + c_h) / self.image_h]
+                        idx += 1
+
+                    # rest of priors
+                    for r in range(len(self.real_aspect_ratios)):
+                        ar = self.real_aspect_ratios[r]
+                        if math.fabs(ar - 1.) < 1e-6:
+                            continue
+                        c_w = min_size * math.sqrt(ar) / 2
+                        c_h = (min_size / math.sqrt(ar)) / 2
+                        out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
+                                                   (c_y - c_h) / self.image_h,
+                                                   (c_x + c_w) / self.image_w,
+                                                   (c_y + c_h) / self.image_h]
+                        idx += 1
+        # clip the prior's coordidate such that it is within[0, 1]
+        if self.clip:
+            out_boxes = np.clip(out_boxes, 0.0, 1.0)
+        # set the variance.
+        out_var = np.tile(self.variances, (self.layer_h, self.layer_w,
+                                           self.num_priors, 1))
+        self.out_boxes = out_boxes.astype('float32')
+        self.out_var = out_var.astype('float32')
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/image.py

@@ -319,11 +319,11 @@ def simple_transform(im,
     """
     im = resize_short(im, resize_size)
     if is_train:
-        im = random_crop(im, crop_size)
+        im = random_crop(im, crop_size, is_color=is_color)
         if np.random.randint(2) == 0:
             im = left_right_flip(im)
     else:
-        im = center_crop(im, crop_size)
+        im = center_crop(im, crop_size, is_color=is_color)
     if len(im.shape) == 3:
         im = to_chw(im)