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

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
     -   id: clang-format-with-version-check
         name: clang-format
         description: Format files with ClangFormat.
-        entry: bash ./.clang_format.hook -i
+        entry: bash ./tools/codestyle/clang_format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
 -   repo: local
@@ -52,7 +52,7 @@ repos:
     hooks:
     -   id: copyright_checker
         name: copyright_checker
-        entry: python ./.copyright.hook
+        entry: python ./tools/codestyle/copyright.hook
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
         exclude: (?!.*third_party)^.*$ | (?!.*book)^.*$

benchmark/fluid/Dockerfile

 FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+
+# Use UBUNTU_MIRROR can speed up apt-get speed.
+# ARG UBUNTU_MIRROR
+# RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
+
 RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop python-opencv
 RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
-RUN pip install -U pip
-RUN pip install -U kubernetes paddlepaddle
 
 # IMPORTANT:
 # Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
+# exmaple: unset http_proxy && unset https_proxy && python fluid_benchmark.py ...
+
+RUN pip install -U pip
+RUN pip install -U kubernetes paddlepaddle
 
 RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
 RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
@@ -14,9 +21,11 @@ RUN pip uninstall -y paddlepaddle && mkdir /workspace
 
 ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
 ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
+RUN chmod +x /usr/bin/paddle_k8s
 
 ADD *.whl /
-RUN pip install /*.whl && rm -f /*.whl && chmod +x /usr/bin/paddle_k8s
+RUN pip install /*.whl && rm -f /*.whl 
 
 ENV LD_LIBRARY_PATH=/usr/local/lib
-ADD fluid_benchmark.py recordio_converter.py models/ /workspace/
+ADD fluid_benchmark.py recordio_converter.py args.py recordio_converter.py run.sh run_fluid_benchmark.sh /workspace/
+ADD models/ /workspace/models/

benchmark/fluid/fluid_benchmark.py

@@ -97,7 +97,7 @@ def dist_transpile(trainer_id, args):
         return train_program, fluid.default_startup_program()
     else:
         raise ValueError(
-            'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
+            'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
         )
 
 
@@ -264,8 +264,6 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
                     break
             else:
                 loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
-            if args.update_method == "pserver":
-                exe.bcast_params()
             if args.use_reader_op:
                 num_samples += args.batch_size * args.gpus
             else:
@@ -301,9 +299,18 @@ def print_train_time(start_time, end_time, num_samples):
           (num_samples, train_elapsed, examples_per_sec))
 
 
+def print_paddle_envs():
+    print('----------- Configuration envs -----------')
+    for k in os.environ:
+        if "PADDLE_" in k:
+            print "ENV %s:%s" % (k, os.environ[k])
+    print('------------------------------------------------')
+
+
 def main():
     args = parse_args()
     print_arguments(args)
+    print_paddle_envs()
 
     # the unique trainer id, starting from 0, needed by trainer
     # only

benchmark/fluid/kube_gen_job.py

@@ -17,6 +17,7 @@ import copy
 import argparse
 import random
 import os
+import copy
 from kube_templates import pserver, trainer, envs
 
 
@@ -108,10 +109,9 @@ def gen_job():
     tn_container["ports"][0]["containerPort"] = spreadport
 
     envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
-    envs.append({"name": "TRAINERS", "value": str(args.trainers)})
-    envs.append({"name": "PSERVERS", "value": str(args.pservers)})
+    envs.append({"name": "PADDLE_TRAINERS", "value": str(args.trainers)})
+    envs.append({"name": "PADDLE_PSERVERS", "value": str(args.pservers)})
     envs.append({"name": "ENTRY", "value": args.entry})
-    envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
     envs.append({"name": "PADDLE_PSERVER_PORT", "value": str(args.port)})
     # NOTE: these directories below are cluster specific, please modify
     # this settings before you run on your own cluster.
@@ -166,17 +166,23 @@ def gen_job():
     tn["spec"]["template"]["spec"]["volumes"] = volumes
     tn_container["volumeMounts"] = volumeMounts
 
-    ps_container["env"] = envs
-    ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
+    ps_container["env"] = copy.deepcopy(envs)
+    ps_container["env"].append({
+        "name": "PADDLE_TRAINING_ROLE",
+        "value": "PSERVER"
+    })
     tn_container["env"] = envs
     if args.disttype == "pserver":
         tn_container["env"].append({
-            "name": "TRAINING_ROLE",
+            "name": "PADDLE_TRAINING_ROLE",
             "value": "TRAINER"
         })
     elif args.disttype == "nccl2" or args.disttype == "local":
         # NCCL2 have no training role, set to plain WORKER
-        tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
+        tn_container["env"].append({
+            "name": "PADDLE_TRAINING_ROLE",
+            "value": "WORKER"
+        })
 
     os.mkdir(args.jobname)
     if args.disttype == "pserver":

cmake/external/mkldnn.cmake

@@ -45,7 +45,8 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
 ELSE()
     MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
-SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-unused-result")
+SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result")
+SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
 SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
 SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
 ExternalProject_Add(

doc/fluid/api/gen_doc.sh

 #!/bin/bash
 python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler metric > layers.rst
 
-for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
+for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer transpiler
 do
   python gen_doc.py ${module} > ${module}.rst
 done

doc/fluid/api/transpiler.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+==========
+transpiler
+==========
+
+DistributeTranspiler
+--------------------
+
+..  autoclass:: paddle.fluid.transpiler.DistributeTranspiler
+    :members:
+    :noindex:
+
+InferenceTranspiler
+-------------------
+
+..  autoclass:: paddle.fluid.transpiler.InferenceTranspiler
+    :members:
+    :noindex:
+
+memory_optimize
+---------------
+
+..  autofunction:: paddle.fluid.transpiler.memory_optimize
+    :noindex:
+
+release_memory
+--------------
+
+..  autofunction:: paddle.fluid.transpiler.release_memory
+    :noindex:
+
+HashName
+--------
+
+..  autoclass:: paddle.fluid.transpiler.HashName
+    :members:
+    :noindex:
+
+RoundRobin
+----------
+
+..  autoclass:: paddle.fluid.transpiler.RoundRobin
+    :members:
+    :noindex:

doc/fluid/howto/cluster/fluid_cluster_train_cn.md

@@ -168,13 +168,13 @@ cd /paddle/python/paddle/fluid/tests/book
 
 第二步，启动Parameter Server：
 ```bash
-PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.2 TRAINERS=2 POD_IP=192.168.1.2 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=PSERVER python test_fit_a_line.py
+PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.2 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=192.168.1.2 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=PSERVER python test_fit_a_line.py
 ```
 执行命令后请等待出现提示： ```Server listening on 192.168.1.2:6174 ```, 表示Paramter Server已经正常启动。
 
 第三步，启动Trainer：
 ```bash
-PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.3 TRAINERS=2 POD_IP=192.168.1.3 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=TRAINER python test_fit_a_line.py
+PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.3 PADDLE_TRAINERS=2 PADDLE_CURRENT_IPP=192.168.1.3 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=TRAINER python test_fit_a_line.py
 ```
 由于我们定义的Trainer的数量是2个，因此需要在另外一个计算节点上再启动一个Trainer。
 

doc/fluid/howto/cluster/fluid_recordio.md

@@ -114,8 +114,8 @@ def gen_train_list(file_pattern, trainers, trainer_id):
            ret_list.append(f)
    return ret_list
 
-trainers = int(os.getenv("TRAINERS"))
-trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
+trainers = int(os.getenv("PADDLE_TRAINERS"))
+trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
 data_file = fluid.layers.io.open_files(
     filenames=gen_train_list("./mnist-[0-9]*.recordio", 2, 0),
     thread_num=1,

doc/fluid/howto/inference/build_and_install_lib_cn.rst

@@ -13,6 +13,7 @@ cpu_noavx_openblas       `fluid.tgz <https://guest:@paddleci.ngrok.io/repository
 cuda7.5_cudnn5_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
 cuda8.0_cudnn5_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
 cuda8.0_cudnn7_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
+cuda9.0_cudnn7_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/fluid.tgz>`_
 ======================   ========================================
 
 从源码编译

paddle/contrib/inference/demo/simple_on_word2vec.cc

@@ -40,10 +40,9 @@ void Main(bool use_gpu) {
     //# 2. Prepare input.
     int64_t data[4] = {1, 2, 3, 4};
 
-    PaddleBuf buf{.data = data, .length = sizeof(data)};
     PaddleTensor tensor{.name = "",
                         .shape = std::vector<int>({4, 1}),
-                        .data = buf,
+                        .data = PaddleBuf(data, sizeof(data)),
                         .dtype = PaddleDType::INT64};
 
     // For simplicity, we set all the slots with the same data.
@@ -55,14 +54,12 @@ void Main(bool use_gpu) {
 
     //# 4. Get output.
     ASSERT_EQ(outputs.size(), 1UL);
-    LOG(INFO) << "output buffer size: " << outputs.front().data.length;
-    const size_t num_elements = outputs.front().data.length / sizeof(float);
+    LOG(INFO) << "output buffer size: " << outputs.front().data.length();
+    const size_t num_elements = outputs.front().data.length() / sizeof(float);
     // The outputs' buffers are in CPU memory.
     for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
-      LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
+      LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
     }
-    // TODO(Superjomn): this is should be free automatically
-    free(outputs[0].data.data);
   }
 }
 
@@ -86,10 +83,9 @@ void MainThreads(int num_threads, bool use_gpu) {
       for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
         // 2. Dummy Input Data
         int64_t data[4] = {1, 2, 3, 4};
-        PaddleBuf buf{.data = data, .length = sizeof(data)};
         PaddleTensor tensor{.name = "",
                             .shape = std::vector<int>({4, 1}),
-                            .data = buf,
+                            .data = PaddleBuf(data, sizeof(data)),
                             .dtype = PaddleDType::INT64};
         std::vector<PaddleTensor> inputs(4, tensor);
         std::vector<PaddleTensor> outputs;
@@ -99,13 +95,13 @@ void MainThreads(int num_threads, bool use_gpu) {
         // 4. Get output.
         ASSERT_EQ(outputs.size(), 1UL);
         LOG(INFO) << "TID: " << tid << ", "
-                  << "output buffer size: " << outputs.front().data.length;
-        const size_t num_elements = outputs.front().data.length / sizeof(float);
+                  << "output buffer size: " << outputs.front().data.length();
+        const size_t num_elements =
+            outputs.front().data.length() / sizeof(float);
         // The outputs' buffers are in CPU memory.
         for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
-          LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
+          LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
         }
-        free(outputs[0].data.data);
       }
     });
   }

paddle/contrib/inference/high_level_api.md

+# Inference High-level APIs
+This document describes the high-level inference APIs one can use to easily deploy a Paddle model for an application.
+
+The APIs are described in `paddle_inference_api.h`, just one header file, and two libaries `libpaddle_fluid.so` and `libpaddle_fluid_api.so` are needed.
+
+## PaddleTensor
+We provide the `PaddleTensor` data structure is to give a general tensor interface.
+
+The definition is 
+
+```c++
+struct PaddleTensor {
+  std::string name;  // variable name.
+  std::vector<int> shape;
+  PaddleBuf data;  // blob of data.
+  PaddleDType dtype;
+};
+```
+
+The data is stored in a continuous memory `PaddleBuf`, and tensor's data type is specified by a `PaddleDType`. 
+The `name` field is used to specify the name of input variable, 
+that is important when there are multiple inputs and need to distiuish which variable to set.
+
+## engine
+The inference APIs has two different underlying implementation, currently there are two valid engines:
+
+- the native engine, which is consists of the native operators and framework,
+- the Anakin engine, which is a Anakin library embeded.
+
+The native engine takes a native Paddle model as input, and supports any model that trained by Paddle, 
+but the Anakin engine can only take the Anakin model as input(user need to manully transform the format first) and currently not all Paddle models are supported.
+
+```c++
+enum class PaddleEngineKind {
+  kNative = 0,  // Use the native Fluid facility.
+  kAnakin,      // Use Anakin for inference.
+};
+```
+
+## PaddlePredictor and how to create one
+The main interface is `PaddlePredictor`, there are following methods 
+
+- `bool Run(const std::vector<PaddleTensor>& inputs, std::vector<PaddleTensor>* output_data)`
+  - take inputs and output `output_data`
+- `Clone` to clone a predictor from an existing one, with model parameter shared.
+
+There is a factory method to help create a predictor, and the user takes the ownership of this object.
+
+```c++
+template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
+std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
+```
+
+By specifying the engine kind and config, one can get an specific implementation.
+
+## Reference
+
+- [paddle_inference_api.h](./paddle_inference_api.h)
+- [demos](./demo)

paddle/contrib/inference/paddle_inference_api.cc

@@ -13,3 +13,53 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/contrib/inference/paddle_inference_api.h"
+
+namespace paddle {
+
+PaddleBuf::PaddleBuf(PaddleBuf&& other)
+    : data_(other.data_),
+      length_(other.length_),
+      memory_owned_(other.memory_owned_) {
+  other.memory_owned_ = false;
+  other.data_ = nullptr;
+  other.length_ = 0;
+}
+
+PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
+
+PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
+  // only the buffer with external memory can be copied
+  assert(!other.memory_owned_);
+  data_ = other.data_;
+  length_ = other.length_;
+  memory_owned_ = other.memory_owned_;
+  return *this;
+}
+
+void PaddleBuf::Resize(size_t length) {
+  // Only the owned memory can be reset, the external memory can't be changed.
+  if (length_ == length) return;
+  assert(memory_owned_);
+  Free();
+  data_ = new char[length];
+  length_ = length;
+  memory_owned_ = true;
+}
+
+void PaddleBuf::Reset(void* data, size_t length) {
+  Free();
+  memory_owned_ = false;
+  data_ = data;
+  length_ = length;
+}
+
+void PaddleBuf::Free() {
+  if (memory_owned_ && data_) {
+    assert(length_ > 0);
+    delete static_cast<char*>(data_);
+    data_ = nullptr;
+    length_ = 0;
+  }
+}
+
+}  // namespace paddle
\ No newline at end of file

paddle/contrib/inference/paddle_inference_api.h

@@ -21,6 +21,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <cassert>
 #include <memory>
 #include <string>
 #include <vector>
@@ -32,12 +33,38 @@ enum PaddleDType {
   INT64,
 };
 
-struct PaddleBuf {
-  void* data;     // pointer to the data memory.
-  size_t length;  // number of memory bytes.
+class PaddleBuf {
+ public:
+  PaddleBuf() = default;
+  PaddleBuf(PaddleBuf&& other);
+  // Copy only available when memory is managed externally.
+  explicit PaddleBuf(const PaddleBuf&);
+  PaddleBuf& operator=(const PaddleBuf&);
+  // Do not own the memory.
+  PaddleBuf(void* data, size_t length)
+      : data_(data), length_(length), memory_owned_{false} {}
+  // Own memory.
+  PaddleBuf(size_t length)
+      : data_(new char[length]), length_(length), memory_owned_(true) {}
+  // Resize to `length` bytes.
+  void Resize(size_t length);
+  // Reset to external memory.
+  void Reset(void* data, size_t length);
+  bool empty() const { return length_ == 0; }
+  void* data() const { return data_; }
+  size_t length() const { return length_; }
+
+  ~PaddleBuf() { Free(); }
+
+ private:
+  void Free();
+  void* data_{nullptr};  // pointer to the data memory.
+  size_t length_{0};     // number of memory bytes.
+  bool memory_owned_{true};
 };
 
 struct PaddleTensor {
+  PaddleTensor() = default;
   std::string name;  // variable name.
   std::vector<int> shape;
   // TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
@@ -67,8 +94,9 @@ class PaddlePredictor {
 
   // Predict an record.
   // The caller should be responsible for allocating and releasing the memory of
-  // `inputs`. `inputs` should be alive until Run returns. caller should be
-  // responsible for releasing the memory of `output_data`.
+  // `inputs`. `inputs` should be available until Run returns. Caller should be
+  // responsible for the output tensor's buffer, either allocated or passed from
+  // outside.
   virtual bool Run(const std::vector<PaddleTensor>& inputs,
                    std::vector<PaddleTensor>* output_data) = 0;
 
@@ -81,8 +109,7 @@ class PaddlePredictor {
 
   // The common configs for all the predictors.
   struct Config {
-    std::string model_dir;      // path to the model directory.
-    bool enable_engine{false};  // Enable to execute (part of) the model on
+    std::string model_dir;  // path to the model directory.
   };
 };
 

paddle/contrib/inference/paddle_inference_api_anakin_engine.cc

@@ -48,7 +48,7 @@ bool PaddleInferenceAnakinPredictor::Run(
     auto d_tensor_in_p = executor_.get_in(input.name);
     float *d_data_p = d_tensor_in_p->mutable_data();
     if (cudaMemcpy(d_data_p,
-                   static_cast<float *>(input.data.data),
+                   static_cast<float *>(input.data.data()),
                    d_tensor_in_p->valid_size() * sizeof(float),
                    cudaMemcpyHostToDevice) != 0) {
       LOG(ERROR) << "copy data from CPU to GPU error";
@@ -65,8 +65,11 @@ bool PaddleInferenceAnakinPredictor::Run(
   for (auto &output : *output_data) {
     auto *tensor = executor_.get_out(output.name);
     output.shape = tensor->shape();
+    if (output.data.length() < tensor->valid_size() * sizeof(float)) {
+      output.data.Resize(tensor->valid_size() * sizeof(float));
+    }
     // Copy data from GPU -> CPU
-    if (cudaMemcpy(output.data.data,
+    if (cudaMemcpy(output.data.data(),
                    tensor->mutable_data(),
                    tensor->valid_size() * sizeof(float),
                    cudaMemcpyDeviceToHost) != 0) {

paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc

@@ -37,28 +37,26 @@ TEST(inference, anakin) {
 
   float data[1 * 3 * 224 * 224] = {1.0f};
 
-  PaddleBuf buf{.data = data, .length = sizeof(data)};
   PaddleTensor tensor{.name = "input_0",
                       .shape = std::vector<int>({1, 3, 224, 224}),
-                      .data = buf,
+                      .data = PaddleBuf(data, sizeof(data)),
                       .dtype = PaddleDType::FLOAT32};
 
   // For simplicity, we set all the slots with the same data.
-  std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
+  std::vector<PaddleTensor> paddle_tensor_feeds;
+  paddle_tensor_feeds.emplace_back(std::move(tensor));
 
-  float data_out[1000];
-
-  PaddleBuf buf_out{.data = data_out, .length = sizeof(data)};
   PaddleTensor tensor_out{.name = "prob_out",
                           .shape = std::vector<int>({1000, 1}),
-                          .data = buf_out,
+                          .data = PaddleBuf(),
                           .dtype = PaddleDType::FLOAT32};
 
-  std::vector<PaddleTensor> outputs(1, tensor_out);
+  std::vector<PaddleTensor> outputs;
+  outputs.emplace_back(std::move(tensor_out));
 
   ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
 
-  float* data_o = static_cast<float*>(outputs[0].data.data);
+  float* data_o = static_cast<float*>(outputs[0].data.data());
   for (size_t j = 0; j < 1000; ++j) {
     LOG(INFO) << "output[" << j << "]: " << data_o[j];
   }

paddle/contrib/inference/paddle_inference_api_impl.cc

@@ -178,8 +178,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
 
     // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
     std::memcpy(static_cast<void *>(input_ptr),
-                inputs[i].data.data,
-                inputs[i].data.length);
+                inputs[i].data.data(),
+                inputs[i].data.length());
     feeds->push_back(input);
   }
   return true;
@@ -241,10 +241,11 @@ bool NativePaddlePredictor::GetFetch(
     }
 
     outputs->at(i).shape = shape;
-    outputs->at(i).data.length = sizeof(float) * data.size();
-    outputs->at(i).data.data = malloc(outputs->at(i).data.length);
-    std::memcpy(
-        outputs->at(i).data.data, data.data(), outputs->at(i).data.length);
+    auto &buffer = outputs->at(i).data;
+    if (buffer.empty() || buffer.length() < sizeof(float) * data.size()) {
+      buffer.Resize(sizeof(float) * data.size());
+    }
+    std::memcpy(buffer.data(), data.data(), buffer.length());
     outputs->at(i).dtype = PaddleDType::FLOAT32;
     // TODO(panyx0718): support other types? fill tensor name? avoid a copy.
   }

paddle/contrib/inference/test_paddle_inference_api_impl.cc

@@ -27,13 +27,12 @@ namespace paddle {
 
 PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
   PaddleTensor pt;
-  pt.data.data = t->data<void>();
 
   if (t->type() == typeid(int64_t)) {
-    pt.data.length = t->numel() * sizeof(int64_t);
+    pt.data.Reset(t->data<void>(), t->numel() * sizeof(int64_t));
     pt.dtype = PaddleDType::INT64;
   } else if (t->type() == typeid(float)) {
-    pt.data.length = t->numel() * sizeof(float);
+    pt.data.Reset(t->data<void>(), t->numel() * sizeof(float));
     pt.dtype = PaddleDType::FLOAT32;
   } else {
     LOG(FATAL) << "unsupported type.";
@@ -79,8 +78,8 @@ void MainWord2Vec(bool use_gpu) {
   std::vector<PaddleTensor> outputs;
   ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
   ASSERT_EQ(outputs.size(), 1UL);
-  size_t len = outputs[0].data.length;
-  float* data = static_cast<float*>(outputs[0].data.data);
+  size_t len = outputs[0].data.length();
+  float* data = static_cast<float*>(outputs[0].data.data());
   for (size_t j = 0; j < len / sizeof(float); ++j) {
     ASSERT_LT(data[j], 1.0);
     ASSERT_GT(data[j], -1.0);
@@ -103,8 +102,6 @@ void MainWord2Vec(bool use_gpu) {
     EXPECT_LT(lod_data[i] - data[i], 1e-3);
     EXPECT_GT(lod_data[i] - data[i], -1e-3);
   }
-
-  free(outputs[0].data.data);
 }
 
 void MainImageClassification(bool use_gpu) {
@@ -143,13 +140,12 @@ void MainImageClassification(bool use_gpu) {
   std::vector<PaddleTensor> outputs;
   ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
   ASSERT_EQ(outputs.size(), 1UL);
-  size_t len = outputs[0].data.length;
-  float* data = static_cast<float*>(outputs[0].data.data);
+  size_t len = outputs[0].data.length();
+  float* data = static_cast<float*>(outputs[0].data.data());
   float* lod_data = output1.data<float>();
   for (size_t j = 0; j < len / sizeof(float); ++j) {
     EXPECT_NEAR(lod_data[j], data[j], 1e-3);
   }
-  free(data);
 }
 
 void MainThreadsWord2Vec(bool use_gpu) {
@@ -192,8 +188,8 @@ void MainThreadsWord2Vec(bool use_gpu) {
 
       // check outputs range
       ASSERT_EQ(local_outputs.size(), 1UL);
-      const size_t len = local_outputs[0].data.length;
-      float* data = static_cast<float*>(local_outputs[0].data.data);
+      const size_t len = local_outputs[0].data.length();
+      float* data = static_cast<float*>(local_outputs[0].data.data());
       for (size_t j = 0; j < len / sizeof(float); ++j) {
         ASSERT_LT(data[j], 1.0);
         ASSERT_GT(data[j], -1.0);
@@ -205,7 +201,6 @@ void MainThreadsWord2Vec(bool use_gpu) {
       for (int i = 0; i < refs[tid].numel(); ++i) {
         EXPECT_NEAR(ref_data[i], data[i], 1e-3);
       }
-      free(data);
     });
   }
   for (int i = 0; i < num_jobs; ++i) {
@@ -251,14 +246,13 @@ void MainThreadsImageClassification(bool use_gpu) {
 
       // check outputs correctness
       ASSERT_EQ(local_outputs.size(), 1UL);
-      const size_t len = local_outputs[0].data.length;
-      float* data = static_cast<float*>(local_outputs[0].data.data);
+      const size_t len = local_outputs[0].data.length();
+      float* data = static_cast<float*>(local_outputs[0].data.data());
       float* ref_data = refs[tid].data<float>();
       EXPECT_EQ(refs[tid].numel(), len / sizeof(float));
       for (int i = 0; i < refs[tid].numel(); ++i) {
         EXPECT_NEAR(ref_data[i], data[i], 1e-3);
       }
-      free(data);
     });
   }
   for (int i = 0; i < num_jobs; ++i) {

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -57,6 +57,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
   for (auto &p : params) {
     grad_names_.insert(GradVarName(p));
   }
+  balance_vars_.resize(places_.size(), 0);
 }
 
 void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
@@ -140,11 +141,30 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
          checker(op.InputArgumentNames(), recv_vars);
 }
 
+size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
+    const std::vector<std::string> &var_names) const {
+  int64_t numel_sum = 0;
+  for (auto var_name : var_names) {
+    auto var_desc = all_vars_.at(var_name);
+    PADDLE_ENFORCE_NOT_NULL(var_desc);
+    auto dim = framework::make_ddim(var_desc->GetShape());
+    int64_t numel = framework::product(dim);
+    PADDLE_ENFORCE_GT(numel, 0);
+    numel_sum += numel;
+  }
+
+  auto smallest =
+      std::min_element(std::begin(balance_vars_), std::end(balance_vars_));
+  size_t dev_id =
+      static_cast<size_t>(std::distance(std::begin(balance_vars_), smallest));
+  balance_vars_[dev_id] += numel_sum;
+  return dev_id;
+}
+
 std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     const ProgramDesc &program) const {
-  std::unordered_map<std::string, VarDesc *> all_vars;
   for (auto *var : program.Block(0).AllVars()) {
-    all_vars[var->Name()] = var;
+    all_vars_.emplace(var->Name(), var);
   }
 
   auto graph = new SSAGraph();
@@ -161,35 +181,16 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
   auto send_vars = FindDistTrainSendVars(program);
   auto recv_vars = FindDistTrainRecvVars(program);
 
-  std::vector<std::unordered_set<std::string>> var_name_on_devices;
   std::vector<std::unordered_set<std::string>> bcast_var_name_set;
-  var_name_on_devices.resize(places_.size());
   bcast_var_name_set.resize(places_.size());
 
   size_t cur_device_id = 0;
-  std::vector<int64_t> balance_grads(places_.size(), 0);
-
-  auto get_appropriate_dev = [&](std::string &g_name) -> size_t {
-    auto var_desc = all_vars.at(g_name);
-    PADDLE_ENFORCE_NOT_NULL(var_desc);
-    auto dim = framework::make_ddim(var_desc->GetShape());
-    int64_t numel = framework::product(dim);
-    PADDLE_ENFORCE_GE(numel, 0);
-    auto smallest =
-        std::min_element(std::begin(balance_grads), std::end(balance_grads));
-    size_t dev_id =
-        static_cast<size_t>(std::distance(std::begin(balance_grads), smallest));
-    balance_grads[dev_id] += numel;
-    return dev_id;
-  };
-
   bool is_forwarding = true;
+
   for (auto *op : program.Block(0).AllOps()) {
     if (boost::get<int>(
             op->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
         static_cast<int>(OpRole::kRPC)) {
-      // append rpc op if program is distributed trainer main program.
-      // always use the first device
       CreateRPCOp(&result, *op);
     } else if (IsDistTrainOp(*op, send_vars, recv_vars)) {
       CreateDistTrainOp(&result, *op);
@@ -199,15 +200,19 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
           BuildStrategy::GradientScaleStrategy::kCustomized) {
         CreateScaleLossGradOp(&result);
       }
+      // This assumes the backward generating code will ensure IsScaleLossOp
+      // is true only for the op that scale the final scalar loss.
+      // It also assumes backward op will always follow the forward op in
+      // the block.
       is_forwarding = false;
     } else {
-      int op_dev_id = GetOpDeviceID(var_name_on_devices, *op);
+      int op_dev_id = GetOpDeviceID(*op);
       if (op_dev_id == -1) {  // var on all device
         CreateComputationalOps(&result, *op, places_.size());
       } else {
         CreateComputationalOp(&result, *op, op_dev_id);
         for (auto &var_name : op->OutputArgumentNames()) {
-          var_name_on_devices[op_dev_id].emplace(var_name);
+          var_name_on_devices_.emplace(var_name, op_dev_id);
         }
       }
       if (!is_forwarding && places_.size() > 1) {
@@ -230,19 +235,22 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 
               switch (strategy_.reduce_) {
                 case BuildStrategy::ReduceStrategy::kReduce:
-                  cur_device_id = get_appropriate_dev(g_name);
+                  cur_device_id = GetAppropriateDeviceID({g_name});
                   CreateReduceOp(&result, g_name, cur_device_id);
-                  var_name_on_devices[cur_device_id].emplace(g_name);
+                  var_name_on_devices_.emplace(g_name, cur_device_id);
                   bcast_var_name_set[cur_device_id].emplace(p_name);
                   break;
                 case BuildStrategy::ReduceStrategy::kAllReduce:
-                  if (IsSparseGradient(all_vars, g_name)) {
+                  if (IsSparseGradient(g_name)) {
                     CreateReduceOp(&result, g_name, 0);
                     CreateBroadcastOp(&result, g_name, 0);
                   } else {
                     InsertAllReduceOp(&result, g_name);
                   }
                   break;
+                default:
+                  LOG(FATAL) << "Unknown reduce strategy ";
+                  break;
               }
             }
           } catch (boost::bad_get e) {
@@ -261,7 +269,7 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
   }
   /*
     Dependency graph has been constructed. However, there are still data
-    harzaeds need to be handled.
+    hazards need to be handled.
    */
   PolishGraphToSupportDataHazards(&result);
 
@@ -273,11 +281,9 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
   return std::unique_ptr<SSAGraph>(graph);
 }
 
-bool MultiDevSSAGraphBuilder::IsSparseGradient(
-    const std::unordered_map<std::string, VarDesc *> &all_vars,
-    const std::string &og) const {
-  PADDLE_ENFORCE(all_vars.count(og) != 0);
-  if (all_vars.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
+bool MultiDevSSAGraphBuilder::IsSparseGradient(const std::string &og) const {
+  PADDLE_ENFORCE(all_vars_.count(og) != 0);
+  if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
     return true;
   }
   return false;
@@ -363,24 +369,23 @@ bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
   return is_pg_once;
 }
 
-int MultiDevSSAGraphBuilder::GetOpDeviceID(
-    const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
-    const OpDesc &op) const {
+int MultiDevSSAGraphBuilder::GetOpDeviceID(const OpDesc &op) const {
   if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
     return -1;
   }
 
-  int var_dev_id = -1;
-  for (auto &var_name : op.InputArgumentNames()) {
-    if (var_dev_id != -1) break;
-    for (size_t i = 0; i < var_name_on_devices.size(); ++i) {
-      if (var_name_on_devices[i].count(var_name)) {
-        var_dev_id = static_cast<int>(i);
-        break;
-      }
+  for (auto &varname : op.InputArgumentNames()) {
+    int dev_id = GetVarDeviceID(varname);
+    if (dev_id != -1) {
+      return dev_id;
     }
   }
-  return var_dev_id;
+  return -1;
+}
+
+int MultiDevSSAGraphBuilder::GetVarDeviceID(const std::string &varname) const {
+  auto got = var_name_on_devices_.find(varname);
+  return got == var_name_on_devices_.end() ? -1 : got->second;
 }
 
 void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(SSAGraph *result) const {
@@ -449,6 +454,8 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(SSAGraph *result,
   return var;
 }
 
+// Find the first occurence of `prev_op_name` and make current `op` depend
+// on it.
 void MultiDevSSAGraphBuilder::ConnectOp(SSAGraph *result, OpHandleBase *op,
                                         const std::string &prev_op_name) const {
   for (auto &prev_op : result->ops_) {
@@ -463,16 +470,66 @@ void MultiDevSSAGraphBuilder::ConnectOp(SSAGraph *result, OpHandleBase *op,
 
 void MultiDevSSAGraphBuilder::CreateDistTrainOp(SSAGraph *result,
                                                 const OpDesc &op) const {
-  CreateComputationalOp(result, op, 0);
+  int op_dev_id = -1;
+  if (op.Type() == "split_byref") {
+    op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
+    if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
+      op_dev_id = GetAppropriateDeviceID(op.InputArgumentNames());
+      for (auto &varname : op.InputArgumentNames()) {
+        var_name_on_devices_.emplace(varname, op_dev_id);
+      }
+    }
+    for (auto &varname : op.OutputArgumentNames()) {
+      var_name_on_devices_.emplace(varname, op_dev_id);
+    }
+  } else if (op.Type() == "concat") {
+    op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
+  } else {
+    PADDLE_ENFORCE(
+        "the distribute training related op should be in [split_byref, "
+        "concat].");
+  }
+
+  PADDLE_ENFORCE(op_dev_id != -1,
+                 "can not find right place for distributed op: %s", op.Type());
+
+  CreateComputationalOp(result, op, op_dev_id);
   if (op.Type() == "concat") {
     ConnectOp(result, result->ops_.back().get(), "fetch_barrier");
   }
 }
 
+// Create RPC related op handles that connects its in ops and out ops.
 void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
                                           const OpDesc &op) const {
-  result->ops_.emplace_back(
-      new RPCOpHandle(op, local_scopes_[0], op.Type(), places_[0]));
+  int op_dev_id = -1;
+  if (op.Type() == "send") {
+    op_dev_id = GetVarDeviceID(op.InputArgumentNames()[0]);
+    // the variable name which contains .block means it was splited by
+    // split_byref op
+    // so that we can balance the variable blocks to all the pserver instances.
+    if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
+        op.InputArgumentNames()[0].find(".block") == std::string::npos) {
+      op_dev_id = GetAppropriateDeviceID(op.InputArgumentNames());
+      for (auto &varname : op.InputArgumentNames()) {
+        var_name_on_devices_.emplace(varname, op_dev_id);
+      }
+    }
+  } else if (op.Type() == "recv") {
+    op_dev_id = GetAppropriateDeviceID(op.OutputArgumentNames());
+    for (auto &varname : op.OutputArgumentNames()) {
+      var_name_on_devices_.emplace(varname, op_dev_id);
+    }
+  } else {
+    // send_barrier and fetch_barrier op can be scheduled on device 0
+    op_dev_id = 0;
+  }
+
+  PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
+                 op.Type());
+
+  result->ops_.emplace_back(new RPCOpHandle(op, local_scopes_[op_dev_id],
+                                            op.Type(), places_[op_dev_id]));
 
   if (op.Type() == "send_barrier") {
     ConnectOp(result, result->ops_.back().get(), "send");
@@ -488,9 +545,7 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(SSAGraph *result,
         "send, send_barrier. recv, fetch_barrier]");
   }
 
-  // TODO(Yancey1989): schedule rpc op on different place may
-  // increate throughput
-  CreateOpHandleIOs(result, op, 0);
+  CreateOpHandleIOs(result, op, op_dev_id);
 }
 
 bool MultiDevSSAGraphBuilder::IsScaleLossOp(const OpDesc &op) const {

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -47,10 +47,11 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
 #endif
 
   std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
+  int GetVarDeviceID(const std::string &varname) const;
 
  private:
   void CreateOpHandleIOs(SSAGraph *result, const OpDesc &op,
-                         size_t place_id) const;
+                         size_t device_id) const;
 
  private:
   std::string loss_var_name_;
@@ -96,21 +97,23 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
       const std::string &og,
       std::unordered_set<std::string> *og_has_been_broadcast) const;
 
-  int GetOpDeviceID(
-      const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
-      const OpDesc &op) const;
+  int GetOpDeviceID(const OpDesc &op) const;
 
   void InsertAllReduceOp(SSAGraph *result, const std::string &og) const;
 
   void CreateBroadcastOp(SSAGraph *result, const std::string &p_name,
                          size_t src_dev_id) const;
 
-  bool IsSparseGradient(
-      const std::unordered_map<std::string, VarDesc *> &all_vars,
-      const std::string &og) const;
+  bool IsSparseGradient(const std::string &og) const;
+
+  size_t GetAppropriateDeviceID(
+      const std::vector<std::string> &var_names) const;
 
  private:
   BuildStrategy strategy_;
+  mutable std::unordered_map<std::string, VarDesc *> all_vars_;
+  mutable std::unordered_map<std::string, int> var_name_on_devices_;
+  mutable std::vector<int64_t> balance_vars_;
 
   void SetCommunicationContext(OpHandleBase *op_handle,
                                const platform::Place &p) const;

paddle/fluid/framework/details/ssa_graph_builder.h

@@ -30,6 +30,7 @@ class SSAGraphBuilder {
   SSAGraphBuilder() {}
   virtual ~SSAGraphBuilder() {}
   virtual std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const = 0;
+  virtual int GetVarDeviceID(const std::string &var_name) const { return -1; }
 
   DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
 

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -96,6 +96,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
 
     if (timeout) {
+      std::lock_guard<std::mutex> l(exception_mu_);
       if (exception_) {
         auto exp = *exception_;
         exception_.reset();
@@ -199,6 +200,7 @@ void ThreadedSSAGraphExecutor::RunOp(
       ready_var_q->Extend(op->Outputs());
       VLOG(10) << op << " " << op->Name() << "Signal posted";
     } catch (platform::EnforceNotMet ex) {
+      std::lock_guard<std::mutex> l(exception_mu_);
       exception_.reset(new platform::EnforceNotMet(ex));
     } catch (...) {
       LOG(FATAL) << "Unknown exception catched";

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -56,6 +56,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;
   platform::DeviceContextPool fetch_ctxs_;
+  std::mutex exception_mu_;
   std::unique_ptr<platform::EnforceNotMet> exception_;
   std::atomic<int> running_ops_;
 

paddle/fluid/framework/executor.cc

@@ -21,7 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
 #ifdef PADDLE_WITH_DISTRIBUTE
-#include "paddle/fluid/operators/detail/grpc_client.h"
+#include "paddle/fluid/operators/distributed/grpc_client.h"
 #endif
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -49,8 +49,8 @@ Executor::Executor(const platform::Place& place) : place_(place) {}
 
 #ifdef PADDLE_WITH_DISTRIBUTE
 void Executor::Complete() {
-  ::paddle::operators::detail::RPCClient::GetInstance<
-      ::paddle::operators::detail::GRPCClient>()
+  ::paddle::operators::distributed::RPCClient::GetInstance<
+      ::paddle::operators::distributed::GRPCClient>()
       ->SendComplete();
 }
 #endif
@@ -321,7 +321,8 @@ std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
 }
 
 void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
-                                  bool create_local_scope, bool create_vars) {
+                                  bool create_local_scope, bool create_vars,
+                                  bool keep_kids) {
   Scope* local_scope = scope;
   if (create_vars) {
     if (create_local_scope) {
@@ -344,12 +345,20 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
     }
   }
   platform::DeviceContextPool::Instance().Get(place_)->Wait();
-  if (create_vars && create_local_scope) {
+  if (local_scope != scope) {
     scope->DeleteScope(local_scope);
   } else {
-    // Delete the local scopes created in operators.
-    scope->DropKids();
+    if (!keep_kids) {
+      // By default, we should delete all kid scopes after run executor because
+      // some operators may create local scope when running, such as while_op.
+      // But when while_op also create a local executor to run it's sub block,
+      // the sub scopes it created should not be dropped immediately, because
+      // while_grad_op will use some variables created during while_op run, so
+      // we need to keep the kids and wait for the outer executor to drop them.
+      scope->DropKids();
+    }
   }
+
   if (FLAGS_benchmark) {
     VLOG(2) << "-------------------------------------------------------";
     VLOG(2) << "Memory used after deleting local scope: "

paddle/fluid/framework/executor.h

@@ -78,7 +78,7 @@ class Executor {
 
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           bool create_local_scope = true,
-                          bool create_vars = true);
+                          bool create_vars = true, bool keep_kids = false);
 
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           std::map<std::string, const LoDTensor*>* feed_targets,

paddle/fluid/framework/parallel_executor.cc

@@ -110,7 +110,6 @@ ParallelExecutor::ParallelExecutor(
 
   // Step 3. Convert main_program to SSA form and dependency graph. Also, insert
   // ncclOp
-
   details::SSAGraphBuilderFactory builder_factory(
       member_->places_, loss_var_name, params, member_->local_scopes_,
       build_strategy);
@@ -122,9 +121,10 @@ ParallelExecutor::ParallelExecutor(
 #endif
   }
 
+  builder_ = std::move(builder_factory.Create());
   member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, places,
-      builder_factory.Create()->Build(main_program)));
+      builder_->Build(main_program)));
 
   member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, std::move(var_infos),
@@ -133,10 +133,22 @@ ParallelExecutor::ParallelExecutor(
 
 void ParallelExecutor::BCastParamsToGPUs(
     const std::unordered_set<std::string> &vars) const {
-  auto *main_scope = member_->local_scopes_[0];
+  // the the initialize bcast, all vars would be bcast from device(0), otherwise
+  // bcast from the specified device.
+  bool initialize = builder_.get() == nullptr ? true : false;
 
   for (auto &var : vars) {
-    auto *main_var = main_scope->FindVar(var);
+    int var_dev_id =
+        builder_.get() == nullptr ? -1 : builder_->GetVarDeviceID(var);
+    if (!initialize && var_dev_id == -1) continue;
+
+    framework::Variable *main_var = nullptr;
+    if (initialize) {
+      main_var = member_->local_scopes_[0]->FindVar(var);
+    } else {
+      main_var = member_->local_scopes_[var_dev_id]->FindVar(var);
+    }
+
     if (main_var == nullptr || !main_var->IsType<LoDTensor>()) {
       continue;
     }
@@ -151,7 +163,8 @@ void ParallelExecutor::BCastParamsToGPUs(
       for (size_t i = 0; i < member_->places_.size(); ++i) {
         auto place = member_->places_[i];
         void *buffer;
-        if (i == 0) {
+
+        if ((initialize && i == 0) || (!initialize && i == var_dev_id)) {
           buffer = const_cast<void *>(main_tensor.data<void>());
         } else {
           auto local_scope = member_->local_scopes_[i];

paddle/fluid/framework/parallel_executor.h

@@ -19,12 +19,14 @@ limitations under the License. */
 #include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/details/execution_strategy.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/platform/device_context.h"
+
 namespace paddle {
 namespace framework {
 
@@ -68,6 +70,7 @@ class ParallelExecutor {
 
  private:
   ParallelExecutorPrivate *member_;
+  std::unique_ptr<details::SSAGraphBuilder> builder_;
 };
 
 }  // namespace framework

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc

@@ -27,7 +27,7 @@ void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
   SubGraphFuse(graph, node_inside_subgraph_teller_);
 }
 
-}  // analysis
-}  // inference
+}  // namespace analysis
+}  // namespace inference
 
-}  // paddle
+}  // namespace paddle

paddle/fluid/operators/CMakeLists.txt

@@ -184,9 +184,9 @@ else()
     set(DEPS_OPS ${DEPS_OPS} nccl_op)
 endif()
 
-add_subdirectory(detail)
 if(WITH_DISTRIBUTE)
-
+    add_subdirectory(distributed)
+    
     set(DISTRIBUTE_DEPS "")
     if(WITH_GRPC)
         set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
@@ -195,18 +195,11 @@ if(WITH_DISTRIBUTE)
     endif()
 
     set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
-    op_library(prefetch_op DEPS ${DISTRIBUTE_DEPS})
-    set_source_files_properties(prefetch_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    op_library(recv_op DEPS ${DISTRIBUTE_DEPS})
-    set_source_files_properties(recv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    op_library(listen_and_serv_op DEPS ${DISTRIBUTE_DEPS})
-    set_source_files_properties(listen_and_serv_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    op_library(send_op DEPS ${DISTRIBUTE_DEPS})
-    set_source_files_properties(send_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
-    op_library(fetch_barrier_op DEPS ${DISTRIBUTE_DEPS})
-    set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    set_source_files_properties(fetch_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    foreach(dist_op "prefetch_op" "listen_and_serv_op" "send_op" "recv_op" "send_barrier_op" "fetch_barrier_op")
+        op_library(${dist_op} DEPS ${DISTRIBUTE_DEPS})
+        set_source_files_properties(${dist_op}.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    endforeach()
+    
     #set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     #cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
     #        listen_and_serv_op sum_op executor SERIAL)

paddle/fluid/operators/activation_op.cc

@@ -143,7 +143,7 @@ $$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
 __attribute__((unused)) constexpr char TanhShrinkDoc[] = R"DOC(
 TanhShrink Activation Operator.
 
-$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
+$$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
 
 )DOC";
 
@@ -385,7 +385,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 STanh Activation Operator.
 
-$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
+$$out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
 
 )DOC");
   }

paddle/fluid/operators/batch_norm_mkldnn_op.cc

@@ -21,8 +21,6 @@ namespace operators {
 
 using batch_norm_bwd = mkldnn::batch_normalization_backward;
 using batch_norm_fwd = mkldnn::batch_normalization_forward;
-using framework::DataLayout;
-using framework::Tensor;
 using mkldnn::memory;
 using mkldnn::primitive;
 using mkldnn::reorder;
@@ -31,18 +29,6 @@ using paddle::platform::MKLDNNDeviceContext;
 using paddle::platform::MKLDNNMemDesc;
 using platform::to_void_cast;
 
-template <typename T>
-using EigenArrayMap =
-    Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
-template <typename T>
-using ConstEigenArrayMap =
-    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
-template <typename T>
-using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
-template <typename T>
-using ConstEigenVectorArrayMap =
-    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
-
 namespace {
 template <typename T>
 struct bn_type_traits {

paddle/fluid/operators/batch_norm_op.cc

@@ -22,22 +22,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-using DataLayout = framework::DataLayout;
-
-template <typename T>
-using EigenArrayMap =
-    Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
-template <typename T>
-using ConstEigenArrayMap =
-    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
-template <typename T>
-using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
-template <typename T>
-using ConstEigenVectorArrayMap =
-    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
-
 class BatchNormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;

paddle/fluid/operators/batch_norm_op.h

@@ -19,6 +19,22 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
+template <typename T>
+using EigenArrayMap =
+    Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
+template <typename T>
+using ConstEigenArrayMap =
+    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
+template <typename T>
+using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
+template <typename T>
+using ConstEigenVectorArrayMap =
+    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
+
 template <typename DeviceContext, typename T>
 class BatchNormKernel : public framework::OpKernel<T> {
  public:

paddle/fluid/operators/bilinear_interp_op.cc

@@ -110,6 +110,7 @@ REGISTER_OPERATOR(bilinear_interp, ops::BilinearInterpOp,
                   ops::BilinearInterpOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(bilinear_interp_grad, ops::BilinearInterpOpGrad);
-REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>);
+REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>,
+                       ops::BilinearInterpKernel<uint8_t>);
 REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
                        ops::BilinearInterpGradKernel<float>);

paddle/fluid/operators/bilinear_interp_op.h

@@ -46,8 +46,10 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
     int in_chw = channels * in_hw;
     int out_chw = channels * out_hw;
 
-    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
-    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
       memcpy(output, input, input_t->numel() * sizeof(T));
@@ -56,24 +58,24 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
         for (int i = 0; i < out_h; ++i) {     // loop for images
           int h = ratio_h * i;
           int hid = (h < in_h - 1) ? 1 : 0;
-          T h1lambda = ratio_h * i - h;
-          T h2lambda = 1 - h1lambda;
+          float h1lambda = ratio_h * i - h;
+          float h2lambda = 1.f - h1lambda;
 
           for (int j = 0; j < out_w; ++j) {
             int w = ratio_w * j;
             int wid = (w < in_w - 1) ? 1 : 0;
-            T w1lambda = ratio_w * j - w;
-            T w2lambda = 1 - w1lambda;
+            float w1lambda = ratio_w * j - w;
+            float w2lambda = 1.f - w1lambda;
             // calculate four position for bilinear interpolation
             const T* in_pos = &input[k * in_chw + h * in_w + w];
             T* out_pos = &output[k * out_chw + i * out_w + j];
 
             for (int c = 0; c < channels; ++c) {  // loop for channels
               // bilinear interpolation
-              out_pos[0] =
+              out_pos[0] = static_cast<T>(
                   h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
                   h1lambda * (w2lambda * in_pos[hid * in_w] +
-                              w1lambda * in_pos[hid * in_w + wid]);
+                              w1lambda * in_pos[hid * in_w + wid]));
               in_pos += in_hw;
               out_pos += out_hw;
             }
@@ -117,8 +119,10 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
     int in_chw = channels * in_hw;
     int out_chw = channels * out_hw;
 
-    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
-    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
       memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
@@ -127,22 +131,24 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
         for (int i = 0; i < out_h; ++i) {     // loop for images
           int h = ratio_h * i;
           int hid = (h < in_h - 1) ? 1 : 0;
-          T h1lambda = ratio_h * i - h;
-          T h2lambda = 1 - h1lambda;
+          float h1lambda = ratio_h * i - h;
+          float h2lambda = 1 - h1lambda;
 
           for (int j = 0; j < out_w; ++j) {
             int w = ratio_w * j;
             int wid = (w < in_w - 1) ? 1 : 0;
-            T w1lambda = ratio_w * j - w;
-            T w2lambda = 1 - w1lambda;
+            float w1lambda = ratio_w * j - w;
+            float w2lambda = 1 - w1lambda;
             T* in_pos = &d_input[k * in_chw + h * in_w + w];
             const T* out_pos = &d_output[k * out_chw + i * out_w + j];
 
             for (int c = 0; c < channels; ++c) {  // loop for channels
-              in_pos[0] += h2lambda * w2lambda * out_pos[0];
-              in_pos[wid] += h2lambda * w1lambda * out_pos[0];
-              in_pos[hid * in_w] += h1lambda * w2lambda * out_pos[0];
-              in_pos[hid * in_w + wid] += h1lambda * w1lambda * out_pos[0];
+              in_pos[0] += static_cast<T>(h2lambda * w2lambda * out_pos[0]);
+              in_pos[wid] += static_cast<T>(h2lambda * w1lambda * out_pos[0]);
+              in_pos[hid * in_w] +=
+                  static_cast<T>(h1lambda * w2lambda * out_pos[0]);
+              in_pos[hid * in_w + wid] +=
+                  static_cast<T>(h1lambda * w1lambda * out_pos[0]);
               in_pos += in_hw;
               out_pos += out_hw;
             }

paddle/fluid/operators/detail/macros.h

@@ -15,13 +15,13 @@
 #pragma once
 
 #ifdef PADDLE_WITH_GRPC
-#include "paddle/fluid/operators/detail/grpc_client.h"
-#include "paddle/fluid/operators/detail/grpc_server.h"
-#define RPCSERVER_T detail::AsyncGRPCServer
-#define RPCCLIENT_T detail::GRPCClient
+#include "paddle/fluid/operators/distributed/grpc_client.h"
+#include "paddle/fluid/operators/distributed/grpc_server.h"
+#define RPCSERVER_T distributed::AsyncGRPCServer
+#define RPCCLIENT_T distributed::GRPCClient
 #else
-#include "paddle/fluid/operators/detail/brpc_client.h"
-#include "paddle/fluid/operators/detail/brpc_server.h"
-#define RPCSERVER_T detail::AsyncBRPCServer
-#define RPCCLIENT_T detail::BRPCClient
+#include "paddle/fluid/operators/distributed/brpc_client.h"
+#include "paddle/fluid/operators/distributed/brpc_server.h"
+#define RPCSERVER_T distributed::AsyncBRPCServer
+#define RPCCLIENT_T distributed::BRPCClient
 #endif

paddle/fluid/operators/detection_map_op.cc

@@ -175,12 +175,12 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Detection mAP evaluate operator.
 The general steps are as follows. First, calculate the true positive and
- false positive according to the input of detection and labels, then
- calculate the mAP evaluate value.
- Supporting '11 point' and 'integral' mAP algorithm. Please get more information
- from the following articles:
- https://sanchom.wordpress.com/tag/average-precision/
- https://arxiv.org/abs/1512.02325
+false positive according to the input of detection and labels, then
+calculate the mAP evaluate value.
+Supporting '11 point' and 'integral' mAP algorithm. Please get more information
+from the following articles:
+https://sanchom.wordpress.com/tag/average-precision/
+https://arxiv.org/abs/1512.02325
 
 )DOC");
   }

paddle/fluid/operators/detail/CMakeLists.txt → paddle/fluid/operators/distributed/CMakeLists.txt

-if(NOT WITH_DISTRIBUTE)
-    return()
-endif()
-
-
 if(WITH_GRPC)
   grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
       request_handler_impl.cc rpc_client.cc rpc_server.cc grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor

paddle/fluid/operators/detail/brpc_client.cc → paddle/fluid/operators/distributed/brpc_client.cc

@@ -12,12 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/detail/brpc_client.h"
+#include "paddle/fluid/operators/distributed/brpc_client.h"
 #include "paddle/fluid/framework/threadpool.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 DEFINE_int32(brpc_channel_num, 24,
              "Number of channels to send requests connected to one server");
@@ -175,6 +175,6 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
   return q;
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/brpc_client.h → paddle/fluid/operators/distributed/brpc_client.h

@@ -31,13 +31,13 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
-#include "paddle/fluid/operators/detail/rpc_client.h"
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/rpc_client.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
 #include "paddle/fluid/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 struct ChannelContext {
   brpc::Channel channel;
@@ -95,6 +95,6 @@ class BRPCClient : public RPCClient {
   DISABLE_COPY_AND_ASSIGN(BRPCClient);
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/brpc_server.cc → paddle/fluid/operators/distributed/brpc_server.cc

@@ -12,13 +12,13 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/detail/brpc_server.h"
-#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/distributed/brpc_server.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
 
 namespace sendrecv {
 
 typedef std::unordered_map<std::string,
-                           paddle::operators::detail::RequestHandler*>
+                           paddle::operators::distributed::RequestHandler*>
     HandlerMap;
 
 class BRPCServiceImpl : public SendRecvService {
@@ -27,17 +27,17 @@ class BRPCServiceImpl : public SendRecvService {
       : request_send_h_(nullptr),
         request_get_h_(nullptr),
         request_prefetch_h_(nullptr) {
-    auto it = rpc_call_map.find(paddle::operators::detail::kRequestSend);
+    auto it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
     if (it != rpc_call_map.end()) {
       request_send_h_ = it->second;
     }
 
-    it = rpc_call_map.find(paddle::operators::detail::kRequestSend);
+    it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
     if (it != rpc_call_map.end()) {
       request_get_h_ = it->second;
     }
 
-    it = rpc_call_map.find(paddle::operators::detail::kRequestPrefetch);
+    it = rpc_call_map.find(paddle::operators::distributed::kRequestPrefetch);
     if (it != rpc_call_map.end()) {
       request_prefetch_h_ = it->second;
     }
@@ -88,15 +88,15 @@ class BRPCServiceImpl : public SendRecvService {
   }
 
  private:
-  paddle::operators::detail::RequestHandler* request_send_h_;
-  paddle::operators::detail::RequestHandler* request_get_h_;
-  paddle::operators::detail::RequestHandler* request_prefetch_h_;
+  paddle::operators::distributed::RequestHandler* request_send_h_;
+  paddle::operators::distributed::RequestHandler* request_get_h_;
+  paddle::operators::distributed::RequestHandler* request_prefetch_h_;
 };
 }  // namespace sendrecv
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 void AsyncBRPCServer::StartServer() {
   // Instance of your service.
@@ -139,6 +139,6 @@ void AsyncBRPCServer::WaitServerReady() {
   VLOG(3) << "AsyncGRPCServer WaitSeverReady";
 }
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/detail/brpc_server.h → paddle/fluid/operators/distributed/brpc_server.h

@@ -19,12 +19,12 @@ limitations under the License. */
 #include <string>
 
 #include "brpc/server.h"
-#include "paddle/fluid/operators/detail/rpc_server.h"
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class AsyncBRPCServer final : public RPCServer {
  public:
@@ -48,6 +48,6 @@ class AsyncBRPCServer final : public RPCServer {
   int ready_;
 };
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/detail/bytebuffer_stream.cc → paddle/fluid/operators/distributed/bytebuffer_stream.cc

@@ -17,11 +17,11 @@ limitations under the License. */
 //       file and did some modifications so that we can send gRPC
 //       requests without too much copying of the tensor data.
 
-#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
+#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 GrpcByteBufferSource::GrpcByteBufferSource() {}
 
@@ -83,6 +83,6 @@ google::protobuf::int64 GrpcByteBufferSource::ByteCount() const {
   return byte_count_;
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/bytebuffer_stream.h → paddle/fluid/operators/distributed/bytebuffer_stream.h

@@ -106,7 +106,7 @@ class GrpcBufferReader final
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 // Source provides a way for a particular RPC implementation to provide
 // received data to ParseFrom.
 class Source {
@@ -183,6 +183,6 @@ class GrpcByteSource : public Source {
   char space_[sizeof(Reader)];
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/grpc_client.cc → paddle/fluid/operators/distributed/grpc_client.cc

@@ -12,19 +12,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/detail/grpc_client.h"
+#include "paddle/fluid/operators/distributed/grpc_client.h"
 
 #include <sys/time.h>
 
 #include <limits>
 
 #include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 void GRPCClient::InitImpl() { InitEventLoop(); }
 
@@ -276,6 +276,6 @@ std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
   return ch;
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/grpc_client.h → paddle/fluid/operators/distributed/grpc_client.h

@@ -38,13 +38,13 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
-#include "paddle/fluid/operators/detail/rpc_client.h"
-#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/rpc_client.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 #include "paddle/fluid/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 struct VarHandle {
   std::string ep;
@@ -226,6 +226,6 @@ class GRPCClient : public RPCClient {
   DISABLE_COPY_AND_ASSIGN(GRPCClient);
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/grpc_serde_test.cc → paddle/fluid/operators/distributed/grpc_serde_test.cc

@@ -21,8 +21,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/framework/variable.h"
-#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
-#include "paddle/fluid/operators/detail/variable_response.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/string/printf.h"
@@ -50,7 +50,7 @@ void RunSerdeTestSelectedRows(platform::Place place) {
   for (int i = 0; i < 564; ++i) rows->push_back(i);
 
   ::grpc::ByteBuffer msg;
-  operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
+  operators::distributed::SerializeToByteBuffer("myvar", &var, ctx, &msg);
   EXPECT_GT(msg.Length(), static_cast<size_t>(0));
 
   // deserialize
@@ -81,10 +81,10 @@ void RunSerdeTestSelectedRows(platform::Place place) {
 
   // deserialize zero-copy
   // framework::Variable var2;
-  // operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2);
+  // operators::distributed::DeserializeFromByteBuffer(msg, ctx, &var2);
   framework::Scope scope;
   scope.Var("myvar");
-  operators::detail::VariableResponse resp(&scope, &ctx);
+  operators::distributed::VariableResponse resp(&scope, &ctx);
   EXPECT_EQ(resp.Parse(msg), 0);
 
   framework::Variable* var2 = resp.GetVar();
@@ -128,7 +128,7 @@ void RunTestLodTensor(platform::Place place, int from_type = 0) {
   math::set_constant(ctx, tensor, 31.9);
 
   ::grpc::ByteBuffer msg;
-  operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
+  operators::distributed::SerializeToByteBuffer("myvar", &var, ctx, &msg);
   EXPECT_GT(msg.Length(), static_cast<size_t>(0));
 
   // deserialize
@@ -171,7 +171,7 @@ void RunTestLodTensor(platform::Place place, int from_type = 0) {
   // deserialize zero-copy
   framework::Scope scope;
   scope.Var("myvar");
-  operators::detail::VariableResponse resp(&scope, &ctx);
+  operators::distributed::VariableResponse resp(&scope, &ctx);
   if (from_type == 0) {
     EXPECT_EQ(resp.Parse(msg), 0);
   } else {

paddle/fluid/operators/detail/grpc_server.cc → paddle/fluid/operators/distributed/grpc_server.cc

@@ -15,13 +15,13 @@ limitations under the License. */
 #include <limits>
 #include <string>
 
-#include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/operators/distributed/grpc_server.h"
 
 using ::grpc::ServerAsyncResponseWriter;
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 enum CallStatus { PROCESS = 0, FINISH };
 
 // reference:
@@ -74,7 +74,7 @@ class RequestSend final : public RequestBase {
     request_.reset(new VariableResponse(request_handler->scope(),
                                         request_handler->dev_ctx(),
                                         !request_handler->sync_mode()));
-    int method_id = static_cast<int>(detail::GrpcMethod::kSendVariable);
+    int method_id = static_cast<int>(distributed::GrpcMethod::kSendVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
@@ -106,7 +106,7 @@ class RequestGet final : public RequestBase {
                       ::grpc::ServerCompletionQueue* cq,
                       RequestHandler* request_handler, int req_id)
       : RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
-    auto method_id = static_cast<int>(detail::GrpcMethod::kGetVariable);
+    auto method_id = static_cast<int>(distributed::GrpcMethod::kGetVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, &request_, &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
@@ -150,7 +150,8 @@ class RequestPrefetch final : public RequestBase {
         local_scope_(nullptr) {
     request_.reset(new VariableResponse(request_handler->scope(),
                                         request_handler->dev_ctx(), true));
-    int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
+    int method_id =
+        static_cast<int>(distributed::GrpcMethod::kPrefetchVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
@@ -354,6 +355,6 @@ void AsyncGRPCServer::HandleRequest(
   }
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/grpc_server.h → paddle/fluid/operators/distributed/grpc_server.h

@@ -29,17 +29,17 @@ limitations under the License. */
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/operators/detail/grpc_service.h"
-#include "paddle/fluid/operators/detail/request_handler.h"
-#include "paddle/fluid/operators/detail/rpc_server.h"
-#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
-#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/grpc_service.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
+#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class RequestBase;
 
@@ -84,6 +84,6 @@ class AsyncGRPCServer final : public RPCServer {
   std::map<std::string, std::vector<RequestBase*>> rpc_reqs_;
 };
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/detail/grpc_service.h → paddle/fluid/operators/distributed/grpc_service.h

@@ -23,7 +23,7 @@
 #include <grpc++/impl/codegen/stub_options.h>
 #include <grpc++/impl/codegen/sync_stream.h>
 #include <grpc++/support/byte_buffer.h>
-#include "paddle/fluid/operators/detail/variable_response.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
 
 #include "paddle/fluid/platform/profiler.h"
 
@@ -42,24 +42,25 @@ class ServerContext;
 // Support parsing/unparsing of tensorflow::VariableResponse.
 // Wire-format is identical to RecvVariableResponse.
 template <>
-class SerializationTraits<paddle::operators::detail::VariableResponse> {
+class SerializationTraits<paddle::operators::distributed::VariableResponse> {
  public:
   static Status Serialize(
-      const paddle::operators::detail::VariableResponse& msg,
+      const paddle::operators::distributed::VariableResponse& msg,
       grpc_byte_buffer** bp, bool* own_buffer) {
     PADDLE_ENFORCE(false, "SerializationTraits::Serialize not implemented!");
     return Status();
   }
-  static Status Deserialize(grpc_byte_buffer* buffer,
-                            paddle::operators::detail::VariableResponse* msg,
-                            int max_message_size = INT_MAX) {
+  static Status Deserialize(
+      grpc_byte_buffer* buffer,
+      paddle::operators::distributed::VariableResponse* msg,
+      int max_message_size = INT_MAX) {
     if (buffer == nullptr) {
       return Status(StatusCode::INTERNAL, "No payload");
     }
 
     Status result = g_core_codegen_interface->ok();
     if (result.ok()) {
-      paddle::operators::detail::GrpcByteSource source(buffer);
+      paddle::operators::distributed::GrpcByteSource source(buffer);
       int ret = msg->Parse(&source);
       if (ret != 0) {
         result = Status(StatusCode::INTERNAL, "VariableResponse parse error");
@@ -73,7 +74,7 @@ class SerializationTraits<paddle::operators::detail::VariableResponse> {
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 enum class GrpcMethod {
   kSendVariable,
@@ -118,6 +119,6 @@ class GrpcService final {
   };
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/proto_encoder_helper.h → paddle/fluid/operators/distributed/proto_encoder_helper.h

@@ -26,7 +26,7 @@ limitations under the License. */
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 char* EncodeVarint32(char* dst, uint32_t v) {
   // Operate on characters as unsigneds
@@ -144,6 +144,6 @@ class ProtoEncodeHelper {
   char* limit_;  // Just for CHECKs
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/request_handler.h → paddle/fluid/operators/distributed/request_handler.h

@@ -31,7 +31,7 @@
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 constexpr char kRequestSend[] = "RequestSend";
 constexpr char kRequestGet[] = "RequestGet";
@@ -124,6 +124,6 @@ class RequestHandler {
   RPCServer* rpc_server_;
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/request_handler_impl.cc → paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -20,12 +20,12 @@
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
-#include "paddle/fluid/operators/detail/request_handler_impl.h"
-#include "paddle/fluid/operators/detail/rpc_server.h"
+#include "paddle/fluid/operators/distributed/request_handler_impl.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 bool RequestSendHandler::Handle(const std::string& varname,
                                 framework::Scope* scope,
@@ -119,6 +119,6 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
   return true;
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/request_handler_impl.h → paddle/fluid/operators/distributed/request_handler_impl.h

@@ -28,11 +28,11 @@
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class RequestSendHandler final : public RequestHandler {
  public:
@@ -66,6 +66,6 @@ class RequestPrefetchHandler final : public RequestHandler {
               const std::string& out_var_name = "") override;
 };
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/rpc_client.cc → paddle/fluid/operators/distributed/rpc_client.cc

@@ -12,15 +12,15 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/detail/rpc_client.h"
+#include "paddle/fluid/operators/distributed/rpc_client.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 std::once_flag RPCClient::init_flag_;
 std::unique_ptr<RPCClient> RPCClient::rpc_client_(nullptr);
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/rpc_client.h → paddle/fluid/operators/distributed/rpc_client.h

@@ -22,7 +22,7 @@
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class RPCClient {
  public:
@@ -84,6 +84,6 @@ class RPCClient {
   static std::once_flag init_flag_;
   static std::unique_ptr<RPCClient> rpc_client_;
 };
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/rpc_server.cc → paddle/fluid/operators/distributed/rpc_server.cc

@@ -17,11 +17,11 @@
 #include <limits>
 #include <string>
 
-#include "paddle/fluid/operators/detail/rpc_server.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 void RPCServer::ShutDown() {
   LOG(INFO) << "RPCServer ShutDown ";
@@ -112,6 +112,6 @@ void RPCServer::WaitCond(const std::string& rpc_name) {
       lock, [=] { return (cur_cond_.load() == cond || exit_flag_.load()); });
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/rpc_server.h → paddle/fluid/operators/distributed/rpc_server.h

@@ -19,11 +19,11 @@
 #include <thread>  // NOLINT
 #include <utility>
 #include <vector>
-#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class RPCServer {
  public:
@@ -86,6 +86,6 @@ class RPCServer {
   friend class RequestHandler;
 };
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/detail/rpc_server_test.cc → paddle/fluid/operators/distributed/rpc_server_test.cc

@@ -22,18 +22,18 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 
 #include "paddle/fluid/operators/detail/macros.h"
-#include "paddle/fluid/operators/detail/request_handler_impl.h"
-#include "paddle/fluid/operators/detail/rpc_client.h"
-#include "paddle/fluid/operators/detail/rpc_server.h"
+#include "paddle/fluid/operators/distributed/request_handler_impl.h"
+#include "paddle/fluid/operators/distributed/rpc_client.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
 
 namespace framework = paddle::framework;
 namespace platform = paddle::platform;
-namespace detail = paddle::operators::detail;
+namespace distributed = paddle::operators::distributed;
 
 USE_OP(lookup_table);
 
-std::unique_ptr<detail::RPCServer> g_rpc_service;
-std::unique_ptr<detail::RequestHandler> g_req_handler;
+std::unique_ptr<distributed::RPCServer> g_rpc_service;
+std::unique_ptr<distributed::RequestHandler> g_req_handler;
 
 framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
   auto root_block = program->MutableBlock(0);
@@ -113,19 +113,21 @@ void StartServer() {
   g_req_handler->SetScope(&scope);
   g_req_handler->SetExecutor(&exe);
 
-  g_rpc_service->RegisterRPC(detail::kRequestPrefetch, g_req_handler.get());
+  g_rpc_service->RegisterRPC(distributed::kRequestPrefetch,
+                             g_req_handler.get());
   g_req_handler->SetRPCServer(g_rpc_service.get());
 
   std::thread server_thread(
-      std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
+      std::bind(&distributed::RPCServer::StartServer, g_rpc_service.get()));
 
   server_thread.join();
 }
 
 TEST(PREFETCH, CPU) {
-  g_req_handler.reset(new detail::RequestPrefetchHandler(true));
+  g_req_handler.reset(new distributed::RequestPrefetchHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
-  detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
+  distributed::RPCClient* client =
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
   std::thread server_thread(StartServer);
   g_rpc_service->WaitServerReady();

paddle/fluid/operators/detail/sendrecvop_utils.cc → paddle/fluid/operators/distributed/sendrecvop_utils.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 
 #ifdef PADDLE_WITH_CUDA
 #include <nccl.h>
@@ -23,14 +23,14 @@ limitations under the License. */
 #include "google/protobuf/io/coded_stream.h"
 #include "google/protobuf/io/zero_copy_stream.h"
 #include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
-#include "paddle/fluid/operators/detail/proto_encoder_helper.h"
-#include "paddle/fluid/operators/detail/variable_response.h"
+#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
+#include "paddle/fluid/operators/distributed/proto_encoder_helper.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 using VarMsg = sendrecv::VariableMessage;
 
@@ -222,11 +222,11 @@ void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
                                framework::Variable** var) {
-  operators::detail::VariableResponse resp(scope, &ctx);
+  operators::distributed::VariableResponse resp(scope, &ctx);
   PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
   *var = resp.GetVar();
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/sendrecvop_utils.h → paddle/fluid/operators/distributed/sendrecvop_utils.h

@@ -25,12 +25,12 @@ limitations under the License. */
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/framework/var_type.h"
 
-#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 typedef void (*DestroyCallback)(void*);
 
@@ -61,6 +61,6 @@ inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) {
   }
 }
 
-}  // namespace detail
+}  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/variable_response.cc → paddle/fluid/operators/distributed/variable_response.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/detail/variable_response.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
 
 #include <string>
 #include <utility>
@@ -22,12 +22,12 @@
 #endif
 #include "paddle/fluid/platform/profiler.h"
 
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
-#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 enum WireType {
   WIRETYPE_VARINT = 0,
@@ -158,13 +158,13 @@ bool VariableResponse::CopySelectRowsTensorData(
   slr->set_height(meta_.slr_height());
   auto* tensor = slr->mutable_value();
   tensor->Resize(dims);
-  PADDLE_ENFORCE_EQ(
-      static_cast<size_t>(tensor->numel()),
-      length / framework::SizeOfType(
-                   paddle::operators::detail::ToTypeIndex(meta_.data_type())));
+  PADDLE_ENFORCE_EQ(static_cast<size_t>(tensor->numel()),
+                    length / framework::SizeOfType(
+                                 paddle::operators::distributed::ToTypeIndex(
+                                     meta_.data_type())));
   void* tensor_data = tensor->mutable_data(
       ctx.GetPlace(),
-      paddle::operators::detail::ToTypeIndex(meta_.data_type()));
+      paddle::operators::distributed::ToTypeIndex(meta_.data_type()));
 
   if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
     return false;
@@ -480,6 +480,6 @@ int VariableResponse::Parse(Source* source) {
   return 0;
 }
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/detail/variable_response.h → paddle/fluid/operators/distributed/variable_response.h

@@ -22,17 +22,17 @@
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/var_type.h"
 
-#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
-#include "paddle/fluid/operators/detail/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/send_recv.grpc.pb.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
 
 #include "google/protobuf/io/coded_stream.h"
 #include "google/protobuf/io/zero_copy_stream.h"
 #include "paddle/fluid/framework/tensor.h"
-#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
+#include "paddle/fluid/operators/distributed/bytebuffer_stream.h"
 
 namespace paddle {
 namespace operators {
-namespace detail {
+namespace distributed {
 
 class VariableResponse {
  public:
@@ -99,6 +99,6 @@ class VariableResponse {
   sendrecv::VariableMessage meta_;
 };
 
-};  // namespace detail
+};  // namespace distributed
 };  // namespace operators
 };  // namespace paddle

paddle/fluid/operators/fetch_barrier_op.cc

@@ -42,8 +42,8 @@ class FetchBarrierOp : public framework::OperatorBase {
     // For profiling
     platform::RecordEvent record_event(Type(), &ctx);
 
-    detail::RPCClient* rpc_client =
-        detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* rpc_client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     rpc_client->Wait();
 

paddle/fluid/operators/gaussian_random_mkldnn_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <string>
+#include "paddle/fluid/operators/mean_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::DataLayout;
+template <typename T>
+class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    float mean = context.Attr<float>("mean");
+    float std = context.Attr<float>("std");
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
+    std::minstd_rand engine;
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    engine.seed(seed);
+    std::normal_distribution<T> dist(mean, std);
+    int64_t size = tensor->numel();
+    for (int64_t i = 0; i < size; ++i) {
+      data[i] = dist(engine);
+    }
+
+    // The format of output is set as the mkldnn's format
+    // TODO(@mozga-intel) The format of matrix sets inside the another layers.
+    tensor->set_layout(DataLayout::kMKLDNN);
+    tensor->set_format(mkldnn::memory::format::oihw);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(gaussian_random, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::GaussianMKLDNNKernel<float>);

paddle/fluid/operators/gaussian_random_op.cc

@@ -15,6 +15,10 @@ limitations under the License. */
 #include <random>
 #include "paddle/fluid/framework/op_registry.h"
 
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
 namespace paddle {
 namespace operators {
 
@@ -62,9 +66,20 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library{framework::LibraryType::kPlain};
+    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
+
+#ifdef PADDLE_WITH_MKLDNN
+    if (library == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library = framework::LibraryType::kMKLDNN;
+      layout = framework::DataLayout::kMKLDNN;
+    }
+#endif
+
     return framework::OpKernelType(
         static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
-        ctx.device_context());
+        ctx.device_context(), layout, library);
   }
 };
 
@@ -95,7 +110,9 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
                  "(int, default 5(FP32)) "
                  "Output data type.")
         .SetDefault(framework::proto::VarType::FP32);
-
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
     AddComment(R"DOC(
 GaussianRandom Operator.
 

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -22,7 +22,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/operators/detail/macros.h"
-#include "paddle/fluid/operators/detail/request_handler_impl.h"
+#include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/platform/nccl_helper.h"
 
 namespace paddle {
@@ -60,7 +60,8 @@ class GenNCCLIdOp : public framework::OperatorBase {
 
     std::vector<std::string> endpoint_list =
         Attr<std::vector<std::string>>("endpoint_list");
-    detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     for (auto& ep : endpoint_list) {
       VLOG(3) << "sending nccl id to " << ep;
@@ -80,11 +81,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
     // NOTE: Can not use unique_ptr here because the default
     // deleter will call GRPC Server's base class's dtor and
     // that will cause a wired crash.
-    detail::RequestSendHandler rpc_h(true);
-    std::unique_ptr<detail::RPCServer> rpc_service(
+    distributed::RequestSendHandler rpc_h(true);
+    std::unique_ptr<distributed::RPCServer> rpc_service(
         new RPCSERVER_T(endpoint, 1));
 
-    rpc_service->RegisterRPC(detail::kRequestSend, &rpc_h);
+    rpc_service->RegisterRPC(distributed::kRequestSend, &rpc_h);
     rpc_h.SetRPCServer(rpc_service.get());
 
     framework::ProgramDesc empty_program;
@@ -95,11 +96,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
     rpc_h.SetExecutor(&executor);
 
     std::thread server_thread(
-        std::bind(&detail::RPCServer::StartServer, rpc_service.get()));
+        std::bind(&distributed::RPCServer::StartServer, rpc_service.get()));
 
-    rpc_service->SetCond(detail::kRequestSend);
+    rpc_service->SetCond(distributed::kRequestSend);
     VLOG(3) << "start getting nccl id from trainer 0...";
-    rpc_service->WaitBarrier(detail::kRequestSend);
+    rpc_service->WaitBarrier(distributed::kRequestSend);
     VLOG(3) << "got nccl id and stop server...";
     rpc_service->ShutDown();
     VLOG(3) << "rpc server stopped";

paddle/fluid/operators/listen_and_serv_op.cc

@@ -21,14 +21,14 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/detail/macros.h"
 
-#include "paddle/fluid/operators/detail/request_handler_impl.h"
+#include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
 
-void RunServer(std::shared_ptr<detail::RPCServer> service) {
+void RunServer(std::shared_ptr<distributed::RPCServer> service) {
   service->StartServer();
   VLOG(4) << "RunServer thread end";
 }
@@ -121,12 +121,12 @@ void ListenAndServOp::RunSyncLoop(
   while (true) {
     // Get from multiple trainers, we don't care about the order in which
     // the gradients arrives, just add suffix 0~n and merge the gradient.
-    rpc_service_->SetCond(detail::kRequestSend);
-    rpc_service_->WaitBarrier(detail::kRequestSend);
+    rpc_service_->SetCond(distributed::kRequestSend);
+    rpc_service_->WaitBarrier(distributed::kRequestSend);
 
     if (rpc_service_->IsExit()) {
       LOG(WARNING) << "get exit!rpc_processor break!";
-      rpc_service_->SetCond(detail::kRequestGet);
+      rpc_service_->SetCond(distributed::kRequestGet);
       break;
     }
 
@@ -154,11 +154,11 @@ void ListenAndServOp::RunSyncLoop(
                           recv_scope);
     VLOG(2) << "run all blocks spent " << GetTimestamp() - ts << "(ms)";
 
-    rpc_service_->SetCond(detail::kRequestGet);
-    rpc_service_->WaitBarrier(detail::kRequestGet);
+    rpc_service_->SetCond(distributed::kRequestGet);
+    rpc_service_->WaitBarrier(distributed::kRequestGet);
     rpc_service_->ResetBarrierCounter();
     // reset received sparse vars to avoid reuse it in the next mini-batch
-    dynamic_cast<detail::RequestSendHandler *>(request_send_handler_.get())
+    dynamic_cast<distributed::RequestSendHandler *>(request_send_handler_.get())
         ->ResetSparseVarRecorder();
   }  // while(true)
 }
@@ -215,13 +215,13 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
 }
 
 static void FillRequestCtx(
-    detail::RequestHandler *h, framework::Scope *scope,
+    distributed::RequestHandler *h, framework::Scope *scope,
     platform::DeviceContext *dev_ctx, framework::Executor *executor,
     framework::ProgramDesc *program,
     std::unordered_map<std::string,
                        std::shared_ptr<framework::ExecutorPrepareContext>>
         *prefetch_ctx,
-    detail::RPCServer *rpc_server) {
+    distributed::RPCServer *rpc_server) {
   h->SetScope(scope);
   h->SetDevCtx(dev_ctx);
   h->SetExecutor(executor);
@@ -249,14 +249,16 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
 
   rpc_service_.reset(new RPCSERVER_T(endpoint, fan_in));
 
-  request_send_handler_.reset(new detail::RequestSendHandler(sync_mode));
-  request_get_handler_.reset(new detail::RequestGetHandler(sync_mode));
+  request_send_handler_.reset(new distributed::RequestSendHandler(sync_mode));
+  request_get_handler_.reset(new distributed::RequestGetHandler(sync_mode));
   request_prefetch_handler_.reset(
-      new detail::RequestPrefetchHandler(sync_mode));
+      new distributed::RequestPrefetchHandler(sync_mode));
 
-  rpc_service_->RegisterRPC(detail::kRequestSend, request_send_handler_.get());
-  rpc_service_->RegisterRPC(detail::kRequestGet, request_get_handler_.get());
-  rpc_service_->RegisterRPC(detail::kRequestPrefetch,
+  rpc_service_->RegisterRPC(distributed::kRequestSend,
+                            request_send_handler_.get());
+  rpc_service_->RegisterRPC(distributed::kRequestGet,
+                            request_get_handler_.get());
+  rpc_service_->RegisterRPC(distributed::kRequestPrefetch,
                             request_prefetch_handler_.get());
 
   auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);

paddle/fluid/operators/listen_and_serv_op.h

@@ -24,8 +24,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/request_handler.h"
-#include "paddle/fluid/operators/detail/rpc_server.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
+#include "paddle/fluid/operators/distributed/rpc_server.h"
 
 namespace paddle {
 namespace operators {
@@ -33,7 +33,7 @@ namespace operators {
 constexpr char kOptimizeBlock[] = "OptimizeBlock";
 constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
 
-void RunServer(std::shared_ptr<detail::RPCServer> service);
+void RunServer(std::shared_ptr<distributed::RPCServer> service);
 
 class ListenAndServOp : public framework::OperatorBase {
  public:
@@ -62,10 +62,11 @@ class ListenAndServOp : public framework::OperatorBase {
                const platform::Place& dev_place) const override;
 
  protected:
-  mutable std::shared_ptr<detail::RPCServer> rpc_service_;
-  mutable std::shared_ptr<detail::RequestHandler> request_send_handler_;
-  mutable std::shared_ptr<detail::RequestHandler> request_get_handler_;
-  mutable std::shared_ptr<detail::RequestHandler> request_prefetch_handler_;
+  mutable std::shared_ptr<distributed::RPCServer> rpc_service_;
+  mutable std::shared_ptr<distributed::RequestHandler> request_send_handler_;
+  mutable std::shared_ptr<distributed::RequestHandler> request_get_handler_;
+  mutable std::shared_ptr<distributed::RequestHandler>
+      request_prefetch_handler_;
 
   mutable std::shared_ptr<std::thread> server_thread_;
 };

paddle/fluid/operators/logical_op.cc

@@ -146,6 +146,6 @@ REGISTER_UNARY_LOGICAL_OP(logical_not, "$$Out = !X$$");
 REGISTER_UNARY_LOGICAL_KERNEL(logical_not, CPU,
                               paddle::operators::LogicalNotFunctor);
 REGISTER_BINARY_LOGICAL_OP(logical_xor,
-                           "$$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$");
+                           "$$Out = (X || Y) \\&\\& !(X \\&\\& Y)$$");
 REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, CPU,
                                paddle::operators::LogicalXorFunctor);

paddle/fluid/operators/math/concat.cc

@@ -93,10 +93,10 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
     auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
 
     // computation
-    for (size_t k = 0; k < input_rows; ++k) {
+    for (int k = 0; k < input_rows; ++k) {
       const T* src_ptr = input.data<T>() + k * input_cols;
       int col_idx = 0;
-      for (int j = 0; j < num; ++j) {
+      for (size_t j = 0; j < num; ++j) {
         int col_len = output_cols[j];
         auto* out_tensor = outputs->at(j);
         if (out_tensor != nullptr) {

paddle/fluid/operators/math/concat.cu

@@ -22,43 +22,24 @@ namespace paddle {
 namespace operators {
 namespace math {
 
-template <typename T>
-__device__ T upper_bound(const T* first, T count, T val) {
-  const T* orig = first;
-  const T* it = nullptr;
-  T step = 0;
-  while (count > 0) {
-    it = first;
-    step = count / 2;
-    it += step;
-    if (!(val < *it)) {
-      first = ++it;
-      count -= step + 1;
-    } else {
-      count = step;
-    }
-  }
-  return first - orig;
-}
-
 template <typename T>
 __global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
                              const int output_rows, const int output_cols,
                              T* output) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
-  int segment = upper_bound<int>(input_cols, col_size, tid_x) - 1;
-
-  int curr_offset = input_cols[segment];
-  int curr_segment = segment;
+  int curr_segment = 0;
+  int curr_offset = input_cols[0];
   for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
-    T curr_col_offset;
-    while ((curr_col_offset = input_cols[curr_segment + 1]) <= tid_x) {
+    int curr_col_offset = input_cols[curr_segment + 1];
+    while (curr_col_offset <= tid_x) {
       curr_offset = curr_col_offset;
       ++curr_segment;
+      curr_col_offset = input_cols[curr_segment + 1];
     }
 
     int local_col = tid_x - curr_offset;
     int segment_width = curr_col_offset - curr_offset;
+
     T* input_ptr = inputs[curr_segment];
     int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
     for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y)
@@ -89,14 +70,14 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
                                  const int in_col, const int* out_cols,
                                  int out_cols_size, T** outputs_data) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
-  int segment = upper_bound<int>(out_cols, out_cols_size, tid_x) - 1;
-  int curr_offset = out_cols[segment];
-  int curr_segment = segment;
+  int curr_segment = 0;
+  int curr_offset = out_cols[0];
   for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
-    T curr_col_offset;
-    while ((curr_col_offset = out_cols[curr_segment + 1]) <= tid_x) {
+    int curr_col_offset = out_cols[curr_segment + 1];
+    while (curr_col_offset <= tid_x) {
       curr_offset = curr_col_offset;
       ++curr_segment;
+      curr_col_offset = out_cols[curr_segment + 1];
     }
 
     int local_col = tid_x - curr_offset;
@@ -228,7 +209,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
 
     outputs_cols[0] = 0;
     for (int i = 0; i < o_num; ++i) {
-      int t_col = outputs->at(i)->numel() / out_row;
+      int t_col = ref_inputs.at(i)->numel() / out_row;
       if (sameShape) {
         if (t_col != out0_col) sameShape = false;
       }

paddle/fluid/operators/math/math_function.cc

@@ -30,6 +30,7 @@ template struct SetConstant<platform::CPUDeviceContext, double>;
 template struct SetConstant<platform::CPUDeviceContext, int>;
 template struct SetConstant<platform::CPUDeviceContext, int64_t>;
 template struct SetConstant<platform::CPUDeviceContext, bool>;
+template struct SetConstant<platform::CPUDeviceContext, uint8_t>;
 
 #define DEFINE_CPU_TRANS(RANK)                                             \
   template struct Transpose<platform::CPUDeviceContext, platform::float16, \

paddle/fluid/operators/prefetch_op.cc

@@ -41,8 +41,8 @@ class PrefetchOp : public framework::OperatorBase {
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
 
-    detail::RPCClient* rpc_client =
-        detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* rpc_client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     for (size_t i = 0; i < ins.size(); i++) {
       if (NeedSend(scope, ins[i])) {

paddle/fluid/operators/recv_op.cc

@@ -43,8 +43,8 @@ class RecvOp : public framework::OperatorBase {
     // For profiling
     platform::RecordEvent record_event(Type(), &ctx);
 
-    detail::RPCClient* rpc_client =
-        detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* rpc_client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     for (size_t i = 0; i < outs.size(); i++) {
       VLOG(3) << "getting " << outs[i] << " from " << epmap[i];

paddle/fluid/operators/send_barrier_op.cc

@@ -44,8 +44,8 @@ class SendBarrierOp : public framework::OperatorBase {
     // For profiling
     platform::RecordEvent record_event(Type(), &ctx);
 
-    detail::RPCClient* rpc_client =
-        detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* rpc_client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
 

paddle/fluid/operators/send_op.cc

@@ -45,8 +45,8 @@ class SendOp : public framework::OperatorBase {
     // For profiling
     platform::RecordEvent record_event(Type(), &ctx);
 
-    detail::RPCClient* rpc_client =
-        detail::RPCClient::GetInstance<RPCCLIENT_T>();
+    distributed::RPCClient* rpc_client =
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
     for (size_t i = 0; i < ins.size(); i++) {
       if (NeedSend(scope, ins[i])) {

paddle/fluid/operators/tensorrt_engine_op.cc

@@ -14,11 +14,14 @@
 
 #ifdef PADDLE_WITH_CUDA
 
-#include "paddle/fluid/operators/tensorrt_engine_op.h"
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 #include "paddle/fluid/inference/tensorrt/engine.h"
 #include "paddle/fluid/inference/utils/singleton.h"
+#include "paddle/fluid/operators/tensorrt_engine_op.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/tensorrt_engine_op.h

@@ -16,10 +16,12 @@
 
 #ifdef PADDLE_WITH_CUDA
 
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/inference/analysis/helper.h"
 #include "paddle/fluid/inference/tensorrt/engine.h"
-#include "paddle/fluid/inference/tensorrt/engine.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/tensorrt_engine_op_test.cc

@@ -179,7 +179,6 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
                         const std::string& z_name, bool x_created,
                         const shape_t& x_shape, const shape_t& y_shape,
                         const shape_t& z_shape) {
-
     LOG(INFO) << "create fc op";
     auto* fc = block_desc.AppendOp();
     fc->SetType("mul");

paddle/fluid/operators/test_send_nccl_id.cc

@@ -21,7 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/operators/detail/macros.h"
-#include "paddle/fluid/operators/detail/request_handler_impl.h"
+#include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
@@ -37,11 +37,11 @@ USE_NO_KERNEL_OP(listen_and_serv);
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 namespace m = paddle::operators::math;
-namespace detail = paddle::operators::detail;
+namespace distributed = paddle::operators::distributed;
 namespace string = paddle::string;
 
-std::unique_ptr<detail::RPCServer> g_rpc_service;
-std::unique_ptr<detail::RequestHandler> g_req_handler;
+std::unique_ptr<distributed::RPCServer> g_rpc_service;
+std::unique_ptr<distributed::RequestHandler> g_req_handler;
 
 void StartServer() {
   f::Scope scope;
@@ -57,14 +57,14 @@ void StartServer() {
   g_req_handler->SetProgram(&empty_program);
   g_req_handler->SetExecutor(&executor);
 
-  g_rpc_service->RegisterRPC(detail::kRequestSend, g_req_handler.get());
+  g_rpc_service->RegisterRPC(distributed::kRequestSend, g_req_handler.get());
   g_req_handler->SetRPCServer(g_rpc_service.get());
 
   std::thread server_thread(
-      std::bind(&detail::RPCServer::StartServer, g_rpc_service.get()));
+      std::bind(&distributed::RPCServer::StartServer, g_rpc_service.get()));
 
-  g_rpc_service->SetCond(detail::kRequestSend);
-  g_rpc_service->WaitBarrier(detail::kRequestSend);
+  g_rpc_service->SetCond(distributed::kRequestSend);
+  g_rpc_service->WaitBarrier(distributed::kRequestSend);
 
   LOG(INFO) << "got nccl id and stop server...";
   g_rpc_service->ShutDown();
@@ -72,7 +72,7 @@ void StartServer() {
 }
 
 TEST(SendNcclId, RPCServer) {
-  g_req_handler.reset(new detail::RequestSendHandler(true));
+  g_req_handler.reset(new distributed::RequestSendHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
 
   std::thread server_thread(StartServer);
@@ -91,7 +91,8 @@ TEST(SendNcclId, RPCServer) {
 
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
 
-  detail::RPCClient* client = detail::RPCClient::GetInstance<RPCCLIENT_T>();
+  distributed::RPCClient* client =
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
   LOG(INFO) << "connect to server" << ep;
   client->AsyncSendVar(ep, dev_ctx, scope, NCCL_ID_VARNAME);

paddle/fluid/pybind/pybind.cc

@@ -159,6 +159,11 @@ PYBIND11_PLUGIN(core) {
              new (&instance) LoDTensor(new_offset_lod);
            })
       .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
+      // We implement offset based LOD in C++ while we use length based with
+      // Python API. So we changed set_lod to set_recursive_sequence_lengths to
+      // avoid misuse.
+      // The discussion is here:
+      // https://github.com/PaddlePaddle/Paddle/issues/10855
       .def("set_lod",
            [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
              // the input lod is offset-based level-of-detail info
@@ -199,6 +204,7 @@ PYBIND11_PLUGIN(core) {
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
              return new_lod;
            })
+      // Set above comments of set_lod.
       .def("recursive_sequence_lengths",
            [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
              // output the length-based lod info

paddle/fluid/pybind/tensor_py.h

@@ -97,7 +97,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
 inline pybind11::buffer_info CastToPyBuffer(const framework::Tensor &tensor) {
   auto buffer_info =
       details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
-                                  platform::float16>()(tensor);
+                                  uint8_t, platform::float16>()(tensor);
   return buffer_info;
 }
 

python/paddle/fluid/__init__.py

@@ -44,7 +44,7 @@ import metrics
 import transpiler
 from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
-from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
+from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
 from transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory
 from concurrency import (Go, make_channel, channel_send, channel_recv,
@@ -83,6 +83,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
               'profiler',
               'unique_name',
               'recordio_writer',
+              'Scope',
           ]
 
 

python/paddle/fluid/average.py

@@ -36,6 +36,25 @@ def _is_number_or_matrix_(var):
 
 
 class WeightedAverage(object):
+    """
+    Calculate weighted average.
+
+    The average calculating is accomplished via Python totally. 
+    They do not change Paddle's Program, nor do anything to
+    modify NN model's configuration. They are completely 
+    wrappers of Python functions.
+
+    Examples:
+        .. code-block:: python
+            avg = fluid.average.WeightedAverage()
+            avg.add(value=2.0, weight=1)
+            avg.add(value=4.0, weight=2)
+            avg.eval()
+
+            # The result is 3.333333333.
+            # For (2.0 * 1 + 4.0 * 2) / (1 + 2) = 3.333333333
+    """
+
     def __init__(self):
         warnings.warn(
             "The %s is deprecated, please use fluid.metrics.Accuracy instead." %

python/paddle/fluid/backward.py

@@ -147,7 +147,7 @@ def _addup_repetitive_outputs_(op_descs):
             else:
                 if len(renamed_vars[var_name]) == 1:
                     new_name = var_name + "@RENAME@" + \
-                               str(var_rename_count[var_name])
+                        str(var_rename_count[var_name])
                     var_rename_count[var_name] += 1
                     # rename original var_name
                     renamed_vars[var_name][0] = new_name
@@ -155,7 +155,7 @@ def _addup_repetitive_outputs_(op_descs):
                     _rename_arg_(pending_sum_ops, var_name, new_name)
 
                 new_name = var_name + "@RENAME@" + \
-                           str(var_rename_count[var_name])
+                    str(var_rename_count[var_name])
                 var_rename_count[var_name] += 1
                 op_desc.rename_output(var_name, new_name)
                 renamed_vars[var_name].append(new_name)
@@ -434,18 +434,65 @@ def _get_stop_gradients_(program):
 def append_backward(loss, parameter_list=None, no_grad_set=None,
                     callbacks=None):
     """
-    Append backward part to main_program
+    Append backward part to main_program.
 
-    Args:
-        loss(Variable): The variable generated by cost function.
-        parameter_list(list[string]): Parameters that need to be updated by
-            optimizer. If None, it means all parameters need to be updated.
-        no_grad_set(set): Variables that have no gradients in Block 0.
-            All variables with `step_gradient=True` from all blocks will be
-            automatically added.
+    A complete neural network training is made up of forward and backward 
+    propagation. However, when we configure a network, we only need to 
+    specify its forwrd part. The backward part is generated automatically 
+    according to the forward part by this function.
 
-    Return:
-        (list[(Variable,Variable)]): list of (parameter, gradient) pair.
+    In most cases, users do not need to invoke this function manually. It 
+    will be automatically invoked by the optimizer's `minimize` function.
+
+    Args:
+        loss(Variable): The loss variable of the network.
+        parameter_list(list[string]|None): Names of parameters that need 
+                                           to be updated by optimizers. 
+                                           If it is None, all parameters 
+                                           will be updated.
+                                           Default: None
+        no_grad_set(set|None): Variables in the Block 0 whose gradients 
+                               should be ignored. All variables with 
+                               `step_gradient=True` from all blocks will 
+                               be automatically added into this set.
+                               Default: None
+        callbacks(list[callable object]|None): The callbacks are used for 
+                                               doing some custom jobs during 
+                                               backward part building. All 
+                                               callable objects in it will 
+                                               be invoked once each time a 
+                                               new gradient operator is added 
+                                               into the program. The callable 
+                                               object must has two input 
+                                               parameters: 'block' and 'context'. 
+                                               The 'block' is the block which 
+                                               the new gradient operator will 
+                                               be added to. The 'context' is a 
+                                               map, whose keys are gradient 
+                                               variable names and values are 
+                                               corresponding original variables.
+                                               In addition to this, the 'context' 
+                                               has another special key-value pair: 
+                                               the key is string '__current_op_desc__' 
+                                               and the value is the op_desc of the 
+                                               gradient operator who has just 
+                                               triggered the callable object. 
+
+    Returns:
+        list[(Variable,Variable)]: Pairs of parameter and its 
+        corresponding gradients. The key is the parameter and the 
+        value is gradient variable.
+
+    Raises:
+        AssertionError: If `loss` is not an instance of Variable.
+
+    Examples:
+        .. code-block:: python
+
+            # network configuration code
+            # ...
+            avg_loss = fluid.layers.mean(loss)
+            param_grad_list = fluid.backward.append_backward(loss=avg_loss)
     """
     assert isinstance(loss, framework.Variable)
 

python/paddle/fluid/clip.py

@@ -24,8 +24,6 @@ __all__ = [
     'GradientClipByValue',
     'GradientClipByNorm',
     'GradientClipByGlobalNorm',
-    'append_gradient_clip_ops',
-    'error_clip_callback',
 ]
 
 
@@ -38,6 +36,25 @@ class BaseErrorClipAttr(object):
 
 
 class ErrorClipByValue(BaseErrorClipAttr):
+    """
+    Clips tensor values to the range [min, max].
+
+    Given a tensor t, this operation clips its value to min and max inplace.
+
+    - Any values less than min are set to min.
+    - Any values greater than max are set to max.
+
+    Args:
+        max (float): The maximum value to clip by.
+        min (float, optional): The minimum value to clip by. if not set by user, \
+        will be set to -max by framework.
+
+    Examples:
+        .. code-block:: python
+
+            var = fluid.framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...)
+    """
+
     def __init__(self, max, min=None):
         max = float(max)
         if min is None:
@@ -99,6 +116,31 @@ class NullGradientClipAttr(BaseGradientClipAttr):
 
 
 class GradientClipByValue(BaseGradientClipAttr):
+    """
+    Clips gradient values to the range [min, max].
+
+    Given a tensor t, this operation clips its value to min and max inplace.
+
+    - Any values less than min are set to min.
+    - Any values greater than max are set to max.
+
+    Args:
+        max (float): The maximum value to clip by.
+        min (float, optional): The minimum value to clip by. if not set by user, \
+        will be set to -max by framework.
+
+    Examples:
+        .. code-block:: python
+
+            w_param_attrs = ParamAttr(name=None,
+              initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
+              learning_rate=1.0,
+              regularizer=L1Decay(1.0),
+              trainable=True,
+              clip=GradientClipByValue(-1.0, 1.0))
+            y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
+    """
+
     def __init__(self, max, min=None):
         max = float(max)
         if min is None:
@@ -120,6 +162,37 @@ class GradientClipByValue(BaseGradientClipAttr):
 
 
 class GradientClipByNorm(BaseGradientClipAttr):
+    """
+    Clips tensor values to a maximum L2-norm.
+
+    This operator limits the L2 norm of the input :math:`X` within :math:`max\_norm`.
+    If the L2 norm of :math:`X` is less than or equal to :math:`max\_norm`, :math:`Out`
+    will be the same as :math:`X`. If the L2 norm of :math:`X` is greater than
+    :math:`max\_norm`, :math:`X` will be linearly scaled to make the L2 norm of
+    :math:`Out` equal to :math:`max\_norm`, as shown in the following formula:
+
+    .. math::
+
+        Out = \\frac{max\_norm * X}{norm(X)},
+
+    where :math:`norm(X)` represents the L2 norm of :math:`X`.
+
+    Args:
+        clip_norm (float): The maximum norm value
+
+    Examples:
+        .. code-block:: python
+
+            w_param_attrs = ParamAttr(name=None,
+              initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
+              learning_rate=1.0,
+              regularizer=L1Decay(1.0),
+              trainable=True,
+              clip=GradientClipByNorm(clip_norm=2.0))
+            y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
+
+    """
+
     def __init__(self, clip_norm):
         self.clip_norm = clip_norm
 
@@ -135,6 +208,44 @@ class GradientClipByNorm(BaseGradientClipAttr):
 
 
 class GradientClipByGlobalNorm(BaseGradientClipAttr):
+    """
+    Clips values of multiple tensors by the ratio of the sum of their norms.
+
+    Given a list of tensors t_list, and a clipping ratio clip_norm, this
+    operation returns a list of clipped tensors list_clipped and the global
+    norm (global_norm) of all tensors in t_list.
+
+    To perform the clipping, the values :math:`t\_list[i]` are set to:
+
+    .. math::
+
+        t\_list[i] = t\_list[i] * \\frac{clip\_norm}{\max(global\_norm, clip\_norm)}
+
+    where:
+
+    .. math::
+
+        global\_norm = \sqrt{\sum_{i=0}^{N-1}(l2norm(t\_list[i]))^2}
+
+    If :math:`clip\_norm > global\_norm` then the entries in t_list remain as they are,
+    otherwise they're all shrunk by the global ratio.
+
+    Args:
+        clip_norm (float): The maximum norm value
+        group_name (str, optional): The group name for this clip.
+
+    Examples:
+        .. code-block:: python
+
+            p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
+
+            with fluid.program_guard(main_program=prog_clip):
+                fluid.clip.set_gradient_clip(
+                    fluid.clip.GradientClipByGlobalNorm(clip_norm=2.0))
+                p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
+
+    """
+
     def __init__(self, clip_norm, group_name="default_group"):
         if not isinstance(group_name, basestring):
             raise TypeError("'group_name' must be a basestring.")
@@ -183,15 +294,16 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
 
 def set_gradient_clip(clip, param_list=None, program=None):
     """
-        To specify parameters that require gradient clip.
-        Args:
-            clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr, 
-                    which describes the type and detailed attributes of required gradient clip.
-            param_list(list, None by default): Parameters that require gradient clip. 
-                    It can be a list of parameter or a list of parameter's name. 
-                    When it's None, all parameters in the program will be included. 
-            program(Program, None by default): The program where parameters are. 
-                    Will be the default main program when assigned with None.
+    To specify parameters that require gradient clip.
+
+    Args:
+        clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr,
+                which describes the type and detailed attributes of required gradient clip.
+        param_list(list(Variable)): Parameters that require gradient clip.
+                It can be a list of parameter or a list of parameter's name.
+                When it's None, all parameters in the program will be included.
+        program(Program): The program where parameters are.
+                Will be the default main program when assigned with None.
     """
     if not isinstance(clip, BaseGradientClipAttr):
         raise TypeError(

python/paddle/fluid/data_feeder.py

@@ -29,6 +29,13 @@ class DataToLoDTensorConverter(object):
         self.place = place
         self.lod_level = lod_level
         self.shape = shape
+        negtive_count = 0
+        for s in self.shape:
+            if s < 0:
+                negtive_count += 1
+            if negtive_count > 1:
+                self.shape = None
+                break
         if dtype == core.VarDesc.VarType.FP32:
             self.dtype = 'float32'
         elif dtype == core.VarDesc.VarType.INT64:
@@ -61,7 +68,9 @@ class DataToLoDTensorConverter(object):
                 self._feed_impl_(each_data, lod[1:], lod_level - 1)
 
     def done(self):
-        arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)
+        arr = numpy.array(self.data, dtype=self.dtype)
+        if self.shape:
+            arr = arr.reshape(self.shape)
         t = core.LoDTensor()
         t.set(arr, self.place)
         if self.lod_level > 0:
@@ -70,6 +79,61 @@ class DataToLoDTensorConverter(object):
 
 
 class DataFeeder(object):
+    """
+    DataFeeder converts the data that returned by a reader into a data
+    structure that can feed into Executor and ParallelExecutor. The reader
+    usually returns a list of mini-batch data entries. Each data entry in
+    the list is one sample. Each sample is a list or a tuple with one
+    feature or multiple features.
+
+    The simple usage shows below:
+
+    ..  code-block:: python
+
+        place = fluid.CPUPlace()
+        img = fluid.layers.data(name='image', shape=[1, 28, 28])
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
+        result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
+
+
+    If you want to feed data into GPU side separately in advance when you
+    use multi-GPU to train a model, you can use `decorate_reader` function.
+
+    ..  code-block:: python
+
+        place=fluid.CUDAPlace(0)
+        feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
+        reader = feeder.decorate_reader(
+            paddle.batch(flowers.train(), batch_size=16))
+
+    Args:
+        feed_list(list): The Variables or Variables'name that will
+            feed into model.
+        place(Place): place indicates feed data into CPU or GPU, if you want to
+            feed data into GPU, please using `fluid.CUDAPlace(i)` (`i` represents
+            the GPU id), or if you want to feed data into CPU, please using
+            `fluid.CPUPlace()`.
+        program(Program): The Program that will feed data into, if program
+            is None, it will use default_main_program(). Default None.
+
+    Raises:
+        ValueError: If some Variable is not in this Program.
+
+    Examples:
+        .. code-block:: python
+
+            # ...
+            place = fluid.CPUPlace()
+            feed_list = [
+                main_program.global_block().var(var_name) for var_name in feed_vars_name
+            ] # feed_vars_name is a list of variables' name.
+            feeder = fluid.DataFeeder(feed_list, place)
+            for data in reader():
+                outs = exe.run(program=main_program,
+                               feed=feeder.feed(data))
+    """
+
     def __init__(self, feed_list, place, program=None):
         self.feed_dtypes = []
         self.feed_names = []
@@ -99,6 +163,16 @@ class DataFeeder(object):
         self.place = place
 
     def feed(self, iterable):
+        """
+        According to feed_list and iterable, converters the input into
+        a data structure that can feed into Executor and ParallelExecutor.
+
+        Args:
+            iterable(list|tuple): the input data.
+
+        Returns:
+            dict: the result of conversion.
+        """
         converter = []
         for lod_level, shape, dtype in six.zip(
                 self.feed_lod_level, self.feed_shapes, self.feed_dtypes):
@@ -121,6 +195,20 @@ class DataFeeder(object):
         return ret_dict
 
     def feed_parallel(self, iterable, num_places=None):
+        """
+        Takes multiple mini-batches. Each mini-batch will be feed on each
+        device in advance.
+
+        Args:
+            iterable(list|tuple): the input data.
+            num_places(int): the number of devices. Default None.
+
+        Returns:
+            dict: the result of conversion.
+
+        Notes:
+            The number of devices and number of mini-batches must be same.
+        """
         if isinstance(self.place, core.CUDAPlace):
             places = [
                 core.CUDAPlace(i)
@@ -159,6 +247,24 @@ class DataFeeder(object):
                         multi_devices,
                         num_places=None,
                         drop_last=True):
+        """
+        Converter the input data into a data that returned by reader into
+        multiple mini-batches. Each mini-batch will be feed on each device.
+
+        Args:
+            reader(fun): the input data.
+            multi_devices(bool): the number of places. Default None.
+            num_places(int): the number of places. Default None.
+            drop_last(bool): the number of places. Default None.
+
+        Returns:
+            dict: the result of conversion.
+
+        Raises:
+            ValueError: If drop_last is False and the data batch which cannot
+            fit for devices.
+        """
+
         def __reader_creator__():
             if not multi_devices:
                 for item in reader():

python/paddle/fluid/evaluator.py

@@ -41,7 +41,12 @@ def _clone_var_(block, var):
 
 class Evaluator(object):
     """
-    Base Class for all evaluators
+    Warning: better to use the fluid.metrics.* things, more
+    flexible support via pure Python and Operator, and decoupled
+    with executor. Short doc are intended to urge new user
+    start from Metrics.
+
+    Base Class for all evaluators.
 
     Args:
         name(str): The name of evaluator. such as, "accuracy". Used for generate
@@ -69,6 +74,10 @@ class Evaluator(object):
     def reset(self, executor, reset_program=None):
         """
         reset metric states at the begin of each pass/user specified batch
+
+        Args:
+            executor(Executor|ParallelExecutor): a executor for executing the reset_program
+            reset_program(Program): a single Program for reset process
         """
         if reset_program is None:
             reset_program = Program()
@@ -85,15 +94,16 @@ class Evaluator(object):
     def eval(self, executor, eval_program=None):
         """
         Evaluate the statistics merged by multiple mini-batches.
+        Args:
+            executor(Executor|ParallelExecutor): a executor for executing the eval_program
+            eval_program(Program): a single Program for eval process
         """
         raise NotImplementedError()
 
-    def create_state(self, suffix, dtype, shape):
+    def _create_state(self, suffix, dtype, shape):
         """
         Create state variable.
 
-        NOTE: It is not a public API.
-
         Args:
             suffix(str): the state suffix.
             dtype(str|core.VarDesc.VarType): the state data type
@@ -113,9 +123,35 @@ class Evaluator(object):
 
 class ChunkEvaluator(Evaluator):
     """
+    Warning: This would be deprecated in the future. Please use fluid.metrics.ChunkEvaluator 
+    instead.
+
     Accumulate counter numbers output by chunk_eval from mini-batches and
     compute the precision recall and F1-score using the accumulated counter
     numbers.
+    For some basics of chunking, please refer to
+    'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
+
+    Args:
+        input (Variable): prediction output of the network.
+        label (Variable): label of the test data set.
+        chunk_scheme (str): can be IOB/IOE/IOBES and IO. See the chunk_eval op for details.
+        num_chunk_types (int): the number of chunk type.
+        excluded_chunk_types (list): A list including chunk type ids, indicating chunk types that are not counted.
+
+    Returns:
+        tuple: tuple containing: precision, recall, f1_score
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.executor(place)
+            evaluator = fluid.Evaluator.ChunkEvaluator(input, label)
+            for epoch in PASS_NUM:
+                evaluator.reset(exe)
+                for data in batches:
+                    loss = exe.run(fetch_list=[cost])
+                distance, instance_error = distance_evaluator.eval(exe)
     """
 
     def __init__(
@@ -130,11 +166,11 @@ class ChunkEvaluator(Evaluator):
         if main_program.current_block().idx != 0:
             raise ValueError("You can only invoke Evaluator in root block")
 
-        self.num_infer_chunks = self.create_state(
+        self.num_infer_chunks = self._create_state(
             dtype='int64', shape=[1], suffix='num_infer_chunks')
-        self.num_label_chunks = self.create_state(
+        self.num_label_chunks = self._create_state(
             dtype='int64', shape=[1], suffix='num_label_chunks')
-        self.num_correct_chunks = self.create_state(
+        self.num_correct_chunks = self._create_state(
             dtype='int64', shape=[1], suffix='num_correct_chunks')
         precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval(
             input=input,
@@ -178,6 +214,8 @@ class ChunkEvaluator(Evaluator):
 
 class EditDistance(Evaluator):
     """
+    Warning: This would be deprecated in the future. Please use fluid.metrics.EditDistance
+    instead.
     Accumulate edit distance sum and sequence number from mini-batches and
     compute the average edit_distance and instance error of all batches.
 
@@ -188,15 +226,16 @@ class EditDistance(Evaluator):
         ignored_tokens(list of int): Tokens that should be removed before
         calculating edit distance.
 
-    Example:
+    Examples:
+        .. code-block:: python
 
-        exe = fluid.executor(place)
-        distance_evaluator = fluid.Evaluator.EditDistance(input, label)
-        for epoch in PASS_NUM:
-            distance_evaluator.reset(exe)
-            for data in batches:
-                loss = exe.run(fetch_list=[cost])
-            distance, instance_error = distance_evaluator.eval(exe)
+            exe = fluid.executor(place)
+            distance_evaluator = fluid.Evaluator.EditDistance(input, label)
+            for epoch in PASS_NUM:
+                distance_evaluator.reset(exe)
+                for data in batches:
+                    loss = exe.run(fetch_list=[cost])
+                distance, instance_error = distance_evaluator.eval(exe)
 
         In the above example:
         'distance' is the average of the edit distance in a pass.
@@ -210,11 +249,11 @@ class EditDistance(Evaluator):
         if main_program.current_block().idx != 0:
             raise ValueError("You can only invoke Evaluator in root block")
 
-        self.total_distance = self.create_state(
+        self.total_distance = self._create_state(
             dtype='float32', shape=[1], suffix='total_distance')
-        self.seq_num = self.create_state(
+        self.seq_num = self._create_state(
             dtype='int64', shape=[1], suffix='seq_num')
-        self.instance_error = self.create_state(
+        self.instance_error = self._create_state(
             dtype='int64', shape=[1], suffix='instance_error')
         distances, seq_num = layers.edit_distance(
             input=input, label=label, ignored_tokens=ignored_tokens)
@@ -256,9 +295,10 @@ class EditDistance(Evaluator):
 
 class DetectionMAP(Evaluator):
     """
+    Warning: This would be deprecated in the future. Please use fluid.metrics.DetectionMAP
+    instead.
     Calculate the detection mean average precision (mAP).
 
-    TODO (Dang Qingqing): update the following doc.
     The general steps are as follows:
     1. calculate the true positive and false positive according to the input
         of detection and labels.
@@ -293,17 +333,18 @@ class DetectionMAP(Evaluator):
             - 11point: the 11-point interpolated average precision.
             - integral: the natural integral of the precision-recall curve.
 
-    Example:
+    Examples:
+        .. code-block:: python
 
-        exe = fluid.executor(place)
-        map_evaluator = fluid.Evaluator.DetectionMAP(input,
-            gt_label, gt_box, gt_difficult)
-        cur_map, accum_map = map_evaluator.get_map_var()
-        fetch = [cost, cur_map, accum_map]
-        for epoch in PASS_NUM:
-            map_evaluator.reset(exe)
-            for data in batches:
-                loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)
+            exe = fluid.executor(place)
+            map_evaluator = fluid.Evaluator.DetectionMAP(input,
+                gt_label, gt_box, gt_difficult)
+            cur_map, accum_map = map_evaluator.get_map_var()
+            fetch = [cost, cur_map, accum_map]
+            for epoch in PASS_NUM:
+                map_evaluator.reset(exe)
+                for data in batches:
+                    loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)
 
         In the above example:
 
@@ -340,9 +381,10 @@ class DetectionMAP(Evaluator):
             evaluate_difficult=evaluate_difficult,
             ap_version=ap_version)
 
-        self.create_state(dtype='int32', shape=None, suffix='accum_pos_count')
-        self.create_state(dtype='float32', shape=None, suffix='accum_true_pos')
-        self.create_state(dtype='float32', shape=None, suffix='accum_false_pos')
+        self._create_state(dtype='int32', shape=None, suffix='accum_pos_count')
+        self._create_state(dtype='float32', shape=None, suffix='accum_true_pos')
+        self._create_state(
+            dtype='float32', shape=None, suffix='accum_false_pos')
 
         self.has_state = None
         var = self.helper.create_variable(

python/paddle/fluid/executor.py

@@ -18,17 +18,24 @@ from framework import Program, default_main_program, Variable
 from . import core
 
 __all__ = [
-    'Executor', 'global_scope', 'scope_guard', 'switch_scope', 'fetch_var'
+    'Executor', 'global_scope', 'scope_guard', '_switch_scope', 'fetch_var'
 ]
 
 g_scope = core.Scope()
 
 
 def global_scope():
+    """
+    Get the global/default scope instance. There are a lot of APIs use
+    :code:`global_scope` as its default value, e.g., :code:`Executor.run`
+
+    Returns:
+        Scope: The global/default scope instance.
+    """
     return g_scope
 
 
-def switch_scope(scope):
+def _switch_scope(scope):
     global g_scope
     ex = g_scope
     g_scope = scope
@@ -37,12 +44,40 @@ def switch_scope(scope):
 
 @contextlib.contextmanager
 def scope_guard(scope):
-    ex = switch_scope(scope)
+    """
+    Change the global/default scope instance by Python `with` statement. All
+    variable in runtime will assigned to the new scope.
+
+    Examples:
+        >>> import paddle.fluid as fluid
+        >>> new_scope = fluid.Scope()
+        >>> with fluid.scope_guard(new_scope):
+        >>>     ...
+
+    Args:
+        scope: The new global/default scope.
+    """
+    ex = _switch_scope(scope)
     yield
-    switch_scope(ex)
+    _switch_scope(ex)
 
 
 def as_numpy(tensor):
+    """
+    Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information.
+    For higher dimensional sequence data, please use LoDTensor directly.
+    Examples:
+        >>> import paddle.fluid as fluid
+        >>> outs = executor.run(...)
+        >>> np_outs = map(lambda x: as_numpy(x), outs)
+        >>>     ...
+
+    Args:
+       tensor(Variable): a instance of Tensor
+
+    Returns:
+        numpy.ndarray
+    """
     if isinstance(tensor, list):
         return [as_numpy(t) for t in tensor]
     assert isinstance(tensor, core.LoDTensor)
@@ -135,14 +170,18 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
 
 def fetch_var(name, scope=None, return_numpy=True):
     """
-    Fetch the value of the variable with the given name from the given scope
+    Fetch the value of the variable with the given name from the
+    given scope.
+
     Args:
         name(str): name of the variable. Typically, only persistable variables
             can be found in the scope used for running the program.
         scope(core.Scope|None): scope object. It should be the scope where
             you pass to Executor.run() when running your program.
-            If None, global_scope() will be used.
-        return_numpy(bool): whether convert the tensor to numpy.ndarray
+            If None, global_scope() will be used. Default None.
+        return_numpy(bool): whether convert the tensor to numpy.ndarray.
+            Default True.
+
     Returns:
        LodTensor|numpy.ndarray
     """
@@ -162,7 +201,7 @@ def fetch_var(name, scope=None, return_numpy=True):
     return tensor
 
 
-def get_program_cache_key(feed, fetch_list):
+def _get_program_cache_key(feed, fetch_list):
     feed_var_names = feed.keys()
 
     def to_name_str(var):
@@ -181,6 +220,25 @@ def get_program_cache_key(feed, fetch_list):
 
 
 class Executor(object):
+    """
+    An Executor in Python, only support the single-GPU running. For multi-cards, please refer to
+    ParallelExecutor.
+    Python executor takes a program, add feed operators and fetch operators to this program according
+    to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
+    the variables(or names) that user want to get after program run. Note: the executor will run all
+    operators in the program but not only the operators dependent by the fetch_list.
+    It store the global variables into the global scope, and create a local scope for the temporary 
+    variables. The local scope contents will be discarded after every minibatch forward/backward finished. 
+    But the global scope variables will be persistent through different runs.
+    All of ops in program will be running in sequence.
+
+    Args:
+        place(core.CPUPlace|core.CUDAPlace(n)): indicate the executor run on which device
+
+    Note: For debugging complicated network in parallel-GPUs, you can test it on the executor.
+    They has the exactly same arguments, and expected the same results.
+    """
+
     def __init__(self, place):
         self.place = place
         p = core.Place()
@@ -189,6 +247,23 @@ class Executor(object):
         self.program_caches = dict()
 
     def as_lodtensor(self, data):
+        """
+        Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
+        For higher dimensional sequence data, please use LoDTensor directly.
+
+        Examples:
+            >>> import paddle.fluid as fluid
+            >>> exe = fluid.executor(fluid.CPUPlace())
+            >>> data = np.array(size=(100, 200, 300))
+            >>> np_outs = map(lambda x: exe.as_lodtensor(x), data)
+            >>>     ...
+
+        Args:
+            data(numpy.ndarray): a instance of array
+
+        Returns:
+            LoDTensor
+        """
         if isinstance(data, list):
             raise RuntimeError("Some of your feed data hold LoD information. \
                 They can not be completely cast from a list of Python \
@@ -280,23 +355,47 @@ class Executor(object):
             scope=None,
             return_numpy=True,
             use_program_cache=False):
-        """ Run program by this Executor. Feed data by feed map, fetch result by fetch_list.
-
+        """
+        Run program by this Executor. Feed data by feed map, fetch result by fetch_list.
         Python executor takes a program, add feed operators and fetch operators to this program according
         to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
-        the variables(or names) that user want to get after program run. Note: the executor will run all
+        the variables(or names) that user want to get after program run.
+
+        Note: the executor will run all
         operators in the program but not only the operators dependent by the fetch_list
 
-        :param program: the program that need to run, if not provied, then default_main_program will be used.
-        :param feed: feed variable map, e.g. {"image": ImageData, "label": LableData}
-        :param fetch_list: a list of variable or variable names that user want to get, run will return them according
-        to this list.
-        :param feed_var_name: the name for the input variable of feed Operator.
-        :param fetch_var_name: the name for the output variable of feed Operator.
-        :param scope: the scope used to run this program, you can switch it to different scope. default is global_scope
-        :param return_numpy: if convert the fetched tensor to numpy
-        :param use_program_cache: set use_program_cache to true if program not changed compare to the last step.
-        :return: result according to fetch_list.
+        Args:
+            program(Program): the program that need to run, if not provied, then default_main_program will be used.
+            feed(dict): feed variable map, e.g. {"image": ImageData, "label": LableData}
+            fetch_list(list): a list of variable or variable names that user want to get, run will return them according to this list.
+            feed_var_name(str): the name for the input variable of feed Operator.
+            fetch_var_name(str): the name for the output variable of fetch Operator.
+            scope(Scope): the scope used to run this program, you can switch it to different scope. default is global_scope
+            return_numpy(bool): if convert the fetched tensor to numpy
+            use_program_cache(bool): set use_program_cache to true if program not changed compare to the last step.
+
+        Returns:
+
+            list(numpy.array): fetch result according to fetch_list.
+
+
+        Examples:
+
+            >>> data = layers.data(name='X', shape=[1], dtype='float32')
+            >>> hidden = layers.fc(input=data, size=10)
+            >>> layers.assign(hidden, out)
+            >>> loss = layers.mean(out)
+            >>> adam = fluid.optimizer.Adam()
+            >>> adam.minimize(loss)
+
+            >>> cpu = core.CPUPlace()
+            >>> exe = Executor(cpu)
+            >>> exe.run(default_startup_program())
+
+            >>> x = numpy.random.random(size=(10, 1)).astype('float32')
+            >>> outs = exe.run(
+            >>>     feed={'X': x},
+            >>>     fetch_list=[loss.name])
         """
         if feed is None:
             feed = {}
@@ -317,7 +416,7 @@ class Executor(object):
         if scope is None:
             scope = global_scope()
 
-        cache_key = get_program_cache_key(feed, fetch_list)
+        cache_key = _get_program_cache_key(feed, fetch_list)
         if use_program_cache:
             cached_program = self._get_program_cache(cache_key)
             if cached_program is None:

python/paddle/fluid/framework.py

@@ -30,8 +30,6 @@ __all__ = [
     'default_startup_program',
     'default_main_program',
     'program_guard',
-    'switch_startup_program',
-    'switch_main_program',
     'get_var',
 ]
 
@@ -43,7 +41,8 @@ ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
 
 def grad_var_name(var_name):
     """
-    return gradient name for a certain var name
+    Returns:
+        str: gradient name for a certain var name
     """
     return var_name + GRAD_VAR_SUFFIX
 
@@ -51,10 +50,12 @@ def grad_var_name(var_name):
 def convert_np_dtype_to_dtype_(np_dtype):
     """
     Convert the data type in numpy to the data type in Paddle
+
     Args:
-        np_dtype(np.dtype): the data type in numpy
+        np_dtype(np.dtype): the data type in numpy.
 
-    Returns(core.VarDesc.VarType): the data type in Paddle
+    Returns:
+        core.VarDesc.VarType: the data type in Paddle.
 
     """
     dtype = np.dtype(np_dtype)
@@ -120,37 +121,53 @@ def _debug_string_(proto, throw_on_error=True):
 
 class Variable(object):
     """
-    Python variable. Every input and output of an operator is a variable. Every
-    variable belongs to a block. The variable has a name and two variables in
-    different blocks could have the same name.
+    In Fluid, every input and output of an operator is a variable. In most 
+    cases, variables are used for holding different kinds of data or training 
+    labels. A variable belongs to a block. All variable has its own name and 
+    two variables in different blocks could have the same name.
 
-    There are many kinds of variables. Please reference the framework.proto for
-    details.
+    There are many kinds of variables. Each kind of them has its own attributes 
+    and usages. Please reference the framework.proto for details. 
 
-    Notes: The constructor of Variable should not be invoked directly. Please
-    use `Block.create_var` to create a variable.
-
-    >>> cur_program = Program()
-    >>> cur_block = cur_program.current_block()
-    >>> new_variable = cur_block.create_var(
-    >>>                    name="X", shape=[-1, 23, 48], dtype='float32')
+    Most of a Variable's member variables can be setted to be None. It mean 
+    it is not available or will be specified later.
 
     Args:
-        block(Block): The associated block. It will be passed by
-            `Block.create_var` automatically.
+        block(Block): The block that the variable belongs to.
         type(core.VarDesc.VarType): Variable type. Please reference the
             framework.proto for details.
-        shape(tuple|list|None): The shape of variable. -1 means the batch size.
+        name(str|None): The name of the variable. If setted None, it will be
+            generated automatically. Default: None
+        shape(tuple|list|None): The shape of the variable. -1 means the batch size.
             Some kinds of variable do not contain shape, just set it to None.
-        dtype(np.dtype|core.VarDesc.VarType|str): The data type of variable.
-        lod_level(int): The level of lod tensor. 0 means it is not a time
+            Default: None
+        dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
+            Default: None
+        lod_level (int|None): The level of lod tensor. 0 means it is not a time
             series data.
-        capacity(int): The capacity of Channel variable. Ignored
-            for other types.
-        persistable(bool): True if the variable should be saved as check point.
-            Defaults to False.
-        stop_gradient(bool): True if the variable will stop to calculate
-            gradients when backward. Defaults to False.
+            Default: None
+        capacity (int|None): The capacity of Channel variable. Ignored for other
+            types. Default: None
+        persistable (bool|None): True if the variable is persistable. A persistable
+            variable will not be deleted after an iteration ending. Defaults: None.
+        error_clip (BaseErrorClipAttr|None): The error clip attributes of the
+            corresponding gradient variable. Default: None
+        stop_gradient (bool): True if the variable will stop to calculate its
+            gradients when backward. Default: False.
+        is_data (bool): True if the variable is an input data. Default: False
+
+    Notes:
+        The constructor of Variable should not be invoked directly. Please
+        use `Block.create_var` to create a variable.
+
+    Examples:
+        .. code-block:: python
+
+            cur_program = Program()
+            cur_block = cur_program.current_block()
+            new_variable = cur_block.create_var(name="X",
+                                                shape=[-1, 23, 48],
+                                                dtype='float32')
     """
 
     def __init__(self,
@@ -253,13 +270,14 @@ class Variable(object):
         Get debug string.
 
         Args:
-            throw_on_error(bool): True if raise an exception when self is not
-                intialized.
+            throw_on_error(bool): True if raise an exception when self is
+                not initialized.
             with_details(bool): more details about variables and parameters
-                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
-
-        Returns(str): The debug string.
+                (e.g. trainable, optimize_attr, ...) will be printed when
+                with_details is True. Default False;
 
+        Returns:
+            str: The debug string.
         """
         assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                bool)
@@ -276,6 +294,15 @@ class Variable(object):
     __repr__ = __str__
 
     def set_desc(self, input):
+        """
+        Set the variable description.
+
+        Args:
+            input(core.VarDesc): The new VarDesc.
+
+        Returns:
+            None
+        """
         self.desc = input
 
     @property
@@ -312,6 +339,15 @@ class Variable(object):
         return self.desc.type()
 
     def set_error_clip(self, error_clip):
+        """
+        Set the error_clip.
+
+        Args:
+            error_clip(BaseErrorClipAttr) : The new error_clip.
+
+        Returns:
+            None
+        """
         self.error_clip = error_clip
 
 
@@ -319,8 +355,8 @@ def get_all_op_protos():
     """
     Get all registered op proto from PaddlePaddle C++ end.
 
-    Returns(list): list of OpProto
-
+    Returns:
+       list: list of OpProto.
     """
     protostrs = core.get_all_op_protos()
     ret_values = []
@@ -373,9 +409,45 @@ class OpProtoHolder(object):
 
 class Operator(object):
     """
-    Python Operator class. The operator represents the build in instructions in a
-    Block. Users can use the build in instructions to describe their neural
-    network.
+    In Fluid, all the operation are represented by Operator, and Operator
+    is regarded as a build in an instruction of a Block. Users can use the
+    build in instructions to describe their neural network.
+
+    Args:
+        block(Block): The block has the current operator.
+        desc(core.OpDesc): The protobuf description of Operator.
+        type(str): The type of operator. Default None.
+        inputs(dict): The input of this Operator. it is a dictionary, for every
+            element, key is the input parameter name, and value is a list of
+            variables. Default None.
+        outputs(dict): The output of this Operator. it is a dictionary, for
+            every element, key is the input parameter name, and value is a list
+            of variables. Default None.
+        attrs(dict): The attributes of this Operator. it is a dictionary, for
+            every element, key is attribute name, and value is the attribute value.
+            The attribute type should be as same as the type registered in C++ side.
+            Default None.
+
+    Returns:
+        Operator: The initialized Operator.
+
+    Raises:
+        ValueError: If the passed input, output and attrs doesn't match the
+            initializing Operator's that registered in C++ side.
+
+    Notes:
+        The constructor of operator should not be invoked directly. Use
+        Block.append_op or Block.prepend_op instead.
+
+    Examples:
+        .. code-block:: python
+
+            cur_program = Program()
+            cur_block = cur_program.current_block()
+            # var1 += var2 + var3
+            cur_block.append_op(type="sum",
+                                inputs={"X": [var1, var2, var3]},
+                                outputs={"Out": [var1]})
     """
     OP_WITHOUT_KERNEL_SET = {
         'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
@@ -392,31 +464,7 @@ class Operator(object):
                  inputs=None,
                  outputs=None,
                  attrs=None):
-        """
-        Constructor.
 
-        Notes: The constructor of operator should not be invoked directly. Use
-        Block.append_op or Block.prepend_op instead.
-
-        >>> cur_program = Program()
-        >>> cur_block = cur_program.current_block()
-        >>> # var1 += var2 + var3
-        >>> cur_block.append_op(type="sum",
-        >>>                     inputs={"X": [var1, var2, var3]},
-        >>>                     outputs={"Out": [var1]})
-
-        Args:
-            block(Block): The block has the current operator.
-            desc(core.OpDesc): The protobuf description.
-            type(str): The type of operator.
-            inputs(dict): The input dictionary. Key is the input parameter name.
-                Value is a list of variables.
-            outputs(dict): The output dictionary which has the same format with
-                           inputs.
-            attrs(dict): The attributes dictionary. Key is attribute name. Value
-                is the attribute value. The attribute type should be as same as
-                the type registered in C++
-        """
         self.block = block
         self.desc = desc
         self.attrs = attrs
@@ -529,12 +577,14 @@ class Operator(object):
 
     def to_string(self, throw_on_error):
         """
-        To debug string.
+        Get debug string.
+
         Args:
-            throw_on_error(bool): raise exception when self is not initialized
-                when throw_on_error is True
+            throw_on_error(bool): Whether to raise exception if self is not
+                initialized.
 
-        Returns(str): The debug string.
+        Returns:
+            str: The debug string.
 
         """
         protostr = self.desc.serialize_to_string()
@@ -552,29 +602,45 @@ class Operator(object):
 
     def input(self, name):
         """
-        Get input arguments by the input parameter name
-        Args:
-            name(str): The input parameter name
+        Get the input arguments according to the input parameter name.
 
-        Returns(list): return the list of argument names associated with the
-            specific parameter name.
+        Args:
+            name(str): The input parameter name.
 
+        Returns:
+            list: return the list of argument names that associated with \
+                the specific parameter name.
         """
         return self.desc.input(name)
 
     def rename_input(self, old_name, new_name):
+        """
+        Rename the `old_name` to `new_name`.
+
+        Args:
+            old_name(str): The old name of the Operator's input.
+            new_name(str): The new name of the Operator's input.
+
+        Returns:
+            None
+        """
         self.desc.rename_input(old_name, new_name)
 
     def rename_output(self, old_name, new_name):
+        """
+        Rename the `old_name` to `new_name`.
+
+        Args:
+            old_name(str): The old name of the Operator's output.
+            new_name(str): The new name of the Operator's output.
+
+        Returns:
+            None
+        """
         self.desc.rename_output(old_name, new_name)
 
     @property
     def input_names(self):
-        """
-        Get all input parameter names
-        Returns(list): return a list of input parameter names
-
-        """
         return self.desc.input_names()
 
     @property
@@ -587,33 +653,23 @@ class Operator(object):
 
     def output(self, name):
         """
-        Get output arguments by the output parameter name
-        Args:
-            name(str): The output parameter name
+        Get output arguments by the output parameter name.
 
-        Returns(list): return the list of argument names associated with the
-            specific parameter name.
+        Args:
+            name(str): The output parameter name.
 
+        Returns:
+            list: return the list of argument names associated with \
+                the specific parameter name.
         """
         return self.desc.output(name)
 
     @property
     def output_names(self):
-        """
-        Get all output parameter names
-        Returns(list): return a list of output parameter names
-
-        """
         return self.desc.output_names()
 
     @property
     def idx(self):
-        """
-        Return the array index of current operator.
-        Returns(int): The array index in block.ops array
-        Raises:
-            ValueError: when the operator is not found.
-        """
         for i, op in enumerate(self.block.ops):
             if op == self:
                 return i
@@ -622,27 +678,40 @@ class Operator(object):
 
     def has_attr(self, name):
         """
-        operator has the attribute with name or not.
+        Whether this Operator has the attribute with name or not.
+
         Args:
-            name(str): the attribute name
+            name(str): the attribute name.
 
-        Returns(bool): True if has this attribute.
+        Returns:
+            bool: True if has this attribute.
 
         """
         return self.desc.has_attr(name)
 
     def attr_type(self, name):
         """
-        Get the type of attribute by attribute name
-        Args:
-            name(str): the attribute name
+        Get the type of attribute by attribute's name.
 
-        Returns(core.AttrType): the attribute type
+        Args:
+            name(str): the attribute name.
 
+        Returns:
+            core.AttrType: the attribute type.
         """
         return self.desc.attr_type(name)
 
     def set_attr(self, name, val):
+        """
+        Set the value of attribute by attribute's name.
+
+        Args:
+            name(str): the attribute name.
+            val(bool|int|str|float|list): the value of the attribute.
+
+        Raises:
+            ValueError: If the type of value doesn't match with desc.attr_type(name).
+        """
         self.attrs[name] = val
         if isinstance(val, Block):
             self.desc.set_block_attr(name, val.desc)
@@ -654,40 +723,39 @@ class Operator(object):
 
     @property
     def attr_names(self):
-        """
-        Get all attribute names
-        Returns(list): The list of attribute name
-
-        """
         return self.desc.attr_names()
 
     def attr(self, name):
         """
-        Get attribute by name
+        Get the attribute by name.
+
         Args:
-            name(str): the attribute name
+            name(str): the attribute name.
 
-        Returns(bool|int|str|float|list): The attribute value. The return value
+        Returns:
+            bool|int|str|float|list: The attribute value. The return value
             can be any valid attribute type.
-
         """
         return self.desc.attr(name)
 
     def block_attr(self, name):
         """
-        Get the block attribute by name
-        Args:
-            name(str): the attribute name
+        Get the block attribute by name.
 
-        Returns(int): the block index
+        Args:
+            name(str): the attribute name.
 
+        Returns:
+            int: the block index.
         """
         return self.desc.block_attr(name)
 
     def all_attrs(self):
         """
-        Get the attribute dict
-        Returns(dict): The Operator's attribute dict
+        Get the attribute dict.
+
+        Returns:
+            dict: The Operator's attribute dict.
         """
         attr_names = self.attr_names
         attr_map = {}
@@ -700,6 +768,35 @@ class Operator(object):
 
 
 class Block(object):
+    """
+    In Fluid, a Program is consistence of multi-Block, and Block stores
+    VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
+    One block could have some child blocks, and child block's name scopes
+    should inherit the parent's so that OpDesc in child block can reference
+    a VarDesc that is stored in the parent block.
+    Please reference the framework.proto for details.
+
+    Args:
+        program(Program): The Program that the Block belongs to.
+        idx(int): The block's id in the Program.
+
+    Notes:
+        The constructor of Block should not be invoked directly. Please
+        use `Program.create_block()` to create a block.
+
+    Examples:
+        .. code-block:: python
+
+            cur_program = Program()
+            cur_block = cur_program.current_block()
+            var = cur_block.create_var(name="X",
+                                       shape=[-1, 23, 48],
+                                       dtype='float32')
+            cur_block.append_op(type="abs",
+                                inputs={"X": [var]},
+                                outputs={"Out": [var]})
+    """
+
     def __init__(self, program, idx):
         self.desc = program.desc.block(idx)
         self.vars = collections.OrderedDict()  # var_name --> var
@@ -712,15 +809,17 @@ class Block(object):
 
     def to_string(self, throw_on_error, with_details=False):
         """
-        To debug string.
+        Get debug string.
+
         Args:
             throw_on_error(bool): raise exception when self is not initialized
-                when throw_on_error is True
+                when throw_on_error is True.
             with_details(bool): more details about variables and parameters
-                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
-
-        Returns(str): The debug string.
+                (e.g. trainable, optimize_attr, ...) will be printed when
+                with_details is True. Default False.
 
+        Returns:
+            str: The debug string.
         """
         assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                bool)
@@ -752,6 +851,15 @@ class Block(object):
         return self.desc.get_forward_block_idx()
 
     def set_forward_block_idx(self, idx):
+        """
+        Set the forward block Idx.
+
+        Args:
+            idx(int): the block index.
+
+        Returns:
+            None
+        """
         self.desc.set_forward_block_idx(idx)
 
     @property
@@ -759,6 +867,19 @@ class Block(object):
         return self.desc.id
 
     def var(self, name):
+        """
+        Get a Variable by name from this block.
+
+        Args:
+            name(str): the Variable's name.
+
+        Raises:
+            ValueError: The If input's type is not str, or this block
+                doesn't have a Variable with the giving name.
+
+        Returns:
+            Variable: the Variable with the giving name.
+        """
         if not isinstance(name, basestring):
             raise TypeError(
                 "var require string as parameter, but get %s instead." %
@@ -769,6 +890,19 @@ class Block(object):
         return v
 
     def var_recursive(self, name):
+        """
+        Get a Variable by name from this block recursively.
+
+        Args:
+            name(str): the Variable's name.
+
+        Raises:
+            ValueError: this block and this parent block doesn't
+                have a Variable with the giving name.
+
+        Returns:
+            Variable: the Variable with the giving name.
+        """
         frontier = list()
         visited = set()
 
@@ -815,6 +949,18 @@ class Block(object):
     def rename_var(self, name, new_name):
         """
         Rename variable in vars and ops' inputs and outputs
+
+        Args:
+            name(str): the name that need to be renamed.
+            new_name(str): the name that need to rename to.
+
+        Raises:
+            ValueError: If this block doesn't have this the giving name,
+                or the type of the var with the giving name is not Parameter
+                or Variable.
+
+        Returns:
+            Variable: the Variable with the giving name.
         """
         if not self.has_var(name):
             raise ValueError("var %s is not in current block" % name)
@@ -878,12 +1024,27 @@ class Block(object):
         return param
 
     def append_op(self, *args, **kwargs):
+        """
+        Appends a new Operator according to the giving arguments.
+
+        Returns:
+            Operator: the append Operator.
+        """
         op_desc = self.desc.append_op()
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
         self.ops.append(op)
         return op
 
     def insert_op(self, index, *args, **kwargs):
+        """
+        Insert a Operator according to the giving arguments.
+
+        Args:
+            index(int): the place that the operator to insert.
+
+        Returns:
+            Operator: the insert Operator.
+        """
         self.sync_with_cpp()
         op_desc = self.desc.insert_op(index)
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
@@ -891,11 +1052,30 @@ class Block(object):
         return op
 
     def remove_op(self, index):
+        """
+        Remove the specific position operator.
+
+        Args:
+            index(int): the position that the operator to insert.
+
+        Returns:
+            None
+        """
         self.sync_with_cpp()
         self.desc.remove_op(index, index + 1)
         del self.ops[index]
 
     def slice_ops(self, start, end):
+        """
+        Return the Operator between start and end.
+
+        Args:
+            start(int): the start position.
+            end(int): the end position.
+
+        Returns:
+            list: the Operators between start and end.
+        """
         return self.ops[start:end]
 
     def prepend_op(self, *args, **kwargs):
@@ -906,9 +1086,8 @@ class Block(object):
 
     def sync_with_cpp(self):
         """
-        Sync from the desc on the c++ end.
-
-        This method is used to synchronize the c++ desc instance generated by backward.
+        Sync from the desc on the c++ end. This method is used to synchronize
+        the c++ desc instance generated by backward.
         """
         # sync variables from cpp
         for var in self.desc.all_vars():
@@ -973,9 +1152,14 @@ class Block(object):
 
     def copy_param_info_from(self, other):
         """
-        Copy the information of parameters from the other block
+        Copy the information of parameters from the other block.
+
         Args:
-            other(Block): the other block
+            other(Block): the other block.
+
+        Raises:
+            ValueError: If type of input is not Block, or the `other` and this
+                block is not in the same topology.
 
         Returns:
             None
@@ -1007,11 +1191,12 @@ class Block(object):
     def clone_variable(self, var):
         """
         Clone a variable into current block.
+
         Args:
             var: the variable to be cloned.
 
         Returns:
-            The new  variable cloned from 'var' in current block.
+            Variable: the new  variable cloned from 'var' in current block.
         """
         assert isinstance(var, Variable)
         ret_var = None
@@ -1051,23 +1236,18 @@ class Program(object):
     Notes: we have default_startup_program and default_main_program
     by default, a pair of them will shared the parameters.
     The default_startup_program only run once to initialize parameters,
-    default_main_program run in every minibatch and adjust the weights.
-
-    Args:
-        None
+    default_main_program run in every mini batch and adjust the weights.
 
     Returns:
-        Python Program
+        A empty program.
 
     Examples:
-       .. code-block:: python
-
-         main_program = Program()
-         startup_program = Program()
-         with fluid.program_guard(main_program=main_program, startup_program=startup_program):
-            fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
-            fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
-            fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
+        >>> main_program = fluid.Program()
+        >>> startup_program = fluid.Program()
+        >>> with fluid.program_guard(main_program=main_program, startup_program=startup_program):
+        >>>     fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
+        >>>     fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
+        >>>     fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
 
     """
 
@@ -1081,6 +1261,19 @@ class Program(object):
 
     @property
     def op_role(self):
+        """
+        The operator role. In a enum {Forward, Backward, Optimize}.
+
+        Notes: this is a low level API. It is used only for ParallelExecutor to
+        duplicate or schedule operator to devices.
+
+        For example, the forward operator should be executed on every device.
+        The backward operator should be executed on every device and the
+        parameter gradient of backward (use :code:`op_role_var` to get this
+        variable) operator should be merged to one device. The optimization
+        operators should be executed on only one device and broadcast the
+        optimization result, i.e., the new parameter, to every other device.
+        """
         return self._current_role
 
     @op_role.setter
@@ -1089,6 +1282,13 @@ class Program(object):
 
     @property
     def op_role_var(self):
+        """
+        The auxiliary variables for :code:`op_role` property.
+
+        See Also: :code:`Program.op_role`'s documentation for details.
+
+        Notes: This is a very low-level API. Users should not use it directly.
+        """
         return self._op_role_var
 
     @op_role_var.setter
@@ -1097,6 +1297,21 @@ class Program(object):
 
     @contextlib.contextmanager
     def optimized_guard(self, var):
+        """
+        A with guard to set :code:`Optimization` :code:`OpRole` and
+        :code:`OpRoleVar` automatically.
+
+        Notes: This is a very low level API. Users should not use it directly.
+
+        Args:
+            var(Variable|str): The variable (name) to be optimized.
+
+        Examples:
+
+            >>> p, g = backward(...)
+            >>> with program.optimized_guard(p):
+            >>>     p = p - 0.001 * g
+        """
         OpRole = core.op_proto_and_checker_maker.OpRole
         self._current_role = OpRole.Optimize
         self._op_role_var = [var.name if isinstance(var, Variable) else var]
@@ -1105,18 +1320,35 @@ class Program(object):
         self._current_role = OpRole.Forward
 
     def __str__(self):
+        """
+        Get the protobuf debug string of this Program.
+
+        Returns:
+            (str): The protobuf debug string.
+
+        Raises:
+            ValueError: If any of required fields is not set.
+        """
         return self.to_string(True)
 
     def to_string(self, throw_on_error, with_details=False):
         """
         To debug string.
+
         Args:
-            throw_on_error(bool): raise exception when self is not initialized
-                when throw_on_error is True
-            with_details(bool): more details about variables and parameters
-                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+            throw_on_error(bool): raise Value error when any of required fields
+                is not set.
 
-        Returns(str): The debug string.
+            with_details(bool): True if more details about variables and
+                parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need
+                to print.
+
+        Returns
+            (str): The debug string.
+
+        Raises:
+            ValueError: If any of required fields is not set and throw_on_error is
+                True.
 
         """
         assert isinstance(throw_on_error, bool) and isinstance(with_details,
@@ -1132,25 +1364,89 @@ class Program(object):
         return res_str
 
     def get_desc(self):
+        """
+        Get the C++ side of `ProgramDesc` object pointer. The C++ object is
+        exposed by :code:`pybind`.
+
+        Notes: This is a very low level API. Users should not use this API
+        directly.
+        """
         return self.desc
 
     def clone(self, for_test=False):
-        """Clone the Program object
-        Args:
-           for_test(bool): indicate whether clone for test.
+        """
+        Create a new, duplicated program.
+
 
-        Set for_test to False when we want to clone the program for training.
-        Set for_test to True when we want to clone the program for testing.
+        Some operators, e.g., :code:`batch_norm`, behave differently between
+        training and testing. They have an attribute, :code:`is_test`, to
+        control this behaviour. This method will change the :code:`is_test`
+        attribute of them to :code:`True` when :code:`for_test=True`.
+
+        * Set for_test to False when we want to clone the program for training.
+        * Set for_test to True when we want to clone the program for testing.
+
+        Notes: This API DOES NOT prune any operator. Use
+        :code:`clone(for_test=True)` before backward and optimization please.
 
         Args:
-            for_test(bool): Some operators, such as batch_norm and drop_out ops,
-                behave differently in training and testing. If for_test is True,
-                the is_test attributes in these operators will be set to True for
-                testing purposes, otherwise, they remain unchanged.
+            for_test(bool): True if change the :code:`is_test` attribute of
+                operators to :code:`True`.
 
         Returns:
-            Program: The cloned Program object.
-
+            Program: The new, duplicated Program object.
+
+        Examples:
+
+            1. To clone a test program, the sample code is:
+
+            >>> import paddle.fluid as fluid
+            >>> train_program = fluid.Program()
+            >>> startup_program = fluid.Program()
+            >>> with fluid.program_guard(train_program, startup_program):
+            >>>     img = fluid.layers.data(name='image', shape=[784])
+            >>>     hidden = fluid.layers.fc(input=img, size=200, act='relu')
+            >>>     hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
+            >>>     loss = fluid.layers.cross_entropy(
+            >>>                 input=fluid.layers.fc(hidden, size=10, act='softmax'),
+            >>>                 label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
+            >>>
+            >>> test_program = train_program.clone(for_test=True)
+            >>>
+            >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3)
+            >>> with fluid.program_guard(train_program, startup_program):
+            >>>     sgd.minimize(loss)
+
+            2. The :code:`clone` method can be avoid if you create program for
+            training and program for testing individually.
+
+            >>> import paddle.fluid as fluid
+            >>>
+            >>> def network(is_test):
+            >>>     img = fluid.layers.data(name='image', shape=[784])
+            >>>     hidden = fluid.layers.fc(input=img, size=200, act='relu')
+            >>>     hidden = fluid.layers.dropout(hidden, dropout_prob=0.5, is_test=is_test)
+            >>>     loss = fluid.layers.cross_entropy(
+            >>>                 input=fluid.layers.fc(hidden, size=10, act='softmax'),
+            >>>                 label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
+            >>>     return loss
+            >>>
+            >>> train_program = fluid.Program()
+            >>> startup_program = fluid.Program()
+            >>> test_program = fluid.Program()
+            >>>
+            >>> with fluid.program_guard(train_program, startup_program):
+            >>>     with fluid.unique_name.guard():
+            >>>         loss = network(is_test=False)
+            >>>         sgd = fluid.optimizer.SGD(learning_rate=1e-3)
+            >>>         sgd.minimize(loss)
+            >>>
+            >>> # the test startup program is not used.
+            >>> with fluid.program_guard(test_program, fluid.Program()):
+            >>>     with fluid.unique_name.guard():
+            >>>         loss = network(is_test=True)
+
+            The two code snippets above will generate same programs.
         """
         if for_test:
             p = self.inference_optimize()
@@ -1165,6 +1461,21 @@ class Program(object):
         return p
 
     def prune(self, targets):
+        """
+        Prune operators and variables which are not needed to generate
+        :code:`targets`.
+
+        Notes: This is a very low level API. Users should not use this API
+        directly. This API is in flux and not stable.
+
+        Args:
+            targets(list|Variable|Operator): A list of variables or operators
+                need to be pruned
+
+        Returns:
+            Program:  A new, pruned program.
+
+        """
         if not isinstance(targets, list):
             targets = [targets]
         targets_idx = []
@@ -1199,6 +1510,17 @@ class Program(object):
         return res
 
     def inference_optimize(self):
+        """
+        This method will create a new program and change the :code:`is_test`
+        attribute of operators to :code:`True`. All the :code:`Parameter`
+        information will be lost.
+
+        Notes: This API is a very low level API. Use
+        :code:`Program.clone(for_test=True)` instead.
+
+        Returns:
+            Program: The new program.
+        """
         # this is an alternative implement before
         # core.inference_optimize being fixed.
         res = Program()
@@ -1215,6 +1537,18 @@ class Program(object):
 
     @staticmethod
     def parse_from_string(binary_str):
+        """
+        Deserialize a program desc from protobuf binary string.
+
+        Notes: All information about parameters will be lost after serialization
+        and deserialization.
+
+        Args:
+            binary_str(str): The binary prootbuf string.
+
+        Returns:
+            Program: A deserialized program desc.
+        """
         p = Program()
         p.desc = core.ProgramDesc(binary_str)
         p.blocks = [Block(p, i) for i in xrange(p.desc.num_blocks())]
@@ -1223,10 +1557,19 @@ class Program(object):
 
     @property
     def random_seed(self):
+        """
+        The default random seed for random operators in Program. Zero means get
+        the random seed from random device.
+
+        Notes: It must be set before the operators have been added.
+        """
         return self._seed
 
     @property
     def num_blocks(self):
+        """
+        The number of blocks in this program.
+        """
         return self.desc.num_blocks()
 
     @random_seed.setter
@@ -1239,15 +1582,40 @@ class Program(object):
         return str(self)
 
     def global_block(self):
+        """
+        Get the first block of this program.
+        """
         return self.blocks[0]
 
     def block(self, index):
+        """
+        Get the :code:`index` block of this program
+        Args:
+            index(int): The index of block to get
+
+        Returns:
+            Block: The :code:`index` block
+        """
         return self.blocks[index]
 
     def current_block(self):
+        """
+        Get the current block. The :code:`current` block is the block to append
+        operators.
+        """
         return self.blocks[self.current_block_idx]
 
     def create_block(self, parent_idx=None):
+        """
+        Create a new block with the :code:`parent_idx` and change the current block
+        to new block.
+
+        Args:
+            parent_idx(int): The parent block index.
+
+        Returns:
+            Block: The new block.
+        """
         new_block_idx = len(self.blocks)
         parent = self.current_block() if parent_idx is None else self.block(
             parent_idx)
@@ -1257,9 +1625,24 @@ class Program(object):
         return self.current_block()
 
     def rollback(self):
+        """
+        Exit a code block, i.e., roll back to the parent block.
+        Returns:
+            None
+        """
         self.current_block_idx = self.current_block().parent_idx
 
     def sync_with_cpp(self):
+        """
+        Synchronize Python instance to its binding C++ object instance.
+        If the program is modified in C++ space, this method should be invoked.
+
+        Notes: This is a very low level API. Users should not invoke it
+        directly.
+
+        Returns:
+            None
+        """
         for block_idx in range(len(self.blocks), self.desc.num_blocks()):
             self.blocks.append(Block(self, block_idx))
         for block in self.blocks:
@@ -1269,6 +1652,9 @@ class Program(object):
         """
         Copy the information of parameters from other program.
 
+        Notes: This is a very low level API. Users should not invoke it
+        directly.
+
         Args:
             other(Program): Other program
 
@@ -1288,6 +1674,9 @@ class Program(object):
         """
         Copy the information of data variables from other program.
 
+        Notes: This is a very low level API. Users should not invoke it
+        directly.
+
         Args:
             other(Program): Other program
 
@@ -1306,12 +1695,41 @@ class Program(object):
                 self.global_block().var(var.name).is_data = True
 
     def list_vars(self):
+        """
+        Get all variables from this Program. A iterable object is returned.
+
+        Returns:
+            iterable: The generator will yield every variable in this program.
+        """
         for each_block in self.blocks:
             for each_var in each_block.vars.itervalues():
                 yield each_var
 
 
 class Parameter(Variable):
+    """
+    Parameter is derived from Variable. A parameter is a persistable 
+    Variable, and will be updated by optimizers after each iteration.
+    The training of a neural network is essentially the updating of 
+    its parameters.
+
+    Relative to a general Variable, a Parameter has several its own
+    member variables:
+
+    Args:
+        trainable(bool): True if the parameter need to be updated after
+            iterations.
+        optimize_attr(map): Parameter attributes related with optimizing.
+            Currently, it only contains 'learning_rate'.
+            Default: {'learning_rate': 1.0}
+        regularizer(WeightDecayRegularizer): The Regularizer which will
+            be applied on the parameter. Default: None
+        gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
+            which will be applied on the parameter. Default: None
+        do_model_average(bool): True if the model average strategy will
+            be applied on this parameter.
+    """
+
     def __init__(self, block, shape, dtype, **kwargs):
         if shape is None or dtype is None:
             raise ValueError("Parameter must set shape and dtype")
@@ -1374,8 +1792,15 @@ _startup_program_ = Program()
 
 def default_startup_program():
     """
-    Get default startup program. In startup program, Paddle will initialize
-    parameters, initialize nccl handle, etc.
+    Get default/global startup program.
+
+    The layer function in :code:`fluid.layers` will create parameters, readers,
+    NCCL handles as global variables. The :code:`startup_program` will
+    initialize them by the operators in startup program. The layer function will
+    append these initialization operators into startup program.
+
+    This method will return the :code:`default` or the :code:`current` startup
+    program. Users can use :code:`fluid.program_guard` to switch program.
 
     Returns:
         Program: startup program
@@ -1385,7 +1810,15 @@ def default_startup_program():
 
 def default_main_program():
     """
-    Get default main program. The main program is used for training or testing.
+    Get default/global main program. The main program is used for training or
+    testing.
+
+    All layer function in :code:`fluid.layers` will append operators and
+    variables to the :code:`default_main_program`.
+
+    The :code:`default_main_program` is the default program in a lot of APIs.
+    For example, the :code:`Executor.run()` will execute the
+    :code:`default_main_program` when the program is not specified.
 
     Returns:
         Program: main program
@@ -1427,20 +1860,34 @@ def switch_startup_program(program):
 @contextlib.contextmanager
 def program_guard(main_program, startup_program=None):
     """
-    Switch program with `with` statement
+    Change the global main program and startup program with `with` statement.
+    Layer functions in the Python `with` block will append operators and
+    variables to the new main programs.
 
     Examples:
-        >>> with program_guard(Program()):
-        >>>   data = fluid.layers.data(...)
-        >>>   hidden = fluid.layers.fc(...)
+
+        >>> import paddle.fluid as fluid
+        >>> main_program = fluid.Program()
+        >>> startup_program = fluid.Program()
+        >>> with fluid.program_guard(main_program, startup_program):
+        >>>     data = fluid.layers.data(...)
+        >>>     hidden = fluid.layers.fc(...)
+
+    Notes: The temporary :code:`Program` can be used if the user does not need
+    to construct either of startup program or main program.
+
+    Examples:
+
+        >>> import paddle.fluid as fluid
+        >>> main_program = fluid.Program()
+        >>> # does not care about startup program. Just pass a temporary value.
+        >>> with fluid.program_guard(main_program, fluid.Program()):
+        >>>     data = ...
 
     Args:
-        main_program(Program): New main program inside `with` statement
+        main_program(Program): New main program inside `with` statement.
         startup_program(Program): New startup program inside `with` statement.
             None means do not change startup program.
-
-    Returns:
-        None
     """
     if not isinstance(main_program, Program):
         raise TypeError("main_program should be Program")
@@ -1457,7 +1904,8 @@ def program_guard(main_program, startup_program=None):
 
 def get_var(name, program=None):
     """
-    Get a variable by name from the global block of a program
+    Get a variable by name from the global block of a program.
+    
     Args:
         name(str): name of the variable
         program(Program|None): program object.

python/paddle/fluid/inferencer.py

@@ -27,13 +27,30 @@ __all__ = ['Inferencer', ]
 
 
 class Inferencer(object):
+    """
+    Inferencer High Level API.
+
+    Args:
+        infer_func (Python func): Infer function that will return predict Variable
+        param_path (str): The path where the inference model is saved by fluid.io.save_params
+        place (Place): place to do the inference
+        parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
+
+    Examples:
+        .. code-block:: python
+
+            def inference_program():
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                return y_predict
+
+            place = fluid.CPUPlace()
+            inferencer = fluid.Inferencer(
+                infer_func=inference_program, param_path="/tmp/model", place=place)
+
+    """
+
     def __init__(self, infer_func, param_path, place=None, parallel=False):
-        """
-        :param infer_func: a function that will return predict Variable
-        :param param_path: the path where the inference model is saved by fluid.io.save_params
-        :param place: place to do the inference
-        :param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
-        """
         self.param_path = param_path
         self.scope = core.Scope()
         self.parallel = parallel
@@ -60,9 +77,20 @@ class Inferencer(object):
 
     def infer(self, inputs, return_numpy=True):
         """
-        :param inputs: a map of {"input_name": input_var} that will be feed into the inference program
-        to get the predict value
-        :return: the predict value of the inference model
+        Do Inference for Inputs
+
+        Args:
+            inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
+            return_numpy (bool): transform return value into numpy or not
+
+        Returns:
+            Tensor or Numpy: the predict value of the inference model for the inputs
+
+        Examples:
+            .. code-block:: python
+
+                tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+                results = inferencer.infer({'x': tensor_x})
         """
         if not isinstance(inputs, dict):
             raise ValueError(

python/paddle/fluid/initializer.py

@@ -19,26 +19,39 @@ from framework import convert_np_dtype_to_dtype_
 from core import VarDesc
 
 __all__ = [
-    'Constant', 'Uniform', 'Normal', 'Xavier', 'Bilinear', 'force_init_on_cpu',
-    'init_on_cpu', 'ConstantInitializer', 'UniformInitializer',
-    'NormalInitializer', 'XavierInitializer', 'BilinearInitializer'
+    'Constant', 'Uniform', 'Normal', 'Xavier', 'Bilinear', 'MSRA',
+    'force_init_on_cpu', 'init_on_cpu', 'ConstantInitializer',
+    'UniformInitializer', 'NormalInitializer', 'XavierInitializer',
+    'BilinearInitializer', 'MSRAInitializer'
 ]
 
 _force_init_on_cpu_ = False
 
 
 def force_init_on_cpu():
+    """
+    The flag of whether force to init variables on CPU.
+
+    Examples:
+        .. code-block:: python
+
+            if force_init_on_cpu():
+                pass
+
+    """
     return _force_init_on_cpu_
 
 
 @contextlib.contextmanager
 def init_on_cpu():
     """
-    Switch program with `with` statement
+    Force the variable to be inited on CPU.
 
     Examples:
-        >>> with init_on_cpu():
-        >>>   step = layers.create_global_var()
+        .. code-block:: python
+
+            with init_on_cpu():
+                step = layers.create_global_var()
 
     """
     global _force_init_on_cpu_
@@ -104,14 +117,18 @@ class Initializer(object):
 
 class ConstantInitializer(Initializer):
     """Implements the constant initializer
+
+    Args:
+        value (float): constant value to initialize the variable
+
+    Examples:
+        .. code-block:: python
+
+            fc = fluid.layers.fc(input=x, size=10,
+                param_attr=fluid.initializer.Constant(value=2.0))
     """
 
     def __init__(self, value=0.0, force_cpu=False):
-        """Constructor for ConstantInitializer
-
-        Args:
-            value: constant value to initialize the variable
-        """
         assert value is not None
         super(ConstantInitializer, self).__init__()
         self._value = value
@@ -146,16 +163,20 @@ class ConstantInitializer(Initializer):
 
 class UniformInitializer(Initializer):
     """Implements the random uniform distribution initializer
+
+    Args:
+        low (float): lower boundary of the uniform distribution
+        high (float): upper boundary of the uniform distribution
+        seed (int): random seed
+
+    Examples:
+        .. code-block:: python
+
+            fc = fluid.layers.fc(input=x, size=10,
+                param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5))
     """
 
     def __init__(self, low=-1.0, high=1.0, seed=0):
-        """Constructor for UniformInitializer
-
-        Args:
-            low: lower boundary of the uniform distribution
-            high: upper boundary of the uniform distribution
-            seed: random seed
-        """
         assert low is not None
         assert high is not None
         assert high >= low
@@ -196,17 +217,21 @@ class UniformInitializer(Initializer):
 
 
 class NormalInitializer(Initializer):
-    """Implements the  random Normal(Gaussian) distribution initializer
+    """Implements the Random Normal(Gaussian) distribution initializer
+
+    Args:
+        loc (float): mean of the normal distribution
+        scale (float): standard deviation of the normal distribution
+        seed (int): random seed
+
+    Examples:
+        .. code-block:: python
+
+            fc = fluid.layers.fc(input=x, size=10,
+                param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
     """
 
     def __init__(self, loc=0.0, scale=1.0, seed=0):
-        """Constructor for NormalInitializer
-
-        Args:
-            loc: mean of the normal distribution
-            scale: standard deviation of the normal distribution
-            seed: random seed
-        """
         assert loc is not None
         assert scale is not None
         assert seed is not None
@@ -246,39 +271,49 @@ class NormalInitializer(Initializer):
 
 
 class XavierInitializer(Initializer):
-    """Implements the Xavier initializer
-
+    """
     This class implements the Xavier weight initializer from the paper
-    Understanding the difficulty of training deep feedforward neural
-    networks[1] by Xavier Glorot and Yoshua Bengio.
+    `Understanding the difficulty of training deep feedforward neural
+    networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
+    by Xavier Glorot and Yoshua Bengio.
 
     This initializer is designed to keep the scale of the gradients
     approximately same in all the layers. In case of Uniform distribution,
-    the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)).
+    the range is [-x, x], where
+
+    .. math::
+
+        x = \sqrt{\\frac{6.0}{fan\_in + fan\_out}}
+
     In case of Normal distribution, the mean is 0 and the standard deviation
-    is sqrt(2/ (fan_in + fan_out)).
+    is
+
+    .. math::
+
+        \sqrt{\\frac{2.0}{fan\_in + fan\_out}}
+
+
+    Args:
+        uniform (bool): whether to use uniform or normal distribution
+        fan_in (float): fan_in for Xavier initialization. If None, it is
+                inferred from the variable.
+        fan_out (float): fan_out for Xavier initialization. If None, it is
+                 inferred from the variable.
+        seed (int): random seed
+
+    Note:
+        It is recommended to set fan_in and fan_out to None for most cases.
+
+    Examples:
+        .. code-block:: python
+
+            fc = fluid.layers.fc(
+                input=queries, size=10,
+                param_attr=fluid.initializer.Xavier(uniform=False))
 
-    References:
-        [1] Understanding the difficulty of training deep feedforward neural
-            networks. International conference on artificial intelligence and
-            statistics.
-            (http://proceedings.mlr.press/v9/glorot10a.html)
     """
 
     def __init__(self, uniform=True, fan_in=None, fan_out=None, seed=0):
-        """Constructor for XavierInitializer
-
-        Args:
-            uniform: whether to use uniform or normal distribution
-            fan_in: fan_in for Xavier initialization. If None, it is
-                    inferred from the variable.
-            fan_out: fan_out for Xavier initialization. If None, it is
-                     inferred from the variable.
-            seed: random seed
-
-        Note: It is recommended to set fan_in and fan_out to None for
-              most cases.
-        """
         assert uniform is not None
         assert seed is not None
         super(XavierInitializer, self).__init__()
@@ -342,30 +377,42 @@ class MSRAInitializer(Initializer):
     """Implements the MSRA initializer a.k.a. Kaiming Initializer
 
     This class implements the weight initialization from the paper
-    Delving Deep into Rectifiers: Surpassing Human-Level Performance on
-    ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
-    and Jian Sun. This is a robust initialization method that particularly
-    considers the rectifier nonlinearities. In case of Uniform distribution,
-    the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
-    distribution, the mean is 0 and the standard deviation
-    is sqrt(2/ fan_in).
-
-    References:
-        [1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
-            on ImageNet Classification
-            (https://arxiv.org/abs/1502.01852)
+    `Delving Deep into Rectifiers: Surpassing Human-Level Performance on
+    ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
+    by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
+    robust initialization method that particularly considers the rectifier
+    nonlinearities. In case of Uniform distribution, the range is [-x, x], where
+
+    .. math::
+
+        x = \sqrt{\\frac{6.0}{fan\_in}}
+
+    In case of Normal distribution, the mean is 0 and the standard deviation
+    is
+
+    .. math::
+
+        \sqrt{\\frac{2.0}{fan\_in}}
+
+    Args:
+        uniform (bool): whether to use uniform or normal distribution
+        fan_in (float): fan_in for MSRAInitializer. If None, it is\
+        inferred from the variable.
+        seed (int): random seed
+
+    Note:
+        It is recommended to set fan_in to None for most cases.
+
+    Examples:
+        .. code-block:: python
+
+            fc = fluid.layers.fc(
+                input=queries, size=10,
+                param_attr=fluid.initializer.MSRA(uniform=False))
     """
 
     def __init__(self, uniform=True, fan_in=None, seed=0):
         """Constructor for MSRAInitializer
-
-        Args:
-            uniform: whether to use uniform or normal distribution
-            fan_in: fan_in for MSRAInitializer. If None, it is
-                    inferred from the variable.
-            seed: random seed
-
-        Note: It is recommended to set fan_in to None for most cases.
         """
         assert uniform is not None
         assert seed is not None
@@ -425,34 +472,37 @@ class MSRAInitializer(Initializer):
 
 
 class BilinearInitializer(Initializer):
-    """Implements the bilinear initializer.
-
+    """
     This initializer can be used in transposed convolution operator to
     act as upsampling. Users can upsample a feature map with shape of
     (B, C, H, W) by any integer factor. The usage is:
-  
-    >>>  factor = 2
-    >>>  w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
-    >>>                     initializer=Bilinear())
-    >>>  conv_up = fluid.layers.conv2d_transpose(
-    >>>      input,
-    >>>      num_filters=C,
-    >>>      output_size=None,
-    >>>      filter_size=2 * factor - factor % 2,
-    >>>      padding=ceil((factor - 1) / 2.),
-    >>>      stride=factor,
-    >>>      groups=C,
-    >>>      param_attr=w_attr,
-    >>>      bias_attr=False)
-
-
-    Where, `num_filters=C` and `groups=C` means this is channel-wise tranposed
+
+    Examples:
+
+        .. code-block:: python
+
+            factor = 2
+            w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
+                               initializer=Bilinear())
+            conv_up = fluid.layers.conv2d_transpose(
+                input,
+                num_filters=C,
+                output_size=None,
+                filter_size=2 * factor - factor % 2,
+                padding=ceil((factor - 1) / 2.),
+                stride=factor,
+                groups=C,
+                param_attr=w_attr,
+                bias_attr=False)
+
+    Where, `num_filters=C` and `groups=C` means this is channel-wise transposed
     convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`,
     This initializer will set a (K, K) interpolation kernel for every channel
     of the filter identically. The resulting shape of the output feature map
     will be (B, C, factor * H, factor * W). Note that the learning rate and the
     weight decay are set to 0 in order to keep coefficient values of bilinear
-    interpolation unchanged during training. 
+    interpolation unchanged during training.
+
     """
 
     def __init__(self):
@@ -469,7 +519,7 @@ class BilinearInitializer(Initializer):
                            be added.
 
         Returns:
-            the initialization op
+            Operator: the initialization op
 
         Raises:
             ValueError: If type of `var` and `block` is not right.

python/paddle/fluid/io.py

@@ -30,20 +30,42 @@ __all__ = [
 
 
 def is_parameter(var):
-    """Check whether the variable is a Parameter.
-
-    This function checks whether the input variable is a Parameter.
+    """
+    Check whether the given variable is an instance of Parameter.
 
     Args:
-        var : The input variable.
+        var(Variable): The variable to be checked.
 
     Returns:
-        boolean result whether the variable is a Parameter.
+        bool: True if the given `var` is an instance of Parameter,
+        False if not.
+
+    Examples:
+        .. code-block:: python
+
+            param = fluid.default_main_program().global_block().var('fc.w')
+            res = fluid.io.is_parameter(param)
     """
     return isinstance(var, Parameter)
 
 
 def is_persistable(var):
+    """
+    Check whether the given variable is persistable.
+
+    Args:
+        var(Variable): The variable to be checked.
+
+    Returns:
+        bool: True if the given `var` is persistable
+        False if not.
+
+    Examples:
+        .. code-block:: python
+
+            param = fluid.default_main_program().global_block().var('fc.w')
+            res = fluid.io.is_persistable(param)
+    """
     if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
             var.desc.type() == core.VarDesc.VarType.FETCH_LIST:
         return False
@@ -68,20 +90,69 @@ def save_vars(executor,
               predicate=None,
               filename=None):
     """
-    Save variables to directory by executor.
+    Save variables to the given directory by executor.
+
+    There are two ways to specify variables to be saved: The first way, list 
+    variables in a list and assign it to the `vars`. The second way, assign the 
+    `main_program` with an existing program, then all variables in the program 
+    will be saved. The first way has a higher priority. In other words, if `vars` 
+    are assigned, the `main_program` and the `predicate` will be ignored.
 
-    :param executor: executor that save variable
-    :param dirname: directory path
-    :param main_program: program. If vars is None, then filter all variables in this
-    program which fit `predicate`. Default default_main_program.
-    :param predicate: The Predicate describes a callable that returns a variable
-    as a bool. If it returns true, the corresponding input variable will be saved.
-    :param vars: variables need to be saved. If vars is specified, program & predicate
-    will be ignored
-    :param filename: The name of a single file that all vars are saved to.
-        If it is None, save variables to separate files.
+    The `dirname` are used to specify the folder where to save variables. 
+    If you prefer to save variables in separate files in the folder `dirname`, 
+    set `filename` None; if you prefer to save all variables in a single file, 
+    use `filename` to specify it.
 
-    :return: None
+    Args:
+        executor(Executor): The executor to run for saving variables.
+        dirname(str): The directory path.
+        main_program(Program|None): The program whose variables will be saved. 
+                                    If it is None, the default main program will 
+                                    be used automatically.
+                                    Default: None
+        vars(list[Variable]|None): The list that contains all variables to save. 
+                                   It has a higher priority than the `main_program`.
+                                   Default: None
+        predicate(function|None): If it is not None, only variables in the 
+                                  `main_program` that makes predicate(variable)==True 
+                                  will be saved. It only works when we are using the 
+                                  `main_program` to specify variables (In other words 
+                                  `vars` is None).
+                                  Default: None
+        filename(str|None): The file which to save all variables. If you prefer to save 
+                            variables separately, set it to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Raises:
+        TypeError: If `main_program` is not an instance of Program nor None.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+
+            # The first usage: using `main_program` to specify variables
+            def name_has_fc(var):
+                res = "fc" in var.name
+                return res
+
+            prog = fluid.default_main_program()
+            fluid.io.save_vars(executor=exe, dirname=path, main_program=prog,
+                               vars=None)
+            # All variables in `main_program` whose name includes "fc" will be saved.
+            # And variables are going to be saved separately.
+
+
+            # The second usage: using `vars` to specify variables
+            var_list = [var_a, var_b, var_c]
+            fluid.io.save_vars(executor=exe, dirname=path, vars=var_list, 
+                               filename="vars_file")
+            # var_a, var_b and var_c will be saved. And they are going to be
+            # saved in the same file named 'var_file' in the path "./my_paddle_model".
     """
     if vars is None:
         if main_program is None:
@@ -129,7 +200,42 @@ def save_vars(executor,
 
 def save_params(executor, dirname, main_program=None, filename=None):
     """
-    Save all parameters to directory with executor.
+    This function filters out all parameters from the give `main_program`
+    and then save them to the folder `dirname` or the file `filename`.
+
+    Use the `dirname` to specify the saving folder. If you would like to 
+    save parameters in separate files, set `filename` None; if you would 
+    like to save all parameters in a single file, use `filename` to specify 
+    the file name.
+
+    NOTICE: Some variables are not Parameter while they are necessary for 
+    training. So you can NOT save and continue your training just by 
+    `save_params()` and `load_params()`. Please use `save_persistables()` 
+    and `load_persistables()` instead.
+
+    Args:
+        executor(Executor): The executor to run for saving parameters.
+        dirname(str): The saving directory path.
+        main_program(Program|None): The program whose parameters will be
+                                    saved. If it is None, the default
+                                    main program will be used automatically.
+                                    Default: None
+        filename(str|None): The file to save all parameters. If you prefer 
+                            to save parameters in differnet files, set it 
+                            to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.save_params(executor=exe, dirname=param_path, 
+                                 main_program=None)
     """
     save_vars(
         executor,
@@ -142,7 +248,37 @@ def save_params(executor, dirname, main_program=None, filename=None):
 
 def save_persistables(executor, dirname, main_program=None, filename=None):
     """
-    Save all persistables to directory with executor.
+    This function filters out all variables with `persistable==True` from the 
+    give `main_program` and then saves these variables to the folder `dirname` 
+    or file `filename`.
+
+    The `dirname` is used to specify the folder where persistable variables 
+    are going to be saved. If you would like to save variables in separate 
+    files, set `filename` None; if you would like to save all variables in a 
+    single file, use `filename` to specify the file name.
+
+    Args:
+        executor(Executor): The executor to run for saving persistable variables.
+        dirname(str): The directory path.
+        main_program(Program|None): The program whose persistbale variables will 
+                                    be saved. If it is None, the default main 
+                                    program will be used automatically.
+                                    Default: None
+        filename(str|None): The file to saved all variables. If you prefer to 
+                            save variables in differnet files, set it to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.save_persistables(executor=exe, dirname=param_path, 
+                                       main_program=None)
     """
     save_vars(
         executor,
@@ -160,20 +296,69 @@ def load_vars(executor,
               predicate=None,
               filename=None):
     """
-    Load variables from directory by executor.
+    Load variables from the given directory by executor.
+
+    There are two ways to specify variables to be loaded: The first way, list 
+    variables in a list and assign it to the `vars`. The second way, assign the 
+    `main_program` with an existing program, then all variables in the program 
+    will be loaded. The first way has a higher priority. In other words if `vars` 
+    are assigned, the `main_program` and the `predicate` will be ignored.
+
+    The `dirname` are used to specify the folder where to load variables. 
+    If variables were saved in separate files in the folder `dirname`, 
+    set `filename` None; if all variables were saved in a single file, 
+    use `filename` to specify it.
 
-    :param executor: executor that load variable
-    :param dirname: directory path
-    :param main_program: program. If vars is None, then filter all variables in this
-    program which fit `predicate`. Default default_main_program().
-    :param predicate: The Predicate describes a callable that returns a variable
-    as a bool. If it returns true, the corresponding input variable will be loaded.
-    :param vars: variables need to be loaded. If vars is specified, program &
-    predicate will be ignored
-    :param filename: The name of the single file that all vars are loaded from.
-        If it is None, load variables from separate files.
+    Args:
+        executor(Executor): The executor to run for loading variables.
+        dirname(str): The directory path.
+        main_program(Program|None): The program whose variables will be loaded. 
+                                    If it is None, the default main program will 
+                                    be used automatically.
+                                    Default: None
+        vars(list[Variable]|None): The list that contains all variables to load. 
+                                   It has a higher priority than the `main_program`.
+                                   Default: None
+        predicate(function|None): If it is not None, only variables in the 
+                                  `main_program` that makes predicate(variable)==True 
+                                  will be loaded. It only works when we are using the 
+                                  `main_program` to specify variables (In other words 
+                                  `vars` is None).
+                                  Default: None
+        filename(str|None): The file which saved all required variables. If variables 
+                            were saved in differnet files, set it to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Raises:
+        TypeError: If `main_program` is not an instance of Program nor None.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+
+            # The first usage: using `main_program` to specify variables
+            def name_has_fc(var):
+                res = "fc" in var.name
+                return res
 
-    :return: None
+            prog = fluid.default_main_program()
+            fluid.io.load_vars(executor=exe, dirname=path, main_program=prog,
+                               vars=None)
+            # All variables in `main_program` whose name includes "fc" will be loaded.
+            # And all the variables are supposed to have been saved in differnet files.
+
+
+            # The second usage: using `vars` to specify variables
+            var_list = [var_a, var_b, var_c]
+            fluid.io.load_vars(executor=exe, dirname=path, vars=var_list, 
+                               filename="vars_file")
+            # var_a, var_b and var_c will be loaded. And they are supposed to haven 
+            # been saved in the same file named 'var_file' in the path "./my_paddle_model".
     """
     if vars is None:
         if main_program is None:
@@ -221,7 +406,42 @@ def load_vars(executor,
 
 def load_params(executor, dirname, main_program=None, filename=None):
     """
-    load all parameters from directory by executor.
+    This function filters out all parameters from the give `main_program`
+    and then trys to load these parameters from the folder `dirname` or
+    the file `filename`.
+
+    Use the `dirname` to specify the folder where parameters were saved. If 
+    parameters were saved in separate files in the folder `dirname`, set 
+    `filename` None; if all parameters were saved in a single file, use 
+    `filename` to specify the file name.
+
+    NOTICE: Some variables are not Parameter while they are necessary for 
+    training. So you can NOT save and continue your training just by 
+    `save_params()` and `load_params()`. Please use `save_persistables()` 
+    and `load_persistables()` instead. 
+
+    Args:
+        executor(Executor): The executor to run for loading parameters.
+        dirname(str): The directory path.
+        main_program(Program|None): The program whose parameters will be
+                                    loaded. If it is None, the default
+                                    main program will be used automatically.
+                                    Default: None
+        filename(str|None): The file which saved all parameters. If parameters 
+                            were saved in differnet files, set it to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.load_params(executor=exe, dirname=param_path, 
+                                main_program=None)
     """
     load_vars(
         executor,
@@ -233,7 +453,37 @@ def load_params(executor, dirname, main_program=None, filename=None):
 
 def load_persistables(executor, dirname, main_program=None, filename=None):
     """
-    load all persistables from directory by executor.
+    This function filters out all variables with `persistable==True` from the 
+    give `main_program` and then trys to load these variables from the folder 
+    `dirname` or the file `filename`.
+
+    Use the `dirname` to specify the folder where persistable variables were 
+    saved. If variables were saved in separate files, set `filename` None; 
+    if all variables were saved in a single file, use `filename` to specify 
+    the file name.
+
+    Args:
+        executor(Executor): The executor to run for loading persistable variables.
+        dirname(str): The directory path.
+        main_program(Program|None): The program whose persistbale variables will 
+                                    be loaded. If it is None, the default main 
+                                    program will be used automatically.
+                                    Default: None
+        filename(str|None): The file which saved all variables. If variables were 
+                            saved in differnet files, set it to None.
+                            Default: None
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.load_persistables(executor=exe, dirname=param_path, 
+                                       main_program=None)
     """
     load_vars(
         executor,
@@ -306,22 +556,48 @@ def save_inference_model(dirname,
                          model_filename=None,
                          params_filename=None):
     """
-    Build a model especially for inference,
-    and save it to directory by the executor.
+    Prune the given `main_program` to build a new program especially for inference,
+    and then save it and all related parameters to given `dirname` by the `executor`.
+
+    Args:
+        dirname(str): The directory path to save the inference model.
+        feeded_var_names(list[str]): Names of variables that need to be feeded data 
+                                     during inference.
+        target_vars(list[Variable]): Variables from which we can get inference 
+                                     results.
+        executor(Executor): The executor that saves the inference model.
+        main_program(Program|None): The original program, which will be pruned to 
+                                    build the inference model. If is setted None, 
+                                    the default main program will be used.
+                                    Default: None.
+        model_filename(str|None): The name of file to save the inference program 
+                                  itself. If is setted None, a default filename 
+                                  `__model__` will be used.
+        params_filename(str|None): The name of file to save all related parameters. 
+                                   If it is setted None, parameters will be saved 
+                                   in separate files .
 
-    :param dirname: directory path
-    :param feeded_var_names: Names of variables that need to be feeded data during inference
-    :param target_vars: Variables from which we can get inference results.
-    :param executor: executor that save inference model
-    :param main_program: original program, which will be pruned to build the inference model.
-            Default default_main_program().
-    :param model_filename: The name of file to save inference program.
-        If not specified, default filename `__model__` will be used.
-    :param params_filename: The name of file to save parameters.
-        It is used for the case that all parameters are saved in a single binary file.
-        If not specified, parameters are considered saved in separate files.
+    Returns:
+        None
+
+    Raises:
+        ValueError: If `feed_var_names` is not a list of basestring.
+        ValueError: If `target_vars` is not a list of Variable.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            path = "./infer_model"
+            fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'],
+                         target_vars=[predict_var], executor=exe)
+
+            # In this exsample, the function will prune the default main program 
+            # to make it suitable for infering the `predict_var`. The pruned 
+            # inference program is going to be saved in the "./infer_model/__model__" 
+            # and parameters are going to be saved in separate files under folder
+            # "./infer_model". 
 
-    :return: None
     """
     if isinstance(feeded_var_names, basestring):
         feeded_var_names = [feeded_var_names]
@@ -382,18 +658,49 @@ def load_inference_model(dirname,
     """
     Load inference model from a directory
 
-    :param dirname: directory path
-    :param executor: executor that load inference model
-    :param model_filename: The name of file to load inference program.
-        If not specified, default filename `__model__` will be used.
-    :param params_filename: The name of file to load parameters.
-        It is used for the case that all parameters are saved in a single binary file.
-        If not specified, parameters are considered saved in separate files.
+    Args:
+        dirname(str): The directory path
+        executor(Executor): The executor to run for loading inference model.
+        model_filename(str|None): The name of file to load inference program.
+                                  If it is None, the default filename 
+                                  '__model__' will be used.
+                                  Default: None
+        params_filename(str|None): The name of file to load all parameters.
+                                   It is only used for the case that all 
+                                   parameters were saved in a single binary 
+                                   file. If parameters were saved in separate 
+                                   files, set it as 'None'.
+
+    Returns:
+        tuple: The return of this function is a tuple with three elements:
+        (program, feed_target_names, fetch_targets). The `program` is a 
+        Program, it's the program for inference. The `feed_target_names` is 
+        a list of str, it contains Names of variables that need to feed 
+        data in the inference program. The `fetch_targets` is a list of 
+        Variable. It contains variables from which we can get inference 
+        results.
+
+    Raises:
+        ValueError: If `dirname` is not a existing directory.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            path = "./infer_model"
+            [inference_program, feed_target_names, fetch_targets] = 
+                fluid.io.load_inference_model(dirname=path, executor=exe)
+            results = exe.run(inference_program,
+                          feed={feed_target_names[0]: tensor_img},
+                          fetch_list=fetch_targets)
+
+            # In this exsample, the inference program was saved in the 
+            # "./infer_model/__model__" and parameters were saved in 
+            # separate files in ""./infer_model". 
+            # After getting inference program, feed target names and 
+            # fetch targets, we can use an Executor to run the inference 
+            # program to get the inference result.
 
-    :return: [program, feed_target_names, fetch_targets]
-             program: program especially for inference.
-             feed_target_names: Names of variables that need to feed data
-             fetch_targets: Variables from which we can get inference results.
     """
     if not os.path.isdir(dirname):
         raise ValueError("There is no directory named '%s'", dirname)
@@ -424,12 +731,25 @@ def load_inference_model(dirname,
 
 def get_parameter_value(para, executor):
     """
-    Get the LoDTensor for the parameter
+    Get the LoDTensor value of the given parameter.
+
+    Args:
+        para(Parameter): The parameter to get value from.
+        executor(Executor): The executor to run for retrieving the value.
+
+    Returns:
+        numpy.array: The given parameter's values.
+
+    Raises:
+        AssertionError: If the `para` is not an instance of Parameter.
 
-    :param executor: executor for retrieving the value
-    :param para: the given parameter
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param = fluid.default_main_program().global_block().var('fc.w')
+            p = fluid.io.get_parameter_value(param, exe)
 
-    :return: the LoDTensor for the parameter
     """
     assert is_parameter(para)
 
@@ -441,14 +761,30 @@ def get_parameter_value(para, executor):
 
 def get_parameter_value_by_name(name, executor, program=None):
     """
-    Get the LoDTensor for paramter with the given name
+    Get the LoDTensor value of a certain parameter by its name.
+
+    Args:
+        name(str): The parameter's name.
+        executor(Executor): The executor to run for retrieving the value.
+        program(Program | None): The program where to find the parameter.
+                               If it's set to be None, the function will
+                               try to find the parameter in the default
+                               main program.
 
-    :param executor: executor for retrieving the value
-    :param name: the name of the parameter
-    :param program: the program where the variable is found
-            Default default_main_program().
+    Returns:
+        numpy.array: The parameter's values.
 
-    :return: the LoDTensor for the variable
+    Raises:
+        TypeError: If given `name` is not an instance of basestring.
+        TypeError: If the parameter with the given name doesn't exist.
+        AssertionError: If there is a varibale named `name` in the
+                        given program but it is not a Parameter.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            p = fluid.io.get_parameter_value('fc.w', exe)
     """
     if program is None:
         program = default_main_program()
@@ -470,16 +806,58 @@ def save_checkpoint(executor,
                     main_program=None,
                     max_num_checkpoints=3):
     """
-    Save Checkpoint will save persistable LodTensor variables from main_program in checkpoint directory,
-    the directory named by serial number from 0 to (n -1), save_checkpoint use LRU strategy
-    to keep numbers of checkpoint directory,  the numbers of checkpoint directory are max_num_checkpoints at most,
-    The interval between two saved checkpoints must greater than save_interval_secs.
+    This function filters out all checkpoint variables from the give
+    main_program and then saves these variables to the `checkpoint_dir` 
+    directory.
+
+    In the training precess, we generally save a checkpoint in each
+    iteration. So there might be a lot of checkpoints in the 
+    `checkpoint_dir`. To avoid them taking too much disk space, the 
+    `max_num_checkpoints` are introduced to limit the total number of 
+    checkpoints. If the number of existing checkpints is greater than 
+    the `max_num_checkpoints`, oldest ones will be scroll deleted.
+
+    A variable is a checkpoint variable and will be saved if it meets
+    all following conditions:
+        1. It's persistable.
+        2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
+        3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
 
-    :param executor executor for save the value
-    :param checkpoint_dir the checkpoint directory 
-    :param trainer_id currect trainer id, if id is equal to 0, the trainer is chief
-    :param main_program   will save all variables in program 
-    :param max_num_checkpoints will keep numbers of checkpoint serials not bigger than max_num_checkpoints
+    Args:
+        executor(Executor): The executor to run for save checkpoint.
+        checkpoint_dir(str): The folder where to save checkpoints.
+        trainer_id(int): currect trainer id, if id is equal to 0, the trainer 
+            is chief.
+        trainer_args(dict|None): Current training arguments. Such as 'epoch_id' 
+            and 'step_id'.
+            Defaut: None
+        main_program(Program|None): The program whose checkpoint variables will
+            be saved. If it is None, the default main program will be used.
+        max_num_checkpoints(int): The max number of total number of existing 
+            checkpoints.
+            Default: 3
+
+    Returns:
+        None
+
+    Raises:
+        ValueError: If `checkpoint_dir` is None.
+        AssertionError: If `trainer_args` is not a dict.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            path = "./checkpoints"
+            prog = fluid.default_main_program()
+            trainer_args = {"epoch_id": 200,
+                            "step_id": 20} # just an example
+            fluid.io.save_checkpoint(executor=exe,
+                                     checkpoint_dir=path,
+                                     trainer_id=0,
+                                     trainer_args=trainer_args,
+                                     main_program=prog,
+                                     max_num_checkpoints=3)
     """
     if checkpoint_dir is None:
         raise ValueError("'checkpoint_dir' should not be None")
@@ -503,13 +881,50 @@ def save_checkpoint(executor,
 
 def load_checkpoint(executor, checkpoint_dir, serial, main_program):
     """
-    Load checkpoint from a directory by executor,
-    it will find  the most recent saved checkpoint file and load it auto.
+    This function filters out all checkpoint variables from the give
+    main_program and then try to load these variables from the
+    `checkpoint_dir` directory.
+
+    In the training precess, we generally save a checkpoint in each
+    iteration. So there are more than one checkpoint in the 
+    `checkpoint_dir` (each checkpoint has its own sub folder), use 
+    `serial` to specify which serial of checkpoint you would like to
+    load.
+
+    A variable is a checkpoint variable and will be loaded if it meets
+    all following conditions:
+        1. It's persistable.
+        2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
+        3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
+
+    Args:
+        executor(Executor): The executor to run for loading checkpoint.
+        checkpoint_dir(str): The folder where all checkpoints are.
+        serial(int): The serial of checkpoint you would like to load.
+        main_program(Program): The program whose checkpoint variables will
+                               be loaded.
 
-    :param executor executor for load the value
-    :param checkpoint_dir  the checkpoint directory 
-    :param serial the serial folder in checkpoint directory will be load
-    :param main_program  will load all variables in program 
+    Returns:
+        None
+
+    Raises:
+        ValueError: If `checkpoint_dir` is None.
+        ValueError: If `serial` is None or `serial` is less than 0.
+        ValueError: If `main_program` is None.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            path = "./checkpoints"
+            prog = fluid.default_main_program()
+            fluid.io.load_checkpoint(executor=exe, checkpoint_dir=path,
+                    serial=9, main_program=prog)
+
+            # In this example, `load_checkpoint` function
+            # will first filters out all checkpoint variables in the default
+            # main program, and then try to load these variables form the
+            # folder "./checkpoints/checkpoint_9/__model__".
     """
 
     if checkpoint_dir is None:
@@ -528,10 +943,10 @@ def load_checkpoint(executor, checkpoint_dir, serial, main_program):
 def clean_checkpoint(checkpoint_dir, delete_dir=False):
     """
     clean the checkpoint dir, when the train exits normally, the trainer will call clean_checkpoint to delete checkpoint directory saved before.
-    delete_dir only works when the directory is empty, otherwise, OSError is raised.  
+    delete_dir only works when the directory is empty, otherwise, OSError is raised.
 
-    :param checkpoint_dir
-    :param delete_dir
+    : param checkpoint_dir
+    : param delete_dir
     """
 
     if checkpoint_dir is None:
@@ -547,13 +962,40 @@ def load_persist_vars_without_grad(executor,
                                    program,
                                    has_model_dir=False):
     """
-    load_persist_vars_without_grad will load variables from a directory by an executor,
-    the variable named end with "@GRAD" will not be loaded.
+    This function filters out all checkpoint variables from the give
+    program and then trys to load these variables from the given directory.
+
+    A variable is a checkpoint variable if it meets all following
+    conditions:
+        1. It's persistable.
+        2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
+        3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
 
-    :param executor  executor for load the value
-    :param dirname the checkpoint directory 
-    :param program   will load all variables in program 
-    :param has_model_dir if has_model_dir is True, will load variables from  sub directory named __model__
+    Args:
+        executor(Executor): The executor to run for loading variables.
+        dirname(str): The directory path.
+        program(Program): The program whose checkpoint variables will
+                          be loaded.
+        has_model_dir(bool): if True, the function loads variables
+                             from a sub directory named '__model__'.
+                             Default: False
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.load_persist_vars_without_grad(executor=exe,
+                    dirname=param_path, program=prog, has_model_dir=True)
+
+            # In this example, `load_persist_vars_without_grad` function
+            # will first filters out all checkpoint variables in the default
+            # main program, and then trys to load these variables form the
+            # folder "./my_paddle_model/__model__".
     """
 
     if has_model_dir:
@@ -569,12 +1011,38 @@ def load_persist_vars_without_grad(executor,
 
 def save_persist_vars_without_grad(executor, dirname, program):
     """
-    save_persist_vars_without_grad  will save variables to a directory by an executor,
-    the variable named end with "@GRAD" will not be saved.
+    This function filters out all checkpoint variables from the give
+    program and then save these variables to a sub-folder '__model__' of 
+    the given directory.
+
+    A variable is a checkpoint variable if it meets all following
+    conditions:
+        1. It's persistable.
+        2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
+        3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
+
+    Args:
+        executor(Executor): The executor to run for saving variables.
+        dirname(str): The directory path.
+        program(Program): The program whose checkpoint variables will
+                          be saved.
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            fluid.io.save_persist_vars_without_grad(executor=exe,
+                    dirname=param_path, program=prog)
 
-    :param executor  executor for load the value
-    :param dirname the checkpoint directory 
-    :param program   will load all variables in program
+            # In this example, `save_persist_vars_without_grad` function
+            # will first filters out all checkpoint variables in the default
+            # main program, and then saves these variables to the folder 
+            # "./my_paddle_model/__model__".
     """
     cur_dir = _get_model_dir(dirname)
     save_vars(
@@ -620,7 +1088,7 @@ def _is_checkpoint_var(var):
     the checkpoint will not save or load all the variables.
     var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
 
-    :param var
+    : param var
     """
     if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
             var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
@@ -701,7 +1169,7 @@ def _write_success(dirname):
     """
     write an empty file named "_SUCCESS" in checkpoint dir, indicate this checkpoint is correct.
 
-    :param dirname
+    : param dirname
     """
     success_file = os.path.join(dirname, SUCCESS_MARK_FILENAME)
     with open(success_file, 'a') as f:
@@ -713,7 +1181,7 @@ def get_latest_checkpoint_serial(checkpoint_dir):
     """
     get the latest file in checkpoint directory, the _SUCCESS file must exist in the directory
 
-    :param checkpoint_dir
+    : param checkpoint_dir
     """
     if not checkpoint_dir:
         return -1

python/paddle/fluid/layers/__init__.py

@@ -28,8 +28,8 @@ import math_op_patch
 from math_op_patch import *
 import detection
 from detection import *
-import metric
-from metric import *
+import metric_op
+from metric_op import *
 from learning_rate_scheduler import *
 
 __all__ = []
@@ -41,5 +41,5 @@ __all__ += control_flow.__all__
 __all__ += ops.__all__
 __all__ += device.__all__
 __all__ += detection.__all__
-__all__ += metric.__all__
+__all__ += metric_op.__all__
 __all__ += learning_rate_scheduler.__all__

python/paddle/fluid/layers/control_flow.py

@@ -185,12 +185,14 @@ def Print(input,
     Returns:
         Variable: Output tensor, same data with input tensor.
 
+
     Examples:
+
         .. code-block:: python
 
-        value = some_layer(...)
-        Print(value, summarize=10,
-              message="The content of some_layer: ")
+           value = some_layer(...)
+           Print(value, summarize=10,
+               message="The content of some_layer: ")
     '''
     helper = LayerHelper('print', **locals())
     out = helper.create_tmp_variable(dtype=helper.input_dtype())
@@ -1201,6 +1203,31 @@ class ConditionalBlockGuard(BlockGuard):
 
 
 class ConditionalBlock(object):
+    '''
+    **ConditionalBlock**
+
+    ConditionalBlock is an operator that bind a block to a specific condition,
+    if the condition matches, the corresponding block will be executed.
+
+    Args:
+        inputs (Variable): bool conditions.
+        is_scalar_condition (bool): whether the branch is controled by a scalar.
+        name(str): name of this ConditionalBlock.
+
+    Examples:
+        .. code-block:: python
+
+             cond = layers.less_than(x=label, y=limit)
+             true_image, false_image = layers.split_lod_tensor(
+                 input=image, mask=cond)
+             true_cond = layers.ConditionalBlock([true_image])
+
+             with true_cond.block():
+                 ...
+             with false_cond.block():
+                 ...
+    '''
+
     def __init__(self, inputs, is_scalar_condition=False, name=None):
         for each_input in inputs:
             if not isinstance(each_input, Variable):

python/paddle/fluid/layers/detection.py

@@ -16,7 +16,7 @@ All layers just related to the detection neural network.
 """
 
 from layer_function_generator import generate_layer_fn
-from layer_function_generator import autodoc
+from layer_function_generator import autodoc, templatedoc
 from ..layer_helper import LayerHelper
 import tensor
 import nn
@@ -155,7 +155,7 @@ def detection_output(loc,
     return nmsed_outs
 
 
-@autodoc()
+@templatedoc()
 def detection_map(detect_res,
                   label,
                   class_num,
@@ -166,6 +166,47 @@ def detection_map(detect_res,
                   input_states=None,
                   out_states=None,
                   ap_version='integral'):
+    """
+    ${comment}
+
+    Args:
+        detect_res: ${detect_res_comment}
+        label:  ${label_comment}
+        class_num: ${class_num_comment}
+        background_label: ${background_label_comment}
+        overlap_threshold: ${overlap_threshold_comment}
+        evaluate_difficult: ${evaluate_difficult_comment}
+        has_state: ${has_state_comment}
+        input_states: If not None, It contains 3 elements:
+            1. pos_count ${pos_count_comment}.
+            2. true_pos ${true_pos_comment}.
+            3. false_pos ${false_pos_comment}.
+        out_states: If not None, it contains 3 elements.
+            1. accum_pos_count ${accum_pos_count_comment}.
+            2. accum_true_pos ${accum_true_pos_comment}.
+            3. accum_false_pos ${accum_false_pos_comment}.
+        ap_version: ${ap_type_comment}
+
+    Returns:
+        ${map_comment}
+
+
+    Examples:
+          .. code-block:: python
+
+            detect_res = fluid.layers.data(
+                name='detect_res',
+                shape=[10, 6],
+                append_batch_size=False,
+                dtype='float32')
+            label = fluid.layers.data(
+                name='label',
+                shape=[10, 6],
+                append_batch_size=False,
+                dtype='float32')
+
+            map_out = fluid.layers.detection_map(detect_res, label, 21)
+    """
     helper = LayerHelper("detection_map", **locals())
 
     def __create_var(type):

python/paddle/fluid/layers/metric.py → python/paddle/fluid/layers/metric_op.py

@@ -126,7 +126,7 @@ def auc(input, label, curve='ROC', num_thresholds=200):
     topk_out, topk_indices = nn.topk(input, k=k)
     auc_out = helper.create_tmp_variable(dtype="float32")
     helper.append_op(
-        type="accuracy",
+        type="auc",
         inputs={
             "Out": [topk_out],
             "Indices": [topk_indices],

python/paddle/fluid/layers/nn.py

@@ -94,6 +94,7 @@ __all__ = [
     'mean_iou',
     'relu',
     'log',
+    'crop',
 ]
 
 
@@ -2675,18 +2676,35 @@ def sequence_expand(x, y, ref_level=-1, name=None):
 
 def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
     '''
+    **beam search**
+
     This function implements the beam search algorithm.
 
+    Beam search is a classical algorithm for selecting candidate words
+    in a machine translation task.
+
+    Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
+    for more details.
+
     Args:
-        pre_ids (Variable): ${pre_ids_comment}
-        ids (Variable): ${ids_comment}
-        scores (Variable): ${scores_comment}
-        beam_size (int): ${beam_size_comment}
-        end_id (int): ${end_id_comment}
-        level (int): ${level_comment}
+        pre_ids (Variable): ids in previous step.
+        ids (Variable): a LoDTensor of shape of [None,k]
+        scores (Variable): a LoDTensor that has the same shape and LoD with `ids`
+        beam_size (int): beam size for beam search
+        end_id (int): the token id which indicates the end of a sequence
+        level (int): the level of LoDTensor
 
     Returns:
-        tuple: a tuple of beam_search output variables: selected_ids, selected_scores
+        tuple: a tuple of beam_search output variables: `selected_ids`, `selected_scores`
+
+    Examples:
+        .. code-block:: python
+
+             # current_score is a Tensor of shape (num_batch_size, embed_size), which
+             # consists score of each candidate word.
+             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)
     '''
     helper = LayerHelper('beam_search', **locals())
     score_type = scores.dtype
@@ -4996,3 +5014,101 @@ def mean_iou(input, label, num_classes):
         },
         attrs={"num_classes": num_classes})
     return out_mean_iou, out_wrong, out_correct
+
+
+def crop(x, shape=None, offsets=None, name=None):
+    """
+    Crop input into output, as specified by offsets and shape.
+
+    .. code-block:: text
+
+        * Case 1:
+            Given
+                X = [[0, 1, 2, 0, 0]
+                     [0, 3, 4, 0, 0]
+                     [0, 0, 0, 0, 0]],
+            and
+                shape = [2, 2],
+                offsets = [0, 1],
+            output is:
+                Out = [[1, 2],
+                       [3, 4]].
+        * Case 2:
+            Given
+                X = [[0, 1, 2, 5, 0]
+                     [0, 3, 4, 6, 0]
+                     [0, 0, 0, 0, 0]],
+            and shape is tensor
+                shape = [[0, 0, 0]
+                         [0, 0, 0]]
+            and
+                offsets = [0, 1],
+
+            output is:
+                Out = [[1, 2, 5],
+                       [3, 4, 6]].
+
+    Args:
+        x (Variable): The input tensor variable.
+        shape (Variable|list/tuple of integer): The output shape is specified
+            by `shape`, which can a Variable or a list/tupe of integer.
+            If a tensor Variable, it's rank must be the same as `x`. This way
+            is suitable for the case that the output shape may be changed each
+            iteration. If a list/tupe of integer, it's length must be the same
+            as the rank of `x`
+        offsets (Variable|list/tuple of integer|None): Specifies the copping
+            offsets at each dimension. It can be a Variable or or a list/tupe
+            of integer. If a tensor Variable, it's rank must be the same as `x`.
+            This way is suitable for the case that the offsets may be changed
+            each iteration. If a list/tupe of integer, it's length must be the
+            same as the rank of `x`. If None, the offsets are 0 at each
+            dimension.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
+
+    Returns:
+        Variable: The cropped tensor variable.
+
+    Raises:
+        ValueError: If shape is not a list, tuple or Variable.
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
+            y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
+            crop = fluid.layers.crop(x, shape=y)
+
+            # or
+            z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
+            crop = fluid.layers.crop(z, shape=[2, 3])
+
+    """
+    helper = LayerHelper('crop', **locals())
+
+    if not (isinstance(shape, list) or isinstance(shape, tuple) or \
+        isinstance(shape, Variable)):
+        raise ValueError("The shape should be a list, tuple or Variable.")
+
+    if offsets is None:
+        offsets = [0] * len(x.shape)
+
+    out = helper.create_tmp_variable(x.dtype)
+    ipts = {'X': x}
+    attrs = {}
+    if isinstance(shape, Variable):
+        ipts['Y'] = shape
+    else:
+        attrs['shape'] = shape
+    if isinstance(offsets, Variable):
+        ipts['Offsets'] = offsets
+    else:
+        attrs['offsets'] = offsets
+
+    helper.append_op(
+        type='crop',
+        inputs=ipts,
+        outputs={'Out': out},
+        attrs=None if len(attrs) == 0 else attrs)
+    return out

python/paddle/fluid/lod_tensor.py

@@ -19,33 +19,41 @@ __all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
 
 
 def create_lod_tensor(data, lod, place):
-    """Create a lod tensor from a numpy array, a list, or an existing lod tensor.
+    """
+    Create a lod tensor from a numpy array, a list, or an existing lod tensor.
 
     Create a lod tensor by doing the following:
+
     1. Check that the length-based input lod is valid.
+
     2. Convert the length-based lod to a offset-based LoD.
-    3. Copy the data from a numpy array, a list or a existing lod tensor to 
+
+    3. Copy the data from a numpy array, a list or a existing lod tensor to
        CPU or GPU device (based on input place).
+
     4. Set the level of detail (LoD) using the offset-based LoD.
     
-    Use example:
-    Suppose we want LoDTensor to hold data for sequences of word, where each word is
-    represented by an integer. If we want to create a LoDTensor to represent two 
-    sentences, one of 2 words, and one of 3 words. 
+    Examples:
 
-    Then 'data' can be a numpy array of integers with shape (5, 1).
-    'lod' will be [[2, 3]], indicating the length(# of words) in each sentence.
-    This length-based input lod [[2, 3]] will be converted to offset-based lod [[0, 2, 5]]
-    inside the function call.
+        Suppose we want LoDTensor to hold data for sequences of word, where each
+        word is represented by an integer. If we want to create a LoDTensor to
+        represent two  sentences, one of 2 words, and one of 3 words.
 
-    Please refer to 
-    github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md
-    for more details regarding LoD.
+        Then :code:`data` can be a numpy array of integers with shape (5, 1).
+        :code:`lod` will be [[2, 3]], indicating the length(# of words) in each
+        sentence. This length-based input lod [[2, 3]] will be converted to
+        offset-based lod [[0, 2, 5]] inside the function call.
+
+    Please reference :ref:`api_guide_low_level_lod_tensor` for more details
+    regarding LoD.
 
     Args:
-        data: a numpy array or a LoDTensor or a list holding the data to be copied.
-        lod: a list of lists indicating the length-based LoD info specified by the user. 
-        place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
+        data(numpy.ndarray|list|LoDTensor): a numpy array or a LoDTensor or a
+            list holding the data to be  copied.
+        lod(list): a list of lists indicating the length-based LoD info
+            specified by the user.
+        place(Place): CPU or GPU place indicating where the data in the new
+            LoDTensor will be stored.
 
     Returns:
         A fluid LoDTensor object with tensor data and lod info.
@@ -77,31 +85,38 @@ def create_lod_tensor(data, lod, place):
 
 
 def create_random_int_lodtensor(lod, base_shape, place, low, high):
-    """Create a LoDTensor containing random integers.
+    """
+    Create a LoDTensor containing random integers.
 
-    This function is frequently used in the book examples. So we revised it based on 
-    the new create_lod_tensor API and put it here in the lod_tensor module to simplify 
-    the code. 
+    This function is frequently used in the book examples. So we revised it
+    based on the new create_lod_tensor API and put it here in the lod_tensor
+    module to simplify the code.
 
     The function does the following:
-    1. Calculate the overall shape of the LoDTensor based on the length-based 'lod' input 
-    and the shape of the basic element in 'base_shape'.
+
+    1. Calculate the overall shape of the LoDTensor based on the length-based
+       :code:`lod` input and the shape of the basic element in
+       :code:`base_shape`.
+
     2. Create a numpy array of this shape.
+
     3. Create the LoDTensor using create_lod_tensor API.
 
-    Suppose we want LoDTensor to hold data for sequences of word, where each word is
-    represented by an integer. If we want to create a LoDTensor to represent two 
-    sentences, one of 2 words, and one of 3 words. Then 'base_shape' is [1], input 
-    length-based 'lod' is [[2, 3]]. Then the overall shape of the LoDTensor would be 
-    [5, 1], holding 5 words for two sentences. 
+    Suppose we want LoDTensor to hold data for sequences of word, where each
+    word is represented by an integer. If we want to create a LoDTensor to
+    represent two sentences, one of 2 words, and one of 3 words. Then
+    'base_shape' is [1], input length-based 'lod' is [[2, 3]]. Then the overall
+    shape of the LoDTensor would be [5, 1], holding 5 words for two sentences.
 
     Args:
-        data: a numpy array or a LoDTensor holding the data to be copied.
-        lod: a list of lists indicating the length-based LoD info specified by the user.
-        base_shape: the shape of the basic element to be held by the LoDTensor. 
-        place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
-        low: the lower bound of the random integers.
-        high: the upper bound of the random integers.
+        lod(list): a list of lists indicating the length-based LoD info
+            specified by the user.
+        base_shape(list): the shape of the basic element to be held by the
+            LoDTensor.
+        place(Place): CPU or GPU place indicating where the data in the new
+            LoDTensor will be stored.
+        low(int): the lower bound of the random integers.
+        high(int): the upper bound of the random integers.
 
     Returns:
         A fluid LoDTensor object with tensor data and lod info. 

python/paddle/fluid/metrics.py

@@ -23,6 +23,8 @@ import warnings
 __all__ = [
     'MetricBase',
     'CompositeMetric',
+    'Precision',
+    'Recall',
     'Accuracy',
     'ChunkEvaluator',
     'EditDistance',
@@ -46,33 +48,34 @@ def _is_number_or_matrix_(var):
 
 class MetricBase(object):
     """
-    Base Class for all evaluators
+    Base Class for all Metrics.
+    MetricBase define a group of interfaces for the
+    model evaluation methods. Metrics accumulate metric states between
+    consecutive minibatches, at every minibatch, use update
+    interface to add current minibatch value to global states.
+    Use eval to compute accumative metric value from last reset()
+    or from scratch on.
+    If you need to custom a new metric, please inherit from MetricBase and
+    custom implementation.
 
     Args:
-        name(str): The name of evaluator. such as, "accuracy". Used for generate
-            temporary variable name.
-    Interface:
-        Note(*) : the states is the attributes who not has _ prefix.
-
-        get_config(): print current states and configuration
-        reset(): clear the states. If the Metrics states type is not (int, float, np.ndarray),
-                Please override this method.
-        update(): update states at every minibatch
-        eval(): get metric evaluation in numpy type.
+        name(str): The name of metric instance. such as, "accuracy".
+                  It needed if you want to distinct different metrics in a model.
+
     """
 
-    def __init__(self, name, **kwargs):
+    def __init__(self, name):
         self._name = str(name) if name != None else self.__class__.__name__
-        self._kwargs = kwargs if kwargs != None else dict()
-        self.reset()
 
     def __str__(self):
         return self._name
 
     def reset(self):
         """
-        states is the attributes who not has _ prefix.
-        reset the states of metrics.
+        reset clear the states of metrics. By default, the states
+        are the members who do not has _ prefix, reset set them to inital states.
+        If you violate the implicit name rule, please also custom the reset
+        interface.
         """
         states = {
             attr: value
@@ -90,61 +93,231 @@ class MetricBase(object):
                 setattr(self, attr, None)
 
     def get_config(self):
+        """
+        Get the metric and current states.
+        The states are the members who do not has "_" prefix.
+
+        Args:
+            None
+
+        Returns:
+            dict: a dict of metric and states
+        """
         states = {
             attr: value
             for attr, value in self.__dict__.iteritems()
             if not attr.startswith("_")
         }
-        config = copy.deepcopy(self._kwargs)
+        config = {}
         config.update({"name": self._name, "states": copy.deepcopy(states)})
         return config
 
-    def update(self):
-        raise NotImplementedError()
+    def update(self, preds, labels):
+        """
+        Updates the metric states at every minibatch.
+        One user can compute the minibatch metric via pure Python, or
+        via a c++ operator.
+
+        Args:
+            preds(numpy.array): the predictions of current minibatch
+            labels(numpy.array): the labels of current minibatch, if the label is one-hot
+                               or soft-label, should custom the corresponding update rule.
+        """
+        raise NotImplementedError(
+            "Should not use it directly, please extend it.")
 
     def eval(self):
-        raise NotImplementedError()
+        """
+        Evalute the current metrics based the accumulated states.
+
+        Returns:
+            float|list(float)|numpy.array: the metrics via Python.
+        """
+        raise NotImplementedError(
+            "Should not use it directly, please extend it.")
 
 
 class CompositeMetric(MetricBase):
     """
-    Compute multiple metrics in each minibatch.
+    Composite multiple metrics in one instance.
     for example, merge F1, accuracy, recall into one Metric.
+    
+    Examples:
+        .. code-block:: python
+    
+          labels = fluid.layers.data(name="data", shape=[1], dtype="int32")
+          data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32")
+          pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+          comp = fluid.metrics.CompositeMetric()
+          acc = fluid.metrics.Precision()
+          recall = fluid.metrics.Recall()
+          comp.add_metric(acc)
+          comp.add_metric(recall)
+          for pass in range(PASSES):
+            comp.reset()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+            comp.update(preds=preds, labels=labels)
+            numpy_acc, numpy_recall = comp.eval()
     """
 
-    def __init__(self, name=None, **kwargs):
-        super(CompositeMetric, self).__init__(name, kwargs)
+    def __init__(self, name=None):
+        super(CompositeMetric, self).__init__(name)
         self._metrics = []
 
     def add_metric(self, metric):
+        """
+        add one metric instance to CompositeMetric.
+
+        Args:
+            metric: a instance of MetricBase.
+        """
         if not isinstance(metric, MetricBase):
             raise ValueError("SubMetric should be inherit from MetricBase.")
         self._metrics.append(metric)
 
+    def update(self, preds, labels):
+        """
+        Update every metrics in sequence.
+
+        Args:
+            preds(numpy.array): the predictions of current minibatch
+            labels(numpy.array): the labels of current minibatch, if the label is one-hot
+                               or soft-label, should custom the corresponding update rule.
+        """
+        for m in self._metrics:
+            ans.append(m.update(preds, labels))
+
     def eval(self):
+        """
+        Evaluate every metrics in sequence.
+
+        Returns:
+            list(float|numpy.array): a list of metrics value in Python.
+        """
         ans = []
         for m in self._metrics:
             ans.append(m.eval())
         return ans
 
 
+class Precision(MetricBase):
+    """
+    Precision (also called positive predictive value) is the fraction of
+    relevant instances among the retrieved instances.
+    https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers
+
+    Note Precision is different with Accuracy in binary classifiers.
+    accuracy = true positive / total instances
+    precision = true positive / all positive instance
+
+    Examples:
+        .. code-block:: python
+
+        metric = fluid.metrics.Precision()
+        for pass in range(PASSES):
+            metric.reset()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+            metric.update(preds=preds, labels=labels)
+            numpy_precision = metric.eval()
+    """
+
+    def __init__(self, name=None):
+        super(Precision, self).__init__(name)
+        self.tp = 0  # true positive
+        self.fp = 0  # false positive
+
+    def update(self, preds, labels):
+        if not _is_numpy_(preds):
+            raise ValueError("The 'preds' must be a numpy ndarray.")
+        if not _is_numpy_(labels):
+            raise ValueError("The 'labels' must be a numpy ndarray.")
+        sample_num = labels[0]
+        for i in range(sample_num):
+            pred = preds[i].astype("int32")
+            label = labels[i]
+            if label == 1:
+                if pred == label:
+                    self.tp += 1
+                else:
+                    self.fp += 1
+
+    def eval(self):
+        ap = self.tp + self.fp
+        return float(self.tp) / ap if ap != 0 else .0
+
+
+class Recall(MetricBase):
+    """
+    Recall (also known as sensitivity) is the fraction of
+    relevant instances that have been retrieved over the
+    total amount of relevant instances
+
+    https://en.wikipedia.org/wiki/Precision_and_recall
+
+    Examples:
+        .. code-block:: python
+
+        metric = fluid.metrics.Recall()
+        for pass in range(PASSES):
+            metric.reset()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+            metric.update(preds=preds, labels=labels)
+            numpy_recall = metric.eval()
+    """
+
+    def __init__(self, name=None):
+        super(Recall, self).__init__(name)
+        self.tp = 0  # true positive
+        self.fn = 0  # false negtive
+
+    def update(self, preds, labels):
+        if not _is_numpy_(preds):
+            raise ValueError("The 'preds' must be a numpy ndarray.")
+        if not _is_numpy_(labels):
+            raise ValueError("The 'labels' must be a numpy ndarray.")
+        sample_num = labels[0]
+        for i in range(sample_num):
+            pred = preds[i].astype("int32")
+            label = labels[i]
+            if label == 1:
+                if pred == label:
+                    self.tp += 1
+            else:
+                if pred != label:
+                    self.fn += 1
+
+    def eval(self):
+        recall = self.tp + self.fn
+        return float(self.tp) / recall if recall != 0 else .0
+
+
 class Accuracy(MetricBase):
     """
     Accumulate the accuracy from minibatches and compute the average accuracy
     for every pass.
+    https://en.wikipedia.org/wiki/Accuracy_and_precision
 
     Args:
        name: the metrics name
 
-    Example:
-        minibatch_accuracy = fluid.layers.accuracy(pred, label)
-        accuracy_evaluator = fluid.metrics.Accuracy()
-        for epoch in PASS_NUM:
-            accuracy_evaluator.reset()
-            for data in batches:
-                loss = exe.run(fetch_list=[cost, minibatch_accuracy])
-            accuracy_evaluator.update(value=minibatch_accuracy, weight=batches)
-            accuracy = accuracy_evaluator.eval()
+    Examples:
+        .. code-block:: python
+
+            labels = fluid.layers.data(name="data", shape=[1], dtype="int32")
+            data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32")
+            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+            minibatch_accuracy = fluid.layers.accuracy(pred, label)
+            accuracy_evaluator = fluid.metrics.Accuracy()
+            for pass in range(PASSES):
+                accuracy_evaluator.reset()
+                for data in train_reader():
+                    batch_size = data[0]
+                    loss = exe.run(fetch_list=[cost, minibatch_accuracy])
+                accuracy_evaluator.update(value=minibatch_accuracy, weight=batch_size)
+                numpy_acc = accuracy_evaluator.eval()
     """
 
     def __init__(self, name=None):
@@ -153,6 +326,13 @@ class Accuracy(MetricBase):
         self.weight = .0
 
     def update(self, value, weight):
+        """
+        Update minibatch states.
+
+        Args:
+            value(float|numpy.array): accuracy of one minibatch.
+            weight(int|float): batch size.
+        """
         if not _is_number_or_matrix_(value):
             raise ValueError(
                 "The 'value' must be a number(int, float) or a numpy ndarray.")
@@ -163,9 +343,8 @@ class Accuracy(MetricBase):
 
     def eval(self):
         if self.weight == 0:
-            raise ValueError(
-                "There is no data in Accuracy Metrics. Please check layers.accuracy output has added to Accuracy."
-            )
+            raise ValueError("There is no data in Accuracy Metrics. \
+                Please check layers.accuracy output has added to Accuracy.")
         return self.value / self.weight
 
 
@@ -174,6 +353,25 @@ class ChunkEvaluator(MetricBase):
     Accumulate counter numbers output by chunk_eval from mini-batches and
     compute the precision recall and F1-score using the accumulated counter
     numbers.
+    For some basics of chunking, please refer to
+    'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
+    ChunkEvalEvaluator computes the precision, recall, and F1-score of chunk detection,
+    and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
+
+    Examples:
+        .. code-block:: python
+
+            labels = fluid.layers.data(name="data", shape=[1], dtype="int32")
+            data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32")
+            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+            precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval(
+                input=pred,
+                label=label)
+            metric = fluid.metrics.ChunkEvaluator()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+                metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks)
+                numpy_precision, numpy_recall, numpy_f1 = metric.eval()
     """
 
     def __init__(self, name=None):
@@ -183,9 +381,17 @@ class ChunkEvaluator(MetricBase):
         self.num_correct_chunks = 0
 
     def update(self, num_infer_chunks, num_label_chunks, num_correct_chunks):
+        """
+        Update the states based on the layers.chunk_eval() ouputs.
+        Args:
+            num_infer_chunks(int|numpy.array): The number of chunks in Inference on the given minibatch.
+            num_label_chunks(int|numpy.array): The number of chunks in Label on the given mini-batch.
+            num_correct_chunks(int|float|numpy.array): The number of chunks both in Inference and Label on the
+                                                  given mini-batch.
+        """
         if not _is_number_or_matrix_(num_infer_chunks):
             raise ValueError(
-                "The 'num_infer_chunks' must be a number(int, float) or a numpy ndarray."
+                "The 'num_infer_chunks' must be a number(int) or a numpy ndarray."
             )
         if not _is_number_or_matrix_(num_label_chunks):
             raise ValueError(
@@ -212,21 +418,28 @@ class ChunkEvaluator(MetricBase):
 
 class EditDistance(MetricBase):
     """
+    Edit distance is a way of quantifying how dissimilar two strings
+    (e.g., words) are to one another by counting the minimum number
+    of operations required to transform one string into the other.
+    Refer to https://en.wikipedia.org/wiki/Edit_distance
+
     Accumulate edit distance sum and sequence number from mini-batches and
     compute the average edit_distance and instance error of all batches.
 
     Args:
         name: the metrics name
 
-    Example:
-        edit_distance_metrics = fluid.layers.edit_distance(input, label)
-        distance_evaluator = fluid.metrics.EditDistance()
-        for epoch in PASS_NUM:
-            distance_evaluator.reset()
-            for data in batches:
-                loss = exe.run(fetch_list=[cost] + list(edit_distance_metrics))
-            distance_evaluator.update(*edit_distance_metrics)
-            distance, instance_error = distance_evaluator.eval()
+    Examples:
+        .. code-block:: python
+
+            distances, seq_num = fluid.layers.edit_distance(input, label)
+            distance_evaluator = fluid.metrics.EditDistance()
+            for epoch in PASS_NUM:
+                distance_evaluator.reset()
+                for data in batches:
+                    loss = exe.run(fetch_list=[cost] + list(edit_distance_metrics))
+                distance_evaluator.update(distances, seq_num)
+                distance, instance_error = distance_evaluator.eval()
 
         In the above example:
         'distance' is the average of the edit distance in a pass.
@@ -264,16 +477,38 @@ class EditDistance(MetricBase):
 class DetectionMAP(MetricBase):
     """
     Calculate the detection mean average precision (mAP).
-
-    TODO (Dang Qingqing): update the following doc.
-    The general steps are as follows:
-    1. calculate the true positive and false positive according to the input
-        of detection and labels.
-    2. calculate mAP value, support two versions: '11 point' and 'integral'.
-
+    mAP is the metric to measure the accuracy of object detectors
+    like Faster R-CNN, SSD, etc.
+    It is the average of the maximum precisions at different recall values.
     Please get more information from the following articles:
       https://sanchom.wordpress.com/tag/average-precision/
+
       https://arxiv.org/abs/1512.02325
+
+    The general steps are as follows:
+
+        1. calculate the true positive and false positive according to the input
+            of detection and labels.
+        2. calculate mAP value, support two versions: '11 point' and 'integral'.
+
+    Examples:
+        .. code-block:: python
+
+            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+            batch_map = layers.detection_map(
+                input,
+                label,
+                class_num,
+                background_label,
+                overlap_threshold=overlap_threshold,
+                evaluate_difficult=evaluate_difficult,
+                ap_version=ap_version)
+            metric = fluid.metrics.DetectionMAP()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, batch_map])
+                batch_size = data[0]
+                metric.update(value=batch_map, weight=batch_size)
+                numpy_map = metric.eval()
     """
 
     def __init__(self, name=None):
@@ -302,17 +537,18 @@ class DetectionMAP(MetricBase):
 
 class Auc(MetricBase):
     """
-    Auc Metrics which adapts to binary classification.
-    Need to note that auc metrics compute the value via Python natively.
+    Auc metric adapts to the binary classification.
+    Refer to https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
+    Need to note that auc metric compute the value via Python natively.
     If you concern the speed, please use the fluid.layers.auc instead.
 
     The `auc` function creates four local variables, `true_positives`,
-      `true_negatives`, `false_positives` and `false_negatives` that are used to
-      compute the AUC. To discretize the AUC curve, a linearly spaced set of
-      thresholds is used to compute pairs of recall and precision values. The area
-      under the ROC-curve is therefore computed using the height of the recall
-      values by the false positive rate, while the area under the PR-curve is the
-      computed using the height of the precision values by the recall.
+    `true_negatives`, `false_positives` and `false_negatives` that are used to
+    compute the AUC. To discretize the AUC curve, a linearly spaced set of
+    thresholds is used to compute pairs of recall and precision values. The area
+    under the ROC-curve is therefore computed using the height of the recall
+    values by the false positive rate, while the area under the PR-curve is the
+    computed using the height of the precision values by the recall.
 
     Args:
         name: metric name
@@ -322,22 +558,32 @@ class Auc(MetricBase):
             curve.
 
     "NOTE: only implement the ROC curve type via Python now."
+
+    Examples:
+        .. code-block:: python
+
+            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+            metric = fluid.metrics.Auc()
+            for data in train_reader():
+                loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+                metric.update(preds, labels)
+                numpy_auc = metric.eval()
     """
 
     def __init__(self, name, curve='ROC', num_thresholds=200):
-        super(MetricBase, self).__init__(name, curve, num_thresholds)
+        super(Auc, self).__init__(name=name)
         self._curve = curve
         self._num_thresholds = num_thresholds
         self._epsilon = 1e-6
-        self.tp_list = np.ndarray((num_thresholds, ))
-        self.fn_list = np.ndarray((num_thresholds, ))
-        self.tn_list = np.ndarray((num_thresholds, ))
-        self.fp_list = np.ndarray((num_thresholds, ))
+        self.tp_list = np.zeros((num_thresholds, ))
+        self.fn_list = np.zeros((num_thresholds, ))
+        self.tn_list = np.zeros((num_thresholds, ))
+        self.fp_list = np.zeros((num_thresholds, ))
 
-    def update(self, labels, predictions, axis=1):
+    def update(self, preds, labels):
         if not _is_numpy_(labels):
             raise ValueError("The 'labels' must be a numpy ndarray.")
-        if not _is_numpy_(predictions):
+        if not _is_numpy_(preds):
             raise ValueError("The 'predictions' must be a numpy ndarray.")
 
         kepsilon = 1e-7  # to account for floating point imprecisions
@@ -350,12 +596,12 @@ class Auc(MetricBase):
             tp, fn, tn, fp = 0, 0, 0, 0
             for i, lbl in enumerate(labels):
                 if lbl:
-                    if predictions[i, 0] >= thresh:
+                    if predictions[i, 1] >= thresh:
                         tp += 1
                     else:
                         fn += 1
                 else:
-                    if predictions[i, 0] >= thresh:
+                    if predictions[i, 1] >= thresh:
                         fp += 1
                     else:
                         tn += 1

python/paddle/fluid/nets.py

@@ -26,16 +26,87 @@ def simple_img_conv_pool(input,
                          filter_size,
                          pool_size,
                          pool_stride,
-                         act,
-                         param_attr=None,
+                         pool_padding=0,
                          pool_type='max',
+                         global_pooling=False,
+                         conv_stride=1,
+                         conv_padding=0,
+                         conv_dilation=1,
+                         conv_groups=1,
+                         param_attr=None,
+                         bias_attr=None,
+                         act=None,
                          use_cudnn=True,
                          use_mkldnn=False):
+    """
+    The simple_img_conv_pool is composed with one Convolution2d and one Pool2d.
+
+    Args:
+        input (Variable): The input image with [N, C, H, W] format.
+        num_filters(int): The number of filter. It is as same as the output
+            feature channel.
+        filter_size (int|list|tuple): The filter size. If filter_size is a list or
+            tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise,
+            the filter_size_H = filter_size_W = filter_size.
+        pool_size (int|list|tuple): The pooling size of Pool2d layer. If pool_size
+            is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
+            Otherwise, the pool_size_H = pool_size_W = pool_size.
+        pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
+            is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W).
+            Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
+        pool_padding (int|list|tuple): The padding of Pool2d layer. If pool_padding is a list or
+            tuple, it must contain two integers, (pool_padding_H, pool_padding_W).
+            Otherwise, the pool_padding_H = pool_padding_W = pool_padding. Default 0.
+        pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
+            average-pooling. Default :math:`max`.
+        global_pooling (bool): Whether to use the global pooling. If global_pooling = true,
+            pool_size and pool_padding while be ignored. Default False
+        conv_stride (int|list|tuple): The stride size of the Conv2d Layer. If stride is a
+            list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise,
+            the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1.
+        conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
+            a list or  tuple, it must contain two integers, (conv_padding_H, conv_padding_W).
+            Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0.
+        conv_dilation (int|list|tuple): The dilation size of the Conv2d Layer. If dilation is
+            a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W).
+            Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1.
+        conv_groups (int): The groups number of the Conv2d Layer. According to grouped
+            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
+            the first half of the filters is only connected to the first half
+            of the input channels, while the second half of the filters is only
+            connected to the second half of the input channels. Default: groups=1
+        param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
+        bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
+        act (str): Activation type for Conv2d. Default: None
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
+            with mkldnn library. Default: False
+
+    Return:
+        Variable: The result of input after Convolution2d and Pool2d.
+
+    Examples:
+        .. code-block:: python
+
+            img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+            conv_pool = fluid.nets.simple_img_conv_pool(input=img,
+                                                        filter_size=5,
+                                                        num_filters=20,
+                                                        pool_size=2,
+                                                        pool_stride=2,
+                                                        act="relu")
+    """
     conv_out = layers.conv2d(
         input=input,
         num_filters=num_filters,
         filter_size=filter_size,
+        stride=conv_stride,
+        padding=conv_padding,
+        dilation=conv_dilation,
+        groups=conv_groups,
         param_attr=param_attr,
+        bias_attr=bias_attr,
         act=act,
         use_cudnn=use_cudnn,
         use_mkldnn=use_mkldnn)
@@ -45,6 +116,8 @@ def simple_img_conv_pool(input,
         pool_size=pool_size,
         pool_type=pool_type,
         pool_stride=pool_stride,
+        pool_padding=pool_padding,
+        global_pooling=global_pooling,
         use_cudnn=use_cudnn,
         use_mkldnn=use_mkldnn)
     return pool_out
@@ -60,11 +133,65 @@ def img_conv_group(input,
                    conv_with_batchnorm=False,
                    conv_batchnorm_drop_rate=0.0,
                    pool_stride=1,
-                   pool_type=None,
+                   pool_type="max",
                    use_cudnn=True,
                    use_mkldnn=False):
     """
-    Image Convolution Group, Used for vgg net.
+    The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut,
+    and Pool2d. According to the input arguments, img_conv_group will do serials of
+    computation for Input using Convolution2d, BatchNorm, DropOut, and pass the last
+    result to Pool2d.
+
+    Args:
+        input (Variable): The input image with [N, C, H, W] format.
+        conv_num_filter(list|tuple): Indicates the numbers of filter of this group.
+        pool_size (int|list|tuple): The pooling size of Pool2d Layer. If pool_size
+            is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
+            Otherwise, the pool_size_H = pool_size_W = pool_size.
+        conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
+            a list or tuple, its length must be equal to the length of conv_num_filter.
+            Otherwise the conv_padding of all Conv2d Layers are the same. Default 1.
+        conv_filter_size (int|list|tuple): The filter size. If filter_size is a list or
+            tuple, its length must be equal to the length of conv_num_filter.
+            Otherwise the conv_filter_size of all Conv2d Layers are the same. Default 3.
+        conv_act (str): Activation type for Conv2d Layer that is not followed by BatchNorm.
+            Default: None.
+        param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
+        conv_with_batchnorm (bool|list): Indicates whether to use BatchNorm after Conv2d Layer.
+            If conv_with_batchnorm is a list, its length must be equal to the length of
+            conv_num_filter. Otherwise, conv_with_batchnorm indicates whether all the
+            Conv2d Layer follows a BatchNorm. Default False.
+        conv_batchnorm_drop_rate (float|list): Indicates the drop_rate of Dropout Layer
+            after BatchNorm. If conv_batchnorm_drop_rate is a list, its length must be
+            equal to the length of conv_num_filter. Otherwise, drop_rate of all Dropout
+            Layers is conv_batchnorm_drop_rate. Default 0.0.
+        pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
+            is a list or tuple, it must contain two integers, (pooling_stride_H,
+            pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
+            Default 1.
+        pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
+            average-pooling. Default :math:`max`.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
+            with mkldnn library. Default: False
+
+    Return:
+        Variable: The final result after serial computation using Convolution2d,
+            BatchNorm, DropOut, and Pool2d.
+
+    Examples:
+        .. code-block:: python
+
+            img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+            conv_pool = fluid.nets.img_conv_group(input=img,
+                                                  num_channels=3,
+                                                  conv_padding=1,
+                                                  conv_num_filter=[3, 3],
+                                                  conv_filter_size=3,
+                                                  conv_act="relu",
+                                                  pool_size=2,
+                                                  pool_stride=2)
     """
     tmp = input
     assert isinstance(conv_num_filter, list) or \
@@ -74,6 +201,7 @@ def img_conv_group(input,
         if not hasattr(obj, '__len__'):
             return [obj] * len(conv_num_filter)
         else:
+            assert len(obj) == len(conv_num_filter)
             return obj
 
     conv_padding = __extend_list__(conv_padding)
@@ -119,6 +247,39 @@ def sequence_conv_pool(input,
                        param_attr=None,
                        act="sigmoid",
                        pool_type="max"):
+    """
+    The sequence_conv_pool is composed with Sequence Convolution and Pooling.
+
+    Args:
+        input (Variable): The input of sequence_conv, which supports variable-time
+            length input sequence. The underlying of input is a matrix with shape
+            (T, N), where T is the total time steps in this mini-batch and N is
+            the input_hidden_size
+        num_filters(int): The number of filter.
+        filter_size (int): The filter size.
+        param_attr (ParamAttr): The parameters to the Sequence_conv Layer. Default: None.
+        act (str): Activation type for Sequence_conv Layer. Default: "sigmoid".
+        pool_type (str): Pooling type can be :math:`max` for max-pooling, :math:`average` for
+            average-pooling, :math:`sum` for sum-pooling, :math:`sqrt` for sqrt-pooling.
+            Default :math:`max`.
+
+    Return:
+        Variable: The final result after Sequence Convolution and Pooling.
+
+    Examples:
+        .. code-block:: python
+
+            input_dim = len(word_dict)
+            emb_dim = 128
+            hid_dim = 512
+            data = fluid.layers.data( ame="words", shape=[1], dtype="int64", lod_level=1)
+            emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
+            seq_conv = fluid.nets.sequence_conv_pool(input=emb,
+                                                     num_filters=hid_dim,
+                                                     filter_size=3,
+                                                     act="tanh",
+                                                     pool_type="sqrt")
+    """
     conv_out = layers.sequence_conv(
         input=input,
         num_filters=num_filters,
@@ -132,9 +293,9 @@ 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 Units(GLU) composed by split, sigmoid activation and element-wise
+    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::
@@ -147,16 +308,16 @@ def glu(input, dim=-1):
     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
-            dimension to split along is :math:`rank(input) + dim`.
+            dimension to split along is :math:`rank(input) + dim`. Default -1.
 
     Returns:
-        Variable: The Tensor variable with half the size of input.
+        Variable: Variable with half the size of input.
 
     Examples:
         .. code-block:: python
 
-            # x is a Tensor variable with shape [3, 6, 9]
-            fluid.nets.glu(input=x, dim=1)  # shape of output: [3, 3, 9]
+            data = fluid.layers.data(name="words", shape=[3, 6, 9], dtype="float32")
+            output = fluid.nets.glu(input=data, dim=1)  # shape of output: [3, 3, 9]
     """
 
     a, b = layers.split(input, num_or_sections=2, dim=dim)
@@ -189,40 +350,48 @@ def scaled_dot_product_attention(queries,
     <https://arxiv.org/pdf/1706.03762.pdf>`_.
 
     Args:
-
         queries (Variable): The input variable which should be a 3-D Tensor.
         keys (Variable): The input variable which should be a 3-D Tensor.
         values (Variable): The input variable which should be a 3-D Tensor.
         num_heads (int): Head number to compute the scaled dot product
-                         attention. Default value is 1.
+            attention. Default: 1.
         dropout_rate (float): The dropout rate to drop the attention weight.
-                              Default value is 0.
+            Default: 0.0.
 
     Returns:
-
-        Variable: A 3-D Tensor computed by multi-head scaled dot product \
-                  attention.
+        Variable: A 3-D Tensor computed by multi-head scaled dot product\
+            attention.
 
     Raises:
-
         ValueError: If input queries, keys, values are not 3-D Tensors.
 
-    NOTE:
+    NOTES:
         1. When num_heads > 1, three linear projections are learned respectively
-        to map input queries, keys and values into queries', keys' and values'.
-        queries', keys' and values' have the same shapes with queries, keys
-        and values.
-
-        1. When num_heads == 1, scaled_dot_product_attention has no learnable
-        parameters.
+           to map input queries, keys and values into queries', keys' and values'.
+           queries', keys' and values' have the same shapes with queries, keys
+           and values.
+        2. When num_heads == 1, scaled_dot_product_attention has no learnable
+           parameters.
 
     Examples:
         .. code-block:: python
 
-            # Suppose q, k, v are Tensors with the following shape:
-            # q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
-
-            contexts = fluid.nets.scaled_dot_product_attention(q, k, v)
+            queries = fluid.layers.data(name="queries",
+                                        shape=[3, 5, 9],
+                                        dtype="float32",
+                                        append_batch_size=False)
+            queries.stop_gradient = False
+            keys = fluid.layers.data(name="keys",
+                                     shape=[3, 6, 9],
+                                     dtype="float32",
+                                     append_batch_size=False)
+            keys.stop_gradient = False
+            values = fluid.layers.data(name="values",
+                                       shape=[3, 6, 10],
+                                       dtype="float32",
+                                       append_batch_size=False)
+            values.stop_gradient = False
+            contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
             contexts.shape  # [3, 5, 10]
     """
     if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):

python/paddle/fluid/optimizer.py

@@ -26,10 +26,10 @@ from clip import append_gradient_clip_ops, error_clip_callback
 from contextlib import contextmanager
 
 __all__ = [
-    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad',
+    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
     'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
     'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
-    'Adadelta', 'ModelAverage', 'Optimizer'
+    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'Optimizer', 'RMSPropOptimizer'
 ]
 
 
@@ -192,15 +192,15 @@ class Optimizer(object):
         """Add optimization operators to update gradients to variables.
 
         Args:
-          loss: the target that this optimization is for.
-          parameters_and_grads: a list of (variable, gradient) pair to update.
+          loss(Variable): the target that this optimization is for.
+          parameters_and_grads(list(tuple(Variable, Variable))):
+          a list of (variable, gradient) pair to update.
 
         Returns:
           return_op_list: a list of operators that will complete one step of
           optimization. This will include parameter update ops, global step
           update ops and any other custom ops required by subclasses to manage
           their internal state.
-          :param startup_program:
         """
         # This is a default implementation of create_optimization_pass that
         # can be shared by most optimizers. This implementation assumes that
@@ -268,7 +268,22 @@ class Optimizer(object):
 
 
 class SGDOptimizer(Optimizer):
-    """ Simple SGD optimizer without any state.
+    """
+    Optimizer of the stochastic gradient descent algorithm.
+
+    .. math::
+
+        param\_out = param - learning\_rate * grad
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+
+    Examples:
+        .. code-block:: python
+
+            sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2)
+            sgd_optimizer.minimize(cost)
     """
 
     def __init__(self, learning_rate, **kwargs):
@@ -294,7 +309,37 @@ class SGDOptimizer(Optimizer):
 
 
 class MomentumOptimizer(Optimizer):
-    """Simple Momentum optimizer with velocity state
+    """
+
+    Simple Momentum optimizer with velocity state
+
+    This optimizer has a flag for Nestrov Momentum.
+
+    The update equations are as follows:
+
+    .. math::
+
+        & velocity = mu * velocity + gradient
+
+        & if (use\_nesterov):
+
+        &\quad   param = param - gradient * learning\_rate + mu * velocity * learning\_rate
+
+        & else:
+
+        &\quad   param = param - learning\_rate * velocity
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        momentum (float): momentum factor
+        use_nesterov (bool): enables Nesterov momentum
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
+            optimizer.minimize(cost)
     """
     _velocity_acc_str = "velocity"
 
@@ -338,7 +383,32 @@ class MomentumOptimizer(Optimizer):
 
 
 class AdagradOptimizer(Optimizer):
-    """Simple Adagrad optimizer with moment state
+    """
+    **Adaptive Gradient Algorithm (Adagrad)**
+
+    The update is done as follows:
+
+    .. math::
+
+        moment\_out &= moment + grad * grad
+
+        param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
+
+    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
+    does not have the epsilon attribute. It is added here in our implementation
+    as also proposed here: http://cs231n.github.io/neural-networks-3/#ada
+    for numerical stability to avoid the division by zero error.
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        epsilon (float): a small float value for numerical stability.
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
+            optimizer.minimize(cost)
     """
     _moment_acc_str = "moment"
 
@@ -379,7 +449,40 @@ class AdagradOptimizer(Optimizer):
 
 
 class AdamOptimizer(Optimizer):
-    """Implements the Adam Optimizer
+    """
+    This implements the Adam optimizer from Section 2 of the Adam
+    paper : https://arxiv.org/abs/1412.6980.
+    Adam is a first-order gradient-based optimization method based on
+    adaptive estimates of lower-order moments.
+
+    Adam updates:
+
+    .. math::
+
+        t & = t + 1
+
+        moment\_1\_out & = {\\beta}_1 * moment\_1 + (1 - {\\beta}_1) * grad
+
+        moment\_2\_out & = {\\beta}_2 * moment\_2 + (1 - {\\beta}_2) * grad * grad
+
+        learning\_rate & = learning\_rate * \\
+                          \\frac{\sqrt{1 - {\\beta}_2^t}}{1 - {\\beta}_1^t}
+
+        param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        beta1 (float): The exponential decay rate for the 1st moment estimates.
+        beta2 (float): The exponential decay rate for the 2nd moment estimates.
+        epsilon (float): a small float value for numerical stability.
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Adam(learning_rate=0.2)
+            optimizer.minimize(cost)
+
     """
     _moment1_acc_str = "moment1"
     _moment2_acc_str = "moment2"
@@ -484,7 +587,42 @@ class AdamOptimizer(Optimizer):
 
 
 class AdamaxOptimizer(Optimizer):
-    """Implements the Adamax Optimizer
+    """
+    We implement the Adamax optimizer from Section 7 of the Adam
+    paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the
+    Adam algorithm based on the infinity norm.
+
+    Adamax updates:
+
+    .. math::
+
+        t & = t + 1
+
+        moment\_out & = {\\beta}_1 * moment + (1 - {\\beta}_1) * grad
+
+        inf\_norm\_out & = max({\\beta}_2 * inf\_norm + \epsilon, |grad|)
+
+        learning\_rate & = \\frac{learning\_rate}{1 - {\\beta}_1^t}
+
+        param\_out & = param - learning\_rate * \\frac{moment\_out}{inf\_norm\_out}
+
+
+    The original paper does not have an epsilon attribute.
+    However, it is added here for numerical stability to prevent the
+    division by 0 error.
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        beta1 (float): The exponential decay rate for the 1st moment estimates.
+        beta2 (float): The exponential decay rate for the 2nd moment estimates.
+        epsilon (float): a small float value for numerical stability.
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Adamax(learning_rate=0.2)
+            optimizer.minimize(cost)
     """
     _moment_acc_str = "moment"
     _inf_norm_acc_str = "inf_norm"
@@ -568,7 +706,34 @@ class AdamaxOptimizer(Optimizer):
 
 
 class DecayedAdagradOptimizer(Optimizer):
-    """Simple Decayed Adagrad optimizer with moment state
+    """
+    **Decayed Adagrad Optimizer**
+
+    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
+
+    The update is done as follows:
+
+    .. math::
+
+        moment\_out & = decay * moment + (1 - decay) * grad * grad
+
+        param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
+
+    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
+    does not have an epsilon attribute. It is added here for numerical
+    stability to avoid the division by zero error.
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        decay (float): decay rate.
+        epsilon (float): a small float value for numerical stability.
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
+            optimizer.minimize(cost)
     """
     _moment_acc_str = "moment"
 
@@ -614,6 +779,7 @@ class DecayedAdagradOptimizer(Optimizer):
 class AdadeltaOptimizer(Optimizer):
     """
     **Adadelta Optimizer**
+
     Simple Adadelta optimizer with average squared grad state and
     average squared update state.
     The details of adadelta please refer to this
@@ -628,7 +794,7 @@ class AdadeltaOptimizer(Optimizer):
         E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\\_rate)^2
 
     Args:
-        learning_rate(float): global leraning rate
+        learning_rate(float): global learning rate
         rho(float): rho in equation
         epsilon(float): epsilon in equation
 
@@ -703,37 +869,37 @@ class RMSPropOptimizer(Optimizer):
 
     ..  math::
 
-        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
+        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
 
         w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w)
 
     The first equation calculates moving average of the squared gradient for
-    each weight. Then dividing the gradient by :math: `sqrt{v(w,t)}`.
+    each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
 
     In some cases, adding a momentum term :math: `\\beta` is beneficial.
     In our implementation, Nesterov momentum is used:
 
     ..  math::
 
-        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
+        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
 
         v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{v(w,t) +
             \\epsilon}} \\nabla Q_{i}(w)
 
         w & = w - v(w, t)
 
-    where, :math: `\\rho` is a hyperparameter and typical values are 0.9, 0.95
+    where, :math:`\\rho` is a hyperparameter and typical values are 0.9, 0.95
     and so on. :math: `beta` is the momentum term. :math: `\\epsilon` is a
     smoothing term to avoid division by zero, usually set somewhere in range
     from 1e-4 to 1e-8.
 
 
     Args:
-        learning_rate(float): global leraning rate.
+        learning_rate(float): global learning rate.
         rho(float): rho is :math: `\\rho` in equation, set 0.95 by default.
         epsilon(float): :math: `\\epsilon` in equation is smoothing term to
             avoid division by zero, set 1e-6 by default.
-        momentum(float): :math: `\\beta` in equation is the momentum term,
+        momentum(float): :math:`\\beta` in equation is the momentum term,
             set 0.0 by default.
 
     Raises:
@@ -810,6 +976,113 @@ class RMSPropOptimizer(Optimizer):
         return rmsprop_op
 
 
+class FtrlOptimizer(Optimizer):
+    """
+    FTRL (Follow The Regularized Leader) Optimizer.
+
+    The paper that proposed Follow The Regularized Leader (FTRL):
+    (https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
+
+    ..  math::
+
+        &new\_accum = squared\_accum + grad^2
+
+        &if (lr\_power == -0.5):
+
+        &\quad  linear\_accum += grad - \\frac{\\sqrt{new\_accum} - \\sqrt{squared\_accum}}{learning\_rate * param}
+
+        &else:
+
+        &\quad   linear\_accum += grad - \\frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param}
+
+
+        &x = l1 * sign(linear\_accum) - linear\_accum
+
+        &if (lr\_power == -0.5):
+
+        &\quad   y = \\frac{\\sqrt{new\_accum}}{learning\_rate} + (2 * l2)
+
+        &\quad   pre\_shrink = \\frac{x}{y}
+
+        &\quad   param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
+
+        &else:
+
+        &\quad   y = \\frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2)
+
+        &\quad   pre\_shrink = \\frac{x}{y}
+
+        &\quad   param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
+
+        &squared\_accum += grad^2
+
+    Args:
+        learning_rate (float|Variable): global learning rate.
+        l1 (float):
+        l2 (float):
+        lr_power (float):
+
+    Raises:
+        ValueError: If learning_rate, rho, epsilon, momentum are None.
+
+    Examples:
+          .. code-block:: python
+
+              optimizer = fluid.optimizer.Ftrl(0.0001)
+              _, params_grads = optimizer.minimize(cost)
+    """
+
+    _squared_acc_str = "squared"
+    _linear_acc_str = "linear"
+
+    def __init__(self, learning_rate, l1=0.0, l2=0.0, lr_power=-0.5, **kwargs):
+        super(FtrlOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
+        if learning_rate is None:
+            raise ValueError("learning_rate is not set.")
+
+        self.type = "ftrl"
+        self._l1 = l1
+        self._l2 = l2
+        self._lr_power = lr_power
+
+    def _create_accumulators(self, block, parameters):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        for p in parameters:
+            self._add_accumulator(self._squared_acc_str, p)
+            self._add_accumulator(self._linear_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        squared_acc = self._get_accumulator(self._squared_acc_str,
+                                            param_and_grad[0])
+        linear_acc = self._get_accumulator(self._linear_acc_str,
+                                           param_and_grad[0])
+        ftrl_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "SquaredAccumulator": squared_acc,
+                "LinearAccumulator": linear_acc,
+                "LearningRate": self._create_param_lr(param_and_grad),
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "SquaredAccumOut": squared_acc,
+                "LinearAccumOut": linear_acc
+            },
+            attrs={"l1": self._l1,
+                   "l2": self._l1,
+                   "lr_power": self._lr_power})
+
+        return ftrl_op
+
+
 # We short the class name, since users will use the optimizer with the package
 # name. The sample code:
 #
@@ -826,6 +1099,7 @@ Adamax = AdamaxOptimizer
 DecayedAdagrad = DecayedAdagradOptimizer
 Adadelta = AdadeltaOptimizer
 RMSProp = RMSPropOptimizer
+Ftrl = FtrlOptimizer
 
 
 class ModelAverage(Optimizer):
@@ -844,7 +1118,9 @@ class ModelAverage(Optimizer):
         max_average_window: The maximum size of average window.
 
     Examples:
-        ...
+
+      .. code-block:: python
+
         optimizer = fluid.optimizer.Momentum()
         _, params_grads = optimizer.minimize(cost)
         model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,

python/paddle/fluid/parallel_executor.py

@@ -27,6 +27,40 @@ BuildStrategy = core.ParallelExecutor.BuildStrategy
 
 
 class ParallelExecutor(object):
+    """
+    ParallelExecutor can run program in parallel.
+
+    Args:
+        use_cuda (bool): Whether to use CUDA or not.
+        loss_name (str): The loss name must set in training. Default None.
+        main_program (Program): The program that need to run, if not provided,
+            then default_main_program will be used. Default None.
+        share_vars_from(ParallelExecutor): If provied, it will share variables
+            from the specified ParallelExecutor. Default None.
+        num_trainers(int): If greater than 1, NCCL will be initialized with
+            multiple rank of nodes, each node should have same number of GPUs.
+            Distributed training will be enabled then. Default 1.
+        trainer_id(int: Must use together with num_trainers. trainer_id is the
+            "rank" of current node starts from 0. Default 0.
+
+    Returns:
+        ParallelExecutor: The initialized ParallelExecutor object.
+
+    Raises:
+        TypeError: If share_vars_from is provided, but not ParallelExecutor object.
+
+    Examples:
+        .. code-block:: python
+
+          train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
+          test_exe = fluid.ParallelExecutor(use_cuda=True,
+                                            main_program=test_program,
+                                            share_vars_from=train_exe)
+
+          train_loss, = train_exe.run([loss.name], feed=feed_dict)
+          test_loss, = test_exe.run([loss.name], feed=feed_dict)
+    """
+
     def __init__(self,
                  use_cuda,
                  loss_name=None,
@@ -37,42 +71,6 @@ class ParallelExecutor(object):
                  num_trainers=1,
                  trainer_id=0,
                  **kwargs):
-        """
-        ParallelExecutor can run program in parallel.
-
-        Args:
-            use_cuda(bool): Whether to use CUDA or not.
-            loss_name(str, default None): The loss name must set in training.
-            main_program(Program, default None): The program that need to run,
-                if not provided, then default_main_program will be used.
-            share_vars_from(ParallelExecutor, default None): If provied,
-                it will share variables from the specified ParallelExecutor.
-            num_trainers(int, default 1): If greater than 1, NCCL will be
-                initialized with multpile rank of nodes, each node should have
-                same number of GPUs. Distributed training will be enabled then.
-            trainer_id(int, default 0): Must use together with num_trainers.
-                trainer_id is the "rank" of current node starts from 0.
-
-        Returns:
-            A ParallelExecutor object.
-
-        Raises:
-            TypeError: If share_vars_from is provided, but not ParallelExecutor
-                object.
-
-        Examples:
-            .. code-block:: python
-
-              train_exe = fluid.ParallelExecutor(
-                  use_cuda=True, loss_name=loss.name)
-              test_exe = fluid.ParallelExecutor(
-                  use_cuda=True,
-                  main_program=test_program,
-                  share_vars_from=train_exe)
-
-              train_loss, = train_exe.run([loss.name], feed=feed_dict)
-              test_loss, = test_exe.run([loss.name], feed=feed_dict)
-        """
         if len(kwargs) != 0:
             err_msg = ""
             for key in kwargs:
@@ -131,10 +129,16 @@ class ParallelExecutor(object):
         main = main_program
         main = main if main else framework.default_main_program()
         scope = executor.global_scope()
+        # FIXME(Yancey1989): it's a temporary approach to determinate the distribute
+        # train program, call self.bcast_param() at the end of each mini-batch.
+        self.is_dist = True if "recv" in [
+            op.type for op in main.global_block().ops
+        ] else False
 
         if share_vars_from and not isinstance(share_vars_from,
                                               ParallelExecutor):
             raise TypeError("share_vars_from must be ParallelExecutor.")
+
         local_scopes = share_vars_from.executor.local_scopes(
         ) if share_vars_from else []
 
@@ -166,12 +170,14 @@ class ParallelExecutor(object):
         element in the list will be copied to each device directly.
 
         For example, if the feed is a dict:
+
         >>> exe = ParallelExecutor()
         >>> # the image will be splitted into devices. If there is two devices
         >>> # each device will process an image with shape (24, 1, 28, 28)
         >>> exe.run(feed={'image': numpy.random.random(size=(48, 1, 28, 28))})
 
         For example, if the feed is a list:
+
         >>> exe = ParallelExecutor()
         >>> # each device will process each element in the list.
         >>> # the 1st device will process an image with shape (48, 1, 28, 28)
@@ -182,18 +188,40 @@ class ParallelExecutor(object):
         >>>               {"image": numpy.random.random(size=(32, 1, 28, 28))},
         >>>              ])
 
-
         Args:
             fetch_list(list): The fetched variable names
             feed(list|dict|None): The feed variables. If the feed is a dict,
                 tensors in that dict will be splitted into each devices. If
                 the feed is a list, each element of the list will be copied
-                to each device.
+                to each device. Default None.
             feed_dict: Alias for feed parameter, for backward compatibility.
-                This parameter is deprecated.
+                This parameter has been deprecated. Default None.
+
+        Returns:
+            List: The fetched result list.
+
+        Raises:
+            ValueError: If the feed is a list, but its length is not equal the
+                length of active places, or its element's is not dict.
+
+        NOTES:
+            1. If the feed's type is dict, the number of data that feeds to
+               ParallelExecutor must be bigger than active places. Otherwise,
+               it will throw exception from C++ side. Special attention should be
+               paid to check whether the last batch of the dataset is bigger
+               than active places.
+            2. If active places are more than one, the fetch results for each
+               variable is a list, and each element of this list is the variable of
+               respective active place.
 
-        Returns: fetched result list.
+        Examples:
+            .. code-block:: python
 
+                pe = fluid.ParallelExecutor(use_cuda=use_cuda,
+                                            loss_name=avg_cost.name,
+                                            main_program=fluid.default_main_program())
+                loss = pe.run(feed=feeder.feed(cur_batch),
+                              fetch_list=[avg_cost.name]))
         """
         if feed is None and feed_dict is not None:
             feed = feed_dict
@@ -238,9 +266,17 @@ class ParallelExecutor(object):
         fetch_var_name = '@FETCHED_VAR_NAME@'
         self.executor.run(fetch_list, fetch_var_name)
         arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
+
+        if self.is_dist:
+            self.bcast_params()
+
         return [arr[i] for i in range(len(arr))]
 
     def bcast_params(self):
+        """
+        Broadcast the parameters to other devices. It is used during
+        distributed training.
+        """
         self.executor.bcast_params(set(self.persistable_vars))
 
     @property

python/paddle/fluid/param_attr.py

@@ -22,6 +22,35 @@ __all__ = [
 
 
 class ParamAttr(object):
+    """
+    Parameter attributes object. To fine-tuning network training process, user
+    can set parameter's attributes to control training details. Such as learning rate,
+    regularization, trainable, do_model_average and the method to initialize param.
+
+
+    Args:
+        name(str): The parameter's name. Default None.
+        initializer(Initializer): The method to initial this parameter. Default None.
+        learning_rate(float): The parameter's learning rate. The learning rate when
+            optimize is :math:`global\_lr * parameter\_lr * scheduler\_factor`.
+            Default 1.0.
+        regularizer(WeightDecayRegularizer): Regularization factor. Default None.
+        trainable(bool): Whether this parameter is trainable. Default True.
+        gradient_clip(BaseGradientClipAttr): The method to clip this parameter's
+            gradient. Default None.
+        do_model_average(bool): Whether this parameter should do model average.
+            Default False.
+
+    Examples:
+        .. code-block:: python
+
+            w_param_attrs = fluid.ParamAttr(name="fc_weight",
+                                            learning_rate=0.5,
+                                            regularizer=fluid.L2Decay(1.0),
+                                            trainable=True)
+            y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
+    """
+
     def __init__(self,
                  name=None,
                  initializer=None,
@@ -29,7 +58,7 @@ class ParamAttr(object):
                  regularizer=None,
                  trainable=True,
                  gradient_clip=None,
-                 do_model_average=None):
+                 do_model_average=False):
         self.name = name
         self.initializer = initializer
         self.learning_rate = learning_rate
@@ -39,6 +68,16 @@ class ParamAttr(object):
         self.model_average = do_model_average
 
     def set_default_initializer(self, initializer):
+        """
+        Set the default initializer, the initializer should be Constant,
+        Uniform, Normal, Xavier, MSRA.
+
+        Args:
+            initializer(Initializer): the initializer to set.
+
+        Returns:
+            None
+        """
         if initializer is None:
             if self.initializer is None:
                 raise ValueError("ParamAttr.initializer is not set")
@@ -50,13 +89,45 @@ class ParamAttr(object):
         self.initializer = initializer
 
     def set_default_param_initializer(self):
+        """
+        Set the default initializer for the parameter with Xavier.
+
+        Args:
+            None.
+
+        Returns:
+            None.
+        """
         self.set_default_initializer(Xavier())
 
     def set_default_bias_initializer(self):
+        """
+        Set the default initializer for the bias with Constant(0.0).
+
+        Args:
+            None.
+
+        Returns:
+            None.
+        """
         self.set_default_initializer(Constant(0.0))
 
     @staticmethod
     def to_attr(arg):
+        """
+        Create ParamAttr[s].
+
+        Args:
+            arg: Arguments to initialize ParamAttr[s]. arg's type can be
+                str, Initializer, float, WeightDecayRegularizer, BaseGradientClipAttr,
+                bool, ParamAttr, or a list of above type.
+
+        Returns:
+            ParamAttr[s]: ParamAttr[s] initialized with arg.
+
+        Raises:
+            arg can not initialize a ParamAttr.
+        """
         if arg is None:
             return ParamAttr()
         elif isinstance(arg, list) or isinstance(arg, tuple):
@@ -75,6 +146,15 @@ class ParamAttr(object):
             raise TypeError("{0} cast to ParamAttr".format(type(arg)))
 
     def to_kwargs(self, with_initializer=False):
+        """
+        Returns the attributes of this parameter.
+
+        Args:
+            with_initializer(bool): Whether to add initializer attr.
+
+        Returns:
+            Parameter attributes(map): The attributes of this parameter.
+        """
         kwargs = {
             'name': self.name,
             'optimize_attr': {
@@ -92,9 +172,27 @@ class ParamAttr(object):
 
 class WeightNormParamAttr(ParamAttr):
     """
-    Used for weight normalization. Any field in ParamAttr can also be set here.
-    Besides, an extra field dim can be set to indicate the dimension except
-    which to normalize.
+    Used for weight Norm. Weight Norm is a reparameterization of the weight vectors
+    in a neural network that decouples the length of those weight vectors from
+    their direction. Weight Norm has been implemented as discussed in this
+    paper: `Weight Normalization: A Simple Reparameterization to Accelerate
+    Training of Deep Neural Networks
+    <https://arxiv.org/pdf/1602.07868.pdf>`_.
+
+    Args:
+        dim(list): The parameter's name. Default None.
+        kwargs: Any field in ParamAttr. Default None.
+
+    Examples:
+        .. code-block:: python
+
+            data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
+            fc = fluid.layers.fc(input=data,
+                                 size=1000,
+                                 param_attr=WeightNormParamAttr(
+                                      dim=None,
+                                      name='weight_norm_param'))
+
     """
     # List to record the parameters reparameterized by weight normalization.
     # If these parameters are treated as Variable rather than Parameter,

python/paddle/fluid/profiler.py

@@ -42,6 +42,9 @@ def cuda_profiler(output_file, output_mode=None, config=None):
     counters/options for profiling by `config` argument. The default config
     is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
     'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
+    Then users can use NVIDIA Visual Profiler
+    (https://developer.nvidia.com/nvidia-visual-profiler) tools to load this
+    this output file to visualize results.
 
     Args:
         output_file (string) : The output file name, the result will be
@@ -50,6 +53,33 @@ def cuda_profiler(output_file, output_mode=None, config=None):
             Comma separated values format. It should be 'kvp' or 'csv'.
         config (list of string) : The profiler options and counters can refer
             to "Compute Command Line Profiler User Guide".
+
+    Raises:
+        ValueError: If `output_mode` is not in ['kvp', 'csv'].
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle.fluid as fluid
+            import paddle.fluid.profiler as profiler
+
+            epoc = 8
+            dshape = [4, 3, 28, 28]
+            data = fluid.layers.data(name='data', shape=[3, 28, 28], dtype='float32')
+            conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
+
+            place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+
+            output_file = 'cuda_profiler.txt'
+            with profiler.cuda_profiler(output_file, 'csv') as nvprof:
+                for i in range(epoc):
+                    input = np.random.random(dshape).astype('float32')
+                    exe.run(fluid.default_main_program(), feed={'data': input})
+            # then use  NVIDIA Visual Profiler (nvvp) to load this output file
+            # to visualize results.
     """
     if output_mode is None:
         output_mode = 'csv'
@@ -69,19 +99,52 @@ def cuda_profiler(output_file, output_mode=None, config=None):
 
 
 def reset_profiler():
-    """The profiler clear interface.
-    reset_profiler will clear the previous time record.
+    """
+    Clear the previous time record. This interface does not work for
+    `fluid.profiler.cuda_profiler`, it only works for
+    `fluid.profiler.start_profiler`, `fluid.profiler.stop_profiler`,
+    and `fluid.profiler.profiler`.
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle.fluid.profiler as profiler
+            with profiler.profiler(state, 'total', '/tmp/profile'):
+                for iter in range(10):
+                    if iter == 2:
+                        profiler.reset_profiler()
+                    # ...
     """
     core.reset_profiler()
 
 
 def start_profiler(state):
-    """Enable the profiler.
+    """
+    Enable the profiler. Uers can use `fluid.profiler.start_profiler` and
+    `fluid.profiler.stop_profiler` to insert the code, except the usage of
+    `fluid.profiler.profiler` interface.
 
     Args:
         state (string) : The profiling state, which should be 'CPU', 'GPU'
             or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling
             GPU as well. 'All' also generates timeline.
+
+    Raises:
+        ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle.fluid.profiler as profiler
+
+            profiler.start_profiler('GPU')
+            for iter in range(10):
+                if iter == 2:
+                    profiler.reset_profiler()
+                # except each iteration
+            profiler.stop_profiler('total', '/tmp/profile')
     """
     if core.is_profiler_enabled():
         return
@@ -97,7 +160,10 @@ def start_profiler(state):
 
 
 def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
-    """Stop the profiler.
+    """
+    Stop the profiler. Uers can use `fluid.profiler.start_profiler` and
+    `fluid.profiler.stop_profiler` to insert the code, except the usage of
+    `fluid.profiler.profiler` interface.
 
     Args:
         sorted_key (string) : If None, the profiling results will be printed
@@ -111,6 +177,23 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
             The `ave` means sorting by the average execution time.
         profile_path (string) : If state == 'All', it will write a profile
             proto output file.
+
+    Raises:
+        ValueError: If `sorted_key` is not in
+            ['calls', 'total', 'max', 'min', 'ave'].
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle.fluid.profiler as profiler
+
+            profiler.start_profiler('GPU')
+            for iter in range(10):
+                if iter == 2:
+                    profiler.reset_profiler()
+                # except each iteration
+            profiler.stop_profiler('total', '/tmp/profile')
     """
     if not core.is_profiler_enabled():
         return
@@ -137,7 +220,12 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
     Different from cuda_profiler, this profiler can be used to profile both CPU
     and GPU program. By defalut, it records the CPU and GPU operator kernels,
     if you want to profile other program, you can refer the profiling tutorial
-    to add more records.
+    to add more records in C++ code.
+
+    If the state == 'All', a profile proto file will be written to
+    `profile_path`. This file records timeline information during the execution.
+    Then users can visualize this file to see the timeline, please refer 
+    https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/timeline.md
 
     Args:
         state (string) : The profiling state, which should be 'CPU' or 'GPU',
@@ -156,6 +244,25 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
             The `ave` means sorting by the average execution time.
         profile_path (string) : If state == 'All', it will write a profile
             proto output file.
+
+    Raises:
+        ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is
+            not in ['calls', 'total', 'max', 'min', 'ave'].
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle.fluid.profiler as profiler
+
+            with profiler.profiler('All', 'total', '/tmp/profile') as prof:
+                for pass_id in range(pass_num):
+                    for batch_id, data in enumerate(train_reader()):
+                        exe.run(fluid.default_main_program(),
+                                feed=feeder.feed(data),
+                                fetch_list=[],
+                                use_program_cache=True)
+                        # ...
     """
     start_profiler(state)
     yield

python/paddle/fluid/recordio_writer.py

@@ -36,6 +36,45 @@ def convert_reader_to_recordio_file(
         compressor=core.RecordIOWriter.Compressor.Snappy,
         max_num_records=1000,
         feed_order=None):
+    """
+    Convert a Python Reader to a recordio file.
+
+    Please see :ref:`api_guide_python_reader` and :ref:`api_guide_reader_op` for
+    details.
+
+    Examples:
+
+        >>> import paddle.fluid as fluid
+        >>> import paddle.dataset.mnist as mnist
+        >>> import paddle
+        >>>
+        >>> tmp_program = fluid.Program()
+        >>> with fluid.program_guard(tmp_program):
+        >>>     img = fluid.layers.data(name='img', shape=[784])
+        >>>     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        >>> feeder = fluid.DataFeeder(feed_list=[img, label], place=fluid.CPUPlace())
+        >>> # mnist.recordio will be generated in current directory
+        >>> fluid.recordio_writer.convert_reader_to_recordio_file(
+        >>>                     filename="mnist.recordio",
+        >>>                     reader_creator=paddle.batch(mnist.train(), batch_size=32),
+        >>>                     feeder=feeder)
+
+    Args:
+        filename(str): The recordio filename.
+        reader_creator(callable): The Python Reader Creator. See
+            :ref:`api_guide_python_reader`.
+        feeder(DataFeeder): The DataFeeder instance. Used to convert
+            :code:`reader_creator` to :code: `lod_tensor`
+        compressor: Must in fluid.core.RecordIOWriter.Compressor.Snappy or
+            fluid.core.RecordIOWriter.Compressor.NoCompress. Use :code:`Snappy`
+            by default.
+        max_num_records(int): Maximum number of records in one chuck. Each record
+            is each return value from reader function
+        feed_order(list): The order of variable names that the reader returns
+
+    Returns:
+        int: the number of record that saved.
+    """
     if feed_order is None:
         feed_order = feeder.feed_names
     counter = 0
@@ -58,6 +97,17 @@ def convert_reader_to_recordio_files(
         compressor=core.RecordIOWriter.Compressor.Snappy,
         max_num_records=1000,
         feed_order=None):
+    """
+    convert a python reader to many recordio files.
+
+    This API is basically same as :code:`convert_reader_to_recordio_file`,
+    instead of it will create many recordio files. Each file contains at
+    most :code:`batch_per_file` records.
+
+    Please reference
+    :ref:`api_fluid_recordio_writer_convert_reader_to_recordio_file` for more
+    details.
+    """
     if feed_order is None:
         feed_order = feeder.feed_names
     f_name, f_ext = os.path.splitext(filename)

python/paddle/fluid/regularizer.py

@@ -16,8 +16,8 @@ import framework
 from . import core
 
 __all__ = [
-    'append_regularization_ops', 'WeightDecayRegularizer', 'L1Decay', 'L2Decay',
-    'L1DecayRegularizer', 'L2DecayRegularizer'
+    'append_regularization_ops', 'L1Decay', 'L2Decay', 'L1DecayRegularizer',
+    'L2DecayRegularizer'
 ]
 
 
@@ -36,7 +36,8 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
                         set. It will be applied with regularizer.
 
     Returns:
-        list of (parameters, gradients) pair with the regularized gradient
+        list[(Variable, Variable)]: list of (parameters, gradients) \
+        pair with the regularized gradient
 
     Raises:
         Exception: Unknown regularization type
@@ -100,6 +101,24 @@ class WeightDecayRegularizer(object):
 
 class L2DecayRegularizer(WeightDecayRegularizer):
     """Implements the L2 Weight Decay Regularization
+
+    Small values of L2 can help prevent over fitting the training data.
+
+    .. math::
+
+        L2WeightDecay = reg\_coeff * parameter
+
+    Args:
+        regularization_coeff(float): regularization coeff
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Adagrad(
+                learning_rate=1e-4,
+                regularization=fluid.regularizer.L2DecayRegularizer(
+                    regularization_coeff=0.1))
+            optimizer.minimize(avg_cost)
     """
 
     def __init__(self, regularization_coeff=0.0):
@@ -154,6 +173,27 @@ class L2DecayRegularizer(WeightDecayRegularizer):
 
 class L1DecayRegularizer(WeightDecayRegularizer):
     """Implements the L1 Weight Decay Regularization
+
+    L1 regularization encourages sparsity.
+
+    .. math::
+
+        L1WeightDecay = reg\_coeff * sign(parameter)
+
+    Args:
+        regularization_coeff(float): regularization coeff
+
+    Examples:
+        .. code-block:: python
+
+            program = fluid.framework.Program()
+            block = program.global_block()
+            mul_x = block.create_parameter(
+                dtype="float32",
+                shape=[5, 10],
+                lod_level=0,
+                name="mul.x",
+                regularizer=fluid.regularizer.L1DecayRegularizer(0.5))
     """
 
     def __init__(self, regularization_coeff=0.0):

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -194,16 +194,16 @@ def train(word_dict,
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

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

@@ -69,16 +69,16 @@ def train(use_cuda, save_dirname, is_local):
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/book/test_image_classification.py

@@ -178,16 +178,16 @@ def train(net_type, use_cuda, save_dirname, is_local):
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -209,16 +209,16 @@ def train(use_cuda, save_dirname=None, is_local=True):
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

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

@@ -200,16 +200,16 @@ def train_main(use_cuda, is_sparse, is_local=True):
     if is_local:
         train_loop(framework.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -151,16 +151,16 @@ def train(nn_type,
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -220,16 +220,16 @@ def train(use_cuda, save_dirname, is_local=True):
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/book/test_word2vec.py

@@ -125,16 +125,16 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
     if is_local:
         train_loop(fluid.default_main_program())
     else:
-        port = os.getenv("PADDLE_INIT_PORT", "6174")
-        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # ip,ip...
+        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
         eplist = []
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        trainers = int(os.getenv("TRAINERS"))
+        trainers = int(os.getenv("PADDLE_TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
-        trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
-        training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -15,6 +15,7 @@
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid.core as core
 
 
 def bilinear_interp_np(input, out_h, out_w, out_size):
@@ -45,9 +46,9 @@ def bilinear_interp_np(input, out_h, out_w, out_size):
 
             out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
                                         w1lambda*input[:, :, h, w+wid]) + \
-                              h1lambda*(w2lambda*input[:, :, h+hid, w] +
-                                        w1lambda*input[:, :, h+hid, w+wid])
-    return out.astype("float32")
+                h1lambda*(w2lambda*input[:, :, h+hid, w] +
+                          w1lambda*input[:, :, h+hid, w+wid])
+    return out.astype(input.dtype)
 
 
 class TestBilinearInterpOp(OpTest):
@@ -122,5 +123,44 @@ class TestCase6(TestBilinearInterpOp):
         self.out_size = np.array([65, 129]).astype("int32")
 
 
+class TestBilinearInterpOpUint8(OpTest):
+    def setUp(self):
+        self.out_size = None
+        self.init_test_case()
+        self.op_type = "bilinear_interp"
+        input_np = np.random.randint(
+            low=0, high=256, size=self.input_shape).astype("uint8")
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
+                                       self.out_size)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(place=core.CPUPlace(), atol=1)
+
+    def init_test_case(self):
+        self.input_shape = [1, 3, 9, 6]
+        self.out_h = 10
+        self.out_w = 9
+
+
+class TestCase1Uint8(TestBilinearInterpOpUint8):
+    def init_test_case(self):
+        self.input_shape = [2, 3, 128, 64]
+        self.out_h = 120
+        self.out_w = 50
+
+
+class TestCase2Uint8(TestBilinearInterpOpUint8):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 5
+        self.out_w = 13
+        self.out_size = np.array([6, 15]).astype("int32")
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_concat_op.py

@@ -43,7 +43,7 @@ class TestConcatOp(OpTest):
         self.axis = 1
 
 
-class TestConcatOp2(OpTest):
+class TestConcatOp2(TestConcatOp):
     def init_test_data(self):
         self.x0 = np.random.random((2, 3, 4, 5)).astype('float32')
         self.x1 = np.random.random((2, 3, 4, 5)).astype('float32')
@@ -51,5 +51,16 @@ class TestConcatOp2(OpTest):
         self.axis = 1
 
 
+class TestConcatOp3(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = np.random.random((1, 256, 170, 256)).astype('float32')
+        self.x1 = np.random.random((1, 128, 170, 256)).astype('float32')
+        self.x2 = np.random.random((1, 128, 170, 256)).astype('float32')
+        self.axis = 1
+
+    def test_check_grad(self):
+        pass
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from test_gaussian_random_op import TestGaussianRandomOp
+
+
+class TestMKLDNN(TestGaussianRandomOp):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_gaussian_random_op.py

@@ -25,7 +25,15 @@ class TestGaussianRandomOp(unittest.TestCase):
     def setUp(self):
         self.op_type = "gaussian_random"
         self.inputs = {}
-        self.attrs = {"shape": [1000, 784], "mean": .0, "std": 1., "seed": 10}
+        self.use_mkldnn = False
+        self.init_kernel_type()
+        self.attrs = {
+            "shape": [1000, 784],
+            "mean": .0,
+            "std": 1.,
+            "seed": 10,
+            "use_mkldnn": self.use_mkldnn
+        }
 
         self.outputs = ["Out"]
 
@@ -58,6 +66,9 @@ class TestGaussianRandomOp(unittest.TestCase):
         self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
         self.assertAlmostEqual(numpy.std(tensor), 1., delta=0.1)
 
+    def init_kernel_type(self):
+        pass
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -401,6 +401,15 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
+    def test_maxout(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 5], dtype="float32")
+            y = layers.data(name='y', shape=[2, 3], dtype="float32")
+            output = layers.crop(x, shape=y)
+            self.assertIsNotNone(output)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_optimizer.py

@@ -434,5 +434,71 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
         self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
 
 
+class TestFtrlOptimizer(unittest.TestCase):
+    class MockFtrl(optimizer.FtrlOptimizer):
+        def get_accumulators(self):
+            return self._accumulators
+
+        def get_squared_str(self):
+            return self._squared_acc_str
+
+        def get_linear_str(self):
+            return self._linear_acc_str
+
+    def test_ftrl_optimizer(self):
+        init_program = framework.Program()
+        program = framework.Program()
+        block = program.global_block()
+        mul_x = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10],
+            lod_level=0,
+            name="mul.x",
+            optimize_attr={'learning_rate': 1.1})
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        block.append_op(
+            type="mul",
+            inputs={"X": mul_x,
+                    "Y": mul_y},
+            outputs={"Out": mul_out},
+            attrs={"x_num_col_dims": 1})
+        mean_out = block.create_var(
+            dtype="float32", shape=[1], lod_level=0, name="mean.out")
+        block.append_op(
+            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
+        learning_rate = 0.01
+        ftrl_optimizer = self.MockFtrl(
+            learning_rate=learning_rate, l1=0.0, l2=0.0, lr_power=-0.5)
+        params_grads = append_backward(mean_out)
+        self.assertEqual(len(params_grads), 1)
+        self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
+        opts = ftrl_optimizer.create_optimization_pass(params_grads, mul_out,
+                                                       init_program)
+        self.assertEqual(len(opts), 3)
+        self.assertEqual([op.type for op in opts],
+                         ["fill_constant", "elementwise_mul", "ftrl"])
+
+        # Check accumulators
+        accumulators = ftrl_optimizer.get_accumulators()
+        self.assertEqual(len(accumulators), 2)
+        self.assertTrue(ftrl_optimizer.get_squared_str() in accumulators)
+        self.assertTrue(ftrl_optimizer.get_linear_str() in accumulators)
+        squared_acc = accumulators[ftrl_optimizer.get_squared_str()]
+        linear_acc = accumulators[ftrl_optimizer.get_linear_str()]
+        self.assertEqual(len(squared_acc), 1)
+        self.assertEqual(len(linear_acc), 1)
+        self.assertTrue(mul_x.name in squared_acc)
+        self.assertTrue(mul_x.name in linear_acc)
+
+        # Check init_program
+        init_ops = init_program.global_block().ops
+        self.assertEqual(len(init_ops), 3)
+        self.assertEqual(init_ops[0].type, "fill_constant")
+        self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/trainer.py

@@ -33,23 +33,59 @@ __all__ = [
 
 
 class BeginEpochEvent(object):
+    """
+    The begin of a training epoch.
+
+    Args:
+        epoch_id(int): The current epoch ID.
+    """
+
     def __init__(self, epoch_id):
         self.epoch = epoch_id
 
 
 class EndEpochEvent(object):
+    """
+    The end of a training epoch.
+
+    Args:
+        epoch_id(int): The current epoch ID.
+    """
+
     def __init__(self, epoch_id):
         self.epoch = epoch_id
 
 
 class BeginStepEvent(object):
+    """
+    The begin of a training epoch.
+
+    Args:
+        epoch_id(int): The current epoch ID.
+        step_id(int): The current step ID.
+    """
+
     def __init__(self, epoch_id, step_id):
         self.epoch = epoch_id
         self.step = step_id
         self.fetch_metrics = True
+        """
+        If fetch_metrics is true, the metrics will be fetched at the 
+        EndStepEvent. Default is True.
+        """
 
 
 class EndStepEvent(object):
+    """
+    The end of a training step.
+
+    Args:
+        epoch_id(int): The current epoch ID.
+        step_id(int): The current step ID.
+        metrics(list): A list of fetched tensor. The order of this list is same
+            as the :code:`train_func` returns.
+    """
+
     def __init__(self, epoch_id, step_id, metrics):
         self.epoch = epoch_id
         self.step = step_id
@@ -57,6 +93,27 @@ class EndStepEvent(object):
 
 
 class CheckpointConfig(object):
+    """
+    Parameter object for :code:`fluid.io.save_checkpoint` and
+    :code:`fluid.Trainer`. Used to configuration how to save checkpoint.
+
+    Args:
+        checkpoint_dir(str): Directory path to save check point. Default is the
+            current directory.
+
+        max_num_checkpoints(int): The max number of local check points.
+        epoch_interval(int): Every number of epoch to save check point.
+        step_interval(int): Every number of step to save check point.
+
+    Examples:
+        >>> config = fluid.CheckpointConfig("./checkpoints")
+        >>> trainer = fluid.Trainer(train_func=train_program,
+        >>>                         place=place,
+        >>>                         optimizer_func=optimizer_func,
+        >>>                         checkpoint_config=config)
+        >>> trainer.train(...)
+    """
+
     def __init__(self,
                  checkpoint_dir=None,
                  max_num_checkpoints=3,
@@ -113,11 +170,62 @@ def check_and_get_place(place):
 
 class Trainer(object):
     """
+    A trainer wraps MultiGPU/MultiNode training loops and can be used to train a
+    simple neural network easily.
+
+    This API takes a :code:`train_func`. A :code:`train_func` is a function that
+    return loss as it first return value. The reset value can be fetched by
+    EndStepEvent.metrics
+
+    This API also takes a :code:`optimizer_func` that will return an optimizer
+    instance.
+
+    For example, to train a MLP for MNIST dataset, the sample program is
+
+    >>> import paddle.fluid as fluid
+    >>>
+    >>> def mlp(image, layer_sizes=[200, 100], activation="relu", num_classes=10):
+    >>>     hidden = image
+    >>>     for layer_size in layer_sizes:
+    >>>         hidden = fluid.layers.fc(input=hidden, size=layer_size, act=activation)
+    >>>     return fluid.layers.fc(input=hidden, size=num_classes, act="softmax")
+    >>>
+    >>> def train_mnist_mlp():
+    >>>     img = fluid.layers.data(name='image', shape=[784])
+    >>>     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    >>>     prediction = mlp(img)
+    >>>     return fluid.layers.mean(fluid.layers.cross_entropy(prediction, label))
+    >>>
+    >>> def optimizer():
+    >>>     return fluid.optimizer.Adam()
+    >>>
+    >>> trainer = Trainer(train_func=train_mnist_mlp,
+    >>>                   optimizer_func=optimizer,
+    >>>                   place=fluid.CUDAPlace(0),
+    >>>                   parallel=True)
+    >>>
+    >>> def train_callback(event):
+    >>>     if isinstance(event, fluid.EndStepEvent):
+    >>>         print "Epoch ID", event.epoch, "Step ID",\
+    >>>             event.step, "AvgLoss", event.metrics[0]
+    >>>     elif isinstance(event, fluid.EndEpochEvent):
+    >>>         trainer.save_params("./model_{0}".format(event.epoch))
+    >>>
+    >>> trainer.train(num_epochs=100, event_handler=train_callback)
+
+    For more example, please see :ref:`api_guide_high_level_api`.
+
 
     Args:
-        train_func(callable): A function which will return loss. The loss must be a scalar.
+        train_func(callable): A function which will return loss. The loss must be
+            a scalar tensor.
         optimizer_func(callable): A function that returns an Optimizer object.
-        place: The device place of this trainer.
+        place(CUDAPlace|CPUPlace): The device place of this trainer. If
+            :code:`parallel=True,` all CUDA Places will be used if :code:`place`
+            is a :code:`CUDAPlace`.
+        parallel(bool): True if use multiple devices.
+        checkpoint_config(CheckpointConfig): Configuration about how to save
+            checkpoints.
     """
 
     def __init__(self,
@@ -129,9 +237,6 @@ class Trainer(object):
                  checkpoint_config=None):
         self.__stop = False
         self.parallel = parallel
-        # 1. we need to generate a framework.Program by calling
-        # program_func. Reference: fluid.program_guard in
-        # test_word2vec.py
 
         # config for checkpoint
         # only chief worker will save variables
@@ -145,6 +250,10 @@ class Trainer(object):
 
         self.scope = core.Scope()
 
+        # 1. we need to generate a framework.Program by calling
+        # program_func. Reference: fluid.program_guard in
+        # test_word2vec.py
+
         self.startup_program = framework.Program()
         self.train_program = framework.Program()
 
@@ -277,17 +386,18 @@ class Trainer(object):
 
     def train(self, num_epochs, event_handler, reader=None, feed_order=None):
         """
-        Train the model.
+        Start the train loop to train the model.
 
         Args:
-            num_epochs: The number of epoch. An epoch will process all data in reader
-            event_handler: The event handler. A function with type (ev:Event)->void
-            reader:
-            feed_order: Feeding order of reader. None will following the defining
+            num_epochs(int): The number of epoch. An epoch will process all data in reader
+            event_handler(callable): The event handler. A function with type (ev:Event)->void
+            reader(callable): A reader creator object. See also
+                :ref:`api_guide_python_reader` .
+            feed_order(list): Feeding order of reader. None will following the defining
                 order in program
 
         Returns:
-
+            None
         """
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
         if training_role == "PSERVER":
@@ -307,16 +417,24 @@ class Trainer(object):
         Test the model on given test data
 
         Args:
-            reader: The reader that yields test data.
-            feed_order: Feeding order of reader. None will following the defining
-                order in program
+            reader(callable): The reader that yields test data.
+            feed_order(list): Feeding order of reader. None will following the
+                defining order in program
         """
 
         return self._test_by_executor(reader, feed_order,
                                       self.train_func_outputs)
 
     def save_params(self, param_path):
-        # reference: save_persistables in io.py
+        """
+        Save all parameters into :code:`param_path`.
+
+        Args:
+            param_path(str): The path to save parameters.
+
+        Returns:
+            None
+        """
         with self._prog_and_scope_guard():
             exe = executor.Executor(self.place)
             io.save_persistables(exe, dirname=param_path)

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -12,14 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Transpile the program to distributed data-parallelism programs.
-The main_program will be transformed to use a remote parameter server
-to do parameter optimization. And the optimization graph will be put
-into a parameter server program.
-
-Use different methods to split trainable variables to different
-parameter servers.
-
 Steps to transpile trainer:
 1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
 2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
@@ -117,129 +109,41 @@ def slice_variable(var_list, slice_count, min_block_size=8192):
     return blocks
 
 
-class DistributeTranspiler:
-    def _has_distributed_lookup_table(self):
-        # process lookup_table_op
-        # 1. check all lookup_table_op is distributed
-        # 2. check all lookup_table_op share the same table.
-        distributed_lookup_table_ops = []
-        # support only one distributed_lookup_table now
-        self.table_name = None
-        for op in self.origin_program.global_block().ops:
-            if op.type == LOOKUP_TABLE_TYPE:
-                if op.attrs['is_distributed'] is True:
-                    if self.table_name is None:
-                        self.table_name = op.input("W")[0]
-                    if self.table_name != op.input("W")[0]:
-                        raise RuntimeError("all distributed lookup_table_ops"
-                                           " should have only one table")
-                    distributed_lookup_table_ops.append(op)
-                else:
-                    if self.table_name is not None:
-                        assert op.input("W")[0] != self.table_name
-
-        return len(distributed_lookup_table_ops) > 0
-
-    def _update_dist_lookup_table_vars(self, param_list, grad_list,
-                                       params_grads):
-        # TODO(wuyi): put find a way to put dist lookup table stuff all together.
-        # update self.table_param_grad and self.trainer_side_table_grad_list
-        program = self.origin_program
-        if self.has_distributed_lookup_table:
-            param_list = [
-                param for param in param_list if param.name != self.table_name
-            ]
-            grad_list = [
-                grad for grad in grad_list
-                if grad.name != grad_var_name(self.table_name)
-            ]
-            self.table_param_grad = [
-                param_grad for param_grad in params_grads
-                if param_grad[0].name == self.table_name
-            ][0]
-            table_grad_var = self.table_param_grad[1]
-            if self.sync_mode:
-                self.trainer_side_table_grad_list = [
-                    program.global_block().create_var(
-                        name="%s.trainer_%d.pserver_%d" %
-                        (table_grad_var.name, self.trainer_id, index),
-                        type=table_grad_var.type,
-                        shape=table_grad_var.shape,
-                        dtype=table_grad_var.dtype)
-                    for index in range(len(self.pserver_endpoints))
-                ]
-            else:
-                self.trainer_side_table_grad_list = [
-                    program.global_block().create_var(
-                        name="%s.pserver_%d" % (table_grad_var.name, index),
-                        type=table_grad_var.type,
-                        shape=table_grad_var.shape,
-                        dtype=table_grad_var.dtype)
-                    for index in range(len(self.pserver_endpoints))
-                ]
-        return param_list, grad_list
-
-    def _init_splited_vars(self, slice_var_up):
-        # update these mappings for further transpile:
-        # 1. param_var_mapping: param var name -> [splited params vars]
-        # 2. grad_var_mapping: grad var name -> [splited grads vars]
-        # 3. grad_param_mapping: grad.blockx -> param.blockx
-        # 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
-
-        param_list = []
-        grad_list = []
-        param_grad_set = set()
-        for p, g in self.params_grads:
-            # skip parameter marked not trainable
-            if type(p) == Parameter and p.trainable == False:
-                continue
-            if p.name not in param_grad_set:
-                param_list.append(p)
-                param_grad_set.add(p.name)
-            if g.name not in param_grad_set:
-                grad_list.append(g)
-                param_grad_set.add(g.name)
-
-        param_list, grad_list = self._update_dist_lookup_table_vars(
-            param_list, grad_list, self.params_grads)
-
-        if slice_var_up:
-            # when we slice var up into blocks, we will slice the var according to
-            # pserver services' count. A pserver may have two or more listening ports.
-            grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
-            param_blocks = slice_variable(param_list,
-                                          len(self.pserver_endpoints))
-        else:
-            # when we do NOT slice var up into blocks, we will always slice params
-            # grads into one block.
-            grad_blocks = slice_variable(grad_list, 1)
-            param_blocks = slice_variable(param_list, 1)
-        assert (len(grad_blocks) == len(param_blocks))
-
-        # origin_varname -> [splited_var]
-        self.param_var_mapping = self._create_vars_from_blocklist(
-            self.origin_program, param_blocks)
-        self.grad_var_mapping = self._create_vars_from_blocklist(
-            self.origin_program,
-            grad_blocks,
-            add_trainer_suffix=self.trainer_num > 1)
-        self.grad_param_mapping = dict()
-        for g, p in zip(grad_blocks, param_blocks):
-            g_name, g_bid, _ = g.split(":")
-            p_name, p_bid, _ = p.split(":")
-            self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] =  \
-                    self.param_var_mapping[p_name][int(p_bid)]
-
-        # create mapping of endpoint -> split var to create pserver side program
-        self.param_grad_ep_mapping = dict()
-        [
-            self.param_grad_ep_mapping.update({
-                ep: {
-                    "params": [],
-                    "grads": []
-                }
-            }) for ep in self.pserver_endpoints
-        ]
+class DistributeTranspiler(object):
+    """
+    **DistributeTranspiler**
+
+    Convert the fluid program to distributed data-parallelism programs.
+
+    The main_program will be transformed to use a remote parameter server
+    to do parameter optimization. And the optimization graph will be put
+    into a parameter server program.
+
+    Examples:
+        .. code-block:: python
+
+           # Define your model before these codes.
+           port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+           pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
+           eplist = []
+           for ip in pserver_ips.split(","):
+                eplist.append(':'.join([ip, port]))
+           pserver_endpoints = ",".join(eplist)
+           trainers = int(os.getenv("PADDLE_TRAINERS"))
+           current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
+           trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+           role = os.getenv("PADDLE_TRAINING_ROLE")
+
+           t = distribute_transpiler.DistributeTranspiler()
+           t.transpile(
+                trainer_id, pservers=pserver_endpoints, trainers=trainers)
+           if role == "PSERVER":
+                pserver_program = t.get_pserver_program(current_endpoint)
+                pserver_startup_program = t.get_startup_program(current_endpoint,
+                                                                pserver_program)
+           elif role == "TRAINER":
+                trainer_program = t.get_trainer_program()
+    """
 
     def transpile(self,
                   trainer_id,
@@ -250,20 +154,20 @@ class DistributeTranspiler:
                   split_method=RoundRobin,
                   sync_mode=True):
         """
-        :param trainer_id: one unique id for each trainer in a job.
-        :type trainer_id: int
-        :param program: program to transpile, default is default_main_program
-        :type program: Program
-        :param pservers: parameter server endpoints like "m1:6174,m2:6174"
-        :type pservers: string
-        :param trainers: total number of workers/trainers in the job
-        :type trainers: int
-        :param split_method: A function to determin how to split variables
-            to different servers equally.
-        :type split_method: function
-        :type sync_mode: boolean default True
-        :param sync_mode: if sync_mode is set True, it means that dist transpiler
-        will transpile the program into sync_mode pserver and trainer program.
+        Run the transpiler.
+
+        Args:
+            trainer_id (int): id for current trainer worker, if you have
+                n workers, the id may range from 0 ~ n-1
+            program (Program|None): program to transpile,
+                default is fluid.default_main_program().
+            pservers (str): comma separated ip:port string for the pserver
+                list.
+            trainers (int): number of trainers in the distributed job.
+            slice_var_up (bool): Do Tensor slice for pservers, default is True.
+            split_method (PSDispatcher): RoundRobin or HashName can be used
+                try to choose the best method to balance loads for pservers.
+            sync_mode (bool): Do sync training or not, default is True.
         """
         assert (split_method.__bases__[0] == PSDispatcher)
         if program is None:
@@ -390,6 +294,12 @@ class DistributeTranspiler:
             self._split_table_grad_and_add_send_vars(program, pserver_endpoints)
 
     def get_trainer_program(self):
+        """
+        Get transpiled trainer side program.
+
+        Returns:
+            Program: trainer side program.
+        """
         # remove optimize ops and add a send op to main_program
         delete_ops(self.origin_program.global_block(), self.optimize_ops)
         # FIXME(typhoonzero): serialize once will fix error occurs when clone.
@@ -398,12 +308,19 @@ class DistributeTranspiler:
 
     def get_pserver_program(self, endpoint):
         """
-        Get pserver side program using the endpoint.
-        TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers.
-        NOTE: assume blocks of the same variable is not distributed
-        on the same pserver, only change param/grad varnames for
-        trainers to fetch.
+        Get parameter server side program.
+        
+        Args:
+            endpoint (str): current parameter server endpoint.
+        
+        Returns:
+            Program: the program for current parameter server to run.
         """
+        # TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers.
+        # NOTE: assume blocks of the same variable is not distributed
+        # on the same pserver, only change param/grad varnames for
+        # trainers to fetch.
+
         # step1
         pserver_program = Program()
         # step2: Create vars to receive vars at parameter servers.
@@ -582,6 +499,14 @@ class DistributeTranspiler:
         Get startup program for current parameter server.
         Modify operator input variables if there are variables that
         were split to several blocks.
+
+        Args:
+            endpoint (str): current pserver endpoint.
+            pserver_program (Program): call get_pserver_program first and
+                pass the result here.
+        
+        Returns:
+            Program: parameter server side startup program.
         """
         s_prog = Program()
         orig_s_prog = default_startup_program()
@@ -633,6 +558,129 @@ class DistributeTranspiler:
 
     # ====================== private transpiler functions =====================
 
+    def _has_distributed_lookup_table(self):
+        # process lookup_table_op
+        # 1. check all lookup_table_op is distributed
+        # 2. check all lookup_table_op share the same table.
+        distributed_lookup_table_ops = []
+        # support only one distributed_lookup_table now
+        self.table_name = None
+        for op in self.origin_program.global_block().ops:
+            if op.type == LOOKUP_TABLE_TYPE:
+                if op.attrs['is_distributed'] is True:
+                    if self.table_name is None:
+                        self.table_name = op.input("W")[0]
+                    if self.table_name != op.input("W")[0]:
+                        raise RuntimeError("all distributed lookup_table_ops"
+                                           " should have only one table")
+                    distributed_lookup_table_ops.append(op)
+                else:
+                    if self.table_name is not None:
+                        assert op.input("W")[0] != self.table_name
+
+        return len(distributed_lookup_table_ops) > 0
+
+    def _update_dist_lookup_table_vars(self, param_list, grad_list,
+                                       params_grads):
+        # TODO(wuyi): put find a way to put dist lookup table stuff all together.
+        # update self.table_param_grad and self.trainer_side_table_grad_list
+        program = self.origin_program
+        if self.has_distributed_lookup_table:
+            param_list = [
+                param for param in param_list if param.name != self.table_name
+            ]
+            grad_list = [
+                grad for grad in grad_list
+                if grad.name != grad_var_name(self.table_name)
+            ]
+            self.table_param_grad = [
+                param_grad for param_grad in params_grads
+                if param_grad[0].name == self.table_name
+            ][0]
+            table_grad_var = self.table_param_grad[1]
+            if self.sync_mode:
+                self.trainer_side_table_grad_list = [
+                    program.global_block().create_var(
+                        name="%s.trainer_%d.pserver_%d" %
+                        (table_grad_var.name, self.trainer_id, index),
+                        type=table_grad_var.type,
+                        shape=table_grad_var.shape,
+                        dtype=table_grad_var.dtype)
+                    for index in range(len(self.pserver_endpoints))
+                ]
+            else:
+                self.trainer_side_table_grad_list = [
+                    program.global_block().create_var(
+                        name="%s.pserver_%d" % (table_grad_var.name, index),
+                        type=table_grad_var.type,
+                        shape=table_grad_var.shape,
+                        dtype=table_grad_var.dtype)
+                    for index in range(len(self.pserver_endpoints))
+                ]
+        return param_list, grad_list
+
+    def _init_splited_vars(self, slice_var_up):
+        # update these mappings for further transpile:
+        # 1. param_var_mapping: param var name -> [splited params vars]
+        # 2. grad_var_mapping: grad var name -> [splited grads vars]
+        # 3. grad_param_mapping: grad.blockx -> param.blockx
+        # 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
+
+        param_list = []
+        grad_list = []
+        param_grad_set = set()
+        for p, g in self.params_grads:
+            # skip parameter marked not trainable
+            if type(p) == Parameter and p.trainable == False:
+                continue
+            if p.name not in param_grad_set:
+                param_list.append(p)
+                param_grad_set.add(p.name)
+            if g.name not in param_grad_set:
+                grad_list.append(g)
+                param_grad_set.add(g.name)
+
+        param_list, grad_list = self._update_dist_lookup_table_vars(
+            param_list, grad_list, self.params_grads)
+
+        if slice_var_up:
+            # when we slice var up into blocks, we will slice the var according to
+            # pserver services' count. A pserver may have two or more listening ports.
+            grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
+            param_blocks = slice_variable(param_list,
+                                          len(self.pserver_endpoints))
+        else:
+            # when we do NOT slice var up into blocks, we will always slice params
+            # grads into one block.
+            grad_blocks = slice_variable(grad_list, 1)
+            param_blocks = slice_variable(param_list, 1)
+        assert (len(grad_blocks) == len(param_blocks))
+
+        # origin_varname -> [splited_var]
+        self.param_var_mapping = self._create_vars_from_blocklist(
+            self.origin_program, param_blocks)
+        self.grad_var_mapping = self._create_vars_from_blocklist(
+            self.origin_program,
+            grad_blocks,
+            add_trainer_suffix=self.trainer_num > 1)
+        self.grad_param_mapping = dict()
+        for g, p in zip(grad_blocks, param_blocks):
+            g_name, g_bid, _ = g.split(":")
+            p_name, p_bid, _ = p.split(":")
+            self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] =  \
+                    self.param_var_mapping[p_name][int(p_bid)]
+
+        # create mapping of endpoint -> split var to create pserver side program
+        self.param_grad_ep_mapping = dict()
+        [
+            self.param_grad_ep_mapping.update({
+                ep: {
+                    "params": [],
+                    "grads": []
+                }
+            }) for ep in self.pserver_endpoints
+        ]
+
     # transpiler function for dis lookup_table
     def _replace_lookup_table_op_with_prefetch(self, program,
                                                pserver_endpoints):

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -383,6 +383,16 @@ def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
 
 
 def release_memory(input_program, skip_opt_set=None):
+    """
+    Modify the input program and insert :code:`delete_op` to early drop not used
+    variables. The modification will be performed inplace.
+
+    Notes: This is an experimental API and could be removed in next few
+    releases. Users should not use this API.
+
+    Args:
+        input_program(Program): The program will be inserted :code:`delete_op`.
+    """
     cfgs = _get_cfgs(input_program)
     for cfg in cfgs:
         cfg.release_memory(skip_opt_set=skip_opt_set)

python/paddle/fluid/transpiler/ps_dispatcher.py

@@ -33,15 +33,21 @@ class PSDispatcher(object):
 
     def dispatch(self, varlist):
         """
-        :param varlist: a list of Variables
-        :return: a map of pserver endpoint -> varname 
+        Args:
+            varlist(list): a list of Variables
+        Returns:
+            a map of pserver endpoint -> varname
         """
         AssertionError("Interface has not been implemented.")
 
 
 class HashName(PSDispatcher):
     """
-      Hash variable names to several endpoints
+    Hash variable names to several endpoints using python
+    "hash()" function.
+
+    Args:
+        pserver_endpoints (list): list of endpoint(ip:port).
     """
 
     def __init__(self, pserver_endpoints):
@@ -61,7 +67,11 @@ class HashName(PSDispatcher):
 
 class RoundRobin(PSDispatcher):
     """
-    Distribute variables to serveral endpoints.
+    Distribute variables to serveral endpoints using
+    RondRobin<https://en.wikipedia.org/wiki/Round-robin_scheduling> method.
+
+    Args:
+        pserver_endpoints (list): list of endpoint(ip:port).
     """
 
     def __init__(self, pserver_endpoints):

python/paddle/fluid/unique_name.py

@@ -16,7 +16,7 @@ import collections
 import contextlib
 import sys
 
-__all__ = ['generate', 'switch', 'guard', 'UniqueNameGenerator']
+__all__ = ['generate', 'switch', 'guard']
 
 
 class UniqueNameGenerator(object):

tools/check_ctest_hung.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import re
+
+
+def escape(input):
+    o = input.replace("\n", "")
+    o = o.replace("\r", "")
+    return o
+
+
+def main():
+    usage = """Usage:
+1. Download the Paddle_PR_CI_*.log from TeamCity
+2. run: python check_ctest_hung.py Paddle_PR_CI_*.log
+3. If there is hung ctest, the result likes:
+Diff:  set(['test_parallel_executor_crf'])
+    """
+    if len(sys.argv) < 2:
+        print(usage)
+        exit(0)
+
+    logfile = sys.argv[1]
+    started = set()
+    passed = set()
+    with open(logfile, "r") as fn:
+        for l in fn.readlines():
+            if l.find("Test ") != -1 and \
+                l.find("Passed") != -1:
+                m = re.search("Test\s+#[0-9]*\:\s([a-z0-9_]+)", escape(l))
+                passed.add(m.group(1))
+            if l.find("Start ") != -1:
+                start_parts = escape(l).split(" ")
+                m = re.search("Start\s+[0-9]+\:\s([a-z0-9_]+)", escape(l))
+                started.add(m.group(1))
+    print "Diff: ", started - passed
+
+
+if __name__ == "__main__":
+    main()

tools/codestyle/docstring_checker.py

@@ -291,6 +291,8 @@ class DocstringChecker(BaseChecker):
             True if successful otherwise False.
         """
 
+        if node.name.startswith("__") or node.name.startswith("_"):
+            return True
         find = False
         for t in node.body:
             if not isinstance(t, astroid.Return):
@@ -316,6 +318,8 @@ class DocstringChecker(BaseChecker):
         Returns:
             True if successful otherwise False.
         """
+        if node.name.startswith("__") or node.name.startswith("_"):
+            return True
         args = []
         for arg in node.args.get_children():
             if (not isinstance(arg, astroid.AssignName)) \

tools/print_signatures.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Print all signature of a python module in alphabet order.
+
+Usage:
+    ./print_signature  "paddle.fluid" > signature.txt
+"""
+import importlib
+import inspect
+import collections
+import sys
+import pydoc
+
+member_dict = collections.OrderedDict()
+
+
+def visit_member(parent_name, member):
+    cur_name = ".".join([parent_name, member.__name__])
+    if inspect.isclass(member):
+        for name, value in inspect.getmembers(member):
+            if hasattr(value, '__name__') and (not name.startswith("_") or
+                                               name == "__init__"):
+                visit_member(cur_name, value)
+    elif callable(member):
+        try:
+            member_dict[cur_name] = inspect.getargspec(member)
+        except TypeError:  # special for PyBind method
+            member_dict[cur_name] = "  ".join([
+                line.strip() for line in pydoc.render_doc(member).split('\n')
+                if "->" in line
+            ])
+
+    else:
+        raise RuntimeError("Unsupported generate signature of member, type {0}".
+                           format(str(type(member))))
+
+
+def visit_all_module(mod):
+    for member_name in (
+            name
+            for name in (mod.__all__ if hasattr(mod, "__all__") else dir(mod))
+            if not name.startswith("_")):
+        instance = getattr(mod, member_name, None)
+        if instance is None:
+            continue
+        if inspect.ismodule(instance):
+            visit_all_module(instance)
+        else:
+            visit_member(mod.__name__, instance)
+
+
+visit_all_module(importlib.import_module(sys.argv[1]))
+
+for name in member_dict:
+    print name, member_dict[name]