Merge remote-tracking branch 'ups/develop' into refine/ut/lac

Dockerfile

@@ -53,7 +53,7 @@ RUN curl -s -q https://glide.sh/get | sh
 #    and its size is only one-third of the official one.
 # 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
 #    See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
-RUN wget -qO- http://paddlepaddledeps.bj.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
+RUN wget -qO- http://paddlepaddledeps.cdn.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
     tar -xz -C /usr/local && \
     cp -rf /usr/local/TensorRT/include /usr && \
     cp -rf /usr/local/TensorRT/lib /usr

cmake/inference_lib.cmake

@@ -128,16 +128,13 @@ set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
 set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
 set(module "framework")
 if (NOT WIN32)
-copy(framework_lib DEPS framework_py_proto 
-  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
-)
-else()
-copy(framework_lib
+set(framework_lib_deps framework_py_proto)
+endif(NOT WIN32)
+copy(framework_lib DEPS ${framework_lib_deps}
   SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
+       ${src_dir}/${module}/ir/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module} ${dst_dir}/${module}/ir
 )
-endif(NOT WIN32)
 
 set(module "memory")
 copy(memory_lib
@@ -161,7 +158,8 @@ set(module "inference")
 copy(inference_lib DEPS ${inference_deps}
   SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
        ${src_dir}/${module}/api/paddle_inference_api.h ${src_dir}/${module}/api/demo_ci
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
+       ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
 )
 
 set(module "platform")

doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md

@@ -60,6 +60,7 @@
 图3. 编码器-解码器框架
 </div>
 
+<a name="编码器"></a>
 #### 编码器
 
 编码阶段分为三步：
@@ -81,7 +82,7 @@
 机器翻译任务的训练过程中，解码阶段的目标是最大化下一个正确的目标语言词的概率。思路是：
 1. 每一个时刻，根据源语言句子的编码信息（又叫上下文向量，context vector）`$c$`、真实目标语言序列的第`$i$`个词`$u_i$`和`$i$`时刻RNN的隐层状态`$z_i$`，计算出下一个隐层状态`$z_{i+1}$`。计算公式如下：
 $$z_{i+1}=\phi_{\theta '} \left ( c,u_i,z_i \right )$$
-其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用[注意力机制](#注意力机制)时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记`<s>`，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
+其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用注意力机制时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记`<s>`，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
 
 2. 将`$z_{i+1}$`通过`softmax`归一化，得到目标语言序列的第`$i+1$`个单词的概率分布`$p_{i+1}$`。概率分布公式如下：
 $$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
@@ -93,6 +94,7 @@ $$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
 
 机器翻译任务的生成过程，通俗来讲就是根据预先训练的模型来翻译源语言句子。生成过程中的解码阶段和上述训练过程的有所差异，具体介绍请见[柱搜索算法](#柱搜索算法)。
 
+<a name="柱搜索算法"></a>
 ### 柱搜索算法
 
 柱搜索（[beam search](http://en.wikipedia.org/wiki/Beam_search)）是一种启发式图搜索算法，用于在图或树中搜索有限集合中的最优扩展节点，通常用在解空间非常大的系统（如机器翻译、语音识别）中，原因是内存无法装下图或树中所有展开的解。如在机器翻译任务中希望翻译“`<s>你好<e>`”，就算目标语言字典中只有3个词（`<s>`, `<e>`, `hello`），也可能生成无限句话（`hello`循环出现的次数不定），为了找到其中较好的翻译结果，我们可采用柱搜索算法。

doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md

@@ -149,6 +149,8 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
 
 网络的输入`input_dim`表示的是词典的大小，`class_dim`表示类别数。这里，我们使用[`sequence_conv_pool`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/trainer_config_helpers/networks.py) API实现了卷积和池化操作。
 
+<a name="栈值双向LSTM"></a>
+
 ### 栈式双向LSTM
 
 栈式双向神经网络`stacked_lstm_net`的代码片段如下：

doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md

@@ -50,7 +50,7 @@ similarity: -0.0997506977351
 
 ```
 
-以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[应用模型](#应用模型)中详细描述用法。
+以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[模型应用](#模型应用)中详细描述用法。
 
 
 ## 模型概览
@@ -189,6 +189,7 @@ dream that one day <e>
 
 最后，每个输入会按其单词次在字典里的位置，转化成整数的索引序列，作为PaddlePaddle的输入。
 
+<a name="训练模型"></a>
 ## 编程实现
 
 本配置的模型结构如下图所示：
@@ -349,6 +350,7 @@ Step 20: Average Cost 5.766995
 ...
 ```
 
+<a name="模型应用"></a>
 ## 模型应用
 在模型训练后，我们可以用它做一些预测。
 

doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md

@@ -102,7 +102,7 @@ Softmax回归模型采用了最简单的两层神经网络，即只有输入层
 
 池化是非线性下采样的一种形式，主要作用是通过减少网络的参数来减小计算量，并且能够在一定程度上控制过拟合。通常在卷积层的后面会加上一个池化层。池化包括最大池化、平均池化等。其中最大池化是用不重叠的矩形框将输入层分成不同的区域，对于每个矩形框的数取最大值作为输出层，如图6所示。
 
-更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md)教程。
+更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类]( https://github.com/PaddlePaddle/book/tree/develop/03.image_classification )教程。
 
 ### 常见激活函数介绍  
 - sigmoid激活函数： $ f(x) = sigmoid(x) = \frac{1}{1+e^{-x}} $

doc/fluid/new_docs/user_guides/howto/debug/visualdl.md

@@ -149,7 +149,7 @@ python setup.py bdist_wheel
 pip install --upgrade dist/visualdl-*.whl
 ```
 
-如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/how_to_dev_frontend_en.md)
+如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/develop/how_to_dev_frontend_cn.md)
 
 
 ## SDK

doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst

@@ -4,13 +4,12 @@ Paddle 预测 API
 为了更简单方便的预测部署，Fluid 提供了一套高层 API
 用来隐藏底层不同的优化实现。
 
-`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference>`__
+`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api>`_
 包括
 
 -  头文件 ``paddle_inference_api.h`` 定义了所有的接口
 -  库文件\ ``libpaddle_fluid.so`` 或 ``libpaddle_fluid.a``
--  库文件 ``libpaddle_inference_api.so`` 或
-   ``libpaddle_inference_api.a``
+
 
 编译和依赖可以参考 :ref:`install_or_build_cpp_inference_lib` 。
 
@@ -97,8 +96,7 @@ engine
     CHECK(predictor->Run(slots, &outputs));
     // 获取 outputs ...
 
-编译时，联编 ``libpaddle_fluid.a/.so`` 和
-``libpaddle_inference_api.a/.so`` 便可。
+编译时，联编 ``libpaddle_fluid.a/.so`` 便可。
 
 详细代码参考
 ------------

paddle/fluid/API.spec

@@ -312,7 +312,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kw
 paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.polygon_box_transform ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
-paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 200, 1))
+paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
 paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
@@ -376,7 +376,7 @@ paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'l
 paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power'], varargs=None, keywords='kwargs', defaults=(0.0, 0.0, -0.5))
 paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0))
+paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0, False))
 paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho'], varargs=None, keywords='kwargs', defaults=(1e-06, 0.95))
 paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -326,7 +326,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
   ir::Graph &result = *graph;
 
   for (auto &node : nodes) {
-    if (node->NodeType() == ir::Node::Type::kVariable && node->Var()) {
+    if (node->IsVar() && node->Var()) {
       all_vars_.emplace(node->Name(), node->Var());
     }
   }
@@ -583,18 +583,6 @@ void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
   }
 }
 
-bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
-    const std::string &og,
-    std::unordered_set<std::string> *og_has_been_broadcast) const {
-  bool is_pg_once =
-      grad_names_.count(og) != 0 && og_has_been_broadcast->count(og) == 0;
-  if (is_pg_once) {
-    // Insert NCCL AllReduce Op
-    og_has_been_broadcast->insert(og);
-  }
-  return is_pg_once;
-}
-
 int MultiDevSSAGraphBuilder::GetOpDeviceID(const ir::Graph &graph,
                                            ir::Node *node) const {
   if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
@@ -688,20 +676,6 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
   return var;
 }
 
-// Find the first occurence of `prev_op_name` and make current `op` depend
-// on it.
-void MultiDevSSAGraphBuilder::ConnectOp(ir::Graph *result, OpHandleBase *op,
-                                        const std::string &prev_op_name) const {
-  for (auto &prev_op : result->Get<GraphOps>(kGraphOps)) {
-    if (prev_op->Name() == prev_op_name) {
-      auto *dep_var = new DummyVarHandle(result->CreateControlDepVar());
-      prev_op->AddOutput(dep_var);
-      result->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
-      op->AddInput(dep_var);
-    }
-  }
-}
-
 void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
                                                 ir::Node *node) const {
   int op_dev_id = -1;

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -69,9 +69,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   std::vector<std::string> FindDistTrainRecvVars(
       const std::vector<ir::Node *> &nodes) const;
 
-  void ConnectOp(ir::Graph *result, OpHandleBase *op,
-                 const std::string &prev_op_name) const;
-
   void CreateComputationalOps(ir::Graph *result, ir::Node *node,
                               size_t num_places) const;
 
@@ -83,10 +80,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   void CreateComputationalOp(ir::Graph *result, ir::Node *node,
                              int dev_id) const;
 
-  bool IsParameterGradientOnce(
-      const std::string &og,
-      std::unordered_set<std::string> *og_has_been_broadcast) const;
-
   int GetOpDeviceID(const ir::Graph &graph, ir::Node *node) const;
 
   void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;

paddle/fluid/framework/ir/CMakeLists.txt

+set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
+file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt.  DO NOT EDIT!\n\n")
+file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
+function(pass_library TARGET)
+    set(options "")
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DEPS)
+    cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+    cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass)
+    file(APPEND ${pass_file} "USE_PASS(${TARGET});\n")
+    set(PASS_LIBRARY ${TARGET} ${PASS_LIBRARY} PARENT_SCOPE)
+endfunction()
+
 cc_library(node SRCS node.cc DEPS proto_desc)
 cc_library(graph SRCS graph.cc DEPS node)
 cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
 cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
-cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
-cc_library(graph_to_program_pass SRCS graph_to_program_pass.cc DEPS graph pass graph_helper)
 cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
 cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph graph_helper graph_traits)
-cc_library(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(attention_lstm_fuse_pass SRCS attention_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass)
-cc_library(fc_lstm_fuse_pass SRCS fc_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(seq_concat_fc_fuse_pass SRCS seq_concat_fc_fuse_pass.cc DEPS graph graph_pattern_detector)
+
+pass_library(graph_to_program_pass)
+pass_library(graph_viz_pass)
+pass_library(fc_fuse_pass)
+pass_library(attention_lstm_fuse_pass)
+pass_library(infer_clean_graph_pass)
+pass_library(fc_lstm_fuse_pass)
+pass_library(seq_concat_fc_fuse_pass)
+set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
 
 cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
 cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
 cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
 cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
 cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
-cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detector graph pass graph_traits framework_proto)
+cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)

paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc

@@ -99,17 +99,13 @@ void FindWhileOp(Graph* graph) {
   auto* cell_init = graph->RetriveNode(6);
   auto* hidden_init = graph->RetriveNode(8);
 
-#define LINK_TO(node0, node1)      \
-  node0->outputs.push_back(node1); \
-  node1->inputs.push_back(node0);
-
   auto* lstm_op = graph->CreateOpNode(&op_desc);
   PrepareParameters(graph, param);
 
-  LINK_TO(X, lstm_op);
-  LINK_TO(cell_init, lstm_op);
-  LINK_TO(hidden_init, lstm_op);
-  LINK_TO(lstm_op, LSTMOUT);
+  IR_NODE_LINK_TO(X, lstm_op);
+  IR_NODE_LINK_TO(cell_init, lstm_op);
+  IR_NODE_LINK_TO(hidden_init, lstm_op);
+  IR_NODE_LINK_TO(lstm_op, LSTMOUT);
 
   GraphSafeRemoveNodes(graph, marked_nodes);
 }

paddle/fluid/framework/ir/fc_fuse_pass.cc

@@ -21,74 +21,26 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-bool VarOutLinksToOp(Node* node, const std::string& op_type) {
-  for (auto* out : node->outputs) {
-    if (out->IsOp() && out->Op()->Type() == op_type) {
-      return true;
-    }
-  }
-  return false;
-}
-
-void BuildFCPattern(PDPattern* pattern) {
-  // Create Operators
-  auto* mul_op = pattern->NewNode("mul")->assert_is_op("mul");
-  auto* elementwise_add_op =
-      pattern->NewNode("elementwise_add")->assert_is_op("elementwise_add");
-  // Create variables
-  // w
-  auto* mul_weight_var = pattern->NewNode("mul_weight")
-                             ->AsInput()
-                             ->assert_is_op_nth_input("mul", "Y", 0);
-  // x
-  auto* mul_tmp_var = pattern->NewNode("mul_tmp_var")
-                          ->AsInput()
-                          ->assert_is_op_nth_input("mul", "X", 0);
-  // intermediate variable, will be removed in the IR after fuse.
-  auto* mul_out_var = pattern->NewNode("mul_out")
-                          ->AsIntermediate()
-                          ->assert_is_only_output_of_op("mul")
-                          ->assert_is_op_input("elementwise_add");
-  // bias
-  auto* elementwise_add_tmp_var = pattern->NewNode("elementwise_add_tmpvar")
-                                      ->assert_is_op_input("elementwise_add")
-                                      ->AsInput();
-  // output
-  auto* elementwise_add_out_var = pattern->NewNode("elementwise_add_out")
-                                      ->AsOutput()
-                                      ->assert_is_op_output("elementwise_add");
-
-  mul_op->LinksFrom({mul_weight_var, mul_tmp_var}).LinksTo({mul_out_var});
-  elementwise_add_op->LinksFrom({mul_out_var, elementwise_add_tmp_var})
-      .LinksTo({elementwise_add_out_var});
-}
-
-// Replace the node `from` in the links to `to`
-bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
-  for (auto*& n : *links) {
-    if (n == from) {
-      n = to;
-      return true;
-    }
-  }
-  return false;
-}
-
 std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   PADDLE_ENFORCE(graph.get());
-  FusePassBase::Init("fc", graph.get());
+  FusePassBase::Init("fc_fuse", graph.get());
 
   std::unordered_set<Node*> nodes2delete;
 
   GraphPatternDetector gpd;
-  BuildFCPattern(gpd.mutable_pattern());
-
-#define GET_NODE(id)                                              \
-  PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode(#id)), \
-                 "pattern has no Node called %s", #id);           \
-  auto* id = subgraph.at(gpd.pattern().RetrieveNode(#id));        \
-  PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
+  // BuildFCPattern(gpd.mutable_pattern());
+  auto* x = gpd.mutable_pattern()
+                ->NewNode("fc_fuse/x")
+                ->AsInput()
+                ->assert_is_op_input("mul", "X");
+  patterns::FC(gpd.mutable_pattern(), "fc_fuse", x, true /*with bias*/);
+
+#define GET_NODE(id)                                                         \
+  PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode("fc_fuse/" #id)), \
+                 "pattern has no Node called %s", #id);                      \
+  auto* id = subgraph.at(gpd.pattern().RetrieveNode("fc_fuse/" #id));        \
+  PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", "fc_fuse/" #id);
 
   int found_fc_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
@@ -98,43 +50,33 @@ std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
     // scenerio.
     // FC's fusion is simple, just op fuse, no need to process the
     // parameters.
-    GET_NODE(mul_tmp_var);             // x
-    GET_NODE(mul_weight);              // Y
-    GET_NODE(elementwise_add_tmpvar);  // bias
-    GET_NODE(elementwise_add_out);     // Out
-    GET_NODE(mul);                     // MUL op
-    GET_NODE(elementwise_add);         // ELEMENT_ADD op
-    GET_NODE(mul_out);                 // tmp
+    GET_NODE(x);                // x
+    GET_NODE(w);                // Y
+    GET_NODE(fc_bias);          // bias
+    GET_NODE(fc_out);           // Out
+    GET_NODE(mul);              // MUL op
+    GET_NODE(elementwise_add);  // ELEMENT_ADD op
+    GET_NODE(mul_out);          // tmp
 #undef GET_NODE
 
     // Create an FC Node.
     OpDesc desc;
-    std::string fc_x_in = mul_tmp_var->Name();
-    std::string fc_Y_in = mul_weight->Name();
-    std::string fc_bias_in = elementwise_add_tmpvar->Name();
-    std::string fc_out = elementwise_add_out->Name();
+    std::string fc_x_in = x->Name();
+    std::string fc_Y_in = w->Name();
+    std::string fc_bias_in = fc_bias->Name();
+    std::string fc_out_out = fc_out->Name();
     desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
     desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
     desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
-    desc.SetOutput("Out", std::vector<std::string>({fc_out}));
+    desc.SetOutput("Out", std::vector<std::string>({fc_out_out}));
     desc.SetType("fc");
     auto fc_node = g->CreateOpNode(&desc);  // OpDesc will be copied.
-    fc_node->inputs =
-        std::vector<Node*>({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
-    fc_node->outputs.push_back(elementwise_add_out);
-
-    // Update link relatons
-    PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
-    PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
-    PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
-                                elementwise_add, fc_node));
-    PADDLE_ENFORCE(
-        LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
+    GraphSafeRemoveNodes(graph.get(), {mul, elementwise_add, mul_out});
 
-    // Drop old nodes
-    graph->RemoveNode(mul);
-    graph->RemoveNode(elementwise_add);
-    graph->RemoveNode(mul_out);  // tmp variable
+    IR_NODE_LINK_TO(x, fc_node);
+    IR_NODE_LINK_TO(w, fc_node);
+    IR_NODE_LINK_TO(fc_bias, fc_node);
+    IR_NODE_LINK_TO(fc_node, fc_out);
 
     found_fc_count++;
   };

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -11,7 +11,6 @@
 // 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/framework/ir/fc_lstm_fuse_pass.h"
 #include <string>
 #include "paddle/fluid/framework/lod_tensor.h"
@@ -87,15 +86,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
       }
       op_desc.SetInput("Bias", {new_bias_var});
     }
-
 #undef GET_NODE
 
+    // Create temp variables.
+    scope->Var(name_scope + "/BatchedInput.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchCellPreAct.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchedGate.new")
+        ->GetMutable<framework::LoDTensor>();
+
     op_desc.SetInput("H0", {});
     op_desc.SetInput("C0", {});
     op_desc.SetOutput("Hidden", {hidden_n->Name()});
     op_desc.SetOutput("Cell", {cell_n->Name()});
     op_desc.SetOutput("XX", {xx_n->Name()});
-    op_desc.SetOutput("BatchedInput", {"blstm_0.tmp_2"});
+    op_desc.SetOutput("BatchedGate", {name_scope + "/BatchedGate.new"});
+    op_desc.SetOutput("BatchCellPreAct", {name_scope + "/BatchCellPreAct.new"});
+    op_desc.SetOutput("BatchedInput", {name_scope + "/BatchedInput.new"});
     op_desc.SetAttr("is_reverse", lstm_n->Op()->GetAttr("is_reverse"));
     op_desc.SetAttr("use_peepholes", lstm_n->Op()->GetAttr("use_peepholes"));
     // TODO(TJ): get from attr
@@ -121,22 +129,18 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
 #undef TMP_NEW
 #undef TMP_NAME
 
-#define LINK_TO(a, b)      \
-  a->outputs.push_back(b); \
-  b->inputs.push_back(a);
-    LINK_TO(input_n, op);
-    LINK_TO(weight_x_n, op);
-    LINK_TO(weight_h_n, op);
-    LINK_TO(bias_n, op);
-    LINK_TO(op, hidden_n);
-#undef LINK_TO
+    IR_NODE_LINK_TO(input_n, op);
+    IR_NODE_LINK_TO(weight_x_n, op);
+    IR_NODE_LINK_TO(weight_h_n, op);
+    IR_NODE_LINK_TO(bias_n, op);
+    IR_NODE_LINK_TO(op, hidden_n);
     return op;
   };
 
   int fusion_count{0};
 
-  auto fc_no_bias_handler = [&](
-      const GraphPatternDetector::subgraph_t& subgraph, Graph* g) {
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
 #define GET_NODE(name__)                                \
   std::string name__##key = name_scope + "/" + #name__; \
   auto* name__##n = pattern->RetrieveNode(name__##key); \
@@ -157,21 +161,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
 
     if (with_fc_bias) {
       GET_NODE(fc_bias);
+      GET_NODE(elementwise_add);
       lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, fc_bias);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes(
+          {mul_n, lstm_n, elementwise_add_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
     } else {
       lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, -1);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes({mul_n, lstm_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
     }
 #undef GET_NODE
 
-    // Remove unneeded nodes.
-    std::unordered_set<const Node*> marked_nodes({mul_n, lstm_n});
-
-    GraphSafeRemoveNodes(graph, marked_nodes);
-
     ++fusion_count;
   };
 
-  gpd(graph, fc_no_bias_handler);
+  gpd(graph, handler);
 
   return fusion_count;
 }

paddle/fluid/framework/ir/fc_lstm_fuse_pass.h

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#pragma once
+
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -73,7 +73,6 @@ void PDPattern::AddEdge(PDNode* a, PDNode* b) {
 void GraphPatternDetector::operator()(Graph* graph,
                                       GraphPatternDetector::handle_t handler) {
   if (!MarkPDNodesInGraph(*graph)) {
-    LOG(INFO) << "Mark failed";
     return;
   }
 
@@ -111,6 +110,11 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph& graph) {
       return false;
     }
   }
+  for (auto& item : pdnodes2nodes_) {
+    for (auto& n : item.second) {
+      GetMarkedNodes(const_cast<Graph*>(&graph)).insert(n);
+    }
+  }
   VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
 
   return !pdnodes2nodes_.empty();
@@ -278,7 +282,7 @@ void GraphPatternDetector::RemoveOverlappedMatch(
   for (const auto& subgraph : *subgraphs) {
     bool valid = true;
     for (auto& item : subgraph) {
-      if (node_set.count(item.second)) {
+      if (item.first->IsIntermediate() && node_set.count(item.second)) {
         valid = false;
         break;
       }
@@ -334,22 +338,22 @@ PDNode& PDNode::LinksFrom(const std::vector<PDNode*>& others) {
 }
 
 PDNode* PDNode::assert_is_op() {
-  asserts_.emplace_back([this](Node* x) { return x && x->IsOp(); });
+  asserts_.emplace_back([](Node* x) { return x && x->IsOp(); });
   return this;
 }
 PDNode* PDNode::assert_is_op(const std::string& op_type) {
-  asserts_.emplace_back([this, op_type](Node* x) {
+  asserts_.emplace_back([op_type](Node* x) {
     return x && x->IsOp() && x->Op()->Type() == op_type;
   });
   return this;
 }
 PDNode* PDNode::assert_is_var() {
-  asserts_.emplace_back([this](Node* x) { return x && x->IsVar(); });
+  asserts_.emplace_back([](Node* x) { return x && x->IsVar(); });
   return this;
 }
 PDNode* PDNode::assert_var_not_persistable() {
   assert_is_var();
-  asserts_.emplace_back([this](Node* x) { return !x->Var()->Persistable(); });
+  asserts_.emplace_back([](Node* x) { return !x->Var()->Persistable(); });
   return this;
 }
 PDNode* PDNode::assert_is_persistable_var() {
@@ -491,14 +495,16 @@ void GraphSafeRemoveNodes(Graph* graph,
     for (auto it = node->inputs.begin(); it != node->inputs.end();) {
       if (nodes.count(*it)) {
         it = const_cast<Node*>(node)->inputs.erase(it);
-      } else
+      } else {
         it++;
+      }
     }
     for (auto it = node->outputs.begin(); it != node->outputs.end();) {
       if (nodes.count(*it)) {
         it = const_cast<Node*>(node)->outputs.erase(it);
-      } else
+      } else {
         it++;
+      }
     }
   }
 }

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -19,6 +19,9 @@
 #endif
 
 #include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/inference/analysis/dot.h"
@@ -245,6 +248,8 @@ class GraphPatternDetector {
   void UniquePatterns(std::vector<subgraph_t>* subgraphs);
 
   // Remove overlapped match subgraphs, when overlapped, keep the previous one.
+  // The intermediate PDNodes will be removed, so can't shared by multiple
+  // patterns.
   void RemoveOverlappedMatch(std::vector<subgraph_t>* subgraphs);
 
   // Validate whether the intermediate nodes are linked by external nodes.
@@ -295,6 +300,10 @@ PDNode* LSTM(PDPattern* pattern, const std::string& name_scope, PDNode* x);
 
 }  // namespace patterns
 
+#define IR_NODE_LINK_TO(a, b) \
+  a->outputs.push_back(b);    \
+  b->inputs.push_back(a);
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector_tester.cc

@@ -140,8 +140,9 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
         return node->IsOp() && (node->Name() == "op2" || node->Name() == "op3");
       },
       "OP0");
-  auto* any_var = x.mutable_pattern()->NewNode(
-      [](Node* node) { return node->IsVar(); }, "VAR");
+  auto* any_var = x.mutable_pattern()
+                      ->NewNode([](Node* node) { return node->IsVar(); }, "VAR")
+                      ->AsIntermediate();
   auto* any_op1 = x.mutable_pattern()->NewNode(
       [](Node* node) { return node->IsOp(); }, "OP1");
 

paddle/fluid/framework/ir/infer_clean_graph_pass.cc

@@ -13,42 +13,41 @@
 // limitations under the License.
 
 #include <algorithm>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 
 namespace paddle {
 namespace framework {
 namespace ir {
 
-class InferCleanGraphPass : public Pass {
+class InferCleanGraphPass : public FusePassBase {
  public:
   virtual ~InferCleanGraphPass() {}
 
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const {
+    FusePassBase::Init("original_graph", graph.get());
     PADDLE_ENFORCE(graph.get());
 
     auto is_valid_node = [](Node* x) {
       return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
     };
 
-    std::unordered_set<Node*> invalid_nodes;
+    std::unordered_set<const Node*> invalid_nodes;
+    int valid_op = 0;
     for (auto* node : graph->Nodes()) {
       if (is_valid_node(node)) {
         invalid_nodes.insert(node);
+      } else if (node->IsOp()) {
+        // Collect all the operators to help tracking number of operators.
+        ++valid_op;
       }
     }
 
-    // remove nodes from the graph.
-    for (auto* node : invalid_nodes) {
-      graph->RemoveNode(node);
-    }
+    GraphSafeRemoveNodes(graph.get(), invalid_nodes);
 
-    // clean edges.
-    for (auto* node : graph->Nodes()) {
-      CleanEdges(&node->inputs, invalid_nodes);
-      CleanEdges(&node->outputs, invalid_nodes);
-    }
+    AddStatis(valid_op);
 
     return graph;
   }

paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc

@@ -219,16 +219,13 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
     op_desc.SetAttr("fc_activation", act->Op()->Type());
 
     auto* op_node = graph->CreateOpNode(&op_desc);
-// Add links
-#define NODE_LINKS(a, b)   \
-  a->outputs.push_back(b); \
-  b->inputs.push_back(a);
-    NODE_LINKS(fc_w, op_node);
-    NODE_LINKS(fc_bias, op_node);
-    NODE_LINKS(concat_in0, op_node);
-    NODE_LINKS(sequence_expand0_in, op_node);
-    NODE_LINKS(sequence_expand1_in, op_node);
-    NODE_LINKS(op_node, fc_out);
+    // Add links
+    IR_NODE_LINK_TO(fc_w, op_node);
+    IR_NODE_LINK_TO(fc_bias, op_node);
+    IR_NODE_LINK_TO(concat_in0, op_node);
+    IR_NODE_LINK_TO(sequence_expand0_in, op_node);
+    IR_NODE_LINK_TO(sequence_expand1_in, op_node);
+    IR_NODE_LINK_TO(op_node, fc_out);
 
     // Clean nodes.
     std::unordered_set<const Node*> marked_nodes;
@@ -241,7 +238,6 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
     marked_nodes.erase(sequence_expand0_in);
     marked_nodes.erase(sequence_expand1_in);
     marked_nodes.erase(fc_out);
-
     GraphSafeRemoveNodes(graph, marked_nodes);
   });
 

paddle/fluid/inference/CMakeLists.txt

@@ -10,7 +10,7 @@ set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor)
 # TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?
 cc_library(paddle_fluid_api
     SRCS io.cc
-    DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} graph_to_program_pass)
+    DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB}) 
 
 get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
 
@@ -22,7 +22,7 @@ cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
 #endif()
 
 # Create static library
-cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api)
+cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api analysis_predictor)
 if(NOT APPLE)
   # TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
   set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
@@ -32,6 +32,7 @@ endif()
 # Create shared library
 cc_library(paddle_fluid_shared SHARED
     SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
+    ${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
     DEPS ${fluid_modules} paddle_fluid_api)
 
 set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -33,7 +33,7 @@ function (inference_analysis_test TARGET)
         endif()
         cc_test(${TARGET}
                 SRCS "${analysis_test_SRCS}"
-                DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detector pass ${analysis_test_EXTRA_DEPS}
+                DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
                 ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
         set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
     endif(WITH_TESTING)
@@ -56,25 +56,13 @@ if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
 endif()
 
 inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
-    EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
-    analysis_predictor
-		# ir
-		fc_fuse_pass
-		fc_lstm_fuse_pass
-    seq_concat_fc_fuse_pass
-		graph_viz_pass
-		infer_clean_graph_pass
-		graph_pattern_detector
-    infer_clean_graph_pass
-    attention_lstm_fuse_pass
-    paddle_inference_api
-		pass
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
     ARGS --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
-        --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
+         --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
 
 inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
-inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc EXTRA_DEPS paddle_inference_api)
-inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc EXTRA_DEPS paddle_fluid)
+inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
+inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc)
 inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
 inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
 inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
@@ -86,7 +74,7 @@ inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
 set(CHINESE_NER_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner_model.tar.gz")
 set(CHINESE_NER_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner-data.txt.tar.gz")
 set(CHINESE_NER_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/chinese_ner" CACHE PATH "Chinese ner model and data root." FORCE)
-if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_MODEL_URL} "chinese_ner_model.tar.gz")
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_DATA_URL} "chinese_ner-data.txt.tar.gz")
 endif()
@@ -99,7 +87,7 @@ inference_analysis_test(test_analyzer_ner SRCS analyzer_ner_tester.cc
 set(LAC_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/lac_model.tar.gz")
 set(LAC_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/lac_data.txt.tar.gz")
 set(LAC_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/lac" CACHE PATH "LAC model and data root." FORCE)
-if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
     inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_MODEL_URL} "lac_model.tar.gz")
     inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
 endif()
@@ -120,3 +108,15 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
     pass
     ARGS --infer_model=${LAC_INSTALL_DIR}/model
         --infer_data=${LAC_INSTALL_DIR}/data.txt)
+
+
+set(TEXT_CLASSIFICATION_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/text-classification-Senta.tar.gz")
+set(TEXT_CLASSIFICATION_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/text_classification" CACHE PATH "Text Classification model and data root." FORCE)
+
+if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
+  inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
+endif()
+
+inference_analysis_test(test_text_classification SRCS test_text_classification.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
+    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)

paddle/fluid/inference/analysis/analyzer.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include <string>
+#include <vector>
 #include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
 #include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
 #include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
@@ -41,20 +42,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
  public:
   DfgPassManagerImpl() {
     // TODO(Superjomn) set the key with pass reprs.
-    LOG(INFO)
-        << "-----------------------------------------------------------------";
-    if (FLAGS_IA_enable_ir) {
-      AddPass("fluid-to-ir-pass", new FluidToIrPass);
-    } else {
+    if (!FLAGS_IA_enable_ir) {
       AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
+    } else {
+      AddPass("fluid-to-ir-pass", new FluidToIrPass);
     }
     TryAddTensorRtPass();
     AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
     if (!FLAGS_IA_output_storage_path.empty()) {
       AddPass("model-store-pass", new ModelStorePass);
     }
-    LOG(INFO)
-        << "-----------------------------------------------------------------";
   }
 
   std::string repr() const override { return "dfg-pass-manager"; }
@@ -101,19 +98,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
 Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
 
 void Analyzer::Run(Argument* argument) {
+  std::vector<std::string> passes;
+  for (auto& pass : all_ir_passes_) {
+    if (!disabled_ir_passes_.count(pass)) {
+      passes.push_back(pass);
+      passes.push_back("graph_viz_pass");  // add graphviz for debug.
+    }
+  }
+  passes.push_back("graph_viz_pass");
   // Ugly support fluid-to-ir-pass
-  argument->Set(kFluidToIrPassesAttr,
-                new std::vector<std::string>({
-                    // Manual update the passes here.
-                    "graph_viz_pass",                              //
-                    "infer_clean_graph_pass", "graph_viz_pass",    //
-                    "attention_lstm_fuse_pass", "graph_viz_pass",  //
-                    "fc_lstm_fuse_pass", "graph_viz_pass",         //
-                    "mul_lstm_fuse_pass", "graph_viz_pass",        //
-                    "seq_concat_fc_fuse_pass", "graph_viz_pass",   //
-                    "fc_fuse_pass", "graph_viz_pass"               //
-
-                }));
+  argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
 
   for (auto& x : data_) {
     PADDLE_ENFORCE(x->Initialize(argument));
@@ -122,6 +116,11 @@ void Analyzer::Run(Argument* argument) {
   }
 }
 
+Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
+  disabled_ir_passes_.insert(passes.begin(), passes.end());
+  return *this;
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/analyzer.h

@@ -36,16 +36,10 @@ limitations under the License. */
  */
 
 #include <gflags/gflags.h>
+#include "paddle/fluid/inference/analysis/flags.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 
-// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
-// flag if not available.
-DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
-DECLARE_string(IA_graphviz_log_root);
-DECLARE_string(IA_output_storage_path);
-DECLARE_bool(IA_enable_ir);
-
 namespace paddle {
 namespace inference {
 namespace analysis {
@@ -57,7 +51,26 @@ class Analyzer : public OrderedRegistry<PassManager> {
 
   void Run(Argument* argument);
 
+  Analyzer& DisableIrPasses(const std::vector<std::string>& passes);
+
   DISABLE_COPY_AND_ASSIGN(Analyzer);
+
+ private:
+  // All avaiable IR passes.
+  // The bigger fuse comes first, so that the small operators prefer to be
+  // merged in a larger fuse op. The small fusion will not break the pattern of
+  // larger fusion.
+  const std::vector<std::string> all_ir_passes_{{
+      // Manual update the passes here.
+      "infer_clean_graph_pass",    //
+      "attention_lstm_fuse_pass",  //
+      "fc_lstm_fuse_pass",         //
+      "mul_lstm_fuse_pass",        //
+      "seq_concat_fc_fuse_pass",   //
+      "fc_fuse_pass",              //
+  }};
+
+  std::unordered_set<std::string> disabled_ir_passes_;
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -22,6 +22,7 @@
 #include "paddle/fluid/inference/api/analysis_predictor.h"
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
 #include "paddle/fluid/inference/utils/singleton.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -270,17 +271,22 @@ void TestDituRNNPrediction(const std::string &model_path,
                            const std::string &data_path, int batch_size,
                            bool use_analysis, bool activate_ir,
                            int num_times = 1) {
-  NativeConfig config;
+  AnalysisConfig config;
   config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
   config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
   config.use_gpu = false;
   config.device = 0;
   config.specify_input_name = true;
+  config.enable_ir_optim = activate_ir;
+  PADDLE_ENFORCE(config.ir_mode ==
+                 AnalysisConfig::IrPassMode::kExclude);  // default
+  config.ir_passes.clear();  // Do not exclude any pass.
 
   auto base_predictor =
       CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
   auto predictor =
-      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kAnalysis>(config);
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+          config);
   std::vector<PaddleTensor> input_slots;
   DataRecord data(data_path, batch_size);
   // Prepare inputs.
@@ -327,9 +333,20 @@ void TestDituRNNPrediction(const std::string &model_path,
       LOG(INFO) << "fused " << item.first << " " << item.second;
     }
 
-    ASSERT_TRUE(fuse_statis.count("fc"));
-    EXPECT_EQ(fuse_statis.at("fc"), 1);
-    EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 1);
+    int num_ops = 0;
+    for (auto &node :
+         analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
+      if (node->IsFunction()) {
+        ++num_ops;
+      }
+    }
+    LOG(INFO) << "has num ops: " << num_ops;
+
+    ASSERT_TRUE(fuse_statis.count("fc_fuse"));
+    EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
+    EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2);  // bi-directional LSTM
+    EXPECT_EQ(num_ops,
+              13);  // After graph optimization, only 13 operators exists.
   }
 }
 
@@ -357,10 +374,3 @@ TEST(Analyzer, DituRNN_with_analysis_with_IR) {
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle
-
-USE_PASS(fc_fuse_pass);
-USE_PASS(seq_concat_fc_fuse_pass);
-USE_PASS(fc_lstm_fuse_pass);
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);
-USE_PASS(attention_lstm_fuse_pass);

paddle/fluid/inference/analysis/flags.h

+// 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.
+
+#include <gflags/gflags.h>
+
+// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
+// flag if not available.
+DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
+DECLARE_string(IA_graphviz_log_root);
+DECLARE_string(IA_output_storage_path);
+DECLARE_bool(IA_enable_ir);

paddle/fluid/inference/analysis/fluid_to_ir_pass.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/inference/analysis/flags.h"
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 
@@ -85,9 +86,11 @@ class FluidToIrPass final : public DataFlowGraphPass {
           new Scope *(&argument_->Get<Scope>(ir::kParamScopeAttr)));
     }
 
-    const auto &ir_passes_to_apply =
-        argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
-    ir_passes.Apply(ir_passes_to_apply);
+    if (FLAGS_IA_enable_ir) {
+      const auto &ir_passes_to_apply =
+          argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
+      ir_passes.Apply(ir_passes_to_apply);
+    }
 
     PADDLE_ENFORCE(argument_->main_dfg.get());
     argument_->main_dfg->Build(ir_passes.graph());

paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc

@@ -16,6 +16,7 @@
 
 #include <gtest/gtest.h>
 #include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
 
 namespace paddle {
 namespace inference {
@@ -33,10 +34,3 @@ TEST(FluidToIrPass, Test) {
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle
-
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);
-USE_PASS(attention_lstm_fuse_pass);
-USE_PASS(fc_lstm_fuse_pass);
-USE_PASS(seq_concat_fc_fuse_pass);
-USE_PASS(fc_fuse_pass);

paddle/fluid/inference/analysis/test_text_classification.cc

+// 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.
+
+#include <gflags/gflags.h>
+#include <glog/logging.h>  // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/timer.h"
+
+DEFINE_string(infer_model, "", "Directory of the inference model.");
+DEFINE_string(infer_data, "", "Path of the dataset.");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(repeat, 1, "How many times to repeat run.");
+
+namespace paddle {
+
+template <typename T>
+std::string to_string(const std::vector<T> &vec) {
+  std::stringstream ss;
+  for (const auto &c : vec) {
+    ss << c << " ";
+  }
+  return ss.str();
+}
+
+void PrintTime(const double latency, const int bs, const int repeat) {
+  LOG(INFO) << "===========profile result===========";
+  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
+            << ", avg latency: " << latency / repeat << "ms";
+  LOG(INFO) << "=====================================";
+}
+
+void Main(int batch_size) {
+  // Three sequence inputs.
+  std::vector<PaddleTensor> input_slots(1);
+  // one batch starts
+  // data --
+  int64_t data0[] = {0, 1, 2};
+  for (auto &input : input_slots) {
+    input.data.Reset(data0, sizeof(data0));
+    input.shape = std::vector<int>({3, 1});
+    // dtype --
+    input.dtype = PaddleDType::INT64;
+    // LoD --
+    input.lod = std::vector<std::vector<size_t>>({{0, 3}});
+  }
+
+  // shape --
+  // Create Predictor --
+  AnalysisConfig config;
+  config.model_dir = FLAGS_infer_model;
+  config.use_gpu = false;
+  config.enable_ir_optim = true;
+  config.ir_passes.push_back("fc_lstm_fuse_pass");
+  auto predictor =
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+          config);
+
+  inference::Timer timer;
+  double sum = 0;
+  std::vector<PaddleTensor> output_slots;
+  for (int i = 0; i < FLAGS_repeat; i++) {
+    timer.tic();
+    CHECK(predictor->Run(input_slots, &output_slots));
+    sum += timer.toc();
+  }
+  PrintTime(sum, batch_size, FLAGS_repeat);
+
+  // Get output
+  LOG(INFO) << "get outputs " << output_slots.size();
+
+  for (auto &output : output_slots) {
+    LOG(INFO) << "output.shape: " << to_string(output.shape);
+    // no lod ?
+    CHECK_EQ(output.lod.size(), 0UL);
+    LOG(INFO) << "output.dtype: " << output.dtype;
+    std::stringstream ss;
+    for (int i = 0; i < 5; i++) {
+      ss << static_cast<float *>(output.data.data())[i] << " ";
+    }
+    LOG(INFO) << "output.data summary: " << ss.str();
+    // one batch ends
+  }
+}
+
+TEST(text_classification, basic) { Main(FLAGS_batch_size); }
+
+}  // namespace paddle
+
+USE_PASS(fc_fuse_pass);
+USE_PASS(seq_concat_fc_fuse_pass);
+USE_PASS(fc_lstm_fuse_pass);
+USE_PASS(graph_viz_pass);
+USE_PASS(infer_clean_graph_pass);
+USE_PASS(attention_lstm_fuse_pass);

paddle/fluid/inference/api/CMakeLists.txt

@@ -18,10 +18,7 @@ if(APPLE)
 endif(APPLE)
 
 
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
-  graph_viz_pass fc_fuse_pass
-  infer_clean_graph_pass
-  )
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${GLOB_PASS_LIB})
 
 if(WITH_GPU AND TENSORRT_FOUND)
     set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
@@ -47,7 +44,19 @@ function(inference_api_test TARGET_NAME)
 endfunction(inference_api_test)
 
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api
+          analysis
+          ir_pass_manager
+          pass
+          fc_fuse_pass
+          fc_lstm_fuse_pass
+          seq_concat_fc_fuse_pass
+          graph_viz_pass
+          infer_clean_graph_pass
+          graph_pattern_detector
+          infer_clean_graph_pass
+          attention_lstm_fuse_pass
+)
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc

paddle/fluid/inference/api/analysis_predictor.cc

@@ -14,10 +14,13 @@
 
 #include "paddle/fluid/inference/api/analysis_predictor.h"
 #include <memory>
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
 #include "paddle/fluid/inference/utils/singleton.h"
 
 namespace paddle {
@@ -27,6 +30,8 @@ bool AnalysisPredictor::Init(
   VLOG(3) << "Predictor::init()";
   if (config_.use_gpu) {
     place_ = paddle::platform::CUDAPlace(config_.device);
+    LOG(WARNING) << "ir optimize only supports CPU currently";
+    config_.enable_ir_optim = false;
   } else {
     place_ = paddle::platform::CPUPlace();
   }
@@ -72,7 +77,7 @@ bool AnalysisPredictor::Init(
 
 void AnalysisPredictor::OptimizeInferenceProgram() {
   LOG(INFO) << "optimize begin";
-  FLAGS_IA_enable_ir = true;
+  FLAGS_IA_enable_ir = config_.enable_ir_optim;
   FLAGS_IA_enable_tensorrt_subgraph_engine = false;
   FLAGS_IA_output_storage_path = "";  // Don't output the model.
   // Analyze inference_program
@@ -89,24 +94,26 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   }
   argument_.origin_program_desc.reset(
       new ProgramDesc(*inference_program_->Proto()));
-  Analyzer().Run(&argument_);
+  PADDLE_ENFORCE(config_.ir_mode == AnalysisConfig::IrPassMode::kExclude,
+                 "Only kExclude is supported yet.");
+  Analyzer().DisableIrPasses(config_.ir_passes).Run(&argument_);
+
   CHECK(argument_.transformed_program_desc);
   VLOG(5) << "to prepare executor";
-  // LOG(INFO) << "transformed_parogram_desc " <<
-  // argument.transformed_program_desc->DebugString();
   inference_program_.reset(
       new framework::ProgramDesc(*argument_.transformed_program_desc));
-  PADDLE_ENFORCE(argument_.Has(framework::ir::kParamScopeAttr));
-  // Update scope.
-  scope_.reset(
-      argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
-  LOG(INFO) << "optimize end ==";
+  if (argument_.Has(framework::ir::kParamScopeAttr)) {
+    // Update scope.
+    scope_.reset(
+        argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
+  }
+  LOG(INFO) << "== optimize end ==";
 }
 
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
-    NativeConfig, PaddleEngineKind::kAnalysis>(const NativeConfig& config) {
-  VLOG(3) << "create NativePredictor";
+    AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig& config) {
+  VLOG(3) << "create AnalysisConfig";
   if (config.use_gpu) {
     // 1. GPU memeroy
     PADDLE_ENFORCE_GT(
@@ -133,7 +140,3 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
 }
 
 }  // namespace paddle
-
-USE_PASS(fc_fuse_pass);
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);

paddle/fluid/inference/api/analysis_predictor.h

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <string>
+#include <vector>
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
@@ -28,7 +30,7 @@ using framework::proto::ProgramDesc;
  */
 class AnalysisPredictor : public NativePaddlePredictor {
  public:
-  explicit AnalysisPredictor(const NativeConfig& config)
+  explicit AnalysisPredictor(const AnalysisConfig& config)
       : NativePaddlePredictor(config), config_(config) {}
 
   bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
@@ -44,7 +46,7 @@ class AnalysisPredictor : public NativePaddlePredictor {
   Argument& analysis_argument() { return argument_; }
 
  private:
-  NativeConfig config_;
+  AnalysisConfig config_;
   Argument argument_;
 };
 

paddle/fluid/inference/api/api_impl.cc

@@ -176,7 +176,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                                     framework::Scope *scope) {
   VLOG(3) << "Predictor::set_feed";
   if (inputs.size() != feeds_.size()) {
-    LOG(ERROR) << "wrong feed input size.";
+    LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
+               << inputs.size();
     return false;
   }
   for (size_t i = 0; i < inputs.size(); ++i) {

paddle/fluid/inference/api/demo_ci/run.sh

@@ -14,7 +14,7 @@ else
 fi
 
 PREFIX=inference-vis-demos%2F
-URL_ROOT=http://paddlemodels.bj.bcebos.com/${PREFIX}
+URL_ROOT=http://paddlemodels.cdn.bcebos.com/${PREFIX}
 
 # download vis_demo data
 function download() {

paddle/fluid/inference/api/helper.h

@@ -16,6 +16,7 @@
 
 #include <sys/time.h>
 #include <algorithm>
+#include <numeric>
 #include <sstream>
 #include <string>
 #include <vector>

paddle/fluid/inference/api/paddle_inference_api.h

@@ -150,6 +150,21 @@ struct TensorRTConfig : public NativeConfig {
   int workspace_size{1 << 30};
 };
 
+// NOTE WIP, not stable yet.
+struct AnalysisConfig : public NativeConfig {
+  //
+  enum class IrPassMode {
+    kSystem,   // Use system default passes, not customize.
+    kInclude,  // Specify the passes in `ir_passes`.
+    kExclude   // Specify the disabled passes in `ir_passes`.
+  };
+
+  bool enable_ir_optim = true;
+  IrPassMode ir_mode{IrPassMode::kExclude};
+  // attention lstm fuse works only on some specific models, disable as default.
+  std::vector<std::string> ir_passes{"attention_lstm_fuse_pass"};
+};
+
 // A factory to help create different predictors.
 //
 // FOR EXTENSION DEVELOPER:

paddle/fluid/inference/paddle_fluid.map

 {
 	global:
 		*paddle*;
+                *Pass*;
 	local:
 		*;
 };

paddle/fluid/operators/auc_op.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/auc_op.h"
-#include <string>
 
 namespace paddle {
 namespace operators {
@@ -36,15 +35,12 @@ class AucOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(predict_height, label_height,
                       "Out and Label should have same height.");
 
-    int num_thres = ctx->Attrs().Get<int>("num_thresholds");
+    int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
 
     ctx->SetOutputDim("AUC", {1});
-    ctx->SetOutputDim("TPOut", {num_thres});
-    ctx->SetOutputDim("TNOut", {num_thres});
-    ctx->SetOutputDim("FPOut", {num_thres});
-    ctx->SetOutputDim("FNOut", {num_thres});
-
-    ctx->ShareLoD("Predict", /*->*/ "AUC");
+    ctx->SetOutputDim("BatchAUC", {1});
+    ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
+    ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
   }
 
  protected:
@@ -66,25 +62,24 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Label",
              "A 2D int tensor indicating the label of the training data. "
              "shape: [batch_size, 1]");
-    AddInput("TP", "True-Positive value.");
-    AddInput("FP", "False-Positive value.");
-    AddInput("TN", "True-Negative value.");
-    AddInput("FN", "False-Negative value.");
     // TODO(typhoonzero): support weight input
+    AddInput("StatPos", "Statistic value when label = 1");
+    AddInput("StatNeg", "Statistic value when label = 0");
+
     AddOutput("AUC",
               "A scalar representing the "
               "current area-under-the-curve.");
-    AddOutput("TPOut", "True-Positive value.");
-    AddOutput("FPOut", "False-Positive value.");
-    AddOutput("TNOut", "True-Negative value.");
-    AddOutput("FNOut", "False-Negative value.");
+    AddOutput("BatchAUC", "The AUC for current batch");
+    AddOutput("StatPosOut", "Statistic value when label = 1");
+    AddOutput("StatNegOut", "Statistic value when label = 0");
 
     AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
         .SetDefault("ROC");
+
     AddAttr<int>("num_thresholds",
                  "The number of thresholds to use when discretizing the"
                  " roc curve.")
-        .SetDefault(200);
+        .SetDefault((2 << 12) - 1);
 
     AddComment(R"DOC(
 Area Under The Curve (AUC) Operator.

paddle/fluid/operators/auc_op.h

@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
 #include <string>
 #include <vector>
-#include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
@@ -23,106 +23,85 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-
 template <typename DeviceContext, typename T>
 class AucKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* predict = ctx.Input<Tensor>("Predict");
-    auto* label = ctx.Input<Tensor>("Label");
-    auto* auc = ctx.Output<Tensor>("AUC");
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *predict = ctx.Input<Tensor>("Predict");
+    auto *label = ctx.Input<Tensor>("Label");
+
+    std::string curve = ctx.Attr<std::string>("curve");
+    int num_thresholds = ctx.Attr<int>("num_thresholds");
+    int num_pred_buckets = num_thresholds + 1;
+
     // Only use output var for now, make sure it's persistable and
     // not cleaned up for each batch.
-    auto* true_positive = ctx.Output<Tensor>("TPOut");
-    auto* false_positive = ctx.Output<Tensor>("FPOut");
-    auto* true_negative = ctx.Output<Tensor>("TNOut");
-    auto* false_negative = ctx.Output<Tensor>("FNOut");
+    auto *auc = ctx.Output<Tensor>("AUC");
+    auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
+    auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
 
-    auto* auc_data = auc->mutable_data<double>(ctx.GetPlace());
+    auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
+    auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
+    calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
+            auc);
 
-    std::string curve = ctx.Attr<std::string>("curve");
-    int num_thresholds = ctx.Attr<int>("num_thresholds");
-    std::vector<double> thresholds_list;
-    thresholds_list.reserve(num_thresholds);
-    for (int i = 1; i < num_thresholds - 1; i++) {
-      thresholds_list[i] = static_cast<double>(i) / (num_thresholds - 1);
-    }
-    const double kEpsilon = 1e-7;
-    thresholds_list[0] = 0.0f - kEpsilon;
-    thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
+    auto *batch_auc = ctx.Output<Tensor>("BatchAUC");
+    std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
+    std::vector<int64_t> stat_neg_batch(num_pred_buckets, 0);
+    calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
+            num_thresholds, batch_auc);
+  }
 
+ private:
+  inline static double trapezoidArea(double X1, double X2, double Y1,
+                                     double Y2) {
+    return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
+  }
+
+  inline static void calcAuc(const framework::ExecutionContext &ctx,
+                             const framework::Tensor *label,
+                             const framework::Tensor *predict,
+                             int64_t *stat_pos, int64_t *stat_neg,
+                             int num_thresholds,
+                             framework::Tensor *auc_tensor) {
     size_t batch_size = predict->dims()[0];
     size_t inference_width = predict->dims()[1];
+    const T *inference_data = predict->data<T>();
+    const auto *label_data = label->data<int64_t>();
+
+    auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
 
-    const T* inference_data = predict->data<T>();
-    const auto* label_data = label->data<int64_t>();
-
-    auto* tp_data = true_positive->mutable_data<int64_t>(ctx.GetPlace());
-    auto* fn_data = false_negative->mutable_data<int64_t>(ctx.GetPlace());
-    auto* tn_data = true_negative->mutable_data<int64_t>(ctx.GetPlace());
-    auto* fp_data = false_positive->mutable_data<int64_t>(ctx.GetPlace());
-
-    for (int idx_thresh = 0; idx_thresh < num_thresholds; idx_thresh++) {
-      // calculate TP, FN, TN, FP for current thresh
-      int64_t tp = 0, fn = 0, tn = 0, fp = 0;
-      for (size_t i = 0; i < batch_size; i++) {
-        // NOTE: label_data used as bool, labels > 0 will be treated as true.
-        if (label_data[i]) {
-          if (inference_data[i * inference_width + 1] >=
-              (thresholds_list[idx_thresh])) {
-            tp++;
-          } else {
-            fn++;
-          }
-        } else {
-          if (inference_data[i * inference_width + 1] >=
-              (thresholds_list[idx_thresh])) {
-            fp++;
-          } else {
-            tn++;
-          }
-        }
+    for (size_t i = 0; i < batch_size; i++) {
+      uint32_t binIdx = static_cast<uint32_t>(
+          inference_data[i * inference_width + 1] * num_thresholds);
+      if (label_data[i]) {
+        stat_pos[binIdx] += 1.0;
+      } else {
+        stat_neg[binIdx] += 1.0;
       }
-      // store rates
-      tp_data[idx_thresh] += tp;
-      fn_data[idx_thresh] += fn;
-      tn_data[idx_thresh] += tn;
-      fp_data[idx_thresh] += fp;
     }
-    // epsilon to avoid divide by zero.
-    double epsilon = 1e-6;
-    // Riemann sum to caculate auc.
-    Tensor tp_rate, fp_rate, rec_rate;
-    tp_rate.Resize({num_thresholds});
-    fp_rate.Resize({num_thresholds});
-    rec_rate.Resize({num_thresholds});
-    auto* tp_rate_data = tp_rate.mutable_data<double>(ctx.GetPlace());
-    auto* fp_rate_data = fp_rate.mutable_data<double>(ctx.GetPlace());
-    auto* rec_rate_data = rec_rate.mutable_data<double>(ctx.GetPlace());
-    for (int i = 0; i < num_thresholds; i++) {
-      tp_rate_data[i] = (static_cast<double>(tp_data[i]) + epsilon) /
-                        (tp_data[i] + fn_data[i] + epsilon);
-      fp_rate_data[i] =
-          static_cast<double>(fp_data[i]) / (fp_data[i] + tn_data[i] + epsilon);
-      rec_rate_data[i] = (static_cast<double>(tp_data[i]) + epsilon) /
-                         (tp_data[i] + fp_data[i] + epsilon);
+
+    *auc = 0.0f;
+
+    double totPos = 0.0;
+    double totNeg = 0.0;
+    double totPosPrev = 0.0;
+    double totNegPrev = 0.0;
+
+    int idx = num_thresholds;
+
+    while (idx >= 0) {
+      totPosPrev = totPos;
+      totNegPrev = totNeg;
+      totPos += stat_pos[idx];
+      totNeg += stat_neg[idx];
+      *auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
+
+      --idx;
     }
-    *auc_data = 0.0f;
-    if (curve == "ROC") {
-      for (int i = 0; i < num_thresholds - 1; i++) {
-        auto dx = fp_rate_data[i] - fp_rate_data[i + 1];
-        auto y = (tp_rate_data[i] + tp_rate_data[i + 1]) / 2.0f;
-        *auc_data = *auc_data + dx * y;
-      }
-    } else if (curve == "PR") {
-      for (int i = 1; i < num_thresholds; i++) {
-        auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
-        auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
-        *auc_data = *auc_data + dx * y;
-      }
+
+    if (totPos > 0.0 && totNeg > 0.0) {
+      *auc = *auc / totPos / totNeg;
     }
   }
 };

paddle/fluid/operators/detection/bbox_util.h

+/* 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. */
+#pragma once
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace operators {
+
+/*
+ * transform that computes target bounding-box regression deltas
+ * given proposal boxes and ground-truth boxes.
+ */
+template <typename T>
+inline void BoxToDelta(const int box_num, const framework::Tensor& ex_boxes,
+                       const framework::Tensor& gt_boxes, const T* weights,
+                       const bool normalized, framework::Tensor* box_delta) {
+  auto ex_boxes_et = framework::EigenTensor<T, 2>::From(ex_boxes);
+  auto gt_boxes_et = framework::EigenTensor<T, 2>::From(gt_boxes);
+  auto trg = framework::EigenTensor<T, 2>::From(*box_delta);
+  T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
+  for (int64_t i = 0; i < box_num; ++i) {
+    ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + (normalized == false);
+    ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + (normalized == false);
+    ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
+    ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
+
+    gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + (normalized == false);
+    gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + (normalized == false);
+    gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
+    gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
+
+    trg(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w;
+    trg(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h;
+    trg(i, 2) = std::log(gt_w / ex_w);
+    trg(i, 3) = std::log(gt_h / ex_h);
+
+    if (weights) {
+      trg(i, 0) = trg(i, 0) / weights[0];
+      trg(i, 1) = trg(i, 1) / weights[1];
+      trg(i, 2) = trg(i, 2) / weights[2];
+      trg(i, 3) = trg(i, 3) / weights[3];
+    }
+  }
+}
+
+template <typename T>
+void Gather(const T* in, const int in_stride, const int* index, const int num,
+            T* out) {
+  const int stride_bytes = in_stride * sizeof(T);
+  for (int i = 0; i < num; ++i) {
+    int id = index[i];
+    memcpy(out + i * in_stride, in + id * in_stride, stride_bytes);
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detection/generate_proposal_labels_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
 #include "paddle/fluid/operators/gather.h"
 #include "paddle/fluid/operators/math/concat.h"
 #include "paddle/fluid/operators/math/math_function.h"
@@ -133,31 +134,6 @@ void BboxOverlaps(const Tensor& r_boxes, const Tensor& c_boxes,
   }
 }
 
-template <typename T>
-void BoxToDelta(int box_num, const Tensor& ex_boxes, const Tensor& gt_boxes,
-                const std::vector<float>& weights, Tensor* box_delta) {
-  auto ex_boxes_et = framework::EigenTensor<T, 2>::From(ex_boxes);
-  auto gt_boxes_et = framework::EigenTensor<T, 2>::From(gt_boxes);
-  auto box_delta_et = framework::EigenTensor<T, 2>::From(*box_delta);
-  T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
-  for (int64_t i = 0; i < box_num; ++i) {
-    ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + 1;
-    ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + 1;
-    ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
-    ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
-
-    gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + 1;
-    gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + 1;
-    gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
-    gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
-
-    box_delta_et(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0];
-    box_delta_et(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1];
-    box_delta_et(i, 2) = log(gt_w / ex_w) / ex_w / weights[2];
-    box_delta_et(i, 3) = log(gt_h / ex_h) / ex_h / weights[3];
-  }
-}
-
 template <typename T>
 std::vector<std::vector<int>> SampleFgBgGt(
     const platform::CPUDeviceContext& context, Tensor* iou,
@@ -243,12 +219,11 @@ void GatherBoxesLabels(const platform::CPUDeviceContext& context,
                        Tensor* sampled_labels, Tensor* sampled_gts) {
   int fg_num = fg_inds.size();
   int bg_num = bg_inds.size();
-  int gt_num = fg_num + bg_num;
   Tensor fg_inds_t, bg_inds_t, gt_box_inds_t, gt_label_inds_t;
   int* fg_inds_data = fg_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
   int* bg_inds_data = bg_inds_t.mutable_data<int>({bg_num}, context.GetPlace());
   int* gt_box_inds_data =
-      gt_box_inds_t.mutable_data<int>({gt_num}, context.GetPlace());
+      gt_box_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
   int* gt_label_inds_data =
       gt_label_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
   std::copy(fg_inds.begin(), fg_inds.end(), fg_inds_data);
@@ -303,18 +278,20 @@ std::vector<Tensor> SampleRoisForOneImage(
 
   // Gather boxes and labels
   Tensor sampled_boxes, sampled_labels, sampled_gts;
-  int boxes_num = fg_inds.size() + bg_inds.size();
+  int fg_num = fg_inds.size();
+  int bg_num = bg_inds.size();
+  int boxes_num = fg_num + bg_num;
   framework::DDim bbox_dim({boxes_num, kBoxDim});
   sampled_boxes.mutable_data<T>(bbox_dim, context.GetPlace());
   sampled_labels.mutable_data<int>({boxes_num}, context.GetPlace());
-  sampled_gts.mutable_data<T>(bbox_dim, context.GetPlace());
+  sampled_gts.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
   GatherBoxesLabels<T>(context, boxes, *gt_boxes, *gt_classes, fg_inds, bg_inds,
                        gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
 
   // Compute targets
   Tensor bbox_targets_single;
   bbox_targets_single.mutable_data<T>(bbox_dim, context.GetPlace());
-  BoxToDelta<T>(boxes_num, sampled_boxes, sampled_gts, bbox_reg_weights,
+  BoxToDelta<T>(fg_num, sampled_boxes, sampled_gts, nullptr, false,
                 &bbox_targets_single);
 
   // Scale rois
@@ -427,7 +404,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
     auto rpn_rois_lod = rpn_rois->lod().back();
     auto gt_classes_lod = gt_classes->lod().back();
     auto gt_boxes_lod = gt_boxes->lod().back();
-    for (size_t i = 0; i < n; ++i) {
+    for (int i = 0; i < n; ++i) {
       Tensor rpn_rois_slice =
           rpn_rois->Slice(rpn_rois_lod[i], rpn_rois_lod[i + 1]);
       Tensor gt_classes_slice =

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -311,8 +311,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
 
     rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
                               context.GetPlace());
-    rpn_roi_probs->mutable_data<T>({scores->numel() / 4, 1},
-                                   context.GetPlace());
+    rpn_roi_probs->mutable_data<T>({scores->numel(), 1}, context.GetPlace());
 
     Tensor bbox_deltas_swap, scores_swap;
     bbox_deltas_swap.mutable_data<T>({num, h_bbox, w_bbox, c_bbox},
@@ -421,7 +420,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
     CPUGather<T>(ctx, proposals, keep, &bbox_sel);
     CPUGather<T>(ctx, scores_sel, keep, &scores_filter);
     if (nms_thresh <= 0) {
-      return std::make_pair(bbox_sel, scores_sel);
+      return std::make_pair(bbox_sel, scores_filter);
     }
 
     Tensor keep_nms = NMS<T>(ctx, &bbox_sel, &scores_filter, nms_thresh, eta);

paddle/fluid/operators/detection/rpn_target_assign_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <random>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
 namespace paddle {
@@ -46,156 +47,219 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
     auto in_dims = ctx->GetInputDim("DistMat");
     PADDLE_ENFORCE_EQ(in_dims.size(), 2,
                       "The rank of Input(DistMat) must be 2.");
+
+    ctx->SetOutputDim("LocationIndex", {-1});
+    ctx->SetOutputDim("ScoreIndex", {-1});
+    ctx->SetOutputDim("TargetLabel", {-1, 1});
+    ctx->SetOutputDim("TargetBBox", {-1, 4});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::LoDTensor>("DistMat")->type()),
+        platform::CPUPlace());
   }
 };
 
 template <typename T>
 class RpnTargetAssignKernel : public framework::OpKernel<T> {
  public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* anchor_t = context.Input<Tensor>("Anchor");  // (H*W*A) * 4
+    auto* gt_bbox_t = context.Input<Tensor>("GtBox");
+    auto* dist_t = context.Input<LoDTensor>("DistMat");
+
+    auto* loc_index_t = context.Output<Tensor>("LocationIndex");
+    auto* score_index_t = context.Output<Tensor>("ScoreIndex");
+    auto* tgt_bbox_t = context.Output<Tensor>("TargetBBox");
+    auto* tgt_lbl_t = context.Output<Tensor>("TargetLabel");
+
+    auto lod = dist_t->lod().back();
+    int64_t batch_num = static_cast<int64_t>(lod.size() - 1);
+    int64_t anchor_num = dist_t->dims()[1];
+    PADDLE_ENFORCE_EQ(anchor_num, anchor_t->dims()[0]);
+
+    int rpn_batch_size = context.Attr<int>("rpn_batch_size_per_im");
+    float pos_threshold = context.Attr<float>("rpn_positive_overlap");
+    float neg_threshold = context.Attr<float>("rpn_negative_overlap");
+    float fg_fraction = context.Attr<float>("fg_fraction");
+
+    int fg_num_per_batch = static_cast<int>(rpn_batch_size * fg_fraction);
+
+    int64_t max_num = batch_num * anchor_num;
+    auto place = context.GetPlace();
+
+    tgt_bbox_t->mutable_data<T>({max_num, 4}, place);
+    auto* loc_index = loc_index_t->mutable_data<int>({max_num}, place);
+    auto* score_index = score_index_t->mutable_data<int>({max_num}, place);
+
+    Tensor tmp_tgt_lbl;
+    auto* tmp_lbl_data = tmp_tgt_lbl.mutable_data<int64_t>({max_num}, place);
+    auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
+    math::SetConstant<platform::CPUDeviceContext, int64_t> iset;
+    iset(dev_ctx, &tmp_tgt_lbl, static_cast<int64_t>(-1));
+
+    std::random_device rnd;
+    std::minstd_rand engine;
+    int seed =
+        context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+    engine.seed(seed);
+
+    int fg_num = 0;
+    int bg_num = 0;
+    for (int i = 0; i < batch_num; ++i) {
+      Tensor dist = dist_t->Slice(lod[i], lod[i + 1]);
+      Tensor gt_bbox = gt_bbox_t->Slice(lod[i], lod[i + 1]);
+      auto fg_bg_gt = SampleFgBgGt(dev_ctx, dist, pos_threshold, neg_threshold,
+                                   rpn_batch_size, fg_num_per_batch, engine,
+                                   tmp_lbl_data + i * anchor_num);
+
+      int cur_fg_num = fg_bg_gt[0].size();
+      int cur_bg_num = fg_bg_gt[1].size();
+      std::transform(fg_bg_gt[0].begin(), fg_bg_gt[0].end(), loc_index,
+                     [i, anchor_num](int d) { return d + i * anchor_num; });
+      memcpy(score_index, loc_index, cur_fg_num * sizeof(int));
+      std::transform(fg_bg_gt[1].begin(), fg_bg_gt[1].end(),
+                     score_index + cur_fg_num,
+                     [i, anchor_num](int d) { return d + i * anchor_num; });
+
+      // get target bbox deltas
+      if (cur_fg_num) {
+        Tensor fg_gt;
+        T* gt_data = fg_gt.mutable_data<T>({cur_fg_num, 4}, place);
+        Tensor tgt_bbox = tgt_bbox_t->Slice(fg_num, fg_num + cur_fg_num);
+        T* tgt_data = tgt_bbox.data<T>();
+        Gather<T>(anchor_t->data<T>(), 4,
+                  reinterpret_cast<int*>(&fg_bg_gt[0][0]), cur_fg_num,
+                  tgt_data);
+        Gather<T>(gt_bbox.data<T>(), 4, reinterpret_cast<int*>(&fg_bg_gt[2][0]),
+                  cur_fg_num, gt_data);
+        BoxToDelta<T>(cur_fg_num, tgt_bbox, fg_gt, nullptr, false, &tgt_bbox);
+      }
+
+      loc_index += cur_fg_num;
+      score_index += cur_fg_num + cur_bg_num;
+      fg_num += cur_fg_num;
+      bg_num += cur_bg_num;
+    }
+
+    int lbl_num = fg_num + bg_num;
+    PADDLE_ENFORCE_LE(fg_num, max_num);
+    PADDLE_ENFORCE_LE(lbl_num, max_num);
+
+    tgt_bbox_t->Resize({fg_num, 4});
+    loc_index_t->Resize({fg_num});
+    score_index_t->Resize({lbl_num});
+    auto* lbl_data = tgt_lbl_t->mutable_data<int64_t>({lbl_num, 1}, place);
+    Gather<int64_t>(tmp_lbl_data, 1, score_index_t->data<int>(), lbl_num,
+                    lbl_data);
+  }
+
+ private:
   void ScoreAssign(const T* dist_data, const Tensor& anchor_to_gt_max,
                    const int row, const int col, const float pos_threshold,
-                   const float neg_threshold, int64_t* target_label_data,
+                   const float neg_threshold, int64_t* target_label,
                    std::vector<int>* fg_inds, std::vector<int>* bg_inds) const {
-    int fg_offset = fg_inds->size();
-    int bg_offset = bg_inds->size();
+    float epsilon = 0.0001;
     for (int64_t i = 0; i < row; ++i) {
       const T* v = dist_data + i * col;
-      T max_dist = *std::max_element(v, v + col);
+      T max = *std::max_element(v, v + col);
       for (int64_t j = 0; j < col; ++j) {
-        T val = dist_data[i * col + j];
-        if (val == max_dist) target_label_data[j] = 1;
+        if (std::abs(max - v[j]) < epsilon) {
+          target_label[j] = 1;
+        }
       }
     }
 
-    // Pick the fg/bg and count the number
+    // Pick the fg/bg
+    const T* anchor_to_gt_max_data = anchor_to_gt_max.data<T>();
     for (int64_t j = 0; j < col; ++j) {
-      if (anchor_to_gt_max.data<T>()[j] > pos_threshold) {
-        target_label_data[j] = 1;
-      } else if (anchor_to_gt_max.data<T>()[j] < neg_threshold) {
-        target_label_data[j] = 0;
+      if (anchor_to_gt_max_data[j] >= pos_threshold) {
+        target_label[j] = 1;
+      } else if (anchor_to_gt_max_data[j] < neg_threshold) {
+        target_label[j] = 0;
       }
-      if (target_label_data[j] == 1) {
-        fg_inds->push_back(fg_offset + j);
-      } else if (target_label_data[j] == 0) {
-        bg_inds->push_back(bg_offset + j);
+      if (target_label[j] == 1) {
+        fg_inds->push_back(j);
+      } else if (target_label[j] == 0) {
+        bg_inds->push_back(j);
       }
     }
   }
 
-  void ReservoirSampling(const int num, const int offset,
-                         std::minstd_rand engine,
+  void ReservoirSampling(const int num, std::minstd_rand engine,
                          std::vector<int>* inds) const {
     std::uniform_real_distribution<float> uniform(0, 1);
-    const int64_t size = static_cast<int64_t>(inds->size() - offset);
-    if (size > num) {
-      for (int64_t i = num; i < size; ++i) {
+    size_t len = inds->size();
+    if (len > static_cast<size_t>(num)) {
+      for (size_t i = num; i < len; ++i) {
         int rng_ind = std::floor(uniform(engine) * i);
         if (rng_ind < num)
-          std::iter_swap(inds->begin() + rng_ind + offset,
-                         inds->begin() + i + offset);
+          std::iter_swap(inds->begin() + rng_ind, inds->begin() + i);
       }
+      inds->resize(num);
     }
   }
 
-  void RpnTargetAssign(const framework::ExecutionContext& ctx,
-                       const Tensor& dist, const float pos_threshold,
-                       const float neg_threshold, const int rpn_batch_size,
-                       const int fg_num, std::minstd_rand engine,
-                       std::vector<int>* fg_inds, std::vector<int>* bg_inds,
-                       int64_t* target_label_data) const {
+  // std::vector<std::vector<int>> RpnTargetAssign(
+  std::vector<std::vector<int>> SampleFgBgGt(
+      const platform::CPUDeviceContext& ctx, const Tensor& dist,
+      const float pos_threshold, const float neg_threshold,
+      const int rpn_batch_size, const int fg_num, std::minstd_rand engine,
+      int64_t* target_label) const {
     auto* dist_data = dist.data<T>();
-    int64_t row = dist.dims()[0];
-    int64_t col = dist.dims()[1];
-    int fg_offset = fg_inds->size();
-    int bg_offset = bg_inds->size();
+    int row = dist.dims()[0];
+    int col = dist.dims()[1];
+
+    std::vector<int> fg_inds;
+    std::vector<int> bg_inds;
+    std::vector<int> gt_inds;
 
     // Calculate the max IoU between anchors and gt boxes
-    Tensor anchor_to_gt_max;
-    anchor_to_gt_max.mutable_data<T>(
-        framework::make_ddim({static_cast<int64_t>(col), 1}),
-        platform::CPUPlace());
-    auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
-                       .eigen_device();
-    auto x = EigenMatrix<T>::From(dist);
-    auto x_col_max = EigenMatrix<T>::From(anchor_to_gt_max);
-    x_col_max.device(place) =
-        x.maximum(Eigen::DSizes<int, 1>(0))
-            .reshape(Eigen::DSizes<int, 2>(static_cast<int64_t>(col), 1));
+    // Map from anchor to gt box that has highest overlap
+    auto place = ctx.GetPlace();
+    Tensor anchor_to_gt_max, anchor_to_gt_argmax;
+    anchor_to_gt_max.mutable_data<T>({col}, place);
+    int* argmax = anchor_to_gt_argmax.mutable_data<int>({col}, place);
+
+    auto x = framework::EigenMatrix<T>::From(dist);
+    auto x_col_max = framework::EigenVector<T>::Flatten(anchor_to_gt_max);
+    auto x_col_argmax =
+        framework::EigenVector<int>::Flatten(anchor_to_gt_argmax);
+    x_col_max = x.maximum(Eigen::DSizes<int, 1>(0));
+    x_col_argmax = x.argmax(0).template cast<int>();
+
     // Follow the Faster RCNN's implementation
     ScoreAssign(dist_data, anchor_to_gt_max, row, col, pos_threshold,
-                neg_threshold, target_label_data, fg_inds, bg_inds);
+                neg_threshold, target_label, &fg_inds, &bg_inds);
     // Reservoir Sampling
-    ReservoirSampling(fg_num, fg_offset, engine, fg_inds);
-    int bg_num = rpn_batch_size - (fg_inds->size() - fg_offset);
-    ReservoirSampling(bg_num, bg_offset, engine, bg_inds);
-  }
+    ReservoirSampling(fg_num, engine, &fg_inds);
+    int fg_num2 = static_cast<int>(fg_inds.size());
+    int bg_num = rpn_batch_size - fg_num2;
+    ReservoirSampling(bg_num, engine, &bg_inds);
 
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* dist = context.Input<LoDTensor>("DistMat");
-    auto* loc_index = context.Output<Tensor>("LocationIndex");
-    auto* score_index = context.Output<Tensor>("ScoreIndex");
-    auto* tgt_lbl = context.Output<Tensor>("TargetLabel");
-
-    auto col = dist->dims()[1];
-    int64_t n = dist->lod().size() == 0UL
-                    ? 1
-                    : static_cast<int64_t>(dist->lod().back().size() - 1);
-    if (dist->lod().size()) {
-      PADDLE_ENFORCE_EQ(dist->lod().size(), 1UL,
-                        "Only support 1 level of LoD.");
+    gt_inds.reserve(fg_num2);
+    for (int i = 0; i < fg_num2; ++i) {
+      gt_inds.emplace_back(argmax[fg_inds[i]]);
     }
-    int rpn_batch_size = context.Attr<int>("rpn_batch_size_per_im");
-    float pos_threshold = context.Attr<float>("rpn_positive_overlap");
-    float neg_threshold = context.Attr<float>("rpn_negative_overlap");
-    float fg_fraction = context.Attr<float>("fg_fraction");
-
-    int fg_num = static_cast<int>(rpn_batch_size * fg_fraction);
-
-    int64_t* target_label_data =
-        tgt_lbl->mutable_data<int64_t>({n * col, 1}, context.GetPlace());
+    std::vector<std::vector<int>> fg_bg_gt;
+    fg_bg_gt.emplace_back(fg_inds);
+    fg_bg_gt.emplace_back(bg_inds);
+    fg_bg_gt.emplace_back(gt_inds);
 
-    auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
-    math::SetConstant<platform::CPUDeviceContext, int64_t> iset;
-    iset(dev_ctx, tgt_lbl, static_cast<int>(-1));
-
-    std::vector<int> fg_inds;
-    std::vector<int> bg_inds;
-    std::random_device rnd;
-    std::minstd_rand engine;
-    int seed =
-        context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
-    engine.seed(seed);
-
-    if (n == 1) {
-      RpnTargetAssign(context, *dist, pos_threshold, neg_threshold,
-                      rpn_batch_size, fg_num, engine, &fg_inds, &bg_inds,
-                      target_label_data);
-    } else {
-      auto lod = dist->lod().back();
-      for (size_t i = 0; i < lod.size() - 1; ++i) {
-        Tensor one_ins = dist->Slice(lod[i], lod[i + 1]);
-        RpnTargetAssign(context, one_ins, pos_threshold, neg_threshold,
-                        rpn_batch_size, fg_num, engine, &fg_inds, &bg_inds,
-                        target_label_data + i * col);
-      }
-    }
-    int* loc_index_data = loc_index->mutable_data<int>(
-        {static_cast<int>(fg_inds.size())}, context.GetPlace());
-    int* score_index_data = score_index->mutable_data<int>(
-        {static_cast<int>(fg_inds.size() + bg_inds.size())},
-        context.GetPlace());
-    memcpy(loc_index_data, reinterpret_cast<int*>(&fg_inds[0]),
-           fg_inds.size() * sizeof(int));
-    memcpy(score_index_data, reinterpret_cast<int*>(&fg_inds[0]),
-           fg_inds.size() * sizeof(int));
-    memcpy(score_index_data + fg_inds.size(),
-           reinterpret_cast<int*>(&bg_inds[0]), bg_inds.size() * sizeof(int));
+    return fg_bg_gt;
   }
 };
 
 class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
+    AddInput("Anchor",
+             "(Tensor) input anchor is a 2-D Tensor with shape [H*W*A, 4].");
+    AddInput("GtBox", "(LoDTensor) input groud-truth bbox with shape [K, 4].");
     AddInput(
         "DistMat",
         "(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape "
@@ -241,12 +305,15 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
         "ScoreIndex",
         "(Tensor), The indexes of foreground and background anchors in all "
         "RPN anchors(The rest anchors are ignored). The shape of the "
-        "ScoreIndex is [F + B], F and B depend on the value of input "
-        "tensor and attributes.");
-    AddOutput("TargetLabel",
-              "(Tensor<int64_t>), The target labels of each anchor with shape "
-              "[K * M, 1], "
-              "K and M is the same as they are in DistMat.");
+        "ScoreIndex is [F + B], F and B are sampled foreground and backgroud "
+        " number.");
+    AddOutput("TargetBBox",
+              "(Tensor<int64_t>), The target bbox deltas with shape "
+              "[F, 4], F is the sampled foreground number.");
+    AddOutput(
+        "TargetLabel",
+        "(Tensor<int64_t>), The target labels of each anchor with shape "
+        "[F + B, 1], F and B are sampled foreground and backgroud number.");
     AddComment(R"DOC(
 This operator can be, for given the IoU between the ground truth bboxes and the
 anchors, to assign classification and regression targets to each prediction.

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -39,19 +39,6 @@ bool RequestSendHandler::Handle(const std::string& varname,
                                 const std::string& out_var_name) {
   VLOG(4) << "RequestSendHandler:" << varname;
 
-  // Async
-  if (!sync_mode_) {
-    rpc_server_->Profiler().OneStep();
-    try {
-      executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
-                                    scope);
-    } catch (std::exception& e) {
-      LOG(ERROR) << "async: run sub program error " << e.what();
-      return false;
-    }
-    return true;
-  }
-
   // Sync
   if (varname == BATCH_BARRIER_MESSAGE) {
     VLOG(3) << "sync: recv BATCH_BARRIER_MESSAGE";
@@ -60,17 +47,31 @@ bool RequestSendHandler::Handle(const std::string& varname,
     VLOG(3) << "sync: recv complete message";
     rpc_server_->Complete();
   } else {
-    VLOG(3) << "sync: received var_name: " << varname;
-    rpc_server_->WaitCond(kRequestSend);
-    VLOG(3) << "sync: processing received var: " << varname;
-
-    if (invar == nullptr) {
-      LOG(FATAL) << "sync: Can not find server side var: " << varname;
-      return false;
-    }
-    if (invar->IsType<framework::SelectedRows>()) {
-      std::unique_lock<std::mutex> lock(mutex_sparse_vars_);
-      sparse_vars_.push_back(invar);
+    // Async
+    if (!sync_mode_) {
+      VLOG(3) << "async process var: " << varname;
+      rpc_server_->Profiler().OneStep();
+      try {
+        executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
+                                      scope);
+      } catch (std::exception& e) {
+        LOG(ERROR) << "async: run sub program error " << e.what();
+        return false;
+      }
+      return true;
+    } else {  // sync
+      rpc_server_->WaitCond(kRequestSend);
+      VLOG(3) << "sync: processing received var: " << varname;
+
+      if (invar == nullptr) {
+        LOG(FATAL) << "sync: Can not find server side var: " << varname;
+        return false;
+      }
+
+      if (invar->IsType<framework::SelectedRows>()) {
+        std::unique_lock<std::mutex> lock(mutex_sparse_vars_);
+        sparse_vars_.push_back(invar);
+      }
     }
   }
   return true;

paddle/fluid/operators/fusion_lstm_op.cc

@@ -89,12 +89,12 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE_EQ(b_dims[0], 1,
                     "The first dimension of Input(Bias) should be 1.");
 
-  PADDLE_ENFORCE(!ctx->Attrs().Get<bool>("use_peepholes"),
-                 "Do not support peephole yet.");
-  PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
+  auto use_peepholes = ctx->Attrs().Get<bool>("use_peepholes");
+  PADDLE_ENFORCE_EQ(b_dims[1], (use_peepholes ? 7 : 4) * frame_size,
                     "The second dimension of Input(Bias) should be "
-                    "4 * %d if disable peepholes connection",
-                    frame_size);
+                    "7 * %d if enable peepholes connection or"
+                    "4 * %d if disable peepholes",
+                    frame_size, frame_size);
 
   framework::DDim out_dims({x_dims[0], frame_size});
   ctx->SetOutputDim("Hidden", out_dims);
@@ -232,16 +232,17 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     act_cand = act_functor(act_cand_str);                                      \
   }
 
-#define INIT_BASE_INPUT_OUTPUT                        \
-  auto* x = ctx.Input<LoDTensor>("X");                \
-  auto* h0 = ctx.Input<Tensor>("H0");                 \
-  auto* c0 = ctx.Input<Tensor>("C0");                 \
-  auto* wx = ctx.Input<Tensor>("WeightX");            \
-  auto* wh = ctx.Input<Tensor>("WeightH");            \
-  auto* bias = ctx.Input<Tensor>("Bias");             \
-  auto* xx = ctx.Output<LoDTensor>("XX");             \
-  auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
-  auto* cell_out = ctx.Output<LoDTensor>("Cell");     \
+#define INIT_BASE_INPUT_OUTPUT                          \
+  auto* x = ctx.Input<LoDTensor>("X");                  \
+  auto* h0 = ctx.Input<Tensor>("H0");                   \
+  auto* c0 = ctx.Input<Tensor>("C0");                   \
+  auto* wx = ctx.Input<Tensor>("WeightX");              \
+  auto* wh = ctx.Input<Tensor>("WeightH");              \
+  auto* bias = ctx.Input<Tensor>("Bias");               \
+  auto* xx = ctx.Output<LoDTensor>("XX");               \
+  auto* hidden_out = ctx.Output<LoDTensor>("Hidden");   \
+  auto* cell_out = ctx.Output<LoDTensor>("Cell");       \
+  bool use_peepholes = ctx.Attr<bool>("use_peepholes"); \
   bool is_reverse = ctx.Attr<bool>("is_reverse");
 
 #define INIT_BASE_SIZES                  \
@@ -266,12 +267,21 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     const T* x_data = x->data<T>();
     const T* h0_data = h0 ? h0->data<T>() : nullptr;
     const T* c0_data = c0 ? c0->data<T>() : nullptr;
+    const T* bias_data = bias->data<T>();
+    const T* wc_data = bias_data + D4;  // w_ic, w_fc, w_oc
     const T* wx_data = wx->data<T>();
     const T* wh_data = wh->data<T>();
+
     T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
     T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
     T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
 
+    // use local variable
+    framework::DDim check_dims({3, D});
+    Tensor checked_cell;  // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
+    auto checked_cell_data =
+        checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
+
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     math::FCCompute<DeviceContext, T>(blas, total_T, D4, M, x_data, wx_data,
                                       xx_data, bias->data<T>());
@@ -297,46 +307,86 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
       int seq_len = x_lod[0][bid + 1] - x_lod[0][bid];
       const T* prev_c_data = nullptr;
       const T* prev_h_data = nullptr;
+
       int tstart = 0;
       if (h0_data) {
         prev_h_data = h0_data + bid * D;
         prev_c_data = c0_data + bid * D;
       } else {
-        // W_ch, W_ih, W_fh, W_oh
-        act_gate(D3, xx_data + D, xx_data + D);
+        // If step == 0 and there is no initialized hidden state, that is to say
+        // the H0 is zeros. Then W_h * H_t-1 can be skipped
+
+        // ~C_t
         act_cand(D, xx_data, xx_data);
-        // cell out= input*tilde
+        if (use_peepholes) {
+          // I_t, F_t
+          act_gate(D2, xx_data + D, xx_data + D);
+        } else {
+          // I_t, F_t, O_t
+          act_gate(D3, xx_data + D, xx_data + D);
+        }
+        // C_t = I_t * ~C_t
         blas.VMUL(D, xx_data, xx_data + D, cell_out_data);
+
+        if (use_peepholes) {
+          // + W_oc * C_t for peephole connection
+          blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
+          blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
+          // O_t
+          act_gate(D, xx_data + D3, xx_data + D3);
+        }
+
         // hidden out= act_state(cellout) * outgate
         act_cell(D, cell_out_data, xx_data + D2);
+        // H_t = O_t * act_state(C_t)
         blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
 
         // prev
         prev_h_data = hidden_out_data;
         prev_c_data = cell_out_data;
-        tstart = 1;
 
+        tstart = 1;
         move_step();
       }
+
       for (int step = tstart; step < seq_len; ++step) {
+        // + W_h * H_t-1
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
                   prev_h_data, D, wh_data, D4, static_cast<T>(1), xx_data, D4);
 
-        // W_ch, W_ih, W_fh, W_oh
-        act_gate(D3, xx_data + D, xx_data + D);
+        // ~C_t
         act_cand(D, xx_data, xx_data);
 
-        // a = forget * prev_cell
+        if (use_peepholes) {
+          // + W_ic|W_fc * C_t-1 for peephole connection
+          blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
+          blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
+          blas.VADD(D2, xx_data + D, checked_cell_data, xx_data + D);
+          // I_t, F_t
+          act_gate(D2, xx_data + D, xx_data + D);
+        } else {
+          // I_t, F_t, O_t
+          act_gate(D3, xx_data + D, xx_data + D);
+        }
+
+        // F_t * C_t-1
         blas.VMUL(D, xx_data + D2, prev_c_data, xx_data + D2);
-
-        // b = input * tilde
+        // I_t * ~C_t
         blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
-
-        // cell out= a+b
+        // C_t = F_t * C_t-1 + I_t * ~C_t
         blas.VADD(D, xx_data + D, xx_data + D2, cell_out_data);
 
+        if (use_peepholes) {
+          // + W_oc * C_t for peephole connection
+          blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
+          blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
+          // O_t
+          act_gate(D, xx_data + D3, xx_data + D3);
+        }
+
         // hidden out= act_state(cellout) * outgate
         act_cell(D, cell_out_data, xx_data + D2);
+        // H_t = O_t * act_state(C_t)
         blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
 
         // prev
@@ -344,14 +394,14 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         prev_c_data = cell_out_data;
 
         move_step();
-      }
-    }
+      }  // for each step in batch
+    }    // for each batch
   }
 
   void BatchCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = platform::CPUDeviceContext;
     INIT_BASE_INPUT_OUTPUT
-    if (x->lod()[0].size() == 2) {
+    if (x->lod()[0].size() == 2) {  // batch size == 1
       SeqCompute(ctx);
       return;
     }
@@ -367,6 +417,8 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     const T* x_data = x->data<T>();
     const T* wx_data = wx->data<T>();
     const T* wh_data = wh->data<T>();
+    const T* bias_data = bias->data<T>();
+    const T* wc_data = bias_data + D4;  // w_ic, w_fc, w_oc
     auto place = ctx.GetPlace();
     T* xx_data = xx->mutable_data<T>(place);
     T* batched_input_data = batched_input->mutable_data<T>(place);
@@ -375,6 +427,12 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     hidden_out->mutable_data<T>(place);
     cell_out->mutable_data<T>(place);
 
+    // use local variable
+    framework::DDim check_dims({3, D});
+    Tensor checked_cell;  // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
+    auto checked_cell_data =
+        checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
+
     math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
@@ -396,17 +454,27 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     reordered_h0->Resize({max_bs, D});
     reordered_c0->Resize({max_bs, D});
 
+    T* prev_batch_h_data = nullptr;
+    T* prev_batch_c_data = nullptr;
+    T* cur_batch_in_data = batched_input_data;
+    T* cur_batch_h_out_data = batched_h_out_data;
+    T* cur_batch_c_out_data = batched_c_out_data;
+
+    auto move_step = [&](int bs) {
+      cur_batch_in_data += bs * D4;
+      cur_batch_c_out_data += bs * D;
+      cur_batch_h_out_data += bs * D;
+    };
+
     int tstart = 0;
-    T* prev_h_data = nullptr;
-    T* prev_c_data = nullptr;
     if (h0) {
       // reorder h0, c0
       T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
       T* reordered_c0_data = reordered_c0->mutable_data<T>(place);
       const T* h0_data = h0->data<T>();
       const T* c0_data = c0->data<T>();
-      prev_h_data = reordered_h0_data;
-      prev_c_data = reordered_c0_data;
+      prev_batch_h_data = reordered_h0_data;
+      prev_batch_c_data = reordered_c0_data;
       size_t sz = sizeof(T) * D;
       for (int i = 0; i < max_bs; ++i) {
         std::memcpy(reordered_h0_data, h0_data + seq_order[i] * D, sz);
@@ -415,71 +483,122 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         reordered_c0_data += D;
       }
     } else {
-      // compute without h0, c0
-      T* cur_in_data = batched_input_data;
-      T* cur_h_out_data = batched_h_out_data;
-      T* cur_c_out_data = batched_c_out_data;
-      // W_ch, W_ih, W_fh, W_oh
-      for (int i = 0; i < max_bs; ++i) {
-        act_gate(D3, cur_in_data + D, cur_in_data + D);
+      // Compute with no H0/C0
+      T* cur_in_data = cur_batch_in_data;
+      T* cur_c_out_data = cur_batch_c_out_data;
+      T* cur_h_out_data = cur_batch_h_out_data;
+
+      // If step == 0 and there is no initialized hidden state, that is to say
+      // the H0 is zeros. Then W_h * H_t-1 can be skiped
+
+      for (int i = 0; i < max_bs; ++i) {  // iterate each data in 1st batch
+        // ~C_t
         act_cand(D, cur_in_data, cur_in_data);
-        // cell out= input*tilde
+
+        if (use_peepholes) {
+          // I_t, F_t
+          act_gate(D2, cur_in_data + D, cur_in_data + D);
+        } else {
+          // I_t, F_t, O_t
+          act_gate(D3, cur_in_data + D, cur_in_data + D);
+        }
+
+        // C_t = I_t * ~C_t
         blas.VMUL(D, cur_in_data, cur_in_data + D, cur_c_out_data);
+
+        if (use_peepholes) {
+          // + W_oc * C_t for peephole connection
+          blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
+          blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
+                    cur_in_data + D3);
+          // O_t
+          act_gate(D, cur_in_data + D3, cur_in_data + D3);
+        }
+
         // hidden out= act_state(cellout) * outgate
         act_cell(D, cur_c_out_data, cur_in_data + D2);
+        // H_t = O_t * act_state(C_t)
         blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
 
-        // add offset
+        // move to next data in the same batch
         cur_in_data += D4;
         cur_c_out_data += D;
         cur_h_out_data += D;
       }
+
+      // move to data for next timestep
+      prev_batch_h_data = cur_batch_h_out_data;
+      prev_batch_c_data = cur_batch_c_out_data;
+      move_step(max_bs);
       tstart = 1;
-      prev_h_data = batched_h_out_data;
-      prev_c_data = batched_c_out_data;
     }
-    // Then start from next
+
     const auto& batch_starts = batched_lod[0];
     const int max_seq_len = batch_starts.size() - 1;
-    const int offset = tstart * max_bs * D;
-    batched_input_data = batched_input_data + offset * 4;
-    batched_h_out_data = batched_h_out_data + offset;
-    batched_c_out_data = batched_c_out_data + offset;
     for (int step = tstart; step < max_seq_len; ++step) {
       const int cur_bs = batch_starts[step + 1] - batch_starts[step];
+      // + W_h * H_t-1
       blas.GEMM(CblasNoTrans, CblasNoTrans, cur_bs, D4, D, static_cast<T>(1),
-                prev_h_data, D, wh_data, D4, static_cast<T>(1),
-                batched_input_data, D4);
-
-      T* cur_in_data = batched_input_data;
-      T* cur_prev_c_data = prev_c_data;
-      T* cur_c_out_data = batched_c_out_data;
-      T* cur_h_out_data = batched_h_out_data;
-      for (int i = 0; i < cur_bs; ++i) {
-        // W_ch, W_ih, W_fh, W_oh
-        act_gate(D3, cur_in_data + D, cur_in_data + D);
+                prev_batch_h_data, D, wh_data, D4, static_cast<T>(1),
+                cur_batch_in_data, D4);
+
+      T* cur_in_data = cur_batch_in_data;
+      T* cur_c_out_data = cur_batch_c_out_data;
+      T* cur_h_out_data = cur_batch_h_out_data;
+      T* prev_c_data = prev_batch_c_data;  // NULL if no C0 in step0
+      T* prev_h_data = prev_batch_h_data;  // NULL if no H0 in step0
+      auto next_data_in_batch = [&]() {
+        cur_in_data += D4;
+        cur_c_out_data += D;
+        cur_h_out_data += D;
+        prev_c_data = prev_c_data ? prev_c_data + D : nullptr;
+        prev_h_data = prev_h_data ? prev_h_data + D : nullptr;
+      };
+
+      for (int i = 0; i < cur_bs; ++i) {  // iterate each data in same batch
+        // ~C_t
         act_cand(D, cur_in_data, cur_in_data);
-        // a = forget * prev_cell
-        blas.VMUL(D, cur_in_data + D2, cur_prev_c_data, cur_in_data + D2);
-        // b = input * tilde
+
+        if (use_peepholes) {
+          // + W_ic|W_fc * C_t-1 for peephole connection
+          blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
+          blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
+          blas.VADD(D2, cur_in_data + D, checked_cell_data, cur_in_data + D);
+          // I_t, F_t
+          act_gate(D2, cur_in_data + D, cur_in_data + D);
+        } else {
+          // I_t, F_t, O_t
+          act_gate(D3, cur_in_data + D, cur_in_data + D);
+        }
+
+        // F_t * C_t-1
+        blas.VMUL(D, cur_in_data + D2, prev_c_data, cur_in_data + D2);
+        // I_t * ~C_t
         blas.VMUL(D, cur_in_data, cur_in_data + D, cur_in_data + D);
-        // cell out= a+b
+        // C_t = F_t * C_t-1 + I_t * ~C_t
         blas.VADD(D, cur_in_data + D, cur_in_data + D2, cur_c_out_data);
+
+        if (use_peepholes) {
+          // + W_oc * C_t for peephole connection
+          blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
+          blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
+                    cur_in_data + D3);
+          // O_t
+          act_gate(D, cur_in_data + D3, cur_in_data + D3);
+        }
+
         // hidden out= act_state(cellout) * outgate
         act_cell(D, cur_c_out_data, cur_in_data + D2);
+        // H_t = O_t * act_state(C_t)
         blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
 
-        cur_in_data += D4;
-        cur_prev_c_data += D;
-        cur_c_out_data += D;
-        cur_h_out_data += D;
+        // move to next data in same batch
+        next_data_in_batch();
       }
-
-      prev_c_data = batched_c_out_data;
-      prev_h_data = batched_h_out_data;
-      batched_c_out_data = cur_c_out_data;
-      batched_h_out_data = cur_h_out_data;
-      batched_input_data = cur_in_data;
+      // move to data for next timestep
+      prev_batch_h_data = cur_batch_h_out_data;
+      prev_batch_c_data = cur_batch_c_out_data;
+      move_step(cur_bs);
     }
 
     math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;

paddle/fluid/operators/gru_unit_op.h

@@ -92,12 +92,12 @@ class GRUUnitKernel : public framework::OpKernel<T> {
               gate_data, frame_size * 3);
 
     // calculate activited gate
-    Eigen::array<int, 2> extents = {batch_size, frame_size};
-    Eigen::array<int, 2> u_offsets = {0, 0};
+    Eigen::array<int, 2> extents{{batch_size, frame_size}};
+    Eigen::array<int, 2> u_offsets{{0, 0}};
     ActCompute(context.Attr<int>("gate_activation"), place,
                g.slice(u_offsets, extents), g.slice(u_offsets, extents));
     auto u = g.slice(u_offsets, extents);  // update gate
-    Eigen::array<int, 2> r_offsets = {0, frame_size};
+    Eigen::array<int, 2> r_offsets{{0, frame_size}};
     ActCompute(context.Attr<int>("gate_activation"), place,
                g.slice(r_offsets, extents), g.slice(r_offsets, extents));
     auto r = g.slice(r_offsets, extents);  // reset gate
@@ -107,7 +107,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
               weight_data + frame_size * frame_size * 2, frame_size, 1,
               gate_data + frame_size * 2, frame_size * 3);
 
-    Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
+    Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
     ActCompute(context.Attr<int>("activation"), place,
                g.slice(c_offsets, extents), g.slice(c_offsets, extents));
     auto c = g.slice(c_offsets, extents);  // output candidate
@@ -171,12 +171,12 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
     int batch_size = input->dims()[0];
     int frame_size = hidden_prev->dims()[1];
 
-    Eigen::array<int, 2> extents = {batch_size, frame_size};
-    Eigen::array<int, 2> u_offsets = {0, 0};
+    Eigen::array<int, 2> extents{{batch_size, frame_size}};
+    Eigen::array<int, 2> u_offsets{{0, 0}};
     auto u = g.slice(u_offsets, extents);  // update gate
-    Eigen::array<int, 2> r_offsets = {0, frame_size};
+    Eigen::array<int, 2> r_offsets{{0, frame_size}};
     auto r = g.slice(r_offsets, extents);  // reset gate
-    Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
+    Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
     auto c = g.slice(c_offsets, extents);  // output candidate
 
     // backward for unactivated update gate

paddle/fluid/operators/lookup_table_op.h

@@ -57,7 +57,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
           memset(output + i * row_width, 0, row_width * sizeof(T));
         } else {
           PADDLE_ENFORCE_LT(ids[i], row_number);
-          PADDLE_ENFORCE_GE(ids[i], 0);
+          PADDLE_ENFORCE_GE(ids[i], 0, "ids %d", i);
           memcpy(output + i * row_width, table + ids[i] * row_width,
                  row_width * sizeof(T));
         }

paddle/fluid/operators/rmsprop_op.cc

@@ -36,9 +36,13 @@ class RmspropOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(param_out) of RmspropOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
-                   "Output(Momentum_out) of RmspropOp should not be null.");
+                   "Output(MomentOut) of RmspropOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("MeanSquareOut"),
                    "Output(MeanSquareOut) of RmspropOp should not be null.");
+    if (ctx->Attrs().Get<bool>("centered")) {
+      PADDLE_ENFORCE(ctx->HasOutput("MeanGradOut"),
+                     "Output(MeanGradOut) of RmspropOp should not be null.");
+    }
 
     auto param_dim = ctx->GetInputDim("Param");
     PADDLE_ENFORCE_EQ(
@@ -58,6 +62,9 @@ class RmspropOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("ParamOut", param_dim);
     ctx->SetOutputDim("MomentOut", param_dim);
     ctx->SetOutputDim("MeanSquareOut", param_dim);
+    if (ctx->Attrs().Get<bool>("centered")) {
+      ctx->SetOutputDim("MeanGradOut", param_dim);
+    }
   }
 };
 
@@ -70,6 +77,10 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("MeanSquare",
              "(Tensor, default Tensor<float>)"
              " The mean square value that gets updated.");
+    AddInput("MeanGrad",
+             "(Tensor, default Tensor<float>)"
+             " The moving average of gradient")
+        .AsDispensable();
     AddInput("LearningRate",
              "(Tensor, default Tensor<float>) "
              "The learning rate should be a tensor of size 1.");
@@ -82,6 +93,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("ParamOut", "(Tensor) Output updated parameter value.");
     AddOutput("MomentOut", "(Tensor) Output updated moment.");
     AddOutput("MeanSquareOut", "(Tensor) Output Mean squared updated value.");
+    AddOutput("MeanGradOut",
+              "(Tensor) Output moving average of gradient updated value.");
 
     AddAttr<float>("epsilon",
                    "(float, default 1e-10) Constant "
@@ -93,6 +106,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(0.9f);
     AddAttr<float>("momentum", "(float, default 0.0) Constant value.")
         .SetDefault(0.0f);
+    AddAttr<bool>("centered", "(bool, default false) use centered rmsprop.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Rmsprop Optimizer. 
 
@@ -103,6 +118,14 @@ MomentOut = momentum * Moment +
 ParamOut = Param -  MomentOut
 $$
 
+if centered is true:
+
+mean_grad = decay * mean_square{t-1} + (1-decay) * gradient
+mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
+mom = momentum * mom{t-1} + learning_rate * g_t /
+    sqrt(mean_square - mean_grad**2 + epsilon)
+param -= mom
+
 The original slides that proposed Rmsprop: Slide 29 of
 http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
 

paddle/fluid/operators/rmsprop_op.h

@@ -41,6 +41,7 @@ class RmspropOpKernel : public framework::OpKernel<T> {
     float epsilon = ctx.Attr<float>("epsilon");
     float rho = ctx.Attr<float>("decay");
     float momentum = ctx.Attr<float>("momentum");
+    bool centered = ctx.Attr<bool>("centered");
 
     auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
     auto ms = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanSquare"));
@@ -53,12 +54,24 @@ class RmspropOpKernel : public framework::OpKernel<T> {
     auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
-    Eigen::DSizes<int, 1> grad_dsize(grad->numel());
+    Eigen::DSizes<int, 1> grad_dsize(static_cast<int>(grad->numel()));
 
     ms_out.device(place) = rho * ms + (1 - rho) * g * g;
-    mom_out.device(place) =
-        momentum * mom +
-        lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
+    if (centered) {
+      auto mg = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanGrad"));
+      auto* mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
+      mean_grad_out->mutable_data<T>(ctx.GetPlace());
+      auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
+
+      mg_out.device(place) = rho * mg + (1 - rho) * g;
+      mom_out.device(place) = momentum * mom +
+                              lr.broadcast(grad_dsize) * g /
+                                  (ms_out - mg_out.square() + epsilon).sqrt();
+    } else {
+      mom_out.device(place) =
+          momentum * mom +
+          lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
+    }
     p_out.device(place) = p - mom_out;
   }
 };

paddle/fluid/operators/roi_pool_op.cu

@@ -31,7 +31,7 @@ static inline int NumBlocks(const int N) {
 
 template <typename T>
 __global__ void GPUROIPoolForward(
-    const int nthreads, const T* input_data, const int64_t* input_rois,
+    const int nthreads, const T* input_data, const T* input_rois,
     const float spatial_scale, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     int* roi_batch_id_data, T* output_data, int64_t* argmax_data) {
@@ -43,7 +43,7 @@ __global__ void GPUROIPoolForward(
     int c = (i / pooled_width / pooled_height) % channels;
     int n = i / pooled_width / pooled_height / channels;
 
-    const int64_t* offset_input_rois = input_rois + n * kROISize;
+    const T* offset_input_rois = input_rois + n * kROISize;
     int roi_batch_ind = roi_batch_id_data[n];
     int roi_start_w = round(offset_input_rois[0] * spatial_scale);
     int roi_start_h = round(offset_input_rois[1] * spatial_scale);
@@ -93,7 +93,7 @@ __global__ void GPUROIPoolForward(
 
 template <typename T>
 __global__ void GPUROIPoolBackward(
-    const int nthreads, const int64_t* input_rois, const T* output_grad,
+    const int nthreads, const T* input_rois, const T* output_grad,
     const int64_t* argmax_data, const int num_rois, const float spatial_scale,
     const int channels, const int height, const int width,
     const int pooled_height, const int pooled_width, int* roi_batch_id_data,
@@ -174,8 +174,8 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
 
     GPUROIPoolForward<
         T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
-        output_size, in->data<T>(), rois->data<int64_t>(), spatial_scale,
-        channels, height, width, pooled_height, pooled_width,
+        output_size, in->data<T>(), rois->data<T>(), spatial_scale, channels,
+        height, width, pooled_height, pooled_width,
         roi_batch_id_list_gpu.data<int>(), out->mutable_data<T>(ctx.GetPlace()),
         argmax->mutable_data<int64_t>(ctx.GetPlace()));
   }
@@ -228,7 +228,7 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
       if (output_grad_size > 0) {
         GPUROIPoolBackward<
             T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
-            output_grad_size, rois->data<int64_t>(), out_grad->data<T>(),
+            output_grad_size, rois->data<T>(), out_grad->data<T>(),
             argmax->data<int64_t>(), rois_num, spatial_scale, channels, height,
             width, pooled_height, pooled_width,
             roi_batch_id_list_gpu.data<int>(),

paddle/fluid/operators/roi_pool_op.h

@@ -72,7 +72,7 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
     T* output_data = out->mutable_data<T>(ctx.GetPlace());
     int64_t* argmax_data = argmax->mutable_data<int64_t>(ctx.GetPlace());
 
-    const int64_t* rois_data = rois->data<int64_t>();
+    const T* rois_data = rois->data<T>();
     for (int n = 0; n < rois_num; ++n) {
       int roi_batch_id = roi_batch_id_data[n];
       int roi_start_w = round(rois_data[0] * spatial_scale);
@@ -171,7 +171,7 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
         }
       }
 
-      const int64_t* rois_data = rois->data<int64_t>();
+      const T* rois_data = rois->data<T>();
       const T* out_grad_data = out_grad->data<T>();
       const int64_t* argmax_data = argmax->data<int64_t>();
       T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());

python/paddle/fluid/layers/detection.py

@@ -145,26 +145,23 @@ def rpn_target_assign(loc,
     """
 
     helper = LayerHelper('rpn_target_assign', **locals())
-    # 1. Compute the regression target bboxes
-    target_bbox = box_coder(
-        prior_box=anchor_box,
-        prior_box_var=anchor_var,
-        target_box=gt_box,
-        code_type='encode_center_size',
-        box_normalized=False)
-    # 2. Compute overlaps between the prior boxes and the gt boxes overlaps
+    # Compute overlaps between the prior boxes and the gt boxes overlaps
     iou = iou_similarity(x=gt_box, y=anchor_box)
-    # 3. Assign target label to anchors
-    loc_index = helper.create_tmp_variable(dtype=anchor_box.dtype)
-    score_index = helper.create_tmp_variable(dtype=anchor_box.dtype)
-    target_label = helper.create_tmp_variable(dtype=anchor_box.dtype)
+    # Assign target label to anchors
+    loc_index = helper.create_tmp_variable(dtype='int32')
+    score_index = helper.create_tmp_variable(dtype='int32')
+    target_label = helper.create_tmp_variable(dtype='int64')
+    target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
     helper.append_op(
         type="rpn_target_assign",
-        inputs={'DistMat': iou},
+        inputs={'Anchor': anchor_box,
+                'GtBox': gt_box,
+                'DistMat': iou},
         outputs={
             'LocationIndex': loc_index,
             'ScoreIndex': score_index,
-            'TargetLabel': target_label
+            'TargetLabel': target_label,
+            'TargetBBox': target_bbox,
         },
         attrs={
             'rpn_batch_size_per_im': rpn_batch_size_per_im,
@@ -173,16 +170,16 @@ def rpn_target_assign(loc,
             'fg_fraction': fg_fraction
         })
 
-    # 4. Reshape and gather the target entry
-    scores = nn.reshape(x=scores, shape=(-1, 2))
-    loc = nn.reshape(x=loc, shape=(-1, 4))
-    target_label = nn.reshape(x=target_label, shape=(-1, 1))
-    target_bbox = nn.reshape(x=target_bbox, shape=(-1, 4))
+    loc_index.stop_gradient = True
+    score_index.stop_gradient = True
+    target_label.stop_gradient = True
+    target_bbox.stop_gradient = True
 
+    scores = nn.reshape(x=scores, shape=(-1, 1))
+    loc = nn.reshape(x=loc, shape=(-1, 4))
     predicted_scores = nn.gather(scores, score_index)
     predicted_location = nn.gather(loc, loc_index)
-    target_label = nn.gather(target_label, score_index)
-    target_bbox = nn.gather(target_bbox, loc_index)
+
     return predicted_scores, predicted_location, target_label, target_bbox
 
 

python/paddle/fluid/layers/metric_op.py

@@ -78,7 +78,7 @@ def accuracy(input, label, k=1, correct=None, total=None):
     return acc_out
 
 
-def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
+def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
     """
     **Area Under the Curve (AUC) Layer**
 
@@ -118,16 +118,14 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
     """
     helper = LayerHelper("auc", **locals())
     auc_out = helper.create_tmp_variable(dtype="float64")
+    batch_auc_out = helper.create_tmp_variable(dtype="float64")
     # make tp, tn, fp, fn persistable, so that can accumulate all batches.
-    tp = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds])
-    tn = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds])
-    fp = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds])
-    fn = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds])
-    for var in [tp, tn, fp, fn]:
+    stat_pos = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds + 1])
+    stat_neg = helper.create_global_variable(
+        persistable=True, dtype='int64', shape=[num_thresholds + 1])
+
+    for var in [stat_pos, stat_neg]:
         helper.set_variable_initializer(
             var, Constant(
                 value=0.0, force_cpu=True))
@@ -137,18 +135,15 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
         inputs={
             "Predict": [input],
             "Label": [label],
-            "TP": [tp],
-            "TN": [tn],
-            "FP": [fp],
-            "FN": [fn]
+            "StatPos": [stat_pos],
+            "StatNeg": [stat_neg]
         },
         attrs={"curve": curve,
                "num_thresholds": num_thresholds},
         outputs={
             "AUC": [auc_out],
-            "TPOut": [tp],
-            "TNOut": [tn],
-            "FPOut": [fp],
-            "FNOut": [fn]
+            "BatchAUC": [batch_auc_out],
+            "StatPosOut": [stat_pos],
+            "StatNegOut": [stat_neg]
         })
-    return auc_out, [tp, tn, fp, fn]
+    return auc_out, batch_auc_out, [stat_pos, stat_neg]

python/paddle/fluid/layers/nn.py

@@ -3546,11 +3546,6 @@ def topk(input, k, name=None):
 
             top5_values, top5_indices = layers.topk(input, k=5)
     """
-    shape = input.shape
-    if k < 1 or k >= shape[-1]:
-        raise ValueError("k must be greater than 0 and less than %d." %
-                         (shape[-1]))
-
     helper = LayerHelper("top_k", **locals())
     values = helper.create_tmp_variable(dtype=input.dtype)
     indices = helper.create_tmp_variable(dtype="int64")

python/paddle/fluid/metrics.py

@@ -558,8 +558,6 @@ class Auc(MetricBase):
         name: metric name
         curve: Specifies the name of the curve to be computed, 'ROC' [default] or
           'PR' for the Precision-Recall-curve.
-        num_thresholds: The number of thresholds to use when discretizing the roc
-            curve.
 
     "NOTE: only implement the ROC curve type via Python now."
 
@@ -574,15 +572,14 @@ class Auc(MetricBase):
                 numpy_auc = metric.eval()
     """
 
-    def __init__(self, name, curve='ROC', num_thresholds=200):
+    def __init__(self, name, curve='ROC', num_thresholds=4095):
         super(Auc, self).__init__(name=name)
         self._curve = curve
         self._num_thresholds = num_thresholds
-        self._epsilon = 1e-6
-        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, ))
+
+        _num_pred_buckets = num_thresholds + 1
+        self._stat_pos = [0] * _num_pred_buckets
+        self._stat_neg = [0] * _num_pred_buckets
 
     def update(self, preds, labels):
         if not _is_numpy_(labels):
@@ -590,41 +587,32 @@ class Auc(MetricBase):
         if not _is_numpy_(preds):
             raise ValueError("The 'predictions' must be a numpy ndarray.")
 
-        kepsilon = 1e-7  # to account for floating point imprecisions
-        thresholds = [(i + 1) * 1.0 / (self._num_thresholds - 1)
-                      for i in range(self._num_thresholds - 2)]
-        thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
-
-        # calculate TP, FN, TN, FP count
-        for idx_thresh, thresh in enumerate(thresholds):
-            tp, fn, tn, fp = 0, 0, 0, 0
-            for i, lbl in enumerate(labels):
-                if lbl:
-                    if preds[i, 1] >= thresh:
-                        tp += 1
-                    else:
-                        fn += 1
-                else:
-                    if preds[i, 1] >= thresh:
-                        fp += 1
-                    else:
-                        tn += 1
-            self.tp_list[idx_thresh] += tp
-            self.fn_list[idx_thresh] += fn
-            self.tn_list[idx_thresh] += tn
-            self.fp_list[idx_thresh] += fp
+        for i, lbl in enumerate(labels):
+            value = preds[i, 1]
+            bin_idx = int(value * self._num_thresholds)
+            assert bin_idx <= self._num_thresholds
+            if lbl:
+                self._stat_pos[bin_idx] += 1.0
+            else:
+                self._stat_neg[bin_idx] += 1.0
+
+    @staticmethod
+    def trapezoid_area(x1, x2, y1, y2):
+        return abs(x1 - x2) * (y1 + y2) / 2.0
 
     def eval(self):
-        epsilon = self._epsilon
-        num_thresholds = self._num_thresholds
-        tpr = (self.tp_list.astype("float32") + epsilon) / (
-            self.tp_list + self.fn_list + epsilon)
-        fpr = self.fp_list.astype("float32") / (
-            self.fp_list + self.tn_list + epsilon)
-        rec = (self.tp_list.astype("float32") + epsilon) / (
-            self.tp_list + self.fp_list + epsilon)
-
-        x = fpr[:num_thresholds - 1] - fpr[1:]
-        y = (tpr[:num_thresholds - 1] + tpr[1:]) / 2.0
-        auc_value = np.sum(x * y)
-        return auc_value
+        tot_pos = 0.0
+        tot_neg = 0.0
+        auc = 0.0
+
+        idx = self._num_thresholds
+        while idx >= 0:
+            tot_pos_prev = tot_pos
+            tot_neg_prev = tot_neg
+            tot_pos += self._stat_pos[idx]
+            tot_neg += self._stat_neg[idx]
+            auc += self.trapezoid_area(tot_neg, tot_neg_prev, tot_pos,
+                                       tot_pos_prev)
+            idx -= 1
+
+        return auc / tot_pos / tot_neg if tot_pos > 0.0 and tot_neg > 0.0 else 0.0

python/paddle/fluid/optimizer.py

@@ -897,7 +897,20 @@ class RMSPropOptimizer(Optimizer):
 
         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) +
+        v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) +
+            \\epsilon}} \\nabla Q_{i}(w)
+
+        w & = w - v(w, t)
+
+    if centered is True:
+
+    ..  math::
+
+        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
+
+        g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w)
+
+        v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 +
             \\epsilon}} \\nabla Q_{i}(w)
 
         w & = w - v(w, t)
@@ -915,6 +928,10 @@ class RMSPropOptimizer(Optimizer):
             avoid division by zero, set 1e-6 by default.
         momentum(float): :math:`\\beta` in equation is the momentum term,
             set 0.0 by default.
+        centered(bool): If True, gradients are normalized by the estimated variance of
+            the gradient; if False, by the uncentered second moment. Setting this to
+            True may help with training, but is slightly more expensive in terms of
+            computation and memory. Defaults to False.
 
     Raises:
         ValueError: If learning_rate, rho, epsilon, momentum are None.
@@ -928,12 +945,14 @@ class RMSPropOptimizer(Optimizer):
 
     _momentum_acc_str = "momentum"
     _mean_square_acc_str = "mean_square"
+    _mean_grad_acc_str = "mean_grad"
 
     def __init__(self,
                  learning_rate,
                  rho=0.95,
                  epsilon=1.0e-6,
                  momentum=0.0,
+                 centered=False,
                  **kwargs):
         super(RMSPropOptimizer, self).__init__(
             learning_rate=learning_rate, **kwargs)
@@ -950,6 +969,7 @@ class RMSPropOptimizer(Optimizer):
         self._rho = rho
         self._epsilon = epsilon
         self._momentum = momentum
+        self._centered = centered
 
     def _create_accumulators(self, block, parameters):
         if not isinstance(block, framework.Block):
@@ -958,6 +978,7 @@ class RMSPropOptimizer(Optimizer):
         for p in parameters:
             self._add_accumulator(self._momentum_acc_str, p)
             self._add_accumulator(self._mean_square_acc_str, p)
+            self._add_accumulator(self._mean_grad_acc_str, p)
 
     def _append_optimize_op(self, block, param_and_grad):
         if not isinstance(block, framework.Block):
@@ -967,6 +988,8 @@ class RMSPropOptimizer(Optimizer):
                                              param_and_grad[0])
         mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
                                                 param_and_grad[0])
+        mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
+                                              param_and_grad[0])
         rmsprop_op = block.append_op(
             type=self.type,
             inputs={
@@ -974,17 +997,20 @@ class RMSPropOptimizer(Optimizer):
                 "Grad": param_and_grad[1],
                 "Moment": momentum_acc,
                 "MeanSquare": mean_square_acc,
+                "MeanGrad": mean_grad_acc,
                 "LearningRate": self._create_param_lr(param_and_grad),
             },
             outputs={
                 "ParamOut": param_and_grad[0],
                 "MomentOut": momentum_acc,
-                "MeanSquareOut": mean_square_acc
+                "MeanSquareOut": mean_square_acc,
+                "MeanGradOut": mean_grad_acc
             },
             attrs={
                 "epsilon": self._epsilon,
                 "decay": self._rho,
-                "momentum": self._momentum
+                "momentum": self._momentum,
+                "centered": self._centered
             })
 
         return rmsprop_op

python/paddle/fluid/tests/test_detection.py

@@ -281,7 +281,7 @@ class TestRpnTargetAssign(unittest.TestCase):
             gt_box = layers.data(
                 name='gt_box', shape=[4], lod_level=1, dtype='float32')
 
-            predicted_scores, predicted_location, target_label, target_bbox = layers.rpn_target_assign(
+            pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
                 loc=loc,
                 scores=scores,
                 anchor_box=anchor_box,
@@ -292,15 +292,13 @@ class TestRpnTargetAssign(unittest.TestCase):
                 rpn_positive_overlap=0.7,
                 rpn_negative_overlap=0.3)
 
-            self.assertIsNotNone(predicted_scores)
-            self.assertIsNotNone(predicted_location)
-            self.assertIsNotNone(target_label)
-            self.assertIsNotNone(target_bbox)
-            assert predicted_scores.shape[1] == 2
-            assert predicted_location.shape[1] == 4
-            assert predicted_location.shape[1] == target_bbox.shape[1]
-
-        print(str(program))
+            self.assertIsNotNone(pred_scores)
+            self.assertIsNotNone(pred_loc)
+            self.assertIsNotNone(tgt_lbl)
+            self.assertIsNotNone(tgt_bbox)
+            assert pred_scores.shape[1] == 1
+            assert pred_loc.shape[1] == 4
+            assert pred_loc.shape[1] == tgt_bbox.shape[1]
 
 
 class TestGenerateProposals(unittest.TestCase):

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

@@ -291,7 +291,7 @@ class OpTest(unittest.TestCase):
                             return_numpy=False)
         return outs, fetch_list
 
-    def check_output_with_place(self, place, atol):
+    def check_output_with_place(self, place, atol, equal_nan=False):
         outs, fetch_list = self._calc_output(place)
         for out_name, out_dup in Operator.get_op_outputs(self.op_type):
             if out_name not in self.outputs:
@@ -321,7 +321,7 @@ class OpTest(unittest.TestCase):
                         if isinstance(expect, tuple) else expect
                     self.assertTrue(
                         np.allclose(
-                            actual_t, expect_t, atol=atol),
+                            actual_t, expect_t, atol=atol, equal_nan=equal_nan),
                         "Output (" + sub_out_name + ") has diff at " +
                         str(place))
                     if isinstance(expect, tuple):
@@ -337,7 +337,7 @@ class OpTest(unittest.TestCase):
                 expect_t = expect[0] if isinstance(expect, tuple) else expect
                 self.assertTrue(
                     np.allclose(
-                        actual_t, expect_t, atol=atol),
+                        actual_t, expect_t, atol=atol, equal_nan=equal_nan),
                     "Output (" + out_name + ") has diff at " + str(place) +
                     "\nExpect " + str(expect_t) + "\n" + "But Got" +
                     str(actual_t))
@@ -360,10 +360,10 @@ class OpTest(unittest.TestCase):
             places.append(core.CUDAPlace(0))
         return places
 
-    def check_output(self, atol=1e-5):
+    def check_output(self, atol=1e-5, equal_nan=False):
         places = self._get_places()
         for place in places:
-            self.check_output_with_place(place, atol)
+            self.check_output_with_place(place, atol, equal_nan)
 
     def check_output_customized(self, checker):
         places = self._get_places()

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

@@ -26,18 +26,15 @@ class TestAucOp(OpTest):
         pred = np.random.random((128, 2)).astype("float32")
         labels = np.random.randint(0, 2, (128, 1))
         num_thresholds = 200
-        tp = np.zeros((num_thresholds, )).astype("int64")
-        tn = np.zeros((num_thresholds, )).astype("int64")
-        fp = np.zeros((num_thresholds, )).astype("int64")
-        fn = np.zeros((num_thresholds, )).astype("int64")
+
+        stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")
+        stat_neg = np.zeros((num_thresholds + 1, )).astype("int64")
 
         self.inputs = {
             'Predict': pred,
             'Label': labels,
-            'TP': tp,
-            'TN': tn,
-            'FP': fp,
-            'FN': fn
+            "StatPos": stat_pos,
+            "StatNeg": stat_neg
         }
         self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
 
@@ -47,11 +44,10 @@ class TestAucOp(OpTest):
         python_auc.update(pred, labels)
 
         self.outputs = {
-            'AUC': python_auc.eval(),
-            'TPOut': python_auc.tp_list,
-            'FNOut': python_auc.fn_list,
-            'TNOut': python_auc.tn_list,
-            'FPOut': python_auc.fp_list
+            'AUC': np.array(python_auc.eval()),
+            'BatchAUC': np.array(python_auc.eval()),
+            'StatPosOut': np.array(python_auc._stat_pos),
+            'StatNegOut': np.array(python_auc._stat_neg)
         }
 
     def test_check_output(self):

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

@@ -58,6 +58,7 @@ class TestFusionLSTMOp(OpTest):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
         self.use_peepholes = False
+        self.use_seq = False
         self.set_conf()
 
         T = sum(self.lod[0])
@@ -107,6 +108,7 @@ class TestFusionLSTMOp(OpTest):
         }
         self.attrs = {
             'use_peepholes': self.use_peepholes,
+            'use_seq': self.use_seq,
             'is_reverse': self.is_reverse,
             'gate_activation': self.act_gate,
             'cell_activation': self.act_cell,
@@ -159,5 +161,68 @@ class TestFusionLSTMOpBS1(TestFusionLSTMOp):
         self.D = 16
 
 
+class TestFusionLSTMOpPeepholes(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_peepholes = True
+
+
+class TestFusionLSTMOpPeepholesInit(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_peepholes = True
+        self.has_initial_state = True
+
+
+class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_peepholes = True
+        self.is_reverse = True
+
+
+class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_peepholes = True
+        self.lod = [[3]]
+        self.D = 16
+
+
+class TestFusionLSTMOpSeqInit(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.has_initial_state = True
+
+
+class TestFusionLSTMOpSeqReverse(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.is_reverse = True
+
+
+class TestFusionLSTMOpSeqInitReverse(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.has_initial_state = True
+        self.is_reverse = True
+
+
+class TestFusionLSTMOpSeqPeepholes(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.use_peepholes = True
+
+
+class TestFusionLSTMOpSeqPeepholesInit(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.use_peepholes = True
+        self.has_initial_state = True
+
+
+class TestFusionLSTMOpSeqPeepholesReverse(TestFusionLSTMOp):
+    def set_conf(self):
+        self.use_seq = True
+        self.use_peepholes = True
+        self.is_reverse = True
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -177,8 +177,8 @@ def _box_to_delta(ex_boxes, gt_boxes, weights):
 
     dx = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0]
     dy = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1]
-    dw = (np.log(gt_w / ex_w)) / ex_w / weights[2]
-    dh = (np.log(gt_h / ex_h)) / ex_h / weights[3]
+    dw = (np.log(gt_w / ex_w)) / weights[2]
+    dh = (np.log(gt_h / ex_h)) / weights[3]
 
     targets = np.vstack([dx, dy, dw, dh]).transpose()
     return targets

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

@@ -15,90 +15,164 @@
 from __future__ import print_function
 
 import unittest
+
 import numpy as np
-from op_test import OpTest
-
-
-class TestRmspropOp1(OpTest):
-    ''' Test RMSProp with explicit inputs
-    '''
-
-    def setUp(self):
-        self.op_type = "rmsprop"
-
-        param = np.random.random((123, 321)).astype("float32")
-        mean_square = np.random.random((123, 321)).astype("float32")
-        learning_rate = np.array([0.01]).astype("float32")
-        grad = np.random.random((123, 321)).astype("float32")
-        moment = np.zeros((123, 321)).astype("float32")
-
-        epsilon = 1e-6
-        decay = 0.9
-        momentum = 0.0
-
-        self.inputs = {
-            'Param': param,
-            'MeanSquare': mean_square,
-            'LearningRate': learning_rate,
-            'Grad': grad,
-            'Moment': moment,
-        }
-
-        self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum}
-
-        ms_out = decay * mean_square + (1 - decay) * grad * grad
-        moment_out = momentum * moment + \
-            learning_rate * grad / np.sqrt(ms_out + epsilon)
-        param_out = param - moment_out
-
-        self.outputs = {
-            'ParamOut': param_out,
-            'MomentOut': moment_out,
-            'MeanSquareOut': ms_out
-        }
-
-    def test_check_output(self):
-        self.check_output()
-
-
-class TestRmspropOp2(OpTest):
-    '''Test RMSProp with default values for attributes
-    '''
-
-    def setUp(self):
-        self.op_type = "rmsprop"
-
-        param = np.random.random((123, 321)).astype("float32")
-        mean_square = np.random.random((123, 321)).astype("float32")
-        learning_rate = np.array([0.01]).astype("float32")
-        grad = np.random.random((123, 321)).astype("float32")
-        moment = np.zeros((123, 321)).astype("float32")
-
-        epsilon = 1.0e-10
-        decay = 0.9
-        momentum = 0.0
-
-        self.inputs = {
-            'Param': param,
-            'MeanSquare': mean_square,
-            'LearningRate': learning_rate,
-            'Grad': grad,
-            'Moment': moment,
-        }
-
-        ms_out = decay * mean_square + (1 - decay) * grad * grad
-        moment_out = momentum * moment + \
-            learning_rate * grad / np.sqrt(ms_out + epsilon)
-        param_out = param - moment_out
-
-        self.outputs = {
-            'ParamOut': param_out,
-            'MomentOut': moment_out,
-            'MeanSquareOut': ms_out
-        }
-
-    def test_check_output(self):
-        self.check_output()
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+
+
+class TestBase(unittest.TestCase):
+    def setup(self, centered, epsilon=1e-6):
+        np.random.seed(5)  # fix seed
+
+        self.param_name = "param"
+        self.param = np.random.random((123, 321)).astype("float32")
+
+        self.mean_square_name = "mean_square"
+        self.mean_square = np.random.random((123, 321)).astype("float32")
+
+        self.mean_grad_name = "mean_grad"
+        self.mean_grad = np.random.random((123, 321)).astype("float32")
+
+        self.lr_name = "lr"
+        self.learning_rate = np.array([0.01]).astype("float32")
+
+        self.grad_name = "grad"
+        self.grad = np.random.random((123, 321)).astype("float32")
+
+        self.moment_name = "moment"
+        self.moment = np.zeros((123, 321)).astype("float32")
+
+        self.epsilon = epsilon
+        self.decay = 0.9
+        self.momentum = 0.0
+        self.centered = centered
+
+        self.ms_out = self.decay * self.mean_square + (1 - self.decay
+                                                       ) * self.grad * self.grad
+        if centered:
+            self.mg_out = self.decay * self.mean_grad + (1 - self.decay
+                                                         ) * self.grad
+            self.moment_out = self.momentum * self.moment + \
+                              self.learning_rate * self.grad / np.sqrt(self.ms_out - np.square(self.mg_out) + self.epsilon)
+        else:
+            self.moment_out = self.momentum * self.moment + \
+                              self.learning_rate * self.grad / np.sqrt(self.ms_out + self.epsilon)
+
+        self.param_out = self.param - self.moment_out
+
+    def check(self,
+              actual_t,
+              expect_t,
+              place,
+              out_name,
+              atol=1e-5,
+              equal_nan=False):
+        self.assertTrue(
+            np.allclose(
+                actual_t, expect_t, atol=atol, equal_nan=equal_nan),
+            "Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
+            + str(expect_t) + "\n" + "But Got" + str(actual_t))
+
+
+class TestRmspropOp(TestBase):
+    def check_with_place(self, place, centered, epsilon):
+        self.setup(centered, epsilon)
+        scope = core.Scope()
+
+        # create and initialize Param Variable
+        param = scope.var(self.param_name).get_tensor()
+        param.set(self.param, place)
+
+        mean_square = scope.var(self.mean_square_name).get_tensor()
+        mean_square.set(self.mean_square, place)
+
+        lr = scope.var(self.lr_name).get_tensor()
+        lr.set(self.learning_rate, place)
+
+        grad = scope.var(self.grad_name).get_tensor()
+        grad.set(self.grad, place)
+
+        moment = scope.var(self.moment_name).get_tensor()
+        moment.set(self.moment, place)
+
+        # create and run sgd operator
+
+        if self.centered:
+            mean_grad = scope.var(self.mean_grad_name).get_tensor()
+            mean_grad.set(self.mean_grad, place)
+
+            rmsprop_op = Operator(
+                "rmsprop",
+                Param=self.param_name,
+                Grad=self.grad_name,
+                MeanSquare=self.mean_square_name,
+                MeanGrad=self.mean_grad_name,
+                Moment=self.moment_name,
+                LearningRate=self.lr_name,
+                ParamOut=self.param_name,
+                MeanSquareOut=self.mean_square_name,
+                MomentOut=self.moment_name,
+                MeanGradOut=self.mean_grad_name,
+                epsilon=self.epsilon,
+                decay=self.decay,
+                momentum=self.momentum,
+                centered=True)
+        else:
+            rmsprop_op = Operator(
+                "rmsprop",
+                Param=self.param_name,
+                Grad=self.grad_name,
+                MeanSquare=self.mean_square_name,
+                Moment=self.moment_name,
+                LearningRate=self.lr_name,
+                ParamOut=self.param_name,
+                MeanSquareOut=self.mean_square_name,
+                MomentOut=self.moment_name,
+                epsilon=self.epsilon,
+                decay=self.decay,
+                momentum=self.momentum,
+                centered=False)
+
+        rmsprop_op.run(scope, place)
+
+        atol = 1e-5
+        equal_nan = False
+
+        if self.centered:
+            atol = 1e-3
+            equal_nan = True
+
+        self.check(
+            np.array(mean_square), self.ms_out, place, self.mean_square_name)
+        self.check(
+            np.array(moment),
+            self.moment_out,
+            place,
+            self.moment_name,
+            atol=atol,
+            equal_nan=equal_nan)
+        self.check(
+            np.array(param),
+            self.param_out,
+            place,
+            self.param_name,
+            atol=atol,
+            equal_nan=equal_nan)
+
+        if self.centered:
+            self.check(
+                np.array(mean_grad), self.mg_out, place, self.mean_grad_name)
+
+    def test_rmsprop(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            self.check_with_place(place, False, 1e-6)
+            self.check_with_place(place, False, 1e-10)
+            self.check_with_place(place, True, 1e-6)
+            self.check_with_place(place, True, 1e-10)
 
 
 if __name__ == "__main__":

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

@@ -61,7 +61,7 @@ class TestROIPoolOp(OpTest):
 
         for i in range(self.rois_num):
             roi = self.rois[i]
-            roi_batch_id = roi[0]
+            roi_batch_id = int(roi[0])
             roi_start_w = int(cpt.round(roi[1] * self.spatial_scale))
             roi_start_h = int(cpt.round(roi[2] * self.spatial_scale))
             roi_end_w = int(cpt.round(roi[3] * self.spatial_scale))
@@ -125,7 +125,7 @@ class TestROIPoolOp(OpTest):
                 roi = [bno, x1, y1, x2, y2]
                 rois.append(roi)
         self.rois_num = len(rois)
-        self.rois = np.array(rois).astype("int64")
+        self.rois = np.array(rois).astype("float32")
 
     def setUp(self):
         self.op_type = "roi_pool"

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

@@ -18,12 +18,17 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest
+from test_anchor_generator_op import anchor_generator_in_python
+from test_generate_proposal_labels import _generate_groundtruth
+from test_generate_proposal_labels import _bbox_overlaps, _box_to_delta
 
 
-def rpn_target_assign(iou, rpn_batch_size_per_im, rpn_positive_overlap,
-                      rpn_negative_overlap, fg_fraction):
-    iou = np.transpose(iou)
+def rpn_target_assign(gt_anchor_iou, rpn_batch_size_per_im,
+                      rpn_positive_overlap, rpn_negative_overlap, fg_fraction):
+    iou = np.transpose(gt_anchor_iou)
     anchor_to_gt_max = iou.max(axis=1)
+    anchor_to_gt_argmax = iou.argmax(axis=1)
+
     gt_to_anchor_argmax = iou.argmax(axis=0)
     gt_to_anchor_max = iou[gt_to_anchor_argmax, np.arange(iou.shape[1])]
     anchors_with_max_overlap = np.where(iou == gt_to_anchor_max)[0]
@@ -42,59 +47,113 @@ def rpn_target_assign(iou, rpn_batch_size_per_im, rpn_positive_overlap,
 
     num_bg = rpn_batch_size_per_im - np.sum(tgt_lbl == 1)
     bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
+    tgt_lbl[bg_inds] = 0
     if len(bg_inds) > num_bg:
         enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
         tgt_lbl[enable_inds] = 0
     bg_inds = np.where(tgt_lbl == 0)[0]
+    tgt_lbl[bg_inds] = 0
 
     loc_index = fg_inds
     score_index = np.hstack((fg_inds, bg_inds))
     tgt_lbl = np.expand_dims(tgt_lbl, axis=1)
-    return loc_index, score_index, tgt_lbl
+
+    gt_inds = anchor_to_gt_argmax[fg_inds]
+
+    return loc_index, score_index, tgt_lbl, gt_inds
+
+
+def get_anchor(n, c, h, w):
+    input_feat = np.random.random((n, c, h, w)).astype('float32')
+    anchors, _ = anchor_generator_in_python(
+        input_feat=input_feat,
+        anchor_sizes=[32., 64.],
+        aspect_ratios=[0.5, 1.0],
+        variances=[1.0, 1.0, 1.0, 1.0],
+        stride=[16.0, 16.0],
+        offset=0.5)
+    return anchors
+
+
+def rpn_blob(anchor, gt_boxes, iou, lod, rpn_batch_size_per_im,
+             rpn_positive_overlap, rpn_negative_overlap, fg_fraction):
+
+    loc_indexes = []
+    score_indexes = []
+    tmp_tgt_labels = []
+    tgt_bboxes = []
+    anchor_num = anchor.shape[0]
+
+    batch_size = len(lod) - 1
+    for i in range(batch_size):
+        b, e = lod[i], lod[i + 1]
+        iou_slice = iou[b:e, :]
+        bboxes_slice = gt_boxes[b:e, :]
+
+        loc_idx, score_idx, tgt_lbl, gt_inds = rpn_target_assign(
+            iou_slice, rpn_batch_size_per_im, rpn_positive_overlap,
+            rpn_negative_overlap, fg_fraction)
+
+        fg_bboxes = bboxes_slice[gt_inds]
+        fg_anchors = anchor[loc_idx]
+        box_deltas = _box_to_delta(fg_anchors, fg_bboxes, [1., 1., 1., 1.])
+
+        if i == 0:
+            loc_indexes = loc_idx
+            score_indexes = score_idx
+            tmp_tgt_labels = tgt_lbl
+            tgt_bboxes = box_deltas
+        else:
+            loc_indexes = np.concatenate(
+                [loc_indexes, loc_idx + i * anchor_num])
+            score_indexes = np.concatenate(
+                [score_indexes, score_idx + i * anchor_num])
+            tmp_tgt_labels = np.concatenate([tmp_tgt_labels, tgt_lbl])
+            tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
+
+    tgt_labels = tmp_tgt_labels[score_indexes]
+    return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
 
 
 class TestRpnTargetAssignOp(OpTest):
     def setUp(self):
-        iou = np.random.random((10, 8)).astype("float32")
-        self.op_type = "rpn_target_assign"
-        self.inputs = {'DistMat': iou}
-        self.attrs = {
-            'rpn_batch_size_per_im': 256,
-            'rpn_positive_overlap': 0.95,
-            'rpn_negative_overlap': 0.3,
-            'fg_fraction': 0.25,
-            'fix_seed': True
-        }
-        loc_index, score_index, tgt_lbl = rpn_target_assign(iou, 256, 0.95, 0.3,
-                                                            0.25)
-        self.outputs = {
-            'LocationIndex': loc_index,
-            'ScoreIndex': score_index,
-            'TargetLabel': tgt_lbl,
-        }
+        n, c, h, w = 2, 4, 14, 14
+        anchor = get_anchor(n, c, h, w)
+        gt_num = 10
+        anchor = anchor.reshape(-1, 4)
+        anchor_num = anchor.shape[0]
 
-    def test_check_output(self):
-        self.check_output()
+        im_shapes = [[64, 64], [64, 64]]
+        gt_box, lod = _generate_groundtruth(im_shapes, 3, 4)
+        bbox = np.vstack([v['boxes'] for v in gt_box])
 
+        iou = _bbox_overlaps(bbox, anchor)
+
+        anchor = anchor.astype('float32')
+        bbox = bbox.astype('float32')
+        iou = iou.astype('float32')
+
+        loc_index, score_index, tgt_bbox, tgt_lbl = rpn_blob(
+            anchor, bbox, iou, [0, 4, 8], 25600, 0.95, 0.03, 0.25)
 
-class TestRpnTargetAssignOp2(OpTest):
-    def setUp(self):
-        iou = np.random.random((10, 20)).astype("float32")
         self.op_type = "rpn_target_assign"
-        self.inputs = {'DistMat': iou}
+        self.inputs = {
+            'Anchor': anchor,
+            'GtBox': (bbox, [[4, 4]]),
+            'DistMat': (iou, [[4, 4]]),
+        }
         self.attrs = {
-            'rpn_batch_size_per_im': 128,
-            'rpn_positive_overlap': 0.5,
-            'rpn_negative_overlap': 0.5,
-            'fg_fraction': 0.5,
+            'rpn_batch_size_per_im': 25600,
+            'rpn_positive_overlap': 0.95,
+            'rpn_negative_overlap': 0.03,
+            'fg_fraction': 0.25,
             'fix_seed': True
         }
-        loc_index, score_index, tgt_lbl = rpn_target_assign(iou, 128, 0.5, 0.5,
-                                                            0.5)
         self.outputs = {
-            'LocationIndex': loc_index,
-            'ScoreIndex': score_index,
-            'TargetLabel': tgt_lbl,
+            'LocationIndex': loc_index.astype('int32'),
+            'ScoreIndex': score_index.astype('int32'),
+            'TargetBBox': tgt_bbox.astype('float32'),
+            'TargetLabel': tgt_lbl.astype('int64'),
         }
 
     def test_check_output(self):