use for_range

cmake/external/anakin.cmake

@@ -2,6 +2,11 @@ if (NOT WITH_ANAKIN)
   return()
 endif()
 
+option(ANAKIN_ENABLE_OP_TIMER      "Get more detailed information with Anakin op time"        OFF)
+if(ANAKIN_ENABLE_OP_TIMER)
+  add_definitions(-DPADDLE_ANAKIN_ENABLE_OP_TIMER)
+endif()
+
 INCLUDE(ExternalProject)
 set(ANAKIN_SOURCE_DIR  ${THIRD_PARTY_PATH}/anakin)
 # the anakin install dir is only default one now
@@ -11,23 +16,34 @@ set(ANAKIN_LIBRARY     ${ANAKIN_INSTALL_DIR})
 set(ANAKIN_SHARED_LIB  ${ANAKIN_LIBRARY}/libanakin.so)
 set(ANAKIN_SABER_LIB   ${ANAKIN_LIBRARY}/libanakin_saber_common.so)
 
-# TODO(luotao): ANAKIN_MODLE_URL will move to demo ci later.
-set(ANAKIN_MODLE_URL "http://paddle-inference-dist.bj.bcebos.com/mobilenet_v2.anakin.bin")
+# TODO(luotao): ANAKIN_MODLE_URL etc will move to demo ci later.
+set(INFERENCE_URL "http://paddle-inference-dist.bj.bcebos.com")
+set(ANAKIN_MODLE_URL "${INFERENCE_URL}/mobilenet_v2.anakin.bin")
+set(ANAKIN_RNN_MODLE_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn.anakin2.model.bin")
+set(ANAKIN_RNN_DATA_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn_data.txt")
 execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_SOURCE_DIR}")
-execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL}")
+execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL} -N")
+execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_MODLE_URL} -N")
+execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_DATA_URL} -N")
 
 include_directories(${ANAKIN_INCLUDE})
 include_directories(${ANAKIN_INCLUDE}/saber/)
+include_directories(${ANAKIN_INCLUDE}/saber/core/)
+include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/x86/)
+include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/cuda/base/cuda_c/)
 
 set(ANAKIN_COMPILE_EXTRA_FLAGS
     -Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
     -Wno-error=unused-variable -Wno-unused-variable
     -Wno-error=format-extra-args -Wno-format-extra-args
-    -Wno-error=comment -Wno-comment
-    -Wno-error=format -Wno-format
+    -Wno-error=comment -Wno-comment 
+    -Wno-error=format -Wno-format 
+    -Wno-error=maybe-uninitialized -Wno-maybe-uninitialized
     -Wno-error=switch -Wno-switch
     -Wno-error=return-type -Wno-return-type
     -Wno-error=non-virtual-dtor -Wno-non-virtual-dtor
+    -Wno-error=ignored-qualifiers
+    -Wno-ignored-qualifiers
     -Wno-sign-compare
     -Wno-reorder
     -Wno-error=cpp)
@@ -38,7 +54,7 @@ ExternalProject_Add(
     DEPENDS             ${MKLML_PROJECT}
     # Anakin codes error on Intel(R) Xeon(R) Gold 5117 CPU, temporary do not compile avx512 related code.
     GIT_REPOSITORY      "https://github.com/luotao1/Anakin"
-    GIT_TAG             "bcf17aabe7921ceb7bce591244b4f9dce7dba5c8"
+    GIT_TAG             "211d1fc5d813d70c0c14072f9083cf25f40940ea"
     PREFIX              ${ANAKIN_SOURCE_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DUSE_GPU_PLACE=YES
@@ -48,6 +64,7 @@ ExternalProject_Add(
                         -DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
                         -DCUDNN_ROOT=${CUDNN_ROOT}
                         -DCUDNN_INCLUDE_DIR=${CUDNN_INCLUDE_DIR}
+                        -DENABLE_OP_TIMER=${ANAKIN_ENABLE_OP_TIMER}
                         ${EXTERNAL_OPTIONAL_ARGS}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
 )

doc/fluid/dev/new_op_cn.md

@@ -119,10 +119,29 @@ $$Out = scale*X$$
 
 这个例子有`AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
 
+### 定义GradProtoMaker类
+每个Op的必须有一个对应的GraProtoMaker，若未定制对应前向Op的GradProtoMaker，fluid提供了DefaultGradProtoMaker，默认注册会使用全部输入输出，包括Input, Output, Output@Grad等，使用不需要的变量的会造成显存浪费。
+下面示例定义了ScaleOp的GradProtoMaker。
+
+```cpp
+class ScaleGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto *grad_op = new framework::OpDesc();
+    grad_op->SetType("scale");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttr("scale", GetAttr("scale"));
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+```
 
 ### 定义Operator类
 
-下面的点实现了MulOp的定义：
+下面实现了MulOp的定义：
 
 ```cpp
 class MulOp : public framework::OperatorWithKernel {
@@ -383,6 +402,19 @@ PADDLE_ENFORCE(forward_pd != nullptr,
                     "Fail to find eltwise_fwd_pd in device context");  //eltwise_fwd_pd用户可能看不懂
 ```
 
+3. OP内部调用非法接口：Op内部如果出现Output = ShareDataWith(Input) 
+问题示例：
+```cpp
+auto *out = ctx.Output<framework::LoDTensor>("Out");
+auto *in = ctx.Input<framework::LoDTensor>("X");
+out->ShareDataWith(*in);
+```
+Op内部如果出现Output = ShareDataWith(Input)，相当于operator图的中有一条隐藏边，连接了Input和Output，这条边无法在图分析中表达，引发基于图优化的错误。
+
+4. OP实现的性能实践
+调用了eigen的broadcast, chop等操作，性能会比手写cuda kernel差几倍以上。此时cpu的实现可以复用eigen，gpu实现可以实现cuda kernel.
+
+
 #### OP InferShape检查提示信息特别说明
 
 - 检查输入输出变量，请统一遵循以下格式

paddle/fluid/framework/ir/graph_helper.cc

@@ -104,7 +104,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
       for (auto &adj_n : var->inputs) {
         PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
         adj_list[n].insert(adj_n);
-        VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
+        VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
                 << " -> " << n->Name() << reinterpret_cast<void *>(n)
                 << "  via " << var->Name() << reinterpret_cast<void *>(var);
       }

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -22,7 +22,7 @@ function (inference_analysis_test TARGET)
     if(WITH_TESTING)
         set(options "")
         set(oneValueArgs "")
-        set(multiValueArgs SRCS)
+        set(multiValueArgs SRCS EXTRA_DEPS)
         cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
         set(mem_opt "")
@@ -31,22 +31,43 @@ 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_detecter pass
+                DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass ${analysis_test_EXTRA_DEPS}
                 ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt})
         set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
     endif(WITH_TESTING)
 endfunction(inference_analysis_test)
 
-cc_test(test_analyzer SRCS analyzer_tester.cc DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
-		  # ir
-		  fc_fuse_pass
-		  graph_viz_pass
-		  infer_clean_graph_pass
-		  graph_pattern_detecter
-		  pass
-        ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
-#set_tests_properties(test_analyzer PROPERTIES DEPENDS test_word2vec)
-#inference_api_test(test_analyzer SRC analyzer_tester.cc ARGS test_word2vec)
+set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
+set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
+set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
+set(DITU_RNN_MODEL ${DITU_INSTALL_DIR}/model)
+set(DITU_RNN_DATA ${DITU_INSTALL_DIR}/data.txt)
+
+function (inference_download_and_uncompress target url gz_filename)
+    message(STATUS "Download inference test stuff ${gz_filename} from ${url}")
+    execute_process(COMMAND bash -c "mkdir -p ${DITU_INSTALL_DIR}")
+    execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && wget -q ${url}")
+    execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && tar xzf ${gz_filename}")
+    message(STATUS "finish downloading ${gz_filename}")
+endfunction(inference_download_and_uncompress)
+
+if (NOT EXISTS ${DITU_INSTALL_DIR})
+    inference_download_and_uncompress(ditu_rnn_model ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
+    inference_download_and_uncompress(ditu_rnn_data ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
+endif()
+
+inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
+		# ir
+		fc_fuse_pass
+		graph_viz_pass
+		infer_clean_graph_pass
+		graph_pattern_detecter
+        infer_clean_graph_pass
+		pass
+    ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model
+        --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
+        --infer_ditu_rnn_data=${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)

paddle/fluid/inference/analysis/analyzer.cc

@@ -23,8 +23,6 @@
 #include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
 #include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
 
-namespace paddle {
-
 DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
             "Enable subgraph to TensorRT engine for acceleration");
 
@@ -35,6 +33,7 @@ DEFINE_string(IA_graphviz_log_root, "./",
 
 DEFINE_string(IA_output_storage_path, "", "optimized model output path");
 
+namespace paddle {
 namespace inference {
 namespace analysis {
 

paddle/fluid/inference/analysis/analyzer.h

@@ -39,8 +39,6 @@ limitations under the License. */
 #include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 
-namespace paddle {
-
 // TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
 // flag if not available.
 DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
@@ -48,6 +46,7 @@ DECLARE_string(IA_graphviz_log_root);
 DECLARE_string(IA_output_storage_path);
 DECLARE_bool(IA_enable_ir);
 
+namespace paddle {
 namespace inference {
 namespace analysis {
 

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -13,11 +13,17 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/analysis/analyzer.h"
+
 #include <google/protobuf/text_format.h>
+#include <gtest/gtest.h>
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 
+DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
+DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
+
 namespace paddle {
 namespace inference {
 namespace analysis {
@@ -38,7 +44,7 @@ TEST(Analyzer, analysis_with_tensorrt) {
   analyser.Run(&argument);
 }
 
-void TestWord2vecPrediction(const std::string& model_path) {
+void TestWord2vecPrediction(const std::string &model_path) {
   NativeConfig config;
   config.model_dir = model_path;
   config.use_gpu = false;
@@ -69,12 +75,245 @@ void TestWord2vecPrediction(const std::string& model_path) {
   // The outputs' buffers are in CPU memory.
   for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
     LOG(INFO) << "data: "
-              << static_cast<float*>(outputs.front().data.data())[i];
-    PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
+              << static_cast<float *>(outputs.front().data.data())[i];
+    PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
                    result[i]);
   }
 }
 
+namespace {
+
+struct DataRecord {
+  std::vector<std::vector<std::vector<float>>> link_step_data_all;
+  std::vector<std::vector<float>> week_data_all, minute_data_all;
+  std::vector<size_t> lod1, lod2, lod3;
+  std::vector<std::vector<float>> rnn_link_data, rnn_week_datas,
+      rnn_minute_datas;
+  size_t batch_iter{0};
+  size_t batch_size{1};
+  DataRecord() = default;
+  DataRecord(const std::string &path, int batch_size = 1)
+      : batch_size(batch_size) {
+    Load(path);
+  }
+  DataRecord NextBatch() {
+    DataRecord data;
+    size_t batch_end = batch_iter + batch_size;
+    // NOTE skip the final batch, if no enough data is provided.
+    if (batch_end <= link_step_data_all.size()) {
+      data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
+                                     link_step_data_all.begin() + batch_end);
+      data.week_data_all.assign(week_data_all.begin() + batch_iter,
+                                week_data_all.begin() + batch_end);
+      data.minute_data_all.assign(minute_data_all.begin() + batch_iter,
+                                  minute_data_all.begin() + batch_end);
+      // Prepare LoDs
+      data.lod1.push_back(0);
+      data.lod2.push_back(0);
+      data.lod3.push_back(0);
+      CHECK(!data.link_step_data_all.empty()) << "empty";
+      CHECK(!data.week_data_all.empty());
+      CHECK(!data.minute_data_all.empty());
+      CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size());
+      CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size());
+      for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
+        for (const auto &d : data.link_step_data_all[j]) {
+          data.rnn_link_data.push_back(d);
+        }
+        data.rnn_week_datas.push_back(data.week_data_all[j]);
+        data.rnn_minute_datas.push_back(data.minute_data_all[j]);
+        // calculate lod
+        data.lod1.push_back(data.lod1.back() +
+                            data.link_step_data_all[j].size());
+        data.lod3.push_back(data.lod3.back() + 1);
+        for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) {
+          data.lod2.push_back(data.lod2.back() +
+                              data.link_step_data_all[j].size());
+        }
+      }
+    }
+    batch_iter += batch_size;
+    return data;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    int num_lines = 0;
+    while (std::getline(file, line)) {
+      num_lines++;
+      std::vector<std::string> data;
+      split(line, ':', &data);
+      std::vector<std::vector<float>> link_step_data;
+      std::vector<std::string> link_datas;
+      split(data[0], '|', &link_datas);
+      for (auto &step_data : link_datas) {
+        std::vector<float> tmp;
+        split_to_float(step_data, ',', &tmp);
+        link_step_data.push_back(tmp);
+      }
+      // load week data
+      std::vector<float> week_data;
+      split_to_float(data[2], ',', &week_data);
+      // load minute data
+      std::vector<float> minute_data;
+      split_to_float(data[1], ',', &minute_data);
+      link_step_data_all.push_back(std::move(link_step_data));
+      week_data_all.push_back(std::move(week_data));
+      minute_data_all.push_back(std::move(minute_data));
+    }
+  }
+};
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                   int batch_size) {
+  // DataRecord data(FLAGS_datapath, batch_size);
+  PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
+      week_tensor, minute_tensor;
+  lod_attention_tensor.name = "data_lod_attention";
+  init_zero_tensor.name = "cell_init";
+  lod_tensor_tensor.name = "data";
+  week_tensor.name = "week";
+  minute_tensor.name = "minute";
+  auto one_batch = data->NextBatch();
+  // clang-format off
+  std::vector<int> rnn_link_data_shape
+      ({static_cast<int>(one_batch.rnn_link_data.size()), static_cast<int>(one_batch.rnn_link_data.front().size())});
+  lod_attention_tensor.shape.assign({1, 2});
+  lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
+  init_zero_tensor.shape.assign({batch_size, 15});
+  init_zero_tensor.lod.assign({one_batch.lod3});
+  lod_tensor_tensor.shape = rnn_link_data_shape;
+  lod_tensor_tensor.lod.assign({one_batch.lod1});
+  week_tensor.shape.assign({(int) one_batch.rnn_week_datas.size(), (int) one_batch.rnn_week_datas.front().size()});
+  week_tensor.lod.assign({one_batch.lod3});
+  minute_tensor.shape.assign({(int) one_batch.rnn_minute_datas.size(),
+                              (int) one_batch.rnn_minute_datas.front().size()});
+  minute_tensor.lod.assign({one_batch.lod3});
+  // assign data
+  TensorAssignData(&lod_attention_tensor, std::vector<std::vector<float>>({{0, 0}}));
+  std::vector<float> tmp_zeros(batch_size * 15, 0.);
+  TensorAssignData(&init_zero_tensor, {tmp_zeros});
+  TensorAssignData(&lod_tensor_tensor, one_batch.rnn_link_data);
+  TensorAssignData(&week_tensor, one_batch.rnn_week_datas);
+  TensorAssignData(&minute_tensor, one_batch.rnn_minute_datas);
+  // clang-format on
+  // Set inputs.
+  auto init_zero_tensor1 = init_zero_tensor;
+  init_zero_tensor1.name = "hidden_init";
+  input_slots->assign({week_tensor, init_zero_tensor, minute_tensor,
+                       init_zero_tensor1, lod_attention_tensor,
+                       lod_tensor_tensor});
+  for (auto &tensor : *input_slots) {
+    tensor.dtype = PaddleDType::FLOAT32;
+  }
+}
+
+std::string DescribeTensor(const PaddleTensor &tensor) {
+  std::stringstream os;
+  os << "Tensor [" << tensor.name << "]\n";
+  os << " - type: ";
+  switch (tensor.dtype) {
+    case PaddleDType::FLOAT32:
+      os << "float32";
+      break;
+    case PaddleDType::INT64:
+      os << "int64";
+      break;
+    default:
+      os << "unset";
+  }
+  os << '\n';
+
+  os << " - shape: " << to_string(tensor.shape) << '\n';
+  os << " - lod: ";
+  for (auto &l : tensor.lod) {
+    os << to_string(l) << "; ";
+  }
+  os << "\n";
+  os << " - data: ";
+
+  // clang-format off
+  int dim = std::accumulate(tensor.shape.begin(),
+                            tensor.shape.end(),
+                            1,
+                            [](int a, int b) { return a * b; });  // clang-format on
+  for (size_t i = 0; i < dim; i++) {
+    os << static_cast<float *>(tensor.data.data())[i] << " ";
+  }
+  os << '\n';
+  return os.str();
+}
+
+}  // namespace
+
+const float ditu_rnn_target_data[] = {
+    104.711, 11.2431, 1.35422, 0,       0,       0,       0,       0,
+    27.7039, 1.41486, 7.09526, 0,       0,       0,       0,       0,
+    7.6481,  6.5324,  56.383,  2.88018, 8.92918, 132.007, 4.27429, 2.02934,
+    14.1727, 10.7461, 25.0616, 16.0197, 14.4163, 16.9199, 6.75517, 0,
+    80.0249, 4.77739, 0,       0,       0,       0,       0,       0,
+    47.5643, 2.67029, 8.76252, 0,       0,       0,       0,       0,
+    51.8822, 4.4411,  0,       0,       0,       0,       0,       0,
+    10.7286, 12.0595, 10.6672, 0,       0,       0,       0,       0,
+    93.5771, 3.84641, 0,       0,       0,       0,       0,       0,
+    169.426, 0,       0,       0,       0,       0,       0,       0};
+// Test with a really complicate model.
+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) {
+  FLAGS_IA_enable_ir = activate_ir;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
+  FLAGS_IA_output_storage_path = "./analysis.out";
+
+  std::string model_out;
+  if (use_analysis) {
+    Argument argument(model_path);
+    argument.model_output_store_path.reset(new std::string("./analysis.out"));
+
+    Analyzer analyzer;
+    analyzer.Run(&argument);
+
+    // Should get the transformed model stored to ./analysis.out
+    model_out = "./analysis.out";
+    ASSERT_TRUE(PathExists(model_out));
+  } else {
+    model_out = FLAGS_infer_ditu_rnn_model;
+  }
+
+  NativeConfig config;
+  config.prog_file = model_out + "/__model__";
+  config.param_file = model_out + "/param";
+  config.use_gpu = false;
+  config.device = 0;
+  config.specify_input_name = true;
+
+  auto predictor =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
+  std::vector<PaddleTensor> input_slots;
+  DataRecord data(data_path, batch_size);
+  // Prepare inputs.
+  PrepareInputs(&input_slots, &data, batch_size);
+  std::vector<PaddleTensor> outputs;
+
+  Timer timer;
+  timer.tic();
+  for (int i = 0; i < num_times; i++) {
+    predictor->Run(input_slots, &outputs);
+  }
+  LOG(INFO) << "time/batch: " << timer.toc() / num_times;
+
+  for (auto &out : outputs) {
+    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                  [](int a, int b) { return a * b; });
+    float *data = static_cast<float *>(out.data.data());
+    for (int i = 0;
+         i < std::min(sizeof(ditu_rnn_target_data) / sizeof(float), size);
+         i++) {
+      EXPECT_NEAR(data[i], ditu_rnn_target_data[i], 1e-3);
+    }
+  }
+}
+
 // Turn on the IR pass supportion, run a real inference and check the result.
 TEST(Analyzer, SupportIRPass) {
   FLAGS_IA_enable_ir = true;
@@ -94,6 +333,27 @@ TEST(Analyzer, SupportIRPass) {
   TestWord2vecPrediction("./analysis.out");
 }
 
+// Directly infer with the original model.
+TEST(Analyzer, DituRNN_without_analysis) {
+  TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
+                        10, false, false);
+}
+
+// Inference with the original model with the analysis turned on, the analysis
+// module will transform the program to a data flow graph.
+TEST(Analyzer, DituRNN_with_analysis) {
+  LOG(INFO) << "ditu rnn with analysis";
+  TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
+                        10, true, false, 1);
+}
+
+// Inference with analysis and IR. The IR module will fuse some large kernels.
+TEST(Analyzer, DituRNN_with_analysis_with_IR) {
+  LOG(INFO) << "ditu rnn with analysis and IR fuse";
+  TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
+                        10, true, true, 1);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/api/CMakeLists.txt

@@ -18,7 +18,10 @@ if(APPLE)
 endif(APPLE)
 
 
-set(inference_deps paddle_inference_api paddle_fluid_api)
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
+  graph_viz_pass fc_fuse_pass
+    infer_clean_graph_pass
+  )
 
 if(WITH_GPU AND TENSORRT_FOUND)
     set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
@@ -62,7 +65,7 @@ endif()
 
 if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
     # compile the libinference_anakin_api.a and anakin.so.
-    cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
+    cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml)
     cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
     function(anakin_target target_name)
       target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
@@ -70,9 +73,12 @@ if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
     anakin_target(inference_anakin_api)
     anakin_target(inference_anakin_api_shared)
     if (WITH_TESTING)
-        cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
+        cc_test(api_anakin_engine_tester SRCS api_anakin_engine_tester.cc 
                 ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
-                DEPS inference_anakin_api dynload_cuda SERIAL)
-        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+                DEPS inference_anakin_api_shared dynload_cuda SERIAL)
+        cc_test(api_anakin_engine_rnn_tester SRCS api_anakin_engine_rnn_tester.cc 
+                ARGS --model=${ANAKIN_SOURCE_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin 
+                     --datapath=${ANAKIN_SOURCE_DIR}/anakin_test%2Fditu_rnn_data.txt
+                DEPS inference_anakin_api_shared dynload_cuda SERIAL)
     endif(WITH_TESTING)
 endif()

paddle/fluid/inference/api/api.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.

paddle/fluid/inference/api/api_anakin_engine.cc

@@ -13,9 +13,22 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/api/api_anakin_engine.h"
+
+#ifdef PADDLE_WITH_CUDA
 #include <cuda.h>
+#endif
+
+#include <mkl_service.h>
+#include <omp.h>
+#include <map>
+#include <string>
+#include <utility>
 #include <vector>
 
+#include "framework/core/net/net.h"
+#include "framework/operators/ops.h"
+#include "saber/funcs/timer.h"
+
 namespace paddle {
 
 template <typename Target>
@@ -23,16 +36,24 @@ PaddleInferenceAnakinPredictor<Target>::PaddleInferenceAnakinPredictor(
     const AnakinConfig &config) {
   CHECK(Init(config));
 }
-
+template <>
+PaddleInferenceAnakinPredictor<anakin::X86>::PaddleInferenceAnakinPredictor(
+    const AnakinConfig &config) {
+  omp_set_dynamic(0);
+  omp_set_num_threads(1);
+  mkl_set_num_threads(1);
+  CHECK(Init(config));
+}
 template <typename Target>
 bool PaddleInferenceAnakinPredictor<Target>::Init(const AnakinConfig &config) {
   if (!(graph_.load(config.model_file))) {
-    LOG(FATAL) << "fail to load graph from " << config.model_file;
+    VLOG(3) << "fail to load graph from " << config.model_file;
     return false;
   }
   auto inputs = graph_.get_ins();
   for (auto &input_str : inputs) {
     graph_.ResetBatchSize(input_str, config.max_batch_size);
+    max_batch_size_ = config.max_batch_size;
   }
   // optimization for graph
   if (!(graph_.Optimize())) {
@@ -52,15 +73,15 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
     std::vector<PaddleTensor> *output_data, int batch_size) {
   for (const auto &input : inputs) {
     if (input.dtype != PaddleDType::FLOAT32) {
-      LOG(ERROR) << "Only support float type inputs. " << input.name
-                 << "'s type is not float";
+      VLOG(3) << "Only support float type inputs. " << input.name
+              << "'s type is not float";
       return false;
     }
     auto d_tensor_in_p = executor_p_->get_in(input.name);
-    auto net_shape = d_tensor_in_p->valid_shape();
+    auto net_shape = d_tensor_in_p->shape();
     if (net_shape.size() != input.shape.size()) {
-      LOG(ERROR) << " input  " << input.name
-                 << "'s shape size should be equal to that of net";
+      VLOG(3) << " input  " << input.name
+              << "'s shape size should be equal to that of net";
       return false;
     }
     int sum = 1;
@@ -79,21 +100,45 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
     }
     d_tensor_in_p->reshape(tmp_shape);
 
+    if (input.lod.size() > 0) {
+      if (input.lod.size() > 1) {
+        VLOG(3) << " input lod first dim should <=1, but you set "
+                << input.lod.size();
+        return false;
+      }
+      std::vector<int> offset(input.lod[0].begin(), input.lod[0].end());
+      d_tensor_in_p->set_seq_offset(offset);
+      VLOG(3) << "offset.size(): " << offset.size();
+      for (int i = 0; i < offset.size(); i++) {
+        VLOG(3) << offset[i];
+      }
+    }
+
     float *d_data_p = d_tensor_in_p->mutable_data();
-    if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
-                   d_tensor_in_p->valid_size() * sizeof(float),
-                   cudaMemcpyHostToDevice) != 0) {
-      LOG(ERROR) << "copy data from CPU to GPU error";
-      return false;
+
+#ifdef PADDLE_WITH_CUDA
+    if (std::is_same<anakin::NV, Target>::value) {
+      if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
+                     d_tensor_in_p->valid_size() * sizeof(float),
+                     cudaMemcpyHostToDevice) != 0) {
+        VLOG(3) << "copy data from CPU to GPU error";
+        return false;
+      }
+    }
+#endif
+    if (std::is_same<anakin::X86, Target>::value) {
+      memcpy(d_data_p, static_cast<float *>(input.data.data()),
+             d_tensor_in_p->valid_size() * sizeof(float));
     }
-    cudaStreamSynchronize(NULL);
   }
+#ifdef PADDLE_WITH_CUDA
   cudaDeviceSynchronize();
   executor_p_->prediction();
   cudaDeviceSynchronize();
+#endif
 
   if (output_data->empty()) {
-    LOG(ERROR) << "At least one output should be set with tensors' names.";
+    VLOG(3) << "At least one output should be set with tensors' names.";
     return false;
   }
   for (auto &output : *output_data) {
@@ -102,14 +147,22 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
     if (output.data.length() < tensor->valid_size() * sizeof(float)) {
       output.data.Resize(tensor->valid_size() * sizeof(float));
     }
-    // Copy data from GPU -> CPU
-    if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
-                   tensor->valid_size() * sizeof(float),
-                   cudaMemcpyDeviceToHost) != 0) {
-      LOG(ERROR) << "copy data from GPU to CPU error";
-      return false;
+
+#if PADDLE_WITH_CUDA
+    if (std::is_same<anakin::NV, Target>::value) {
+      // Copy data from GPU -> CPU
+      if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
+                     tensor->valid_size() * sizeof(float),
+                     cudaMemcpyDeviceToHost) != 0) {
+        VLOG(3) << "copy data from GPU to CPU error";
+        return false;
+      }
+    }
+#endif
+    if (std::is_same<anakin::X86, Target>::value) {
+      memcpy(output.data.data(), tensor->mutable_data(),
+             tensor->valid_size() * sizeof(float));
     }
-    cudaStreamSynchronize(NULL);
   }
   return true;
 }
@@ -132,7 +185,7 @@ PaddleInferenceAnakinPredictor<Target>::Clone() {
   auto anakin_predictor_p =
       dynamic_cast<PaddleInferenceAnakinPredictor<Target> *>(cls.get());
   if (!anakin_predictor_p) {
-    LOG(ERROR) << "fail to call Init";
+    VLOG(3) << "fail to call Init";
     return nullptr;
   }
   anakin_predictor_p->get_executer().init(graph_);
@@ -162,6 +215,44 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
     VLOG(3) << "Anakin Predictor create on unknown platform.";
     return nullptr;
   }
-};
+}
+
+#ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER
+template <typename Target>
+using executor_t =
+    anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>;
+
+template <typename Target>
+void DisplayOpTimer(executor_t<Target> *net_executor, int epoch) {
+  std::vector<float> op_time = net_executor->get_op_time();
+  auto exec_funcs = net_executor->get_exec_funcs();
+  auto op_param = net_executor->get_op_param();
+  for (int i = 0; i < op_time.size(); i++) {
+    LOG(INFO) << "name: " << exec_funcs[i].name
+              << " op_type: " << exec_funcs[i].op_name
+              << " op_param: " << op_param[i] << " time " << op_time[i] / epoch;
+  }
+  std::map<std::string, float> op_map;
+  for (int i = 0; i < op_time.size(); i++) {
+    auto it = op_map.find(op_param[i]);
+    if (it != op_map.end())
+      op_map[op_param[i]] += op_time[i];
+    else
+      op_map.insert(std::pair<std::string, float>(op_param[i], op_time[i]));
+  }
+  for (auto it = op_map.begin(); it != op_map.end(); ++it) {
+    LOG(INFO) << it->first << "  " << (it->second) / epoch << " ms";
+  }
+}
+#endif
+
+template <typename Target>
+PaddleInferenceAnakinPredictor<Target>::~PaddleInferenceAnakinPredictor() {
+#ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER
+  DisplayOpTimer<Target>(executor_p_, max_batch_size_);
+#endif
+  delete executor_p_;
+  executor_p_ = nullptr;
+}
 
 }  // namespace paddle

paddle/fluid/inference/api/api_anakin_engine.h

@@ -47,10 +47,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
   anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>&
   get_executer();
 
-  ~PaddleInferenceAnakinPredictor() override {
-    delete executor_p_;
-    executor_p_ = nullptr;
-  };
+  ~PaddleInferenceAnakinPredictor() override;
 
  private:
   bool Init(const AnakinConfig& config);
@@ -60,6 +57,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
   anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>*
       executor_p_{nullptr};
   AnakinConfig config_;
+  int max_batch_size_{0};
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/api_anakin_engine_rnn_tester.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 <sys/time.h>
+#include <time.h>
+#include <algorithm>
+#include <fstream>
+#include <iostream>
+#include <thread>  // NOLINT
+#include <vector>
+#include "framework/core/net/net.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+DEFINE_string(model, "", "Directory of the inference model.");
+DEFINE_string(datapath, "", "Path of the dataset.");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
+
+// Timer for timer
+class Timer {
+ public:
+  double start;
+  double startu;
+  void tic() {
+    struct timeval tp;
+    gettimeofday(&tp, NULL);
+    start = tp.tv_sec;
+    startu = tp.tv_usec;
+  }
+  double toc() {
+    struct timeval tp;
+    gettimeofday(&tp, NULL);
+    double used_time_ms =
+        (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
+    return used_time_ms;
+  }
+};
+
+std::vector<std::string> string_split(std::string in_str,
+                                      std::string delimiter) {
+  std::vector<std::string> seq;
+  int found = in_str.find(delimiter);
+  int pre_found = -1;
+  while (found != std::string::npos) {
+    if (pre_found == -1) {
+      seq.push_back(in_str.substr(0, found));
+    } else {
+      seq.push_back(in_str.substr(pre_found + delimiter.length(),
+                                  found - delimiter.length() - pre_found));
+    }
+    pre_found = found;
+    found = in_str.find(delimiter, pre_found + delimiter.length());
+  }
+  seq.push_back(
+      in_str.substr(pre_found + 1, in_str.length() - (pre_found + 1)));
+  return seq;
+}
+std::vector<std::string> string_split(
+    std::string in_str, std::vector<std::string>& delimiter) {  // NOLINT
+  std::vector<std::string> in;
+  std::vector<std::string> out;
+  out.push_back(in_str);
+  for (auto del : delimiter) {
+    in = out;
+    out.clear();
+    for (auto s : in) {
+      auto out_s = string_split(s, del);
+      for (auto o : out_s) {
+        out.push_back(o);
+      }
+    }
+  }
+  return out;
+}
+
+class Data {
+ public:
+  Data(std::string file_name, int batch_size)
+      : _batch_size(batch_size), _total_length(0) {
+    _file.open(file_name);
+    _file.seekg(_file.end);
+    _total_length = _file.tellg();
+    _file.seekg(_file.beg);
+  }
+  void get_batch_data(std::vector<std::vector<float>>& fea,         // NOLINT
+                      std::vector<std::vector<float>>& week_fea,    // NOLINT
+                      std::vector<std::vector<float>>& time_fea,    // NOLINT
+                      std::vector<long unsigned int>& seq_offset);  // NOLINT
+
+ private:
+  std::fstream _file;
+  int _total_length;
+  int _batch_size;
+};
+
+void Data::get_batch_data(
+    std::vector<std::vector<float>>& fea,          // NOLINT
+    std::vector<std::vector<float>>& week_fea,     // NOLINT
+    std::vector<std::vector<float>>& time_fea,     // NOLINT
+    std::vector<long unsigned int>& seq_offset) {  // NOLINT
+  int seq_num = 0;
+  long unsigned int cum = 0;  // NOLINT
+
+  char buf[10000];
+  seq_offset.clear();
+  seq_offset.push_back(0);
+  fea.clear();
+  week_fea.clear();
+  time_fea.clear();
+  while (_file.getline(buf, 10000)) {
+    std::string s = buf;
+    std::vector<std::string> deli_vec = {":"};
+    std::vector<std::string> data_vec = string_split(s, deli_vec);
+
+    std::vector<std::string> seq;
+    seq = string_split(data_vec[0], {"|"});
+
+    for (auto link : seq) {
+      std::vector<std::string> data = string_split(link, ",");
+      std::vector<float> vec;
+      for (int i = 0; i < data.size(); i++) {
+        vec.push_back(atof(data[i].c_str()));
+      }
+      fea.push_back(vec);
+    }
+    std::vector<std::string> week_data;
+    std::vector<std::string> time_data;
+
+    week_data = string_split(data_vec[2], ",");
+    std::vector<float> vec_w;
+    for (int i = 0; i < week_data.size(); i++) {
+      vec_w.push_back(atof(week_data[i].c_str()));
+    }
+    week_fea.push_back(vec_w);
+
+    time_data = string_split(data_vec[1], ",");
+    std::vector<float> vec_t;
+    for (int i = 0; i < time_data.size(); i++) {
+      vec_t.push_back(atof(time_data[i].c_str()));
+    }
+    time_fea.push_back(vec_t);
+
+    cum += seq.size();
+    seq_offset.push_back(cum);
+
+    seq_num++;
+    if (seq_num >= _batch_size) {
+      break;
+    }
+  }
+}
+
+namespace paddle {
+
+AnakinConfig GetConfig() {
+  AnakinConfig config;
+  // using AnakinConfig::X86 if you need to use cpu to do inference
+  config.target_type = AnakinConfig::X86;
+  config.model_file = FLAGS_model;
+  config.device = 0;
+  config.max_batch_size = 1000;  // the max number of token
+  return config;
+}
+
+void set_tensor(std::string name, std::vector<int> shape,
+                std::vector<PaddleTensor>& vec) {  // NOLINT
+  int sum = 1;
+  std::for_each(shape.begin(), shape.end(), [&](int n) { sum *= n; });
+  float* data = new float[sum];
+  PaddleTensor tensor;
+  tensor.name = name;
+  tensor.shape = shape;
+  tensor.data = PaddleBuf(data, sum);
+  tensor.dtype = PaddleDType::FLOAT32;
+  vec.push_back(tensor);
+}
+
+void single_test() {
+  AnakinConfig config = GetConfig();
+  auto predictor =
+      CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(config);
+
+  int max_batch_size = 1000;
+  std::string feature_file = FLAGS_datapath;
+  Data map_data(feature_file, FLAGS_batch_size);
+  std::vector<std::vector<float>> fea;
+  std::vector<std::vector<float>> week_fea;
+  std::vector<std::vector<float>> time_fea;
+  std::vector<long unsigned int> seq_offset;  // NOLINT
+
+  paddle::PaddleTensor tensor_0, tensor_1, tensor_2;
+  tensor_0.name = "input_0";
+  tensor_1.name = "input_4";
+  tensor_2.name = "input_5";
+
+  PaddleTensor tensor_out;
+  tensor_out.name = "final_output.tmp_1_gout";
+  tensor_out.shape = std::vector<int>({});
+  tensor_out.data = PaddleBuf();
+  tensor_out.dtype = PaddleDType::FLOAT32;
+
+  std::vector<PaddleTensor> inputs;
+  std::vector<PaddleTensor> outputs(1, tensor_out);
+
+  int data_0_dim = 38;
+  int data_1_dim = 10;
+  int data_2_dim = 10;
+  float data_0[max_batch_size * data_0_dim];  // NOLINT
+  float data_1[max_batch_size * data_1_dim];  // NOLINT
+  float data_2[max_batch_size * data_2_dim];  // NOLINT
+
+  int count = 0;
+  while (true) {
+    if (count++ > 0) break;  // only run the first batch in ci.
+    seq_offset.clear();
+    map_data.get_batch_data(fea, week_fea, time_fea, seq_offset);
+    if (seq_offset.size() <= 1) {
+      LOG(FATAL) << "seq_offset.size() <= 1, exit.";
+      break;
+    }
+
+    std::vector<std::vector<long unsigned int>> seq_offset_vec;  // NOLINT
+    seq_offset_vec.push_back(seq_offset);
+    tensor_0.lod = seq_offset_vec;
+
+    int p_shape_0[] = {(int)fea.size(), 1, 1, data_0_dim};       // NOLINT
+    int p_shape_1[] = {(int)week_fea.size(), data_1_dim, 1, 1};  // NOLINT
+    int p_shape_2[] = {(int)time_fea.size(), data_2_dim, 1, 1};  // NOLINT
+
+    std::vector<int> shape_0(p_shape_0, p_shape_0 + 4);
+    std::vector<int> shape_1(p_shape_1, p_shape_1 + 4);
+    std::vector<int> shape_2(p_shape_2, p_shape_2 + 4);
+
+    tensor_0.shape = shape_0;
+    tensor_1.shape = shape_1;
+    tensor_2.shape = shape_2;
+
+    for (int i = 0; i < fea.size(); i++) {
+      memcpy(data_0 + i * data_0_dim, &fea[i][0], sizeof(float) * data_0_dim);
+    }
+    for (int i = 0; i < week_fea.size(); i++) {
+      memcpy(data_1 + i * data_1_dim, &week_fea[i][0],
+             sizeof(float) * data_1_dim);
+    }
+    for (int i = 0; i < time_fea.size(); i++) {
+      memcpy(data_2 + i * data_2_dim, &time_fea[i][0],
+             sizeof(float) * data_2_dim);
+    }
+
+    tensor_0.data =
+        paddle::PaddleBuf(data_0, fea.size() * sizeof(float) * data_0_dim);
+    tensor_1.data =
+        paddle::PaddleBuf(data_1, week_fea.size() * sizeof(float) * data_1_dim);
+    tensor_2.data =
+        paddle::PaddleBuf(data_2, time_fea.size() * sizeof(float) * data_2_dim);
+
+    tensor_0.dtype = paddle::PaddleDType::FLOAT32;
+    tensor_1.dtype = paddle::PaddleDType::FLOAT32;
+    tensor_2.dtype = paddle::PaddleDType::FLOAT32;
+
+    inputs.clear();
+    inputs.push_back(tensor_1);
+    inputs.push_back(tensor_2);
+    inputs.push_back(tensor_0);
+
+    Timer timer;
+    timer.tic();
+    for (int i = 0; i < FLAGS_repeat; i++) predictor->Run(inputs, &outputs);
+
+    LOG(INFO) << "batch_size = " << FLAGS_batch_size
+              << ", repeat = " << FLAGS_repeat
+              << ", sequence_length = " << seq_offset[seq_offset.size() - 1]
+              << ", latency: " << timer.toc() / FLAGS_repeat << "ms";
+
+    float* data_o = static_cast<float*>(outputs[0].data.data());
+    VLOG(3) << "outputs[0].data.length() = " << outputs[0].data.length();
+    for (size_t j = 0; j < outputs[0].data.length(); ++j) {
+      VLOG(3) << "output[" << j << "]: " << data_o[j];
+    }
+  }
+}
+}  // namespace paddle
+
+int main(int argc, char** argv) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  logger::init(argv[0]);
+
+  paddle::single_test();
+  /* multi-threads
+  std::vector<std::thread> threads;
+  int num = 1;
+  for (int i = 0; i < num; i++) {
+    LOG(INFO) << " thread id : " << i;
+    threads.emplace_back(paddle::single_test);
+  }
+  for (int i = 0; i < num; i++) {
+    threads[i].join();
+  }
+  threads.clear();
+  */
+
+  return 0;
+}

paddle/fluid/inference/api/api_impl.cc

@@ -137,8 +137,11 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
     return false;
   }
   for (size_t i = 0; i < feed_target_names_.size(); ++i) {
-    VLOG(4) << "setting " << i << "-th target";
-    feed_targets[feed_target_names_[i]] = &feeds[i];
+    if (config_.specify_input_name) {
+      feed_targets[inputs[i].name] = &feeds[i];
+    } else {
+      feed_targets[feed_target_names_[i]] = &feeds[i];
+    }
   }
   // get fetch variable
   std::map<std::string, framework::LoDTensor *> fetch_targets;

paddle/fluid/inference/api/helper.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 <sys/time.h>
+#include <algorithm>
+#include <sstream>
+#include <string>
+#include <vector>
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+namespace paddle {
+namespace inference {
+
+// Timer for timer
+class Timer {
+ public:
+  double start;
+  double startu;
+  void tic() {
+    struct timeval tp;
+    gettimeofday(&tp, NULL);
+    start = tp.tv_sec;
+    startu = tp.tv_usec;
+  }
+  double toc() {
+    struct timeval tp;
+    gettimeofday(&tp, NULL);
+    double used_time_ms =
+        (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
+    return used_time_ms;
+  }
+};
+
+void split(const std::string &str, char sep, std::vector<std::string> *pieces) {
+  pieces->clear();
+  if (str.empty()) {
+    return;
+  }
+  size_t pos = 0;
+  size_t next = str.find(sep, pos);
+  while (next != std::string::npos) {
+    pieces->push_back(str.substr(pos, next - pos));
+    pos = next + 1;
+    next = str.find(sep, pos);
+  }
+  if (!str.substr(pos).empty()) {
+    pieces->push_back(str.substr(pos));
+  }
+}
+void split_to_float(const std::string &str, char sep, std::vector<float> *fs) {
+  std::vector<std::string> pieces;
+  split(str, sep, &pieces);
+  std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs),
+                 [](const std::string &v) { return std::stof(v); });
+}
+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();
+}
+template <>
+std::string to_string<std::vector<float>>(
+    const std::vector<std::vector<float>> &vec) {
+  std::stringstream ss;
+  for (const auto &piece : vec) {
+    ss << to_string(piece) << "\n";
+  }
+  return ss.str();
+}
+template <>
+std::string to_string<std::vector<std::vector<float>>>(
+    const std::vector<std::vector<std::vector<float>>> &vec) {
+  std::stringstream ss;
+  for (const auto &line : vec) {
+    for (const auto &rcd : line) {
+      ss << to_string(rcd) << ";\t";
+    }
+    ss << '\n';
+  }
+  return ss.str();
+}
+// clang-format off
+void TensorAssignData(PaddleTensor *tensor, const std::vector<std::vector<float>> &data) {
+  // Assign buffer
+  int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; });
+  tensor->data.Resize(sizeof(float) * dim);
+  int c = 0;
+  for (const auto &f : data) {
+    for (float v : f) { static_cast<float *>(tensor->data.data())[c++] = v; }
+  }
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/api/paddle_inference_api.h

@@ -45,7 +45,7 @@ class PaddleBuf {
   PaddleBuf(void* data, size_t length)
       : data_(data), length_(length), memory_owned_{false} {}
   // Own memory.
-  explicit PaddleBuf(size_t length)
+  PaddleBuf(size_t length)
       : data_(new char[length]), length_(length), memory_owned_(true) {}
   // Resize to `length` bytes.
   void Resize(size_t length);
@@ -70,7 +70,7 @@ struct PaddleTensor {
   std::vector<int> shape;
   PaddleBuf data;  // blob of data.
   PaddleDType dtype;
-  std::vector<std::vector<uint64_t>> lod;  // lod data
+  std::vector<std::vector<size_t>> lod;  // Tensor+LoD equals LoDTensor
 };
 
 enum class PaddleEngineKind {
@@ -120,6 +120,8 @@ struct NativeConfig : public PaddlePredictor::Config {
   bool use_gpu{false};
   int device{0};
   float fraction_of_gpu_memory{-1.f};  // Negative to notify initialization.
+  // Specify the variable's name of each input.
+  bool specify_input_name{false};
 
   std::string prog_file;
   std::string param_file;

paddle/fluid/operators/mul_op.cc

@@ -54,9 +54,9 @@ class MulOp : public framework::OperatorWithKernel {
     auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
     auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
 
-    PADDLE_ENFORCE_EQ(
-        x_mat_dims[1], y_mat_dims[0],
-        "First matrix's width must be equal with second matrix's height.");
+    PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
+                      "First matrix's width must be equal with second matrix's "
+                      "height. %s, %s");
     std::vector<int64_t> output_dims;
     output_dims.reserve(
         static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));

paddle/fluid/operators/stack_op.cc

@@ -14,53 +14,15 @@
 
 #include "paddle/fluid/operators/stack_op.h"
 
-namespace paddle {
-namespace operators {
-
-struct CPUStackFunctor {
-  template <typename DeviceContext, typename T>
-  void operator()(const DeviceContext& ctx, const std::vector<const T*>& x,
-                  T* y, int pre, int n, int post) const {
-    int total_num = pre * post * n;
-    for (int idx = 0; idx < total_num; ++idx) {
-      int i = idx / (n * post);
-      int which_x = idx / post - i * n;
-      int x_index = i * post + idx % post;
-      y[idx] = x[which_x][x_index];
-    }
-  }
-};
-
-struct CPUStackGradFunctor {
-  template <typename DeviceContext, typename T>
-  void operator()(const DeviceContext& ctx, std::vector<T*>& dx,  // NOLINT
-                  const T* dy, int pre, int n, int post) const {
-    int total_num = pre * post * n;
-    for (int idx = 0; idx < total_num; ++idx) {
-      int i = idx / (n * post);
-      int which_x = idx / post - i * n;
-      int x_index = i * post + idx % post;
-      dx[which_x][x_index] = dy[idx];
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
 namespace plat = paddle::platform;
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(stack, ops::StackOp, ops::StackOpMaker,
                   ops::StackGradOpDescMaker);
 REGISTER_OPERATOR(stack_grad, ops::StackOpGrad);
 
-REGISTER_OP_CPU_KERNEL(
-    stack,
-    ops::StackKernel<plat::CPUDeviceContext, float, ops::CPUStackFunctor>,
-    ops::StackKernel<plat::CPUDeviceContext, double, ops::CPUStackFunctor>);
+REGISTER_OP_CPU_KERNEL(stack, ops::StackKernel<plat::CPUDeviceContext, float>,
+                       ops::StackKernel<plat::CPUDeviceContext, double>);
 
 REGISTER_OP_CPU_KERNEL(stack_grad,
-                       ops::StackGradKernel<plat::CPUDeviceContext, float,
-                                            ops::CPUStackGradFunctor>,
-                       ops::StackGradKernel<plat::CPUDeviceContext, double,
-                                            ops::CPUStackGradFunctor>);
+                       ops::StackGradKernel<plat::CPUDeviceContext, float>,
+                       ops::StackGradKernel<plat::CPUDeviceContext, double>);

paddle/fluid/operators/stack_op.cu

@@ -12,98 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <thrust/device_vector.h>
-#include "paddle/fluid/framework/array.h"
 #include "paddle/fluid/operators/stack_op.h"
 
-namespace paddle {
-namespace operators {
-
-template <typename T, typename VecXType>
-__global__ void StackCUDAKernel(VecXType x, T* y, int total_num, int n,
-                                int post) {
-  int idx = threadIdx.x + blockIdx.x * blockDim.x;
-  if (idx < total_num) {
-    int i = idx / (n * post);
-    int which_x = idx / post - i * n;
-    int x_index = i * post + idx % post;
-    y[idx] = x[which_x][x_index];
-  }
-}
-
-template <typename T, typename VecDxType>
-__global__ void StackGradCUDAKernel(VecDxType dx, const T* dy, int total_num,
-                                    int n, int post) {
-  int idx = threadIdx.x + blockIdx.x * blockDim.x;
-  if (idx < total_num) {
-    int i = idx / (n * post);
-    int which_x = idx / post - i * n;
-    int x_index = i * post + idx % post;
-    dx[which_x][x_index] = dy[idx];
-  }
-}
-
-struct GPUStackFunctor {
-  template <typename DeviceContext, typename T>
-  void operator()(const DeviceContext& ctx, const std::vector<const T*>& x,
-                  T* y, int pre, int n, int post) const {
-    int total_num = pre * post * n;
-    int threads = 512;
-    int grid = (total_num + threads - 1) / threads;
-
-    constexpr auto kMaxThreshold = 16;
-    if (n <= kMaxThreshold) {
-      framework::Array<const T*, kMaxThreshold> arr;
-      for (int i = 0; i < n; ++i) arr[i] = x[i];
-      StackCUDAKernel<<<grid, threads, 0, ctx.stream()>>>(arr, y, total_num, n,
-                                                          post);
-    } else {
-      VLOG(10) << "Stack more than " << kMaxThreshold
-               << " tensors may be slow on GPU.";
-      thrust::device_vector<const T*> dev_x(x);
-      StackCUDAKernel<<<grid, threads, 0, ctx.stream()>>>(dev_x.data().get(), y,
-                                                          total_num, n, post);
-    }
-  }
-};
-
-struct GPUStackGradFunctor {
-  template <typename DeviceContext, typename T>
-  void operator()(const DeviceContext& ctx, std::vector<T*>& dx,  // NOLINT
-                  const T* dy, int pre, int n, int post) const {
-    int total_num = pre * post * n;
-    int threads = 512;
-    int grid = (total_num + threads - 1) / threads;
-
-    constexpr auto kMaxThreshold = 16;
-    if (n <= kMaxThreshold) {
-      framework::Array<T*, kMaxThreshold> arr;
-      for (int i = 0; i < n; ++i) arr[i] = dx[i];
-      StackGradCUDAKernel<<<grid, threads, 0, ctx.stream()>>>(
-          arr, dy, total_num, n, post);
-    } else {
-      VLOG(10) << "Stack more than " << kMaxThreshold
-               << " tensors may be slow on GPU.";
-      thrust::device_vector<T*> dev_dx(dx);
-      StackGradCUDAKernel<<<grid, threads, 0, ctx.stream()>>>(
-          dev_dx.data().get(), dy, total_num, n, post);
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
 namespace plat = paddle::platform;
 namespace ops = paddle::operators;
 
-REGISTER_OP_CUDA_KERNEL(
-    stack,
-    ops::StackKernel<plat::CUDADeviceContext, float, ops::GPUStackFunctor>,
-    ops::StackKernel<plat::CUDADeviceContext, double, ops::GPUStackFunctor>);
+REGISTER_OP_CUDA_KERNEL(stack, ops::StackKernel<plat::CUDADeviceContext, float>,
+                        ops::StackKernel<plat::CUDADeviceContext, double>);
 
 REGISTER_OP_CUDA_KERNEL(stack_grad,
-                        ops::StackGradKernel<plat::CUDADeviceContext, float,
-                                             ops::GPUStackGradFunctor>,
-                        ops::StackGradKernel<plat::CUDADeviceContext, double,
-                                             ops::GPUStackGradFunctor>);
+                        ops::StackGradKernel<plat::CUDADeviceContext, float>,
+                        ops::StackGradKernel<plat::CUDADeviceContext, double>);

paddle/fluid/operators/stack_op.h

@@ -11,20 +11,20 @@
 // 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/op_registry.h"
+#include "paddle/fluid/platform/for_range.h"
+
+#ifdef __NVCC__
+#include <thrust/device_vector.h>
+#include "paddle/fluid/framework/array.h"
+#endif
 
 namespace paddle {
 namespace operators {
 
-inline void GetPrePostForStackOp(const framework::DDim &dim, int axis, int *pre,
-                                 int *post) {
-  *pre = 1;
-  for (auto i = 0; i < axis; ++i) (*pre) *= dim[i];
-  *post = 1;
-  for (auto i = axis; i < dim.size(); ++i) (*post) *= dim[i];
-}
-
 class StackOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -72,7 +72,61 @@ class StackOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-template <typename DeviceContext, typename T, typename Functor>
+template <typename VecXType, typename T>
+struct StackFunctor {
+  HOSTDEVICE StackFunctor(const VecXType &x, T *y, int n, int post)
+      : x_(x), y_(y), n_(n), post_(post) {}
+
+  HOSTDEVICE void operator()(int idx) {
+    int i = idx / (n_ * post_);
+    int which_x = idx / post_ - i * n_;
+    int x_index = i * post_ + idx % post_;
+    y_[idx] = x_[which_x][x_index];
+  }
+
+ private:
+  VecXType x_;
+  T *y_;
+  int n_;
+  int post_;
+};
+
+template <typename VecDxType, typename T>
+struct StackGradFunctor {
+  HOSTDEVICE StackGradFunctor(const VecDxType &dx, const T *dy, int n, int post)
+      : dx_(dx), dy_(dy), n_(n), post_(post) {}
+
+  HOSTDEVICE void operator()(int idx) {
+    int i = idx / (n_ * post_);
+    int which_x = idx / post_ - i * n_;
+    int x_index = i * post_ + idx % post_;
+    dx_[which_x][x_index] = dy_[idx];
+  }
+
+ private:
+  VecDxType dx_;
+  const T *dy_;
+  int n_;
+  int post_;
+};
+
+template <typename DeviceContext, typename VecXType, typename T>
+static inline void StackFunctorForRange(const DeviceContext &ctx,
+                                        const VecXType &x, T *y, int total_num,
+                                        int n, int post) {
+  platform::ForRange<DeviceContext> for_range(ctx, total_num);
+  for_range(StackFunctor<VecXType, T>(x, y, n, post));
+}
+
+template <typename DeviceContext, typename VecDxType, typename T>
+static inline void StackGradFunctorForRange(const DeviceContext &ctx,
+                                            const VecDxType &dx, const T *dy,
+                                            int total_num, int n, int post) {
+  platform::ForRange<DeviceContext> for_range(ctx, total_num);
+  for_range(StackGradFunctor<VecDxType, T>(dx, dy, n, post));
+}
+
+template <typename DeviceContext, typename T>
 class StackKernel : public framework::OpKernel<T> {
   using Tensor = framework::LoDTensor;
 
@@ -93,10 +147,29 @@ class StackKernel : public framework::OpKernel<T> {
     auto &dim = x[0]->dims();
     for (auto i = 0; i < axis; ++i) pre *= dim[i];
     for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
+    int total_num = pre * n * post;
 
-    Functor functor;
-    functor(ctx.template device_context<DeviceContext>(), x_datas, y_data, pre,
-            n, post);
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    constexpr auto kMaxThreshold = 16;
+    if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value ||
+        n > kMaxThreshold) {
+#ifdef __NVCC__
+      thrust::device_vector<const T *> device_x_vec(x_datas);
+      auto x_data_arr = device_x_vec.data().get();
+#else
+      auto x_data_arr = x_datas.data();
+#endif
+      StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
+    }
+#ifdef __NVCC__
+    else {  // NOLINT
+      VLOG(10) << "Stack more than " << kMaxThreshold
+               << " tensors on GPU may be slow.";
+      framework::Array<const T *, kMaxThreshold> x_data_arr;
+      for (int i = 0; i < n; ++i) x_data_arr[i] = x_datas[i];
+      StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
+    }
+#endif
   }
 };
 
@@ -127,31 +200,11 @@ class StackOpGrad : public framework::OperatorWithKernel {
   }
 };
 
-class StackGradOpDescMaker
-    : public framework::
-          SingleGradOpDescMaker /*framework::GradOpDescMakerBase*/ {
+class StackGradOpDescMaker : public framework::SingleGradOpDescMaker {
  public:
   using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
-  /*
-  using framework::GradOpDescMakerBase::GradOpDescMakerBase;
-
-  std::vector<std::unique_ptr<framework::OpDesc>> operator ()() const override {
-                auto x_grads = InputGrad("X", false);
-    std::vector<std::unique_ptr<framework::OpDesc>> grad_ops;
-    grad_ops.reserve(x_grads.size());
-    auto og = OutputGrad("Y");
-    std::transform(x_grads.begin(), x_grads.end(), std::back_inserter(grad_ops),
-                   [&og](const std::string& x_grad) {
-                     auto* grad_op = new framework::OpDesc();
-                     grad_op->SetInput("X", og);
-                     grad_op->SetOutput("Y", {x_grad});
-                     grad_op->SetAttrMap(Attrs());
-                     return std::unique_ptr<framework::OpDesc>(grad_op);
-                   });
-    return grad_ops;
-  }
-  */
 
+ protected:
   std::unique_ptr<framework::OpDesc> Apply() const override {
     std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
     op->SetType("stack_grad");
@@ -162,7 +215,7 @@ class StackGradOpDescMaker
   }
 };
 
-template <typename DeviceContext, typename T, typename GradFunctor>
+template <typename DeviceContext, typename T>
 class StackGradKernel : public framework::OpKernel<T> {
   using Tensor = framework::LoDTensor;
 
@@ -175,16 +228,39 @@ class StackGradKernel : public framework::OpKernel<T> {
 
     int n = dy->dims()[axis];
     std::vector<T *> dx_datas(n);  // NOLINT
-    for (int i = 0; i < n; i++)
+    for (int i = 0; i < n; i++) {
       dx_datas[i] = dx[i]->mutable_data<T>(ctx.GetPlace());
+    }
     auto dy_data = dy->data<T>();
 
     int pre = 1;
     for (int i = 0; i < axis; ++i) pre *= dy->dims()[i];
-    int post = dy->numel() / (n * pre);
-    GradFunctor functor;
-    functor(ctx.template device_context<DeviceContext>(), dx_datas, dy_data,
-            pre, n, post);
+    int total_num = dy->numel();
+    int post = total_num / (n * pre);
+
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    constexpr auto kMaxThreshold = 16;
+    if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value ||
+        n > kMaxThreshold) {
+#ifdef __NVCC__
+      thrust::device_vector<T *> device_dx_vec(dx_datas);
+      auto dx_data_arr = device_dx_vec.data().get();
+#else
+      auto dx_data_arr = dx_datas.data();
+#endif
+      StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n,
+                               post);
+    }
+#ifdef __NVCC__
+    else {  // NOLINT
+      VLOG(10) << "Stack more than " << kMaxThreshold
+               << " tensors on GPU may be slow.";
+      framework::Array<T *, kMaxThreshold> dx_data_arr;
+      for (int i = 0; i < n; ++i) dx_data_arr[i] = dx_datas[i];
+      StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n,
+                               post);
+    }
+#endif
   }
 };
 

python/paddle/fluid/io.py

@@ -406,6 +406,9 @@ def load_vars(executor,
                 attrs={'file_path': os.path.join(dirname, filename)})
         executor.run(load_prog)
 
+        if main_program is None:
+            main_program = default_main_program()
+
         # load slice vars on pserver, if have it.
         _load_slice_up_vars(executor, dirname,
                             main_program._slice_vars_and_attrs)

python/paddle/fluid/layers/nn.py

@@ -29,21 +29,81 @@ from .. import unique_name
 from functools import reduce
 
 __all__ = [
-    'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
-    'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
-    'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
-    'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
-    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
-    'sequence_expand', 'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max',
-    'reduce_min', 'reduce_prod', 'sequence_first_step', 'sequence_last_step',
-    'dropout', 'split', 'ctc_greedy_decoder', 'edit_distance', 'l2_normalize',
-    'matmul', 'topk', 'warpctc', 'sequence_reshape', 'transpose', 'im2sequence',
-    'nce', 'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
-    'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
-    'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
-    'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
-    'image_resize_short', 'resize_bilinear', 'gather', 'scatter', 'random_crop',
-    'mean_iou', 'relu', 'log', 'crop', 'rank_loss', 'prelu', 'flatten', 'stack'
+    'fc',
+    'embedding',
+    'dynamic_lstm',
+    'dynamic_lstmp',
+    'dynamic_gru',
+    'gru_unit',
+    'linear_chain_crf',
+    'crf_decoding',
+    'cos_sim',
+    'cross_entropy',
+    'square_error_cost',
+    'chunk_eval',
+    'sequence_conv',
+    'conv2d',
+    'conv3d',
+    'sequence_pool',
+    'sequence_softmax',
+    'softmax',
+    'pool2d',
+    'pool3d',
+    'batch_norm',
+    'beam_search_decode',
+    'conv2d_transpose',
+    'conv3d_transpose',
+    'sequence_expand',
+    'lstm_unit',
+    'reduce_sum',
+    'reduce_mean',
+    'reduce_max',
+    'reduce_min',
+    'reduce_prod',
+    'sequence_first_step',
+    'sequence_last_step',
+    'dropout',
+    'split',
+    'ctc_greedy_decoder',
+    'edit_distance',
+    'l2_normalize',
+    'matmul',
+    'topk',
+    'warpctc',
+    'sequence_reshape',
+    'transpose',
+    'im2sequence',
+    'nce',
+    'hsigmoid',
+    'beam_search',
+    'row_conv',
+    'multiplex',
+    'layer_norm',
+    'softmax_with_cross_entropy',
+    'smooth_l1',
+    'one_hot',
+    'autoincreased_step_counter',
+    'reshape',
+    'lod_reset',
+    'lrn',
+    'pad',
+    'label_smooth',
+    'roi_pool',
+    'dice_loss',
+    'image_resize',
+    'image_resize_short',
+    'resize_bilinear',
+    'gather',
+    'scatter',
+    'random_crop',
+    'mean_iou',
+    'relu',
+    'log',
+    'crop',
+    'rank_loss',
+    'prelu',
+    'flatten',
+    'stack',
 ]
 
 
@@ -5469,5 +5529,6 @@ def stack(x, axis=0):
 
     out = helper.create_tmp_variable(x[0].dtype)
     helper.append_op(
-        type='stack', inputs={'X': x}, outpus={'Y': out}, attrs={'axis': axis})
+        type='stack', inputs={'X': x}, outputs={'Y': out},
+        attrs={'axis': axis})
     return out

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

@@ -25,9 +25,6 @@ import paddle.fluid.core as core
 
 class TestProfiler(unittest.TestCase):
     def net_profiler(self, state, profile_path='/tmp/profile'):
-        enable_if_gpu = state == 'GPU' or state == "All"
-        if enable_if_gpu and not core.is_compiled_with_cuda():
-            return
         startup_program = fluid.Program()
         main_program = fluid.Program()
 
@@ -81,8 +78,6 @@ class TestProfiler(unittest.TestCase):
                 pass_acc_calculator.add(value=acc, weight=b_size)
                 pass_acc = pass_acc_calculator.eval()
 
-    @unittest.skipIf(not core.is_compiled_with_cuda(),
-                     "profiler is enabled only with GPU")
     def test_cpu_profiler(self):
         self.net_profiler('CPU')