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

CMakeLists.txt

@@ -213,9 +213,11 @@ include(configure)          # add paddle env configuration
 if(WITH_GPU)
     include(cuda)
     include(tensorrt)
+endif()
+if(WITH_MKL OR WITH_MKLML)
     include(external/anakin)
 elseif()
-    set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in GPU only now." FORCE)
+    set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in MKL only now." FORCE)
 endif()
 
 include(generic)            # simplify cmake module

cmake/external/anakin.cmake

@@ -16,16 +16,6 @@ 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 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} -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/)
@@ -48,6 +38,11 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
     -Wno-reorder
     -Wno-error=cpp)
 
+if(WITH_GPU)
+    set(CMAKE_ARGS_PREFIX -DUSE_GPU_PLACE=YES -DCUDNN_ROOT=${CUDNN_ROOT} -DCUDNN_INCLUDE_DIR=${CUDNN_INCLUDE_DIR})
+else()
+    set(CMAKE_ARGS_PREFIX -DUSE_GPU_PLACE=NO)
+endif()
 ExternalProject_Add(
     extern_anakin
     ${EXTERNAL_PROJECT_LOG_ARGS}
@@ -56,13 +51,11 @@ ExternalProject_Add(
     GIT_TAG             "9424277cf9ae180a14aff09560d3cd60a49c76d2"
     PREFIX              ${ANAKIN_SOURCE_DIR}
     UPDATE_COMMAND      ""
-    CMAKE_ARGS          -DUSE_GPU_PLACE=YES
+    CMAKE_ARGS          ${CMAKE_ARGS_PREFIX}
                         -DUSE_X86_PLACE=YES
                         -DBUILD_WITH_UNIT_TEST=NO
                         -DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
                         -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}

cmake/inference_lib.cmake

@@ -145,7 +145,7 @@ copy(memory_lib
 set(inference_deps paddle_fluid_shared paddle_fluid)
 
 set(module "inference/api")
-if (WITH_ANAKIN AND WITH_GPU)
+if (WITH_ANAKIN AND WITH_MKL)
     copy(anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
         SRCS
         ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api

doc/fluid/api/layers.rst

@@ -822,6 +822,14 @@ pad
 ..  autofunction:: paddle.fluid.layers.pad
     :noindex:
 
+.. _api_fluid_layers_pad_constant_like:
+
+pad_constant_like
+---
+
+..  autofunction:: paddle.fluid.layers.pad_constant_like
+    :noindex:
+
 .. _api_fluid_layers_label_smooth:
 
 label_smooth
@@ -1145,6 +1153,14 @@ sigmoid
 ..  autofunction:: paddle.fluid.layers.sigmoid
     :noindex:
 
+.. _api_fluid_layers_hsigmoid:
+
+hsigmoid
+-------
+
+..  autofunction:: paddle.fluid.layers.hsigmoid
+    :noindex:
+
 .. _api_fluid_layers_logsigmoid:
 
 logsigmoid

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

@@ -104,6 +104,7 @@ visualDL --logdir=scratch_log --port=8080
 
 # 访问 http://127.0.0.1:8080
 ```
+如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上，运行`pip install --upgrade protobuf`就能解决。
 
 如果在虚拟环境下仍然遇到安装问题，请尝试以下方法。
 

paddle/fluid/API.spec

@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list',
 paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
+paddle.fluid.Trainer.save_inference_model ArgSpec(args=['self', 'param_path', 'feeded_var_names', 'target_var_indexes'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc

@@ -13,13 +13,10 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/ir/attention_lstm_fuse_pass.h"
-
 #include <string>
-
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 #include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/inference/api/helper.h"
 
 namespace paddle {
 namespace framework {

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -11,6 +11,7 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
+
 #include "paddle/fluid/framework/ir/fc_lstm_fuse_pass.h"
 #include <string>
 #include "paddle/fluid/framework/lod_tensor.h"

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -85,7 +85,7 @@ void GraphPatternDetector::operator()(Graph* graph,
   LOG(INFO) << "detect " << subgraphs.size() << " subgraph matches the pattern";
   int id = 0;
   for (auto& g : subgraphs) {
-    LOG(INFO) << "optimizing #" << id++ << " subgraph";
+    VLOG(3) << "optimizing #" << id++ << " subgraph";
     handler(g, graph);
   }
 }

paddle/fluid/framework/ir/graph_viz_pass.cc

@@ -50,20 +50,37 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
 
   Dot dot;
 
-  std::vector<Dot::Attr> op_attrs({Dot::Attr("style", "filled"),
-                                   Dot::Attr("shape", "box"),
-                                   Dot::Attr("fillcolor", "red")});
-  std::vector<Dot::Attr> var_attrs({Dot::Attr("style", "filled,rounded"),
-                                    // Dot::Attr("shape", "diamond"),
+  const std::vector<Dot::Attr> op_attrs({
+      Dot::Attr("style", "rounded,filled,bold"),  //
+      Dot::Attr("shape", "box"),                  //
+      Dot::Attr("color", "#303A3A"),              //
+      Dot::Attr("fontcolor", "#ffffff"),          //
+      Dot::Attr("width", "1.3"),                  //
+      Dot::Attr("height", "0.84"),                //
+      Dot::Attr("fontname", "Arial"),             //
+  });
+  const std::vector<Dot::Attr> arg_attrs({
+      Dot::Attr("shape", "box"),                  //
+      Dot::Attr("style", "rounded,filled,bold"),  //
+      Dot::Attr("fontname", "Arial"),             //
+      Dot::Attr("fillcolor", "#999999"),          //
+      Dot::Attr("color", "#dddddd"),              //
+  });
+
+  const std::vector<Dot::Attr> param_attrs({
+      Dot::Attr("shape", "box"),                  //
+      Dot::Attr("style", "rounded,filled,bold"),  //
+      Dot::Attr("fontname", "Arial"),             //
+      Dot::Attr("color", "#148b97"),              //
+      Dot::Attr("fontcolor", "#ffffff"),          //
+  });
+
+  const std::vector<Dot::Attr> marked_op_attrs(
+      {Dot::Attr("style", "rounded,filled,bold"), Dot::Attr("shape", "box"),
+       Dot::Attr("fillcolor", "yellow")});
+  const std::vector<Dot::Attr> marked_var_attrs(
+      {Dot::Attr("style", "filled,rounded"), Dot::Attr("shape", "box"),
        Dot::Attr("fillcolor", "yellow")});
-
-  std::vector<Dot::Attr> marked_op_attrs({Dot::Attr("style", "filled"),
-                                          Dot::Attr("shape", "box"),
-                                          Dot::Attr("fillcolor", "lightgray")});
-  std::vector<Dot::Attr> marked_var_attrs(
-      {Dot::Attr("style", "filled,rounded"),
-       // Dot::Attr("shape", "diamond"),
-       Dot::Attr("fillcolor", "lightgray")});
 
   auto marked_nodes = ConsumeMarkedNodes(graph.get());
   // Create nodes
@@ -74,9 +91,17 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
           marked_nodes.count(n) ? marked_op_attrs : op_attrs;
       dot.AddNode(node_id, attr, node_id);
     } else if (n->IsVar()) {
-      decltype(op_attrs) attr =
-          marked_nodes.count(n) ? marked_var_attrs : var_attrs;
-      dot.AddNode(node_id, attr, node_id);
+      decltype(op_attrs)* attr;
+      if (marked_nodes.count(n)) {
+        attr = &marked_var_attrs;
+      } else if (const_cast<Node*>(n)->Var() &&
+                 const_cast<Node*>(n)->Var()->Persistable()) {
+        attr = &param_attrs;
+      } else {
+        attr = &arg_attrs;
+      }
+
+      dot.AddNode(node_id, *attr, node_id);
     }
     node2dot[n] = node_id;
   }

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -6,6 +6,7 @@ cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits
   analyzer.cc
   helper.cc
   # passes
+  analysis_pass.cc
   fluid_to_data_flow_graph_pass.cc
   data_flow_graph_to_fluid_pass.cc
   dfg_graphviz_draw_pass.cc
@@ -99,12 +100,17 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
 
 
 set(TEXT_CLASSIFICATION_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/text-classification-Senta.tar.gz")
+set(TEXT_CLASSIFICATION_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/text_classification_data.txt.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} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
+  inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_DATA_URL} "text_classification_data.txt.tar.gz")
 endif()
 
-inference_analysis_test(test_text_classification SRCS test_text_classification.cc
+inference_analysis_test(test_text_classification SRCS analyzer_text_classification_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
-    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
+    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta
+         --infer_data=${TEXT_CLASSIFICATION_INSTALL_DIR}/data.txt
+         --topn=1 # Just run top 1 batch.
+    )

paddle/fluid/inference/analysis/pass.cc → paddle/fluid/inference/analysis/analysis_pass.cc

@@ -12,4 +12,4 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/pass.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"

paddle/fluid/inference/analysis/pass.h → paddle/fluid/inference/analysis/analysis_pass.h

@@ -28,10 +28,10 @@ namespace paddle {
 namespace inference {
 namespace analysis {
 
-class Pass {
+class AnalysisPass {
  public:
-  Pass() = default;
-  virtual ~Pass() = default;
+  AnalysisPass() = default;
+  virtual ~AnalysisPass() = default;
   // Mutable Pass.
   virtual bool Initialize(Argument *argument) { return false; }
   // Readonly Pass.
@@ -42,23 +42,16 @@ class Pass {
   virtual bool Finalize() { return false; }
 
   // Get a Pass appropriate to print the Node this pass operates on.
-  virtual Pass *CreatePrinterPass(std::ostream &os,
+  virtual AnalysisPass *CreatePrinterPass(std::ostream &os,
                                           const std::string &banner) const {
     return nullptr;
   }
 
   // Create a debugger Pass that draw the DFG by graphviz toolkit.
-  virtual Pass *CreateGraphvizDebugerPass() const { return nullptr; }
+  virtual AnalysisPass *CreateGraphvizDebugerPass() const { return nullptr; }
 
-  virtual void Run() { LOG(FATAL) << "not valid"; }
-  // Run on a single Node.
-  virtual void Run(Node *x) { LOG(FATAL) << "not valid"; }
-  // Run on a single Function.
-  virtual void Run(Function *x) { LOG(FATAL) << "not valid"; }
-  // Run on a single FunctionBlock.
-  virtual void Run(FunctionBlock *x) { LOG(FATAL) << "not valid"; }
   // Run on a single DataFlowGraph.
-  virtual void Run(DataFlowGraph *x) { LOG(FATAL) << "not valid"; }
+  virtual void Run(DataFlowGraph *x) = 0;
 
   // Human-readable short representation.
   virtual std::string repr() const = 0;
@@ -66,29 +59,8 @@ class Pass {
   virtual std::string description() const { return "No DOC"; }
 };
 
-// NodePass process on any Node types.
-class NodePass : public Pass {
- public:
-  virtual void Run(Node *node) = 0;
-};
-
-// NodePass process on any Function node types.
-class FunctionPass : public Pass {
- public:
-  virtual void Run(Function *node) = 0;
-};
-
-// NodePass process on any FunctionBlock node types.
-class FunctionBlockPass : public Pass {
- public:
-  virtual void Run(FunctionBlock *node) = 0;
-};
-
 // GraphPass processes on any GraphType.
-class DataFlowGraphPass : public Pass {
- public:
-  virtual void Run(DataFlowGraph *graph) = 0;
-};
+class DataFlowGraphPass : public AnalysisPass {};
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/analyzer.cc

@@ -15,6 +15,7 @@
 #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"
@@ -58,7 +59,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
   std::string description() const override { return "DFG pass manager."; }
 
  private:
-  void AddPass(const std::string& name, Pass* pass) {
+  void AddPass(const std::string& name, AnalysisPass* pass) {
     VLOG(3) << "Adding pass " << name;
     Register(name, pass);
     AddGraphvizDebugerPass(pass);
@@ -87,7 +88,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
   }
 
   // Add the graphviz debuger pass if the parent pass has one.
-  void AddGraphvizDebugerPass(Pass* pass) {
+  void AddGraphvizDebugerPass(AnalysisPass* pass) {
     auto* debuger_pass = pass->CreateGraphvizDebugerPass();
     if (debuger_pass) {
       Register(debuger_pass->repr(), debuger_pass);
@@ -106,7 +107,6 @@ void Analyzer::Run(Argument* argument) {
     }
   }
   passes.push_back("graph_viz_pass");
-  // Ugly support fluid-to-ir-pass
   argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
 
   for (auto& x : data_) {

paddle/fluid/inference/analysis/analyzer.h

@@ -38,8 +38,9 @@ limitations under the License. */
 #include <gflags/gflags.h>
 #include <string>
 #include <vector>
+
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/flags.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 
 namespace paddle {

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -16,6 +16,7 @@
 
 #include <google/protobuf/text_format.h>
 #include <gtest/gtest.h>
+#include <thread>  // NOLINT
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
@@ -24,12 +25,12 @@
 #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"
 
 DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
 DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
 DEFINE_int32(batch_size, 10, "batch size.");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
+DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
 
 namespace paddle {
 namespace inference {
@@ -220,39 +221,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   }
 }
 
-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: ";
-
-  int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
-                            [](int a, int b) { return a * b; });
-  for (int 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[] = {
@@ -266,11 +234,29 @@ const float ditu_rnn_target_data[] = {
     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};
+void CompareResult(const std::vector<PaddleTensor> &outputs,
+                   const std::vector<PaddleTensor> &base_outputs) {
+  PADDLE_ENFORCE_GT(outputs.size(), 0);
+  PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
+  for (size_t i = 0; i < outputs.size(); i++) {
+    auto &out = outputs[i];
+    auto &base_out = base_outputs[i];
+    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                  [](int a, int b) { return a * b; });
+    size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
+                                   1, [](int a, int b) { return a * b; });
+    PADDLE_ENFORCE_EQ(size, size1);
+    PADDLE_ENFORCE_GT(size, 0);
+    float *data = static_cast<float *>(out.data.data());
+    float *base_data = static_cast<float *>(base_out.data.data());
+    for (size_t i = 0; i < size; i++) {
+      EXPECT_NEAR(data[i], base_data[i], 1e-3);
+    }
+  }
+}
 // 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) {
+void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
+                           int num_threads) {
   AnalysisConfig config;
   config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
   config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
@@ -281,6 +267,8 @@ void TestDituRNNPrediction(const std::string &model_path,
   PADDLE_ENFORCE(config.ir_mode ==
                  AnalysisConfig::IrPassMode::kExclude);  // default
   config.ir_passes.clear();  // Do not exclude any pass.
+  int batch_size = FLAGS_batch_size;
+  int num_times = FLAGS_repeat;
 
   auto base_predictor =
       CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
@@ -288,40 +276,55 @@ void TestDituRNNPrediction(const std::string &model_path,
       CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
           config);
   std::vector<PaddleTensor> input_slots;
-  DataRecord data(data_path, batch_size);
+  DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
   // Prepare inputs.
   PrepareInputs(&input_slots, &data, batch_size);
   std::vector<PaddleTensor> outputs, base_outputs;
 
   base_predictor->Run(input_slots, &base_outputs);
 
+  LOG(INFO) << "===========profile result===========";
+  if (num_threads == 1) {
+    // Prepare inputs.
     Timer timer;
     timer.tic();
     for (int i = 0; i < num_times; i++) {
       predictor->Run(input_slots, &outputs);
     }
-  LOG(INFO) << "===========profile result===========";
-  LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << num_times
-            << ", latency: " << timer.toc() / num_times << "ms";
-  LOG(INFO) << "=====================================";
-
-  PADDLE_ENFORCE_GT(outputs.size(), 0);
-  PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
-  for (size_t i = 0; i < outputs.size(); i++) {
-    auto &out = outputs[i];
-    auto &base_out = base_outputs[i];
-    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
-                                  [](int a, int b) { return a * b; });
-    size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
-                                   1, [](int a, int b) { return a * b; });
-    PADDLE_ENFORCE_EQ(size, size1);
-    PADDLE_ENFORCE_GT(size, 0);
-    float *data = static_cast<float *>(out.data.data());
-    float *base_data = static_cast<float *>(base_out.data.data());
-    for (size_t j = 0; j < size; j++) {
-      EXPECT_NEAR(data[j], base_data[j], 1e-3);
+    PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times);
+    CompareResult(outputs, base_outputs);
+  } else {
+    std::vector<std::thread> threads;
+    std::vector<std::unique_ptr<PaddlePredictor>> predictors;
+    // TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
+    // because AttentionLSTM's hard code nodeid will be damanged.
+    for (int tid = 0; tid < num_threads; ++tid) {
+      predictors.emplace_back(
+          CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+              config));
+    }
+    for (int tid = 0; tid < num_threads; ++tid) {
+      threads.emplace_back([&, tid]() {
+        // Each thread should have local input_slots and outputs.
+        std::vector<PaddleTensor> input_slots;
+        DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
+        PrepareInputs(&input_slots, &data, batch_size);
+        std::vector<PaddleTensor> outputs;
+        Timer timer;
+        timer.tic();
+        for (int i = 0; i < num_times; i++) {
+          predictors[tid]->Run(input_slots, &outputs);
+        }
+        PrintTime(batch_size, num_times, num_threads, tid,
+                  timer.toc() / num_times);
+        CompareResult(outputs, base_outputs);
+      });
     }
+    for (int i = 0; i < num_threads; ++i) {
+      threads[i].join();
     }
+  }
+  LOG(INFO) << "=====================================";
 
   if (use_analysis && activate_ir) {
     AnalysisPredictor *analysis_predictor =
@@ -350,25 +353,26 @@ void TestDituRNNPrediction(const std::string &model_path,
   }
 }
 
-// Directly infer with the original model.
-TEST(Analyzer, DituRNN_without_analysis) {
-  TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
-                        FLAGS_batch_size, false, false, FLAGS_repeat);
+// Inference with analysis and IR, easy for profiling independently.
+TEST(Analyzer, DituRNN) {
+  TestDituRNNPrediction(true, true, FLAGS_num_threads);
 }
 
-// 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,
-                        FLAGS_batch_size, true, false, FLAGS_repeat);
-}
-
-// 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,
-                        FLAGS_batch_size, true, true, FLAGS_repeat);
+// Other unit-tests of DituRNN, test different options of use_analysis,
+// activate_ir and multi-threads.
+TEST(Analyzer, DituRNN_tests) {
+  int num_threads[2] = {1, 4};
+  for (auto i : num_threads) {
+    // Directly infer with the original model.
+    TestDituRNNPrediction(false, false, i);
+    // Inference with the original model with the analysis turned on, the
+    // analysis
+    // module will transform the program to a data flow graph.
+    TestDituRNNPrediction(true, false, i);
+    // Inference with analysis and IR. The IR module will fuse some large
+    // kernels.
+    TestDituRNNPrediction(true, true, i);
+  }
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/test_text_classification.cc → paddle/fluid/inference/analysis/analyzer_text_classification_tester.cc

@@ -12,19 +12,23 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/fluid/inference/analysis/analyzer.h"
 #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 <fstream>
 #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/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.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.");
+DEFINE_int32(topn, -1, "Run top n batches of data to save time");
 
 namespace paddle {
 
@@ -44,41 +48,67 @@ void PrintTime(const double latency, const int bs, const int repeat) {
   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}});
+struct DataReader {
+  DataReader(const std::string &path) : file(new std::ifstream(path)) {}
+
+  bool NextBatch(PaddleTensor *tensor, int batch_size) {
+    PADDLE_ENFORCE_EQ(batch_size, 1);
+    std::string line;
+    tensor->lod.clear();
+    tensor->lod.emplace_back(std::vector<size_t>({0}));
+    std::vector<int64_t> data;
+
+    for (int i = 0; i < batch_size; i++) {
+      if (!std::getline(*file, line)) return false;
+      inference::split_to_int64(line, ' ', &data);
+    }
+    tensor->lod.front().push_back(data.size());
+
+    tensor->data.Resize(data.size() * sizeof(int64_t));
+    memcpy(tensor->data.data(), data.data(), data.size() * sizeof(int64_t));
+    tensor->shape.clear();
+    tensor->shape.push_back(data.size());
+    tensor->shape.push_back(1);
+    return true;
   }
 
+  std::unique_ptr<std::ifstream> file;
+};
+
+void Main(int batch_size) {
   // 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);
 
+  std::vector<PaddleTensor> input_slots(1);
+  // one batch starts
+  // data --
+  auto &input = input_slots[0];
+  input.dtype = PaddleDType::INT64;
+
   inference::Timer timer;
   double sum = 0;
   std::vector<PaddleTensor> output_slots;
-  for (int i = 0; i < FLAGS_repeat; i++) {
+
+  int num_batches = 0;
+  for (int t = 0; t < FLAGS_repeat; t++) {
+    DataReader reader(FLAGS_infer_data);
+    while (reader.NextBatch(&input, FLAGS_batch_size)) {
+      if (FLAGS_topn > 0 && num_batches > FLAGS_topn) break;
       timer.tic();
       CHECK(predictor->Run(input_slots, &output_slots));
       sum += timer.toc();
+      ++num_batches;
+    }
   }
-  PrintTime(sum, batch_size, FLAGS_repeat);
+
+  PrintTime(sum, batch_size, num_batches);
 
   // Get output
   LOG(INFO) << "get outputs " << output_slots.size();
@@ -100,10 +130,3 @@ void Main(int batch_size) {
 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/analysis/data_flow_graph_to_fluid_pass.cc

@@ -263,7 +263,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
 };
 }  // namespace
 
-Pass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
+AnalysisPass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
   return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
       FLAGS_IA_graphviz_log_root,
       "data_flow_graph_to_fluid_graphviz_debugger"));

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h

@@ -21,8 +21,8 @@
 
 #include <string>
 #include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 
 namespace paddle {
 namespace inference {
@@ -42,7 +42,7 @@ class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
     return "Transform a DFG to a Fluid ProgramDesc";
   }
 
-  Pass *CreateGraphvizDebugerPass() const override;
+  AnalysisPass *CreateGraphvizDebugerPass() const override;
 
  protected:
   // Add a Fluid Op into the ProgramDesc.

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h

@@ -21,8 +21,8 @@ limitations under the License. */
 
 #include <fstream>
 #include <string>
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/dot.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 
 namespace paddle {
 namespace inference {

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc

@@ -66,7 +66,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
 };
 }
 
-Pass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
+AnalysisPass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
   return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
       FLAGS_IA_graphviz_log_root, "fluid-to-dfg-debuger"));
 }

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h

@@ -22,8 +22,8 @@
 #include <string>
 
 #include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 
 namespace paddle {
 namespace inference {
@@ -46,7 +46,7 @@ class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
     return "transform a fluid ProgramDesc to a data flow graph.";
   }
 
-  Pass *CreateGraphvizDebugerPass() const override;
+  AnalysisPass *CreateGraphvizDebugerPass() const override;
 
  private:
   framework::proto::ProgramDesc const *desc_;

paddle/fluid/inference/analysis/fluid_to_ir_pass.h

@@ -14,15 +14,17 @@
 
 #pragma once
 
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/flags.h"
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
-using namespace framework;
 
 static const char kFluidToIrPassesAttr[] = "__fluid_to_ir_passes__";
 
@@ -48,7 +50,8 @@ class FluidToIrPass final : public DataFlowGraphPass {
     ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_program_path);
     // Load program.
     auto program = LoadProgramDesc(*argument->fluid_model_program_path);
-    argument->origin_program_desc.reset(new proto::ProgramDesc(program));
+    argument->origin_program_desc.reset(
+        new framework::proto::ProgramDesc(program));
     // Create main data flow graph.
     if (!argument->main_dfg) {
       argument->main_dfg.reset(new DataFlowGraph);
@@ -78,12 +81,13 @@ class FluidToIrPass final : public DataFlowGraphPass {
     IRPassManager ir_passes(argument_->Get<ProgramDesc>("ir_program_desc"),
                             nullptr);
     // Pass the scope from analysis to IR if needed.
-    if (argument_->Has(ir::kParamScopeAttr)) {
+    if (argument_->Has(framework::ir::kParamScopeAttr)) {
       // Here the address is passed, attention that IR doesn't own the scope, so
       // the real scope in analysis should live during the IR phase.
       ir_passes.graph().Set(
-          ir::kParamScopeAttr,
-          new Scope *(&argument_->Get<Scope>(ir::kParamScopeAttr)));
+          framework::ir::kParamScopeAttr,
+          new framework::Scope *(&argument_->Get<framework::Scope>(
+              framework::ir::kParamScopeAttr)));
     }
 
     if (FLAGS_IA_enable_ir) {
@@ -95,12 +99,12 @@ class FluidToIrPass final : public DataFlowGraphPass {
     PADDLE_ENFORCE(argument_->main_dfg.get());
     argument_->main_dfg->Build(ir_passes.graph());
     // inherit the arguments from ir.
-    if (ir_passes.graph().Has(ir::kFuseStatisAttr)) {
+    if (ir_passes.graph().Has(framework::ir::kFuseStatisAttr)) {
       argument_->Set(
-          ir::kFuseStatisAttr,
+          framework::ir::kFuseStatisAttr,
           new std::unordered_map<std::string, int>(
               ir_passes.graph().Get<std::unordered_map<std::string, int>>(
-                  ir::kFuseStatisAttr)));
+                  framework::ir::kFuseStatisAttr)));
     }
   }
 
@@ -112,7 +116,7 @@ class FluidToIrPass final : public DataFlowGraphPass {
 
  private:
   // Load parameters from a single file or from a directory.
-  bool LoadParams(Scope *scope, const std::string &dir,
+  bool LoadParams(framework::Scope *scope, const std::string &dir,
                   const std::string &prog_file, const std::string &param_file);
 
  private:

paddle/fluid/inference/analysis/model_store_pass.h

@@ -19,7 +19,7 @@
 
 #pragma once
 #include <string>
-#include "paddle/fluid/inference/analysis/pass.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 
 namespace paddle {
 namespace inference {

paddle/fluid/inference/analysis/pass_manager.cc

@@ -40,17 +40,6 @@ void DfgPassManager::RunAll() {
   }
 }
 
-void NodePassManager::RunAll() {
-  PADDLE_ENFORCE(argument_);
-  PADDLE_ENFORCE(argument_->main_dfg.get());
-  auto trait = GraphTraits<DataFlowGraph>(*argument_->main_dfg).nodes_in_DFS();
-  for (auto& node : trait) {
-    for (auto& pass : data_) {
-      pass->Run(&node);
-    }
-  }
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/pass_manager.h

@@ -33,7 +33,7 @@ limitations under the License. */
 
 #include <string>
 #include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/pass.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 
 namespace paddle {
 namespace inference {
@@ -43,7 +43,7 @@ namespace analysis {
  * PassManager is the base class for all pass managers, a pass manager has
  * several Pass-es registered, and execute them in the linear order.
  */
-class PassManager : public OrderedRegistry<Pass> {
+class PassManager : public OrderedRegistry<AnalysisPass> {
  public:
   PassManager() = default;
   // Call all the passes' Initialize methods. The desc and data_flow_graph are
@@ -89,18 +89,6 @@ class DfgPassManager : public PassManager {
   virtual ~DfgPassManager() = default;
 };
 
-/*
- * A pass manager that process a Node each time.
- */
-class NodePassManager : public PassManager {
- public:
-  NodePassManager() = default;
-
-  void RunAll() override;
-
-  virtual ~NodePassManager() = default;
-};
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/pass_manager_tester.cc

@@ -34,28 +34,6 @@ class TestDfgPassManager final : public DfgPassManager {
   std::string description() const override { return "test doc"; }
 };
 
-class TestNodePassManager final : public NodePassManager {
- public:
-  virtual ~TestNodePassManager() = default;
-
-  std::string repr() const override { return "test-node-pass-manager"; }
-  std::string description() const override { return "test doc"; }
-};
-
-class TestNodePass final : public NodePass {
- public:
-  virtual ~TestNodePass() = default;
-
-  bool Initialize(Argument* argument) override { return true; }
-
-  void Run(Node* node) override {
-    LOG(INFO) << "- Processing node " << node->repr();
-  }
-
-  std::string repr() const override { return "test-node"; }
-  std::string description() const override { return "some doc"; }
-};
-
 TEST(PassManager, DFG_pass_manager) {
   TestDfgPassManager manager;
   DFG_GraphvizDrawPass::Config config("./", "dfg.dot");
@@ -71,19 +49,6 @@ TEST(PassManager, DFG_pass_manager) {
   manager.RunAll();
 }
 
-TEST(PassManager, Node_pass_manager) {
-  Argument argument(FLAGS_inference_model_dir);
-  // Pre-process: initialize the DFG with the ProgramDesc first.
-  FluidToDataFlowGraphPass pass0;
-  pass0.Initialize(&argument);
-  pass0.Run(argument.main_dfg.get());
-
-  TestNodePassManager manager;
-  manager.Register("test-node-pass", new TestNodePass);
-  ASSERT_TRUE(manager.Initialize(&argument));
-  manager.RunAll();
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.cc

@@ -68,7 +68,7 @@ class DfgDebuggerPass : public DFG_GraphvizDrawPass {
   }
 };
 
-Pass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
+AnalysisPass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
   DFG_GraphvizDrawPass::Config config(FLAGS_IA_graphviz_log_root,
                                       "tensorrt_marked_node");
   return new DfgDebuggerPass(config);

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h

@@ -20,7 +20,7 @@
 #pragma once
 
 #include <string>
-#include "paddle/fluid/inference/analysis/pass.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/subgraph_splitter.h"
 
 namespace paddle {
@@ -48,7 +48,7 @@ class TensorRTSubgraphNodeMarkPass : public DataFlowGraphPass {
     return "tensorrt sub-graph mark pass";
   }
 
-  Pass* CreateGraphvizDebugerPass() const override;
+  AnalysisPass* CreateGraphvizDebugerPass() const override;
   bool Finalize() override;
 
  private:

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h

@@ -15,8 +15,8 @@ limitations under the License. */
 #pragma once
 
 #include <string>
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/node.h"
-#include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/subgraph_splitter.h"
 
 namespace paddle {

paddle/fluid/inference/api/CMakeLists.txt

@@ -44,20 +44,7 @@ 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
-          ir_pass_manager
-          pass
-          fc_fuse_pass
-          fc_lstm_fuse_pass
-          fc_gru_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_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc
@@ -74,7 +61,7 @@ cc_library(paddle_inference_tensorrt_subgraph_engine
 inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
 endif()
 
-if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
+if (WITH_ANAKIN AND WITH_MKL) # 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 mklml)
     cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
@@ -84,12 +71,24 @@ if (WITH_ANAKIN AND WITH_GPU) # only needed in CI
     anakin_target(inference_anakin_api)
     anakin_target(inference_anakin_api_shared)
     if (WITH_TESTING)
+        # TODO(luotao): ANAKIN_MODLE_URL etc will move to demo ci later.
+        set(INFERENCE_URL "http://paddle-inference-dist.bj.bcebos.com")
+        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_RNN_MODLE_URL} -N")
+        execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_DATA_URL} -N")
+        if(WITH_GPU)
+            set(anakin_test_extra_deps dynload_cuda)
+            set(ANAKIN_MODLE_URL "${INFERENCE_URL}/mobilenet_v2.anakin.bin")
+            execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL} -N")
             cc_test(api_anakin_engine_tester SRCS api_anakin_engine_tester.cc 
                     ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
-                DEPS inference_anakin_api_shared dynload_cuda SERIAL)
+                    DEPS inference_anakin_api_shared ${anakin_test_extra_deps} SERIAL)
+        endif()
         cc_test(api_anakin_engine_rnn_tester SRCS api_anakin_engine_rnn_tester.cc 
                 ARGS --model=${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)
+                DEPS inference_anakin_api_shared ${anakin_test_extra_deps} SERIAL)
     endif(WITH_TESTING)
 endif()

paddle/fluid/inference/api/analysis_predictor.cc

@@ -48,7 +48,6 @@ bool AnalysisPredictor::Init(
   } else {
     place_ = paddle::platform::CPUPlace();
   }
-  PADDLE_ENFORCE(!parent_scope);
   if (parent_scope) {
     scope_ = parent_scope;
     sub_scope_ = &(parent_scope->NewScope());

paddle/fluid/inference/api/api_anakin_engine.cc

@@ -193,7 +193,9 @@ PaddleInferenceAnakinPredictor<Target>::Clone() {
   return std::move(cls);
 }
 
+#ifdef PADDLE_WITH_CUDA
 template class PaddleInferenceAnakinPredictor<anakin::NV>;
+#endif
 template class PaddleInferenceAnakinPredictor<anakin::X86>;
 
 // A factory to help create difference predictor.
@@ -202,10 +204,15 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
     AnakinConfig, PaddleEngineKind::kAnakin>(const AnakinConfig &config) {
   VLOG(3) << "Anakin Predictor create.";
   if (config.target_type == AnakinConfig::NVGPU) {
+#ifdef PADDLE_WITH_CUDA
     VLOG(3) << "Anakin Predictor create on [ NVIDIA GPU ].";
     std::unique_ptr<PaddlePredictor> x(
         new PaddleInferenceAnakinPredictor<anakin::NV>(config));
     return x;
+#else
+    LOG(ERROR) << "AnakinConfig::NVGPU could not used in ONLY-CPU environment";
+    return nullptr;
+#endif
   } else if (config.target_type == AnakinConfig::X86) {
     VLOG(3) << "Anakin Predictor create on [ Intel X86 ].";
     std::unique_ptr<PaddlePredictor> x(

paddle/fluid/inference/api/helper.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <glog/logging.h>
 #include <sys/time.h>
 #include <algorithm>
 #include <numeric>
@@ -88,5 +89,45 @@ static void TensorAssignData(PaddleTensor *tensor,
   }
 }
 
+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: ";
+
+  int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
+                            [](int a, int b) { return a * b; });
+  for (int i = 0; i < dim; i++) {
+    os << static_cast<float *>(tensor.data.data())[i] << " ";
+  }
+  os << '\n';
+  return os.str();
+}
+
+void PrintTime(int batch_size, int repeat, int num_threads, int tid,
+               double latency) {
+  LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << repeat
+            << ", threads: " << num_threads << ", thread id: " << tid
+            << ", latency: " << latency << "ms";
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/operators/fake_quantize_op.cu

@@ -119,7 +119,8 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
                   const framework::Tensor& last_scale,
                   const framework::Tensor& iter, const int window_size,
                   framework::Tensor* scales_arr, framework::Tensor* out_scale) {
-    auto& gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+    const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
+
     T* scale_arr = scales_arr->mutable_data<T>(gpu_place);
     T* out_scale_data = out_scale->mutable_data<T>(gpu_place);
 

paddle/fluid/operators/flatten_op.cc

@@ -157,6 +157,116 @@ class FlattenGradOp : public framework::OperatorBase {
   }
 };
 
+// FIXME(zcd): flatten2 adds an intermediate output(XShape) based on flatten,
+// the XShape is used to carry the shape and lod of X which will be used in
+// flatten_grad, in this way, the framework can reuse the memory of X
+// immediately the flatten2_op is finished.
+// Considering compatibility issues, we could not fix flatten2_op
+class Flatten2OpInferShape : public FlattenOpInferShape {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    FlattenOpInferShape::operator()(ctx);
+    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
+                   "Output (XShape) of Flatten op should not be null.");
+    const auto &in_dims = ctx->GetInputDim("X");
+    std::vector<int64_t> xshape_dims(in_dims.size() + 1);
+    xshape_dims[0] = 0;
+    for (int i = 0; i < in_dims.size(); ++i) {
+      xshape_dims[i + 1] = in_dims[i];
+    }
+    ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
+    ctx->ShareLoD("X", "XShape");
+  }
+};
+
+class Flatten2Op : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto &axis = Attr<int>("axis");
+    auto in_dims =
+        scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = out_dims;
+    attrs["inplace"] = false;
+    // Invoke Reshape Op
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
+        {{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+class Flatten2OpMaker : public FlattenOpMaker {
+ public:
+  void Make() override {
+    FlattenOpMaker::Make();
+    AddOutput("XShape",
+              "XShape is just used to store the shape and lod of X, which will "
+              "be used in FlattenGradOp.")
+        .AsIntermediate();
+  }
+};
+
+class Flatten2GradOpMaker : 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("flatten2_grad");
+    grad_op->SetInput("XShape", Output("XShape"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+class Flatten2GradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("XShape"),
+                   "Input(XShape) shouldn't be null.");
+    PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    auto xshape_dims = context->GetInputDim("XShape");
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+    context->SetOutputDim(framework::GradVarName("X"), x_dims);
+    context->ShareLoD("XShape", framework::GradVarName("X"));
+  }
+};
+
+class Flatten2GradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+    auto xshape_name = Input("XShape");
+    auto xshape_dims =
+        scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(x_dims);
+    attrs["inplace"] = false;
+
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {dout_name}}, {"Shape", {}}},
+        {{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -167,3 +277,8 @@ REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
                   ops::FlattenOpInferShape,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
+
+REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
+                  ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
+REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
+                  ops::Flatten2GradInferShape);

paddle/fluid/operators/fusion_gru_op.cc

@@ -30,14 +30,7 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
                  "Input(WeightX) of GRU should not be null.");
   PADDLE_ENFORCE(ctx->HasInput("WeightH"),
                  "Input(WeightH) of GRU should not be null.");
-
   PADDLE_ENFORCE(ctx->HasOutput("XX"), "Output(XX) of GRU should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
-                 "Output(ReorderedH0) of GRU should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
-                 "Output(BatchedInput) of GRU should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
-                 "Output(BatchedOut) of GRU should not be null.");
   PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
                  "Output(Hidden) of GRU should not be null.");
 
@@ -80,15 +73,20 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
   }
   framework::DDim out_dims({x_dims[0], frame_size});
   ctx->SetOutputDim("Hidden", out_dims);
-  ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
-  ctx->SetOutputDim("BatchedOut", out_dims);
   ctx->ShareLoD("X", "Hidden");
-
   int xx_width;
   if (ctx->Attrs().Get<bool>("use_seq")) {
     xx_width = wx_dims[1];
   } else {
     xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
+    PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
+                   "Output(ReorderedH0) of GRU should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
+                   "Output(BatchedInput) of GRU should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
+                   "Output(BatchedOut) of GRU should not be null.");
+    ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
+    ctx->SetOutputDim("BatchedOut", out_dims);
   }
   ctx->SetOutputDim("XX", {x_dims[0], xx_width});
   ctx->ShareLoD("X", "XX");

paddle/fluid/operators/fusion_lstm_op.cc

@@ -38,16 +38,6 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
                  "Output(Hidden) of LSTM should not be null.");
   PADDLE_ENFORCE(ctx->HasOutput("Cell"),
                  "Output(Cell) of LSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
-                 "Output(BatchedInput) of LSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("BatchedHidden"),
-                 "Output(BatchedHidden) of LSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("BatchedCell"),
-                 "Output(BatchedCell) of LSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
-                 "Output(ReorderedH0) of LSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("ReorderedC0"),
-                 "Output(ReorderedC0) of LSTM should not be null.");
 
   auto x_dims = ctx->GetInputDim("X");
   PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
@@ -88,9 +78,8 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
   PADDLE_ENFORCE_EQ(b_dims[0], 1,
                     "The first dimension of Input(Bias) should be 1.");
-
-  auto use_peepholes = ctx->Attrs().Get<bool>("use_peepholes");
-  PADDLE_ENFORCE_EQ(b_dims[1], (use_peepholes ? 7 : 4) * frame_size,
+  PADDLE_ENFORCE_EQ(
+      b_dims[1], (ctx->Attrs().Get<bool>("use_peepholes") ? 7 : 4) * frame_size,
       "The second dimension of Input(Bias) should be "
       "7 * %d if enable peepholes connection or"
       "4 * %d if disable peepholes",
@@ -99,17 +88,26 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   framework::DDim out_dims({x_dims[0], frame_size});
   ctx->SetOutputDim("Hidden", out_dims);
   ctx->SetOutputDim("Cell", out_dims);
-  ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
-  ctx->SetOutputDim("BatchedHidden", out_dims);
-  ctx->SetOutputDim("BatchedCell", out_dims);
   ctx->ShareLoD("X", "Hidden");
   ctx->ShareLoD("X", "Cell");
-
   int xx_width;
   if (ctx->Attrs().Get<bool>("use_seq")) {
     xx_width = wx_dims[1];
   } else {
     xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
+    PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
+                   "Output(BatchedInput) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchedHidden"),
+                   "Output(BatchedHidden) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchedCell"),
+                   "Output(BatchedCell) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
+                   "Output(ReorderedH0) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("ReorderedC0"),
+                   "Output(ReorderedC0) of LSTM should not be null.");
+    ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
+    ctx->SetOutputDim("BatchedHidden", out_dims);
+    ctx->SetOutputDim("BatchedCell", out_dims);
   }
   ctx->SetOutputDim("XX", {x_dims[0], xx_width});
   ctx->ShareLoD("X", "XX");
@@ -242,8 +240,8 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
   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");
+  bool is_reverse = ctx.Attr<bool>("is_reverse");     \
+  bool use_peepholes = ctx.Attr<bool>("use_peepholes");
 
 #define INIT_BASE_SIZES                  \
   auto x_dims = x->dims();   /* T x M*/  \
@@ -254,172 +252,183 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
   const int D3 = D * 3;                  \
   const int D4 = wh_dims[1];
 
+#define INIT_BASE_INPUT_DATAS                                        \
+  const T* x_data = x->data<T>();                                    \
+  const T* wx_data = wx->data<T>();                                  \
+  const T* wh_data = wh->data<T>();                                  \
+  /* diagonal weight*/                                               \
+  const T* wc_data = bias->data<T>() + D4;                           \
+  /* for peephole only*/                                             \
+  Tensor checked_cell;                                               \
+  T* checked_cell_data = nullptr;                                    \
+  auto place = ctx.GetPlace();                                       \
+  if (use_peepholes) {                                               \
+    /* w_ic * Ct-1, w_fc * Ct-1  ; w_oc * Ct => ih*/                 \
+    checked_cell_data = checked_cell.mutable_data<T>({2, D}, place); \
+  }
+
+/// Compute LSTM
+#define GEMM_WH_ADDON(bs, prev, out)                                           \
+  blas.GEMM(CblasNoTrans, CblasNoTrans, bs, D4, D, static_cast<T>(1), prev, D, \
+            wh_data, D4, static_cast<T>(1), out, D4)
+
+// gates: W_ch, W_ih, W_fh, W_oh
+#define GET_Ct(ct_1, gates, ct)                   \
+  /* C_t = C_t-1 * fgated + cand_gated * igated*/ \
+  act_cand(D, gates, gates);                      \
+  blas.VMUL(D, gates, gates + D, gates + D);      \
+  blas.VMUL(D, ct_1, gates + D2, gates + D2);     \
+  blas.VADD(D, gates + D, gates + D2, ct)
+
+#define GET_Ht(ct, gates, ht)        \
+  /* H_t = act_cell(C_t) * ogated */ \
+  act_cell(D, ct, gates + D2);       \
+  blas.VMUL(D, gates + D2, gates + D3, ht)
+
+#define GET_Ct_NOH0C0(gates, ct)     \
+  /* C_t = igated * cgated*/         \
+  act_gate(D, gates + D, gates + D); \
+  act_cand(D, gates, gates);         \
+  blas.VMUL(D, gates, gates + D, ct)
+
+#define COMPUTE_CtHt_NOH0C0(gates, ct, ht) \
+  GET_Ct_NOH0C0(gates, ct);                \
+  act_gate(D, gates + D3, gates + D3);     \
+  GET_Ht(ct, gates, ht)
+
+#define COMPUTE_CtHt_PEEPHOLE_NOH0C0(gates, ct, ht) \
+  GET_Ct_NOH0C0(gates, ct);                         \
+  /* get outgated, put W_oc * C_t on igated */      \
+  blas.VMUL(D, wc_data + D2, ct, gates + D);        \
+  blas.VADD(D, gates + D, gates + D3, gates + D3);  \
+  act_gate(D, gates + D3, gates + D3);              \
+  GET_Ht(ct, gates, ht)
+
+#define COMPUTE_CtHt(gates, ct_1, ct, ht) \
+  act_gate(D3, gates + D, gates + D);     \
+  GET_Ct(ct_1, gates, ct);                \
+  GET_Ht(ct, gates, ht)
+
+#define COMPUTE_CtHt_PEEPHOLE(gates, ct_1, ct, ht)        \
+  /* get fgated and igated*/                              \
+  blas.VMUL(D, wc_data, ct_1, checked_cell_data);         \
+  blas.VMUL(D, wc_data + D, ct_1, checked_cell_data + D); \
+  blas.VADD(D2, checked_cell_data, gates + D, gates + D); \
+  act_gate(D2, gates + D, gates + D);                     \
+  GET_Ct(ct_1, gates, ct);                                \
+  /* get ogated*/                                         \
+  blas.VMUL(D, wc_data + D2, ct, gates + D);              \
+  blas.VADD(D, gates + D, gates + D3, gates + D3);        \
+  act_gate(D, gates + D3, gates + D3);                    \
+  GET_Ht(ct, gates, ht)
+
   void SeqCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
     INIT_BASE_INPUT_OUTPUT
     INIT_BASE_SIZES
     INIT_VEC_FUNC
+    INIT_BASE_INPUT_DATAS
 
     auto x_lod = x->lod();
     const int total_T = x_dims[0];
-    const int N = x_lod[0].size() - 1;  // batch size
-
-    const T* x_data = x->data<T>();
+    const int N = x_lod[0].size() - 1;
     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());
-
+    T* xx_data = xx->mutable_data<T>(place);
+    T* h_out_data = hidden_out->mutable_data<T>(place);
+    T* c_out_data = cell_out->mutable_data<T>(place);
     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>());
+
     int xx_offset = D4;
     int gate_offset = D;
     if (is_reverse) {
       const int offset = (total_T - 1) * D;
       xx_data = xx_data + offset * 4;
-      hidden_out_data = hidden_out_data + offset;
-      cell_out_data = cell_out_data + offset;
+      h_out_data = h_out_data + offset;
+      c_out_data = c_out_data + offset;
       xx_offset = -D4;
       gate_offset = -D;
     }
 
-    auto move_step = [&]() {
-      xx_data = xx_data + xx_offset;
-      hidden_out_data = hidden_out_data + gate_offset;
-      cell_out_data = cell_out_data + gate_offset;
-    };
-
-    for (int i = 0; i < N; ++i) {
-      int bid = is_reverse ? N - 1 - i : i;
-      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 {
-        // 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
+#define MOVE_ONE_STEP                    \
+  prev_h_data = h_out_data;              \
+  prev_c_data = c_out_data;              \
+  xx_data = xx_data + xx_offset;         \
+  h_out_data = h_out_data + gate_offset; \
+  c_out_data = c_out_data + gate_offset
+
+#define PROCESS_H0C0_DEFINES                       \
+  int bid = is_reverse ? N - 1 - i : i;            \
+  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
+
+#define PROCESS_H0C0_PEEPHOLE                                      \
+  PROCESS_H0C0_DEFINES;                                            \
+  if (h0_data) {                                                   \
+    prev_h_data = h0_data + bid * D;                               \
+    prev_c_data = c0_data + bid * D;                               \
+  } else {                                                         \
+    COMPUTE_CtHt_PEEPHOLE_NOH0C0(xx_data, c_out_data, h_out_data); \
+    MOVE_ONE_STEP;                                                 \
+    tstart = 1;                                                    \
+  }
 
-        // ~C_t
-        act_cand(D, xx_data, xx_data);
-        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);
+#define PROCESS_H0C0                                      \
+  PROCESS_H0C0_DEFINES;                                   \
+  if (h0_data) {                                          \
+    prev_h_data = h0_data + bid * D;                      \
+    prev_c_data = c0_data + bid * D;                      \
+  } else {                                                \
+    COMPUTE_CtHt_NOH0C0(xx_data, c_out_data, h_out_data); \
+    MOVE_ONE_STEP;                                        \
+    tstart = 1;                                           \
   }
-        // 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);
+      for (int i = 0; i < N; ++i) {
+        PROCESS_H0C0_PEEPHOLE
+        for (int step = tstart; step < seq_len; ++step) {
+          GEMM_WH_ADDON(1, prev_h_data, xx_data);
+          COMPUTE_CtHt_PEEPHOLE(xx_data, prev_c_data, c_out_data, h_out_data);
+          MOVE_ONE_STEP;
         }
-
-        // 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;
-        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);
-
-        // ~C_t
-        act_cand(D, xx_data, xx_data);
-
-        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);
+      for (int i = 0; i < N; ++i) {
+        PROCESS_H0C0
+        for (int step = tstart; step < seq_len; ++step) {
+          GEMM_WH_ADDON(1, prev_h_data, xx_data);
+          COMPUTE_CtHt(xx_data, prev_c_data, c_out_data, h_out_data);
+          MOVE_ONE_STEP;
         }
-
-        // F_t * C_t-1
-        blas.VMUL(D, xx_data + D2, prev_c_data, xx_data + D2);
-        // I_t * ~C_t
-        blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
-        // 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
-        prev_h_data = hidden_out_data;
-        prev_c_data = cell_out_data;
-
-        move_step();
-      }  // for each step in batch
-    }    // for each batch
+    }
+#undef PROCESS_H0C0_DEFINES
+#undef PROCESS_H0C0_PEEPHOLE
+#undef PROCESS_H0C0
+#undef MOVE_ONE_STEP
   }
 
   void BatchCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = platform::CPUDeviceContext;
     INIT_BASE_INPUT_OUTPUT
-    if (x->lod()[0].size() == 2) {  // batch size == 1
+    if (x->lod()[0].size() == 2) {
       SeqCompute(ctx);
       return;
     }
     INIT_BASE_SIZES
     INIT_VEC_FUNC
+    INIT_BASE_INPUT_DATAS
 
     auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
     auto* reordered_c0 = ctx.Output<Tensor>("ReorderedC0");
     auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
     auto* batched_c_out = ctx.Output<LoDTensor>("BatchedCell");
     auto* batched_h_out = ctx.Output<LoDTensor>("BatchedHidden");
-
-    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);
     T* batched_c_out_data = batched_c_out->mutable_data<T>(place);
@@ -427,12 +436,6 @@ 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);
@@ -454,27 +457,17 @@ 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_batch_h_data = reordered_h0_data;
-      prev_batch_c_data = reordered_c0_data;
+      prev_h_data = reordered_h0_data;
+      prev_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);
@@ -483,123 +476,80 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         reordered_c0_data += D;
       }
     } else {
-      // 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);
-
+      // 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;
+      for (int i = 0; i < max_bs; ++i) {
+        GET_Ct_NOH0C0(cur_in_data, cur_c_out_data);
         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);
+          blas.VMUL(D, wc_data + D2, cur_c_out_data, cur_in_data + D);
+          blas.VADD(D, cur_in_data + D, cur_in_data + D3, cur_in_data + D3);
         }
-
-        // 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);
-
-        // move to next data in the same batch
+        GET_Ht(cur_c_out_data, cur_in_data, cur_h_out_data);
         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;
     }
-
     const auto& batch_starts = batched_lod[0];
     const int max_seq_len = batch_starts.size() - 1;
-    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_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);
+    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;
+
+#define DEFINE_CUR                        \
+  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
+
+#define MOVE_ONE_BATCH  \
+  cur_in_data += D4;    \
+  cur_prev_c_data += D; \
+  cur_c_out_data += D;  \
+  cur_h_out_data += D
+
+#define MOVE_ONE_STEP                  \
+  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
 
     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);
+      for (int step = tstart; step < max_seq_len; ++step) {
+        const int cur_bs = batch_starts[step + 1] - batch_starts[step];
+        GEMM_WH_ADDON(cur_bs, prev_h_data, batched_input_data);
+        DEFINE_CUR;
+        for (int i = 0; i < cur_bs; ++i) {
+          COMPUTE_CtHt_PEEPHOLE(cur_in_data, cur_prev_c_data, cur_c_out_data,
+                                cur_h_out_data);
+          MOVE_ONE_BATCH;
         }
-
-        // 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);
-        // 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);
+        MOVE_ONE_STEP;
       }
-
-        // 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);
-
-        // move to next data in same batch
-        next_data_in_batch();
+    } else {
+      for (int step = tstart; step < max_seq_len; ++step) {
+        const int cur_bs = batch_starts[step + 1] - batch_starts[step];
+        GEMM_WH_ADDON(cur_bs, prev_h_data, batched_input_data);
+        DEFINE_CUR;
+        for (int i = 0; i < cur_bs; ++i) {
+          COMPUTE_CtHt(cur_in_data, cur_prev_c_data, cur_c_out_data,
+                       cur_h_out_data);
+          MOVE_ONE_BATCH;
+        }
+        MOVE_ONE_STEP;
       }
-      // 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);
     }
+#undef MOVE_ONE_STEP
+#undef MOVE_ONE_BATCH
+#undef DEFINE_CUR
 
     math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
     batched_h_out->set_lod(batched_lod);
@@ -615,6 +565,16 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
       BatchCompute(ctx);
     }
   }
+
+#undef COMPUTE_CtHt_PEEPHOLE
+#undef COMPUTE_CtHt
+#undef GET_Ct_NOH0C0
+#undef COMPUTE_CtHt_NOH0C0
+#undef COMPUTE_CtHt_PEEPHOLE_NOH0C0
+#undef GET_Ht
+#undef GET_Ct
+#undef GEMM_WH_ADDON
+#undef INIT_BASE_INPUT_DATAS
 #undef INIT_BASE_SIZES
 #undef INIT_BASE_INPUT_OUTPUT
 #undef INIT_VEC_FUNC

paddle/fluid/operators/layer_norm_op.cu

@@ -67,27 +67,27 @@ template <typename T, int BlockDim>
 __global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
                                  T *y, T *mean, T *var, float epsilon,
                                  int feature_size) {
-  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<double>, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
 
   int beg_idx = blockIdx.x * feature_size + threadIdx.x;
   int end_idx = (blockIdx.x + 1) * feature_size;
 
   // Step 1: Reduce to calculate mean and var
-  T mean_val = static_cast<T>(0);
-  T var_val = static_cast<T>(0);
+  double mean_val = 0;
+  double var_val = 0;
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
     T tmp = x[i];
     mean_val += tmp;
     var_val += (tmp * tmp);
   }
   auto pair = BlockReduce(temp_storage)
-                  .Reduce(PairForLayerNorm<T>(mean_val, var_val),
-                          PairForLayerNormAddFunctor<T>());
+                  .Reduce(PairForLayerNorm<double>(mean_val, var_val),
+                          PairForLayerNormAddFunctor<double>());
   if (threadIdx.x == 0) {
     auto tmp = pair.first_ / feature_size;
-    mean[blockIdx.x] = tmp;
-    var[blockIdx.x] = pair.second_ / feature_size - tmp * tmp;
+    mean[blockIdx.x] = static_cast<T>(tmp);
+    var[blockIdx.x] = static_cast<T>(pair.second_ / feature_size - tmp * tmp);
   }
   __syncthreads();
   mean_val = mean[blockIdx.x];

paddle/fluid/operators/reshape_op.cc

@@ -246,6 +246,88 @@ class ReshapeGradKernel {
   }
 };
 
+// FIXME(zcd): reshape2 adds an intermediate output(XShape) based on reshape,
+// the XShape is used to carry the shape and lod of X which will be used in
+// reshape_grad, in this way, the framework can reuse the memory of X
+// immediately the reshape_op is finished.
+// Considering compatibility issues, we could not fix reshape_op
+class Reshape2Op : public ReshapeOp {
+ public:
+  Reshape2Op(const std::string &type, const framework::VariableNameMap &inputs,
+             const framework::VariableNameMap &outputs,
+             const framework::AttributeMap &attrs)
+      : ReshapeOp(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    ReshapeOp::InferShape(ctx);
+    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
+                   "Output(XShape) of ReshapeOp should not be null.");
+    const auto &x_dims = ctx->GetInputDim("X");
+    std::vector<int64_t> xshape_dims(x_dims.size() + 1);
+    xshape_dims[0] = 0;
+    for (int i = 0; i < x_dims.size(); ++i) {
+      xshape_dims[i + 1] = x_dims[i];
+    }
+    ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
+    ctx->ShareLoD("X", /*->*/ "XShape");
+  }
+};
+
+class Reshape2OpMaker : public ReshapeOpMaker {
+ public:
+  void Make() override {
+    ReshapeOpMaker::Make();
+    AddOutput("XShape",
+              "XShape is just used to store the shape and lod of X, which will "
+              "be used in FlattenGradOp.")
+        .AsIntermediate();
+  }
+};
+
+class Reshape2GradMaker : 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("reshape2_grad");
+    grad_op->SetInput("XShape", Output("XShape"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+class Reshape2GradOp : public framework::OperatorWithKernel {
+ public:
+  Reshape2GradOp(const std::string &type,
+                 const framework::VariableNameMap &inputs,
+                 const framework::VariableNameMap &outputs,
+                 const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    auto xshape_dims = ctx->GetInputDim("XShape");
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    ctx->ShareLoD("XShape", framework::GradVarName("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
+                ->type()),
+        ctx.device_context());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;
@@ -261,6 +343,17 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
                                ops::ReshapeGradKernel, int64_t,
                                ops::ReshapeGradKernel);
 
+REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
+                  ops::Reshape2GradMaker);
+REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
+                               ops::ReshapeKernel, int, ops::ReshapeKernel,
+                               int64_t, ops::ReshapeKernel);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
+                               double, ops::ReshapeGradKernel, int,
+                               ops::ReshapeGradKernel, int64_t,
+                               ops::ReshapeGradKernel);
+
 #ifdef PADDLE_WITH_CUDA
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
                                 ops::ReshapeKernel, int, ops::ReshapeKernel,
@@ -269,4 +362,11 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
                                 double, ops::ReshapeGradKernel, int,
                                 ops::ReshapeGradKernel, int64_t,
                                 ops::ReshapeGradKernel);
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
+                                ops::ReshapeKernel, int, ops::ReshapeKernel,
+                                int64_t, ops::ReshapeKernel);
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
+                                double, ops::ReshapeGradKernel, int,
+                                ops::ReshapeGradKernel, int64_t,
+                                ops::ReshapeGradKernel);
 #endif

paddle/fluid/operators/squeeze_op.cc

@@ -181,6 +181,113 @@ class SqueezeGradOp : public framework::OperatorBase {
   }
 };
 
+// FIXME(zcd): squeeze2 adds an intermediate output(XShape) based on squeeze,
+// the XShape is used to carry the shape and lod of X which will be used in
+// squeeze_grad, in this way, the framework can reuse the memory of X
+// immediately the squeeze2_op is finished.
+// Considering compatibility issues, we could not fix squeeze2_op
+class Squeeze2OpMaker : public SqueezeOpMaker {
+ public:
+  void Make() override {
+    SqueezeOpMaker::Make();
+    AddOutput("XShape",
+              "XShape is just used to store the shape and lod of X, which will "
+              "be used in SqueezeGradOp.")
+        .AsIntermediate();
+  }
+};
+
+class Squeeze2OpInferShape : public SqueezeOpInferShape {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    SqueezeOpInferShape::operator()(ctx);
+    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
+                   "Output(XShape) of Squeeze operator should not be null.");
+    const auto &x_dims = ctx->GetInputDim("X");
+    std::vector<int64_t> xshape_dims(x_dims.size() + 1);
+    xshape_dims[0] = 0;
+    for (int i = 0; i < x_dims.size(); ++i) {
+      xshape_dims[i + 1] = x_dims[i];
+    }
+    ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
+    ctx->ShareLoD("X", /*->*/ "XShape");
+  }
+};
+
+class Squeeze2Op : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto &axes = Attr<std::vector<int>>("axes");
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims);
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(out_dims);
+    // Invoke Reshape Op
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
+        {{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+class Squeeze2GradOpMaker : 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("squeeze2_grad");
+    grad_op->SetInput("XShape", Output("XShape"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+class Squeeze2GradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("XShape"),
+                   "Input(XShape) shouldn't be null.");
+    PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    auto xshape_dims = context->GetInputDim("XShape");
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+    context->SetOutputDim(framework::GradVarName("X"), x_dims);
+    context->ShareLoD("XShape", framework::GradVarName("X"));
+  }
+};
+
+class Squeeze2GradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+    auto xshape_name = Input("XShape");
+    auto xshape_dims =
+        scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(x_dims);
+
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {dout_name}}, {"Shape", {}}},
+        {{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -192,3 +299,8 @@ REGISTER_OPERATOR(squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
                   ops::SqueezeOpInferShape,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp, ops::SqueezeGradInferShape);
+
+REGISTER_OPERATOR(squeeze2, ops::Squeeze2Op, ops::Squeeze2OpMaker,
+                  ops::Squeeze2OpInferShape, ops::Squeeze2GradOpMaker);
+REGISTER_OPERATOR(squeeze2_grad, ops::Squeeze2GradOp,
+                  ops::Squeeze2GradInferShape);

paddle/fluid/operators/transpose_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/transpose_op.h"
+#include <string>
 #include <vector>
 
 namespace paddle {
@@ -24,7 +25,7 @@ class TransposeOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
     auto x_dims = ctx->GetInputDim("X");
@@ -101,7 +102,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) should not be null");
@@ -113,6 +114,93 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
   }
 };
 
+// FIXME(zcd): transpose2 adds an intermediate output(XShape) based on
+// transpose, the XShape is used to carry the shape and lod of X which
+// will be used in transpose_grad, in this way, the framework can reuse
+// the memory of X immediately the transpose2_op is finished.
+// Considering compatibility issues, we could not fix transpose2_op
+class Transpose2Op : public TransposeOp {
+ public:
+  Transpose2Op(const std::string &type,
+               const framework::VariableNameMap &inputs,
+               const framework::VariableNameMap &outputs,
+               const framework::AttributeMap &attrs)
+      : TransposeOp(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    TransposeOp::InferShape(ctx);
+    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
+                   "Output(XShape) should not be null");
+    const auto &in_dims = ctx->GetInputDim("X");
+    std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
+    x_shape_dim[0] = 0;
+    for (int i = 0; i < in_dims.size(); ++i) {
+      x_shape_dim[i + 1] = in_dims[i];
+    }
+    ctx->SetOutputDim("XShape", framework::make_ddim(x_shape_dim));
+    ctx->ShareLoD("X", /*->*/ "XShape");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
+class Transpose2OpMaker : public TransposeOpMaker {
+ public:
+  void Make() override {
+    TransposeOpMaker::Make();
+    AddOutput("XShape", "(Tensor)The output tensor.").AsIntermediate();
+  }
+};
+
+class Transpose2GradMaker : 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("transpose2_grad");
+    grad_op->SetInput("XShape", Output("XShape"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+class Transpose2OpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) should not be null");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) should not be null");
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      auto xshape_dim = ctx->GetInputDim("XShape");
+      auto x_shape_dim =
+          framework::slice_ddim(xshape_dim, 1, xshape_dim.size());
+      ctx->SetOutputDim(framework::GradVarName("X"), x_shape_dim);
+      ctx->ShareLoD("XShape", framework::GradVarName("X"));
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
+                ->type()),
+        ctx.device_context());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -120,8 +208,20 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
+
 REGISTER_OP_CPU_KERNEL(
     transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
 REGISTER_OP_CPU_KERNEL(
     transpose_grad,
     ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
+
+REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
+                  ops::Transpose2GradMaker);
+REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    transpose2,
+    ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
+REGISTER_OP_CPU_KERNEL(
+    transpose2_grad,
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/transpose_op.cu.cc

@@ -21,3 +21,10 @@ REGISTER_OP_CUDA_KERNEL(
 REGISTER_OP_CUDA_KERNEL(
     transpose_grad,
     ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
+
+REGISTER_OP_CUDA_KERNEL(
+    transpose2,
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
+REGISTER_OP_CUDA_KERNEL(
+    transpose2_grad,
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/unsqueeze_op.cc

@@ -168,6 +168,112 @@ class UnsqueezeGradOp : public framework::OperatorBase {
   }
 };
 
+// FIXME(zcd): unsqueeze2 adds an intermediate output(XShape) based on
+// unsqueeze, the XShape is used to carry the shape and lod of X which
+// will be used in unsqueeze_grad, in this way, the framework can reuse
+// the memory of X immediately the unsqueeze2_op is finished.
+// Considering compatibility issues, we could not fix unsqueeze2_op
+class Unsqueeze2OpInferShape : public UnsqueezeOpInferShape {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    UnsqueezeOpInferShape::operator()(ctx);
+    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
+                   "Output(XShape) of Unsqueeze operator should not be null.");
+    const auto &x_dims = ctx->GetInputDim("X");
+    std::vector<int64_t> xshape_dims(x_dims.size() + 1);
+    xshape_dims[0] = 0;
+    for (int i = 0; i < x_dims.size(); ++i) {
+      xshape_dims[i + 1] = x_dims[i];
+    }
+    ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
+    ctx->ShareLoD("X", /*->*/ "XShape");
+  }
+};
+
+class Unsqueeze2OpMaker : public UnsqueezeOpMaker {
+ public:
+  void Make() override {
+    UnsqueezeOpMaker::Make();
+    AddOutput("XShape",
+              "XShape is just used to store the shape and lod of X, which will "
+              "be used in UnsqueezeGradOp.")
+        .AsIntermediate();
+  }
+};
+
+class Unsqueeze2Op : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto &axes = Attr<std::vector<int>>("axes");
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    auto out_dims = Unsqueeze2OpInferShape::GetOutputShape(axes, x_dims);
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(out_dims);
+    // Invoke Reshape op.
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
+        {{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+class Unsqueeze2GradOpMaker : 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("unsqueeze2_grad");
+    grad_op->SetInput("XShape", Output("XShape"));
+    grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+class Unsqueeze2GradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("XShape"),
+                   "Input(XShape) shouldn't be null.");
+    PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    auto xshape_dims = context->GetInputDim("XShape");
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+    context->SetOutputDim(framework::GradVarName("X"), x_dims);
+    context->ShareLoD("XShape", framework::GradVarName("X"));
+  }
+};
+
+class Unsqueeze2GradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+    auto xshape_name = Input("XShape");
+    auto xshape_dims =
+        scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(x_dims);
+
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape2", {{"X", {dout_name}}, {"Shape", {}}},
+        {{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
@@ -180,3 +286,8 @@ REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
                   ops::UnsqueezeGradInferShape);
+
+REGISTER_OPERATOR(unsqueeze2, ops::Unsqueeze2Op, ops::Unsqueeze2OpMaker,
+                  ops::Unsqueeze2OpInferShape, ops::Unsqueeze2GradOpMaker);
+REGISTER_OPERATOR(unsqueeze2_grad, ops::Unsqueeze2GradOp,
+                  ops::Unsqueeze2GradInferShape);

paddle/fluid/platform/dynload/dynamic_loader.cc

@@ -121,6 +121,12 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
     if (nullptr == dso_handle) {
       LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
                    << dlerror() << ")";
+      if (dlPath.find("nccl") != std::string::npos) {
+        std::cout
+            << "You may need to install 'nccl2' from NVIDIA official website: "
+            << "https://developer.nvidia.com/nccl/nccl-download"
+            << "before install PaddlePaddle" << std::endl;
+      }
       dlPath = dso_name;
       dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
     }

paddle/scripts/paddle_build.sh

@@ -115,6 +115,7 @@ function cmake_gen() {
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON}
+        -DWITH_INFERENCE=${WITH_INFERENCE:-ON}
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
         -DPY_VERSION=${PY_VERSION:-2.7}
     ========================================
@@ -144,6 +145,7 @@ EOF
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
+        -DWITH_INFERENCE=${WITH_INFERENCE:-ON} \
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
         -DPY_VERSION=${PY_VERSION:-2.7}
 }
@@ -498,7 +500,7 @@ EOF
 EOF
 
     if [[ ${WITH_GPU} == "ON"  ]]; then
-        NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.1.2-1+cuda${CUDA_MAJOR} libnccl-dev=2.1.2-1+cuda${CUDA_MAJOR} &&"
+        NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
     else
         NCCL_DEPS=""
     fi

python/paddle/dataset/image.py

@@ -104,7 +104,7 @@ def batch_images_from_tar(data_file,
                 pickle.dump(
                     output,
                     open('%s/batch_%d' % (out_path, file_id), 'wb'),
-                    protocol=pickle.HIGHEST_PROTOCOL)
+                    protocol=2)
                 file_id += 1
                 data = []
                 labels = []
@@ -113,9 +113,7 @@ def batch_images_from_tar(data_file,
         output['label'] = labels
         output['data'] = data
         pickle.dump(
-            output,
-            open('%s/batch_%d' % (out_path, file_id), 'wb'),
-            protocol=pickle.HIGHEST_PROTOCOL)
+            output, open('%s/batch_%d' % (out_path, file_id), 'wb'), protocol=2)
 
     with open(meta_file, 'a') as meta:
         for file in os.listdir(out_path):

python/paddle/fluid/layers/nn.py

@@ -4025,10 +4025,12 @@ def transpose(x, perm, name=None):
 
     helper = LayerHelper('transpose', **locals())
     out = helper.create_tmp_variable(x.dtype)
+    x_shape = helper.create_tmp_variable(x.dtype)
     helper.append_op(
-        type='transpose',
+        type='transpose2',
         inputs={'X': [x]},
-        outputs={'Out': [out]},
+        outputs={'Out': [out],
+                 'XShape': [x_shape]},
         attrs={'axis': perm})
     return out
 
@@ -4520,13 +4522,15 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
                 "Each dimension size given in shape must not be negtive "
                 "except one unknown dimension.")
 
-    helper = LayerHelper("reshape", **locals())
+    helper = LayerHelper("reshape2", **locals())
     out = helper.create_tmp_variable(dtype=x.dtype)
+    x_shape = helper.create_tmp_variable(dtype=x.dtype)
     helper.append_op(
-        type="reshape",
+        type="reshape2",
         inputs=inputs,
         attrs={"shape": shape},
-        outputs={"Out": out})
+        outputs={"Out": out,
+                 "XShape": x_shape})
 
     return helper.append_activation(out)
 
@@ -4570,11 +4574,13 @@ def squeeze(input, axes, name=None):
     """
     helper = LayerHelper("squeeze", **locals())
     out = helper.create_tmp_variable(dtype=input.dtype)
+    x_shape = helper.create_tmp_variable(dtype=input.dtype)
     helper.append_op(
-        type="squeeze",
+        type="squeeze2",
         inputs={"X": input},
         attrs={"axes": axes},
-        outputs={"Out": out})
+        outputs={"Out": out,
+                 "XShape": x_shape})
 
     return out
 
@@ -4605,11 +4611,13 @@ def unsqueeze(input, axes, name=None):
     """
     helper = LayerHelper("unsqueeze", **locals())
     out = helper.create_tmp_variable(dtype=input.dtype)
+    x_shape = helper.create_tmp_variable(dtype=input.dtype)
     helper.append_op(
-        type="unsqueeze",
+        type="unsqueeze2",
         inputs={"X": input},
         attrs={"axes": axes},
-        outputs={"Out": out})
+        outputs={"Out": out,
+                 "XShape": x_shape})
 
     return out
 
@@ -5811,10 +5819,12 @@ def flatten(x, axis=1, name=None):
         raise ValueError("The axis should be a int, and in range [0, rank(x)]")
 
     out = helper.create_tmp_variable(x.dtype)
+    x_shape = helper.create_tmp_variable(x.dtype)
     helper.append_op(
-        type='flatten',
+        type='flatten2',
         inputs={"X": x},
-        outputs={'Out': out},
+        outputs={'Out': out,
+                 'XShape': x_shape},
         attrs={"axis": axis})
     return out
 

python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py

@@ -47,14 +47,14 @@ def train_program():
     loss = fluid.layers.square_error_cost(input=y_predict, label=y)
     avg_loss = fluid.layers.mean(loss)
 
-    return avg_loss
+    return [avg_loss, y_predict]
 
 
 def optimizer_func():
     return fluid.optimizer.SGD(learning_rate=0.001)
 
 
-def train(use_cuda, train_program, params_dirname):
+def train(use_cuda, train_program, params_dirname, inference_model_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     trainer = fluid.Trainer(
@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname):
                 '''
                 if params_dirname is not None:
                     trainer.save_params(params_dirname)
+                    trainer.save_inference_model(inference_model_dirname,
+                                                 ['x'], [1])
                 trainer.stop()
 
     trainer.train(
@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None):
     print("infer results: ", results[0])
 
 
+def infer_by_saved_model(use_cuda, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
+        # Use fluid.io.load_inference_model to obtain the inference program desc,
+        # the feed_target_names (the names of variables that will be feeded
+        # data using feed operators), and the fetch_targets (variables that
+        # we want to obtain data from using fetch operators).
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+        # The input's dimension should be 2-D and the second dim is 13
+        # The input data should be >= 0
+        batch_size = 10
+
+        test_reader = paddle.batch(
+            paddle.dataset.uci_housing.test(), batch_size=batch_size)
+
+        test_data = next(test_reader())
+        test_feat = numpy.array(
+            [data[0] for data in test_data]).astype("float32")
+        test_label = numpy.array(
+            [data[1] for data in test_data]).astype("float32")
+
+        assert feed_target_names[0] == 'x'
+        results = exe.run(inference_program,
+                          feed={feed_target_names[0]: numpy.array(test_feat)},
+                          fetch_list=fetch_targets)
+        print("infer shape: ", results[0].shape)
+        print("infer results: ", results[0])
+        print("ground truth: ", test_label)
+
+
 def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
 
     # Directory for saving the trained model
-    params_dirname = "fit_a_line.inference.model"
+    params_dirname = "fit_a_line.model"
+    inference_model_dirname = "fit_a_line.inference_model"
 
-    train(use_cuda, train_program, params_dirname)
+    train(use_cuda, train_program, params_dirname, inference_model_dirname)
     infer(use_cuda, inference_program, params_dirname)
+    infer_by_saved_model(use_cuda, inference_model_dirname)
 
 
 class TestFitALine(unittest.TestCase):

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

@@ -36,6 +36,7 @@ import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid import core
 from test_dist_base import TestDistRunnerBase, runtime_main
+import paddle.compat as cpt
 from paddle.compat import long_type
 
 import hashlib
@@ -315,7 +316,8 @@ def pad_batch_data(insts,
     """
     return_list = []
     max_len = max(len(inst) for inst in insts)
-    num_token = reduce(lambda x, y: x + y,
+    num_token = six.moves.reduce(
+        lambda x, y: x + y,
         [len(inst) for inst in insts]) if return_num_token else 0
     # Any token included in dict can be used to pad, since the paddings' loss
     # will be masked out by weights and make no effect on parameter gradients.
@@ -328,7 +330,7 @@ def pad_batch_data(insts,
         return_list += [inst_weight.astype("float32").reshape([-1, 1])]
     else:  # position data
         inst_pos = np.array([
-            range(1, len(inst) + 1) + [0] * (max_len - len(inst))
+            list(range(1, len(inst) + 1)) + [0] * (max_len - len(inst))
             for inst in insts
         ])
         return_list += [inst_pos.astype("int64").reshape([-1, 1])]
@@ -385,10 +387,11 @@ def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
         return_num_token=True)
 
     data_input_dict = dict(
+        list(
             zip(data_input_names, [
                 src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
                 trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
-        ]))
+            ])))
     return data_input_dict, np.asarray([num_token], dtype="float32")
 
 
@@ -561,7 +564,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                         np.log(TrainTaskConfig.label_smooth_eps / (
                             ModelHyperParams.trg_vocab_size - 1) + 1e-20))
     init = False
-    for pass_id in xrange(TrainTaskConfig.pass_num):
+    for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
         pass_start_time = time.time()
         for batch_id, data in enumerate(train_data()):
             if batch_id >= 5:
@@ -587,11 +590,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                     ModelHyperParams.eos_idx, ModelHyperParams.n_head,
                     ModelHyperParams.d_model)
                 total_num_token += num_token
-                feed_kv_pairs = data_input_dict.items()
+                feed_kv_pairs = list(data_input_dict.items())
                 if TrainTaskConfig.local:
-                    feed_kv_pairs += {
+                    feed_kv_pairs += list({
                         lr_scheduler.learning_rate.name: lr_rate
-                    }.items()
+                    }.items())
                 feed_list.append(dict(feed_kv_pairs))
 
                 if not init:
@@ -873,6 +876,7 @@ class DataReader(object):
 
             f = tarfile.open(fpaths[0], "r")
             for line in f.extractfile(tar_fname):
+                line = cpt.to_text(line)
                 fields = line.strip("\n").split(self._field_delimiter)
                 if (not self._only_src and len(fields) == 2) or (
                         self._only_src and len(fields) == 1):
@@ -882,8 +886,9 @@ class DataReader(object):
                 if not os.path.isfile(fpath):
                     raise IOError("Invalid file: %s" % fpath)
 
-                with open(fpath, "r") as f:
+                with open(fpath, "rb") as f:
                     for line in f:
+                        line = cpt.to_text(line)
                         fields = line.strip("\n").split(self._field_delimiter)
                         if (not self._only_src and len(fields) == 2) or (
                                 self._only_src and len(fields) == 1):
@@ -892,8 +897,9 @@ class DataReader(object):
     @staticmethod
     def load_dict(dict_path, reverse=False):
         word_dict = {}
-        with open(dict_path, "r") as fdict:
+        with open(dict_path, "rb") as fdict:
             for idx, line in enumerate(fdict):
+                line = cpt.to_text(line)
                 if reverse:
                     word_dict[idx] = line.strip("\n")
                 else:
@@ -1034,7 +1040,7 @@ def multi_head_attention(queries,
         # size of the input as the output dimension size.
         return layers.reshape(
             x=trans_x,
-            shape=map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]]))
+            shape=list(map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]])))
 
     def scaled_dot_product_attention(q, k, v, attn_bias, d_model, dropout_rate):
         """

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

@@ -249,7 +249,7 @@ class OpTest(unittest.TestCase):
         outs, _ = self._calc_output(place)
         return outs
 
-    def _calc_output(self, place, parallel=False):
+    def _calc_output(self, place, parallel=False, no_check_set=None):
 
         program = Program()
         block = program.global_block()
@@ -273,6 +273,8 @@ class OpTest(unittest.TestCase):
         # if not, fill the fetch_list by the user configured outputs in test.
         if len(fetch_list) == 0:
             for var_name, var in six.iteritems(outputs):
+                if no_check_set is not None and var_name in no_check_set:
+                    continue
                 if isinstance(var, list):
                     for v in var:
                         fetch_list.append(v)
@@ -291,11 +293,17 @@ class OpTest(unittest.TestCase):
                             return_numpy=False)
         return outs, fetch_list
 
-    def check_output_with_place(self, place, atol, equal_nan=False):
-        outs, fetch_list = self._calc_output(place)
+    def check_output_with_place(self,
+                                place,
+                                atol,
+                                no_check_set=None,
+                                equal_nan=False):
+        outs, fetch_list = self._calc_output(place, no_check_set=no_check_set)
         for out_name, out_dup in Operator.get_op_outputs(self.op_type):
             if out_name not in self.outputs:
                 continue
+            if no_check_set is not None and out_name in no_check_set:
+                continue
 
             def find_actual(target_name, fetch_list):
                 found = [
@@ -360,10 +368,10 @@ class OpTest(unittest.TestCase):
             places.append(core.CUDAPlace(0))
         return places
 
-    def check_output(self, atol=1e-5, equal_nan=False):
+    def check_output(self, atol=1e-5, no_check_set=None, equal_nan=False):
         places = self._get_places()
         for place in places:
-            self.check_output_with_place(place, atol, equal_nan)
+            self.check_output_with_place(place, atol, no_check_set, equal_nan)
 
     def check_output_customized(self, checker):
         places = self._get_places()

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

@@ -55,6 +55,7 @@ class TestDistRunnerBase(object):
         pserver_prog = t.get_pserver_program(args.current_endpoint)
         startup_prog = t.get_startup_program(args.current_endpoint,
                                              pserver_prog)
+
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
         exe.run(startup_prog)
@@ -147,6 +148,8 @@ def runtime_main(test_class):
 
 
 import paddle.compat as cpt
+import socket
+from contextlib import closing
 
 
 class TestDistBase(unittest.TestCase):
@@ -156,13 +159,19 @@ class TestDistBase(unittest.TestCase):
     def setUp(self):
         self._trainers = 2
         self._pservers = 2
-        self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
+        self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
+            self._find_free_port(), self._find_free_port())
         self._python_interp = "python"
         self._sync_mode = True
         self._mem_opt = False
         self._use_reduce = False
         self._setup_config()
 
+    def _find_free_port(self):
+        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
+            s.bind(('', 0))
+            return s.getsockname()[1]
+
     def start_pserver(self, model_file, check_error_log):
         ps0_ep, ps1_ep = self._ps_endpoints.split(",")
         ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"

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

@@ -22,14 +22,17 @@ from op_test import OpTest
 
 class TestFlattenOp(OpTest):
     def setUp(self):
-        self.op_type = "flatten"
+        self.op_type = "flatten2"
         self.init_test_case()
         self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
         self.init_attrs()
-        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.in_shape).astype("float32")
+        }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=["XShape"])
 
     def test_check_grad(self):
         self.check_grad(["X"], "Out")

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

@@ -53,12 +53,11 @@ class TestFusionLSTMOp(OpTest):
         self.M = 8
         self.D = 16
         self.has_initial_state = False
+        self.use_peepholes = False
         self.is_reverse = False
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
-        self.use_peepholes = False
-        self.use_seq = False
         self.set_conf()
 
         T = sum(self.lod[0])
@@ -108,7 +107,6 @@ 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,
@@ -178,50 +176,18 @@ class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
         self.is_reverse = True
 
 
-class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
+class TestFusionLSTMOpPeepholesInitReverse(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):
+class TestFusionLSTMOpPeepholesBS1(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
+        self.lod = [[2]]
+        self.D = 8
 
 
 if __name__ == '__main__':

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

@@ -85,6 +85,7 @@ class TestFetchOp(unittest.TestCase):
                     assert not math.isnan(np.sum(ret[i])) and \
                            not math.isinf(np.sum(ret[i]))
 
+    @unittest.skip(reason="CI timeout")
     def test_fetch_op(self):
         tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
         tst_reader_iter = tst_reader()
@@ -139,6 +140,7 @@ class TestFeedParallel(unittest.TestCase):
             if batch_id == 2:
                 break
 
+    @unittest.skip(reason="CI timeout")
     def test_feed_op(self):
         os.environ['CPU_NUM'] = str(4)
         if core.is_compiled_with_cuda():

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
+import six
 from op_test import OpTest
 
 
@@ -62,17 +63,20 @@ class PReluTest(OpTest):
 
 
 # TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
-#  class TestCase1(PReluTest):
-#  def initTestCase(self):
-#  self.attrs = {'mode': "all"}
+if six.PY2:
 
-#  class TestCase2(PReluTest):
-#  def initTestCase(self):
-#  self.attrs = {'mode': "channel"}
+    class TestCase1(PReluTest):
+        def initTestCase(self):
+            self.attrs = {'mode': "all"}
+
+    class TestCase2(PReluTest):
+        def initTestCase(self):
+            self.attrs = {'mode': "channel"}
+
+    class TestCase3(PReluTest):
+        def initTestCase(self):
+            self.attrs = {'mode': "element"}
 
-#  class TestCase3(PReluTest):
-#  def initTestCase(self):
-#  self.attrs = {'mode': "element"}
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -22,106 +22,39 @@ from op_test import OpTest
 
 class TestReshapeOp(OpTest):
     def setUp(self):
-        ori_shape = (2, 25)
-        new_shape = (5, 10)
-
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Out")
-
-
-class TestReshapeOpDimInfer1(OpTest):
-    def setUp(self):
-        ori_shape = (5, 10)
-        new_shape = (5, -1, 5)
-
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Out")
-
-
-class TestReshapeOpDimInfer2(OpTest):
-    def setUp(self):
-        ori_shape = (2, 2, 6)
-        new_shape = (2, 0, 3, -1)
-        infered_shape = (2, 2, 3, -1)
-
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Out")
-
-
-class TestReshapeOpInplace(OpTest):
-    def setUp(self):
-        ori_shape = (2, 25)
-        new_shape = (5, 10)
-
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(["X"], "Out")
-
-
-class TestReshapeOpDimInferInplace1(OpTest):
-    def setUp(self):
-        ori_shape = (5, 10)
-        new_shape = (5, -1, 5)
+        self.init_data()
+        self.op_type = "reshape2"
+        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
+        self.attrs = {"shape": self.new_shape}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.infered_shape),
+            'XShape': np.random.random(self.ori_shape).astype("float32")
+        }
 
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
+    def init_data(self):
+        self.ori_shape = (2, 25)
+        self.new_shape = (5, 10)
+        self.infered_shape = (5, 10)
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
         self.check_grad(["X"], "Out")
 
 
-class TestReshapeOpDimInferInplace2(OpTest):
-    def setUp(self):
-        ori_shape = (2, 2, 6)
-        new_shape = (2, 0, 3, -1)
-        infered_shape = (2, 2, 3, -1)
-
-        self.op_type = "reshape"
-        self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
-        self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
+class TestReshapeOpDimInfer1(TestReshapeOp):
+    def init_data(self):
+        self.ori_shape = (5, 10)
+        self.new_shape = (5, -1, 5)
+        self.infered_shape = (5, -1, 5)
 
-    def test_check_output(self):
-        self.check_output()
 
-    def test_check_grad(self):
-        self.check_grad(["X"], "Out")
+class TestReshapeOpDimInfer2(TestReshapeOp):
+    def init_data(self):
+        self.ori_shape = (2, 2, 6)
+        self.new_shape = (2, 0, 3, -1)
+        self.infered_shape = (2, 2, 3, -1)
 
 
 class TestReshapeOpWithInputShape(OpTest):
@@ -130,20 +63,23 @@ class TestReshapeOpWithInputShape(OpTest):
         new_shape = (0, -1, 5)
         actual_shape = (2, 3, 5)
 
-        self.op_type = "reshape"
+        self.op_type = "reshape2"
         self.inputs = {
             "X": np.random.random(ori_shape).astype("float32"),
             "Shape": np.array(
                 actual_shape, dtype="int32")
         }
         self.attrs = {"shape": new_shape}
-        self.outputs = {"Out": self.inputs["X"].reshape(actual_shape)}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(actual_shape),
+            'XShape': np.random.random(ori_shape).astype("float32")
+        }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
-        self.check_grad(["X"], "Out")
+        self.check_grad(["X"], "Out", sum_outputs=["Out"])
 
 
 if __name__ == "__main__":

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

@@ -23,14 +23,17 @@ from op_test import OpTest
 # Correct: General.
 class TestSqueezeOp(OpTest):
     def setUp(self):
-        self.op_type = "squeeze"
+        self.op_type = "squeeze2"
         self.init_test_case()
         self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
         self.init_attrs()
-        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.ori_shape).astype("float32")
+        }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
         self.check_grad(["X"], "Out")

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

@@ -22,16 +22,19 @@ from op_test import OpTest
 class TestTransposeOp(OpTest):
     def setUp(self):
         self.initTestCase()
-        self.op_type = "transpose"
+        self.op_type = "transpose2"
         self.inputs = {'X': np.random.random(self.shape).astype("float32")}
         self.attrs = {'axis': list(self.axis)}
-        self.outputs = {'Out': self.inputs['X'].transpose(self.axis)}
+        self.outputs = {
+            'XShape': np.random.random(self.shape).astype("float32"),
+            'Out': self.inputs['X'].transpose(self.axis)
+        }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+        self.check_grad(['X'], 'Out', sum_outputs=['Out'])
 
     def initTestCase(self):
         self.shape = (3, 4)

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

@@ -24,13 +24,16 @@ from op_test import OpTest
 class TestUnsqueezeOp(OpTest):
     def setUp(self):
         self.init_test_case()
-        self.op_type = "unsqueeze"
+        self.op_type = "unsqueeze2"
         self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
         self.init_attrs()
-        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.ori_shape).astype("float32")
+        }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(no_check_set=["XShape"])
 
     def test_check_grad(self):
         self.check_grad(["X"], "Out")

python/paddle/fluid/trainer.py

@@ -431,6 +431,28 @@ class Trainer(object):
             exe = executor.Executor(self.place)
             io.save_persistables(exe, dirname=param_path)
 
+    def save_inference_model(self, param_path, feeded_var_names,
+                             target_var_indexes):
+        """
+        Save model for cpp inference into :code:`param_path`.
+
+        Args:
+            param_path(str): The path to save parameters.
+            feeded_var_names(list(str)): The name of the vars that you
+                need to feed in before run program.
+            target_var_indexes(list(int)): the index of target var that
+                you need to return in trainer.train_func.
+        Returns:
+            None
+        """
+        with self._prog_and_scope_guard():
+            exe = executor.Executor(self.place)
+            target_vars = [
+                self.train_func_outputs[index] for index in target_var_indexes
+            ]
+            io.save_inference_model(param_path, feeded_var_names, target_vars,
+                                    exe)
+
     @contextlib.contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(

python/paddle/fluid/transpiler/details/program_utils.py

@@ -153,7 +153,7 @@ def block_to_code(block, block_idx):
 
     indent += 1
     # sort all vars
-    all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
+    all_vars = sorted(six.iteritems(block.vars), key=lambda x: x[0])
     for var in all_vars:
         print("{}{}".format(get_indent_space(indent), variable_to_code(var[1])))
 

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -300,7 +300,7 @@ class DistributeTranspiler(object):
             input_deps = grad_name_to_send_dummy_out.values()
             program.global_block().append_op(
                 type="send_barrier",
-                inputs={"X": input_deps},
+                inputs={"X": list(input_deps)},
                 outputs={"Out": send_barrier_out},
                 attrs={
                     "endpoints": pserver_endpoints,
@@ -455,7 +455,7 @@ class DistributeTranspiler(object):
             if len(splited_var) <= 1:
                 continue
             # NOTE: if enable memory optimization, origin vars maybe removed.
-            if startup_program.global_block().vars.has_key(varname):
+            if varname in startup_program.global_block().vars:
                 orig_param = startup_program.global_block().vars[varname]
             else:
                 origin_param_var = self.origin_program.global_block().vars[