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

paddle/fluid/framework/framework.proto

@@ -80,7 +80,6 @@ message OpProto {
     optional bool duplicable = 3 [ default = false ];
     optional bool intermediate = 4 [ default = false ];
     optional bool dispensable = 5 [ default = false ];
-    optional string reuse = 6;
   }
 
   // AttrProto describes the C++ type Attribute.

paddle/fluid/framework/ir/CMakeLists.txt

@@ -42,12 +42,10 @@ if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
     pass_library(conv_bias_mkldnn_fuse_pass inference)
     pass_library(conv_relu_mkldnn_fuse_pass inference)
+    pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
 endif()
 
 cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
-if(WITH_MKLDNN)
-  pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
-endif()
 
 set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
 

paddle/fluid/framework/ir/graph_helper_test.cc

@@ -200,15 +200,15 @@ TEST(GraphHelperTest, GraphNum) {
 
   Graph g(prog);
   BuildZeroGraph(&g);
-  ASSERT_EQ(GraphNum(g), 0);
+  ASSERT_EQ(GraphNum(g), 0UL);
 
   Graph g2(prog);
   BuildOneGraph(&g2);
-  ASSERT_EQ(GraphNum(g2), 1);
+  ASSERT_EQ(GraphNum(g2), 1UL);
 
   Graph g3(prog);
   BuildTwoGraphs(&g3);
-  ASSERT_EQ(GraphNum(g3), 2);
+  ASSERT_EQ(GraphNum(g3), 2UL);
 }
 
 }  // namespace ir

paddle/fluid/framework/ir/graph_test.cc

@@ -124,7 +124,7 @@ TEST(GraphTest, Basic) {
       ASSERT_EQ(n->outputs.size(), 0UL);
     }
   }
-  ASSERT_EQ(nodes.size(), 5);
+  ASSERT_EQ(nodes.size(), 5UL);
 }
 
 TEST(GraphTest, WriteAfterRead) {

paddle/fluid/framework/op_desc.cc

@@ -515,20 +515,14 @@ void OpDesc::InferShape(const BlockDesc &block) const {
 }
 
 void OpDesc::InferVarType(BlockDesc *block) const {
+  // There are a few places that var type can be set.
+  // When VarDesc is created, default set to LOD_TENSOR.
+  // When output variable is created, default is defaut set to LOD_TENSOR.
+  // We limit here to be the only place that operator defines its customized
+  // var type inference. Hence, we don't do any "default" setting here.
   auto &info = OpInfoMap::Instance().Get(this->Type());
   if (info.infer_var_type_) {
     info.infer_var_type_(*this, block);
-  } else {
-    // all output type is LoDTensor by default
-    VLOG(10) << this->Type()
-             << " has not registered InferVarType. Set output variables to "
-                "LOD_TENSOR";
-    for (auto &out_pair : this->outputs_) {
-      for (auto &out_var_name : out_pair.second) {
-        block->FindRecursiveOrCreateVar(out_var_name)
-            .SetType(proto::VarType::LOD_TENSOR);
-      }
-    }
   }
 }
 

paddle/fluid/framework/op_proto_maker.cc

@@ -21,7 +21,6 @@ namespace framework {
 void OpProtoAndCheckerMaker::Validate() {
   validated_ = true;
   CheckNoDuplicatedInOutAttrs();
-  CheckReuseVars();
 }
 
 OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
   return OpProtoAndCheckerMaker::VariableBuilder{output};
 }
 
-void OpProtoAndCheckerMaker::Reuse(const std::string& name,
-                                   const std::string& reused_name) {
-  bool found = false;
-  proto::OpProto::Var* var;
-
-  for (auto& var : proto_->inputs()) {
-    if (var.name() == reused_name) {
-      found = true;
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found == true,
-                 "Input/Output name: %s reused_name: %s, one of them is not "
-                 "exists or not matched.",
-                 name, reused_name);
-
-  found = false;
-  for (int i = 0; i < proto_->outputs().size(); ++i) {
-    var = proto_->mutable_outputs()->Mutable(i);
-    if (var->name() == name) {
-      PADDLE_ENFORCE(!var->has_reuse(),
-                     "Output(%s) has been set reused var of %s", name,
-                     var->reuse());
-      found = true;
-      var->set_reuse(reused_name);
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found == true,
-                 "Input/Output name: %s reused_name: %s, one of them is not "
-                 "exists or not matched.",
-                 name, reused_name);
-}
-
 void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   std::unordered_set<std::string> names;
   auto checker = [&](const std::string& name) {
@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   }
 }
 
-void OpProtoAndCheckerMaker::CheckReuseVars() {
-  std::unordered_set<std::string> names;
-  for (auto& input : proto_->inputs()) {
-    names.insert(input.name());
-  }
-  auto checker = [&](const std::string& name, const std::string& reused) {
-    PADDLE_ENFORCE(
-        names.count(reused),
-        "Output [%s] reuse Input [%s], but the input is not registered.", name,
-        reused);
-  };
-  for (auto& output : proto_->outputs()) {
-    if (output.has_reuse()) {
-      checker(output.name(), output.reuse());
-    }
-  }
-}
-
 void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
                                         OpAttrChecker* attr_checker) {
   proto_ = proto;

paddle/fluid/framework/op_proto_maker.h

@@ -14,8 +14,6 @@ limitations under the License. */
 #pragma once
 
 #include <string>
-#include <unordered_set>
-
 #include "glog/logging.h"
 #include "paddle/fluid/framework/attribute.h"
 #include "paddle/fluid/framework/framework.pb.h"
@@ -73,11 +71,6 @@ class OpProtoAndCheckerMaker {
       var_->set_dispensable(true);
       return *this;
     }
-
-    VariableBuilder &Reuse(const std::string &name) {
-      var_->set_reuse(name);
-      return *this;
-    }
   };
 
   VariableBuilder AddInput(const std::string &name, const std::string &comment);
@@ -85,8 +78,6 @@ class OpProtoAndCheckerMaker {
   VariableBuilder AddOutput(const std::string &name,
                             const std::string &comment);
 
-  void Reuse(const std::string &name, const std::string &reused_name);
-
   template <typename T>
   TypedAttrChecker<T> &AddAttr(const std::string &name,
                                const std::string &comment,
@@ -105,8 +96,6 @@ class OpProtoAndCheckerMaker {
   void CheckNoDuplicatedInOutAttrs();
   void Validate();
 
-  void CheckReuseVars();
-
   proto::OpProto *proto_;
   OpAttrChecker *op_checker_;
   bool validated_{false};

paddle/fluid/framework/op_proto_maker_test.cc

@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
   ASSERT_THROW(proto_maker(&op_proto, &op_checker),
                paddle::platform::EnforceNotMet);
 }
-
-class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddOutput("XOut", "output of test op").Reuse("X");
-  }
-};
-
-class TestInplaceProtoMaker2
-    : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddOutput("XOut", "output of test op").Reuse("X");
-    AddOutput("NoOut", "output of test op").Reuse("NotExists");
-  }
-};
-
-TEST(ProtoMaker, InplaceOutput) {
-  paddle::framework::proto::OpProto op_proto, op_proto2;
-  paddle::framework::OpAttrChecker op_checker;
-  TestInplaceProtoMaker proto_maker;
-  TestInplaceProtoMaker2 proto_maker2;
-
-  proto_maker(&op_proto, &op_checker);
-
-  ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
-               paddle::platform::EnforceNotMet);
-}
-
-// normal reuse
-class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddInput("Y", "input of test op");
-    AddOutput("Out", "output of test op");
-    AddOutput("XOut", "output of test op");
-    // avoid destructor exception.
-    // Validate();
-    TestReuse();
-  }
-
-  virtual void TestReuse() {}
-};
-
-// test duplicate reuse error
-class TestReuseProtoMaker2 : public TestReuseProtoMaker {
- public:
-  void TestReuse() {
-    Reuse("Out", "X");
-    Reuse("Out", "Y");
-  }
-};
-
-// NotExists Input
-class TestReuseProtoMaker3 : public TestReuseProtoMaker {
- public:
-  void TestReuse() {
-    Reuse("Out", "NotExists");
-    Reuse("XOut", "X");
-  }
-};
-
-// NotExists Output
-class TestReuseProtoMaker4 : public TestReuseProtoMaker {
- public:
-  void TestReuse() { Reuse("NotExists", "X"); }
-};
-
-TEST(ProtoMaker, Reuse) {
-  paddle::framework::proto::OpProto op_proto;
-  paddle::framework::OpAttrChecker op_checker;
-  TestReuseProtoMaker proto_maker;
-  proto_maker(&op_proto, &op_checker);
-}
-
-// NOTE(dzhwinter):
-// There is a Fatal CHECK on base class destructor, which will call abort inside
-// instead of
-// throw an exception. If we throw an exception in Make(), we will trigger the
-// CHECK and terminate the tests.
-//
-// I had tried to replace the default CHECK with a exception, however, it's
-// still not supported by glog.
-// the details:
-// https://github.com/google/glog/issues/249
-// https://github.com/facebookresearch/TensorComprehensions/issues/351
-/*
-TEST(ProtoMaker, ReuseWithException) {
-  paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
-  paddle::framework::OpAttrChecker op_checker;
-  TestReuseProtoMaker2 proto_maker2;
-  TestReuseProtoMaker3 proto_maker3;
-  TestReuseProtoMaker4 proto_maker4;
-  EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
-               paddle::platform::EnforceNotMet);
-
-  EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
-               paddle::platform::EnforceNotMet);
-
-  EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
-               paddle::platform::EnforceNotMet);
-}
-
-void FailureFunction() {
-  throw std::runtime_error("Check failed in destructor.");
-  // return 0;
-}
-
-int main(int argc, char** argv) {
-  testing::InitGoogleTest(&argc, argv);
-  google::InstallFailureFunction(&FailureFunction);
-  return RUN_ALL_TESTS();
-}
-*/

paddle/fluid/framework/parallel_executor.cc

@@ -156,12 +156,10 @@ ParallelExecutor::ParallelExecutor(
                            params, member_->local_scopes_, member_->use_cuda_);
 #endif
 
-  if (VLOG_IS_ON(5)) {
-    // If the loss_var_name is given, the number of graph should be only one.
-    if (loss_var_name.size()) {
-      PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
-                        "The number of graph should be only one");
-    }
+  // If the loss_var_name is given, the number of graph should be only one.
+  if (loss_var_name.size()) {
+    PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
+                      "The number of graph should be only one");
   }
 
   if (exec_strategy.type_ == ExecutionStrategy::kDefault) {

paddle/fluid/framework/program_desc_test.cc

@@ -103,7 +103,7 @@ TEST(ProgramDesc, copy_ctor) {
       ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
       found_sub_block = true;
 
-      ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
+      ASSERT_EQ(2UL, op->GetBlocksAttrIds("sub_blocks").size());
       found_sub_blocks = true;
     }
   }

paddle/fluid/framework/reader_test.cc

@@ -40,7 +40,7 @@ TEST(READER, decorate_chain) {
     auto endpoints = root->GetEndPoints();
     ASSERT_EQ(endpoints.size(), 2U);
     ASSERT_NE(endpoints.count(end_point1.get()), 0UL);
-    ASSERT_NE(endpoints.count(end_point2.get()), 0);
+    ASSERT_NE(endpoints.count(end_point2.get()), 0UL);
   }
 
   {

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

@@ -21,7 +21,7 @@ else
 fi
 
 USE_TENSORRT=OFF
-if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
+if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
   USE_TENSORRT=ON
 fi
 

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
         boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
     std::vector<int> paddings =
         boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
+    bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
 
+    nvinfer1::Dims input_shape = input1->getDimensions();
+    int nbDims = input_shape.nbDims;
     nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
+    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
+    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
+
     if (global_pooling == true) {
-      nvinfer1::Dims input_shape = input1->getDimensions();
-      int nbDims = input_shape.nbDims;
       nv_ksize.d[0] = input_shape.d[nbDims - 2];
       nv_ksize.d[1] = input_shape.d[nbDims - 1];
+      nv_strides.h() = 1;
+      nv_strides.w() = 1;
+      nv_paddings.h() = 0;
+      nv_paddings.w() = 0;
     }
-    const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
-    const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
 
     PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
 
@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
       PADDLE_THROW("TensorRT unsupported pooling type!");
     }
 
+    if (ceil_mode) {
+      nvinfer1::DimsHW pre_pad(0, 0);
+      nvinfer1::DimsHW post_pad(0, 0);
+      int input_height = input_shape.d[nbDims - 2];
+      int input_width = input_shape.d[nbDims - 1];
+      int floor_h_output_size =
+          (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+      int ceil_h_output_size =
+          (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
+              strides[0] +
+          1;
+
+      int floor_w_output_size =
+          (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+      int ceil_w_output_size =
+          (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
+              strides[1] +
+          1;
+      if (floor_h_output_size != ceil_h_output_size) {
+        post_pad.h() = strides[0] - 1;
+      }
+
+      if (floor_w_output_size != ceil_w_output_size) {
+        post_pad.w() = strides[1] - 1;
+      }
+      auto* layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
+          post_pad);
+      input1 = layer->getOutput(0);
+    }
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
                                        *const_cast<nvinfer1::ITensor*>(input1),
                                        nv_pool_type, nv_ksize);

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -20,18 +20,20 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-void test_pool2d(bool global_pooling) {
+void test_pool2d(bool global_pooling, bool ceil_mode) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
   TRTConvertValidation validator(5, parameters, scope, 1 << 15);
 
   // The ITensor's Dims should not contain the batch size.
   // So, the ITensor's Dims of input and output should be C * H * W.
-  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
+  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
   if (global_pooling)
     validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
+  else if (ceil_mode)
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
   else
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
 
   // Prepare Op description
   framework::OpDesc desc;
@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
   desc.SetInput("X", {"pool2d-X"});
   desc.SetOutput("Out", {"pool2d-Out"});
 
-  std::vector<int> ksize({2, 2});
+  std::vector<int> ksize({3, 3});
   std::vector<int> strides({2, 2});
   std::vector<int> paddings({0, 0});
   std::string pooling_t = "max";
@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
   desc.SetAttr("strides", strides);
   desc.SetAttr("paddings", paddings);
   desc.SetAttr("global_pooling", global_pooling);
+  desc.SetAttr("ceil_mode", ceil_mode);
 
   LOG(INFO) << "set OP";
   validator.SetOp(*desc.Proto());
@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
   validator.Execute(3);
 }
 
-TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
+TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
+TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
 
-TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
+TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
 
 }  // namespace tensorrt
 }  // namespace inference

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -71,7 +71,7 @@ void profile(bool use_mkldnn = false) {
 }
 
 TEST(Analyzer_resnet50, profile) { profile(); }
-#ifndef PADDLE_WITH_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
 TEST(Analyzer_resnet50, profile_mkldnn) { profile(true /* use_mkldnn */); }
 #endif
 

paddle/fluid/inference/tests/api/tester_helper.h

@@ -50,7 +50,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
     auto &ref_out = ref_outputs[i];
     size_t size = VecReduceToInt(out.shape);
     size_t ref_size = VecReduceToInt(ref_out.shape);
-    EXPECT_GT(size, 0);
+    EXPECT_GT(size, 0UL);
     EXPECT_EQ(size, ref_size);
     EXPECT_EQ(out.dtype, ref_out.dtype);
     switch (out.dtype) {

paddle/fluid/operators/CMakeLists.txt

@@ -284,10 +284,10 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
 op_library(sequence_conv_op DEPS context_project)
 op_library(sequence_pool_op DEPS sequence_pooling)
 if (NOT WIN32)
-op_library(lstm_op DEPS sequence2batch lstm_compute)
-op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
-op_library(lstmp_op DEPS sequence2batch lstm_compute)
-op_library(gru_op DEPS sequence2batch gru_compute)
+    op_library(lstm_op DEPS sequence2batch lstm_compute)
+    op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
+    op_library(lstmp_op DEPS sequence2batch lstm_compute)
+    op_library(gru_op DEPS sequence2batch gru_compute)
 endif(NOT WIN32)
 op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
@@ -316,7 +316,7 @@ op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
 op_library(save_combine_op DEPS lod_tensor)
 op_library(load_combine_op DEPS lod_tensor)
-op_library(concat_op DEPS concat)
+op_library(concat_op DEPS concat_and_split)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 
@@ -348,6 +348,6 @@ cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
 cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
 cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
 if(NOT WIN32)
-nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
+    nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
 endif()
 nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)

paddle/fluid/operators/activation_op.cc

@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
    public:                                                              \
     void Make() override {                                              \
       AddInput("X", "Input of " #OP_NAME " operator");                  \
-      AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X");   \
+      AddOutput("Out", "Output of " #OP_NAME " operator");              \
       AddAttr<bool>("use_mkldnn",                                       \
                     "(bool, default false) Only used in mkldnn kernel") \
           .SetDefault(false);                                           \

paddle/fluid/operators/adam_op.cc

@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
     AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
 
-    AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
-    AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
-    AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
+    AddOutput("ParamOut", "(Tensor) Output parameter");
+    AddOutput("Moment1Out", "(Tensor) Output first moment");
+    AddOutput("Moment2Out", "(Tensor) Output second moment");
 
     AddAttr<float>("beta1",
                    "(float, default 0.9) "

paddle/fluid/operators/array_to_lod_tensor_op.cc

@@ -11,7 +11,7 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
-#include <paddle/fluid/operators/math/concat.h>
+#include <paddle/fluid/operators/math/concat_and_split.h>
 #include <numeric>
 
 #include "paddle/fluid/framework/lod_rank_table.h"

paddle/fluid/operators/batch_norm_op.cc

@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Variance",
              "The global variance (for training) "
              "or estimated Variance (for testing)");
-    AddOutput("Y", "result after normalization").Reuse("X");
+    AddOutput("Y", "result after normalization");
     AddOutput("MeanOut",
               "Share memory with Mean. "
-              "Store the global mean when training")
-        .Reuse("Mean");
+              "Store the global mean when training");
     AddOutput("VarianceOut",
               "Share memory with Variance. "
-              "Store the global Variance when training")
-        .Reuse("Variance");
+              "Store the global Variance when training");
     AddOutput("SavedMean",
               "Mean of the current mini batch, "
               "will apply to output when training")

paddle/fluid/operators/concat_op.h

@@ -17,7 +17,7 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/operators/strided_memcpy.h"
 
 namespace paddle {
@@ -89,29 +89,17 @@ class ConcatGradKernel : public framework::OpKernel<T> {
         outputs.push_back(nullptr);
       }
     }
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
 
     // Sometimes direct copies will be faster, this maybe need deeply analysis.
     if (axis == 0 && outs.size() < 10) {
-      size_t input_offset = 0;
-      const auto in_stride = framework::stride_numel(out_grad->dims());
-
-      for (size_t i = 0; i < outs.size(); ++i) {
-        auto out_stride = framework::stride_numel(ins[i]->dims());
-        auto* out = outputs[i];
-        if (out != nullptr) {
-          StridedNumelCopyWithAxis<T>(
-              ctx.device_context(), axis, out->data<T>(), out_stride,
-              out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
-        }
-        input_offset += out_stride[axis];
-      }
+      std::vector<const framework::Tensor*> ref_shape;
+      ref_shape.insert(ref_shape.begin(), ins.begin(), ins.end());
+      StridedMemcpyWithAxis0<T>(dev_ctx, *out_grad, ref_shape, &outputs);
     } else {
-      auto& dev_ctx = ctx.template device_context<DeviceContext>();
-      paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
-          concat_grad_functor;
-      concat_grad_functor(dev_ctx, *out_grad,
-                          ctx.MultiInput<framework::Tensor>("X"),
-                          static_cast<int>(axis), &outputs);
+      math::SplitFunctor<DeviceContext, T> split_functor;
+      split_functor(dev_ctx, *out_grad, ctx.MultiInput<framework::Tensor>("X"),
+                    static_cast<int>(axis), &outputs);
     }
   }
 };

paddle/fluid/operators/conv_op.cc

@@ -130,8 +130,7 @@ void Conv2DOpMaker::Make() {
       .AsDispensable();
   AddOutput("Output",
             "(Tensor) The output tensor of convolution operator. "
-            "The format of output tensor is also NCHW.")
-      .Reuse("Input");
+            "The format of output tensor is also NCHW.");
   AddInput("ResidualData",
            "(Tensor) Tensor with residual data "
            "to which convolution output will be added."
@@ -238,8 +237,7 @@ void Conv3DOpMaker::Make() {
            "input image channels divided by the groups.");
   AddOutput("Output",
             "(Tensor) The output tensor of convolution operator."
-            "The format of output tensor is also NCDHW.")
-      .Reuse("Input");
+            "The format of output tensor is also NCDHW.");
   AddAttr<std::vector<int>>("strides",
                             "(vector<int>, default:{1, 1, 1}), the "
                             "strides(d_stride, h_stride, w_stride) of "

paddle/fluid/operators/detection/generate_proposal_labels_op.cc

@@ -16,7 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detection/bbox_util.h"
 #include "paddle/fluid/operators/gather.h"
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
 namespace paddle {

paddle/fluid/operators/detection/rpn_target_assign_op.cc

@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->HasOutput("TargetBBox"),
         "Output(TargetBBox) of RpnTargetAssignOp should not be null");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("BBoxInsideWeight"),
+        "Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
 
     auto anchor_dims = ctx->GetInputDim("Anchor");
     auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("ScoreIndex", {-1});
     ctx->SetOutputDim("TargetLabel", {-1, 1});
     ctx->SetOutputDim("TargetBBox", {-1, 4});
+    ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
   }
 
  protected:
@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
                  const float rpn_positive_overlap,
                  const float rpn_negative_overlap, std::vector<int>* fg_inds,
                  std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
+                 std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
                  std::minstd_rand engine, bool use_random) {
   float epsilon = 0.00001;
   int anchor_num = anchor_to_gt_max.dims()[0];
@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
   // Reservoir Sampling
   int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
   ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
-  fg_num = static_cast<int>(fg_inds_fake.size());
-  for (int64_t i = 0; i < fg_num; ++i) {
+  int fg_fake_num = static_cast<int>(fg_inds_fake.size());
+  for (int64_t i = 0; i < fg_fake_num; ++i) {
     target_label[fg_inds_fake[i]] = 1;
   }
 
-  int bg_num = rpn_batch_size_per_im - fg_num;
+  int bg_num = rpn_batch_size_per_im - fg_fake_num;
   for (int64_t i = 0; i < anchor_num; ++i) {
     if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
       bg_inds_fake.push_back(i);
@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
   }
   ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
   bg_num = static_cast<int>(bg_inds_fake.size());
+  int fake_num = 0;
   for (int64_t i = 0; i < bg_num; ++i) {
+    // fg fake found
+    if (target_label[bg_inds_fake[i]] == 1) {
+      fake_num++;
+      fg_fake->emplace_back(fg_inds_fake[0]);
+      for (int j = 0; j < 4; ++j) {
+        bbox_inside_weight->emplace_back(T(0.));
+      }
+    }
     target_label[bg_inds_fake[i]] = 0;
   }
 
+  for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
+    bbox_inside_weight->emplace_back(T(1.));
+  }
+
   for (int64_t i = 0; i < anchor_num; ++i) {
-    if (target_label[i] == 1) fg_inds->emplace_back(i);
+    if (target_label[i] == 1) {
+      fg_inds->emplace_back(i);
+      fg_fake->emplace_back(i);
+    }
     if (target_label[i] == 0) bg_inds->emplace_back(i);
   }
   fg_num = fg_inds->size();
@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
   std::vector<int> bg_inds;
   std::vector<int> gt_inds;
   std::vector<int> tgt_lbl;
-
+  std::vector<int> fg_fake;
+  std::vector<T> bbox_inside_weight;
   // Calculate the max IoU between anchors and gt boxes
   // Map from anchor to gt box that has highest overlap
   auto place = ctx.GetPlace();
@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
   // Follow the Faster RCNN's implementation
   ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
               rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
-              rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
-              use_random);
+              rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
+              &bbox_inside_weight, engine, use_random);
 
   int fg_num = fg_inds.size();
   int bg_num = bg_inds.size();
-  gt_inds.reserve(fg_num);
-  for (int i = 0; i < fg_num; ++i) {
-    gt_inds.emplace_back(argmax[fg_inds[i]]);
+  int fg_fake_num = fg_fake.size();
+  gt_inds.reserve(fg_fake_num);
+  for (int i = 0; i < fg_fake_num; ++i) {
+    gt_inds.emplace_back(argmax[fg_fake[i]]);
   }
-
-  Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
-  int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
+  Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
+  int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
   int* score_index_data =
       score_index_t.mutable_data<int>({fg_num + bg_num}, place);
   int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
-  int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
-  std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
+  int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
+  T* bbox_inside_weight_data =
+      bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
+  std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
   std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
   std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
   std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
   std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
+  std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
+            bbox_inside_weight_data);
   std::vector<Tensor> loc_score_tgtlbl_gt;
   loc_score_tgtlbl_gt.emplace_back(loc_index_t);
   loc_score_tgtlbl_gt.emplace_back(score_index_t);
   loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
   loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
+  loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
 
   return loc_score_tgtlbl_gt;
 }
@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     auto* score_index = context.Output<LoDTensor>("ScoreIndex");
     auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
     auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
+    auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
 
     PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
                       "RpnTargetAssignOp gt_boxes needs 1 level of LoD");
@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     score_index->mutable_data<int>({max_num}, place);
     tgt_bbox->mutable_data<T>({max_num, 4}, place);
     tgt_lbl->mutable_data<int>({max_num, 1}, place);
-
+    bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
     auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
 
     std::random_device rnd;
@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
       Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
       Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
       Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
+      Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
 
       int loc_num = sampled_loc_index.dims()[0];
       int score_num = sampled_score_index.dims()[0];
@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
       AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
       AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
       AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
+      AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
+                    &sampled_bbox_inside_weight);
       total_loc_num += loc_num;
 
       total_score_num += score_num;
@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     score_index->set_lod(loc_score);
     tgt_bbox->set_lod(lod_loc);
     tgt_lbl->set_lod(loc_score);
+    bbox_inside_weight->set_lod(lod_loc);
     loc_index->Resize({total_loc_num});
     score_index->Resize({total_score_num});
     tgt_bbox->Resize({total_loc_num, 4});
     tgt_lbl->Resize({total_score_num, 1});
+    bbox_inside_weight->Resize({total_loc_num, 4});
   }
 };
 
@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
         "TargetLabel",
         "(Tensor<int>), The target labels of each anchor with shape "
         "[F + B, 1], F and B are sampled foreground and backgroud number.");
+    AddOutput("BBoxInsideWeight",
+              "(Tensor), The bbox inside weight with shape "
+              "[F, 4], F is the sampled foreground number.");
     AddComment(R"DOC(
 This operator can be, for a given set of ground truth bboxes and the
 anchors, to assign classification and regression targets to each prediction.

paddle/fluid/operators/elementwise_op.h

@@ -80,8 +80,6 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() final {
     AddInput("X", "(Tensor), The first input tensor of elementwise op.");
     AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
-    // AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
-    // memory.").AsIntermediate();
     AddOutput("Out", "The output of elementwise op.");
     AddAttr<int>("axis",
                  "(int, default -1). The start dimension index "
@@ -129,13 +127,11 @@ But the output only shares the LoD information with the input $X$.
 
 )DOC",
                                GetName(), GetEquation()));
-    SetReuse();
   }
 
  protected:
   virtual std::string GetName() const = 0;
   virtual std::string GetEquation() const = 0;
-  virtual void SetReuse() {}
 };
 
 class ElementwiseOpGrad : public framework::OperatorWithKernel {
@@ -269,7 +265,6 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
    protected:                                                          \
     virtual std::string GetName() const { return op_name; }            \
     virtual std::string GetEquation() const { return equation; }       \
-    virtual void SetReuse() { Reuse(__VA_ARGS__); }                    \
   };                                                                   \
   REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp,       \
                     __ElemwiseOp##op_type##Maker__,                    \

paddle/fluid/operators/fusion_gru_op.cc

@@ -16,10 +16,9 @@ limitations under the License. */
 #include <cstring>  // for memcpy
 #include <string>
 #include "paddle/fluid/operators/math/blas.h"
-#include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/operators/math/fc_compute.h"
+#include "paddle/fluid/operators/math/jit_kernel.h"
 #include "paddle/fluid/operators/math/sequence2batch.h"
-#include "paddle/fluid/platform/cpu_info.h"
 
 namespace paddle {
 namespace operators {
@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     }
   }
 
-#define INIT_VEC_FUNC                                                     \
-  std::function<void(const int, const T *, T *)> act_gate, act_state;     \
-  std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
-  auto& act_gate_str = ctx.Attr<std::string>("gate_activation");          \
-  auto& act_state_str = ctx.Attr<std::string>("activation");              \
-  if (platform::jit::MayIUse(platform::jit::avx)) {                       \
-    math::VecActivations<T, platform::jit::avx> act_functor;              \
-    act_gate = act_functor(act_gate_str);                                 \
-    act_state = act_functor(act_state_str);                               \
-    cross = math::vec_cross<T, platform::jit::avx>;                       \
-  } else {                                                                \
-    math::VecActivations<T, platform::jit::isa_any> act_functor;          \
-    act_gate = act_functor(act_gate_str);                                 \
-    act_state = act_functor(act_state_str);                               \
-    cross = math::vec_cross<T, platform::jit::isa_any>;                   \
-  }
-
-#define INIT_BASE_INPUT_OUTPUT                        \
-  auto* h0 = ctx.Input<Tensor>("H0");                 \
-  auto* wx = ctx.Input<Tensor>("WeightX");            \
-  auto* wh = ctx.Input<Tensor>("WeightH");            \
-  auto* bias = ctx.Input<Tensor>("Bias");             \
-  auto* xx = ctx.Output<LoDTensor>("XX");             \
-  auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
-  bool is_reverse = ctx.Attr<bool>("is_reverse");
-
-#define INIT_BASE_SIZES                  \
-  auto x_dims = x->dims();   /* T x M*/  \
-  auto wh_dims = wh->dims(); /* D x 3D*/ \
-  const int total_T = x_dims[0];         \
-  const int M = x_dims[1];               \
-  const int D = wh_dims[0];              \
-  const int D3 = wh_dims[1];             \
-  const int D2 = D * 2;
+#define INIT_BASE_DEFINES                  \
+  auto* x = ctx.Input<LoDTensor>("X");     \
+  auto* wh = ctx.Input<Tensor>("WeightH"); \
+  auto* xx = ctx.Output<LoDTensor>("XX");  \
+  auto x_lod = x->lod();                   \
+  auto x_dims = x->dims();   /* T x M*/    \
+  auto wh_dims = wh->dims(); /* D x 3D*/   \
+  const int total_T = x_dims[0];           \
+  const int D3 = wh_dims[1]
+
+#define INIT_OTHER_DEFINES                                                     \
+  auto* h0 = ctx.Input<Tensor>("H0");                                          \
+  auto* wx = ctx.Input<Tensor>("WeightX");                                     \
+  auto* bias = ctx.Input<Tensor>("Bias");                                      \
+  auto* hidden_out = ctx.Output<LoDTensor>("Hidden");                          \
+  bool is_reverse = ctx.Attr<bool>("is_reverse");                              \
+  const int M = x_dims[1];                                                     \
+  const int D = wh_dims[0];                                                    \
+  const int D2 = D * 2;                                                        \
+  const auto& ker = math::jitkernel::KernelPool::Instance()                    \
+                        .template Get<math::jitkernel::GRUKernel<T>,           \
+                                      const std::string&, const std::string&>( \
+                            ctx.Attr<std::string>("gate_activation"),          \
+                            ctx.Attr<std::string>("activation"), D);           \
+  const T* x_data = x->data<T>();                                              \
+  const T* wx_data = wx->data<T>();                                            \
+  const T* wh_data = wh->data<T>();                                            \
+  auto place = ctx.GetPlace();                                                 \
+  T* xx_data = xx->mutable_data<T>(place)
 
   void SeqCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    INIT_BASE_INPUT_OUTPUT
-    INIT_BASE_SIZES
-    INIT_VEC_FUNC
-
-    auto x_lod = x->lod();
+    INIT_BASE_DEFINES;
+    INIT_OTHER_DEFINES;
     const int N = x_lod[0].size() - 1;
-    const T* x_data = x->data<T>();
     const T* h0_data = h0 ? h0->data<T>() : nullptr;
-    const T* wx_data = wx->data<T>();
-    const T* wh_data = wh->data<T>();
     const T* wh_state_data = wh_data + D * D2;
-    T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
-    T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
-
+    T* hidden_out_data = hidden_out->mutable_data<T>(place);
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
                                       xx_data,
@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       if (h0_data) {
         prev_hidden_data = h0_data + bid * D;
       } else {
-        // W: {W_update, W_reset; W_state}
-        // update gate
-        act_gate(D, xx_data, xx_data);
-        // state gate
-        act_state(D, xx_data + D2, xx_data + D2);
-        // out = a*b
-        blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
-        // save prev
+        ker->ComputeH1(xx_data, hidden_out_data);
         prev_hidden_data = hidden_out_data;
         tstart = 1;
         move_step();
@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
                   prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
                   D3);
-        act_gate(D2, xx_data, xx_data);
-        // rt = rt*ht_1 inplace result
-        blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
-
+        ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
         // gemm rt * Ws
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
                   hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
                   xx_data + D2, D3);
-        act_state(D, xx_data + D2, xx_data + D2);
-        // out = zt*ht~ + (1-zt)*ht_1
-        cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
+        ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
         // save prev
         prev_hidden_data = hidden_out_data;
         move_step();
@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
 
   void BatchCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    INIT_BASE_INPUT_OUTPUT
-    INIT_BASE_SIZES
-    if (x->lod()[0].size() == 2) {
+    INIT_BASE_DEFINES;
+    if (x_lod[0].size() == 2) {
       xx->Resize({total_T, D3});
       SeqCompute(ctx);
       return;
     }
-    INIT_VEC_FUNC
-
+    INIT_OTHER_DEFINES;
     auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
     auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
     auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
-
-    const T* x_data = x->data<T>();
-    const T* wx_data = wx->data<T>();
-    const T* wh_data = wh->data<T>();
-    T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
-    T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
-    T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
-    hidden_out->mutable_data<T>(ctx.GetPlace());
-
+    T* batched_input_data = batched_input->mutable_data<T>(place);
+    T* batched_out_data = batched_out->mutable_data<T>(place);
+    hidden_out->mutable_data<T>(place);
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     T* prev_hidden_data = nullptr;
     if (h0) {
       // reorder h0
-      T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
+      T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
       const T* h0_data = h0->data<T>();
       prev_hidden_data = reordered_h0_data;
       size_t sz = sizeof(T) * D;
@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       T* cur_out_data = batched_out_data;
       // W: {W_update, W_reset; W_state}
       for (int i = 0; i < max_bs; ++i) {
-        // update gate
-        act_gate(D, cur_in_data, cur_in_data);
-        // state gate
-        act_state(D, cur_in_data + D2, cur_in_data + D2);
-        // out = a*b
-        blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
+        ker->ComputeH1(cur_in_data, cur_out_data);
         // add offset
         cur_in_data += D3;
         cur_out_data += D;
@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       T* cur_out_data = batched_out_data;
       T* cur_prev_hidden_data = prev_hidden_data;
       for (int i = 0; i < cur_bs; ++i) {
-        act_gate(D2, cur_batched_data, cur_batched_data);
-        // rt = rt*ht_1 inplace result
-        blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
-
+        ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
+                            cur_out_data);
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
         cur_out_data += D;
@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
 
       cur_prev_hidden_data = prev_hidden_data;
       for (int i = 0; i < cur_bs; ++i) {
-        // ht~ = act_state(...)
-        act_state(D, cur_batched_data + D2, cur_batched_data + D2);
-        // out = zt*ht~ + (1-zt)*ht_1
-        cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
-              cur_out_data);
-
+        ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
+                            cur_out_data);
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
         cur_out_data += D;
@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     batched_out->set_lod(batched_lod);
     to_seq(dev_ctx, *batched_out, hidden_out);
   }
-#undef INIT_VEC_FUNC
-#undef INIT_BASE_SIZES
-#undef INIT_BASE_INPUT_OUTPUT
+#undef INIT_OTHER_DEFINES
+#undef INIT_BASE_DEFINES
 };
 
 }  // namespace operators

paddle/fluid/operators/lod_tensor_to_array_op.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/port.h"
 
@@ -79,7 +79,7 @@ struct LoDTensorToArrayFunctor : public boost::static_visitor<void> {
 template <typename DeviceContext>
 template <typename T>
 void LoDTensorToArrayFunctorImpl<DeviceContext>::apply() {
-  math::ConcatGradFunctor<DeviceContext, T> func;
+  math::SplitFunctor<DeviceContext, T> func;
   func(*dev_ctx_, prev_functor_->input_, prev_functor_->ref_inputs_, 0,
        &prev_functor_->outputs_);
 }

paddle/fluid/operators/math/CMakeLists.txt

 if (NOT WIN32)
-add_subdirectory(detail)
+    add_subdirectory(detail)
 endif(NOT WIN32)
 
 function(math_library TARGET)
@@ -35,7 +35,7 @@ function(math_library TARGET)
 endfunction()
 
 # please add new math_library in alphabetical order
-math_library(concat)
+math_library(concat_and_split)
 math_library(context_project DEPS im2col math_function)
 math_library(cross_entropy)
 math_library(cos_sim_functor)
@@ -43,8 +43,8 @@ math_library(depthwise_conv)
 math_library(im2col)
 
 if (NOT WIN32) # windows do not support avx functions yet.
-math_library(gru_compute DEPS activation_functions math_function)
-math_library(lstm_compute DEPS activation_functions)
+    math_library(gru_compute DEPS activation_functions math_function)
+    math_library(lstm_compute DEPS activation_functions)
 endif (NOT WIN32)
 
 cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
@@ -58,7 +58,7 @@ math_library(sequence_pooling DEPS math_function)
 math_library(sequence_scale)
 math_library(softmax DEPS math_function)
 if (NOT WIN32)
-math_library(matrix_bit_code)
+    math_library(matrix_bit_code)
 endif (NOT WIN32)
 math_library(unpooling)
 math_library(vol2col)
@@ -68,13 +68,14 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
 cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
 cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
 cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
+cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
 if(WITH_GPU)
     nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
     nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
 endif()
-cc_test(concat_test SRCS concat_test.cc DEPS concat)
+cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
 cc_library(jit_kernel 
-    SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
+    SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
     DEPS cpu_info cblas)
 cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)

paddle/fluid/operators/math/concat.cc → paddle/fluid/operators/math/concat_and_split.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include <vector>
 
 namespace paddle {
@@ -67,7 +67,7 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
  * each dimension must be the same, except the axis dimension.
  */
 template <typename T>
-class ConcatGradFunctor<platform::CPUDeviceContext, T> {
+class SplitFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::Tensor& input,
@@ -111,7 +111,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
 };
 #define DEFINE_FUNCTOR(type)                                      \
   template class ConcatFunctor<platform::CPUDeviceContext, type>; \
-  template class ConcatGradFunctor<platform::CPUDeviceContext, type>;
+  template class SplitFunctor<platform::CPUDeviceContext, type>;
 
 FOR_ALL_TYPES(DEFINE_FUNCTOR);
 

paddle/fluid/operators/math/concat.cu → paddle/fluid/operators/math/concat_and_split.cu

@@ -15,7 +15,7 @@ limitations under the License. */
 #include <algorithm>
 #include <vector>
 #include "paddle/fluid/framework/mixed_vector.h"
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
 #include "paddle/fluid/platform/float16.h"
 
@@ -24,7 +24,7 @@ namespace operators {
 namespace math {
 
 template <typename T>
-__global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
+__global__ void ConcatKernel(T** inputs, const int* input_cols, int col_size,
                              const int output_rows, const int output_cols,
                              T* output) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
@@ -50,7 +50,7 @@ __global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
 }
 
 template <typename T>
-__global__ void KernelConcat(T** inputs_data, const int fixed_in_col,
+__global__ void ConcatKernel(T** inputs_data, const int fixed_in_col,
                              const int out_rows, const int out_cols,
                              T* output_data) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
@@ -67,9 +67,9 @@ __global__ void KernelConcat(T** inputs_data, const int fixed_in_col,
 }
 
 template <typename T>
-__global__ void KernelConcatGrad(const T* input_data, const int in_row,
-                                 const int in_col, const int* out_cols,
-                                 int out_cols_size, T** outputs_data) {
+__global__ void SplitKernel(const T* input_data, const int in_row,
+                            const int in_col, const int* out_cols,
+                            int out_cols_size, T** outputs_data) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
   int curr_segment = 0;
   int curr_offset = out_cols[0];
@@ -94,9 +94,9 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
 }
 
 template <typename T>
-__global__ void KernelConcatGrad(const T* input_data, const int in_row,
-                                 const int in_col, const int fixed_out_col,
-                                 T** outputs_data) {
+__global__ void SplitKernel(const T* input_data, const int in_row,
+                            const int in_col, const int fixed_out_col,
+                            T** outputs_data) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
   for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
     int split = tid_x / fixed_out_col;
@@ -170,11 +170,11 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
     dim3 grid_size = dim3(grid_cols, grid_rows, 1);
 
     if (sameShape) {
-      KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
+      ConcatKernel<<<grid_size, block_size, 0, context.stream()>>>(
           dev_ins_data, in_col, out_row, out_col, output->data<T>());
     } else {
       const int* dev_ins_col_data = inputs_col.CUDAData(context.GetPlace());
-      KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
+      ConcatKernel<<<grid_size, block_size, 0, context.stream()>>>(
           dev_ins_data, dev_ins_col_data, static_cast<int>(inputs_col.size()),
           out_row, out_col, output->data<T>());
     }
@@ -189,7 +189,7 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
  * each dimension must be the same, except the axis dimension.
  */
 template <typename T>
-class ConcatGradFunctor<platform::CUDADeviceContext, T> {
+class SplitFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::Tensor& input,
@@ -248,11 +248,11 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
     dim3 grid_size = dim3(grid_cols, grid_rows, 1);
 
     if (sameShape) {
-      KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
+      SplitKernel<<<grid_size, block_size, 0, context.stream()>>>(
           input.data<T>(), in_row, in_col, out0_col, dev_out_gpu_data);
     } else {
       const int* dev_outs_col_data = outputs_cols.CUDAData(context.GetPlace());
-      KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
+      SplitKernel<<<grid_size, block_size, 0, context.stream()>>>(
           input.data<T>(), in_row, in_col, dev_outs_col_data,
           static_cast<int>(outputs_cols.size()), dev_out_gpu_data);
     }
@@ -264,7 +264,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
 
 #define DEFINE_FUNCTOR(type)                                       \
   template class ConcatFunctor<platform::CUDADeviceContext, type>; \
-  template class ConcatGradFunctor<platform::CUDADeviceContext, type>
+  template class SplitFunctor<platform::CUDADeviceContext, type>
 
 FOR_ALL_TYPES(DEFINE_FUNCTOR);
 

paddle/fluid/operators/math/concat.h → paddle/fluid/operators/math/concat_and_split.h

@@ -54,7 +54,7 @@ class ConcatFunctor {
  *     Output[1] = [[5,6]]
  */
 template <typename DeviceContext, typename T>
-class ConcatGradFunctor {
+class SplitFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor& input,
                   const std::vector<const framework::Tensor*>& ref_inputs,

paddle/fluid/operators/math/concat_test.cc

@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/math/concat.h"
 #include <gtest/gtest.h>
 #include <vector>
 #include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 
 template <typename DeviceContext, typename Place>
 void testConcat() {

paddle/fluid/operators/math/jit_kernel.h

@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
                            const T *wp_data = nullptr) const = 0;
 };
 
+template <typename T>
+class GRUKernel : public Kernel {
+ public:
+  // compute h1 without h0
+  virtual void ComputeH1(T *gates, T *ht) const = 0;
+  virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
+  virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
+};
+
 }  // namespace jitkernel
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
   return nullptr;
 }
 
+#ifdef __AVX__
+template <jit::cpu_isa_t isa>
+static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
+  if (type == "sigmoid") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
+  } else if (type == "relu") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
+  } else if (type == "tanh") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
+  } else if (type == "identity" || type == "") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
+  }
+  PADDLE_THROW("Not support type: %s", type);
+  return nullptr;
+}
+#endif
+
 /* LSTM JitKernel */
 template <typename T, jit::cpu_isa_t isa, jit_block>
 class LSTMKernelImpl : public LSTMKernel<T> {
@@ -192,61 +209,49 @@ class LSTMKernelImpl : public LSTMKernel<T> {
 #endif
 };
 
-#define INTRI8_FLOAT(isa)                                                      \
-  template <>                                                                  \
-  LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl(                            \
-      const std::string& act_gate, const std::string& act_cand,                \
-      const std::string& act_cell, int d)                                      \
-      : LSTMKernel<float>() {                                                  \
-    auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
-      if (type == "sigmoid") {                                                 \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());       \
-      } else if (type == "relu") {                                             \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());          \
-      } else if (type == "tanh") {                                             \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());          \
-      } else if (type == "identity" || type == "") {                           \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());      \
-      }                                                                        \
-      PADDLE_THROW("Not support type: %s", type);                              \
-    };                                                                         \
-    avx_act_gate_ = GetAVXAct(act_gate);                                       \
-    avx_act_cand_ = GetAVXAct(act_cand);                                       \
-    avx_act_cell_ = GetAVXAct(act_cell);                                       \
-  }                                                                            \
-  template <>                                                                  \
-  void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt(                          \
-      float* gates, const float* ct_1, float* ct, float* ht,                   \
-      const float* wp_data, float* checked) const {                            \
-    /* gates: W_ch, W_ih, W_fh, W_oh */                                        \
-    __m256 c, i, f, o;                                                         \
-    c = _mm256_loadu_ps(gates);                                                \
-    i = _mm256_loadu_ps(gates + 8);                                            \
-    f = _mm256_loadu_ps(gates + 16);                                           \
-    o = _mm256_loadu_ps(gates + 24);                                           \
-    /* C_t = C_t-1 * fgated + cand_gated * igated*/                            \
-    c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i));   \
-    i = _mm256_loadu_ps(ct_1);                                                 \
-    f = _mm256_mul_ps(i, avx_act_gate_->Compute(f));                           \
-    f = _mm256_add_ps(c, f);                                                   \
-    _mm256_storeu_ps(ct, f);                                                   \
-    /* H_t = act_cell(C_t) * ogated */                                         \
-    o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o));   \
-    _mm256_storeu_ps(ht, o);                                                   \
-  }                                                                            \
-  template <>                                                                  \
-  void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1(                          \
-      float* gates, float* ct, float* ht, const float* wp_data) const {        \
-    __m256 c, i, o;                                                            \
-    c = _mm256_loadu_ps(gates);                                                \
-    i = _mm256_loadu_ps(gates + 8);                                            \
-    o = _mm256_loadu_ps(gates + 24);                                           \
-    /* C_t = igated * cgated*/                                                 \
-    c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c));   \
-    _mm256_storeu_ps(ct, c);                                                   \
-    /* H_t = act_cell(C_t) * ogated */                                         \
-    o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o));   \
-    _mm256_storeu_ps(ht, o);                                                   \
+#define INTRI8_FLOAT(isa)                                                    \
+  template <>                                                                \
+  LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl(                          \
+      const std::string& act_gate, const std::string& act_cand,              \
+      const std::string& act_cell, int d)                                    \
+      : LSTMKernel<float>() {                                                \
+    avx_act_gate_ = GetAVXAct<isa>(act_gate);                                \
+    avx_act_cand_ = GetAVXAct<isa>(act_cand);                                \
+    avx_act_cell_ = GetAVXAct<isa>(act_cell);                                \
+  }                                                                          \
+  template <>                                                                \
+  void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt(                        \
+      float* gates, const float* ct_1, float* ct, float* ht,                 \
+      const float* wp_data, float* checked) const {                          \
+    /* gates: W_ch, W_ih, W_fh, W_oh */                                      \
+    __m256 c, i, f, o;                                                       \
+    c = _mm256_loadu_ps(gates);                                              \
+    i = _mm256_loadu_ps(gates + 8);                                          \
+    f = _mm256_loadu_ps(gates + 16);                                         \
+    o = _mm256_loadu_ps(gates + 24);                                         \
+    /* C_t = C_t-1 * fgated + cand_gated * igated*/                          \
+    c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
+    i = _mm256_loadu_ps(ct_1);                                               \
+    f = _mm256_mul_ps(i, avx_act_gate_->Compute(f));                         \
+    f = _mm256_add_ps(c, f);                                                 \
+    _mm256_storeu_ps(ct, f);                                                 \
+    /* H_t = act_cell(C_t) * ogated */                                       \
+    o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
+    _mm256_storeu_ps(ht, o);                                                 \
+  }                                                                          \
+  template <>                                                                \
+  void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1(                        \
+      float* gates, float* ct, float* ht, const float* wp_data) const {      \
+    __m256 c, i, o;                                                          \
+    c = _mm256_loadu_ps(gates);                                              \
+    i = _mm256_loadu_ps(gates + 8);                                          \
+    o = _mm256_loadu_ps(gates + 24);                                         \
+    /* C_t = igated * cgated*/                                               \
+    c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
+    _mm256_storeu_ps(ct, c);                                                 \
+    /* H_t = act_cell(C_t) * ogated */                                       \
+    o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
+    _mm256_storeu_ps(ht, o);                                                 \
   }
 
 // TODO(TJ): optimize keq16
@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
 #undef JITKERNEL_DECLARE_LSTM
 #undef JITKERNEL_KEY_LSTM
 #undef JITKERNEL_NEW_LSTM_IMPL
+
+/* GRU JitKernel */
+template <typename T, jit::cpu_isa_t isa, jit_block>
+class GRUKernelImpl : public GRUKernel<T> {
+ public:
+  explicit GRUKernelImpl(const std::string& act_gate,
+                         const std::string& act_state, int d)
+      : GRUKernel<T>() {
+    d_ = d;
+    d2_ = d * 2;
+    act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
+    act_gate_d_ = GetActKernel<T>(act_gate, d);
+    act_state_d_ = GetActKernel<T>(act_state, d);
+    vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
+  }
+
+  void ComputeH1(T* gates, T* ht) const override {
+    act_gate_d_->Compute(gates, gates);
+    act_state_d_->Compute(gates + d2_, gates + d2_);
+    vmul_d_->Compute(gates, gates + d2_, ht);
+  }
+
+  void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
+    // W: {W_update, W_reset; W_state}
+    act_gate_d2_->Compute(gates, gates);
+    vmul_d_->Compute(ht_1, gates + d_, ht);
+  }
+
+  void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
+    T* y = gates + d2_;
+    act_state_d_->Compute(y, y);
+    // out = zt*ht~ + (1-zt)*ht_1
+    for (int i = 0; i < d_; ++i) {
+      ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
+    }
+  }
+
+ private:
+  int d_, d2_;
+  std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
+  std::shared_ptr<const VMulKernel<T>> vmul_d_;
+#ifdef __AVX__
+  std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
+#endif
+};
+
+#define INTRI8_FLOAT(isa)                                                     \
+  template <>                                                                 \
+  GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl(                             \
+      const std::string& act_gate, const std::string& act_state, int d)       \
+      : GRUKernel<float>() {                                                  \
+    avx_act_gate_ = GetAVXAct<isa>(act_gate);                                 \
+    avx_act_state_ = GetAVXAct<isa>(act_state);                               \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht)    \
+      const {                                                                 \
+    __m256 u, s;                                                              \
+    /* W: {W_update, W_reset; W_state} */                                     \
+    u = _mm256_loadu_ps(gates);                                               \
+    s = _mm256_loadu_ps(gates + 16);                                          \
+    s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
+    _mm256_storeu_ps(ht, s);                                                  \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1(                       \
+      float* gates, const float* ht_1, float* ht) const {                     \
+    /* not exactly equal the any implementation */                            \
+    __m256 r, ht0;                                                            \
+    r = _mm256_loadu_ps(gates + 8);                                           \
+    ht0 = _mm256_loadu_ps(ht_1);                                              \
+    r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0);                        \
+    _mm256_storeu_ps(ht, r);                                                  \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2(                       \
+      float* gates, const float* ht_1, float* ht) const {                     \
+    /* not exactly equal the any implementation */                            \
+    __m256 u, s, ht0;                                                         \
+    u = _mm256_loadu_ps(gates);                                               \
+    s = _mm256_loadu_ps(gates + 16);                                          \
+    ht0 = _mm256_loadu_ps(ht_1);                                              \
+    u = avx_act_gate_->Compute(u);                                            \
+    s = _mm256_mul_ps(u, avx_act_state_->Compute(s));                         \
+    u = _mm256_sub_ps(_mm256_set1_ps(1.f), u);                                \
+    u = _mm256_mul_ps(u, ht0);                                                \
+    u = _mm256_add_ps(s, u);                                                  \
+    _mm256_storeu_ps(ht, u);                                                  \
+  }
+
+#ifdef __AVX__
+INTRI8_FLOAT(jit::avx);
+#endif
+#ifdef __AVX2__
+INTRI8_FLOAT(jit::avx2);
+#endif
+#ifdef __AVX512F__
+INTRI8_FLOAT(jit::avx512f);
+#endif
+
+#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype)                       \
+  template <>                                                             \
+  std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get<            \
+      GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
+      const std::string& act_gate, const std::string& act_state, int d)
+
+#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
+  #ker_key #dtype_key + std::to_string(d) + act_gate + act_state
+
+#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
+  p = std::dynamic_pointer_cast<ker<dtype>>(       \
+      std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
+
+REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
+                        JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
+
+#undef INTRI8_FLOAT
+#undef JITKERNEL_NEW_GRU_IMPL
+#undef JITKERNEL_KEY_GRU
+#undef JITKERNEL_DECLARE_GRU
 }  // namespace jitkernel
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/sequence_pooling.cc

@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
   }
 };
 
+template <typename T>
+class SumSeqPoolGradFunctor {
+ public:
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::Tensor& out_grad,
+                  framework::LoDTensor* in_grad) {
+    auto lod = in_grad->lod()[0];
+    int64_t out_w = out_grad.numel() / out_grad.dims()[0];
+    int64_t in_w = in_grad->numel() / in_grad->dims()[0];
+    PADDLE_ENFORCE(in_w == out_w);
+    const T* out_g_data = out_grad.data<T>();
+    T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+    for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
+      int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
+      int64_t in_offset = lod[i] * in_w;
+      const T* out_pos = out_g_data + i * out_w;
+      T* in_pos = in_g_data + in_offset;
+      for (int r = 0; r != h; ++r) {
+        blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
+      }
+    }
+  }
+};
+
 template <typename T>
 class SequencePoolFunctor<platform::CPUDeviceContext, T> {
  public:
@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
       math::SetConstant<platform::CPUDeviceContext, T> functor;
       functor(context, in_grad, 0);
     }
+
+    if (pooltype == "SUM") {
+      math::SumSeqPoolGradFunctor<T> sum_pool_grad;
+      sum_pool_grad(context, out_grad, in_grad);
+      return;
+    }
+
     auto lod = in_grad->lod()[0];
     auto& place = *context.eigen_device();
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
     for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
       auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
                                    static_cast<int>(lod[i + 1]));
@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
 
       if (pooltype == "AVERAGE") {
         in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
-      } else if (pooltype == "SUM") {
-        const T* out_g_data = out_g_t.data<T>();
-        T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
-        for (int r = 0; r != h; ++r) {
-          blas.VCOPY(w, out_g_data, in_g_data + r * w);
-        }
       } else if (pooltype == "SQRT") {
         in_g_e.device(place) =
             (out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);

paddle/fluid/operators/math/sequence_pooling_test.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/math/sequence_pooling.h"
+#include <gtest/gtest.h>
+#include <vector>
+
+template <typename DeviceContext, typename Place, typename T>
+void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
+  paddle::framework::LoDTensor cpu_out_grad;
+  paddle::framework::LoDTensor cpu_in_grad;
+  paddle::framework::LoDTensor out_grad;
+  paddle::framework::LoDTensor in_grad;
+  const size_t second_dim = 128u;
+
+  // construct out_grad's tensor in cpu
+  const size_t out_first_dim = lod[0].size() - 1;
+  auto out_dims = paddle::framework::make_ddim(
+      {static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
+
+  cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
+  for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
+    cpu_out_grad.data<T>()[i] = static_cast<T>(i);
+  }
+
+  // copy to dst out_grad
+  auto* place = new Place();
+  DeviceContext* context = new DeviceContext(*place);
+  if (paddle::platform::is_cpu_place(*place)) {
+    out_grad = cpu_out_grad;
+  } else {
+    TensorCopySync(cpu_out_grad, *place, &out_grad);
+  }
+
+  // construct in_grad
+  in_grad.set_lod(lod);
+  auto in_dims = paddle::framework::make_ddim(
+      {static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
+  in_grad.mutable_data<T>(in_dims, context->GetPlace());
+
+  // check tensor contruction result
+  PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
+  for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
+    PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
+  }
+
+  // call functor
+  paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
+      *context, "SUM", out_grad, &in_grad);
+
+  if (paddle::platform::is_cpu_place(*place)) {
+    cpu_in_grad = in_grad;
+  } else {
+    TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
+    cpu_in_grad.set_lod(in_grad.lod());
+  }
+
+  EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
+  EXPECT_EQ(in_grad.lod(), lod);
+
+  if (paddle::platform::is_cpu_place(*place)) {
+    for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
+      int64_t begin = in_grad.lod()[0][i];
+      int64_t end = in_grad.lod()[0][i + 1];
+      paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
+      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+        for (int64_t m = 0; m != second_dim; ++m) {
+          EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
+                    out_grad.data<T>()[m + i * second_dim]);
+        }
+      }
+    }
+  } else {
+    for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
+      int64_t begin = cpu_in_grad.lod()[0][i];
+      int64_t end = cpu_in_grad.lod()[0][i + 1];
+      paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
+      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+        for (int64_t m = 0; m != second_dim; ++m) {
+          EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
+                    cpu_out_grad.data<T>()[m + i * second_dim]);
+        }
+      }
+    }
+  }
+
+  delete place;
+  delete context;
+}
+
+TEST(SequencePoolingGrad, CPU_SUM) {
+  paddle::framework::LoD lod1;
+  lod1.push_back(std::vector<size_t>{0, 10});
+  TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
+                         paddle::platform::CPUPlace, float>(lod1);
+
+  paddle::framework::LoD lod2;
+  lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
+  TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
+                         paddle::platform::CPUPlace, float>(lod2);
+}
+
+#ifdef PADDLE_WITH_CUDA
+TEST(SequencePoolingGrad, CUDA_SUM) {
+  paddle::framework::LoD lod1;
+  lod1.push_back(std::vector<size_t>{0, 10});
+  TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
+                         paddle::platform::CUDAPlace, float>(lod1);
+
+  paddle::framework::LoD lod2;
+  lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
+  TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
+                         paddle::platform::CUDAPlace, float>(lod2);
+}
+#endif

paddle/fluid/operators/mean_op.cc

@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X", "(Tensor) The input of mean op");
-    AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
+    AddOutput("Out", "(Tensor) The output of mean op");
     AddComment(R"DOC(
 Mean Operator calculates the mean of all elements in X.
 

paddle/fluid/operators/pool_op.cc

@@ -151,8 +151,7 @@ void Pool2dOpMaker::Make() {
             "The format of output tensor is also NCHW, "
             "where N is batch size, C is the number of channels, "
             "H is the height of the feature, "
-            "and W is the width of the feature.")
-      .Reuse("X");
+            "and W is the width of the feature.");
 
   AddAttr<std::string>("pooling_type",
                        "(string), pooling type, can be \"max\" for max-pooling "
@@ -252,8 +251,7 @@ void Pool3dOpMaker::Make() {
             "The format of output tensor is also NCDHW, "
             "where N is batch size, C is "
             "the number of channels, and D, H and W is the depth, height and "
-            "width of the feature, respectively.")
-      .Reuse("X");
+            "width of the feature, respectively.");
 
   AddAttr<std::string>("pooling_type",
                        "(string) Pooling type, can be \"max\" for max-pooling "

paddle/fluid/operators/reader/reader_blocking_queue_test.cc

@@ -237,7 +237,7 @@ TEST(BlockingQueue, speed_test_mode) {
   }
   for (size_t i = 0; i < queue_size; ++i) {
     q2.Receive(&b);
-    EXPECT_EQ(b, 0);
+    EXPECT_EQ(b, 0UL);
   }
   EXPECT_EQ(q2.Size(), queue_size);
 }

paddle/fluid/operators/sequence_concat_op.h

@@ -17,7 +17,7 @@
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
-#include "paddle/fluid/operators/math/concat.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 
 namespace paddle {
 namespace operators {
@@ -106,7 +106,7 @@ class SeqConcatGradKernel : public framework::OpKernel<T> {
       }
     }
 
-    math::ConcatGradFunctor<DeviceContext, T> functor;
+    math::SplitFunctor<DeviceContext, T> functor;
     std::vector<const framework::Tensor *> sliced_x_ptr;
     std::vector<framework::Tensor *> sliced_dx_ptr;
     for (auto &x : sliced_x) {

paddle/fluid/operators/sgd_op.cc

@@ -77,8 +77,7 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
     AddOutput("ParamOut",
               "(Tensor or SelectedRows, same with Param) "
-              "Output parameter, should share the same memory with Param")
-        .Reuse("Param");
+              "Output parameter, should share the same memory with Param");
     AddComment(R"DOC(
 
 SGD operator

paddle/fluid/operators/softmax_op.cc

@@ -80,8 +80,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "The input tensor of softmax, "
              "whose last dimension is the input_feature_dimensions.");
-    AddOutput("Out", "The normalized values with the same shape as X.")
-        .Reuse("X");
+    AddOutput("Out", "The normalized values with the same shape as X.");
     AddAttr<bool>(
         "use_cudnn",
         "(bool, default false) Only used in cudnn kernel, need install cudnn")

paddle/fluid/operators/split_op.cc

@@ -111,11 +111,10 @@ Example:
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-USE_CPU_ONLY_OP(concat);
 
 REGISTER_OPERATOR(split, ops::SplitOp, ops::SplitOpMaker, ops::SplitGradMaker);
-REGISTER_OP_CPU_KERNEL(split,
-                       ops::SplitOpKernel<paddle::platform::CPUPlace, double>,
-                       ops::SplitOpKernel<paddle::platform::CPUPlace, float>,
-                       ops::SplitOpKernel<paddle::platform::CPUPlace, int64_t>,
-                       ops::SplitOpKernel<paddle::platform::CPUPlace, int>);
+REGISTER_OP_CPU_KERNEL(
+    split, ops::SplitOpKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SplitOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SplitOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::SplitOpKernel<paddle::platform::CPUDeviceContext, int>);

paddle/fluid/operators/split_op.h

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <chrono>  // NOLINT
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/operators/strided_memcpy.h"
 
 namespace paddle {
@@ -28,18 +29,22 @@ class SplitOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
     auto outs = ctx.MultiOutput<framework::Tensor>("Out");
-    auto in_stride = framework::stride_numel(in->dims());
-    int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
+    int axis = ctx.Attr<int>("axis");
     auto place = ctx.GetPlace();
 
-    size_t input_offset = 0;
-    for (auto& out : outs) {
-      out->mutable_data<T>(ctx.GetPlace());
-      auto out_stride = framework::stride_numel(out->dims());
-      StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
-                                  out_stride, in->data<T>() + input_offset,
-                                  in_stride, out_stride[axis]);
-      input_offset += out_stride[axis];
+    std::vector<const framework::Tensor*> shape_refer;
+    for (size_t j = 0; j < outs.size(); ++j) {
+      outs[j]->mutable_data<T>(ctx.GetPlace());
+      shape_refer.emplace_back(outs[j]);
+    }
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    // Sometimes direct copies will be faster, this maybe need deeply analysis.
+    if (axis == 0 && outs.size() < 10) {
+      StridedMemcpyWithAxis0<T>(dev_ctx, *in, shape_refer, &outs);
+    } else {
+      math::SplitFunctor<DeviceContext, T> functor;
+      functor(dev_ctx, *in, shape_refer, axis, &outs);
     }
   }
 };

paddle/fluid/operators/strided_memcpy.h

@@ -13,8 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <vector>
+#include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/detail/strided_memcpy.h"
-
 namespace paddle {
 namespace operators {
 
@@ -98,5 +99,26 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
   }
 }
 
+template <typename T>
+inline void StridedMemcpyWithAxis0(
+    const platform::DeviceContext& dev_ctx, const framework::Tensor& input,
+    const std::vector<const framework::Tensor*>& shape_refer,
+    std::vector<framework::Tensor*>* outputs) {
+  const framework::DDim in_stride = stride_numel(input.dims());
+  const int axis = 0;
+  size_t input_offset = 0;
+
+  for (size_t i = 0; i < outputs->size(); ++i) {
+    auto out_stride = stride_numel(shape_refer[i]->dims());
+    auto out = outputs->at(i);
+    if (out != nullptr) {
+      StridedNumelCopyWithAxis<T>(dev_ctx, axis, out->data<T>(), out_stride,
+                                  input.data<T>() + input_offset, in_stride,
+                                  out_stride[axis]);
+    }
+    input_offset += out_stride[axis];
+  }
+}
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/sum_op.cc

@@ -132,7 +132,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
         .AsDuplicable();
-    AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
+    AddOutput("Out", "(Tensor) The output tensor of sum operator.");
     AddAttr<bool>("use_mkldnn",
                   "(bool, default false) Only used in mkldnn kernel")
         .SetDefault(false);

paddle/fluid/operators/top_k_op.cc

@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X", "(Tensor) The input of Topk op");
-    AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
+    AddOutput("Out", "(Tensor) The output tensor of Topk op");
     AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
     AddComment(R"DOC(
 Top K operator

paddle/fluid/operators/top_k_op.cu

@@ -262,31 +262,31 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
                              const T* src, int lds, int dim, int k,
                              int grid_dim, int num) {
   __shared__ Pair<T> sh_topk[BlockSize];
-  __shared__ int maxid[BlockSize / 2];
   const int tid = threadIdx.x;
   const int warp = threadIdx.x / 32;
 
   const int bid = blockIdx.x;
   for (int i = bid; i < num; i += grid_dim) {
-    output += i * output_stride;
-    indices += i * k;
-
+    int top_num = k;
+    __shared__ int maxid[BlockSize / 2];
+    T* out = output + i * output_stride;
+    int64_t* inds = indices + i * k;
     Pair<T> topk[MaxLength];
     int beam = MaxLength;
     Pair<T> max;
     bool is_empty = false;
     bool firststep = true;
 
-    for (int k = 0; k < MaxLength; k++) {
-      topk[k].set(-INFINITY, -1);
+    for (int j = 0; j < MaxLength; j++) {
+      topk[j].set(-INFINITY, -1);
     }
-    while (k) {
+    while (top_num) {
       ThreadGetTopK<T, MaxLength, BlockSize>(
           topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
 
       sh_topk[tid] = topk[0];
-      BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
-                                           &indices, &beam, &k, tid, warp);
+      BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &out, &inds,
+                                           &beam, &top_num, tid, warp);
     }
   }
 }
@@ -327,13 +327,15 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     size_t k = static_cast<int>(ctx.Attr<int>("k"));
 
     const T* input_data = input->data<T>();
-
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     // FIXME(typhoonzero): data is always converted to type T?
     int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
-    size_t input_height = input->dims()[0];
-    size_t input_width = input->dims()[1];
+    framework::DDim inputdims = input->dims();
+    const size_t input_height = framework::product(
+        framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
+    const size_t input_width = inputdims[inputdims.size() - 1];
+
     if (k > input_width) k = input_width;
 
     // NOTE: pass lds and dim same to input width.
@@ -342,14 +344,12 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     const int kMaxHeight = 2048;
     int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
     auto& dev_ctx = ctx.cuda_device_context();
-
     switch (GetDesiredBlockDim(input_width)) {
       FIXED_BLOCK_DIM(
           KeMatrixTopK<T, 5,
                        kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
-              output_data, output->dims()[1], indices_data, input_data,
-              input_width, input_width, static_cast<int>(k), gridx,
-              input_height));
+              output_data, k, indices_data, input_data, input_width,
+              input_width, static_cast<int>(k), gridx, input_height));
       default:
         PADDLE_THROW("Error");
     }

paddle/fluid/operators/top_k_op.h

@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     // Get the top k elements of each row of input tensor
-    // FIXME: only deal with matrix(2d tensor).
     auto* input = ctx.Input<Tensor>("X");
     auto* output = ctx.Output<Tensor>("Out");
     auto* indices = ctx.Output<Tensor>("Indices");
@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
-    auto eg_input = EigenMatrix<T>::From(*input);
-
     // reshape input to a flattern matrix(like flat_inner_dims)
     framework::DDim inputdims = input->dims();
     const size_t row = framework::product(
@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
     const size_t col = inputdims[inputdims.size() - 1];
     Eigen::DSizes<int, 2> flat2dims(row, col);
     // NOTE: eigen shape doesn't affect paddle tensor.
-    eg_input.reshape(flat2dims);
+    auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
 
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for

python/paddle/fluid/layer_helper.py

@@ -324,10 +324,19 @@ class LayerHelper(object):
             raise ValueError("no Parameter name %s found" % name)
         return param
 
-    def create_tmp_variable(self, dtype, stop_gradient=False):
+    def create_variable_for_type_inference(self, dtype, stop_gradient=False):
+        """Create a temporary variable that should be type inferred layer.
+
+        Note:
+            The default type will be set to LOD_TENSOR. However, when
+            the var is used as operator output, its type will be updated
+            based on operator's `VarTypeInference` implementation in
+            infer_var_type.
+        """
         return self.main_program.current_block().create_var(
             name=unique_name.generate(".".join([self.name, 'tmp'])),
             dtype=dtype,
+            type=core.VarDesc.VarType.LOD_TENSOR,
             persistable=False,
             stop_gradient=stop_gradient)
 
@@ -388,7 +397,7 @@ class LayerHelper(object):
 
         b = self.create_parameter(
             attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True)
-        tmp = self.create_tmp_variable(dtype=input_var.dtype)
+        tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
         self.append_op(
             type='elementwise_add',
             inputs={'X': [input_var],
@@ -414,7 +423,7 @@ class LayerHelper(object):
         tmp = input_var
         # NOTE(dzhwinter): some activation support inplace compution.
         if not core.IsInplace(act_type):
-            tmp = self.create_tmp_variable(dtype=input_var.dtype)
+            tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
         self.append_op(
             type=act_type,
             inputs={"X": [input_var]},

python/paddle/fluid/layers/control_flow.py

@@ -80,8 +80,8 @@ def split_lod_tensor(input, mask, level=0):
 
     """
     helper = LayerHelper('split_lod_tensor', **locals())
-    out_true = helper.create_tmp_variable(dtype=input.dtype)
-    out_false = helper.create_tmp_variable(dtype=input.dtype)
+    out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
+    out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type='split_lod_tensor',
         inputs={
@@ -131,7 +131,7 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
                 in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
     """
     helper = LayerHelper('merge_lod_tensor', **locals())
-    out = helper.create_tmp_variable(dtype=in_true.dtype)
+    out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
     helper.append_op(
         type='merge_lod_tensor',
         inputs={'X': x,
@@ -524,7 +524,7 @@ class StaticRNN(object):
         if not isinstance(o, Variable):
             raise TypeError("step output takes a Variable")
 
-        tmp_o = self.helper.create_tmp_variable(dtype=o.dtype)
+        tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
         self.helper.append_op(
             type='rnn_memory_helper',
             inputs={'X': [o]},
@@ -606,7 +606,8 @@ class StaticRNN(object):
             pre_memories.append(mem.pre_mem.name)
             mem_var = rnn_block.var(mem.mem.name)
             assert isinstance(mem_var, Variable)
-            new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype)
+            new_mem = self.helper.create_variable_for_type_inference(
+                dtype=mem_var.dtype)
 
             rnn_block.append_op(
                 type='rnn_memory_helper',
@@ -813,7 +814,7 @@ def max_sequence_len(rank_table):
         ${out_comment}.
     """
     helper = LayerHelper("max_seqence_len", **locals())
-    res = helper.create_tmp_variable(dtype="int64")
+    res = helper.create_variable_for_type_inference(dtype="int64")
     helper.append_op(
         type="max_sequence_len",
         inputs={"RankTable": rank_table},
@@ -884,7 +885,7 @@ def array_to_lod_tensor(x, table):
           lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
     """
     helper = LayerHelper("array_to_lod_tensor", **locals())
-    tmp = helper.create_tmp_variable(dtype=x.dtype)
+    tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type="array_to_lod_tensor",
         inputs={'X': x,
@@ -915,7 +916,7 @@ def increment(x, value=1.0, in_place=True):
     """
     helper = LayerHelper("increment", **locals())
     if not in_place:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = x
     helper.append_op(
@@ -1012,7 +1013,7 @@ def less_than(x, y, force_cpu=None, cond=None, **ignored):
     """
     helper = LayerHelper("less_than", **locals())
     if cond is None:
-        cond = helper.create_tmp_variable(dtype='bool')
+        cond = helper.create_variable_for_type_inference(dtype='bool')
         cond.stop_gradient = True
 
     attrs = dict()
@@ -1051,7 +1052,7 @@ def equal(x, y, cond=None, **ignored):
     """
     helper = LayerHelper("equal", **locals())
     if cond is None:
-        cond = helper.create_tmp_variable(dtype='bool')
+        cond = helper.create_variable_for_type_inference(dtype='bool')
         cond.stop_gradient = True
 
     helper.append_op(
@@ -1098,7 +1099,7 @@ def array_read(array, i):
             array,
             Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
         raise TypeError("array should be tensor array vairable")
-    out = helper.create_tmp_variable(dtype=array.dtype)
+    out = helper.create_variable_for_type_inference(dtype=array.dtype)
     helper.append_op(
         type='read_from_array',
         inputs={'X': [array],
@@ -1133,7 +1134,7 @@ def shrink_memory(x, i, table):
         usage.
     """
     helper = LayerHelper('shrink_memory', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='shrink_rnn_memory',
         inputs={'X': [x],
@@ -1170,7 +1171,7 @@ def array_length(array):
 
     """
     helper = LayerHelper('array_length', **locals())
-    tmp = helper.create_tmp_variable(dtype='int64')
+    tmp = helper.create_variable_for_type_inference(dtype='int64')
     tmp.stop_gradient = True
     helper.append_op(
         type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
@@ -1590,7 +1591,7 @@ class DynamicRNN(object):
         self.mem_dict = dict()
         self.output_array = []
         self.outputs = []
-        self.cond = self.helper.create_tmp_variable(dtype='bool')
+        self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
         self.cond.stop_gradient = False
         self.while_op = While(self.cond)
         self.input_array = []
@@ -1924,7 +1925,7 @@ def reorder_lod_tensor_by_rank(x, rank_table):
     helper.is_instance('x', Variable)
     helper.is_instance('rank_table', Variable)
 
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='reorder_lod_tensor_by_rank',
         inputs={'X': [x],
@@ -1958,7 +1959,7 @@ def is_empty(x, cond=None, **ignored):
     """
     helper = LayerHelper("is_empty", **locals())
     if cond is None:
-        cond = helper.create_tmp_variable(dtype='bool')
+        cond = helper.create_variable_for_type_inference(dtype='bool')
         cond.stop_gradient = True
     elif not isinstance(cond, Variable):
         raise TypeError("cond takes a variable")

python/paddle/fluid/layers/detection.py

@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
     Returns:
         tuple:
                A tuple(predicted_scores, predicted_location, target_label,
-               target_bbox) is returned. The predicted_scores and
-               predicted_location is the predicted result of the RPN.
+               target_bbox, bbox_inside_weight) is returned. The predicted_scores 
+               and predicted_location is the predicted result of the RPN.
                The target_label and target_bbox is the ground truth,
                respectively. The predicted_location is a 2D Tensor with shape
                [F, 4], and the shape of target_bbox is same as the shape of
@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
                [F + B, 1], and the shape of target_label is same as the shape
                of the predicted_scores, B is the number of the background
                anchors, the F and B is depends on the input of this operator.
+               Bbox_inside_weight represents whether the predicted loc is fake_fg
+               or not and the shape is [F, 4].
 
     Examples:
         .. code-block:: python
@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
                           append_batch_size=False, dtype='float32')
         gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
                          append_batch_size=False, dtype='float32')
-        loc_pred, score_pred, loc_target, score_target =
+        loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
             fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
                                           cls_logits=cls_logits,
                                           anchor_box=anchor_box,
@@ -147,10 +149,13 @@ def rpn_target_assign(bbox_pred,
 
     helper = LayerHelper('rpn_target_assign', **locals())
     # Assign target label to anchors
-    loc_index = helper.create_tmp_variable(dtype='int32')
-    score_index = helper.create_tmp_variable(dtype='int32')
-    target_label = helper.create_tmp_variable(dtype='int32')
-    target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
+    loc_index = helper.create_variable_for_type_inference(dtype='int32')
+    score_index = helper.create_variable_for_type_inference(dtype='int32')
+    target_label = helper.create_variable_for_type_inference(dtype='int32')
+    target_bbox = helper.create_variable_for_type_inference(
+        dtype=anchor_box.dtype)
+    bbox_inside_weight = helper.create_variable_for_type_inference(
+        dtype=anchor_box.dtype)
     helper.append_op(
         type="rpn_target_assign",
         inputs={
@@ -163,7 +168,8 @@ def rpn_target_assign(bbox_pred,
             'LocationIndex': loc_index,
             'ScoreIndex': score_index,
             'TargetLabel': target_label,
-            'TargetBBox': target_bbox
+            'TargetBBox': target_bbox,
+            'BBoxInsideWeight': bbox_inside_weight
         },
         attrs={
             'rpn_batch_size_per_im': rpn_batch_size_per_im,
@@ -178,13 +184,14 @@ def rpn_target_assign(bbox_pred,
     score_index.stop_gradient = True
     target_label.stop_gradient = True
     target_bbox.stop_gradient = True
+    bbox_inside_weight.stop_gradient = True
 
     cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
     bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
     predicted_cls_logits = nn.gather(cls_logits, score_index)
     predicted_bbox_pred = nn.gather(bbox_pred, loc_index)
 
-    return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox
+    return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
 
 
 def detection_output(loc,
@@ -282,7 +289,8 @@ def detection_output(loc,
     scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
     scores = nn.transpose(scores, perm=[0, 2, 1])
     scores.stop_gradient = True
-    nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
+    nmsed_outs = helper.create_variable_for_type_inference(
+        dtype=decoded_box.dtype)
     helper.append_op(
         type="multiclass_nms",
         inputs={'Scores': scores,
@@ -314,7 +322,7 @@ def iou_similarity(x, y, name=None):
     """
     helper = LayerHelper("iou_similarity", **locals())
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -351,7 +359,8 @@ def box_coder(prior_box,
     helper = LayerHelper("box_coder", **locals())
 
     if name is None:
-        output_box = helper.create_tmp_variable(dtype=prior_box.dtype)
+        output_box = helper.create_variable_for_type_inference(
+            dtype=prior_box.dtype)
     else:
         output_box = helper.create_variable(
             name=name, dtype=prior_box.dtype, persistable=False)
@@ -382,7 +391,7 @@ def polygon_box_transform(input, name=None):
     """
     helper = LayerHelper("polygon_box_transform", **locals())
     if name is None:
-        output = helper.create_tmp_variable(dtype=input.dtype)
+        output = helper.create_variable_for_type_inference(dtype=input.dtype)
     else:
         output = helper.create_variable(
             name=name, dtype=prior_box.input, persistable=False)
@@ -450,7 +459,7 @@ def detection_map(detect_res,
     helper = LayerHelper("detection_map", **locals())
 
     def __create_var(type):
-        return helper.create_tmp_variable(dtype=type)
+        return helper.create_variable_for_type_inference(dtype=type)
 
     map_out = __create_var('float32')
     accum_pos_count_out = out_states[0] if out_states else __create_var('int32')
@@ -557,8 +566,9 @@ def bipartite_match(dist_matrix,
         >>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
     """
     helper = LayerHelper('bipartite_match', **locals())
-    match_indices = helper.create_tmp_variable(dtype='int32')
-    match_distance = helper.create_tmp_variable(dtype=dist_matrix.dtype)
+    match_indices = helper.create_variable_for_type_inference(dtype='int32')
+    match_distance = helper.create_variable_for_type_inference(
+        dtype=dist_matrix.dtype)
     helper.append_op(
         type='bipartite_match',
         inputs={'DistMat': dist_matrix},
@@ -644,8 +654,8 @@ def target_assign(input,
                             gt, matched_indices, mismatch_value=0)
     """
     helper = LayerHelper('target_assign', **locals())
-    out = helper.create_tmp_variable(dtype=input.dtype)
-    out_weight = helper.create_tmp_variable(dtype='float32')
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    out_weight = helper.create_variable_for_type_inference(dtype='float32')
     helper.append_op(
         type='target_assign',
         inputs={
@@ -816,9 +826,10 @@ def ssd_loss(location,
     conf_loss = nn.reshape(
         x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
     conf_loss.stop_gradient = True
-    neg_indices = helper.create_tmp_variable(dtype='int32')
+    neg_indices = helper.create_variable_for_type_inference(dtype='int32')
     dtype = matched_indices.dtype
-    updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
+    updated_matched_indices = helper.create_variable_for_type_inference(
+        dtype=dtype)
     helper.append_op(
         type='mine_hard_examples',
         inputs={
@@ -998,8 +1009,8 @@ def prior_box(input,
             max_sizes = [max_sizes]
         attrs['max_sizes'] = max_sizes
 
-    box = helper.create_tmp_variable(dtype)
-    var = helper.create_tmp_variable(dtype)
+    box = helper.create_variable_for_type_inference(dtype)
+    var = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="prior_box",
         inputs={"Input": input,
@@ -1337,8 +1348,8 @@ def anchor_generator(input,
         'offset': offset
     }
 
-    anchor = helper.create_tmp_variable(dtype)
-    var = helper.create_tmp_variable(dtype)
+    anchor = helper.create_variable_for_type_inference(dtype)
+    var = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="anchor_generator",
         inputs={"Input": input},
@@ -1384,7 +1395,7 @@ def roi_perspective_transform(input,
     """
     helper = LayerHelper('roi_perspective_transform', **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="roi_perspective_transform",
         inputs={"X": input,
@@ -1418,11 +1429,15 @@ def generate_proposal_labels(rpn_rois,
 
     helper = LayerHelper('generate_proposal_labels', **locals())
 
-    rois = helper.create_tmp_variable(dtype=rpn_rois.dtype)
-    labels_int32 = helper.create_tmp_variable(dtype=gt_classes.dtype)
-    bbox_targets = helper.create_tmp_variable(dtype=rpn_rois.dtype)
-    bbox_inside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
-    bbox_outside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
+    rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
+    labels_int32 = helper.create_variable_for_type_inference(
+        dtype=gt_classes.dtype)
+    bbox_targets = helper.create_variable_for_type_inference(
+        dtype=rpn_rois.dtype)
+    bbox_inside_weights = helper.create_variable_for_type_inference(
+        dtype=rpn_rois.dtype)
+    bbox_outside_weights = helper.create_variable_for_type_inference(
+        dtype=rpn_rois.dtype)
 
     helper.append_op(
         type="generate_proposal_labels",
@@ -1504,8 +1519,10 @@ def generate_proposals(scores,
     """
     helper = LayerHelper('generate_proposals', **locals())
 
-    rpn_rois = helper.create_tmp_variable(dtype=bbox_deltas.dtype)
-    rpn_roi_probs = helper.create_tmp_variable(dtype=scores.dtype)
+    rpn_rois = helper.create_variable_for_type_inference(
+        dtype=bbox_deltas.dtype)
+    rpn_roi_probs = helper.create_variable_for_type_inference(
+        dtype=scores.dtype)
     helper.append_op(
         type="generate_proposals",
         inputs={

python/paddle/fluid/layers/io.py

@@ -954,7 +954,7 @@ def read_file(reader):
     """
     helper = LayerHelper('read_file')
     out = [
-        helper.create_tmp_variable(
+        helper.create_variable_for_type_inference(
             stop_gradient=True, dtype='float32')
         for _ in range(len(reader.desc.shapes()))
     ]

python/paddle/fluid/layers/layer_function_generator.py

@@ -202,10 +202,12 @@ def generate_layer_fn(op_type):
             out_var = out[0] if (isinstance(out, list) or
                                  isinstance(out, tuple)) else out
         else:
-            out_var = helper.create_tmp_variable(dtype=dtype)
+            out_var = helper.create_variable_for_type_inference(dtype=dtype)
         outputs[o_name] = [out_var]
         for name in intermediate_output_names:
-            outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
+            outputs[name] = [
+                helper.create_variable_for_type_inference(dtype=dtype)
+            ]
         helper.append_op(
             type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
         return helper.append_activation(out_var)
@@ -229,7 +231,7 @@ def generate_layer_fn_noattr(op_type):
 
     def func(x, name=None):
         helper = LayerHelper(op_type, **locals())
-        output = helper.create_tmp_variable(dtype=x.dtype)
+        output = helper.create_variable_for_type_inference(dtype=x.dtype)
         helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
         return output
 

python/paddle/fluid/layers/metric_op.py

@@ -58,11 +58,11 @@ def accuracy(input, label, k=1, correct=None, total=None):
     """
     helper = LayerHelper("accuracy", **locals())
     topk_out, topk_indices = nn.topk(input, k=k)
-    acc_out = helper.create_tmp_variable(dtype="float32")
+    acc_out = helper.create_variable_for_type_inference(dtype="float32")
     if correct is None:
-        correct = helper.create_tmp_variable(dtype="int64")
+        correct = helper.create_variable_for_type_inference(dtype="int64")
     if total is None:
-        total = helper.create_tmp_variable(dtype="int64")
+        total = helper.create_variable_for_type_inference(dtype="int64")
     helper.append_op(
         type="accuracy",
         inputs={
@@ -124,8 +124,8 @@ def auc(input,
             auc_out=fluid.layers.auc(input=prediction, label=label)
     """
     helper = LayerHelper("auc", **locals())
-    auc_out = helper.create_tmp_variable(dtype="float64")
-    batch_auc_out = helper.create_tmp_variable(dtype="float64")
+    auc_out = helper.create_variable_for_type_inference(dtype="float64")
+    batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
     # make tp, tn, fp, fn persistable, so that can accumulate all batches.
 
     # for batch auc

python/paddle/fluid/layers/nn.py

@@ -242,7 +242,7 @@ def fc(input,
 
         w = helper.create_parameter(
             attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
-        tmp = helper.create_tmp_variable(dtype)
+        tmp = helper.create_variable_for_type_inference(dtype)
         helper.append_op(
             type="mul",
             inputs={"X": input_var,
@@ -255,7 +255,7 @@ def fc(input,
     if len(mul_results) == 1:
         pre_bias = mul_results[0]
     else:
-        pre_bias = helper.create_tmp_variable(dtype)
+        pre_bias = helper.create_variable_for_type_inference(dtype)
         helper.append_op(
             type="sum",
             inputs={"X": mul_results},
@@ -314,7 +314,7 @@ def embedding(input,
     helper = LayerHelper('embedding', **locals())
     w = helper.create_parameter(
         attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
-    tmp = helper.create_tmp_variable(dtype)
+    tmp = helper.create_variable_for_type_inference(dtype)
     padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
         size[0] + padding_idx)
     helper.append_op(
@@ -418,10 +418,10 @@ def dynamic_lstm(input,
     bias = helper.create_parameter(
         attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
 
-    hidden = helper.create_tmp_variable(dtype)
-    cell = helper.create_tmp_variable(dtype)
-    batch_gate = helper.create_tmp_variable(dtype)
-    batch_cell_pre_act = helper.create_tmp_variable(dtype)
+    hidden = helper.create_variable_for_type_inference(dtype)
+    cell = helper.create_variable_for_type_inference(dtype)
+    batch_gate = helper.create_variable_for_type_inference(dtype)
+    batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
     inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
     batch_size = input.shape[0]
     if h_0:
@@ -621,12 +621,12 @@ def dynamic_lstmp(input,
     bias = helper.create_parameter(
         attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
 
-    projection = helper.create_tmp_variable(dtype)
-    cell = helper.create_tmp_variable(dtype)
-    ordered_proj0 = helper.create_tmp_variable(dtype)
-    batch_hidden = helper.create_tmp_variable(dtype)
-    batch_gate = helper.create_tmp_variable(dtype)
-    batch_cell_pre_act = helper.create_tmp_variable(dtype)
+    projection = helper.create_variable_for_type_inference(dtype)
+    cell = helper.create_variable_for_type_inference(dtype)
+    ordered_proj0 = helper.create_variable_for_type_inference(dtype)
+    batch_hidden = helper.create_variable_for_type_inference(dtype)
+    batch_gate = helper.create_variable_for_type_inference(dtype)
+    batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type='lstmp',
@@ -751,10 +751,10 @@ def dynamic_gru(input,
         ), 'The shape of h0 should be(batch_size, %d)' % size
         inputs['H0'] = h_0
 
-    hidden = helper.create_tmp_variable(dtype)
-    batch_gate = helper.create_tmp_variable(dtype)
-    batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
-    batch_hidden = helper.create_tmp_variable(dtype)
+    hidden = helper.create_variable_for_type_inference(dtype)
+    batch_gate = helper.create_variable_for_type_inference(dtype)
+    batch_reset_hidden_prev = helper.create_variable_for_type_inference(dtype)
+    batch_hidden = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type='gru',
@@ -844,9 +844,9 @@ def gru_unit(input,
     weight = helper.create_parameter(
         attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
 
-    gate = helper.create_tmp_variable(dtype)
-    reset_hidden_pre = helper.create_tmp_variable(dtype)
-    updated_hidden = helper.create_tmp_variable(dtype)
+    gate = helper.create_variable_for_type_inference(dtype)
+    reset_hidden_pre = helper.create_variable_for_type_inference(dtype)
+    updated_hidden = helper.create_variable_for_type_inference(dtype)
     inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
     # create bias
     if helper.bias_attr:
@@ -896,10 +896,14 @@ def linear_chain_crf(input, label, param_attr=None):
         attr=helper.param_attr,
         shape=[size + 2, size],
         dtype=helper.input_dtype())
-    alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
-    emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
-    transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
-    log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
+    alpha = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
+    emission_exps = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
+    transition_exps = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
+    log_likelihood = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
     helper.append_op(
         type='linear_chain_crf',
         inputs={"Emission": [input],
@@ -938,7 +942,8 @@ def crf_decoding(input, param_attr, label=None):
     """
     helper = LayerHelper('crf_decoding', **locals())
     transition = helper.get_parameter(param_attr.name)
-    viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
+    viterbi_path = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
     helper.append_op(
         type='crf_decoding',
         inputs={"Emission": [input],
@@ -962,9 +967,9 @@ def cos_sim(X, Y):
         Variable: the output of cosine(X, Y).
     """
     helper = LayerHelper('cos_sim', **locals())
-    out = helper.create_tmp_variable(dtype=X.dtype)
-    xnorm = helper.create_tmp_variable(dtype=X.dtype)
-    ynorm = helper.create_tmp_variable(dtype=X.dtype)
+    out = helper.create_variable_for_type_inference(dtype=X.dtype)
+    xnorm = helper.create_variable_for_type_inference(dtype=X.dtype)
+    ynorm = helper.create_variable_for_type_inference(dtype=X.dtype)
     helper.append_op(
         type='cos_sim',
         inputs={'X': [X],
@@ -1008,8 +1013,9 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
     """
 
     helper = LayerHelper('dropout', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
-    mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    mask = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True)
 
     if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
         seed = helper.main_program.random_seed
@@ -1094,7 +1100,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
           cost = fluid.layers.cross_entropy(input=predict, label=label)
     """
     helper = LayerHelper('cross_entropy', **locals())
-    out = helper.create_tmp_variable(dtype=input.dtype)
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type='cross_entropy',
         inputs={'X': [input],
@@ -1141,14 +1147,14 @@ def square_error_cost(input, label):
 
     """
     helper = LayerHelper('square_error_cost', **locals())
-    minus_out = helper.create_tmp_variable(dtype=input.dtype)
+    minus_out = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type='elementwise_sub',
         inputs={'X': [input],
                 'Y': [label]},
         outputs={'Out': [minus_out]})
 
-    square_out = helper.create_tmp_variable(dtype=input.dtype)
+    square_out = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type='square', inputs={'X': [minus_out]},
         outputs={'Out': [square_out]})
@@ -1254,12 +1260,13 @@ def chunk_eval(input,
     helper = LayerHelper("chunk_eval", **locals())
 
     # prepare output
-    precision = helper.create_tmp_variable(dtype="float32")
-    recall = helper.create_tmp_variable(dtype="float32")
-    f1_score = helper.create_tmp_variable(dtype="float32")
-    num_infer_chunks = helper.create_tmp_variable(dtype="int64")
-    num_label_chunks = helper.create_tmp_variable(dtype="int64")
-    num_correct_chunks = helper.create_tmp_variable(dtype="int64")
+    precision = helper.create_variable_for_type_inference(dtype="float32")
+    recall = helper.create_variable_for_type_inference(dtype="float32")
+    f1_score = helper.create_variable_for_type_inference(dtype="float32")
+    num_infer_chunks = helper.create_variable_for_type_inference(dtype="int64")
+    num_label_chunks = helper.create_variable_for_type_inference(dtype="int64")
+    num_correct_chunks = helper.create_variable_for_type_inference(
+        dtype="int64")
 
     helper.append_op(
         type="chunk_eval",
@@ -1326,7 +1333,7 @@ def sequence_conv(input,
     filter_shape = [filter_size * input.shape[1], num_filters]
     filter_param = helper.create_parameter(
         attr=helper.param_attr, shape=filter_shape, dtype=dtype)
-    pre_bias = helper.create_tmp_variable(dtype)
+    pre_bias = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type='sequence_conv',
@@ -1382,7 +1389,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
     """
     helper = LayerHelper('sequence_softmax', **locals())
     dtype = helper.input_dtype()
-    softmax_out = helper.create_tmp_variable(dtype)
+    softmax_out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="sequence_softmax",
         inputs={"X": input},
@@ -1436,7 +1443,7 @@ def softmax(input, use_cudnn=True, name=None):
     """
     helper = LayerHelper('softmax', **locals())
     dtype = helper.input_dtype()
-    softmax_out = helper.create_tmp_variable(dtype)
+    softmax_out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="softmax",
         inputs={"X": input},
@@ -1599,7 +1606,7 @@ def conv2d(input,
         dtype=dtype,
         default_initializer=_get_default_param_initializer())
 
-    pre_bias = helper.create_tmp_variable(dtype)
+    pre_bias = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type=l_type,
@@ -1770,7 +1777,7 @@ def conv3d(input,
         dtype=dtype,
         default_initializer=_get_default_param_initializer())
 
-    pre_bias = helper.create_tmp_variable(dtype)
+    pre_bias = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type=l_type,
@@ -1849,8 +1856,8 @@ def sequence_pool(input, pool_type):
     """
     helper = LayerHelper('sequence_pool', **locals())
     dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
-    max_index = helper.create_tmp_variable(dtype)
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    max_index = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type="sequence_pool",
@@ -1886,7 +1893,7 @@ def sequence_concat(input, name=None):
            out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
     """
     helper = LayerHelper('sequence_concat', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     helper.append_op(
         type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
     return out
@@ -2013,7 +2020,7 @@ def sequence_slice(input, offset, length, name=None):
     """
     helper = LayerHelper("sequence_slice", **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
 
     offset.stop_gradient = True
     length.stop_gradient = True
@@ -2099,7 +2106,7 @@ def pool2d(input,
 
     helper = LayerHelper(l_type, **locals())
     dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
+    pool_out = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type=l_type,
@@ -2167,7 +2174,7 @@ def pool3d(input,
     l_type = "pool3d"
     helper = LayerHelper(l_type, **locals())
     dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
+    pool_out = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type=l_type,
@@ -2310,10 +2317,13 @@ def batch_norm(input,
     mean_out = mean
     # variance and variance out share the same memory
     variance_out = variance
-    saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
-    saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
+    saved_mean = helper.create_variable_for_type_inference(
+        dtype=dtype, stop_gradient=True)
+    saved_variance = helper.create_variable_for_type_inference(
+        dtype=dtype, stop_gradient=True)
 
-    batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
+    batch_norm_out = input if in_place else helper.create_variable_for_type_inference(
+        dtype)
 
     helper.append_op(
         type="batch_norm",
@@ -2430,9 +2440,11 @@ def layer_norm(input,
         inputs['Bias'] = bias
 
     # create output
-    mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
-    variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
-    layer_norm_out = helper.create_tmp_variable(dtype)
+    mean_out = helper.create_variable_for_type_inference(
+        dtype=dtype, stop_gradient=True)
+    variance_out = helper.create_variable_for_type_inference(
+        dtype=dtype, stop_gradient=True)
+    layer_norm_out = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type="layer_norm",
@@ -2619,7 +2631,7 @@ def conv2d_transpose(input,
     img_filter = helper.create_parameter(
         dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
 
-    pre_bias = helper.create_tmp_variable(dtype=input.dtype)
+    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type=op_type,
         inputs={'Input': [input],
@@ -2797,7 +2809,7 @@ def conv3d_transpose(input,
     img_filter = helper.create_parameter(
         dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
 
-    pre_bias = helper.create_tmp_variable(dtype=input.dtype)
+    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type=l_type,
         inputs={'Input': [input],
@@ -2876,7 +2888,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
     """
     helper = LayerHelper('sequence_expand', input=x, **locals())
     dtype = helper.input_dtype()
-    tmp = helper.create_tmp_variable(dtype)
+    tmp = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='sequence_expand',
         inputs={'X': x,
@@ -2942,7 +2954,7 @@ def sequence_expand_as(x, y, name=None):
     """
     helper = LayerHelper('sequence_expand_as', input=x, **locals())
     dtype = helper.input_dtype()
-    tmp = helper.create_tmp_variable(dtype)
+    tmp = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='sequence_expand_as',
         inputs={'X': x,
@@ -2987,8 +2999,8 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
 
     helper = LayerHelper('sequence_pad', input=x, **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
-    length = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
+    length = helper.create_variable_for_type_inference(dtype)
 
     pad_value.stop_gradient = True
     length.stop_gradient = True
@@ -3053,7 +3065,7 @@ def sequence_unpad(x, length, name=None):
 
     helper = LayerHelper('sequence_unpad', input=x, **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
 
     length.stop_gradient = True
 
@@ -3152,8 +3164,9 @@ def beam_search(pre_ids,
     score_type = scores.dtype
     id_type = ids.dtype
 
-    selected_scores = helper.create_tmp_variable(dtype=score_type)
-    selected_ids = helper.create_tmp_variable(dtype=id_type)
+    selected_scores = helper.create_variable_for_type_inference(
+        dtype=score_type)
+    selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
 
     helper.append_op(
         type='beam_search',
@@ -3210,8 +3223,8 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
                 ids, scores, beam_size=5, end_id=0)
     """
     helper = LayerHelper('beam_search_decode', **locals())
-    sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
-    sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
+    sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
+    sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
 
     helper.append_op(
         type="beam_search_decode",
@@ -3341,8 +3354,8 @@ def lstm_unit(x_t,
                 param_attr=param_attr,
                 bias_attr=bias_attr)
     dtype = x_t.dtype
-    c = helper.create_tmp_variable(dtype)
-    h = helper.create_tmp_variable(dtype)
+    c = helper.create_variable_for_type_inference(dtype)
+    h = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type='lstm_unit',
@@ -3396,7 +3409,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
 
     """
     helper = LayerHelper('reduce_sum', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
     helper.append_op(
@@ -3453,7 +3466,7 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
     """
     helper = LayerHelper('reduce_mean', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
     helper.append_op(
@@ -3508,7 +3521,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
     """
     helper = LayerHelper('reduce_max', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
     helper.append_op(
@@ -3563,7 +3576,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
     """
     helper = LayerHelper('reduce_min', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
     helper.append_op(
@@ -3619,7 +3632,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
     """
     helper = LayerHelper('reduce_prod', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     if dim is not None and not isinstance(dim, list):
         dim = [dim]
     helper.append_op(
@@ -3679,7 +3692,7 @@ def split(input, num_or_sections, dim=-1, name=None):
             dim], 'len(num_or_sections) must not be more than input.shape[dim].'
         num = len(num_or_sections)
     outs = [
-        helper.create_tmp_variable(dtype=helper.input_dtype())
+        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
         for i in range(num)
     ]
     helper.append_op(
@@ -3736,8 +3749,8 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
         axis = 0
     helper = LayerHelper("l2_normalize", **locals())
 
-    out = helper.create_tmp_variable(dtype=x.dtype)
-    norm = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    norm = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type="norm",
         inputs={"X": x},
@@ -3846,7 +3859,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
     __check_input(x, y)
 
     helper = LayerHelper('matmul', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='matmul',
         inputs={'X': x,
@@ -3917,8 +3930,8 @@ def topk(input, k, name=None):
             top5_values, top5_indices = layers.topk(input, k=5)
     """
     helper = LayerHelper("top_k", **locals())
-    values = helper.create_tmp_variable(dtype=input.dtype)
-    indices = helper.create_tmp_variable(dtype="int64")
+    values = helper.create_variable_for_type_inference(dtype=input.dtype)
+    indices = helper.create_variable_for_type_inference(dtype="int64")
     helper.append_op(
         type="top_k",
         inputs={"X": [input]},
@@ -3976,8 +3989,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
 
     # remove some tokens from input and labels
     if ignored_tokens is not None and len(ignored_tokens) > 0:
-        erased_input = helper.create_tmp_variable(dtype="int64")
-        erased_label = helper.create_tmp_variable(dtype="int64")
+        erased_input = helper.create_variable_for_type_inference(dtype="int64")
+        erased_label = helper.create_variable_for_type_inference(dtype="int64")
 
         helper.append_op(
             type="sequence_erase",
@@ -3994,8 +4007,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
         label = erased_label
 
     # edit distance op
-    edit_distance_out = helper.create_tmp_variable(dtype="int64")
-    sequence_num = helper.create_tmp_variable(dtype="int64")
+    edit_distance_out = helper.create_variable_for_type_inference(dtype="int64")
+    sequence_num = helper.create_variable_for_type_inference(dtype="int64")
     helper.append_op(
         type="edit_distance",
         inputs={"Hyps": [input],
@@ -4070,7 +4083,7 @@ def ctc_greedy_decoder(input, blank, name=None):
     _, topk_indices = topk(input, k=1)
 
     # ctc align op
-    ctc_out = helper.create_tmp_variable(dtype="int64")
+    ctc_out = helper.create_variable_for_type_inference(dtype="int64")
     helper.append_op(
         type="ctc_align",
         inputs={"Input": [topk_indices]},
@@ -4120,8 +4133,8 @@ def warpctc(input, label, blank=0, norm_by_times=False):
 
     """
     helper = LayerHelper('warpctc', **locals())
-    loss_out = helper.create_tmp_variable(dtype=input.dtype)
-    grad_out = helper.create_tmp_variable(dtype=input.dtype)
+    loss_out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    grad_out = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type='warpctc',
         inputs={'Logits': [input],
@@ -4182,7 +4195,7 @@ def sequence_reshape(input, new_dim):
             x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
     """
     helper = LayerHelper('sequence_reshape', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype())
+    out = helper.create_variable_for_type_inference(helper.input_dtype())
     helper.append_op(
         type='sequence_reshape',
         inputs={'X': [input]},
@@ -4279,9 +4292,9 @@ def nce(input,
             is_bias=True,
             dtype=input.dtype)
         inputs['Bias'] = b
-    cost = helper.create_tmp_variable(dtype=input.dtype)
-    sample_logits = helper.create_tmp_variable(dtype=input.dtype)
-    sample_labels = helper.create_tmp_variable(dtype=label.dtype)
+    cost = helper.create_variable_for_type_inference(dtype=input.dtype)
+    sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
+    sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
 
     if num_neg_samples is None:
         num_neg_samples = 10
@@ -4357,8 +4370,8 @@ def hsigmoid(input,
 
     helper = LayerHelper('hierarchical_sigmoid', **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
-    pre_out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
+    pre_out = helper.create_variable_for_type_inference(dtype)
     dim = input.shape[1]
     if num_classes < 2:
         raise ValueError("num_classes must not be less than 2.")
@@ -4418,8 +4431,8 @@ def transpose(x, perm, name=None):
                 (idx, perm[idx], len(x.shape)))
 
     helper = LayerHelper('transpose', **locals())
-    out = helper.create_tmp_variable(x.dtype)
-    x_shape = helper.create_tmp_variable(x.dtype)
+    out = helper.create_variable_for_type_inference(x.dtype)
+    x_shape = helper.create_variable_for_type_inference(x.dtype)
     helper.append_op(
         type='transpose2',
         inputs={'X': [x]},
@@ -4561,7 +4574,7 @@ def im2sequence(input,
         inputs["Y"] = input_image_size
         attrs["out_stride"] = out_stride
     helper = LayerHelper('im2sequence', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     helper.append_op(
         type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
     return out
@@ -4594,7 +4607,7 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
     filter_shape = [future_context_size + 1, input.shape[1]]
     filter_param = helper.create_parameter(
         attr=helper.param_attr, shape=filter_shape, dtype=dtype)
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='row_conv',
         inputs={'X': [input],
@@ -4627,7 +4640,7 @@ def multiplex(inputs, index):
         raise ValueError("inputs should be a list object and contains at least "
                          "2 elements.")
 
-    out = helper.create_tmp_variable(inputs[0].dtype)
+    out = helper.create_variable_for_type_inference(inputs[0].dtype)
     helper.append_op(
         type='multiplex',
         inputs={'X': inputs,
@@ -4698,8 +4711,8 @@ def softmax_with_cross_entropy(logits,
                 logits=fc, label=label)
     """
     helper = LayerHelper('softmax_with_cross_entropy', **locals())
-    softmax = helper.create_tmp_variable(dtype=logits.dtype)
-    loss = helper.create_tmp_variable(dtype=logits.dtype)
+    softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
+    loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
     helper.append_op(
         type='softmax_with_cross_entropy',
         inputs={'Logits': logits,
@@ -4749,8 +4762,8 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
     """
 
     helper = LayerHelper('smooth_l1_loss', **locals())
-    diff = helper.create_tmp_variable(dtype=x.dtype)
-    loss = helper.create_tmp_variable(dtype=x.dtype)
+    diff = helper.create_variable_for_type_inference(dtype=x.dtype)
+    loss = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='smooth_l1_loss',
         inputs={
@@ -4783,7 +4796,7 @@ def one_hot(input, depth):
             one_hot_label = layers.one_hot(input=label, depth=10)
     """
     helper = LayerHelper("one_hot", **locals())
-    one_hot_out = helper.create_tmp_variable(dtype='float32')
+    one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
     helper.append_op(
         type="one_hot",
         inputs={'X': input},
@@ -4925,8 +4938,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
                 "except one unknown dimension.")
 
     helper = LayerHelper("reshape2", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
-    x_shape = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type="reshape2",
         inputs=inputs,
@@ -4975,8 +4988,8 @@ def squeeze(input, axes, name=None):
             y = layers.sequeeze(input=x, axes=[1])
     """
     helper = LayerHelper("squeeze", **locals())
-    out = helper.create_tmp_variable(dtype=input.dtype)
-    x_shape = helper.create_tmp_variable(dtype=input.dtype)
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type="squeeze2",
         inputs={"X": input},
@@ -5012,8 +5025,8 @@ def unsqueeze(input, axes, name=None):
             y = layers.unsequeeze(input=x, axes=[1])
     """
     helper = LayerHelper("unsqueeze", **locals())
-    out = helper.create_tmp_variable(dtype=input.dtype)
-    x_shape = helper.create_tmp_variable(dtype=input.dtype)
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
         type="unsqueeze2",
         inputs={"X": input},
@@ -5103,7 +5116,7 @@ def lod_reset(x, y=None, target_lod=None):
             out = layers.lod_reset(x=x, y=y)
     """
     helper = LayerHelper("lod_reset", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     if y is not None:
         helper.append_op(
             type="lod_reset", inputs={'X': x,
@@ -5172,8 +5185,9 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
             "dims of input must be 4(not %d), and it's order must be NCHW" %
             (dims))
 
-    mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
-    lrn_out = helper.create_tmp_variable(dtype)
+    mid_out = helper.create_variable_for_type_inference(
+        dtype=dtype, stop_gradient=True)
+    lrn_out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="lrn",
         inputs={"X": input},
@@ -5238,7 +5252,7 @@ def pad(x, paddings, pad_value=0., name=None):
     """
     helper = LayerHelper('pad', input=x, **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='pad',
         inputs={'X': x},
@@ -5318,7 +5332,7 @@ def pad_constant_like(x, y, pad_value=0., name=None):
     """
     helper = LayerHelper('pad_constant_like', input=x, **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='pad_constant_like',
         inputs={'X': x,
@@ -5383,7 +5397,7 @@ def label_smooth(label,
         raise ValueError("The value of epsilon must be between 0 and 1.")
     helper = LayerHelper("label_smooth", **locals())
     label.stop_gradient = True
-    smooth_label = helper.create_tmp_variable(dtype)
+    smooth_label = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="label_smooth",
         inputs={"X": label,
@@ -5415,8 +5429,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
     """
     helper = LayerHelper('roi_pool', **locals())
     dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
-    argmaxes = helper.create_tmp_variable(dtype='int32')
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    argmaxes = helper.create_variable_for_type_inference(dtype='int32')
     helper.append_op(
         type="roi_pool",
         inputs={"X": input,
@@ -5464,7 +5478,7 @@ def roi_align(input,
     """
     helper = LayerHelper('roi_align', **locals())
     dtype = helper.input_dtype()
-    align_out = helper.create_tmp_variable(dtype)
+    align_out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="roi_align",
         inputs={"X": input,
@@ -5589,7 +5603,7 @@ def image_resize(input,
         out_h = int(input.shape[2] * scale)
         out_w = int(input.shape[3] * scale)
 
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type=resample_methods[resample],
         inputs=inputs,
@@ -5698,7 +5712,7 @@ def gather(input, index):
     """
     helper = LayerHelper('gather', **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="gather",
         inputs={"X": input,
@@ -5738,7 +5752,7 @@ def scatter(input, index, updates, name=None):
     """
     helper = LayerHelper('scatter', **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="scatter",
         inputs={"X": input,
@@ -5798,7 +5812,7 @@ def sequence_scatter(input, index, updates, name=None):
     """
     helper = LayerHelper('sequence_scatter', **locals())
     dtype = helper.input_dtype()
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="sequence_scatter",
         inputs={"X": input,
@@ -5828,7 +5842,7 @@ def random_crop(x, shape, seed=None):
     """
     helper = LayerHelper("random_crop", **locals())
     dtype = x.dtype
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     if seed is None:
         seed = np.random.randint(-65536, 65536)
     op_attrs = {"shape": shape}
@@ -5874,7 +5888,7 @@ def log(x, name=None):
     """
     helper = LayerHelper('log', **locals())
     dtype = helper.input_dtype(input_param_name='x')
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
     return out
 
@@ -5905,7 +5919,7 @@ def relu(x, name=None):
     """
     helper = LayerHelper('relu', **locals())
     dtype = helper.input_dtype(input_param_name='x')
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
     return out
 
@@ -5944,9 +5958,9 @@ def mean_iou(input, label, num_classes):
     """
     helper = LayerHelper('mean_iou', **locals())
     dtype = helper.input_dtype()
-    out_mean_iou = helper.create_tmp_variable(dtype='float32')
-    out_wrong = helper.create_tmp_variable(dtype='int32')
-    out_correct = helper.create_tmp_variable(dtype='int32')
+    out_mean_iou = helper.create_variable_for_type_inference(dtype='float32')
+    out_wrong = helper.create_variable_for_type_inference(dtype='int32')
+    out_correct = helper.create_variable_for_type_inference(dtype='int32')
     helper.append_op(
         type="mean_iou",
         inputs={"Predictions": input,
@@ -6038,7 +6052,7 @@ def crop(x, shape=None, offsets=None, name=None):
     if offsets is None:
         offsets = [0] * len(x.shape)
 
-    out = helper.create_tmp_variable(x.dtype)
+    out = helper.create_variable_for_type_inference(x.dtype)
     ipts = {'X': x}
     attrs = {}
     if isinstance(shape, Variable):
@@ -6118,7 +6132,7 @@ def rank_loss(label, left, right, name=None):
     if not (isinstance(right, Variable)):
         raise ValueError("The right should be a Variable")
 
-    out = helper.create_tmp_variable("float32")
+    out = helper.create_variable_for_type_inference("float32")
 
     helper.append_op(
         type='rank_loss',
@@ -6164,8 +6178,8 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
         raise ValueError("The left should be a Variable.")
     if not isinstance(right, Variable):
         raise ValueError("The right should be a Variable.")
-    out = helper.create_tmp_variable(left.dtype)
-    act = helper.create_tmp_variable(left.dtype)
+    out = helper.create_variable_for_type_inference(left.dtype)
+    act = helper.create_variable_for_type_inference(left.dtype)
     helper.append_op(
         type='margin_rank_loss',
         inputs={"Label": label,
@@ -6250,7 +6264,7 @@ def pad2d(input,
 
     helper = LayerHelper('pad2d', **locals())
     dtype = helper.input_dtype(input_param_name='input')
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='pad2d',
         inputs={'X': input},
@@ -6279,7 +6293,7 @@ def elu(x, alpha=1.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('elu', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='elu',
         inputs={'X': x},
@@ -6302,7 +6316,7 @@ def relu6(x, threshold=6.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('relu6', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='relu6',
         inputs={'X': x},
@@ -6325,7 +6339,7 @@ def pow(x, factor=1.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('pow', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='pow',
         inputs={'X': x},
@@ -6349,7 +6363,7 @@ def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('stanh', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='stanh',
         inputs={'X': x},
@@ -6374,7 +6388,7 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('hard_sigmoid', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='hard_sigmoid',
         inputs={'X': x},
@@ -6398,7 +6412,7 @@ def swish(x, beta=1.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('swish', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='swish',
         inputs={'X': x},
@@ -6450,7 +6464,7 @@ def prelu(x, mode, param_attr=None, name=None):
         dtype='float32',
         is_bias=False,
         default_initializer=Constant(1.0))
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="prelu",
         inputs={"X": x,
@@ -6474,7 +6488,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('brelu', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='brelu',
         inputs={'X': x},
@@ -6497,7 +6511,7 @@ def leaky_relu(x, alpha=0.02, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('leaky_relu', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='leaky_relu',
         inputs={'X': x},
@@ -6519,7 +6533,7 @@ def soft_relu(x, threshold=40.0, name=None):
         output(${out_type}): ${out_comment}
     """
     helper = LayerHelper('soft_relu', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='soft_relu',
         inputs={'X': x},
@@ -6586,8 +6600,8 @@ def flatten(x, axis=1, name=None):
     if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
         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)
+    out = helper.create_variable_for_type_inference(x.dtype)
+    x_shape = helper.create_variable_for_type_inference(x.dtype)
     helper.append_op(
         type='flatten2',
         inputs={"X": x},
@@ -6633,7 +6647,8 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
             out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
     """
     helper = LayerHelper('sequence_enumerate', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
+    out = helper.create_variable_for_type_inference(
+        helper.input_dtype(), stop_gradient=True)
     helper.append_op(
         type='sequence_enumerate',
         inputs={'X': input},
@@ -6673,9 +6688,9 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
 
     helper = LayerHelper('sequence_mask', **locals())
     if name is None:
-        out = helper.create_tmp_variable(dtype=dtype)
+        out = helper.create_variable_for_type_inference(dtype=dtype)
     else:
-        out = helper.create_tmp_variable(dtype=dtype, name=name)
+        out = helper.create_variable_for_type_inference(dtype=dtype, name=name)
 
     helper.append_op(
         type='sequence_mask',
@@ -6718,7 +6733,7 @@ def stack(x, axis=0):
     if not isinstance(x, list) and not isinstance(x, tuple):
         x = [x]
 
-    out = helper.create_tmp_variable(x[0].dtype)
+    out = helper.create_variable_for_type_inference(x[0].dtype)
     helper.append_op(
         type='stack', inputs={'X': x}, outputs={'Y': out},
         attrs={'axis': axis})
@@ -6756,7 +6771,7 @@ def unstack(x, axis=0, num=None):
 
     outs = []
     for _ in num:
-        outs.append(helper.create_tmp_variable(x.dtype))
+        outs.append(helper.create_variable_for_type_inference(x.dtype))
 
     helper.append_op(
         type='unstack',
@@ -6808,7 +6823,7 @@ def expand(x, expand_times, name=None):
     """
     helper = LayerHelper('expand', input=x, **locals())
     dtype = helper.input_dtype(input_param_name='x')
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='expand',
         inputs={'X': x},
@@ -6847,7 +6862,7 @@ def uniform_random_batch_size_like(input,
     """
 
     helper = LayerHelper('uniform_random_batch_size_like', **locals())
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     c_dtype = convert_np_dtype_to_dtype_(dtype)
     helper.append_op(
         type='uniform_random_batch_size_like',
@@ -6884,7 +6899,7 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
     """
 
     helper = LayerHelper('gaussian_random', **locals())
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     c_dtype = convert_np_dtype_to_dtype_(dtype)
     helper.append_op(
         type='gaussian_random',
@@ -6919,7 +6934,7 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
     """
 
     helper = LayerHelper('sampling_id', **locals())
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type='sampling_id',
         inputs={'X': x},
@@ -6958,7 +6973,7 @@ def gaussian_random_batch_size_like(input,
     """
 
     helper = LayerHelper('gaussian_random_batch_size_like', **locals())
-    out = helper.create_tmp_variable(dtype)
+    out = helper.create_variable_for_type_inference(dtype)
     c_dtype = convert_np_dtype_to_dtype_(dtype)
     helper.append_op(
         type='gaussian_random_batch_size_like',
@@ -6990,7 +7005,8 @@ def sum(x):
     """
 
     helper = LayerHelper('sum', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
+    out = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype('x'))
     helper.append_op(
         type='sum',
         inputs={'X': x},
@@ -7017,7 +7033,8 @@ def slice(input, axes, starts, ends):
     """
 
     helper = LayerHelper('slice', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
+    out = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype('input'))
     helper.append_op(
         type='slice',
         inputs={'Input': input},
@@ -7043,7 +7060,8 @@ def shape(input):
     """
 
     helper = LayerHelper('shape', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
+    out = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype('input'))
     helper.append_op(
         type='shape', inputs={'Input': input}, outputs={'Out': out})
 
@@ -7060,7 +7078,7 @@ def _elementwise_op(helper):
     use_mkldnn = helper.kwargs.get('use_mkldnn', False)
     name = helper.kwargs.get('name', None)
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7094,7 +7112,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
 
     helper = LayerHelper('scale', **locals())
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7160,7 +7178,7 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
 
     if out is None:
         if name is None:
-            out = helper.create_tmp_variable(dtype=x.dtype)
+            out = helper.create_variable_for_type_inference(dtype=x.dtype)
         else:
             out = helper.create_variable(
                 name=name, dtype=x.dtype, persistable=False)
@@ -7268,7 +7286,7 @@ def clip(x, min, max, name=None):
     helper = LayerHelper("clip", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7300,7 +7318,7 @@ def clip_by_norm(x, max_norm, name=None):
     helper = LayerHelper("clip_by_norm", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7330,7 +7348,7 @@ def mean(x, name=None):
     helper = LayerHelper("mean", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7360,7 +7378,7 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
     helper = LayerHelper("mul", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7394,7 +7412,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
     helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7424,7 +7442,7 @@ def maxout(x, groups, name=None):
     helper = LayerHelper("maxout", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)
@@ -7463,7 +7481,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
     helper = LayerHelper("affine_channel", **locals())
 
     if name is None:
-        out = helper.create_tmp_variable(dtype=x.dtype)
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
         out = helper.create_variable(
             name=name, dtype=x.dtype, persistable=False)

python/paddle/fluid/layers/tensor.py

@@ -152,7 +152,7 @@ def cast(x, dtype):
             result = fluid.layers.cast(x=data, dtype='float64')
     """
     helper = LayerHelper('cast', **locals())
-    out = helper.create_tmp_variable(dtype=dtype)
+    out = helper.create_variable_for_type_inference(dtype=dtype)
     helper.append_op(
         type='cast',
         inputs={'X': [x]},
@@ -184,7 +184,7 @@ def concat(input, axis=0, name=None):
            out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
     """
     helper = LayerHelper('concat', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     helper.append_op(
         type='concat',
         inputs={'X': input},
@@ -221,7 +221,8 @@ def sums(input, out=None):
     """
     helper = LayerHelper('sum', **locals())
     if out is None:
-        out = helper.create_tmp_variable(dtype=helper.input_dtype())
+        out = helper.create_variable_for_type_inference(
+            dtype=helper.input_dtype())
     helper.append_op(
         type='sum',
         inputs={'X': input},
@@ -252,7 +253,7 @@ def assign(input, output=None):
     """
     helper = LayerHelper('assign', **locals())
     if output is None:
-        output = helper.create_tmp_variable(dtype=input.dtype)
+        output = helper.create_variable_for_type_inference(dtype=input.dtype)
     if isinstance(input, Variable):
         helper.append_op(
             type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
@@ -311,7 +312,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
 
     helper = LayerHelper("fill_constant", **locals())
     if out is None:
-        out = helper.create_tmp_variable(dtype=dtype)
+        out = helper.create_variable_for_type_inference(dtype=dtype)
     helper.append_op(
         type='fill_constant',
         inputs={},
@@ -358,7 +359,7 @@ def fill_constant_batch_size_like(input,
         ${out_comment}.
     """
     helper = LayerHelper("fill_constant_batch_size_like", **locals())
-    out = helper.create_tmp_variable(dtype=dtype)
+    out = helper.create_variable_for_type_inference(dtype=dtype)
     helper.append_op(
         type='fill_constant_batch_size_like',
         inputs={'Input': input},
@@ -396,7 +397,7 @@ def argmin(x, axis=0):
           out = fluid.layers.argmin(x=in, axis=-1)
     """
     helper = LayerHelper("arg_min", **locals())
-    out = helper.create_tmp_variable(VarDesc.VarType.INT64)
+    out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
     helper.append_op(
         type='arg_min',
         inputs={'X': x},
@@ -427,7 +428,7 @@ def argmax(x, axis=0):
           out = fluid.layers.argmax(x=in, axis=-1)
     """
     helper = LayerHelper("arg_max", **locals())
-    out = helper.create_tmp_variable(VarDesc.VarType.INT64)
+    out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
     helper.append_op(
         type='arg_max',
         inputs={'X': x},
@@ -477,8 +478,10 @@ def argsort(input, axis=-1, name=None):
             out, indices = fluid.layers.argsort(input, axis=0)
     """
     helper = LayerHelper("argsort", **locals())
-    out = helper.create_tmp_variable(dtype=input.dtype, stop_gradient=True)
-    ids = helper.create_tmp_variable(VarDesc.VarType.INT64, stop_gradient=True)
+    out = helper.create_variable_for_type_inference(
+        dtype=input.dtype, stop_gradient=True)
+    ids = helper.create_variable_for_type_inference(
+        VarDesc.VarType.INT64, stop_gradient=True)
     helper.append_op(
         type='argsort',
         inputs={'X': input},
@@ -562,7 +565,7 @@ def reverse(x, axis):
     if isinstance(axis, int):
         axis = [axis]
     helper = LayerHelper("reverse", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type='reverse',
         inputs={'Input': x},
@@ -654,7 +657,7 @@ def has_inf(x):
         Variable: The tensor variable storing the output, only a bool value.
     """
     helper = LayerHelper("isinf", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
     return out
 
@@ -670,7 +673,7 @@ def has_nan(x):
         Variable: The tensor variable storing the output, only a bool value.
     """
     helper = LayerHelper("isnan", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
     return out
 
@@ -687,6 +690,6 @@ def isfinite(x):
         Variable: The tensor variable storing the output, contains a bool value.
     """
     helper = LayerHelper("isfinite", **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
     return out

python/paddle/fluid/regularizer.py

@@ -151,7 +151,7 @@ class L2DecayRegularizer(WeightDecayRegularizer):
             decay = block.create_var(
                 dtype="float32",
                 shape=param.shape,
-                type=core.VarDesc.VarType.SELECTED_ROWS)
+                type=core.VarDesc.VarType.LOD_TENSOR)
             block.append_op(
                 type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
             block.append_op(
@@ -228,7 +228,7 @@ class L1DecayRegularizer(WeightDecayRegularizer):
             decay = block.create_var(
                 dtype="float32",
                 shape=param.shape,
-                type=core.VarDesc.VarType.SELECTED_ROWS)
+                type=core.VarDesc.VarType.LOD_TENSOR)
             block.append_op(
                 type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
             block.append_op(

python/paddle/fluid/tests/CMakeLists.txt

-if(NOT APPLE)
-  set(PYTHON_TESTS_DIR ${CMAKE_CURRENT_BINARY_DIR} CACHE PATH "python tests directory")
-else()
-  set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
-endif(NOT APPLE)
+set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
 
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")

python/paddle/fluid/tests/test_detection.py

@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
                 dtype='float32',
                 lod_level=1,
                 append_batch_size=False)
-            pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
+            pred_scores, pred_loc, tgt_lbl, tgt_bbox, bbox_inside_weight = layers.rpn_target_assign(
                 bbox_pred=bbox_pred,
                 cls_logits=cls_logits,
                 anchor_box=anchor_box,
@@ -313,15 +313,18 @@ class TestRpnTargetAssign(unittest.TestCase):
                 rpn_straddle_thresh=0.0,
                 rpn_fg_fraction=0.5,
                 rpn_positive_overlap=0.7,
-                rpn_negative_overlap=0.3)
+                rpn_negative_overlap=0.3,
+                use_random=False)
 
             self.assertIsNotNone(pred_scores)
             self.assertIsNotNone(pred_loc)
             self.assertIsNotNone(tgt_lbl)
             self.assertIsNotNone(tgt_bbox)
+            self.assertIsNotNone(bbox_inside_weight)
             assert pred_scores.shape[1] == 1
             assert pred_loc.shape[1] == 4
             assert pred_loc.shape[1] == tgt_bbox.shape[1]
+            print(str(program))
 
 
 class TestGenerateProposals(unittest.TestCase):

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
         set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
         py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
         set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
-        # TODO: fix this test
-        #py_test_modules(test_dist_transformer MODULES test_dist_transformer)
-        #set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
+
+        py_test_modules(test_dist_transformer MODULES test_dist_transformer)
+        set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
     endif(NOT APPLE)
     py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
 endif()

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

@@ -35,7 +35,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid import core
-from test_dist_base import TestDistRunnerBase, runtime_main
+from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
 import paddle.compat as cpt
 from paddle.compat import long_type
 
@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
     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:
+            if batch_id >= RUN_STEP:
                 break
 
             feed_list = []
             total_num_token = 0
 
-            #if TrainTaskConfig.local:
-            #    lr_rate = lr_scheduler.update_learning_rate()
-            #for place_id, data_buffer in enumerate(
-            #        split_data(
-            #            data, num_part=dev_count)):
-
             if TrainTaskConfig.local:
                 lr_rate = lr_scheduler.update_learning_rate()
 
@@ -619,12 +613,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
             init = True
 
             # Validate and save the model for inference.
-            if batch_id == 0 or batch_id == 4:
-                if TrainTaskConfig.val_file_pattern is not None:
-                    val_avg_cost, val_ppl = test()
-                    print("[%f]" % val_avg_cost)
-                else:
-                    assert (False)
+            if TrainTaskConfig.val_file_pattern is not None:
+                val_avg_cost, val_ppl = test()
+                print("[%f]" % val_avg_cost)
+            else:
+                assert (False)
 
 
 #import transformer_reader as reader
@@ -1701,7 +1694,7 @@ class DistTransformer2x2(TestDistRunnerBase):
 
     def run_trainer(self, args):
         TrainTaskConfig.use_gpu = args.use_cuda
-        sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
+        sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
             args.is_dist, not args.sync_mode)
 
         if args.is_dist:

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

@@ -40,7 +40,8 @@ class TestDistMnistAsync(TestDistBase):
         self._sync_mode = False
         self._use_reduce = False
 
-    def test_dist_train(self):
+    # FIXME(typhoonzero): fix async mode test later
+    def no_test_dist_train(self):
         self.check_with_place("dist_mnist.py", delta=200)
 
 

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

@@ -40,7 +40,8 @@ class TestDistSeResneXt2x2Async(TestDistBase):
         self._sync_mode = False
         self._use_reader_alloc = False
 
-    def test_dist_train(self):
+    #FIXME(typhoonzero): fix async mode later
+    def no_test_dist_train(self):
         self.check_with_place("dist_se_resnext.py", delta=100)
 
 

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

@@ -42,7 +42,8 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    def test_simnet_bow(self):
+    #FIXME(typhoonzero): fix async tests later
+    def no_test_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '0',
@@ -78,7 +79,8 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    def test_simnet_bow(self):
+    #FIXME(typhoonzero): fix async tests later
+    def no_test_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '1',

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

@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
 
     def test_dist_train(self):
         download_files()
-        self.check_with_place("dist_transformer.py", delta=1e-5)
+        self.check_with_place(
+            "dist_transformer.py", delta=1e-5, check_error_log=False)
 
 
 class TestDistTransformer2x2Async(TestDistBase):
@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
 
     def test_dist_train(self):
         download_files()
-        self.check_with_place("dist_transformer.py", delta=1.0)
+        self.check_with_place(
+            "dist_transformer.py", delta=1.0, check_error_log=False)
 
 
 if __name__ == "__main__":

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

@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
         self.D = 8
 
 
+class TestFusionGRUOpMD3(TestFusionGRUOp):
+    def set_confs(self):
+        self.M = 17
+        self.D = 15
+
+
 class TestFusionGRUOpBS1(TestFusionGRUOp):
     def set_confs(self):
         self.lod = [[3]]

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

@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
             fg_inds, size=(len(fg_inds) - num_fg), replace=False)
     else:
         disable_inds = fg_inds[num_fg:]
+
     labels[disable_inds] = -1
     fg_inds = np.where(labels == 1)[0]
+    bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32)
 
     num_bg = rpn_batch_size_per_im - np.sum(labels == 1)
     bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
         enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
     else:
         enable_inds = bg_inds[:num_bg]
+
+    fg_fake_inds = np.array([], np.int32)
+    fg_value = np.array([fg_inds[0]], np.int32)
+    fake_num = 0
+    for bg_id in enable_inds:
+        if bg_id in fg_inds:
+            fake_num += 1
+            fg_fake_inds = np.hstack([fg_fake_inds, fg_value])
     labels[enable_inds] = 0
+
+    bbox_inside_weight[fake_num:, :] = 1
     fg_inds = np.where(labels == 1)[0]
     bg_inds = np.where(labels == 0)[0]
-
-    loc_index = fg_inds
-    score_index = np.hstack((fg_inds, bg_inds))
+    loc_index = np.hstack([fg_fake_inds, fg_inds])
+    score_index = np.hstack([fg_inds, bg_inds])
     labels = labels[score_index]
     assert not np.any(labels == -1), "Wrong labels with -1"
 
-    gt_inds = anchor_to_gt_argmax[fg_inds]
+    gt_inds = anchor_to_gt_argmax[loc_index]
 
-    return loc_index, score_index, labels, gt_inds
+    return loc_index, score_index, labels, gt_inds, bbox_inside_weight
 
 
 def get_anchor(n, c, h, w):
@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
         gt_boxes_slice = gt_boxes_slice[not_crowd_inds]
         iou = _bbox_overlaps(inside_anchors, gt_boxes_slice)
 
-        loc_inds, score_inds, labels, gt_inds = rpn_target_assign(
-            iou, rpn_batch_size_per_im, rpn_positive_overlap,
-            rpn_negative_overlap, rpn_fg_fraction, use_random)
+        loc_inds, score_inds, labels, gt_inds, bbox_inside_weight = \
+                         rpn_target_assign(iou, rpn_batch_size_per_im,
+                                           rpn_positive_overlap,
+                                           rpn_negative_overlap,
+                                           rpn_fg_fraction,
+                                           use_random)
         # unmap to all anchor 
         loc_inds = inds_inside[loc_inds]
         score_inds = inds_inside[score_inds]
@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
             score_indexes = score_inds
             tgt_labels = labels
             tgt_bboxes = box_deltas
+            bbox_inside_weights = bbox_inside_weight
         else:
             loc_indexes = np.concatenate(
                 [loc_indexes, loc_inds + i * anchor_num])
@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
                 [score_indexes, score_inds + i * anchor_num])
             tgt_labels = np.concatenate([tgt_labels, labels])
             tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
+            bbox_inside_weights = np.vstack([bbox_inside_weights, \
+                                             bbox_inside_weight])
 
-    return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
+    return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights
 
 
 class TestRpnTargetAssignOp(OpTest):
@@ -182,10 +199,12 @@ class TestRpnTargetAssignOp(OpTest):
         rpn_fg_fraction = 0.5
         use_random = False
 
-        loc_index, score_index, tgt_bbox, labels = rpn_target_assign_in_python(
-            all_anchors, gt_boxes, is_crowd, im_info, lod, rpn_straddle_thresh,
-            rpn_batch_size_per_im, rpn_positive_overlap, rpn_negative_overlap,
-            rpn_fg_fraction, use_random)
+        loc_index, score_index, tgt_bbox, labels, bbox_inside_weights = \
+            rpn_target_assign_in_python(all_anchors, gt_boxes, is_crowd,
+                                   im_info, lod, rpn_straddle_thresh,
+                                   rpn_batch_size_per_im, rpn_positive_overlap,
+                                   rpn_negative_overlap,
+                                   rpn_fg_fraction, use_random)
         labels = labels[:, np.newaxis]
 
         self.op_type = "rpn_target_assign"
@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
             'LocationIndex': loc_index.astype('int32'),
             'ScoreIndex': score_index.astype('int32'),
             'TargetBBox': tgt_bbox.astype('float32'),
-            'TargetLabel': labels.astype('int32')
+            'TargetLabel': labels.astype('int32'),
+            'BBoxInsideWeight': bbox_inside_weights.astype('float32')
         }
 
     def test_check_output(self):

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

@@ -30,7 +30,6 @@ class TestSliceVar(unittest.TestCase):
             var = program.global_block().create_var(
                 name=str(random.randint(10000, 99999)),
                 persistable=True,
-                # dtype=core.VarDesc.VarType.LOD_TENSOR,
                 shape=shape)
             var_list.append(var)
         blocks = slice_variable(var_list, 10, min_size)

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

@@ -21,22 +21,27 @@ from op_test import OpTest
 
 class TestTopkOp(OpTest):
     def setUp(self):
+        self.set_args()
         self.op_type = "top_k"
-        k = 1
-        input = np.random.random((32, 84)).astype("float32")
-        output = np.ndarray((32, k))
-        indices = np.ndarray((32, k)).astype("int64")
+        k = self.top_k
+        input = np.random.random((self.row, k)).astype("float32")
+        output = np.ndarray((self.row, k))
+        indices = np.ndarray((self.row, k)).astype("int64")
 
         self.inputs = {'X': input}
         self.attrs = {'k': k}
 
-        for rowid in range(32):
+        for rowid in range(self.row):
             row = input[rowid]
-            output[rowid] = np.sort(row)[-k:]
-            indices[rowid] = row.argsort()[-k:]
+            output[rowid] = np.sort(row)[::-1][:k]
+            indices[rowid] = row.argsort()[::-1][:k]
 
         self.outputs = {'Out': output, 'Indices': indices}
 
+    def set_args(self):
+        self.row = 32
+        self.top_k = 1
+
     def test_check_output(self):
         self.check_output()
 
@@ -50,14 +55,39 @@ class TestTopkOp3d(OpTest):
         output = np.ndarray((64, k))
         indices = np.ndarray((64, k)).astype("int64")
 
-        # FIXME: should use 'X': input for a 3d input
-        self.inputs = {'X': input_flat_2d}
+        self.inputs = {'X': input}
         self.attrs = {'k': k}
 
         for rowid in range(64):
             row = input_flat_2d[rowid]
-            output[rowid] = np.sort(row)[-k:]
-            indices[rowid] = row.argsort()[-k:]
+            output[rowid] = np.sort(row)[::-1][:k]
+            indices[rowid] = row.argsort()[::-1][:k]
+
+        self.outputs = {
+            'Out': output.reshape((32, 2, k)),
+            'Indices': indices.reshape((32, 2, k))
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestTopkOp2(OpTest):
+    def setUp(self):
+        self.op_type = "top_k"
+        k = 1
+        m = 2056
+        input = np.random.random((m, 84)).astype("float32")
+        output = np.ndarray((m, k))
+        indices = np.ndarray((m, k)).astype("int64")
+
+        self.inputs = {'X': input}
+        self.attrs = {'k': k}
+
+        for rowid in range(m):
+            row = input[rowid]
+            output[rowid] = -np.sort(-row)[:k]
+            indices[rowid] = (-row).argsort()[:k]
 
         self.outputs = {'Out': output, 'Indices': indices}
 
@@ -65,5 +95,17 @@ class TestTopkOp3d(OpTest):
         self.check_output()
 
 
+class TestTopkOp3(TestTopkOp):
+    def set_args(self):
+        self.row = 2056
+        self.top_k = 3
+
+
+class TestTopkOp4(TestTopkOp):
+    def set_args(self):
+        self.row = 40000
+        self.top_k = 1
+
+
 if __name__ == "__main__":
     unittest.main()