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

lite/CMakeLists.txt

@@ -45,6 +45,7 @@ if (WITH_TESTING)
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "resnet50.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
+        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "step_rnn.tar.gz")
     endif()
 endif()
 

lite/api/CMakeLists.txt

@@ -143,6 +143,11 @@ if(WITH_TESTING)
            ${ops} ${host_kernels} ${x86_kernels}
            ARGS --model_dir=${LITE_MODEL_DIR}/resnet50)
         add_dependencies(test_resnet50_lite_x86 extern_lite_download_resnet50_tar_gz)
+        lite_cc_test(test_step_rnn_lite_x86 SRCS test_step_rnn_lite_x86.cc
+           DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
+           ${ops} ${host_kernels} ${x86_kernels}
+           ARGS --model_dir=${LITE_MODEL_DIR}/step_rnn)
+        add_dependencies(test_step_rnn_lite_x86 extern_lite_download_step_rnn_tar_gz)
     endif()
 endif()
 

lite/api/cxx_api.h

@@ -15,6 +15,7 @@
 #pragma once
 #include <map>
 #include <memory>
+#include <mutex>  //NOLINT
 #include <string>
 #include <utility>
 #include <vector>
@@ -126,6 +127,8 @@ class CxxPaddleApiImpl : public lite_api::PaddlePredictor {
 
   void Run() override;
 
+  std::shared_ptr<lite_api::PaddlePredictor> Clone() override;
+
   std::string GetVersion() const override;
 
   // get inputs names and get outputs names
@@ -146,6 +149,8 @@ class CxxPaddleApiImpl : public lite_api::PaddlePredictor {
 
  private:
   Predictor raw_predictor_;
+  lite_api::CxxConfig config_;
+  std::mutex mutex_;
 };
 
 /*

lite/api/cxx_api_impl.cc

@@ -13,6 +13,8 @@
 // limitations under the License.
 
 #include "lite/api/cxx_api.h"
+#include <memory>
+#include <mutex>  //NOLINT
 #include <string>
 #include "lite/api/paddle_api.h"
 #include "lite/core/device_info.h"
@@ -22,6 +24,7 @@ namespace paddle {
 namespace lite {
 
 void CxxPaddleApiImpl::Init(const lite_api::CxxConfig &config) {
+  config_ = config;
 #ifdef LITE_WITH_CUDA
   Env<TARGET(kCUDA)>::Init();
 #endif
@@ -50,6 +53,13 @@ std::vector<std::string> CxxPaddleApiImpl::GetOutputNames() {
 
 void CxxPaddleApiImpl::Run() { raw_predictor_.Run(); }
 
+std::shared_ptr<lite_api::PaddlePredictor> CxxPaddleApiImpl::Clone() {
+  std::lock_guard<std::mutex> lock(mutex_);
+  auto predictor = std::make_shared<lite::CxxPaddleApiImpl>();
+  predictor->Init(config_);
+  return predictor;
+}
+
 std::string CxxPaddleApiImpl::GetVersion() const { return version(); }
 
 std::unique_ptr<const lite_api::Tensor> CxxPaddleApiImpl::GetTensor(

lite/api/light_api.h

@@ -96,6 +96,8 @@ class LightPredictorImpl : public lite_api::PaddlePredictor {
 
   void Run() override;
 
+  std::shared_ptr<lite_api::PaddlePredictor> Clone() override;
+
   std::string GetVersion() const override;
   std::vector<std::string> GetInputNames() override;
   std::vector<std::string> GetOutputNames() override;

lite/api/light_api_impl.cc

@@ -44,6 +44,10 @@ std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetOutput(
 
 void LightPredictorImpl::Run() { raw_predictor_->Run(); }
 
+std::shared_ptr<lite_api::PaddlePredictor> LightPredictorImpl::Clone() {
+  LOG(FATAL) << "The Clone API is not supported in LigthPredictor";
+}
+
 std::string LightPredictorImpl::GetVersion() const { return lite::version(); }
 
 std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetTensor(

lite/api/paddle_api.cc

@@ -46,6 +46,10 @@ template <>
 const int8_t *Tensor::data() const {
   return ctensor(raw_tensor_)->data<int8_t>();
 }
+template <>
+const int64_t *Tensor::data() const {
+  return ctensor(raw_tensor_)->data<int64_t>();
+}
 
 template <>
 const int32_t *Tensor::data() const {
@@ -64,6 +68,10 @@ template <>
 int8_t *Tensor::mutable_data(TargetType type) const {
   return tensor(raw_tensor_)->mutable_data<int8_t>(type);
 }
+template <>
+int64_t *Tensor::mutable_data(TargetType type) const {
+  return tensor(raw_tensor_)->mutable_data<int64_t>(type);
+}
 
 template <typename T, TargetType type>
 void Tensor::CopyFromCpu(const T *src_data) {

lite/api/paddle_api.h

@@ -78,6 +78,7 @@ class LITE_API PaddlePredictor {
   virtual std::unique_ptr<const Tensor> GetOutput(int i) const = 0;
 
   virtual void Run() = 0;
+  virtual std::shared_ptr<PaddlePredictor> Clone() = 0;
 
   virtual std::string GetVersion() const = 0;
 

lite/api/paddle_place.h

@@ -103,6 +103,8 @@ static size_t PrecisionTypeLength(PrecisionType type) {
       return 1;
     case PrecisionType::kInt32:
       return 4;
+    case PrecisionType::kInt64:
+      return 8;
     case PrecisionType::kFP16:
       return 2;
     default:

lite/api/test_step_rnn_lite_x86.cc

+// Copyright (c) 2019 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.
+
+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/utils/cp_logging.h"
+
+namespace paddle {
+namespace lite {
+
+TEST(Step_rnn, test_step_rnn_lite_x86) {
+  std::string model_dir = FLAGS_model_dir;
+  lite_api::CxxConfig config;
+  config.set_model_dir(model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kX86), PRECISION(kInt64)},
+                           lite_api::Place{TARGET(kX86), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kHost), PRECISION(kFloat)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::vector<std::string> target_names = {"item_type_id",
+                                           "mthid_id",
+                                           "source_id_id",
+                                           "layout_id",
+                                           "mark_id",
+                                           "category_id",
+                                           "subcategory_id",
+                                           "score_segment_id",
+                                           "item_attention_id",
+                                           "queue_num_id",
+                                           "micro_video_id",
+                                           "vertical_type_id"};
+
+  for (int i = 0; i < target_names.size(); ++i) {
+    auto input_tensor = predictor->GetInput(i);
+    int size = 0;
+    if (i == 6 || i == 8) {
+      input_tensor->Resize(std::vector<int64_t>{5, 1});
+      input_tensor->SetLoD({{0, 5}});
+      size = 5;
+    } else {
+      input_tensor->Resize(std::vector<int64_t>{1, 1});
+      input_tensor->SetLoD({{0, 1}});
+      size = 1;
+    }
+    auto* data = input_tensor->mutable_data<int64_t>();
+    for (int i = 0; i < size; i++) data[i] = 1;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    predictor->Run();
+  }
+
+  auto start = GetCurrentUS();
+  for (int i = 0; i < FLAGS_repeats; ++i) {
+    predictor->Run();
+  }
+
+  //  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
+            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
+            << " ms in average.";
+
+  std::vector<std::vector<float>> results;
+  // i = 1
+  results.emplace_back(std::vector<float>({0.5030127, 0.496987}));
+  auto out = predictor->GetOutput(0);
+
+  std::vector<int64_t> out_shape = out->shape();
+
+  for (int i = 0; i < results.size(); ++i) {
+    for (int j = 0; j < results[i].size(); ++j) {
+      EXPECT_NEAR(
+          out->data<float>()[j + (out_shape[1] * i)], results[i][j], 1e-6);
+    }
+  }
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/core/context.h

@@ -255,8 +255,6 @@ class Context<TargetType::kX86> {
  public:
   Context() {}
 
-  Context(Context&& ctx) {}
-
   // NOTE: InitOnce should only be used by ContextScheduler
   void InitOnce() {}
 

lite/core/mir/fusion/quant_dequant_fuse_pass.cc

@@ -54,4 +54,5 @@ void QuantDequantFusePass::Apply(const std::unique_ptr<SSAGraph>& graph) {
 
 REGISTER_MIR_PASS(lite_quant_dequant_fuse_pass,
                   paddle::lite::mir::QuantDequantFusePass)
-    .BindTargets({TARGET(kAny)});
+    .BindTargets({TARGET(kAny)})
+    .BindKernel("calib");

lite/core/mir/subgraph/subgraph_program_pass.cc

@@ -229,6 +229,8 @@ void SubgraphProgramPass::InferOnce(const std::unique_ptr<SSAGraph>& graph) {
     }
     op->CheckShape();
     op->InferShape();
+
+#ifndef LITH_WITH_XPU
     // TOOD(xxx): remove Launch() at last
     auto& kkks = stmt.kernels();
     if (!kkks.empty()) {
@@ -237,6 +239,7 @@ void SubgraphProgramPass::InferOnce(const std::unique_ptr<SSAGraph>& graph) {
         kk->Launch();
       }
     }
+#endif
   }
 }
 

lite/core/op_registry.cc

@@ -54,6 +54,8 @@ std::list<std::unique_ptr<KernelBase>> KernelRegistry::Create(
       CREATE_KERNEL1(target__, kFP16);                  \
     case PRECISION(kAny):                               \
       CREATE_KERNEL1(target__, kAny);                   \
+    case PRECISION(kInt64):                             \
+      CREATE_KERNEL1(target__, kInt64);                 \
     default:                                            \
       CHECK(false) << "not supported kernel precision " \
                    << PrecisionToStr(precision);        \
@@ -126,6 +128,7 @@ KernelRegistry::KernelRegistry()
   INIT_FOR(kX86, kFloat, kNCHW);
   INIT_FOR(kX86, kAny, kNCHW);
   INIT_FOR(kX86, kAny, kAny);
+  INIT_FOR(kX86, kInt64, kNCHW);
 
   INIT_FOR(kARM, kFloat, kNCHW);
   INIT_FOR(kARM, kInt8, kNCHW);

lite/fluid/eigen.h

@@ -32,7 +32,7 @@ struct EigenDim {
   static Type From(const lite::DDim& dims) {
     PADDLE_ENFORCE(dims.size() == D, "D must match DDim::size");
     Type ret;
-    for (int64_t d = 0; d < dims.size(); d++) {
+    for (size_t d = 0; d < dims.size(); d++) {
       ret[d] = dims[d];
     }
     return ret;
@@ -118,7 +118,9 @@ struct EigenScalar {
   using ConstType = Eigen::TensorMap<
       Eigen::TensorFixedSize<const T, Eigen::Sizes<>, MajorType, IndexType>>;
 
-  static Type From(Tensor& tensor) { return Type(tensor.data<T>()); }  // NOLINT
+  static Type From(Tensor* tensor) {
+    return Type(const_cast<T*>(tensor->data<T>()));
+  }  // NOLINT
 
   static ConstType From(const Tensor& tensor) {
     return ConstType(tensor.data<T>());

lite/kernels/x86/CMakeLists.txt

@@ -36,6 +36,9 @@ add_kernel(sequence_pool_compute_x86 X86 basic SRCS sequence_pool_compute.cc DEP
 add_kernel(softmax_compute_x86 X86 basic SRCS softmax_compute.cc DEPS ${lite_kernel_deps} softmax)
 add_kernel(elementwise_compute_x86 X86 basic SRCS elementwise_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(batch_norm_compute_x86 X86 basic SRCS batch_norm_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(reduce_sum_compute_x86 X86 basic SRCS reduce_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(lookup_table_compute_x86 X86 basic SRCS lookup_table_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(sequence_reshape_compute_x86 X86 basic SRCS sequence_reshape_compute.cc DEPS ${lite_kernel_deps})
 
 if(NOT LITE_WITH_X86)
     return()

lite/kernels/x86/activation_compute.cc

@@ -57,3 +57,13 @@ REGISTER_LITE_KERNEL(gelu,
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(softsign,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::SoftsignCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/activation_compute.h

@@ -187,6 +187,31 @@ class GeluCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   virtual ~GeluCompute() = default;
 };
 
+// softsign(x) = x / (1 + |x|)
+template <typename T>
+struct SoftsignFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) {
+    out.device(d) = x / (static_cast<T>(1) + x.abs());
+  }
+};
+
+template <typename T>
+class SoftsignCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ActivationParam;
+
+  void Run() override {
+    // auto& context = ctx_->As<X86Context>();
+    auto& param = *param_.get_mutable<operators::ActivationParam>();
+    param.Out->template mutable_data<T>();
+
+    Activate<SoftsignFunctor<T>>(param.X, param.Out);
+  }
+
+  virtual ~SoftsignCompute() = default;
+};
+
 }  // namespace x86
 }  // namespace kernels
 }  // namespace lite

lite/kernels/x86/concat_compute.h

@@ -42,7 +42,10 @@ class ConcatCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     int64_t axis = static_cast<int64_t>(param.axis);
     auto x_dims = param.x[0]->dims();
     auto out = param.output;
-    if (param.x.size() == 1) return;
+    if (param.x.size() == 1) {
+      param.output->ShareDataWith(*param.x[0]);
+      return;
+    }
 
     auto output_data = param.output->template mutable_data<T>();
     int offset_concat_axis = 0;

lite/kernels/x86/lookup_table_compute.cc

+// Copyright (c) 2019 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 "lite/kernels/x86/lookup_table_compute.h"
+
+// REGISTER_LITE_KERNEL(lookup_table, kX86, kFloat, kNCHW,
+//                     paddle::lite::kernels::x86::LookupTableCompute<float>,
+//                     def)
+//    .BindInput("W", {LiteType::GetTensorTy(TARGET(kX86))})
+//    .BindInput("Ids", {LiteType::GetTensorTy(TARGET(kX86))})
+//    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+//    .Finalize();
+//,
+REGISTER_LITE_KERNEL(lookup_table,
+                     kX86,
+                     kInt64,
+                     kNCHW,
+                     paddle::lite::kernels::x86::LookupTableCompute<float>,
+                     def)
+    .BindInput("W", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("Ids", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/lookup_table_compute.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+#include <vector>
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+#include "lite/fluid/eigen.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class LookupTableCompute : public KernelLite<TARGET(kX86), PRECISION(kInt64)> {
+ public:
+  using param_t = operators::LookupTableParam;
+
+  void Run() override {
+    auto &param = *param_.get_mutable<operators::LookupTableParam>();
+    // auto& context = context_->As<X86Context>();
+    auto *ids_t = param.Ids;
+    auto *output_t = param.Out;
+    int64_t padding_idx = param.padding_idx;
+    auto *ids = ids_t->data<int64_t>();
+    int64_t ids_numel = ids_t->dims().production();
+
+    auto *table_t = param.W;
+    int64_t row_number = table_t->dims()[0];
+    int64_t row_width = table_t->dims()[1];
+
+    auto *table = table_t->data<float>();
+    auto *output = output_t->mutable_data<float>();
+    memset(output, 0, output_t->dims().production() * sizeof(float));
+    for (int64_t i = 0; i < ids_numel; ++i) {
+      if (padding_idx != -1 && ids[i] == padding_idx) {
+        memset(output + i * row_width, 0, row_width * sizeof(float));
+      } else {
+        CHECK_LT(ids[i], row_number);
+        CHECK_GE(ids[i], 0);
+        memcpy(output + i * row_width,
+               table + ids[i] * row_width,
+               row_width * sizeof(float));
+      }
+    }
+  }
+
+  virtual ~LookupTableCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/reduce_compute.cc

+// Copyright (c) 2019 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 "lite/kernels/x86/reduce_compute.h"
+
+REGISTER_LITE_KERNEL(reduce_sum,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::ReduceSumCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/reduce_compute.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+#include <vector>
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+#include "lite/fluid/eigen.h"
+#include "lite/kernels/x86/reduce_op_function.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+struct SumFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(X* x, Y* y, const Dim& dim) {
+    y->device(lite::fluid::EigenDeviceType<TARGET(kX86)>()) = x->sum(dim);
+  }
+};
+
+#define HANDLE_DIM(NDIM, RDIM)                                            \
+  if (ndim == NDIM && rdim == RDIM) {                                     \
+    paddle::lite::kernels::x86::                                          \
+        ReduceFunctor<lite::TargetType::kX86, T, NDIM, RDIM, SumFunctor>( \
+            *input, output, dims, keep_dim);                              \
+  }
+
+template <typename T>
+class ReduceSumCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ReduceParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::ReduceParam>();
+    // auto& context = ctx_->As<X86Context>();
+    bool reduce_all = param.reduce_all;
+    auto* input = param.x;
+    auto* output = param.output;
+    param.output->mutable_data<T>();
+
+    auto dims = param.dim;
+    bool keep_dim = param.keep_dim;
+    if (reduce_all) {
+      // Flatten and reduce 1-D tensor
+      auto x = lite::fluid::EigenVector<T>::Flatten(*input);
+      auto out = lite::fluid::EigenScalar<T>::From(output);
+      // auto& place = *platform::CPUDeviceContext().eigen_device();
+      auto reduce_dim = Eigen::array<int, 1>({{0}});
+      SumFunctor functor;
+      functor(&x, &out, reduce_dim);
+    } else {
+      int ndim = input->dims().size();
+      int rdim = dims.size();
+      HANDLE_DIM(4, 3);
+      HANDLE_DIM(4, 2);
+      HANDLE_DIM(4, 1);
+      HANDLE_DIM(3, 2);
+      HANDLE_DIM(3, 1);
+      HANDLE_DIM(2, 1);
+      HANDLE_DIM(1, 1);
+    }
+  }
+
+  virtual ~ReduceSumCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/reduce_op_function.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+#include "lite/core/op_registry.h"
+#include "lite/fluid/eigen.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T,
+          size_t D,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = lite::fluid::EigenTensor<T, D, MajorType, IndexType>;
+template <typename T,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenScalar = lite::fluid::EigenScalar<T, MajorType, IndexType>;
+template <typename T,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = lite::fluid::EigenVector<T, MajorType, IndexType>;
+
+template <lite::TargetType Target,
+          typename T,
+          size_t D,
+          size_t R_D,
+          typename Functor>
+// const lite::Context<Target>& context,
+void ReduceFunctor(const lite::Tensor& input,
+                   lite::Tensor* output,
+                   const std::vector<int>& dims,
+                   bool keep_dim) {
+  auto x = EigenTensor<T, D>::From(input);
+  auto x_rank = static_cast<int>(x.dimensions().size());
+  auto reduce_dim = Eigen::array<int, R_D>();
+  std::vector<int> dims_ref = dims;
+  for (size_t i = 0; i < dims_ref.size(); ++i) {
+    if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
+    reduce_dim[i] = dims_ref[i];
+  }
+  // construct the squeezed output tensor
+  lite::DDim out_dims = output->dims();
+  if (keep_dim && x_rank > 1) {
+    const int kDelFlag = -2;
+    auto dims_vector = out_dims.Vectorize();
+    for (size_t i = 0; i < dims_ref.size(); ++i) {
+      dims_vector[dims_ref[i]] = kDelFlag;
+    }
+    dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
+                      dims_vector.end());
+    out_dims = lite::DDim(dims_vector);
+  }
+  // auto& place = *context.eigen_device();
+  Functor functor;
+
+  if (D == 1) {
+    auto out = EigenScalar<T>::From(output);
+    functor(&x, &out, reduce_dim);
+  } else {
+    auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
+    functor(&x, &out, reduce_dim);
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/reshape_compute.cc

@@ -34,3 +34,14 @@ REGISTER_LITE_KERNEL(reshape2,
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput("XShape", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
+REGISTER_LITE_KERNEL(reshape2,
+                     kX86,
+                     kInt64,
+                     kNCHW,
+                     paddle::lite::kernels::x86::Reshape2Compute<int64_t>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .BindOutput("XShape",
+                {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .Finalize();

lite/kernels/x86/sequence_reshape_compute.cc

+// Copyright (c) 2019 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 "lite/kernels/x86/sequence_reshape_compute.h"
+
+REGISTER_LITE_KERNEL(
+    sequence_reshape,
+    kX86,
+    kInt64,
+    kNCHW,
+    paddle::lite::kernels::x86::SequenceReshapeCompute<int64_t>,
+    def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kInt64))})
+    .Finalize();

lite/kernels/x86/sequence_reshape_compute.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+#include <vector>
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+#include "lite/fluid/eigen.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class SequenceReshapeCompute
+    : public KernelLite<TARGET(kX86), PRECISION(kInt64)> {
+ public:
+  using param_t = operators::SequenceReshapeParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::SequenceReshapeParam>();
+    // auto& context = context_->As<X86Context>();
+    auto* in = param.x;
+    auto* out = param.output;
+    int out_width = param.new_dim;
+
+    auto in_dims = in->dims();
+    int64_t in_width = in_dims[1];
+    // LOG(INFO)<<"sequence_reshape in tensor:"<<*in;
+    auto& in_lod = in->lod();
+
+    CHECK_EQ(in_lod.size(), 1UL);
+    CHECK_EQ((uint64_t)in_dims[0], in_lod[0].back());
+
+    auto in_lod_l0 = in_lod[0];
+    int seq_num = in_lod_l0.size() - 1;
+
+    if (in_width == out_width) {
+      out->set_lod(in->lod());
+    } else {
+      auto& out_lod = *out->mutable_lod();
+      out_lod.resize(1);
+      out_lod[0].resize(seq_num + 1);
+      out_lod[0][0] = 0;
+      for (int i = 0; i < seq_num; ++i) {
+        size_t seq_len = in_lod_l0[i + 1] - in_lod_l0[i];
+        size_t offset = 0;
+        offset = (seq_len * in_width) / out_width;
+        CHECK_EQ(offset * out_width, seq_len * in_width);
+        out_lod[0][i + 1] = out_lod[0][i] + offset;
+      }
+    }
+
+    out->Resize(in_dims);
+    auto* dst_ptr = out->mutable_data<T>();
+    auto size = in->numel() * sizeof(T);
+    std::memcpy(dst_ptr, in->data<T>(), size);
+    std::vector<int64_t> out_shape{static_cast<int64_t>(out->lod()[0].back()),
+                                   out_width};
+    out->Resize(lite::DDim(out_shape));
+  }
+
+  virtual ~SequenceReshapeCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/operators/CMakeLists.txt

@@ -76,6 +76,8 @@ add_operator(sequence_expand_as_op_lite extra SRCS sequence_expand_as_op.cc DEPS
 add_operator(range_op extra SRCS range_op.cc DEPS ${op_DEPS})
 add_operator(assign_value_op extra SRCS assign_value_op.cc DEPS ${op_DEPS})
 add_operator(fake_quantize_dequantize_moving_avg_abs_max_op extra SRCS fake_quantize_dequantize_moving_avg_max_abs.cc DEPS ${op_DEPS})
+add_operator(sequence_reshape_op_lite extra SRCS sequence_reshape_op.cc DEPS ${op_DEPS})
+add_operator(reduce_sum_op_lite extra SRCS reduce_ops.cc DEPS ${op_DEPS})
 
 # for OCR specific
 add_operator(while_op extra SRCS while_op.cc DEPS ${op_DEPS})

lite/operators/activation_ops.cc

@@ -118,6 +118,7 @@ REGISTER_LITE_OP(exp, paddle::lite::operators::ActivationOp);
 REGISTER_LITE_OP(floor, paddle::lite::operators::ActivationOp);
 REGISTER_LITE_OP(hard_sigmoid, paddle::lite::operators::ActivationOp);
 REGISTER_LITE_OP(rsqrt, paddle::lite::operators::ActivationOp);
+REGISTER_LITE_OP(softsign, paddle::lite::operators::ActivationOp);
 
 #ifdef LITE_WITH_TRAIN
 REGISTER_LITE_OP(square_grad, paddle::lite::operators::ActivationGradOp);

lite/operators/concat_op.cc

@@ -21,7 +21,7 @@ namespace lite {
 namespace operators {
 
 bool ConcatOpLite::CheckShape() const {
-  CHECK_GT_OR_FALSE(param_.x.size(), 1UL);
+  CHECK_GE_OR_FALSE(param_.x.size(), 1UL);
   CHECK_OR_FALSE(param_.output);
   return true;
 }

lite/operators/lookup_table_op.cc

@@ -50,6 +50,7 @@ bool LookupTableOpLite::InferShape() const {
   }
   out_dims.push_back(table_dims[1]);
   param_.Out->Resize(lite::DDim{out_dims});
+  param_.Out->set_lod(param_.Ids->lod());
   return true;
 }
 

lite/operators/op_params.h

@@ -721,6 +721,12 @@ struct SequencePoolParam {
 #endif
 };
 
+struct SequenceReshapeParam {
+  lite::Tensor* x{};
+  lite::Tensor* output{};
+  int new_dim;
+};
+
 struct SequenceExpandParam {
   const lite::Tensor* X{};
   const lite::Tensor* Y{};
@@ -753,6 +759,15 @@ struct IsEmptyParam {
   const lite::Tensor* X{};
   lite::Tensor* Out{};
 };
+
+struct ReduceParam {
+  lite::Tensor* x{};
+  lite::Tensor* output{};
+  std::vector<int> dim{0};
+  bool keep_dim{false};
+  bool reduce_all{false};
+};
+
 /// ----------------------- shape operators ----------------------
 struct ShapeParam {
   const lite::Tensor* X{};

lite/operators/reduce_ops.cc

+// Copyright (c) 2019 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 "lite/operators/reduce_ops.h"
+#include <algorithm>
+#include "lite/core/op_registry.h"
+namespace paddle {
+namespace lite {
+namespace operators {
+
+bool ReduceOp::CheckShape() const {
+  CHECK_OR_FALSE(param_.x);
+  CHECK_OR_FALSE(param_.output);
+  auto x_dims = param_.x->dims();
+  auto x_rank = x_dims.size();
+  CHECK_LE(x_rank, 6UL) << "Tensors with rank at most 6 are supported.";
+  return true;
+}
+
+bool ReduceOp::InferShape() const {
+  auto x_dims = param_.x->dims();
+  auto x_rank = x_dims.size();
+  auto dims = param_.dim;
+  for (size_t i = 0; i < dims.size(); ++i) {
+    if (dims[i] < 0) dims[i] = x_rank + dims[i];
+    CHECK_LT(dims[i], x_rank)
+        << "The dim should be in the range [-rank(input), rank(input).";
+  }
+  sort(dims.begin(), dims.end());
+  bool reduce_all = param_.reduce_all;
+  bool keep_dim = param_.keep_dim;
+
+  if (reduce_all) {
+    if (keep_dim)
+      param_.output->Resize(lite::DDim(std::vector<int64_t>(x_rank, 1)));
+    else
+      param_.output->Resize(lite::DDim(std::vector<int64_t>{1}));
+  } else {
+    auto dims_vector = x_dims.Vectorize();
+    if (keep_dim) {
+      for (size_t i = 0; i < dims.size(); ++i) {
+        dims_vector[dims[i]] = 1;
+      }
+    } else {
+      const int kDelFlag = -2;
+      for (size_t i = 0; i < dims.size(); ++i) {
+        dims_vector[dims[i]] = kDelFlag;
+      }
+      dims_vector.erase(
+          remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
+          dims_vector.end());
+    }
+    auto out_dims = lite::DDim(dims_vector);
+    param_.output->Resize(out_dims);
+    if (dims[0] != 0) {
+      param_.output->set_lod(param_.x->lod());
+    }
+  }
+  return true;
+}
+
+bool ReduceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
+  param_.x =
+      scope->FindVar(opdesc.Input("X").front())->GetMutable<lite::Tensor>();
+  param_.output =
+      scope->FindVar(opdesc.Output("Out").front())->GetMutable<lite::Tensor>();
+
+  param_.dim = opdesc.GetAttr<std::vector<int>>("dim");
+  param_.reduce_all = opdesc.GetAttr<bool>("reduce_all");
+  param_.keep_dim = opdesc.GetAttr<bool>("keep_dim");
+  return true;
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_OP(reduce_sum, paddle::lite::operators::ReduceOp);

lite/operators/reduce_ops.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+#include "lite/core/op_lite.h"
+#include "lite/core/scope.h"
+#include "lite/utils/all.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+class ReduceOp : public OpLite {
+ public:
+  ReduceOp() {}
+  explicit ReduceOp(const std::string &op_type) : OpLite(op_type) {}
+
+  bool CheckShape() const override;
+
+  bool InferShape() const override;
+
+  bool AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) override;
+
+  void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
+  std::string DebugString() const override { return "reduce"; }
+
+ private:
+  mutable ReduceParam param_;
+};
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle

lite/operators/sequence_reshape_op.cc

+// Copyright (c) 2019 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 "lite/operators/sequence_reshape_op.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+bool SequenceReshapeOp::CheckShape() const {
+  CHECK_OR_FALSE(param_.x);
+  CHECK_OR_FALSE(param_.output);
+  auto x_dims = param_.x->dims();
+  CHECK_EQ_OR_FALSE(x_dims.size(), 2U);
+  return true;
+}
+
+bool SequenceReshapeOp::InferShape() const {
+  int new_dim = param_.new_dim;
+  auto x_numel = param_.x->dims().production();
+  std::vector<int64_t> out_shape{x_numel / new_dim,
+                                 static_cast<int64_t>(new_dim)};
+  param_.output->Resize(lite::DDim(out_shape));
+  return true;
+}
+
+bool SequenceReshapeOp::AttachImpl(const cpp::OpDesc &opdesc,
+                                   lite::Scope *scope) {
+  param_.x =
+      scope->FindVar(opdesc.Input("X").front())->GetMutable<lite::Tensor>();
+  param_.output =
+      scope->FindVar(opdesc.Output("Out").front())->GetMutable<lite::Tensor>();
+
+  param_.new_dim = opdesc.GetAttr<int>("new_dim");
+  return true;
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_OP(sequence_reshape, paddle::lite::operators::SequenceReshapeOp);

lite/operators/sequence_reshape_op.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "lite/core/op_lite.h"
+#include "lite/core/scope.h"
+#include "lite/utils/all.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+class SequenceReshapeOp : public OpLite {
+ public:
+  SequenceReshapeOp() {}
+  explicit SequenceReshapeOp(const std::string &op_type) : OpLite(op_type) {}
+
+  bool CheckShape() const override;
+
+  bool InferShape() const override;
+
+  bool AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) override;
+
+  void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
+  std::string DebugString() const override { return "sequence_reshape"; }
+
+ private:
+  mutable SequenceReshapeParam param_;
+};
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle

lite/utils/any.h

@@ -22,6 +22,14 @@ namespace lite {
 
 class Any {
  public:
+  Any() = default;
+  explicit Any(const Any& other) {
+    type_ = other.type_;
+    data_ = other.clone_data_(other.data_);
+    deleter_ = other.deleter_;
+    clone_data_ = other.clone_data_;
+  }
+
   template <typename T>
   void set(const T& v) {
     set<T>();
@@ -34,7 +42,16 @@ class Any {
       CHECK(type_ == typeid(T).hash_code());
     } else {
       type_ = typeid(T).hash_code();
-      deleter_ = [&] { delete static_cast<T*>(data_); };
+      deleter_ = [&](void** data) {
+        delete static_cast<T*>(*data);
+        *data = nullptr;
+      };
+      clone_data_ = [&](void* data) {
+        T* res = new T;
+        CHECK(data) << "data pointer is nullptr";
+        *res = *static_cast<T*>(data);
+        return res;
+      };
     }
     data_ = new T;
   }
@@ -54,17 +71,18 @@ class Any {
 
   bool valid() const { return (data_ != nullptr); }
 
-  // ~Any() {
-  //    if (valid()) {
-  //      deleter_();
-  //    }
-  //  }
+  ~Any() {
+    if (valid()) {
+      deleter_(&data_);
+    }
+  }
 
  private:
   static size_t kInvalidType;
   size_t type_{kInvalidType};
   void* data_{nullptr};
-  std::function<void()> deleter_;
+  std::function<void(void**)> deleter_;
+  std::function<void*(void*)> clone_data_;
 };
 
 }  // namespace lite