add asr related kernel test=develop

lite/api/paddle_place.h

@@ -101,6 +101,8 @@ 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/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);        \
@@ -123,6 +125,7 @@ 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(const 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

@@ -35,6 +35,9 @@ 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

@@ -35,3 +35,13 @@ REGISTER_LITE_KERNEL(relu,
     .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

@@ -115,6 +115,31 @@ class ReluCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   virtual ~ReluCompute() = 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/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<int64_t>,
+                     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 <iostream>
+#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 {
+
+/*struct LookupTableTimer {
+  std::chrono::time_point<std::chrono::high_resolution_clock> timer_{};
+  uint64_t total_{};
+
+  void Start() { timer_ = std::chrono::high_resolution_clock::now(); }
+  void Stop() {
+    auto duration = std::chrono::duration_cast<std::chrono::microseconds>(
+        std::chrono::high_resolution_clock::now() - timer_);
+    Log(duration.count());
+  }
+  void Log(uint32_t timespan) { total_ += timespan; }
+  ~LookupTableTimer() {
+    LOG(INFO) << "lookup table timer: [" << total_ << "us]";
+  }
+};*/
+
+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;
+    LOG(INFO) << "lookup_table input ids tensor address: " << ids_t;
+    int64_t padding_idx = param.padding_idx;
+    auto *ids = ids_t->data<int64_t>();
+    LOG(INFO) << "ids data address: " << ids;
+    int64_t ids_numel = ids_t->dims().production();
+    std::cout << "ids_numel: " << ids_numel << std::endl;
+    std::cout << "ids tensor info: [";
+    for (size_t i = 0; i < ids_numel; ++i) {
+      std::cout << ids[i] << ",";
+    }
+    std::cout << "]" << std::endl;
+
+    auto *table_t = param.W;
+    int64_t row_number = table_t->dims()[0];
+    int64_t row_width = table_t->dims()[1];
+    std::cout << "row_number: " << row_number << std::endl;
+    std::cout << "row_width: " << row_width << std::endl;
+
+    auto *table = table_t->data<float>();
+    auto *output = output_t->mutable_data<float>();
+    memset(output, 0, output_t->dims().production() * sizeof(T));
+    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 {
+        std::cout << "*************************" << std::endl;
+        std::cout << "ids[i]: " << ids[i] << std::endl;
+        std::cout << "row_number: " << row_number << std::endl;
+        std::cout << "*************************" << std::endl;
+        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/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,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::SequenceReshapeCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kFloat))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86), PRECISION(kFloat))})
+    .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(kFloat)> {
+ 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 basic SRCS sequence_expand_as_op.cc DEPS
 add_operator(range_op basic SRCS range_op.cc DEPS ${op_DEPS})
 add_operator(assign_value_op basic SRCS assign_value_op.cc DEPS ${op_DEPS})
 add_operator(fake_quantize_dequantize_moving_avg_abs_max_op basic SRCS fake_quantize_dequantize_moving_avg_max_abs.cc DEPS ${op_DEPS})
+add_operator(sequence_reshape_op_lite basic SRCS sequence_reshape_op.cc DEPS ${op_DEPS})
+add_operator(reduce_sum_op_lite basic 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/op_params.h

@@ -717,6 +717,12 @@ struct SequencePoolParam {
 #endif
 };
 
+struct SequenceReshapeParam {
+  lite::Tensor* x{};
+  lite::Tensor* output{};
+  int new_dim;
+};
+
 struct SequenceExpandParam {
   const lite::Tensor* X{};
   const lite::Tensor* Y{};
@@ -749,6 +755,15 @@ 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