add lite x86 ops for ASR test=develop (#1981)

* add lite x86 ops for ASR test=develop

* add lite x86 ops for ASR test=develop

* fix x86 ci run test problems test=develop

* fix mkl path for CI test=develop

lite/backends/x86/CMakeLists.txt

@@ -6,7 +6,7 @@ configure_file(cupti_lib_path.h.in ${CMAKE_CURRENT_BINARY_DIR}/cupti_lib_path.h)
 configure_file(warpctc_lib_path.h.in ${CMAKE_CURRENT_BINARY_DIR}/warpctc_lib_path.h)
 
 lite_cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags)
-#lite_cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
+lite_cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
 lite_cc_library(target_wrapper_x86 SRCS target_wrapper.cc)
 lite_cc_library(x86_cpu_info SRCS cpu_info.cc DEPS xbyak)
 

lite/backends/x86/dynamic_loader.cc

@@ -54,8 +54,8 @@ DEFINE_string(
 DEFINE_string(mklml_dir, "", "Specify path for loading libmklml_intel.so.");
 
 namespace paddle {
-namespace platform {
-namespace dynload {
+namespace lite {
+namespace x86 {
 static constexpr char cupti_lib_path[] = CUPTI_LIB_PATH;
 static constexpr char warpctc_lib_path[] = WARPCTC_LIB_PATH;
 
@@ -258,6 +258,6 @@ void* GetMKLMLDsoHandle() {
 #endif
 }
 
-}  // namespace dynload
-}  // namespace platform
+}  // namespace x86
+}  // namespace lite
 }  // namespace paddle

lite/backends/x86/math/CMakeLists.txt

@@ -16,7 +16,7 @@ function(math_library TARGET)
     endif()
 
     list(LENGTH cc_srcs cc_srcs_len)
-    lite_cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${math_library_DEPS} ${math_common_deps} eigen3)
+    lite_cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${math_library_DEPS} ${math_common_deps} eigen3 dynload_mklml)
 endfunction()
 
 # please add new math_library in alphabetical order

lite/backends/x86/math/blas_impl.h

@@ -483,7 +483,7 @@ void Blas<Target>::MatMul(const lite::Tensor &mat_a,
              mat_a.data<T>(),
              mat_b.data<T>(),
              beta,
-             mat_out->data<T>());
+             mat_out->mutable_data<T>());
 }
 
 template <>
@@ -759,7 +759,7 @@ void Blas<Target>::MatMul(const lite::Tensor &mat_a,
                            mat_a.data<T>(),
                            mat_b.data<T>(),
                            beta,
-                           mat_out->data<T>());
+                           mat_out->mutable_data<T>());
   } else {
     PADDLE_ENFORCE(dim_a.batch_size_ == dim_b.batch_size_ ||
                    dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0);
@@ -773,7 +773,7 @@ void Blas<Target>::MatMul(const lite::Tensor &mat_a,
         mat_a.data<T>(),
         mat_b.data<T>(),
         beta,
-        mat_out->data<T>(),
+        mat_out->mutable_data<T>(),
         dim_a.batch_size_ == 0 ? dim_b.batch_size_ : dim_a.batch_size_,
         dim_a.stride_,
         dim_b.stride_);

lite/core/tensor.h

@@ -218,16 +218,22 @@ R *TensorLite::mutable_data(TargetType target) {
 
 template <typename T>
 TensorLite TensorLite::Slice(int64_t begin, int64_t end) const {
-  int64_t base = numel() / dims_[0];
-
-  TensorLite dst;
-  dst.buffer_ = buffer_;
-  dst.target_ = target_;
-  auto dst_dims = dims_;
-  dst_dims[0] = end - begin;
-  dst.Resize(dst_dims);
-  dst.offset_ = offset_ + static_cast<size_t>(begin * base) * sizeof(T);
-  return dst;
+  CHECK_GE(begin, 0);
+  CHECK_LE(end, dims_[0]);
+  CHECK_LT(begin, end);
+  if (dims_[0] == 1) {
+    return *this;
+  } else {
+    int64_t base = numel() / dims_[0];
+    TensorLite dst;
+    dst.buffer_ = buffer_;
+    dst.target_ = target_;
+    auto dst_dims = dims_;
+    dst_dims[0] = end - begin;
+    dst.Resize(dst_dims);
+    dst.offset_ = offset_ + static_cast<size_t>(begin * base) * sizeof(T);
+    return dst;
+  }
 }
 
 template <typename TensorT>

lite/kernels/x86/CMakeLists.txt

@@ -23,11 +23,18 @@ add_kernel(scale_compute_x86 X86 basic SRCS scale_compute.cc DEPS ${lite_kernel_
 # lite_cc_test(test_fc_compute_x86 SRCS fc_compute_test.cc DEPS fc_compute_x86)
 # lite_cc_test(test_conv2d_compute_x86 SRCS conv_compute_test.cc DEPS conv_compute_x86)
 # lite_cc_test(test_pool2d_compute_x86 SRCS pool_compute_test.cc DEPS pool_compute_x86)
-# lite_cc_test(test_concat_compute_x86 SRCS concat_compute_test.cc DEPS concat_compute_x86)
 # lite_cc_test(test_softmax_compute_x86 SRCS softmax_compute_test.cc DEPS softmax_compute_x86)
 # lite_cc_test(test_elementwise_compute_x86 SRCS elementwise_compute_test.cc DEPS elementwise_compute_x86)
 # lite_cc_test(test_relu_compute_x86 SRCS relu_compute_test.cc DEPS relu_compute_x86)
-# lite_cc_test(test_mul_compute_x86 SRCS mul_compute_test.cc DEPS mul_compute_x86 operator)
 # lite_cc_test(test_scale_compute_x86 SRCS scale_compute_test.cc DEPS scale_compute_x86)
 # lite_cc_test(test_dropout_compute_x86 SRCS dropout_compute_test.cc DEPS dropout_compute_x86)
 # lite_cc_test(test_batch_norm_compute_x86 SRCS batch_norm_compute_test.cc DEPS batch_norm_compute_x86)
+add_kernel(mul_compute_x86 X86 basic SRCS mul_compute.cc DEPS ${lite_kernel_deps} blas)
+add_kernel(concat_compute_x86 X86 basic SRCS concat_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(shape_compute_x86 X86 basic SRCS shape_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(sequence_pool_compute_x86 X86 basic SRCS sequence_pool_compute.cc DEPS ${lite_kernel_deps} sequence_pooling)
+
+lite_cc_test(test_mul_compute_x86 SRCS mul_compute_test.cc DEPS mul_compute_x86)
+lite_cc_test(test_concat_compute_x86 SRCS concat_compute_test.cc DEPS concat_compute_x86)
+lite_cc_test(test_sequence_pool_compute_x86 SRCS sequence_pool_compute_test.cc DEPS sequence_pool_compute_x86)
+lite_cc_test(test_shape_compute_x86 SRCS shape_compute_test.cc DEPS shape_compute_x86)

lite/kernels/x86/concat_compute.h

@@ -18,13 +18,20 @@
 #include "lite/core/kernel.h"
 #include "lite/core/op_registry.h"
 #include "lite/core/types.h"
-#include "paddle/fluid/operators/strided_memcpy.h"
 
 namespace paddle {
 namespace lite {
 namespace kernels {
 namespace x86 {
 
+inline int count(int start_axis, int end_axis, const lite::DDim& dim) {
+  int count = 1;
+  for (int i = start_axis; i < end_axis; ++i) {
+    count *= dim[i];
+  }
+  return count;
+}
+
 template <typename T>
 class ConcatCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
  public:
@@ -33,67 +40,28 @@ class ConcatCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   void Run() override {
     auto& param = *param_.get_mutable<param_t>();
     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 (axis == 0 && param.x.size() < 10) {
-      size_t output_offset = 0;
-      for (auto* in : param.x) {
-        if (!in || in->dims().production() == 0UL) {
-          continue;
-        }
-        auto in_stride = framework::stride_numel(in->dims().data());
-        auto out_stride = framework::stride_numel(out->dims().data());
-        paddle::operators::StridedNumelCopyWithAxis<T>(
-            platform::CPUDeviceContext(),
-            axis,
-            out->mutable_data<T>() + output_offset,
-            out_stride,
-            in->data<T>(),
-            in_stride,
-            in_stride[axis]);
-
-        output_offset += in_stride[axis];
-      }
-    } else {
-      std::vector<lite::Tensor> inputs;
-      for (size_t j = 0; j < param.x.size(); ++j) {
-        if (param.x[j] && param.x[j]->dims().production() > 0) {
-          inputs.push_back(*param.x[j]);
-        } else {
-          continue;
-        }
-      }
-
-      int num = inputs.size();
-      int rows = 1;
-      auto dim_0 = inputs[0].dims();
-      for (int i = 0; i < axis; ++i) {
-        rows *= dim_0[i];
-      }
-      int out_rows = rows, out_cols = 0;
-
-      std::vector<int64_t> input_cols(inputs.size());
-      for (int i = 0; i < num; ++i) {
-        int t_cols = inputs[i].dims().production() / rows;
-        out_cols += t_cols;
-        input_cols[i] = t_cols;
-      }
-      // computation
-      auto output_data = param.output->template mutable_data<T>();
-      int col_idx = 0;
-      for (int j = 0; j < num; ++j) {
-        int col_len = input_cols[j];
-        auto input_data = inputs[j].data<float>();
-        for (int k = 0; k < out_rows; ++k) {
-          std::memcpy(output_data + k * out_cols + col_idx,
-                      input_data + k * col_len,
-                      sizeof(T) * col_len);
-        }
-        col_idx += col_len;
+    auto output_data = param.output->template mutable_data<T>();
+    int offset_concat_axis = 0;
+    int num_concat = count(0, axis, x_dims);
+    int concat_input_size = count(axis + 1, x_dims.size(), x_dims);
+    const int top_concat_axis = out->dims()[axis];
+    for (size_t i = 0; i < param.x.size(); ++i) {
+      auto bottom_data = param.x[i]->data<T>();
+      const int64_t bottom_concat_axis = param.x[i]->dims()[axis];
+      for (int n = 0; n < num_concat; ++n) {
+        std::memcpy(
+            output_data +
+                (n * top_concat_axis + offset_concat_axis) * concat_input_size,
+            bottom_data + n * bottom_concat_axis * concat_input_size,
+            (bottom_concat_axis * concat_input_size) * sizeof(T));
       }
+      offset_concat_axis += bottom_concat_axis;
     }
   }
-
   virtual ~ConcatCompute() = default;
 };
 

lite/kernels/x86/concat_compute_test.cc

@@ -14,7 +14,6 @@
 
 #include "lite/kernels/x86/concat_compute.h"
 #include <gtest/gtest.h>
-#include <iostream>
 #include <vector>
 #include "lite/core/op_registry.h"
 
@@ -68,11 +67,11 @@ TEST(concat_x86, run_test) {
   concat.SetParam(param);
   concat.Run();
 
-  std::cout << "output: ";
+  std::vector<float> ref_results = {
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2};
   for (int i = 0; i < out.dims().production(); i++) {
-    std::cout << out_data[i] << " ";
+    EXPECT_NEAR(out_data[i], ref_results[i], 1e-3);
   }
-  std::cout << std::endl;
 }
 
 }  // namespace x86

lite/kernels/x86/mul_compute.cc

@@ -25,6 +25,7 @@ REGISTER_LITE_KERNEL(mul,
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
 
+#ifdef LITE_WITH_TRAIN
 REGISTER_LITE_KERNEL(mul_grad,
                      kX86,
                      kFloat,
@@ -40,3 +41,4 @@ REGISTER_LITE_KERNEL(mul_grad,
     .BindOutput(paddle::framework::GradVarName("Y"),
                 {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
+#endif

lite/kernels/x86/mul_compute.h

@@ -13,17 +13,26 @@
 // limitations under the License.
 #pragma once
 
+#include "lite/backends/x86/math/blas.h"
 #include "lite/core/kernel.h"
 #include "lite/core/op_registry.h"
 #include "lite/core/types.h"
-#include "paddle/fluid/operators/math/blas.h"
-
 namespace paddle {
 namespace lite {
 namespace kernels {
 namespace x86 {
 
-using Tensor = framework::Tensor;
+// using Tensor = framework::Tensor;
+inline lite::Tensor ReshapeToMatrix(const lite::Tensor& src, int num_col_dims) {
+  int rank = src.dims().size();
+  if (rank == 2) {
+    return src;
+  }
+  lite::Tensor res;
+  res.ShareDataWith(src);
+  res.Resize(src.dims().Flatten2D(num_col_dims));
+  return res;
+}
 
 template <typename T>
 class MulCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
@@ -33,36 +42,35 @@ class MulCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   void Run() override {
     auto& context = ctx_->As<X86Context>();
     auto& param = *param_.get_mutable<operators::MulParam>();
-    CHECK(context.x86_device_context());
+    // CHECK(context.x86_device_context());
 
-    param.output->template mutable_data<T>();
+    auto* z = param.output;
 
-    auto* x = &param.x->raw_tensor();
-    auto* y = &param.y->raw_tensor();
+    auto* x = param.x;
+    auto* y = param.y;
 
     Tensor x_matrix, y_matrix;
 
     if (x->dims().size() > 2) {
-      x_matrix = framework::ReshapeToMatrix(*x, param.x_num_col_dims);
+      x_matrix = ReshapeToMatrix(*x, param.x_num_col_dims);
     } else {
       x_matrix = *x;
     }
 
     if (y->dims().size() > 2) {
-      y_matrix = framework::ReshapeToMatrix(*y, param.y_num_col_dims);
+      y_matrix = ReshapeToMatrix(*y, param.y_num_col_dims);
 
     } else {
       y_matrix = *y;
     }
 
-    auto* z = &param.output->raw_tensor();
+    z->mutable_data<T>();
     auto z_dim = z->dims();
     if (z_dim.size() != 2) {
       z->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
     }
 
-    auto blas = paddle::operators::math::GetBlas<platform::CPUDeviceContext, T>(
-        *context.x86_device_context());
+    auto blas = lite::x86::math::GetBlas<lite::TargetType::kX86, T>(context);
 
     blas.MatMul(x_matrix, y_matrix, z);
     if (z_dim.size() != 2) {
@@ -73,6 +81,7 @@ class MulCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   virtual ~MulCompute() = default;
 };
 
+#ifdef LITE_WITH_TRAIN
 template <typename T>
 class MulGradCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
  public:
@@ -142,6 +151,7 @@ class MulGradCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
 
   virtual ~MulGradCompute() = default;
 };
+#endif
 
 }  // namespace x86
 }  // namespace kernels

lite/kernels/x86/mul_compute_test.cc

@@ -19,7 +19,6 @@
 #include <utility>
 #include <vector>
 #include "lite/core/op_registry.h"
-
 namespace paddle {
 namespace lite {
 namespace kernels {
@@ -33,7 +32,7 @@ TEST(mul_x86, retrive_op) {
 }
 
 TEST(mul_x86, init) {
-  MulCompute<float> mul;
+  lite::kernels::x86::MulCompute<float> mul;
   ASSERT_EQ(mul.precision(), PRECISION(kFloat));
   ASSERT_EQ(mul.target(), TARGET(kX86));
 }
@@ -72,9 +71,10 @@ TEST(mul_x86, run_test) {
   mul.SetParam(param);
   mul.Run();
 
-  LOG(INFO) << "output: ";
+  std::vector<float> ref_result = {20, 23, 26, 29};
+
   for (int i = 0; i < out.dims().production(); i++) {
-    LOG(INFO) << out_data[i];
+    EXPECT_NEAR(out_data[i], ref_result[i], 1e-3);
   }
 }
 

lite/kernels/x86/sequence_pool_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_pool_compute.h"
+
+REGISTER_LITE_KERNEL(sequence_pool,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::SequencePoolCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/sequence_pool_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/backends/x86/math/math_function.h"
+#include "lite/backends/x86/math/sequence_pooling.h"
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class SequencePoolCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::SequencePoolParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::SequencePoolParam>();
+    auto& context = ctx_->As<X86Context>();
+    auto* out = param.Out;
+    auto dims = param.X->dims();
+    auto lod = param.X->lod();
+    CHECK_EQ(lod.size(), 1UL);
+    CHECK_GE(dims[0], static_cast<int64_t>(lod[0].size() - 1));
+
+    dims[0] = lod[0].size() - 1;
+    out->Resize({dims});
+    out->mutable_data<T>();
+    lite::Tensor* index = nullptr;
+
+    const bool is_test = true;
+    float pad_value = 0.0;
+
+    lite::x86::math::SequencePoolFunctor<lite::TargetType::kX86, T> pool;
+    pool(context, param.pool_type, pad_value, *param.X, out, is_test, index);
+  }
+
+  virtual ~SequencePoolCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/sequence_pool_compute_test.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_pool_compute.h"
+#include <gtest/gtest.h>
+#include <memory>
+#include <utility>
+#include <vector>
+#include "lite/core/op_registry.h"
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(sequence_pool_x86, retrive_op) {
+  auto sequence_pool =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "sequence_pool");
+  ASSERT_FALSE(sequence_pool.empty());
+  ASSERT_TRUE(sequence_pool.front());
+}
+
+TEST(sequence_pool_x86, init) {
+  SequencePoolCompute<float> sequence_pool;
+  ASSERT_EQ(sequence_pool.precision(), PRECISION(kFloat));
+  ASSERT_EQ(sequence_pool.target(), TARGET(kX86));
+}
+
+TEST(sequence_pool_x86, run_test) {
+  lite::Tensor x, out;
+  lite::LoD lod;
+  lod.push_back(std::vector<uint64_t>{0, 10});
+
+  x.set_lod(lod);
+  const size_t second_dim = 8u;
+  std::vector<int64_t> input_shape{static_cast<int64_t>(lod[0].back()),
+                                   static_cast<int64_t>(second_dim)};
+  lite::DDim in_dims(input_shape);
+  x.Resize(in_dims);
+
+  const size_t out_first_dim = lod[0].size() - 1;
+  std::vector<int64_t> output_shape{static_cast<int64_t>(out_first_dim),
+                                    static_cast<int64_t>(second_dim)};
+  lite::DDim out_dims(output_shape);
+  out.Resize(out_dims);
+
+  auto x_data = x.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = 1.1f * i;
+  }
+
+  SequencePoolCompute<float> sequence_pool;
+  operators::SequencePoolParam param;
+  param.X = &x;
+  param.Out = &out;
+
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  sequence_pool.SetContext(std::move(ctx));
+  sequence_pool.SetParam(param);
+  sequence_pool.Run();
+
+  std::vector<float> ref_results = {
+      39.6, 40.7, 41.8, 42.9, 44, 45.1, 46.2, 47.3};
+  for (int i = 0; i < out.dims().production(); i++) {
+    EXPECT_NEAR(out_data[i], ref_results[i], 1e-3);
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(sequence_pool, kX86, kFloat, kNCHW, def);

lite/kernels/x86/shape_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/shape_compute.h"
+
+REGISTER_LITE_KERNEL(shape,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::ShapeCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/shape_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"
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class ShapeCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ShapeParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::ShapeParam>();
+    // auto& context = context_->As<X86Context>();
+    auto out_data = param.Out->mutable_data<int32_t>();
+    auto in_dims = param.X->dims();
+    for (int i = 0; i < in_dims.size(); ++i) {
+      out_data[i] = in_dims[i];
+    }
+  }
+
+  virtual ~ShapeCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/shape_compute_test.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/shape_compute.h"
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(shape_x86, retrive_op) {
+  auto shape =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>("shape");
+  ASSERT_FALSE(shape.empty());
+  ASSERT_TRUE(shape.front());
+}
+
+TEST(shape_x86, init) {
+  ShapeCompute<float> shape;
+  ASSERT_EQ(shape.precision(), PRECISION(kFloat));
+  ASSERT_EQ(shape.target(), TARGET(kX86));
+}
+
+TEST(shape_x86, run_test) {
+  lite::Tensor x, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 1, 3, 3};
+  x.Resize(lite::DDim(x_shape));
+
+  std::vector<int64_t> out_shape{4};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto out_data = out.mutable_data<int32_t>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = 1;
+  }
+
+  ShapeCompute<float> shape;
+  operators::ShapeParam param;
+  param.X = &x;
+  param.Out = &out;
+
+  shape.SetParam(param);
+  shape.Run();
+
+  std::vector<float> ref_results = {1, 1, 3, 3};
+  for (int i = 0; i < out.dims().production(); i++) {
+    EXPECT_NEAR(out_data[i], ref_results[i], 1e-3);
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(shape, kX86, kFloat, kNCHW, def);

lite/operators/CMakeLists.txt

@@ -88,7 +88,7 @@ add_operator(greater_than  extra SRCS compare_op.cc DEPS ${op_DEPS})
 add_operator(greater_equal  extra SRCS compare_op.cc DEPS ${op_DEPS})
 add_operator(read_from_array_op extra SRCS read_from_array_op.cc DEPS ${op_DEPS})
 add_operator(beam_search_op extra SRCS beam_search_op.cc DEPS ${op_DEPS})
-add_operator(sequence_pool_op_lite extra SRCS sequence_pool_op.cc DEPS ${op_DEPS})
+add_operator(sequence_pool extra SRCS sequence_pool_op.cc DEPS ${op_DEPS})
 add_operator(lod_reset_op extra SRCS lod_reset_op.cc DEPS ${op_DEPS})
 add_operator(is_empty extra SRCS is_empty_op.cc DEPS ${op_DEPS})
 add_operator(slice_op_lite extra SRCS slice_op.cc DEPS ${op_DEPS})

lite/operators/op_params.h

@@ -666,7 +666,10 @@ struct BeamSearchParam {
 struct SequencePoolParam {
   const lite::Tensor* X{};
   lite::Tensor* Out{};
-  std::string pool_type;
+  std::string pool_type{"AVERAGE"};
+#ifdef LITE_WITH_X86
+  float pad_value{0.0};
+#endif
 };
 
 struct SequenceExpandParam {

lite/tools/ci_build.sh

@@ -151,7 +151,7 @@ function build_opencl {
 # This method is only called in CI.
 function cmake_x86_for_CI {
     prepare_workspace # fake an empty __generated_code__.cc to pass cmake.
-    cmake ..  -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DLITE_WITH_X86=ON ${common_flags} -DLITE_WITH_PROFILE=ON -DWITH_MKL=OFF \
+    cmake ..  -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DLITE_WITH_X86=ON ${common_flags} -DLITE_WITH_PROFILE=ON -DWITH_MKL=ON \
         -DLITE_BUILD_EXTRA=ON \
 
     # Compile and execute the gen_code related test, so it will generate some code, and make the compilation reasonable.
@@ -219,7 +219,7 @@ function test_server {
 function build_test_server {
     mkdir -p ./build
     cd ./build
-    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/paddle/build/third_party/install/mklml/lib"
+    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/third_party/install/mklml/lib"
     cmake_x86_for_CI
     build
 

lite/tools/cmake_tools/parse_kernel_registry.py

@@ -71,6 +71,7 @@ for line in lines:
             alias = fields[-1]
             key = "USE_LITE_KERNEL(%s, %s, %s, %s, %s);" % (
                 op, target, precision, layout, alias)
+            if "_grad" in key: continue
             out_lines.append(key)