Merge branch 'add-kernel-unitest' into 'incubate/lite'

add GoogleNet realted kernels unitest

See merge request inference/paddlelite!27

paddle/fluid/lite/kernels/x86/CMakeLists.txt

@@ -18,6 +18,18 @@ cc_library(concat_compute_x86 SRCS concat_compute.cc DEPS ${lite_kernel_deps} )
 cc_library(conv_compute_x86 SRCS conv_compute.cc DEPS ${lite_kernel_deps} blas im2col vol2col)
 cc_library(pool_compute_x86 SRCS pool_compute.cc DEPS ${lite_kernel_deps} pooling)
 
+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)
+
+
 set(x86_kernels
     activation_compute_x86
     elementwise_compute_x86

paddle/fluid/lite/kernels/x86/concat_compute.cc

@@ -12,88 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/types.h"
-#include "paddle/fluid/operators/strided_memcpy.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-class ConcatCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ConcatParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    int64_t axis = static_cast<int64_t>(param.axis);
-    auto out = param.output;
-
-    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;
-      }
-    }
-  }
-
-  virtual ~ConcatCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/concat_compute.h"
 
 REGISTER_LITE_KERNEL(concat, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::ConcatCompute<float>, def)

paddle/fluid/lite/kernels/x86/concat_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 <Eigen/Core>
+#include <vector>
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/types.h"
+#include "paddle/fluid/operators/strided_memcpy.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class ConcatCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ConcatParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    int64_t axis = static_cast<int64_t>(param.axis);
+    auto out = param.output;
+
+    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;
+      }
+    }
+  }
+
+  virtual ~ConcatCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/concat_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 "paddle/fluid/lite/kernels/x86/concat_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(concat_x86, retrive_op) {
+  auto concat =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "concat");
+  ASSERT_FALSE(concat.empty());
+  ASSERT_TRUE(concat.front());
+}
+
+TEST(concat_x86, init) {
+  ConcatCompute<float> concat;
+  ASSERT_EQ(concat.precision(), PRECISION(kFloat));
+  ASSERT_EQ(concat.target(), TARGET(kX86));
+}
+
+TEST(concat_x86, run_test) {
+  lite::Tensor x1, x2, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x1_shape{batch_size, 1, 3, 3};
+  x1.Resize(lite::DDim(x1_shape));
+  std::vector<int64_t> x2_shape{batch_size, 1, 3, 3};
+  x2.Resize(lite::DDim(x2_shape));
+
+  std::vector<lite::Tensor*> x = {&x1, &x2};
+
+  std::vector<int64_t> out_shape{batch_size, 2, 3, 3};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x1_data = x1.mutable_data<float>();
+  auto x2_data = x2.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x1.dims().production(); i++) {
+    x1_data[i] = 1;
+    x2_data[i] = 2;
+  }
+
+  ConcatCompute<float> concat;
+  operators::ConcatParam param;
+  param.x = x;
+  param.output = &out;
+  param.axis = 1;
+
+  concat.SetParam(param);
+  concat.Run();
+
+  std::cout << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    std::cout << out_data[i] << " ";
+  }
+  std::cout << std::endl;
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(concat, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/conv_compute.cc

@@ -12,144 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include <string>
-#include <vector>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/types.h"
-#include "paddle/fluid/lite/operators/conv_op.h"
-#include "paddle/fluid/operators/math/blas.h"
-#include "paddle/fluid/operators/math/depthwise_conv.h"
-#include "paddle/fluid/operators/math/im2col.h"
-#include "paddle/fluid/operators/math/vol2col.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-inline bool IsExpand(const std::vector<int64_t>& filter_dim,
-                     const std::vector<int>& strides,
-                     const std::vector<int>& paddings,
-                     const std::vector<int>& dilations) {
-  bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
-  for (size_t j = 0; j < strides.size(); ++j) {
-    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
-    strides_1 = strides_1 && (strides[j] == 1);
-    padding_0 = padding_0 && (paddings[j] == 0);
-    dilation_1 = dilation_1 && (dilations[j] == 1);
-  }
-  return !(filter_1 && strides_1 && padding_0 && dilation_1);
-}
-
-template <typename T>
-class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ConvParam;
-  void Run() override {
-    auto& param = *param_.get_mutable<operators::ConvParam>();
-    lite::Tensor filter = *param.filter;
-    param.output->template mutable_data<T>();
-
-    const int batch_size = static_cast<int>(param.x->dims()[0]);
-
-    std::vector<int64_t> filter_shape_vec(filter.dims().Vectorize());
-    std::vector<int64_t> output_shape_vec(param.output->dims().Vectorize());
-
-    size_t data_dim = filter_shape_vec.size() - 2;
-    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
-    col_shape_vec[0] = param.x->dims()[1] / param.groups;
-    for (size_t j = 0; j < data_dim; ++j) {
-      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
-      col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
-    }
-    lite::DDim col_shape(col_shape_vec);
-    lite::DDim col_matrix_shape = col_shape.Flattern2D(data_dim + 1);
-    bool is_expand = IsExpand(filter_shape_vec, param.strides, param.paddings,
-                              param.dilations);
-
-    lite::Tensor col;
-    lite::Tensor col_matrix;
-    if (is_expand) {
-      col.Resize(col_shape);
-      col.mutable_data<T>();
-      col_matrix.ShareDataWith(col);
-      col_matrix.Resize(col_matrix_shape);
-    }
-    lite::DDim input_shape = param.x->dims().Slice(1, param.x->dims().size());
-
-    lite::DDim filter_matrix_shape(std::vector<int64_t>{
-        filter.dims()[0], filter.dims().production() / filter.dims()[0]});
-    filter.Resize(filter_matrix_shape);
-
-    lite::DDim output_matrix_shape(std::vector<int64_t>{
-        param.output->dims()[1],
-        param.output->dims().production() /
-            (param.output->dims()[0] * param.output->dims()[1])});
-
-    int in_step = static_cast<int>(param.x->dims()[1]) / param.groups;
-    int out_step = static_cast<int>(param.output->dims()[1]) / param.groups;
-
-    paddle::operators::math::Vol2ColFunctor<platform::CPUDeviceContext, T>
-        vol2col;
-    paddle::operators::math::Im2ColFunctor<
-        paddle::operators::math::ColFormat::kCFO, platform::CPUDeviceContext, T>
-        im2col;
-    auto blas = paddle::operators::math::GetBlas<platform::CPUDeviceContext, T>(
-        platform::CPUDeviceContext());
-    for (int i = 0; i < batch_size; i++) {
-      lite::Tensor in_batch;
-      in_batch.ShareDataWith(
-          param.x->raw_tensor().Slice(i, i + 1).Resize(input_shape.data()));
-      lite::Tensor out_batch;
-      out_batch.ShareDataWith(param.output->raw_tensor().Slice(i, i + 1).Resize(
-          output_matrix_shape.data()));
-
-      for (int g = 0; g < param.groups; g++) {
-        lite::Tensor in_slice;
-        in_slice.ShareDataWith(
-            in_batch.raw_tensor().Slice(g * in_step, (g + 1) * in_step));
-
-        if (!is_expand) {
-          col.ShareDataWith(in_slice);
-          col_matrix.ShareDataWith(col);
-          col_matrix.Resize(col_matrix_shape);
-        } else if (data_dim == 2U) {
-          // im2col
-          im2col(platform::CPUDeviceContext(), in_slice.raw_tensor(),
-                 param.dilations, param.strides,
-                 std::vector<int>{param.paddings[0], param.paddings[1],
-                                  param.paddings[0], param.paddings[1]},
-                 &(col.raw_tensor()));
-        } else if (data_dim == 3U) {
-          // vol2col
-          vol2col(platform::CPUDeviceContext(), in_slice.raw_tensor(),
-                  param.dilations, param.strides, param.paddings,
-                  &(col.raw_tensor()));
-        }
-
-        // gemm
-        lite::Tensor out_slice;
-        out_slice.ShareDataWith(
-            out_batch.raw_tensor().Slice(g * out_step, (g + 1) * out_step));
-        lite::Tensor filter_slice;
-        filter_slice.ShareDataWith(
-            filter.raw_tensor().Slice(g * out_step, (g + 1) * out_step));
-        blas.MatMul(filter_slice.raw_tensor(), false, col_matrix.raw_tensor(),
-                    false, T(1.0), &(out_slice.raw_tensor()), T(0.0));
-      }
-    }
-  }
-
-  virtual ~Conv2dCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/conv_compute.h"
 
 REGISTER_LITE_KERNEL(conv2d, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::Conv2dCompute<float>, def)

paddle/fluid/lite/kernels/x86/conv_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 <Eigen/Core>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/types.h"
+#include "paddle/fluid/lite/operators/conv_op.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/depthwise_conv.h"
+#include "paddle/fluid/operators/math/im2col.h"
+#include "paddle/fluid/operators/math/vol2col.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+inline bool IsExpand(const std::vector<int64_t>& filter_dim,
+                     const std::vector<int>& strides,
+                     const std::vector<int>& paddings,
+                     const std::vector<int>& dilations) {
+  bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
+  for (size_t j = 0; j < strides.size(); ++j) {
+    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
+    strides_1 = strides_1 && (strides[j] == 1);
+    padding_0 = padding_0 && (paddings[j] == 0);
+    dilation_1 = dilation_1 && (dilations[j] == 1);
+  }
+  return !(filter_1 && strides_1 && padding_0 && dilation_1);
+}
+
+template <typename T>
+class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ConvParam;
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::ConvParam>();
+    lite::Tensor filter = *param.filter;
+    param.output->template mutable_data<T>();
+
+    const int batch_size = static_cast<int>(param.x->dims()[0]);
+
+    std::vector<int64_t> filter_shape_vec(filter.dims().Vectorize());
+    std::vector<int64_t> output_shape_vec(param.output->dims().Vectorize());
+
+    size_t data_dim = filter_shape_vec.size() - 2;
+    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+    col_shape_vec[0] = param.x->dims()[1] / param.groups;
+    for (size_t j = 0; j < data_dim; ++j) {
+      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+      col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
+    }
+    lite::DDim col_shape(col_shape_vec);
+    lite::DDim col_matrix_shape = col_shape.Flattern2D(data_dim + 1);
+    bool is_expand = IsExpand(filter_shape_vec, param.strides, param.paddings,
+                              param.dilations);
+
+    lite::Tensor col;
+    lite::Tensor col_matrix;
+    if (is_expand) {
+      col.Resize(col_shape);
+      col.mutable_data<T>();
+      col_matrix.ShareDataWith(col);
+      col_matrix.Resize(col_matrix_shape);
+    }
+    lite::DDim input_shape = param.x->dims().Slice(1, param.x->dims().size());
+
+    lite::DDim filter_matrix_shape(std::vector<int64_t>{
+        filter.dims()[0], filter.dims().production() / filter.dims()[0]});
+    filter.Resize(filter_matrix_shape);
+
+    lite::DDim output_matrix_shape(std::vector<int64_t>{
+        param.output->dims()[1],
+        param.output->dims().production() /
+            (param.output->dims()[0] * param.output->dims()[1])});
+
+    int in_step = static_cast<int>(param.x->dims()[1]) / param.groups;
+    int out_step = static_cast<int>(param.output->dims()[1]) / param.groups;
+
+    paddle::operators::math::Vol2ColFunctor<platform::CPUDeviceContext, T>
+        vol2col;
+    paddle::operators::math::Im2ColFunctor<
+        paddle::operators::math::ColFormat::kCFO, platform::CPUDeviceContext, T>
+        im2col;
+    auto blas = paddle::operators::math::GetBlas<platform::CPUDeviceContext, T>(
+        platform::CPUDeviceContext());
+    for (int i = 0; i < batch_size; i++) {
+      lite::Tensor in_batch;
+      in_batch.ShareDataWith(
+          param.x->raw_tensor().Slice(i, i + 1).Resize(input_shape.data()));
+      lite::Tensor out_batch;
+      out_batch.ShareDataWith(param.output->raw_tensor().Slice(i, i + 1).Resize(
+          output_matrix_shape.data()));
+
+      for (int g = 0; g < param.groups; g++) {
+        lite::Tensor in_slice;
+        in_slice.ShareDataWith(
+            in_batch.raw_tensor().Slice(g * in_step, (g + 1) * in_step));
+
+        if (!is_expand) {
+          col.ShareDataWith(in_slice);
+          col_matrix.ShareDataWith(col);
+          col_matrix.Resize(col_matrix_shape);
+        } else if (data_dim == 2U) {
+          // im2col
+          im2col(platform::CPUDeviceContext(), in_slice.raw_tensor(),
+                 param.dilations, param.strides,
+                 std::vector<int>{param.paddings[0], param.paddings[1],
+                                  param.paddings[0], param.paddings[1]},
+                 &(col.raw_tensor()));
+        } else if (data_dim == 3U) {
+          // vol2col
+          vol2col(platform::CPUDeviceContext(), in_slice.raw_tensor(),
+                  param.dilations, param.strides, param.paddings,
+                  &(col.raw_tensor()));
+        }
+
+        // gemm
+        lite::Tensor out_slice;
+        out_slice.ShareDataWith(
+            out_batch.raw_tensor().Slice(g * out_step, (g + 1) * out_step));
+        lite::Tensor filter_slice;
+        filter_slice.ShareDataWith(
+            filter.raw_tensor().Slice(g * out_step, (g + 1) * out_step));
+        blas.MatMul(filter_slice.raw_tensor(), false, col_matrix.raw_tensor(),
+                    false, T(1.0), &(out_slice.raw_tensor()), T(0.0));
+      }
+    }
+  }
+
+  virtual ~Conv2dCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/conv_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 "paddle/fluid/lite/kernels/x86/conv_compute.h"
+#include <gtest/gtest.h>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(conv_x86, retrive_op) {
+  auto conv2d =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "conv2d");
+  ASSERT_FALSE(conv2d.empty());
+  ASSERT_TRUE(conv2d.front());
+}
+
+TEST(conv2d_x86, init) {
+  Conv2dCompute<float> conv2d;
+  ASSERT_EQ(conv2d.precision(), PRECISION(kFloat));
+  ASSERT_EQ(conv2d.target(), TARGET(kX86));
+}
+
+TEST(conv2d_x86, run_test) {
+  lite::Tensor x, filter, b, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 3, 3};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> filter_shape{1, 3, 3, 3};
+  filter.Resize(lite::DDim(filter_shape));
+  std::vector<int64_t> b_shape{1, 3, 1, 1};
+  b.Resize(lite::DDim(b_shape));
+  std::vector<int64_t> out_shape{batch_size, 1, 1, 1};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto filter_data = filter.mutable_data<float>();
+  auto b_data = b.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = 1;
+  }
+  for (int64_t i = 0; i < filter.dims().production(); i++) {
+    filter_data[i] = 1;
+  }
+  for (int64_t i = 0; i < b.dims().production(); i++) {
+    b_data[i] = 0;
+  }
+
+  Conv2dCompute<float> conv2d;
+  operators::ConvParam param;
+
+  param.x = &x;
+  param.filter = &filter;
+  param.bias = &b;
+  param.output = &out;
+  param.strides = {1, 1};
+  param.paddings = {0, 0};
+  param.groups = 1;
+  param.dilations = {1, 1};
+
+  conv2d.SetParam(param);
+  conv2d.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i] << " ";
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(conv2d, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/dropout_compute.cc

@@ -12,72 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <random>
-#include <string>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
-
-template <typename T>
-class DropoutCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::DropoutParam;
-  void Run() override {
-    auto& param = *param_.get_mutable<operators::DropoutParam>();
-    const auto* x_data = param.x->data<T>();
-    auto* out_data = param.output->template mutable_data<T>();
-    if (!param.is_test) {
-      auto* mask_data = param.mask->template mutable_data<T>();
-      std::random_device rnd;
-      std::minstd_rand engine;
-      int seed = param.fix_seed ? param.seed : rnd();
-      engine.seed(seed);
-      std::uniform_real_distribution<float> dist(0, 1);
-
-      size_t size = framework::product(param.mask->dims().data());
-      for (size_t i = 0; i < size; ++i) {
-        if (dist(engine) < param.dropout_prob) {
-          mask_data[i] = 0;
-          out_data[i] = 0;
-        } else {
-          if (param.dropout_implementation == "upscale_in_train") {
-            mask_data[i] = 1.0f / static_cast<T>(1.0f - param.dropout_prob);
-            out_data[i] = x_data[i] / static_cast<T>(1.0f - param.dropout_prob);
-          } else {
-            mask_data[i] = 1;
-            out_data[i] = x_data[i];
-          }
-        }
-      }
-    } else {
-      auto X = EigenMatrix<T>::Reshape(param.x->raw_tensor(), 1);
-      auto Y = EigenMatrix<T>::Reshape(param.output->raw_tensor(), 1);
-      auto& place = *platform::CPUDeviceContext().eigen_device();
-      if (param.dropout_implementation == "upscale_in_train") {
-        Y.device(place) = X;
-      } else {
-        Y.device(place) = X * static_cast<T>(1.0f - param.dropout_prob);
-      }
-    }
-  }
-
-  virtual ~DropoutCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/dropout_compute.h"
 
 REGISTER_LITE_KERNEL(dropout, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::DropoutCompute<float>, def)

paddle/fluid/lite/kernels/x86/dropout_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 <random>
+#include <string>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename T>
+class DropoutCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::DropoutParam;
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::DropoutParam>();
+    const auto* x_data = param.x->data<T>();
+    auto* out_data = param.output->template mutable_data<T>();
+    if (!param.is_test) {
+      auto* mask_data = param.mask->template mutable_data<T>();
+      std::random_device rnd;
+      std::minstd_rand engine;
+      int seed = param.fix_seed ? param.seed : rnd();
+      engine.seed(seed);
+      std::uniform_real_distribution<float> dist(0, 1);
+
+      size_t size = framework::product(param.mask->dims().data());
+      for (size_t i = 0; i < size; ++i) {
+        if (dist(engine) < param.dropout_prob) {
+          mask_data[i] = 0;
+          out_data[i] = 0;
+        } else {
+          if (param.dropout_implementation == "upscale_in_train") {
+            mask_data[i] = 1.0f / static_cast<T>(1.0f - param.dropout_prob);
+            out_data[i] = x_data[i] / static_cast<T>(1.0f - param.dropout_prob);
+          } else {
+            mask_data[i] = 1;
+            out_data[i] = x_data[i];
+          }
+        }
+      }
+    } else {
+      auto X = EigenMatrix<T>::Reshape(param.x->raw_tensor(), 1);
+      auto Y = EigenMatrix<T>::Reshape(param.output->raw_tensor(), 1);
+      auto& place = *platform::CPUDeviceContext().eigen_device();
+      if (param.dropout_implementation == "upscale_in_train") {
+        Y.device(place) = X;
+      } else {
+        Y.device(place) = X * static_cast<T>(1.0f - param.dropout_prob);
+      }
+    }
+  }
+
+  virtual ~DropoutCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/dropout_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 "paddle/fluid/lite/kernels/x86/dropout_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(dropout_x86, retrive_op) {
+  auto dropout =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "dropout");
+  ASSERT_FALSE(dropout.empty());
+  ASSERT_TRUE(dropout.front());
+}
+
+TEST(dropout_x86, init) {
+  DropoutCompute<float> dropout;
+  ASSERT_EQ(dropout.precision(), PRECISION(kFloat));
+  ASSERT_EQ(dropout.target(), TARGET(kX86));
+}
+
+TEST(dropout_x86, run_test) {
+  lite::Tensor x, y, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 2, 2};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 2, 2};
+  out.Resize(lite::DDim(out_shape));
+
+  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] = static_cast<float>(i);
+  }
+  // DropoutCompute dropout;
+  DropoutCompute<float> dropout;
+  operators::DropoutParam param;
+
+  param.x = &x;
+  param.dropout_prob = 0.25;
+  param.is_test = true;
+  param.fix_seed = true;
+  param.output = &out;
+
+  dropout.SetParam(param);
+  dropout.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(dropout, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/elementwise_compute.cc

@@ -12,113 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/operators/activation_op.h"
-#include "paddle/fluid/operators/elementwise/elementwise_op.h"
-#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/lite/kernels/x86/elementwise_compute.h"
 
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-struct SubFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
-};
-
-template <typename T>
-struct AddFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
-};
-
-template <typename T>
-class ElementwiseSubCompute
-    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ElementwiseParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    auto& context = ctx_->As<X86Context>();
-    CHECK(context.x86_device_context());
-
-    param.Out->template mutable_data<T>();
-    paddle::operators::ElementwiseComputeEx<SubFunctor<T>,
-                                            platform::CPUDeviceContext, T>(
-        *context.x86_execution_context(), &param.X->raw_tensor(),
-        &param.Y->raw_tensor(), param.axis, SubFunctor<T>(),
-        &param.Out->raw_tensor());
-  }
-
-  virtual ~ElementwiseSubCompute() = default;
-};
-
-template <typename T>
-struct SubGradDX {
-  T operator()(T x, T y, T out, T dout) const { return dout; }
-};
-
-template <typename T>
-struct SubGradDY {
-  T operator()(T x, T y, T out, T dout) const { return -dout; }
-};
-
-template <typename T>
-class ElementwiseSubGradCompute
-    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ElementwiseGradParam;
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    auto& context = ctx_->As<X86Context>();
-    CHECK(context.x86_device_context());
-
-    param.X_grad->template mutable_data<T>();
-    param.Y_grad->template mutable_data<T>();
-    // skip out, x, y
-    auto dout = param.Out_grad->raw_tensor();
-    auto dx = param.X_grad->raw_tensor();
-    auto dy = param.Y_grad->raw_tensor();
-    auto& skip = dout;
-    paddle::operators::ElemwiseExplicitGradCompute<
-        platform::CPUDeviceContext, T, SubGradDX<T>, SubGradDY<T>>(
-        *context.x86_execution_context(), skip, skip, skip, dout, param.axis,
-        &dx, &dy, SubGradDX<T>(), SubGradDY<T>());
-  }
-
-  virtual ~ElementwiseSubGradCompute() = default;
-};
-
-template <typename T>
-class ElementwiseAddCompute
-    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ElementwiseParam;
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    auto& context = ctx_->As<X86Context>();
-    CHECK(context.x86_device_context());
-    param.Out->template mutable_data<T>();
-    paddle::operators::ElementwiseComputeEx<AddFunctor<T>,
-                                            platform::CPUDeviceContext, T>(
-        *context.x86_execution_context(), &param.X->raw_tensor(),
-        &param.Y->raw_tensor(), param.axis, AddFunctor<T>(),
-        &param.Out->raw_tensor());
-  }
-
-  virtual ~ElementwiseAddCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
-
-// float
 REGISTER_LITE_KERNEL(elementwise_sub, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::ElementwiseSubCompute<float>,
                      def)

paddle/fluid/lite/kernels/x86/elementwise_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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/operators/activation_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+struct SubFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
+};
+
+template <typename T>
+struct AddFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
+};
+
+template <typename T>
+class ElementwiseSubCompute
+    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ElementwiseParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto& context = ctx_->As<X86Context>();
+    CHECK(context.x86_device_context());
+
+    param.Out->template mutable_data<T>();
+    paddle::operators::ElementwiseComputeEx<SubFunctor<T>,
+                                            platform::CPUDeviceContext, T>(
+        *context.x86_execution_context(), &param.X->raw_tensor(),
+        &param.Y->raw_tensor(), param.axis, SubFunctor<T>(),
+        &param.Out->raw_tensor());
+  }
+
+  virtual ~ElementwiseSubCompute() = default;
+};
+
+template <typename T>
+struct SubGradDX {
+  T operator()(T x, T y, T out, T dout) const { return dout; }
+};
+
+template <typename T>
+struct SubGradDY {
+  T operator()(T x, T y, T out, T dout) const { return -dout; }
+};
+
+template <typename T>
+class ElementwiseSubGradCompute
+    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ElementwiseGradParam;
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto& context = ctx_->As<X86Context>();
+    CHECK(context.x86_device_context());
+
+    param.X_grad->template mutable_data<T>();
+    param.Y_grad->template mutable_data<T>();
+    // skip out, x, y
+    auto dout = param.Out_grad->raw_tensor();
+    auto dx = param.X_grad->raw_tensor();
+    auto dy = param.Y_grad->raw_tensor();
+    auto& skip = dout;
+    paddle::operators::ElemwiseExplicitGradCompute<
+        platform::CPUDeviceContext, T, SubGradDX<T>, SubGradDY<T>>(
+        *context.x86_execution_context(), skip, skip, skip, dout, param.axis,
+        &dx, &dy, SubGradDX<T>(), SubGradDY<T>());
+  }
+
+  virtual ~ElementwiseSubGradCompute() = default;
+};
+
+template <typename T>
+class ElementwiseAddCompute
+    : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ElementwiseParam;
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto& context = ctx_->As<X86Context>();
+    CHECK(context.x86_device_context());
+    param.Out->template mutable_data<T>();
+    paddle::operators::ElementwiseComputeEx<AddFunctor<T>,
+                                            platform::CPUDeviceContext, T>(
+        *context.x86_execution_context(), &param.X->raw_tensor(),
+        &param.Y->raw_tensor(), param.axis, AddFunctor<T>(),
+        &param.Out->raw_tensor());
+  }
+
+  virtual ~ElementwiseAddCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/elementwise_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 "paddle/fluid/lite/kernels/x86/elementwise_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(elementwise_add_x86, retrive_op) {
+  auto elementwise_add =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "elementwise_add");
+  ASSERT_FALSE(elementwise_add.empty());
+  ASSERT_TRUE(elementwise_add.front());
+}
+
+TEST(elementwise_add_x86, init) {
+  ElementwiseAddCompute<float> elementwise_add;
+  ASSERT_EQ(elementwise_add.precision(), PRECISION(kFloat));
+  ASSERT_EQ(elementwise_add.target(), TARGET(kX86));
+}
+
+TEST(elementwise_add_x86, run_test) {
+  lite::Tensor x, y, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 2, 2};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> y_shape{batch_size, 3, 2, 2};
+  y.Resize(lite::DDim(y_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 2, 2};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto y_data = y.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = 1;
+  }
+  for (int64_t i = 0; i < y.dims().production(); i++) {
+    y_data[i] = 2;
+  }
+
+  // ElementwiseAddCompute elementwise_add;
+  ElementwiseAddCompute<float> elementwise_add;
+  operators::ElementwiseParam param;
+
+  param.X = &x;
+  param.Y = &y;
+  param.Out = &out;
+
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  elementwise_add.SetParam(param);
+  elementwise_add.SetContext(std::move(ctx));
+  elementwise_add.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(elementwise_add, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/fc_compute.cc

@@ -12,89 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_lite.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/type_system.h"
-#include "paddle/fluid/lite/operators/fc_op.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-void fc_compute_eigen(const T* x, int x_h, int x_w,  //
-                      const T* w, int w_h, int w_w,  //
-                      const T* b,                    //
-                      T* out) {
-  using matrix_t =
-      Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
-
-  Eigen::Map<const matrix_t> X(x, x_h, x_w);
-  Eigen::Map<const matrix_t> W(w, w_h, w_w);
-  Eigen::Map<matrix_t> Out(out, x_h, w_w);
-
-  Out = X * W;
-
-  if (b) {
-    Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>> B(b, w_w);
-    Out = Out.array().rowwise() + B.transpose().array();
-  }
-}
-
-template <typename T>
-void fc_compute_naive(const T* x, int x_h, int x_w,  //
-                      const T* w, int w_h, int w_w,  //
-                      const T* b,                    //
-                      T* out) {
-  CHECK_EQ(x_w, w_h);
-  // out shape: (x_h, w_w)
-  memset(out, 0, x_h * w_w * sizeof(T));
-  for (int i = 0; i < x_h; i++) {
-    for (int j = 0; j < w_w; j++) {
-      T tmp = static_cast<T>(0);
-      for (int k = 0; k < x_w; k++) {
-        tmp += x[i * x_w + k] * w[k * w_w + j];
-      }
-      out[i * w_w + j] = tmp + b[j];
-    }
-  }
-}
-
-template <typename T>
-class FcCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::FcParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    CHECK_GE(param.input->dims().size(), 2UL);
-    CHECK_EQ(param.output->dims().size(), 2UL);
-
-    fc_compute_eigen(
-        param.input->data<T>(),  // x
-        param.input->dims().Slice(0, param.in_num_col_dims).production(),
-        param.input->dims()
-            .Slice(param.in_num_col_dims, param.input->dims().size())
-            .production(),
-        param.w->data<T>(),     // w
-        param.w->dims()[0],     // w_h
-        param.w->dims()[1],     // w_w
-        param.bias->data<T>(),  // b
-        param.output->mutable_data<T>());
-  }
-
-  virtual ~FcCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/fc_compute.h"
 
 REGISTER_LITE_KERNEL(fc, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::FcCompute<float>, def)

paddle/fluid/lite/kernels/x86/fc_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 <Eigen/Core>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/type_system.h"
+#include "paddle/fluid/lite/operators/fc_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+void fc_compute_eigen(const T* x, int x_h, int x_w,  //
+                      const T* w, int w_h, int w_w,  //
+                      const T* b,                    //
+                      T* out) {
+  using matrix_t =
+      Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+
+  Eigen::Map<const matrix_t> X(x, x_h, x_w);
+  Eigen::Map<const matrix_t> W(w, w_h, w_w);
+  Eigen::Map<matrix_t> Out(out, x_h, w_w);
+
+  Out = X * W;
+
+  if (b) {
+    Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>> B(b, w_w);
+    Out = Out.array().rowwise() + B.transpose().array();
+  }
+}
+
+template <typename T>
+void fc_compute_naive(const T* x, int x_h, int x_w,  //
+                      const T* w, int w_h, int w_w,  //
+                      const T* b,                    //
+                      T* out) {
+  CHECK_EQ(x_w, w_h);
+  // out shape: (x_h, w_w)
+  memset(out, 0, x_h * w_w * sizeof(T));
+  for (int i = 0; i < x_h; i++) {
+    for (int j = 0; j < w_w; j++) {
+      T tmp = static_cast<T>(0);
+      for (int k = 0; k < x_w; k++) {
+        tmp += x[i * x_w + k] * w[k * w_w + j];
+      }
+      out[i * w_w + j] = tmp + b[j];
+    }
+  }
+}
+
+template <typename T>
+class FcCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::FcParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    CHECK_GE(param.input->dims().size(), 2UL);
+    CHECK_EQ(param.output->dims().size(), 2UL);
+
+    fc_compute_eigen(
+        param.input->data<T>(),  // x
+        param.input->dims().Slice(0, param.in_num_col_dims).production(),
+        param.input->dims()
+            .Slice(param.in_num_col_dims, param.input->dims().size())
+            .production(),
+        param.w->data<T>(),     // w
+        param.w->dims()[0],     // w_h
+        param.w->dims()[1],     // w_w
+        param.bias->data<T>(),  // b
+        param.output->mutable_data<T>());
+  }
+
+  virtual ~FcCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/fc_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 "paddle/fluid/lite/kernels/x86/fc_compute.h"
+#include <gtest/gtest.h>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(fc_x86, retrive_op) {
+  auto fc =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>("fc");
+  ASSERT_FALSE(fc.empty());
+  ASSERT_TRUE(fc.front());
+}
+
+TEST(fc_x86, init) {
+  FcCompute<float> fc;
+  ASSERT_EQ(fc.precision(), PRECISION(kFloat));
+  ASSERT_EQ(fc.target(), TARGET(kX86));
+}
+
+TEST(fc_x86, run_test) {
+  lite::Tensor x, w, b, out;
+  constexpr int batch_size = 2;
+  std::vector<int64_t> x_shape{batch_size, 3};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> w_shape{3, 4};
+  w.Resize(lite::DDim(w_shape));
+  std::vector<int64_t> b_shape{1, 4};
+  b.Resize(lite::DDim(b_shape));
+  std::vector<int64_t> out_shape{1, 4};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto w_data = w.mutable_data<float>();
+  auto b_data = b.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = static_cast<float>(i);
+  }
+  for (int64_t i = 0; i < w.dims().production(); i++) {
+    w_data[i] = static_cast<float>(i);
+  }
+  for (int64_t i = 0; i < b.dims().production(); i++) {
+    b_data[i] = static_cast<float>(i);
+  }
+
+  /* lite::x86::math::fc_compute_eigen(x_data, batch_size, 3,  //
+                                     w_data, 3, 4,           //
+                                     b_data, ref_data); */
+
+  // FcCompute fc;
+  FcCompute<float> fc;
+  operators::FcParam param;
+
+  param.in_num_col_dims = 1;
+  param.input = &x;
+  param.w = &w;
+  param.bias = &b;
+  param.output = &out;
+  param.in_mat_dims = x.dims();
+
+  // std::unique_ptr<KernelContext> ctx(new KernelContext);
+  // ctx->As<X86Context>();
+  fc.SetParam(param);
+  // fc.SetContext(std::move(ctx));
+  fc.Run();
+
+  VLOG(3) << "output vs ref";
+  for (int i = 0; i < out.dims().production(); i++) {
+    VLOG(3) << out_data[i];
+  }
+
+  /* for (int i = 0; i < out.dims().product(); ++i) {
+     EXPECT_NEAR(out_data[i], ref_data[i], 1e-5);
+   }*/
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(fc, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/mul_compute.cc

@@ -12,122 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/types.h"
-#include "paddle/fluid/operators/math/blas.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-using Tensor = framework::Tensor;
-
-template <typename T>
-class MulCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::MulParam;
-
-  void Run() override {
-    auto& context = ctx_->As<X86Context>();
-    auto& param = *param_.get_mutable<operators::MulParam>();
-    CHECK(context.x86_device_context());
-
-    param.output->template mutable_data<T>();
-
-    auto* x = &param.x->raw_tensor();
-    auto* y = &param.y->raw_tensor();
-
-    const Tensor x_matrix = x->dims().size() > 2 ? framework::ReshapeToMatrix(
-                                                       *x, param.x_num_col_dims)
-                                                 : *x;
-    const Tensor y_matrix = y->dims().size() > 2 ? framework::ReshapeToMatrix(
-                                                       *y, param.y_num_col_dims)
-                                                 : *y;
-
-    auto* z = &param.output->raw_tensor();
-    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());
-
-    blas.MatMul(x_matrix, y_matrix, z);
-    if (z_dim.size() != 2) {
-      z->Resize(z_dim);
-    }
-  }
-
-  virtual ~MulCompute() = default;
-};
-
-template <typename T>
-class MulGradCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  void Run() override {
-    auto& context = ctx_->As<X86Context>();
-    auto& param = *param_.get_mutable<operators::MulGradParam>();
-    CHECK(context.x86_device_context());
-
-    auto* x = &param.x->raw_tensor();
-    auto* y = &param.y->raw_tensor();
-    auto x_matrix = x->dims().size() > 2
-                        ? framework::ReshapeToMatrix(*x, param.x_num_col_dims)
-                        : static_cast<const Tensor&>(*x);
-    auto y_matrix = y->dims().size() > 2
-                        ? framework::ReshapeToMatrix(*y, param.y_num_col_dims)
-                        : static_cast<const Tensor&>(*y);
-    auto* dout = &param.output_grad->raw_tensor();
-
-    Tensor dout_mat;
-    dout_mat.ShareDataWith(*dout);
-    dout_mat.Resize(
-        {framework::flatten_to_2d(x->dims(), param.x_num_col_dims)[0],
-         framework::flatten_to_2d(y->dims(), param.y_num_col_dims)[1]});
-
-    auto* dx = &param.x_grad->raw_tensor();
-    auto* dy = &param.y_grad->raw_tensor();
-
-    if (dx != nullptr) {
-      dx->set_lod(x->lod());
-    }
-    if (dy != nullptr) {
-      dy->set_lod(y->lod());
-    }
-
-    auto blas = paddle::operators::math::GetBlas<platform::CPUDeviceContext, T>(
-        *context.x86_device_context());
-    if (dx) {
-      // dx->mutable_data<T>(context.x86_device_context->GetPlace());
-      param.x_grad->template mutable_data<T>();
-      Tensor dx_matrix = dx->dims().size() > 2 ? framework::ReshapeToMatrix(
-                                                     *dx, param.x_num_col_dims)
-                                               : *dx;
-
-      // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
-      blas.MatMul(dout_mat, false, y_matrix, true, &dx_matrix);
-    }
-    if (dy) {
-      // dy->yutable_data<T>(context.x86_device_context->GetPlace());
-      param.y_grad->template mutable_data<T>();
-      Tensor dy_matrix = dy->dims().size() > 2 ? framework::ReshapeToMatrix(
-                                                     *dy, param.y_num_col_dims)
-                                               : *dy;
-      // dy = x' * dout. dy K x N, dout : M x N, x : M x K
-      blas.MatMul(x_matrix, true, dout_mat, false, &dy_matrix);
-    }
-  }
-
-  virtual ~MulGradCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/mul_compute.h"
 
 REGISTER_LITE_KERNEL(mul, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::MulCompute<float>, def)

paddle/fluid/lite/kernels/x86/mul_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 "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/types.h"
+#include "paddle/fluid/operators/math/blas.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class MulCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::MulParam;
+
+  void Run() override {
+    auto& context = ctx_->As<X86Context>();
+    auto& param = *param_.get_mutable<operators::MulParam>();
+    CHECK(context.x86_device_context());
+
+    param.output->template mutable_data<T>();
+
+    auto* x = &param.x->raw_tensor();
+    auto* y = &param.y->raw_tensor();
+
+    const Tensor x_matrix = x->dims().size() > 2 ? framework::ReshapeToMatrix(
+                                                       *x, param.x_num_col_dims)
+                                                 : *x;
+    const Tensor y_matrix = y->dims().size() > 2 ? framework::ReshapeToMatrix(
+                                                       *y, param.y_num_col_dims)
+                                                 : *y;
+
+    auto* z = &param.output->raw_tensor();
+    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());
+
+    blas.MatMul(x_matrix, y_matrix, z);
+    if (z_dim.size() != 2) {
+      z->Resize(z_dim);
+    }
+  }
+
+  virtual ~MulCompute() = default;
+};
+
+template <typename T>
+class MulGradCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  void Run() override {
+    auto& context = ctx_->As<X86Context>();
+    auto& param = *param_.get_mutable<operators::MulGradParam>();
+    CHECK(context.x86_device_context());
+
+    auto* x = &param.x->raw_tensor();
+    auto* y = &param.y->raw_tensor();
+    auto x_matrix = x->dims().size() > 2
+                        ? framework::ReshapeToMatrix(*x, param.x_num_col_dims)
+                        : static_cast<const Tensor&>(*x);
+    auto y_matrix = y->dims().size() > 2
+                        ? framework::ReshapeToMatrix(*y, param.y_num_col_dims)
+                        : static_cast<const Tensor&>(*y);
+    auto* dout = &param.output_grad->raw_tensor();
+
+    Tensor dout_mat;
+    dout_mat.ShareDataWith(*dout);
+    dout_mat.Resize(
+        {framework::flatten_to_2d(x->dims(), param.x_num_col_dims)[0],
+         framework::flatten_to_2d(y->dims(), param.y_num_col_dims)[1]});
+
+    auto* dx = &param.x_grad->raw_tensor();
+    auto* dy = &param.y_grad->raw_tensor();
+
+    if (dx != nullptr) {
+      dx->set_lod(x->lod());
+    }
+    if (dy != nullptr) {
+      dy->set_lod(y->lod());
+    }
+
+    auto blas = paddle::operators::math::GetBlas<platform::CPUDeviceContext, T>(
+        *context.x86_device_context());
+    if (dx) {
+      // dx->mutable_data<T>(context.x86_device_context->GetPlace());
+      param.x_grad->template mutable_data<T>();
+      Tensor dx_matrix = dx->dims().size() > 2 ? framework::ReshapeToMatrix(
+                                                     *dx, param.x_num_col_dims)
+                                               : *dx;
+
+      // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
+      blas.MatMul(dout_mat, false, y_matrix, true, &dx_matrix);
+    }
+    if (dy) {
+      // dy->yutable_data<T>(context.x86_device_context->GetPlace());
+      param.y_grad->template mutable_data<T>();
+      Tensor dy_matrix = dy->dims().size() > 2 ? framework::ReshapeToMatrix(
+                                                     *dy, param.y_num_col_dims)
+                                               : *dy;
+      // dy = x' * dout. dy K x N, dout : M x N, x : M x K
+      blas.MatMul(x_matrix, true, dout_mat, false, &dy_matrix);
+    }
+  }
+
+  virtual ~MulGradCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/mul_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 "paddle/fluid/lite/kernels/x86/mul_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(mul_x86, retrive_op) {
+  auto mul =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>("mul");
+  ASSERT_FALSE(mul.empty());
+  ASSERT_TRUE(mul.front());
+}
+
+TEST(mul_x86, init) {
+  MulCompute<float> mul;
+  ASSERT_EQ(mul.precision(), PRECISION(kFloat));
+  ASSERT_EQ(mul.target(), TARGET(kX86));
+}
+
+TEST(mul_x86, run_test) {
+  lite::Tensor x, y, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> y_shape{3, 4};
+  y.Resize(lite::DDim(y_shape));
+  std::vector<int64_t> out_shape{batch_size, 4};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto y_data = y.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    x_data[i] = static_cast<float>(i);
+  }
+  for (int64_t i = 0; i < y.dims().production(); i++) {
+    y_data[i] = static_cast<float>(i);
+  }
+  // MulCompute mul;
+  MulCompute<float> mul;
+  operators::MulParam param;
+
+  param.x = &x;
+  param.y = &y;
+  param.output = &out;
+
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  mul.SetContext(std::move(ctx));
+  mul.SetParam(param);
+  mul.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(mul, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/pool_compute.cc

@@ -12,69 +12,10 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/types.h"
-#include "paddle/fluid/operators/math/math_function.h"
-#include "paddle/fluid/operators/math/pooling.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-class PoolCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::PoolParam;
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    if (param.global_pooling) {
-      for (size_t i = 0; i < param.ksize.size(); ++i) {
-        param.paddings[i] = 0;
-        param.ksize[i] = static_cast<int>(param.x->dims()[i + 2]);
-      }
-    }
-    switch (param.ksize.size()) {
-      case 2: {
-        if (param.pooling_type == "max") {
-          paddle::operators::math::Pool2dFunctor<
-              platform::CPUDeviceContext, paddle::operators::math::MaxPool<T>,
-              T>
-              pool2d_forward;
-          paddle::operators::math::MaxPool<T> pool_process;
-          pool2d_forward(platform::CPUDeviceContext(), param.x->raw_tensor(),
-                         param.ksize, param.strides, param.paddings,
-                         pool_process, true, false,
-                         &(param.output->raw_tensor()));
-        } else if (param.pooling_type == "avg") {
-          paddle::operators::math::Pool2dFunctor<
-              platform::CPUDeviceContext, paddle::operators::math::AvgPool<T>,
-              T>
-              pool2d_forward;
-          paddle::operators::math::AvgPool<T> pool_process;
-          pool2d_forward(platform::CPUDeviceContext(), param.x->raw_tensor(),
-                         param.ksize, param.strides, param.paddings,
-                         pool_process, param.exclusive, param.adaptive,
-                         &(param.output->raw_tensor()));
-        }
-      } break;
-      case 3: {
-      } break;
-    }
-  }
-  virtual ~PoolCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/pool_compute.h"
 
 REGISTER_LITE_KERNEL(pool2d, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::PoolCompute<float>, def)
-    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("x", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();

paddle/fluid/lite/kernels/x86/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 <Eigen/Core>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/types.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/pooling.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class PoolCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::PoolParam;
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    if (param.global_pooling) {
+      for (size_t i = 0; i < param.ksize.size(); ++i) {
+        param.paddings[i] = 0;
+        param.ksize[i] = static_cast<int>(param.x->dims()[i + 2]);
+      }
+    }
+    switch (param.ksize.size()) {
+      case 2: {
+        if (param.pooling_type == "max") {
+          paddle::operators::math::Pool2dFunctor<
+              platform::CPUDeviceContext, paddle::operators::math::MaxPool<T>,
+              T>
+              pool2d_forward;
+          paddle::operators::math::MaxPool<T> pool_process;
+          pool2d_forward(platform::CPUDeviceContext(), param.x->raw_tensor(),
+                         param.ksize, param.strides, param.paddings,
+                         pool_process, true, false,
+                         &(param.output->raw_tensor()));
+        } else if (param.pooling_type == "avg") {
+          paddle::operators::math::Pool2dFunctor<
+              platform::CPUDeviceContext, paddle::operators::math::AvgPool<T>,
+              T>
+              pool2d_forward;
+          paddle::operators::math::AvgPool<T> pool_process;
+          pool2d_forward(platform::CPUDeviceContext(), param.x->raw_tensor(),
+                         param.ksize, param.strides, param.paddings,
+                         pool_process, param.exclusive, param.adaptive,
+                         &(param.output->raw_tensor()));
+        }
+      } break;
+      case 3: {
+      } break;
+    }
+  }
+  virtual ~PoolCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/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 "paddle/fluid/lite/kernels/x86/pool_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(pool_x86, retrive_op) {
+  auto pool2d =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "pool2d");
+  ASSERT_FALSE(pool2d.empty());
+  ASSERT_TRUE(pool2d.front());
+}
+
+TEST(pool2d_x86, init) {
+  PoolCompute<float> pool2d;
+  ASSERT_EQ(pool2d.precision(), PRECISION(kFloat));
+  ASSERT_EQ(pool2d.target(), TARGET(kX86));
+}
+
+TEST(pool2d_x86, run_test) {
+  lite::Tensor x, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 4, 4};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 2, 2};
+  out.Resize(lite::DDim(out_shape));
+
+  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] = static_cast<float>(i);
+  }
+
+  PoolCompute<float> pool2d;
+  operators::PoolParam param;
+
+  param.x = &x;
+  param.output = &out;
+  param.strides = {2, 2};
+  param.paddings = {0, 0};
+  param.ksize = {2, 2};
+  param.pooling_type = "max";
+
+  pool2d.SetParam(param);
+  pool2d.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(pool2d, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/relu_compute.cc

@@ -12,42 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_lite.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/type_system.h"
-#include "paddle/fluid/lite/operators/relu_op.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-class ReluCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ReluParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    auto n = param.input->dims().production();
-    const float* input = param.input->data<float>();
-    float* output = param.output->mutable_data<float>();
-    for (int i = 0; i < n; i++) {
-      output[i] = std::max(0.f, input[i]);
-    }
-  }
-
-  virtual ~ReluCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/relu_compute.h"
 
 REGISTER_LITE_KERNEL(relu, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::ReluCompute<float>, def)

paddle/fluid/lite/kernels/x86/relu_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 <Eigen/Core>
+#include <algorithm>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/type_system.h"
+#include "paddle/fluid/lite/operators/relu_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class ReluCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ReluParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto n = param.input->dims().production();
+    const float* input = param.input->data<float>();
+    float* output = param.output->mutable_data<float>();
+    for (int i = 0; i < n; i++) {
+      output[i] = std::max(0.f, input[i]);
+    }
+  }
+
+  virtual ~ReluCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/relu_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 "paddle/fluid/lite/kernels/x86/relu_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(relu_x86, retrive_op) {
+  auto relu =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>("relu");
+  ASSERT_FALSE(relu.empty());
+  ASSERT_TRUE(relu.front());
+}
+
+TEST(relu_x86, init) {
+  ReluCompute<float> relu;
+  ASSERT_EQ(relu.precision(), PRECISION(kFloat));
+  ASSERT_EQ(relu.target(), TARGET(kX86));
+}
+
+TEST(relu_x86, run_test) {
+  lite::Tensor x, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 2, 2};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 2, 2};
+  out.Resize(lite::DDim(out_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); i++) {
+    int sign = i % 2 == 0 ? 1 : -1;
+    x_data[i] = static_cast<float>(i * sign);
+  }
+  // ReluCompute relu;
+  ReluCompute<float> relu;
+  operators::ReluParam param;
+
+  param.input = &x;
+  param.output = &out;
+
+  relu.SetParam(param);
+  relu.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(relu, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/scale_compute.cc

@@ -12,48 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_lite.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/type_system.h"
-#include "paddle/fluid/lite/operators/relu_op.h"
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-template <typename T>
-void scale_compute(const T* x, T* out, int size, float scale, float bias,
-                   bool bias_before) {
-  if (bias_before) bias *= scale;
-  for (int i = 0; i < size; i++) {
-    out[i] = x[i] * scale + bias;
-  }
-}
-
-template <typename T>
-class ScaleCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ScaleParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<param_t>();
-    scale_compute(param.x->data<T>(), param.output->mutable_data<T>(),
-                  param.x->dims().production(), param.scale, param.bias,
-                  param.bias_after_scale);
-  }
-
-  virtual ~ScaleCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/scale_compute.h"
 
 REGISTER_LITE_KERNEL(scale, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::ScaleCompute<float>, def)

paddle/fluid/lite/kernels/x86/scale_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 <Eigen/Core>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/type_system.h"
+#include "paddle/fluid/lite/operators/relu_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+void scale_compute(const T* x, T* out, int size, float scale, float bias,
+                   bool bias_before) {
+  if (bias_before) bias *= scale;
+  for (int i = 0; i < size; i++) {
+    out[i] = x[i] * scale + bias;
+  }
+}
+
+template <typename T>
+class ScaleCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ScaleParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    scale_compute(param.x->data<T>(), param.output->mutable_data<T>(),
+                  param.x->dims().production(), param.scale, param.bias,
+                  param.bias_after_scale);
+  }
+
+  virtual ~ScaleCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/scale_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 "paddle/fluid/lite/kernels/x86/scale_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(scale_x86, retrive_op) {
+  auto scale =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>("scale");
+  ASSERT_FALSE(scale.empty());
+  ASSERT_TRUE(scale.front());
+}
+
+TEST(scale_x86, init) {
+  ScaleCompute<float> scale;
+  ASSERT_EQ(scale.precision(), PRECISION(kFloat));
+  ASSERT_EQ(scale.target(), TARGET(kX86));
+}
+
+TEST(scale_x86, run_test) {
+  lite::Tensor x, y, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 2, 2};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 2, 2};
+  out.Resize(lite::DDim(out_shape));
+
+  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] = static_cast<float>(i);
+  }
+  // ScaleCompute scale;
+  ScaleCompute<float> scale;
+  operators::ScaleParam param;
+
+  param.x = &x;
+  param.scale = 0.5;
+  param.bias = 0;
+  param.output = &out;
+
+  scale.SetParam(param);
+  scale.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(scale, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/x86/softmax_compute.cc

@@ -12,76 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/math/softmax.h"
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/lite/core/kernel.h"
-#include "paddle/fluid/lite/core/op_registry.h"
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace x86 {
-
-static inline int CanonicalAxis(const int axis, const int rank) {
-  if (axis < 0) {
-    return axis + rank;
-  }
-  return axis;
-}
-
-static inline int SizeToAxis(const int axis, lite::DDim dims) {
-  int size = 1;
-  for (int i = 0; i < axis; i++) {
-    size *= dims[i];
-  }
-  return size;
-}
-
-static inline int SizeFromAxis(const int axis, lite::DDim dims) {
-  int size = 1;
-  for (int i = axis; i < dims.size(); i++) {
-    size *= dims[i];
-  }
-  return size;
-}
-
-template <typename T>
-class SoftmaxCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
- public:
-  using param_t = operators::SoftmaxParam;
-
-  void Run() override {
-    auto& param = *param_.get_mutable<operators::SoftmaxParam>();
-    // auto& context = context_->As<X86Context>();
-    CHECK(param.output);
-    CHECK(param.x);
-    const int rank = param.x->dims().size();
-    const int axis = CanonicalAxis(param.axis, rank);
-    int axis_dim = param.x->dims()[axis];
-    const int n = SizeToAxis(axis, param.x->dims());
-    const int d = SizeFromAxis(axis, param.x->dims());
-    std::vector<int64_t> shape{n, d};
-
-    lite::Tensor input_2d, out_2d;
-    input_2d.ShareDataWith(*param.x);
-    input_2d.Resize(lite::DDim(shape));
-    out_2d.ShareDataWith(*param.output);
-    out_2d.Resize(lite::DDim(shape));
-
-    paddle::operators::math::SoftmaxFunctor<platform::CPUDeviceContext, T,
-                                            true>()(
-        platform::CPUDeviceContext(), axis_dim, &input_2d.raw_tensor(),
-        &out_2d.raw_tensor());
-  }
-
-  virtual ~SoftmaxCompute() = default;
-};
-
-}  // namespace x86
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle
+#include "paddle/fluid/lite/kernels/x86/softmax_compute.h"
 
 REGISTER_LITE_KERNEL(softmax, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::SoftmaxCompute<float>, def)

paddle/fluid/lite/kernels/x86/softmax_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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/operators/math/softmax.h"
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+static inline int CanonicalAxis(const int axis, const int rank) {
+  if (axis < 0) {
+    return axis + rank;
+  }
+  return axis;
+}
+
+static inline int SizeToAxis(const int axis, lite::DDim dims) {
+  int size = 1;
+  for (int i = 0; i < axis; i++) {
+    size *= dims[i];
+  }
+  return size;
+}
+
+static inline int SizeFromAxis(const int axis, lite::DDim dims) {
+  int size = 1;
+  for (size_t i = axis; i < dims.size(); i++) {
+    size *= dims[i];
+  }
+  return size;
+}
+
+template <typename T>
+class SoftmaxCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::SoftmaxParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::SoftmaxParam>();
+    // auto& context = context_->As<X86Context>();
+    CHECK(param.output);
+    CHECK(param.x);
+    const int rank = param.x->dims().size();
+    const int axis = CanonicalAxis(param.axis, rank);
+    int axis_dim = param.x->dims()[axis];
+    const int n = SizeToAxis(axis, param.x->dims());
+    const int d = SizeFromAxis(axis, param.x->dims());
+    std::vector<int64_t> shape{n, d};
+
+    lite::Tensor input_2d, out_2d;
+    input_2d.ShareDataWith(*param.x);
+    input_2d.Resize(lite::DDim(shape));
+    out_2d.ShareDataWith(*param.output);
+    out_2d.Resize(lite::DDim(shape));
+
+    paddle::operators::math::SoftmaxFunctor<platform::CPUDeviceContext, T,
+                                            true>()(
+        platform::CPUDeviceContext(), axis_dim, &input_2d.raw_tensor(),
+        &out_2d.raw_tensor());
+  }
+
+  virtual ~SoftmaxCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/kernels/x86/softmax_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 "paddle/fluid/lite/kernels/x86/softmax_compute.h"
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+TEST(softmax_x86, retrive_op) {
+  auto softmax =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "softmax");
+  ASSERT_FALSE(softmax.empty());
+  ASSERT_TRUE(softmax.front());
+}
+
+TEST(softmax_x86, init) {
+  SoftmaxCompute<float> softmax;
+  ASSERT_EQ(softmax.precision(), PRECISION(kFloat));
+  ASSERT_EQ(softmax.target(), TARGET(kX86));
+}
+
+TEST(softmax_x86, run_test) {
+  lite::Tensor x, out;
+  constexpr int batch_size = 1;
+  std::vector<int64_t> x_shape{batch_size, 3, 3, 3};
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape{batch_size, 3, 3, 3};
+  out.Resize(lite::DDim(out_shape));
+
+  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] = static_cast<float>(i);
+  }
+  SoftmaxCompute<float> softmax;
+  operators::SoftmaxParam param;
+
+  param.x = &x;
+  param.output = &out;
+
+  softmax.SetParam(param);
+  softmax.Run();
+
+  LOG(INFO) << "output: ";
+  for (int i = 0; i < out.dims().production(); i++) {
+    LOG(INFO) << out_data[i];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(softmax, kX86, kFloat, kNCHW, def);

paddle/fluid/lite/operators/dropout_op.cc

@@ -52,7 +52,7 @@ class DropoutOpLite : public OpLite {
     param_.mask = GetMutableVar<lite::Tensor>(scope, Mask);
 
     param_.dropout_prob = op_desc.GetAttr<float>("dropout_prob");
-    if (op_desc.HasAttr("axis")) {
+    if (op_desc.HasAttr("is_test")) {
       param_.is_test = op_desc.GetAttr<bool>("is_test");
     }
     param_.fix_seed = op_desc.GetAttr<bool>("fix_seed");