Enable pool2d, dropout, transpose and transpose2 op on x86 (#2226)

* enable pool2d op on x86 and add its unit tests, test=develop

* enable dropout op and add its unit tests, test=develop

* add tranpose, transpose2 op and add their unit tests, test=develop

lite/backends/x86/math/pooling.cc

@@ -30,7 +30,7 @@ template <typename PoolProcess, typename T>
 class Pool2dFunctor<lite::TargetType::kX86, PoolProcess, T> {
  public:
   void operator()(const lite::X86Context& context,
-                  const lite::Tensor& input,
+                  const lite::Tensor* input,
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings,
@@ -38,9 +38,9 @@ class Pool2dFunctor<lite::TargetType::kX86, PoolProcess, T> {
                   bool exclusive,
                   bool adaptive,
                   lite::Tensor* output) {
-    const int batch_size = input.dims()[0];
-    const int input_height = input.dims()[2];
-    const int input_width = input.dims()[3];
+    const int batch_size = input->dims()[0];
+    const int input_height = input->dims()[2];
+    const int input_width = input->dims()[3];
     const int output_channels = output->dims()[1];
     const int output_height = output->dims()[2];
     const int output_width = output->dims()[3];
@@ -54,7 +54,7 @@ class Pool2dFunctor<lite::TargetType::kX86, PoolProcess, T> {
     const int input_stride = input_height * input_width;
     const int output_stride = output_height * output_width;
 
-    const T* input_data = input.data<T>();
+    const T* input_data = input->data<T>();
     T* output_data = output->mutable_data<T>(lite::TargetType::kX86);
 
     int hstart, hend;

lite/backends/x86/math/pooling.h

@@ -94,24 +94,12 @@ HOSTDEVICE inline int AdaptEndIndex(int ph, int input_size, int output_size) {
  * This is different from average pooling. So we rewrite the max_pool_grad:
  * MaxPool2dGradFunctor, MaxPool3dGradFunctor.
  */
-//#ifdef PADDLE_WITH_CUDA
-// template <typename PoolProcess, typename T>
-// class Pool2dDirectCUDAFunctor {
-// public:
-//  void operator()(const T* input, const std::vector<int>& input_shape,
-//                  const std::vector<int>& output_shape,
-//                  const std::vector<int>& ksize,
-//                  const std::vector<int>& strides,
-//                  const std::vector<int>& paddings, PoolProcess pool_compute,
-//                  bool exclusive, T* output, cudaStream_t stream);
-//};
-//#endif
 
 template <lite::TargetType Target, typename PoolProcess, typename T>
 class Pool2dFunctor {
  public:
   void operator()(const lite::Context<Target>& context,
-                  const lite::Tensor& input,
+                  const lite::Tensor* input,
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings,

lite/kernels/x86/CMakeLists.txt

@@ -4,7 +4,6 @@ add_kernel(activation_compute_x86 X86 basic SRCS activation_compute.cc DEPS ${li
 # lite_cc_library(sgd_compute_x86 SRCS sgd_compute.cc DEPS ${lite_kernel_deps})
 
 # lite_cc_library(fc_compute_x86 SRCS fc_compute.cc DEPS ${lite_kernel_deps})
-# lite_cc_library(relu_compute_x86 SRCS relu_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(scale_compute_x86 X86 basic SRCS scale_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(slice_compute_x86 X86 basic SRCS slice_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(squeeze_compute_x86 X86 basic SRCS squeeze_compute.cc DEPS ${lite_kernel_deps})
@@ -15,7 +14,10 @@ add_kernel(conv_compute_x86 X86 basic SRCS conv_compute.cc DEPS ${lite_kernel_de
 # lite_cc_library(softmax_compute_x86 SRCS softmax_compute.cc DEPS ${lite_kernel_deps} softmax)
 # lite_cc_library(dropout_compute_x86 SRCS dropout_compute.cc DEPS ${lite_kernel_deps} )
 # lite_cc_library(conv_compute_x86 SRCS conv_compute.cc DEPS ${lite_kernel_deps} blas im2col vol2col)
-# lite_cc_library(pool_compute_x86 SRCS pool_compute.cc DEPS ${lite_kernel_deps} pooling)
+add_kernel(pool_compute_x86 X86 basic SRCS pool_compute.cc DEPS ${lite_kernel_deps} pooling)
+add_kernel(dropout_compute_x86 X86 basic SRCS dropout_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(transpose_compute_x86 X86 basic SRCS transpose_compute.cc DEPS ${lite_kernel_deps} math_function)
+# add_kernel(fc_compute_x86 X86 basic SRCS fc_compute.cc DEPS ${lite_kernel_deps})
 # lite_cc_library(batch_norm_compute_x86 SRCS batch_norm_compute.cc DEPS ${lite_kernel_deps})
 # lite_cc_library(uniform_random_compute_x86 SRCS uniform_random_compute.cc DEPS ${lite_kernel_deps} )
 add_kernel(gru_compute_x86 X86 basic SRCS gru_compute.cc DEPS ${lite_kernel_deps} blas math_function sequence2batch gru_compute)
@@ -24,7 +26,6 @@ add_kernel(sequence_expand_as_compute_x86 X86 basic SRCS sequence_expand_as_comp
 
 # 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_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)
@@ -57,3 +58,7 @@ lite_cc_test(test_relu_compute_x86 SRCS relu_compute_test.cc DEPS activation_com
 lite_cc_test(test_sequence_expand_as_compute_x86 SRCS sequence_expand_as_compute_test.cc DEPS sequence_expand_as_compute_x86)
 lite_cc_test(test_gru_compute_x86 SRCS gru_compute_test.cc DEPS gru_compute_x86)
 lite_cc_test(test_matmul_compute_x86 SRCS matmul_compute_test.cc DEPS matmul_compute_x86)
+
+lite_cc_test(test_pool2d_compute_x86 SRCS pool_compute_test.cc DEPS pool_compute_x86)
+lite_cc_test(test_dropout_compute_x86 SRCS dropout_compute_test.cc DEPS dropout_compute_x86)
+lite_cc_test(test_transpose_compute_x86 SRCS transpose_compute_test.cc DEPS transpose_compute_x86)

lite/kernels/x86/dropout_compute.h

@@ -13,12 +13,14 @@
 // limitations under the License.
 #pragma once
 
+#include <Eigen/Core>
 #include <random>
 #include <string>
 #include "lite/core/kernel.h"
 #include "lite/core/op_registry.h"
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
+#include "lite/core/types.h"
+#include "lite/fluid/eigen.h"
+#include "lite/operators/dropout_op.h"
 
 namespace paddle {
 namespace lite {
@@ -28,7 +30,7 @@ namespace x86 {
 template <typename T,
           int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+using EigenMatrix = lite::fluid::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename T>
 class DropoutCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
@@ -37,16 +39,16 @@ class DropoutCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   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>();
+    auto* out_data = param.output->mutable_data<T>();
     if (!param.is_test) {
-      auto* mask_data = param.mask->template mutable_data<T>();
+      auto* mask_data = param.mask->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());
+      size_t size = param.mask->dims().production();
       for (size_t i = 0; i < size; ++i) {
         if (dist(engine) < param.dropout_prob) {
           mask_data[i] = 0;
@@ -62,13 +64,13 @@ class DropoutCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
         }
       }
     } 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();
+      auto X = EigenMatrix<T>::Reshape(*param.x, 1);
+      auto Y = EigenMatrix<T>::Reshape(*param.output, 1);
       if (param.dropout_implementation == "upscale_in_train") {
-        Y.device(place) = X;
+        Y.device(lite::fluid::EigenDeviceType<lite::TargetType::kX86>()) = X;
       } else {
-        Y.device(place) = X * static_cast<T>(1.0f - param.dropout_prob);
+        Y.device(lite::fluid::EigenDeviceType<lite::TargetType::kX86>()) =
+            X * static_cast<T>(1.0f - param.dropout_prob);
       }
     }
   }

lite/kernels/x86/dropout_compute_test.cc

@@ -15,6 +15,8 @@
 #include "lite/kernels/x86/dropout_compute.h"
 #include <gtest/gtest.h>
 #include <iostream>
+#include <memory>
+#include <utility>
 #include <vector>
 #include "lite/core/op_registry.h"
 
@@ -60,7 +62,9 @@ TEST(dropout_x86, run_test) {
   param.is_test = true;
   param.fix_seed = true;
   param.output = &out;
-
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  dropout.SetContext(std::move(ctx));
   dropout.SetParam(param);
   dropout.Run();
 

lite/kernels/x86/pool_compute.h

@@ -14,12 +14,12 @@
 #pragma once
 
 #include <Eigen/Core>
+#include "lite/backends/x86/math/math_function.h"
+#include "lite/backends/x86/math/pooling.h"
 #include "lite/core/kernel.h"
 #include "lite/core/op_registry.h"
 #include "lite/core/types.h"
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/operators/math/math_function.h"
-#include "paddle/fluid/operators/math/pooling.h"
+#include "lite/fluid/eigen.h"
 
 namespace paddle {
 namespace lite {
@@ -31,6 +31,7 @@ class PoolCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
  public:
   using param_t = operators::PoolParam;
   void Run() override {
+    auto& context = ctx_->As<X86Context>();
     auto& param = *param_.get_mutable<param_t>();
     if (param.global_pooling) {
       for (size_t i = 0; i < param.ksize.size(); ++i) {
@@ -41,37 +42,37 @@ class PoolCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     switch (param.ksize.size()) {
       case 2: {
         if (param.pooling_type == "max") {
-          paddle::operators::math::Pool2dFunctor<
-              platform::CPUDeviceContext,
-              paddle::operators::math::MaxPool<T>,
+          paddle::lite::x86::math::Pool2dFunctor<
+              lite::TargetType::kX86,
+              paddle::lite::x86::math::MaxPool<T>,
               T>
               pool2d_forward;
-          paddle::operators::math::MaxPool<T> pool_process;
-          pool2d_forward(platform::CPUDeviceContext(),
-                         param.x->raw_tensor(),
+          paddle::lite::x86::math::MaxPool<T> pool_process;
+          pool2d_forward(context,
+                         param.x,
                          param.ksize,
                          param.strides,
                          param.paddings,
                          pool_process,
                          true,
                          false,
-                         &(param.output->raw_tensor()));
+                         param.output);
         } else if (param.pooling_type == "avg") {
-          paddle::operators::math::Pool2dFunctor<
-              platform::CPUDeviceContext,
-              paddle::operators::math::AvgPool<T>,
+          paddle::lite::x86::math::Pool2dFunctor<
+              lite::TargetType::kX86,
+              paddle::lite::x86::math::AvgPool<T>,
               T>
               pool2d_forward;
-          paddle::operators::math::AvgPool<T> pool_process;
-          pool2d_forward(platform::CPUDeviceContext(),
-                         param.x->raw_tensor(),
+          paddle::lite::x86::math::AvgPool<T> pool_process;
+          pool2d_forward(context,
+                         param.x,
                          param.ksize,
                          param.strides,
                          param.paddings,
                          pool_process,
                          param.exclusive,
                          param.adaptive,
-                         &(param.output->raw_tensor()));
+                         param.output);
         }
       } break;
       case 3: {

lite/kernels/x86/pool_compute_test.cc

@@ -15,6 +15,8 @@
 #include "lite/kernels/x86/pool_compute.h"
 #include <gtest/gtest.h>
 #include <iostream>
+#include <memory>
+#include <utility>
 #include <vector>
 #include "lite/core/op_registry.h"
 
@@ -61,7 +63,9 @@ TEST(pool2d_x86, run_test) {
   param.paddings = {0, 0};
   param.ksize = {2, 2};
   param.pooling_type = "max";
-
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  pool2d.SetContext(std::move(ctx));
   pool2d.SetParam(param);
   pool2d.Run();
 

lite/kernels/x86/transpose_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/transpose_compute.h"
+
+REGISTER_LITE_KERNEL(transpose,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::TransposeCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(transpose2,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::Transpose2Compute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("XShape", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/transpose_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 "lite/backends/x86/math/math_function.h"
+#include "lite/core/kernel.h"
+#include "lite/core/op_lite.h"
+#include "lite/core/op_registry.h"
+#include "lite/core/type_system.h"
+#include "lite/operators/transpose_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <lite::TargetType Target, typename T>
+inline void TransCompute(const int dim,
+                         const lite::Context<Target>& context,
+                         const lite::Tensor& in,
+                         lite::Tensor* out,
+                         const std::vector<int>& axis) {
+  switch (dim) {
+    case 1:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 1> trans1;
+      trans1(context, in, out, axis);
+      break;
+    case 2:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 2> trans2;
+      trans2(context, in, out, axis);
+      break;
+    case 3:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 3> trans3;
+      trans3(context, in, out, axis);
+      break;
+    case 4:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 4> trans4;
+      trans4(context, in, out, axis);
+      break;
+    case 5:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 5> trans5;
+      trans5(context, in, out, axis);
+      break;
+    case 6:
+      paddle::lite::x86::math::Transpose<lite::TargetType::kX86, T, 6> trans6;
+      trans6(context, in, out, axis);
+      break;
+    default:
+      PADDLE_THROW("Tensors with rank at most 6 are supported");
+  }
+}
+
+template <typename T>
+class TransposeCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::TransposeParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto* x = param.x;
+    auto* out = param.output;
+    out->mutable_data<T>();
+    int ndims = param.axis.size();
+    auto& context = ctx_->As<X86Context>();
+    TransCompute<lite::TargetType::kX86, T>(
+        ndims, context, *x, out, param.axis);
+  }
+
+  virtual ~TransposeCompute() = default;
+};
+
+template <typename T>
+class Transpose2Compute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::TransposeParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<param_t>();
+    auto* x = param.x;
+    auto* out = param.output;
+    out->mutable_data<T>();
+    int ndims = param.axis.size();
+    auto& context = ctx_->As<X86Context>();
+    TransCompute<lite::TargetType::kX86, T>(
+        ndims, context, *x, out, param.axis);
+  }
+
+  virtual ~Transpose2Compute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/x86/transpose_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/transpose_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 {
+
+// transpose
+TEST(transpose_x86, retrive_op) {
+  auto transpose =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "transpose");
+  ASSERT_FALSE(transpose.empty());
+  ASSERT_TRUE(transpose.front());
+}
+
+TEST(transpose_x86, init) {
+  lite::kernels::x86::TransposeCompute<float> transpose;
+  ASSERT_EQ(transpose.precision(), PRECISION(kFloat));
+  ASSERT_EQ(transpose.target(), TARGET(kX86));
+}
+
+TEST(transpose_x86, run_test) {
+  lite::Tensor x;
+  lite::Tensor out;
+  std::vector<int64_t> x_shape({3, 4, 5});
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape({3, 5, 4});
+  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);
+  }
+
+  // TransposeCompute transpose;
+  TransposeCompute<float> transpose;
+  operators::TransposeParam param;
+
+  param.x = &x;
+  param.output = &out;
+  std::vector<int> axis({0, 2, 1});
+  param.axis = axis;
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  transpose.SetContext(std::move(ctx));
+  transpose.SetParam(param);
+  transpose.Run();
+
+  for (int j = 0; j < out.dims().production(); ++j) {
+    // EXPECT_NEAR(out_data[j], x_data[j], 1e-5);
+    LOG(INFO) << out_data[j];
+  }
+}
+
+// transpose2
+TEST(transpose2_x86, retrive_op) {
+  auto transpose2 =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "transpose2");
+  ASSERT_FALSE(transpose2.empty());
+  ASSERT_TRUE(transpose2.front());
+}
+
+TEST(transpose2_x86, init) {
+  lite::kernels::x86::Transpose2Compute<float> transpose2;
+  ASSERT_EQ(transpose2.precision(), PRECISION(kFloat));
+  ASSERT_EQ(transpose2.target(), TARGET(kX86));
+}
+
+TEST(transpose2_x86, run_test) {
+  lite::Tensor x;
+  lite::Tensor xshape;
+  lite::Tensor out;
+  std::vector<int64_t> x_shape({3, 4, 5});
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape({3, 5, 4});
+  out.Resize(lite::DDim(out_shape));
+  std::vector<int64_t> xshape_shape({3, 4, 5});
+  xshape.Resize(lite::DDim(xshape_shape));
+
+  auto x_data = x.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+  auto xshape_data = xshape.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); ++i) {
+    x_data[i] = static_cast<float>(i);
+    xshape_data[i] = static_cast<float>(i);
+  }
+
+  // Transpose2Compute transpose2;
+  Transpose2Compute<float> transpose2;
+  operators::TransposeParam param;
+
+  param.x = &x;
+  param.output = &out;
+  param.xshape = &xshape;
+  std::vector<int> axis({0, 2, 1});
+  param.axis = axis;
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  transpose2.SetContext(std::move(ctx));
+  transpose2.SetParam(param);
+  transpose2.Run();
+
+  for (int j = 0; j < out.dims().production(); ++j) {
+    LOG(INFO) << out_data[j];
+  }
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(transpose, kX86, kFloat, kNCHW, def);
+USE_LITE_KERNEL(transpose2, kX86, kFloat, kNCHW, def);

lite/operators/dropout_op.cc

@@ -11,6 +11,7 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
+#include "lite/operators/dropout_op.h"
 #include <string>
 #include <vector>
 #include "lite/core/op_lite.h"
@@ -20,16 +21,12 @@ namespace paddle {
 namespace lite {
 namespace operators {
 
-class DropoutOpLite : public OpLite {
- public:
-  explicit DropoutOpLite(const std::string& type) : OpLite(type) {}
-
-  bool CheckShape() const override {
+bool DropoutOp::CheckShape() const {
   CHECK_OR_FALSE(param_.x);
   return true;
-  }
+}
 
-  bool InferShape() const override {
+bool DropoutOp::InferShape() const {
   const auto x_dims = param_.x->dims();
   param_.output->Resize(x_dims);
   if (param_.is_test == false) {
@@ -38,11 +35,10 @@ class DropoutOpLite : public OpLite {
   // share LoD
   // param_.output->set_lod(param_.input->lod());
   return true;
-  }
+}
 
-  void AttachKernel(KernelBase* kernel) override { kernel->SetParam(param_); }
-  // TODO(Superjomn) replace framework::OpDesc with a lite one.
-  bool AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) override {
+// TODO(Superjomn) replace framework::OpDesc with a lite one.
+bool DropoutOp::AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) {
   auto input = op_desc.Input("X").front();
   auto out = op_desc.Output("Out").front();
   auto Mask = op_desc.Output("Mask").front();
@@ -63,16 +59,10 @@ class DropoutOpLite : public OpLite {
   param_.dropout_implementation =
       op_desc.GetAttr<std::string>("dropout_implementation");
   return true;
-  }
-
-  std::string DebugString() const override { return "dropout"; }
-
- private:
-  mutable DropoutParam param_;
-};
+}
 
 }  // namespace operators
 }  // namespace lite
 }  // namespace paddle
 
-REGISTER_LITE_OP(dropout, paddle::lite::operators::DropoutOpLite);
+REGISTER_LITE_OP(dropout, paddle::lite::operators::DropoutOp);

lite/operators/dropout_op.h

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

lite/operators/fc_op.cc

@@ -87,6 +87,10 @@ bool FcOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
   param_.output = scope->FindVar(out)->GetMutable<lite::Tensor>();
   param_.in_num_col_dims = op_desc.GetAttr<int>("in_num_col_dims");
 
+  if (op_desc.HasAttr("activation_type")) {
+    param_.activation_type = op_desc.GetAttr<std::string>("activation_type");
+  }
+
   // For Int8
   if (op_desc.HasAttr("enable_int8")) {
     param_.enable_int8 = op_desc.GetAttr<bool>("enable_int8");

lite/operators/op_params.h

@@ -83,6 +83,7 @@ struct FcParam {
   lite::Tensor* output{nullptr};
   lite::DDim in_mat_dims;
   int in_num_col_dims{1};
+  std::string activation_type{""};
   // for int8
   WITH_INT8_CONFIG
 };
@@ -323,6 +324,8 @@ struct SplitParam {
 struct TransposeParam {
   const lite::Tensor* x{};
   lite::Tensor* output{};
+  lite::Tensor* xshape{};
+
   std::vector<int> axis;
   bool use_mkldnn{false};
   std::string data_format{"AnyLayout"};

lite/operators/transpose_op.cc

@@ -154,6 +154,10 @@ bool Transpose2Op::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
   if (op_desc.HasAttr("data_format")) {
     param_.data_format = op_desc.GetAttr<std::string>("data_format");
   }
+  if (op_desc.HasOutput("XShape")) {
+    auto xshape_var = scope->FindVar(op_desc.Output("XShape").front());
+    param_.xshape = xshape_var->GetMutable<lite::Tensor>();
+  }
   return true;
 }