[PHI] Move segment_pool to phi. (#40099)

* move segment_pool to phi.

* mark summed ids as optional tensor.

* fix as reviews.

paddle/fluid/operators/CMakeLists.txt

@@ -161,7 +161,7 @@ cc_library(common_infer_shape_functions SRCS common_infer_shape_functions.cc DEP
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows_utils lapack_function
 lod_tensor maxouting unpooling pooling lod_rank_table context_project
-sequence_pooling segment_pooling executor device_memory_aligment generator)
+sequence_pooling executor device_memory_aligment generator)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler sample_prob tree2col)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search fc matrix_inverse matrix_solve)

paddle/fluid/operators/math/CMakeLists.txt

@@ -46,7 +46,6 @@ math_library(vol2col)
 math_library(prelu)
 math_library(bert_encoder_functor)
 math_library(tree2col DEPS math_function)
-math_library(segment_pooling)
 math_library(matrix_solve)
 
 cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selected_rows_functor)

paddle/fluid/operators/segment_pool_op.cc

@@ -12,9 +12,12 @@ 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/operators/segment_pool_op.h"
 #include <memory>
 #include <string>
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/binary.h"
 
 namespace paddle {
 namespace operators {
@@ -23,22 +26,6 @@ class SegmentPoolOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SegmentPool");
-    OP_INOUT_CHECK(ctx->HasInput("SegmentIds"), "Input", "SegmentIds",
-                   "SegmentPool");
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SegmentPool");
-    auto dims = ctx->GetInputDim("X");
-    dims[0] = -1;
-    ctx->SetOutputDim("Out", dims);
-
-    if (ctx->Attrs().Get<std::string>("pooltype") == "MEAN") {
-      OP_INOUT_CHECK(ctx->HasOutput("SummedIds"), "Output", "SummedIds",
-                     "SegmentPool");
-      ctx->SetOutputDim("SummedIds", {-1, 1});
-    }
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
@@ -150,17 +137,11 @@ class SegmentPoolGradOpMaker : public framework::SingleGradOpMaker<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(segment_pool, SegmentPoolInferShapeFunctor,
+                            PD_INFER_META(phi::SegmentPoolInferMeta));
+
 REGISTER_OPERATOR(segment_pool, ops::SegmentPoolOp, ops::SegmentPoolOpMaker,
                   ops::SegmentPoolGradOpMaker<paddle::framework::OpDesc>,
-                  ops::SegmentPoolGradOpMaker<paddle::imperative::OpBase>);
+                  ops::SegmentPoolGradOpMaker<paddle::imperative::OpBase>,
+                  SegmentPoolInferShapeFunctor);
 REGISTER_OPERATOR(segment_pool_grad, ops::SegmentPoolGradOp);
-
-REGISTER_OP_CPU_KERNEL(
-    segment_pool,
-    ops::SegmentPoolKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::SegmentPoolKernel<paddle::platform::CPUDeviceContext, double>);
-
-REGISTER_OP_CPU_KERNEL(
-    segment_pool_grad,
-    ops::SegmentPoolGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::SegmentPoolGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/segment_pool_op.cu

-/* Copyright (c) 2020 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/operators/segment_pool_op.h"
-#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
-#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    segment_pool,
-    ops::SegmentPoolKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SegmentPoolKernel<paddle::platform::CUDADeviceContext, double>);
-REGISTER_OP_CUDA_KERNEL(
-    segment_pool_grad,
-    ops::SegmentPoolGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SegmentPoolGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/segment_pool_op.h

-/* Copyright (c) 2020 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 "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/segment_pooling.h"
-#include "paddle/fluid/platform/macros.h"
-#include "paddle/phi/common/place.h"
-#include "paddle/phi/kernels/funcs/math_function.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T, typename IndexT>
-void SegmentKernelLaunchHelper(const framework::ExecutionContext& context) {
-  auto* input = context.Input<Tensor>("X");
-  auto* segment = context.Input<Tensor>("SegmentIds");
-  auto* output = context.Output<Tensor>("Out");
-  std::string pooltype = context.Attr<std::string>("pooltype");
-  Tensor* summed_ids = nullptr;
-
-  int64_t num_indices = segment->numel();
-  PADDLE_ENFORCE_EQ(
-      num_indices, input->dims()[0],
-      platform::errors::InvalidArgument(
-          "Segment_ids should be the same size as dimension 0 of input X."));
-  PADDLE_ENFORCE_EQ(num_indices, segment->dims()[0],
-                    platform::errors::InvalidArgument(
-                        "Segment_ids should be 1-D tensor, or it's other "
-                        "dimension size is 1. Segment_ids's shape is: [%s].",
-                        segment->dims()));
-
-  if (input->numel() == 0 || segment->numel() == 0) {
-    return;
-  }
-
-  bool cpu_place = context.GetPlace().GetType() == phi::AllocationType::CPU;
-  if (cpu_place) {
-    auto dims = input->dims();
-    auto* segment_ids = segment->data<IndexT>();
-    dims[0] = static_cast<int64_t>(segment_ids[segment->numel() - 1] + 1);
-    PADDLE_ENFORCE_GT(
-        dims[0], 0,
-        platform::errors::InvalidArgument(
-            "Segment ids must be >= 0, but got last id %d", dims[0]));
-    output->Resize({dims});
-    output->mutable_data<T>(context.GetPlace());
-    phi::funcs::SetConstant<DeviceContext, T> set_zero;
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    set_zero(dev_ctx, output, static_cast<T>(0));
-  }
-#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
-  if (!cpu_place) {
-    Tensor length;
-    length.mutable_data<IndexT>(phi::make_ddim({1}), platform::CPUPlace());
-    IndexT* length_data = length.data<IndexT>();
-    const IndexT* segment_ids = segment->data<IndexT>();
-
-#ifdef PADDLE_WITH_HIP
-    PADDLE_ENFORCE_GPU_SUCCESS(
-        hipMemcpy(length_data, segment_ids + num_indices - 1, sizeof(IndexT),
-                  hipMemcpyDeviceToHost));
-#else
-    PADDLE_ENFORCE_GPU_SUCCESS(
-        cudaMemcpy(length_data, segment_ids + num_indices - 1, sizeof(IndexT),
-                   cudaMemcpyDeviceToHost));
-#endif
-
-    IndexT length_host = length_data[0];
-    length_host++;
-    PADDLE_ENFORCE_GT(
-        length_host, 0,
-        platform::errors::InvalidArgument(
-            "Segment ids must be >= 0, but got last id %d", length_data[0]));
-    auto dims = input->dims();
-    dims[0] = static_cast<int64_t>(length_host);
-    output->Resize({dims});
-    output->mutable_data<T>(context.GetPlace());
-    T init_value = 0;
-    if (pooltype == "MAX") {
-      init_value = static_cast<T>(-FLT_MAX);
-    } else if (pooltype == "MIN") {
-      init_value = static_cast<T>(FLT_MAX);
-    }
-    phi::funcs::SetConstant<DeviceContext, T> setconst;
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    setconst(dev_ctx, output, static_cast<T>(init_value));
-    // the gpu kernel of mean pool record the counts of segment_ids
-    if (pooltype == "MEAN") {
-      summed_ids = context.Output<Tensor>("SummedIds");
-      summed_ids->Resize({dims[0], 1});
-      summed_ids->mutable_data<T>(context.GetPlace());
-      setconst(dev_ctx, summed_ids, static_cast<T>(1e-12));
-    }
-  }
-#endif
-
-  SegmentPoolFunctor<DeviceContext, T, IndexT> pool;
-
-  pool(context.template device_context<DeviceContext>(), *input, *segment,
-       output, summed_ids, pooltype);
-}
-
-template <typename DeviceContext, typename T>
-class SegmentPoolKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* segment = context.Input<Tensor>("SegmentIds");
-    auto index_type = framework::TransToProtoVarType(segment->dtype());
-    if (index_type == framework::proto::VarType::INT32) {
-      SegmentKernelLaunchHelper<DeviceContext, T, int>(context);
-    } else if (index_type == framework::proto::VarType::INT64) {
-      SegmentKernelLaunchHelper<DeviceContext, T, int64_t>(context);
-    } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "Unsupported index type, Expected int, int64, but got %s.",
-          index_type));
-    }
-  }
-};
-
-template <typename DeviceContext, typename T>
-class SegmentPoolGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* input = context.Input<Tensor>("X");
-    auto* output = context.Input<Tensor>("Out");
-    auto* segment = context.Input<Tensor>("SegmentIds");
-    auto* out_g = context.Input<Tensor>(framework::GradVarName("Out"));
-    auto* in_g = context.Output<Tensor>(framework::GradVarName("X"));
-    std::string pooltype = context.Attr<std::string>("pooltype");
-
-    const Tensor* summed_ids = nullptr;
-    if (pooltype == "MEAN") {
-      summed_ids = context.Input<Tensor>("SummedIds");
-    }
-
-    in_g->mutable_data<T>(context.GetPlace());
-    phi::funcs::SetConstant<DeviceContext, T> set_zero;
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    set_zero(dev_ctx, in_g, static_cast<T>(0));
-
-    auto index_type = framework::TransToProtoVarType(segment->dtype());
-    if (index_type == framework::proto::VarType::INT32) {
-      SegmentPoolGradFunctor<DeviceContext, T, int> pool;
-      pool(context.template device_context<DeviceContext>(), *input, *output,
-           *out_g, *segment, in_g, summed_ids, pooltype);
-    } else if (index_type == framework::proto::VarType::INT64) {
-      SegmentPoolGradFunctor<DeviceContext, T, int64_t> pool;
-      pool(context.template device_context<DeviceContext>(), *input, *output,
-           *out_g, *segment, in_g, summed_ids, pooltype);
-    } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "Unsupported index type, Expected int, int64, but got %s.",
-          index_type));
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/unity_build_rule.cmake

@@ -236,7 +236,6 @@ register_unity_group(cc
     scatter_nd_add_op.cc
     scatter_op.cc
     seed_op.cc
-    segment_pool_op.cc
     select_input_op.cc
     select_output_op.cc)
 register_unity_group(cc
@@ -496,8 +495,7 @@ register_unity_group(cu
     scale_op.cu
     scatter_nd_add_op.cu
     scatter_op.cu
-    seed_op.cu
-    segment_pool_op.cu)
+    seed_op.cu)
 register_unity_group(cu
     roi_pool_op.cu
     selu_op.cu

paddle/phi/infermeta/binary.cc

@@ -417,6 +417,25 @@ void Atan2InferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
   out->share_meta(x);
 }
 
+void SegmentPoolInferMeta(const MetaTensor& x,
+                          const MetaTensor& segment_ids,
+                          const std::string& pooltype,
+                          MetaTensor* out,
+                          MetaTensor* summed_ids,
+                          MetaConfig config) {
+  auto dims = x.dims();
+  dims[0] = -1;
+  out->set_dims(dims);
+  out->set_dtype(x.dtype());
+  out->set_layout(x.layout());
+
+  if (pooltype == "MEAN") {
+    summed_ids->set_dims({-1, 1});
+    summed_ids->set_dtype(x.dtype());
+    summed_ids->set_layout(x.layout());
+  }
+}
+
 void BCELossInferMeta(const MetaTensor& input,
                       const MetaTensor& label,
                       MetaTensor* out,

paddle/phi/infermeta/binary.h

@@ -80,6 +80,14 @@ void CrossInferMeta(const MetaTensor& x,
                     MetaTensor* out);
 
 void Atan2InferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out);
+
+void SegmentPoolInferMeta(const MetaTensor& x,
+                          const MetaTensor& segment_ids,
+                          const std::string& pooltype,
+                          MetaTensor* out,
+                          MetaTensor* summed_ids,
+                          MetaConfig config = MetaConfig());
+
 void BCELossInferMeta(const MetaTensor& input,
                       const MetaTensor& label,
                       MetaTensor* out,

paddle/phi/kernels/CMakeLists.txt

@@ -27,7 +27,7 @@ kernel_library(full_kernel DEPS ${COMMON_KERNEL_DEPS} empty_kernel)
 # Some kernels depend on some targets that are not commonly used.
 # These targets are not suitable for common dependencies.
 # In this case, you need to manually generate them here.
-set(MANUAL_BUILD_KERNELS math_kernel softmax_kernel softmax_grad_kernel triangular_solve_grad_kernel maxout_kernel maxout_grad_kernel put_along_axis_kernel put_along_axis_grad_kernel take_along_axis_kernel take_along_axis_grad_kernel eigh_kernel)
+set(MANUAL_BUILD_KERNELS math_kernel softmax_kernel softmax_grad_kernel triangular_solve_grad_kernel maxout_kernel maxout_grad_kernel put_along_axis_kernel put_along_axis_grad_kernel take_along_axis_kernel take_along_axis_grad_kernel eigh_kernel segment_pool_kernel segment_pool_grad_kernel)
 kernel_library(math_kernel DEPS ${COMMON_KERNEL_DEPS} cast_kernel copy_kernel)
 kernel_library(softmax_kernel DEPS ${COMMON_KERNEL_DEPS} softmax)
 kernel_library(softmax_grad_kernel DEPS ${COMMON_KERNEL_DEPS} softmax)
@@ -39,6 +39,8 @@ kernel_library(put_along_axis_grad_kernel DEPS ${COMMON_KERNEL_DEPS} gather_scat
 kernel_library(take_along_axis_kernel DEPS ${COMMON_KERNEL_DEPS} gather_scatter_kernel)
 kernel_library(take_along_axis_grad_kernel DEPS ${COMMON_KERNEL_DEPS} gather_scatter_kernel)
 kernel_library(eigh_kernel DEPS ${COMMON_KERNEL_DEPS} lapack_function)
+kernel_library(segment_pool_kernel DEPS ${COMMON_KERNEL_DEPS} segment_pooling)
+kernel_library(segment_pool_grad_kernel DEPS ${COMMON_KERNEL_DEPS} segment_pooling)
 
 # 4. auto parse and build kernel targets by cmake
 register_kernels(EXCLUDES ${COMMON_BAISC_KERNELS} ${MANUAL_BUILD_KERNELS} DEPS ${COMMON_KERNEL_DEPS} ${COMMON_BAISC_KERNELS} )

paddle/phi/kernels/cpu/segment_pool_grad_kernel.cc

+// Copyright (c) 2022 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/phi/kernels/segment_pool_grad_kernel.h"
+#include "paddle/phi/kernels/impl/segment_pool_grad_kernel_impl.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_REGISTER_KERNEL(segment_pool_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::SegmentPoolGradKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/cpu/segment_pool_kernel.cc

+// Copyright (c) 2022 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/phi/kernels/segment_pool_kernel.h"
+#include "paddle/phi/kernels/impl/segment_pool_kernel_impl.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_REGISTER_KERNEL(
+    segment_pool, CPU, ALL_LAYOUT, phi::SegmentPoolKernel, float, double) {}

paddle/phi/kernels/funcs/CMakeLists.txt

@@ -4,6 +4,7 @@ add_subdirectory(lapack)
 add_subdirectory(detail)
 
 math_library(math_function DEPS blas dense_tensor tensor)
+math_library(segment_pooling)
 math_library(sequence2batch)
 math_library(gru_compute DEPS activation_functions math_function)
 math_library(lstm_compute DEPS activation_functions)

paddle/fluid/operators/math/segment_pooling.cc → paddle/phi/kernels/funcs/segment_pooling.cc

@@ -12,45 +12,52 @@ 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/operators/math/segment_pooling.h"
+#include "paddle/phi/kernels/funcs/segment_pooling.h"
 
 #include <string>
-#include "paddle/fluid/framework/eigen.h"
 
-namespace paddle {
-namespace operators {
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
 
-using Tensor = framework::Tensor;
+namespace phi {
+namespace funcs {
+
+using Tensor = DenseTensor;
 
 template <typename T, typename IndexT>
-class SegmentPoolFunctor<platform::CPUDeviceContext, T, IndexT> {
+class SegmentPoolFunctor<phi::CPUContext, T, IndexT> {
  public:
-  void operator()(const platform::CPUDeviceContext& context,
-                  const framework::Tensor& input,
-                  const framework::Tensor& segments, framework::Tensor* output,
-                  framework::Tensor* index,
+  void operator()(const phi::CPUContext& dev_ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& segments,
+                  DenseTensor* output,
+                  DenseTensor* index,
                   const std::string pooltype = "SUM") {
     const IndexT* segment_ids = segments.data<IndexT>();
     auto curent_id = segment_ids[0];
     int64_t last_idx = 0;
     int64_t w = input.numel() / input.dims()[0];
-    auto& place = *context.eigen_device();
+    auto& place = *dev_ctx.eigen_device();
     for (int64_t idx = 1; idx <= segments.numel(); ++idx) {
       if (idx < segments.numel()) {
         if (segment_ids[idx] == curent_id) continue;
-        PADDLE_ENFORCE_GE(segment_ids[idx], curent_id,
-                          platform::errors::InvalidArgument(
+        PADDLE_ENFORCE_GE(segment_ids[idx],
+                          curent_id,
+                          phi::errors::InvalidArgument(
                               "The segment ids should be sorted, but got "
                               "segment_ids[%d]:%d > segment_ids[%d]:%d.",
-                              idx - 1, curent_id, idx, segment_ids[idx]));
+                              idx - 1,
+                              curent_id,
+                              idx,
+                              segment_ids[idx]));
       }
 
       Tensor out_t = output->Slice(curent_id, curent_id + 1);
       Tensor in_t = input.Slice(last_idx, idx);
 
       int64_t h = idx - last_idx;
-      auto in_e = framework::EigenMatrix<T>::From(in_t, phi::make_ddim({h, w}));
-      auto out_e = framework::EigenVector<T>::Flatten(out_t);
+      auto in_e = EigenMatrix<T>::From(in_t, phi::make_ddim({h, w}));
+      auto out_e = EigenVector<T>::Flatten(out_t);
 
       auto reduce_dim = Eigen::array<int, 1>({{0}});
       if (pooltype == "MEAN") {
@@ -62,7 +69,7 @@ class SegmentPoolFunctor<platform::CPUDeviceContext, T, IndexT> {
       } else if (pooltype == "MIN") {
         out_e.device(place) = in_e.minimum(reduce_dim);
       } else {
-        PADDLE_THROW(platform::errors::InvalidArgument(
+        PADDLE_THROW(phi::errors::InvalidArgument(
             "Unsupported segment pooling type, only MEAN, SUM, MAX, MIN "
             "available, but got %s.",
             pooltype));
@@ -75,36 +82,41 @@ class SegmentPoolFunctor<platform::CPUDeviceContext, T, IndexT> {
 };
 
 template <typename T, typename IndexT>
-class SegmentPoolGradFunctor<platform::CPUDeviceContext, T, IndexT> {
+class SegmentPoolGradFunctor<phi::CPUContext, T, IndexT> {
  public:
-  void operator()(const platform::CPUDeviceContext& context,
-                  const framework::Tensor& input,
-                  const framework::Tensor& output,
-                  const framework::Tensor& out_grad,
-                  const framework::Tensor& segments, framework::Tensor* in_grad,
-                  const framework::Tensor* index = nullptr,
+  void operator()(const phi::CPUContext& dev_ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& output,
+                  const DenseTensor& out_grad,
+                  const DenseTensor& segments,
+                  DenseTensor* in_grad,
+                  paddle::optional<const DenseTensor&> index,
                   const std::string pooltype = "SUM") {
     const IndexT* segment_ids = segments.data<IndexT>();
-    auto& place = *context.eigen_device();
+    auto& place = *dev_ctx.eigen_device();
     auto curent_id = segment_ids[0];
     int64_t last_idx = 0;
     int64_t w = in_grad->numel() / in_grad->dims()[0];
     for (int64_t idx = 1; idx <= segments.numel(); ++idx) {
       if (idx < segments.numel()) {
         if (segment_ids[idx] == curent_id) continue;
-        PADDLE_ENFORCE_GE(segment_ids[idx], curent_id,
-                          platform::errors::InvalidArgument(
+        PADDLE_ENFORCE_GE(segment_ids[idx],
+                          curent_id,
+                          phi::errors::InvalidArgument(
                               "The segment ids should be sorted, but got "
                               "segment_ids[%d]:%d > segment_ids[%d]:%d.",
-                              idx - 1, curent_id, idx, segment_ids[idx]));
+                              idx - 1,
+                              curent_id,
+                              idx,
+                              segment_ids[idx]));
       }
 
       Tensor out_g_t = out_grad.Slice(curent_id, curent_id + 1);
       Tensor in_g_t = in_grad->Slice(last_idx, idx);
 
       int64_t h = idx - last_idx;
-      auto in_g_e = framework::EigenMatrix<T>::From(in_g_t, {h, w});
-      auto out_g_e = framework::EigenMatrix<T>::From(out_g_t, {1, w});
+      auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
+      auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
       Eigen::DSizes<int, 2> bcast(h, 1);
 
       if (pooltype == "MEAN") {
@@ -114,13 +126,13 @@ class SegmentPoolGradFunctor<platform::CPUDeviceContext, T, IndexT> {
       } else if (pooltype == "MAX" || pooltype == "MIN") {
         Tensor out_t = output.Slice(curent_id, curent_id + 1);
         Tensor in_t = input.Slice(last_idx, idx);
-        auto in_e = framework::EigenMatrix<T>::From(in_t, {h, w});
-        auto out_e = framework::EigenMatrix<T>::From(out_t, {1, w});
+        auto in_e = EigenMatrix<T>::From(in_t, {h, w});
+        auto out_e = EigenMatrix<T>::From(out_t, {1, w});
         in_g_e.device(place) =
             (in_e == out_e.broadcast(bcast)).template cast<T>() *
             out_g_e.broadcast(bcast);
       } else {
-        PADDLE_THROW(platform::errors::InvalidArgument(
+        PADDLE_THROW(phi::errors::InvalidArgument(
             "Unsupported segment pooling type, only MEAN, SUM, MAX, MIN "
             "available, but got %s.",
             pooltype));
@@ -132,7 +144,7 @@ class SegmentPoolGradFunctor<platform::CPUDeviceContext, T, IndexT> {
   }
 };
 
-using CPU = platform::CPUDeviceContext;
+using CPU = phi::CPUContext;
 template class SegmentPoolFunctor<CPU, float, int>;
 template class SegmentPoolFunctor<CPU, float, int64_t>;
 template class SegmentPoolFunctor<CPU, double, int>;
@@ -142,5 +154,5 @@ template class SegmentPoolGradFunctor<CPU, float, int64_t>;
 template class SegmentPoolGradFunctor<CPU, double, int>;
 template class SegmentPoolGradFunctor<CPU, double, int64_t>;
 
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

paddle/fluid/operators/math/segment_pooling.cu → paddle/phi/kernels/funcs/segment_pooling.cu

@@ -12,20 +12,24 @@ 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/phi/kernels/funcs/segment_pooling.h"
+
 #include <algorithm>
-#include "paddle/fluid/operators/math/segment_pooling.h"
-#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
+
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/backends/gpu/gpu_launch_config.h"
 #include "paddle/phi/kernels/funcs/gather.cu.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
 
-namespace paddle {
-namespace operators {
+namespace phi {
+namespace funcs {
 
-using Tensor = framework::Tensor;
+using Tensor = DenseTensor;
 
 template <typename T, typename Index, int DimTileSize>
-__global__ void SegmentSumIdsKernel(const Index* segment_ids, T* summed_ids,
+__global__ void SegmentSumIdsKernel(const Index* segment_ids,
+                                    T* summed_ids,
                                     const Index input_length_size,
                                     const Index total_stripe_count) {
   CUDA_KERNEL_LOOP(stripe_index, total_stripe_count) {
@@ -45,16 +49,19 @@ __global__ void SegmentSumIdsKernel(const Index* segment_ids, T* summed_ids,
       PADDLE_ENFORCE(current_segment_id >= last_segment_id,
                      "the segment ids should be sorted, but got "
                      "segment_ids[%d]:%d > segment_ids[%d]:%d.",
-                     dim_index_base + j - 1, dim_index_base + j,
-                     last_segment_id, current_segment_id);
+                     dim_index_base + j - 1,
+                     dim_index_base + j,
+                     last_segment_id,
+                     current_segment_id);
       if (current_segment_id > last_segment_id) {
         for (Index interval_id = last_segment_id + 1;
-             interval_id < current_segment_id; ++interval_id) {
+             interval_id < current_segment_id;
+             ++interval_id) {
           *(summed_ids + interval_id) = 0;
         }
         if (j > 0) {
           if (last_segment_id == first_segment_id) {
-            platform::CudaAtomicAdd(summed_ids + last_segment_id, sum);
+            paddle::platform::CudaAtomicAdd(summed_ids + last_segment_id, sum);
           } else {
             *(summed_ids + last_segment_id) = sum;
           }
@@ -64,13 +71,15 @@ __global__ void SegmentSumIdsKernel(const Index* segment_ids, T* summed_ids,
       sum += T(1);
       last_segment_id = current_segment_id;
     }
-    platform::CudaAtomicAdd(summed_ids + last_segment_id, sum);
+    paddle::platform::CudaAtomicAdd(summed_ids + last_segment_id, sum);
   }
 }
 
 template <typename T, typename Index, int DimTileSize>
-__global__ void SegmentMeanKernel(const Index* segment_ids, const T* input,
-                                  T* output, T* summed_ids,
+__global__ void SegmentMeanKernel(const Index* segment_ids,
+                                  const T* input,
+                                  T* output,
+                                  T* summed_ids,
                                   const Index input_length_size,
                                   const Index inner_dim_size,
                                   const Index output_length_size,
@@ -93,7 +102,8 @@ __global__ void SegmentMeanKernel(const Index* segment_ids, const T* input,
       if (current_segment_id > last_segment_id) {
         // reset the interval value which do not have corresponding ids.
         for (Index interval_id = last_segment_id + 1;
-             interval_id < current_segment_id; ++interval_id) {
+             interval_id < current_segment_id;
+             ++interval_id) {
           *(output + interval_id * inner_dim_size + segment_offset) = T(0);
         }
 
@@ -102,8 +112,8 @@ __global__ void SegmentMeanKernel(const Index* segment_ids, const T* input,
               last_segment_id * inner_dim_size + segment_offset;
 
           if (last_segment_id == first_segment_id) {
-            platform::CudaAtomicAdd(output + output_index,
-                                    sum / *(summed_ids + last_segment_id));
+            paddle::platform::CudaAtomicAdd(
+                output + output_index, sum / *(summed_ids + last_segment_id));
           } else {
             *(output + output_index) = sum / *(summed_ids + last_segment_id);
           }
@@ -114,15 +124,14 @@ __global__ void SegmentMeanKernel(const Index* segment_ids, const T* input,
       last_segment_id = current_segment_id;
     }
     Index output_index = last_segment_id * inner_dim_size + segment_offset;
-    platform::CudaAtomicAdd(output + output_index,
+    paddle::platform::CudaAtomicAdd(output + output_index,
                                     sum / *(summed_ids + last_segment_id));
   }
 }
 
 template <typename T, typename Index, typename Helper, typename Pool>
-__global__ void __launch_bounds__(1024, 1)
-    SegmentOpsKernel(const Index* segment_ids, const T* input, T* output,
-                     Helper h, Pool pool) {
+__global__ void __launch_bounds__(1024, 1) SegmentOpsKernel(
+    const Index* segment_ids, const T* input, T* output, Helper h, Pool pool) {
   CUDA_KERNEL_LOOP(stripe_index, h.total_stripe_count) {
     Index segment_offset, dim_index_base, actual_height;
     Index inner_dim_size = h.inner_dim_size;
@@ -142,13 +151,16 @@ __global__ void __launch_bounds__(1024, 1)
       PADDLE_ENFORCE(current_segment_id >= last_segment_id,
                      "The segment ids should be sorted, but got "
                      "segment_ids[%d]:%d > segment_ids[%d]:%d.",
-                     dim_index_base + j - 1, dim_index_base + j,
-                     last_segment_id, current_segment_id);
+                     dim_index_base + j - 1,
+                     dim_index_base + j,
+                     last_segment_id,
+                     current_segment_id);
 
       if (current_segment_id > last_segment_id) {
         // reset the interval value which do not have corresponding ids.
         for (Index interval_id = last_segment_id + 1;
-             interval_id < current_segment_id; ++interval_id) {
+             interval_id < current_segment_id;
+             ++interval_id) {
           *(output + interval_id * inner_dim_size + segment_offset) = T(0);
         }
         // don't update result when j=0
@@ -175,9 +187,12 @@ __global__ void __launch_bounds__(1024, 1)
 }
 
 template <typename T, typename Index, typename Helper>
-__global__ void SegmentIndexGradKernel(const Index* segment_ids, const T* input,
-                                       const T* output, const T* out_grad,
-                                       T* in_grad, Helper h) {
+__global__ void SegmentIndexGradKernel(const Index* segment_ids,
+                                       const T* input,
+                                       const T* output,
+                                       const T* out_grad,
+                                       T* in_grad,
+                                       Helper h) {
   CUDA_KERNEL_LOOP(stripe_index, h.total_stripe_count) {
     Index segment_offset, dim_index_base, actual_height;
     h.calculate(stripe_index, &segment_offset, &dim_index_base, &actual_height);
@@ -201,7 +216,7 @@ class MaxPool {
   DEVICE inline T initial() { return static_cast<T>(-FLT_MAX); }
   DEVICE inline void compute(const T& x, T* y) { *y = *y > x ? *y : x; }
   DEVICE inline T atomic(T* address, const T val) {
-    return platform::CudaAtomicMax(address, val);
+    return paddle::platform::CudaAtomicMax(address, val);
   }
 };
 
@@ -211,7 +226,7 @@ class MinPool {
   DEVICE inline T initial() { return static_cast<T>(FLT_MAX); }
   DEVICE inline void compute(const T& x, T* y) { *y = *y < x ? *y : x; }
   DEVICE inline T atomic(T* address, const T val) {
-    return platform::CudaAtomicMin(address, val);
+    return paddle::platform::CudaAtomicMin(address, val);
   }
 };
 
@@ -221,7 +236,7 @@ class SumPool {
   DEVICE inline T initial() { return static_cast<T>(0); }
   DEVICE inline void compute(const T& x, T* y) { *y = *y + x; }
   DEVICE inline T atomic(T* address, const T val) {
-    return platform::CudaAtomicAdd(address, val);
+    return paddle::platform::CudaAtomicAdd(address, val);
   }
 };
 
@@ -243,8 +258,10 @@ class ArrangeHelper {
     total_stripe_count = inner_dim_size * input_outer_dim_num_stripe;
   }
 
-  DEVICE inline void calculate(T stripe_index, T* segment_offset,
-                               T* dim_index_base, T* actual_height) {
+  DEVICE inline void calculate(T stripe_index,
+                               T* segment_offset,
+                               T* dim_index_base,
+                               T* actual_height) {
     *segment_offset = stripe_index % inner_dim_size;
     *dim_index_base = stripe_index / inner_dim_size * DimTileSize;
     *actual_height = min(DimTileSize, input_length_size - *dim_index_base);
@@ -252,23 +269,32 @@ class ArrangeHelper {
 };
 
 template <typename T, typename Index>
-void SegmentPoolCUDAGradFunctor(const platform::CUDADeviceContext& ctx,
-                                const framework::Tensor& input,
-                                const framework::Tensor& segment_ids,
-                                const framework::Tensor& output,
-                                const framework::Tensor& out_grad,
-                                framework::Tensor* in_grad,
+void SegmentPoolCUDAGradFunctor(const phi::GPUContext& ctx,
+                                const DenseTensor& input,
+                                const DenseTensor& segment_ids,
+                                const DenseTensor& output,
+                                const DenseTensor& out_grad,
+                                DenseTensor* in_grad,
                                 const std::string pooltype = "SUM") {
-  auto h = ArrangeHelper<Index>(input.numel(), segment_ids.dims()[0],
-                                output.dims()[0]);
-  auto config = platform::GetGpuLaunchConfig1D(ctx, h.total_stripe_count);
+  auto h = ArrangeHelper<Index>(
+      input.numel(), segment_ids.dims()[0], output.dims()[0]);
+  auto config =
+      phi::backends::gpu::GetGpuLaunchConfig1D(ctx, h.total_stripe_count);
   if (pooltype == "MAX" || pooltype == "MIN") {
-    SegmentIndexGradKernel<T, Index, ArrangeHelper<Index>><<<
-        config.block_per_grid.x, config.thread_per_block.x, 0, ctx.stream()>>>(
-        segment_ids.data<Index>(), input.data<T>(), output.data<T>(),
-        out_grad.data<T>(), in_grad->data<T>(), h);
+    SegmentIndexGradKernel<T,
+                           Index,
+                           ArrangeHelper<Index>><<<config.block_per_grid.x,
+                                                   config.thread_per_block.x,
+                                                   0,
+                                                   ctx.stream()>>>(
+        segment_ids.data<Index>(),
+        input.data<T>(),
+        output.data<T>(),
+        out_grad.data<T>(),
+        in_grad->data<T>(),
+        h);
   } else {
-    PADDLE_THROW(platform::errors::InvalidArgument(
+    PADDLE_THROW(phi::errors::InvalidArgument(
         "Unsupported segment pooling grad operation, Only MAX, MIN "
         "available, but got %s.",
         pooltype));
@@ -291,13 +317,13 @@ __global__ void SimpleDiv(T* x, const T* y, const int len, const int dim) {
 }
 
 template <typename T, typename IndexT>
-class SegmentPoolFunctor<platform::CUDADeviceContext, T, IndexT> {
+class SegmentPoolFunctor<phi::GPUContext, T, IndexT> {
  public:
-  void operator()(const platform::CUDADeviceContext& ctx,
-                  const framework::Tensor& input,
-                  const framework::Tensor& segment_ids,
-                  framework::Tensor* output,
-                  framework::Tensor* summed_ids = nullptr,
+  void operator()(const phi::GPUContext& ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& segment_ids,
+                  DenseTensor* output,
+                  DenseTensor* summed_ids = nullptr,
                   const std::string pooltype = "SUM") {
     if (pooltype == "MEAN") {
       // Sum the segment id num first
@@ -305,50 +331,76 @@ class SegmentPoolFunctor<platform::CUDADeviceContext, T, IndexT> {
       auto input_length_size = segment_ids.numel();
       auto total_stripe_count =
           (input_length_size + DimTileSize - 1) / DimTileSize;
-      auto config = platform::GetGpuLaunchConfig1D(ctx, total_stripe_count);
-      SegmentSumIdsKernel<
-          T, IndexT, IndexT(8)><<<config.block_per_grid.x,
-                                  config.thread_per_block.x, 0, ctx.stream()>>>(
-          segment_ids.data<IndexT>(), summed_ids->data<T>(), input_length_size,
+      auto config =
+          phi::backends::gpu::GetGpuLaunchConfig1D(ctx, total_stripe_count);
+      SegmentSumIdsKernel<T, IndexT, IndexT(8)><<<config.block_per_grid.x,
+                                                  config.thread_per_block.x,
+                                                  0,
+                                                  ctx.stream()>>>(
+          segment_ids.data<IndexT>(),
+          summed_ids->data<T>(),
+          input_length_size,
           total_stripe_count);
     }
 
-    auto h = ArrangeHelper<IndexT>(input.numel(), segment_ids.dims()[0],
-                                   output->dims()[0]);
-    auto config = platform::GetGpuLaunchConfig1D(ctx, h.total_stripe_count);
+    auto h = ArrangeHelper<IndexT>(
+        input.numel(), segment_ids.dims()[0], output->dims()[0]);
+    auto config =
+        phi::backends::gpu::GetGpuLaunchConfig1D(ctx, h.total_stripe_count);
     if (pooltype == "MEAN") {
-      SegmentMeanKernel<
-          T, IndexT, IndexT(8)><<<config.block_per_grid.x,
-                                  config.thread_per_block.x, 0, ctx.stream()>>>(
-          segment_ids.data<IndexT>(), input.data<T>(), output->data<T>(),
-          summed_ids->data<T>(), h.input_length_size, h.inner_dim_size,
-          h.output_length_size, h.total_stripe_count);
+      SegmentMeanKernel<T, IndexT, IndexT(8)><<<config.block_per_grid.x,
+                                                config.thread_per_block.x,
+                                                0,
+                                                ctx.stream()>>>(
+          segment_ids.data<IndexT>(),
+          input.data<T>(),
+          output->data<T>(),
+          summed_ids->data<T>(),
+          h.input_length_size,
+          h.inner_dim_size,
+          h.output_length_size,
+          h.total_stripe_count);
     } else if (pooltype == "SUM") {
       SumPool<T> pool;
-      SegmentOpsKernel<
-          T, IndexT, ArrangeHelper<IndexT>,
-          SumPool<T>><<<config.block_per_grid.x, config.thread_per_block.x, 0,
+      SegmentOpsKernel<T,
+                       IndexT,
+                       ArrangeHelper<IndexT>,
+                       SumPool<T>><<<config.block_per_grid.x,
+                                     config.thread_per_block.x,
+                                     0,
                                      ctx.stream()>>>(segment_ids.data<IndexT>(),
-                                        input.data<T>(), output->data<T>(), h,
+                                                     input.data<T>(),
+                                                     output->data<T>(),
+                                                     h,
                                                      pool);
     } else if (pooltype == "MAX") {
       MaxPool<T> pool;
-      SegmentOpsKernel<
-          T, IndexT, ArrangeHelper<IndexT>,
-          MaxPool<T>><<<config.block_per_grid.x, config.thread_per_block.x, 0,
+      SegmentOpsKernel<T,
+                       IndexT,
+                       ArrangeHelper<IndexT>,
+                       MaxPool<T>><<<config.block_per_grid.x,
+                                     config.thread_per_block.x,
+                                     0,
                                      ctx.stream()>>>(segment_ids.data<IndexT>(),
-                                        input.data<T>(), output->data<T>(), h,
+                                                     input.data<T>(),
+                                                     output->data<T>(),
+                                                     h,
                                                      pool);
     } else if (pooltype == "MIN") {
       MinPool<T> pool;
-      SegmentOpsKernel<
-          T, IndexT, ArrangeHelper<IndexT>,
-          MinPool<T>><<<config.block_per_grid.x, config.thread_per_block.x, 0,
+      SegmentOpsKernel<T,
+                       IndexT,
+                       ArrangeHelper<IndexT>,
+                       MinPool<T>><<<config.block_per_grid.x,
+                                     config.thread_per_block.x,
+                                     0,
                                      ctx.stream()>>>(segment_ids.data<IndexT>(),
-                                        input.data<T>(), output->data<T>(), h,
+                                                     input.data<T>(),
+                                                     output->data<T>(),
+                                                     h,
                                                      pool);
     } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
+      PADDLE_THROW(phi::errors::InvalidArgument(
           "Unsupported segment pooling operation, Only MEAN, SUM, MAX, MIN "
           "available, but got %s.",
           pooltype));
@@ -357,33 +409,38 @@ class SegmentPoolFunctor<platform::CUDADeviceContext, T, IndexT> {
 };
 
 template <typename T, typename IndexT>
-class SegmentPoolGradFunctor<platform::CUDADeviceContext, T, IndexT> {
+class SegmentPoolGradFunctor<phi::GPUContext, T, IndexT> {
  public:
-  void operator()(const platform::CUDADeviceContext& context,
-                  const framework::Tensor& input,
-                  const framework::Tensor& output,
-                  const framework::Tensor& out_grad,
-                  const framework::Tensor& segments, framework::Tensor* in_grad,
-                  const framework::Tensor* summed_ids = nullptr,
+  void operator()(const phi::GPUContext& dev_ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& output,
+                  const DenseTensor& out_grad,
+                  const DenseTensor& segments,
+                  DenseTensor* in_grad,
+                  paddle::optional<const DenseTensor&> summed_ids,
                   const std::string pooltype = "SUM") {
     if (pooltype == "MAX" || pooltype == "MIN") {
-      SegmentPoolCUDAGradFunctor<T, IndexT>(context, input, segments, output,
-                                            out_grad, in_grad, pooltype);
+      SegmentPoolCUDAGradFunctor<T, IndexT>(
+          dev_ctx, input, segments, output, out_grad, in_grad, pooltype);
     } else if (pooltype == "MEAN") {
-      framework::Tensor mean_grad;
-      mean_grad.mutable_data<T>(input.dims(), context.GetPlace());
-      framework::TensorCopy(out_grad, context.GetPlace(), context, &mean_grad);
+      DenseTensor mean_grad;
+      mean_grad.Resize(input.dims());
+      dev_ctx.template Alloc<T>(&mean_grad);
+      paddle::framework::TensorCopy(
+          out_grad, dev_ctx.GetPlace(), dev_ctx, &mean_grad);
       int len = output.dims()[0];
       int dim = output.numel() / len;
-      auto config = platform::GetGpuLaunchConfig1D(context, len);
-      SimpleDiv<T><<<config.block_per_grid.x, config.thread_per_block.x, 0,
-                     context.stream()>>>(mean_grad.data<T>(),
-                                         summed_ids->data<T>(), len, dim);
-      phi::funcs::GPUGather<T, IndexT>(context, mean_grad, segments, in_grad);
+      auto config = phi::backends::gpu::GetGpuLaunchConfig1D(dev_ctx, len);
+      SimpleDiv<T><<<config.block_per_grid.x,
+                     config.thread_per_block.x,
+                     0,
+                     dev_ctx.stream()>>>(
+          mean_grad.data<T>(), summed_ids->data<T>(), len, dim);
+      phi::funcs::GPUGather<T, IndexT>(dev_ctx, mean_grad, segments, in_grad);
     } else if (pooltype == "SUM") {
-      phi::funcs::GPUGather<T, IndexT>(context, out_grad, segments, in_grad);
+      phi::funcs::GPUGather<T, IndexT>(dev_ctx, out_grad, segments, in_grad);
     } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
+      PADDLE_THROW(phi::errors::InvalidArgument(
           "Unsupported segment pooling operation, Only MEAN, SUM, MAX, MIN "
           "available, but got %s.",
           pooltype));
@@ -391,15 +448,15 @@ class SegmentPoolGradFunctor<platform::CUDADeviceContext, T, IndexT> {
   }
 };
 
-using CUDA = paddle::platform::CUDADeviceContext;
-template class SegmentPoolFunctor<CUDA, float, int>;
-template class SegmentPoolFunctor<CUDA, float, int64_t>;
-template class SegmentPoolFunctor<CUDA, double, int>;
-template class SegmentPoolFunctor<CUDA, double, int64_t>;
-template class SegmentPoolGradFunctor<CUDA, float, int>;
-template class SegmentPoolGradFunctor<CUDA, float, int64_t>;
-template class SegmentPoolGradFunctor<CUDA, double, int>;
-template class SegmentPoolGradFunctor<CUDA, double, int64_t>;
-
-}  // namespace operators
-}  // namespace paddle
+using GPU = phi::GPUContext;
+template class SegmentPoolFunctor<GPU, float, int>;
+template class SegmentPoolFunctor<GPU, float, int64_t>;
+template class SegmentPoolFunctor<GPU, double, int>;
+template class SegmentPoolFunctor<GPU, double, int64_t>;
+template class SegmentPoolGradFunctor<GPU, float, int>;
+template class SegmentPoolGradFunctor<GPU, float, int64_t>;
+template class SegmentPoolGradFunctor<GPU, double, int>;
+template class SegmentPoolGradFunctor<GPU, double, int64_t>;
+
+}  // namespace funcs
+}  // namespace phi

paddle/fluid/operators/math/segment_pooling.h → paddle/phi/kernels/funcs/segment_pooling.h

@@ -14,33 +14,36 @@ limitations under the License. */
 
 #pragma once
 #include <string>
-#include "paddle/fluid/framework/tensor.h"
-#include "paddle/fluid/platform/device_context.h"
+#include "paddle/phi/core/dense_tensor.h"
 
-namespace paddle {
-namespace operators {
+namespace phi {
+namespace funcs {
 
-template <typename DeviceContext, typename T, typename IndexT>
+template <typename Context, typename T, typename IndexT>
 class SegmentPoolFunctor {
  public:
   /* mean pool has summed_ids output */
-  void operator()(const DeviceContext& context, const framework::Tensor& input,
-                  const framework::Tensor& segments, framework::Tensor* output,
-                  framework::Tensor* summed_ids = nullptr,
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& segments,
+                  DenseTensor* output,
+                  DenseTensor* summed_ids = nullptr,
                   const std::string pooltype = "SUM");
 };
 
-template <typename DeviceContext, typename T, typename IndexT>
+template <typename Context, typename T, typename IndexT>
 class SegmentPoolGradFunctor {
  public:
   /* mean pool has summed_ids output */
-  void operator()(const DeviceContext& context, const framework::Tensor& input,
-                  const framework::Tensor& output,
-                  const framework::Tensor& out_grad,
-                  const framework::Tensor& segments, framework::Tensor* in_grad,
-                  const framework::Tensor* summed_ids = nullptr,
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor& input,
+                  const DenseTensor& output,
+                  const DenseTensor& out_grad,
+                  const DenseTensor& segments,
+                  DenseTensor* in_grad,
+                  paddle::optional<const DenseTensor&> summed_ids,
                   const std::string pooltype = "SUM");
 };
 
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

paddle/phi/kernels/gpu/segment_pool_grad_kernel.cu

+// Copyright (c) 2022 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/phi/kernels/impl/segment_pool_grad_kernel_impl.h"
+#include "paddle/phi/kernels/segment_pool_grad_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_REGISTER_KERNEL(segment_pool_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::SegmentPoolGradKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/gpu/segment_pool_kernel.cu

+// Copyright (c) 2022 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/phi/kernels/impl/segment_pool_kernel_impl.h"
+#include "paddle/phi/kernels/segment_pool_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_REGISTER_KERNEL(
+    segment_pool, GPU, ALL_LAYOUT, phi::SegmentPoolKernel, float, double) {}

paddle/phi/kernels/impl/segment_pool_grad_kernel_impl.h

+// Copyright (c) 2022 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 "paddle/fluid/framework/eigen.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/segment_pooling.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SegmentPoolGradKernel(const Context& dev_ctx,
+                           const DenseTensor& x,
+                           const DenseTensor& segment_ids,
+                           const DenseTensor& out,
+                           paddle::optional<const DenseTensor&> summed_ids,
+                           const DenseTensor& out_grad,
+                           const std::string& pooltype,
+                           DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+  phi::funcs::SetConstant<Context, T> set_zero;
+  set_zero(dev_ctx, x_grad, static_cast<T>(0));
+
+  auto index_type = segment_ids.type();
+  if (index_type == DataType::INT32) {
+    phi::funcs::SegmentPoolGradFunctor<Context, T, int> pool;
+    pool(dev_ctx, x, out, out_grad, segment_ids, x_grad, summed_ids, pooltype);
+  } else if (index_type == DataType::INT64) {
+    phi::funcs::SegmentPoolGradFunctor<Context, T, int64_t> pool;
+    pool(dev_ctx, x, out, out_grad, segment_ids, x_grad, summed_ids, pooltype);
+  } else {
+    PADDLE_THROW(phi::errors::InvalidArgument(
+        "Unsupported index type, Expected int, int64, but got %s.",
+        index_type));
+  }
+}
+}  // namespace phi

paddle/phi/kernels/impl/segment_pool_kernel_impl.h

+// Copyright (c) 2022 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 "paddle/fluid/framework/eigen.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/segment_pooling.h"
+
+namespace phi {
+
+template <typename Context, typename T, typename IndexT>
+void SegmentKernelLaunchHelper(const Context& dev_ctx,
+                               const DenseTensor& x,
+                               const DenseTensor& segment_ids,
+                               const std::string& pooltype,
+                               DenseTensor* out,
+                               DenseTensor* summed_ids) {
+  int64_t num_indices = segment_ids.numel();
+  PADDLE_ENFORCE_EQ(
+      num_indices,
+      x.dims()[0],
+      phi::errors::InvalidArgument(
+          "Segment_ids should be the same size as dimension 0 of input X."));
+  PADDLE_ENFORCE_EQ(num_indices,
+                    segment_ids.dims()[0],
+                    phi::errors::InvalidArgument(
+                        "Segment_ids should be 1-D tensor, or it's other "
+                        "dimension size is 1. Segment_ids's shape is: [%s].",
+                        segment_ids.dims()));
+
+  if (x.numel() == 0 || segment_ids.numel() == 0) {
+    return;
+  }
+
+  bool cpu_place = dev_ctx.GetPlace().GetType() == phi::AllocationType::CPU;
+  if (cpu_place) {
+    auto dims = x.dims();
+    auto* segment_ids_ptr = segment_ids.data<IndexT>();
+    dims[0] =
+        static_cast<int64_t>(segment_ids_ptr[segment_ids.numel() - 1] + 1);
+    PADDLE_ENFORCE_GT(
+        dims[0],
+        0,
+        phi::errors::InvalidArgument(
+            "Segment ids must be >= 0, but got last id %d", dims[0]));
+
+    out->Resize({dims});
+    dev_ctx.template Alloc<T>(out);
+
+    phi::funcs::SetConstant<Context, T> set_zero;
+    set_zero(dev_ctx, out, static_cast<T>(0));
+  }
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  if (!cpu_place) {
+    DenseTensor length;
+    length.Resize(phi::make_ddim({1}));
+    IndexT* length_data = dev_ctx.template HostAlloc<IndexT>(&length);
+
+    const IndexT* segment_ids_ptr = segment_ids.data<IndexT>();
+
+#ifdef PADDLE_WITH_HIP
+    PADDLE_ENFORCE_GPU_SUCCESS(hipMemcpy(length_data,
+                                         segment_ids_ptr + num_indices - 1,
+                                         sizeof(IndexT),
+                                         hipMemcpyDeviceToHost));
+#else
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpy(length_data,
+                                          segment_ids_ptr + num_indices - 1,
+                                          sizeof(IndexT),
+                                          cudaMemcpyDeviceToHost));
+#endif
+
+    IndexT length_host = length_data[0];
+    length_host++;
+    PADDLE_ENFORCE_GT(
+        length_host,
+        0,
+        phi::errors::InvalidArgument(
+            "Segment ids must be >= 0, but got last id %d", length_data[0]));
+    auto dims = x.dims();
+    dims[0] = static_cast<int64_t>(length_host);
+    out->Resize({dims});
+    dev_ctx.template Alloc<T>(out);
+
+    T init_value = 0;
+    if (pooltype == "MAX") {
+      init_value = static_cast<T>(-FLT_MAX);
+    } else if (pooltype == "MIN") {
+      init_value = static_cast<T>(FLT_MAX);
+    }
+    phi::funcs::SetConstant<Context, T> setconst;
+    setconst(dev_ctx, out, static_cast<T>(init_value));
+    // the gpu kernel of mean pool record the counts of segment_ids
+    if (pooltype == "MEAN") {
+      summed_ids->Resize({dims[0], 1});
+      dev_ctx.template Alloc<T>(summed_ids);
+      setconst(dev_ctx, summed_ids, static_cast<T>(1e-12));
+    }
+  }
+#endif
+
+  phi::funcs::SegmentPoolFunctor<Context, T, IndexT> pool;
+
+  pool(dev_ctx, x, segment_ids, out, summed_ids, pooltype);
+}
+
+template <typename T, typename Context>
+void SegmentPoolKernel(const Context& dev_ctx,
+                       const DenseTensor& x,
+                       const DenseTensor& segment_ids,
+                       const std::string& pooltype,
+                       DenseTensor* out,
+                       DenseTensor* summed_ids) {
+  auto index_type = segment_ids.dtype();
+  if (index_type == DataType::INT32) {
+    SegmentKernelLaunchHelper<Context, T, int>(
+        dev_ctx, x, segment_ids, pooltype, out, summed_ids);
+  } else if (index_type == DataType::INT64) {
+    SegmentKernelLaunchHelper<Context, T, int64_t>(
+        dev_ctx, x, segment_ids, pooltype, out, summed_ids);
+  } else {
+    PADDLE_THROW(phi::errors::InvalidArgument(
+        "Unsupported index type, Expected int, int64, but got %s.",
+        index_type));
+  }
+}
+
+}  // namespace phi

paddle/phi/kernels/segment_pool_grad_kernel.h

+// Copyright (c) 2022 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/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SegmentPoolGradKernel(const Context& dev_ctx,
+                           const DenseTensor& x,
+                           const DenseTensor& segment_ids,
+                           const DenseTensor& out,
+                           paddle::optional<const DenseTensor&> summed_ids,
+                           const DenseTensor& out_grad,
+                           const std::string& pooltype,
+                           DenseTensor* x_grad);
+
+}  // namespace phi

paddle/phi/kernels/segment_pool_kernel.h

+// Copyright (c) 2022 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/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SegmentPoolKernel(const Context& dev_ctx,
+                       const DenseTensor& x,
+                       const DenseTensor& segment_ids,
+                       const std::string& pooltype,
+                       DenseTensor* out,
+                       DenseTensor* summed_ids);
+
+}  // namespace phi

paddle/phi/ops/compat/segment_pool_sig.cc

+// Copyright (c) 2022 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/phi/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature SegmentPoolGradOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature(
+      "segment_pool_grad",
+      {
+          "X", "SegmentIds", "Out", "SummedIds", GradVarName("Out"),
+      },
+      {"pooltype"},
+      {GradVarName("X")});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(segment_pool_grad,
+                           phi::SegmentPoolGradOpArgumentMapping);