[phi] move sequence_pool to phi - Step 2 : sequence_pool_op (#52750)

* [phi] move sequence_pool kernel to phi

* [phi] mv sequence_pooling to phi funcs

* [phi] mv sequence_pooling_test

* [phi] RollBACK `paddle/fluid/operators/sequence_ops/sequence_pool_op.cc`

* [phi][funcs] fix mutable_data

* [phi][funcs] fix mutable_data

paddle/fluid/operators/math/CMakeLists.txt

@@ -15,7 +15,6 @@ math_library(sampler DEPS generator)
 
 # math_library(math_function DEPS blas dense_tensor tensor)
 
-math_library(sequence_pooling DEPS math_function jit_kernel_helper)
 if(WITH_XPU)
   math_library(beam_search DEPS math_function beam_search_xpu)
 else()

paddle/fluid/operators/sequence_ops/sequence_pool_op.cc

@@ -196,9 +196,6 @@ REGISTER_OPERATOR(sequence_pool,
 REGISTER_OPERATOR(sequence_pool_grad,
                   ops::SequencePoolGradOp,
                   ops::SequencePoolGradOpNoNeedBufferVarsInferer);
-REGISTER_OP_CPU_KERNEL(sequence_pool,
-                       ops::SequencePoolKernel<phi::CPUContext, float>,
-                       ops::SequencePoolKernel<phi::CPUContext, double>);
 
 REGISTER_OP_CPU_KERNEL(sequence_pool_grad,
                        ops::SequencePoolGradKernel<phi::CPUContext, float>,

paddle/fluid/operators/sequence_ops/sequence_pool_op.cu

@@ -14,7 +14,5 @@ limitations under the License. */
 #include "paddle/fluid/operators/sequence_ops/sequence_pool_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(sequence_pool,
-                        ops::SequencePoolKernel<phi::GPUContext, float>);
 REGISTER_OP_CUDA_KERNEL(sequence_pool_grad,
                         ops::SequencePoolGradKernel<phi::GPUContext, float>);

paddle/fluid/operators/sequence_ops/sequence_pool_op.h

@@ -17,81 +17,12 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/sequence_pooling.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/sequence_pooling.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename DeviceContext, typename T>
-class SequencePoolKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* in = context.Input<phi::DenseTensor>("X");
-    auto* out = context.Output<phi::DenseTensor>("Out");
-    std::string pooltype = context.Attr<std::string>("pooltype");
-    T pad_value = static_cast<T>(context.Attr<float>("pad_value"));
-
-    auto dims = in->dims();
-    auto lod = in->lod();
-    auto lod_level = lod.size();
-    // InferShape by lod
-    PADDLE_ENFORCE_GT(lod_level,
-                      0,
-                      platform::errors::InvalidArgument(
-                          "Input(X) phi::DenseTensor of SequencePoolOp "
-                          "does not contain LoD information."));
-    PADDLE_ENFORCE_LE(lod_level,
-                      2UL,
-                      platform::errors::InvalidArgument(
-                          "The lod level of input shall be no more than 2."
-                          "Received lod level is %d.",
-                          lod_level));
-    PADDLE_ENFORCE_GE(
-        dims[0],
-        /*batch size = */ static_cast<int64_t>(lod[lod_level - 1].size() - 1),
-        platform::errors::InvalidArgument(
-            "The first dimension of Input(X) must be large than batch size."
-            "But received first dimension of Input(X) is %d, while batch"
-            "size is %d.",
-            dims[0],
-            static_cast<int64_t>(lod[lod_level - 1].size() - 1)));
-    if (lod_level > 1UL) {
-      PADDLE_ENFORCE_EQ(lod[0][lod[0].size() - 1],
-                        lod[1].size() - 1,
-                        platform::errors::InvalidArgument(
-                            "The input lod information is illegal."));
-      framework::LoD out_lod;
-      out_lod.push_back(lod[0]);
-      out->set_lod(out_lod);
-    }
-    dims[0] = lod[lod_level - 1].size() - 1;
-    out->Resize({dims});
-    out->mutable_data<T>(context.GetPlace());
-    phi::DenseTensor* index = nullptr;
-
-    bool is_test =
-        context.HasAttr("is_test") ? context.Attr<bool>("is_test") : false;
-
-    // Do not create index buffer for inference mode
-    if (pooltype == "MAX" &&
-        (is_test == false ||
-         platform::is_cpu_place(context.GetPlace()) == false)) {
-      index = context.Output<phi::DenseTensor>("MaxIndex");
-      index->Resize({dims});
-      index->mutable_data<int>(context.GetPlace());
-    }
-    math::SequencePoolFunctor<DeviceContext, T> pool;
-    pool(context.template device_context<DeviceContext>(),
-         pooltype,
-         pad_value,
-         *in,
-         out,
-         is_test,
-         index);
-  }
-};
-
 template <typename DeviceContext, typename T>
 class SequencePoolGradKernel : public framework::OpKernel<T> {
  public:
@@ -105,7 +36,7 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
       index = context.Input<phi::DenseTensor>("MaxIndex");
     }
     in_g->mutable_data<T>(context.GetPlace());
-    math::SequencePoolGradFunctor<DeviceContext, T> pool;
+    phi::funcs::SequencePoolGradFunctor<DeviceContext, T> pool;
     pool(context.template device_context<DeviceContext>(),
          pooltype,
          *out_g,

paddle/phi/kernels/CMakeLists.txt

@@ -74,6 +74,7 @@ set(COMMON_KERNEL_DEPS
     phi_dynload_warpctc
     phi_dynload_warprnnt
     sequence_padding
+    sequence_pooling
     sequence_scale
     fft
     phi_data_layout_transform

paddle/phi/kernels/cpu/sequence_pool_kernel.cc

+/* Copyright (c) 2023 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/sequence_pool_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/sequence_pool_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    sequence_pool, CPU, ALL_LAYOUT, phi::SequencePoolKernel, float, double) {}

paddle/phi/kernels/funcs/CMakeLists.txt

@@ -25,6 +25,7 @@ math_library(maxouting)
 math_library(matrix_bit_code)
 math_library(sequence_scale)
 math_library(sequence_padding DEPS lod_utils)
+math_library(sequence_pooling DEPS math_function jit_kernel_helper)
 
 cc_library(
   phi_data_layout_transform

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

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2023 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.
@@ -12,7 +12,7 @@ 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 "paddle/fluid/operators/math/sequence_pooling.h"
+#include "paddle/phi/kernels/funcs/sequence_pooling.h"
 
 #include <string>
 
@@ -21,9 +21,8 @@ limitations under the License. */
 #include "paddle/phi/kernels/funcs/jit/kernels.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
 
-namespace paddle {
-namespace operators {
-namespace math {
+namespace phi {
+namespace funcs {
 
 template <typename T,
           int MajorType = Eigen::RowMajor,
@@ -47,13 +46,13 @@ class MaxSeqPoolFunctor {
     auto idx_dims = index->dims();
     PADDLE_ENFORCE_GT(in_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of input shall be greater than 1, but got "
                           "the rank is %ld. Please check the input value",
                           in_dims.size()));
     PADDLE_ENFORCE_GT(out_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of output shall be greater than 1, but got "
                           "the rank is %ld. Please check the input value",
                           out_dims.size()));
@@ -61,7 +60,7 @@ class MaxSeqPoolFunctor {
       PADDLE_ENFORCE_EQ(
           in_dims[i],
           out_dims[i],
-          platform::errors::InvalidArgument(
+          errors::InvalidArgument(
               "The dimension of input and output shall be same. Expected %ld "
               "== %ld, but got %ld != %ld. Please check the input value.",
               in_dims[i],
@@ -72,7 +71,7 @@ class MaxSeqPoolFunctor {
     PADDLE_ENFORCE_EQ(
         idx_dims,
         out_dims,
-        platform::errors::InvalidArgument(
+        errors::InvalidArgument(
             "The dimension of index and output shall be same. Expected %ld == "
             "%ld, but got %ld != %ld. Please check the input value.",
             idx_dims,
@@ -125,13 +124,13 @@ class MaxSeqPoolFunctor<T, true> {
     auto out_dims = output->dims();
     PADDLE_ENFORCE_GT(in_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of input shall be greater than 1, but got "
                           "%ld <= 1. Please check the input value.",
                           in_dims.size()));
     PADDLE_ENFORCE_GT(out_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of output shall be greater than 1, but got "
                           "%ld <= 1. Please check the input value.",
                           out_dims.size()));
@@ -139,7 +138,7 @@ class MaxSeqPoolFunctor<T, true> {
       PADDLE_ENFORCE_EQ(
           in_dims[i],
           out_dims[i],
-          platform::errors::InvalidArgument(
+          errors::InvalidArgument(
               "The dimension of input and output shall be same. Expected %ld "
               "== %ld, but got %ld != %ld. Please check the input value.",
               in_dims[i],
@@ -186,20 +185,20 @@ class MaxSeqPoolGradFunctor {
     auto idx_dims = index.dims();
     PADDLE_ENFORCE_GT(og_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of output@Grad shall be greater than 1, "
                           "but got %ld <= 1. Please check the input value.",
                           og_dims.size()));
     PADDLE_ENFORCE_GT(ig_dims.size(),
                       1,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The rank of input@Grad shall be greater than 1, but "
                           "got %ld <= 1. Please check the input value.",
                           ig_dims.size()));
     for (int64_t i = 1; i < og_dims.size(); ++i) {
       PADDLE_ENFORCE_EQ(og_dims[i],
                         ig_dims[i],
-                        platform::errors::InvalidArgument(
+                        errors::InvalidArgument(
                             "The dimension of input@Grad and output@Grad shall "
                             "be same. Expected %ld == %ld, but got %ld != %ld. "
                             "Please check the input value.",
@@ -211,7 +210,7 @@ class MaxSeqPoolGradFunctor {
     PADDLE_ENFORCE_EQ(
         idx_dims,
         og_dims,
-        platform::errors::InvalidArgument(
+        errors::InvalidArgument(
             "The dimension of index and output@Grad shall be same. Expected "
             "%ld == %ld, but got %ld != %ld. Please check the input value.",
             idx_dims,
@@ -317,7 +316,7 @@ class SumSeqPoolGradFunctor {
     int64_t in_w = in_grad->numel() / in_grad->dims()[0];
     PADDLE_ENFORCE_EQ(in_w,
                       out_w,
-                      platform::errors::InvalidArgument(
+                      errors::InvalidArgument(
                           "The feature size of input@Grad and output@Grad "
                           "shall be same. Expected %ld == %ld, but got %ld != "
                           "%ld. Please check the input value.",
@@ -326,7 +325,7 @@ class SumSeqPoolGradFunctor {
                           in_w,
                           out_w));
     const T* out_g_data = out_grad.data<T>();
-    T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
+    T* in_g_data = context.template Alloc<T>(in_grad);
     auto blas = phi::funcs::GetBlas<phi::CPUContext, T>(context);
     for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
       int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
@@ -354,21 +353,21 @@ class SequencePoolFunctor<phi::CPUContext, T> {
                   phi::DenseTensor* index = nullptr) {
     if (pooltype == "MAX") {
       if (is_test) {
-        math::MaxSeqPoolFunctor<T, true> max_pool;
+        phi::funcs::MaxSeqPoolFunctor<T, true> max_pool;
         max_pool(context, input, pad_value, output, index);
       } else {
-        math::MaxSeqPoolFunctor<T, false> max_pool;
+        phi::funcs::MaxSeqPoolFunctor<T, false> max_pool;
         max_pool(context, input, pad_value, output, index);
       }
       return;
     }
     if (pooltype == "LAST") {
-      math::LastSeqPoolFunctor<T> last_pool;
+      phi::funcs::LastSeqPoolFunctor<T> last_pool;
       last_pool(context, input, pad_value, output);
       return;
     }
     if (pooltype == "FIRST") {
-      math::FirstSeqPoolFunctor<T> first_pool;
+      phi::funcs::FirstSeqPoolFunctor<T> first_pool;
       first_pool(context, input, pad_value, output);
       return;
     }
@@ -377,17 +376,17 @@ class SequencePoolFunctor<phi::CPUContext, T> {
     if (pooltype == "SUM") {
       auto place = context.GetPlace();
       PADDLE_ENFORCE_EQ(
-          platform::is_cpu_place(place),
+          place == phi::CPUPlace(),
           true,
-          platform::errors::InvalidArgument(
+          errors::InvalidArgument(
               "Sequence_pool should run on CPU Device when pooltype is SUM"));
       const T* src = input.data<T>();
-      T* dst = output->mutable_data<T>(place);
+      T* dst = context.template Alloc<T>(output);
       phi::jit::seq_pool_attr_t attr(
           static_cast<int>(input.numel() / input.dims()[0]),
           phi::jit::SeqPoolType::kSum);
       auto seqpool = phi::jit::KernelFuncs<phi::jit::SeqPoolTuple<T>,
-                                           platform::CPUPlace>::Cache()
+                                           phi::CPUPlace>::Cache()
                          .At(attr);
       for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
         attr.h = static_cast<int>(lod[i + 1] - lod[i]);
@@ -424,7 +423,7 @@ class SequencePoolFunctor<phi::CPUContext, T> {
         out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
                               std::sqrt(static_cast<T>(h));
       } else {
-        PADDLE_THROW(platform::errors::InvalidArgument(
+        PADDLE_THROW(errors::InvalidArgument(
             "unsupported pooling pooltype: %s. Only support \"AVERAGE\" and "
             "\"SQRT\"",
             pooltype));
@@ -443,7 +442,7 @@ class SequencePoolGradFunctor<phi::CPUContext, T> {
                   /* max pool has index */
                   const phi::DenseTensor* index = nullptr) {
     if (pooltype == "MAX") {
-      math::MaxSeqPoolGradFunctor<T> max_pool_grad;
+      phi::funcs::MaxSeqPoolGradFunctor<T> max_pool_grad;
       max_pool_grad(context, out_grad, *index, in_grad);
       return;
     }
@@ -455,7 +454,7 @@ class SequencePoolGradFunctor<phi::CPUContext, T> {
     }
 
     if (pooltype == "SUM") {
-      math::SumSeqPoolGradFunctor<T> sum_pool_grad;
+      phi::funcs::SumSeqPoolGradFunctor<T> sum_pool_grad;
       sum_pool_grad(context, out_grad, in_grad);
       return;
     }
@@ -485,7 +484,7 @@ class SequencePoolGradFunctor<phi::CPUContext, T> {
       } else if (pooltype == "FIRST") {
         in_g_e.chip(0, 0).device(place) = out_g_e_v;
       } else {
-        PADDLE_THROW(platform::errors::InvalidArgument(
+        PADDLE_THROW(errors::InvalidArgument(
             "unsupported pooling pooltype: %s. Only support \"AVERAGE\", "
             "\"SQRT\", \"LAST\" and \"FIRST\"",
             pooltype));
@@ -499,6 +498,5 @@ template class SequencePoolFunctor<phi::CPUContext, double>;
 template class SequencePoolGradFunctor<phi::CPUContext, float>;
 template class SequencePoolGradFunctor<phi::CPUContext, double>;
 
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

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

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2023 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.
@@ -15,14 +15,14 @@ limitations under the License. */
 #include <algorithm>
 #include <string>
 
-#include "paddle/fluid/operators/math/sequence_pooling.h"
-#include "paddle/fluid/platform/macros.h"
 #include "paddle/phi/backends/gpu/gpu_primitives.h"
+#include "paddle/phi/core/macros.h"
+#include "paddle/phi/core/mixed_vector.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/sequence_pooling.h"
 
-namespace paddle {
-namespace operators {
-namespace math {
+namespace phi {
+namespace funcs {
 
 template <typename T>
 struct MaxPoolFunctor {
@@ -213,7 +213,7 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               index->data<int>());
     } else if (pooltype == "AVERAGE") {
       sequence_pool_kernel<T, AvgPoolFunctor<T>>
@@ -224,7 +224,7 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               nullptr);
     } else if (pooltype == "SUM") {
       sequence_pool_kernel<T, SumPoolFunctor<T>>
@@ -235,7 +235,7 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               nullptr);
     } else if (pooltype == "SQRT") {
       sequence_pool_kernel<T, SqrtPoolFunctor<T>>
@@ -246,7 +246,7 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               nullptr);
     } else if (pooltype == "LAST") {
       sequence_pool_kernel<T, LastPoolFunctor<T>>
@@ -257,7 +257,7 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               nullptr);
     } else if (pooltype == "FIRST") {
       sequence_pool_kernel<T, FirstPoolFunctor<T>>
@@ -268,10 +268,10 @@ class SequencePoolFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              output->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(output),
               nullptr);
     } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
+      PADDLE_THROW(errors::InvalidArgument(
           "unsupported pooling pooltype: %s. Only support \"MAX\", "
           "\"AVERAGE\", \"SUM\", \"SQRT\", \"LAST\" and \"FIRST\"",
           pooltype));
@@ -430,7 +430,7 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               index->data<int>());
     } else if (pooltype == "AVERAGE") {
       sequence_pool_grad_kernel<T, AvgPoolGradFunctor<T>>
@@ -440,7 +440,7 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               nullptr);
     } else if (pooltype == "SUM") {
       sequence_pool_grad_kernel<T, SumPoolGradFunctor<T>>
@@ -450,7 +450,7 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               nullptr);
     } else if (pooltype == "SQRT") {
       sequence_pool_grad_kernel<T, SqrtPoolGradFunctor<T>>
@@ -460,7 +460,7 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               nullptr);
     } else if (pooltype == "LAST") {
       sequence_pool_grad_kernel<T, LastPoolGradFunctor<T>>
@@ -470,7 +470,7 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               nullptr);
     } else if (pooltype == "FIRST") {
       sequence_pool_grad_kernel<T, FirstPoolGradFunctor<T>>
@@ -480,11 +480,11 @@ class SequencePoolGradFunctor<phi::GPUContext, T> {
               mix_vector.CUDAData(context.GetPlace()),
               lod.size(),
               item_dim,
-              in_grad->mutable_data<T>(context.GetPlace()),
+              context.template Alloc<T>(in_grad),
               nullptr);
 
     } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
+      PADDLE_THROW(errors::InvalidArgument(
           "unsupported pooling pooltype: %s. Only support \"MAX\", "
           "\"AVERAGE\", \"SUM\", \"SQRT\", \"LAST\" and \"FIRST\"",
           pooltype));
@@ -498,6 +498,5 @@ template class SequencePoolFunctor<phi::GPUContext, double>;
 template class SequencePoolGradFunctor<phi::GPUContext, float>;
 template class SequencePoolGradFunctor<phi::GPUContext, double>;
 
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

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

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2023 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.
@@ -15,13 +15,10 @@ limitations under the License. */
 #pragma once
 #include <string>
 
-#include "paddle/fluid/framework/lod_tensor.h"
-#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 math {
+namespace phi {
+namespace funcs {
 
 template <typename DeviceContext, typename T>
 class SequencePoolFunctor {
@@ -47,6 +44,5 @@ class SequencePoolGradFunctor {
                   const phi::DenseTensor* index = nullptr);
 };
 
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

paddle/phi/kernels/gpu/sequence_pool_kernel.cu

+/* Copyright (c) 2023 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/sequence_pool_kernel.h"
+#include "paddle/phi/kernels/impl/sequence_pool_kernel_impl.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_REGISTER_KERNEL(
+    sequence_pool, GPU, ALL_LAYOUT, phi::SequencePoolKernel, float) {}

paddle/phi/kernels/impl/sequence_pool_kernel_impl.h

+/* Copyright (c) 2023 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/kernels/funcs/sequence_pooling.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SequencePoolKernel(const Context& ctx,
+                        const DenseTensor& x,
+                        bool is_test,
+                        const std::string& pooltype,
+                        float pad_value,
+                        DenseTensor* out,
+                        DenseTensor* max_index) {
+  T pad_value_ = static_cast<T>(pad_value);
+
+  auto dims = x.dims();
+  auto lod = x.lod();
+  auto lod_level = lod.size();
+  // InferShape by lod
+  PADDLE_ENFORCE_GT(
+      lod_level,
+      0,
+      errors::InvalidArgument("Input(X) phi::DenseTensor of SequencePoolOp "
+                              "does not contain LoD information."));
+  PADDLE_ENFORCE_LE(
+      lod_level,
+      2UL,
+      errors::InvalidArgument("The lod level of input shall be no more than 2."
+                              "Received lod level is %d.",
+                              lod_level));
+  PADDLE_ENFORCE_GE(
+      dims[0],
+      /*batch size = */ static_cast<int64_t>(lod[lod_level - 1].size() - 1),
+      errors::InvalidArgument(
+          "The first dimension of Input(X) must be large than batch size."
+          "But received first dimension of Input(X) is %d, while batch"
+          "size is %d.",
+          dims[0],
+          static_cast<int64_t>(lod[lod_level - 1].size() - 1)));
+  if (lod_level > 1UL) {
+    PADDLE_ENFORCE_EQ(
+        lod[0][lod[0].size() - 1],
+        lod[1].size() - 1,
+        errors::InvalidArgument("The input lod information is illegal."));
+    phi::LoD out_lod;
+    out_lod.push_back(lod[0]);
+    out->set_lod(out_lod);
+  }
+  dims[0] = lod[lod_level - 1].size() - 1;
+  out->Resize({dims});
+  ctx.template Alloc<T>(out);
+  phi::DenseTensor* index = nullptr;
+
+  // Do not create index buffer for inference mode
+  if (pooltype == "MAX" &&
+      (is_test == false || (ctx.GetPlace() == phi::CPUPlace()) == false)) {
+    index = max_index;
+    index->Resize({dims});
+    ctx.template Alloc<int>(index);
+  }
+  phi::funcs::SequencePoolFunctor<Context, T> pool;
+  pool(ctx, pooltype, pad_value_, x, out, is_test, index);
+}
+
+}  // namespace phi

paddle/phi/kernels/sequence_pool_kernel.h

+/* Copyright (c) 2023 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 SequencePoolKernel(const Context& ctx,
+                        const DenseTensor& x,
+                        bool is_test,
+                        const std::string& pooltype,
+                        float pad_value,
+                        DenseTensor* out,
+                        DenseTensor* max_index);
+
+}  // namespace phi

paddle/phi/ops/compat/sequence_pool_sig.cc

+/* Copyright (c) 2023 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 SequencePoolOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature("sequence_pool",
+                         {"X"},
+                         {"is_test", "pooltype", "pad_value"},
+                         {"Out", "MaxIndex"});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(sequence_pool, phi::SequencePoolOpArgumentMapping);

test/cpp/phi/kernels/sequence_pooling_test.cc

@@ -14,11 +14,10 @@ limitations under the License. */
 
 #include <gtest/gtest.h>
 
-#include "paddle/fluid/operators/math/sequence_pooling.h"
-
 #include "paddle/phi/backends/context_pool.h"
 #include "paddle/phi/common/place.h"
 #include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/funcs/sequence_pooling.h"
 
 template <typename DeviceContext, typename T>
 void TestSequencePoolingSum(const DeviceContext &context,
@@ -78,7 +77,7 @@ void TestSequencePoolingSum(const DeviceContext &context,
   }
 
   // call functor
-  paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
+  phi::funcs::SequencePoolGradFunctor<DeviceContext, T>()(
       context, "SUM", out_grad, &in_grad);
 
   if (place == phi::CPUPlace()) {