[Phi] move flip op to phi kernel (#39822)

paddle/fluid/operators/flip_op.cc

@@ -12,12 +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/flip_op.h"
 #include <string>
 #include <unordered_map>
 #include <vector>
+
+#include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/op_version_registry.h"
-#include "paddle/fluid/platform/complex.h"
 
 namespace paddle {
 namespace operators {
@@ -29,6 +29,7 @@ class FlipOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
+  // TODO move to phi kernel
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE_EQ(
         ctx->HasInput("X"), true,
@@ -150,14 +151,6 @@ namespace plat = paddle::platform;
 REGISTER_OPERATOR(flip, ops::FlipOp, ops::FlipOpMaker, ops::FlipOpInferVarType,
                   ops::FlipOpGradMaker<paddle::framework::OpDesc>,
                   ops::FlipOpGradMaker<paddle::imperative::OpBase>);
-REGISTER_OP_CPU_KERNEL(
-    flip, ops::FlipKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, int32_t>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, int64_t>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, bool>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, plat::complex<float>>,
-    ops::FlipKernel<paddle::platform::CPUDeviceContext, plat::complex<double>>);
 
 /* ==========================  register checkpoint ===========================*/
 REGISTER_OP_VERSION(flip)

paddle/fluid/operators/flip_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/flip_op.h"
-
-#include <vector>
-#include "paddle/fluid/memory/malloc.h"
-#include "paddle/fluid/platform/complex.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-using CUDADeviceContext = paddle::platform::CUDADeviceContext;
-
-template <typename T>
-__global__ void flip_cuda_kernel(const int N, const T* in_data, T* out_data,
-                                 int64_t* x_shape, int64_t* x_stride,
-                                 int* flip_dims, int flip_dims_size,
-                                 int total_dims) {
-  int idx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (idx >= N) {
-    return;
-  }
-
-  int cur_indices = idx, rem = 0, dst_offset = 0;
-  for (int i = 0; i < total_dims; ++i) {
-    int64_t temp = cur_indices;
-    cur_indices = cur_indices / x_stride[i];
-    rem = temp - cur_indices * x_stride[i];
-    // flip the indices if it is in flip_dims
-    for (int j = 0; j < flip_dims_size; ++j) {
-      if (i == flip_dims[j]) {
-        cur_indices = x_shape[i] - 1 - cur_indices;
-      }
-    }
-    dst_offset += cur_indices * x_stride[i];
-    cur_indices = rem;
-  }
-  out_data[idx] = in_data[dst_offset];
-}
-
-template <typename T>
-class FlipKernel<platform::CUDADeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    const auto gplace = ctx.GetPlace();
-    auto cplace = platform::CPUPlace();
-    auto& dev_ctx = ctx.template device_context<CUDADeviceContext>();
-
-    const Tensor* x = ctx.Input<Tensor>("X");
-    Tensor* out = ctx.Output<Tensor>("Out");
-    auto* in_data = x->data<T>();
-    auto* out_data = out->mutable_data<T>(ctx.GetPlace());
-    auto flip_dims = ctx.template Attr<std::vector<int>>("axis");
-
-    const int flip_dims_size = static_cast<int>(flip_dims.size());
-    auto x_dims = x->dims();
-    const int total_dims = x_dims.size();
-    const int N = x->numel();
-
-    int block_size = 512;
-    dim3 dim_block(block_size);
-    dim3 dim_grid((N + block_size - 1) / block_size);
-
-    for (size_t i = 0; i < flip_dims.size(); ++i) {
-      if (flip_dims[i] < 0) {
-        flip_dims[i] += total_dims;
-      }
-    }
-
-    auto x_stride = phi::stride(x_dims);
-    std::vector<int64_t> x_dims_v = phi::vectorize(x_dims);
-    std::vector<int64_t> x_stride_v = phi::vectorize(x_stride);
-
-    int bytes = total_dims * sizeof(int64_t);
-    auto x_strides_array_tmp = memory::Alloc(dev_ctx, bytes);
-    int64_t* x_strides_array_gpu =
-        reinterpret_cast<int64_t*>(x_strides_array_tmp->ptr());
-    memory::Copy(gplace, x_strides_array_gpu, cplace, x_stride_v.data(), bytes,
-                 dev_ctx.stream());
-
-    auto x_shape_array_tmp = memory::Alloc(dev_ctx, bytes);
-    int64_t* x_shape_array_gpu =
-        reinterpret_cast<int64_t*>(x_shape_array_tmp->ptr());
-    memory::Copy(gplace, x_shape_array_gpu, cplace, x_dims_v.data(), bytes,
-                 dev_ctx.stream());
-
-    bytes = flip_dims_size * sizeof(int);
-    auto flip_dims_array_tmp = memory::Alloc(dev_ctx, bytes);
-    int* flip_dims_array_gpu =
-        reinterpret_cast<int*>(flip_dims_array_tmp->ptr());
-    memory::Copy(gplace, flip_dims_array_gpu, cplace, flip_dims.data(), bytes,
-                 dev_ctx.stream());
-
-    flip_cuda_kernel<
-        T><<<dim_grid, dim_block, 0, ctx.cuda_device_context().stream()>>>(
-        N, in_data, out_data, x_shape_array_gpu, x_strides_array_gpu,
-        flip_dims_array_gpu, flip_dims_size, total_dims);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-REGISTER_OP_CUDA_KERNEL(
-    flip, ops::FlipKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, plat::float16>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, int64_t>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, bool>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext, plat::complex<float>>,
-    ops::FlipKernel<paddle::platform::CUDADeviceContext,
-                    plat::complex<double>>);

paddle/fluid/operators/flip_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 <algorithm>
-#include <bitset>
-#include <vector>
-
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/operator.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-constexpr size_t dim_bitset_size = 64;
-
-template <typename DeviceContext, typename T>
-class FlipKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override;
-};
-
-template <typename T>
-class FlipKernel<platform::CPUDeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    const Tensor* x = ctx.Input<Tensor>("X");
-    Tensor* out = ctx.Output<Tensor>("Out");
-    auto flip_dims = ctx.template Attr<std::vector<int>>("axis");
-
-    auto x_dims = x->dims();
-    const int total_dims = x_dims.size();
-    std::bitset<dim_bitset_size> dim_bitset;
-    for (size_t i = 0; i < flip_dims.size(); ++i) {
-      int dim = flip_dims[i];
-      if (flip_dims[i] < 0) {
-        dim += total_dims;
-      }
-      dim_bitset[dim] = true;
-    }
-    auto x_strides = phi::stride(x_dims);
-    auto numel = x->numel();
-    const T* x_data = x->data<T>();
-    T* out_data = out->mutable_data<T>(ctx.GetPlace());
-#ifdef PADDLE_WITH_MKLML
-#pragma omp parallel for
-#endif
-    for (int64_t i = 0; i < numel; ++i) {
-      int64_t cur_indices = i;
-      int64_t rem = 0;
-      int64_t dst_offset = 0;
-
-      for (int d = 0; d < total_dims; ++d) {
-        int64_t temp = cur_indices;
-        cur_indices = cur_indices / x_strides[d];
-        rem = temp - cur_indices * x_strides[d];
-        dst_offset += dim_bitset[d]
-                          ? (x_dims[d] - 1 - cur_indices) * x_strides[d]
-                          : cur_indices * x_strides[d];
-        cur_indices = rem;
-      }
-      out_data[i] = x_data[dst_offset];
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/phi/kernels/cpu/flip_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/flip_kernel.h"
+
+#include <bitset>
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+constexpr size_t dim_bitset_size = 64;
+
+template <typename T, typename Context>
+void FlipKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const std::vector<int>& axis,
+                DenseTensor* out) {
+  auto x_dims = x.dims();
+  const int total_dims = x_dims.size();
+  std::bitset<dim_bitset_size> dim_bitset;
+  for (size_t i = 0; i < axis.size(); ++i) {
+    int dim = axis[i];
+    if (axis[i] < 0) {
+      dim += total_dims;
+    }
+    dim_bitset[dim] = true;
+  }
+  auto x_strides = phi::stride(x_dims);
+  auto numel = x.numel();
+  const T* x_data = x.data<T>();
+  T* out_data = dev_ctx.template Alloc<T>(out);
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (int64_t i = 0; i < numel; ++i) {
+    int64_t cur_indices = i;
+    int64_t rem = 0;
+    int64_t dst_offset = 0;
+
+    for (int d = 0; d < total_dims; ++d) {
+      int64_t temp = cur_indices;
+      cur_indices = cur_indices / x_strides[d];
+      rem = temp - cur_indices * x_strides[d];
+      dst_offset += dim_bitset[d] ? (x_dims[d] - 1 - cur_indices) * x_strides[d]
+                                  : cur_indices * x_strides[d];
+      cur_indices = rem;
+    }
+    out_data[i] = x_data[dst_offset];
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(flip,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::FlipKernel,
+                   float,
+                   double,
+                   int32_t,
+                   int64_t,
+                   bool,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}

paddle/phi/kernels/flip_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 <vector>
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void FlipKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const std::vector<int>& axis,
+                DenseTensor* out);
+
+}  // namespace phi

paddle/phi/kernels/gpu/flip_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/flip_kernel.h"
+
+#include "paddle/fluid/memory/malloc.h"
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T>
+__global__ void flip_cuda_kernel(const int N,
+                                 const T* in_data,
+                                 T* out_data,
+                                 int64_t* x_shape,
+                                 int64_t* x_stride,
+                                 int* flip_dims,
+                                 int flip_dims_size,
+                                 int total_dims) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx >= N) {
+    return;
+  }
+
+  int cur_indices = idx, rem = 0, dst_offset = 0;
+  for (int i = 0; i < total_dims; ++i) {
+    int64_t temp = cur_indices;
+    cur_indices = cur_indices / x_stride[i];
+    rem = temp - cur_indices * x_stride[i];
+    // flip the indices if it is in flip_dims
+    for (int j = 0; j < flip_dims_size; ++j) {
+      if (i == flip_dims[j]) {
+        cur_indices = x_shape[i] - 1 - cur_indices;
+      }
+    }
+    dst_offset += cur_indices * x_stride[i];
+    cur_indices = rem;
+  }
+  out_data[idx] = in_data[dst_offset];
+}
+
+template <typename T, typename Context>
+void FlipKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const std::vector<int>& axis,
+                DenseTensor* out) {
+  const auto gplace = dev_ctx.GetPlace();
+  auto cplace = phi::CPUPlace();
+  std::vector<int> flip_dims = axis;
+
+  auto* in_data = x.data<T>();
+  auto* out_data = dev_ctx.template Alloc<T>(out);
+
+  const int flip_dims_size = static_cast<int>(flip_dims.size());
+  auto x_dims = x.dims();
+  const int total_dims = x_dims.size();
+  const int N = x.numel();
+
+  int block_size = 512;
+  dim3 dim_block(block_size);
+  dim3 dim_grid((N + block_size - 1) / block_size);
+
+  for (size_t i = 0; i < flip_dims.size(); ++i) {
+    if (flip_dims[i] < 0) {
+      flip_dims[i] += total_dims;
+    }
+  }
+
+  auto x_stride = phi::stride(x_dims);
+  std::vector<int64_t> x_dims_v = phi::vectorize(x_dims);
+  std::vector<int64_t> x_stride_v = phi::vectorize(x_stride);
+
+  int bytes = total_dims * sizeof(int64_t);
+  auto x_strides_array_tmp = paddle::memory::Alloc(dev_ctx, bytes);
+  int64_t* x_strides_array_gpu =
+      reinterpret_cast<int64_t*>(x_strides_array_tmp->ptr());
+  paddle::memory::Copy(gplace,
+                       x_strides_array_gpu,
+                       cplace,
+                       x_stride_v.data(),
+                       bytes,
+                       dev_ctx.stream());
+
+  auto x_shape_array_tmp = paddle::memory::Alloc(dev_ctx, bytes);
+  int64_t* x_shape_array_gpu =
+      reinterpret_cast<int64_t*>(x_shape_array_tmp->ptr());
+  paddle::memory::Copy(gplace,
+                       x_shape_array_gpu,
+                       cplace,
+                       x_dims_v.data(),
+                       bytes,
+                       dev_ctx.stream());
+
+  bytes = flip_dims_size * sizeof(int);
+  auto flip_dims_array_tmp = paddle::memory::Alloc(dev_ctx, bytes);
+  int* flip_dims_array_gpu = reinterpret_cast<int*>(flip_dims_array_tmp->ptr());
+  paddle::memory::Copy(gplace,
+                       flip_dims_array_gpu,
+                       cplace,
+                       flip_dims.data(),
+                       bytes,
+                       dev_ctx.stream());
+
+  flip_cuda_kernel<T><<<dim_grid, dim_block, 0, dev_ctx.stream()>>>(
+      N,
+      in_data,
+      out_data,
+      x_shape_array_gpu,
+      x_strides_array_gpu,
+      flip_dims_array_gpu,
+      flip_dims_size,
+      total_dims);
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(flip,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::FlipKernel,
+                   float,
+                   double,
+                   phi::dtype::float16,
+                   int,
+                   int64_t,
+                   bool,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}