wehub-resource-sync
127 lines
4.6 KiB
C++
127 lines
4.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/repeat_interleave_grad_kernel.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/utils/data_type.h"
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#include "paddle/phi/kernels/cpu/index_select_impl.h"
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#include "paddle/phi/kernels/funcs/repeat_tensor2index_tensor.h"
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namespace phi {
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template <typename T, typename Context>
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void RepeatInterleaveWithTensorIndexGradKernel(
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const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& repeats_tensor,
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const DenseTensor& out_grad,
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int dim,
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int64_t output_size UNUSED,
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DenseTensor* x_grad) {
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auto input_dim = x_grad->dims();
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if (dim < 0) {
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dim += input_dim.size();
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}
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DenseTensor index;
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PADDLE_ENFORCE_EQ(repeats_tensor.dims()[0] == x_grad->dims()[dim],
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true,
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common::errors::InvalidArgument(
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"The length of Input(RepeatsTensor) must be the "
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"same as length of Input(X) in axis. "
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"But received: [%s], required: [%d].",
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repeats_tensor.dims()[0],
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x_grad->dims()[dim]));
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const auto& index_type = repeats_tensor.dtype();
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bool index_type_match =
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index_type == DataType::INT32 || index_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(index_type_match,
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true,
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common::errors::InvalidArgument(
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"Input(Repeats) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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DataTypeToString(index_type),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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DeviceContextPool::Instance().Get(repeats_tensor.place());
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if (index_type == DataType::INT32) {
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funcs::RepeatsTensor2IndexTensorFunctor<Context, int>()(
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dev_ctx, repeats_tensor, &index);
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IndexSelectGradInner<Context, T, int>(
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dev_ctx, out_grad, index, x_grad, dim);
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} else if (index_type == DataType::INT64) {
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funcs::RepeatsTensor2IndexTensorFunctor<Context, int64_t>()(
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dev_ctx, repeats_tensor, &index);
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IndexSelectGradInner<Context, T, int64_t>(
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dev_ctx, out_grad, index, x_grad, dim);
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}
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}
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template <typename T, typename Context>
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void RepeatInterleaveGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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int repeats,
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int dim,
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int64_t output_size UNUSED,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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auto input_dim = x_grad->dims();
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if (dim < 0) {
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dim += input_dim.size();
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}
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DenseTensor index;
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int64_t index_size = x_grad->dims()[dim] * repeats;
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std::vector<int> index_vec(index_size);
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for (int i = 0; i < x_grad->dims()[dim]; i++) {
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std::fill_n(index_vec.begin() + i * repeats, repeats, i);
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}
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index.Resize({index_size});
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TensorFromVector<int>(index_vec, dev_ctx, &index);
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const DenseTensor index_copy = index;
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IndexSelectGradInner<Context, T, int>(
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dev_ctx, out_grad, index_copy, x_grad, dim);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(repeat_interleave_with_tensor_index_grad,
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CPU,
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ALL_LAYOUT,
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phi::RepeatInterleaveWithTensorIndexGradKernel,
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float,
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double,
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int,
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int64_t,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(repeat_interleave_grad,
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CPU,
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ALL_LAYOUT,
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phi::RepeatInterleaveGradKernel,
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float,
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double,
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int,
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int64_t,
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phi::bfloat16) {}
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