wehub-resource-sync
103 lines
3.5 KiB
Plaintext
103 lines
3.5 KiB
Plaintext
// 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/core/kernel_registry.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/gather.cu.h"
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#include "paddle/phi/kernels/funcs/scatter.cu.h"
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#include "paddle/phi/kernels/gather_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void GatherGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& index,
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const DenseTensor& out_grad,
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const Scalar& axis,
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DenseTensor* x_grad) {
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// x [4, 2], index [2, 0], out [2, 0], x_grad [4, 2]
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if (out_grad.numel() == 0 || (x_grad && x_grad->numel() == 0)) {
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if (x_grad) {
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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}
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return;
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}
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const auto& index_type = index.dtype();
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auto axis_v = axis.to<int>();
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if (axis_v < 0) {
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axis_v += static_cast<int>(x.dims().size());
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}
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if (axis_v != 0) {
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if (index_type == DataType::INT32) {
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funcs::GatherV2GradCUDAFunction<T, int32_t>(
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&out_grad, &index, axis_v, x_grad, dev_ctx);
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} else if (index_type == DataType::INT64) {
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funcs::GatherV2GradCUDAFunction<T, int64_t>(
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&out_grad, &index, axis_v, x_grad, dev_ctx);
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}
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return;
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}
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dev_ctx.template Alloc<T>(x_grad);
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funcs::set_constant(dev_ctx, x_grad, static_cast<float>(0));
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if (out_grad.numel() == 0) {
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return;
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}
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if (index.dims().size() != 0) {
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if (index_type == DataType::INT32) {
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DenseTensor index_int64 =
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Cast<int32_t, Context>(dev_ctx, index, DataType::INT64);
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funcs::GPUScatterAdd<T, int64_t>(
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dev_ctx, out_grad, index_int64, x_grad, axis_v);
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} else if (index_type == DataType::INT64) {
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funcs::GPUScatterAdd<T, int64_t>(
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dev_ctx, out_grad, index, x_grad, axis_v);
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} else {
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PADDLE_THROW(common::errors::InvalidArgument(
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"The data type of Input(Index) of gather_grad must be int32 or int64 "
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"on GPU."));
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}
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} else {
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if (index_type == DataType::INT32) {
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funcs::GPUScatterAssign<T, int>(dev_ctx, out_grad, index, x_grad, false);
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} else if (index_type == DataType::INT64) {
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funcs::GPUScatterAssign<T, int64_t>(
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dev_ctx, out_grad, index, x_grad, false);
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} else {
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PADDLE_THROW(common::errors::InvalidArgument(
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"The data type of Input(Index) of gather_grad must be int32 or int64 "
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"on GPU."));
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(gather_grad,
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GPU,
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ALL_LAYOUT,
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phi::GatherGradKernel,
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float,
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double,
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int64_t,
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int,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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