wehub-resource-sync
99 lines
3.5 KiB
C++
99 lines
3.5 KiB
C++
// Copyright (c) 2025 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/take_along_axis_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void TakeAlongAxisGradKernel(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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int axis,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) {
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return;
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}
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const auto& index_dtype = index.dtype();
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bool index_dtype_match =
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index_dtype == DataType::INT32 || index_dtype == DataType::INT64;
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PADDLE_ENFORCE_EQ(index_dtype_match,
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true,
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errors::InvalidArgument(
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"Input(Index) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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DataTypeToString(index_dtype),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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int r = xpu::constant(dev_ctx.x_context(),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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x_grad->numel(),
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XPUType(0));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
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if (out_grad.numel() == 0 || index.numel() == 0) {
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return;
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}
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auto x_shape = vectorize<int64_t>(x.dims());
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auto out_grad_shape = vectorize<int64_t>(out_grad.dims());
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auto index_shape = vectorize<int64_t>(index.dims());
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if (index_dtype == DataType::INT32) {
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r = xpu::paddle_put_along_axis<XPUType, int>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_grad->data<T>()),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<const int*>(index.data<int>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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x_shape,
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out_grad_shape,
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index_shape,
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axis,
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1,
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false);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "paddle_put_along_axis");
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} else {
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r = xpu::paddle_put_along_axis<XPUType, int64_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_grad->data<T>()),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<const int64_t*>(index.data<int64_t>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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x_shape,
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out_grad_shape,
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index_shape,
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axis,
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1,
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false);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "paddle_put_along_axis");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(take_along_axis_grad,
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XPU,
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ALL_LAYOUT,
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phi::TakeAlongAxisGradKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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