wehub-resource-sync
125 lines
3.8 KiB
C++
125 lines
3.8 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/top_k_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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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/xpu/xpu_mem_util.h"
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namespace {
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inline void GetDims(
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const phi::DDim& dim, int axis, int64_t* pre, int64_t* n, int64_t* post) {
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*pre = 1;
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*post = 1;
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*n = dim[axis];
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for (int i = 0; i < axis; ++i) {
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(*pre) *= dim[i];
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}
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for (int i = axis + 1; i < dim.size(); ++i) {
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(*post) *= dim[i];
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}
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}
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} // namespace
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namespace phi {
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template <typename T, typename Context>
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void TopkGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out_grad,
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const Scalar& k_scalar,
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int axis,
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bool largest,
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bool sorted,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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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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const auto& in_dims = x.dims();
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// get the real the axis and the k
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if (axis < 0) {
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axis += in_dims.size();
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}
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// allocate the xpu memory for the x_grad
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T* x_grad_data = dev_ctx.template Alloc<T>(x_grad);
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const T* out_grad_data = out_grad.data<T>();
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const int64_t* indices_data = indices.data<int64_t>();
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if (in_dims.size() == 0) {
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Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
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return;
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}
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int64_t pre, n, post;
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GetDims(in_dims, axis, &pre, &n, &post);
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Full<T, Context>(dev_ctx, x_grad->dims(), 0.0f, x_grad);
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// launch the xpu kernel to assign the grad
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int ret = xpu::scatter_element<XPUType, int64_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_grad_data),
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reinterpret_cast<const XPUType*>(out_grad_data),
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indices_data,
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reinterpret_cast<XPUType*>(x_grad_data),
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vectorize(x_grad->dims()),
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vectorize(out_grad.dims()),
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vectorize(indices.dims()),
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axis,
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/*reduction=override*/ 0);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "scatter");
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}
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template <typename T, typename Context>
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void TopkV1GradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out_grad,
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const Scalar& k_scalar,
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DenseTensor* x_grad) {
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TopkGradKernel<T, Context>(
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dev_ctx, x, indices, out_grad, k_scalar, -1, true, true, x_grad);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(topk_grad,
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XPU,
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ALL_LAYOUT,
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phi::TopkGradKernel,
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float,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(topk_v1_grad,
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XPU,
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ALL_LAYOUT,
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phi::TopkV1GradKernel,
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float,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16) {}
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