wehub-resource-sync
128 lines
4.7 KiB
C++
128 lines
4.7 KiB
C++
// Copyright (c) 2023 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/scatter_nd_add_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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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void ScatterNdAddGradKernel(const Context &dev_ctx,
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const DenseTensor &index,
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const DenseTensor &updates,
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const DenseTensor &out_grad,
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DenseTensor *x_grad,
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DenseTensor *updates_grad) {
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if (out_grad.numel() == 0) {
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if (x_grad) {
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dev_ctx.template Alloc<T>(x_grad);
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}
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if (updates_grad) {
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Full<T, Context>(dev_ctx, updates_grad->dims(), 0, updates_grad);
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}
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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int ret = 0;
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const T *out_grad_data = out_grad.data<T>();
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if (x_grad) {
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auto *x_grad_data = dev_ctx.template Alloc<T>(x_grad);
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ret = xpu::copy<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(out_grad_data),
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reinterpret_cast<XPUType *>(x_grad_data),
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out_grad.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "copy");
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}
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if (updates_grad) {
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auto *updates_grad_data = dev_ctx.template Alloc<T>(updates_grad);
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if (updates_grad->numel() == 0) {
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return;
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}
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if (index.numel() == 0) {
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auto index_dims = index.dims();
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auto index_dims_size = index_dims.size();
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int64_t end_size = index_dims[index_dims_size - 1];
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PADDLE_ENFORCE_EQ(
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end_size,
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0,
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errors::InvalidArgument(
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"Size of the last dim of the index tensor [%d] should be 0",
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end_size));
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auto remain_dims = slice_ddim(index_dims, 0, index_dims_size - 1);
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int64_t remain_numel = common::product(remain_dims);
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int64_t updates_grad_numel = updates_grad->numel();
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int64_t out_grad_numel = out_grad.numel();
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PADDLE_ENFORCE_EQ(
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remain_numel * out_grad_numel,
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updates_grad_numel,
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errors::InvalidArgument("out_grad numel[%d] * remain numel[%d] "
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"should math updates_grad numel[%d]",
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out_grad_numel,
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remain_numel,
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updates_grad_numel));
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ret = xpu::broadcast<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(out_grad_data),
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reinterpret_cast<XPUType *>(updates_grad_data),
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{1, out_grad_numel},
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{remain_numel, out_grad_numel});
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "broadcast");
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return;
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}
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auto index_shape_vec = vectorize<int64_t>(index.dims());
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if (index_shape_vec.size() == 1) {
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index_shape_vec.insert(index_shape_vec.begin(), 1);
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}
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auto out_grad_shape_vec = vectorize<int64_t>(out_grad.dims());
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xpu::VectorParam<int64_t> out_grad_shape_param = {
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out_grad_shape_vec.data(),
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static_cast<int64_t>(out_grad_shape_vec.size()),
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nullptr};
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if (index.dtype() == DataType::INT32) {
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ret = xpu::gather_nd<XPUType, int>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(out_grad_data),
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index.data<int>(),
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reinterpret_cast<XPUType *>(updates_grad_data),
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out_grad_shape_param,
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index_shape_vec);
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} else {
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ret = xpu::gather_nd<XPUType, int64_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(out_grad_data),
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index.data<int64_t>(),
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reinterpret_cast<XPUType *>(updates_grad_data),
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out_grad_shape_param,
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index_shape_vec);
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}
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "gather_nd");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(scatter_nd_add_grad,
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XPU,
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ALL_LAYOUT,
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phi::ScatterNdAddGradKernel,
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phi::float16,
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phi::bfloat16,
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float,
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int,
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int64_t) {}
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