wehub-resource-sync
140 lines
4.7 KiB
C++
140 lines
4.7 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/gather_nd_grad_kernel.h"
|
|
|
|
#include "paddle/phi/backends/xpu/enforce_xpu.h"
|
|
#include "paddle/phi/backends/xpu/xpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void GatherNdGradKernel(const Context &dev_ctx,
|
|
const DenseTensor &x,
|
|
const DenseTensor &index,
|
|
const DenseTensor &out_grad,
|
|
DenseTensor *x_grad) {
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
if (x_grad->numel() == 0) {
|
|
return;
|
|
}
|
|
int r = 0;
|
|
XPUType *dx_data = reinterpret_cast<XPUType *>(x_grad->data<T>());
|
|
r = xpu::constant<XPUType>(
|
|
dev_ctx.x_context(), dx_data, x_grad->numel(), static_cast<XPUType>(0));
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
|
|
|
|
if (out_grad.numel() == 0) {
|
|
return;
|
|
}
|
|
|
|
if (index.numel() == 0) {
|
|
auto index_dims = index.dims();
|
|
auto index_dims_size = index_dims.size();
|
|
// final dim
|
|
int64_t end_size = index_dims[index_dims_size - 1];
|
|
PADDLE_ENFORCE_EQ(
|
|
end_size,
|
|
0,
|
|
common::errors::InvalidArgument("end_size[%d] should be 0", end_size));
|
|
// remain dim
|
|
auto remain_ddim = slice_ddim(index_dims, 0, index_dims_size - 1);
|
|
int64_t remain_numel = common::product(remain_ddim);
|
|
|
|
int64_t x_numel = x.numel();
|
|
int64_t out_grad_numel = out_grad.numel();
|
|
PADDLE_ENFORCE_EQ(
|
|
x_numel * remain_numel,
|
|
out_grad_numel,
|
|
common::errors::InvalidArgument(
|
|
"x_numel[%d] * remain_numel[%d] should match out_grad_numel[%d]",
|
|
x_numel,
|
|
remain_numel,
|
|
out_grad_numel));
|
|
|
|
// int reduce_sum(Context* xpu_ctx, const T* x, T* y, const
|
|
// std::vector<int>& xshape, const std::vector<int>& rdims)
|
|
int r =
|
|
xpu::reduce_sum(dev_ctx.x_context(),
|
|
reinterpret_cast<const XPUType *>(out_grad.data<T>()),
|
|
reinterpret_cast<XPUType *>(x_grad->data<T>()),
|
|
{(int64_t)remain_numel, (int64_t)x_numel},
|
|
{0LL});
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
|
|
return;
|
|
}
|
|
|
|
auto index_type = index.dtype();
|
|
bool index_type_match =
|
|
index_type == DataType::INT32 || index_type == DataType::INT64;
|
|
PADDLE_ENFORCE_EQ(index_type_match,
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"Index holds the wrong type, it holds [%s],"
|
|
"but desires to be [%s] or [%s]",
|
|
index_type,
|
|
DataType::INT32,
|
|
DataType::INT64));
|
|
|
|
auto x_shape = vectorize<int64_t>(x_grad->dims());
|
|
auto index_shape = vectorize<int64_t>(index.dims());
|
|
if (index_shape.size() == 1) {
|
|
index_shape.insert(index_shape.begin(), 1);
|
|
}
|
|
xpu::VectorParam<int64_t> x_vec = {
|
|
x_shape.data(), static_cast<int64_t>(x_shape.size()), nullptr};
|
|
|
|
int64_t index_size = index.numel();
|
|
if (index_type == DataType::INT32) {
|
|
auto index_data = const_cast<int *>(index.data<int>());
|
|
xpu::VectorParam<int> index_vec{nullptr, index_size, index_data};
|
|
r = xpu::scatter_nd<XPUType, int>(
|
|
dev_ctx.x_context(),
|
|
nullptr,
|
|
reinterpret_cast<const XPUType *>(out_grad.data<T>()),
|
|
dx_data,
|
|
index_vec,
|
|
x_vec,
|
|
index_shape,
|
|
false);
|
|
} else {
|
|
auto index_data = const_cast<int64_t *>(index.data<int64_t>());
|
|
xpu::VectorParam<int64_t> index_vec{nullptr, index_size, index_data};
|
|
r = xpu::scatter_nd<XPUType, int64_t>(
|
|
dev_ctx.x_context(),
|
|
nullptr,
|
|
reinterpret_cast<const XPUType *>(out_grad.data<T>()),
|
|
dx_data,
|
|
index_vec,
|
|
x_vec,
|
|
index_shape,
|
|
false);
|
|
}
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "scatter_nd");
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(gather_nd_grad,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::GatherNdGradKernel,
|
|
float,
|
|
int,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
int64_t) {}
|