wehub-resource-sync
121 lines
4.5 KiB
C++
121 lines
4.5 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/scatter_nd_add_kernel.h"
|
|
|
|
#include "paddle/phi/backends/xpu/enforce_xpu.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
|
|
namespace phi {
|
|
template <typename T, typename Context>
|
|
void ScatterNdAddKernel(const Context &dev_ctx,
|
|
const DenseTensor &x,
|
|
const DenseTensor &index,
|
|
const DenseTensor &updates,
|
|
DenseTensor *out) {
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
if (out && out->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
return;
|
|
}
|
|
const XPUType *x_ptr = reinterpret_cast<const XPUType *>(x.data<T>());
|
|
const XPUType *updates_ptr =
|
|
reinterpret_cast<const XPUType *>(updates.data<T>());
|
|
|
|
dev_ctx.template Alloc<T>(out);
|
|
XPUType *out_ptr = reinterpret_cast<XPUType *>(out->data<T>());
|
|
int r = xpu::copy(dev_ctx.x_context(), x_ptr, out_ptr, x.numel());
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
|
|
|
|
if (updates.numel() == 0) return;
|
|
|
|
if (index.numel() == 0) {
|
|
int64_t index_dims_size = index.dims().size();
|
|
int64_t loop_time =
|
|
index_dims_size == 0
|
|
? 1
|
|
: common::product(slice_ddim(index.dims(), 0, index_dims_size - 1));
|
|
|
|
for (int64_t i = 0; i < loop_time; i++) {
|
|
r = xpu::broadcast_add<XPUType>(dev_ctx.x_context(),
|
|
updates_ptr + out->numel() * i,
|
|
out_ptr,
|
|
out_ptr,
|
|
{out->numel()},
|
|
{out->numel()});
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
|
|
}
|
|
return;
|
|
}
|
|
|
|
const DataType 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.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,
|
|
updates_ptr,
|
|
out_ptr,
|
|
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,
|
|
updates_ptr,
|
|
out_ptr,
|
|
index_vec,
|
|
x_vec,
|
|
index_shape,
|
|
false);
|
|
}
|
|
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "scatter_nd_add");
|
|
}
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(scatter_nd_add,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::ScatterNdAddKernel,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
float,
|
|
int,
|
|
int64_t) {}
|