wehub-resource-sync
134 lines
4.7 KiB
C++
134 lines
4.7 KiB
C++
// Copyright (c) 2022 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_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 ScatterKernel(const Context &dev_ctx,
|
|
const DenseTensor &x,
|
|
const DenseTensor &index,
|
|
const DenseTensor &updates,
|
|
bool overwrite,
|
|
DenseTensor *out) {
|
|
if (index.numel() == 0) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
|
|
return;
|
|
}
|
|
if (out && out->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
return;
|
|
}
|
|
using XPUTypeT = typename XPUTypeTrait<T>::Type;
|
|
out->Resize(x.dims());
|
|
auto *x_data = reinterpret_cast<const XPUTypeT *>(x.data<T>());
|
|
auto *updates_data = reinterpret_cast<const XPUTypeT *>(updates.data<T>());
|
|
auto *out_data = reinterpret_cast<XPUTypeT *>(dev_ctx.template Alloc<T>(out));
|
|
int ret = xpu::copy(dev_ctx.x_context(), x_data, out_data, x.numel());
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(ret, "copy");
|
|
|
|
// Apply ScatterUpdate: Out[index] = Updates[:]
|
|
const 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));
|
|
|
|
// check index of shape 1-D
|
|
PADDLE_ENFORCE_EQ(
|
|
index.dims().size() == 1 || index.dims().size() == 0 ||
|
|
(index.dims().size() == 2 && index.dims()[1] == 1),
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"index's shape is error, "
|
|
"expect index'dims shape is 0, 1, 2 (index.dims[1] should "
|
|
"be 1), 0 but got index'dims shape is %d",
|
|
index.dims().size()));
|
|
|
|
int64_t index_size = index.dims().size() == 0 ? 1 : index.dims()[0];
|
|
auto x_dims = x.dims();
|
|
auto update_dims = updates.dims();
|
|
if (index.dims().size() != 0) {
|
|
// only check when the updates tensor is not a 0D tensor
|
|
for (int i = 1; i < x_dims.size(); i++)
|
|
PADDLE_ENFORCE_EQ(
|
|
x_dims[i],
|
|
update_dims[i],
|
|
common::errors::InvalidArgument(
|
|
"The dimensions of the source tensor and target tensor should"
|
|
" match, but received source tensor's %d-th dimension is %d,"
|
|
"target tensor's %d-th dimension is %d.",
|
|
i,
|
|
x_dims[i],
|
|
i,
|
|
update_dims[i]));
|
|
}
|
|
|
|
int64_t dim0 = x.dims()[0];
|
|
int64_t dim1 = common::product(slice_ddim(x_dims, 1, x_dims.size()));
|
|
|
|
DenseTensor indices_cpu(index.type());
|
|
Copy(dev_ctx, index, CPUPlace(), true, &indices_cpu);
|
|
|
|
int r = 0;
|
|
if (index_type == DataType::INT32) {
|
|
auto index_data = const_cast<int *>(index.data<int>());
|
|
xpu::VectorParam<int> indices{
|
|
indices_cpu.data<int>(), index_size, index_data};
|
|
r = xpu::scatter(dev_ctx.x_context(),
|
|
updates_data,
|
|
out_data,
|
|
indices,
|
|
dim0,
|
|
dim1,
|
|
overwrite);
|
|
} else {
|
|
auto index_data = const_cast<int64_t *>(index.data<int64_t>());
|
|
xpu::VectorParam<int64_t> indices{
|
|
indices_cpu.data<int64_t>(), index_size, index_data};
|
|
r = xpu::scatter(dev_ctx.x_context(),
|
|
updates_data,
|
|
out_data,
|
|
indices,
|
|
dim0,
|
|
dim1,
|
|
overwrite);
|
|
}
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "scatter");
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(scatter,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::ScatterKernel,
|
|
float,
|
|
int32_t,
|
|
int64_t,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|