未验证 提交 b33673be 编写于 作者: Z zhangyikun02 提交者: GitHub

add index_select_grad for xpu (#51342)

上级 374e757f
......@@ -8,7 +8,7 @@ set(XPU_API_LIB_NAME "libxpuapi.so")
set(XPU_RT_LIB_NAME "libxpurt.so")
set(XPU_XFT_LIB_NAME "libxft.so")
set(XPU_BASE_DATE "20230227")
set(XPU_BASE_DATE "20230308")
set(XPU_XCCL_BASE_VERSION "1.0.10")
set(XPU_XFT_BASE_VERSION "latest")
......
......@@ -391,6 +391,7 @@ XPUOpMap& get_kl2_ops() {
phi::DataType::FLOAT16,
phi::DataType::FLOAT32,
phi::DataType::BOOL})},
{"index_select_grad", XPUKernelSet({phi::DataType::FLOAT32})},
{"index_select",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::INT32,
......
// 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/index_select_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
namespace phi {
template <typename T, typename Context>
void IndexSelectGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& index,
const DenseTensor& out_grad,
int dim,
DenseTensor* x_grad) {
if (dim < 0) {
dim += out_grad.dims().size();
}
const auto& index_type = index.dtype();
bool index_type_match =
index_type == phi::DataType::INT32 || index_type == phi::DataType::INT64;
PADDLE_ENFORCE_EQ(index_type_match,
true,
phi::errors::InvalidArgument(
"Input(Index) holds the wrong type, it holds %s, but "
"desires to be %s or %s",
index_type,
phi::DataType::INT32,
phi::DataType::INT64));
T* x_grad_data = ctx.template Alloc<T>(x_grad);
const T* out_grad_data = out_grad.data<T>();
auto out_grad_shape = phi::vectorize<int64_t>(out_grad.dims());
auto x_grad_shape = phi::vectorize<int64_t>(x_grad->dims());
int r = xpu::Error_t::SUCCESS;
if (index_type == phi::DataType::INT32) {
const int* index_data = index.data<int>();
r = xpu::index_select_grad<T, int>(ctx.x_context(),
nullptr,
index_data,
out_grad_data,
dim,
x_grad_data,
out_grad_shape,
x_grad_shape);
} else if (index_type == phi::DataType::INT64) {
const int64_t* index_data = index.data<int64_t>();
r = xpu::index_select_grad<T, int64_t>(ctx.x_context(),
nullptr,
index_data,
out_grad_data,
dim,
x_grad_data,
out_grad_shape,
x_grad_shape);
}
PADDLE_ENFORCE_XDNN_SUCCESS(r, "index_select_grad");
}
} // namespace phi
PD_REGISTER_KERNEL(
index_select_grad, XPU, ALL_LAYOUT, phi::IndexSelectGradKernel, float) {}
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