wehub-resource-sync
96 lines
3.5 KiB
C++
96 lines
3.5 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/nll_loss_grad_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 NllLossGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& label,
|
|
const optional<DenseTensor>& weight,
|
|
const DenseTensor& total_weight,
|
|
const DenseTensor& d_out,
|
|
int64_t ignore_index,
|
|
const std::string& reduction,
|
|
DenseTensor* d_x) {
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
const auto& label_type = label.dtype();
|
|
bool label_type_match =
|
|
label_type == DataType::INT32 || label_type == DataType::INT64;
|
|
PADDLE_ENFORCE_EQ(label_type_match,
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"Input(Label) holds the wrong type, it holds %s, but "
|
|
"desires to be %s or %s",
|
|
label_type,
|
|
DataType::INT32,
|
|
DataType::INT64));
|
|
|
|
auto d_out_data = d_out.data<XPUType>();
|
|
auto d_x_data = dev_ctx.template Alloc<XPUType>(d_x);
|
|
|
|
auto d_x_dims = d_x->dims();
|
|
std::vector<int64_t> d_x_shape = vectorize<int64_t>(d_x_dims);
|
|
|
|
auto weight_data =
|
|
weight.get_ptr() ? weight.get_ptr()->data<float>() : nullptr;
|
|
|
|
int64_t reduction_id = 0;
|
|
if (reduction == "none") {
|
|
reduction_id = 0;
|
|
} else if (reduction == "mean") {
|
|
reduction_id = 1;
|
|
} else if (reduction == "sum") {
|
|
reduction_id = 2;
|
|
}
|
|
|
|
auto total_weight_data = total_weight.data<XPUType>();
|
|
|
|
int r;
|
|
if (label_type == DataType::INT32) {
|
|
const int* label_data = label.data<int>();
|
|
r = xpu::nll_loss_grad(dev_ctx.x_context(),
|
|
d_out_data,
|
|
d_x_data,
|
|
d_x_shape,
|
|
label_data,
|
|
weight_data,
|
|
reduction_id,
|
|
ignore_index,
|
|
total_weight_data);
|
|
} else if (label_type == DataType::INT64) {
|
|
const int64_t* label_data = label.data<int64_t>();
|
|
r = xpu::nll_loss_grad(dev_ctx.x_context(),
|
|
d_out_data,
|
|
d_x_data,
|
|
d_x_shape,
|
|
label_data,
|
|
weight_data,
|
|
reduction_id,
|
|
ignore_index,
|
|
total_weight_data);
|
|
}
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "nll_loss_grad");
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
// TODO(xiongkun): add the non-raw kernel register here.
|
|
PD_REGISTER_KERNEL(
|
|
nll_loss_grad, XPU, ALL_LAYOUT, phi::NllLossGradKernel, float) {}
|