// Copyright (c) 2023 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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 "op_plugin/AclOpsInterface.h"
#include "op_plugin/utils/OpAdapter.h"
#include "op_plugin/utils/custom_functions/aclops/inner_compute.h"
namespace acl_op {
using npu_preparation = at_npu::native::OpPreparation;
std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> npu_deep_norm_backward(const at::Tensor& dy,
const at::Tensor& x,
const at::Tensor& gx,
const at::Tensor& gamma,
const at::Tensor& mean,
const at::Tensor& rstd,
double alpha)
{
at::Tensor dx = npu_preparation::apply_tensor(x);
at::Tensor dgx = npu_preparation::apply_tensor(gx);
at::Tensor dbeta = npu_preparation::apply_tensor(gamma.sizes(), gamma.options().dtype(at::kFloat), gamma);
at::Tensor dgamma = npu_preparation::apply_tensor(gamma.sizes(), gamma.options().dtype(at::kFloat), gamma);
at_npu::native::OpCommand cmd;
cmd.Name("DeepNormGrad")
.Input(dy, "dy")
.Input(x, "x")
.Input(gx, "gx")
.Input(gamma, "gamma")
.Input(mean, "mean")
.Input(rstd, "rstd")
.Output(dx, "dx")
.Output(dgx, "dgx")
.Output(dbeta, "dbeta")
.Output(dgamma, "dgamma")
.Attr("alpha", static_cast<float>(alpha))
.Run();
return std::make_tuple(dx, dgx, dbeta, dgamma);
}
} // namespace acl_op