Files
paddlepaddle--paddle/paddle/phi/kernels/onednn/squeeze_grad_kernel.cc
T
2026-07-13 12:40:42 +08:00

62 lines
2.3 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/squeeze_grad_kernel.h"
#include "paddle/phi/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void SqueezeGradKernel(const Context& dev_ctx,
const DenseTensor& xshape,
const DenseTensor& dout,
const IntArray& axes UNUSED,
DenseTensor* dx) {
auto dout_vec_dims = dout.dims().size() != 0 ? vectorize(dout.dims())
: std::vector<int64_t>{1};
auto dout_type = funcs::ToOneDNNDataType(dout.dtype());
funcs::ReorderOneDNNHandler reorder_handler(
dout_vec_dims, dout.dtype(), dout_type, dev_ctx.GetEngine());
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
dout.mem_desc(), funcs::to_void_cast(dout.data<T>()));
auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
dx,
funcs::GetPlainOneDNNFormat(static_cast<int>(dout_vec_dims.size())),
dev_ctx.GetPlace());
auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
reorder_src_memory_p);
auto& astream = OneDNNContext::tls().get_stream();
reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
astream.wait();
auto dx_dims = slice_ddim(xshape.dims(), 1, xshape.dims().size());
dx->Resize(dx_dims);
reorder_dst_memory_p->get_desc().reshape(vectorize(dx_dims));
}
} // namespace phi
PD_REGISTER_KERNEL(squeeze_grad,
OneDNN,
ONEDNN,
phi::SqueezeGradKernel,
float,
phi::bfloat16) {}