// 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/clip_grad_kernel.h" #include "paddle/phi/backends/onednn/onednn_reuse.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void ClipGradKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& out_grad, const Scalar& min, const Scalar& max, DenseTensor* x_grad) { const auto& onednn_engine = dev_ctx.GetEngine(); funcs::ClipOneDNNHandler handler( min, max, onednn_engine, dev_ctx.GetPlace(), &x, &out_grad); auto src_memory_p = handler.AcquireBackwardSrcMemory(&x); auto diff_dst_memory_p = handler.AcquireDiffDstMemory(&out_grad); auto diff_src_memory_p = handler.AcquireDiffSrcMemory(x_grad); auto activation_backward_p = handler.AcquireBackwardPrimitive(); auto& astream = OneDNNContext::tls().get_stream(); activation_backward_p->execute(astream, {{DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DIFF_DST, *diff_dst_memory_p}, {DNNL_ARG_DIFF_SRC, *diff_src_memory_p}}); astream.wait(); x_grad->set_mem_desc(diff_dst_memory_p->get_desc()); } } // namespace phi PD_REGISTER_KERNEL(clip_grad, OneDNN, ONEDNN, phi::ClipGradKernel, float, phi::dtype::bfloat16) {}