wehub-resource-sync
101 lines
4.0 KiB
C++
101 lines
4.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/expand_grad_kernel.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void ExpandGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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const IntArray& shape UNUSED,
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DenseTensor* in_grad) {
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const auto& onednn_engine = dev_ctx.GetEngine();
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if ((in_grad && in_grad->numel() == 0) || out_grad.numel() == 0) {
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dev_ctx.template Alloc<T>(in_grad);
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Full<T, Context>(dev_ctx, in_grad->dims(), 0, in_grad);
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return;
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}
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auto in_grad_vec_dims = vectorize(in_grad->dims());
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auto out_grad_vec_dims = vectorize(out_grad.dims());
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if (in_grad_vec_dims.size() != out_grad_vec_dims.size()) {
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in_grad_vec_dims.insert(in_grad_vec_dims.begin(),
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out_grad_vec_dims.size() - in_grad_vec_dims.size(),
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1);
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}
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auto& astream = OneDNNContext::tls().get_stream();
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if (out_grad_vec_dims == in_grad_vec_dims) {
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dnnl::memory::data_type out_grad_type =
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funcs::ToOneDNNDataType(out_grad.dtype());
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if (out_grad_vec_dims.empty()) {
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out_grad_vec_dims = std::vector<int64_t>{1};
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}
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funcs::ReorderOneDNNHandler reorder_handler(
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out_grad_vec_dims, out_grad.dtype(), out_grad_type, onednn_engine);
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auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
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out_grad.mem_desc(), funcs::to_void_cast(out_grad.data<T>()));
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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in_grad,
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funcs::GetPlainOneDNNFormat(static_cast<int>(in_grad_vec_dims.size())),
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dev_ctx.GetPlace());
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auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p,
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reorder_dst_memory_p);
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reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
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astream.wait();
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in_grad->set_mem_desc(reorder_dst_memory_p->get_desc());
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} else {
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funcs::ReductionOneDNNHandler<T> handler(dnnl::algorithm::reduction_sum,
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0.0f,
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0.0f,
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onednn_engine,
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dev_ctx.GetPlace(),
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&out_grad,
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in_grad,
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in_grad_vec_dims);
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auto src_memory_p = handler.AcquireSrcMemory(&out_grad);
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auto dst_memory_p = handler.AcquireDstMemory(in_grad);
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std::unordered_map<int, dnnl::memory> reduction_args = {
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{DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DST, *dst_memory_p}};
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auto reduction_p = handler.AcquireForwardPrimitive();
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reduction_p->execute(astream, reduction_args);
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astream.wait();
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in_grad->set_layout(DataLayout::ONEDNN);
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const auto in_grad_md_dims = in_grad->dims().size() != 0
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? vectorize<int64_t>(in_grad->dims())
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: std::vector<int64_t>{1};
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in_grad->set_mem_desc(dst_memory_p->get_desc().reshape(in_grad_md_dims));
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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expand_grad, OneDNN, ONEDNN, phi::ExpandGradKernel, float, phi::bfloat16) {}
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