wehub-resource-sync
81 lines
2.8 KiB
C++
81 lines
2.8 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/concat_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/funcs/concat_funcs.h"
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namespace phi {
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template <typename T, typename Context>
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void ConcatGradKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const DenseTensor& out_grad,
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const Scalar& axis_scalar,
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std::vector<DenseTensor*> x_grad) {
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const auto& onednn_engine = dev_ctx.GetEngine();
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auto& astream = OneDNNContext::tls().get_stream();
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for (size_t i = 0; i < x_grad.size(); ++i) {
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if (x_grad[i] != nullptr) {
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x_grad[i]->set_lod(x[i]->lod());
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}
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}
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int axis = axis_scalar.to<int>();
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auto out_grad_vec_dims = vectorize(out_grad.dims());
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axis = static_cast<int>(funcs::ComputeAxis(axis, out_grad_vec_dims.size()));
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std::vector<int64_t> offset(out_grad_vec_dims.size(), 0);
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dnnl::memory::data_type out_grad_type =
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funcs::ToOneDNNDataType(out_grad.dtype());
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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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for (auto& grad : x_grad) {
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if (grad && grad->numel() != 0UL) {
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auto x_grad_vec_dims = vectorize(grad->dims());
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auto slice_mem_p = reorder_handler.AcquireSubmemory(
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x_grad_vec_dims, offset, reorder_src_memory_p);
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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grad,
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x_grad_vec_dims,
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funcs::GetPlainOneDNNFormat(static_cast<int>(x_grad_vec_dims.size())),
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dev_ctx.GetPlace());
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auto reorder_p =
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reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);
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reorder_p->execute(astream, *slice_mem_p, *reorder_dst_memory_p);
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offset[axis] += grad->dims()[axis];
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grad->set_mem_desc(reorder_dst_memory_p->get_desc());
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}
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}
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astream.wait();
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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concat_grad, OneDNN, ONEDNN, phi::ConcatGradKernel, float, phi::bfloat16) {}
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