wehub-resource-sync
92 lines
3.3 KiB
C++
92 lines
3.3 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/slice_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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namespace phi {
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bool SliceGradCheckIfOneDNNSupport(const KernelContext* dev_ctx) {
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if (dev_ctx->InputAt<DenseTensor>(1).mem_desc().get_inner_nblks() == 0) {
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return true;
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}
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return false;
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}
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template <typename T, typename Context>
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void SliceGradKernel(const Context& dev_ctx,
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const DenseTensor& input UNUSED,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends,
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const std::vector<int64_t>& infer_flags UNUSED,
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const std::vector<int64_t>& decrease_axis UNUSED,
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DenseTensor* input_grad) {
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const auto& onednn_engine = dev_ctx.GetEngine();
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auto dx_dims = vectorize(input_grad->dims());
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auto starts_vec = starts.GetData();
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auto ends_vec = ends.GetData();
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std::vector<int64_t> offsets(dx_dims.size(), 0);
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std::vector<int64_t> slice_dims(dx_dims);
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for (size_t i = 0; i < axes.size(); ++i) {
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starts_vec[i] =
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starts_vec[i] < 0 ? dx_dims[axes[i]] + starts_vec[i] : starts_vec[i];
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ends_vec[i] = ends_vec[i] < 0 ? dx_dims[axes[i]] + ends_vec[i]
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: std::min(ends_vec[i], dx_dims[axes[i]]);
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offsets[axes[i]] = starts_vec[i];
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slice_dims[axes[i]] = ends_vec[i] - starts_vec[i];
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}
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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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slice_dims, out_grad.dtype(), out_grad_type, onednn_engine);
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auto reorder_src_memory_p =
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reorder_handler.AcquireSrcMemory(out_grad.mem_desc().reshape(slice_dims),
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funcs::to_void_cast(out_grad.data<T>()));
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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input_grad,
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dx_dims,
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funcs::GetPlainOneDNNFormat(static_cast<int>(dx_dims.size())),
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dev_ctx.GetPlace());
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memset(input_grad->data<T>(), 0, reorder_dst_memory_p->get_desc().get_size());
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auto slice_mem_p = reorder_handler.AcquireSubmemory(
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slice_dims, offsets, reorder_dst_memory_p);
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auto reorder_p =
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reorder_handler.AcquireReorder(slice_mem_p, reorder_src_memory_p);
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auto& astream = OneDNNContext::tls().get_stream();
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reorder_p->execute(astream, *reorder_src_memory_p, *slice_mem_p);
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astream.wait();
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input_grad->set_mem_desc(reorder_dst_memory_p->get_desc());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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slice_grad, OneDNN, ONEDNN, phi::SliceGradKernel, float, phi::bfloat16) {
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kernel->check_if_onednn_kernel_support_ = phi::SliceGradCheckIfOneDNNSupport;
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}
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