wehub-resource-sync
86 lines
3.1 KiB
C++
86 lines
3.1 KiB
C++
// Copyright (c) 2024 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/flatten_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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template <typename T, typename Context>
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void ExecuteFlatten(const Context& dev_ctx,
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const DenseTensor& x,
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const DDim& x_dims,
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const DDim& out_dims,
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DenseTensor* out) {
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auto x_vec_dims = vectorize(x_dims);
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funcs::ReorderOneDNNHandler reorder_handler(
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x_vec_dims,
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x.dtype(),
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funcs::ToOneDNNDataType(x.dtype()),
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dev_ctx.GetEngine());
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auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
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x.mem_desc(), funcs::to_void_cast(x.data<T>()));
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out->Resize(x_dims); // to match x numel, format is changed later
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// reorder is done into a plain tag to allow usage with blocked formats
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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out, funcs::GetPlainOneDNNFormat(x_dims.size()), dev_ctx.GetPlace());
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auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
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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, *reorder_dst_memory_p);
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astream.wait();
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out->Resize(out_dims);
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auto reshape_dims =
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out_dims.size() != 0 ? vectorize(out_dims) : std::vector<int64_t>{1};
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out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(reshape_dims));
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}
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template <typename T, typename Context>
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void FlattenKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int start_axis,
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int stop_axis,
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DenseTensor* out) {
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auto x_dims = x.dims();
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auto out_dims = out->dims();
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ExecuteFlatten<T, Context>(dev_ctx, x, x_dims, out_dims, out);
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}
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template <typename T, typename Context>
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void FlattenWithXShapeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int start_axis,
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int stop_axis,
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DenseTensor* out,
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DenseTensor* xshape UNUSED) {
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FlattenKernel<T, Context>(dev_ctx, x, start_axis, stop_axis, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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flatten, OneDNN, ONEDNN, phi::FlattenKernel, float, phi::bfloat16) {}
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PD_REGISTER_KERNEL(flatten_with_xshape,
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OneDNN,
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ONEDNN,
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phi::FlattenWithXShapeKernel,
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float,
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phi::bfloat16) {}
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