wehub-resource-sync
97 lines
3.4 KiB
C++
97 lines
3.4 KiB
C++
// Copyright (c) 2023 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/transpose_kernel.h"
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#include "glog/logging.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 TransposeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int>& axis,
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DenseTensor* out) {
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// Here we need to match dims to paddle layout
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// as we are producing non-oneDNN result
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auto x_dims = x.dims();
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if ((x_dims.size() >= 3) &&
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(OneDNNContext::tls().get_cur_paddle_data_layout() == DataLayout::NHWC)) {
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int axis_size = static_cast<int>(axis.size());
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std::vector<int> formatted_axis = axis;
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std::vector<int> count(axis_size, 0);
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for (int i = 0; i < axis_size; i++) {
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if (axis[i] < 0) {
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formatted_axis[i] = axis[i] + axis_size;
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}
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}
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auto dims = vectorize<int>(x_dims);
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std::rotate(dims.begin() + 1, dims.begin() + 2, dims.end());
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x_dims = x_dims.reshape(dims);
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VLOG(3) << "Rotating Shape in Transpose from: ONEDNN to: NHWC output_shape";
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DDim out_dims(x_dims);
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for (size_t i = 0; i < axis.size(); i++) {
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out_dims[i] = x_dims[formatted_axis[i]]; // NOLINT
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}
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out->Resize(out_dims);
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}
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PADDLE_ENFORCE_EQ(
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dev_ctx.GetPlace().GetType(),
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AllocationType::CPU,
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errors::PreconditionNotMet("oneDNN Transpose kernel must use CPUPlace"));
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if (axis.size() == 1 || axis.empty()) {
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Copy<Context>(dev_ctx, x, x.place(), false, out);
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out->set_mem_desc(x.mem_desc());
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return;
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}
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auto x_vec_dims = vectorize(x.dims());
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auto x_type = funcs::ToOneDNNDataType(x.dtype());
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funcs::ReorderOneDNNHandler reorder_handler(
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x_vec_dims, x.dtype(), x_type, 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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auto fake_strides = funcs::FakeTransposeStrides(x_vec_dims, axis);
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auto dst_md = dnnl::memory::desc(
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x_vec_dims, x.mem_desc().get_data_type(), fake_strides);
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auto reorder_dst_memory_p =
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reorder_handler.AcquireDstMemory(out, dst_md, 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->set_mem_desc(reorder_dst_memory_p->get_desc().permute_axes(
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funcs::TransposeToPermuteAxes(axis)));
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}
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} // namespace phi
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PD_REGISTER_KERNEL(transpose,
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OneDNN,
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ONEDNN,
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phi::TransposeKernel,
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float,
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uint8_t,
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int8_t,
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phi::bfloat16) {}
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