wehub-resource-sync
62 lines
2.0 KiB
C++
62 lines
2.0 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/shape_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 ShapeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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DDim x_dims = x.dims();
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// Output of shape op is often fed as x to fill_constant ops
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// and we need to rotate a shape otherwise Tensors of wrong shape may be
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// allocated
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if (OneDNNContext::tls().get_cur_paddle_data_layout() == DataLayout::NHWC &&
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x_dims.size() >= 3) {
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auto rdims = vectorize<int>(x_dims);
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std::rotate(rdims.begin() + 1, rdims.begin() + 2, rdims.end());
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x_dims = make_ddim(rdims);
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}
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out->Resize({x_dims.size()});
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auto out_data = dev_ctx.template Alloc<int32_t>(out);
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for (int i = 0; i < x_dims.size(); ++i) {
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out_data[i] = x_dims[i];
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}
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dnnl::memory::desc out_mem_desc(
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vectorize(out->dims()),
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funcs::ToOneDNNDataType(out->dtype()),
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funcs::GetPlainOneDNNFormat(out->dims().size()));
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out->set_mem_desc(out_mem_desc);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(shape,
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OneDNN,
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ONEDNN,
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phi::ShapeKernel,
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float,
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phi::bfloat16,
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int8_t,
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uint8_t) {
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kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
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}
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