wehub-resource-sync
62 lines
2.1 KiB
C++
62 lines
2.1 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/flip_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.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 FlipKernel(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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using XPUInTDType = typename XPUTypeTrait<T>::Type;
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int x_rank = x.dims().size();
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std::vector<int64_t> formatted_axis(std::begin(axis), std::end(axis));
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for (size_t i = 0; i < axis.size(); i++) {
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if (axis[i] < 0) {
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formatted_axis[i] = static_cast<int64_t>(axis[i] + x_rank);
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}
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}
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dev_ctx.template Alloc<T>(out);
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if (out->numel() == 0) {
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return;
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}
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if (formatted_axis.size() == 0) {
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Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), false, out);
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return;
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}
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std::vector<int64_t> x_shape = vectorize(x.dims());
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auto x_data = reinterpret_cast<const XPUInTDType*>(x.data<T>());
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auto out_data = reinterpret_cast<XPUInTDType*>(out->data<T>());
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auto numel = x.numel();
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if (numel <= 0) {
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return;
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}
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int r = xpu::flip<XPUInTDType>(
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/* Context* xpu_ctx */ dev_ctx.x_context(),
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/* const T* x */ x_data,
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/* T* y */ out_data,
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/* const std::vector<int64_t>& xshape */ x_shape,
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/* const std::vector<int64_t>& axis */ formatted_axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "flip");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(flip, XPU, ALL_LAYOUT, phi::FlipKernel, float) {}
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