wehub-resource-sync
50 lines
1.7 KiB
C++
50 lines
1.7 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/meshgrid_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/backends/xpu/xpu_context.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 MeshgridKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& inputs,
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std::vector<DenseTensor*> outputs) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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std::vector<const XPUType*> x_list;
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std::vector<XPUType*> y_list;
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std::vector<std::vector<int64_t>> xshape_list;
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for (const auto& x : inputs) {
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x_list.push_back(reinterpret_cast<const XPUType*>(x->data<T>()));
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xshape_list.emplace_back(vectorize<int64_t>(x->dims()));
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}
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for (auto& x : outputs) {
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dev_ctx.template Alloc<T>(x);
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y_list.push_back(reinterpret_cast<XPUType*>(x->data<T>()));
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}
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int r =
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xpu::meshgrid<XPUType>(dev_ctx.x_context(), x_list, y_list, xshape_list);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "meshgrid");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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meshgrid, XPU, ALL_LAYOUT, phi::MeshgridKernel, float, int, int64_t) {}
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