wehub-resource-sync
64 lines
2.4 KiB
C++
64 lines
2.4 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/fill_diagonal_tensor_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 FillDiagonalTensorKernel(const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &y,
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int64_t offset,
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int dim1,
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int dim2,
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DenseTensor *out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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T *out_data = dev_ctx.template Alloc<T>(out);
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int r = xpu::copy(dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(x.data<T>()),
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reinterpret_cast<XPUType *>(out_data),
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x.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
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std::vector<int64_t> xshape = vectorize<int64_t>(x.dims());
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std::vector<int64_t> yshape = vectorize<int64_t>(y.dims());
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r = xpu::fill_diagonal_tensor(dev_ctx.x_context(),
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reinterpret_cast<const XPUType *>(x.data<T>()),
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reinterpret_cast<const XPUType *>(y.data<T>()),
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reinterpret_cast<XPUType *>(out_data),
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xshape,
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yshape,
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dim1,
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dim2,
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offset);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "fill_diagonal_tensor");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(fill_diagonal_tensor,
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XPU,
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ALL_LAYOUT,
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phi::FillDiagonalTensorKernel,
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float,
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int64_t,
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int,
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phi::float16,
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bool) {}
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