wehub-resource-sync
65 lines
2.2 KiB
C++
65 lines
2.2 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/diag_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 DiagKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int offset,
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float padding_value,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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dev_ctx.template Alloc<T>(out);
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if (out && out->numel() == 0) return;
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auto* out_data = reinterpret_cast<XPUType*>(out->data<T>());
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auto x_shape = vectorize<int64_t>(x.dims());
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auto out_shape = vectorize<int64_t>(out->dims());
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if (x.dims().size() == 0) {
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x_shape = std::vector<int64_t>({1});
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}
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if (x.numel() == 0) {
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int r_fill = xpu::constant<XPUType>(dev_ctx.x_context(),
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out_data,
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out->numel(),
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static_cast<XPUType>(padding_value));
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PADDLE_ENFORCE_XDNN_SUCCESS(r_fill, "constant");
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return;
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}
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int r = xpu::diag<XPUType>(dev_ctx.x_context(),
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x_data,
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out_data,
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x_shape,
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out_shape,
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offset,
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padding_value);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "diag");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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diag, XPU, ALL_LAYOUT, phi::DiagKernel, phi::float16, int, float, int64_t) {
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}
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