wehub-resource-sync
64 lines
2.1 KiB
C++
64 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/squared_l2_norm_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 SquaredL2NormKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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T* data = dev_ctx.template Alloc<T>(out);
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using XPUType = typename XPUTypeTrait<T>::Type;
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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float* y_for_xdnn = nullptr;
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if (std::is_same<T, float>::value) {
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y_for_xdnn = reinterpret_cast<float*>(data);
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} else {
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y_for_xdnn = RAII_GUARD.alloc_l3_or_gm<float>(1);
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}
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// int square_reduce_sum(Context* xpu_ctx, const T* x, float* y, int64_t len,
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// bool is_sqrt=false);
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int r = xpu::square_reduce_sum<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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y_for_xdnn,
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x.numel(),
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false);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "square_reduce_sum");
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if (!std::is_same<T, float>::value) {
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// int cast(Context* xpu_ctx, const TX* x, TY* y, int64_t len);
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int r = xpu::cast<float, XPUType>(
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dev_ctx.x_context(), y_for_xdnn, reinterpret_cast<XPUType*>(data), 1);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(squared_l2_norm,
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XPU,
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ALL_LAYOUT,
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phi::SquaredL2NormKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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