wehub-resource-sync
54 lines
1.9 KiB
C++
54 lines
1.9 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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#ifndef _USE_MATH_DEFINES
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#define _USE_MATH_DEFINES // use M_2_SQRTPI on Windows
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#endif
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#include "paddle/phi/kernels/erfinv_kernel.h"
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#include <limits>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void ErfinvKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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if (out && out->numel() == 0) {
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return;
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}
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auto eigen_in = EigenVector<T>::Flatten(x);
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auto eigen_out = EigenVector<T>::Flatten(*out);
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auto& place = *dev_ctx.eigen_device();
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constexpr T half = static_cast<T>(0.5);
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constexpr T half_sqrt = static_cast<T>(M_SQRT1_2);
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constexpr T one = static_cast<T>(1);
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const T nan_val = std::numeric_limits<T>::quiet_NaN();
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// erfinv is only defined on [-1, 1]; align with PyTorch/scipy by returning
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// NaN for |x| > 1 (boundary +/-1 still yields +/-inf through ndtri).
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eigen_out.device(place) =
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(eigen_in.abs() > eigen_in.constant(one))
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.select(eigen_in.constant(nan_val),
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(eigen_in * half + half).ndtri() * half_sqrt);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(erfinv, CPU, ALL_LAYOUT, phi::ErfinvKernel, float, double) {}
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