chore: import upstream snapshot with attribution
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// 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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#pragma once
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#include <unsupported/Eigen/SpecialFunctions>
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/for_range.h"
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namespace phi {
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template <typename T>
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struct DigammaFunctor {
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DigammaFunctor(const T* input, T* output, int64_t numel)
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: input_(input), output_(output), numel_(numel) {}
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HOSTDEVICE void operator()(int64_t idx) const {
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using MPType = typename MPTypeTrait<T>::Type;
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const MPType mp_input = static_cast<MPType>(input_[idx]);
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MPType eigen_out = Eigen::numext::digamma(mp_input);
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constexpr MPType mp_inf = std::numeric_limits<MPType>::infinity();
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MPType mp_out =
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mp_input == 0 ? (std::signbit(mp_input) ? mp_inf : -mp_inf) : eigen_out;
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output_[idx] = static_cast<T>(mp_out);
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}
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private:
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const T* input_;
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T* output_;
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int64_t numel_;
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};
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template <typename T, typename Context>
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void DigammaKernel(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* x_data = x.data<T>();
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auto* out_data = out->data<T>();
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auto numel = x.numel();
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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DigammaFunctor<T> functor(x_data, out_data, numel);
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for_range(functor);
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}
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} // namespace phi
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