wehub-resource-sync
111 lines
3.2 KiB
C++
111 lines
3.2 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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#pragma once
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/device_context.h"
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#include "paddle/phi/kernels/atan2_kernel.h"
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#include "paddle/phi/kernels/broadcast_tensors_kernel.h"
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#include "paddle/phi/kernels/funcs/common_shape.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 Atan2Out {
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using type = T;
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};
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template <>
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struct Atan2Out<int32_t> {
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using type = double;
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};
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template <>
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struct Atan2Out<int64_t> {
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using type = double;
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};
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template <typename T>
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struct Atan2Functor {
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Atan2Functor(const T* x1,
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const T* x2,
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typename Atan2Out<T>::type* out,
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int64_t numel)
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: x1_(x1), x2_(x2), out_(out), numel_(numel) {}
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HOSTDEVICE void operator()(int64_t idx) const {
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out_[idx] = static_cast<typename Atan2Out<T>::type>(
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::atan2f(static_cast<float>(x1_[idx]), static_cast<float>(x2_[idx])));
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}
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const T* x1_;
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const T* x2_;
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typename Atan2Out<T>::type* out_;
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int64_t numel_;
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};
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template <>
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struct Atan2Functor<double> {
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Atan2Functor(const double* x1, const double* x2, double* out, int64_t numel)
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: x1_(x1), x2_(x2), out_(out), numel_(numel) {}
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HOSTDEVICE void operator()(int64_t idx) const {
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out_[idx] = ::atan2(x1_[idx], x2_[idx]);
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}
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const double* x1_;
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const double* x2_;
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double* out_;
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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 Atan2Kernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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dev_ctx.template Alloc<typename Atan2Out<T>::type>(out);
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if (out->numel() == 0) return;
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if (x.dims() == y.dims()) {
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const auto numel = out->numel();
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const auto* x_data = x.data<T>();
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const auto* y_data = y.data<T>();
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auto* out_data = out->data<typename Atan2Out<T>::type>();
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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Atan2Functor<T> functor(x_data, y_data, out_data, numel);
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for_range(functor);
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} else {
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DenseTensor b_x, b_y;
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// Calculate broadcasted dims
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b_x.Resize(out->dims());
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b_y.Resize(out->dims());
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std::vector<const DenseTensor*> inputs = {&x, &y};
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std::vector<DenseTensor*> outputs = {&b_x, &b_y};
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BroadcastTensorsKernel<T, Context>(dev_ctx, inputs, outputs);
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const auto numel = out->numel();
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const auto* x_data = b_x.data<T>();
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const auto* y_data = b_y.data<T>();
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auto* out_data = out->data<typename Atan2Out<T>::type>();
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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Atan2Functor<T> functor(x_data, y_data, out_data, numel);
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for_range(functor);
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}
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}
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} // namespace phi
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