sigmoid_cross_entropy_with_logits.h

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#pragma once

#include <algorithm>
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/math.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_helper.h"
#include "paddle/phi/core/hostdevice.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/copy_kernel.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/gpu/reduce.h"

#ifdef __NVCC__
#include "cub/cub.cuh"
#endif
#ifdef __HIPCC__
#include <hipcub/hipcub.hpp>
namespace cub = hipcub;
#endif

namespace phi {

#ifdef __HIPCC__
static constexpr int kNumCUDAThreads = 256;
#else
static constexpr int kNumCUDAThreads = 512;
#endif
static constexpr int kNumMaxinumNumBlocks = 4096;

static inline int NumBlocks(const int N) {
  return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
                  kNumMaxinumNumBlocks);
}

template <typename T>
struct NonzeroFunctor {
  HOSTDEVICE explicit inline NonzeroFunctor() {}
  HOSTDEVICE inline T operator()(const T x) const {
    return static_cast<T>(static_cast<double>(x) != 0);
  }
};

template <typename T>
struct DivFunctor {
  const T norm_;
  HOSTDEVICE inline DivFunctor(const T norm) : norm_(norm) {}

  HOSTDEVICE inline T operator()(T loss) {
    loss /= norm_;
    return loss;
  }
};

}  // namespace phi