#pragma once

#include <ATen/detail/FunctionTraits.h>
#include <ATen/native/TensorIterator.h>
#include <ATen/native/TensorIteratorDynamicCasting.h>
#include <ATen/cuda/detail/OffsetCalculator.cuh>
#include <ATen/OpMathType.h>
#include <ATen/native/cuda/thread_constants.h>

#include <thrust/tuple.h>

#include <ATen/native/cuda/MemoryAccess.cuh>


namespace at { namespace native {

template<int N>
static OffsetCalculator<N> make_input_offset_calculator(const TensorIteratorBase& iter) {
  // array size can not be 0, this happens when N == 0
  constexpr int array_size = std::max<int>(N, 1);
  TORCH_INTERNAL_ASSERT(N == iter.ntensors() - iter.noutputs());
  std::array<const int64_t*, array_size> strides;
  int64_t element_sizes[array_size];
  for (int i = 0; i < N; i++) {
    strides[i] = iter.strides(i + iter.noutputs()).data();
    element_sizes[i] = iter.element_size(i + iter.noutputs());
  }
  return OffsetCalculator<N>(iter.ndim(), iter.shape().data(), strides.data(), element_sizes);
}

template <int num_outputs = 1>
static OffsetCalculator<num_outputs> make_output_offset_calculator(const TensorIteratorBase& iter) {
  TORCH_INTERNAL_ASSERT(num_outputs == iter.noutputs());
  std::array<const int64_t*, num_outputs> strides;
  int64_t element_sizes[num_outputs];
  for (int i = 0; i < num_outputs; i++) {
    strides[i] = iter.strides(i).data();
    element_sizes[i] = iter.element_size(i);
  }
  return OffsetCalculator<num_outputs>(iter.ndim(), iter.shape().data(), strides.data(), element_sizes);
}

template<typename func_t, typename policy_t>
__device__ inline void elementwise_kernel_helper(func_t f, policy_t policy) {
  using traits = function_traits<func_t>;
  using return_t = typename traits::result_type;
  using args_t = typename traits::ArgsTuple;

  int idx = blockIdx.x;

  return_t results[thread_work_size()];
  args_t args[thread_work_size()];

  // load
  policy.load(args, idx);

  // compute
  #pragma unroll
  for (int i = 0; i < thread_work_size(); i++) {
    if (policy.check_inbounds(i)) {
      results[i] = c10::guts::apply(f, args[i]);
    }
  }

  // store
  policy.store(results, idx);
}

}}  // namespace at::native

// Note:
// CUDA and ROCm get diverged in this PR:
//   https://github.com/pytorch/pytorch/pull/32383
// Because for some reason trying to enable vectorized
// memory access introduce regression on ROCm.

#if !defined(USE_ROCM)
  #include <ATen/native/cuda/CUDALoops.cuh>
#else
  #include <ATen/native/cuda/ROCmLoops.cuh>
#endif

namespace at { namespace native {

template <typename func_t>
void gpu_kernel(TensorIteratorBase& iter, const func_t& f) {

  for (int arg = 0; arg < iter.ntensors(); arg++) {
    TORCH_INTERNAL_ASSERT(
      iter.device(arg).is_cuda(),
      "argument ", arg, ": expected a CUDA device but found ", iter.device(arg));
  }

  if (iter.numel() == 0) {
    return;
  }

  if (!iter.can_use_32bit_indexing()) {
    for (auto& sub_iter : iter.with_32bit_indexing()) {
      gpu_kernel(sub_iter, f);
    }
    return;
  }

  gpu_kernel_impl(iter, f);
}

template<typename arg1_t, typename arg2_t, typename return_t, typename func_t>
struct AUnaryFunctor {
  using traits = function_traits<func_t>;
  using opmath_arg1_t = typename traits::template arg<0>::type;
  __device__ return_t operator()(arg2_t b) const {
    return f(a, b);
  }
  // NB: scalar is stored in higher precision!
  AUnaryFunctor(func_t f_, opmath_arg1_t a_): f(f_), a(a_) {}
  private:
    func_t f;
    opmath_arg1_t a;
};

template<typename arg1_t, typename arg2_t, typename return_t, typename func_t>
struct BUnaryFunctor {
  using traits = function_traits<func_t>;
  using opmath_arg2_t = typename traits::template arg<1>::type;
  __device__ return_t operator()(arg1_t a) const {
    return f(a, b);
  }
  // NB: scalar is stored in higher precision!
  BUnaryFunctor(func_t f_, opmath_arg2_t b_): f(f_), b(b_) {}
  private:
    func_t f;
    opmath_arg2_t b;
};

// Though seemingly noop, this inserts casts from arg1_t to func_t's type
// (which may be higher precision), as well as casts to return_t
template <typename arg1_t, typename arg2_t, typename return_t, typename func_t>
struct BinaryFunctor {
  __device__ return_t operator()(arg1_t a, arg2_t b) const {
    return f(a, b);
  }
  BinaryFunctor(func_t f_): f(f_) {}
  private:
    func_t f;
};

// Unlike gpu_kernel_with_scalars, this allows you to pass a func_t which
// accepts inputs at higher precision (typically opmath_t), but then
// ensure that we load from memory at the correct precision (scalar_t)
// to avoid expensive loads.  For the whole sordid story see
// https://dev-discuss.pytorch.org/t/cuda-loops-case-study-code-generation-vs-templates/302
template <typename arg1_t, typename arg2_t = arg1_t, typename return_t = arg1_t, typename func_t>
void opmath_gpu_kernel_with_scalars(TensorIteratorBase& iter, const func_t& f) {
  TORCH_INTERNAL_ASSERT(iter.ntensors() == 3);

  using traits = function_traits<func_t>;
  using opmath_arg1_t = typename traits::template arg<0>::type;
  using opmath_arg2_t = typename traits::template arg<1>::type;
  static_assert(
      traits::arity == 2,
      "gpu_kernel_with_scalars only supports two input arguments");

  if (iter.is_cpu_scalar(1)) {
    AUnaryFunctor<arg1_t, arg2_t, return_t, func_t> af(f, iter.scalar_value<opmath_arg1_t>(1));
    iter.remove_operand(1);
    // TODO: When all kernels that use gpu_kernel_with_scalars are
    // ported to structured, this device guard can be deleted.  This
    // works around incorrect device guard generation for pre-structured
    // kernels device guards, but structured kernels do it right and
    // we can assume the device is already set correctly
    const OptionalDeviceGuard device_guard(iter.device(1));
    gpu_kernel(iter, af);
  } else if (iter.is_cpu_scalar(2)) {
    BUnaryFunctor<arg1_t, arg2_t, return_t, func_t> bf(f, iter.scalar_value<opmath_arg2_t>(2));
    iter.remove_operand(2);
    gpu_kernel(iter, bf);
  } else {
    gpu_kernel(iter, BinaryFunctor<arg1_t, arg2_t, return_t, func_t>(f));
  }
}

template <typename scalar_t, typename return_t = scalar_t, typename func_t>
void opmath_symmetric_gpu_kernel_with_scalars(TensorIteratorBase& iter, const func_t& f) {
  // Use symmetric property of the functor to reduce number of kernels,
  // requires f(a, b) == f(b, a)
  TORCH_INTERNAL_ASSERT(iter.ntensors() == 3);

  using traits = function_traits<func_t>;
  using opmath_arg_t = typename traits::template arg<0>::type;
  static_assert(
      traits::arity == 2,
      "gpu_kernel_with_scalars only supports two input arguments");
  static_assert(std::is_same<opmath_arg_t, typename traits::template arg<1>::type>::value,
                "f is not symmetric");

  OptionalDeviceGuard device_guard;
  opmath_arg_t scalar_val{};

  if (iter.is_cpu_scalar(1)) {
    scalar_val = iter.scalar_value<opmath_arg_t>(1);
    iter.remove_operand(1);

    // TODO: When all kernels that use gpu_kernel_with_scalars are
    // ported to structured, this device guard can be deleted.  This
    // works around incorrect device guard generation for pre-structured
    // kernels device guards, but structured kernels do it right and
    // we can assume the device is already set correctly
    device_guard.reset_device(iter.device(1));
  } else if (iter.is_cpu_scalar(2)) {
    scalar_val = iter.scalar_value<opmath_arg_t>(2);
    iter.remove_operand(2);
  }

  if (iter.ninputs() == 2) {
    gpu_kernel(iter, BinaryFunctor<scalar_t, scalar_t, return_t, func_t>(f));
  } else {
    AUnaryFunctor<scalar_t, scalar_t, return_t, func_t> unary_f(f, scalar_val);
    gpu_kernel(iter, unary_f);
  }
}

// Legacy variant that assumes that func_t has the correct types
// that we expect to load from memory
template <typename func_t>
void gpu_kernel_with_scalars(TensorIteratorBase& iter, const func_t& f) {
  using traits = function_traits<func_t>;
  static_assert(
      traits::arity == 2,
      "gpu_kernel_with_scalars only supports two input arguments");
  using arg1_t = typename traits::template arg<0>::type;
  using arg2_t = typename traits::template arg<1>::type;
  using return_t = typename traits::result_type;
  opmath_gpu_kernel_with_scalars<arg1_t, arg2_t, return_t, func_t>(iter, f);
}

namespace { // functions for `gpu_kernel_multiple_outputs`.

// check the return type is `thrust::tuple`, not `std::tuple`.
template <typename T> struct is_tuple: std::false_type {};

template <typename ...T> struct is_tuple<thrust::tuple<T...>>: std::true_type {};

template <int num_outputs, typename func_t, typename array_t, typename inp_calc_t, typename out_calc_t>
C10_LAUNCH_BOUNDS_1(num_threads())
__global__ void unrolled_elementwise_kernel_for_multi_outputs(int N, func_t f, array_t data, inp_calc_t ic, out_calc_t oc) {
  int remaining = N - block_work_size() * blockIdx.x;
  elementwise_kernel_helper(f, memory::policies::multi_outputs_unroll<array_t, inp_calc_t, out_calc_t, num_outputs>(data, remaining, ic, oc));
}

template <int num_outputs, typename func_t, typename array_t, typename inp_calc_t, typename out_calc_t>
static inline void launch_unrolled_kernel_for_multi_outputs(int64_t N, const func_t& f, array_t data, inp_calc_t ic, out_calc_t oc) {
  TORCH_INTERNAL_ASSERT(N > 0 && N <= std::numeric_limits<int32_t>::max());
  int64_t grid = (N + block_work_size() - 1) / block_work_size();
  auto stream = at::cuda::getCurrentCUDAStream();
  unrolled_elementwise_kernel_for_multi_outputs<num_outputs, func_t, array_t><<<grid, num_threads(), 0, stream>>>(N, f, data, ic, oc);
  C10_CUDA_KERNEL_LAUNCH_CHECK();
}

template <typename func_t>
void gpu_kernel_multiple_outputs_impl(TensorIteratorBase& iter, const func_t& f) {
  using traits = function_traits<func_t>;
  using output_t = typename traits::result_type;
  static_assert(is_tuple<output_t>::value, "f's return type must be `thrust::tuple`");
  constexpr int num_outputs = thrust::tuple_size<output_t>::value;
  constexpr int num_inputs = traits::arity;
  constexpr int ntensors = num_outputs + num_inputs;

  TORCH_INTERNAL_ASSERT(iter.can_use_32bit_indexing());
  TORCH_INTERNAL_ASSERT(iter.ntensors() == ntensors);

  at::detail::Array<char*, ntensors> data;
  for (int i = 0; i < ntensors; i++) {
    data[i] = (char*)iter.data_ptr(i);
  }

  int64_t numel = iter.numel();

  if (iter.is_contiguous()) {
    auto input_calc = TrivialOffsetCalculator<num_inputs>();
    auto output_calc = TrivialOffsetCalculator<num_outputs>();
    launch_unrolled_kernel_for_multi_outputs<num_outputs>(numel, f, data, input_calc, output_calc);
  } else {
    auto input_calc = make_input_offset_calculator<num_inputs>(iter);
    auto output_calc = make_output_offset_calculator<num_outputs>(iter);
    launch_unrolled_kernel_for_multi_outputs<num_outputs>(numel, f, data, input_calc, output_calc);
  }
}
} // namespace

template <typename func_t>
void gpu_kernel_multiple_outputs(TensorIteratorBase& iter, const func_t& f) {
  ASSERT_HOST_DEVICE_LAMBDA(func_t);

  for (int arg = 0; arg < iter.ntensors(); arg++) {
    TORCH_INTERNAL_ASSERT(iter.device(arg).is_cuda());
  }

  if (iter.numel() == 0) {
    return;
  }

  if (!iter.can_use_32bit_indexing()) {
    for (auto& sub_iter : iter.with_32bit_indexing()) {
      gpu_kernel_multiple_outputs(sub_iter, f);
    }
    return;
  }

  gpu_kernel_multiple_outputs_impl(iter, f);
}

}} //namespace at::native