/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#pragma once

#include <cmath>

#include <cute/tensor.hpp>

#include <cutlass/cutlass.h>
#include <cutlass/array.h>

#include "utils.h"

namespace flash {

using namespace cute;

////////////////////////////////////////////////////////////////////////////////////////////////////

template <bool zero_init = true, typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
__device__ inline void thread_reduce_(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1>& summary, Operator& op) {
  static_assert(Layout0::rank == 2, "Only support 2D Tensor");
  static_assert(Layout1::rank == 1, "Only support 1D Tensor");
  CUTE_STATIC_ASSERT_V(size<0>(summary) == size<0>(tensor));
#pragma unroll
  for (int mi = 0; mi < size<0>(tensor); mi++) {
    summary(mi) = zero_init ? tensor(mi, 0) : op(summary(mi), tensor(mi, 0));
#pragma unroll
    for (int ni = 1; ni < size<1>(tensor); ni++) {
      summary(mi) = op(summary(mi), tensor(mi, ni));
    }
  }
}

template <typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
__device__ inline void quad_allreduce_(Tensor<Engine0, Layout0>& dst, Tensor<Engine1, Layout1>& src, Operator& op) {
  CUTE_STATIC_ASSERT_V(size(dst) == size(src));
#pragma unroll
  for (int i = 0; i < size(dst); i++) {
    dst(i) = Allreduce<4>::run(src(i), op);
  }
}

template <bool zero_init = true, typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
__device__ inline void reduce_(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1>& summary, Operator& op) {
  thread_reduce_<zero_init>(tensor, summary, op);
  quad_allreduce_(summary, summary, op);
}

template <bool zero_init = true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
__device__ inline void reduce_max(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1>& max) {
  MaxOp<float> max_op;
  reduce_<zero_init>(tensor, max, max_op);
}

template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
__device__ inline void reduce_sum(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1>& sum) {
  SumOp<float> sum_op;
  reduce_(tensor, sum, sum_op);
}

// Apply the exp to all the elements.
template <bool Scale_max = true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
inline __device__ void scale_apply_exp2(Tensor<Engine0, Layout0>& tensor, Tensor<Engine1, Layout1> const& max, const float scale) {
  static_assert(Layout0::rank == 2, "Only support 2D Tensor");
  static_assert(Layout1::rank == 1, "Only support 1D Tensor");
  CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
#pragma unroll
  for (int mi = 0; mi < size<0>(tensor); ++mi) {
    // If max is -inf, then all elements must have been -inf (possibly due to masking).
    // We don't want (-inf - (-inf)) since that would give NaN.
    // If we don't have float around M_LOG2E the multiplication is done in fp64.
    const float max_scaled = max(mi) == -INFINITY ? 0.f : max(mi) * (Scale_max ? scale : float(M_LOG2E));
#pragma unroll
    for (int ni = 0; ni < size<1>(tensor); ++ni) {
      // Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
      // max * log_2(e)) This allows the compiler to use the ffma
      // instruction instead of fadd and fmul separately.
      tensor(mi, ni) = exp2f(tensor(mi, ni) * scale - max_scaled);
    }
  }
}

// Apply the exp to all the elements.
template <bool zero_init = true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
inline __device__ void max_scale_exp2_sum(Tensor<Engine0, Layout0>& tensor, Tensor<Engine1, Layout1>& max, Tensor<Engine1, Layout1>& sum, const float scale) {
  static_assert(Layout0::rank == 2, "Only support 2D Tensor");
  static_assert(Layout1::rank == 1, "Only support 1D Tensor");
  CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
#pragma unroll
  for (int mi = 0; mi < size<0>(tensor); ++mi) {
    MaxOp<float> max_op;
    max(mi) = zero_init ? tensor(mi, 0) : max_op(max(mi), tensor(mi, 0));
#pragma unroll
    for (int ni = 1; ni < size<1>(tensor); ni++) {
      max(mi) = max_op(max(mi), tensor(mi, ni));
    }
    max(mi) = Allreduce<4>::run(max(mi), max_op);
    // If max is -inf, then all elements must have been -inf (possibly due to masking).
    // We don't want (-inf - (-inf)) since that would give NaN.
    const float max_scaled = max(mi) == -INFINITY ? 0.f : max(mi) * scale;
    sum(mi) = 0;
#pragma unroll
    for (int ni = 0; ni < size<1>(tensor); ++ni) {
      // Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
      // max * log_2(e)) This allows the compiler to use the ffma
      // instruction instead of fadd and fmul separately.
      tensor(mi, ni) = exp2f(tensor(mi, ni) * scale - max_scaled);
      sum(mi) += tensor(mi, ni);
    }
    SumOp<float> sum_op;
    sum(mi) = Allreduce<4>::run(sum(mi), sum_op);
  }
}

template <typename Engine, typename Layout>
inline __device__ void apply_mask(Tensor<Engine, Layout>& tensor, const int max_seqlen_k,
                                  const int col_idx_offset_ = 0) {
  // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
  static_assert(Layout::rank == 2, "Only support 2D Tensor");
  const int lane_id = threadIdx.x % 32;
  const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
#pragma unroll
  for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
    const int col_idx_base = col_idx_offset + nj * 8;
#pragma unroll
    for (int j = 0; j < size<1, 0>(tensor); ++j) {
      const int col_idx = col_idx_base + j;
      if (col_idx >= max_seqlen_k) {
// Without the "make_coord" we get wrong results
#pragma unroll
        for (int mi = 0; mi < size<0>(tensor); ++mi) {
          tensor(mi, make_coord(j, nj)) = -INFINITY;
        }
      }
    }
  }
}

template <bool HasWSLeft = true, typename Engine, typename Layout>
inline __device__ void apply_mask_local(Tensor<Engine, Layout>& tensor, const int col_idx_offset_,
                                        const int max_seqlen_k, const int row_idx_offset_,
                                        const int max_seqlen_q, const int warp_row_stride,
                                        const int window_size_left, const int window_size_right) {
  // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
  static_assert(Layout::rank == 2, "Only support 2D Tensor");
  const int lane_id = threadIdx.x % 32;
  // const int row_idx_offset = row_idx_offset_ + lane_id / 4;
  const int row_idx_offset = row_idx_offset_;
  const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
#pragma unroll
  for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
    const int row_idx_base = row_idx_offset + mi * warp_row_stride;
#pragma unroll
    for (int i = 0; i < size<0, 0>(tensor); ++i) {
      const int row_idx = row_idx_base + i * 8;
      const int col_idx_limit_left = std::max(0, row_idx + max_seqlen_k - max_seqlen_q - window_size_left);
      const int col_idx_limit_right = std::min(max_seqlen_k, row_idx + 1 + max_seqlen_k - max_seqlen_q + window_size_right);
#pragma unroll
      for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
        const int col_idx_base = col_idx_offset + nj * 8;
#pragma unroll
        for (int j = 0; j < size<1, 0>(tensor); ++j) {
          const int col_idx = col_idx_base + j;
          if (col_idx >= col_idx_limit_right || (HasWSLeft && col_idx < col_idx_limit_left)) {
            tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
          }
        }
      }
      // if (cute::thread0()) {
      //     printf("mi = %d, i = %d, row_idx = %d, max_seqlen_k = %d\n", mi, i, row_idx, max_seqlen_k);
      //     print(tensor(make_coord(i, mi), _));
      //     // print(tensor(_, j + nj * size<1, 0>(tensor)));
      // }
    }
  }
}

template <typename Engine, typename Layout>
inline __device__ void apply_mask_causal(Tensor<Engine, Layout>& tensor, const int col_idx_offset_,
                                         const int max_seqlen_k, const int row_idx_offset_,
                                         const int max_seqlen_q, const int warp_row_stride) {
  // Causal masking is equivalent to local masking with window_size_left = infinity and window_size_right = 0
  apply_mask_local</*HasWSLeft=*/false>(tensor, col_idx_offset_, max_seqlen_k, row_idx_offset_,
                                        max_seqlen_q, warp_row_stride, -1, 0);
}

template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
inline __device__ void apply_mask_causal_w_idx(
    Tensor<Engine0, Layout0>& tensor, Tensor<Engine1, Layout1> const& idx_rowcol,
    const int col_idx_offset_, const int max_seqlen_k, const int row_idx_offset_) {
  // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
  static_assert(Layout0::rank == 2, "Only support 2D Tensor");
  static_assert(Layout1::rank == 2, "Only support 2D Tensor");
  CUTE_STATIC_ASSERT_V(size<0>(tensor) == size<0>(idx_rowcol));
  CUTE_STATIC_ASSERT_V(size<1>(tensor) == size<1>(idx_rowcol));
#pragma unroll
  for (int mi = 0; mi < size<0>(tensor); ++mi) {
    const int col_idx_limit = std::min(max_seqlen_k, 1 + row_idx_offset_ + get<0>(idx_rowcol(mi, 0)));
#pragma unroll
    for (int ni = 0; ni < size<1, 1>(tensor); ++ni) {
      if (col_idx_offset_ + get<1>(idx_rowcol(0, ni)) >= col_idx_limit) {
        tensor(mi, ni) = -INFINITY;
      }
    }
    // if (cute::thread0()) {
    //     printf("ni = %d, j = %d, col_idx = %d, max_seqlen_k = %d\n", ni, j, col_idx, max_seqlen_k);
    //     print(tensor(_, make_coord(j, ni)));
    //     // print(tensor(_, j + ni * size<1, 0>(tensor)));
    // }
  }
}

}  // namespace flash
