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#pragma once

#include <cuda.h>
#include <vector>

#ifdef OLD_GENERATOR_PATH
#include <ATen/CUDAGeneratorImpl.h>
#else
#include <ATen/cuda/CUDAGeneratorImpl.h>
#endif

#include <ATen/cuda/CUDAGraphsUtils.cuh>

#include <fmha_utils.h>


constexpr int TOTAL_DIM = 0;
constexpr int THREE_DIM = 1;
constexpr int H_DIM = 2;
constexpr int D_DIM = 3;

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

struct Qkv_params {
    // The QKV matrices.
    void *qkv_ptr;

    // The stride between rows of the Q, K and V matrices.
    size_t qkv_stride_in_bytes;

    // The number of heads.
    int h;
};

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

struct Fused_multihead_attention_fprop_params : public Qkv_params {

    // The dQKV matrices.
    void *dqkv_ptr;

    // Temporary for dKV.
    void *dkv_ptr;

    // The O matrix (output).
    void *o_ptr;

    // The stride between rows of O.
    int64_t o_stride_in_bytes;

    // The pointer to the S matrix, overwritten by the dP matrix (bwd).
    void *s_ptr;
    // The stride between rows of the S matrix.
    int64_t s_stride_in_bytes;

    // The dimensions.
    int b, s, d;

    // The scaling factors for the kernel.
    uint32_t scale_bmm1, scale_softmax, scale_bmm2;

    // array of length b+1 holding starting offset of each sequence.
    int *cu_seqlens;

    // The dropout probability (probability of keeping an activation).
    float p_dropout;

    // Scale factor of 1 / (1 - p_dropout).
    float rp_dropout;

    // Scale factor of 1 / (1 - p_dropout), in half2.
    uint32_t scale_dropout;

    // Random state.
    at::PhiloxCudaState philox_args;
};

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

void run_fmha_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
void run_fmha_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
void run_fmha_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);

void run_fmha_dgrad_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
void run_fmha_dgrad_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
void run_fmha_dgrad_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
void run_fmha_dgrad_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);

void run_fmha_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const bool is_training, const int num_chunks, cudaStream_t stream); 

void run_fmha_dgrad_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const int num_chunks, cudaStream_t stream);

void fmha_run_noloop_reduce(void *out,
                            const void *in,
                            const int *cu_seqlens,
                            const int hidden_size,
                            const int batch_size,
                            const int total,
                            const int num_chunks,
                            cudaStream_t stream);