import torch, math
import triton
import triton.language as tl

# Autotune Here
configs = [
    triton.Config({'BLOCK_M': BM, 'BLOCK_N': BN, 'STAGE':S, 'waves_per_eu':wpe}, num_warps=nw, num_stages=ns) \
    for BM in [32]\
    for BN in [16]\
    for nw in[2, 4]\
    for ns in [1]\
    for S in [1]\
    for wpe in [3,4]
]

def keep(conf):
    BLOCK_M = conf.kwargs["BLOCK_M"]
    BLOCK_N = conf.kwargs["BLOCK_N"]
    BLOCK_AREA = BLOCK_M * BLOCK_N

    # do not keep too high block area, any higher doesnt seem to help for navi21
    if (BLOCK_AREA > 1024):
        return False

    # do not keep 'mirror image' configs (ie keep [64,32] and discard [32,64])
    if (BLOCK_M < BLOCK_N):
        return False

    # do not keep skinny sizes for now
    if (BLOCK_M//BLOCK_N >= 8):
        return False
    
    # do not keep configs where num_warps is too high or low
    if (BLOCK_AREA >= 1024 and conf.num_warps != 2):
        return False
    if (BLOCK_AREA >= 2048 and conf.num_warps != 4):
        return False

    return True


@triton.jit
def _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len,
                    K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn, 
                    start_m,  
                    BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,  
                    STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,  
                    ):
    lo, hi = 0, kv_len
    for start_n in range(lo, hi, BLOCK_N):
        start_n = tl.multiple_of(start_n, BLOCK_N)
        k_mask = offs_n[None, :] < (kv_len - start_n)   
        k = tl.load(K_ptrs, mask = k_mask)
        k_scale = tl.load(K_scale_ptr)
        qk = tl.dot(q, k).to(tl.float32) * q_scale * k_scale 
        m_ij = tl.maximum(m_i, tl.max(qk, 1))
        qk = qk - m_ij[:, None]
        p = tl.math.exp2(qk)
        l_ij = tl.sum(p, 1)
        
        alpha = tl.math.exp2(m_i - m_ij)
        l_i = l_i * alpha + l_ij
        
        acc = acc * alpha[:, None]
        
        v = tl.load(V_ptrs, mask = offs_n[:, None] < (kv_len - start_n))
        p = p.to(tl.float16)
        
        acc += tl.dot(p, v, out_dtype=tl.float32)   
        m_i = m_ij
        K_ptrs += BLOCK_N * stride_kn
        K_scale_ptr += 1
        V_ptrs += BLOCK_N * stride_vn
    return acc, l_i

@triton.autotune(
    list(filter(keep, configs)), 
    key=['qo_len', 'kv_len', 'h_qo']
)
@triton.jit
def _attn_fwd(Q, K, V, Q_scale, K_scale, Out,  
              stride_qz, stride_qh, stride_qn,
              stride_kz, stride_kh, stride_kn,  
              stride_vz, stride_vh, stride_vn,  
              stride_oz, stride_oh, stride_on,  
              qo_len, kv_len, H: tl.constexpr, num_kv_groups: tl.constexpr,
              HEAD_DIM: tl.constexpr,  
              BLOCK_M: tl.constexpr,  
              BLOCK_N: tl.constexpr,  
              STAGE: tl.constexpr
              ):
    start_m = tl.program_id(0)

    off_z = tl.program_id(2).to(tl.int64)
    off_h = tl.program_id(1).to(tl.int64)

    q_scale_offset = (off_z * H + off_h) * tl.cdiv(qo_len, BLOCK_M)
    k_scale_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * tl.cdiv(kv_len, BLOCK_N)  
    
    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, HEAD_DIM)
    Q_ptrs = Q + (off_z * stride_qz + off_h * stride_qh) + offs_m[:, None] * stride_qn + offs_k[None, :]
    Q_scale_ptr = Q_scale + q_scale_offset + start_m
    K_ptrs = K + (off_z * stride_kz + (off_h // num_kv_groups) * stride_kh) + offs_n[None, :] * stride_kn + offs_k[:, None] 
    K_scale_ptr = K_scale + k_scale_offset
    V_ptrs = V + (off_z * stride_vz + (off_h // num_kv_groups) * stride_vh) + offs_n[:, None] * stride_vn + offs_k[None, :]
    O_block_ptr = Out + (off_z * stride_oz + off_h * stride_oh) + offs_m[:, None] * stride_on + offs_k[None, :]
    
    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
    
    q = tl.load(Q_ptrs, mask = offs_m[:, None] < qo_len)
    q_scale = tl.load(Q_scale_ptr)
    acc, l_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
                                    start_m,  
                                    BLOCK_M, HEAD_DIM, BLOCK_N,  
                                    4 - STAGE, offs_m, offs_n 
                                    )
    acc = acc / l_i[:, None]
    tl.store(O_block_ptr, acc.to(Out.type.element_ty), mask = (offs_m[:, None] < qo_len))

def forward(q, k, v, q_scale, k_scale, tensor_layout="HND", output_dtype=torch.float16):
    BLOCK_M = 128
    BLOCK_N = 64
    stage = 1

    o = torch.empty(q.shape, dtype=output_dtype, device=q.device)

    if tensor_layout == "HND":
        b, h_qo, qo_len, head_dim = q.shape
        _, h_kv, kv_len, _ = k.shape

        stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
        stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
        stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(1), v.stride(2)
        stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(1), o.stride(2)
    elif tensor_layout == "NHD":
        b, qo_len, h_qo, head_dim = q.shape
        _, kv_len, h_kv, _ = k.shape

        stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
        stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
        stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(2), v.stride(1)
        stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(2), o.stride(1)
    else:
        raise ValueError(f"tensor_layout {tensor_layout} not supported")
    
    HEAD_DIM_K = head_dim
    num_kv_groups = h_qo // h_kv

    grid = lambda META: (triton.cdiv(qo_len,  META['BLOCK_M']), h_qo, b)
    _attn_fwd[grid](
        q, k, v, q_scale, k_scale, o,  
        stride_bz_q, stride_h_q, stride_seq_q, 
        stride_bz_k, stride_h_k, stride_seq_k,  
        stride_bz_v, stride_h_v, stride_seq_v,  
        stride_bz_o, stride_h_o, stride_seq_o,
        qo_len, kv_len,
        h_qo, num_kv_groups,
        HEAD_DIM=HEAD_DIM_K,  
        )
    return o