import torch
import torch.nn.functional as F
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
from einops import rearrange, einsum
import argparse


@tilelang.jit(
    out_idx=[-1], pass_configs={
        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
    })
def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H):
    scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504  # log2(e)
    dtype = "float16"
    q_dtype = "float8_e4m3"
    accum_dtype = "float"
    kv_group_num = heads // kv_head_num
    VALID_BLOCK_H = min(block_H, kv_group_num)
    assert kv_head_num == 1, "kv_head_num must be 1"

    @T.prim_func
    def main_no_split(
            Q: T.Tensor([batch, heads, dim], dtype),
            Q_pe: T.Tensor([batch, heads, pe_dim], dtype),
            KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], q_dtype),
            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
            Output: T.Tensor([batch, heads, dim], dtype),
    ):
        with T.Kernel(batch, heads // min(block_H, kv_group_num), threads=256) as (bx, by):
            Q_shared = T.alloc_shared([block_H, dim], dtype)
            S_shared = T.alloc_shared([block_H, block_N], dtype)
            Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype)
            qKV_shared = T.alloc_shared([block_N, dim], q_dtype)
            KV_shared = T.alloc_shared([block_N, dim], dtype)
            K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype)
            O_shared = T.alloc_shared([block_H, dim], dtype)
            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
            acc_o = T.alloc_fragment([block_H, dim], accum_dtype)
            scores_max = T.alloc_fragment([block_H], accum_dtype)
            scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
            scores_scale = T.alloc_fragment([block_H], accum_dtype)
            scores_sum = T.alloc_fragment([block_H], accum_dtype)
            logsum = T.alloc_fragment([block_H], accum_dtype)

            cur_kv_head = by // (kv_group_num // block_H)
            T.use_swizzle(10)
            T.annotate_layout({
                O_shared: tilelang.layout.make_swizzled_layout(O_shared),
            })

            T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_shared)
            T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_shared)
            T.fill(acc_o, 0)
            T.fill(logsum, 0)
            T.fill(scores_max, -T.infinity(accum_dtype))
            T.disable_warp_group_reg_alloc()
            loop_range = T.ceildiv(seqlen_kv, block_N)
            for k in T.Pipelined(loop_range, num_stages=2):
                T.copy(KV[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], qKV_shared)
                T.copy(K_pe[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared)
                T.copy(qKV_shared, KV_shared)

                T.clear(acc_s)
                T.gemm(
                    Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol)
                T.gemm(
                    Q_pe_shared,
                    K_pe_shared,
                    acc_s,
                    transpose_B=True,
                    policy=T.GemmWarpPolicy.FullCol)
                T.copy(scores_max, scores_max_prev)
                T.fill(scores_max, -T.infinity(accum_dtype))
                T.reduce_max(acc_s, scores_max, dim=1, clear=False)
                for i in T.Parallel(block_H):
                    scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
                for i, j in T.Parallel(block_H, block_N):
                    acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
                T.reduce_sum(acc_s, scores_sum, dim=1)
                T.copy(acc_s, S_shared)
                for i in T.Parallel(block_H):
                    logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
                for i, j in T.Parallel(block_H, dim):
                    acc_o[i, j] *= scores_scale[i]
                T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol)
            for i, j in T.Parallel(block_H, dim):
                acc_o[i, j] /= logsum[i]
            T.copy(acc_o, O_shared)
            T.copy(O_shared, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :])

    return main_no_split


def ref_program(q, q_pe, kv, k_pe):
    #     """
    #     Inputs:
    #     - q (Tensor): [batch, heads, dim]
    #     - q_pe (Tensor): [batch, heads, pe_dim]
    #     - kv (Tensor): [batch, seqlen_kv, kv_head_num, dim]
    #     - k_pe (Tensor): [batch, seqlen_kv, kv_head_num, pe_dim]
    #     Outputs:
    #     - output (Tensor): [batch, heads, dim]
    #     """
    dim = q.shape[-1]
    pe_dim = q_pe.shape[-1]
    num_head_groups = q.shape[1] // kv.shape[2]
    scale = (dim + pe_dim)**0.5
    q = rearrange(
        q, 'b (h g) d -> b g h d', g=num_head_groups)  # [batch_size, num_head_groups, groups, dim]

    q_pe = rearrange(
        q_pe, 'b (h g) d -> b g h d',
        g=num_head_groups)  # [batch_size, num_head_groups, groups, pe_dim]

    kv = rearrange(kv, 'b n h d -> b h n d')  # [batch_size, groups, seqlen_kv, dim]

    k_pe = rearrange(k_pe, 'b n h d -> b h n d')  # [batch_size, num_head_groups, groups, pe_dim]

    query = torch.concat([q, q_pe], dim=-1)
    key = torch.concat([kv, k_pe], dim=-1)

    scores = einsum(
        query, key,
        'b g h d, b h s d -> b g h s')  # [batch_size, num_head_groups, groups, seqlen_kv]

    attention = F.softmax(
        scores / scale, dim=-1)  # [batch_size, num_head_groups, groups, seqlen_kv]

    out = einsum(attention, kv,
                 'b g h s, b h s d -> b g h d')  # [batch_size, num_head_groups, groups, dim]
    out = rearrange(out, 'b g h d -> b (h g) d')  # [batch_size, heads, dim]
    return out


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch', type=int, default=128, help='batch size')
    parser.add_argument('--heads', type=int, default=128, help='q heads number')
    parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number')
    parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length')
    parser.add_argument('--dim', type=int, default=512, help='head dim')
    parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim')
    args = parser.parse_args()
    batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim
    qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim)
    pv_flops = 2 * batch * heads * kv_ctx * dim
    total_flops = qk_flops + pv_flops
    BLOCK_N = 64
    BLOCK_H = 64

    kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H)
    profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn)
    latency = profiler.do_bench(warmup=500)
    print(f"Latency: {latency} ms")
    print(f"TFlops: {total_flops / latency * 1e-9} TFlops")
