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.disable_cache()


def flashmla_decode(batch,
                    heads,
                    kv_head_num,
                    seqlen_kv,
                    dim,
                    pe_dim,
                    block_N,
                    block_H,
                    num_split,
                    threads=128):
    scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504  # log2(e)
    dtype = "float16"
    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.macro
    def flash_attn(
            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], 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=threads) as (bx, by):
            Q_local = T.alloc_fragment([block_H, dim], dtype)
            Q_pe_local = T.alloc_fragment([block_H, pe_dim], dtype)
            KV_shared = T.alloc_shared([block_N, dim], dtype)
            K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype)
            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
            acc_s_cast = T.alloc_fragment([block_H, block_N], 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.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_local)
            T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_local)
            T.fill(acc_o, 0)
            T.fill(logsum, 0)
            T.fill(scores_max, -T.infinity(accum_dtype))

            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, :], KV_shared)
                T.copy(K_pe[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared)
                T.clear(acc_s)
                T.gemm(Q_local, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
                T.gemm(
                    Q_pe_local,
                    K_pe_shared,
                    acc_s,
                    transpose_B=True,
                    policy=T.GemmWarpPolicy.FullRow)
                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)
                T.copy(acc_s, acc_s_cast)
                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(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
            for i, j in T.Parallel(block_H, dim):
                acc_o[i, j] /= logsum[i]
            T.copy(acc_o, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :])

    @T.macro
    def flash_attn_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], dtype),
            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
            glse: T.Tensor([batch, heads, num_split], dtype),
            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
    ):
        with T.Kernel(
                batch, heads // min(block_H, kv_group_num), num_split,
                threads=threads) as (bx, by, bz):
            Q_local = T.alloc_fragment([block_H, dim], dtype)
            Q_pe_local = T.alloc_fragment([block_H, pe_dim], dtype)
            KV_shared = T.alloc_shared([block_N, dim], dtype)
            K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype)
            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
            acc_s_cast = T.alloc_fragment([block_H, block_N], 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.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_local)
            T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_local)
            T.fill(acc_o, 0)
            T.fill(logsum, 0)
            T.fill(scores_max, -T.infinity(accum_dtype))

            loop_range = T.ceildiv((seqlen_kv // num_split), block_N)
            for k in T.Pipelined(loop_range, num_stages=0):
                kv_start = (seqlen_kv // num_split) * bz + k * block_N
                kv_end = (seqlen_kv // num_split) * bz + (k + 1) * block_N
                T.copy(KV[bx, kv_start:kv_end, cur_kv_head, :], KV_shared)
                T.copy(K_pe[bx, kv_start:kv_end, cur_kv_head, :], K_pe_shared)
                T.clear(acc_s)
                T.gemm(Q_local, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
                T.gemm(
                    Q_pe_local,
                    K_pe_shared,
                    acc_s,
                    transpose_B=True,
                    policy=T.GemmWarpPolicy.FullRow)
                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, acc_s_cast)
                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(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
            for i, j in T.Parallel(block_H, dim):
                acc_o[i, j] /= logsum[i]
            for i in T.Parallel(block_H):
                logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
            T.copy(logsum, glse[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz])
            T.copy(acc_o, Output_partial[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz, :])

    @T.macro
    def combine(
            glse: T.Tensor([batch, heads, num_split], dtype),
            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
            Output: T.Tensor([batch, heads, dim], dtype),
    ):
        with T.Kernel(heads, batch, threads=128) as (by, bz):
            po_local = T.alloc_fragment([dim], dtype)
            o_accum_local = T.alloc_fragment([dim], accum_dtype)
            lse_local_split = T.alloc_local([1], accum_dtype)
            lse_logsum_local = T.alloc_local([1], accum_dtype)
            lse_max_local = T.alloc_local([1], accum_dtype)
            scale_local = T.alloc_local([1], accum_dtype)

            T.annotate_layout({
                lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
            })

            T.clear(lse_logsum_local)
            T.clear(o_accum_local)
            lse_max_local[0] = -T.infinity(accum_dtype)
            for k in T.serial(num_split):
                lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k])
            for k in T.Pipelined(num_split, num_stages=1):
                lse_local_split[0] = glse[bz, by, k]
                lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
            lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
            for k in T.serial(num_split):
                for i in T.Parallel(dim):
                    po_local[i] = Output_partial[bz, by, k, i]
                lse_local_split[0] = glse[bz, by, k]
                scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
                for i in T.Parallel(dim):
                    o_accum_local[i] += po_local[i] * scale_local[0]
            for i in T.Parallel(dim):
                Output[bz, by, i] = o_accum_local[i]

    @T.prim_func
    def main_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], dtype),
            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
            glse: T.Tensor([batch, heads, num_split], dtype),
            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
            Output: T.Tensor([batch, heads, dim], dtype),
    ):
        flash_attn_split(Q, Q_pe, KV, K_pe, glse, Output_partial)
        combine(glse, Output_partial, Output)

    @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], dtype),
            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
            glse: T.Tensor([batch, heads, num_split], dtype),
            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
            Output: T.Tensor([batch, heads, dim], dtype),
    ):
        flash_attn(Q, Q_pe, KV, K_pe, Output)

    if num_split > 1:
        return main_split
    else:
        return main_no_split


def ref_program(q, q_pe, kv, k_pe, glse, Output_partial):
    #     """
    #     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]
    #     - glse (Tensor): [batch, heads, num_split]
    #     - Output_partial (Tensor): [batch, heads, num_split, 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=1, 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=1024, 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')
    parser.add_argument('--auto_tune', action='store_true', help='auto tune')
    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
    enable_autotune = args.auto_tune

    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 = 32
    BLOCK_H = 64
    num_split = 4

    program = flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H,
                              num_split)
    kernel = tilelang.compile(program, out_idx=[6])
    profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn)
    input_tensors = profiler._get_inputs()
    tilelang_output = kernel(*input_tensors)
    ref_output = ref_program(*input_tensors)
    print(f"Tilelang output: {tilelang_output}")
    print(f"Ref output: {ref_output}")
    torch.testing.assert_close(tilelang_output, ref_output, rtol=0.01, atol=0.01)
    latency = profiler.do_bench(warmup=500)
    print(f"Latency: {latency} ms")
    print(f"TFlops: {total_flops / latency * 1e-9} TFlops")

    # Enable Auto Tuning


    def get_configs():
        import itertools
        BLOCK_N = [16, 32, 64, 128]
        BLOCK_H = [16, 32, 64, 128]
        num_split = [1, 2, 4, 8, 16, 32]
        thread_num = [128, 256]

        _configs = list(itertools.product(BLOCK_N, BLOCK_H, num_split, thread_num))

        return [{
            "block_N": c[0],
            "block_H": c[1],
            "num_split": c[2],
            "thread_num": c[3],
        } for c in _configs]

    def wrapped_kernel(block_N=None, block_H=None, num_split=None, thread_num=None):
        return flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim, block_N, block_H,
                               num_split, thread_num)

    if enable_autotune:
        autotuner = AutoTuner.from_kernel(
            kernel=wrapped_kernel, configs=get_configs()).set_compile_args(
                supply_type=tilelang.TensorSupplyType.Integer,
                target="auto",
            )
        tune_result = autotuner.run(warmup=3, rep=20)
        best_latency = tune_result.latency
        best_config = tune_result.config
        print(f"Best latency: {best_latency} ms")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9} TFlops")
        print(f"Best config: {best_config}")
