import torch
import torch.nn.functional as F
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
import itertools
import argparse
from functools import partial


def get_configs():
    iter_params = dict(
        block_M=[128],
        block_N=[128],
        num_stages=[2],
        threads=[256],
    )
    return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]


@autotune(
    configs=get_configs(),
    warmup=10,
    rep=10,
)
@tilelang.jit(
    out_idx=[3], pass_configs={
        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
    })
def flashattn(
    batch,
    heads,
    seq_len,
    dim,
    is_causal,
    groups=1,
    block_M=64,
    block_N=64,
    num_stages=0,
    threads=128,
):
    scale = (1.0 / dim)**0.5 * 1.44269504  # log2(e)
    head_kv = heads // groups
    q_shape = [batch, seq_len, heads, dim]
    kv_shape = [batch, seq_len, head_kv, dim]
    dtype = "float16"
    accum_dtype = "float"

    @T.macro
    def MMA0(
        K: T.Tensor(kv_shape, dtype),
        Q_shared: T.SharedBuffer([block_M, dim], dtype),
        K_shared: T.SharedBuffer([block_N, dim], dtype),
        acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
        k: T.int32,
        bx: T.int32,
        by: T.int32,
        bz: T.int32,
    ):
        T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared)
        if is_causal:
            for i, j in T.Parallel(block_M, block_N):
                acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0,
                                             -T.infinity(acc_s.dtype))
        else:
            T.clear(acc_s)
        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)

    @T.macro
    def MMA1(
        V: T.Tensor(kv_shape, dtype),
        V_shared: T.SharedBuffer([block_N, dim], dtype),
        acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
        acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
        k: T.int32,
        by: T.int32,
        bz: T.int32,
    ):
        T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared)
        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)

    @T.macro
    def Softmax(
            acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
            acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
            scores_max: T.FragmentBuffer([block_M], accum_dtype),
            scores_max_prev: T.FragmentBuffer([block_M], accum_dtype),
            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
            scores_sum: T.FragmentBuffer([block_M], accum_dtype),
            logsum: T.FragmentBuffer([block_M], accum_dtype),
    ):
        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)
        # To do causal softmax, we need to set the scores_max to 0 if it is -inf
        # This process is called Check_inf in FlashAttention3 code, and it only need to be done
        # in the first ceil_div(kBlockM, kBlockN) steps.
        # for i in T.Parallel(block_M):
        #     scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i])
        for i in T.Parallel(block_M):
            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
        for i, j in T.Parallel(block_M, block_N):
            # 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.
            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
        T.reduce_sum(acc_s, scores_sum, dim=1)
        for i in T.Parallel(block_M):
            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
        T.copy(acc_s, acc_s_cast)

    @T.macro
    def Rescale(
            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
    ):
        for i, j in T.Parallel(block_M, dim):
            acc_o[i, j] *= scores_scale[i]

    @T.prim_func
    def main(
            Q: T.Tensor(q_shape, dtype),
            K: T.Tensor(kv_shape, dtype),
            V: T.Tensor(kv_shape, dtype),
            Output: T.Tensor(q_shape, dtype),
    ):
        with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz):
            Q_shared = T.alloc_shared([block_M, dim], dtype)
            K_shared = T.alloc_shared([block_N, dim], dtype)
            V_shared = T.alloc_shared([block_N, dim], dtype)
            O_shared = T.alloc_shared([block_M, dim], dtype)
            acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
            acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
            acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
            scores_max = T.alloc_fragment([block_M], accum_dtype)
            scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
            scores_scale = T.alloc_fragment([block_M], accum_dtype)
            scores_sum = T.alloc_fragment([block_M], accum_dtype)
            logsum = T.alloc_fragment([block_M], accum_dtype)

            T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared)
            T.fill(acc_o, 0)
            T.fill(logsum, 0)
            T.fill(scores_max, -T.infinity(accum_dtype))

            loop_range = (
                T.min(T.ceildiv(seq_len, block_N), T.ceildiv(
                    (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N))

            for k in T.Pipelined(
                    loop_range,
                    num_stages=num_stages,
                    order=[-1, 0, 3, 1, -1, 2],
                    stage=[-1, 0, 0, 1, -1, 1],
                    group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]):
                MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz)
                Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum,
                        logsum)
                Rescale(acc_o, scores_scale)
                MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz)
            for i, j in T.Parallel(block_M, dim):
                acc_o[i, j] /= logsum[i]
            T.copy(acc_o, O_shared)
            T.copy(O_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :])

    return main


def ref_program(Q, K, V, is_causal, groups=1):
    # Q: [B, T, HQ, D]
    # K: [B, T, HK, D]
    # V: [B, T, HV, D]
    # HQ = HKV * groups
    assert Q.size(2) == K.size(
        2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}"
    assert Q.size(2) == V.size(
        2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}"

    dim = Q.size(-1)
    K = K.repeat_interleave(groups, dim=2)
    V = V.repeat_interleave(groups, dim=2)
    scores = torch.einsum('bqhd,bkhd->bhqk', Q, K)
    scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype))
    if is_causal:
        seq_len = Q.size(1)
        mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device))
        mask = mask.unsqueeze(0).unsqueeze(0)
        scores = scores.masked_fill(mask == 0, float('-inf'))
    attention_weights = F.softmax(scores, dim=-1)
    output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V)
    return output


def main(
    batch: int = 1,
    heads: int = 64,
    seq_len: int = 4096,
    dim: int = 128,
    is_causal: bool = False,
    groups: int = 16,
    tune: bool = False,
):
    flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim
    total_flops = 2 * flops_per_matmul
    if is_causal:
        total_flops *= 0.5

    if (not tune):
        kernel = flashattn(
            batch,
            heads,
            seq_len,
            dim,
            is_causal,
            groups=groups,
            block_M=128,
            block_N=128,
            num_stages=2,
            threads=256)
        ref_program_processed = partial(ref_program, is_causal=is_causal, groups=groups)
        profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
        profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01)
        print("All checks pass.")
        latency = profiler.do_bench(ref_program_processed, warmup=500)
        print("Ref: {:.2f} ms".format(latency))
        print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
        latency = profiler.do_bench(warmup=500)
        print("Tile-lang: {:.2f} ms".format(latency))
        print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
    else:
        kernel = flashattn(batch, heads, seq_len, dim, is_causal)
        best_latency = kernel.latency
        best_config = kernel.config
        ref_latency = kernel.ref_latency
        print(f"Best latency: {best_latency}")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
        print(f"Best config: {best_config}")
        print(f"Ref latency: {ref_latency}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch', type=int, default=1, help='batch size')
    parser.add_argument('--heads', type=int, default=64, help='heads')
    parser.add_argument('--seq_len', type=int, default=4096, help='sequence length')
    parser.add_argument('--dim', type=int, default=128, help='dim')
    parser.add_argument('--is_causal', action='store_true', help='causal')
    parser.add_argument('--tune', action='store_true', help='tune configs')
    parser.add_argument('--groups', type=int, default=16, help='groups')
    args = parser.parse_args()
    main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups, args.tune)
