example_gqa_fwd_varlen.py

# ruff: noqa
import argparse
import torch
import tilelang
import tilelang.language as T
import tilelang.testing
from einops import rearrange, repeat
from tilelang.profiler import do_bench
from varlen_utils import generate_random_padding_mask, generate_qkv


def attention_ref(
    q,
    k,
    v,
    query_padding_mask=None,
    key_padding_mask=None,
    causal=False,
    window_size=(-1, -1),
    upcast=True,
):
    if causal:
        window_size = (window_size[0], 0)
    dtype_og = q.dtype
    if upcast:
        q, k, v = q.float(), k.float(), v.float()
    b, T, Hq, D = q.shape
    S = k.shape[1]
    scale = (1.0 / D) ** 0.5
    k = repeat(k, "b s h d -> b s (h g) d", g=Hq // k.shape[2])
    v = repeat(v, "b s h d -> b s (h g) d", g=Hq // v.shape[2])
    scores = torch.einsum("bthd,bshd->bhts", q, k)
    left, right = window_size
    left = S if left is None or left < 0 else int(left)
    right = S if right is None or right < 0 else int(right)
    t_idx = torch.arange(T, device=scores.device)[:, None]
    s_idx = torch.arange(S, device=scores.device)[None, :]
    visible_ts = (s_idx >= (t_idx - left)) & (s_idx <= (t_idx + right))
    visible_mask = visible_ts.unsqueeze(0).unsqueeze(0)
    if key_padding_mask is not None:
        k_keep = rearrange(key_padding_mask, "b s -> b 1 1 s")
        visible_mask = visible_mask & k_keep
    neg_inf = torch.finfo(scores.dtype).min
    scores = scores * scale
    scores = scores.masked_fill(~visible_mask, neg_inf)
    attention = torch.softmax(scores, dim=-1).to(v.dtype)
    if query_padding_mask is not None:
        q_keep = rearrange(query_padding_mask, "b t -> b 1 t 1")
        attention = attention.masked_fill(~q_keep, 0.0)
    output = torch.einsum("bhts,bshd->bthd", attention, v)
    if query_padding_mask is not None:
        output = output.masked_fill(rearrange(~query_padding_mask, "b t -> b t 1 1"), 0.0)
    return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)


@tilelang.jit(
    out_idx=[6],
    pass_configs={
        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
    },
)
def flashattn(batch_size, groups, UQ, UKV, heads, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128):
    scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
    head_kv = heads // groups
    q_shape = [UQ, heads, dim]
    kv_shape = [UKV, head_kv, dim]
    o_shape = [UQ, heads, dim]
    dtype = T.float16
    accum_dtype = T.float32

    @T.prim_func
    def main(
        Q_unpad: T.Tensor(q_shape, dtype),
        K_unpad: T.Tensor(kv_shape, dtype),
        V_unpad: T.Tensor(kv_shape, dtype),
        cu_seqlens_q: T.Tensor([batch_size + 1], T.int32),
        cu_seqlens_k: T.Tensor([batch_size + 1], T.int32),
        max_seqlen_q: T.int32,
        Output_unpad: T.Tensor(o_shape, dtype),
    ):
        with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, 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.annotate_layout(
                {
                    O_shared: tilelang.layout.make_swizzled_layout(O_shared),
                    Q_shared: tilelang.layout.make_swizzled_layout(Q_shared),
                }
            )

            batch_idx = bz
            head_idx = by
            kv_head_idx = head_idx // groups

            q_start_idx = cu_seqlens_q[batch_idx]
            kv_start_idx = cu_seqlens_k[batch_idx]
            q_end_idx = cu_seqlens_q[batch_idx + 1]
            k_end_idx = cu_seqlens_k[batch_idx + 1]

            q_current_seqlen = q_end_idx - q_start_idx
            kv_current_seqlen = k_end_idx - kv_start_idx

            T.copy(Q_unpad[q_start_idx + bx * block_M : q_start_idx + (bx + 1) * block_M, head_idx, :], 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(q_current_seqlen + (bx + 1) * block_M, block_N), T.ceildiv(kv_current_seqlen, block_N))
                if is_causal
                else T.ceildiv(kv_current_seqlen, block_N)
            )

            for k in T.Pipelined(loop_range, num_stages=num_stages):
                T.copy(K_unpad[kv_start_idx + k * block_N : kv_start_idx + (k + 1) * block_N, kv_head_idx, :], 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)
                            or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen),
                            -1e9,
                            0,
                        )
                else:
                    for i, j in T.Parallel(block_M, block_N):
                        acc_s[i, j] = T.if_then_else(
                            (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen), -1e9, 0
                        )

                T.gemm(Q_shared, K_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_M):
                    scores_max[i] = T.max(scores_max[i], scores_max_prev[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):
                    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)

                for i, j in T.Parallel(block_M, dim):
                    acc_o[i, j] *= scores_scale[i]

                T.copy(V_unpad[kv_start_idx + k * block_N : kv_start_idx + (k + 1) * block_N, kv_head_idx, :], V_shared)

                T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)

            for i, j in T.Parallel(block_M, dim):
                acc_o[i, j] /= logsum[i]
            T.copy(acc_o, O_shared)

            for i, d in T.Parallel(block_M, dim):
                if bx * block_M + i < q_current_seqlen:
                    Output_unpad[q_start_idx + bx * block_M + i, head_idx, d] = O_shared[i, d]

    return main


def main(
    batch: int = 1, heads: int = 64, q_seqlen: int = 2048, k_seqlen: int = 2048, dim: int = 128, groups: int = 16, is_causal: bool = False
):
    assert heads % groups == 0, "heads must be divisible by groups"

    flops_per_matmul = 2.0 * batch * heads * q_seqlen * k_seqlen * dim
    total_flops = 2 * flops_per_matmul

    tilelang.testing.set_random_seed(0)

    if is_causal:
        total_flops *= 0.5

    tilelang.testing.set_random_seed(0)

    dtype = torch.float16
    device = torch.device("cuda")

    head_kv = heads // groups
    q = torch.randn(batch, q_seqlen, heads, dim, dtype=dtype, device=device)
    k = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device)
    v = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device)

    query_padding_mask = generate_random_padding_mask(q_seqlen, batch, device, mode="random")
    key_padding_mask = generate_random_padding_mask(k_seqlen, batch, device, mode="random")

    (
        q_unpad,
        k_unpad,
        v_unpad,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        q,
        k,
        v,
        output_pad_fn,
        _,
        _,
    ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)

    UQ = q_unpad.shape[0]
    UKV = k_unpad.shape[0]

    kernel = flashattn(batch, groups, UQ, UKV, heads, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256)

    out_unpad = kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q)
    out = output_pad_fn(out_unpad)

    out_ref, _ = attention_ref(
        q,
        k,
        v,
        query_padding_mask=query_padding_mask,
        key_padding_mask=key_padding_mask,
        causal=is_causal,
    )
    torch.testing.assert_close(out, out_ref, rtol=1e-2, atol=1e-2)
    print("All checks passed.✅")
    latency = do_bench(lambda: kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q), _n_warmup=5, _n_repeat=5)
    print("Tile-lang: {:.2f} ms".format(latency))
    print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--batch", type=int, default=8, help="batch size")
    parser.add_argument("--heads", type=int, default=64, help="query heads")
    parser.add_argument("--groups", type=int, default=16, help="groups")
    parser.add_argument("--q_seqlen", type=int, default=2048, help="query sequence length")
    parser.add_argument("--k_seqlen", type=int, default=2048, help="key/value sequence length")
    parser.add_argument("--dim", type=int, default=128, help="head dim")
    parser.add_argument("--is_causal", action="store_true", help="causal attention")
    args = parser.parse_args()
    main(args.batch, args.heads, args.q_seqlen, args.k_seqlen, args.dim, args.groups, args.is_causal)