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b66f552 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import torch
import triton
import triton.language as tl
from fla.ops.utils.cumsum import chunk_global_cumsum
from fla.ops.utils.op import exp
from fla.utils import autotune_cache_kwargs, check_shared_mem
@triton.heuristics({
'USE_G': lambda args: args['g_cumsum'] is not None,
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4] + ([] if check_shared_mem('hopper') else [8])
for num_stages in [2, 3, 4, 5]
],
key=['H', 'G', 'K', 'V', 'BK', 'BV', 'USE_G'],
**autotune_cache_kwargs,
)
@triton.jit
def naive_attn_decoding_kernel(
q,
k,
v,
o,
g_cumsum,
scale,
gate_scale,
cu_seqlens,
T,
B: tl.constexpr,
H: tl.constexpr,
HQ: tl.constexpr,
G: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BS: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_G: tl.constexpr,
):
i_v, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_hq = i_bh // HQ, i_bh % HQ
i_h = i_hq // G
bos, eos = tl.load(cu_seqlens + i_b).to(tl.int32), tl.load(cu_seqlens + i_b + 1).to(tl.int32)
T = eos - bos
p_q = tl.make_block_ptr(q + i_bh * K, (K,), (1, ), (0, ), (BK,), (0,))
p_o = tl.make_block_ptr(o + i_bh * V, (V,), (1, ), (0, ), (BV,), (0,))
b_q = tl.load(p_q, boundary_check=(0,))
b_q = (b_q * scale).to(b_q.dtype)
b_o = tl.zeros([BV ], dtype=tl.float32)
b_m = tl.full([1], float('-inf'), dtype=tl.float32)
b_acc = tl.zeros([1], dtype=tl.float32)
if USE_G:
p_g = tl.make_block_ptr(g_cumsum + bos * HQ + i_hq, (T,), (HQ,), (T-1,), (1,), (0,))
b_gq = tl.load(p_g, boundary_check=(0,)).to(tl.float32)
else:
b_gq = None
for i_s in range(0, T, BS):
p_k = tl.make_block_ptr(k + (bos * H + i_h) * K, (T, K), (H*K, 1), (i_s, 0), (BS, BK), (1, 0))
p_v = tl.make_block_ptr(v + (bos * H + i_h) * V, (T, V), (H*V, 1), (i_s, i_v * BV), (BS, BV), (1, 0))
# [BK, BS]
b_k = tl.load(p_k, boundary_check=(0, 1))
# [BS, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
# [BT, BS]
b_s = tl.sum(b_q[None, :] * b_k, 1)
mask = i_s + tl.arange(0, BS) < T
b_s = tl.where(mask, b_s, float('-inf'))
if USE_G:
p_gk = tl.make_block_ptr(g_cumsum + bos * HQ + i_hq, (T,), (HQ,), (i_s,), (BS,), (0,))
b_gk = tl.load(p_gk, boundary_check=(0,)).to(tl.float32)
b_s += (b_gq - b_gk) * gate_scale
# [BT, BS]
b_m, b_mp = tl.maximum(b_m, tl.max(b_s)), b_m
b_r = exp(b_mp - b_m)
# [BT, BS]
b_p = exp(b_s - b_m)
# [BT]
b_acc = b_acc * b_r + tl.sum(b_p, 0)
# [BT, BV]
b_o = b_o * b_r + tl.sum(b_p[:, None] * b_v, 0)
b_mp = b_m
b_o = b_o / b_acc
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, ))
def attn_decoding_one_step(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor | None = None,
scale: float | None = None,
cu_seqlens: torch.LongTensor = None,
do_gate_scale: bool = False,
):
r"""
Args:
q (torch.Tensor):
query of shape `[1, B, HQ, K]`.
k (torch.Tensor):
keys of shape `[1, T, H, K]`.
GQA will be applied if HQ is divisible by H. T is the cumulative length for all batch.
v (torch.Tensor):
values of shape `[1, T, H, V]`.
g (Optional[torch.Tensor]):
log decay factors of shape `[1, T, H]`. Default: `None`.
scale (Optional[float]):
Scale factor for attention scores.
If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
cu_seqlens (torch.LongTensor):
Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
consistent with the FlashAttention API.
do_gate_scale (bool):
Whether to apply gate scale. Default: `False`. If `True`, the attention scale will also be applied
to the gating bias term in Forgetting Transformer or PaTH-FoX.
Returns:
o (torch.Tensor):
Outputs of shape `[B, 1, HQ, V]`.
"""
assert cu_seqlens is not None, "The cu_seqlens must be provided for varlen decoding"
B, T, H, K, V = *k.shape, v.shape[-1]
N = len(cu_seqlens) - 1
HQ = q.shape[2]
G = HQ // H
if scale is None:
scale = K ** -0.5
BK = max(triton.next_power_of_2(K), 16)
if check_shared_mem('hopper', q.device.index):
BS = min(64, max(16, triton.next_power_of_2(T)))
BV = min(256, max(16, triton.next_power_of_2(V)))
elif check_shared_mem('ampere', q.device.index):
BS = min(32, max(16, triton.next_power_of_2(T)))
BV = min(128, max(16, triton.next_power_of_2(V)))
else:
BS = min(32, max(16, triton.next_power_of_2(T)))
BV = min(64, max(16, triton.next_power_of_2(V)))
g_cumsum = chunk_global_cumsum(g, cu_seqlens=cu_seqlens, output_dtype=torch.float32) if g is not None else None
NV = triton.cdiv(V, BV)
o = torch.empty(*q.shape[:-1], V, dtype=v.dtype, device=q.device)
gate_scale = 1.0 if not do_gate_scale else scale
grid = (NV, N * HQ)
naive_attn_decoding_kernel[grid](
q=q,
k=k,
v=v,
o=o,
g_cumsum=g_cumsum,
scale=scale,
gate_scale=gate_scale,
cu_seqlens=cu_seqlens,
B=B,
T=T,
H=H,
HQ=HQ,
G=G,
K=K,
V=V,
BS=BS,
BK=BK,
BV=BV,
)
return o
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