File size: 6,029 Bytes
df13683 | 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 |
import torch
import triton
import triton.language as tl
from fla.ops.utils import prepare_chunk_indices
@triton.heuristics({
"USE_G": lambda args: args['g_cumsum'] is not None,
"IS_VARLEN": lambda args: args['offsets'] is not None,
})
@triton.jit(do_not_specialize=['T'])
def intra_chunk_preprocess_fwd_kernel(
q,
k,
v,
w,
beta,
g_cumsum,
o,
A,
L,
M,
w2,
q_new,
k_new,
scale,
indices, # varlen helper
offsets, # varlen helper
T,
H: tl.constexpr,
G: tl.constexpr,
HQ: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
BT: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_G: tl.constexpr,
):
i_t, i_nh = tl.program_id(0), tl.program_id(1)
i_n, i_hq = i_nh // HQ, i_nh % HQ
i_h = i_hq // G
if IS_VARLEN:
i_n, i_t = tl.load(indices + i_t * 2).to(tl.int32), tl.load(indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_n * T, i_n * T + T
sm_scale = scale * 1.44269504
# offset calculations
A += (bos*H + i_h) * BT
q += (bos*HQ + i_hq) * K
q_new += (bos*HQ + i_hq) * K
k += (bos*H + i_h) * K
k_new += (bos*H + i_h) * K
w2 += (bos*H + i_h) * K
w += (bos*H + i_h) * K
v += (bos*H + i_h) * V
o += (bos*HQ + i_hq) * V
beta += (bos*H + i_h)
if USE_G:
g_cumsum += (bos*HQ + i_hq)
L += (bos*HQ + i_hq)
M += (bos*HQ + i_hq)
p_q = tl.make_block_ptr(q, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_k = tl.make_block_ptr(k, (K, T), (1, H*K), (0, i_t * BT), (BK, BT), (0, 1))
p_w = tl.make_block_ptr(w, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_beta = tl.make_block_ptr(beta, (T, ), (H, ), (i_t * BT, ), (BT, ), (0, ))
p_T = tl.make_block_ptr(A, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0))
b_beta = tl.load(p_beta, boundary_check=(0, ))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_kt = tl.load(p_k, boundary_check=(0, 1))
b_v = tl.load(p_v, boundary_check=(0, 1))
b_w = tl.load(p_w, boundary_check=(0, 1))
b_T = tl.load(p_T, boundary_check=(0, 1))
b_T = b_T * b_beta[None, :]
o_i = tl.arange(0, BT)
m_t = o_i[:, None] >= o_i[None, :]
b_qw = tl.where(m_t, tl.dot(b_q, tl.trans(b_w.to(b_q.dtype))), 0).to(b_q.dtype)
b_qwT = tl.dot(b_qw, b_T.to(b_q.dtype)).to(b_q.dtype)
b_wbk = tl.where(o_i[:, None] > o_i[None, :], tl.dot(b_w.to(b_q.dtype), b_kt), 0).to(b_q.dtype)
b_A = tl.where(m_t, tl.dot(b_q, b_kt) - tl.dot(b_qwT.to(b_q.dtype), b_wbk), 0)
b_q = b_q.to(tl.float32) - tl.dot(b_qwT, b_w.to(b_q.dtype))
p_q_new = tl.make_block_ptr(q_new, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, K), (1, 0))
tl.store(p_q_new, b_q.to(p_q_new.dtype.element_ty), boundary_check=(0, 1))
if i_hq % G == 0:
b_Twb = tl.dot(b_T, b_w)
p_w2 = tl.make_block_ptr(w2, (T, K), (K*H, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_w2, b_Twb.to(p_w2.dtype.element_ty), boundary_check=(0, 1))
b_T_wbk = tl.dot(b_T.to(b_kt.dtype), b_wbk).to(b_kt.dtype)
p_k_new = tl.make_block_ptr(k_new, (K, T), (1, K*H), (0, i_t * BT), (BK, BT), (0, 1))
tl.store(p_k_new, (b_kt - tl.dot(tl.trans(b_w.to(b_kt.dtype)), b_T_wbk)
).to(p_k_new.dtype.element_ty), boundary_check=(0, 1))
if USE_G:
p_g_cumsum = tl.make_block_ptr(g_cumsum, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, ))
b_g_cumsum = tl.load(p_g_cumsum, boundary_check=(0, ))
b_A = b_A + (b_g_cumsum[:, None] - b_g_cumsum[None, :])
b_A = tl.where((i_t * BT + tl.arange(0, BT) < T)[:, None], b_A, float("-inf")) # avoid nan
b_qkT_softmax = tl.where(o_i[:, None] >= o_i[None, :], b_A * sm_scale, float("-inf"))
m_i = tl.max(b_qkT_softmax, 1)
b_qkT_softmax = tl.math.exp2(b_qkT_softmax - m_i[:, None])
l_i = tl.sum(b_qkT_softmax, 1)
b_o = tl.dot(b_qkT_softmax.to(b_v.dtype), b_v)
p_o = tl.make_block_ptr(o, (T, V), (V*HQ, 1), (i_t * BT, 0), (BT, BV), (1, 0))
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
p_l = tl.make_block_ptr(L, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, ))
p_m = tl.make_block_ptr(M, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, ))
tl.store(p_m, m_i.to(p_m.dtype.element_ty), boundary_check=(0,))
tl.store(p_l, l_i.to(p_l.dtype.element_ty), boundary_check=(0,))
def intra_chunk_preprocess_fwd_fn(q, k, v, w, beta, g_cumsum, A, scale, BT, cu_seqlens,
chunk_indices: torch.LongTensor | None = None):
HQ = q.shape[-2]
B, T, H, K = k.shape
V = v.shape[-1]
q_new = torch.empty_like(q, dtype=torch.float32) # for stability
k_new = torch.empty_like(k)
o = torch.empty(B, T, HQ, V, device=q.device, dtype=torch.float32)
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
indices = chunk_indices
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(indices)
grid = (NT, B*HQ)
L = torch.empty(B, T, HQ, dtype=torch.float32, device=q.device)
M = torch.empty(B, T, HQ, dtype=torch.float32, device=q.device)
w2 = torch.empty_like(w)
G = HQ//H
intra_chunk_preprocess_fwd_kernel[grid](
q=q,
k=k,
v=v,
w=w,
beta=beta,
g_cumsum=g_cumsum,
o=o,
A=A,
L=L,
M=M,
w2=w2,
q_new=q_new,
k_new=k_new,
scale=scale,
offsets=cu_seqlens,
indices=indices,
T=T,
H=H,
G=G,
HQ=HQ,
K=K,
V=V,
BK=triton.next_power_of_2(K),
BV=triton.next_power_of_2(V),
BT=BT,
num_warps=4 if BT == 64 else 2,
)
return q_new, k_new, w2, o, L, M
|