File size: 3,449 Bytes
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 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import torch
import triton
import triton.language as tl
from fla.ops.utils import get_max_num_splits, prepare_chunk_indices
@triton.heuristics({
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.jit(do_not_specialize=['T'])
def transform_q_fwd_kernel(
q,
q_new,
w1,
w2,
cu_seqlens,
indices,
T,
S: tl.constexpr,
G: tl.constexpr,
HQ: tl.constexpr,
H: tl.constexpr,
K: tl.constexpr,
BT: tl.constexpr,
BS: tl.constexpr,
BK: tl.constexpr,
NUM_BLOCKS: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_t, 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
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(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
i_n = i_b
bos, eos = i_n * T, i_n * T + T
# boh = i_n * tl.cdiv(T, BS)
p_q = tl.make_block_ptr(q + (bos * HQ + i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_q = tl.zeros([BT, BK], dtype=tl.float32)
b_q += tl.load(p_q, boundary_check=(0, 1))
if BS == BT:
if (i_t * BT) % S == 0:
p_q_new = tl.make_block_ptr(q_new + ((bos * NUM_BLOCKS + (i_t * BT // S)) * HQ + i_hq) * K,
(T, K), (HQ*K*NUM_BLOCKS, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_q_new, b_q.to(q_new.dtype.element_ty), boundary_check=(0, 1))
for offset in range((i_t + 1) * BT - 2 * BS, S-BS, -BS):
p_w1 = tl.make_block_ptr(w1 + (bos * H + i_h) * K, (K, T), (1, K*H), (0, offset), (BK, BS), (0, 1))
p_w2 = tl.make_block_ptr(w2 + (bos * H + i_h) * K, (T, K), (K*H, 1), (offset, 0), (BS, BK), (1, 0))
b_w1 = tl.load(p_w1, boundary_check=(0, 1))
b_w2 = tl.load(p_w2, boundary_check=(0, 1))
m_s = i_t * BT + tl.arange(0, BT) >= (offset + BS)
b_s2 = tl.dot(b_q.to(b_w1.dtype), b_w1)
b_s2 = tl.where(m_s[:, None], b_s2, 0)
b_q -= tl.dot(b_s2.to(b_w2.dtype), b_w2)
if offset % S == 0:
p_q_new = tl.make_block_ptr(q_new + ((bos * NUM_BLOCKS + (offset // S)) * HQ + i_hq) * K,
(T, K), (HQ*K*NUM_BLOCKS, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_q_new, b_q.to(q_new.dtype.element_ty), boundary_check=(0, 1))
def transform_q_fwd_fn(
q,
w1,
w2,
cu_seqlens,
BT,
BS,
S,
):
B, T, HQ, K = q.shape
H = w1.shape[-2]
G = HQ // H
indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(indices)
num_blocks = triton.cdiv(T, S) if cu_seqlens is None else get_max_num_splits(cu_seqlens, S)
q_new = torch.zeros(B, T, num_blocks, HQ, K, dtype=q.dtype, device=q.device)
transform_q_fwd_kernel[(NT, B * HQ)](
q=q,
q_new=q_new,
w1=w1,
w2=w2,
cu_seqlens=cu_seqlens,
indices=indices,
T=T,
K=K,
BK=triton.next_power_of_2(K),
G=G,
HQ=HQ,
H=H,
BS=BS,
BT=BT,
S=S,
NUM_BLOCKS=num_blocks,
num_warps=8 if (BT == 128 and K == 128) else 4,
)
return q_new
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