File size: 6,946 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import torch
import triton
import triton.language as tl
from fla.ops.utils import prepare_chunk_indices
from fla.utils import check_shared_mem
# episold
@triton.heuristics({
'IS_VARLEN': lambda args: args['offsets'] is not None,
})
@triton.jit(do_not_specialize=['T'])
def intra_chunk_preprocess_bwd_kernel(
q, k, w, w2, beta,
AT,
dA_local, dq, dq_new, dk, dk_new, dw, dbeta, dw1, dw2, T,
offsets, indices,
HQ: tl.constexpr, G: tl.constexpr, H: tl.constexpr,
K: tl.constexpr, BT: tl.constexpr, BK: tl.constexpr,
IS_VARLEN: 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
b_dk = tl.zeros([BT, BK], dtype=tl.float32)
b_dw_beta = tl.zeros([BT, BK], dtype=tl.float32)
b_dw = tl.zeros([BT, BK], dtype=tl.float32)
b_dT = tl.zeros([BT, BT], dtype=tl.float32)
p_q = tl.make_block_ptr(q + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_k = tl.make_block_ptr(k + (bos * H + i_h) * K, (T, K), (K*H, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_w = tl.make_block_ptr(w + (bos * H + i_h) * K, (T, K), (K*H, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_w2 = tl.make_block_ptr(w2 + (bos * H + i_h) * K, (T, K), (K*H, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_beta = tl.make_block_ptr(beta + (bos * H + i_h), (T, ), (H, ), (i_t * BT, ), (BT, ), (0, ))
p_T = tl.make_block_ptr(AT + (bos * H + i_h) * BT, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0))
b_w = tl.load(p_w, boundary_check=(0, 1))
b_Twb = tl.load(p_w2, boundary_check=(0, 1))
b_beta = tl.load(p_beta, boundary_check=(0, ))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
b_T = tl.load(p_T, boundary_check=(0, 1))
b_w_beta = (b_w * b_beta[:, None]).to(b_w.dtype)
o_i = tl.arange(0, BT)
b_qw = tl.where(o_i[:, None] >= o_i[None, :], tl.dot(b_q, tl.trans(b_w)), 0).to(b_q.dtype)
b_wbk = tl.where(o_i[:, None] > o_i[None, :], tl.dot(b_w_beta, tl.trans(b_k)), 0).to(b_k.dtype)
b_Twbk = tl.dot(b_T, b_wbk).to(b_w.dtype)
p_dA_local = tl.make_block_ptr(dA_local + (bos * HQ + i_hq) * BT, (T, BT), (BT*HQ, 1), (i_t * BT, 0), (BT, BT), (1, 0))
b_dA_local = tl.load(p_dA_local, boundary_check=(0, 1))
# # Twb part qw part.
p_dq = tl.make_block_ptr(dq + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_dq = tl.load(p_dq, boundary_check=(0, 1))
p_dw1 = tl.make_block_ptr(dw1 + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_dw += tl.load(p_dw1, boundary_check=(0, 1))
b_dqw = -tl.dot(b_dA_local, tl.trans(b_Twbk)) - tl.dot(b_dq.to(b_Twb.dtype), tl.trans(b_Twb))
p_dw2 = tl.make_block_ptr(dw2 + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_dTwb = -tl.dot(tl.trans(b_qw), b_dq) + tl.load(p_dw2, boundary_check=(0, 1))
b_dT += tl.dot(b_dTwb.to(b_w_beta.dtype), tl.trans(b_w_beta))
b_dw_beta += tl.dot(tl.trans(b_T), b_dTwb.to(b_T.dtype))
b_dqw = tl.where(tl.arange(0, BT)[:, None] >= tl.arange(0, BT)[None, :], b_dqw, 0)
b_dq += tl.dot(b_dA_local.to(b_k.dtype), b_k)
b_dq += tl.dot(b_dqw.to(b_w.dtype), b_w)
b_dw += tl.dot(tl.trans(b_dqw.to(b_q.dtype)), b_q)
p_q_new = tl.make_block_ptr(dq_new + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_q_new, b_dq.to(dq_new.dtype.element_ty), boundary_check=(0, 1))
# Twbk part
p_dk = tl.make_block_ptr(dk + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_dk = tl.load(p_dk, boundary_check=(0, 1))
b_dTwbk = -tl.dot(tl.trans(b_qw), b_dA_local.to(b_qw.dtype)) - tl.dot(b_w, tl.trans(b_dk.to(b_w.dtype)))
b_dw -= tl.dot(b_Twbk, b_dk.to(b_w.dtype))
b_dT += tl.dot(b_dTwbk.to(b_wbk.dtype), tl.trans(b_wbk))
b_dwbk = tl.where(o_i[:, None] > o_i[None, :], tl.dot(tl.trans(b_T), b_dTwbk.to(b_T.dtype)), 0).to(b_w.dtype)
b_dw_beta += tl.dot(b_dwbk, b_k)
b_dk += tl.dot(tl.trans(b_dwbk), b_w_beta)
b_dk += tl.dot(tl.trans(b_dA_local), b_q)
p_dk_new = tl.make_block_ptr(dk_new + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_dk_new, b_dk.to(dk_new.dtype.element_ty), boundary_check=(0, 1))
# matrix inverse's gradient
p_T = tl.make_block_ptr(AT + (bos * H + i_h) * BT, (BT, T), (1, BT*H), (0, i_t * BT), (BT, BT), (0, 1))
b_Tt = tl.load(p_T, boundary_check=(0, 1))
b_dT = tl.where(tl.arange(0, BT)[:, None] > tl.arange(0, BT)[None, :], b_dT, 0).to(b_w.dtype)
b_dT = tl.dot(b_Tt, b_dT).to(b_w.dtype)
b_dT = tl.dot(b_dT, b_Tt)
b_dT = tl.where(tl.arange(0, BT)[:, None] > tl.arange(0, BT)[None, :], -b_dT, 0).to(b_k.dtype)
b_dw_beta += tl.dot(b_dT, b_w)
b_dw += tl.dot(tl.trans(b_dT), b_w_beta)
b_dw += b_dw_beta * b_beta[:, None]
b_dbeta = tl.sum(b_dw_beta * b_w, axis=1)
p_dw = tl.make_block_ptr(dw + (bos * HQ + i_hq) * K, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
tl.store(p_dw, b_dw.to(dw.dtype.element_ty), boundary_check=(0, 1))
p_dbeta = tl.make_block_ptr(dbeta + (bos * HQ + i_hq), (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, ))
tl.store(p_dbeta, b_dbeta.to(dbeta.dtype.element_ty), boundary_check=(0, ))
def intra_chunk_preprocess_bwd_fn(q, k, w, w2, beta,
dq, dk, dA_local,
dw1, dw2,
A, L, D, do, scale, cu_seqlens=None):
BT = A.shape[-1]
HQ = q.shape[-2]
B, T, H, K = k.shape
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)
grid = (NT, B*HQ)
# better precision because h would be of norm smaller than 1 anyways
dbeta = torch.empty(B, T, HQ, device=q.device, dtype=k.dtype if G == 1 else torch.float32)
dw = torch.empty(B, T, HQ, K, device=q.device, dtype=k.dtype if G == 1 else torch.float32)
dk_new = torch.empty_like(dk, dtype=k.dtype if G == 1 else torch.float32) # float32 reduction
dq_new = torch.empty_like(dq, dtype=q.dtype)
intra_chunk_preprocess_bwd_kernel[grid](
q=q, k=k, w=w, w2=w2, beta=beta,
AT=A,
dA_local=dA_local, dq=dq, dq_new=dq_new, dk=dk, dk_new=dk_new, dw=dw, dbeta=dbeta, dw1=dw1, dw2=dw2, T=T,
offsets=cu_seqlens, indices=indices,
HQ=HQ, G=G, H=H,
K=K, BT=BT, BK=triton.next_power_of_2(K),
num_stages=3 if check_shared_mem('hopper') else 1,
)
return dq_new, dk_new, dbeta, dw
|