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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 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import torch
import triton
import triton.language as tl
from fla.ops.utils import prepare_chunk_offsets
from fla.ops.utils.op import exp
from fla.utils import autotune_cache_kwargs
@triton.heuristics({
'USE_INITIAL_STATE': lambda args: args['h_init'] is not None,
'STORE_FINAL_STATE': lambda args: args['h_final'] is not None,
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4, 8]
for num_stages in [2, 3, 4]
],
key=['BT'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_mesa_net_fwd_kernel_h(
k,
v,
beta,
g,
h,
h_kv,
h_init,
h_kv_init,
h_final,
h_kv_final,
cu_seqlens,
split_offsets,
T,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BS: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
STORE_FINAL_STATE: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
i_n, i_h = i_nh // H, i_nh % H
if IS_VARLEN:
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
NT = tl.cdiv(T, BT)
NS = tl.cdiv(T, BS)
boh = tl.load(split_offsets + i_n).to(tl.int32)
else:
bos, eos = i_n * T, i_n * T + T
NT = tl.cdiv(T, BT)
NS = tl.cdiv(T, BS)
boh = i_n * NS
# [BK, BV]
b_h = tl.zeros([BK, BV], dtype=tl.float32)
b_h_kv = tl.zeros([BK, BV], dtype=tl.float32)
if USE_INITIAL_STATE:
p_h0 = tl.make_block_ptr(h_init + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32)
p_h_kv0 = tl.make_block_ptr(h_kv_init + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_h_kv = tl.load(p_h_kv0, boundary_check=(0, 1)).to(tl.float32)
for i_t in range(NT):
i_s = i_t // (BS // BT)
p_k = tl.make_block_ptr(k + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
p_k2 = tl.make_block_ptr(k + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_v = tl.make_block_ptr(v + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_beta = tl.make_block_ptr(beta + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT, ), (0,))
b_beta = tl.load(p_beta, boundary_check=(0,))
o_h = ((boh + i_s) * H + i_h).to(tl.int64) * K*V
p_h = tl.make_block_ptr(h + o_h, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
p_h_kv = tl.make_block_ptr(h_kv + o_h, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
if i_t % (BS // BT) == 0:
tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_h_kv, b_h_kv.to(p_h_kv.dtype.element_ty), boundary_check=(0, 1))
# [BK, BT]
b_k = tl.load(p_k, boundary_check=(0, 1))
b_k2 = tl.load(p_k2, boundary_check=(0, 1))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
last_idx = min((i_t + 1) * BT, T) - 1
# scalar decay
b_g_last = tl.load(g + bos * H + last_idx * H + i_h)
p_g = g + bos*H + (i_t * BT + tl.arange(0, BT)) * H + i_h
b_h *= exp(b_g_last)
b_h_kv *= exp(b_g_last)
b_g = tl.load(p_g, mask=(i_t * BT + tl.arange(0, BT) < T), other=0.)
b_k_decay = ((b_k * exp(b_g_last - b_g)[:, None]) * b_beta[:, None]).to(b_k2.dtype)
b_h += tl.dot(tl.trans(b_k_decay), b_k2)
b_h_kv += tl.dot(tl.trans(b_k_decay), b_v.to(b_k2.dtype))
if STORE_FINAL_STATE:
p_ht = tl.make_block_ptr(h_final + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
p_h_kv_final = tl.make_block_ptr(h_kv_final + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_h_kv_final, b_h_kv.to(p_h_kv_final.dtype.element_ty), boundary_check=(0, 1))
def chunk_mesa_fwd_h(
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
h_init: torch.Tensor,
h_kv_init: torch.Tensor,
output_final_state: bool,
cu_seqlens: torch.Tensor | None = None,
chunk_size: int = 64,
split_size: int | None = None,
states_in_fp32: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
B, T, H, K, V = *k.shape, v.shape[-1]
assert K == V, "K must be equal to V for now"
BT = chunk_size
BS = BT if split_size is None else split_size
assert BS % BT == 0, f"The `split_size` (got {BS}) must be a multiple of `chunk_size` {BT}"
# N: the actual number of sequences in the batch with either equal or variable lengths
if cu_seqlens is None:
N, NS, split_offsets = B, triton.cdiv(T, BS), None
else:
split_offsets = prepare_chunk_offsets(cu_seqlens, BS)
N, NS = len(cu_seqlens) - 1, split_offsets[-1].item()
h = k.new_empty(B, NS, H, K, V, dtype=k.dtype if not states_in_fp32 else torch.float)
h_kv = k.new_empty(B, NS, H, K, V, dtype=k.dtype if not states_in_fp32 else torch.float)
h_final = k.new_empty(N, H, K, V, dtype=torch.float) if output_final_state else None
h_kv_final = k.new_empty(N, H, K, V, dtype=torch.float)
def grid(meta): return (triton.cdiv(K, 64), triton.cdiv(V, 64), N * H)
chunk_mesa_net_fwd_kernel_h[grid](
k=k,
v=v,
beta=beta,
g=g,
h=h,
h_kv=h_kv,
h_init=h_init,
h_kv_init=h_kv_init,
h_final=h_final,
h_kv_final=h_kv_final,
cu_seqlens=cu_seqlens,
split_offsets=split_offsets,
T=T,
H=H,
K=K,
V=V,
BT=BT,
BS=BS,
BK=64,
BV=64,
)
return h, h_kv, h_final, h_kv_final
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