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775b8fb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 | """Deployment engine for NonCausalViTDecoder: same math (bf16 GEMMs, fp32 residual), batch 1, fixed (T, H, W).
* every (temporal window, spatial window) is one FlashAttention-4 varlen sequence: queries = its valid tokens,
keys = its valid tokens + the global tokens of the native blocks it overlaps (no padding, no mask);
* global-token attention is one more varlen call: per native block, queries = its 5 global tokens, keys = all
tokens of the block + its global tokens;
* the 5 global rows of every block are appended to the token rows, so each linear layer is a single GEMM;
* residual add + RMSNorm, SwiGLU in the up-projection GEMM (quack), fp32 head as one bf16 GEMM on a hi/lo split
(fused_kernels.py);
* the whole decode is one CUDA graph.
"""
from __future__ import annotations
import site
import sys
import torch
from torch import nn
if "cutlass" not in sys.modules:
for p in list(sys.path):
if p.endswith("site-packages"):
site.addsitedir(p)
from flash_attn.cute.interface import _flash_attn_fwd
from quack.gemm_interface import gemm as qgemm, gemm_act as qgemm_act
from fused_kernels import _interleave, _resid_ln_split_kernel, _resid_norm_kernel
from noncausal_vit_decoder import OUT_CH, PATCH, PATCH_T, NonCausalViTDecoder
BF16 = torch.bfloat16
import triton
import triton.language as tl
@triton.jit
def _nc_qkv_post_kernel(qkv, qo, kp, vp, kgp, vgp, cos, sin, qpos, kpos, kgpos, gwin, N, S, MAXW,
HEADS: tl.constexpr, DH: tl.constexpr, RD: tl.constexpr, EPS: tl.constexpr):
"""One row of qkv: qk-RMSNorm, rotary (token rows only), then scatter q to its window-ordered slot, k/v to its
slot in the window key pool and in the global-attention key pool. A global row is also copied into every
window that overlaps its block (gwin[b, :] = slot bases, -1 = unused)."""
r = tl.program_id(0)
D3: tl.constexpr = 3 * DH
ROW: tl.constexpr = HEADS * DH
HALF: tl.constexpr = RD // 2
hh = tl.arange(0, HEADS)[:, None]
d = tl.arange(0, DH)[None, :]
src = qkv + r.to(tl.int64) * (HEADS * D3)
dp = tl.where(d < HALF, d + HALF, tl.where(d < RD, d - HALF, d))
sg = tl.where(d < HALF, -1.0, 1.0)
oq = hh * D3 + d
op = hh * D3 + dp
q = tl.load(src + oq).to(tl.float32)
qp = tl.load(src + op).to(tl.float32)
k = tl.load(src + DH + oq).to(tl.float32)
kq = tl.load(src + DH + op).to(tl.float32)
v = tl.load(src + 2 * DH + oq)
rq = tl.rsqrt(tl.sum(q * q, axis=1) / DH + EPS)[:, None]
rk = tl.rsqrt(tl.sum(k * k, axis=1) / DH + EPS)[:, None]
qn = (q * rq).to(tl.bfloat16).to(tl.float32)
kn = (k * rk).to(tl.bfloat16).to(tl.float32)
oo = hh * DH + d
if r < N:
qr = (qp * rq).to(tl.bfloat16).to(tl.float32) * sg
kr = (kq * rk).to(tl.bfloat16).to(tl.float32) * sg
rm = d < RD
dc = tl.where(rm, d, 0)
c = tl.load(cos + r.to(tl.int64) * RD + dc)
s_ = tl.load(sin + r.to(tl.int64) * RD + dc)
qv = tl.where(rm, (qn * c).to(tl.bfloat16).to(tl.float32) + (qr * s_).to(tl.bfloat16).to(tl.float32), qn)
kv = tl.where(rm, (kn * c).to(tl.bfloat16).to(tl.float32) + (kr * s_).to(tl.bfloat16).to(tl.float32), kn)
qv = qv.to(tl.bfloat16)
kv = kv.to(tl.bfloat16)
qd = tl.load(qpos + r).to(tl.int64)
tl.store(qo + qd * ROW + oo, qv)
kd = tl.load(kpos + r).to(tl.int64)
tl.store(kp + kd * ROW + oo, kv)
tl.store(vp + kd * ROW + oo, v)
else:
qv = qn.to(tl.bfloat16)
kv = kn.to(tl.bfloat16)
tl.store(qo + r.to(tl.int64) * ROW + oo, qv)
i = r - N
b = i // S
slot = i % S
for w in range(MAXW):
base = tl.load(gwin + b * MAXW + w)
if base >= 0:
pos = (base + slot).to(tl.int64)
tl.store(kp + pos * ROW + oo, kv)
tl.store(vp + pos * ROW + oo, v)
kg = tl.load(kgpos + r).to(tl.int64)
tl.store(kgp + kg * ROW + oo, kv)
tl.store(vgp + kg * ROW + oo, v)
@triton.jit
def _nc_unpatch_kernel(G, bias, out, H, W, NF, TB, NTRIM, BLOCK: tl.constexpr):
pid = tl.program_id(0)
py = pid % 16
tmp = pid // 16
pt = tmp % 4
tmp = tmp // 4
oc = tmp % 3
tmp = tmp // 3
y = tmp % H
t = tmp // H
drop = ((t % TB) == 0) & (pt < NTRIM)
if not drop:
fo = t * 4 + pt - NTRIM * (t // TB + 1)
e = tl.arange(0, BLOCK)
em = e < W * 16
x = e // 16
px = e % 16
ch = oc * 1024 + pt * 256 + py * 16 + px
gi = ((t * H + y) * W + x).to(tl.int64) * 3072 + ch
v = tl.load(G + gi, mask=em, other=0.0) + tl.load(bias + ch, mask=em, other=0.0)
orow = ((oc * NF + fo) * (16 * H) + y * 16 + py).to(tl.int64) * (16 * W)
tl.store(out + orow + e, v, mask=em)
class NonCausalFast(nn.Module):
def __init__(self, ref: NonCausalViTDecoder, T: int, H: int, W: int, *, cuda_graph: bool = True):
super().__init__()
r = ref
self.r, self.T, self.H, self.W = r, T, H, W
self.heads, self.dh, self.D = r.heads, r.dh, r.heads * r.dh
dev = r.register_tokens.device
self.dev = dev
tb = r.block
self.nb = nb = (T + tb - 1) // tb
self.S = S = r.register_tokens.shape[1] + 1
self.N = N = T * H * W
self.M = M = N + nb * S
self.L = L = len(r.blocks)
self.lw = []
for blk in r.blocks:
assert isinstance(blk.norm1, nn.RMSNorm) and blk.attn.norm_q is not None
self.lw.append(dict(
wqkv=blk.attn.to_qkv.weight.detach().to(BF16).contiguous(), bqkv=blk.attn.to_qkv.bias.detach().to(BF16),
wo=blk.attn.to_out.weight.detach().to(BF16).contiguous(), bo=blk.attn.to_out.bias.detach().to(BF16),
w1=_interleave(blk.ff.w1.weight.detach().to(BF16)), b1=_interleave(blk.ff.w1.bias.detach().to(BF16)),
w2=blk.ff.w2.weight.detach().to(BF16).contiguous(), b2=blk.ff.w2.bias.detach().to(BF16),
n1=blk.norm1.weight.detach().float().contiguous(), n2=blk.norm2.weight.detach().float().contiguous(),
s1=blk.scale1.detach().float().contiguous(), s2=blk.scale2.detach().float().contiguous(),
eps1=blk.norm1.eps if blk.norm1.eps is not None else torch.finfo(torch.float32).eps,
eps2=blk.norm2.eps if blk.norm2.eps is not None else torch.finfo(torch.float32).eps,
epsq=blk.attn.norm_q.eps if blk.attn.norm_q.eps is not None else torch.finfo(torch.float32).eps))
self.inter = r.blocks[0].ff.w2.weight.shape[1]
wh = r.proj_out.weight.detach().float(); whi = wh.to(BF16); wlo = (wh - whi.float()).to(BF16)
self.whead = torch.cat([whi, whi, wlo], 1).contiguous()
self.bhead = r.proj_out.bias.detach().float().contiguous()
self.ln_w, self.ln_b, self.ln_eps = r.norm_out.weight.detach().float().contiguous(), r.norm_out.bias.detach().float().contiguous(), r.norm_out.eps
self.g0 = torch.cat([r.register_tokens[0].detach(), r.register_tokens.new_zeros(1, self.D)], 0).to(BF16).float().repeat(nb, 1).contiguous()
i32 = dict(dtype=torch.int32, device=dev)
# global-token attention: per block, keys = the block's tokens (x order) + its S global rows ---------
HW = H * W
kgpos = torch.empty(M, dtype=torch.int64); cu_q, cu_k = [0], [0]
for b in range(nb):
t0, t1 = b * tb * HW, min((b + 1) * tb, T) * HW
kgpos[t0:t1] = torch.arange(t1 - t0) + cu_k[-1]
kgpos[N + b * S: N + (b + 1) * S] = torch.arange(S) + cu_k[-1] + (t1 - t0)
cu_q.append(cu_q[-1] + S); cu_k.append(cu_k[-1] + (t1 - t0) + S)
self.kgpos = kgpos.to(**i32)
self.g_cuq, self.g_cuk = torch.tensor(cu_q, **i32), torch.tensor(cu_k, **i32)
self.g_maxk = max(b - a for a, b in zip(cu_k, cu_k[1:]))
self.kgp = torch.zeros(cu_k[-1], self.heads, self.dh, dtype=BF16, device=dev)
self.vgp = torch.zeros_like(self.kgp)
# window attention geometry per shift -------------------------------------------------------------
self.shift = [r._shifts(li) for li in range(L)]
self.geo = {sh: self._geometry(*sh, dev) for sh in sorted(set(self.shift))}
tk = max(g["total_k"] for g in self.geo.values())
self.kp = torch.zeros(tk, self.heads, self.dh, dtype=BF16, device=dev)
self.vp = torch.zeros_like(self.kp)
# buffers ------------------------------------------------------------------------------------------
self.z_in = torch.zeros(1, T, r.latents_mean.numel(), H, W, device=dev)
self.x = torch.zeros(M, self.D, device=dev)
self.h = torch.zeros(M, self.D, dtype=BF16, device=dev)
self.qkv = torch.zeros(M, 3 * self.D, dtype=BF16, device=dev)
self.qo = torch.zeros(M, self.heads, self.dh, dtype=BF16, device=dev)
self.obuf = torch.zeros(M, self.D, dtype=BF16, device=dev)
self.ybuf = torch.zeros(M, self.D, dtype=BF16, device=dev)
self.abuf = torch.zeros(M, self.inter, dtype=BF16, device=dev)
self.hsplit = torch.zeros(N, 3 * self.D, dtype=BF16, device=dev)
self.G = torch.zeros(N, self.whead.shape[0], device=dev)
self.ident = torch.arange(M, **i32)
self.NF = T * PATCH_T - r.frames_to_trim * nb
self.cuda_graph, self.graph = cuda_graph, None
with torch.no_grad():
self._step()
torch.cuda.synchronize()
if cuda_graph:
self.graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(self.graph):
self._step()
torch.cuda.synchronize()
def _geometry(self, s, st, dev):
r, T, H, W, tb, ws, S, N = self.r, self.T, self.H, self.W, self.r.block, self.r.window, self.S, self.N
HW = H * W
cos, sin = r._rope(T, H, W, s, st, dev) # (1, N, 1, rd)
rd = cos.shape[-1]
qpos = torch.empty(N, dtype=torch.int64); kpos = torch.empty(N, dtype=torch.int64)
gw = [[] for _ in range(self.nb)]
cu_q, cu_k = [0], [0]
Tp = ((T + st + tb - 1) // tb) * tb; Hp = ((H + s + ws - 1) // ws) * ws; Wp = ((W + s + ws - 1) // ws) * ws
for j in range(Tp // tb):
t_lo, t_hi = max(j * tb - st, 0), min((j + 1) * tb - st, T)
if t_hi <= t_lo: continue
blocks = sorted({t_lo // tb, (t_hi - 1) // tb})
for i in range(Hp // ws):
y_lo, y_hi = max(i * ws - s, 0), min((i + 1) * ws - s, H)
if y_hi <= y_lo: continue
for k in range(Wp // ws):
x_lo, x_hi = max(k * ws - s, 0), min((k + 1) * ws - s, W)
if x_hi <= x_lo: continue
tt, yy, xx = torch.meshgrid(torch.arange(t_lo, t_hi), torch.arange(y_lo, y_hi), torch.arange(x_lo, x_hi), indexing="ij")
toks = (tt * HW + yy * W + xx).flatten(); n = len(toks)
qpos[toks] = torch.arange(n) + cu_q[-1]
kpos[toks] = torch.arange(n) + cu_k[-1]
for jj, b in enumerate(blocks):
gw[b].append(cu_k[-1] + n + jj * S)
cu_q.append(cu_q[-1] + n); cu_k.append(cu_k[-1] + n + len(blocks) * S)
assert cu_q[-1] == N and len(torch.unique(qpos)) == N
maxw = max(len(x) for x in gw)
gwin = torch.full((self.nb, maxw), -1, dtype=torch.int64)
for b, x in enumerate(gw):
gwin[b, :len(x)] = torch.tensor(x)
i32 = dict(dtype=torch.int32, device=dev)
src = torch.cat([qpos, torch.arange(N, self.M)])
lens_q = [b - a for a, b in zip(cu_q, cu_q[1:])]; lens_k = [b - a for a, b in zip(cu_k, cu_k[1:])]
return {"cos": cos[0, :, 0].to(BF16).float().contiguous(), "sin": sin[0, :, 0].to(BF16).float().contiguous(), "rd": rd,
"qpos": qpos.to(**i32), "kpos": kpos.to(**i32), "src": src.to(**i32), "gwin": gwin.to(**i32), "maxw": maxw,
"cu_q": torch.tensor(cu_q, **i32), "cu_k": torch.tensor(cu_k, **i32), "total_k": cu_k[-1],
"max_q": max(lens_q), "max_k": max(lens_k)}
@torch.no_grad()
def _step(self):
r, T, H, W, N, M = self.r, self.T, self.H, self.W, self.N, self.M
with torch.autocast("cuda", dtype=BF16):
raw = self.z_in * r.latents_std.view(1, 1, -1, 1, 1) + r.latents_mean.view(1, 1, -1, 1, 1)
raw = r.post_quant_conv(raw.transpose(1, 2))
e = r.x_embedder(raw.permute(0, 2, 3, 4, 1).float()).reshape(N, -1)
self.x[:N].copy_(e.to(BF16))
self.x[N:].copy_(self.g0)
lw0 = self.lw[0]
_resid_norm_kernel[(M,)](self.x, self.ybuf, self.ident, lw0["s1"], lw0["n1"], self.h, D=self.D, EPS=lw0["eps1"], ADD=False, num_warps=8)
for li in range(self.L):
lw, g = self.lw[li], self.geo[self.shift[li]]
torch.addmm(lw["bqkv"], self.h, lw["wqkv"].t(), out=self.qkv)
_nc_qkv_post_kernel[(M,)](self.qkv, self.qo, self.kp, self.vp, self.kgp, self.vgp, g["cos"], g["sin"], g["qpos"],
g["kpos"], self.kgpos, g["gwin"], N, self.S, g["maxw"],
HEADS=self.heads, DH=self.dh, RD=g["rd"], EPS=lw["epsq"], num_warps=4)
_flash_attn_fwd(self.qo[:N], self.kp[:g["total_k"]], self.vp[:g["total_k"]], cu_seqlens_q=g["cu_q"], cu_seqlens_k=g["cu_k"],
max_seqlen_q=g["max_q"], max_seqlen_k=g["max_k"], out=self.obuf[:N].view(N, self.heads, self.dh))
if li < self.L - 1: # the last layer's global tokens are never read
_flash_attn_fwd(self.qo[N:], self.kgp, self.vgp, cu_seqlens_q=self.g_cuq, cu_seqlens_k=self.g_cuk,
max_seqlen_q=self.S, max_seqlen_k=self.g_maxk, out=self.obuf[N:].view(M - N, self.heads, self.dh))
qgemm(self.obuf, lw["wo"].t(), bias=lw["bo"], out=self.ybuf)
_resid_norm_kernel[(M,)](self.x, self.ybuf, g["src"], lw["s1"], lw["n2"], self.h, D=self.D, EPS=lw["eps2"], ADD=True, num_warps=8)
qgemm_act(self.h, lw["w1"].t(), bias=lw["b1"], activation="swiglu", store_preact=False, postact_out=self.abuf)
torch.addmm(lw["b2"], self.abuf, lw["w2"].t(), out=self.ybuf)
if li < self.L - 1:
nxt = self.lw[li + 1]
_resid_norm_kernel[(M,)](self.x, self.ybuf, self.ident, lw["s2"], nxt["n1"], self.h, D=self.D, EPS=nxt["eps1"], ADD=True, num_warps=8)
else:
_resid_ln_split_kernel[(N,)](self.x, self.ybuf, lw["s2"], self.ln_w, self.ln_b, self.hsplit, D=self.D, EPS=self.ln_eps, num_warps=8)
torch.mm(self.hsplit, self.whead.t(), out_dtype=torch.float32, out=self.G)
@torch.no_grad()
def decode(self, z):
"""z: normalized latents (1, T, 24, H, W), t = 0 a block start -> decode_base pixels (1, 3, F, 16H, 16W)."""
assert tuple(z.shape) == tuple(self.z_in.shape)
self.z_in.copy_(z)
if self.graph is not None:
self.graph.replay()
else:
self._step()
H, W = self.H, self.W
out = torch.empty(1, OUT_CH, self.NF, H * PATCH, W * PATCH, device=self.dev)
_nc_unpatch_kernel[(self.T * H * OUT_CH * PATCH_T * PATCH,)](self.G, self.bhead, out, H, W, self.NF, self.r.block,
self.r.frames_to_trim, BLOCK=triton.next_power_of_2(W * PATCH), num_warps=4)
return out
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