File size: 11,205 Bytes
9287d39 | 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 | from dataclasses import dataclass, asdict
import hashlib
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
@dataclass(frozen=True)
class Config:
arm: str = 'mha_gated_ffn_router'
vocab: int = 16384
width: int = 256
layers: int = 7
hidden: int = 896
q_heads: int = 8
kv_heads: int = 8
head_dim: int = 32
eps: float = 1e-6
seed: int = 20260907
backend: str = 'sdpa'
dropout: float = 0.
value_variance: bool = True
variance_delivery: str = 'full_width_direct'
def validate(self):
assert self.arm == 'mha_gated_ffn_router'
assert self.backend in ('sdpa','flash')
assert self.width==self.q_heads*self.head_dim and self.q_heads==self.kv_heads
assert self.head_dim%2==0 and self.layers>0
assert 0<=self.dropout<1
assert self.variance_delivery=='full_width_direct'
def sphere(x,eps=1e-6):
dtype=x.dtype
y=x.double() if dtype==torch.float64 else x.float()
return (y*torch.rsqrt(y.square().mean(-1,keepdim=True)+eps)).to(dtype)
@dataclass
class Layout:
mask: torch.Tensor | None
positions: torch.Tensor
indices: torch.Tensor
cu: torch.Tensor
max_length: int
def prepare_layout(segments,device,backend='sdpa'):
"""CPU metadata boundary: positive contiguous document IDs, padding0."""
if segments.device.type!='cpu':raise ValueError('prepare metadata on CPU before transfer')
if segments.ndim!=2 or bool((segments<0).any()):raise ValueError('bad segment IDs')
b,t=segments.shape
# Vectorized host metadata; no per-token Python loop or device synchronization.
raw=segments.numpy()
valid=raw>0
starts=valid.copy();starts[:,1:] &= raw[:,1:]!=raw[:,:-1]
cols=np.arange(t)[None,:]
origins=np.maximum.accumulate(np.where(starts,cols,0),axis=1)
positions=torch.from_numpy(np.where(valid,cols-origins,0).astype(np.int64))
indices=np.flatnonzero(valid.reshape(-1))
start_offsets=np.flatnonzero(starts.reshape(-1)[indices])
lengths=np.diff(np.append(start_offsets,len(indices)))
mask=None
if backend=='sdpa':
causal=torch.arange(t)[None,:]<=torch.arange(t)[:,None]
mask=((segments[:,:,None]==segments[:,None,:]) & (segments[:,:,None]>0) & causal).unsqueeze(1).to(device)
cu=torch.from_numpy(np.concatenate(([0],np.cumsum(lengths))).astype(np.int32))
return Layout(mask,positions.to(device),torch.tensor(indices,dtype=torch.long,device=device),cu.to(device),int(max(lengths,default=0)))
class Norm(nn.Module):
def __init__(self,d,eps):
super().__init__();self.weight=nn.Parameter(torch.ones(d));self.eps=eps
def forward(self,x):return sphere(x,self.eps)*self.weight.to(x.dtype)
class Router(nn.Module):
def __init__(self,c):
super().__init__();self.eps=c.eps
self.query=nn.Linear(c.width,16,bias=False)
self.key=nn.Linear(c.width,16,bias=False)
def forward(self,current,s1,s2):
q=self.query(sphere(current,self.eps)).float()
k1,k2=self.key(s1).float(),self.key(s2).float()
scores=torch.stack(((q*k1).sum(-1),(q*k2).sum(-1)),dim=-1)/4.
return scores.softmax(-1)
class DeltaReader(nn.Module):
def __init__(self,c,out_width):
super().__init__()
self.aux1=nn.Linear(c.width,out_width,bias=False)
self.aux2=nn.Linear(c.width,out_width,bias=False)
self.router=Router(c)
def forward(self,current,s1,s2):
v1,v2=self.aux1(s1),self.aux2(s2)
p=self.router(current,s1,s2).to(v1.dtype)
return p[...,0:1]*v1+p[...,1:2]*v2
def spread_features(variance,eps=1e-6):
"""Separate direction and smooth magnitude; zero spread maps exactly to zero."""
work=variance.double() if variance.dtype==torch.float64 else variance.float()
r=torch.log1p(work/0.1)
mean_square=r.square().mean(-1,keepdim=True)
energy=mean_square+eps
inverse=torch.rsqrt(energy)
direction=r*inverse
# Rationalized difference avoids cancellation and is exactly zero at r=0.
magnitude=torch.log1p(mean_square/((energy.sqrt()+math.sqrt(eps))*(1+math.sqrt(eps))))
return torch.cat((direction,magnitude),-1)
class FFN(nn.Module):
def __init__(self,c,enabled=True):
super().__init__();self.c=c
self.base=nn.Linear(c.width,2*c.hidden,bias=False)
self.down=nn.Linear(c.hidden,c.width,bias=False)
self.reader=DeltaReader(c,2*c.hidden) if enabled else None
self.spread_input=nn.Linear(c.width+1,2*c.hidden,bias=False) if c.value_variance else None
def forward(self,x,current,s1,s2,variance=None):
pre=self.base(x)
if self.reader is not None:pre=pre+self.reader(current,s1,s2)
if self.spread_input is not None:
pre=pre+self.spread_input(spread_features(variance,self.c.eps).to(x.dtype))
g,u=pre.chunk(2,-1)
return self.down(F.dropout(F.silu(g)*u,self.c.dropout,self.training))
class AttentionGate(nn.Module):
"""Head-local elementwise write gate, kept as a compilable fusion boundary."""
def __init__(self,c):
super().__init__();self.c=c
self.proj=nn.Linear(c.width,c.q_heads*c.head_dim,bias=False)
def forward(self,out,xq):
b,t,h,d=out.shape
gate=torch.sigmoid(self.proj(xq).view(b,t,h,d))
return out*gate
class Block(nn.Module):
def __init__(self,c,index):
super().__init__();self.c=c
self.an=Norm(c.width,c.eps);self.fn=Norm(c.width,c.eps)
self.qk=nn.Linear(c.width,(c.q_heads+c.kv_heads)*c.head_dim,bias=False)
self.v=nn.Linear(c.width,c.kv_heads*c.head_dim,bias=False)
# Head-specific elementwise gate from the same pre-normalized query state.
# Applied after SDPA and before heads are concatenated/output-projected.
self.attn_gate=AttentionGate(c)
self.out=nn.Linear(c.width,c.width,bias=False)
self.ffn=FFN(c,index>0)
def project_attention(self,h,cos,sin):
c=self.c;b,t,d=h.shape
xq=xv=self.an(h)
q,k=self.qk(xq).split((c.q_heads*c.head_dim,c.kv_heads*c.head_dim),-1)
q=q.view(b,t,c.q_heads,c.head_dim);k=k.view(b,t,c.kv_heads,c.head_dim)
v=self.v(xv).view(b,t,c.kv_heads,c.head_dim)
def rope(x):
a,z=x.chunk(2,-1)
co,si=cos.to(x.dtype),sin.to(x.dtype)
return torch.cat((a*co-z*si,z*co+a*si),-1)
q,k=rope(q),rope(k)
if c.value_variance:
q,k=F.pad(q,(0,c.head_dim)),F.pad(k,(0,c.head_dim))
# Keep double for the high precision CPU reference.
vv=v.double() if v.dtype==torch.float64 else v.float()
v=torch.cat((v,vv.square().to(v.dtype)),-1)
return q,k,v,xq
def finish_attention(self,h,paired,xq):
c=self.c;b,t,d=h.shape
if c.value_variance:
out,second=paired.split(c.head_dim,-1)
mean=out.double() if out.dtype==torch.float64 else out.float()
moment=second.double() if second.dtype==torch.float64 else second.float()
variance=(moment-mean.square()).clamp_min(0).reshape(b,t,c.width)
else:out=paired;variance=None
out=self.attn_gate(out,xq).reshape(b,t,d)
a=sphere(h+self.out(out),c.eps)
return a,self.fn(a),variance
def forward(self,h,history,layout,cos,sin):
c=self.c;b,t,d=h.shape
q,k,v,xq=self.project_attention(h,cos,sin)
attention_width=c.head_dim*(2 if c.value_variance else 1)
if c.backend=='flash':
from flash_attn import flash_attn_varlen_func
idx=layout.indices
qp=q.reshape(-1,c.q_heads,attention_width)[idx];kp=k.reshape(-1,c.kv_heads,attention_width)[idx];vp=v.reshape(-1,c.kv_heads,attention_width)[idx]
y=flash_attn_varlen_func(qp,kp,vp,layout.cu,layout.cu,layout.max_length,layout.max_length,softmax_scale=c.head_dim**-.5,dropout_p=0.,causal=True,deterministic=True)
# Scatter the paired moments once; calculate variance after unpacking.
paired=torch.zeros_like(q.reshape(-1,c.q_heads,attention_width)).index_copy(0,idx,y).view(b,t,c.q_heads,attention_width)
else:
paired=F.scaled_dot_product_attention(q.transpose(1,2),k.transpose(1,2),v.transpose(1,2),attn_mask=layout.mask,dropout_p=0.,enable_gqa=False,scale=c.head_dim**-.5).transpose(1,2)
a,normalized,variance=self.finish_attention(h,paired,xq)
if variance is not None:
# A causal document's first token has exactly one Value: variance is
# identically zero. Do not normalize BF16 moment cancellation noise.
variance=torch.where((layout.positions>0).unsqueeze(-1),variance,0.)
if history is None:s1=s2=torch.zeros_like(a)
else:s1,s2=history
y=sphere(a+self.ffn(normalized,a,s1,s2,variance),c.eps)
return y,a
class DenseLM(nn.Module):
def __init__(self,c):
super().__init__();c.validate();self.cfg=c
self.embed=nn.Embedding(c.vocab,c.width)
self.blocks=nn.ModuleList([Block(c,i) for i in range(c.layers)])
self.norm=Norm(c.width,c.eps);self.lm_head=nn.Linear(c.width,c.vocab,bias=False)
self.register_buffer('inv_freq',10000.**(-torch.arange(0,c.head_dim,2).float()/c.head_dim),persistent=True)
for name,p in self.named_parameters():
if p.is_meta:continue
if name.endswith('spread_input.weight'):nn.init.zeros_(p);continue
if p.ndim==1:nn.init.ones_(p);continue
seed=(int.from_bytes(hashlib.sha256(name.encode()).digest()[:8],'little')+c.seed)%(2**63-1)
gen=torch.Generator(device=p.device).manual_seed(seed)
scale=.02/math.sqrt(2*c.layers) if name.endswith(('down.weight','out.weight')) else .02
nn.init.normal_(p,std=scale,generator=gen)
def hidden(self,ids,layout):
h=sphere(self.embed(ids),self.cfg.eps)
# Keep residual states and their differences in FP32 to avoid cancellation
# before RMS normalization; autocast still handles matrix multiplications.
history=None
angles=layout.positions[...,None].float()*self.inv_freq
cos,sin=angles.cos().to(h.dtype).unsqueeze(2),angles.sin().to(h.dtype).unsqueeze(2)
for i,block in enumerate(self.blocks):
before=h;h,attention_after=block(h,history,layout,cos,sin)
if i+1<len(self.blocks):
d1,d2=attention_after-before,h-attention_after
history=(sphere(d1,self.cfg.eps),sphere(d2,self.cfg.eps))
return self.norm(h)
def forward(self,ids,layout,labels=None):
logits=self.lm_head(self.hidden(ids,layout))
if labels is None:return logits
# labels are explicitly pre-shifted; padding and document boundaries=-100.
loss=F.cross_entropy(logits.float().flatten(0,1),labels.flatten(),ignore_index=-100,reduction='sum')
return loss/(labels!=-100).sum().clamp_min(1)
def optimizer(model):
return torch.optim.AdamW([{'params':[p for p in model.parameters() if p.ndim>=2],'weight_decay':.1},{'params':[p for p in model.parameters() if p.ndim<2],'weight_decay':0.}],lr=8e-4,betas=(.9,.95),eps=1e-8)
|