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f37be5a | 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 | from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.fog.config import FOGConfig
from src.fog.model_structured_v2 import LayerGeometryV2, build_layer_geometries_v2
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
scale = torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
return x * scale * self.weight
class RuntimeStructuredAttention(nn.Module):
def __init__(self, d_model: int, d_compare: int, d_memory: int, n_heads: int) -> None:
super().__init__()
assert d_compare % n_heads == 0
assert d_memory % n_heads == 0
self.n_heads = n_heads
self.compare_head_dim = d_compare // n_heads
self.memory_head_dim = d_memory // n_heads
self.d_compare = d_compare
self.d_memory = d_memory
self.in_proj = nn.Linear(d_model, (2 * d_compare) + d_memory)
self.out_proj = nn.Linear(d_memory, d_model)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b, t, _ = x.shape
packed = self.in_proj(x)
q, k, v = packed.split([self.d_compare, self.d_compare, self.d_memory], dim=-1)
q = q.view(b, t, self.n_heads, self.compare_head_dim).transpose(1, 2)
k = k.view(b, t, self.n_heads, self.compare_head_dim).transpose(1, 2)
v = v.view(b, t, self.n_heads, self.memory_head_dim).transpose(1, 2)
y = F.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=True)
y = y.transpose(1, 2).contiguous().view(b, t, self.d_memory)
return self.out_proj(y)
class RuntimeStructuredFFN(nn.Module):
def __init__(self, d_model: int, geometry: LayerGeometryV2, dropout: float) -> None:
super().__init__()
self.stage = geometry.stage
self.d_expand = geometry.d_expand
self.d_gate = geometry.d_gate
self.fused_in = nn.Linear(d_model, geometry.d_expand + geometry.d_gate)
self.gate_up = nn.Linear(geometry.d_gate, geometry.d_expand)
self.compress = nn.Linear(geometry.d_expand, d_model)
self.drop = nn.Dropout(dropout)
if self.stage == "middle":
self.stage_proj = nn.Linear(geometry.d_expand, geometry.d_expand)
self.stage_scale = 0.35
elif self.stage == "late":
self.stage_proj = nn.Linear(geometry.d_expand, geometry.d_expand)
self.stage_scale = 0.25
else:
self.stage_proj = None
self.stage_scale = 0.0
def forward(self, x: torch.Tensor) -> torch.Tensor:
expanded, gate_seed = self.fused_in(x).split([self.d_expand, self.d_gate], dim=-1)
h = F.silu(expanded)
gate_hidden = F.silu(gate_seed)
h = h * torch.sigmoid(self.gate_up(gate_hidden))
if self.stage_proj is not None:
if self.stage == "middle":
h = h + self.stage_scale * F.silu(self.stage_proj(h))
else:
h = h + self.stage_scale * torch.tanh(self.stage_proj(h))
h = self.drop(h)
return self.compress(h)
class RuntimeStructuredBlock(nn.Module):
def __init__(self, d_model: int, n_heads: int, geometry: LayerGeometryV2, dropout: float) -> None:
super().__init__()
self.geometry = geometry
self.norm1 = RMSNorm(d_model)
self.norm2 = RMSNorm(d_model)
self.attn = RuntimeStructuredAttention(d_model, geometry.d_compare, geometry.d_memory, n_heads)
self.ffn = RuntimeStructuredFFN(d_model, geometry, dropout)
self.drop = nn.Dropout(dropout)
self.attn_scale = nn.Parameter(torch.tensor(float(geometry.residual_scale)))
self.ffn_scale = nn.Parameter(torch.tensor(float(geometry.residual_scale)))
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn_scale * self.drop(self.attn(self.norm1(x)))
x = x + self.ffn_scale * self.drop(self.ffn(self.norm2(x)))
return x
class RuntimeStructuredMotifTransformer(nn.Module):
def __init__(self, cfg: FOGConfig) -> None:
super().__init__()
self.cfg = cfg
self.layer_geometries = build_layer_geometries_v2(cfg)
self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.d_model)
self.pos_emb = nn.Embedding(cfg.max_seq_len, cfg.d_model)
self.drop = nn.Dropout(cfg.dropout)
self.blocks = nn.ModuleList(
[
RuntimeStructuredBlock(
d_model=cfg.d_model,
n_heads=cfg.n_heads,
geometry=geometry,
dropout=cfg.dropout,
)
for geometry in self.layer_geometries
]
)
self.norm_f = RMSNorm(cfg.d_model)
self.head = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
self.tok_emb.weight = self.head.weight
def forward(
self,
input_ids: torch.Tensor,
targets: torch.Tensor | None = None,
loss_mask: torch.Tensor | None = None,
) -> dict[str, torch.Tensor | list[dict[str, int | str]]]:
_, t = input_ids.shape
pos = torch.arange(t, device=input_ids.device).unsqueeze(0)
x = self.drop(self.tok_emb(input_ids) + self.pos_emb(pos))
for block in self.blocks:
x = block(x)
x = self.norm_f(x)
logits = self.head(x)
loss = None
if targets is not None:
if loss_mask is not None:
flat_logits = logits.view(-1, logits.size(-1))
flat_targets = targets.view(-1)
flat_mask = loss_mask.view(-1).bool()
loss = F.cross_entropy(flat_logits[flat_mask], flat_targets[flat_mask]) if flat_mask.any() else torch.tensor(0.0, device=logits.device)
else:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
geometry_summary = [
{
"stage": g.stage,
"d_compare": g.d_compare,
"d_memory": g.d_memory,
"d_expand": g.d_expand,
"d_gate": g.d_gate,
}
for g in self.layer_geometries
]
return {"logits": logits, "loss": loss, "geometry": geometry_summary}
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