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f86dc09 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | """tilelli.core.tilelli_block — heterogeneous-pathway block with a per-token
soft router.
Up to five structurally-different operations run in parallel on the same
input, mixed by a per-token softmax router. Optional Ternary Dispenser
(n_banks > 1) replicates each pathway across n_banks weight banks; the
router dispatches both pathway and bank per token. Compute per token stays
constant; parameter capacity multiplies by n_banks.
"""
from __future__ import annotations
import torch
from torch import Tensor, nn
from tilelli.core.sparse_attention import SparseCausalAttention
from tilelli.core.ssm import DiagonalSSM
from tilelli.core.ternary_conv import TernaryCausalConv1d
from tilelli.core.ternary_linear import TernaryLinear
PATHWAY_NAMES_3 = ("local", "state", "sparse")
PATHWAY_NAMES_5 = ("local", "wide", "state", "sparse", "dense")
class TernaryFFN(nn.Module):
"""Tiny feed-forward network with ternary weights: d → expand·d → d."""
def __init__(
self,
d_model: int,
expand: int = 2,
quantize: bool = True,
per_row: bool = False,
hadamard: bool = False,
lsq: bool = False,
) -> None:
super().__init__()
d_inner = d_model * expand
self.up = TernaryLinear(
d_model, d_inner,
quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
)
self.down = TernaryLinear(
d_inner, d_model,
quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
)
def forward(self, x: Tensor) -> Tensor:
return self.down(torch.nn.functional.gelu(self.up(x)))
def _make_pathway(
kind: str,
d_model: int,
d_head: int,
kernel_size: int,
wide_kernel_size: int,
top_k: int,
quantize: bool,
per_row: bool,
hadamard: bool,
lsq: bool,
dense_expand: int,
fp_attention: bool,
) -> nn.Module:
"""Build a single pathway module of the named kind.
fp_attention=True forces the Sparse pathway's Q/K/V projections to FP32
even when the global quantize is True. From the Spectrum spinoff insight:
attention is the precision-critical operation where ternary hurts most.
"""
if kind == "local":
return TernaryCausalConv1d(
d_model, kernel_size=kernel_size,
quantize=quantize, per_row=per_row, lsq=lsq,
)
if kind == "wide":
return TernaryCausalConv1d(
d_model, kernel_size=wide_kernel_size,
quantize=quantize, per_row=per_row, lsq=lsq,
)
if kind == "state":
return DiagonalSSM(d_model)
if kind == "sparse":
attn_quantize = False if fp_attention else quantize
return SparseCausalAttention(
d_model, d_head=d_head, top_k=top_k, quantize=attn_quantize,
)
if kind == "dense":
return TernaryFFN(
d_model, expand=dense_expand,
quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
)
raise ValueError(f"unknown pathway kind: {kind}")
class TilelliBlock(nn.Module):
"""One Tilelli block: parallel heterogeneous pathways mixed by a router.
Parameters
----------
n_banks : int, default 1
Number of weight banks per pathway (Ternary Dispenser). 1 = original.
>1 = MoE at the weight level: each pathway holds n_banks copies, the
router argmax-picks one bank per token. Adds a load-balancing aux
loss accessible via .aux_loss after each forward.
per_row, hadamard, lsq : bool
Ternary-quantization tricks forwarded to TernaryLinear / Conv. All
default off so the existing aurora-ternary baseline stays identical.
skip_threshold, skip_mode : as before — only used by .infer().
"""
def __init__(
self,
d_model: int,
d_head: int = 32,
kernel_size: int = 5,
wide_kernel_size: int = 21,
top_k: int = 8,
pathways: int = 5,
n_banks: int = 1,
skip_threshold: float = 0.05,
skip_mode: str = "per_call",
quantize: bool = True,
per_row: bool = False,
hadamard: bool = False,
lsq: bool = False,
dense_expand: int = 2,
fp_attention: bool = False,
top_k_routing: int = 0,
) -> None:
super().__init__()
if pathways not in (3, 5):
raise ValueError(f"pathways must be 3 or 5, got {pathways}")
if skip_mode not in ("per_call", "per_token"):
raise ValueError(f"skip_mode must be 'per_call' or 'per_token', got {skip_mode!r}")
if n_banks < 1:
raise ValueError(f"n_banks must be >= 1, got {n_banks}")
self.d_model = d_model
self.pathways = pathways
self.n_banks = n_banks
self.skip_threshold = skip_threshold
self.skip_mode = skip_mode
self.quantize = quantize
self.top_k_routing = top_k_routing
self.pathway_names = PATHWAY_NAMES_5 if pathways == 5 else PATHWAY_NAMES_3
self.norm = nn.LayerNorm(d_model)
def _build(kind: str) -> nn.Module | nn.ModuleList:
mk = lambda: _make_pathway(
kind, d_model, d_head, kernel_size, wide_kernel_size,
top_k, quantize, per_row, hadamard, lsq, dense_expand,
fp_attention,
)
if n_banks <= 1:
return mk()
return nn.ModuleList([mk() for _ in range(n_banks)])
self.local = _build("local")
self.state = _build("state")
self.sparse = _build("sparse")
if pathways == 5:
self.wide = _build("wide")
self.dense = _build("dense")
# Router: routes over (pathway × bank) when n_banks > 1, else pathways.
n_router_outputs = pathways * n_banks
self.router = TernaryLinear(
d_model, n_router_outputs,
quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
)
self._aux_loss = torch.tensor(0.0)
def _pathway_modules(self) -> list[tuple[str, nn.Module | nn.ModuleList]]:
if self.pathways == 5:
return [
("local", self.local),
("wide", self.wide),
("state", self.state),
("sparse", self.sparse),
("dense", self.dense),
]
return [
("local", self.local),
("state", self.state),
("sparse", self.sparse),
]
def _compute_single_bank(self, h: Tensor, r: Tensor) -> Tensor:
outputs = [mod(h) for _, mod in self._pathway_modules()]
return sum(r[..., i:i + 1] * outputs[i] for i in range(len(outputs)))
def _compute_multi_bank(self, h: Tensor, r: Tensor) -> Tensor:
"""Multi-bank dispenser: per-token top-1 bank selection per pathway.
r shape: (B, L, n_pathways * n_banks)
"""
B, L, _ = r.shape
plist = self._pathway_modules()
n_paths = len(plist)
r_2d = r.view(B, L, n_paths, self.n_banks)
pathway_weights = r_2d.sum(dim=-1) # (B, L, n_paths)
bank_idx = r_2d.argmax(dim=-1) # (B, L, n_paths)
# Load balance: each bank should be selected ~1/n_banks of the time.
bank_probs = r_2d.mean(dim=(0, 1)) # (n_paths, n_banks)
target = 1.0 / self.n_banks
self._aux_loss = ((bank_probs - target) ** 2).mean() * 0.01
mixed = torch.zeros(B, L, self.d_model, device=h.device, dtype=h.dtype)
for p_idx, (_name, banks) in enumerate(plist):
pw = pathway_weights[..., p_idx:p_idx + 1] # (B, L, 1)
bidx = bank_idx[..., p_idx] # (B, L)
for b in range(self.n_banks):
mask = (bidx == b)
if not mask.any():
continue
out = banks[b](h)
mixed = mixed + pw * out * mask.unsqueeze(-1).to(out.dtype)
return mixed
def _maybe_topk_route(self, r: Tensor) -> Tensor:
"""Optionally restrict routing to the top-k pathways per token (Mixtral-style)."""
if self.top_k_routing <= 0 or self.top_k_routing >= r.shape[-1]:
return r
top_vals, top_idx = r.topk(self.top_k_routing, dim=-1)
mask = torch.zeros_like(r)
mask.scatter_(-1, top_idx, top_vals)
return mask / mask.sum(dim=-1, keepdim=True).clamp(min=1e-12)
def forward(self, x: Tensor) -> Tensor:
h = self.norm(x)
r = torch.softmax(self.router(h), dim=-1)
r = self._maybe_topk_route(r)
if self.n_banks <= 1:
mixed = self._compute_single_bank(h, r)
else:
mixed = self._compute_multi_bank(h, r)
return x + mixed
@property
def aux_loss(self) -> Tensor:
"""Load-balancing loss for multi-bank. Add to main loss during training."""
return self._aux_loss
@torch.no_grad()
def infer(self, x: Tensor) -> Tensor:
h = self.norm(x)
r = torch.softmax(self.router(h), dim=-1)
if self.n_banks > 1:
return x + self._compute_multi_bank(h, r)
y = torch.zeros_like(x)
if self.skip_mode == "per_call":
r_max = r.amax(dim=(0, 1))
for i, (_, mod) in enumerate(self._pathway_modules()):
if r_max[i].item() >= self.skip_threshold:
step = mod.infer(h) if hasattr(mod, "infer") else mod(h)
y = y + r[..., i:i + 1] * step
return x + y
for i, (_, mod) in enumerate(self._pathway_modules()):
step = mod.infer(h) if hasattr(mod, "infer") else mod(h)
mask = (r[..., i:i + 1] >= self.skip_threshold).to(step.dtype)
y = y + mask * r[..., i:i + 1] * step
return x + y
@torch.no_grad()
def router_weights(self, x: Tensor) -> Tensor:
"""Per-token router distribution.
For single-bank: shape (B, L, n_pathways).
For multi-bank: pathway-level weights (banks summed). Shape (B, L, n_pathways).
"""
r = torch.softmax(self.router(self.norm(x)), dim=-1)
if self.n_banks > 1:
B, L, _ = r.shape
n_paths = len(self._pathway_modules())
return r.view(B, L, n_paths, self.n_banks).sum(dim=-1)
return r
@torch.no_grad()
def router_entropy(self, x: Tensor) -> Tensor:
r = self.router_weights(x).clamp_min(1e-12)
return -(r * r.log()).sum(dim=-1)
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