File size: 8,018 Bytes
ca3d977 | 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 | """Frozen-Pythia attachment for the split canonical router/translator."""
from __future__ import annotations
import torch
from torch import Tensor, nn
from pathlib import Path
from pcm.planner.canonical import CanonicalPStore
from pcm.planner.split_translator import (
ByteEntityEncoder,
CanonicalPRouter,
CanonicalRouterIndex,
RouteResult,
FactorizedCanonicalQuery,
SplitPTranslatePackage,
config_checksum,
)
def pythia_model_identifier(base_model: nn.Module) -> str:
configured = str(getattr(base_model.config, "_name_or_path", "")).strip()
if configured and Path(configured).is_absolute():
configured = Path(configured).name
return configured or str(getattr(base_model.config, "model_type", "gpt_neox"))
class PythiaSplitTranslatedModel(nn.Module):
def __init__(
self,
base_model: nn.Module,
package: SplitPTranslatePackage,
router: CanonicalPRouter,
entity_encoder: ByteEntityEncoder,
) -> None:
super().__init__()
if not hasattr(base_model, "gpt_neox"):
raise TypeError("base model must expose GPT-NeoX transformer layers")
package.validate_compatibility(
model_id=pythia_model_identifier(base_model),
model_hidden_width=int(base_model.config.hidden_size),
attachment_layers=package.config.attachment_layers,
model_config_sha256=config_checksum(base_model.config),
)
self.base_model = base_model
self.package = package
self.router = router
self.entity_encoder = entity_encoder
for parameter in base_model.parameters():
parameter.requires_grad_(False)
layers = base_model.gpt_neox.layers
if any(index < 0 or index >= len(layers) for index in package.config.attachment_layers):
raise IndexError("split translator attachment layer is outside Pythia depth")
self._store: CanonicalPStore | None = None
self._index: CanonicalRouterIndex | None = None
self._oracle_indices: Tensor | None = None
self._query_entity_anchor: Tensor | None = None
self._gate_enabled = True
self._injection_enabled = True
self._collect = False
self._gate_telemetry: list[Tensor] = []
self._route_telemetry: list[RouteResult] = []
self._query_telemetry: list[FactorizedCanonicalQuery] = []
self._handles = [
layers[index].register_forward_hook(self._hook)
for index in package.config.attachment_layers
]
self.base_model.eval()
def _oracle_route(self, query, hidden: Tensor) -> RouteResult:
assert self._index is not None and self._oracle_indices is not None
scores, features = self.router.all_scores(query, self._index)
batch, sequence = hidden.shape[:2]
indices = self._oracle_indices.to(hidden.device).view(batch, 1, 1).expand(batch, sequence, 1)
selected_scores = scores.gather(-1, indices)
selected_features = features.gather(
-2, indices.unsqueeze(-1).expand(batch, sequence, 1, 4)
)
return RouteResult(
indices=indices,
scores=selected_scores,
weights=torch.ones_like(selected_scores),
features=selected_features,
accepted=torch.ones((batch, sequence), dtype=torch.bool, device=hidden.device),
has_valid=True,
)
def _hook(self, _module, _inputs, hidden: Tensor):
if self._store is None or self._store.cache.occupied == 0:
return hidden
assert self._index is not None
entity_anchor = None
if self._query_entity_anchor is not None:
entity_anchor = self._query_entity_anchor[:, None, :].expand(
hidden.shape[0], hidden.shape[1], -1
)
query = self.package.query_projector(hidden, entity_anchor=entity_anchor)
if self._collect:
self._query_telemetry.append(FactorizedCanonicalQuery(
entity=query.entity.detach(),
relation_logits=query.relation_logits.detach(),
metadata_logits=query.metadata_logits.detach(),
))
route = (
self._oracle_route(query, hidden)
if self._oracle_indices is not None
else self.router.route(query, self._index, top_k=self.package.config.top_k)
)
if self._collect:
self._route_telemetry.append(RouteResult(
indices=route.indices.detach(), scores=route.scores.detach(),
weights=route.weights.detach(), features=route.features.detach(),
accepted=route.accepted.detach(),
has_valid=route.has_valid,
))
if not self._injection_enabled or not route.has_valid:
return hidden
canonical = self._store.canonical_values.to(
device=hidden.device, dtype=route.weights.dtype
)
selected = canonical[route.indices]
pooled = torch.einsum("...k,...kd->...d", route.weights, selected)
translated = self.package.value_translator(pooled)
route_features = torch.einsum(
"...k,...kf->...f", route.weights, route.features
)
gate = (
self.package.gate(hidden, translated, route_features)
if self._gate_enabled
else torch.ones(hidden.shape[:-1], device=hidden.device, dtype=translated.dtype)
)
gate = gate * route.accepted.to(gate.dtype)
if self._collect:
self._gate_telemetry.append(gate.detach())
return hidden + (gate.unsqueeze(-1) * translated).to(hidden.dtype)
def train(self, mode: bool = True):
super().train(mode)
self.base_model.eval()
self.package.train(mode)
self.router.train(mode)
return self
def forward(
self,
*args,
p_store: CanonicalPStore | None = None,
query_entity_surfaces: list[str] | tuple[str, ...] | None = None,
oracle_indices: Tensor | None = None,
gate_enabled: bool = True,
injection_enabled: bool = True,
collect_telemetry: bool = False,
**kwargs,
):
if self._store is not None:
raise RuntimeError("PythiaSplitTranslatedModel is not reentrant")
self._store = p_store
self._oracle_indices = oracle_indices
if query_entity_surfaces is not None:
if "input_ids" in kwargs and len(query_entity_surfaces) != kwargs["input_ids"].shape[0]:
raise ValueError("query entity surface count must match the input batch")
self._query_entity_anchor = self.entity_encoder(
list(query_entity_surfaces)
).to(next(self.package.parameters()).device)
else:
self._query_entity_anchor = None
self._gate_enabled = gate_enabled
self._injection_enabled = injection_enabled
self._collect = collect_telemetry
self._gate_telemetry.clear()
self._route_telemetry.clear()
self._query_telemetry.clear()
if p_store is not None and p_store.cache.occupied:
self._index = self.router.build_index(
p_store, self.entity_encoder, device=next(self.package.parameters()).device
)
try:
return self.base_model(*args, **kwargs)
finally:
self._store = None
self._index = None
self._oracle_indices = None
self._query_entity_anchor = None
self._collect = False
@property
def gate_telemetry(self):
return tuple(self._gate_telemetry)
@property
def route_telemetry(self):
return tuple(self._route_telemetry)
@property
def query_telemetry(self):
return tuple(self._query_telemetry)
def close(self):
for handle in self._handles:
handle.remove()
self._handles.clear()
|