File size: 7,653 Bytes
906715b | 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 | """attach / AttachHandle / detach — the runtime verbs.
attach(model, anchor) wraps every bound decoder layer; adapters follow
their block's device (device_map="auto" compatible). detach(verify=True)
restores the retained layers and asserts BIT-EXACT equality with a
pre-attach probe (fp32; dtype-conditional under bf16).
"""
from __future__ import annotations
import contextlib
from typing import Sequence
import torch
import torch.nn as nn
from ..binding.resolver import resolve
from ..core.adapter import AdapterSpec, BlockWithAdapter, RelayPatchwork
from ..core.dispatch import AnchorDispatch, BlockWithDispatch
from ..io.checkpoint import (AnchorCheckpoint, DispatchCheckpoint,
load_anchor, load_dispatch)
def _per_block_state(adapters: dict, i: int) -> dict:
pre = f"{i}."
return {k[len(pre):]: v for k, v in adapters.items()
if k.startswith(pre)}
def _probe(model, binding) -> torch.Tensor:
"""Deterministic logits fingerprint on a tiny fixed input."""
dev = next(model.parameters()).device
ids = torch.arange(8, dtype=torch.long, device=dev).unsqueeze(0) % 7 + 1
with torch.no_grad():
out = model(input_ids=ids)
logits = out.logits if hasattr(out, "logits") else out[0]
return logits.detach().float().cpu()
class AttachHandle:
def __init__(self, model, binding, original_layers, names,
per_block_modules, fingerprint):
self.model = model
self.binding = binding
self._original = original_layers
self.names = list(names)
self._blocks = per_block_modules # BlockWithAdapter|BlockWithDispatch list
self._fingerprint = fingerprint
# -- masking -------------------------------------------------------
def set_mask(self, mask: dict[str, bool]) -> None:
enabled = [mask.get(n, True) for n in self.names]
for b in self._blocks:
if isinstance(b, BlockWithDispatch):
b.disp.enabled = list(enabled)
else:
b.enabled = enabled[0]
def enable(self, *names):
self.set_mask({n: (n in names or not names) for n in self.names})
def disable(self, *names):
self.set_mask({n: n not in names for n in self.names})
@contextlib.contextmanager
def only(self, *names):
prev = self._snapshot()
self.set_mask({n: n in names for n in self.names})
try:
yield self
finally:
self._restore(prev)
@contextlib.contextmanager
def all_off(self):
prev = self._snapshot()
self.set_mask({n: False for n in self.names})
try:
yield self
finally:
self._restore(prev)
def _snapshot(self):
out = []
for b in self._blocks:
out.append(list(b.disp.enabled)
if isinstance(b, BlockWithDispatch) else b.enabled)
return out
def _restore(self, snap):
for b, s in zip(self._blocks, snap):
if isinstance(b, BlockWithDispatch):
b.disp.enabled = list(s)
else:
b.enabled = s
# -- telemetry -----------------------------------------------------
def telemetry(self, on: bool) -> None:
for b in self._blocks:
if isinstance(b, BlockWithDispatch):
b.disp.rec = [] if on else None
def usage(self) -> dict[str, float]:
import torch as _t
ids = [b.disp._last_shadow.flatten() for b in self._blocks
if isinstance(b, BlockWithDispatch)
and b.disp._last_shadow is not None]
if not ids:
return {}
counts = _t.bincount(_t.cat(ids), minlength=len(self.names)).float()
p = counts / counts.sum()
return {n: round(float(v), 4) for n, v in zip(self.names, p)}
def amplitude(self) -> dict[str, float]:
recs = [b.disp.rec for b in self._blocks
if isinstance(b, BlockWithDispatch) and b.disp.rec]
if not recs:
return {}
amp = torch.stack([torch.stack(r).mean(0) for r in recs]).mean(0)
return {n: round(float(a), 5) for n, a in zip(self.names, amp)}
# -- detach --------------------------------------------------------
def detach(self, *, verify: bool = True) -> nn.Module:
self.binding.set_layers(self.model, list(self._original))
if verify:
post = _probe(self.model, self.binding)
if not torch.equal(post, self._fingerprint):
raise RuntimeError(
"detach verification FAILED: post-detach logits are "
"not bit-exact with the pre-attach fingerprint")
return self.model
def attach(model, anchors, dispatch=None, *, binding=None,
spec: AdapterSpec | None = None,
strict: bool = True) -> AttachHandle:
b = resolve(model, binding)
layers = list(b.layers(model))
d = b.hidden_size(model)
fingerprint = _probe(model, b)
def _as_ckpt(a):
if isinstance(a, AnchorCheckpoint):
return a
return load_anchor(a)
single = not isinstance(anchors, (list, tuple))
anchor_list = [anchors] if single else list(anchors)
ckpts = [_as_ckpt(a) for a in anchor_list]
names = [c.meta.get("name", f"anchor{i}")
for i, c in enumerate(ckpts)]
if strict:
for c in ckpts:
bid = c.meta.get("base_model_id")
live = getattr(model.config, "_name_or_path", None)
if bid and live and bid not in str(live):
raise ValueError(
f"anchor '{c.meta.get('name')}' was trained on "
f"{bid}, live model is {live} (pass strict=False "
"to override)")
blocks = []
if single and dispatch is None:
ck = ckpts[0]
for i, layer in enumerate(layers):
st = _per_block_state(ck.adapters, i)
if not st:
blocks.append(layer)
continue
a = RelayPatchwork(d, spec)
a.load_state_dict(st)
a.to(next(layer.parameters()).device) # device-following
wrapped = BlockWithAdapter(layer, a)
blocks.append(wrapped)
else:
if dispatch is None:
raise ValueError("multiple anchors require a dispatch "
"(align one, or pass dispatch='init')")
disp_ck = (None if dispatch == "init"
else dispatch if isinstance(dispatch, DispatchCheckpoint)
else load_dispatch(dispatch))
for i, layer in enumerate(layers):
stack = nn.ModuleList()
for ck in ckpts:
a = RelayPatchwork(d, spec)
a.load_state_dict(_per_block_state(ck.adapters, i))
for p in a.parameters():
p.requires_grad_(False)
stack.append(a)
dev = next(layer.parameters()).device
dp = AnchorDispatch(stack.to(dev), d).to(dev)
if disp_ck is not None:
dp.load_state_dict(disp_ck.dispatch[i], strict=False)
blocks.append(BlockWithDispatch(layer, dp))
b.set_layers(model, blocks)
wrapped_blocks = [x for x in blocks
if isinstance(x, (BlockWithAdapter,
BlockWithDispatch))]
return AttachHandle(model, b, layers, names, wrapped_blocks,
fingerprint)
def detach(handle: AttachHandle, *, verify: bool = True) -> nn.Module:
return handle.detach(verify=verify)
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