File size: 7,343 Bytes
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import json
from pathlib import Path
from typing import Optional
import torch
EMBEDDING_FILENAME = "embeddings.pt"
BACKBONE_REF_FILENAME = "backbone.json"
LLOPA_SPECIALS_FILENAME = "llopa_specials.pt"
def write_backbone_ref(best_dir: str | Path, backbone: str | None) -> None:
if not backbone:
return
out = Path(best_dir)
out.mkdir(parents=True, exist_ok=True)
payload = {"backbone": str(backbone)}
(out / BACKBONE_REF_FILENAME).write_text(
json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8"
)
def read_backbone_ref(best_dir: str | Path) -> Optional[str]:
path = Path(best_dir) / BACKBONE_REF_FILENAME
if not path.is_file():
return None
try:
data = json.loads(path.read_text(encoding="utf-8"))
except Exception:
return None
val = data.get("backbone")
return str(val) if val else None
def save_embedding_layer(model, best_dir: str | Path, *, include_lm_head: bool = True) -> bool:
out = Path(best_dir)
out.mkdir(parents=True, exist_ok=True)
try:
emb = model.get_input_embeddings()
except Exception:
emb = None
if emb is None or not hasattr(emb, "weight"):
return False
payload: dict[str, torch.Tensor] = {
"input_embeddings": emb.weight.detach().cpu()
}
if include_lm_head:
head = getattr(model, "lm_head", None)
if head is None and hasattr(model, "get_output_embeddings"):
try:
head = model.get_output_embeddings()
except Exception:
head = None
if head is not None and hasattr(head, "weight"):
try:
same_storage = head.weight.data_ptr() == emb.weight.data_ptr()
except Exception:
same_storage = False
if not same_storage and head.weight.shape == emb.weight.shape:
payload["lm_head"] = head.weight.detach().cpu()
torch.save(payload, out / EMBEDDING_FILENAME)
return True
def load_embedding_layer(model, best_dir: str | Path) -> bool:
path = Path(best_dir) / EMBEDDING_FILENAME
if not path.is_file():
return False
try:
payload = torch.load(path, map_location="cpu")
except Exception:
return False
weight = payload.get("input_embeddings")
if weight is None:
return False
try:
cur_emb = model.get_input_embeddings()
except Exception:
cur_emb = None
if cur_emb is None or not hasattr(cur_emb, "weight"):
return False
if cur_emb.weight.shape[0] != weight.shape[0]:
try:
model.resize_token_embeddings(weight.shape[0])
except Exception:
pass
cur_emb = model.get_input_embeddings()
cur_emb.weight.data.copy_(weight.to(cur_emb.weight.dtype))
head_weight = payload.get("lm_head")
if head_weight is not None:
head = getattr(model, "lm_head", None)
if head is None and hasattr(model, "get_output_embeddings"):
try:
head = model.get_output_embeddings()
except Exception:
head = None
if head is not None and hasattr(head, "weight") and head.weight.shape == head_weight.shape:
head.weight.data.copy_(head_weight.to(head.weight.dtype))
return True
def _get_llopa_specials_module(model):
for cand in (model, getattr(model, "base_model", None), getattr(model, "model", None), getattr(model, "transformer", None)):
if cand is not None and hasattr(cand, "llopa_specials"):
return cand
return None
def save_llopa_specials(model, best_dir: str | Path) -> bool:
out = Path(best_dir)
out.mkdir(parents=True, exist_ok=True)
core = _get_llopa_specials_module(model)
if core is None:
return False
specials = getattr(core, "llopa_specials", None)
if specials is None:
return False
try:
tensors = [p.detach().cpu() for p in specials]
except Exception:
return False
if not tensors:
return False
payload = {
"llopa_num_specials": int(getattr(core, "llopa_num_specials", 0) or 0),
"llopa_num_layers": len(tensors),
"tensors": tensors,
}
torch.save(payload, out / LLOPA_SPECIALS_FILENAME)
return True
def load_llopa_specials(model, best_dir: str | Path) -> bool:
path = Path(best_dir) / LLOPA_SPECIALS_FILENAME
if not path.is_file():
return False
try:
payload = torch.load(path, map_location="cpu")
except Exception:
return False
tensors = payload.get("tensors")
if not tensors:
return False
core = _get_llopa_specials_module(model)
if core is None:
return False
specials = getattr(core, "llopa_specials", None)
if specials is None:
return False
if len(specials) != len(tensors):
return False
try:
for dst, src in zip(specials, tensors):
if dst.shape != src.shape:
return False
dst.data.copy_(src.to(dst.dtype))
except Exception:
return False
return True
def init_llopa_specials_with_mean(model, *, chunk_size: int = 8192) -> bool:
core = _get_llopa_specials_module(model)
if core is None:
return False
specials = getattr(core, "llopa_specials", None)
if specials is None or len(specials) == 0:
return False
emb = None
try:
emb = model.get_input_embeddings()
except Exception:
emb = None
if emb is None and hasattr(core, "get_input_embeddings"):
try:
emb = core.get_input_embeddings()
except Exception:
emb = None
if emb is None:
emb = getattr(core, "embed_tokens", None)
if emb is None or not hasattr(emb, "weight"):
return False
weight = emb.weight.detach()
if weight.ndim != 2:
return False
old_num_tokens, hidden_size = weight.shape
if old_num_tokens <= 0:
return False
emb_cpu = weight.float().cpu()
mean = emb_cpu.mean(dim=0)
cov = torch.zeros((hidden_size, hidden_size), dtype=torch.float32)
step = max(1, int(chunk_size) if chunk_size else int(old_num_tokens))
for start in range(0, int(old_num_tokens), step):
chunk = emb_cpu[start : start + step]
centered = chunk - mean
cov += centered.t().matmul(centered)
cov /= float(old_num_tokens)
epsilon = 1e-9
cov_eps = cov * epsilon
try:
is_psd = torch.distributions.constraints.positive_definite.check(cov_eps).all()
except Exception:
is_psd = False
with torch.no_grad():
if bool(is_psd):
dist = torch.distributions.multivariate_normal.MultivariateNormal(
mean, covariance_matrix=cov_eps
)
for p in specials:
if p.numel() == 0:
continue
samples = dist.sample(sample_shape=(p.shape[0],))
p.copy_(samples.to(dtype=p.dtype, device=p.device))
else:
mean_row = mean.unsqueeze(0)
for p in specials:
if p.numel() == 0:
continue
p.copy_(mean_row.expand_as(p).to(dtype=p.dtype, device=p.device))
return True
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