File size: 2,505 Bytes
91ac3cc | 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 | from __future__ import annotations
from pathlib import Path
from typing import Any
import yaml
from transformers import LlamaConfig
def load_yaml(path: str | Path) -> dict[str, Any]:
config_path = Path(path)
with config_path.open("r", encoding="utf-8") as handle:
data = yaml.safe_load(handle)
if not isinstance(data, dict):
raise ValueError(f"YAML root must be a mapping: {config_path}")
return data
def _positive_int(value: Any, name: str) -> int:
if not isinstance(value, int) or isinstance(value, bool) or value <= 0:
raise ValueError(f"{name} must be a positive integer, got {value!r}")
return value
def build_llama_config(
model_data: dict[str, Any],
*,
tokenizer_vocab_size: int | None = None,
) -> tuple[LlamaConfig, int | None]:
"""Validate a model mapping and return a Hugging Face LlamaConfig."""
if "model" in model_data:
model_data = model_data["model"]
if not isinstance(model_data, dict):
raise ValueError("model configuration must be a mapping")
values = dict(model_data)
expected_parameters = values.pop("expected_parameters", None)
required = (
"vocab_size",
"hidden_size",
"intermediate_size",
"num_hidden_layers",
"num_attention_heads",
"num_key_value_heads",
"max_position_embeddings",
)
for name in required:
values[name] = _positive_int(values.get(name), name)
if values["hidden_size"] % values["num_attention_heads"] != 0:
raise ValueError("hidden_size must be divisible by num_attention_heads")
if values["num_attention_heads"] % values["num_key_value_heads"] != 0:
raise ValueError(
"num_attention_heads must be divisible by num_key_value_heads"
)
if tokenizer_vocab_size is not None and values["vocab_size"] != tokenizer_vocab_size:
raise ValueError(
"Tokenizer/model vocabulary mismatch: "
f"tokenizer={tokenizer_vocab_size}, model={values['vocab_size']}"
)
config = LlamaConfig(**values)
return config, expected_parameters
def count_unique_parameters(model: Any) -> int:
"""Count parameters without double-counting tied tensors."""
seen: set[int] = set()
total = 0
for parameter in model.parameters():
pointer = parameter.untyped_storage().data_ptr()
if pointer not in seen:
seen.add(pointer)
total += parameter.numel()
return total
|