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import inspect
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
def make_config_class(model_args: dict, model_type: str) -> PretrainedConfig:
model_type_ = model_type
class Config(PretrainedConfig):
model_type = model_type_
def __init__(self, **kwargs):
for k, v in model_args.items():
setattr(self, k, kwargs.get(k, v))
super().__init__(**kwargs)
return Config
def make_model_class(base_class: nn.Module, config_attributes: list[str] = None) -> PreTrainedModel:
base_init_signature = inspect.signature(base_class.__init__)
base_params = set(base_init_signature.parameters.keys()) - {"self"}
class Model(PreTrainedModel):
config_class: PretrainedConfig
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
if config_attributes is not None:
model_kwargs = {a: getattr(config, a) for a in config_attributes if hasattr(config, a)}
else:
model_kwargs = {}
for param_name in base_params:
if hasattr(config, param_name):
model_kwargs[param_name] = getattr(config, param_name)
filtered_kwargs = {k: v for k, v in kwargs.items() if k in base_params}
if "config" in base_params:
self._model = base_class(config, **model_kwargs, **filtered_kwargs)
else:
self._model = base_class(**model_kwargs, **filtered_kwargs)
def forward(self, *args, **kwargs):
return self._model(*args, **kwargs)
return Model
def make_tokenizer_class(
vocab: list[str],
special_tokens: dict[str, str]
) -> PreTrainedTokenizer:
for key in special_tokens:
if key not in ["unk", "pad", "bos", "eos", "sep", "cls", "mask"]:
raise ValueError(f"unrecognized special token key: `{key}`")
unk_token = special_tokens.get("unk", vocab[0])
token_to_idx = {k: v for v, k in enumerate(vocab)}
idx_to_token = {v: k for k, v in token_to_idx.items()}
# I have no idea how this class works, I copied from somewhere else and forgot
class Tokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids"]
def __init__(
self,
model_max_length: int | None = None,
split_special_tokens: bool = True,
**kwargs
):
self.model_max_length = model_max_length
self._vocab = token_to_idx
self._inv_vocab = idx_to_token
tokens = dict(
unk_token=special_tokens.get("unk"),
pad_token=special_tokens.get("pad"),
bos_token=special_tokens.get("bos"),
eos_token=special_tokens.get("eos"),
sep_token=special_tokens.get("sep"),
cls_token=special_tokens.get("cls"),
mask_token=special_tokens.get("mask"),
)
tokens = {k: v for k, v in tokens.items() if v is not None}
super().__init__(
model_max_length=model_max_length,
split_special_tokens=split_special_tokens,
**tokens,
**kwargs,
)
def _tokenize(self, seq: str) -> list[str]:
# TODO This only handles characters, not subwords
return list(seq)
def _convert_token_to_id(self, token: str) -> int:
return self._vocab.get(token, self._vocab[unk_token])
def _convert_id_to_token(self, idx: int) -> str:
return self._inv_vocab[idx]
@property
def vocab_size(self) -> int:
return len(self._vocab)
def get_vocab(self) -> dict[str, int]:
return self._vocab
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple:
return ()
return Tokenizer
def register_auto_classes(
config_class: PretrainedConfig,
model_class: PreTrainedModel = None,
tokenizer_class: PreTrainedTokenizer = None
):
model_type = getattr(config_class, "model_type", None)
if model_type is None:
raise ValueError("`config_class` must have a `model_type` attribute")
AutoConfig.register(model_type, config_class)
config_class.register_for_auto_class()
if model_class is not None:
if not hasattr(model_class, "config_class") or model_class.config_class is None:
model_class.config_class = config_class
AutoModel.register(config_class, model_class)
model_class.register_for_auto_class("AutoModel")
if tokenizer_class is not None:
AutoTokenizer.register(config_class, tokenizer_class)
tokenizer_class.register_for_auto_class("AutoTokenizer")
def push_model_to_hub(
config_class: PretrainedConfig,
model_class: PreTrainedModel,
model_args: dict,
state_dict: dict,
id_: str,
commit_message: str = "Upload model",
) -> str:
config = config_class(**model_args)
huggingface_model = model_class(config)
pytorch_model = getattr(huggingface_model, "_model")
pytorch_model.load_state_dict(state_dict)
config.save_pretrained(id_)
huggingface_model.save_pretrained(id_)
return huggingface_model.push_to_hub(id_, commit_message=commit_message)
def push_tokenizer_to_hub(
tokenizer_class: PreTrainedTokenizer,
id_: str,
commit_message: str = "Upload tokenizer",
**kwargs,
) -> str:
tokenizer = tokenizer_class(**kwargs)
tokenizer.save_pretrained(id_)
return tokenizer.push_to_hub(id_, commit_message=commit_message) |