237 lines
8.0 KiB
Python
237 lines
8.0 KiB
Python
import json
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import logging
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import os
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import pathlib
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from hashlib import sha256
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import requests
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from huggingface_hub import login, model_info
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from sentencepiece import SentencePieceProcessor
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from .constants import (
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GPT_PRE_TOKENIZER_DEFAULT,
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HF_TOKENIZER_BPE_FILES,
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HF_TOKENIZER_SPM_FILES,
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MODEL_FILE_TYPE_NAMES,
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VOCAB_TYPE_NAMES,
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ModelFileType,
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VocabType,
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)
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class HFHubRequest:
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def __init__(self, auth_token: None | str, logger: None | logging.Logger):
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# Set headers if authentication is available
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if auth_token is None:
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self._headers = None
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else:
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self._headers = {"Authorization": f"Bearer {auth_token}"}
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# Set the logger
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if logger is None:
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logger = logging.getLogger(__name__)
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self.logger = logger
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# Persist across requests
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self._session = requests.Session()
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# This is read-only
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self._base_url = "https://huggingface.co"
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@property
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def headers(self) -> str:
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return self._headers
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@property
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def save_path(self) -> pathlib.Path:
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return self._save_path
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@property
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def session(self) -> requests.Session:
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return self._session
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@property
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def base_url(self) -> str:
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return self._base_url
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def write_file(self, content: bytes, file_path: pathlib.Path) -> None:
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with open(file_path, 'wb') as f:
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f.write(content)
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self.logger.info(f"Wrote {len(content)} bytes to {file_path} successfully")
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def resolve_url(self, repo: str, filename: str) -> str:
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return f"{self._base_url}/{repo}/resolve/main/{filename}"
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def download_file(self, url: str) -> requests.Response:
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response = self._session.get(url, headers=self.headers)
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self.logger.info(f"Response status was {response.status_code}")
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response.raise_for_status()
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return response
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class HFHub:
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def __init__(
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self,
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model_path: None | str | pathlib.Path,
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auth_token: str,
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logger: None | logging.Logger
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):
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# Set the model path
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if model_path is None:
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model_path = "models"
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self._model_path = model_path
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# Set the logger
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if logger is None:
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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self.logger = logger
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# Set the hub api
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self._hub = HFHubRequest(auth_token, logger)
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@property
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def hub(self) -> HFHubRequest:
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return self._hub
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@property
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def model_path(self) -> pathlib.Path:
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return pathlib.Path(self._model_path)
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@model_path.setter
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def model_path(self, value: pathlib.Path):
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self._model_path = value
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class HFTokenizer(HFHub):
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def __init__(self, model_path: str, auth_token: str, logger: logging.Logger):
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super().__init__(model_path, auth_token, logger)
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@staticmethod
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def get_vocab_filenames(vocab_type: VocabType) -> tuple[str]:
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if vocab_type == VocabType.SPM:
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return HF_TOKENIZER_SPM_FILES
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# NOTE: WPM and BPE are equivalent
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return HF_TOKENIZER_BPE_FILES
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@staticmethod
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def get_vocab_name(vocab_type: VocabType) -> str:
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return VOCAB_TYPE_NAMES.get(vocab_type)
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@staticmethod
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def get_vocab_enum(vocab_name: str) -> VocabType:
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return {
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"SPM": VocabType.SPM,
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"BPE": VocabType.BPE,
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"WPM": VocabType.WPM,
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}.get(vocab_name, VocabType.NON)
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def config(self, model_repo: str) -> dict[str, object]:
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path = self.model_path / model_repo / "config.json"
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return json.loads(path.read_text(encoding='utf-8'))
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def tokenizer_model(self, model_repo: str) -> SentencePieceProcessor:
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path = self.model_path / model_repo / "tokenizer.model"
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processor = SentencePieceProcessor()
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processor.LoadFromFile(path.read_bytes())
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return processor
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def tokenizer_config(self, model_repo: str) -> dict[str, object]:
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path = self.model_path / model_repo / "tokenizer_config.json"
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return json.loads(path.read_text(encoding='utf-8'))
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def tokenizer_json(self, model_repo: str) -> dict[str, object]:
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path = self.model_path / model_repo / "tokenizer.json"
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return json.loads(path.read_text(encoding='utf-8'))
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def get_normalizer(self, model_repo: str) -> None | dict[str, object]:
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normalizer = self.tokenizer_json(model_repo).get("normalizer", dict())
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if normalizer:
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self.logger.info(f"JSON:Normalizer: {json.dumps(normalizer, indent=2)}")
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else:
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self.logger.warn(f"WARN:Normalizer: {normalizer}")
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return normalizer
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def get_pre_tokenizer(self, model_repo: str) -> None | dict[str, object]:
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pre_tokenizer = self.tokenizer_json(model_repo).get("pre_tokenizer", dict())
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if pre_tokenizer:
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self.logger.info(f"JSON:PreTokenizer: {json.dumps(pre_tokenizer, indent=2)}")
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else:
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self.logger.warn(f"WARN:PreTokenizer: {pre_tokenizer}")
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return pre_tokenizer
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def get_added_tokens(self, model_repo: str) -> None | list[dict[str, object]]:
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added_tokens = self.tokenizer_json(model_repo).get("pre_tokenizer", list())
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if added_tokens:
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self.logger.info(f"JSON:AddedTokens: {json.dumps(added_tokens, indent=2)}")
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else:
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self.logger.warn(f"WARN:PreTokenizer: {added_tokens}")
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return added_tokens
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def get_tokenizer_json_hash(self, model_repo: str) -> str:
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tokenizer = self.tokenizer_json(model_repo)
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tokenizer_path = self.model_path / model_repo / "tokenizer.json"
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sha256sum = sha256(str(tokenizer).encode()).hexdigest()
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self.logger.info(f"Hashed '{tokenizer_path}' as {sha256sum}")
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return sha256sum
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def log_tokenizer_json_info(self, model_repo: str) -> None:
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self.logger.info(f"{model_repo}")
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tokenizer = self.tokenizer_json(model_repo)
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for k, v in tokenizer.items():
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if k not in ["added_tokens", "model"]:
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self.logger.info(f"{k}:{json.dumps(v, indent=2)}")
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if k == "model":
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for x, y in v.items():
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if x not in ["vocab", "merges"]:
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self.logger.info(f"{k}:{x}:{json.dumps(y, indent=2)}")
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class HFModel(HFHub):
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def __init__(
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self,
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auth_token: str,
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model_path: None | str | pathlib.Path,
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logger: None | logging.Logger
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):
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super().__init__(model_path, auth_token, logger)
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self._tokenizer = HFTokenizer(model_path, auth_token, logger)
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login(auth_token) # NOTE: Required for using model_info
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@staticmethod
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def get_model_info(repo_id: str) -> list[str]:
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# NOTE: Get repository metadata to extract remote filenames
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return [x.rfilename for x in model_info(repo_id).siblings]
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@property
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def model_type(self) -> ModelFileType:
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return ModelFileType
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@property
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def tokenizer(self) -> HFTokenizer:
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return self._tokenizer
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def get_vocab_file(
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self, model_repo: str, file_name: str, file_path: pathlib.Path,
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) -> bool:
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# NOTE: Do not use bare exceptions! They mask issues!
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# Allow the exception to occur or explicitly handle it.
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resolve_url = self.hub.resolve_url(model_repo, file_name)
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response = self.hub.download_file(resolve_url)
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self.hub.write_file(response.content, file_path)
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self.logger.info(f"Downloaded tokenizer {file_name} from {model_repo}")
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def get_all_vocab_files(self, model_repo: str, vocab_type: VocabType) -> None:
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vocab_list = self.tokenizer.get_vocab_filenames(vocab_type)
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for vocab_file in vocab_list:
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dir_path = self.model_path / model_repo
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file_path = dir_path / vocab_file
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os.makedirs(dir_path, exist_ok=True)
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self.get_vocab_file(model_repo, vocab_file, file_path)
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def get_model_file(self, model_repo: str, file_name: str, file_path: pathlib.Path) -> bool:
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pass
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def get_all_model_files(self) -> None:
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pass
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