refactor: Add a custom tokenizer component and fix vocab request class

gguf-py/gguf/huggingface_hub.py

@@ -5,6 +5,7 @@ import pathlib
 from hashlib import sha256
 
 import requests
+from sentencepiece import SentencePieceProcessor
 from transformers import AutoTokenizer
 
 from .constants import (
@@ -103,6 +104,71 @@ class HFHubBase:
         self._model_path = value
 
 
+class HFTokenizer(HFHubBase):
+    def __init__(self, model_path: str, auth_token: str, logger: logging.Logger):
+        super().__init__(model_path, auth_token, logger)
+        self._model_path = model_path
+
+    @staticmethod
+    def get_vocab_filenames(vocab_type: VocabType) -> tuple[str]:
+        if vocab_type == VocabType.SPM:
+            return HF_TOKENIZER_SPM_FILES
+        # NOTE: WPM and BPE are equivalent
+        return HF_TOKENIZER_BPE_FILES
+
+    @staticmethod
+    def get_vocab_name(vocab_type: VocabType) -> str:
+        return VOCAB_TYPE_NAMES.get(vocab_type)
+
+    @staticmethod
+    def get_vocab_enum(vocab_name: str) -> VocabType:
+        return {
+            "SPM": VocabType.SPM,
+            "BPE": VocabType.BPE,
+            "WPM": VocabType.WPM,
+        }.get(vocab_name, VocabType.NON)
+
+    def config(self, model_repo: str) -> dict[str, object]:
+        path = self.model_path / model_repo / "config.json"
+        with path.read_text(encoding='utf-8') as file:
+            return json.loads(file)
+
+    def tokenizer_config(self, model_repo: str) -> dict[str, object]:
+        path = self.model_path / model_repo / "tokenizer_config.json"
+        with path.read_text(encoding='utf-8') as file:
+            return json.loads(file)
+
+    def tokenizer_json(self, model_repo: str) -> dict[str, object]:
+        path = self.model_path / model_repo / "tokenizer.json"
+        with path.read_text(encoding='utf-8') as file:
+            return json.loads(file)
+
+    def tokenizer_model(self, model_repo: str) -> SentencePieceProcessor:
+        path = self.model_path / model_repo / "tokenizer.model"
+        processor = SentencePieceProcessor()
+        processor.LoadFromFile(path.read_bytes())
+        return processor
+
+    def get_tokenizer_json_hash(self, model_repo: str) -> str:
+        tokenizer = self.tokenizer_json(model_repo)
+        tokenizer_path = self.model_path / model_repo / "tokenizer.json"
+        sha256sum = sha256(str(tokenizer).encode()).hexdigest()
+        self.logger.info(f"Hashed '{tokenizer_path}' as {sha256sum}")
+        return sha256sum
+
+    def log_tokenizer_json_info(self, model_repo: str) -> None:
+        tokenizer = self.tokenizer_json(model_repo)
+        self.logger.info(f"JSON:ModelRepo: {model_repo}")
+        for k, v in tokenizer.get("model", {}).items():
+            if k == "vocab":
+                continue  # NOTE: Do not pollute the output
+            self.logger.info(f"JSON:Model: {k}: {json.dumps(v, indent=2)}")
+        for k, v in tokenizer.get("normalizer", {}).items():
+            self.logger.info(f"JSON:Normalizer: {k}: {json.dumps(v, indent=2)}")
+        for k, v in tokenizer.get("pre_tokenizer", {}).items():
+            self.logger.info(f"JSON:PreTokenizer: {k}: {json.dumps(v, indent=2)}")
+
+
 class HFVocabRequest(HFHubBase):
     def __init__(
         self,
@@ -111,94 +177,45 @@ class HFVocabRequest(HFHubBase):
         logger: None | logging.Logger
     ):
         super().__init__(model_path, auth_token, logger)
+        self._tokenizer = HFTokenizer(model_path, auth_token, logger)
 
     @property
-    def tokenizer_type(self) -> VocabType:
+    def tokenizer(self) -> HFTokenizer:
-        return VocabType
+        return self._tokenizer
-
-    @property
-    def tokenizer_path(self) -> pathlib.Path:
-        return self.model_path / "tokenizer.json"
-
-    def get_vocab_name(self, vocab_type: VocabType) -> str:
-        return VOCAB_TYPE_NAMES.get(vocab_type)
-
-    def get_vocab_enum(self, vocab_name: str) -> VocabType:
-        return {
-            "SPM": VocabType.SPM,
-            "BPE": VocabType.BPE,
-            "WPM": VocabType.WPM,
-        }.get(vocab_name, VocabType.NON)
-
-    def get_vocab_filenames(self, vocab_type: VocabType) -> tuple[str]:
-        if vocab_type == self.tokenizer_type.SPM:
-            return HF_TOKENIZER_SPM_FILES
-        # NOTE: WPM and BPE are equivalent
-        return HF_TOKENIZER_BPE_FILES
 
     def get_vocab_file(
         self, model_repo: str, file_name: str, file_path: pathlib.Path,
     ) -> bool:
         # NOTE: Do not use bare exceptions! They mask issues!
-        # Allow the exception to occur or handle it explicitly.
+        # Allow the exception to occur or explicitly handle it.
         resolve_url = self.hub.resolve_url(model_repo, file_name)
         response = self.hub.download_file(resolve_url)
         self.hub.write_file(response.content, file_path)
         self.logger.info(f"Downloaded tokenizer {file_name} from {model_repo}")
 
     def get_all_vocab_files(self, model_repo: str, vocab_type: VocabType) -> None:
-        vocab_list = self.get_vocab_filenames(vocab_type)
+        vocab_list = HFTokenizer.get_vocab_filenames(vocab_type)
         for vocab_file in vocab_list:
             dir_path = self.model_path / model_repo
             file_path = dir_path / vocab_file
             os.makedirs(dir_path, exist_ok=True)
             self.get_vocab_file(model_repo, vocab_file, file_path)
 
-    def get_normalizer(self) -> None | dict[str, object]:
+    def get_normalizer(self, model_repo: str) -> None | dict[str, object]:
-        with open(self.tokenizer_path, mode="r") as file:
+        normalizer = self.tokenizer.tokenizer_json(model_repo).get("normalizer", dict())
-            tokenizer_json = json.load(file)
+        if normalizer:
-        return tokenizer_json.get("normalizer")
+            self.logger.info(f"JSON:Normalizer: {json.dumps(normalizer, indent=2)}")
+        else:
+            self.logger.warn(f"WARN:Normalizer: {normalizer}")
+        return normalizer
 
-    def get_pre_tokenizer(self) -> None | dict[str, object]:
+    def get_pre_tokenizer(self, model_repo: str) -> None | dict[str, object]:
-        with open(self.tokenizer_path, mode="r") as file:
+        pre_tokenizer = self.tokenizer.tokenizer_json(model_repo).get("pre_tokenizer", dict())
-            tokenizer_json = json.load(file)
+        if pre_tokenizer:
-        return tokenizer_json.get("pre_tokenizer")
+            self.logger.info(f"JSON:PreTokenizer: {json.dumps(pre_tokenizer, indent=2)}")
-
+        else:
-    def generate_checksum(self) -> None:
+            self.logger.warn(f"WARN:PreTokenizer: {pre_tokenizer}")
-        checksums = []
+        return pre_tokenizer
-        for model in self.models:
-            mapping = {}
-            file_path = f"{self.model_path}/{model['repo']}"
-
-            try:
-                tokenizer = AutoTokenizer.from_pretrained(file_path, trust_remote=True)
-            except OSError as e:
-                self.logger.error(f"Failed to hash tokenizer {model['repo']}: {e}")
-                continue
-
-            mapping.update(model)
-            mapping['checksum'] = sha256(str(tokenizer.vocab).encode()).hexdigest()
-            self.logger.info(f"Hashed {mapping['repo']} as {mapping['checksum']}")
-            checksums.append(mapping)
-
-        with open(f"{self.model_path}/checksums.json", mode="w") as file:
-            json.dump(checksums, file)
-
-    def log_pre_tokenizer_info(self) -> None:
-        for model in self.models:
-            try:
-                with open(f"{self.model_path}/{model['repo']}/tokenizer.json", "r", encoding="utf-8") as f:
-                    self.logger.info(f"Start: {model['repo']}")
-                    cfg = json.load(f)
-                    self.logger.info(f"normalizer: {json.dumps(cfg['normalizer'], indent=4)}")
-                    self.logger.info(f"pre_tokenizer: {json.dumps(cfg['pre_tokenizer'], indent=4)}")
-                    if "type" in cfg["model"]:
-                        self.logger.info(f"type: {json.dumps(cfg['model']['type'])}")
-                    if "ignore_merges" in cfg["model"]:
-                        self.logger.info(f"ignore_merges: {json.dumps(cfg['model']['ignore_merges'], indent=4)}")
-                self.logger.info(f"End: {model['repo']}")
-            except FileNotFoundError as e:
-                self.logger.error(f"Failed to log tokenizer {model['repo']}: {e}")
 
 
 # TODO: