chore: 重命名目录

astrai/tokenize/tokenizer.py

@@ -0,0 +1,212 @@
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
+from tokenizers.models import BPE
+from tokenizers.trainers import BpeTrainer as BpeTrainerImpl
+
+
+class BaseTokenizer(ABC):
+    @abstractmethod
+    def _init_tokenizer(self):
+        pass
+
+    @abstractmethod
+    def save(self, path):
+        pass
+
+    @abstractmethod
+    def load(self, path):
+        pass
+
+    @abstractmethod
+    def encode(
+        self,
+        tokens: Union[str, List[str]],
+        out_ids: bool = True,
+        add_special_tokens: bool = False,
+    ) -> List:
+        pass
+
+    @abstractmethod
+    def decode(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+        pass
+
+    @abstractmethod
+    def __len__(self) -> int:
+        pass
+
+    @property
+    @abstractmethod
+    def stop_ids(self) -> List[int]:
+        pass
+
+    @property
+    @abstractmethod
+    def bos_id(self) -> int:
+        pass
+
+    @property
+    @abstractmethod
+    def eos_id(self) -> int:
+        pass
+
+    @property
+    @abstractmethod
+    def pad_id(self) -> int:
+        pass
+
+
+class BaseTrainer(ABC):
+    def __init__(self, tokenizer: BaseTokenizer):
+        self.tokenizer = tokenizer
+
+    @abstractmethod
+    def train(self, files, vocab_size, min_freq, **kwargs):
+        pass
+
+    @abstractmethod
+    def train_from_iterator(self, iterator, vocab_size, min_freq, **kwargs):
+        pass
+
+
+class BpeTokenizer(BaseTokenizer):
+    def __init__(
+        self,
+        control_tokens: List[str] = None,
+        special_tokens: List[str] = None,
+        path=None,
+    ):
+        self._control_tokens = control_tokens or [
+            "<｜begin▁of▁sentence｜>",
+            "<｜end▁of▁sentence｜>",
+            "<｜▁pad▁｜>",
+        ]
+        self._special_tokens = special_tokens or [
+            "<｜im▁start｜>",
+            "<｜im▁end｜>",
+        ]
+        self._tokenizer = None
+        self._init_tokenizer()
+        if path is not None:
+            self.load(path)
+
+    def _init_tokenizer(self):
+        model = BPE()
+        self._tokenizer = Tokenizer(model)
+        self._tokenizer.normalizer = normalizers.Sequence(
+            [normalizers.NFC(), normalizers.Strip()]
+        )
+        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
+            [
+                pre_tokenizers.UnicodeScripts(),
+                pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=True),
+            ]
+        )
+        self._tokenizer.decoder = decoders.ByteLevel()
+        self._tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
+
+    def save(self, path):
+        self._tokenizer.save(path)
+
+    def load(self, path):
+        self._tokenizer = Tokenizer.from_file(path)
+
+    def encode(
+        self,
+        tokens: Union[str, List[str]],
+        out_ids: bool = True,
+        add_special_tokens: bool = False,
+    ) -> List:
+        if isinstance(tokens, str):
+            encoded = self._tokenizer.encode(
+                tokens, add_special_tokens=add_special_tokens
+            )
+            return encoded.ids if out_ids else encoded.tokens
+        else:
+            encoded_list = self._tokenizer.encode_batch(
+                tokens, add_special_tokens=add_special_tokens
+            )
+            return [
+                encoded.ids if out_ids else encoded.tokens for encoded in encoded_list
+            ]
+
+    def decode(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+
+    def __len__(self) -> int:
+        return self._tokenizer.get_vocab_size()
+
+    @property
+    def stop_ids(self) -> List[int]:
+        stop_token = self._control_tokens + self._special_tokens
+        return [self._tokenizer.token_to_id(tok) for tok in stop_token]
+
+    @property
+    def bos_id(self) -> int:
+        return self._tokenizer.token_to_id(self._control_tokens[0])
+
+    @property
+    def eos_id(self) -> int:
+        return self._tokenizer.token_to_id(self._control_tokens[1])
+
+    @property
+    def pad_id(self) -> int:
+        return self._tokenizer.token_to_id(self._control_tokens[2])
+
+
+class BpeTrainer(BaseTrainer):
+    def __init__(self, tokenizer: BaseTokenizer):
+        super().__init__(tokenizer)
+
+    def _prepare_trainer(
+        self,
+        vocab_size: int,
+        min_freq: int,
+        reserved_token_size: int,
+        max_token_length=18,
+    ):
+        assert reserved_token_size > len(self.tokenizer._special_tokens)
+        reserved_tokens = [
+            f"<|reserve{i:02d}|>"
+            for i in range(reserved_token_size - len(self.tokenizer._special_tokens))
+        ]
+        detail_vocab_size = vocab_size - (
+            len(reserved_tokens) + len(self.tokenizer._special_tokens)
+        )
+        alphabet = pre_tokenizers.ByteLevel.alphabet()
+        min_size = len(alphabet) + len(self.tokenizer._control_tokens)
+        assert detail_vocab_size > min_size
+
+        trainer = BpeTrainerImpl(
+            vocab_size=detail_vocab_size,
+            min_frequency=min_freq,
+            limit_alphabet=detail_vocab_size // 6,
+            max_token_length=max_token_length,
+            special_tokens=self.tokenizer._control_tokens,
+            initial_alphabet=alphabet,
+            show_progress=True,
+        )
+        return trainer, reserved_tokens
+
+    def train(self, files, vocab_size, min_freq, reserved_token_size=100, **kwargs):
+        trainer, reserved_tokens = self._prepare_trainer(
+            vocab_size, min_freq, reserved_token_size, **kwargs
+        )
+        self.tokenizer._tokenizer.train(files=files, trainer=trainer)
+        self.tokenizer._tokenizer.add_special_tokens(
+            self.tokenizer._special_tokens + reserved_tokens
+        )
+
+    def train_from_iterator(
+        self, iterator, vocab_size, min_freq, reserved_token_size=100, **kwargs
+    ):
+        trainer, reserved_tokens = self._prepare_trainer(
+            vocab_size, min_freq, reserved_token_size, **kwargs
+        )
+        self.tokenizer._tokenizer.train_from_iterator(
+            iterator=iterator, trainer=trainer
+        )
+        self.tokenizer._tokenizer.add_special_tokens(
+            self.tokenizer._special_tokens + reserved_tokens
+        )