sds

tokenizeConfig.py

@@ -1,90 +1,215 @@
-from transformers import PreTrainedTokenizer
-from tokenizers import Tokenizer, models, pre_tokenizers, trainers, decoders
-import json
-from typing import List, Optional, Union, Dict
-from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
-from transformers.utils import PaddingStrategy
+"""Tokenization classes for IntermLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
 
 class OBITokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
     def __init__(
         self,
+        vocab_file,
         unk_token="<unk>",
         bos_token="<s>",
         eos_token="</s>",
-        pad_token=None,
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
         add_bos_token=True,
         add_eos_token=False,
+        decode_with_prefix_space=False,
         clean_up_tokenization_spaces=False,
-        auto_map={"AutoTokenizer": ["tokenizeConfig.OBITokenizer"]},
-        tokenizer_class="OBITokenizer",
         **kwargs,
     ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
         super().__init__(
-            unk_token=unk_token,
             bos_token=bos_token,
             eos_token=eos_token,
+            unk_token=unk_token,
             pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
             **kwargs,
         )
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
 
-        # Initialize a BPE model for tokenization
-        bpe_model = models.BPE()
-        self.tokenizer = Tokenizer(bpe_model)
+        """ Initialisation"""
 
-        # Add pre-tokenization and decoding steps if needed
-        self.tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel()
-        self.tokenizer.decoder = decoders.ByteLevel()
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
+        return self._no_prefix_space_tokens
 
-        # Set the padding token
-        self.pad_token = "[PAD]"
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
 
-        # Set the special tokens
-        self.cls_token = "[CLS]"
-        self.sep_token = "[SEP]"
-        self.unk_token = "[UNK]"
-        self.mask_token = "[MASK]"
-        self.bos_token = "[CLS]"
-        self.eos_token = "[SEP]"
-        self.pad_token = "[PAD]"
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
 
-        # Call the add_special_tokens method on the tokenizer object
-        self.tokenizer.add_special_tokens([self.cls_token, self.sep_token, self.unk_token, self.mask_token])
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
 
     def _tokenize(self, text):
-        # Implement your custom tokenization logic here
-        # In this example, we split the text into tokens using whitespace
-        return text.split()
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
 
     def _convert_token_to_id(self, token):
-        # Implement mapping from token to ID using your tokenizer's vocabulary
-        # Use the tokenizer's method to convert tokens to IDs
-        return self.tokenizer.encode(token).ids
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
 
     def _convert_id_to_token(self, index):
-        # Implement mapping from ID to token using your tokenizer's vocabulary
-        # Use the tokenizer's method to convert IDs to tokens
-        return self.tokenizer.decode([index])
-
-    def encode(self, text):
-        # Encode text using the custom tokenizer
-        input_ids = [self._convert_token_to_id(token) for token in self._tokenize(text)]
-        attention_mask = [1] * len(input_ids)
-
-        return {"input_ids": input_ids, "attention_mask": attention_mask}
-
-    def decode(self, ids):
-        # Decode IDs to text using the custom tokenizer
-        tokens = [self._convert_id_to_token(token_id) for token_id in ids]
-        return " ".join(tokens)
-    
-    def get_vocab(self):
-        # Return the tokenizer's vocabulary
-        return self.tokenizer.get_vocab()
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
 
-    def save_vocabulary(self, vocab_path):
-        # Save the tokenizer's vocabulary to a file
-        with open(vocab_path, "w") as f:
-            json.dump(self.tokenizer.get_vocab(), f)
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]