Fix llama tokenizer

src/transformers/models/llama/convert_llama_weights_to_hf.py

@@ -20,7 +20,7 @@
 
 import torch
 
-from transformers import LlamaConfig, LlamaForCausalLM
+from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
 
 
 """
@@ -233,19 +233,9 @@ def permute(w):
 
 def write_tokenizer(tokenizer_path, input_tokenizer_path):
     print(f"Fetching the tokenizer from {input_tokenizer_path}.")
-    os.makedirs(tokenizer_path, exist_ok=True)
-    write_json({}, os.path.join(tokenizer_path, "special_tokens_map.json"))
-    write_json(
-        {
-            "bos_token": "",
-            "eos_token": "",
-            "model_max_length": int(1e30),
-            "tokenizer_class": "LlamaTokenizer",
-            "unk_token": "",
-        },
-        os.path.join(tokenizer_path, "tokenizer_config.json"),
-    )
-    shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model"))
+    # Initialize the tokenizer based on the `spm` model
+    tokenizer = LlamaTokenizer(input_tokenizer_path)
+    tokenizer.save_pretrained(tokenizer_path)
 
 
 def main():

src/transformers/models/llama/tokenization_llama.py

@@ -25,15 +25,25 @@
 
 import sentencepiece as spm
 
-from ...tokenization_utils import PreTrainedTokenizer
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
 from ...utils import logging
 
 
 logger = logging.get_logger(__name__)
 
 VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
 
-PRETRAINED_VOCAB_FILES_MAP = {}
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
+    },
+    "tokenizer_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
+    },
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "hf-internal-testing/llama-tokenizer": 2048,
+}
 
 
 class LlamaTokenizer(PreTrainedTokenizer):
@@ -47,6 +57,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
 
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
@@ -55,51 +66,50 @@ def __init__(
         unk_token="<unk>",
         bos_token="<s>",
         eos_token="</s>",
+        pad_token=None,
         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,
         **kwargs,
     ):
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
         super().__init__(
             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,
+            sp_model_kwargs=self.sp_model_kwargs,
             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
 
-        """ Initialisation"""
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
 
-    @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
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
 
     @property
     def vocab_size(self):
         """Returns vocab size"""
         return self.sp_model.get_piece_size()
 
-    @property
-    def bos_token_id(self) -> Optional[int]:
-        return self.sp_model.bos_id()
-
-    @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)}
@@ -119,21 +129,15 @@ def _convert_id_to_token(self, index):
         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:
+        for i, token in enumerate(tokens):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
-                if not prev_is_special:
+                if not prev_is_special and i != 0:
                     out_string += " "
                 out_string += self.sp_model.decode(current_sub_tokens) + token
                 prev_is_special = True
@@ -142,7 +146,6 @@ def convert_tokens_to_string(self, tokens):
                 current_sub_tokens.append(token)
                 prev_is_special = False
         out_string += self.sp_model.decode(current_sub_tokens)
-        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
         return out_string
 
     def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
@@ -173,18 +176,13 @@ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None)
         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 = []
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
 
-        output = bos_token_ids + token_ids_0
+        output = bos_token_id + token_ids_0 + eos_token_id
 
         if token_ids_1 is not None:
-            output = output + token_ids_1
-
-        if self.add_eos_token:
-            output = output + [self.eos_token_id]
+            output = output + bos_token_id + token_ids_1 + eos_token_id
 
         return output
 
@@ -211,28 +209,46 @@ def get_special_tokens_mask(
                 token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
 
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
         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]
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
 
     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.
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
 
         Args:
             token_ids_0 (`List[int]`):
-                List of IDs.
+                List of ids.
             token_ids_1 (`List[int]`, *optional*):
                 Optional second list of IDs for sequence pairs.
 
         Returns:
-            `List[int]`: List of zeros.
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
         """
-        eos = [self.eos_token_id]
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
 
         if token_ids_1 is None:
-            return len(token_ids_0 + eos) * [0]
-        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]