fix tokenizer #417

src/llmtuner/chat/stream_chat.py

@@ -5,7 +5,7 @@ from threading import Thread
 from transformers import PreTrainedModel, TextIteratorStreamer
 
 from llmtuner.extras.misc import dispatch_model, get_logits_processor, get_stopping_criteria
-from llmtuner.extras.template import get_template
+from llmtuner.extras.template import get_template_and_fix_tokenizer
 from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
 
 
@@ -16,7 +16,7 @@ class ChatModel:
         self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
         self.model = dispatch_model(self.model)
         self.model = self.model.eval() # change to eval mode
-        self.template = get_template(data_args.template)
+        self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
         self.source_prefix = data_args.source_prefix
         self.stop_ids = self.tokenizer.convert_tokens_to_ids(self.template.stop_words)
         self.tokenizer.add_special_tokens(dict(additional_special_tokens=self.template.stop_words))

src/llmtuner/dsets/preprocess.py

@@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal
 from itertools import chain
 
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import get_template
+from llmtuner.extras.template import get_template_and_fix_tokenizer
 
 if TYPE_CHECKING:
     from datasets import Dataset
@@ -19,7 +19,7 @@ def preprocess_dataset(
     stage: Literal["pt", "sft", "rm", "ppo"]
 ) -> "Dataset":
     column_names = list(dataset.column_names)
-    template = get_template(data_args.template)
+    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
 
     def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
         for i in range(len(examples["prompt"])):
@@ -119,10 +119,9 @@ def preprocess_dataset(
         print("input_ids:\n{}".format(example["input_ids"]))
         print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
         print("label_ids:\n{}".format(example["labels"]))
-        print("labels:\n{}".format(''.join([
-            tokenizer.decode(d, skip_special_tokens=False)
-            if d != IGNORE_INDEX else '-100' for d in example["labels"]
-        ])))
+        print("labels:\n{}".format(tokenizer.decode([
+            token_id if token_id != IGNORE_INDEX else tokenizer.pad_token_id for token_id in example["labels"]
+        ], skip_special_tokens=False)))
 
     def print_pairwise_dataset_example(example):
         print("accept_ids:\n{}".format(example["accept_ids"]))

src/llmtuner/extras/template.py

@@ -67,15 +67,15 @@ class Template:
         self,
         tokenizer: "PreTrainedTokenizer"
     ) -> Tuple[List[int], List[int]]:
-        if tokenizer.bos_token_id and getattr(tokenizer, "add_bos_token", False):
+        if tokenizer.bos_token_id and getattr(tokenizer, "add_bos_token", True):
             bos_ids = [tokenizer.bos_token_id]
-        else: # bos token is optional
-            bos_ids = []
+        else:
+            bos_ids = [] # bos token is optional
 
-        if tokenizer.eos_token_id and getattr(tokenizer, "add_eos_token", False):
+        if tokenizer.eos_token_id and getattr(tokenizer, "add_eos_token", True):
             eos_ids = [tokenizer.eos_token_id]
-        else: # use the first stop word as the eos token
-            eos_ids = [tokenizer.convert_tokens_to_ids(self.stop_words[0])]
+        else:
+            raise ValueError("EOS token is required.")
 
         return bos_ids, eos_ids
 
@@ -172,9 +172,19 @@ def register_template(
     )
 
 
-def get_template(name: str) -> Template:
+def get_template_and_fix_tokenizer(
+    name: str,
+    tokenizer: "PreTrainedTokenizer"
+) -> Template:
     template = templates.get(name, None)
     assert template is not None, "Template {} does not exist.".format(name)
+
+    if tokenizer.eos_token_id is None and len(template.stop_words): # inplace method
+        tokenizer.eos_token = template.stop_words[0]
+
+    if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None:
+        tokenizer.pad_token = tokenizer.eos_token
+
     return template
 
 

src/llmtuner/tuner/core/loader.py

@@ -68,8 +68,6 @@ def load_model_and_tokenizer(
         padding_side=model_args.padding_side,
         **config_kwargs
     )
-    if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None: # add pad token
-        tokenizer.pad_token = tokenizer.eos_token
 
     if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
         model_to_load = model_args.checkpoint_dir[0]