Update qwen model.

qwen/modeling_qwen.py

@@ -34,7 +34,6 @@ from qwen_generation_utils import (
     HistoryType,
     make_context,
     decode_tokens,
-    get_stop_words_ids,
     StopWordsLogitsProcessor,
 )
 
@@ -416,19 +415,16 @@ class QWenLMHeadModel(QWenPreTrainedModel):
         query_assistant: str,
         history: Optional[HistoryType],
         system: str = "You are a helpful assistant.",
-        stop_words_ids: Optional[List[List[int]]] = None,
-        generation_config: Optional[GenerationConfig] = None,
         **kwargs,
     ) -> Tuple[str, HistoryType]:
-        generation_config = generation_config if generation_config is not None else self.generation_config
+        generation_config = self.generation_config
 
         if history is None:
             history = []
         else:
             history = copy.deepcopy(history)
 
-        if stop_words_ids is None:
+        stop_words_ids = []
-            stop_words_ids = []
 
         max_window_size = kwargs.get("max_window_size", None)
         if max_window_size is None:
@@ -442,12 +438,11 @@ class QWenLMHeadModel(QWenPreTrainedModel):
             max_window_size=max_window_size
         )
 
-        stop_words_ids.extend(get_stop_words_ids(tokenizer))
+        stop_words_ids.extend([[tokenizer.im_end_id], [tokenizer.im_start_id]])
         input_ids = torch.tensor([context_tokens]).to(self.device)
         outputs = self.generate(
             input_ids,
             stop_words_ids=stop_words_ids,
-            generation_config=generation_config,
             **kwargs,
         )
         decoded, response, end_reason = decode_tokens(
@@ -463,57 +458,20 @@ class QWenLMHeadModel(QWenPreTrainedModel):
     def generate(
         self,
         inputs: Optional[torch.Tensor] = None,
-        generation_config: Optional[GenerationConfig] = None,
+        stop_words_ids = [],
-        logits_processor: Optional[LogitsProcessorList] = None,
-        stopping_criteria: Optional[StoppingCriteriaList] = None,
         prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
-        assistant_model: Optional["PreTrainedModel"] = None,
         streamer: Optional["BaseStreamer"] = None,
         **kwargs,
     ) -> Union[GenerateOutput, torch.LongTensor]:
-        generation_config = generation_config if generation_config is not None else self.generation_config
+        generation_config = self.generation_config
 
         # Process stop_words_ids.
-        stop_words_ids = kwargs.pop("stop_words_ids", None)
+        stop_words_logits_processor = StopWordsLogitsProcessor(
-        if stop_words_ids is None and generation_config is not None:
+            stop_words_ids=stop_words_ids,
-            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
+            eos_token_id=generation_config.eos_token_id,
-        if stop_words_ids is None:
-            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
-
-        if stop_words_ids is not None:
-            stop_words_logits_processor = StopWordsLogitsProcessor(
-                stop_words_ids=stop_words_ids,
-                eos_token_id=generation_config.eos_token_id,
-            )
-            if logits_processor is None:
-                logits_processor = LogitsProcessorList([stop_words_logits_processor])
-            else:
-                logits_processor.append(stop_words_logits_processor)
-
-        return self.generate_base(
-            inputs,
-            generation_config=generation_config,
-            logits_processor=logits_processor,
-            stopping_criteria=stopping_criteria,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-            assistant_model=assistant_model,
-            streamer=streamer,
-            **kwargs,
         )
+        logits_processor = LogitsProcessorList([stop_words_logits_processor])
 
-    def generate_base(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        generation_config: Optional[GenerationConfig] = None,
-        logits_processor: Optional[LogitsProcessorList] = None,
-        stopping_criteria: Optional[StoppingCriteriaList] = None,
-        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
-        assistant_model: Optional["PreTrainedModel"] = None,
-        streamer: Optional["BaseStreamer"] = None,
-        negative_prompt_ids: Optional[torch.Tensor] = None,
-        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> Union[GenerateOutput, torch.LongTensor]:
         # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
         self._validate_model_class()
 
@@ -523,8 +481,7 @@ class QWenLMHeadModel(QWenPreTrainedModel):
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 2. Set generation parameters if not already defined
-        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+
-        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
         if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
             if model_kwargs.get("attention_mask", None) is None:
@@ -574,11 +531,13 @@ class QWenLMHeadModel(QWenPreTrainedModel):
             prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
             logits_processor=logits_processor,
             model_kwargs=model_kwargs,
-            negative_prompt_ids=negative_prompt_ids,
+            negative_prompt_ids=None,
-            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            negative_prompt_attention_mask=None,
         )
 
         # 9. prepare stopping criteria
+        
+        stopping_criteria = StoppingCriteriaList()
         stopping_criteria = self._get_stopping_criteria(
             generation_config=generation_config, stopping_criteria=stopping_criteria
         )

qwen/qwen_generation_utils.py

@@ -105,12 +105,6 @@ def get_batch(context_tokens: torch.LongTensor, eod_id: int):
     )
     return tokens, attention_mask, position_ids
 
-
-def get_stop_words_ids(tokenizer):
-    stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
-    return stop_words_ids
-
-
 def make_context(
     tokenizer: PreTrainedTokenizer,
     query: str,