Refine model of qwen.
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@ -465,12 +465,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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) -> Union[GenerateOutput, torch.LongTensor]:
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generation_config = self.generation_config
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# Process stop_words_ids.
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stop_words_logits_processor = StopWordsLogitsProcessor(
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stop_words_ids=stop_words_ids,
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eos_token_id=generation_config.eos_token_id,
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)
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logits_processor = LogitsProcessorList([stop_words_logits_processor])
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# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
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self._validate_model_class()
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@ -523,7 +517,12 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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generation_config.max_length = generation_config.max_new_tokens + input_ids_length
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self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
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# 8. prepare distribution pre_processing samplers
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stop_words_logits_processor = StopWordsLogitsProcessor(
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stop_words_ids=stop_words_ids,
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eos_token_id=generation_config.eos_token_id,
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)
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logits_processor = LogitsProcessorList([stop_words_logits_processor])
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logits_processor = self._get_logits_processor(
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generation_config=generation_config,
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input_ids_seq_length=input_ids_length,
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@ -535,16 +534,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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negative_prompt_attention_mask=None,
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)
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# 9. prepare stopping criteria
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stopping_criteria = StoppingCriteriaList()
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stopping_criteria = self._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=stopping_criteria
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)
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# 10. go into different generation modes
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# 11. prepare logits warper
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logits_warper = self._get_logits_warper(generation_config)
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# 12. expand input_ids with `num_return_sequences` additional sequences per batch
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input_ids, model_kwargs = self._expand_inputs_for_generation(
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@ -555,42 +544,24 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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)
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# 13. run sample
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return self.sample_base(
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input_ids,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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stopping_criteria=stopping_criteria,
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pad_token_id=generation_config.pad_token_id,
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eos_token_id=generation_config.eos_token_id,
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output_scores=generation_config.output_scores,
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streamer=streamer,
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**model_kwargs,
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pad_token_id=generation_config.pad_token_id
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eos_token_id=generation_config.eos_token_id
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streamer=streamer
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# init values
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stopping_criteria = self._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=StoppingCriteriaList()
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)
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def sample_base(
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self,
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input_ids: torch.LongTensor,
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logits_processor: Optional[LogitsProcessorList] = None,
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stopping_criteria: Optional[StoppingCriteriaList] = None,
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logits_warper: Optional[LogitsProcessorList] = None,
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max_length: Optional[int] = None,
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pad_token_id: Optional[int] = None,
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eos_token_id: Optional[Union[int, List[int]]] = None,
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output_scores: Optional[bool] = None,
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streamer: Optional["BaseStreamer"] = None,
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**model_kwargs,
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):
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# init values
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logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
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logits_warper = self._get_logits_warper(generation_config)
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logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
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pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
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eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
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if isinstance(eos_token_id, int):
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eos_token_id = [eos_token_id]
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eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
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output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
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# init attention / hidden states / scores tuples
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scores = None
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@ -607,10 +578,10 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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# forward pass to get next token
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outputs = self(**model_inputs)
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next_token_logits = outputs.logits[:, -1, :]
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next_token_scores = outputs.logits[:, -1, :]
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# pre-process distribution
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next_token_scores = logits_processor(input_ids, next_token_logits)
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next_token_scores = logits_processor(input_ids, next_token_scores)
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next_token_scores = logits_warper(input_ids, next_token_scores)
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# sample
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@ -619,8 +590,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
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# finished sentences should have their next token be a padding token
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if eos_token_id is not None:
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if pad_token_id is None:
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raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
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next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
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# update generated ids, model inputs, and length for next step
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