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Refine model of qwen.

This commit is contained in:
Colin 2024-01-11 07:00:18 +00:00
parent 7d7b4381f8
commit 063f722177
1 changed files with 18 additions and 49 deletions
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67
qwen/modeling_qwen.py
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@ -465,12 +465,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
) -> Union[GenerateOutput, torch.LongTensor]: ) -> Union[GenerateOutput, torch.LongTensor]:
generation_config = self.generation_config generation_config = self.generation_config
# Process stop_words_ids.
stop_words_logits_processor = StopWordsLogitsProcessor(
stop_words_ids=stop_words_ids,
eos_token_id=generation_config.eos_token_id,
)
logits_processor = LogitsProcessorList([stop_words_logits_processor])
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class() self._validate_model_class()
@ -523,7 +517,12 @@ class QWenLMHeadModel(QWenPreTrainedModel):
generation_config.max_length = generation_config.max_new_tokens + input_ids_length generation_config.max_length = generation_config.max_new_tokens + input_ids_length
self._validate_generated_length(generation_config, input_ids_length, has_default_max_length) self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
# 8. prepare distribution pre_processing samplers
stop_words_logits_processor = StopWordsLogitsProcessor(
stop_words_ids=stop_words_ids,
eos_token_id=generation_config.eos_token_id,
)
logits_processor = LogitsProcessorList([stop_words_logits_processor])
logits_processor = self._get_logits_processor( logits_processor = self._get_logits_processor(
generation_config=generation_config, generation_config=generation_config,
input_ids_seq_length=input_ids_length, input_ids_seq_length=input_ids_length,
@ -535,16 +534,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
negative_prompt_attention_mask=None, 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
)
# 10. go into different generation modes
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
# 12. expand input_ids with `num_return_sequences` additional sequences per batch # 12. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation( input_ids, model_kwargs = self._expand_inputs_for_generation(
@ -555,42 +544,24 @@ class QWenLMHeadModel(QWenPreTrainedModel):
) )
# 13. run sample # 13. run sample
return self.sample_base(
input_ids, pad_token_id=generation_config.pad_token_id
logits_processor=logits_processor, eos_token_id=generation_config.eos_token_id
logits_warper=logits_warper, streamer=streamer
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id, # init values
output_scores=generation_config.output_scores, stopping_criteria = self._get_stopping_criteria(
streamer=streamer, generation_config=generation_config, stopping_criteria=StoppingCriteriaList()
**model_kwargs,
) )
def sample_base( logits_warper = self._get_logits_warper(generation_config)
self,
input_ids: torch.LongTensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_scores: Optional[bool] = None,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
):
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int): if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id] eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
# init attention / hidden states / scores tuples # init attention / hidden states / scores tuples
scores = None scores = None
@ -607,10 +578,10 @@ class QWenLMHeadModel(QWenPreTrainedModel):
# forward pass to get next token # forward pass to get next token
outputs = self(**model_inputs) outputs = self(**model_inputs)
next_token_logits = outputs.logits[:, -1, :] next_token_scores = outputs.logits[:, -1, :]
# pre-process distribution # pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits) next_token_scores = logits_processor(input_ids, next_token_scores)
next_token_scores = logits_warper(input_ids, next_token_scores) next_token_scores = logits_warper(input_ids, next_token_scores)
# sample # sample
@ -619,8 +590,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
# finished sentences should have their next token be a padding token # finished sentences should have their next token be a padding token
if eos_token_id is not None: if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences) next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step # update generated ids, model inputs, and length for next step