初始化项目，由ModelHub XC社区提供模型
Model: LeroyDyer/SpydazWebAI_QuietStar_Project Source: Original Platform
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ModelHub XC
219
generate.py
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219
generate.py
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def custom_generate(
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self,
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input_ids,
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attention_mask=None,
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max_new_tokens=None,
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min_length=None,
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do_sample=None,
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early_stopping=None,
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num_beams=None,
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temperature=None,
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top_k=None,
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top_p=None,
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repetition_penalty=None,
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bad_words_ids=None,
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bos_token_id=None,
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pad_token_id=None,
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eos_token_id=None,
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streamer=None,
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length_penalty=None,
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no_repeat_ngram_size=None,
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num_return_sequences=None,
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decoder_start_token_id=None,
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use_cache=None,
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num_beam_groups=None,
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diversity_penalty=None,
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prefix_allowed_tokens_fn=None,
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output_attentions=None,
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output_hidden_states=None,
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output_scores=None,
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return_dict_in_generate=None,
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forced_bos_token_id=None,
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forced_eos_token_id=None,
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remove_invalid_values=None,
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synced_gpus=None,
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**kwargs,
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):
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if input_ids is None or input_ids.nelement() == 0:
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# If input_ids is None or an empty tensor, create a default input tensor
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input_ids = torch.LongTensor([[self.tokenizer.bos_token_id]]).to(self.device)
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attention_mask = torch.ones_like(input_ids).to(self.device)
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device = input_ids.device
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with torch.no_grad():
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batch_size = input_ids.shape[0]
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finished_generating = torch.zeros(batch_size, dtype=torch.bool, device=device)
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generated_token_ids = torch.full((batch_size, max_new_tokens), self.tokenizer.pad_token_id, dtype=torch.long, device=device)
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for cur_token_idx in range(max_new_tokens):
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# Sample the next token
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new_ids = self(
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input_ids[~finished_generating],
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attention_mask=attention_mask[~finished_generating] if attention_mask is not None else None,
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**kwargs
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)['logits']
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# Mask out the start and end thought tokens so we don't accidentally sample them
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new_ids[:, :, self.tokenizer.vocab_size:] = -float("inf")
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for list_idx, answer_idx in enumerate((~finished_generating).nonzero(as_tuple=True)[0]):
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# Find the index of the last token that is not padding
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base_answer_ids = input_ids[answer_idx]
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new_answer_ids = new_ids[list_idx]
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last_token_idx = (base_answer_ids != self.tokenizer.pad_token_id).nonzero(as_tuple=True)[0].max()
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new_ids_sampled = torch.multinomial(
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torch.nn.functional.softmax(new_answer_ids[last_token_idx] / temperature, dim=-1), 1)
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# Assign the new id to the last token
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if last_token_idx + 1 >= len(base_answer_ids):
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# Add padding everywhere
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new_padding = torch.full((batch_size, 1), self.tokenizer.pad_token_id, dtype=torch.long,
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device=device)
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input_ids = torch.cat([input_ids, new_padding], dim=-1)
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if attention_mask is not None:
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attention_mask = torch.cat([attention_mask, torch.zeros_like(new_padding)], dim=-1)
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if attention_mask is not None:
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attention_mask[answer_idx, last_token_idx + 1] = 1
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input_ids[answer_idx, last_token_idx + 1] = new_ids_sampled
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generated_token_ids[answer_idx, cur_token_idx] = new_ids_sampled
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if new_ids_sampled == self.tokenizer.eos_token_id or new_ids_sampled == self.tokenizer.bos_token_id or new_ids_sampled == self.tokenizer.pad_token_id:
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finished_generating[answer_idx] = 1
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# Check if the end token is generated
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if new_ids_sampled == self.tokenizer.convert_tokens_to_ids("</s>"):
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finished_generating[answer_idx] = 1
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if finished_generating.all():
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break
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if streamer is not None:
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streamer.put(new_ids_sampled)
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return generated_token_ids
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def generate(
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self,
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input_ids,
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attention_mask=None,
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max_new_tokens=None,
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min_length=None,
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do_sample=None,
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early_stopping=None,
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num_beams=None,
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temperature=1.1,
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streamer=None,
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top_k=None,
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top_p=None,
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repetition_penalty=None,
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bad_words_ids=None,
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bos_token_id=None,
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pad_token_id=None,
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eos_token_id=None,
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length_penalty=None,
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no_repeat_ngram_size=None,
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num_return_sequences=None,
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decoder_start_token_id=None,
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use_cache=None,
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num_beam_groups=None,
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diversity_penalty=None,
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prefix_allowed_tokens_fn=None,
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output_attentions=None,
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output_hidden_states=None,
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output_scores=None,
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return_dict_in_generate=None,
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forced_bos_token_id=None,
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forced_eos_token_id=None,
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remove_invalid_values=None,
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synced_gpus=None,
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n_ahead=4,
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n_ahead_talk=4,
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merged_talk_heads=True,
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merged_lm_and_talk_heads=False,
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merged_lm_and_think_heads=True,
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use_concat_talk_head=True,
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use_shallow_think=True,
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use_shallow_talk=False,
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use_complex_think_head=False,
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use_complex_talk_head=True,
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use_weighted_talk_head=True,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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**model_kwargs,
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):
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if max_new_tokens is None:
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max_new_tokens = 128
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# Set model attributes
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self.max_thoughts = n_ahead + n_ahead_talk + 1
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self.merged_talk_heads = merged_talk_heads
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self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
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self.merged_lm_and_think_heads = merged_lm_and_think_heads
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self.use_concat_talk_head = use_concat_talk_head
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self.use_shallow_think = use_shallow_think
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self.use_shallow_talk = use_shallow_talk
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self.use_complex_think_head = use_complex_think_head
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self.use_complex_talk_head = use_complex_talk_head
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self.use_weighted_talk_head = use_weighted_talk_head
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# Set model properties
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self.use_end_thought_token = True
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self.use_start_thought_token = True
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self.n_ahead = n_ahead
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self.n_passes = 1
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self.eval_mode = True
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self.first_run = False
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self.rm_initialized = True
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self.original_mode = False
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# Check if the input is a string (for compatibility with text-generation-webui)
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if isinstance(input_ids, str):
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input_ids = self.tokenizer.encode(input_ids, return_tensors='pt')
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# Move input_ids and attention_mask to the same device as the model
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input_ids = input_ids.to(self.device)
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if attention_mask is not None:
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attention_mask = attention_mask.to(self.device)
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generated_token_ids = custom_generate(
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self,
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input_ids=input_ids,
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attention_mask=attention_mask,
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max_new_tokens=max_new_tokens,
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min_length=min_length,
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do_sample=do_sample,
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early_stopping=early_stopping,
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num_beams=num_beams,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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bad_words_ids=bad_words_ids,
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bos_token_id=bos_token_id,
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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length_penalty=length_penalty,
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no_repeat_ngram_size=no_repeat_ngram_size,
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num_return_sequences=num_return_sequences,
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decoder_start_token_id=decoder_start_token_id,
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use_cache=use_cache,
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num_beam_groups=num_beam_groups,
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diversity_penalty=diversity_penalty,
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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output_scores=output_scores,
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return_dict_in_generate=return_dict_in_generate,
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forced_bos_token_id=forced_bos_token_id,
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forced_eos_token_id=forced_eos_token_id,
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remove_invalid_values=remove_invalid_values,
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synced_gpus=synced_gpus,
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streamer=streamer,
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**model_kwargs,
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)
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return generated_token_ids
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