138 lines
5.8 KiB
Python
138 lines
5.8 KiB
Python
import copy
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import warnings
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from dataclasses import dataclass
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from typing import Callable, List, Optional
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import torch
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from torch import nn
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from transformers import AutoModel, AutoTokenizer
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from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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@dataclass
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class GenerationConfig:
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max_length: Optional[int] = None
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top_p: Optional[float] = None
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temperature: Optional[float] = None
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do_sample: Optional[bool] = True
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repetition_penalty: Optional[float] = 1.0
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@torch.inference_mode()
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def generate_interactive(
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model,
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tokenizer,
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prompt,
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generation_config: Optional[GenerationConfig] = None,
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logits_processor: Optional[LogitsProcessorList] = None,
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stopping_criteria: Optional[StoppingCriteriaList] = None,
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prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
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additional_eos_token_id: Optional[int] = None,
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**kwargs,
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):
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inputs = tokenizer([prompt], padding=True, return_tensors="pt")
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input_length = len(inputs["input_ids"][0])
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for k, v in inputs.items():
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inputs[k] = v.cuda()
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input_ids = inputs["input_ids"]
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batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
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if generation_config is None:
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generation_config = model.generation_config
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generation_config = copy.deepcopy(generation_config)
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model_kwargs = generation_config.update(**kwargs)
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bos_token_id, eos_token_id = generation_config.bos_token_id, 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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if additional_eos_token_id is not None:
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eos_token_id.append(additional_eos_token_id)
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has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
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if has_default_max_length and generation_config.max_new_tokens is None:
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warnings.warn(
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f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
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"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
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" recommend using `max_new_tokens` to control the maximum length of the generation.",
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UserWarning,
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)
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elif generation_config.max_new_tokens is not None:
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generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
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if not has_default_max_length:
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logger.warn(
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f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
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f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
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"Please refer to the documentation for more information. "
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"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
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UserWarning,
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)
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if input_ids_seq_length >= generation_config.max_length:
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input_ids_string = "input_ids"
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logger.warning(
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f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
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f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
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" increasing `max_new_tokens`."
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)
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# 2. Set generation parameters if not already defined
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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_processor = model._get_logits_processor(
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generation_config=generation_config,
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input_ids_seq_length=input_ids_seq_length,
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encoder_input_ids=input_ids,
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
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logits_processor=logits_processor,
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)
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stopping_criteria = model._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=stopping_criteria
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)
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logits_warper = model._get_logits_warper(generation_config)
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unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
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scores = None
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while True:
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model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
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# forward pass to get next token
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outputs = model(
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**model_inputs,
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return_dict=True,
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output_attentions=False,
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output_hidden_states=False,
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)
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next_token_logits = 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_warper(input_ids, next_token_scores)
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# sample
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probs = nn.functional.softmax(next_token_scores, dim=-1)
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if generation_config.do_sample:
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next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
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else:
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next_tokens = torch.argmax(probs, dim=-1)
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# update generated ids, model inputs, and length for next step
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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model_kwargs = model._update_model_kwargs_for_generation(
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outputs, model_kwargs, is_encoder_decoder=False
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)
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unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
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output_token_ids = input_ids[0].cpu().tolist()
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output_token_ids = output_token_ids[input_length:]
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for each_eos_token_id in eos_token_id:
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if output_token_ids[-1] == each_eos_token_id:
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output_token_ids = output_token_ids[:-1]
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response = tokenizer.decode(output_token_ids)
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yield response
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# stop when each sentence is finished, or if we exceed the maximum length
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if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
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break
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