generate.py

import torch
from transformers.generation.utils import GenerationMixin, validate_stopping_criteria, StoppingCriteriaList
from transformers import TextStreamer

def custom_generate(
    self,
    input_ids,
    attention_mask=None,
    max_new_tokens=None,
    min_length=None,
    do_sample=None,
    early_stopping=None,
    num_beams=None,
    temperature=None,
    top_k=None,
    top_p=None,
    repetition_penalty=None,
    bad_words_ids=None,
    bos_token_id=None,
    pad_token_id=None,
    eos_token_id=None,
    streamer=None,
    length_penalty=None,
    no_repeat_ngram_size=None,
    num_return_sequences=None,
    decoder_start_token_id=None,
    use_cache=None,
    num_beam_groups=None,
    diversity_penalty=None,
    prefix_allowed_tokens_fn=None,
    output_attentions=None,
    output_hidden_states=None,
    output_scores=None,
    return_dict_in_generate=None,
    forced_bos_token_id=None,
    forced_eos_token_id=None,
    remove_invalid_values=None,
    synced_gpus=None,
    **kwargs,
):
    if input_ids is None or input_ids.nelement() == 0:
        # If input_ids is None or an empty tensor, create a default input tensor
        input_ids = torch.LongTensor([[self.tokenizer.bos_token_id]]).to(self.device)
        attention_mask = torch.ones_like(input_ids).to(self.device)

    device = input_ids.device
    with torch.no_grad():
        batch_size = input_ids.shape[0]
        finished_generating = torch.zeros(batch_size, dtype=torch.bool, device=device)
        generated_token_ids = torch.full((batch_size, max_new_tokens), self.tokenizer.pad_token_id, dtype=torch.long, device=device)

        for cur_token_idx in range(max_new_tokens):
            # Sample the next token
            new_ids = self(
                input_ids[~finished_generating],
                attention_mask=attention_mask[~finished_generating] if attention_mask is not None else None,
                **kwargs
            )['logits']

            # Mask out the start and end thought tokens so we don't accidentally sample them
            new_ids[:, :, self.tokenizer.vocab_size:] = -float("inf")

            for list_idx, answer_idx in enumerate((~finished_generating).nonzero(as_tuple=True)[0]):
                # Find the index of the last token that is not padding
                base_answer_ids = input_ids[answer_idx]
                new_answer_ids = new_ids[list_idx]
                last_token_idx = (base_answer_ids != self.tokenizer.pad_token_id).nonzero(as_tuple=True)[0].max()

                new_ids_sampled = torch.multinomial(
                    torch.nn.functional.softmax(new_answer_ids[last_token_idx] / temperature, dim=-1), 1)

                # Assign the new id to the last token
                if last_token_idx + 1 >= len(base_answer_ids):
                    # Add padding everywhere
                    new_padding = torch.full((batch_size, 1), self.tokenizer.pad_token_id, dtype=torch.long,
                                            device=device)
                    input_ids = torch.cat([input_ids, new_padding], dim=-1)
                    if attention_mask is not None:
                        attention_mask = torch.cat([attention_mask, torch.zeros_like(new_padding)], dim=-1)

                if attention_mask is not None:
                    attention_mask[answer_idx, last_token_idx + 1] = 1
                input_ids[answer_idx, last_token_idx + 1] = new_ids_sampled
                generated_token_ids[answer_idx, cur_token_idx] = new_ids_sampled

                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:
                    finished_generating[answer_idx] = 1

                # Check if the end token is generated
                if new_ids_sampled == self.tokenizer.convert_tokens_to_ids("</s>"):
                    finished_generating[answer_idx] = 1
            
            if finished_generating.all():
                break

            if streamer is not None:
                streamer.put(new_ids_sampled)

        return generated_token_ids


def generate(
    self,
    input_ids,
    attention_mask=None,
    max_new_tokens=None,
    min_length=None,
    do_sample=None,
    early_stopping=None,
    num_beams=None,
    temperature=1.1,
    streamer=None,
    top_k=None,
    top_p=None,
    repetition_penalty=None,
    bad_words_ids=None,
    bos_token_id=None,
    pad_token_id=None,
    eos_token_id=None,
    length_penalty=None,
    no_repeat_ngram_size=None,
    num_return_sequences=None,
    decoder_start_token_id=None,
    use_cache=None,
    num_beam_groups=None,
    diversity_penalty=None,
    prefix_allowed_tokens_fn=None,
    output_attentions=None,
    output_hidden_states=None,
    output_scores=None,
    return_dict_in_generate=None,
    forced_bos_token_id=None,
    forced_eos_token_id=None,
    remove_invalid_values=None,
    synced_gpus=None,
    n_ahead=4,
    n_ahead_talk=4,
    merged_talk_heads=True,
    merged_lm_and_talk_heads=False,
    merged_lm_and_think_heads=True,
    use_concat_talk_head=True,
    use_shallow_think=True,
    use_shallow_talk=False,
    use_complex_think_head=False,
    use_complex_talk_head=True,
    use_weighted_talk_head=True,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    **model_kwargs,
):

    if max_new_tokens is None:
        max_new_tokens = 128 
    
    # Set model attributes
    self.max_thoughts = n_ahead + n_ahead_talk + 1
    self.merged_talk_heads = merged_talk_heads
    self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
    self.merged_lm_and_think_heads = merged_lm_and_think_heads
    self.use_concat_talk_head = use_concat_talk_head
    self.use_shallow_think = use_shallow_think
    self.use_shallow_talk = use_shallow_talk
    self.use_complex_think_head = use_complex_think_head
    self.use_complex_talk_head = use_complex_talk_head
    self.use_weighted_talk_head = use_weighted_talk_head

    # Set model properties
    self.use_end_thought_token = True
    self.use_start_thought_token = True
    self.n_ahead = n_ahead
    self.n_passes = 1
    self.eval_mode = True
    self.first_run = False
    self.rm_initialized = True
    self.original_mode = False

    # Check if the input is a string (for compatibility with text-generation-webui)
    if isinstance(input_ids, str):
        input_ids = self.tokenizer.encode(input_ids, return_tensors='pt')

    # Move input_ids and attention_mask to the same device as the model
    input_ids = input_ids.to(self.device)
    if attention_mask is not None:
        attention_mask = attention_mask.to(self.device)

    generated_token_ids = custom_generate(
        self,
        input_ids=input_ids, 
        attention_mask=attention_mask,
        max_new_tokens=max_new_tokens,
        min_length=min_length,
        do_sample=do_sample,
        early_stopping=early_stopping,
        num_beams=num_beams,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        bad_words_ids=bad_words_ids,
        bos_token_id=bos_token_id,
        pad_token_id=pad_token_id,
        eos_token_id=eos_token_id,
        length_penalty=length_penalty,
        no_repeat_ngram_size=no_repeat_ngram_size,
        num_return_sequences=num_return_sequences,
        decoder_start_token_id=decoder_start_token_id,
        use_cache=use_cache,
        num_beam_groups=num_beam_groups,
        diversity_penalty=diversity_penalty,
        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        output_scores=output_scores,
        return_dict_in_generate=return_dict_in_generate,
        forced_bos_token_id=forced_bos_token_id,
        forced_eos_token_id=forced_eos_token_id,
        remove_invalid_values=remove_invalid_values,
        synced_gpus=synced_gpus,
        streamer=streamer,
        **model_kwargs,
    )

    return generated_token_ids