VTimeLLM/vtimellm/model/vtimellm_llama.py

import torch
import torch.nn as nn
from typing import List, Optional, Tuple, Union
from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from .vtimellm_arch import VTimeLLMMetaModel, VTimeLLMMetaForCausalLM

class VTimeLLMConfig(LlamaConfig):
    model_type = "VTimeLLM"

class VTimeLLMLlamaModel(LlamaModel, VTimeLLMMetaModel):
    config_class = VTimeLLMConfig

    def __init__(self, config: LlamaConfig):
        super(VTimeLLMLlamaModel, self).__init__(config)


class VTimeLLMLlamaForCausalLM(LlamaForCausalLM, VTimeLLMMetaForCausalLM):
    config_class = VTimeLLMConfig

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = VTimeLLMLlamaModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()


    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
        dpo_forward: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                images,
            )

        outputs = super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )
        if dpo_forward:
            return outputs['logits'], labels
        return outputs

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        _inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        if images is not None:
            _inputs['images'] = images
        return _inputs

AutoConfig.register("VTimeLLM", VTimeLLMConfig)
AutoModelForCausalLM.register(VTimeLLMConfig, VTimeLLMLlamaForCausalLM)