File size: 3,372 Bytes
fca4fc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
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)
|