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import torch
import torch.nn as nn
from typing import List, Optional, Tuple, Union
from transformers import AutoConfig, AutoModelForCausalLM
from .chatglm import ChatGLMConfig, ChatGLMModel, ChatGLMForConditionalGeneration
from .vtimellm_arch import VTimeLLMMetaModel, VTimeLLMMetaForCausalLM
class VTimeLLMChatGLMConfig(ChatGLMConfig):
model_type = "VTimeLLM_ChatGLM"
class VTimeLLMChatGLMModel(ChatGLMModel, VTimeLLMMetaModel):
config_class = VTimeLLMChatGLMConfig
def __init__(self, config, empty_init=True, device=None):
super(VTimeLLMChatGLMModel, self).__init__(config, empty_init=empty_init, device=device)
class VTimeLLMChatGLMForCausalLM(ChatGLMForConditionalGeneration, VTimeLLMMetaForCausalLM):
config_class = VTimeLLMChatGLMConfig
def __init__(self, config, empty_init=True, device=None):
super(ChatGLMForConditionalGeneration, self).__init__(config)
self.transformer = VTimeLLMChatGLMModel(config, empty_init=empty_init, device=device)
self.max_sequence_length = config.max_length
self.config = config
self.quantized = False
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.transformer
def forward(
self,
input_ids: torch.LongTensor = None,
position_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = 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,
return_dict: Optional[bool] = None,
return_last_logit: Optional[bool] = False,
images: Optional[torch.FloatTensor] = None,
):
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
)
return 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
)
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_ChatGLM", VTimeLLMChatGLMConfig)
AutoModelForCausalLM.register(VTimeLLMChatGLMConfig, VTimeLLMChatGLMForCausalLM)
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