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from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from .llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from .configuration_baichuan import BaichuanConfig
from .modeling_baichuan import BaichuanModel, BaichuanForCausalLM
class LlavaBaichuanConfig(BaichuanConfig):
model_type = "llava_baichuan"
class LlavaBaichuanModel(LlavaMetaModel, BaichuanModel):
config_class = LlavaBaichuanConfig
def __init__(self, config: BaichuanConfig):
super(LlavaBaichuanModel, self).__init__(config)
class LlavaBaichuanForCausalLM(BaichuanForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaBaichuanConfig
def __init__(self, config):
super(BaichuanForCausalLM, self).__init__(config)
self.model = LlavaBaichuanModel(config)
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,
image_sizes: Optional[List[List[int]]] = None,
return_dict: 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,
image_sizes
)
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
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_multimodal(
inputs,
position_ids,
attention_mask,
None,
None,
images,
image_sizes=image_sizes
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
**kwargs
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", 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
if image_sizes is not None:
inputs['image_sizes'] = image_sizes
return inputs
AutoConfig.register("llava_baichuan", LlavaBaichuanConfig)
AutoModelForCausalLM.register(LlavaBaichuanConfig, LlavaBaichuanForCausalLM)
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