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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is modified from https://github.com/haotian-liu/LLaVA/
from typing import List, Optional, Tuple, Union
import os, os.path as osp
import torch
from transformers import (
LlamaForCausalLM,
LlamaConfig,
PreTrainedModel,
AutoConfig,
AutoModel,
GenerationConfig,
PretrainedConfig,
PreTrainedModel,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from ..multimodal_encoder.builder import build_vision_tower
from ..multimodal_projector.builder import build_mm_projector
from ..configuration_llava import LlavaConfig
from ..utils import get_model_config
from .builder import build_llm_and_tokenizer
class LlavaLlamaConfig(LlavaConfig):
model_type = "llava_llama"
## FIXME we will follow the convention to add a new class for CausalLM in the future
class LlavaLlamaModel(LlavaMetaModel, LlavaMetaForCausalLM, PreTrainedModel):
config_class = LlavaLlamaConfig
main_input_name = "input_embeds"
supports_gradient_checkpointing = True
def __init__(self, config: LlavaLlamaConfig = None, *args, **kwargs) -> None:
super().__init__(config)
return self.init_vlm(config=config, *args, **kwargs)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
*model_args,
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
ignore_mismatched_sizes: bool = False,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
use_safetensors: bool = None,
**kwargs,
):
if hasattr(cls, "load_pretrained"):
return cls.load_pretrained(pretrained_model_name_or_path,
*model_args, config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, force_download=force_download, local_files_only=local_files_only, token=token,
revision=revision, use_safetensors=use_safetensors, **kwargs
)
return super(LlavaLlamaModel).from_pretrained(pretrained_model_name_or_path,
*model_args, config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, force_download=force_download, local_files_only=local_files_only, token=token,
revision=revision, use_safetensors=use_safetensors, **kwargs)
def forward(
self,
input_ids: torch.LongTensor = None,
images: Optional[torch.FloatTensor] = 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,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
self.freezed_module_patch()
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
)
# Note (kentang-mit@): we have a unit test for this function.
if self.training:
(
_,
new_position_ids,
new_attention_mask,
_,
new_inputs_embeds,
new_labels,
sorted_seqlens_in_batch,
) = self.repack_multimodal_data(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels,
)
new_input_ids = None
past_key_values = None
else:
new_attention_mask = attention_mask
new_position_ids = position_ids
new_inputs_embeds = inputs_embeds
new_labels = labels
sorted_seqlens_in_batch = attention_mask.sum(-1).int()
new_input_ids = input_ids
outputs = self.llm.forward(
input_ids=new_input_ids,
attention_mask=new_attention_mask,
position_ids=new_position_ids,
past_key_values=past_key_values,
inputs_embeds=new_inputs_embeds,
labels=new_labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
seqlens_in_batch=sorted_seqlens_in_batch,
)
return outputs
@torch.no_grad()
def generate(
self,
input_ids: Optional[torch.FloatTensor] = None,
images: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
**generation_kwargs,
):
if images is not None:
(
_,
_,
attention_mask,
_,
inputs_embeds,
_,
) = self.prepare_inputs_labels_for_multimodal(
input_ids, None, attention_mask, None, None, images
)
else:
inputs_embeds = self.get_input_embeddings()(input_ids)
inputs_embeds = inputs_embeds.to(self.dtype)
outputs = self.llm.generate(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**generation_kwargs
)
return outputs
AutoConfig.register("llava_llama", LlavaLlamaConfig)
AutoModel.register(LlavaLlamaConfig, LlavaLlamaModel)
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