fix: change device choice in model loader function

Author: gofixyourself

DPF/filters/videos/lita_filter.py

@@ -9,15 +9,22 @@
  DEFAULT_IM_START_TOKEN,
  DEFAULT_IMAGE_TOKEN,
  IMAGE_TOKEN_INDEX,
+ TIME_TOKEN_TEMPLATE,
 )
-from lita.model.builder import load_pretrained_model
+from lita.model.language_model.lita_llama import LitaLlamaForCausalLM
 from lita.utils import load_video
 from llava.conversation import SeparatorStyle, conv_templates
 from llava.mm_utils import (
  KeywordsStoppingCriteria,
  get_model_name_from_path,
  tokenizer_image_token,
 )
+from transformers import (
+ AutoConfig,
+ AutoModelForCausalLM,
+ AutoTokenizer,
+ BitsAndBytesConfig,
+)
 
 from DPF.types import ModalityToDataMapping
 
@@ -29,6 +36,92 @@
  from torch.utils.data import default_collate
 
 
+def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
+ kwargs = {"device_map": device_map}
+
+ if device != "cuda":
+ kwargs['device_map'] = {"": device}
+
+ if load_8bit:
+ kwargs['load_in_8bit'] = True
+ elif load_4bit:
+ kwargs['load_in_4bit'] = True
+ kwargs['quantization_config'] = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_compute_dtype=torch.float16,
+ bnb_4bit_use_double_quant=True,
+ bnb_4bit_quant_type='nf4'
+ )
+ else:
+ kwargs['torch_dtype'] = torch.float16
+
+ if 'lita' not in model_name.lower():
+ warnings.warn("this function is for loading LITA models")
+ if 'lora' in model_name.lower():
+ warnings.warn("lora is currently not supported for LITA")
+ if 'mpt' in model_name.lower():
+ warnings.warn("mpt is currently not supported for LITA")
+
+ if model_base is not None:
+ print('Loading LITA from base model...')
+ tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+ cfg_pretrained = AutoConfig.from_pretrained(model_path)
+ model = LitaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+
+ mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
+ mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items() if 'mm_projector' in k}
+ model.load_state_dict(mm_projector_weights, strict=False)
+ else:
+ tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+ model = LitaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+
+ mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
+ mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", False)
+ if mm_use_im_patch_token:
+ tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+ if mm_use_im_start_end:
+ tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+ model.resize_token_embeddings(len(tokenizer))
+
+ vision_tower = model.get_vision_tower()
+ if not vision_tower.is_loaded:
+ vision_tower.load_model()
+ vision_tower.to(device=device, dtype=torch.float16)
+ image_processor = vision_tower.image_processor
+
+ # time tokens and embeddings
+ num_time_tokens = getattr(model.config, "num_time_tokens", 0)
+ if num_time_tokens > 0:
+ time_tokens = [TIME_TOKEN_TEMPLATE.format(t=x) for x in range(num_time_tokens)]
+ num_new_tokens = tokenizer.add_tokens(time_tokens)
+
+ if model_base is None:
+ assert num_new_tokens == 0, "time tokens should already be in the tokenizer for full finetune model"
+
+ if num_new_tokens > 0:
+ warnings.warn("looking for weights in mm_projector.bin")
+ assert num_new_tokens == num_time_tokens
+ model.resize_token_embeddings(len(tokenizer))
+ input_embeddings = model.get_input_embeddings().weight.data
+ output_embeddings = model.get_output_embeddings().weight.data
+ weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
+ assert 'model.embed_tokens.weight' in weights and 'lm_head.weight' in weights
+ 
+ dtype = input_embeddings.dtype
+ device = input_embeddings.device
+ 
+ tokenizer_time_token_ids = tokenizer.convert_tokens_to_ids(time_tokens)
+ time_token_ids = getattr(model.config, 'time_token_ids', tokenizer_time_token_ids)
+ input_embeddings[tokenizer_time_token_ids] = weights['model.embed_tokens.weight'][time_token_ids].to(dtype).to(device)
+ output_embeddings[tokenizer_time_token_ids] = weights['lm_head.weight'][time_token_ids].to(dtype).to(device)
+ 
+ if hasattr(model.config, "max_sequence_length"):
+ context_len = model.config.max_sequence_length
+ else:
+ context_len = 2048 
+ return tokenizer, model, image_processor, context_len
+
+
 def disable_torch_init() -> None:
  """
  Disable the redundant torch default initialization to accelerate model creation.
@@ -79,7 +172,7 @@ def __init__(
 
  disable_torch_init()
 
- pretrainers = load_pretrained_model(weights_path, model_base, self.model_name, load_8bit, load_4bit)
+ pretrainers = load_pretrained_model(weights_path, model_base, self.model_name, load_8bit, load_4bit, device=self.device)
  self.tokenizer, self.model, self.processor, self.context_len = pretrainers
 
  self.model_num_frames = self.model.config.num_frames