VTimeLLM/vtimellm/model/builder_fixed.py

import os
import shutil

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from vtimellm.model import *
from peft import PeftModel

def load_lora(model, lora_path):
    non_lora_trainables_path = os.path.join(lora_path, 'non_lora_trainables.bin')
    if os.path.exists(non_lora_trainables_path):
        non_lora_trainables = torch.load(non_lora_trainables_path, map_location='cpu')
        non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
        if any(k.startswith('model.model.') for k in non_lora_trainables):
            non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
        model.load_state_dict(non_lora_trainables, strict=False)
    print('Loading LoRA weights...')
    model = PeftModel.from_pretrained(model, lora_path)
    return model

def load_pretrained_model(args, stage2=None, stage3=None, stage4=None, stage5=None):
    """
    Load VTimeLLM model with proper GPU device handling
    
    FIXED VERSION: This function now properly handles GPU device selection
    to prevent multiple GPU detection issues.
    """
    kwargs = {'torch_dtype': torch.float16}

    # model_path = os.path.expanduser(args.model_path)
    model_base = args.model_base

    # FIX: Set up single GPU environment before model loading
    if torch.cuda.is_available():
        # Ensure we're using the correct GPU device
        current_device = torch.cuda.current_device()
        print(f'Using GPU device: {current_device}')
        print(f'GPU name: {torch.cuda.get_device_name(current_device)}')
        
        # Set device for all operations
        device = f'cuda:{current_device}'
    else:
        device = 'cpu'
        print('No CUDA available, using CPU')

    # lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
    print('Loading VTimeLLM from base model...')
    if 'chatglm' in model_base:
        tokenizer = AutoTokenizer.from_pretrained(model_base, trust_remote_code=True)
        model = VTimeLLMChatGLMForCausalLM.from_pretrained(model_base)
    else:
        tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
        model = VTimeLLMLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
        token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
        if model.lm_head.weight.shape[0] != token_num:
            model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
            model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))

    # FIX: Move model to GPU with explicit device selection
    if torch.cuda.is_available():
        model = model.to(device)
        print(f'Model moved to {device}')
    else:
        print('Model loaded on CPU')
    
    model.get_model().initialize_vision_modules(args)

    if stage2 is not None and stage2 != "":
        print('Loading stage2 weights...')
        model = load_lora(model, stage2)
        print('Merging stage2 weights...')
        model = model.merge_and_unload()

        if stage3 is not None and stage3 != "" :
            print('Loading stage3 weights...')
            model = load_lora(model, stage3)
            print('Merging stage3 weights...')
            model = model.merge_and_unload()

        if stage4 is not None and stage4 != "":
            print('Loading stage4 weights...')
            model = load_lora(model, stage4)
            print('Merging stage4 weights...')
            model = model.merge_and_unload()      

        if stage5 is not None and stage5 != "":
            print('Loading stage5 weights...')
            model = load_lora(model, stage5)
            print('Merging stage5 weights...')
            model = model.merge_and_unload()

    if hasattr(model.config, "max_sequence_length"):
        context_len = model.config.max_sequence_length
    else:
        context_len = 2048

    return tokenizer, model, context_len

def load_pretrained_model_single_gpu(args, stage2=None, stage3=None, stage4=None, stage5=None):
    """
    Load VTimeLLM model with forced single GPU usage
    
    This function ensures only one GPU is used by setting environment variables
    and explicitly managing device placement.
    """
    # Force single GPU usage
    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    os.environ['OMPI_COMM_WORLD_SIZE'] = '1'
    
    if torch.cuda.is_available():
        torch.cuda.set_device(0)
        print(f'Forced single GPU usage: {torch.cuda.get_device_name(0)}')
    
    return load_pretrained_model(args, stage2, stage3, stage4, stage5)