tcli.py

import argparse
import torch

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image

import requests
import traceback
from PIL import Image
from io import BytesIO
from transformers import TextStreamer

# Garbage Collector for PyTorch GPU Memory
import gc

def pytorch_gpu_garbage_collector():
    """Explicitly trigger Python's garbage collector and then clear PyTorch's GPU cache for all GPUs."""
    print("Triggering Python's garbage collector...")
    # Collect Python's garbage
    gc.collect()

    # Clear PyTorch's GPU cache for all GPUs
    if torch.cuda.is_available():
        for device_idx in range(torch.cuda.device_count()):
            torch.cuda.set_device(device_idx)
            print(f"Clearing PyTorch GPU cache for GPU {device_idx}...")
            torch.cuda.empty_cache()
    print("Garbage collection completed!")


def load_image(image_file):
    if image_file.startswith('http://') or image_file.startswith('https://'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert('RGB')
    else:
        image = Image.open(image_file).convert('RGB')
    return image


def main(args):
    # Model
    disable_torch_init()

    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
    
    if 'llama-2' in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
    else:
        args.conv_mode = conv_mode


    while True:
        try:            
            conv = conv_templates[args.conv_mode].copy()
            if "mpt" in model_name.lower():
                roles = ('user', 'assistant')
            else:
                roles = conv.roles

            image_file = input('Image path:')
            image = load_image(image_file)

            # Similar operation in model_worker.py
            image_tensor = process_images([image], image_processor, args)
            if type(image_tensor) is list:
                image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
            else:
                image_tensor = image_tensor.to(model.device, dtype=torch.float16)            
            
            inp = input(f"{roles[0]}: ")
        except EOFError:
            inp = ""
        if not inp:
            print("exit...")
            break

        try:
            print(f"{roles[1]}: ", end="")

            if image is not None:
                # first message
                if model.config.mm_use_im_start_end:
                    inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
                else:
                    inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
                conv.append_message(conv.roles[0], inp)
                image = None
            else:
                # later messages
                conv.append_message(conv.roles[0], inp)
            conv.append_message(conv.roles[1], None)
            prompt = conv.get_prompt()



            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
            keywords = [stop_str]
            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

            with torch.inference_mode():
                output_ids = model.generate(
                    input_ids,
                    images=image_tensor,
                    do_sample=True,
                    temperature=args.temperature,
                    max_new_tokens=args.max_new_tokens,
                    streamer=streamer,
                    use_cache=True,
                    stopping_criteria=[stopping_criteria])

            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
            conv.messages[-1][-1] = outputs
            print('------------------------')
            if args.debug:
                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")


            # Assuming you're done with output_ids and other tensors, start cleanup
            del output_ids
            del input_ids
            del image_tensor
            del inp
            del stop_str

            # --- MEMORY CLEANER            
            pytorch_gpu_garbage_collector()

               
        except Exception as e:
            print('Error:',str(e))
            print('Traceback:', traceback.format_exc())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
    args = parser.parse_args()
    main(args)