model/llava/eval/run_llava.py

import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
from llava.conversation import conv_templates, SeparatorStyle
from llava.utils import disable_torch_init
from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
from llava.model import *
from llava.model.utils import KeywordsStoppingCriteria

from PIL import Image

import os
import requests
from PIL import Image
from io import BytesIO


DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"


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 eval_model(args):
    # Model
    disable_torch_init()
    model_name = os.path.expanduser(args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    if "mpt" in model_name.lower():
        model = LlavaMPTForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=torch.float16, use_cache=True).cuda()
    else:
        # model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=torch.float16, use_cache=True).cuda()
        model = LlavaLlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='auto')#.cuda()
    image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=torch.float16)

    mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
    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)

    vision_tower = model.get_model().vision_tower[0]
    if vision_tower.device.type == 'meta':
        vision_tower = CLIPVisionModel.from_pretrained(vision_tower.config._name_or_path, torch_dtype=torch.float16, low_cpu_mem_usage=True).cuda()
        model.get_model().vision_tower[0] = vision_tower
    else:
        vision_tower.to(device='cuda', dtype=torch.float16)
    vision_config = vision_tower.config
    vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
    vision_config.use_im_start_end = mm_use_im_start_end
    if mm_use_im_start_end:
        vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
    image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2

    qs = args.query
    if mm_use_im_start_end:
        qs = qs + '\n' + DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + DEFAULT_IM_END_TOKEN
    else:
        qs = qs + '\n' + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len

    if "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt_multimodal"
    else:
        conv_mode = "multimodal"

    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

    conv = conv_templates[args.conv_mode].copy()
    conv.append_message(conv.roles[0], qs)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    inputs = tokenizer([prompt])

    image = load_image(args.image_file)
    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]

    input_ids = torch.as_tensor(inputs.input_ids).cuda()

    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

    with torch.inference_mode():
        output_ids = model.generate(input_ids, images=image_tensor.unsqueeze(0).half().cuda(), do_sample=True, temperature=0.2, max_new_tokens=1024, stopping_criteria=[stopping_criteria])

    input_token_len = input_ids.shape[1]
    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
    if n_diff_input_output > 0:
        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
    outputs = outputs.strip()
    if outputs.endswith(stop_str):
        outputs = outputs[:-len(stop_str)]
    outputs = outputs.strip()
    print(outputs)

    import pdb; pdb.set_trace()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
    parser.add_argument("--image-file", type=str, required=True)
    parser.add_argument("--query", type=str, required=True)
    parser.add_argument("--conv-mode", type=str, default=None)
    args = parser.parse_args()

    eval_model(args)