model/llava/eval/model_vqa.py

import argparse
import json
import math
import os
import random

import shortuuid
import torch
from llava import LlavaLlamaForCausalLM
from llava.conversation import conv_templates
from llava.utils import disable_torch_init
from PIL import Image
from tqdm import tqdm
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    CLIPImageProcessor,
    CLIPVisionModel,
    StoppingCriteria,
)


def split_list(lst, n):
    """Split a list into n (roughly) equal-sized chunks"""
    chunk_size = math.ceil(len(lst) / n)  # integer division
    return [lst[i : i + chunk_size] for i in range(0, len(lst), chunk_size)]


def get_chunk(lst, n, k):
    chunks = split_list(lst, n)
    return chunks[k]


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


def patch_config(config):
    patch_dict = {
        "use_mm_proj": True,
        "mm_vision_tower": "openai/clip-vit-large-patch14",
        "mm_hidden_size": 1024,
    }

    cfg = AutoConfig.from_pretrained(config)
    if not hasattr(cfg, "mm_vision_tower"):
        print(
            f"`mm_vision_tower` not found in `{config}`, applying patch and save to disk."
        )
        for k, v in patch_dict.items():
            setattr(cfg, k, v)
        cfg.save_pretrained(config)


def eval_model(args):
    # Model
    disable_torch_init()
    model_name = os.path.expanduser(args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    if args.mm_projector is None:
        patch_config(model_name)
        model = LlavaLlamaForCausalLM.from_pretrained(
            model_name, torch_dtype=torch.float16
        ).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.model.vision_tower[0]
        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
    else:
        # in case of using a pretrained model with only a MLP projector weights
        model = LlavaLlamaForCausalLM.from_pretrained(
            model_name, torch_dtype=torch.float16
        ).cuda()

        vision_tower = CLIPVisionModel.from_pretrained(
            args.vision_tower, torch_dtype=torch.float16
        ).cuda()
        image_processor = CLIPImageProcessor.from_pretrained(
            args.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_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

        mm_projector = torch.nn.Linear(
            vision_config.hidden_size, model.config.hidden_size
        )
        mm_projector_weights = torch.load(args.mm_projector, map_location="cpu")
        mm_projector.load_state_dict(
            {k.split(".")[-1]: v for k, v in mm_projector_weights.items()}
        )

        model.model.mm_projector = mm_projector.cuda().half()
        model.model.vision_tower = [vision_tower]

    questions = [
        json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")
    ]
    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
    answers_file = os.path.expanduser(args.answers_file)
    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
    ans_file = open(answers_file, "w")
    for i, line in enumerate(tqdm(questions)):
        idx = line["question_id"]
        image_file = line["image"]
        qs = line["text"]
        cur_prompt = qs
        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 args.conv_mode == "simple_legacy":
            qs += "\n\n### Response:"
        # conv = default_conversation.copy()
        conv = conv_templates[args.conv_mode].copy()
        conv.append_message(conv.roles[0], qs)
        prompt = conv.get_prompt()
        inputs = tokenizer([prompt])

        image = Image.open(os.path.join(args.image_folder, image_file))
        # image.save(os.path.join(save_image_folder, image_file))
        image_tensor = image_processor.preprocess(image, return_tensors="pt")[
            "pixel_values"
        ][0]

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

        # new stopping implementation
        class KeywordsStoppingCriteria(StoppingCriteria):
            def __init__(self, keywords, tokenizer, input_ids):
                self.keywords = keywords
                self.tokenizer = tokenizer
                self.start_len = None
                self.input_ids = input_ids

            def __call__(
                self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
            ) -> bool:
                if self.start_len is None:
                    self.start_len = self.input_ids.shape[1]
                else:
                    outputs = self.tokenizer.batch_decode(
                        output_ids[:, self.start_len :], skip_special_tokens=True
                    )[0]
                    for keyword in self.keywords:
                        if keyword in outputs:
                            return True
                return False

        keywords = ["###"]
        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=0.7,
                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] Sample {i}: {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]

        if args.conv_mode == "simple_legacy" or args.conv_mode == "simple":
            while True:
                cur_len = len(outputs)
                outputs = outputs.strip()
                for pattern in ["###", "Assistant:", "Response:"]:
                    if outputs.startswith(pattern):
                        outputs = outputs[len(pattern) :].strip()
                if len(outputs) == cur_len:
                    break

        try:
            index = outputs.index(conv.sep)
        except ValueError:
            outputs += conv.sep
            index = outputs.index(conv.sep)

        outputs = outputs[:index].strip()

        ans_id = shortuuid.uuid()
        ans_file.write(
            json.dumps(
                {
                    "question_id": idx,
                    "prompt": cur_prompt,
                    "text": outputs,
                    "answer_id": ans_id,
                    "model_id": model_name,
                    "metadata": {},
                }
            )
            + "\n"
        )
        ans_file.flush()
    ans_file.close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
    parser.add_argument("--image-folder", type=str, default="")
    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
    parser.add_argument("--mm-projector", type=str, default=None)
    parser.add_argument("--vision-tower", type=str, default=None)
    parser.add_argument("--conv-mode", type=str, default="simple")
    parser.add_argument("--num-chunks", type=int, default=1)
    parser.add_argument("--chunk-idx", type=int, default=0)
    args = parser.parse_args()

    eval_model(args)