import argparse import itertools import json import os import random import time from functools import partial import torch from datasets import concatenate_datasets, load_dataset from internvl.model import load_model_and_tokenizer from internvl.train.dataset import build_transform, dynamic_preprocess from tqdm import tqdm ds_collections = { 'MathVista_testmini': { 'root': 'AI4Math/MathVista', 'max_new_tokens': 4096, 'min_new_tokens': 1, 'split': 'testmini' }, 'MathVista_test': { 'root': 'AI4Math/MathVista', 'max_new_tokens': 4096, 'min_new_tokens': 1, 'split': 'test' }, } COT_INSTRUCTION = ( 'Your task is to answer the question below. ' "Give step by step reasoning before you answer, and when you're ready to answer, " "please use the format \"Final answer: ..\"" '\n\n' 'Question:' '\n\n' '{question}' ) def collate_fn(batches, tokenizer): pixel_values = torch.cat([_['pixel_values'] for _ in batches], dim=0) data_items = [_['data_item'] for _ in batches] return pixel_values, data_items class MathVistaDataset(torch.utils.data.Dataset): def __init__(self, root, split, input_size=224, dynamic_image_size=False, use_thumbnail=False, max_num=6): dataset = load_dataset(root, cache_dir=os.path.join(os.getcwd(), 'data/MathVista/')) self.data = dataset[split] self.input_size = input_size self.dynamic_image_size = dynamic_image_size self.use_thumbnail = use_thumbnail self.max_num = max_num self.transform = build_transform(is_train=False, input_size=input_size) def __len__(self): return len(self.data) def __getitem__(self, idx): data_item = self.data[idx] image = data_item['decoded_image'] del data_item['decoded_image'] if self.dynamic_image_size: images = dynamic_preprocess(image, image_size=self.input_size, use_thumbnail=self.use_thumbnail, max_num=self.max_num) else: images = [image] pixel_values = [self.transform(image) for image in images] pixel_values = torch.stack(pixel_values) return { 'pixel_values': pixel_values, 'data_item': data_item, } class InferenceSampler(torch.utils.data.sampler.Sampler): def __init__(self, size): self._size = int(size) assert size > 0 self._rank = torch.distributed.get_rank() self._world_size = torch.distributed.get_world_size() self._local_indices = self._get_local_indices(size, self._world_size, self._rank) @staticmethod def _get_local_indices(total_size, world_size, rank): shard_size = total_size // world_size left = total_size % world_size shard_sizes = [shard_size + int(r < left) for r in range(world_size)] begin = sum(shard_sizes[:rank]) end = min(sum(shard_sizes[:rank + 1]), total_size) return range(begin, end) def __iter__(self): yield from self._local_indices def __len__(self): return len(self._local_indices) def evaluate_chat_model(): random.seed(args.seed) for ds_name in args.datasets: dataset = MathVistaDataset( root=ds_collections[ds_name]['root'], split=ds_collections[ds_name]['split'], input_size=image_size, dynamic_image_size=args.dynamic, use_thumbnail=use_thumbnail, max_num=args.max_num ) dataloader = torch.utils.data.DataLoader( dataset=dataset, sampler=InferenceSampler(len(dataset)), batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, drop_last=False, collate_fn=partial(collate_fn, tokenizer=tokenizer), ) outputs = [] for _, (pixel_values, data_items) in tqdm(enumerate(dataloader)): if args.cot: question = COT_INSTRUCTION.format(question=data_items[0]['query']) else: question = data_items[0]['query'] pixel_values = pixel_values.to(torch.bfloat16).cuda() generation_config = dict( num_beams=args.num_beams, max_new_tokens=ds_collections[ds_name]['max_new_tokens'] if not args.cot else 4096, min_new_tokens=ds_collections[ds_name]['min_new_tokens'], do_sample=True if args.temperature > 0 else False, temperature=args.temperature, ) pred = model.chat( tokenizer=tokenizer, pixel_values=pixel_values, question=question, generation_config=generation_config, verbose=True ) data_item = data_items[0] data_item['response'] = pred outputs.append(data_item) torch.distributed.barrier() world_size = torch.distributed.get_world_size() merged_outputs = [None for _ in range(world_size)] torch.distributed.all_gather_object(merged_outputs, json.dumps(outputs)) merged_outputs = [json.loads(_) for _ in merged_outputs] merged_outputs = [_ for _ in itertools.chain.from_iterable(merged_outputs)] if torch.distributed.get_rank() == 0: temp = {} for data_item in merged_outputs: pid = data_item['pid'] temp[pid] = data_item print(f'Evaluating {ds_name} ...') time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime()) results_file = f'{ds_name}_{time_prefix}.json' output_path = os.path.join(args.out_dir, results_file) json.dump(temp, open(output_path, 'w'), indent=4) print('Results saved to {}'.format(output_path)) if args.cot: cmd = f'python eval/mathvista/extract_answer.py --output_file {results_file} --output_dir {args.out_dir} --quick_extract' else: cmd = f'python eval/mathvista/extract_answer.py --output_file {results_file} --output_dir {args.out_dir}' print(cmd) os.system(cmd) cmd = f'python eval/mathvista/calculate_score.py --output_file {results_file} --output_dir {args.out_dir} --score_file {results_file[:-5]}_score.json' print(cmd) os.system(cmd) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--checkpoint', type=str, default='') parser.add_argument('--datasets', type=str, default='MathVista_testmini') parser.add_argument('--batch-size', type=int, default=1) parser.add_argument('--num-workers', type=int, default=1) parser.add_argument('--num-beams', type=int, default=1) parser.add_argument('--temperature', type=float, default=0.0) parser.add_argument('--out-dir', type=str, default='results') parser.add_argument('--seed', type=int, default=0) parser.add_argument('--dynamic', action='store_true') parser.add_argument('--max-num', type=int, default=6) parser.add_argument('--load-in-8bit', action='store_true') parser.add_argument('--load-in-4bit', action='store_true') parser.add_argument('--auto', action='store_true') parser.add_argument('--cot', action='store_true') args = parser.parse_args() model_name = '_'.join(args.checkpoint.split('/')[-2:]) model_name = f'{model_name}_cot' if args.cot else model_name args.out_dir = os.path.join(args.out_dir, model_name) if not os.path.exists(args.out_dir): os.makedirs(args.out_dir, exist_ok=True) args.datasets = args.datasets.split(',') print('datasets:', args.datasets) assert args.batch_size == 1, 'Only batch size 1 is supported' torch.distributed.init_process_group( backend='nccl', world_size=int(os.getenv('WORLD_SIZE', '1')), rank=int(os.getenv('RANK', '0')), ) torch.cuda.set_device(int(os.getenv('LOCAL_RANK', 0))) model, tokenizer = load_model_and_tokenizer(args) image_size = model.config.force_image_size or model.config.vision_config.image_size use_thumbnail = model.config.use_thumbnail total_params = sum(p.numel() for p in model.parameters()) / 1e9 if total_params > 20 or args.dynamic: args.num_beams = 1 print(f'[test] total_params: {total_params}B, use num_beams: {args.num_beams}') else: print(f'[test] total_params: {total_params}B') print(f'[test] image_size: {image_size}') print(f'[test] template: {model.config.template}') print(f'[test] dynamic_image_size: {args.dynamic}') print(f'[test] use_thumbnail: {use_thumbnail}') print(f'[test] max_num: {args.max_num}') evaluate_chat_model()