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import argparse |
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import itertools |
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import json |
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import os |
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import random |
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import time |
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from functools import partial |
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import torch |
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from tqdm import tqdm |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def collate_fn(batches, tokenizer): |
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images = [_['image'] for _ in batches] |
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questions = [_['question'] for _ in batches] |
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input_ids = tokenizer(questions, return_tensors='pt', padding='longest') |
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return images, input_ids.input_ids, input_ids.attention_mask |
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class VQADataset(torch.utils.data.Dataset): |
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def __init__(self, train, test, prompt, few_shot): |
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self.test = json.load(open(test)) |
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self.prompt = prompt |
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self.few_shot = few_shot |
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if few_shot > 0: |
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self.train = open(train).readlines() |
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def __len__(self): |
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return len(self.test) |
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def __getitem__(self, idx): |
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data = self.test[idx] |
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image, question = data['image'], data['question'] |
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few_shot_prompt = '' |
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if self.few_shot > 0: |
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few_shot_samples = random.sample(self.train, self.few_shot) |
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for sample in few_shot_samples: |
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sample = json.loads(sample.strip()) |
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few_shot_prompt += self.prompt.format( |
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sample['image'], |
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sample['question']) + f" {sample['answer']}" |
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return { |
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'image': data['image'], |
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'question': few_shot_prompt + self.prompt.format(image, question), |
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} |
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class InferenceSampler(torch.utils.data.sampler.Sampler): |
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def __init__(self, size): |
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self._size = int(size) |
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assert size > 0 |
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self._rank = torch.distributed.get_rank() |
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self._world_size = torch.distributed.get_world_size() |
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self._local_indices = self._get_local_indices(size, self._world_size, |
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self._rank) |
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@staticmethod |
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def _get_local_indices(total_size, world_size, rank): |
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shard_size = total_size // world_size |
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left = total_size % world_size |
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shard_sizes = [shard_size + int(r < left) for r in range(world_size)] |
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begin = sum(shard_sizes[:rank]) |
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end = min(sum(shard_sizes[:rank + 1]), total_size) |
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return range(begin, end) |
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def __iter__(self): |
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yield from self._local_indices |
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def __len__(self): |
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return len(self._local_indices) |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--checkpoint', type=str, default='') |
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parser.add_argument('--batch-size', type=int, default=1) |
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parser.add_argument('--num-workers', type=int, default=1) |
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parser.add_argument('--few-shot', type=int, default=0) |
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parser.add_argument('--seed', type=int, default=0) |
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args = parser.parse_args() |
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torch.distributed.init_process_group( |
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backend='nccl', |
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world_size=int(os.getenv('WORLD_SIZE', '1')), |
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rank=int(os.getenv('RANK', '0')), |
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) |
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torch.cuda.set_device(torch.distributed.get_rank()) |
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model = AutoModelForCausalLM.from_pretrained( |
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args.checkpoint, device_map='cuda', trust_remote_code=True).eval() |
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tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, |
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trust_remote_code=True) |
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tokenizer.padding_side = 'left' |
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tokenizer.pad_token_id = tokenizer.eod_id |
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prompt = '<img>data/vizwiz/test/{}</img>{} Answer:' |
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random.seed(args.seed) |
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dataset = VQADataset( |
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train='data/vizwiz/vizwiz_train.jsonl', |
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test='data/vizwiz/test.json', |
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prompt=prompt, |
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few_shot=args.few_shot, |
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) |
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dataloader = torch.utils.data.DataLoader( |
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dataset=dataset, |
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sampler=InferenceSampler(len(dataset)), |
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batch_size=args.batch_size, |
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num_workers=args.num_workers, |
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pin_memory=True, |
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drop_last=False, |
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collate_fn=partial(collate_fn, tokenizer=tokenizer), |
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) |
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outputs = [] |
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for _, (images, input_ids, attention_mask) in tqdm(enumerate(dataloader)): |
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pred = model.generate( |
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input_ids=input_ids.cuda(), |
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attention_mask=attention_mask.cuda(), |
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do_sample=False, |
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num_beams=1, |
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max_new_tokens=10, |
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min_new_tokens=1, |
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length_penalty=1, |
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num_return_sequences=1, |
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output_hidden_states=True, |
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use_cache=True, |
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pad_token_id=tokenizer.eod_id, |
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eos_token_id=tokenizer.eod_id, |
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) |
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answers = [ |
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tokenizer.decode(_[input_ids.size(1):].cpu(), |
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skip_special_tokens=True).strip() for _ in pred |
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] |
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for image, answer in zip(images, answers): |
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outputs.append({'image': image, 'answer': answer}) |
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torch.distributed.barrier() |
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world_size = torch.distributed.get_world_size() |
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merged_outputs = [None for _ in range(world_size)] |
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torch.distributed.all_gather_object(merged_outputs, outputs) |
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merged_outputs = [_ for _ in itertools.chain.from_iterable(merged_outputs)] |
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if torch.distributed.get_rank() == 0: |
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time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime()) |
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results_file = f'vizwiz_testdev_{time_prefix}_fs{args.few_shot}_s{args.seed}.json' |
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json.dump(merged_outputs, open(results_file, 'w'), |
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ensure_ascii=False) |
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torch.distributed.barrier() |
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