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import argparse
import itertools
import json
import os
import random
import time
from functools import partial
import torch
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
def collate_fn(batches, tokenizer):
images = [_['image'] for _ in batches]
questions = [_['question'] for _ in batches]
input_ids = tokenizer(questions, return_tensors='pt', padding='longest')
return images, input_ids.input_ids, input_ids.attention_mask
class VQADataset(torch.utils.data.Dataset):
def __init__(self, train, test, prompt, few_shot):
self.test = json.load(open(test))
self.prompt = prompt
self.few_shot = few_shot
if few_shot > 0:
self.train = open(train).readlines()
def __len__(self):
return len(self.test)
def __getitem__(self, idx):
data = self.test[idx]
image, question = data['image'], data['question']
few_shot_prompt = ''
if self.few_shot > 0:
few_shot_samples = random.sample(self.train, self.few_shot)
for sample in few_shot_samples:
sample = json.loads(sample.strip())
few_shot_prompt += self.prompt.format(
sample['image'],
sample['question']) + f" {sample['answer']}"
return {
'image': data['image'],
'question': few_shot_prompt + self.prompt.format(image, question),
}
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)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='')
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--num-workers', type=int, default=1)
parser.add_argument('--few-shot', type=int, default=0)
parser.add_argument('--seed', type=int, default=0)
args = parser.parse_args()
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(torch.distributed.get_rank())
model = AutoModelForCausalLM.from_pretrained(
args.checkpoint, device_map='cuda', trust_remote_code=True).eval()
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint,
trust_remote_code=True)
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eod_id
prompt = '<img>data/vizwiz/test/{}</img>{} Answer:'
random.seed(args.seed)
dataset = VQADataset(
train='data/vizwiz/vizwiz_train.jsonl',
test='data/vizwiz/test.json',
prompt=prompt,
few_shot=args.few_shot,
)
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 _, (images, input_ids, attention_mask) in tqdm(enumerate(dataloader)):
pred = model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=10,
min_new_tokens=1,
length_penalty=1,
num_return_sequences=1,
output_hidden_states=True,
use_cache=True,
pad_token_id=tokenizer.eod_id,
eos_token_id=tokenizer.eod_id,
)
answers = [
tokenizer.decode(_[input_ids.size(1):].cpu(),
skip_special_tokens=True).strip() for _ in pred
]
for image, answer in zip(images, answers):
outputs.append({'image': image, 'answer': answer})
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, outputs)
merged_outputs = [_ for _ in itertools.chain.from_iterable(merged_outputs)]
if torch.distributed.get_rank() == 0:
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = f'vizwiz_testdev_{time_prefix}_fs{args.few_shot}_s{args.seed}.json'
json.dump(merged_outputs, open(results_file, 'w'),
ensure_ascii=False) # save to results
torch.distributed.barrier()
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