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import argparse
import itertools
import json
import os
from functools import partial
import torch
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
multiple_choices = ['A', 'B', 'C', 'D', 'E']
ds_collections = {
'scienceqa_test_img': {
'test': 'data/scienceqa/scienceqa_test_img.jsonl',
}
}
def collate_fn(batches, pad_token_id):
input_tokens = [_['input_tokens'] for _ in batches]
target_lengths = [_['target_lengths'] for _ in batches]
answers = [_['answer'] for _ in batches]
chunk_sizes = [len(_) for _ in input_tokens]
input_tokens = [_ for _ in itertools.chain.from_iterable(input_tokens)]
max_lengths = max([len(_) for _ in input_tokens])
input_tokens = [[pad_token_id] * (max_lengths - len(_)) + _
for _ in input_tokens]
input_tokens = torch.LongTensor(input_tokens)
attention_mask = 1 - input_tokens.eq(pad_token_id).float()
return input_tokens, attention_mask, target_lengths, answers, chunk_sizes
class MultipleChoiceDataste(torch.utils.data.Dataset):
def __init__(self, test, prompt, tokenizer):
self.datas = open(test).readlines()
self.prompt = prompt
self.tokenizer = tokenizer
def __len__(self):
return len(self.datas)
def __getitem__(self, idx):
data = json.loads(self.datas[idx].strip())
image = data['image']
hint = data['hint'] if data['hint'] else 'N/A'
question = data['question']
choices = data['choices']
choice_list = []
for i, c in enumerate(choices):
choice_list.append('{}. {}'.format(multiple_choices[i], c))
choice_txt = '\n'.join(choice_list)
prompt = self.prompt.format(image, hint, question, choice_txt)
prompt_tokens = self.tokenizer(prompt).input_ids
target_tokens = [
self.tokenizer(' ' + _).input_ids
for _ in multiple_choices[:len(choices)]
]
return {
'input_tokens': [prompt_tokens + _ for _ in target_tokens],
'target_lengths': [len(_) for _ in target_tokens],
'answer': data['answer'],
}
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('--dataset', type=str, default='')
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--num-workers', type=int, default=1)
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)
prompt = '<img>{}</img>Context: {}\nQuestion: {}\nOptions: {}\nAnswer:'
dataset = MultipleChoiceDataste(test=ds_collections[args.dataset]['test'],
prompt=prompt,
tokenizer=tokenizer)
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, pad_token_id=tokenizer.eod_id),
)
results = []
with torch.no_grad():
for _, (input_tokens, attention_mask, target_lengths, answer,
chunk_sizes) in tqdm(enumerate(dataloader)):
outputs = model(
input_ids=input_tokens[:, :-1].cuda(),
attention_mask=attention_mask[:, :-1].cuda(),
return_dict=True,
)
losses = torch.nn.functional.cross_entropy(outputs.logits.permute(
0, 2, 1),
input_tokens[:,
1:].cuda(),
reduction='none')
losses = losses.split(chunk_sizes, dim=0)
for loss, target_length, answer in zip(losses, target_lengths,
answer):
target_loss = loss.mean(-1)
for _ in range(len(target_length)):
target_loss[_] = loss[_, -target_length[_]:].mean()
pred = target_loss.argmin().item()
if pred == answer:
results.append(1)
else:
results.append(0)
torch.distributed.barrier()
world_size = torch.distributed.get_world_size()
merged_results = [None for _ in range(world_size)]
torch.distributed.all_gather_object(merged_results, results)
merged_results = [_ for _ in itertools.chain.from_iterable(merged_results)]
if torch.distributed.get_rank() == 0:
print(f'Acc@1: {sum(merged_results) / len(merged_results)}')
torch.distributed.barrier()
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