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import os
from pprint import pprint
import torch
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
kwargs = {
'per_device_train_batch_size': 4,
'per_device_eval_batch_size': 4,
'gradient_accumulation_steps': 4,
'num_train_epochs': 1,
'save_steps': 100,
'max_length': 8192,
}
def calc_acc(infer_result):
n_correct = 0
for res in infer_result:
if res['response'] == res['labels']:
n_correct += 1
return f'acc: {n_correct/len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
def calc_diff(infer_result, infer_result2):
n_correct = 0
for x1, x2 in zip(infer_result, infer_result2):
if x1['response'] == x2['response']:
n_correct += 1
return f'acc: {n_correct/len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
def test_llm():
from swift.llm import sft_main, TrainArguments, infer_main, InferArguments, Template
res = []
for padding_side in ['left', 'right']:
model = 'Qwen/Qwen2.5-0.5B-Instruct'
dataset = ['damo/zh_cls_fudan-news#2000']
result = sft_main(
TrainArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, padding_side=padding_side, **kwargs))
last_model_checkpoint = result['last_model_checkpoint']
infer_result = infer_main(InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True))
res.append(calc_acc(infer_result))
infer_result2 = infer_main(
InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16))
res.append(calc_acc(infer_result2))
pprint(res)
def test_mllm():
from swift.llm import sft_main, TrainArguments, infer_main, InferArguments, Template
res = []
for padding_side in ['left', 'right']:
model = 'Qwen/Qwen2-VL-2B-Instruct'
dataset = ['AI-ModelScope/LaTeX_OCR#2000']
result = sft_main(TrainArguments(model=model, dataset=dataset, padding_side=padding_side, **kwargs))
last_model_checkpoint = result['last_model_checkpoint']
infer_result = infer_main(InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True))
res.append(infer_result)
infer_result2 = infer_main(
InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16))
res.append(infer_result2)
print(calc_diff(res[0], res[1]))
print(calc_diff(res[2], res[3]))
print(calc_diff(res[0], res[2]))
print(calc_diff(res[0], res[3]))
print(calc_diff(res[2], res[1]))
if __name__ == '__main__':
test_llm()
test_mllm()
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