chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,73 @@
+import os
+from pprint import pprint
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
+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 import InferArguments, SftArguments, infer_main, sft_main
+    res = []
+    for padding_side in ['left', 'right']:
+        model = 'Qwen/Qwen2.5-0.5B-Instruct'
+        dataset = ['damo/zh_cls_fudan-news#2000']
+        result = sft_main(
+            SftArguments(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(adapters=[last_model_checkpoint], load_data_args=True))
+        res.append(calc_acc(infer_result))
+        infer_result2 = infer_main(
+            InferArguments(adapters=[last_model_checkpoint], load_data_args=True, max_batch_size=16))
+        res.append(calc_acc(infer_result2))
+    pprint(res)
+
+
+def test_mllm():
+    from swift import InferArguments, SftArguments, infer_main, sft_main
+    from swift.template import Template
+    res = []
+    for padding_side in ['left', 'right']:
+        model = 'Qwen/Qwen2-VL-2B-Instruct'
+        dataset = ['AI-ModelScope/LaTeX_OCR#2000']
+        result = sft_main(
+            SftArguments(model=model, dataset=dataset, split_dataset_ratio=0.01, padding_side=padding_side, **kwargs))
+        last_model_checkpoint = result['last_model_checkpoint']
+        infer_result = infer_main(InferArguments(adapters=[last_model_checkpoint], load_data_args=True))
+        res.append(infer_result)
+        infer_result2 = infer_main(
+            InferArguments(adapters=[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()