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 +1,80 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
+kwargs = {
+    'per_device_train_batch_size': 4,
+    'save_steps': 5,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+}
+
+
+def test_llm():
+    from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
+    result = rlhf_main(
+        RLHFArguments(
+            rlhf_type='gkd',
+            model='Qwen/Qwen2.5-0.5B',
+            teacher_model='Qwen/Qwen2.5-1.5B-Instruct',
+            dataset=['AI-ModelScope/alpaca-gpt4-data-en#2000'],
+            split_dataset_ratio=0.01,
+            load_from_cache_file=False,
+            seq_kd=True,
+            **kwargs,
+        ))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+def test_mllm():
+    from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
+    result = rlhf_main(
+        RLHFArguments(
+            rlhf_type='gkd',
+            model='OpenGVLab/InternVL3-2B-Pretrained',
+            teacher_model='OpenGVLab/InternVL3-8B',
+            dataset=['AI-ModelScope/LaTeX_OCR#2000', 'AI-ModelScope/alpaca-gpt4-data-en#2000'],
+            split_dataset_ratio=0.01,
+            load_from_cache_file=False,
+            **kwargs,
+        ))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+def test_multi_turn():
+    """GKD multi-turn smoke test: verify rollout → encode → loss works with multi_turn_scheduler.
+
+    Uses the built-in ``math_tip_trick`` scheduler with max_turns=2 to keep the test
+    lightweight. The key assertion is that training completes without raising
+    NotImplementedError (the previous block) and that multi-turn response token ids
+    are correctly propagated through the GKD loss pipeline.
+    """
+    from swift import InferArguments, RLHFArguments, infer_main, rlhf_main
+    result = rlhf_main(
+        RLHFArguments(
+            rlhf_type='gkd',
+            model='Qwen/Qwen2.5-0.5B',
+            teacher_model='Qwen/Qwen2.5-1.5B-Instruct',
+            dataset=['AI-ModelScope/alpaca-gpt4-data-en#200'],
+            split_dataset_ratio=0.01,
+            load_from_cache_file=False,
+            multi_turn_scheduler='math_tip_trick',
+            max_turns=2,
+            max_completion_length=256,
+            num_generations=2,
+            per_device_train_batch_size=2,
+            gradient_accumulation_steps=1,
+            save_steps=50,
+            num_train_epochs=1,
+        ))
+    last_model_checkpoint = result['last_model_checkpoint']
+    if last_model_checkpoint is not None:
+        infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+if __name__ == '__main__':
+    # test_llm()
+    # test_mllm()
+    test_multi_turn()