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
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+"""
+Example of using reasoning_parser
+
+This example demonstrates how to use reasoning_parser in Swift's VllmEngine to support reasoning models.
+"""
+
+from swift.infer_engine import InferRequest, RequestConfig, VllmEngine
+
+
+def main(engine: VllmEngine):
+    # Create inference request
+    infer_request = InferRequest(messages=[{'role': 'user', 'content': '9.11 and 9.8, which is greater?'}])
+
+    # Configure request parameters
+    request_config = RequestConfig(
+        max_tokens=8192,
+        temperature=0.7,
+        stream=False  # Non-streaming inference
+    )
+
+    # Execute inference
+    responses = engine.infer(infer_requests=[infer_request], request_config=request_config)
+
+    # Process responses
+    for response in responses:
+        if hasattr(response, 'choices') and response.choices:
+            choice = response.choices[0]
+            message = choice.message
+
+            print('=== Reasoning Content ===')
+            if message.reasoning_content:
+                print(f'Reasoning steps: {message.reasoning_content}')
+            else:
+                print('No reasoning content detected')
+
+            print('\n=== Final Answer ===')
+            print(f'Answer: {message.content}')
+
+            print('\n=== Finish Reason ===')
+            print(f'Reason: {choice.finish_reason}')
+
+
+def streaming_example(engine: VllmEngine):
+    """Streaming inference example"""
+    infer_request = InferRequest(messages=[{'role': 'user', 'content': 'Calculate the result of 15 + 27'}])
+
+    request_config = RequestConfig(
+        max_tokens=8192,
+        temperature=0.7,
+        stream=True  # Enable streaming inference
+    )
+
+    # Streaming inference
+    responses = engine.infer(infer_requests=[infer_request], request_config=request_config)
+
+    print('=== Streaming Inference Results ===')
+    for chunk in responses[0]:  # responses[0] is the streaming generator
+        if chunk and chunk.choices:
+            choice = chunk.choices[0]
+            delta = choice.delta
+
+            if delta.reasoning_content:
+                print(f'Reasoning: {delta.reasoning_content}', end='', flush=True)
+
+            if delta.content:
+                print(f'Content: {delta.content}', end='', flush=True)
+
+    print('\n=== Inference Complete ===')
+
+
+if __name__ == '__main__':
+    # Initialize VllmEngine with reasoning_parser enabled
+    engine = VllmEngine(
+        model_id_or_path='Qwen/Qwen3-8B',
+        reasoning_parser='qwen3',  # Specify reasoning parser
+        gpu_memory_utilization=0.9,
+    )
+
+    print('=== Non-streaming Inference Example ===')
+    main(engine)
+
+    print('\n' + '=' * 50 + '\n')
+
+    print('=== Streaming Inference Example ===')
+    streaming_example(engine)