adapt to sglang v0.5.2rc1 on dcu

examples/runtime/engine/offline_batch_inference.py

@@ -0,0 +1,43 @@
+"""
+Usage:
+python3 offline_batch_inference.py  --model meta-llama/Llama-3.1-8B-Instruct
+"""
+
+import argparse
+import dataclasses
+
+import sglang as sgl
+from sglang.srt.server_args import ServerArgs
+
+
+def main(
+    server_args: ServerArgs,
+):
+    # Sample prompts.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    # Create a sampling params object.
+    sampling_params = {"temperature": 0.8, "top_p": 0.95}
+
+    # Create an LLM.
+    llm = sgl.Engine(**dataclasses.asdict(server_args))
+
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    for prompt, output in zip(prompts, outputs):
+        print("===============================")
+        print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
+
+
+# The __main__ condition is necessary here because we use "spawn" to create subprocesses
+# Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    args = parser.parse_args()
+    server_args = ServerArgs.from_cli_args(args)
+    main(server_args)