66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
"""
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Usage:
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python offline_batch_inference_async.py --model-path Qwen/Qwen2-VL-7B-Instruct
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Note:
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This demo shows the usage of async generation,
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which is useful to implement an online-like generation with batched inference.
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"""
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import argparse
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import asyncio
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import dataclasses
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import time
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import sglang as sgl
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from sglang.srt.server_args import ServerArgs
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class InferenceEngine:
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def __init__(self, **kwargs):
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self.engine = sgl.Engine(**kwargs)
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async def generate(self, prompt, sampling_params):
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result = await self.engine.async_generate(prompt, sampling_params)
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return result
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async def run_server(server_args):
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inference = InferenceEngine(**dataclasses.asdict(server_args))
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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] * 100
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# Create a sampling params object.
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sampling_params = {"temperature": 0.8, "top_p": 0.95}
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# Run the generation tasks concurrently in async mode.
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tasks = []
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for prompt in prompts:
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task = asyncio.create_task(inference.generate(prompt, sampling_params))
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tasks.append(task)
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# Get and print the result
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for task in tasks:
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await task
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while True:
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if not task.done():
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time.sleep(1)
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else:
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result = task.result()
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print(f"Generated text: {result['text']}")
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break
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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ServerArgs.add_cli_args(parser)
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args = parser.parse_args()
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server_args = ServerArgs.from_cli_args(args)
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asyncio.run(run_server(server_args))
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