mirror of https://github.com/vllm-project/vllm.git
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Demonstrates how to achieve reproducibility in vLLM.
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Main article: https://docs.vllm.ai/en/latest/usage/reproducibility.html
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"""
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import os
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import random
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from vllm import LLM, SamplingParams
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# V1 only: Turn off multiprocessing to make the scheduling deterministic.
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os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
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# V0 only: Set the global seed. The default seed is None, which is
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# not reproducible.
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SEED = 42
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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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]
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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def main():
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llm = LLM(model="facebook/opt-125m", seed=SEED)
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outputs = llm.generate(prompts, sampling_params)
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print("-" * 50)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
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print("-" * 50)
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# Try generating random numbers outside vLLM
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# The same number is output across runs, meaning that the random state
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# in the user code has been updated by vLLM
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print(random.randint(0, 100))
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if __name__ == "__main__":
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main()
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