Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from vllm import LLM, EngineArgs
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from vllm.utils.argparse_utils import FlexibleArgumentParser
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def create_parser():
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parser = FlexibleArgumentParser()
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# Add engine args
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EngineArgs.add_cli_args(parser)
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parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
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# Add sampling params
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sampling_group = parser.add_argument_group("Sampling parameters")
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sampling_group.add_argument("--max-tokens", type=int)
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sampling_group.add_argument("--temperature", type=float)
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sampling_group.add_argument("--top-p", type=float)
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sampling_group.add_argument("--top-k", type=int)
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return parser
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def main(args: dict):
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# Pop arguments not used by LLM
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max_tokens = args.pop("max_tokens")
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temperature = args.pop("temperature")
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top_p = args.pop("top_p")
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top_k = args.pop("top_k")
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# Create an LLM
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llm = LLM(**args)
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# Create a sampling params object
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sampling_params = llm.get_default_sampling_params()
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if max_tokens is not None:
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sampling_params.max_tokens = max_tokens
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if temperature is not None:
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sampling_params.temperature = temperature
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if top_p is not None:
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sampling_params.top_p = top_p
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if top_k is not None:
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sampling_params.top_k = top_k
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# Generate texts from the prompts. The output is a list of RequestOutput
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# objects that contain the prompt, generated text, and other information.
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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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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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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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if __name__ == "__main__":
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parser = create_parser()
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args: dict = vars(parser.parse_args())
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main(args)
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