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- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Containing tests that check for regressions in vLLM's behavior.
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It should include tests that are reported by users and making sure they
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will never happen again.
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"""
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import gc
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import torch
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from vllm import LLM, SamplingParams
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def test_duplicated_ignored_sequence_group():
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"""https://github.com/vllm-project/vllm/issues/1655"""
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sampling_params = SamplingParams(temperature=0.01,
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top_p=0.1,
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max_tokens=256)
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llm = LLM(model="facebook/opt-125m",
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max_num_batched_tokens=4096,
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tensor_parallel_size=1)
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prompts = ["This is a short prompt", "This is a very long prompt " * 1000]
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outputs = llm.generate(prompts, sampling_params=sampling_params)
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assert len(prompts) == len(outputs)
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def test_max_tokens_none():
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sampling_params = SamplingParams(temperature=0.01,
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top_p=0.1,
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max_tokens=None)
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llm = LLM(model="facebook/opt-125m",
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max_num_batched_tokens=4096,
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tensor_parallel_size=1)
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prompts = ["Just say hello!"]
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outputs = llm.generate(prompts, sampling_params=sampling_params)
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assert len(prompts) == len(outputs)
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def test_gc():
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llm = LLM("facebook/opt-125m", enforce_eager=True)
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del llm
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gc.collect()
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torch.cuda.empty_cache()
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# The memory allocated for model and KV cache should be released.
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# The memory allocated for PyTorch and others should be less than 50MB.
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# Usually, it's around 10MB.
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allocated = torch.cuda.memory_allocated()
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assert allocated < 50 * 1024 * 1024
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def test_model_from_modelscope(monkeypatch):
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# model: https://modelscope.cn/models/qwen/Qwen1.5-0.5B-Chat/summary
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MODELSCOPE_MODEL_NAME = "qwen/Qwen1.5-0.5B-Chat"
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monkeypatch.setenv("VLLM_USE_MODELSCOPE", "True")
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try:
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llm = LLM(model=MODELSCOPE_MODEL_NAME)
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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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outputs = llm.generate(prompts, sampling_params)
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assert len(outputs) == 4
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finally:
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monkeypatch.delenv("VLLM_USE_MODELSCOPE", raising=False)
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if __name__ == "__main__":
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import pytest
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pytest.main([__file__])
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