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e489ad7a21
- **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>
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import sys
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from contextlib import nullcontext
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from vllm_test_utils import BlameResult, blame
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from vllm import LLM, SamplingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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def run_normal():
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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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# Create an LLM without guided decoding as a baseline.
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llm = LLM(model="facebook/opt-125m",
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enforce_eager=True,
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gpu_memory_utilization=0.3)
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outputs = llm.generate(prompts, sampling_params)
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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}, Generated text: {generated_text!r}")
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# Destroy the LLM object and free up the GPU memory.
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del llm
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cleanup_dist_env_and_memory()
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def run_lmfe(sample_regex):
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# Create an LLM with guided decoding enabled.
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llm = LLM(model="facebook/opt-125m",
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enforce_eager=True,
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guided_decoding_backend="lm-format-enforcer",
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gpu_memory_utilization=0.3)
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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outputs = llm.generate(
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prompts=[
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f"Give an example IPv4 address with this regex: {sample_regex}"
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] * 2,
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sampling_params=sampling_params,
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use_tqdm=True,
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guided_options_request=dict(guided_regex=sample_regex))
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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}, Generated text: {generated_text!r}")
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def test_lazy_outlines(sample_regex):
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"""If users don't use guided decoding, outlines should not be imported.
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"""
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# make sure outlines is not imported
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module_name = "outlines"
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# In CI, we only check finally if the module is imported.
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# If it is indeed imported, we can rerun the test with `use_blame=True`,
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# which will trace every function call to find the first import location,
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# and help find the root cause.
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# We don't run it in CI by default because it is slow.
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use_blame = False
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context = blame(
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lambda: module_name in sys.modules) if use_blame else nullcontext()
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with context as result:
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run_normal()
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run_lmfe(sample_regex)
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if use_blame:
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assert isinstance(result, BlameResult)
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print(f"the first import location is:\n{result.trace_stack}")
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assert module_name not in sys.modules, (
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f"Module {module_name} is imported. To see the first"
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f" import location, run the test with `use_blame=True`.")
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