7c7714d856
Co-authored-by: Nick Hill <nickhill@us.ibm.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Robert Shaw <114415538+robertgshaw2-neuralmagic@users.noreply.github.com> Co-authored-by: Simon Mo <simon.mo@hey.com>
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
"""
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This file test accuracy of the vLLM server via LMEval.
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It uses local-completions, which interacts with vLLM
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through the OAI API with N concurrent connections.
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This simulates real work usage of the API and makes
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sure that the zmq frontend mp RPC message passing and
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AsyncLLMEngine are working correctly.
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"""
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import lm_eval
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import pytest
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from ...utils import RemoteOpenAIServer
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MODEL_NAME = "Qwen/Qwen2-1.5B-Instruct"
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NUM_CONCURRENT = 500
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TASK = "gsm8k"
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FILTER = "exact_match,strict-match"
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RTOL = 0.03
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EXPECTED_VALUE = 0.58
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DEFAULT_ARGS = ["--max-model-len", "4096", "--disable-log-requests"]
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MORE_ARGS_LIST = [["--enable-chunked-prefill"], ["--num-scheduler-steps", "8"]]
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@pytest.mark.parametrize("more_args", MORE_ARGS_LIST)
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def test_lm_eval_accuracy(more_args):
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args = list(DEFAULT_ARGS)
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args.extend(more_args)
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print(f"Running with: {args}")
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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url = f"{remote_server.url_for('v1')}/completions"
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model_args = (
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f"model={MODEL_NAME},"
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f"base_url={url},"
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f"num_concurrent={NUM_CONCURRENT},tokenized_requests=False")
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results = lm_eval.simple_evaluate(
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model="local-completions",
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model_args=model_args,
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tasks=TASK,
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)
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measured_value = results["results"][TASK][FILTER]
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assert (measured_value - RTOL < EXPECTED_VALUE
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and measured_value + RTOL > EXPECTED_VALUE
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), f"Expected: {EXPECTED_VALUE} | Measured: {measured_value}"
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