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f3c378ffa7
Signed-off-by: Ye (Charlotte) Qi <yeq@meta.com> Signed-off-by: zhewenli <zhewenli@meta.com> Co-authored-by: Ye (Charlotte) Qi <yeq@meta.com> Co-authored-by: Ye (Charlotte) Qi <ye.charlotte.qi@gmail.com>
64 lines
2.1 KiB
Python
64 lines
2.1 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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LM eval harness on model to compare vs HF baseline computed offline.
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Configs are found in configs/$MODEL.yaml
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pytest -s -v test_lm_eval_correctness.py \
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--config-list-file=configs/models-small.txt \
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--tp-size=1
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"""
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import lm_eval
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import numpy as np
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import yaml
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RTOL = 0.08
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def launch_lm_eval(eval_config, tp_size):
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trust_remote_code = eval_config.get("trust_remote_code", False)
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max_model_len = eval_config.get("max_model_len", 4096)
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batch_size = eval_config.get("batch_size", "auto")
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backend = eval_config.get("backend", "vllm")
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model_args = (
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f"pretrained={eval_config['model_name']},"
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f"tensor_parallel_size={tp_size},"
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f"enforce_eager=true,"
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f"add_bos_token=true,"
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f"trust_remote_code={trust_remote_code},"
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f"max_model_len={max_model_len},"
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)
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results = lm_eval.simple_evaluate(
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model=backend,
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model_args=model_args,
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tasks=[task["name"] for task in eval_config["tasks"]],
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num_fewshot=eval_config["num_fewshot"],
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limit=eval_config["limit"],
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# TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
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# text models. however, this is regressing measured strict-match for
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# existing text models in CI, so only apply it for mm.
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apply_chat_template=backend == "vllm-vlm",
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batch_size=batch_size,
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)
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return results
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def test_lm_eval_correctness_param(config_filename, tp_size):
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eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
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results = launch_lm_eval(eval_config, tp_size)
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success = True
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for task in eval_config["tasks"]:
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for metric in task["metrics"]:
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ground_truth = metric["value"]
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measured_value = results["results"][task["name"]][metric["name"]]
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print(
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f"{task['name']} | {metric['name']}: "
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f"ground_truth={ground_truth} | measured={measured_value}"
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)
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success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
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assert success
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