1b4a2f3817
### What this PR does / why we need it?
Add accuracy ci for DP and EP and TP
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.9.2
- vLLM main:
35514b682a
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Signed-off-by: hfadzxy <starmoon_zhang@163.com>
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/blob/main/tests/entrypoints/llm/test_accuracy.py
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#
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import gc
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import multiprocessing
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import sys
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from multiprocessing import Queue
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import lm_eval
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import pytest
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import torch
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# pre-trained model path on Hugging Face.
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MODEL_NAME = ["Qwen/Qwen2.5-0.5B-Instruct", "Qwen/Qwen2.5-VL-3B-Instruct"]
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# Benchmark configuration mapping models to evaluation tasks:
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# - Text model: GSM8K (grade school math reasoning)
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# - Vision-language model: MMMU Art & Design validation (multimodal understanding)
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TASK = {
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"Qwen/Qwen2.5-0.5B-Instruct": "gsm8k",
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"Qwen/Qwen2.5-VL-3B-Instruct": "mmmu_val_art_and_design"
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}
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# Answer validation requiring format consistency.
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FILTER = {
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"Qwen/Qwen2.5-0.5B-Instruct": "exact_match,strict-match",
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"Qwen/Qwen2.5-VL-3B-Instruct": "acc,none"
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}
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# 3% relative tolerance for numerical accuracy.
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RTOL = 0.03
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# Baseline accuracy after VLLM optimization.
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EXPECTED_VALUE = {
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"Qwen/Qwen2.5-0.5B-Instruct": 0.316,
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"Qwen/Qwen2.5-VL-3B-Instruct": 0.566
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}
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# Maximum context length configuration for each model.
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MAX_MODEL_LEN = {
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"Qwen/Qwen2.5-0.5B-Instruct": 4096,
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"Qwen/Qwen2.5-VL-3B-Instruct": 8192
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}
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# Model types distinguishing text-only and vision-language models.
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MODEL_TYPE = {
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"Qwen/Qwen2.5-0.5B-Instruct": "vllm",
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"Qwen/Qwen2.5-VL-3B-Instruct": "vllm-vlm"
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}
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# wrap prompts in a chat-style template.
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APPLY_CHAT_TEMPLATE = {"vllm": False, "vllm-vlm": True}
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# Few-shot examples handling as multi-turn dialogues.
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FEWSHOT_AS_MULTITURN = {"vllm": False, "vllm-vlm": True}
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# batch_size
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BATCH_SIZE = {
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"Qwen/Qwen2.5-0.5B-Instruct": "auto",
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"Qwen/Qwen2.5-VL-3B-Instruct": 1
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}
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multiprocessing.set_start_method("spawn", force=True)
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def run_test(queue, model, max_model_len, model_type):
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try:
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if model_type == "vllm-vlm":
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model_args = (f"pretrained={model},max_model_len={max_model_len},"
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"tensor_parallel_size=1,dtype=auto,max_images=2")
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else:
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model_args = (f"pretrained={model},max_model_len={max_model_len},"
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"tensor_parallel_size=1,dtype=auto")
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results = lm_eval.simple_evaluate(
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model=model_type,
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model_args=model_args,
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tasks=TASK[model],
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batch_size=BATCH_SIZE[model],
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apply_chat_template=APPLY_CHAT_TEMPLATE[model_type],
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fewshot_as_multiturn=FEWSHOT_AS_MULTITURN[model_type],
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)
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result = results["results"][TASK[model]][FILTER[model]]
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print("result:", result)
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queue.put(result)
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except Exception as e:
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queue.put(e)
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sys.exit(1)
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finally:
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gc.collect()
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torch.npu.empty_cache()
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@pytest.mark.parametrize("model", MODEL_NAME)
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def test_lm_eval_accuracy(monkeypatch: pytest.MonkeyPatch, model):
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with monkeypatch.context():
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result_queue: Queue[float] = multiprocessing.Queue()
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p = multiprocessing.Process(target=run_test,
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args=(result_queue, model,
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MAX_MODEL_LEN[model],
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MODEL_TYPE[model]))
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p.start()
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p.join()
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result = result_queue.get()
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if isinstance(result, Exception):
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pytest.fail(f"Subprocess failed with exception: {str(result)}")
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print(result)
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assert (EXPECTED_VALUE[model] - RTOL < result < EXPECTED_VALUE[model] + RTOL), \
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f"Expected: {EXPECTED_VALUE[model]}±{RTOL} | Measured: {result}"
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