1165b2c863
### What this PR does / why we need it?
1. Accuracy testing no longer compares eager and graph modes; instead,
it directly extracts the golden result under the graph mode
configuration (the implicit purpose of this case is to verify whether
modifications affect existing results)
2. Next step: finer-grained supervision of logits/sampler results
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
Signed-off-by: wangli <wangli858794774@gmail.com>
108 lines
3.9 KiB
Python
108 lines
3.9 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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#
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"""
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Compare the outputs of vLLM with and without xlite.
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Run `pytest tests/e2e/singlecard/test_xlite.py`.
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"""
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import os
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import pytest
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from vllm import SamplingParams
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from tests.e2e.singlecard.utils import (PROMPTS_SHORT, LLMTestCase,
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gen_and_valid)
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os.environ["VLLM_ASCEND_ENABLE_NZ"] = "2"
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CASE_DECODE_ONLY = LLMTestCase(
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model="Qwen/Qwen3-0.6B",
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prompts=PROMPTS_SHORT,
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golden_answers=[
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"Hello, my name is Lina. I'm a 22-year-old student from China.",
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'The president of the United States is the same as the president of the United Nations. This is because the president',
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'The capital of France is Paris. The capital of France is also the capital of the French Republic.',
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'The future of AI is not just a technological challenge but a profound transformation of how we live, work'
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],
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sampling_params=SamplingParams(
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max_tokens=15,
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temperature=0.0,
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top_p=1.0,
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top_k=0,
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n=1,
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))
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CASE_FULL_DECODE_ONLY = LLMTestCase(
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model="Qwen/Qwen3-0.6B",
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prompts=[
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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],
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golden_answers=[
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" Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the",
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' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
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' Paris. The capital of Italy is Rome. The capital of Spain is Madrid. The capital of China is Beijing. The capital of Japan is Tokyo. The capital',
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" not just about the technology itself, but about how we use it to solve real-world problems. As AI continues to evolve, it's important to consider the ethical"
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],
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sampling_params=SamplingParams(
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max_tokens=32,
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temperature=0.0,
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top_p=1.0,
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top_k=0,
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n=1,
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))
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@pytest.mark.skip(
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reason="TODO: Re-enable xlite_decode_only e2e test when stable.")
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@pytest.mark.parametrize("cur_case", [CASE_DECODE_ONLY])
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def test_models_with_xlite_decode_only(cur_case: LLMTestCase):
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runner_kwargs = {
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"model_name": cur_case.model,
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"max_model_len": 1024,
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"block_size": 128,
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"additional_config": {
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"xlite_graph_config": {
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"enabled": True
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}
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},
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}
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gen_and_valid(runner_kwargs=runner_kwargs,
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prompts=cur_case.prompts,
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sampling_params=cur_case.sampling_params,
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golden_answers=cur_case.golden_answers)
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@pytest.mark.parametrize("cur_case", [CASE_FULL_DECODE_ONLY])
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def test_models_with_xlite_full_mode(cur_case: LLMTestCase):
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runner_kwargs = {
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"model_name": cur_case.model,
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"max_model_len": 1024,
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"block_size": 128,
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"additional_config": {
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"xlite_graph_config": {
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"enabled": True,
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"full_mode": True
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}
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},
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}
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gen_and_valid(runner_kwargs=runner_kwargs,
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prompts=cur_case.prompts,
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sampling_params=cur_case.sampling_params,
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golden_answers=cur_case.golden_answers)
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