6f7a81cd9f
1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
45 lines
1.6 KiB
Python
45 lines
1.6 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/tests/basic_correctness/test_basic_correctness.py
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#
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from modelscope import snapshot_download # type: ignore
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from tests.e2e.conftest import VllmRunner
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def test_qwen2_5_w8a8_external_quantized_tp2():
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example_prompts = [
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"The president of the United States is",
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]
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max_tokens = 5
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with VllmRunner(
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snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
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tensor_parallel_size=2,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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) as vllm_model:
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vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
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golden_results = [
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'The president of the United States is the head of state and',
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]
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for i in range(len(vllm_output)):
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assert golden_results[i] == vllm_output[i][1]
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print(f"Generated text: {vllm_output[i][1]!r}")
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