Files
xc-llm-ascend/tests/e2e/multicard/2-cards/test_quantization.py
wangxiyuan f8e76a49fa [CI] Upgrade trasnformers version (#6307)
Upgrade transformers to >=4.56.4

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-01-28 14:06:39 +08:00

68 lines
2.3 KiB
Python

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
import pytest
from tests.e2e.conftest import VllmRunner
def test_qwen2_5_w8a8_external_quantized_tp2():
example_prompts = [
"The president of the United States is",
]
max_tokens = 5
with VllmRunner(
"neuralmagic/Qwen2.5-3B-quantized.w8a8",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
max_model_len=4096,
gpu_memory_utilization=0.8,
) as vllm_model:
vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
golden_results = [
'The president of the United States is the head of state and',
]
for i in range(len(vllm_output)):
assert golden_results[i] == vllm_output[i][1]
print(f"Generated text: {vllm_output[i][1]!r}")
@pytest.mark.skip(reason="CANN8.5 failed, capture stream failed, fix me")
def test_qwen3_moe_w8a8_dynamic_llm_compressor():
example_prompts = [
"The president of the United States is",
]
max_tokens = 5
with VllmRunner(
"vllm-ascend/Qwen3-30B-A3B-Instruct-2507-quantized.w8a8",
tensor_parallel_size=2,
max_model_len=4096,
gpu_memory_utilization=0.8,
) as vllm_model:
vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
golden_results = [
'The president of the United States is the head of state and',
]
for i in range(len(vllm_output)):
assert golden_results[i] == vllm_output[i][1]
print(f"Generated text: {vllm_output[i][1]!r}")