xc-llm-ascend/tests/e2e/singlecard/test_quantization.py

#
# 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.
#
from modelscope import snapshot_download  # type: ignore[import-untyped]

from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal


def test_qwen3_w8a8_quant():
    max_tokens = 5
    example_prompts = [
        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
    ]
    vllm_target_outputs = [([
        85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
        13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
    ], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
                            )]

    with VllmRunner(
            snapshot_download("vllm-ascend/Qwen3-0.6B-W8A8"),
            max_model_len=8192,
            gpu_memory_utilization=0.7,
            cudagraph_capture_sizes=[1, 2, 4, 8],
            quantization="ascend",
    ) as vllm_model:
        vllm_quant_w8a8_outputs = vllm_model.generate_greedy(
            example_prompts, max_tokens)

    check_outputs_equal(
        outputs_0_lst=vllm_target_outputs,
        outputs_1_lst=vllm_quant_w8a8_outputs,
        name_0="vllm_target_outputs",
        name_1="vllm_w8a16_outputs",
    )


def test_qwen3_dense_w8a16():
    max_tokens = 5
    example_prompts = [
        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
    ]
    vllm_target_outputs = [([
        85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
        13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
    ], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
                            )]

    with VllmRunner(
            snapshot_download("vllm-ascend/Qwen3-0.6B-W8A16"),
            max_model_len=8192,
            enforce_eager=False,
            gpu_memory_utilization=0.7,
            quantization="ascend",
    ) as vllm_model:
        vllm_quant_w8a16_outputs = vllm_model.generate_greedy(
            example_prompts, max_tokens)

    check_outputs_equal(
        outputs_0_lst=vllm_target_outputs,
        outputs_1_lst=vllm_quant_w8a16_outputs,
        name_0="vllm_target_outputs",
        name_1="vllm_w8a16_outputs",
    )