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https://git.datalinker.icu/vllm-project/vllm.git
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97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import random
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import tempfile
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from unittest.mock import patch
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from vllm.config import (
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CacheConfig,
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DeviceConfig,
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ModelConfig,
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ParallelConfig,
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SchedulerConfig,
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VllmConfig,
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)
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from vllm.config.load import LoadConfig
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from vllm.config.lora import LoRAConfig
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from vllm.lora.models import LoRAMapping
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from vllm.lora.request import LoRARequest
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from vllm.v1.worker.gpu_worker import Worker
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MODEL_PATH = "Qwen/Qwen3-0.6B"
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NUM_LORAS = 16
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@patch.dict(os.environ, {"RANK": "0"})
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def test_worker_apply_lora(qwen3_lora_files):
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def set_active_loras(worker: Worker, lora_requests: list[LoRARequest]):
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lora_mapping = LoRAMapping([], [])
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worker.model_runner.lora_manager.set_active_adapters(
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lora_requests, lora_mapping
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)
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vllm_config = VllmConfig(
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model_config=ModelConfig(
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MODEL_PATH,
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seed=0,
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dtype="float16",
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max_model_len=127,
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enforce_eager=True,
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),
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load_config=LoadConfig(
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download_dir=None,
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load_format="dummy",
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),
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parallel_config=ParallelConfig(
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pipeline_parallel_size=1,
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tensor_parallel_size=1,
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data_parallel_size=1,
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),
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scheduler_config=SchedulerConfig("generate", 32, 32, 32),
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device_config=DeviceConfig("cuda"),
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cache_config=CacheConfig(
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block_size=16,
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swap_space=0,
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cache_dtype="auto",
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),
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lora_config=LoRAConfig(
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max_lora_rank=8, max_cpu_loras=NUM_LORAS, max_loras=NUM_LORAS
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),
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)
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worker = Worker(
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vllm_config=vllm_config,
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local_rank=0,
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rank=0,
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distributed_init_method=f"file://{tempfile.mkstemp()[1]}",
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)
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worker.init_device()
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worker.load_model()
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set_active_loras(worker, [])
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assert worker.list_loras() == set()
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lora_requests = [
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LoRARequest(str(i + 1), i + 1, qwen3_lora_files) for i in range(NUM_LORAS)
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]
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set_active_loras(worker, lora_requests)
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assert worker.list_loras() == {
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lora_request.lora_int_id for lora_request in lora_requests
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}
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for i in range(NUM_LORAS):
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random.seed(i)
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iter_lora_requests = random.choices(
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lora_requests, k=random.randint(1, NUM_LORAS)
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)
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random.shuffle(iter_lora_requests)
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iter_lora_requests = iter_lora_requests[: -random.randint(0, NUM_LORAS)]
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set_active_loras(worker, lora_requests)
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assert worker.list_loras().issuperset(
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{lora_request.lora_int_id for lora_request in iter_lora_requests}
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)
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