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https://git.datalinker.icu/vllm-project/vllm.git
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72d14d0eed
Signed-off-by: Sanger Steel <sangersteel@gmail.com> Co-authored-by: Eta <esyra@coreweave.com>
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Callable
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import pytest
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from vllm import LLM, EngineArgs
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from vllm.distributed import cleanup_dist_env_and_memory
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from vllm.model_executor.model_loader import tensorizer as tensorizer_mod
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from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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from vllm.utils import get_distributed_init_method, get_ip, get_open_port
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from vllm.v1.executor.abstract import UniProcExecutor
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from vllm.worker.worker_base import WorkerWrapperBase
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MODEL_REF = "facebook/opt-125m"
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@pytest.fixture()
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def model_ref():
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return MODEL_REF
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@pytest.fixture(autouse=True)
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def allow_insecure_serialization(monkeypatch):
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monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
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@pytest.fixture(autouse=True)
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def cleanup():
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cleanup_dist_env_and_memory(shutdown_ray=True)
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@pytest.fixture()
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def just_serialize_model_tensors(model_ref, monkeypatch, tmp_path):
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def noop(*args, **kwargs):
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return None
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args = EngineArgs(model=model_ref)
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tc = TensorizerConfig(tensorizer_uri=f"{tmp_path}/model.tensors")
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monkeypatch.setattr(tensorizer_mod, "serialize_extra_artifacts", noop)
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tensorizer_mod.tensorize_vllm_model(args, tc)
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yield tmp_path
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@pytest.fixture(autouse=True)
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def tensorizer_config():
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config = TensorizerConfig(tensorizer_uri="vllm")
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return config
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@pytest.fixture()
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def model_path(model_ref, tmp_path):
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yield tmp_path / model_ref / "model.tensors"
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def assert_from_collective_rpc(engine: LLM, closure: Callable,
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closure_kwargs: dict):
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res = engine.collective_rpc(method=closure, kwargs=closure_kwargs)
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return all(res)
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# This is an object pulled from tests/v1/engine/test_engine_core.py
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# Modified to strip the `load_model` method from its `_init_executor`
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# method. It's purely used as a dummy utility to run methods that test
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# Tensorizer functionality
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class DummyExecutor(UniProcExecutor):
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def _init_executor(self) -> None:
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"""Initialize the worker and load the model.
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"""
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self.driver_worker = WorkerWrapperBase(vllm_config=self.vllm_config,
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rpc_rank=0)
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distributed_init_method = get_distributed_init_method(
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get_ip(), get_open_port())
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local_rank = 0
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# set local rank as the device index if specified
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device_info = self.vllm_config.device_config.device.__str__().split(
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":")
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if len(device_info) > 1:
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local_rank = int(device_info[1])
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rank = 0
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is_driver_worker = True
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kwargs = dict(
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vllm_config=self.vllm_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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is_driver_worker=is_driver_worker,
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)
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self.collective_rpc("init_worker", args=([kwargs], ))
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self.collective_rpc("init_device")
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@property
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def max_concurrent_batches(self) -> int:
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return 2
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def shutdown(self):
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if hasattr(self, 'thread_pool'):
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self.thread_pool.shutdown(wait=False)
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