98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
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# SPDX-License-Identifier: Apache-2.0
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"""Test that we handle a startup Error and shutdown."""
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import pytest
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from tests.utils import wait_for_gpu_memory_to_clear
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from tests.v1.shutdown.utils import (SHUTDOWN_TEST_THRESHOLD_BYTES,
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SHUTDOWN_TEST_TIMEOUT_SEC)
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from vllm import LLM, SamplingParams
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.sampling_params import RequestOutputKind
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from vllm.utils import cuda_device_count_stateless
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from vllm.v1.engine.async_llm import AsyncLLM
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MODELS = ["meta-llama/Llama-3.2-1B"]
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@pytest.mark.asyncio
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@pytest.mark.timeout(SHUTDOWN_TEST_TIMEOUT_SEC)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("tensor_parallel_size", [2, 1])
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@pytest.mark.parametrize("send_one_request", [False, True])
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async def test_async_llm_delete(model: str, tensor_parallel_size: int,
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send_one_request: bool) -> None:
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"""Test that AsyncLLM frees GPU memory upon deletion.
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AsyncLLM always uses an MP client.
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Args:
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model: model under test
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tensor_parallel_size: degree of tensor parallelism
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send_one_request: send one request to engine before deleting
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"""
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if cuda_device_count_stateless() < tensor_parallel_size:
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pytest.skip(reason="Not enough CUDA devices")
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engine_args = AsyncEngineArgs(model=model,
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enforce_eager=True,
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tensor_parallel_size=tensor_parallel_size)
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# Instantiate AsyncLLM; make request to complete any deferred
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# initialization; then delete instance
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async_llm = AsyncLLM.from_engine_args(engine_args)
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if send_one_request:
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async for _ in async_llm.generate(
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"Hello my name is",
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request_id="abc",
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sampling_params=SamplingParams(
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max_tokens=1, output_kind=RequestOutputKind.DELTA)):
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pass
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del async_llm
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# Confirm all the processes are cleaned up.
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wait_for_gpu_memory_to_clear(
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devices=list(range(tensor_parallel_size)),
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threshold_bytes=SHUTDOWN_TEST_THRESHOLD_BYTES,
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)
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@pytest.mark.timeout(SHUTDOWN_TEST_TIMEOUT_SEC)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("tensor_parallel_size", [2, 1])
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@pytest.mark.parametrize("enable_multiprocessing", [True])
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@pytest.mark.parametrize("send_one_request", [False, True])
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def test_llm_delete(monkeypatch, model: str, tensor_parallel_size: int,
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enable_multiprocessing: bool,
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send_one_request: bool) -> None:
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"""Test that LLM frees GPU memory upon deletion.
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TODO(andy) - LLM without multiprocessing.
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Args:
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model: model under test
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tensor_parallel_size: degree of tensor parallelism
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enable_multiprocessing: enable workers in separate process(es)
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send_one_request: send one request to engine before deleting
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"""
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if cuda_device_count_stateless() < tensor_parallel_size:
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pytest.skip(reason="Not enough CUDA devices")
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with monkeypatch.context() as m:
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MP_VALUE = "1" if enable_multiprocessing else "0"
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m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", MP_VALUE)
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# Instantiate LLM; make request to complete any deferred
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# initialization; then delete instance
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llm = LLM(model=model,
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enforce_eager=True,
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tensor_parallel_size=tensor_parallel_size)
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if send_one_request:
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llm.generate("Hello my name is",
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sampling_params=SamplingParams(max_tokens=1))
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del llm
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# Confirm all the processes are cleaned up.
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wait_for_gpu_memory_to_clear(
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devices=list(range(tensor_parallel_size)),
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threshold_bytes=SHUTDOWN_TEST_THRESHOLD_BYTES,
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)
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