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vllm-project--vllm/tests/v1/worker/test_gpu_worker.py
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chore: import upstream snapshot with attribution
2026-07-13 12:55:37 +08:00

189 lines
6.3 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from types import SimpleNamespace
from unittest.mock import patch
import pytest
import vllm.v1.worker.gpu_worker as gpu_worker_module
from vllm.multimodal.video import (
PYNVVIDEOCODEC_CUDA_CONTEXT_BYTES,
PYNVVIDEOCODEC_DECODER_GPU_MEMORY_BYTES,
PYNVVIDEOCODEC_MAX_RETAINED_DECODERS,
PYNVVIDEOCODEC_VIDEO_BACKEND,
)
from vllm.utils.mem_constants import GiB_bytes
from vllm.v1.worker import startup_plan
from vllm.v1.worker.gpu_worker import Worker
from vllm.v1.worker.startup_plan import (
maybe_apply_startup_plan,
maybe_save_startup_plan,
)
def _worker_with_mm_config(
mm_config: SimpleNamespace,
*,
api_process_count: int = 1,
) -> Worker:
worker = object.__new__(Worker)
worker.model_config = SimpleNamespace(multimodal_config=mm_config)
worker.parallel_config = SimpleNamespace(_api_process_count=api_process_count)
return worker
def _mm_config(
*,
mm_ipc_gpu_memory_gb: float = 0,
video_backend: str | None = None,
) -> SimpleNamespace:
video_kwargs = {} if video_backend is None else {"video_backend": video_backend}
return SimpleNamespace(
mm_ipc_gpu_memory_gb=mm_ipc_gpu_memory_gb,
media_io_kwargs={"video": video_kwargs} if video_kwargs else {},
)
def _pynvvideocodec_decoder_budget(api_process_count: int = 1) -> int:
return api_process_count * (
PYNVVIDEOCODEC_DECODER_GPU_MEMORY_BYTES * PYNVVIDEOCODEC_MAX_RETAINED_DECODERS
+ PYNVVIDEOCODEC_CUDA_CONTEXT_BYTES
)
@pytest.mark.parametrize("video_backend", [None, "opencv"])
def test_reserve_mm_ipc_gpu_memory_raw_frame_budget_only(
monkeypatch: pytest.MonkeyPatch,
video_backend: str | None,
):
monkeypatch.setattr(
gpu_worker_module.envs,
"VLLM_VIDEO_LOADER_BACKEND",
"opencv",
)
worker = _worker_with_mm_config(
_mm_config(mm_ipc_gpu_memory_gb=0.25, video_backend=video_backend)
)
assert worker._reserve_mm_ipc_gpu_memory(GiB_bytes) == int(0.75 * GiB_bytes)
def test_reserve_mm_ipc_gpu_memory_includes_pynvvideocodec_decoder_budget(
monkeypatch: pytest.MonkeyPatch,
):
monkeypatch.setattr(
gpu_worker_module.envs,
"VLLM_VIDEO_LOADER_BACKEND",
"opencv",
)
worker = _worker_with_mm_config(
_mm_config(
mm_ipc_gpu_memory_gb=0.25,
video_backend=PYNVVIDEOCODEC_VIDEO_BACKEND,
)
)
available_bytes = 4 * GiB_bytes
assert worker._reserve_mm_ipc_gpu_memory(available_bytes) == (
available_bytes - int(0.25 * GiB_bytes) - _pynvvideocodec_decoder_budget()
)
def test_reserve_mm_ipc_gpu_memory_uses_env_video_backend(
monkeypatch: pytest.MonkeyPatch,
):
monkeypatch.setattr(
gpu_worker_module.envs,
"VLLM_VIDEO_LOADER_BACKEND",
PYNVVIDEOCODEC_VIDEO_BACKEND,
)
worker = _worker_with_mm_config(_mm_config())
available_bytes = 4 * GiB_bytes
assert worker._reserve_mm_ipc_gpu_memory(available_bytes) == (
available_bytes - _pynvvideocodec_decoder_budget()
)
def test_reserve_mm_ipc_gpu_memory_scales_pynvvideocodec_budget_by_api_servers(
monkeypatch: pytest.MonkeyPatch,
):
monkeypatch.setattr(
gpu_worker_module.envs,
"VLLM_VIDEO_LOADER_BACKEND",
PYNVVIDEOCODEC_VIDEO_BACKEND,
)
worker = _worker_with_mm_config(_mm_config(), api_process_count=3)
available_bytes = 8 * GiB_bytes
assert worker._reserve_mm_ipc_gpu_memory(available_bytes) == (
available_bytes - _pynvvideocodec_decoder_budget(api_process_count=3)
)
# Startup-plan persistence (vllm/v1/worker/startup_plan.py), applied and
# saved by Worker.determine_available_memory / compile_or_warm_up_model.
def _plan_worker(config_hash="abc123", free_memory=78 * GiB_bytes, kv_bytes=None):
"""The minimal Worker surface the startup-plan entry points touch."""
return SimpleNamespace(
vllm_config=SimpleNamespace(compute_hash=lambda: config_hash),
rank=0,
parallel_config=SimpleNamespace(world_size=1),
init_snapshot=SimpleNamespace(free_memory=free_memory),
cache_config=SimpleNamespace(kv_cache_memory_bytes=kv_bytes),
)
def _plan_platform(name="NVIDIA H100 PCIe"):
return SimpleNamespace(
get_device_name=lambda device_id=0: name,
get_device_total_memory=lambda device_id=0: 80 * GiB_bytes,
get_device_capability=lambda device_id=0: (9, 0),
)
@pytest.fixture
def plan_env(monkeypatch: pytest.MonkeyPatch, tmp_path):
"""Enable the startup plan, isolated under a tmp cache root."""
monkeypatch.setenv("VLLM_ENABLE_STARTUP_PLAN", "1")
monkeypatch.setenv("VLLM_CACHE_ROOT", str(tmp_path))
with patch.object(startup_plan, "current_platform", _plan_platform()):
yield
def test_startup_plan_fingerprint_sensitivity(plan_env):
"""The fingerprint is the OOM-safety key: stable for identical inputs,
different for anything the profiled value depends on."""
fp = startup_plan.compute_plan_fingerprint
base = fp(_plan_worker().vllm_config, 0, 1)
assert base == fp(_plan_worker().vllm_config, 0, 1)
assert base != fp(_plan_worker("other").vllm_config, 0, 1)
assert base != fp(_plan_worker().vllm_config, 1, 2)
with patch.object(startup_plan, "current_platform", _plan_platform("NVIDIA A100")):
assert base != fp(_plan_worker().vllm_config, 0, 1)
with patch("vllm.__version__", "0.0.0+plan-test"):
assert base != fp(_plan_worker().vllm_config, 0, 1)
def test_startup_plan_apply_gate(plan_env):
"""Only a fingerprint-matching, memory-safe plan is ever applied."""
maybe_save_startup_plan(_plan_worker(), 50 * GiB_bytes)
applied = _plan_worker()
maybe_apply_startup_plan(applied)
assert applied.cache_config.kv_cache_memory_bytes == 50 * GiB_bytes
less_memory = _plan_worker(free_memory=60 * GiB_bytes)
other_config = _plan_worker(config_hash="zzz999")
for refused in (less_memory, other_config):
maybe_apply_startup_plan(refused)
assert refused.cache_config.kv_cache_memory_bytes is None
# An explicit --kv-cache-memory is never overridden.
explicit = _plan_worker(kv_bytes=7 * GiB_bytes)
maybe_apply_startup_plan(explicit)
assert explicit.cache_config.kv_cache_memory_bytes == 7 * GiB_bytes