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1 change: 1 addition & 0 deletions .buildkite/test_areas/quantization.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@ steps:
- csrc/
- vllm/model_executor/layers/quantization
- vllm/models/deepseek_v41/quant_config.py
- vllm/utils/humming.py
- tests/quantization
commands:
# Pin torchao to the version compatible with the CI PyTorch and CUDA stack.
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43 changes: 43 additions & 0 deletions tests/quantization/test_humming_compile.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,43 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch

from vllm.platforms import current_platform
from vllm.utils.import_utils import has_humming

pytestmark = pytest.mark.skipif(
not (has_humming() and current_platform.is_cuda()),
reason="requires humming and CUDA",
)


def test_humming_input_quant_compiles_fullgraph():
"""Humming's forward helpers look up ``DeviceInfo`` on every call; vLLM
traces them with ``fullgraph=True`` (vllm-project/humming#147)."""
import vllm.utils.humming as hm

weight_schema = hm.HummingWeightSchema(b_dtype="int4", weight_scale_group_size=128)
input_schema = hm.HummingInputSchema(a_dtype="int8")
from humming.transform import prepare_layer_config

config = prepare_layer_config(
shape_n=256,
shape_k=256,
weight_schema=weight_schema,
input_schema=input_schema,
torch_dtype=torch.bfloat16,
device=torch.device("cuda"),
)
x = torch.randn(16, 256, dtype=torch.bfloat16, device="cuda")

def quant(t: torch.Tensor):
return hm.may_process_input(config, t)

expected = quant(x)
actual = torch.compile(quant, fullgraph=True)(x)
for a, e in zip(actual, expected):
if e is None:
assert a is None
else:
torch.testing.assert_close(a, e)
60 changes: 60 additions & 0 deletions vllm/utils/humming.py
Original file line number Diff line number Diff line change
Expand Up @@ -109,10 +109,70 @@ def create_humming_locks(device: torch.device | str) -> torch.Tensor:
)


_patched = False


def _patch_humming() -> None:
"""Make humming's per-call ``DeviceInfo(device)`` lookups traceable.

The forward path (``LayerConfig.check_device``, PDL heuristics) builds a
``DeviceInfo`` on every call, which breaks vLLM's fullgraph compile:
Dynamo recurses forever in ``DeviceInfo.__getattr__``
(vllm-project/humming#147) and, with that fixed (#148), still cannot
trace the extension's ``_DeviceInfo`` constructor. Hand out one cached
instance per device instead; ``prepare_layer_config`` creates it eagerly
during weight processing.

TODO: remove after bumping humming to a release with
vllm-project/humming#149.
"""
global _patched
if _patched:
return
_patched = True

import humming.config.config
import humming.transform
import humming.tune
import humming.tune.process_input
import humming.utils.smem
from humming.device import DeviceInfo, get_device_index

def __getattr__(self, name: str) -> Any:
if name.startswith("_"):
raise AttributeError(
f"{type(self).__name__!r} object has no attribute {name!r}"
)
return getattr(self._get_value(), name)

DeviceInfo.__getattr__ = __getattr__

device_infos: dict[int, DeviceInfo] = {}

def cached_device_info(index: int | torch.device | None = None) -> DeviceInfo:
if isinstance(index, torch.device) and index.type != "cuda":
return DeviceInfo(index)
device_index = get_device_index(index)
if device_index not in device_infos:
device_infos[device_index] = DeviceInfo(device_index)
return device_infos[device_index]

for module in (
humming.config.config,
humming.transform,
humming.tune,
humming.tune.process_input,
humming.utils.smem,
):
if getattr(module, "DeviceInfo", None) is DeviceInfo:
module.DeviceInfo = cached_device_info


def __getattr__(name: str) -> Any:
spec = _EXPORTS.get(name)
if spec is None:
raise AttributeError(f"module 'vllm.utils.humming' has no attribute {name!r}")
_patch_humming()
if ":" in spec:
mod_path, attr = spec.split(":", 1)
obj = getattr(importlib.import_module(mod_path), attr)
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