mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-08 20:14:27 +08:00
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import contextlib
|
|
import enum
|
|
import json
|
|
from typing import Optional
|
|
|
|
import torch
|
|
|
|
from vllm.config import VllmConfig
|
|
from vllm.logger import init_logger
|
|
from vllm.v1.core.sched.output import SchedulerOutput
|
|
from vllm.v1.metrics.stats import SchedulerStats
|
|
from vllm.version import __version__ as VLLM_VERSION
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
def prepare_object_to_dump(obj) -> str:
|
|
if isinstance(obj, str):
|
|
return "'{obj}'" # Double quotes
|
|
elif isinstance(obj, dict):
|
|
dict_str = ', '.join({f'{str(k)}: {prepare_object_to_dump(v)}' \
|
|
for k, v in obj.items()})
|
|
return f'{{{dict_str}}}'
|
|
elif isinstance(obj, list):
|
|
return f"[{', '.join([prepare_object_to_dump(v) for v in obj])}]"
|
|
elif isinstance(obj, set):
|
|
return f"[{', '.join([prepare_object_to_dump(v) for v in list(obj)])}]"
|
|
# return [prepare_object_to_dump(v) for v in list(obj)]
|
|
elif isinstance(obj, tuple):
|
|
return f"[{', '.join([prepare_object_to_dump(v) for v in obj])}]"
|
|
elif isinstance(obj, enum.Enum):
|
|
return repr(obj)
|
|
elif isinstance(obj, torch.Tensor):
|
|
# We only print the 'draft' of the tensor to not expose sensitive data
|
|
# and to get some metadata in case of CUDA runtime crashed
|
|
return (f"Tensor(shape={obj.shape}, "
|
|
f"device={obj.device},"
|
|
f"dtype={obj.dtype})")
|
|
elif hasattr(obj, 'anon_repr'):
|
|
return obj.anon_repr()
|
|
elif hasattr(obj, '__dict__'):
|
|
items = obj.__dict__.items()
|
|
dict_str = ','.join([f'{str(k)}={prepare_object_to_dump(v)}' \
|
|
for k, v in items])
|
|
return (f"{type(obj).__name__}({dict_str})")
|
|
else:
|
|
# Hacky way to make sure we can serialize the object in JSON format
|
|
try:
|
|
return json.dumps(obj)
|
|
except (TypeError, OverflowError):
|
|
return repr(obj)
|
|
|
|
|
|
def dump_engine_exception(config: VllmConfig,
|
|
scheduler_output: SchedulerOutput,
|
|
scheduler_stats: Optional[SchedulerStats]):
|
|
# NOTE: ensure we can log extra info without risking raises
|
|
# unexpected errors during logging
|
|
with contextlib.suppress(BaseException):
|
|
_dump_engine_exception(config, scheduler_output, scheduler_stats)
|
|
|
|
|
|
def _dump_engine_exception(config: VllmConfig,
|
|
scheduler_output: SchedulerOutput,
|
|
scheduler_stats: Optional[SchedulerStats]):
|
|
logger.error("Dumping input data")
|
|
|
|
logger.error(
|
|
"V1 LLM engine (v%s) with config: %s, ",
|
|
VLLM_VERSION,
|
|
config,
|
|
)
|
|
|
|
try:
|
|
dump_obj = prepare_object_to_dump(scheduler_output)
|
|
logger.error("Dumping scheduler output for model execution:")
|
|
logger.error(dump_obj)
|
|
if scheduler_stats:
|
|
logger.error(scheduler_stats)
|
|
except BaseException as exception:
|
|
logger.error("Error preparing object to dump")
|
|
logger.error(repr(exception))
|