init

logging_utils/dump_input.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+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 f"'{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))