[Misc] Don't dump contents of kvcache tensors on errors

vllm/worker/model_runner_base.py

@@ -3,11 +3,13 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
 from functools import wraps
-from typing import (TYPE_CHECKING, Any, Dict, Generic, List, Optional, Type,
-                    TypeVar)
+from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List,
+                    Optional, Type, TypeVar)
 
 import torch
+from torch import is_tensor
 
+from vllm.logger import init_logger
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
@@ -17,6 +19,8 @@
     from vllm.attention.backends.abstract import AttentionBackend
     from vllm.model_executor import SamplingMetadata
 
+logger = init_logger(__name__)
+
 T = TypeVar('T', bound="BroadcastableModelInput")
 
 
@@ -113,6 +117,8 @@ def _wrapper(*args, **kwargs):
             except Exception as err:
                 timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
                 filename = f"/tmp/err_{func.__name__}_input_{timestamp}.pkl"
+                logger.info("Writing input of failed execution to %s...",
+                            filename)
                 with open(filename, "wb") as filep:
                     dumped_inputs = {
                         k: v
@@ -122,7 +128,19 @@ def _wrapper(*args, **kwargs):
                     for i, arg in enumerate(args):
                         if i not in (exclude_args or []):
                             dumped_inputs[f"arg_{i}"] = arg
+
+                    # Only persist dtype and shape for kvcache tensors
+                    # (can be way to big otherwise)
+                    if (kv_caches := dumped_inputs.get("kv_caches")) \
+                        and isinstance(kv_caches, Iterable):
+                        dumped_inputs["kv_caches"] = [(t.dtype, t.shape)
+                                                      for t in kv_caches
+                                                      if is_tensor(t)]
+
                     pickle.dump(dumped_inputs, filep)
+                    logger.info(
+                        "Completed writing input of failed execution to %s.",
+                        filename)
                 raise type(err)(
                     f"Error in model execution (input dumped to {filename}): "
                     f"{str(err)}") from err