[Core][Feature] Input metadata dump on crash

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -75,7 +75,7 @@ body:
       ```
 
       ```
-      The error message you got, with the full traceback.
+      The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
       ```
   validations:
     required: true

tests/basic_correctness/test_basic_correctness.py

@@ -5,11 +5,13 @@
 """
 import os
 import weakref
+from unittest.mock import Mock
 
 import pytest
 
 from vllm import LLM
 from vllm.platforms import current_platform
+from vllm.v1.engine.llm_engine import LLMEngine as LLMEngineV1
 
 from ..conftest import VllmRunner
 from ..models.utils import check_outputs_equal
@@ -152,9 +154,44 @@ def test_models_distributed(
         with hf_runner(model, dtype=dtype) as hf_model:
             hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
-        check_outputs_equal(
-            outputs_0_lst=hf_outputs,
-            outputs_1_lst=vllm_outputs,
-            name_0="hf",
-            name_1="vllm",
-        )
+    check_outputs_equal(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
+def test_failed_model_execution(vllm_runner, monkeypatch) -> None:
+
+    from vllm.envs import VLLM_USE_V1
+
+    if not VLLM_USE_V1:
+        pytest.skip("Skipping V0 test, dump input not supported")
+
+    # Needed to mock an error in the same process
+    monkeypatch.setenv('VLLM_ENABLE_V1_MULTIPROCESSING', '0')
+
+    with vllm_runner('facebook/opt-125m', enforce_eager=True) as vllm_model:
+        if isinstance(vllm_model.model.llm_engine, LLMEngineV1):
+            v1_test_failed_model_execution(vllm_model)
+
+
+def v1_test_failed_model_execution(vllm_model):
+
+    engine = vllm_model.model.llm_engine
+    mocked_execute_model = Mock(
+        side_effect=RuntimeError("Mocked Critical Error"))
+    engine.engine_core.engine_core.model_executor.execute_model =\
+                mocked_execute_model
+
+    with pytest.raises(RuntimeError) as exc_info:
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        vllm_model.generate_greedy(prompts, 200, use_tqdm=False)
+    assert isinstance(exc_info.value, RuntimeError)
+    assert "Mocked Critical Error" in str(exc_info.value)

vllm/logging_utils/dump_input.py

@@ -0,0 +1,84 @@
+# 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))

vllm/v1/core/sched/output.py

@@ -48,6 +48,35 @@ def from_request(
             lora_request=request.lora_request,
         )
 
+    def __repr__(self):
+        return (f"NewRequestData("
+                f"req_id={self.req_id},"
+                f"prompt_token_ids={self.prompt_token_ids},"
+                f"prompt={self.prompt},"
+                f"mm_inputs={self.mm_inputs},"
+                f"mm_hashes={self.mm_hashes},"
+                f"mm_positions={self.mm_positions},"
+                f"sampling_params={self.sampling_params},"
+                f"block_ids={self.block_ids},"
+                f"num_computed_tokens={self.num_computed_tokens},"
+                f"lora_request={self.lora_request}"
+                ")")
+
+    # Version of __repr__ with the prompt data obfuscated
+    def anon_repr(self):
+        return (f"NewRequestData("
+                f"req_id={self.req_id},"
+                f"prompt_token_ids_len={len(self.prompt_token_ids)},"
+                f"prompt='',"
+                f"mm_inputs={self.mm_inputs},"
+                f"mm_hashes={self.mm_hashes},"
+                f"mm_positions={self.mm_positions},"
+                f"sampling_params={self.sampling_params},"
+                f"block_ids={self.block_ids},"
+                f"num_computed_tokens={self.num_computed_tokens},"
+                f"lora_request={self.lora_request}"
+                ")")
+
 
 @dataclass
 class CachedRequestData:

vllm/v1/engine/core.py

@@ -19,6 +19,7 @@
 from vllm.distributed import stateless_destroy_torch_distributed_process_group
 from vllm.executor.multiproc_worker_utils import _add_prefix
 from vllm.logger import init_logger
+from vllm.logging_utils.dump_input import dump_engine_exception
 from vllm.lora.request import LoRARequest
 from vllm.transformers_utils.config import (
     maybe_register_config_serialize_by_value)
@@ -58,6 +59,7 @@ def __init__(
     ):
         assert vllm_config.model_config.runner_type != "pooling"
 
+        self.vllm_config = vllm_config
         logger.info("Initializing a V1 LLM engine (v%s) with config: %s",
                     VLLM_VERSION, vllm_config)
 
@@ -203,7 +205,15 @@ def step(self) -> EngineCoreOutputs:
                 scheduler_stats=self.scheduler.make_stats(),
             )
         scheduler_output = self.scheduler.schedule()
-        output = self.model_executor.execute_model(scheduler_output)
+        try:
+            output = self.model_executor.execute_model(scheduler_output)
+        except BaseException as err:
+            # NOTE: This method is exception-free
+            dump_engine_exception(self.vllm_config, scheduler_output,
+                                  self.scheduler.make_stats())
+            # Re-raise exception
+            raise err
+
         engine_core_outputs = self.scheduler.update_from_output(
             scheduler_output, output)  # type: ignore