[V1][Frontend] Improve Shutdown And Logs (#11737)

Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Signed-off-by: Andrew Feldman <afeldman@neuralmagic.com>
Signed-off-by: Nick Hill <nhill@redhat.com>
Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Russell Bryant <rbryant@redhat.com>
Co-authored-by: Andrew Feldman <afeldman@neuralmagic.com>
Co-authored-by: afeldman-nm <156691304+afeldman-nm@users.noreply.github.com>
Co-authored-by: Nick Hill <nhill@redhat.com>

tests/v1/shutdown/test_startup_error.py

@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Test that we handle a startup Error and shutdown."""
+
+import pytest
+
+from tests.utils import wait_for_gpu_memory_to_clear
+from tests.v1.shutdown.utils import (SHUTDOWN_TEST_THRESHOLD_BYTES,
+                                     SHUTDOWN_TEST_TIMEOUT_SEC)
+from vllm import LLM
+from vllm.distributed import get_tensor_model_parallel_rank
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.utils import cuda_device_count_stateless
+from vllm.v1.engine.async_llm import AsyncLLM
+
+MODELS = ["meta-llama/Llama-3.2-1B"]
+
+
+def evil_method(self, *args, **kwargs):
+    """Evil method that raises an exception."""
+
+    if get_tensor_model_parallel_rank() == 0:
+        raise Exception("Simulated Error in startup!")
+
+    return self.model(*args, **kwargs, intermediate_tensors=None)
+
+
+@pytest.mark.timeout(SHUTDOWN_TEST_TIMEOUT_SEC)
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tensor_parallel_size", [2, 1])
+@pytest.mark.parametrize("failing_method", ["forward", "load_weights"])
+def test_async_llm_startup_error(monkeypatch, model: str,
+                                 tensor_parallel_size: int,
+                                 failing_method: str) -> None:
+    """Test that AsyncLLM propagates an __init__ error & frees memory.
+    Test profiling (forward()) and load weights failures.
+    AsyncLLM always uses an MP client.
+    """
+    if cuda_device_count_stateless() < tensor_parallel_size:
+        pytest.skip(reason="Not enough CUDA devices")
+
+    # Monkeypatch an error in the model.
+    monkeypatch.setattr(LlamaForCausalLM, failing_method, evil_method)
+
+    engine_args = AsyncEngineArgs(model=model,
+                                  enforce_eager=True,
+                                  tensor_parallel_size=tensor_parallel_size)
+
+    # Confirm we get an exception.
+    with pytest.raises(Exception, match="initialization failed"):
+        _ = AsyncLLM.from_engine_args(engine_args)
+
+    # Confirm all the processes are cleaned up.
+    wait_for_gpu_memory_to_clear(
+        devices=list(range(tensor_parallel_size)),
+        threshold_bytes=SHUTDOWN_TEST_THRESHOLD_BYTES,
+    )
+
+
+@pytest.mark.timeout(SHUTDOWN_TEST_TIMEOUT_SEC)
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tensor_parallel_size", [2, 1])
+@pytest.mark.parametrize("enable_multiprocessing", [True])
+@pytest.mark.parametrize("failing_method", ["forward", "load_weights"])
+def test_llm_startup_error(monkeypatch, model: str, tensor_parallel_size: int,
+                           enable_multiprocessing: bool,
+                           failing_method: str) -> None:
+    """Test that LLM propagates an __init__ error and frees memory.
+    Test profiling (forward()) and load weights failures.
+    TODO(andy) - LLM without multiprocessing.
+    """
+    if model != "meta-llama/Llama-3.2-1B":
+        pytest.skip(reason="Only test meta-llama/Llama-3.2-1B")
+    if cuda_device_count_stateless() < tensor_parallel_size:
+        pytest.skip(reason="Not enough CUDA devices")
+
+    with monkeypatch.context() as m:
+
+        MP_VALUE = "1" if enable_multiprocessing else "0"
+        m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", MP_VALUE)
+
+        # Monkeypatch an error in the model.
+        monkeypatch.setattr(LlamaForCausalLM, failing_method, evil_method)
+
+        with pytest.raises(
+                Exception,
+                match="initialization failed"
+                if enable_multiprocessing else "Simulated Error in startup!"):
+            _ = LLM(model=model,
+                    enforce_eager=True,
+                    tensor_parallel_size=tensor_parallel_size)
+
+        # Confirm all the processes are cleaned up.
+        wait_for_gpu_memory_to_clear(
+            devices=list(range(tensor_parallel_size)),
+            threshold_bytes=SHUTDOWN_TEST_THRESHOLD_BYTES,
+        )