[BugFix] Handle errors when preprocessing added requests (#30895)

Signed-off-by: Nick Hill <nhill@redhat.com>

tests/v1/engine/test_preprocess_error_handling.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch.cuda
+
+from vllm import LLM, SamplingParams
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.core import EngineCore
+
+MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
+
+
+def test_preprocess_error_handling(monkeypatch: pytest.MonkeyPatch):
+    """Test that preprocessing errors are handled gracefully."""
+
+    assert not torch.cuda.is_initialized(), (
+        "fork needs to be used for the engine "
+        "core process and this isn't possible if cuda is already initialized"
+    )
+
+    # Store original method to call for non-failing requests
+    original_preprocess = EngineCore.preprocess_add_request
+
+    # Monkeypatch to make preprocess_add_request raise an exception
+    # only for requests with "FAIL" in the first token
+    def conditional_failing_preprocess(self, request: EngineCoreRequest):
+        # Fail if the first token id is 333
+        if request.prompt_token_ids and request.prompt_token_ids[0] == 333:
+            raise ValueError("Simulated preprocessing error!")
+        return original_preprocess(self, request)
+
+    monkeypatch.setattr(
+        EngineCore, "preprocess_add_request", conditional_failing_preprocess
+    )
+
+    llm = LLM(model=MODEL_NAME)
+
+    # Create a failing request by crafting a request with an invalid token
+    # We need to use a direct approach since LLM.generate tokenizes for us
+    from vllm.inputs import TokensPrompt
+
+    # This should raise an exception due to the preprocessing failure
+    # Special token id to trigger the failure
+    failing_prompt = TokensPrompt(prompt_token_ids=[333])
+    outputs = llm.generate(failing_prompt, SamplingParams(max_tokens=10))  # type: ignore
+    assert len(outputs) == 1
+    assert len(outputs[0].outputs[0].token_ids) == 0
+    assert outputs[0].finished
+    assert outputs[0].outputs[0].finish_reason == "error"
+
+    # Verify the engine is still functional with a normal request
+    outputs = llm.generate("Hello, my name is", SamplingParams(max_tokens=10))
+    assert len(outputs) == 1
+    assert len(outputs[0].outputs[0].token_ids) > 0
+    assert outputs[0].outputs[0].finish_reason in ("stop", "length")