Fix(llm): Abort orphaned requests when llm.chat() batch fails Fixes #26081

tests/entrypoints/llm/test_chat.py

@@ -6,6 +6,7 @@
 
 from vllm import LLM
 from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.sampling_params import SamplingParams
 
 from ..openai.test_vision import TEST_IMAGE_ASSETS
 
@@ -23,6 +24,29 @@ def text_llm():
     cleanup_dist_env_and_memory()
 
 
+@pytest.fixture(scope="function")
+def llm_for_failure_test():
+    """
+    Fixture for testing issue #26081.
+    Uses a small max_model_len to easily trigger length errors.
+    """
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(
+        model="meta-llama/Llama-3.2-1B-Instruct",
+        enforce_eager=True,
+        seed=0,
+        max_model_len=128,
+        disable_log_stats=True,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
 def test_chat(text_llm):
     prompt1 = "Explain the concept of entropy."
     messages = [
@@ -157,3 +181,32 @@ def test_chat_extra_kwargs(thinking_llm, enable_thinking):
     else:
         # The chat template includes dummy thinking process
         assert think_id in prompt_token_ids
+
+
+def test_chat_batch_failure_cleanup(llm_for_failure_test):
+    """
+    Tests that if a batch call to llm.chat() fails mid-way
+    (e.g., due to one invalid prompt), the requests that
+    were already enqueued are properly aborted and do not
+    pollute the queue for subsequent calls.
+    (Fixes Issue #26081)
+    """
+    llm = llm_for_failure_test
+    valid_msg = [{"role": "user", "content": "Hello"}]
+    long_text = "This is a very long text to test the error " * 50
+    invalid_msg = [{"role": "user", "content": long_text}]
+    batch_1 = [
+        valid_msg,
+        valid_msg,
+        invalid_msg,
+    ]
+    batch_2 = [
+        valid_msg,
+        valid_msg,
+    ]
+    sampling_params = SamplingParams(temperature=0, max_tokens=10)
+    with pytest.raises(ValueError, match="longer than the maximum model length"):
+        llm.chat(batch_1, sampling_params=sampling_params)
+    outputs_2 = llm.chat(batch_2, sampling_params=sampling_params)
+    assert len(outputs_2) == len(batch_2)
+    assert llm.llm_engine.get_num_unfinished_requests() == 0

vllm/entrypoints/llm.py

@@ -1588,20 +1588,27 @@ def _validate_and_add_requests(
             tqdm_func = use_tqdm if callable(use_tqdm) else tqdm
             it = tqdm_func(it, desc="Adding requests")
 
-        for i, prompt in enumerate(it):
-            if isinstance(prompt, dict):
-                self._validate_mm_data_and_uuids(
-                    prompt.get("multi_modal_data"), prompt.get("multi_modal_uuids")
-                )
+        added_request_ids: list[str] = []
 
-            self._add_request(
-                prompt,
-                params[i] if isinstance(params, Sequence) else params,
-                lora_request=lora_request[i]
-                if isinstance(lora_request, Sequence)
-                else lora_request,
-                priority=priority[i] if priority else 0,
-            )
+        try:
+            for i, prompt in enumerate(it):
+                if isinstance(prompt, dict):
+                    self._validate_mm_data_and_uuids(
+                        prompt.get("multi_modal_data"), prompt.get("multi_modal_uuids")
+                    )
+                request_id = self._add_request(
+                    prompt,
+                    params[i] if isinstance(params, Sequence) else params,
+                    lora_request=lora_request[i]
+                    if isinstance(lora_request, Sequence)
+                    else lora_request,
+                    priority=priority[i] if priority else 0,
+                )
+                added_request_ids.append(request_id)
+        except Exception as e:
+            if added_request_ids:
+                self.llm_engine.abort_request(added_request_ids)
+            raise e
 
     def _validate_mm_data_and_uuids(
         self,
@@ -1684,7 +1691,7 @@ def _add_request(
         params: SamplingParams | PoolingParams,
         lora_request: LoRARequest | None = None,
         priority: int = 0,
-    ) -> None:
+    ) -> str:
         prompt_text, _, _ = get_prompt_components(prompt)
         request_id = str(next(self.request_counter))
 
@@ -1705,6 +1712,7 @@ def _add_request(
             priority=priority,
             prompt_text=prompt_text,
         )
+        return request_id
 
     def _run_engine(
         self, *, use_tqdm: bool | Callable[..., tqdm] = True