[BugFix] Fix async scheduling + request preemption (#26385)

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

tests/v1/e2e/test_async_sched_and_preempt.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import pytest
+
+from vllm import SamplingParams
+
+from ...conftest import VllmRunner
+from ...models.utils import check_outputs_equal
+
+MODEL = "Qwen/Qwen3-0.6B"
+
+
+def test_preempt_and_async_scheduling_e2e(monkeypatch: pytest.MonkeyPatch):
+    """Test consistency of combos of async scheduling, preemption,
+    uni/multiproc executor, and various sampling parameters."""
+
+    first_prompt = (
+        "The following numbers of the sequence "
+        + ", ".join(str(i) for i in range(10))
+        + " are:"
+    )
+    example_prompts = [first_prompt, "In one word, the capital of France is "] + [
+        f"Tell me about the number {i}: " for i in range(32)
+    ]
+
+    sampling_param_tests: list[dict[str, Any]] = [
+        dict(),
+        # dict(min_tokens=20),
+        # TODO enable these with https://github.com/vllm-project/vllm/pull/26467.
+        # dict(repetition_penalty=0.1),
+        # dict(bad_words=[]),
+    ]
+
+    default_params = dict(
+        temperature=0.0,  # greedy
+        max_tokens=20,
+    )
+
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
+        # m.setenv("VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT", "1")
+
+        outputs = []
+        for test_preemption in [False, True]:
+            for executor in ["uni", "mp"]:
+                for async_scheduling in [False, True]:
+                    cache_arg: dict[str, Any] = (
+                        dict(num_gpu_blocks_override=32)
+                        if test_preemption
+                        else dict(gpu_memory_utilization=0.7)
+                    )
+                    test_config = (
+                        f"executor={executor}, preemption={test_preemption},"
+                        f" async_sched={async_scheduling}"
+                    )
+                    print("-" * 80)
+                    print(f"---- TESTING: {test_config}")
+                    print("-" * 80)
+                    with VllmRunner(
+                        MODEL,
+                        max_model_len=512,
+                        enforce_eager=True,
+                        async_scheduling=async_scheduling,
+                        distributed_executor_backend=executor,
+                        dtype="float32",  # avoid precision errors
+                        **cache_arg,
+                    ) as vllm_model:
+                        results = []
+                        for override_params in sampling_param_tests:
+                            print(f"----------- RUNNING PARAMS: {override_params}")
+                            results.append(
+                                vllm_model.generate(
+                                    example_prompts,
+                                    sampling_params=SamplingParams(
+                                        **default_params, **override_params
+                                    ),
+                                )
+                            )
+                        outputs.append((test_config, results))
+
+    baseline_config, baseline_tests = outputs[0]
+
+    for test_config, test_outputs in outputs[1:]:
+        for base_outs, test_outs, params in zip(
+            baseline_tests, test_outputs, sampling_param_tests
+        ):
+            check_outputs_equal(
+                outputs_0_lst=base_outs,
+                outputs_1_lst=test_outs,
+                name_0=f"baseline=[{baseline_config}], params={params}",
+                name_1=f"config=[{test_config}], params={params}",
+            )
+
+            print(f"PASSED: config=[{test_config}], params={params}")

vllm/v1/worker/gpu_model_runner.py

@@ -754,6 +754,12 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 # Replace the existing block IDs with the new ones.
                 req_state.block_ids = new_block_ids
 
+                if self.use_async_scheduling and num_output_tokens > 0:
+                    # We must recover the output token ids for resumed requests in the
+                    # async scheduling case, so that correct input_ids are obtained.
+                    resumed_token_ids = req_data.resumed_req_token_ids[i]
+                    assert resumed_token_ids is not None
+                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]
             if req_index is None:
                 # The request is not in the persistent batch.
                 # The request was either preempted and resumed later, or was not
@@ -991,7 +997,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
         if num_commmon_tokens == 0:
             # No requests in common with the previous iteration
-            # So input_ids_cpu will have all the input ids.
+            # So input_ids.cpu will have all the input ids.
             return
         if indices_match and max_flattened_index == (num_commmon_tokens - 1):
             # Common-case optimization: the batch is unchanged
@@ -1005,8 +1011,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             if self.enable_prompt_embeds:
                 self.is_token_ids.gpu[:num_commmon_tokens] = True
             return
-        # Upload the index tensors asynchronously
-        # so the scatter can be non-blocking.
+        # Upload the index tensors asynchronously so the scatter can be non-blocking.
         input_ids_index_tensor = torch.tensor(
             flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
         ).to(self.device, non_blocking=True)