fix: resolve silent request skipping in PRIORITY scheduling

tests/v1/core/test_priority_preemption_bug.py

@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.config import (
+    CacheConfig,
+    ModelConfig,
+    ParallelConfig,
+    SchedulerConfig,
+    VllmConfig,
+)
+from vllm.sampling_params import SamplingParams
+from vllm.utils.hashing import sha256
+from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.core.single_type_kv_cache_manager import register_all_kvcache_specs
+from vllm.v1.kv_cache_interface import (
+    FullAttentionSpec,
+    KVCacheConfig,
+    KVCacheGroupSpec,
+)
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.request import Request, RequestStatus
+from vllm.v1.structured_output import StructuredOutputManager
+
+BLOCK_SIZE = 16
+NUM_BLOCKS = 6
+MAX_MODEL_LEN = 1000
+LONG_PREFILL_THRESHOLD = 15
+EOS_TOKEN_ID = 50256
+
+
+def build_scheduler():
+    model_config = ModelConfig(
+        model="facebook/opt-350m",
+        trust_remote_code=True,
+        dtype="float16",
+        seed=42,
+        skip_tokenizer_init=True,
+        max_model_len=MAX_MODEL_LEN,
+    )
+    scheduler_config = SchedulerConfig(
+        max_num_seqs=5,
+        max_num_batched_tokens=200,
+        max_model_len=MAX_MODEL_LEN,
+        long_prefill_token_threshold=LONG_PREFILL_THRESHOLD,
+        enable_chunked_prefill=True,
+        is_encoder_decoder=model_config.is_encoder_decoder,
+        policy="priority",
+        watermark=0.0,
+        scheduler_reserve_full_isl=False,
+    )
+    cache_config = CacheConfig(
+        block_size=BLOCK_SIZE,
+        gpu_memory_utilization=0.9,
+        cache_dtype="auto",
+        enable_prefix_caching=False,
+    )
+    vllm_config = VllmConfig(
+        scheduler_config=scheduler_config,
+        model_config=model_config,
+        cache_config=cache_config,
+        parallel_config=ParallelConfig(),
+    )
+    kv_cache_spec = FullAttentionSpec(
+        block_size=BLOCK_SIZE, num_kv_heads=1, head_size=1, dtype=torch.float32
+    )
+    kv_cache_config = KVCacheConfig(
+        num_blocks=NUM_BLOCKS,
+        kv_cache_tensors=[],
+        kv_cache_groups=[KVCacheGroupSpec(["layer"], kv_cache_spec)],
+    )
+    cache_config.num_gpu_blocks = NUM_BLOCKS
+    register_all_kvcache_specs(vllm_config)
+    return Scheduler(
+        vllm_config=vllm_config,
+        kv_cache_config=kv_cache_config,
+        block_size=BLOCK_SIZE,
+        log_stats=True,
+        structured_output_manager=StructuredOutputManager(vllm_config),
+    )
+
+
+_hash_init = False
+
+
+def make_request(req_id, num_tokens, priority, arrival_time):
+    global _hash_init
+    if not _hash_init:
+        init_none_hash(sha256)
+        _hash_init = True
+    block_hasher = get_request_block_hasher(BLOCK_SIZE, sha256)
+    sp = SamplingParams(ignore_eos=True, max_tokens=50)
+    sp.update_from_generation_config({}, EOS_TOKEN_ID)
+    return Request(
+        request_id=req_id,
+        prompt_token_ids=[7] * num_tokens,
+        sampling_params=sp,
+        pooling_params=None,
+        priority=priority,
+        arrival_time=arrival_time,
+        block_hasher=block_hasher,
+    )
+
+
+def mock_output(out, tok=100):
+    ids = list(out.num_scheduled_tokens.keys())
+    return ModelRunnerOutput(
+        req_ids=ids,
+        req_id_to_index={r: i for i, r in enumerate(ids)},
+        sampled_token_ids=[[tok] for _ in ids],
+        logprobs=None,
+        prompt_logprobs_dict={},
+        pooler_output=[],
+    )
+
+
+def test_priority_scheduler_preempt_skipped_request():
+    scheduler = build_scheduler()
+
+    # R1: Add request A (Worst priority)
+    A = make_request("A", num_tokens=200, priority=9, arrival_time=1.0)
+    scheduler.add_request(A)
+    out = scheduler.schedule()
+    scheduler.update_from_output(out, mock_output(out))
+
+    # R2: Add request B (Best priority)
+    B = make_request("B", num_tokens=15, priority=0, arrival_time=2.0)
+    scheduler.add_request(B)
+    out = scheduler.schedule()
+    scheduler.update_from_output(out, mock_output(out))
+
+    # R3: Add request C (Middle priority)
+    C = make_request("C", num_tokens=1, priority=1, arrival_time=3.0)
+    scheduler.add_request(C)
+    out = scheduler.schedule()
+    scheduler.update_from_output(out, mock_output(out))
+
+    # R4: Trigger preemption
+    out = scheduler.schedule(throttle_prefills=True)
+
+    is_c_scheduled = "C" in out.num_scheduled_tokens
+
+    assert is_c_scheduled, (
+        "Bug present: Request C was silently skipped during scheduling "
+        "because the req_index was not decremented after preempting A."
+    )
+    assert C.status == RequestStatus.RUNNING

vllm/v1/core/sched/scheduler.py

@@ -593,7 +593,16 @@ def schedule(self, throttle_prefills: bool = False) -> SchedulerOutput:
                             self.running,
                             key=lambda r: (r.priority, r.arrival_time),
                         )
-                        self.running.remove(preempted_req)
+                        # Record the index of the preemption victim to
+                        # maintain accurate loop state.
+                        victim_index = self.running.index(preempted_req)
+                        del self.running[victim_index]
+                        # Decrement the loop cursor if the removed request
+                        # preceded the current iteration, preventing the
+                        # silent omission of the subsequent request.
+                        if victim_index < req_index:
+                            req_index -= 1
+
                         if preempted_req in scheduled_running_reqs:
                             preempted_req_id = preempted_req.request_id
                             scheduled_running_reqs.remove(preempted_req)
@@ -611,7 +620,6 @@ def schedule(self, throttle_prefills: bool = False) -> SchedulerOutput:
                                     for i in preempted_encoder_inputs
                                 )
                                 encoder_compute_budget += num_embeds_to_restore
-                            req_index -= 1
                     else:
                         preempted_req = self.running.pop()