[CI/Build] Refresh tags before building macOS wheel (#49901)

Signed-off-by: khluu <khluu000@gmail.com>
Co-authored-by: OpenAI Codex <codex@openai.com>
(cherry picked from commit 0934b26790)

.buildkite/release-pipeline.yaml

@@ -813,8 +813,8 @@ steps:
 
             # Download artifacts from current build
             echo "Downloading artifacts from current build"
-            # buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
-            # buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .
+            buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
+            buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .
 
             # # Run upload script
             bash .buildkite/scripts/upload-rocm-wheels.sh

.buildkite/scripts/build-macos-wheel.sh

@@ -7,6 +7,9 @@
 
 set -euo pipefail
 
+# The macmini queue uses persistent checkouts, so refresh tags for setuptools-scm.
+git fetch --tags --force origin
+
 # The Rust frontend build needs protoc.
 if ! command -v protoc >/dev/null 2>&1; then
   brew install protobuf

.buildkite/test_areas/basic_correctness.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
   key: basic-correctness
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
   device: h200_18gb
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/benchmarks.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
   key: benchmarks-cli-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/engine.yaml

@@ -51,7 +51,7 @@ steps:
 
 - label: e2e Scheduling (1 GPU)
   key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 35
+  timeout_in_minutes: 53
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/

.buildkite/test_areas/entrypoints.yaml

@@ -39,7 +39,7 @@ steps:
 - label: Entrypoints Integration (API Server)
   key: entrypoints-integration-api-server
   device: h200_35gb
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -59,7 +59,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 1)
   device: h200_35gb
   key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -78,7 +78,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 2)
   device: h200_35gb
   key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -156,7 +156,7 @@ steps:
 - label: Entrypoints Integration (Pooling)
   device: h200_35gb
   key: entrypoints-integration-pooling
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/misc.yaml

@@ -31,7 +31,7 @@ steps:
 
 - label: V1 Sample + Logits
   key: v1-sample-logits
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
   device: h200_18gb
   source_file_dependencies:
     - vllm/config/

.buildkite/test_areas/model_executor.yaml

@@ -5,7 +5,7 @@ steps:
 - label: Model Executor
   device: h200_35gb
   key: model-executor
-  timeout_in_minutes: 45
+  timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/engine/arg_utils.py
   - vllm/config/model.py

.buildkite/test_areas/models_language.yaml

@@ -137,7 +137,7 @@ steps:
 
 - label: Language Models Test (MTEB)
   key: language-models-test-mteb
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
   device: h200_18gb
   optional: true
   source_file_dependencies:

.buildkite/test_areas/models_multimodal.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
   key: multi-modal-models-standard-1-qwen2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -20,7 +20,7 @@ steps:
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
   key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -54,7 +54,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
   device: h200_35gb
   key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
@@ -85,7 +85,7 @@ steps:
 
 - label: Multi-Modal Processor # 44min
   key: multi-modal-processor
-  timeout_in_minutes: 65
+  timeout_in_minutes: 98
   device: h200_18gb
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/pytorch.yaml

@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
   device: h200_35gb
   key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 90
+  timeout_in_minutes: 150
   source_file_dependencies:
     - vllm/__init__.py
     - vllm/_aiter_ops.py

tests/evals/gsm8k/configs/GLM-5.2-NVFP4-TP1-PCP4-EP.yaml

@@ -6,6 +6,7 @@ max_concurrency: 100
 server_args: >-
   --enforce-eager
   --max-model-len 4096
+  --max-num-batched-tokens 32768
   --safetensors-load-strategy prefetch
   --moe-backend flashinfer_cutlass
   --prefill-context-parallel-size 4

tests/evals/gsm8k/configs/GLM-5.2-NVFP4-TP2-PCP2-EP.yaml

@@ -6,6 +6,7 @@ max_concurrency: 100
 server_args: >-
   --enforce-eager
   --max-model-len 4096
+  --max-num-batched-tokens 32768
   --safetensors-load-strategy prefetch
   --moe-backend flashinfer_cutlass
   --tensor-parallel-size 2

tests/kernels/attention/test_merge_attn_states.py

@@ -11,6 +11,9 @@ from vllm._custom_ops import (
     scaled_fp8_quant,
 )
 from vllm.platforms import current_platform
+from vllm.v1.attention.ops.triton_merge_attn_states import (
+    mask_empty_context,
+)
 from vllm.v1.attention.ops.triton_merge_attn_states import (
     merge_attn_states as merge_attn_states_triton,
 )
@@ -73,6 +76,59 @@ DTYPES = [torch.float32, torch.half, torch.bfloat16]
 all_case_info: list[tuple] = []
 
 
+def test_mask_empty_context() -> None:
+    query_lens = torch.tensor([2] + [1] * 31 + [131, 1], dtype=torch.int32)
+    query_start_loc = torch.cat(
+        (torch.zeros(1, dtype=torch.int32), query_lens.cumsum(0))
+    ).cuda()
+    context_lens = torch.tensor([4] * 32 + [0, 3], dtype=torch.int32)
+    context_start_loc = torch.cat(
+        (torch.zeros(1, dtype=torch.int32), context_lens.cumsum(0))
+    ).cuda()
+    num_heads, num_tokens, head_dim = 4, 165, 16
+    lse = torch.randn(num_heads, num_tokens, device="cuda")
+    output = torch.randn(num_tokens, num_heads, head_dim, device="cuda")
+    # Empty-context rows carry undefined (possibly non-finite) attention output.
+    output[33:164] = float("nan")
+
+    expected_lse = lse.clone()
+    expected_lse[:, 33:164] = float("-inf")
+    expected_output = output.clone()
+    expected_output[33:164] = 0.0
+
+    mask_empty_context(lse, output, query_start_loc, context_start_loc)
+
+    torch.testing.assert_close(lse, expected_lse)
+    torch.testing.assert_close(output, expected_output)
+
+
+@pytest.mark.parametrize("merge_fn", [merge_attn_states_cuda, merge_attn_states_triton])
+@pytest.mark.parametrize("output_dtype", [torch.float32, torch.half, torch.bfloat16])
+def test_merge_attn_states_both_empty(merge_fn, output_dtype) -> None:
+    """When a token is empty on both sides (both LSE -inf), the 0/0 softmax
+    scales must not surface as NaN in the merged output."""
+    num_tokens, num_heads, head_size = 6, 8, 128
+    prefix_output = torch.zeros(
+        num_tokens, num_heads, head_size, device="cuda", dtype=output_dtype
+    )
+    prefix_lse = torch.randn(num_heads, num_tokens, device="cuda")
+    suffix_output = torch.zeros(
+        num_tokens, num_heads, head_size, device="cuda", dtype=output_dtype
+    )
+    suffix_lse = torch.randn(num_heads, num_tokens, device="cuda")
+
+    # Tokens 2 and 3 are empty on both sides (mask_empty_context already zeroed
+    # their outputs and set both LSEs to -inf).
+    empty = slice(2, 4)
+    prefix_lse[:, empty] = float("-inf")
+    suffix_lse[:, empty] = float("-inf")
+
+    output = torch.empty_like(prefix_output)
+    merge_fn(output, prefix_output, prefix_lse, suffix_output, suffix_lse)
+
+    assert not output.isnan().any()
+
+
 def generate_markdown_table():
     global all_case_info
     table_header = (

tests/plugins_tests/test_bge_m3_sparse_io_processor_plugins.py

@@ -48,12 +48,12 @@ def _check_dense_embedding(data, index=0):
 def _check_sparse_embedding(data, check_tokens=False):
     expected_weights = [
         {"token_id": 32, "weight": 0.0552978515625, "token": "?"},
-        {"token_id": 70, "weight": 0.09808349609375, "token": "the"},
-        {"token_id": 83, "weight": 0.08154296875, "token": "is"},
-        {"token_id": 111, "weight": 0.11810302734375, "token": "of"},
-        {"token_id": 4865, "weight": 0.1171875, "token": "What"},
-        {"token_id": 9942, "weight": 0.292236328125, "token": "France"},
-        {"token_id": 10323, "weight": 0.2802734375, "token": "capital"},
+        {"token_id": 70, "weight": 0.09808349609375, "token": " the"},
+        {"token_id": 83, "weight": 0.08154296875, "token": " is"},
+        {"token_id": 111, "weight": 0.11810302734375, "token": " of"},
+        {"token_id": 4865, "weight": 0.1171875, "token": " What"},
+        {"token_id": 9942, "weight": 0.292236328125, "token": " France"},
+        {"token_id": 10323, "weight": 0.2802734375, "token": " capital"},
     ]
     expected_embed = {x["token_id"]: x for x in expected_weights}
 

tests/v1/core/test_async_scheduler.py

@@ -9,6 +9,7 @@ from vllm.v1.core.sched.async_scheduler import AsyncScheduler
 from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import RequestStatus
+from vllm.v1.structured_output import StructuredOutputGrammar
 from vllm.v1.utils import ConstantList
 
 from .utils import create_requests, create_scheduler
@@ -262,7 +263,7 @@ def test_abort_request_when_structured_output_fsm_cannot_advance():
     scheduler = object.__new__(AsyncScheduler)
     request = create_requests(num_requests=1, num_tokens=1)[0]
     request.structured_output_request = Mock()
-    request.structured_output_request.grammar = Mock()
+    request.structured_output_request.grammar = Mock(spec=StructuredOutputGrammar)
     request.structured_output_request.grammar.accept_tokens.return_value = False
     request.status = RequestStatus.RUNNING
     request.num_computed_tokens = request.num_tokens
@@ -284,6 +285,7 @@ def test_abort_request_when_structured_output_fsm_cannot_advance():
     scheduler.kv_event_publisher = Mock()
     scheduler.finished_req_ids = set()
     scheduler.finished_req_ids_dict = None
+    scheduler.grammar_compile_error_reqs = set()
     scheduler.vllm_config = Mock()
     scheduler.vllm_config.model_config.enable_return_routed_experts = False
     scheduler.enable_return_routed_experts = False

tests/v1/core/test_scheduler.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import dataclasses
+from concurrent.futures import Future
 from unittest.mock import Mock
 
 import pytest
@@ -36,7 +37,7 @@ from vllm.v1.kv_cache_interface import (
 )
 from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus
-from vllm.v1.structured_output import StructuredOutputManager
+from vllm.v1.structured_output import StructuredOutputGrammar, StructuredOutputManager
 
 from .utils import EOS_TOKEN_ID, create_requests, create_scheduler, mock_kv
 
@@ -3006,6 +3007,58 @@ def test_schedule_skip_tokenizer_init_structured_output_request():
     assert len(scheduler.skipped_waiting) == 1
 
 
+@pytest.mark.parametrize("async_grammar", [True, False])
+def test_grammar_compile_error_finishes_only_request(async_grammar: bool):
+    scheduler = create_scheduler()
+    manager = scheduler.structured_output_manager
+    manager.backend = Mock()
+    manager.backend.compile_grammar.side_effect = RuntimeError(
+        "forced FSM compilation error"
+    )
+    manager._use_async_grammar_compilation = async_grammar
+
+    sampling_params = SamplingParams(
+        max_tokens=16,
+        structured_outputs=StructuredOutputsParams(json='{"type": "object"}'),
+    )
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
+    request = Request(
+        request_id="grammar-error",
+        prompt_token_ids=[0, 1],
+        sampling_params=sampling_params,
+        pooling_params=None,
+    )
+
+    manager.grammar_init(request)
+    assert request.structured_output_request is not None
+    grammar_future = request.structured_output_request._grammar
+    assert isinstance(grammar_future, Future)
+    assert isinstance(grammar_future.exception(timeout=5), RuntimeError)
+
+    scheduler.add_request(request)
+    scheduler_output = scheduler.schedule()
+    assert not scheduler_output.num_scheduled_tokens
+
+    engine_core_outputs = scheduler.update_from_output(
+        scheduler_output,
+        ModelRunnerOutput(req_ids=[], req_id_to_index={}),
+    )
+
+    assert request.status == RequestStatus.FINISHED_ERROR
+    assert request.request_id not in scheduler.requests
+    output = engine_core_outputs[0].outputs[0]
+    assert output.request_id == request.request_id
+    assert output.finish_reason == FinishReason.ERROR
+    assert output.stop_reason is None
+
+    healthy_request = create_requests(num_requests=1, req_ids=["healthy-request"])[0]
+    scheduler.add_request(healthy_request)
+    next_output = scheduler.schedule()
+    assert [req.req_id for req in next_output.scheduled_new_reqs] == [
+        healthy_request.request_id
+    ]
+
+
 def test_abort_request_when_structured_output_fsm_cannot_advance():
     scheduler = object.__new__(Scheduler)
     sampling_params = SamplingParams(ignore_eos=True, max_tokens=4)
@@ -3019,7 +3072,7 @@ def test_abort_request_when_structured_output_fsm_cannot_advance():
         pooling_params=None,
     )
     request.structured_output_request = Mock()
-    request.structured_output_request.grammar = Mock()
+    request.structured_output_request.grammar = Mock(spec=StructuredOutputGrammar)
     request.structured_output_request.grammar.accept_tokens.return_value = False
     request.status = RequestStatus.RUNNING
     request.num_computed_tokens = request.num_tokens
@@ -3040,6 +3093,7 @@ def test_abort_request_when_structured_output_fsm_cannot_advance():
     scheduler.kv_event_publisher = Mock()
     scheduler.finished_req_ids = set()
     scheduler.finished_req_ids_dict = None
+    scheduler.grammar_compile_error_reqs = set()
     scheduler.vllm_config = Mock()
     scheduler.vllm_config.model_config.enable_return_routed_experts = False
     scheduler.enable_return_routed_experts = False

vllm/model_executor/layers/attention/mla_attention.py

@@ -277,6 +277,7 @@ from vllm.v1.attention.backends.utils import (
 from vllm.v1.attention.ops.common import cp_lse_ag_out_ar, cp_lse_ag_out_rs
 from vllm.v1.attention.ops.dcp_alltoall import dcp_a2a_lse_reduce
 from vllm.v1.attention.ops.merge_attn_states import merge_attn_states
+from vllm.v1.attention.ops.triton_merge_attn_states import mask_empty_context
 from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
@@ -1342,6 +1343,7 @@ class MLACommonPrefillMetadata:
         workspace: torch.Tensor
         token_to_seq: torch.Tensor
         chunk_total_token: list[int]
+        has_empty_context: list[bool]
 
         # for mla DCP
         padded_local_chunk_seq_lens: list[list[int]] | None = None
@@ -1551,6 +1553,7 @@ def build_mla_chunked_context_metadata(
     )
     chunk_seq_lens = chunk_ends - chunk_starts
     chunk_seq_lens.clamp_(min=0)
+    has_empty_context = torch.any(chunk_seq_lens == 0, dim=1).tolist()
 
     cu_seq_lens_cpu = torch.zeros(
         num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
@@ -1629,6 +1632,7 @@ def build_mla_chunked_context_metadata(
             token_to_seq=token_to_seq_cpu.to(device, non_blocking=True),
             chunk_total_token=chunk_total_token.tolist(),
             workspace=chunked_prefill_workspace,
+            has_empty_context=has_empty_context,
             prefill_tokens_with_context=prefill_tokens_with_context,
             padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(),
             local_context_lens_allranks=local_context_lens_allranks.tolist(),
@@ -1651,6 +1655,7 @@ def build_mla_chunked_context_metadata(
             token_to_seq=token_to_seq_cpu.to(device, non_blocking=True),
             chunk_total_token=chunk_total_token,
             workspace=chunked_prefill_workspace,
+            has_empty_context=has_empty_context,
             prefill_tokens_with_context=prefill_tokens_with_context,
         )
 
@@ -2238,6 +2243,13 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
                     v=v,
                 )
             )
+            if prefill_metadata.chunked_context.has_empty_context[i]:
+                mask_empty_context(
+                    attn_softmax_lse,
+                    attn_output,
+                    prefill_metadata.query_start_loc,
+                    prefill_metadata.chunked_context.cu_seq_lens[i],
+                )
 
             if output is None:
                 output = attn_output
@@ -2388,6 +2400,13 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
                     v=v,
                 )
             )
+            if prefill_metadata.chunked_context.has_empty_context[i]:
+                mask_empty_context(
+                    attn_softmax_lse,
+                    attn_output,
+                    prefill_metadata.query_start_loc,
+                    prefill_metadata.chunked_context.cu_seq_lens[i],
+                )
 
             if output is None:
                 output = attn_output

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -133,6 +133,18 @@ def _extract_data_from_fused_moe_module(
     return w13, w13_s, w2, w2_s, num_topk
 
 
+def _is_deep_gemm_backed_kernel(fp8_linear: object) -> bool:
+    """
+    Return True if the selected linear kernel dispatches to DeepGEMM, either
+    directly or as the fallback branch of a dynamic wrapper.
+    """
+    if isinstance(fp8_linear, DeepGemmFp8BlockScaledMMKernel):
+        return True
+    return isinstance(
+        getattr(fp8_linear, "fallback", None), DeepGemmFp8BlockScaledMMKernel
+    )
+
+
 def _fp8_linear_may_use_deep_gemm(module: torch.nn.Module) -> bool:
     """
     Return True if the input module/layer could be processed with DeepGEMM.
@@ -147,10 +159,8 @@ def _fp8_linear_may_use_deep_gemm(module: torch.nn.Module) -> bool:
     ):
         return False
 
-    if not isinstance(
-        getattr(module.quant_method, "fp8_linear", None),
-        DeepGemmFp8BlockScaledMMKernel,
-    ):
+    fp8_linear = getattr(module.quant_method, "fp8_linear", None)
+    if not _is_deep_gemm_backed_kernel(fp8_linear):
         return False
 
     block_size = get_mk_alignment_for_contiguous_layout()[0]

vllm/platforms/cuda.py

@@ -290,7 +290,8 @@ class CudaPlatformBase(Platform):
             # kernel with limited pinned memory support for CUDA.
             version = _get_wsl_kernel_version()
             if version is None or version < (4, 19, 121):
-                logger.warning_once(
+                # warning_once() causes a circular import on WSL, see #48397.
+                logger.warning(
                     "Using 'pin_memory=False' as WSL is detected and the "
                     "WSL2 kernel version is below 4.19.121. This may slow "
                     "down performance. Please run `wsl --update`."

vllm/platforms/interface.py

@@ -991,7 +991,8 @@ class Platform:
             # Pinned memory support under WSL depends on the vendor and driver
             # version. Conservative default: return False. Platform subclasses
             # that can verify support (e.g. CudaPlatformBase) override this.
-            logger.warning_once(
+            # warning_once() causes a circular import on WSL, see #48397.
+            logger.warning(
                 "Using 'pin_memory=False' as WSL is detected. "
                 "This may slow down performance."
             )

vllm/transformers_utils/config.py

@@ -72,6 +72,7 @@ class LazyConfigDict(dict):
 _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     afmoe="AfmoeConfig",
     arctic="ArcticConfig",
+    axk1="AXK1Config",
     bagel="BagelConfig",
     umm="CheersConfig",
     chatglm="ChatGLMConfig",

vllm/transformers_utils/configs/AXK1.py

@@ -114,7 +114,7 @@ class AXK1Config(PretrainedConfig):
             The dropout ratio for the attention probabilities.
     """
 
-    model_type = "AXK1"
+    model_type = "axk1"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(

vllm/transformers_utils/model_arch_config_convertor.py

@@ -261,7 +261,7 @@ class ModelArchConfigConvertorBase:
         if not hasattr(self.hf_text_config, "model_type"):
             return False
         elif self.hf_text_config.model_type in (
-            "AXK1",
+            "axk1",
             "deepseek_v2",
             "deepseek_v3",
             "deepseek_v32",
@@ -290,7 +290,7 @@ class ModelArchConfigConvertorBase:
             return (
                 self.hf_text_config.model.model_type
                 in (
-                    "AXK1",
+                    "axk1",
                     "deepseek_v2",
                     "deepseek_v3",
                     "deepseek_v32",

vllm/v1/attention/ops/triton_merge_attn_states.py

@@ -9,6 +9,118 @@ from vllm.triton_utils import tl, triton
 float8_info = torch.finfo(current_platform.fp8_dtype())
 
 
+def mask_empty_context(
+    lse: torch.Tensor,
+    output: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    context_start_loc: torch.Tensor,
+) -> None:
+    """Neutralize context chunks that cover no keys before merging.
+
+    A prefill query whose context chunk is empty attended to no keys, so its
+    partial attention is undefined: the backend leaves the output rows as
+    uninitialized scratch (which may hold NaN/Inf) even when it reports an LSE
+    of -inf. Sanitize both here so ``merge_attn_states`` can stay generic:
+    force the LSE to -inf (zero softmax weight) and zero the undefined output
+    rows (so a zero weight cannot combine with NaN/Inf). Emptiness is derived
+    from the context offsets, not from the -inf LSE, so no merge kernel has to
+    reason about undefined partials.
+
+    Args:
+        lse: Chunk log-sum-exp, shape [num_heads, num_tokens].
+        output: Chunk attention output, shape [num_tokens, num_heads, ...].
+        query_start_loc: Prefill query cumulative offsets, shape [num_reqs + 1].
+        context_start_loc: Chunk context cumulative offsets,
+            shape [num_reqs + 1]; an empty chunk has a zero-length span.
+    """
+    num_heads, num_tokens = lse.shape
+    num_reqs = query_start_loc.shape[0] - 1
+    block_size = 128
+    # Reserve the worst-case number of request-local blocks.
+    num_query_blocks = num_tokens // block_size + num_reqs
+    is_empty = torch.zeros(num_tokens, dtype=torch.bool, device=lse.device)
+    mask_empty_context_kernel[(num_query_blocks,)](
+        lse,
+        is_empty,
+        query_start_loc,
+        context_start_loc,
+        lse.stride(0),
+        lse.stride(1),
+        num_reqs,
+        NUM_HEADS=num_heads,
+        BLOCK_SIZE=block_size,
+        BLOCK_HEADS=8,
+        num_warps=8,
+    )
+    output.masked_fill_(is_empty[:, None, None], 0.0)
+
+
+@triton.jit
+def mask_empty_context_kernel(
+    lse,
+    is_empty,
+    query_start_loc,
+    context_start_loc,
+    lse_head_stride,
+    lse_token_stride,
+    num_reqs,
+    NUM_HEADS: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_HEADS: tl.constexpr,
+):
+    query_block_idx = tl.program_id(0)
+
+    lanes = tl.arange(0, 32)
+    chunk_start = 0
+    req_idx = 0
+    req_idx_found = False
+    while (chunk_start < num_reqs) & (not req_idx_found):
+        req_offsets = chunk_start + lanes
+        req_mask = req_offsets < num_reqs
+        query_starts = tl.load(query_start_loc + req_offsets, mask=req_mask)
+        # Assume the worst-case number of blocks for each request.
+        req_block_starts = query_starts // BLOCK_SIZE + req_offsets
+        matched_idx = tl.sum(
+            (req_mask & (req_block_starts <= query_block_idx)).to(tl.int32)
+        )
+        # matched_idx == 32 means the match is past this warp chunk.
+        req_idx = chunk_start + matched_idx - 1
+        req_idx_found = matched_idx < 32
+        chunk_start += 32
+
+    query_start = tl.load(query_start_loc + req_idx)
+    query_end = tl.load(query_start_loc + req_idx + 1)
+    query_len = query_end - query_start
+    req_first_block = query_start // BLOCK_SIZE + req_idx
+    block_in_req = query_block_idx - req_first_block
+    token_offset = block_in_req * BLOCK_SIZE
+    if token_offset >= query_len:
+        return
+
+    context_start = tl.load(context_start_loc + req_idx)
+    context_end = tl.load(context_start_loc + req_idx + 1)
+    if context_start != context_end:
+        return
+
+    token_offsets = token_offset + tl.arange(0, BLOCK_SIZE)
+    token_indices = query_start + token_offsets
+    token_lse_offsets = token_indices * lse_token_stride
+    valid_tokens = token_offsets < query_len
+    tl.store(is_empty + token_indices, True, mask=valid_tokens)
+    head_offsets = tl.arange(0, BLOCK_HEADS)
+    for head_start in range(0, NUM_HEADS, BLOCK_HEADS):
+        head_indices = head_start + head_offsets
+        lse_ptrs = (
+            lse + head_indices[:, None] * lse_head_stride + token_lse_offsets[None, :]
+        )
+        valid_heads = head_indices < NUM_HEADS
+        tl.store(
+            lse_ptrs,
+            float("-inf"),
+            mask=valid_heads[:, None] & valid_tokens[None, :],
+        )
+
+
 # Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005
 # can be used to combine partial attention results (in the split-KV case)
 def merge_attn_states(
@@ -136,6 +248,9 @@ def merge_attn_states_kernel(
 
     if OUTPUT_LSE:
         out_lse = tl.log(out_se) + max_lse
+        # Both sides empty (max_lse == -inf) => undefined merge; keep -inf so
+        # downstream merges continue to treat the token as empty.
+        out_lse = tl.where(max_lse == float("-inf"), float("-inf"), out_lse)
         tl.store(output_lse + head_idx * num_tokens + token_idx, out_lse)
 
     p_out = tl.load(
@@ -159,6 +274,10 @@ def merge_attn_states_kernel(
     p_scale = p_se / out_se
     s_scale = s_se / out_se
     out = p_out * p_scale + s_out * s_scale
+    # If both sides are empty (max_lse == -inf) the scales are 0/0 = NaN; emit
+    # zeros rather than NaN. Callers with empty chunks (see mask_empty_context)
+    # zero those inputs, so this only guards the fully-undefined corner.
+    out = tl.where(max_lse == float("-inf"), 0.0, out)
 
     if USE_FP8:
         out = out * (1.0 / tl.load(output_scale))

vllm/v1/core/sched/interface.py

@@ -145,7 +145,7 @@ class SchedulerInterface(ABC):
         self,
         request_ids: str | Iterable[str] | None,
         finished_status: "RequestStatus",
-    ) -> list[tuple[str, int]]:
+    ) -> "list[Request]":
         """Finish the requests in the scheduler's internal queue. If the request
         is not in the queue, this method will do nothing for that request.
 
@@ -159,8 +159,8 @@ class SchedulerInterface(ABC):
             finished_status: The finished status of the given requests.
 
         Returns:
-            Tuple of (req_id, client_index) for requests that were aborted. Will not
-            include any that were already finished.
+            List of requests that were aborted. Will not include any that were
+            already finished.
         """
         raise NotImplementedError
 

vllm/v1/core/sched/scheduler.py

@@ -61,7 +61,7 @@ from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus, StreamingUpdate
 from vllm.v1.spec_decode.dynamic.utils import build_dynamic_sd_schedule_lookup
 from vllm.v1.spec_decode.metrics import SpecDecodingStats
-from vllm.v1.structured_output import StructuredOutputManager
+from vllm.v1.structured_output import StructuredOutputGrammar, StructuredOutputManager
 from vllm.v1.utils import record_function_or_nullcontext
 
 logger = init_logger(__name__)
@@ -202,6 +202,10 @@ class Scheduler(SchedulerInterface):
         self.finished_recving_kv_req_ids: set[str] = set()
         self.failed_recving_kv_req_ids: set[str] = set()
 
+        # Grammar compilation failures to finish as per-request errors in
+        # update_from_output.
+        self.grammar_compile_error_reqs: set[str] = set()
+
         # Encoder-related.
         # Calculate encoder cache size if applicable
         supports_mm_inputs = mm_registry.supports_multimodal_inputs(
@@ -1729,7 +1733,7 @@ class Scheduler(SchedulerInterface):
                 struct_output_request = request.structured_output_request
                 assert struct_output_request is not None
                 grammar = struct_output_request.grammar
-                assert grammar is not None
+                assert isinstance(grammar, StructuredOutputGrammar)
                 # new_token_ids can be a mixed block of reasoning content, then
                 # the reasoning end marker, then the start of the grammar content.
                 # Trim the reasoning content so the grammar only sees grammar content.
@@ -1859,10 +1863,16 @@ class Scheduler(SchedulerInterface):
             # This is a rare case and unlikely to impact performance.
             self.waiting.remove_requests(stopped_preempted_reqs)
 
+        error_req_ids = set(self.grammar_compile_error_reqs)
+        self.grammar_compile_error_reqs.clear()
         if failed_kv_load_req_ids and not self.recompute_kv_load_failures:
-            requests = [self.requests[req_id] for req_id in failed_kv_load_req_ids]
-            self.finish_requests(failed_kv_load_req_ids, RequestStatus.FINISHED_ERROR)
-            for request in requests:
+            error_req_ids.update(failed_kv_load_req_ids)
+
+        if error_req_ids:
+            error_reqs = self.finish_requests(
+                error_req_ids, RequestStatus.FINISHED_ERROR
+            )
+            for request in error_reqs:
                 outputs[request.client_index].append(
                     EngineCoreOutput(
                         request_id=request.request_id,
@@ -2092,8 +2102,7 @@ class Scheduler(SchedulerInterface):
             # Filter out spec tokens which do not adhere to the grammar.
             if self.structured_output_manager.should_advance(request):
                 metadata = request.structured_output_request
-                assert metadata is not None and metadata.grammar is not None
-                spec_token_ids = metadata.grammar.validate_tokens(spec_token_ids)
+                spec_token_ids = metadata.grammar.validate_tokens(spec_token_ids)  # type: ignore[union-attr]
             # Pad to original number of spec tokens.
             num_invalid_tokens = orig_num_spec_tokens - len(spec_token_ids)
             if num_invalid_tokens:
@@ -2134,7 +2143,7 @@ class Scheduler(SchedulerInterface):
 
     def finish_requests(
         self, request_ids: str | Iterable[str] | None, finished_status: RequestStatus
-    ) -> list[tuple[str, int]]:
+    ) -> list[Request]:
         """Handles the finish signal from outside the scheduler.
 
         For example, the API server can abort a request when the client
@@ -2143,8 +2152,8 @@ class Scheduler(SchedulerInterface):
         If request_ids is None, all requests will be finished.
 
         Returns:
-            Tuple of (req_id, client_index) for requests that were aborted. Will not
-            include any that were already finished.
+            List of requests that were aborted. Will not include any that were
+            already finished.
         """
         assert RequestStatus.is_finished(finished_status)
         if isinstance(request_ids, str):
@@ -2193,7 +2202,7 @@ class Scheduler(SchedulerInterface):
             request.status = finished_status
             self._free_request(request, delay_free_blocks=delay_free_blocks)
 
-        return [(r.request_id, r.client_index) for r in valid_requests]
+        return valid_requests
 
     def _free_request(
         self, request: Request, delay_free_blocks: bool = False
@@ -2593,7 +2602,10 @@ class Scheduler(SchedulerInterface):
 
         if request.status == RequestStatus.WAITING_FOR_STRUCTURED_OUTPUT_GRAMMAR:
             structured_output_req = request.structured_output_request
-            if not (structured_output_req and structured_output_req.grammar):
+            if not structured_output_req or structured_output_req.grammar is None:
+                return False
+            if isinstance(structured_output_req.grammar, Exception):
+                self.grammar_compile_error_reqs.add(request.request_id)
                 return False
             request.status = RequestStatus.WAITING
             return True

vllm/v1/engine/core.py

@@ -1830,13 +1830,13 @@ class EngineCoreProc(EngineCore):
     ) -> None:
         self._send_finish_outputs_to_client(req_ids, client_index, FinishReason.ERROR)
 
-    def _send_abort_outputs(self, aborted_reqs: list[tuple[str, int]]) -> None:
+    def _send_abort_outputs(self, aborted_reqs: list[Request]) -> None:
         # TODO(nick) this will be moved inside the scheduler
         if aborted_reqs:
             # Map client_index to list of request_ids that belong to that client.
             by_client = defaultdict[int, set[str]](set)
-            for req_id, client_index in aborted_reqs:
-                by_client[client_index].add(req_id)
+            for request in aborted_reqs:
+                by_client[request.client_index].add(request.request_id)
             for client_index, req_ids in by_client.items():
                 self._send_abort_outputs_to_client(list(req_ids), client_index)
 

vllm/v1/structured_output/__init__.py

@@ -164,24 +164,33 @@ class StructuredOutputManager:
             else:
                 raise ValueError(f"Unsupported structured output backend: {backend}")
 
+        grammar: Future[StructuredOutputGrammar] | StructuredOutputGrammar
         if self._use_async_grammar_compilation:
             grammar = self.executor.submit(self._create_grammar, request)
         else:
-            grammar = self._create_grammar(request)  # type: ignore[assignment]
-        request.structured_output_request.grammar = grammar  # type: ignore[assignment]
+            try:
+                grammar = self._create_grammar(request)
+            except Exception as e:
+                grammar = Future()
+                grammar.set_exception(e)
+        request.structured_output_request.grammar = grammar
 
     def _create_grammar(self, request: "Request") -> StructuredOutputGrammar:
-        key = request.structured_output_request.structured_output_key  # type: ignore[union-attr]
-
+        struct_request = request.structured_output_request
+        assert struct_request is not None
         # Note that the request was validated in the engine core client,
-        # so at this point we know it is a supported type of request.
-        #
-        # TODO: we still need to handle xgrammar compilation failures,
-        # though it should be unlikely as we test that up front as well.
-        request_type, grammar_spec = key
-
-        assert self.backend is not None
-        return self.backend.compile_grammar(request_type, grammar_spec)
+        # so at this point we know it is a supported type of request. Grammar
+        # compilation may still fail; the Future carries that error to the
+        # scheduler so it can fail only this request.
+        try:
+            request_type, grammar_spec = struct_request.structured_output_key
+            assert self.backend is not None
+            return self.backend.compile_grammar(request_type, grammar_spec)
+        except Exception:
+            logger.exception(
+                "Failed to compile grammar for request %s", request.request_id
+            )
+            raise
 
     def _fill_bitmasks(
         self, batch: Iterable[tuple[StructuredOutputGrammar, int, bool]]
@@ -244,8 +253,9 @@ class StructuredOutputManager:
                 structured_output_request = request.structured_output_request
                 if TYPE_CHECKING:
                     assert structured_output_request is not None
-                    assert structured_output_request.grammar is not None
                 grammar = structured_output_request.grammar
+                if TYPE_CHECKING:
+                    assert isinstance(grammar, StructuredOutputGrammar)
 
                 apply_bitmask = self.should_fill_bitmask(request)
                 batch.append((grammar, cumulative_index, apply_bitmask))
@@ -268,8 +278,9 @@ class StructuredOutputManager:
 
                 if TYPE_CHECKING:
                     assert structured_output_request is not None
-                    assert structured_output_request.grammar is not None
                 grammar = structured_output_request.grammar
+                if TYPE_CHECKING:
+                    assert isinstance(grammar, StructuredOutputGrammar)
                 apply_bitmask = self.should_fill_bitmask(request)
 
                 reasoner = self._get_reasoner(request)

vllm/v1/structured_output/request.py

@@ -21,7 +21,9 @@ if TYPE_CHECKING:
 @dataclasses.dataclass
 class StructuredOutputRequest:
     params: StructuredOutputsParams
-    _grammar: Future[StructuredOutputGrammar] | StructuredOutputGrammar | None = None
+    _grammar: (
+        Future[StructuredOutputGrammar] | StructuredOutputGrammar | Exception | None
+    ) = None
     reasoning_ended: bool | None = None
     # Absolute index into the request's all_token_ids of the last reasoning
     # token (the reasoning-end marker). Tokens at or before this index are
@@ -56,6 +58,8 @@ class StructuredOutputRequest:
                 self.status = RequestStatus.WAITING
             except TimeoutError:
                 return False
+            except Exception as e:
+                self._grammar = e
         return True
 
     @property
@@ -63,11 +67,10 @@ class StructuredOutputRequest:
         return self._check_grammar_completion()
 
     @property
-    def grammar(self) -> StructuredOutputGrammar | None:
-        completed = self._check_grammar_completion()
-        return (
-            cast(StructuredOutputGrammar | None, self._grammar) if completed else None
-        )
+    def grammar(self) -> StructuredOutputGrammar | Exception | None:
+        if not self._check_grammar_completion():
+            return None
+        return cast(StructuredOutputGrammar | Exception | None, self._grammar)
 
     @grammar.setter
     def grammar(