[Structured Outputs][V1] Skipping with models doesn't contain tokenizers (#20365)

Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
Co-authored-by: Nick Hill <nhill@redhat.com>

tests/v1/core/test_scheduler.py

@@ -9,7 +9,7 @@ import torch
 from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
                          SchedulerConfig, SpeculativeConfig, VllmConfig)
 from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
 from vllm.v1.core.sched.scheduler import Scheduler
 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
@@ -17,6 +17,7 @@ from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.structured_output import StructuredOutputManager
+from vllm.v1.structured_output.request import StructuredOutputRequest
 
 EOS_TOKEN_ID = 50256
 
@@ -33,6 +34,7 @@ def create_scheduler(
     block_size: int = 16,
     max_model_len: Optional[int] = None,
     num_speculative_tokens: Optional[int] = None,
+    skip_tokenizer_init: bool = False,
 ) -> Scheduler:
     '''Create scheduler under test.
 
@@ -65,6 +67,7 @@ def create_scheduler(
         trust_remote_code=True,
         dtype="float16",
         seed=42,
+        skip_tokenizer_init=skip_tokenizer_init,
     )
     # Cache config, optionally force APC
     kwargs_cache = ({} if enable_prefix_caching is None else {
@@ -186,7 +189,7 @@ def test_get_num_unfinished_requests():
 ])
 def test_schedule(enable_prefix_caching: Optional[bool],
                   prompt_logprobs: Optional[int]):
-    '''Test scheduling. 
+    '''Test scheduling.
     Two cases: default APC/no prompt logprobs; APC=True + prompt logprobs
     '''
     scheduler = create_scheduler(enable_prefix_caching=enable_prefix_caching)
@@ -1408,7 +1411,7 @@ def create_requests_with_priority(
 
 
 def test_priority_scheduling_basic_ordering():
-    """Test that requests are scheduled in priority order 
+    """Test that requests are scheduled in priority order
     (lower value = higher priority)."""
     scheduler = create_scheduler_with_priority()
 
@@ -1437,7 +1440,7 @@ def test_priority_scheduling_basic_ordering():
 
 
 def test_priority_scheduling_arrival_time_tiebreaker():
-    """Test that arrival time is used 
+    """Test that arrival time is used
     as tiebreaker when priorities are equal."""
     scheduler = create_scheduler_with_priority()
 
@@ -1495,7 +1498,7 @@ def test_priority_scheduling_mixed_priority_and_arrival():
 
 
 def test_priority_scheduling_preemption():
-    """Test that priority scheduling preempts 
+    """Test that priority scheduling preempts
     lower priority requests when memory is constrained."""
     # Create scheduler with very limited memory to force preemption
     scheduler = create_scheduler_with_priority(
@@ -1576,7 +1579,7 @@ def test_priority_scheduling_preemption():
 
 
 def test_priority_scheduling_no_preemption_when_space_available():
-    """Test that preemption doesn't happen 
+    """Test that preemption doesn't happen
     when there's space for new requests."""
     scheduler = create_scheduler_with_priority(
         max_num_seqs=3,  # Allow 3 concurrent requests
@@ -1626,7 +1629,7 @@ def test_priority_scheduling_no_preemption_when_space_available():
 
 
 def test_priority_scheduling_preemption_victim_selection():
-    """Test that the correct victim is selected for 
+    """Test that the correct victim is selected for
     preemption based on priority and arrival time."""
     # This test verifies the priority-based victim selection logic
     # by checking the waiting queue order after adding requests with different
@@ -1743,7 +1746,7 @@ def test_priority_scheduling_waiting_queue_order():
 
 
 def test_priority_scheduling_fcfs_fallback():
-    """Test that FCFS behavior is maintained when all 
+    """Test that FCFS behavior is maintained when all
     requests have same priority."""
     scheduler = create_scheduler_with_priority()
 
@@ -1811,7 +1814,7 @@ def test_priority_scheduling_with_limited_slots():
 
 
 def test_priority_scheduling_heap_property():
-    """Test that the waiting queue maintains heap 
+    """Test that the waiting queue maintains heap
     property for priority scheduling."""
     scheduler = create_scheduler_with_priority(
         max_num_seqs=1,  # Only one request can run at a time
@@ -1857,3 +1860,39 @@ def test_priority_scheduling_heap_property():
     # Verify requests were scheduled in priority order (lowest value first)
     expected_priorities = sorted(priorities)
     assert scheduled_priorities == expected_priorities
+
+
+def test_schedule_skip_tokenizer_init():
+    scheduler = create_scheduler(skip_tokenizer_init=True)
+    requests = create_requests(num_requests=5)
+    for request in requests:
+        scheduler.add_request(request)
+    output = scheduler.schedule()
+    assert len(output.scheduled_new_reqs) == len(requests)
+    assert output.grammar_bitmask is None
+
+
+def test_schedule_skip_tokenizer_init_structured_output_request():
+    scheduler = create_scheduler(skip_tokenizer_init=True)
+    guided_params = GuidedDecodingParams(regex="[0-9]+")
+    sampling_params = SamplingParams(
+        ignore_eos=False,
+        max_tokens=16,
+        guided_decoding=guided_params,
+    )
+    request = Request(
+        request_id="0",
+        prompt_token_ids=[0, 1],
+        multi_modal_inputs=None,
+        multi_modal_hashes=None,
+        multi_modal_placeholders=None,
+        sampling_params=sampling_params,
+        pooling_params=None,
+        eos_token_id=EOS_TOKEN_ID,
+        structured_output_request=StructuredOutputRequest(sampling_params),
+    )
+    scheduler.add_request(request)
+    output = scheduler.schedule()
+    assert len(output.scheduled_new_reqs) == 0
+    assert len(scheduler.running) == 0
+    assert len(scheduler.waiting) == 1

tests/v1/engine/test_llm_engine.py

@@ -1,19 +1,30 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
 
 import random
-from typing import Optional
+from typing import TYPE_CHECKING, Optional
 
 import pytest
 
-from vllm import LLM, SamplingParams
+from vllm import LLM
+from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 from vllm.v1.metrics.reader import Counter, Gauge, Histogram, Metric, Vector
 
+if TYPE_CHECKING:
+    from tests.conftest import VllmRunner
+
 MODEL = "facebook/opt-125m"
 DTYPE = "half"
 
 
-def _vllm_model(apc: bool, vllm_runner, monkeypatch):
+def _vllm_model(
+    apc: bool,
+    vllm_runner: type[VllmRunner],
+    monkeypatch: pytest.MonkeyPatch,
+    *,
+    skip_tokenizer_init: bool = False,
+):
     """Set up VllmRunner instance."""
     monkeypatch.setenv("VLLM_USE_V1", "1")
     return vllm_runner(
@@ -23,6 +34,7 @@ def _vllm_model(apc: bool, vllm_runner, monkeypatch):
         enforce_eager=True,
         enable_prefix_caching=apc,
         gpu_memory_utilization=0.5,
+        skip_tokenizer_init=skip_tokenizer_init,
     )
 
 
@@ -45,9 +57,27 @@ def vllm_model_apc(vllm_runner, monkeypatch):
         yield vllm_model
 
 
+@pytest.fixture(
+    # Function scope decouples tests & allows
+    # env var adjustment via monkeypatch
+    scope="function",
+    # Prefix caching
+    params=[False, True])
+def vllm_model_skip_tokenizer_init(vllm_runner, request, monkeypatch):
+    """VllmRunner test fixture with APC."""
+    with _vllm_model(
+            request.param,
+            vllm_runner,
+            monkeypatch,
+            skip_tokenizer_init=True,
+    ) as vllm_model:
+        yield vllm_model
+
+
 def _get_test_sampling_params(
     prompt_list: list[str],
     seed: Optional[int] = 42,
+    structured_outputs: bool = False,
 ) -> tuple[list[SamplingParams], list[int]]:
     """Generate random sampling params for a batch."""
 
@@ -62,14 +92,34 @@ def _get_test_sampling_params(
     n_list = [get_mostly_n_gt1() for _ in range(len(prompt_list))]
     # High temperature to maximize the chance of unique completions
     return [
-        SamplingParams(temperature=0.95, top_p=0.95, n=n, seed=seed)
-        for n in n_list
+        SamplingParams(
+            temperature=0.95,
+            top_p=0.95,
+            n=n,
+            seed=seed,
+            guided_decoding=GuidedDecodingParams(
+                regex="[0-9]+") if structured_outputs else None,
+        ) for n in n_list
     ], n_list
 
 
+def test_compatibility_with_skip_tokenizer_init(
+    vllm_model_skip_tokenizer_init: VllmRunner,
+    example_prompts: list[str],
+):
+    # Case 1: Structured output request should raise an error.
+    sampling_params_list, _ = _get_test_sampling_params(
+        example_prompts,
+        structured_outputs=True,
+    )
+    model: LLM = vllm_model_skip_tokenizer_init.model
+    with pytest.raises(ValueError):
+        _ = model.generate(example_prompts, sampling_params_list)
+
+
 def test_parallel_sampling(vllm_model, example_prompts) -> None:
     """Test passes if parallel sampling `n>1` yields `n` unique completions.
-    
+
     Args:
       vllm_model: VllmRunner instance under test.
       example_prompt: test fixture providing prompts for testing.

vllm/v1/engine/processor.py

@@ -152,6 +152,11 @@ class Processor:
         if not params.guided_decoding or not self.decoding_config:
             return
 
+        if self.model_config.skip_tokenizer_init and params.guided_decoding:
+            raise ValueError(
+                "Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'"  # noqa: E501
+            )
+
         engine_level_backend = self.decoding_config.backend
         if params.guided_decoding.backend:
             # Request-level backend selection is not supported in V1.

vllm/v1/structured_output/__init__.py

@@ -40,22 +40,25 @@ class StructuredOutputManager:
         self._grammar_bitmask: Optional[torch.Tensor] = None
         self._full_mask = torch.tensor(-1, dtype=torch.int32)
 
-        # The default max_workers if not specified is the number of CPUs * 5,
-        # which is way too high since these tasks are CPU-bound, not I/O bound.
-        # We also know we would never dominate CPU usage with just grammar
-        # compilation, so we set it to half the number of CPUs.
-        max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
-        self.executor = ThreadPoolExecutor(max_workers=max_workers)
-        self.tokenizer = init_tokenizer_from_configs(
-            model_config=self.vllm_config.model_config,
-            scheduler_config=self.vllm_config.scheduler_config,
-            lora_config=self.vllm_config.lora_config,
-        ).get_lora_tokenizer(None)
-        reasoning_backend = vllm_config.decoding_config.reasoning_backend
-        if reasoning_backend:
-            reasoner_cls = ReasoningParserManager.get_reasoning_parser(
-                reasoning_backend)
-            self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
+        if not self.vllm_config.model_config.skip_tokenizer_init:
+            # The default max_workers if not specified is the number of
+            # CPUs * 5, which is way too high since these tasks are CPU-bound,
+            # not I/O bound. We also know we would never dominate CPU usage
+            # with just grammar compilation, so we set it to half the number
+            # of CPUs.
+            max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
+            self.executor = ThreadPoolExecutor(max_workers=max_workers)
+            self.tokenizer = init_tokenizer_from_configs(
+                model_config=self.vllm_config.model_config,
+                scheduler_config=self.vllm_config.scheduler_config,
+                lora_config=self.vllm_config.lora_config,
+            ).get_lora_tokenizer(None)
+            reasoning_backend = \
+                    self.vllm_config.decoding_config.reasoning_backend
+            if reasoning_backend:
+                reasoner_cls = ReasoningParserManager.get_reasoning_parser(
+                    reasoning_backend)
+                self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
 
     def grammar_init(self, request: Request) -> None:
         if request.structured_output_request is None: