[ModelRunner V2] Support spec decode with structured outputs (#33374)

Signed-off-by: Nick Hill <nickhill123@gmail.com>

vllm/v1/worker/gpu/input_batch.py

@@ -75,6 +75,9 @@ class InputBatch:
     cu_num_logits: torch.Tensor
     cu_num_logits_np: np.ndarray
 
+    # Whether any requests in batch use structured output.
+    has_structured_output_reqs: bool
+
     @classmethod
     def make_dummy(
         cls,
@@ -139,6 +142,7 @@ class InputBatch:
             logits_indices=logits_indices,
             cu_num_logits=cu_num_logits,
             cu_num_logits_np=cu_num_logits_np,
+            has_structured_output_reqs=False,
         )
 
 

vllm/v1/worker/gpu/model_runner.py

@@ -20,7 +20,7 @@ from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig
-from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
 from vllm.v1.worker.gpu.async_utils import AsyncOutput
 from vllm.v1.worker.gpu.attn_utils import (
     build_attn_metadata,
@@ -59,6 +59,7 @@ from vllm.v1.worker.gpu.sample.prompt_logprob import PromptLogprobsWorker
 from vllm.v1.worker.gpu.sample.sampler import Sampler
 from vllm.v1.worker.gpu.spec_decode import init_speculator
 from vllm.v1.worker.gpu.spec_decode.rejection_sample import rejection_sample
+from vllm.v1.worker.gpu.spec_decode.utils import DraftTokensHandler
 from vllm.v1.worker.gpu.states import RequestState
 from vllm.v1.worker.gpu.structured_outputs import StructuredOutputsWorker
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
@@ -167,6 +168,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # LoRA-related workers.
         self.lora_state = LoraState(max_num_reqs=self.max_num_reqs)
 
+        # Draft tokens propagation - for spec-dec + struct outputs.
+        self.draft_tokens_handler = DraftTokensHandler(self.device)
+
+        # KV Connector if configured.
         self.kv_connector: KVConnector = NO_OP_KV_CONNECTOR
 
     def update_max_model_len(self, max_model_len: int) -> None:
@@ -638,6 +643,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             logits_indices=logits_indices,
             cu_num_logits=cu_num_logits,
             cu_num_logits_np=cu_num_logits_np,
+            has_structured_output_reqs=scheduler_output.has_structured_output_requests,
         )
 
     @torch.inference_mode()
@@ -938,7 +944,11 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 num_rejected,
             )
             self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
+            self.draft_tokens_handler.set_draft_tokens(input_batch, draft_tokens)
 
         if self.use_async_scheduling:
             return async_output
         return async_output.get_output()
+
+    def take_draft_token_ids(self) -> DraftTokenIds | None:
+        return self.draft_tokens_handler.get_draft_tokens()

vllm/v1/worker/gpu/spec_decode/utils.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.v1.outputs import DraftTokenIds
+from vllm.v1.worker.gpu.async_utils import async_copy_to_np
+from vllm.v1.worker.gpu.input_batch import InputBatch
+
+
+class DraftTokensHandler:
+    def __init__(self, device: torch.device | None = None):
+        self.device = device
+        self.copy_stream = torch.cuda.Stream(device)
+        self.copy_event = torch.cuda.Event()
+
+        self.req_ids: list[str] = []
+        self.draft_tokens_np: np.ndarray | None = None
+
+    def set_draft_tokens(
+        self, input_batch: InputBatch, draft_tokens: torch.Tensor
+    ) -> None:
+        if not input_batch.has_structured_output_reqs:
+            # No draft token validation needs to be performed by
+            # the scheduler for this batch.
+            if self.req_ids:
+                self.req_ids = []
+            self.draft_tokens_np = None
+            return
+
+        # For spec decoding + structured outputs, we must transfer the
+        # draft tokens back to the scheduler for grammar validation.
+        self.req_ids = input_batch.req_ids
+        current_stream = torch.cuda.current_stream(self.device)
+        self.copy_stream.wait_stream(current_stream)
+        with torch.cuda.stream(self.copy_stream):
+            self.draft_tokens_np = async_copy_to_np(draft_tokens)
+            self.copy_event.record()
+
+    def get_draft_tokens(self) -> DraftTokenIds | None:
+        if self.draft_tokens_np is None:
+            return None
+
+        self.copy_event.synchronize()
+        return DraftTokenIds(self.req_ids, self.draft_tokens_np.tolist())