[Perf] Parallelize fill_bitmask to accelerate high-throughput guided decoding (#21862)

Signed-off-by: Benjamin Chislett <benjamin.chislett@centml.ai>

vllm/v1/structured_output/__init__.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import multiprocessing
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import Future, ThreadPoolExecutor
 from typing import TYPE_CHECKING, Optional
 
 from vllm.config import VllmConfig
@@ -40,6 +40,17 @@ class StructuredOutputManager:
         self._grammar_bitmask: Optional[torch.Tensor] = None
         self._full_mask = torch.tensor(-1, dtype=torch.int32)
 
+        max_batch_size = self.vllm_config.scheduler_config.max_num_seqs
+        self.fill_bitmask_parallel_threshold = 128
+        if self.fill_bitmask_parallel_threshold < max_batch_size:
+            self.fill_bitmask_parallel_batch_size = 16
+            # Use:
+            # - at least 1 CPU
+            # - at most half the number of CPUs or 8, whichever is less
+            max_workers = max(1, min(multiprocessing.cpu_count() // 2, 8))
+            self.executor_for_fillmask = ThreadPoolExecutor(
+                max_workers=max_workers)
+
         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,
@@ -120,6 +131,26 @@ class StructuredOutputManager:
         assert self.backend is not None
         return self.backend.compile_grammar(request_type, grammar_spec)
 
+    def _fill_bitmasks(
+        self,
+        batch: list[tuple[StructuredOutputGrammar, int, bool]],
+    ) -> None:
+        assert self._grammar_bitmask is not None
+        for grammar, index, apply_bitmask in batch:
+            if apply_bitmask and not grammar.is_terminated():
+                grammar.fill_bitmask(self._grammar_bitmask, index)
+            else:
+                # Note that for thinking support, we will need to
+                # reset the relevant part of the bitmask for consequent
+                # requests here.
+                self._grammar_bitmask[index].fill_(self._full_mask)
+
+    def _async_submit_fill_bitmask(
+        self,
+        batch: list[tuple[StructuredOutputGrammar, int, bool]],
+    ) -> Future:
+        return self.executor_for_fillmask.submit(self._fill_bitmasks, batch)
+
     def grammar_bitmask(
         self,
         requests: dict[str, Request],
@@ -146,7 +177,6 @@ class StructuredOutputManager:
                 self.backend.allocate_token_bitmask(
                     max_batch_size * (1 + max_num_spec_tokens))
 
-        bitmask_tensor = self._grammar_bitmask
         # Generate a batched bitmask for all structured output requests.
         # When speculative decoding is enabled, we need to include multiple
         # masks for each request, one for each possible bonus token position.
@@ -155,14 +185,35 @@ class StructuredOutputManager:
         ordered_seq = sorted(structured_output_request_ids.items(),
                              key=lambda x: x[1])
 
-        # Note that for thinking support, we will need to
-        # reset the relevant part of the bitmask for consequent
-        # request here.
-        bitmask_tensor[:(len(ordered_seq) * (1 + max_num_spec_tokens))].fill_(
-            self._full_mask)
+        # Optimized parallel filling of bitmasks for
+        # non-spec, large-batch-size cases
+        if len(ordered_seq) > self.fill_bitmask_parallel_threshold and \
+                max_num_spec_tokens == 0:
+            promises = []
+            batch = []
+            for req_id, _ in ordered_seq:
+                request = requests[req_id]
+                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
+
+                apply_bitmask = self.should_fill_bitmask(request)
+                batch.append((structured_output_request.grammar,
+                              cumulative_index, apply_bitmask))
+                if len(batch) == self.fill_bitmask_parallel_batch_size:
+                    promises.append(self._async_submit_fill_bitmask(batch))
+                    batch = []
+
+                cumulative_index += 1
+            if batch:
+                promises.append(self._async_submit_fill_bitmask(batch))
 
-        # NOTE: This outer loop can likely be parallelized to improve
-        # performance of bitmask generation for large batches.
+            # Wait for all bitmask filling tasks to complete.
+            for promise in promises:
+                promise.result()
+        else:
+            # Fallback to serial filling of bitmasks for small-batch-size cases
             for req_id, _ in ordered_seq:
                 request = requests[req_id]
                 structured_output_request = request.structured_output_request
@@ -170,32 +221,25 @@ class StructuredOutputManager:
                 if TYPE_CHECKING:
                     assert structured_output_request is not None
                     assert structured_output_request.grammar is not None
-            apply_bitmask: bool = True
-            if self.reasoner is not None:
-                if structured_output_request.reasoning_ended is None:
-                    structured_output_request.reasoning_ended = \
-                        self.reasoner.is_reasoning_end(request.prompt_token_ids)
-                apply_bitmask = structured_output_request.reasoning_ended
+                apply_bitmask = self.should_fill_bitmask(request)
 
                 state_advancements = 0
-            req_tokens = scheduled_spec_decode_tokens.get(req_id, []) + [None]
-            for i, token in enumerate(req_tokens):
-                if apply_bitmask and not \
-                    structured_output_request.grammar.is_terminated():
-                    structured_output_request.grammar.fill_bitmask(
-                        bitmask_tensor, cumulative_index)
-                    if token is not None:
-                        # In order to generate the correct bitmask for each
-                        # position in the speculative sequence, we advance
-                        # the FSM state for each speculative token and rollback
-                        # to restore the previous state when we are finished.
+                req_tokens = scheduled_spec_decode_tokens.get(req_id, [])
+                for i, token in enumerate(req_tokens + [None]):
+                    self._fill_bitmasks([(structured_output_request.grammar,
+                                          cumulative_index, apply_bitmask)])
+
+                    if apply_bitmask and token is not None and \
+                        not structured_output_request.grammar.is_terminated():
                         assert structured_output_request.grammar.accept_tokens(
                             req_id, [token])
                         state_advancements += 1
                     cumulative_index += 1
                 if state_advancements > 0:
-                structured_output_request.grammar.rollback(state_advancements)
+                    structured_output_request.grammar.rollback(
+                        state_advancements)
 
+        bitmask_tensor = self._grammar_bitmask
         if cumulative_index < bitmask_tensor.shape[0]:
             bitmask_tensor = bitmask_tensor[:cumulative_index]
 
@@ -204,6 +248,15 @@ class StructuredOutputManager:
         # and deserialization when sending this to the GPU workers.
         return bitmask_tensor.numpy()
 
+    def should_fill_bitmask(self, request: Request) -> bool:
+        if self.reasoner is not None:
+            assert request.structured_output_request is not None
+            if request.structured_output_request.reasoning_ended is None:
+                request.structured_output_request.reasoning_ended = \
+                    self.reasoner.is_reasoning_end(request.prompt_token_ids)
+            return request.structured_output_request.reasoning_ended
+        return True
+
     def should_advance(self, request: Request) -> bool:
         if not request.use_structured_output:
             return False

vllm/v1/structured_output/backend_xgrammar.py

@@ -148,6 +148,7 @@ class XgrammarGrammar(StructuredOutputGrammar):
                                       repr=False,
                                       hash=False,
                                       init=False)
+    _is_terminated: bool = field(default=False, repr=False, hash=False)
 
     def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
         """Accepts a list of tokens and advances the FSM.
@@ -155,6 +156,8 @@ class XgrammarGrammar(StructuredOutputGrammar):
         Returns True if the FSM was advanced successfully.
         Returns False if the FSM failed to advance.
         """
+        if self._is_terminated:
+            return False
         for token in tokens:
             if not self.matcher.accept_token(token):
                 logger.error(
@@ -162,6 +165,7 @@ class XgrammarGrammar(StructuredOutputGrammar):
                     "for tokens %s. Please file an issue.", request_id, token)
                 return False
             self.num_processed_tokens += 1
+        self._is_terminated = self.matcher.is_terminated()
         return True
 
     def validate_tokens(self, tokens: list[int]) -> list[int]:
@@ -184,12 +188,13 @@ class XgrammarGrammar(StructuredOutputGrammar):
     def rollback(self, num_tokens: int) -> None:
         self.matcher.rollback(num_tokens)
         self.num_processed_tokens -= num_tokens
+        self._is_terminated = self.matcher.is_terminated()
 
     def fill_bitmask(self, bitmask: torch.Tensor, idx: int) -> None:
         self.matcher.fill_next_token_bitmask(bitmask, idx)
 
     def is_terminated(self) -> bool:
-        return self.matcher.is_terminated()
+        return self._is_terminated
 
     def reset(self):
         self.num_processed_tokens = 0

vllm/v1/worker/gpu_model_runner.py

@@ -1324,9 +1324,14 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             cumulative_index += 1 + num_spec_tokens
         grammar_bitmask = sorted_bitmask
 
+        # If the grammar bitmask and the logits have the same shape
+        # we don't need to pass indices to the kernel,
+        # since the bitmask is already aligned with the logits.
+        skip_out_indices = grammar_bitmask.shape[0] == logits.shape[0]
+
         # Serialization of np.ndarray is much more efficient than a tensor,
         # so we receive it in that format.
-        grammar_bitmask = torch.from_numpy(grammar_bitmask)
+        grammar_bitmask = torch.from_numpy(grammar_bitmask).contiguous()
 
         # Force use of the torch.compile implementation from xgrammar to work
         # around issues with the Triton kernel in concurrent structured output
@@ -1334,7 +1339,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         xgr_torch_compile.apply_token_bitmask_inplace_torch_compile(
             logits,
             grammar_bitmask.to(self.device, non_blocking=True),
-            indices=out_indices,
+            indices=out_indices if not skip_out_indices else None,
         )
 
     def sync_and_slice_intermediate_tensors(