[V1][Structured Output] Add apply_grammar_bitmask() method to model runner (#555)

### What this PR does / why we need it?
Add `apply_grammar_bitmask()` method to model runner.

This method is necessary for `xgrammar` structured output.

---------

Signed-off-by: shen-shanshan <467638484@qq.com>

Author: shen-shanshan

vllm_ascend/worker/model_runner_v1.py

@@ -38,7 +38,7 @@
 from vllm.sampling_params import SamplingType
 from vllm.sequence import IntermediateTensors
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
-                        LayerBlockType, cdiv)
+                        LayerBlockType, LazyLoader, cdiv)
 from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
                                         KVCacheSpec)
@@ -52,7 +52,10 @@
 from vllm_ascend.platform import NPUPlatform
 
 if TYPE_CHECKING:
+    import xgrammar as xgr  # type: ignore[import-untyped]
     from vllm.v1.core.sched.output import SchedulerOutput
+else:
+    xgr = LazyLoader("xgr", globals(), "xgrammar")
 
 
 class NPUModelRunner:
@@ -493,6 +496,60 @@ def _process_reqs(
 
         return hidden_states[sample_indices]
 
+    def apply_grammar_bitmask(
+        self,
+        scheduler_output: "SchedulerOutput",
+        logits: torch.Tensor,
+    ) -> torch.Tensor:
+        # Serialization of np.ndarray is much more efficient than a tensor,
+        # so we receive it in that format.
+        grammar_bitmask = scheduler_output.grammar_bitmask
+        if grammar_bitmask is None:
+            return
+
+        # We receive the structured output bitmask from the scheduler, but the
+        # indices of the requests in the batch may not match the indices of
+        # the bitmask since the scheduler doesn't know how the gpu runner is
+        # ordering the requests in the batch. We need to sort the bitmask to
+        # match the order of the requests used here.
+        struct_out_req_batch_indices: dict[str, int] = {}
+        indices_match = True
+        for req_id in self.input_batch.req_ids:
+            mask_index = scheduler_output.structured_output_request_ids.get(
+                req_id)
+            if mask_index is None:
+                # not a structured output request
+                continue
+            batch_index = self.input_batch.req_id_to_index[req_id]
+            if batch_index != mask_index:
+                indices_match = False
+            struct_out_req_batch_indices[req_id] = batch_index
+
+        if not indices_match:
+            # Sort the bitmask to match the order of the requests
+            sorted_bitmask = np.zeros_like(grammar_bitmask)
+            for req_id, batch_index in struct_out_req_batch_indices.items():
+                orig_index = scheduler_output.structured_output_request_ids[
+                    req_id]
+                sorted_bitmask[batch_index] = grammar_bitmask[orig_index]
+            grammar_bitmask = sorted_bitmask
+
+        grammar_bitmask = torch.from_numpy(grammar_bitmask)
+
+        # TODO: compatibility with spec decode.
+        # NOTE:
+        # 1. XGrammar bitmask applying only supports CPU and GPU.
+        # 2. The logits and bitmask should be on the same device.
+        # 3. XGrammar logits on CPU only supports float32 dtype.
+        logits_dtype = logits.dtype
+        logits = logits.to("cpu").float()
+        xgr.apply_token_bitmask_inplace(
+            logits,
+            grammar_bitmask,
+            indices=list(struct_out_req_batch_indices.values()),
+        )
+        return logits.to(self.device).to(logits_dtype)
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -507,6 +564,10 @@ def execute_model(
                                            intermediate_tensors)
         logits = self.model.compute_logits(hidden_states, None)
 
+        # Apply structured output bitmasks if present
+        if scheduler_output.grammar_bitmask is not None:
+            logits = self.apply_grammar_bitmask(scheduler_output, logits)
+
         # Sample the next token and get logprobs if needed.
         sampling_metadata = self.input_batch.sampling_metadata
         sampler_output = self.model.sample(