[BugFix][NPU] Honor prefer_model_sampler in NPU AR runner

vllm_omni/platforms/npu/worker/npu_ar_model_runner.py

@@ -730,6 +730,39 @@ def execute_model(
 
         return None
 
+    def _sample(
+        self,
+        logits: torch.Tensor | None,
+        spec_decode_metadata: Any,
+    ):
+        sampling_metadata = self.input_batch.sampling_metadata
+        if spec_decode_metadata is None:
+            model_sample = getattr(self.model, "sample", None)
+            self.input_batch.update_async_output_token_ids()
+            if logits is not None and callable(model_sample) and getattr(self.model, "prefer_model_sampler", False):
+                # Apply logit bias (min_tokens, allowed_token_ids) before
+                # the custom model sampler — the standard GPU sampler does
+                # this internally, but prefer_model_sampler bypasses it.
+                if hasattr(self.sampler, "logit_bias_state"):
+                    self.sampler.logit_bias_state.apply_logit_bias(
+                        logits,
+                        self.input_batch.expanded_idx_mapping,
+                        self.input_batch.idx_mapping_np,
+                        self.input_batch.positions[self.input_batch.logits_indices],
+                    )
+                sampler_output = model_sample(
+                    logits,
+                    self._sampling_metadata_for_model_sampler(sampling_metadata),
+                )
+                if sampler_output is not None:
+                    return sampler_output
+            return self.sampler(
+                logits=logits,
+                sampling_metadata=sampling_metadata,
+            )
+
+        return super()._sample(logits, spec_decode_metadata)
+
     @torch.inference_mode()
     def sample_tokens(
         self, grammar_output: GrammarOutput | None

vllm_omni/worker/gpu_ar_model_runner.py

@@ -109,53 +109,6 @@ def _make_buffer(self, *size, dtype, numpy=True):
         with maybe_disable_pin_memory_for_ray(self, total_bytes):
             return super()._make_buffer(*size, dtype=dtype, numpy=numpy)
 
-    def _build_model_sampler_output_token_ids(self) -> list[list[int]]:
-        """Build decoded-token history for custom model samplers.
-
-        vLLM only populates sampling_metadata.output_token_ids when penalties or
-        logits processors require it. CosyVoice3's custom RAS sampler also
-        depends on this history, so we reconstruct it directly from the input
-        batch for prefer_model_sampler models.
-        """
-        req_output_token_ids = getattr(self.input_batch, "req_output_token_ids", [])
-        req_ids = list(getattr(self.input_batch, "req_ids", []))
-        output_token_ids = [list(req_output_token_ids[idx] or []) for idx in range(len(req_ids))]
-
-        sampled_token_ids_cpu = getattr(self.input_batch, "sampled_token_ids_cpu", None)
-        async_copy_ready_event = getattr(self.input_batch, "async_copy_ready_event", None)
-        prev_req_id_to_index = getattr(self.input_batch, "prev_req_id_to_index", None)
-        if sampled_token_ids_cpu is None or not output_token_ids or prev_req_id_to_index is None:
-            return output_token_ids
-
-        sampled_token_ids: list[list[int]] | None = None
-        for index, req_id in enumerate(req_ids):
-            prev_index = prev_req_id_to_index.get(req_id)
-            if prev_index is None:
-                continue
-            req_history = output_token_ids[index]
-            if not req_history or req_history[-1] != -1:
-                continue
-            if sampled_token_ids is None:
-                assert async_copy_ready_event is not None
-                async_copy_ready_event.synchronize()
-                sampled_token_ids = sampled_token_ids_cpu.tolist()
-            new_ids = list(sampled_token_ids[prev_index])
-            if not new_ids:
-                continue
-            num_sampled_ids = len(new_ids) if new_ids[-1] != -1 else new_ids.index(-1)
-            first_placeholder = req_history.index(-1)
-            num_placeholders = len(req_history) - first_placeholder
-            num_to_replace = min(num_sampled_ids, num_placeholders)
-            req_history[first_placeholder : first_placeholder + num_to_replace] = new_ids[:num_to_replace]
-
-        return output_token_ids
-
-    def _sampling_metadata_for_model_sampler(self, sampling_metadata):
-        output_token_ids = self._build_model_sampler_output_token_ids()
-        if output_token_ids == sampling_metadata.output_token_ids:
-            return sampling_metadata
-        return replace(sampling_metadata, output_token_ids=output_token_ids)
-
     def _request_final_stage_id(self, req_id: str) -> int | None:
         info = self.model_intermediate_buffer.get(req_id)
         if not isinstance(info, dict):

vllm_omni/worker/gpu_model_runner.py

@@ -1,5 +1,6 @@
 import contextlib
 from collections.abc import Callable
+from dataclasses import replace
 from typing import TYPE_CHECKING, Any, cast
 
 import numpy as np
@@ -121,6 +122,54 @@ def load_model(self, *args, **kwargs) -> None:
             self.last_talker_hidden = self._make_buffer(max_batch_size, hidden_size, dtype=self.dtype, numpy=False)
             self.text_step = self._make_buffer(max_batch_size, hidden_size, dtype=self.dtype, numpy=False)
 
+    def _build_model_sampler_output_token_ids(self) -> list[list[int]]:
+        """Build decoded-token history for ``prefer_model_sampler`` models.
+
+        vLLM only populates ``sampling_metadata.output_token_ids`` when penalties
+        or logits processors require it. Models that opt into a custom sampler
+        (e.g. CosyVoice3 RAS sampler, HunyuanImage3 stage-transition sampler)
+        also depend on this history, so we reconstruct it directly from the
+        input batch. Shared by GPU and NPU AR runners.
+        """
+        req_output_token_ids = getattr(self.input_batch, "req_output_token_ids", [])
+        req_ids = list(getattr(self.input_batch, "req_ids", []))
+        output_token_ids = [list(req_output_token_ids[idx] or []) for idx in range(len(req_ids))]
+
+        sampled_token_ids_cpu = getattr(self.input_batch, "sampled_token_ids_cpu", None)
+        async_copy_ready_event = getattr(self.input_batch, "async_copy_ready_event", None)
+        prev_req_id_to_index = getattr(self.input_batch, "prev_req_id_to_index", None)
+        if sampled_token_ids_cpu is None or not output_token_ids or prev_req_id_to_index is None:
+            return output_token_ids
+
+        sampled_token_ids: list[list[int]] | None = None
+        for index, req_id in enumerate(req_ids):
+            prev_index = prev_req_id_to_index.get(req_id)
+            if prev_index is None:
+                continue
+            req_history = output_token_ids[index]
+            if not req_history or req_history[-1] != -1:
+                continue
+            if sampled_token_ids is None:
+                assert async_copy_ready_event is not None
+                async_copy_ready_event.synchronize()
+                sampled_token_ids = sampled_token_ids_cpu.tolist()
+            new_ids = list(sampled_token_ids[prev_index])
+            if not new_ids:
+                continue
+            num_sampled_ids = len(new_ids) if new_ids[-1] != -1 else new_ids.index(-1)
+            first_placeholder = req_history.index(-1)
+            num_placeholders = len(req_history) - first_placeholder
+            num_to_replace = min(num_sampled_ids, num_placeholders)
+            req_history[first_placeholder : first_placeholder + num_to_replace] = new_ids[:num_to_replace]
+
+        return output_token_ids
+
+    def _sampling_metadata_for_model_sampler(self, sampling_metadata):
+        output_token_ids = self._build_model_sampler_output_token_ids()
+        if output_token_ids == sampling_metadata.output_token_ids:
+            return sampling_metadata
+        return replace(sampling_metadata, output_token_ids=output_token_ids)
+
     def _init_mrope_positions(self, req_state: CachedRequestState):
         """Initialize M-RoPE positions for multimodal inputs.