[BugFix] Fix the issue of thinker requests being preempted, causing shape mismatch.

tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json

@@ -10,16 +10,16 @@
                 "dataset_name": "random",
                 "backend": "openai-chat-omni",
                 "endpoint": "/v1/chat/completions",
-                "num_prompts": [4, 16, 32, 64],
-                "max_concurrency": [1, 4, 8, 16],
+                "num_prompts": [4, 16, 32, 64, 128],
+                "max_concurrency": [1, 4, 8, 16, 32],
                 "random_input_len": 2500,
                 "random_output_len": 900,
                 "ignore_eos": true,
                 "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
                 "baseline": {
-                    "mean_ttft_ms": [1000, 3000, 5000, 7000],
-                    "mean_audio_ttfp_ms": [1000, 3000, 5000, 7000],
-                    "mean_audio_rtf": [0.2, 0.35, 0.6, 0.85]
+                    "mean_ttft_ms": [1000, 3000, 5000, 7000, 9000],
+                    "mean_audio_ttfp_ms": [1000, 3000, 5000, 7000, 9000],
+                    "mean_audio_rtf": [0.2, 0.35, 0.6, 0.85, 0.9]
                 }
             },
             {

tests/e2e/online_serving/test_qwen3_omni_expansion.py

@@ -44,7 +44,7 @@ def get_batch_token_config(default_path):
     return modify_stage_config(
         default_path,
         updates={
-            "stages": {1: {"max_num_batched_tokens": 64}},
+            "stages": {0: {"max_num_batched_tokens": 64}, 1: {"max_num_batched_tokens": 64}},
         },
     )
 
@@ -95,7 +95,12 @@ def get_default_config(default_path):
 
 test_token_params = [
     pytest.param(
-        OmniServerParams(model=model, stage_config_path=get_batch_token_config(default_path), use_stage_cli=True),
+        OmniServerParams(
+            model=model,
+            stage_config_path=get_batch_token_config(default_path),
+            use_stage_cli=True,
+            server_args=["--async-chunk"],
+        ),
         id="batch_token_64",
     )
 ]

vllm_omni/distributed/omni_connectors/transfer_adapter/chunk_transfer_adapter.py

@@ -62,6 +62,7 @@ def __init__(self, vllm_config: Any):
         self.waiting_for_chunk_running_requests: deque[Any] = deque()
         self.requests_with_ready_chunks = set()
         self.requests_origin_status = {}
+        self.requests_num_chunks_sent: dict[str, int] = defaultdict(int)
 
     @classmethod
     def create_connector(cls, model_config: Any):
@@ -117,6 +118,17 @@ def save_async(
             pooling_output: Partial pooling output dictionary
             request: Request object
         """
+
+        # If the request is preempted, skip the already saved chunks.
+        if request.num_computed_tokens < self.requests_num_chunks_sent.get(request.external_req_id, 0):
+            logger.warning(
+                f"Enqueue save_async for request {request.external_req_id}, "
+                f"request.num_computed_tokens={request.num_computed_tokens}, "
+                f"previous_chunks_sent={self.requests_num_chunks_sent.get(request.external_req_id, 0)}"
+            )
+            return
+
+        self.requests_num_chunks_sent[request.external_req_id] = request.num_computed_tokens
         task = {
             "pooling_output": pooling_output,
             "request": request,
@@ -155,8 +167,7 @@ def _poll_single_request(self, request: Request):
 
             meta = payload_data.get("meta", {})
             if self.model_mode == "ar":
-                merged_payload = self._update_request_payload(external_req_id, payload_data)
-                request.additional_information = merged_payload
+                request.additional_information = payload_data
                 if meta.get("finished"):
                     self.finished_requests.add(req_id)
             else:
@@ -198,42 +209,6 @@ def _poll_single_request(self, request: Request):
 
         return False
 
-    def _update_request_payload(self, req_id: str, payload_data: dict[str, Any]) -> dict[str, Any]:
-        """Update the stored payload for *req_id* with the latest chunk."""
-        if req_id not in self.request_payload:
-            self.request_payload[req_id] = payload_data
-            return payload_data
-        origin = self.request_payload[req_id]
-        raw_ok = payload_data.get("meta", {}).pop("override_keys", [])
-        override_keys = {tuple(k) if isinstance(k, list) else k for k in raw_ok}
-
-        for key, value in payload_data.items():
-            if isinstance(value, dict):
-                origin_sub = origin.get(key)
-                if not isinstance(origin_sub, dict):
-                    continue
-                for qual, qval in value.items():
-                    if key == "meta" and qual == "finished":
-                        continue
-                    if (key, qual) in override_keys:
-                        continue
-                    osv = origin_sub.get(qual)
-                    if isinstance(qval, torch.Tensor) and isinstance(osv, torch.Tensor):
-                        value[qual] = torch.cat([osv, qval], dim=0)
-                    elif isinstance(qval, list) and isinstance(osv, list):
-                        value[qual] = osv + qval
-            else:
-                if key in override_keys:
-                    continue
-                ov = origin.get(key)
-                if isinstance(value, torch.Tensor) and isinstance(ov, torch.Tensor):
-                    payload_data[key] = torch.cat([ov, value], dim=0)
-                elif isinstance(value, list) and isinstance(ov, list):
-                    payload_data[key] = ov + value
-
-        self.request_payload[req_id] = payload_data
-        return payload_data
-
     def _send_single_request(self, task: dict):
         raw_po = task["pooling_output"]
         pooling_output = unflatten_payload(raw_po) if isinstance(raw_po, dict) else raw_po
@@ -290,6 +265,7 @@ def _send_single_request(self, task: dict):
 
         if is_finished:
             self.code_prompt_token_ids.pop(external_req_id, None)
+            self.requests_num_chunks_sent.pop(external_req_id, None)
             cached_ic = getattr(self, "_cached_ic", None)
             if cached_ic is not None:
                 cached_ic.pop(external_req_id, None)
@@ -327,6 +303,7 @@ def cleanup_sender(self, external_req_id: str) -> None:
         self.put_req_chunk.pop(external_req_id, None)
         self.request_payload.pop(external_req_id, None)
         self.code_prompt_token_ids.pop(external_req_id, None)
+        self.requests_num_chunks_sent.pop(external_req_id, None)
 
         cached_ic = getattr(self, "_cached_ic", None)
         if cached_ic is not None:
@@ -399,6 +376,11 @@ def postprocess_scheduler_output(
         Add additional info for cached requests and
         clean up ready chunks from scheduler output.
         """
+        stage_id = self.connector.stage_id
+
+        if stage_id == 0:
+            return
+
         if requests is not None:
             self.attach_cached_additional_information(scheduler_output, requests)
         self._clear_chunk_ready(scheduler_output)
@@ -414,6 +396,8 @@ def attach_cached_additional_information(scheduler_output: Any, requests: dict[s
             request = requests.get(req_id) if req_id else None
             additional_info = getattr(request, "additional_information", None) if request else None
             cached_reqs.additional_information[req_id] = additional_info
+            if request and additional_info:
+                request.additional_information = None
 
     def _process_chunk_queue(
         self,

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py

@@ -982,19 +982,10 @@ def _thinker_decode_to_talker_decode(
         """
         embed = payload.get("embed", {})
         meta = payload.get("meta", {})
-        ids = payload.get("ids", {})
 
         cached_thinker_decode_embeds = embed.get("cached_decode", None)
         thinker_decode_embed = embed.get("decode", None)
         start_index = meta.get("num_processed_tokens", 0)
-        thinker_output_token_ids = ids.get("output", [])
-        if start_index >= len(thinker_output_token_ids) - 1:
-            # When the tokens output by the thinker are exhausted, an EOS token needs to be appended.
-            # Use the finished_flag to mark that all tokens output by thinker have been consumed.
-            if meta.get("eos_emitted", False):
-                return self.tts_pad_embed.to(device)
-            update_dict.setdefault("meta", {})["eos_emitted"] = True
-            return self.tts_eos_embed.to(device)
 
         if cached_thinker_decode_embeds is not None and start_index < cached_thinker_decode_embeds.shape[0]:
             cached_thinker_decode_embeds = cached_thinker_decode_embeds.to(device)
@@ -1003,10 +994,20 @@ def _thinker_decode_to_talker_decode(
                 thinker_decode_embed = thinker_decode_embed.to(device)
                 cached_thinker_decode_embeds = torch.cat([cached_thinker_decode_embeds, thinker_decode_embed], dim=0)
                 update_dict.setdefault("embed", {})["cached_decode"] = cached_thinker_decode_embeds
-        else:
+
+        elif thinker_decode_embed is not None:
             thinker_embed = thinker_decode_embed
             if thinker_embed.device != device:
                 thinker_embed = thinker_embed.to(device)
+
+        else:
+            # When the tokens output by the thinker are exhausted, an EOS token needs to be appended.
+            # Use the finished_flag to mark that all tokens output by thinker have been consumed.
+            if meta.get("eos_emitted", False):
+                return self.tts_pad_embed.to(device)
+            update_dict.setdefault("meta", {})["eos_emitted"] = True
+            return self.tts_eos_embed.to(device)
+
         update_dict.setdefault("embed", {})["decode"] = None
         return self.talker.text_projection(thinker_embed).to(device)
 

vllm_omni/model_executor/stage_input_processors/qwen3_omni.py

@@ -349,27 +349,28 @@ def _maybe_cpu(t: Any) -> torch.Tensor | None:
             payload.hidden_states.output = torch.cat(
                 (save_payload.get("hidden_states", {}).get("output"), payload.hidden_states.output), dim=0
             )
+            prefill_shape = payload.embed.prefill.shape[0]
+            if not is_finished and prefill_shape <= len(prompt_token_ids):
+                transfer_manager.request_payload[request_id] = to_dict(payload)
+                return None
     else:
-        output_token_ids = _ensure_list(request.output_token_ids)
-        meta = MetaStruct(finished=torch.tensor(is_finished, dtype=torch.bool))
-        if output_token_ids:
-            meta.override_keys = [("embed", "decode"), ("ids", "output")]
-            payload = OmniPayloadStruct(
-                meta=meta,
-                embed=EmbeddingsStruct(decode=thinker_emb.detach().cpu()),
-                ids=IdsStruct(output=output_token_ids),
-                speaker=speaker,
-                language=language,
-            )
-        else:
-            # When prefilling a chunked thinker, thinker_hidden_states needs to be updated.
-            payload = OmniPayloadStruct(
-                meta=meta,
-                embed=EmbeddingsStruct(prefill=thinker_emb.detach().cpu()),
-                hidden_states=HiddenStatesStruct(output=thinker_hid.detach().cpu()),
-                speaker=speaker,
-                language=language,
+        if thinker_emb.shape[0] > 1:
+            logger.warning(
+                "Unexpected multiple embeddings in thinker2talker_async_chunk for chunk_id %d: "
+                "request_id %s, num_computed_tokens%d %s. Expected shape [1, D].",
+                chunk_id,
+                request_id,
+                request.num_computed_tokens,
+                thinker_emb.shape,
             )
+            return None
+        meta = MetaStruct(finished=torch.tensor(is_finished, dtype=torch.bool))
+        payload = OmniPayloadStruct(
+            meta=meta,
+            embed=EmbeddingsStruct(decode=thinker_emb.detach().cpu()),
+            speaker=speaker,
+            language=language,
+        )
     return payload