[Feature] Support Qwen3 Omni talker mtp batch inference

.buildkite/scripts/simple_test.sh

@@ -52,3 +52,4 @@ VENV_PYTHON="${VENV_DIR}/bin/python"
 "${VENV_PYTHON}" -m pytest -v -s tests/entrypoints/
 "${VENV_PYTHON}" -m pytest -v -s tests/diffusion/cache/
 "${VENV_PYTHON}" -m pytest -v -s tests/model_executor/models/qwen2_5_omni/test_audio_length.py
+"${VENV_PYTHON}" -m pytest -v -s tests/worker/

tests/worker/test_omni_gpu_model_runner.py

@@ -0,0 +1,123 @@
+from contextlib import contextmanager
+
+import torch
+
+from vllm_omni.worker.gpu_model_runner import OmniGPUModelRunner
+
+
+class DummyBuffer:
+    """A minimal buffer wrapper that exposes the `.gpu` attribute."""
+
+    def __init__(self, t: torch.Tensor):
+        self.gpu = t
+
+
+class DummyInputBatch:
+    """A minimal input batch that only provides `req_ids`."""
+
+    def __init__(self, req_ids):
+        self.req_ids = req_ids
+
+
+class DummyReqState:
+    """A minimal request state container."""
+
+    pass
+
+
+class DummyTalkerMTP(torch.nn.Module):
+    """A fake talker_mtp module for deterministic CPU testing."""
+
+    def forward(self, req_input_ids, req_embeds, last_talker_hidden, text_step):
+        # Deterministic behavior:
+        # - output embeds = input embeds + 1
+        # - output codes = [[0], [1], ...]
+        bsz = req_embeds.shape[0]
+        new_embeds = req_embeds + 1.0
+        codes = torch.arange(bsz, dtype=torch.int64).view(bsz, 1)
+        return new_embeds, codes
+
+
+@contextmanager
+def _noop_forward_context(*args, **kwargs):
+    """A no-op context manager to replace vLLM forward context in CPU tests."""
+    yield
+
+
+def _make_runner(req_ids=("r1", "r2"), hidden_size=4):
+    # Create an instance without calling OmniGPUModelRunner.__init__
+    runner = object.__new__(OmniGPUModelRunner)
+
+    # Minimal attributes used by OmniGPUModelRunner._talker_mtp_forward
+    runner.input_batch = DummyInputBatch(list(req_ids))
+    runner.requests = {rid: DummyReqState() for rid in req_ids}
+
+    # query_start_loc.cpu[req_index] is used to locate the token position
+    # in the flattened `inputs_embeds`.
+    runner.query_start_loc = type("QSL", (), {})()
+    # Map: r1 -> offset 0, r2 -> offset 3
+    runner.query_start_loc.cpu = torch.tensor([0, 3], dtype=torch.int32)
+
+    bsz = len(req_ids)
+    runner.talker_mtp_input_ids = DummyBuffer(torch.zeros((bsz,), dtype=torch.int64))
+    runner.talker_mtp_inputs_embeds = DummyBuffer(torch.zeros((bsz, hidden_size), dtype=torch.float32))
+    runner.last_talker_hidden = DummyBuffer(torch.zeros((bsz, hidden_size), dtype=torch.float32))
+    runner.text_step = DummyBuffer(torch.zeros((bsz, hidden_size), dtype=torch.float32))
+
+    runner.talker_mtp = DummyTalkerMTP()
+    runner.vllm_config = object()
+
+    # Provide a minimal implementation that returns the expected 4-tuple.
+    def _determine_batch_execution_and_padding(**kwargs):
+        return None, object(), None, None
+
+    runner._determine_batch_execution_and_padding = _determine_batch_execution_and_padding
+
+    # Use the real merge method from OmniGPUModelRunner.
+    return runner
+
+
+def test_talker_mtp_forward_cpu_updates_inputs_and_info(monkeypatch):
+    # Patch the module-level `set_forward_context` symbol used inside
+    # OmniGPUModelRunner._talker_mtp_forward.
+    import vllm_omni.worker.gpu_model_runner as mod  # Must be the same module that defines OmniGPUModelRunner
+
+    monkeypatch.setattr(mod, "set_forward_context", _noop_forward_context)
+
+    runner = _make_runner(req_ids=("r1", "r2"), hidden_size=4)
+
+    # Initialize per-request embeds (batch-major inside talker_mtp_inputs_embeds)
+    runner.talker_mtp_inputs_embeds.gpu[0] = torch.tensor([1.0, 2.0, 3.0, 4.0])
+    runner.talker_mtp_inputs_embeds.gpu[1] = torch.tensor([10.0, 20.0, 30.0, 40.0])
+
+    # Flattened `inputs_embeds`: offsets 0 and 3 will be overwritten
+    inputs_embeds = torch.zeros((6, 4), dtype=torch.float32)
+
+    # Call the original implementation from OmniGPUModelRunner (no re-implementation)
+    OmniGPUModelRunner._talker_mtp_forward(runner, ["r1", "r2"], inputs_embeds)
+
+    # Validate embeds were written back (+1)
+    assert torch.allclose(inputs_embeds[0], torch.tensor([2.0, 3.0, 4.0, 5.0]))
+    assert torch.allclose(inputs_embeds[3], torch.tensor([11.0, 21.0, 31.0, 41.0]))
+
+    # Validate per-request additional_information_cpu was updated
+    info_r1 = runner.requests["r1"].additional_information_cpu
+    info_r2 = runner.requests["r2"].additional_information_cpu
+    assert int(info_r1["code_predictor_codes"][0, 0]) == 0
+    assert int(info_r2["code_predictor_codes"][0, 0]) == 1
+
+
+def test_talker_mtp_forward_cpu_empty_batch_noop(monkeypatch):
+    import vllm_omni.worker.gpu_model_runner as mod
+
+    monkeypatch.setattr(mod, "set_forward_context", _noop_forward_context)
+
+    runner = _make_runner(req_ids=("r1",), hidden_size=4)
+
+    inputs_embeds = torch.randn((2, 4))
+    before = inputs_embeds.clone()
+
+    OmniGPUModelRunner._talker_mtp_forward(runner, [], inputs_embeds)
+
+    # Ensure no changes were made
+    assert torch.allclose(inputs_embeds, before)

vllm_omni/entrypoints/omni_stage.py

@@ -951,8 +951,6 @@ async def _stage_worker_async(
     except Exception as e:
         logger.warning("Device setup failed: %s", e)
 
-    max_batch_size = int(runtime_cfg.get("max_batch_size", 1) or 1)
-    engine_args["max_num_seqs"] = max_batch_size
     # Initialize OmniConnectors if configured to match sync worker behavior
     connectors: dict[Any, Any] = {}
     if connectors_config:

vllm_omni/entrypoints/utils.py

@@ -195,6 +195,10 @@ def load_stage_configs_from_yaml(config_path: str, base_engine_args: dict | None
         # Update base_engine_args with stage-specific engine_args if they exist
         if hasattr(stage_arg, "engine_args") and stage_arg.engine_args is not None:
             base_engine_args_tmp = OmegaConf.merge(base_engine_args_tmp, stage_arg.engine_args)
+        if hasattr(stage_arg, "runtime") and stage_arg.runtime is not None:
+            runtime_cfg = stage_arg.runtime
+            max_batch_size = int(runtime_cfg.get("max_batch_size", 1) or 1)
+            base_engine_args_tmp["max_num_seqs"] = max_batch_size
         stage_arg.engine_args = base_engine_args_tmp
     return stage_args
 

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py

@@ -573,30 +573,22 @@ def talker_preprocess(self, input_ids: torch.Tensor, input_embeds: torch.Tensor,
         if input_embeds is None and input_ids is not None:
             input_embeds = self.talker.embed_input_ids(input_ids)
 
-        text_step = torch.zeros(
-            1,
-            self.talker_config.text_config.hidden_size,
-            device=self._module_device(self.talker),
-            dtype=torch.bfloat16,
-        )
-        last_talker_hidden = torch.zeros(
-            1,
-            1,
-            self.talker_config.text_config.hidden_size,
-            device=self._module_device(self.talker),
-            dtype=torch.bfloat16,
-        )
-
         span_len = input_ids.shape[0]
         if span_len > 1:
             # prefill
             input_ids, input_embeds, update_dict = self.talker_preprocess_prefill(input_ids, input_embeds, **info_dict)
+            code_predictor_codes = torch.zeros(
+                (input_embeds.shape[0], self.talker.num_code_groups),
+                device=self._module_device(self.talker),
+                dtype=torch.long,
+            )
+            update_dict["code_predictor_codes"] = code_predictor_codes
         else:
             last_talker_hidden, text_step, update_dict = self.talker_preprocess_decode(
                 input_ids, input_embeds, **info_dict
             )
 
-        update_dict["mtp_inputs"] = last_talker_hidden, text_step
+            update_dict["mtp_inputs"] = last_talker_hidden, text_step
 
         return input_ids, input_embeds, update_dict
 
@@ -608,24 +600,19 @@ def talker_mtp(
         text_step: torch.Tensor,
     ):
         # TODO(Peiqi): not support intermediate_tensors now
-        input_ids = safe_tensor_reshape(input_ids, (1, -1))
+        input_ids = safe_tensor_reshape(input_ids, (input_ids.shape[0], -1))
         inputs_embeds = safe_tensor_reshape(input_embeds, (-1, self.talker_config.text_config.hidden_size))
-        text_step = safe_tensor_reshape(text_step, (1, -1))
-        last_talker_hidden = safe_tensor_reshape(last_talker_hidden, (1, 1, self.talker_config.text_config.hidden_size))
+        text_step = safe_tensor_reshape(text_step, (-1, self.talker_config.text_config.hidden_size))
+        last_talker_hidden = safe_tensor_reshape(
+            last_talker_hidden, (-1, 1, self.talker_config.text_config.hidden_size)
+        )
         # for profiling
         if inputs_embeds.shape[-1] == 2048:
             inputs_embeds = self.text_projection(inputs_embeds)
-        if inputs_embeds.shape[0] == 1:
-            code_predictor_codes, summed_embeddings = self.talker.code_predictor_forward(
-                input_ids, inputs_embeds.clone(), last_talker_hidden=last_talker_hidden
-            )
-            inputs_embeds = summed_embeddings.clone()
-        else:
-            code_predictor_codes = torch.zeros(
-                (inputs_embeds.shape[0], self.talker.num_code_groups),
-                device=self._module_device(self.talker),
-                dtype=torch.long,
-            )
+        code_predictor_codes, summed_embeddings = self.talker.code_predictor_forward(
+            input_ids, inputs_embeds.clone(), last_talker_hidden=last_talker_hidden
+        )
+        inputs_embeds = summed_embeddings.clone()
         inputs_embeds = (inputs_embeds + text_step).reshape(-1, self.talker_config.text_config.hidden_size)
         return inputs_embeds, code_predictor_codes.squeeze(-1)
 
@@ -848,7 +835,7 @@ def talker_preprocess_decode(self, input_ids: torch.Tensor, input_embeds: torch.
                 use_vec = q_tail[0:1, :]
                 new_q_tail = (
                     q_tail[1:, :].detach().to("cpu").contiguous()
-                    if q_tail.shape[1] > 1
+                    if q_tail.shape[0] > 1
                     else self.tts_pad_embed.to(input_embeds.device, dtype=input_embeds.dtype)
                 )
                 text_step = use_vec.to(input_embeds.device, dtype=input_embeds.dtype)

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py

@@ -234,13 +234,7 @@ def code_predictor_forward(
                 # Use the corresponding lm_head for this layer
                 logits = self.code_predictor.lm_head[layer_idx](hidden_state[:, -1:, :])  # [batch, 1, vocab_size]
 
-                if len(pos_codes) > 1:
-                    input_ids_for_logits_processors = torch.cat(pos_codes[1:], dim=1).to(
-                        device=logits.device, dtype=torch.long
-                    )
-                else:
-                    input_ids_for_logits_processors = self.empty_code
-                logits = logits_processors(input_ids_for_logits_processors, logits.squeeze(0)).unsqueeze(0)
+                logits = logits_processors(None, logits[:, -1])
 
                 # Sample from the filtered distribution
                 probs = F.softmax(logits, dim=-1)
@@ -288,7 +282,7 @@ def code_predictor_forward(
             all_summed_embeddings.append(pos_summed)
 
         # Concatenate across positions: [batch, seq_len, hidden_size]
-        summed_embeddings = torch.cat(all_summed_embeddings, dim=1)
+        summed_embeddings = torch.cat(all_summed_embeddings, dim=1).squeeze(1)
 
         return result_codes, summed_embeddings
 

vllm_omni/model_executor/stage_input_processors/qwen3_omni.py

@@ -160,7 +160,7 @@ def talker2code2wav(
     # Process each talker output
     for i, talker_output in enumerate(talker_outputs):
         output = talker_output.outputs[0]
-        seq_len = len(output.token_ids)
+        seq_len = len(output.token_ids) - 1
         # Extract codec codes from talker output
         # Expected shape: [8, seq_len] (8-layer RVQ codes)
         codec_codes = (

vllm_omni/worker/gpu_model_runner.py

@@ -563,11 +563,7 @@ def _dummy_run(
                     ubatch_slices=ubatch_slices,
                 ),
             ):
-                if (
-                    getattr(self.model, "talker", None) is not None
-                    and hasattr(self.model, "talker_mtp")
-                    and num_tokens_padded == 1
-                ):
+                if getattr(self.model, "talker", None) is not None and hasattr(self.model, "talker_mtp"):
                     outputs = self.talker_mtp(
                         self.talker_mtp_input_ids.gpu[:num_tokens_padded],
                         self.talker_mtp_inputs_embeds.gpu[:num_tokens_padded],
@@ -884,6 +880,7 @@ def _preprocess(
         if hasattr(self.model, "has_preprocess") and self.model.has_preprocess:
             # Overlay custom prompt_embeds per request for the prompt portion;
             # collect additional_information (tensor/list) for prefill portion only
+            decode_req_ids = []
             for req_index, req_id in enumerate(self.input_batch.req_ids):
                 req_state = self.requests.get(req_id)
                 req_infos = getattr(req_state, "additional_information_cpu", None) if req_state is not None else None
@@ -897,33 +894,14 @@ def _preprocess(
                 req_input_ids, req_embeds, update_dict = self.model.preprocess(
                     input_ids=input_ids[s:e], input_embeds=inputs_embeds[s:e], **req_infos
                 )
-                # run talker mtp decode
-                if hasattr(self.model, "talker_mtp"):
-                    _cudagraph_mode, batch_desc, _, _ = self._determine_batch_execution_and_padding(
-                        num_tokens=span_len,
-                        num_reqs=1,
-                        num_scheduled_tokens_np=num_scheduled_tokens_np[req_index],
-                        max_num_scheduled_tokens=1,
-                        force_eager=span_len > 1,
-                        use_cascade_attn=False,
-                    )
+                if hasattr(self.model, "talker_mtp") and span_len == 1:
                     last_talker_hidden, text_step = update_dict.pop("mtp_inputs")
-                    if _cudagraph_mode != CUDAGraphMode.NONE:
-                        self.talker_mtp_input_ids.gpu[:span_len].copy_(req_input_ids)
-                        self.talker_mtp_inputs_embeds.gpu[:span_len].copy_(req_embeds)
-                        self.last_talker_hidden.gpu[:span_len].copy_(last_talker_hidden)
-                        self.text_step.gpu[:span_len].copy_(text_step)
-                        req_input_ids = self.talker_mtp_input_ids.gpu[:span_len]
-                        req_embeds = self.talker_mtp_inputs_embeds.gpu[:span_len]
-                        last_talker_hidden = self.last_talker_hidden.gpu[:span_len]
-                        text_step = self.text_step.gpu[:span_len]
-                    with set_forward_context(
-                        None, self.vllm_config, cudagraph_runtime_mode=_cudagraph_mode, batch_descriptor=batch_desc
-                    ):
-                        req_embeds, code_predictor_codes = self.talker_mtp(
-                            req_input_ids, req_embeds, last_talker_hidden, text_step
-                        )
-                        update_dict["code_predictor_codes"] = code_predictor_codes
+                    decode_slice = slice(len(decode_req_ids), len(decode_req_ids) + 1)
+                    self.talker_mtp_input_ids.gpu[decode_slice].copy_(req_input_ids)
+                    self.talker_mtp_inputs_embeds.gpu[decode_slice].copy_(req_embeds)
+                    self.last_talker_hidden.gpu[decode_slice].copy_(last_talker_hidden)
+                    self.text_step.gpu[decode_slice].copy_(text_step)
+                    decode_req_ids.append(req_id)
 
                 # TODO(Peiqi): the merge stage could move out from the critical path
                 self._merge_additional_information_update(req_id, update_dict)
@@ -934,6 +912,10 @@ def _preprocess(
                 if isinstance(req_input_ids, torch.Tensor) and req_input_ids.numel() == seg_len:
                     input_ids[s : s + seg_len] = req_input_ids
 
+            # run talker mtp decode
+            if hasattr(self.model, "talker_mtp"):
+                self._talker_mtp_forward(decode_req_ids, inputs_embeds)
+
         return (
             input_ids,
             inputs_embeds,
@@ -943,6 +925,34 @@ def _preprocess(
             ec_connector_output,
         )
 
+    def _talker_mtp_forward(self, decode_req_ids: list[str], inputs_embeds: torch.Tensor) -> None:
+        decode_batch_size = len(decode_req_ids)
+        if decode_batch_size == 0:
+            return
+        _cudagraph_mode, batch_desc, _, _ = self._determine_batch_execution_and_padding(
+            num_tokens=decode_batch_size,
+            num_reqs=decode_batch_size,
+            num_scheduled_tokens_np=np.ones(decode_batch_size, dtype=np.int32),
+            max_num_scheduled_tokens=1,
+            use_cascade_attn=False,
+        )
+        req_input_ids = self.talker_mtp_input_ids.gpu[:decode_batch_size]
+        req_embeds = self.talker_mtp_inputs_embeds.gpu[:decode_batch_size]
+        last_talker_hidden = self.last_talker_hidden.gpu[:decode_batch_size]
+        text_step = self.text_step.gpu[:decode_batch_size]
+        with set_forward_context(
+            None, self.vllm_config, cudagraph_runtime_mode=_cudagraph_mode, batch_descriptor=batch_desc
+        ):
+            req_embeds, code_predictor_codes = self.talker_mtp(req_input_ids, req_embeds, last_talker_hidden, text_step)
+        # update the inputs_embeds and code_predictor_codes
+        code_predictor_codes_cpu = code_predictor_codes.detach().to("cpu").contiguous()
+        for idx, req_id in enumerate(decode_req_ids):
+            req_index = self.input_batch.req_ids.index(req_id)
+            start_offset = int(self.query_start_loc.cpu[req_index])
+            inputs_embeds[start_offset : start_offset + 1] = req_embeds[idx : idx + 1]
+            update_dict = {"code_predictor_codes": code_predictor_codes_cpu[idx : idx + 1]}
+            self._merge_additional_information_update(req_id, update_dict)
+
     def _model_forward(
         self,
         input_ids: torch.Tensor | None = None,