[Model] Implement LoRA support for Qwen3ASRForConditionalGeneration (#37247)

Signed-off-by: Peter Nguyen <petern0408@gmail.com>

docs/models/supported_models.md

@@ -666,7 +666,7 @@ Speech2Text models trained specifically for Automatic Speech Recognition.
 | `Gemma3nForConditionalGeneration` | Gemma3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
 | `GlmAsrForConditionalGeneration` | GLM-ASR | `zai-org/GLM-ASR-Nano-2512` | ✅︎ | ✅︎ |
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | `ibm-granite/granite-4.0-1b-speech`, `ibm-granite/granite-speech-3.3-2b`, etc. | ✅︎ | ✅︎ |
-| `Qwen3ASRForConditionalGeneration` | Qwen3-ASR | `Qwen/Qwen3-ASR-1.7B`, etc. | | ✅︎ |
+| `Qwen3ASRForConditionalGeneration` | Qwen3-ASR | `Qwen/Qwen3-ASR-1.7B`, etc. | ✅︎ | ✅︎ |
 | `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, etc. | | ✅︎ |
 | `VoxtralForConditionalGeneration` | Voxtral (Mistral format) | `mistralai/Voxtral-Mini-3B-2507`, `mistralai/Voxtral-Small-24B-2507`, etc. | ✅︎ | ✅︎ |
 | `WhisperForConditionalGeneration` | Whisper | `openai/whisper-small`, `openai/whisper-large-v3-turbo`, etc. | | |

vllm/model_executor/models/qwen3_asr.py

@@ -37,6 +37,7 @@ from vllm.inputs import ModalityData, MultiModalDataDict, PromptType, TokensProm
 from vllm.logger import init_logger
 from vllm.model_executor.models.interfaces import (
     MultiModalEmbeddings,
+    SupportsLoRA,
     SupportsMRoPE,
     SupportsMultiModal,
     SupportsPP,
@@ -266,7 +267,21 @@ class Qwen3ASRForConditionalGeneration(
     SupportsPP,
     SupportsMRoPE,
     SupportsTranscription,
+    SupportsLoRA,
 ):
+    # LoRA support
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
     supported_languages = ISO639_1_SUPPORTED_LANGS
 
     hf_to_vllm_mapper = WeightsMapper(
@@ -513,6 +528,17 @@ class Qwen3ASRForConditionalGeneration(
             tower_model=["audio_tower."],
         )
 
+    def get_num_mm_encoder_tokens(self, num_audio_tokens: int) -> int:
+        """Return the number of tokens processed by the audio tower encoder.
+
+        Required for LoRA support on the tower module.
+        """
+        # For Qwen3-ASR, the audio tower produces one embedding per audio
+        # placeholder token inserted into the prompt (no additional
+        # merge/downsample step like vision towers). Therefore, the encoder
+        # token budget is identity.
+        return num_audio_tokens
+
     @classmethod
     def get_speech_to_text_config(
         cls, model_config: ModelConfig, task_type: str

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -57,6 +57,7 @@ from vllm.model_executor.layers.conv import Conv3dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
+    ReplicatedLinear,
     RowParallelLinear,
 )
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -357,7 +358,13 @@ class Qwen3OmniMoeAudioEncoder(nn.Module):
         conv_out_dim = config.downsample_hidden_size * (
             (((config.num_mel_bins + 1) // 2 + 1) // 2 + 1) // 2
         )
-        self.conv_out = nn.Linear(conv_out_dim, config.d_model, bias=False)
+        self.conv_out = ReplicatedLinear(
+            conv_out_dim,
+            config.d_model,
+            bias=False,
+            return_bias=False,
+            prefix=f"{prefix}.conv_out",
+        )
 
         # Transformer encoder layers
         self.layers = nn.ModuleList(
@@ -372,9 +379,21 @@ class Qwen3OmniMoeAudioEncoder(nn.Module):
 
         # Output layers
         self.ln_post = nn.LayerNorm(config.d_model)
-        self.proj1 = nn.Linear(config.d_model, config.d_model)
+        self.proj1 = ReplicatedLinear(
+            config.d_model,
+            config.d_model,
+            bias=True,
+            return_bias=False,
+            prefix=f"{prefix}.proj1",
+        )
         self.act = _ACTIVATION_REGISTRY[config.activation_function]
-        self.proj2 = nn.Linear(config.d_model, config.output_dim)
+        self.proj2 = ReplicatedLinear(
+            config.d_model,
+            config.output_dim,
+            bias=True,
+            return_bias=False,
+            prefix=f"{prefix}.proj2",
+        )
 
         # Get attention backend
         self.attn_backend = get_vit_attn_backend(

vllm/v1/worker/gpu_model_runner.py

@@ -2783,7 +2783,20 @@ class GPUModelRunner(
             )
             self.lora_manager.set_active_adapters(lora_requests, tower_mapping)
 
-            if hasattr(self.model, "get_num_mm_connector_tokens"):
+            # Only set connector mapping if the model actually has a connector.
+            # Some multimodal models inherit a stub `get_num_mm_connector_tokens`
+            # from `SupportsMultiModal`, which returns None and should not be
+            # treated as a signal that connector LoRA is supported.
+            mm_mapping = (
+                self.model.get_mm_mapping()  # type: ignore[attr-defined]
+                if hasattr(self.model, "get_mm_mapping")
+                else None
+            )
+            if (
+                mm_mapping is not None
+                and mm_mapping.connector
+                and hasattr(self.model, "get_num_mm_connector_tokens")
+            ):
                 post_op_counts = [
                     self.model.get_num_mm_connector_tokens(num_tokens)  # type: ignore[attr-defined]
                     for num_tokens in encoder_token_counts