qwen2.5 & 3 vl fixes and tests

Signed-off-by: prashanth058 <prashanth.dannamaneni@uipath.com>

tests/lora/conftest.py

@@ -240,6 +240,16 @@ def qwen2vl_vision_tower_lora_files():
     return snapshot_download(repo_id="prashanth058/qwen2vl-flickr-lora-tower")
 
 
+@pytest.fixture(scope="session")
+def qwen25vl_vision_lora_files():
+    return snapshot_download(repo_id="prashanth058/qwen2.5-3b-vl-flickr-lora-vision")
+
+
+@pytest.fixture(scope="session")
+def qwen3vl_vision_lora_files():
+    return snapshot_download(repo_id="prashanth058/qwen3-4b-vl-lora-vision-connector")
+
+
 @pytest.fixture(scope="session")
 def tinyllama_lora_files():
     return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")

tests/lora/test_qwenvl.py

@@ -14,8 +14,9 @@ class TestConfig:
     lora_path: str
     max_num_seqs: int = 2
     max_loras: int = 2
-    max_lora_rank: int = 16
+    max_lora_rank: int = 32
-    max_model_len: int = 4096
+    max_model_len: int = 8192
+    gpu_memory_utilization: float = 0.85
     mm_processor_kwargs: dict[str, int] | None = None
 
     def __post_init__(self):
@@ -49,6 +50,7 @@ class Qwen2VLTester:
             max_loras=self.config.max_loras,
             max_lora_rank=self.config.max_lora_rank,
             trust_remote_code=True,
+            gpu_memory_utilization=self.config.gpu_memory_utilization,
             mm_processor_kwargs=self.config.mm_processor_kwargs,
             max_model_len=self.config.max_model_len,
         )
@@ -142,6 +144,16 @@ EXPECTED_OUTPUTS_VISION_NO_CONNECTOR = [
     "A closeup shot of the Tokyo Skytree with pink flowers in the foreground.",
 ]
 
+EXPECTED_OUTPUTS_VISION_QWEN2_5_VL = [
+    "A black car is driving past a stop sign and a large red and gold arch.",
+    "A view of the Tokyo Skytree through the branches of a cherry blossom tree.",
+]
+
+EXPECTED_OUTPUTS_VISION_QWEN3_VL = [
+    "A black SUV drives past a stop sign in front of a Chinese gate.",
+    "A white tower is seen through pink flowers.",
+]
+
 # NOTE - beam search .text contains the whole text
 EXPECTED_BEAM_SEARCH_OUTPUTS = [
     [
@@ -152,6 +164,7 @@ EXPECTED_BEAM_SEARCH_OUTPUTS = [
 
 QWEN2VL_MODEL_PATH = "Qwen/Qwen2-VL-2B-Instruct"
 QWEN25VL_MODEL_PATH = "Qwen/Qwen2.5-VL-3B-Instruct"
+QWEN3VL_MODEL_PATH = "Qwen/Qwen3-VL-4B-Instruct"
 
 
 def test_qwen2vl_lora(qwen2vl_lora_files):
@@ -192,10 +205,6 @@ def test_qwen25vl_lora(qwen25vl_lora_files):
         tester.run_test(TEST_IMAGES, expected_outputs=EXPECTED_OUTPUTS, lora_id=lora_id)
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen2vl_language_lora(qwen2vl_language_lora_files):
     """
     Test language-only LoRA adapter.
@@ -210,10 +219,6 @@ def test_qwen2vl_language_lora(qwen2vl_language_lora_files):
         )
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen2vl_vision_lora(qwen2vl_vision_tower_connector_lora_files):
     """
     Test vision tower + connector LoRA adapter.
@@ -229,10 +234,6 @@ def test_qwen2vl_vision_lora(qwen2vl_vision_tower_connector_lora_files):
         )
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen2vl_vision_no_connector_lora(
     qwen2vl_vision_tower_lora_files,
 ):
@@ -251,3 +252,31 @@ def test_qwen2vl_vision_no_connector_lora(
             expected_outputs=EXPECTED_OUTPUTS_VISION_NO_CONNECTOR,
             lora_id=lora_id,
         )
+
+
+def test_qwen25vl_vision_lora(qwen25vl_vision_lora_files):
+    config = TestConfig(
+        model_path=QWEN25VL_MODEL_PATH,
+        lora_path=qwen25vl_vision_lora_files,
+    )
+    tester = Qwen2VLTester(config)
+    for lora_id in [1, 2]:
+        tester.run_test(
+            TEST_IMAGES,
+            expected_outputs=EXPECTED_OUTPUTS_VISION_QWEN2_5_VL,
+            lora_id=lora_id,
+        )
+
+
+def test_qwen3vl_vision_lora(qwen3vl_vision_lora_files):
+    config = TestConfig(
+        model_path=QWEN3VL_MODEL_PATH,
+        lora_path=qwen3vl_vision_lora_files,
+    )
+    tester = Qwen2VLTester(config)
+    for lora_id in [1, 2]:
+        tester.run_test(
+            TEST_IMAGES,
+            expected_outputs=EXPECTED_OUTPUTS_VISION_QWEN3_VL,
+            lora_id=lora_id,
+        )

vllm/lora/layers/column_parallel_linear.py

@@ -340,7 +340,12 @@ class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
         packed_modules_list: list,
         model_config: PretrainedConfig | None,
     ) -> bool:
-        return type(source_layer) is QKVParallelLinear and len(packed_modules_list) == 1
+        # Vision tower QKV has packed_modules_list=[] (already packed in checkpoint)
+        # Language models have packed_modules_list=[module_name]
+        # (single LoRA for qkv_proj)
+        return type(source_layer) is QKVParallelLinear and (
+            len(packed_modules_list) <= 1
+        )
 
 
 class MergedQKVParallelLinearWithLoRA(MergedColumnParallelLinearWithLoRA):

vllm/lora/models.py

@@ -562,10 +562,12 @@ class LoRAModelManager:
         target_wrapper = self.punica_wrapper
 
         if self.supports_mm_lora:
-            if mapping.type == LoRAMappingType.TOWER:
+            if mapping.type == LoRAMappingType.TOWER and self.mm_mapping.tower_model:
                 target_name = self.mm_mapping.tower_model[0]
                 target_wrapper = self.mm_punica_wrapper_mapping[target_name]
-            elif mapping.type == LoRAMappingType.CONNECTOR:
+            elif (
+                mapping.type == LoRAMappingType.CONNECTOR and self.mm_mapping.connector
+            ):
                 target_name = self.mm_mapping.connector[0]
                 target_wrapper = self.mm_punica_wrapper_mapping[target_name]
             else:

vllm/model_executor/models/qwen3_vl.py

@@ -1675,6 +1675,6 @@ class Qwen3VLForConditionalGeneration(
         """
         return MultiModelKeys.from_string_field(
             language_model="language_model",
-            connector="visual.merger",
+            connector=["visual.merger", "visual.deepstack_merger_list"],
             tower_model="visual.",
         )

vllm/v1/worker/gpu_model_runner.py

@@ -2075,7 +2075,9 @@ class GPUModelRunner(
                 req_idx = self.input_batch.req_id_to_index[req_id]
                 lora_id = int(self.input_batch.request_lora_mapping[req_idx])
 
-                num_tokens = self.info.get_num_mm_encoder_tokens(pos_info.length)
+                num_tokens = self.info.get_num_mm_encoder_tokens(  # type: ignore[attr-defined]
+                    pos_info.length
+                )
                 prompt_lora_mapping.append(lora_id)
                 token_lora_mapping.extend([lora_id] * num_tokens)
 
@@ -2095,16 +2097,18 @@ class GPUModelRunner(
             if hasattr(self.info, "get_num_mm_connector_tokens"):
                 num_post_op_tokens = []
                 for _, pos_info in mm_hashes_pos:
-                    mm_token_count = self.info.get_num_mm_encoder_tokens(
+                    mm_token_count = self.info.get_num_mm_encoder_tokens(  # type: ignore[attr-defined]
                         pos_info.length
                     )
-                    post_op_count = self.info.get_num_mm_connector_tokens(
+                    post_op_count = self.info.get_num_mm_connector_tokens(  # type: ignore[attr-defined]
                         mm_token_count
                     )
                     num_post_op_tokens.append(post_op_count)
 
+                last_mapping = self.lora_manager._adapter_manager._last_mapping
+                assert last_mapping is not None
                 lora_ids = np.array(
-                    self.lora_manager._adapter_manager._last_mapping.prompt_mapping,
+                    last_mapping.prompt_mapping,
                     dtype=np.int32,
                 )
                 post_op_counts_np = np.array(num_post_op_tokens, dtype=np.int32)
@@ -2112,8 +2116,8 @@ class GPUModelRunner(
 
                 connector_mapping = LoRAMapping(
                     index_mapping=tuple(new_token_indices.tolist()),
-                    prompt_mapping=self.lora_manager._adapter_manager._last_mapping.prompt_mapping,
+                    prompt_mapping=last_mapping.prompt_mapping,
-                    is_prefill=self.lora_manager._adapter_manager._last_mapping.is_prefill,
+                    is_prefill=last_mapping.is_prefill,
                     type=LoRAMappingType.CONNECTOR,
                 )
 

vllm/v1/worker/lora_model_runner_mixin.py

@@ -33,7 +33,7 @@ class LoRAModelRunnerMixin:
         model: nn.Module,
         vllm_config: VllmConfig,
         device: torch.device,
-        model_config: ModelConfig = None,
+        model_config: ModelConfig | None = None,
     ) -> nn.Module:
         if not supports_lora(model):
             raise ValueError(f"{model.__class__.__name__} does not support LoRA yet.")