[CI/Build] Avoid duplicate empty inputs test for common multimodal generation tests (#29907)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

tests/models/multimodal/generation/test_common.py

@@ -137,7 +137,7 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
-        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
     "qwen2_5_omni": VLMTestInfo(
@@ -152,7 +152,7 @@ VLM_TEST_SETTINGS = {
         auto_cls=AutoModelForTextToWaveform,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
         patch_hf_runner=model_utils.qwen2_5_omni_patch_hf_runner,
-        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
     "qwen3_vl": VLMTestInfo(
@@ -173,7 +173,7 @@ VLM_TEST_SETTINGS = {
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
         patch_hf_runner=model_utils.qwen3_vl_patch_hf_runner,
-        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[
             pytest.mark.core_model,
         ],
@@ -350,7 +350,7 @@ VLM_TEST_SETTINGS = {
         patch_hf_runner=model_utils.deepseekvl2_patch_hf_runner,
         hf_output_post_proc=model_utils.deepseekvl2_trunc_hf_output,
         stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"],
-        image_size_factors=[(), (1.0,), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
+        image_size_factors=[(1.0,), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
     ),
     "fuyu": VLMTestInfo(
         models=["adept/fuyu-8b"],
@@ -707,7 +707,7 @@ VLM_TEST_SETTINGS = {
         max_model_len=8192,
         max_num_seqs=2,
         auto_cls=AutoModelForCausalLM,
-        image_size_factors=[(), (0.25,)],
+        image_size_factors=[(0.25,)],
         marks=[
             pytest.mark.skipif(
                 Version(TRANSFORMERS_VERSION) == Version("4.57.3"),
@@ -760,7 +760,7 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
-        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[pytest.mark.cpu_model],
     ),
     "skywork_r1v": VLMTestInfo(
@@ -812,7 +812,7 @@ VLM_TEST_SETTINGS = {
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
-        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[pytest.mark.skip("Model initialization hangs")],
     ),
     ### Tensor parallel / multi-gpu broadcast tests

tests/models/multimodal/generation/vlm_utils/case_filtering.py

@@ -62,24 +62,14 @@ def get_filtered_test_settings(
     return matching_tests
 
 
-def get_parametrized_options(
-    test_settings: dict[str, VLMTestInfo],
+def get_model_type_cases(
+    model_type: str,
+    test_info: VLMTestInfo,
     test_type: VLMTestType,
-    create_new_process_for_each_test: bool,
 ):
-    """Converts all of our VLMTestInfo into an expanded list of parameters.
-    This is similar to nesting pytest parametrize calls, but done directly
-    through an itertools product so that each test can set things like
-    size factors etc, while still running in isolated test cases.
-    """
-    matching_tests = get_filtered_test_settings(
-        test_settings, test_type, create_new_process_for_each_test
-    )
-
     # Ensure that something is wrapped as an iterable it's not already
     ensure_wrapped = lambda e: e if isinstance(e, (list, tuple)) else (e,)
 
-    def get_model_type_cases(model_type: str, test_info: VLMTestInfo):
     # This is essentially the same as nesting a bunch of mark.parametrize
     # decorators, but we do it programmatically to allow overrides for on
     # a per-model basis, while still being able to execute each of these
@@ -105,7 +95,10 @@ def get_parametrized_options(
         )
 
     # No sizes passed for custom inputs, since inputs are directly provided
-        if test_type not in (VLMTestType.CUSTOM_INPUTS, VLMTestType.AUDIO):
+    if test_type not in (
+        VLMTestType.CUSTOM_INPUTS,
+        VLMTestType.AUDIO,
+    ):
         wrapped_sizes = get_wrapped_test_sizes(test_info, test_type)
         if wrapped_sizes is None:
             raise ValueError(f"Sizes must be set for test type {test_type}")
@@ -121,19 +114,32 @@ def get_parametrized_options(
     return [
         pytest.param(
             model_type,
-                ExpandableVLMTestArgs(
-                    **{k: v for k, v in zip(iter_kwargs.keys(), case)}
-                ),
+            ExpandableVLMTestArgs(**{k: v for k, v in zip(iter_kwargs.keys(), case)}),
             marks=test_info.marks if test_info.marks is not None else [],
         )
         for case in list(itertools.product(*iter_kwargs.values()))
     ]
 
+
+def get_parametrized_options(
+    test_settings: dict[str, VLMTestInfo],
+    test_type: VLMTestType,
+    create_new_process_for_each_test: bool,
+):
+    """Converts all of our VLMTestInfo into an expanded list of parameters.
+    This is similar to nesting pytest parametrize calls, but done directly
+    through an itertools product so that each test can set things like
+    size factors etc, while still running in isolated test cases.
+    """
+    matching_tests = get_filtered_test_settings(
+        test_settings, test_type, create_new_process_for_each_test
+    )
+
     # Get a list per model type, where each entry contains a tuple of all of
     # that model type's cases, then flatten them into the top level so that
     # we can consume them in one mark.parametrize call.
     cases_by_model_type = [
-        get_model_type_cases(model_type, test_info)
+        get_model_type_cases(model_type, test_info, test_type)
         for model_type, test_info in matching_tests.items()
     ]
     return list(itertools.chain(*cases_by_model_type))

tests/models/multimodal/generation/vlm_utils/types.py

@@ -50,8 +50,8 @@ MULTI_IMAGE_BASE_PROMPT = f"Image-1: {TEST_IMG_PLACEHOLDER}Image-2: {TEST_IMG_PL
 VIDEO_BASE_PROMPT = f"{TEST_VIDEO_PLACEHOLDER}Why is this video funny?"
 
 
-IMAGE_SIZE_FACTORS = [(), (1.0,), (1.0, 1.0, 1.0), (0.25, 0.5, 1.0)]
-EMBEDDING_SIZE_FACTORS = [(), (1.0,), (1.0, 1.0, 1.0)]
+IMAGE_SIZE_FACTORS = [(1.0,), (1.0, 1.0, 1.0), (0.25, 0.5, 1.0)]
+EMBEDDING_SIZE_FACTORS = [(1.0,), (1.0, 1.0, 1.0)]
 RunnerOutput = tuple[list[int], str, SampleLogprobs | None]