[Tests] Fixing bug inside MultiModalProfiler. (#21842)

Signed-off-by: Varun Shenoy <varun.vinayak.shenoy@oracle.com>

tests/models/multimodal/processing/test_mllama4.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for mllama's multimodal preprocessing and profiling."""
+import pytest
+from torch import prod
+from transformers import Llama4Config
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.profiling import MultiModalProfiler
+
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["meta-llama/Llama-Guard-4-12B"])
+@pytest.mark.parametrize("max_model_len", [4096, 8192, 25600, 131072])
+def test_profiling(model_id: str, max_model_len: int):
+    model_config_kwargs = {
+        "max_model_len": max_model_len,
+    }
+    ctx = build_model_context(
+        model_id,
+        model_config_kwargs=model_config_kwargs,
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    mm_config = ctx.get_mm_config()
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    profiler = MultiModalProfiler(processor)
+
+    decoder_dummy_data = profiler.get_decoder_dummy_data(
+        max_model_len,
+        mm_counts=mm_config.limit_per_prompt,
+    )
+    dummy_mm_data = processor.dummy_inputs.get_dummy_processor_inputs(
+        max_model_len,
+        mm_counts=mm_config.limit_per_prompt,
+    )
+
+    hf_config = ctx.get_hf_config(Llama4Config)
+
+    mm_kwargs = processor.apply(
+        prompt=dummy_mm_data.prompt,
+        mm_data=dummy_mm_data.mm_data,
+        hf_processor_mm_kwargs=dict(),
+    )["mm_kwargs"]
+
+    image_size = hf_config.vision_config.image_size
+    patch_size = hf_config.vision_config.patch_size
+    downsample_ratio = int(
+        round(1.0 / (hf_config.vision_config.pixel_shuffle_ratio**2)))
+    tokens_per_patch = ((image_size // patch_size)**2) // downsample_ratio
+    chunks_per_image = prod(mm_kwargs["patches_per_image"])
+    total_num_patches = chunks_per_image * tokens_per_patch
+    num_tiles = mm_kwargs["aspect_ratios"][0][0] * mm_kwargs["aspect_ratios"][
+        0][1]  # x-y seperator tokens
+    total_tokens = total_num_patches.item() + num_tiles.item(
+    ) + 3  # image start, image, image end
+
+    profiled_tokens = profiler.get_mm_max_contiguous_tokens(
+        max_model_len,
+        mm_counts=mm_config.limit_per_prompt,
+    )
+
+    assert total_tokens == profiled_tokens["image"]
+    assert total_tokens == sum(
+        placeholder.length for placeholder in
+        decoder_dummy_data.multi_modal_placeholders["image"])

tests/models/registry.py

@@ -391,7 +391,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                       extras={"thinking": "moonshotai/Kimi-VL-A3B-Thinking"},  # noqa: E501
                                                       trust_remote_code=True),
     "Llama4ForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-4-Scout-17B-16E-Instruct",   # noqa: E501
-                                                      max_model_len=10240),
+                                                      max_model_len=10240,
+                                                      extras={"llama-guard-4": "meta-llama/Llama-Guard-4-12B"}, # noqa: E501
+                                                      ),
     "LlavaForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-1.5-7b-hf",
                                                      extras={"mistral": "mistral-community/pixtral-12b", # noqa: E501
                                                              "mistral-fp8": "nm-testing/pixtral-12b-FP8-dynamic"}),  # noqa: E501