[CI/Build] Move model-specific multi-modal processing tests (#11934)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/multimodal/processing/test_common.py

@@ -0,0 +1,201 @@
+from functools import partial
+
+import numpy as np
+import pytest
+from PIL import Image
+
+from vllm.config import ModelConfig
+from vllm.inputs import InputProcessingContext
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.processing import ProcessingCache
+from vllm.multimodal.utils import cached_get_tokenizer
+
+from ....multimodal.utils import random_audio, random_image, random_video
+
+
+def _test_processing_correctness(
+    model_id: str,
+    modalities: dict[str, bool],
+    hit_rate: float,
+    num_batches: int,
+    simplify_rate: float,
+):
+    if model_id == "TIGER-Lab/Mantis-8B-siglip-llama3":
+        hf_overrides = {"architectures": ["MantisForConditionalGeneration"]}
+    else:
+        hf_overrides = {}
+
+    limit_mm_per_prompt = {
+        modality: 3 if supports_multi else 1
+        for modality, supports_multi in modalities.items()
+    }
+
+    model_config = ModelConfig(
+        model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=True,
+        seed=0,
+        dtype="float16",
+        revision=None,
+        hf_overrides=hf_overrides,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+    )
+
+    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
+    factories = MULTIMODAL_REGISTRY._processor_factories[model_cls]
+    ctx = InputProcessingContext(
+        model_config,
+        tokenizer=cached_get_tokenizer(model_config.tokenizer),
+    )
+    # Ensure that it can fit all of the data
+    cache = ProcessingCache(capacity=1 << 30)
+
+    baseline_processor = factories.build_processor(ctx, cache=None)
+    cached_processor = factories.build_processor(ctx, cache=cache)
+    dummy_inputs = baseline_processor.dummy_inputs
+    tokenizer = baseline_processor.info.get_tokenizer()
+
+    rng = np.random.RandomState(0)
+
+    input_to_hit = {
+        "image": Image.new("RGB", size=(128, 128)),
+        "video": np.zeros((4, 128, 128, 3), dtype=np.uint8),
+        "audio": (np.zeros((512, )), 16000),
+    }
+    input_factory = {
+        "image":
+        partial(random_image, rng, min_wh=128, max_wh=256),
+        "video":
+        partial(random_video,
+                rng,
+                min_frames=2,
+                max_frames=8,
+                min_wh=128,
+                max_wh=256),
+        "audio":
+        partial(random_audio, rng, min_len=512, max_len=1024, sr=16000),
+    }
+
+    for batch_idx in range(num_batches):
+        mm_data = {
+            k:
+            [(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
+             for _ in range(rng.randint(limit_mm_per_prompt[k]))]
+            for k in modalities
+        }
+
+        mm_counts = {k: len(vs) for k, vs in mm_data.items()}
+        prompt = dummy_inputs.get_dummy_processor_inputs(
+            model_config.max_model_len,
+            mm_counts,
+        ).prompt_text
+
+        # Drop unnecessary keys and test single -> multi conversion
+        if rng.rand() < simplify_rate:
+            for k in list(mm_data.keys()):
+                if not mm_data[k]:
+                    del mm_data[k]
+                elif len(mm_data[k]) == 1:
+                    mm_data[k] = mm_data[k][0]
+
+        baseline_result = baseline_processor.apply(
+            prompt,
+            mm_data=mm_data,
+            hf_processor_mm_kwargs={},
+        )
+        cached_result = cached_processor.apply(
+            prompt,
+            mm_data=mm_data,
+            hf_processor_mm_kwargs={},
+        )
+
+        assert baseline_result == cached_result, (
+            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+
+        baseline_tokenized_result = baseline_processor.apply(
+            tokenizer.encode(prompt),
+            mm_data=mm_data,
+            hf_processor_mm_kwargs={},
+        )
+
+        assert baseline_result == baseline_tokenized_result, (
+            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+
+        cached_tokenized_result = cached_processor.apply(
+            tokenizer.encode(prompt),
+            mm_data=mm_data,
+            hf_processor_mm_kwargs={},
+        )
+
+        assert cached_result == cached_tokenized_result, (
+            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+
+
+# yapf: disable
+# True if the model supports multiple data items of the modality per request
+@pytest.mark.parametrize(("model_id", "modalities"), [
+    ("rhymes-ai/Aria", {"image": True}),
+    ("Salesforce/blip2-opt-2.7b", {"image": False}),
+    ("facebook/chameleon-7b", {"image": False}),
+    ("adept/fuyu-8b", {"image": False}),
+    ("llava-hf/llava-1.5-7b-hf", {"image": True}),
+    ("llava-hf/llava-v1.6-mistral-7b-hf", {"image": True}),
+    ("llava-hf/LLaVA-NeXT-Video-7B-hf", {"video": False}),
+    ("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", {"image": True, "video": True}),  # noqa: E501
+    ("TIGER-Lab/Mantis-8B-siglip-llama3", {"image": True}),
+    ("mistral-community/pixtral-12b", {"image": True}),
+    ("Qwen/Qwen2-VL-2B-Instruct", {"image": True, "video": True}),
+    ("Qwen/Qwen2-Audio-7B-Instruct", {"audio": True}),
+    ("fixie-ai/ultravox-v0_3", {"audio": True}),
+])
+@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
+@pytest.mark.parametrize("num_batches", [32])
+@pytest.mark.parametrize("simplify_rate", [1.0])
+# yapf: enable
+def test_processing_correctness(
+    model_id: str,
+    modalities: dict[str, bool],
+    hit_rate: float,
+    num_batches: int,
+    simplify_rate: float,
+):
+    _test_processing_correctness(
+        model_id,
+        modalities,
+        hit_rate=hit_rate,
+        num_batches=num_batches,
+        simplify_rate=simplify_rate,
+    )
+
+
+# yapf: disable
+@pytest.mark.parametrize(("model_id", "modalities"), [
+    ("microsoft/Phi-3-vision-128k-instruct", {"image": True}),
+])
+@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
+@pytest.mark.parametrize("num_batches", [32])
+@pytest.mark.parametrize("simplify_rate", [1.0])
+# yapf: enable
+def test_processing_correctness_phi3v(
+    model_id: str,
+    modalities: dict[str, bool],
+    hit_rate: float,
+    num_batches: int,
+    simplify_rate: float,
+):
+    # HACK - this is an attempted workaround for the following bug
+    # https://github.com/huggingface/transformers/issues/34307
+    from transformers import AutoImageProcessor  # noqa: F401
+    from transformers import AutoProcessor  # noqa: F401
+
+    AutoImageProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+    _test_processing_correctness(
+        model_id,
+        modalities,
+        hit_rate=hit_rate,
+        num_batches=num_batches,
+        simplify_rate=simplify_rate,
+    )