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

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

.buildkite/test-pipeline.yaml

@@ -368,6 +368,7 @@ steps:
   - tests/models/encoder_decoder/vision_language
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal
     - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
     - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
     - pytest -v -s models/embedding/vision_language -m core_model

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,
+    )

tests/models/multimodal/processing/test_idefics3.py

@@ -8,8 +8,8 @@ from transformers import AutoImageProcessor, AutoTokenizer
 from vllm.inputs import InputContext, token_inputs
 from vllm.multimodal import MultiModalRegistry
 
-from .....conftest import _ImageAssets
-from ....utils import build_model_context
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
 
 models = ["HuggingFaceM4/Idefics3-8B-Llama3"]
 

tests/models/multimodal/processing/test_internvl.py

@@ -7,8 +7,8 @@ from transformers import AutoTokenizer
 from vllm.inputs import InputContext, token_inputs
 from vllm.multimodal import MultiModalRegistry
 
-from .....conftest import _ImageAssets
-from ....utils import build_model_context
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
 
 models = ["OpenGVLab/InternVL2-2B"]
 

tests/models/multimodal/processing/test_llava_next.py

@@ -10,7 +10,7 @@ from vllm.multimodal.parse import ImageSize
 from vllm.multimodal.processing import BaseMultiModalProcessor
 from vllm.multimodal.utils import cached_get_tokenizer
 
-from ....utils import build_model_context
+from ...utils import build_model_context
 
 
 def _validate_image_prompt_replacements_one(

tests/models/multimodal/processing/test_llava_onevision.py

@@ -10,7 +10,7 @@ from vllm.multimodal.parse import ImageSize
 from vllm.multimodal.processing import BaseMultiModalProcessor
 from vllm.multimodal.utils import cached_get_tokenizer
 
-from ....utils import build_model_context
+from ...utils import build_model_context
 
 
 def _validate_image_prompt_replacements_one(

tests/models/multimodal/processing/test_phi3v.py

@@ -4,8 +4,8 @@ import pytest
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.utils import cached_get_tokenizer
 
-from .....conftest import _ImageAssets
-from ....utils import build_model_context
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
 
 
 @pytest.mark.parametrize("model_id", ["microsoft/Phi-3.5-vision-instruct"])

tests/models/multimodal/processing/test_qwen.py

@@ -9,8 +9,8 @@ from vllm.inputs import InputContext, token_inputs
 from vllm.multimodal import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 
-from .....conftest import IMAGE_ASSETS
-from ....utils import build_model_context
+from ....conftest import IMAGE_ASSETS
+from ...utils import build_model_context
 
 ### Multimodal preprocessing tests
 SAMPLE_IMAGE = IMAGE_ASSETS[0].pil_image

tests/models/multimodal/processing/test_qwen2_vl.py

@@ -3,8 +3,8 @@ import pytest
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.utils import cached_get_tokenizer
 
-from .....conftest import _ImageAssets
-from ....utils import build_model_context
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
 
 
 @pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-2B-Instruct"])

tests/multimodal/test_processing.py

@@ -1,30 +1,25 @@
 from contextlib import nullcontext
-from functools import partial
 from typing import cast
 from unittest.mock import MagicMock
 
 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
-# yapf conflicts with isort for this block
-# yapf: disable
-from vllm.multimodal.processing import (PlaceholderInfo, ProcessingCache,
-                                        PromptReplacement,
+from vllm.multimodal.processing import (PlaceholderInfo, PromptReplacement,
                                         find_mm_placeholders,
                                         find_text_matches, find_token_matches,
                                         iter_token_matches,
                                         replace_text_matches,
                                         replace_token_matches)
-# yapf: enable
 from vllm.multimodal.profiling import MultiModalProfiler
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import full_groupby
 
+from .utils import random_image
+
 
 # yapf: disable
 @pytest.mark.parametrize(
@@ -531,37 +526,6 @@ def test_find_mm_placeholders(
     assert result == expected
 
 
-def _rand_img(rng: np.random.RandomState, min_wh: int, max_wh: int):
-    w, h = rng.randint(min_wh, max_wh, size=(2, ))
-    arr = rng.randint(0, 255, size=(w, h, 3), dtype=np.uint8)
-    return Image.fromarray(arr)
-
-
-def _rand_video(
-    rng: np.random.RandomState,
-    min_frames: int,
-    max_frames: int,
-    min_wh: int,
-    max_wh: int,
-):
-    # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
-    num_frames = rng.randint(min_frames, max_frames)
-    num_frames = (num_frames // 2) * 2
-
-    w, h = rng.randint(min_wh, max_wh, size=(2, ))
-    return rng.randint(0, 255, size=(num_frames, w, h, 3), dtype=np.uint8)
-
-
-def _rand_audio(
-    rng: np.random.RandomState,
-    min_len: int,
-    max_len: int,
-    sr: int,
-):
-    audio_len = rng.randint(min_len, max_len)
-    return rng.rand(audio_len), sr
-
-
 @pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
 @pytest.mark.parametrize(
     ("limit", "num_supported", "is_valid"),
@@ -628,7 +592,7 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
     )
 
     rng = np.random.RandomState(0)
-    image = _rand_img(rng, min_wh=128, max_wh=256)
+    image = random_image(rng, min_wh=128, max_wh=256)
     if num_images == 0:
         mm_data = {}
     elif num_images == 1:
@@ -647,191 +611,3 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
             mm_data=mm_data,
             hf_processor_mm_kwargs={},
         )
-
-
-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(_rand_img, rng, min_wh=128, max_wh=256),
-        "video":
-        partial(_rand_video,
-                rng,
-                min_frames=2,
-                max_frames=8,
-                min_wh=128,
-                max_wh=256),
-        "audio":
-        partial(_rand_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,
-    )

tests/multimodal/utils.py

@@ -0,0 +1,33 @@
+import numpy as np
+from PIL import Image
+
+
+def random_image(rng: np.random.RandomState, min_wh: int, max_wh: int):
+    w, h = rng.randint(min_wh, max_wh, size=(2, ))
+    arr = rng.randint(0, 255, size=(w, h, 3), dtype=np.uint8)
+    return Image.fromarray(arr)
+
+
+def random_video(
+    rng: np.random.RandomState,
+    min_frames: int,
+    max_frames: int,
+    min_wh: int,
+    max_wh: int,
+):
+    # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
+    num_frames = rng.randint(min_frames, max_frames)
+    num_frames = (num_frames // 2) * 2
+
+    w, h = rng.randint(min_wh, max_wh, size=(2, ))
+    return rng.randint(0, 255, size=(num_frames, w, h, 3), dtype=np.uint8)
+
+
+def random_audio(
+    rng: np.random.RandomState,
+    min_len: int,
+    max_len: int,
+    sr: int,
+):
+    audio_len = rng.randint(min_len, max_len)
+    return rng.rand(audio_len), sr