[CI/Build] Refactor image test assets (#5821)

tests/conftest.py

 import contextlib
 import gc
 import os
-from typing import Any, Dict, List, Optional, Tuple, TypeVar
+from collections import UserList
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import (Any, Dict, List, Literal, Optional, Tuple, TypedDict,
+                    TypeVar)
 
 import pytest
 import torch
@@ -28,21 +33,8 @@ _TEST_DIR = os.path.dirname(__file__)
 _TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
 _LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
 
-# Multi modal related
-# You can use `.buildkite/download-images.sh` to download the assets
-PIXEL_VALUES_FILES = [
-    os.path.join(_TEST_DIR, "images", filename) for filename in
-    ["stop_sign_pixel_values.pt", "cherry_blossom_pixel_values.pt"]
-]
-IMAGE_FEATURES_FILES = [
-    os.path.join(_TEST_DIR, "images", filename) for filename in
-    ["stop_sign_image_features.pt", "cherry_blossom_image_features.pt"]
-]
-IMAGE_FILES = [
-    os.path.join(_TEST_DIR, "images", filename)
-    for filename in ["stop_sign.jpg", "cherry_blossom.jpg"]
-]
-assert len(PIXEL_VALUES_FILES) == len(IMAGE_FEATURES_FILES) == len(IMAGE_FILES)
+_IMAGE_DIR = Path(_TEST_DIR) / "images"
+"""You can use `.buildkite/download-images.sh` to download the assets."""
 
 
 def _read_prompts(filename: str) -> List[str]:
@@ -51,6 +43,63 @@ def _read_prompts(filename: str) -> List[str]:
         return prompts
 
 
+@dataclass(frozen=True)
+class ImageAsset:
+    name: Literal["stop_sign", "cherry_blossom"]
+
+    @cached_property
+    def pixel_values(self) -> torch.Tensor:
+        return torch.load(_IMAGE_DIR / f"{self.name}_pixel_values.pt")
+
+    @cached_property
+    def image_features(self) -> torch.Tensor:
+        return torch.load(_IMAGE_DIR / f"{self.name}_image_features.pt")
+
+    @cached_property
+    def pil_image(self) -> Image.Image:
+        return Image.open(_IMAGE_DIR / f"{self.name}.jpg")
+
+    def for_hf(self) -> Image.Image:
+        return self.pil_image
+
+    def for_vllm(self, vision_config: VisionLanguageConfig) -> MultiModalData:
+        image_input_type = vision_config.image_input_type
+        ImageInputType = VisionLanguageConfig.ImageInputType
+
+        if image_input_type == ImageInputType.IMAGE_FEATURES:
+            return ImageFeatureData(self.image_features)
+        if image_input_type == ImageInputType.PIXEL_VALUES:
+            return ImagePixelData(self.pil_image)
+
+        raise NotImplementedError
+
+
+class _ImageAssetPrompts(TypedDict):
+    stop_sign: str
+    cherry_blossom: str
+
+
+class _ImageAssets(UserList[ImageAsset]):
+
+    def __init__(self) -> None:
+        super().__init__(
+            [ImageAsset("stop_sign"),
+             ImageAsset("cherry_blossom")])
+
+    def prompts(self, prompts: _ImageAssetPrompts) -> List[str]:
+        """
+        Convenience method to define the prompt for each test image.
+
+        The order of the returned prompts matches the order of the
+        assets when iterating through this object.
+        """
+        return [prompts["stop_sign"], prompts["cherry_blossom"]]
+
+
+IMAGE_ASSETS = _ImageAssets()
+"""Singleton instance of :class:`_ImageAssets`."""
+
+
 def cleanup():
     destroy_model_parallel()
     destroy_distributed_environment()
@@ -81,31 +130,6 @@ def cleanup_fixture(should_do_global_cleanup_after_test: bool):
         cleanup()
 
 
-@pytest.fixture(scope="session")
-def hf_images() -> List[Image.Image]:
-    return [Image.open(filename) for filename in IMAGE_FILES]
-
-
-@pytest.fixture()
-def vllm_images(request) -> List[MultiModalData]:
-    vision_language_config = request.getfixturevalue("model_and_config")[1]
-    if vision_language_config.image_input_type == (
-            VisionLanguageConfig.ImageInputType.IMAGE_FEATURES):
-        return [
-            ImageFeatureData(torch.load(filename))
-            for filename in IMAGE_FEATURES_FILES
-        ]
-    else:
-        return [
-            ImagePixelData(Image.open(filename)) for filename in IMAGE_FILES
-        ]
-
-
-@pytest.fixture()
-def vllm_image_tensors(request) -> List[torch.Tensor]:
-    return [torch.load(filename) for filename in PIXEL_VALUES_FILES]
-
-
 @pytest.fixture
 def example_prompts() -> List[str]:
     prompts = []
@@ -122,6 +146,11 @@ def example_long_prompts() -> List[str]:
     return prompts
 
 
+@pytest.fixture(scope="session")
+def image_assets() -> _ImageAssets:
+    return IMAGE_ASSETS
+
+
 _STR_DTYPE_TO_TORCH_DTYPE = {
     "half": torch.half,
     "bfloat16": torch.bfloat16,

tests/models/test_llava.py

@@ -5,17 +5,17 @@ from transformers import AutoTokenizer
 
 from vllm.config import VisionLanguageConfig
 
-from ..conftest import IMAGE_FILES
+from ..conftest import IMAGE_ASSETS
 
 pytestmark = pytest.mark.vlm
 
 # The image token is placed before "user" on purpose so that the test can pass
-HF_IMAGE_PROMPTS = [
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
     "<image>\nUSER: What's the content of the image?\nASSISTANT:",
+    "cherry_blossom":
     "<image>\nUSER: What is the season?\nASSISTANT:",
-]
-
-assert len(HF_IMAGE_PROMPTS) == len(IMAGE_FILES)
+})
 
 
 def iter_llava_configs(model_name: str):
@@ -49,28 +49,28 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
     x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
     It also reduces `output_str` from "<image><image>bla" to "bla".
     """
-    input_ids, output_str = vllm_output
+    output_ids, output_str = vllm_output
     image_token_id = vlm_config.image_token_id
 
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     image_token_str = tokenizer.decode(image_token_id)
 
-    hf_input_ids = [
-        input_id for idx, input_id in enumerate(input_ids)
-        if input_id != image_token_id or input_ids[idx - 1] != image_token_id
+    hf_output_ids = [
+        token_id for idx, token_id in enumerate(output_ids)
+        if token_id != image_token_id or output_ids[idx - 1] != image_token_id
     ]
     hf_output_str = output_str \
         .replace(image_token_str * vlm_config.image_feature_size, "")
 
-    return hf_input_ids, hf_output_str
+    return hf_output_ids, hf_output_str
 
 
 # TODO: Add test for `tensor_parallel_size` [ref: PR #3883]
 @pytest.mark.parametrize("model_and_config", model_and_vl_config)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])
-def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
-                model_and_config, dtype: str, max_tokens: int) -> None:
+def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
+                dtype: str, max_tokens: int) -> None:
     """Inference result should be the same between hf and vllm.
 
     All the image fixtures for the test is under tests/images.
@@ -81,6 +81,8 @@ def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
     The text output is sanitized to be able to compare with hf.
     """
     model_id, vlm_config = model_and_config
+    hf_images = [asset.for_hf() for asset in image_assets]
+    vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets]
 
     with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model:
         hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS,

tests/models/test_llava_next.py

@@ -5,7 +5,7 @@ from transformers import AutoTokenizer
 
 from vllm.config import VisionLanguageConfig
 
-from ..conftest import IMAGE_FILES
+from ..conftest import IMAGE_ASSETS
 
 pytestmark = pytest.mark.vlm
 
@@ -15,12 +15,12 @@ _PREFACE = (
     "questions.")
 
 # The image token is placed before "user" on purpose so that the test can pass
-HF_IMAGE_PROMPTS = [
-    f"{_PREFACE} <image>\nUSER: What's the content of the image? ASSISTANT:",
-    f"{_PREFACE} <image>\nUSER: What is the season? ASSISTANT:",
-]
-
-assert len(HF_IMAGE_PROMPTS) == len(IMAGE_FILES)
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    f"{_PREFACE} <image>\nUSER: What's the content of the image?\nASSISTANT:",
+    "cherry_blossom":
+    f"{_PREFACE} <image>\nUSER: What is the season?\nASSISTANT:",
+})
 
 
 def iter_llava_next_configs(model_name: str):
@@ -56,20 +56,20 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
     x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
     It also reduces `output_str` from "<image><image>bla" to "bla".
     """
-    input_ids, output_str = vllm_output
+    output_ids, output_str = vllm_output
     image_token_id = vlm_config.image_token_id
 
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     image_token_str = tokenizer.decode(image_token_id)
 
-    hf_input_ids = [
-        input_id for idx, input_id in enumerate(input_ids)
-        if input_id != image_token_id or input_ids[idx - 1] != image_token_id
+    hf_output_ids = [
+        token_id for idx, token_id in enumerate(output_ids)
+        if token_id != image_token_id or output_ids[idx - 1] != image_token_id
     ]
     hf_output_str = output_str \
         .replace(image_token_str * vlm_config.image_feature_size, " ")
 
-    return hf_input_ids, hf_output_str
+    return hf_output_ids, hf_output_str
 
 
 @pytest.mark.xfail(
@@ -78,8 +78,8 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
 @pytest.mark.parametrize("model_and_config", model_and_vl_config)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])
-def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
-                model_and_config, dtype: str, max_tokens: int) -> None:
+def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
+                dtype: str, max_tokens: int) -> None:
     """Inference result should be the same between hf and vllm.
 
     All the image fixtures for the test is under tests/images.
@@ -90,6 +90,8 @@ def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
     The text output is sanitized to be able to compare with hf.
     """
     model_id, vlm_config = model_and_config
+    hf_images = [asset.for_hf() for asset in image_assets]
+    vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets]
 
     with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model:
         hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS,

tests/models/test_phi3v.py

@@ -6,17 +6,17 @@ from transformers import AutoTokenizer
 from vllm.config import VisionLanguageConfig
 from vllm.utils import is_cpu
 
-from ..conftest import IMAGE_FILES
+from ..conftest import IMAGE_ASSETS
 
 pytestmark = pytest.mark.vlm
 
 # The image token is placed before "user" on purpose so that the test can pass
-HF_IMAGE_PROMPTS = [
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
     "<|user|>\n<|image_1|>\nWhat's the content of the image?<|end|>\n<|assistant|>\n",  # noqa: E501
-    "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n",
-]
-
-assert len(HF_IMAGE_PROMPTS) == len(IMAGE_FILES)
+    "cherry_blossom":
+    "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n",  # noqa: E501
+})
 
 
 def iter_phi3v_configs(model_name: str):
@@ -50,22 +50,22 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
     x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
     It also reduces `output_str` from "<image><image>bla" to "bla".
     """
-    input_ids, output_str = vllm_output
+    output_ids, output_str = vllm_output
     image_token_id = vlm_config.image_token_id
 
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     image_token_str = tokenizer.decode(image_token_id)
 
-    hf_input_ids = [
-        input_id if input_id != image_token_id else 0
-        for idx, input_id in enumerate(input_ids)
+    hf_output_ids = [
+        token_id if token_id != image_token_id else 0
+        for idx, token_id in enumerate(output_ids)
     ]
     hf_output_str = output_str \
         .replace(image_token_str * vlm_config.image_feature_size, "") \
         .replace("<s>", " ").replace("<|user|>", "") \
         .replace("<|end|>\n<|assistant|>", " ")
 
-    return hf_input_ids, hf_output_str
+    return hf_output_ids, hf_output_str
 
 
 target_dtype = "half"
@@ -82,8 +82,8 @@ if is_cpu():
 @pytest.mark.parametrize("model_and_config", model_and_vl_config)
 @pytest.mark.parametrize("dtype", [target_dtype])
 @pytest.mark.parametrize("max_tokens", [128])
-def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
-                model_and_config, dtype: str, max_tokens: int) -> None:
+def test_models(hf_runner, vllm_runner, image_assets, model_and_config,
+                dtype: str, max_tokens: int) -> None:
     """Inference result should be the same between hf and vllm.
 
     All the image fixtures for the test is under tests/images.
@@ -94,6 +94,8 @@ def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
     The text output is sanitized to be able to compare with hf.
     """
     model_id, vlm_config = model_and_config
+    hf_images = [asset.for_hf() for asset in image_assets]
+    vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets]
 
     # use eager mode for hf runner, since phi3_v didn't work with flash_attn
     hf_model_kwargs = {"_attn_implementation": "eager"}

tests/multimodal/test_processor.py

@@ -10,7 +10,7 @@ from ..conftest import _STR_DTYPE_TO_TORCH_DTYPE
 
 
 @pytest.mark.parametrize("dtype", ["half", "float"])
-def test_clip_image_processor(hf_images, dtype):
+def test_clip_image_processor(image_assets, dtype):
     MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
     IMAGE_HEIGHT = IMAGE_WIDTH = 560
 
@@ -35,13 +35,13 @@ def test_clip_image_processor(hf_images, dtype):
         image_processor_revision=None,
     )
 
-    for image in hf_images:
+    for asset in image_assets:
         hf_result = hf_processor.preprocess(
-            image,
+            asset.pil_image,
             return_tensors="pt",
         ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype])
         vllm_result = MULTIMODAL_REGISTRY.process_input(
-            ImagePixelData(image),
+            ImagePixelData(asset.pil_image),
             model_config=model_config,
             vlm_config=vlm_config,
         )
@@ -59,7 +59,7 @@ def test_clip_image_processor(hf_images, dtype):
     reason="Inconsistent image processor being used due to lack "
     "of support for dynamic image token replacement")
 @pytest.mark.parametrize("dtype", ["half", "float"])
-def test_llava_next_image_processor(hf_images, dtype):
+def test_llava_next_image_processor(image_assets, dtype):
     MODEL_NAME = "llava-hf/llava-v1.6-34b-hf"
     IMAGE_HEIGHT = IMAGE_WIDTH = 560
 
@@ -84,13 +84,13 @@ def test_llava_next_image_processor(hf_images, dtype):
         image_processor_revision=None,
     )
 
-    for image in hf_images:
+    for asset in image_assets:
         hf_result = hf_processor.preprocess(
-            image,
+            asset.pil_image,
             return_tensors="pt",
         ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype])
         vllm_result = MULTIMODAL_REGISTRY.process_input(
-            ImagePixelData(image),
+            ImagePixelData(asset.pil_image),
             model_config=model_config,
             vlm_config=vlm_config,
         )
@@ -107,7 +107,7 @@ def test_llava_next_image_processor(hf_images, dtype):
 @pytest.mark.xfail(
     reason="Example image pixels were not processed using HuggingFace")
 @pytest.mark.parametrize("dtype", ["float"])
-def test_image_pixel_types(hf_images, vllm_image_tensors, dtype):
+def test_image_pixel_types(image_assets, dtype):
     MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
     IMAGE_HEIGHT = IMAGE_WIDTH = 560
 
@@ -129,14 +129,14 @@ def test_image_pixel_types(hf_images, vllm_image_tensors, dtype):
         image_processor_revision=None,
     )
 
-    for image, tensor in zip(hf_images, vllm_image_tensors):
+    for asset in image_assets:
         image_result = MULTIMODAL_REGISTRY.process_input(
-            ImagePixelData(image),
+            ImagePixelData(asset.pil_image),
             model_config=model_config,
             vlm_config=vlm_config,
         )
         tensor_result = MULTIMODAL_REGISTRY.process_input(
-            ImagePixelData(tensor),
+            ImagePixelData(asset.pixel_values),
             model_config=model_config,
             vlm_config=vlm_config,
         )