test_common.py 50.4 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4
5
"""Common tests for testing .generate() functionality for single / multiple
image, embedding, and video support for different VLMs in vLLM.
"""
6
import math
7
import os
8
from collections import defaultdict
9
10
11
from pathlib import PosixPath

import pytest
12
from transformers import (AutoModel, AutoModelForImageTextToText,
13
                          AutoModelForTextToWaveform, AutoModelForVision2Seq)
14
15

from vllm.platforms import current_platform
16
from vllm.utils import identity
17

18
19
from ....conftest import (IMAGE_ASSETS, AudioTestAssets, HfRunner,
                          ImageTestAssets, VideoTestAssets, VllmRunner)
20
from ....utils import (create_new_process_for_each_test, large_gpu_mark,
21
                       multi_gpu_marks)
22
23
24
25
26
27
28
29
30
31
32
33
34
from ...utils import check_outputs_equal
from .vlm_utils import custom_inputs, model_utils, runners
from .vlm_utils.case_filtering import get_parametrized_options
from .vlm_utils.types import (CustomTestOptions, ExpandableVLMTestArgs,
                              VLMTestInfo, VLMTestType)

# This hack is needed for phi3v & paligemma models
# ROCm Triton FA can run into shared memory issues with these models,
# use other backends in the meantime
# FIXME (mattwong, gshtrasb, hongxiayan)
if current_platform.is_rocm():
    os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"

35
36
37
REQUIRES_V0_MODELS = [
    # V1 Test: not enough KV cache space in C1.
    "fuyu",
38
39
    # V1 Test: Deadlock issue when processing mm_inputs
    "llava-onevision-transformers",
40
41
]

42
43
44
45
46
47
# yapf: disable
COMMON_BROADCAST_SETTINGS = {
    "test_type": VLMTestType.IMAGE,
    "dtype": "half",
    "max_tokens": 5,
    "tensor_parallel_size": 2,
48
    "hf_model_kwargs": {"device_map": "auto"},
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
    "image_size_factors": [(.25, 0.5, 1.0)],
    "distributed_executor_backend": (
        "ray",
        "mp",
    )
}

### Test configuration for specific models
# NOTE: The convention of the test settings below is to lead each test key
# with the name of the model arch used in the test, using underscores in place
# of hyphens; this makes it more convenient to filter tests for a specific kind
# of model. For example....
#
# To run all test types for a specific key:
#     use the k flag to substring match with a leading square bracket; if the
#     model arch happens to be a substring of another one, you can add a
#     trailing hyphen. E.g.,
#                 - pytest $TEST_FILE -k "[llava-"
#     prevents matching on "[llava_next-" & will match just the enabled cases
#     for llava, i.e., single image, image embedding, and custom input tests.
#
# To run a test for a Test Info for just one of multiple models:
#     use the k flag to substring match the model name, e.g.,
#                 - pytest $TEST_FILE -k OpenGVLab/InternVL2-1B
#     prevents matching on nGVLab/InternVL2-2B.
#
# You can also combine substrings to match more granularly.
#     ex 1:
#        pytest $TEST_FILE -k "test_single_image and OpenGVLab/InternVL2-1B"
#     will run only test_single_image* for OpenGVLab/InternVL2-1B; this would
#     match both wrappers for single image tests, since it also matches
#     test_single_image_heavy (which forks if we have a distributed backend)
#     ex 2:
#        pytest $TEST_FILE -k  "[llava- or [intern_vl-"
#     will run all of the tests for only llava & internvl.
#
# NOTE you can add --collect-only to any of the above commands to see
# which cases would be selected and deselected by pytest. In general,
# this is a good idea for checking your command first, since tests are slow.

VLM_TEST_SETTINGS = {
90
91
92
93
94
95
96
97
98
99
100
    #### Core tests to always run in the CI
    "llava": VLMTestInfo(
        models=["llava-hf/llava-1.5-7b-hf"],
        test_type=(
            VLMTestType.EMBEDDING,
            VLMTestType.IMAGE,
            VLMTestType.CUSTOM_INPUTS
        ),
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        convert_assets_to_embeddings=model_utils.get_llava_embeddings,
        max_model_len=4096,
101
        auto_cls=AutoModelForImageTextToText,
102
103
104
105
106
107
108
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
                formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:"
            ),
            limit_mm_per_prompt={"image": 4},
        )],
109
110
        # TODO: Revert to "auto" when CPU backend can use torch > 2.6
        dtype="bfloat16" if current_platform.is_cpu() else "auto",
111
        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
112
113
114
115
116
117
118
119
120
121
122
    ),
    "paligemma": VLMTestInfo(
        models=["google/paligemma-3b-mix-224"],
        test_type=VLMTestType.IMAGE,
        prompt_formatter=identity,
        img_idx_to_prompt = lambda idx: "",
        # Paligemma uses its own sample prompts because the default one fails
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "caption es",
            "cherry_blossom": "What is in the picture?",
        }),
123
        auto_cls=AutoModelForImageTextToText,
124
        vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
125
126
        dtype="bfloat16",
        marks=[pytest.mark.skip(reason="vLLM does not support PrefixLM attention mask")],  # noqa: E501
127
    ),
Roger Wang's avatar
Roger Wang committed
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
    "qwen2_5_vl": VLMTestInfo(
        models=["Qwen/Qwen2.5-VL-3B-Instruct"],
        test_type=(
            VLMTestType.IMAGE,
            VLMTestType.MULTI_IMAGE,
            VLMTestType.VIDEO
        ),
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForVision2Seq,
        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)],
143
        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
Roger Wang's avatar
Roger Wang committed
144
    ),
145
    "qwen2_5_omni": VLMTestInfo(
146
        models=["Qwen/Qwen2.5-Omni-3B"],
147
148
149
150
151
152
153
154
155
156
        test_type=(
            VLMTestType.IMAGE,
            VLMTestType.MULTI_IMAGE,
            VLMTestType.VIDEO
        ),
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<|vision_bos|><|IMAGE|><|vision_eos|>", # noqa: E501
        video_idx_to_prompt=lambda idx: "<|vision_bos|><|VIDEO|><|vision_eos|>", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
157
        num_logprobs= 6 if current_platform.is_cpu() else 5,
158
        auto_cls=AutoModelForTextToWaveform,
159
        vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
160
        patch_hf_runner=model_utils.qwen2_5_omni_patch_hf_runner,
161
162
163
        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],
    ),
164
165
166
167
168
169
170
171
172
173
174
    "ultravox": VLMTestInfo(
        models = ["fixie-ai/ultravox-v0_5-llama-3_2-1b"],
        test_type=VLMTestType.AUDIO,
        prompt_formatter=lambda audio_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{audio_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
        audio_idx_to_prompt=lambda idx: "<|audio|>",
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModel,
        hf_output_post_proc=model_utils.ultravox_trunc_hf_output,
        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
    ),
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
    #### Transformers fallback to test
    ## To reduce test burden, we only test batching arbitrary image size
    # Dynamic image length and number of patches
    "llava-onevision-transformers": VLMTestInfo(
        models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
        test_type=VLMTestType.IMAGE,
        prompt_formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n",   # noqa: E501
        max_model_len=16384,
        hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),   # noqa: E501
        auto_cls=AutoModelForImageTextToText,
        vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
        image_size_factors=[(0.25, 0.5, 1.0)],
        vllm_runner_kwargs={
            "model_impl": "transformers",
        },
        marks=[pytest.mark.core_model],
    ),
    # FIXME(Isotr0py): Enable this test after
    # https://github.com/huggingface/transformers/pull/39470 released
    # "idefics3-transformers": VLMTestInfo(
    #     models=["HuggingFaceTB/SmolVLM-256M-Instruct"],
    #     test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
    #     prompt_formatter=lambda img_prompt:f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
    #     img_idx_to_prompt=lambda idx: "<image>",
    #     max_model_len=8192,
    #     max_num_seqs=2,
    #     auto_cls=AutoModelForImageTextToText,
    #     hf_output_post_proc=model_utils.idefics3_trunc_hf_output,
    #     image_size_factors=[(0.25, 0.5, 1.0)],
    #     vllm_runner_kwargs={
    #         "model_impl": "transformers",
    #     },
    #     marks=[pytest.mark.core_model],
    # ),
    # Pixel values from processor are not 4D or 5D arrays
    "qwen2_5_vl-transformers": VLMTestInfo(
        models=["Qwen/Qwen2.5-VL-3B-Instruct"],
        test_type=VLMTestType.IMAGE,
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
        image_size_factors=[(0.25, 0.2, 0.15)],
        vllm_runner_kwargs={
            "model_impl": "transformers",
        },
        marks=[large_gpu_mark(min_gb=32)],
    ),
225
    # Check "auto" with fallback to transformers
226
227
228
229
230
231
232
233
234
    "internvl-transformers": VLMTestInfo(
        models=["OpenGVLab/InternVL3-1B-hf"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<IMG_CONTEXT>",
        max_model_len=4096,
        use_tokenizer_eos=True,
        image_size_factors=[(0.25, 0.5, 1.0)],
        vllm_runner_kwargs={
235
            "model_impl": "auto",
236
237
238
239
        },
        auto_cls=AutoModelForImageTextToText,
        marks=[pytest.mark.core_model],
    ),
240
    #### Extended model tests
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
    "aria": VLMTestInfo(
        models=["rhymes-ai/Aria"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n ", # noqa: E501
        img_idx_to_prompt=lambda idx: "<fim_prefix><|img|><fim_suffix>\n",
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<vlm_image>Please describe the image shortly.",
            "cherry_blossom": "<vlm_image>Please infer the season with reason.",  # noqa: E501
        }),
        multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.",    # noqa: E501
        stop_str=["<|im_end|>"],
        image_size_factors=[(0.10, 0.15)],
        max_tokens=64,
        marks=[large_gpu_mark(min_gb=64)],
    ),
Jennifer Zhao's avatar
Jennifer Zhao committed
259
260
    "aya_vision": VLMTestInfo(
        models=["CohereForAI/aya-vision-8b"],
261
        test_type=(VLMTestType.IMAGE),
Jennifer Zhao's avatar
Jennifer Zhao committed
262
263
264
265
266
267
        prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<image>What's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<image>What is the season?",  # noqa: E501
        }),
        multi_image_prompt="<image><image>Describe the two images in detail.",  # noqa: E501
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}},
    ),
    "aya_vision-multi_image": VLMTestInfo(
        models=["CohereForAI/aya-vision-8b"],
        test_type=(VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<image>What's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<image>What is the season?",  # noqa: E501
        }),
        multi_image_prompt="<image><image>Describe the two images in detail.",  # noqa: E501
        max_model_len=4096,
Jennifer Zhao's avatar
Jennifer Zhao committed
283
284
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
285
286
        vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}},
        marks=[large_gpu_mark(min_gb=32)],
Jennifer Zhao's avatar
Jennifer Zhao committed
287
    ),
288
    "blip2": VLMTestInfo(
289
        models=["Salesforce/blip2-opt-2.7b"],
290
291
292
        test_type=VLMTestType.IMAGE,
        prompt_formatter=lambda img_prompt: f"Question: {img_prompt} Answer:",
        img_idx_to_prompt=lambda idx: "",
293
        auto_cls=AutoModelForImageTextToText,
294
        vllm_output_post_proc=model_utils.blip2_vllm_to_hf_output,
295
296
        # FIXME: https://github.com/huggingface/transformers/pull/38510
        marks=[pytest.mark.skip("Model is broken")],
297
298
299
300
301
302
    ),
    "chameleon": VLMTestInfo(
        models=["facebook/chameleon-7b"],
        test_type=VLMTestType.IMAGE,
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        max_model_len=4096,
303
        max_num_seqs=2,
304
        auto_cls=AutoModelForImageTextToText,
305
306
307
308
309
310
311
        # For chameleon, we only compare the sequences
        vllm_output_post_proc = lambda vllm_output, model: vllm_output[:2],
        hf_output_post_proc = lambda hf_output, model: hf_output[:2],
        comparator=check_outputs_equal,
        max_tokens=8,
        dtype="bfloat16",
    ),
312
    "deepseek_vl_v2": VLMTestInfo(
313
        models=["Isotr0py/deepseek-vl2-tiny"], # model repo using dynamic module
314
315
316
317
318
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|User|>: {img_prompt}\n\n<|Assistant|>: ", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        single_image_prompts=IMAGE_ASSETS.prompts({
319
320
            "stop_sign": "<image>\nWhat's the content in the center of the image?", # noqa: E501
            "cherry_blossom": "<image>\nPlease infer the season with reason in details.",   # noqa: E501
321
        }),
322
        multi_image_prompt="image_1:<image>\nimage_2:<image>\nWhich image can we see the car and the tower?",    # noqa: E501
323
324
325
        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|>"],  # noqa: E501
326
        image_size_factors=[(), (1.0, ), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
327
    ),
328
329
330
331
332
333
334
    "fuyu": VLMTestInfo(
        models=["adept/fuyu-8b"],
        test_type=VLMTestType.IMAGE,
        prompt_formatter=lambda img_prompt: f"{img_prompt}\n",
        img_idx_to_prompt=lambda idx: "",
        max_model_len=2048,
        max_num_seqs=2,
335
        auto_cls=AutoModelForImageTextToText,
336
337
338
339
        use_tokenizer_eos=True,
        vllm_output_post_proc=model_utils.fuyu_vllm_to_hf_output,
        num_logprobs=10,
        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
340
341
342
343
        # FIXME(Isotr0py): This model is broken in Transformers v4.54.1, we
        # should enable this again after the fix is released:
        # https://github.com/huggingface/transformers/pull/39915
        marks=[pytest.mark.skip("HF model is broken")],
344
    ),
345
346
347
348
349
350
351
352
353
354
355
    "gemma3": VLMTestInfo(
        models=["google/gemma-3-4b-it"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<start_of_image>What's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<start_of_image>What is the season?",  # noqa: E501
        }),
        multi_image_prompt="<start_of_image><start_of_image>Describe the two images in detail.",  # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
356
        auto_cls=AutoModelForImageTextToText,
357
358
        vllm_runner_kwargs={"mm_processor_kwargs": {"do_pan_and_scan": True}},
        patch_hf_runner=model_utils.gemma3_patch_hf_runner,
359
        num_logprobs=10,
360
    ),
361
    "glm4v": VLMTestInfo(
362
        models=["zai-org/glm-4v-9b"],
363
        test_type=VLMTestType.IMAGE,
364
365
366
367
368
        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",  # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<|begin_of_image|><|endoftext|><|end_of_image|>What's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<|begin_of_image|><|endoftext|><|end_of_image|>What is the season?",  # noqa: E501
        }),
369
370
371
        max_model_len=2048,
        max_num_seqs=2,
        get_stop_token_ids=lambda tok: [151329, 151336, 151338],
372
373
374
375
376
377
        patch_hf_runner=model_utils.glm4v_patch_hf_runner,
        # The image embeddings match with HF but the outputs of the language
        # decoder are only consistent up to 2 decimal places.
        # So, we need to reduce the number of tokens for the test to pass.
        max_tokens=8,
        num_logprobs=10,
378
        marks=[large_gpu_mark(min_gb=32)],
379
    ),
380
    "glm4_1v": VLMTestInfo(
381
        models=["zai-org/GLM-4.1V-9B-Thinking"],
382
383
384
385
386
387
388
389
390
391
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",  # noqa: E501
        img_idx_to_prompt=lambda idx: "<|begin_of_image|><|image|><|end_of_image|>", # noqa: E501
        video_idx_to_prompt=lambda idx: "<|begin_of_video|><|video|><|end_of_video|>", # noqa: E501
        max_model_len=2048,
        max_num_seqs=2,
        get_stop_token_ids=lambda tok: [151329, 151336, 151338],
        num_logprobs=10,
        image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
        auto_cls=AutoModelForImageTextToText,
392
        marks=[large_gpu_mark(min_gb=32)],
393
394
    ),
    "glm4_1v-video": VLMTestInfo(
395
        models=["zai-org/GLM-4.1V-9B-Thinking"],
396
397
398
399
400
401
402
403
404
405
        # GLM4.1V require include video metadata for input
        test_type=VLMTestType.CUSTOM_INPUTS,
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        patch_hf_runner=model_utils.glm4_1v_patch_hf_runner,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.video_with_metadata_glm4_1v(),
            limit_mm_per_prompt={"video": 1},
        )],
406
        marks=[large_gpu_mark(min_gb=32)],
407
    ),
408
409
410
    "h2ovl": VLMTestInfo(
        models = [
            "h2oai/h2ovl-mississippi-800m",
411
            "h2oai/h2ovl-mississippi-2b",
412
413
414
415
416
417
418
419
420
421
        ],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|prompt|>{img_prompt}<|end|><|answer|>", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<image>\nWhat is the season?",
        }),
        multi_image_prompt="Image-1: <image>\nImage-2: <image>\nDescribe the two images in short.",  # noqa: E501
        max_model_len=8192,
        use_tokenizer_eos=True,
422
        num_logprobs=10,
423
        patch_hf_runner=model_utils.h2ovl_patch_hf_runner,
424
    ),
425
    "idefics3": VLMTestInfo(
426
        models=["HuggingFaceTB/SmolVLM-256M-Instruct"],
427
428
429
430
431
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt:f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
        img_idx_to_prompt=lambda idx: "<image>",
        max_model_len=8192,
        max_num_seqs=2,
432
        auto_cls=AutoModelForImageTextToText,
433
        hf_output_post_proc=model_utils.idefics3_trunc_hf_output,
434
    ),
435
436
437
438
    "intern_vl": VLMTestInfo(
        models=[
            "OpenGVLab/InternVL2-1B",
            "OpenGVLab/InternVL2-2B",
439
440
            # FIXME: Config cannot be loaded in transformers 4.52
            # "OpenGVLab/Mono-InternVL-2B",
441
442
443
444
445
446
447
448
449
450
451
452
        ],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<image>\nWhat is the season?",
        }),
        multi_image_prompt="Image-1: <image>\nImage-2: <image>\nDescribe the two images in short.",  # noqa: E501
        max_model_len=4096,
        use_tokenizer_eos=True,
        patch_hf_runner=model_utils.internvl_patch_hf_runner,
    ),
453
454
455
456
457
458
459
460
461
462
463
    "intern_vl-video": VLMTestInfo(
        models=[
            "OpenGVLab/InternVL3-1B",
        ],
        test_type=VLMTestType.VIDEO,
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
        video_idx_to_prompt=lambda idx: "<video>",
        max_model_len=8192,
        use_tokenizer_eos=True,
        patch_hf_runner=model_utils.internvl_patch_hf_runner,
    ),
464
465
466
467
468
469
470
471
472
473
474
475
    "kimi_vl": VLMTestInfo(
        models=["moonshotai/Kimi-VL-A3B-Instruct"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|im_user|>user<|im_middle|>{img_prompt}<|im_end|><|im_assistant|>assistant<|im_middle|>", # noqa: E501
        img_idx_to_prompt=lambda _: "<|media_start|>image<|media_content|><|media_pad|><|media_end|>",  # noqa: E501
        max_model_len=8192,
        max_num_seqs=2,
        dtype="bfloat16",
        tensor_parallel_size=1,
        vllm_output_post_proc=model_utils.kimiv_vl_vllm_to_hf_output,
        marks=[large_gpu_mark(min_gb=48)],
    ),
476
477
478
479
480
481
482
483
484
485
486
487
    "llama4": VLMTestInfo(
        models=["meta-llama/Llama-4-Scout-17B-16E-Instruct"],
        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{img_prompt}<|eot|><|header_start|>assistant<|header_end|>\n\n", # noqa: E501
        img_idx_to_prompt=lambda _: "<|image|>",
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        distributed_executor_backend="mp",
        image_size_factors=[(.25, 0.5, 1.0)],
        hf_model_kwargs={"device_map": "auto"},
        max_model_len=8192,
        max_num_seqs=4,
        dtype="bfloat16",
        auto_cls=AutoModelForImageTextToText,
488
489
        tensor_parallel_size=4,
        marks=multi_gpu_marks(num_gpus=4),
490
    ),
491
492
493
494
495
    "llava_next": VLMTestInfo(
        models=["llava-hf/llava-v1.6-mistral-7b-hf"],
        test_type=(VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS),
        prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]",
        max_model_len=10240,
496
        auto_cls=AutoModelForImageTextToText,
497
498
499
500
501
502
503
504
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
                formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]"
            ),
            limit_mm_per_prompt={"image": 4},
        )],
    ),
505
    "llava_onevision": VLMTestInfo(
506
507
508
509
510
        models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
        test_type=VLMTestType.CUSTOM_INPUTS,
        prompt_formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n",   # noqa: E501
        num_video_frames=16,
        max_model_len=16384,
511
        hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),   # noqa: E501
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
        auto_cls=AutoModelForVision2Seq,
        vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.multi_video_multi_aspect_ratio_inputs(
                formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n",   # noqa: E501
            ),
            limit_mm_per_prompt={"video": 4},
        )],
    ),
    "llava_next_video": VLMTestInfo(
        models=["llava-hf/LLaVA-NeXT-Video-7B-hf"],
        test_type=VLMTestType.VIDEO,
        prompt_formatter=lambda vid_prompt: f"USER: {vid_prompt} ASSISTANT:",
        num_video_frames=16,
        max_model_len=4096,
527
        max_num_seqs=2,
528
529
530
        auto_cls=AutoModelForVision2Seq,
        vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output,
    ),
531
532
533
534
535
536
    "mantis": VLMTestInfo(
        models=["TIGER-Lab/Mantis-8B-siglip-llama3"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
        max_model_len=4096,
        get_stop_token_ids=lambda tok: [128009],
537
        auto_cls=AutoModelForImageTextToText,
538
539
540
        vllm_output_post_proc=model_utils.mantis_vllm_to_hf_output,
        patch_hf_runner=model_utils.mantis_patch_hf_runner,
    ),
541
    "minicpmv_25": VLMTestInfo(
542
        models=["openbmb/MiniCPM-Llama3-V-2_5"],
543
        test_type=VLMTestType.IMAGE,
544
545
546
547
548
        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
        max_model_len=4096,
        max_num_seqs=2,
        get_stop_token_ids=lambda tok: [tok.eos_id, tok.eot_id],
549
        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
550
        patch_hf_runner=model_utils.minicpmv_25_patch_hf_runner,
551
552
        # FIXME: https://huggingface.co/openbmb/MiniCPM-V-2_6/discussions/55
        marks=[pytest.mark.skip("HF import fails")],
553
    ),
554
555
    "minicpmo_26": VLMTestInfo(
        models=["openbmb/MiniCPM-o-2_6"],
556
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
557
558
559
560
561
562
563
        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
        max_model_len=4096,
        max_num_seqs=2,
        get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
        patch_hf_runner=model_utils.minicpmo_26_patch_hf_runner,
564
565
        # FIXME: https://huggingface.co/openbmb/MiniCPM-V-2_6/discussions/55
        marks=[pytest.mark.skip("HF import fails")],
566
    ),
567
568
    "minicpmv_26": VLMTestInfo(
        models=["openbmb/MiniCPM-V-2_6"],
569
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
570
571
572
573
574
575
576
        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
        max_model_len=4096,
        max_num_seqs=2,
        get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
        patch_hf_runner=model_utils.minicpmv_26_patch_hf_runner,
577
578
        # FIXME: https://huggingface.co/openbmb/MiniCPM-V-2_6/discussions/55
        marks=[pytest.mark.skip("HF import fails")],
579
    ),
580
581
582
583
584
585
586
587
588
589
590
591
592
    "minimax_vl_01": VLMTestInfo(
        models=["MiniMaxAI/MiniMax-VL-01"],
        prompt_formatter=lambda img_prompt: f"<beginning_of_sentence>user: {img_prompt} assistant:<end_of_sentence>", # noqa: E501
        img_idx_to_prompt=lambda _: "<image>",
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        max_model_len=8192,
        max_num_seqs=4,
        dtype="bfloat16",
        hf_output_post_proc=model_utils.minimax_vl_01_hf_output,
        patch_hf_runner=model_utils.minimax_vl_01_patch_hf_runner,
        auto_cls=AutoModelForImageTextToText,
        marks=[large_gpu_mark(min_gb=80)],
    ),
593
594
    "molmo": VLMTestInfo(
        models=["allenai/Molmo-7B-D-0924"],
595
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
596
        prompt_formatter=identity,
597
598
        max_model_len=4096,
        max_num_seqs=2,
599
        patch_hf_runner=model_utils.molmo_patch_hf_runner,
600
    ),
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
    "ovis1_6-gemma2": VLMTestInfo(
        models=["AIDC-AI/Ovis1.6-Gemma2-9B"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<image>\n", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        dtype="half",
        # use sdpa mode for hf runner since ovis2 didn't work with flash_attn
        hf_model_kwargs={"llm_attn_implementation": "sdpa"},
        patch_hf_runner=model_utils.ovis_patch_hf_runner,
        marks=[large_gpu_mark(min_gb=32)],
    ),
    "ovis1_6": VLMTestInfo(
        models=["AIDC-AI/Ovis1.6-Llama3.2-3B"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful and honest multimodal assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<image>\n", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        dtype="half",
        # use sdpa mode for hf runner since ovis2 didn't work with flash_attn
        hf_model_kwargs={"llm_attn_implementation": "sdpa"},
        patch_hf_runner=model_utils.ovis_patch_hf_runner,
    ),
626
627
628
629
630
631
632
633
634
635
    "ovis2": VLMTestInfo(
        models=["AIDC-AI/Ovis2-1B"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<image>\n", # noqa: E501
        max_model_len=4096,
        max_num_seqs=2,
        dtype="half",
        # use sdpa mode for hf runner since ovis2 didn't work with flash_attn
        hf_model_kwargs={"llm_attn_implementation": "sdpa"},
636
        patch_hf_runner=model_utils.ovis_patch_hf_runner,
637
    ),
638
639
640
641
642
643
644
    "phi3v": VLMTestInfo(
        models=["microsoft/Phi-3.5-vision-instruct"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|end|>\n<|assistant|>\n", # noqa: E501
        img_idx_to_prompt=lambda idx: f"<|image_{idx}|>\n",
        max_model_len=4096,
        max_num_seqs=2,
645
        runner="generate",
646
647
        # use sdpa mode for hf runner since phi3v didn't work with flash_attn
        hf_model_kwargs={"_attn_implementation": "sdpa"},
648
649
650
651
        use_tokenizer_eos=True,
        vllm_output_post_proc=model_utils.phi3v_vllm_to_hf_output,
        num_logprobs=10,
    ),
652
653
654
655
656
657
658
    "pixtral_hf": VLMTestInfo(
        models=["nm-testing/pixtral-12b-FP8-dynamic"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<s>[INST]{img_prompt}[/INST]",
        img_idx_to_prompt=lambda idx: "[IMG]",
        max_model_len=8192,
        max_num_seqs=2,
659
        auto_cls=AutoModelForImageTextToText,
660
        marks=[large_gpu_mark(min_gb=48)],
661
    ),
662
    "qwen_vl": VLMTestInfo(
663
664
665
666
667
668
669
670
        models=["Qwen/Qwen-VL"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=identity,
        img_idx_to_prompt=lambda idx: f"Picture {idx}: <img></img>\n",
        max_model_len=1024,
        max_num_seqs=2,
        vllm_output_post_proc=model_utils.qwen_vllm_to_hf_output,
        prompt_path_encoder=model_utils.qwen_prompt_path_encoder,
671
672
        # FIXME: https://github.com/huggingface/transformers/issues/38358
        marks=[pytest.mark.skip("Model initialization fails")],
673
    ),
674
675
676
677
678
679
680
681
682
683
    "qwen2_vl": VLMTestInfo(
        models=["Qwen/Qwen2-VL-2B-Instruct"],
        test_type=(
            VLMTestType.IMAGE,
            VLMTestType.MULTI_IMAGE,
            VLMTestType.VIDEO
        ),
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
684
        multi_image_prompt="Picture 1: <vlm_image>\nPicture 2: <vlm_image>\nDescribe these two images with one paragraph respectively.",    # noqa: E501
685
686
687
688
689
690
691
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForVision2Seq,
        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)],
        marks=[pytest.mark.cpu_model],
    ),
692
693
694
695
696
697
698
699
700
701
702
703
704
705
    "skywork_r1v": VLMTestInfo(
        models=["Skywork/Skywork-R1V-38B"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"<|begin▁of▁sentence|><|User|>\n{img_prompt}<|Assistant|><think>\n", # noqa: E501
        single_image_prompts=IMAGE_ASSETS.prompts({
            "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
            "cherry_blossom": "<image>\nWhat is the season?",
        }),
        multi_image_prompt="<image>\n<image>\nDescribe the two images in short.",  # noqa: E501
        max_model_len=4096,
        use_tokenizer_eos=True,
        patch_hf_runner=model_utils.skyworkr1v_patch_hf_runner,
        marks=[large_gpu_mark(min_gb=80)],
    ),
706
707
708
    "smolvlm": VLMTestInfo(
        models=["HuggingFaceTB/SmolVLM2-2.2B-Instruct"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
709
        prompt_formatter=lambda img_prompt: f"<|im_start|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
710
711
712
713
714
715
        img_idx_to_prompt=lambda idx: "<image>",
        max_model_len=8192,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        hf_output_post_proc=model_utils.smolvlm_trunc_hf_output,
    ),
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
    "tarsier": VLMTestInfo(
        models=["omni-research/Tarsier-7b"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt} ASSISTANT:",
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForImageTextToText,
        patch_hf_runner=model_utils.tarsier_patch_hf_runner,
    ),
    "tarsier2": VLMTestInfo(
        models=["omni-research/Tarsier2-Recap-7b"],
        test_type=(
            VLMTestType.IMAGE,
            VLMTestType.MULTI_IMAGE,
            VLMTestType.VIDEO,
        ),
        prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501
        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501
        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)],
        marks=[pytest.mark.skip("Model initialization hangs")],
    ),
741
    ### Tensor parallel / multi-gpu broadcast tests
742
    "chameleon-broadcast": VLMTestInfo(
743
744
745
        models=["facebook/chameleon-7b"],
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        max_model_len=4096,
746
        auto_cls=AutoModelForImageTextToText,
747
748
749
        vllm_output_post_proc = lambda vllm_output, model: vllm_output[:2],
        hf_output_post_proc = lambda hf_output, model: hf_output[:2],
        comparator=check_outputs_equal,
750
        marks=multi_gpu_marks(num_gpus=2),
751
752
        **COMMON_BROADCAST_SETTINGS # type: ignore
    ),
753
    "llava-broadcast": VLMTestInfo(
754
755
756
        models=["llava-hf/llava-1.5-7b-hf"],
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        max_model_len=4096,
757
        auto_cls=AutoModelForImageTextToText,
758
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
759
        marks=multi_gpu_marks(num_gpus=2),
760
761
        **COMMON_BROADCAST_SETTINGS # type: ignore
    ),
762
    "llava_next-broadcast": VLMTestInfo(
763
764
765
        models=["llava-hf/llava-v1.6-mistral-7b-hf"],
        prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]",
        max_model_len=10240,
766
        auto_cls=AutoModelForImageTextToText,
767
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
768
        marks=multi_gpu_marks(num_gpus=2),
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
        **COMMON_BROADCAST_SETTINGS # type: ignore
    ),
    ### Custom input edge-cases for specific models
    "intern_vl-diff-patches": VLMTestInfo(
        models=["OpenGVLab/InternVL2-2B"],
        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
        test_type=VLMTestType.CUSTOM_INPUTS,
        max_model_len=4096,
        use_tokenizer_eos=True,
        patch_hf_runner=model_utils.internvl_patch_hf_runner,
        custom_test_opts=[
            CustomTestOptions(
                inputs=inp,
                limit_mm_per_prompt={"image": 2},
            ) for inp in custom_inputs.different_patch_input_cases_internvl()
        ],
    ),
786
    "llava_onevision-multiple-images": VLMTestInfo(
787
788
789
790
791
        models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],
        test_type=VLMTestType.CUSTOM_INPUTS,
        max_model_len=16384,
        max_num_seqs=2,
        auto_cls=AutoModelForVision2Seq,
792
        hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),   # noqa: E501
793
794
795
796
797
798
799
800
        vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(
                formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
            ),
            limit_mm_per_prompt={"image": 4},
        )],
    ),
801
802
803
804
805
806
807
808
809
810
811
812
813
    # regression test for https://github.com/vllm-project/vllm/issues/15122
    "qwen2_5_vl-windows-attention": VLMTestInfo(
        models=["Qwen/Qwen2.5-VL-3B-Instruct"],
        test_type=VLMTestType.CUSTOM_INPUTS,
        max_model_len=4096,
        max_num_seqs=2,
        auto_cls=AutoModelForVision2Seq,
        vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
        custom_test_opts=[CustomTestOptions(
            inputs=custom_inputs.windows_attention_image_qwen2_5_vl(),
            limit_mm_per_prompt={"image": 1},
        )],
    ),
814
815
816
817
}
# yapf: enable


818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
def _mark_splits(
    test_settings: dict[str, VLMTestInfo],
    *,
    num_groups: int,
) -> dict[str, VLMTestInfo]:
    name_by_test_info_id = {id(v): k for k, v in test_settings.items()}
    test_infos_by_model = defaultdict[str, list[VLMTestInfo]](list)

    for info in test_settings.values():
        for model in info.models:
            test_infos_by_model[model].append(info)

    models = sorted(test_infos_by_model.keys())
    split_size = math.ceil(len(models) / num_groups)

    new_test_settings = dict[str, VLMTestInfo]()

    for i in range(num_groups):
        models_in_group = models[i * split_size:(i + 1) * split_size]

        for model in models_in_group:
            for info in test_infos_by_model[model]:
                new_marks = (info.marks or []) + [pytest.mark.split(group=i)]
                new_info = info._replace(marks=new_marks)
                new_test_settings[name_by_test_info_id[id(info)]] = new_info

    missing_keys = test_settings.keys() - new_test_settings.keys()
    assert not missing_keys, f"Missing keys: {missing_keys}"

    return new_test_settings


VLM_TEST_SETTINGS = _mark_splits(VLM_TEST_SETTINGS, num_groups=2)


853
854
855
856
857
858
### Test wrappers
# Wrappers around the core test running func for:
# - single image
# - multi-image
# - image embeddings
# - video
859
# - audio
860
# - custom inputs
861
862
863
864
865
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.IMAGE,
866
        create_new_process_for_each_test=False,
867
    ))
868
869
def test_single_image_models(tmp_path: PosixPath, model_type: str,
                             test_case: ExpandableVLMTestArgs,
870
871
                             hf_runner: type[HfRunner],
                             vllm_runner: type[VllmRunner],
872
                             image_assets: ImageTestAssets, monkeypatch):
873
874
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
875
876
877
878
879
880
881
882
883
884
885
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_single_image_test(
        tmp_path=tmp_path,
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


886
887
888
889
890
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.MULTI_IMAGE,
891
        create_new_process_for_each_test=False,
892
    ))
893
894
def test_multi_image_models(tmp_path: PosixPath, model_type: str,
                            test_case: ExpandableVLMTestArgs,
895
896
                            hf_runner: type[HfRunner],
                            vllm_runner: type[VllmRunner],
897
                            image_assets: ImageTestAssets, monkeypatch):
898
899
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
900
901
902
903
904
905
906
907
908
909
910
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_multi_image_test(
        tmp_path=tmp_path,
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


911
912
913
914
915
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.EMBEDDING,
916
        create_new_process_for_each_test=False,
917
    ))
918
919
def test_image_embedding_models(model_type: str,
                                test_case: ExpandableVLMTestArgs,
920
921
                                hf_runner: type[HfRunner],
                                vllm_runner: type[VllmRunner],
922
                                image_assets: ImageTestAssets, monkeypatch):
923
924
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
925
926
927
928
929
930
931
932
933
934
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_embedding_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


935
936
937
938
939
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.VIDEO,
940
        create_new_process_for_each_test=False,
941
    ))
942
def test_video_models(model_type: str, test_case: ExpandableVLMTestArgs,
943
                      hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
944
                      video_assets: VideoTestAssets, monkeypatch):
945
946
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
947
948
949
950
951
952
953
954
955
956
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_video_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        video_assets=video_assets,
    )


957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.AUDIO,
        create_new_process_for_each_test=False,
    ))
def test_audio_models(model_type: str, test_case: ExpandableVLMTestArgs,
                      hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
                      audio_assets: AudioTestAssets, monkeypatch):
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_audio_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        audio_assets=audio_assets,
    )


979
980
981
982
983
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.CUSTOM_INPUTS,
984
        create_new_process_for_each_test=False,
985
    ))
986
987
988
def test_custom_inputs_models(
    model_type: str,
    test_case: ExpandableVLMTestArgs,
989
990
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
991
    monkeypatch,
992
):
993
994
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
995
996
997
998
999
1000
1001
1002
1003
1004
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_custom_inputs_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
    )


#### Tests filtering for things running each test as a new process
1005
1006
1007
1008
1009
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.IMAGE,
1010
        create_new_process_for_each_test=True,
1011
    ))
1012
@create_new_process_for_each_test()
1013
1014
def test_single_image_models_heavy(tmp_path: PosixPath, model_type: str,
                                   test_case: ExpandableVLMTestArgs,
1015
1016
                                   hf_runner: type[HfRunner],
                                   vllm_runner: type[VllmRunner],
1017
                                   image_assets: ImageTestAssets, monkeypatch):
1018
1019
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_single_image_test(
        tmp_path=tmp_path,
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


1031
1032
1033
1034
1035
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.MULTI_IMAGE,
1036
        create_new_process_for_each_test=True,
1037
    ))
1038
@create_new_process_for_each_test()
1039
1040
def test_multi_image_models_heavy(tmp_path: PosixPath, model_type: str,
                                  test_case: ExpandableVLMTestArgs,
1041
1042
                                  hf_runner: type[HfRunner],
                                  vllm_runner: type[VllmRunner],
1043
                                  image_assets: ImageTestAssets, monkeypatch):
1044
1045
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_multi_image_test(
        tmp_path=tmp_path,
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


1057
1058
1059
1060
1061
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.EMBEDDING,
1062
        create_new_process_for_each_test=True,
1063
    ))
1064
@create_new_process_for_each_test()
1065
1066
def test_image_embedding_models_heavy(model_type: str,
                                      test_case: ExpandableVLMTestArgs,
1067
1068
                                      hf_runner: type[HfRunner],
                                      vllm_runner: type[VllmRunner],
1069
1070
                                      image_assets: ImageTestAssets,
                                      monkeypatch):
1071
1072
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_embedding_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        image_assets=image_assets,
    )


1083
1084
1085
1086
1087
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.VIDEO,
1088
        create_new_process_for_each_test=True,
1089
    ))
1090
def test_video_models_heavy(model_type: str, test_case: ExpandableVLMTestArgs,
1091
1092
                            hf_runner: type[HfRunner],
                            vllm_runner: type[VllmRunner],
1093
                            video_assets: VideoTestAssets, monkeypatch):
1094
1095
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_video_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        video_assets=video_assets,
    )


1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.AUDIO,
        create_new_process_for_each_test=True,
    ))
def test_audio_models_heavy(model_type: str, test_case: ExpandableVLMTestArgs,
                            hf_runner: type[HfRunner],
                            vllm_runner: type[VllmRunner],
                            audio_assets: AudioTestAssets, monkeypatch):
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_audio_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
        audio_assets=audio_assets,
    )


1129
1130
1131
1132
1133
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.CUSTOM_INPUTS,
1134
        create_new_process_for_each_test=True,
1135
    ))
1136
@create_new_process_for_each_test()
1137
1138
1139
def test_custom_inputs_models_heavy(
    model_type: str,
    test_case: ExpandableVLMTestArgs,
1140
1141
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
1142
    monkeypatch,
1143
):
1144
1145
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1146
1147
1148
1149
1150
1151
1152
    model_test_info = VLM_TEST_SETTINGS[model_type]
    runners.run_custom_inputs_test(
        model_test_info=model_test_info,
        test_case=test_case,
        hf_runner=hf_runner,
        vllm_runner=vllm_runner,
    )