test_common.py 49.5 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
        # FIXME: https://huggingface.co/openbmb/MiniCPM-o-2_6/discussions/49
565
        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
577
        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,
    ),
578
579
580
581
582
583
584
585
586
587
588
589
590
    "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)],
    ),
591
592
    "molmo": VLMTestInfo(
        models=["allenai/Molmo-7B-D-0924"],
593
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
594
        prompt_formatter=identity,
595
596
        max_model_len=4096,
        max_num_seqs=2,
597
        patch_hf_runner=model_utils.molmo_patch_hf_runner,
598
    ),
599
600
601
602
603
604
605
606
607
608
609
610
611
    "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)],
    ),
612
613
614
615
616
617
618
619
620
621
    "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"},
622
        patch_hf_runner=model_utils.ovis_patch_hf_runner,
623
    ),
624
625
626
627
628
629
630
    "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,
631
        runner="generate",
632
633
        # use sdpa mode for hf runner since phi3v didn't work with flash_attn
        hf_model_kwargs={"_attn_implementation": "sdpa"},
634
635
636
637
        use_tokenizer_eos=True,
        vllm_output_post_proc=model_utils.phi3v_vllm_to_hf_output,
        num_logprobs=10,
    ),
638
639
640
641
642
643
644
    "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,
645
        auto_cls=AutoModelForImageTextToText,
646
        marks=[large_gpu_mark(min_gb=48)],
647
    ),
648
    "qwen_vl": VLMTestInfo(
649
650
651
652
653
654
655
656
        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,
657
658
        # FIXME: https://github.com/huggingface/transformers/issues/38358
        marks=[pytest.mark.skip("Model initialization fails")],
659
    ),
660
661
662
663
664
665
666
667
668
669
    "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
670
        multi_image_prompt="Picture 1: <vlm_image>\nPicture 2: <vlm_image>\nDescribe these two images with one paragraph respectively.",    # noqa: E501
671
672
673
674
675
676
677
        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],
    ),
678
679
680
681
682
683
684
685
686
687
688
689
690
691
    "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)],
    ),
692
693
694
    "smolvlm": VLMTestInfo(
        models=["HuggingFaceTB/SmolVLM2-2.2B-Instruct"],
        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
695
        prompt_formatter=lambda img_prompt: f"<|im_start|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
696
697
698
699
700
701
        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,
    ),
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
    "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")],
    ),
727
    ### Tensor parallel / multi-gpu broadcast tests
728
    "chameleon-broadcast": VLMTestInfo(
729
730
731
        models=["facebook/chameleon-7b"],
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        max_model_len=4096,
732
        auto_cls=AutoModelForImageTextToText,
733
734
735
        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,
736
        marks=multi_gpu_marks(num_gpus=2),
737
738
        **COMMON_BROADCAST_SETTINGS # type: ignore
    ),
739
    "llava-broadcast": VLMTestInfo(
740
741
742
        models=["llava-hf/llava-1.5-7b-hf"],
        prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
        max_model_len=4096,
743
        auto_cls=AutoModelForImageTextToText,
744
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
745
        marks=multi_gpu_marks(num_gpus=2),
746
747
        **COMMON_BROADCAST_SETTINGS # type: ignore
    ),
748
    "llava_next-broadcast": VLMTestInfo(
749
750
751
        models=["llava-hf/llava-v1.6-mistral-7b-hf"],
        prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]",
        max_model_len=10240,
752
        auto_cls=AutoModelForImageTextToText,
753
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
754
        marks=multi_gpu_marks(num_gpus=2),
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
        **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()
        ],
    ),
772
    "llava_onevision-multiple-images": VLMTestInfo(
773
774
775
776
777
        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,
778
        hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),   # noqa: E501
779
780
781
782
783
784
785
786
        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},
        )],
    ),
787
788
789
790
791
792
793
794
795
796
797
798
799
    # 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},
        )],
    ),
800
801
802
803
}
# yapf: enable


804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
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)


839
840
841
842
843
844
### Test wrappers
# Wrappers around the core test running func for:
# - single image
# - multi-image
# - image embeddings
# - video
845
# - audio
846
# - custom inputs
847
848
849
850
851
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.IMAGE,
852
        create_new_process_for_each_test=False,
853
    ))
854
855
def test_single_image_models(tmp_path: PosixPath, model_type: str,
                             test_case: ExpandableVLMTestArgs,
856
857
                             hf_runner: type[HfRunner],
                             vllm_runner: type[VllmRunner],
858
                             image_assets: ImageTestAssets, monkeypatch):
859
860
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
861
862
863
864
865
866
867
868
869
870
871
    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,
    )


872
873
874
875
876
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.MULTI_IMAGE,
877
        create_new_process_for_each_test=False,
878
    ))
879
880
def test_multi_image_models(tmp_path: PosixPath, model_type: str,
                            test_case: ExpandableVLMTestArgs,
881
882
                            hf_runner: type[HfRunner],
                            vllm_runner: type[VllmRunner],
883
                            image_assets: ImageTestAssets, monkeypatch):
884
885
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
886
887
888
889
890
891
892
893
894
895
896
    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,
    )


897
898
899
900
901
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.EMBEDDING,
902
        create_new_process_for_each_test=False,
903
    ))
904
905
def test_image_embedding_models(model_type: str,
                                test_case: ExpandableVLMTestArgs,
906
907
                                hf_runner: type[HfRunner],
                                vllm_runner: type[VllmRunner],
908
                                image_assets: ImageTestAssets, monkeypatch):
909
910
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
911
912
913
914
915
916
917
918
919
920
    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,
    )


921
922
923
924
925
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.VIDEO,
926
        create_new_process_for_each_test=False,
927
    ))
928
def test_video_models(model_type: str, test_case: ExpandableVLMTestArgs,
929
                      hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
930
                      video_assets: VideoTestAssets, monkeypatch):
931
932
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
933
934
935
936
937
938
939
940
941
942
    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,
    )


943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
@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,
    )


965
966
967
968
969
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.CUSTOM_INPUTS,
970
        create_new_process_for_each_test=False,
971
    ))
972
973
974
def test_custom_inputs_models(
    model_type: str,
    test_case: ExpandableVLMTestArgs,
975
976
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
977
    monkeypatch,
978
):
979
980
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
981
982
983
984
985
986
987
988
989
990
    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
991
992
993
994
995
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.IMAGE,
996
        create_new_process_for_each_test=True,
997
    ))
998
@create_new_process_for_each_test()
999
1000
def test_single_image_models_heavy(tmp_path: PosixPath, model_type: str,
                                   test_case: ExpandableVLMTestArgs,
1001
1002
                                   hf_runner: type[HfRunner],
                                   vllm_runner: type[VllmRunner],
1003
                                   image_assets: ImageTestAssets, monkeypatch):
1004
1005
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
    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,
    )


1017
1018
1019
1020
1021
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.MULTI_IMAGE,
1022
        create_new_process_for_each_test=True,
1023
    ))
1024
@create_new_process_for_each_test()
1025
1026
def test_multi_image_models_heavy(tmp_path: PosixPath, model_type: str,
                                  test_case: ExpandableVLMTestArgs,
1027
1028
                                  hf_runner: type[HfRunner],
                                  vllm_runner: type[VllmRunner],
1029
                                  image_assets: ImageTestAssets, monkeypatch):
1030
1031
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
    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,
    )


1043
1044
1045
1046
1047
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.EMBEDDING,
1048
        create_new_process_for_each_test=True,
1049
    ))
1050
@create_new_process_for_each_test()
1051
1052
def test_image_embedding_models_heavy(model_type: str,
                                      test_case: ExpandableVLMTestArgs,
1053
1054
                                      hf_runner: type[HfRunner],
                                      vllm_runner: type[VllmRunner],
1055
1056
                                      image_assets: ImageTestAssets,
                                      monkeypatch):
1057
1058
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
    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,
    )


1069
1070
1071
1072
1073
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.VIDEO,
1074
        create_new_process_for_each_test=True,
1075
    ))
1076
def test_video_models_heavy(model_type: str, test_case: ExpandableVLMTestArgs,
1077
1078
                            hf_runner: type[HfRunner],
                            vllm_runner: type[VllmRunner],
1079
                            video_assets: VideoTestAssets, monkeypatch):
1080
1081
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
    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,
    )


1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
@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,
    )


1115
1116
1117
1118
1119
@pytest.mark.parametrize(
    "model_type,test_case",
    get_parametrized_options(
        VLM_TEST_SETTINGS,
        test_type=VLMTestType.CUSTOM_INPUTS,
1120
        create_new_process_for_each_test=True,
1121
    ))
1122
@create_new_process_for_each_test()
1123
1124
1125
def test_custom_inputs_models_heavy(
    model_type: str,
    test_case: ExpandableVLMTestArgs,
1126
1127
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
1128
    monkeypatch,
1129
):
1130
1131
    if model_type in REQUIRES_V0_MODELS:
        monkeypatch.setenv("VLLM_USE_V1", "0")
1132
1133
1134
1135
1136
1137
1138
    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,
    )