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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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92
tests/models/multimodal/processing/test_common.py
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@@ -6,22 +6,27 @@ from typing import Optional, Union
import numpy as np
import pytest
from mistral_common.protocol.instruct.messages import (ImageChunk, TextChunk,
UserMessage)
from mistral_common.protocol.instruct.messages import ImageChunk, TextChunk, UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from PIL import Image
from vllm.config import ModelConfig
from vllm.config.multimodal import (AudioDummyOptions, BaseDummyOptions,
ImageDummyOptions, VideoDummyOptions)
from vllm.config.multimodal import (
AudioDummyOptions,
BaseDummyOptions,
ImageDummyOptions,
VideoDummyOptions,
)
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict
from vllm.multimodal.cache import MultiModalProcessorOnlyCache
from vllm.multimodal.inputs import MultiModalInputs
from vllm.multimodal.processing import (BaseMultiModalProcessor,
InputProcessingContext)
from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
cached_tokenizer_from_config,
encode_tokens)
from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
from vllm.transformers_utils.tokenizer import (
AnyTokenizer,
MistralTokenizer,
cached_tokenizer_from_config,
encode_tokens,
)
from ....multimodal.utils import random_audio, random_image, random_video
from ...registry import HF_EXAMPLE_MODELS
@@ -36,14 +41,17 @@ def glm4_1v_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
# GLM4.1V doesn't support multiple videos
video = mm_data["video"]
num_frames = len(video)
mm_data["video"] = (video, {
"total_num_frames": num_frames,
"fps": num_frames,
"duration": 1,
"frames_indices": [i for i in range(num_frames)],
"video_backend": "opencv",
"do_sample_frames": True,
})
mm_data["video"] = (
video,
{
"total_num_frames": num_frames,
"fps": num_frames,
"duration": 1,
"frames_indices": [i for i in range(num_frames)],
"video_backend": "opencv",
"do_sample_frames": True,
},
)
return mm_data
@@ -102,7 +110,8 @@ def _test_processing_correctness(
mm_processor_cache_gb=2048,
skip_tokenizer_init=model_info.skip_tokenizer_init,
enforce_eager=model_info.enforce_eager,
dtype=model_info.dtype)
dtype=model_info.dtype,
)
model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
factories = MULTIMODAL_REGISTRY._processor_factories[model_cls]
@@ -145,27 +154,22 @@ def _test_processing_correctness(
input_to_hit = {
"image": Image.new("RGB", size=(128, 128)),
"video": np.zeros((4, 128, 128, 3), dtype=np.uint8),
"audio": (np.zeros((512, )), 16000),
"audio": (np.zeros((512,)), 16000),
}
input_factory = {
"image":
partial(random_image, rng, min_wh=128, max_wh=256),
"video":
partial(random_video,
rng,
min_frames=2,
max_frames=16,
min_wh=128,
max_wh=256),
"audio":
partial(random_audio, rng, min_len=512, max_len=1024, sr=16000),
"image": partial(random_image, rng, min_wh=128, max_wh=256),
"video": partial(
random_video, rng, min_frames=2, max_frames=16, min_wh=128, max_wh=256
),
"audio": partial(random_audio, rng, min_len=512, max_len=1024, sr=16000),
}
for batch_idx in range(num_batches):
mm_data = {
k:
[(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
for _ in range(rng.randint(limit + 1))]
k: [
(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
for _ in range(rng.randint(limit + 1))
]
for k, limit in limit_mm_per_prompt_ints.items()
}
@@ -174,12 +178,16 @@ def _test_processing_correctness(
# Mistral chat outputs tokens directly, rather than text prompts
if isinstance(tokenizer, MistralTokenizer):
images = mm_data.get("image", [])
request = ChatCompletionRequest(messages=[
UserMessage(content=[
TextChunk(text=""),
*(ImageChunk(image=image) for image in images),
]),
])
request = ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text=""),
*(ImageChunk(image=image) for image in images),
]
),
]
)
res = tokenizer.mistral.encode_chat_completion(request)
prompt = res.tokens
else:
@@ -303,16 +311,14 @@ def _test_processing_correctness_one(
baseline_text_result,
baseline_tokenized_result,
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {text_prompt=}, "
f"{token_prompt=}, {mm_data=})",
msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
)
_assert_inputs_equal(
cached_text_result,
cached_tokenized_result,
ignore_mm_keys=ignore_mm_keys,
msg=f"Failed ({batch_idx=}, {text_prompt=}, "
f"{token_prompt=}, {mm_data=})",
msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
)