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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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73
tests/models/multimodal/generation/test_granite_speech.py
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@@ -10,8 +10,7 @@ from transformers import AutoModelForSpeechSeq2Seq
from vllm.logprobs import SampleLogprobs
from vllm.lora.request import LoRARequest
from ....conftest import (AudioTestAssets, HfRunner, PromptAudioInput,
VllmRunner)
from ....conftest import AudioTestAssets, HfRunner, PromptAudioInput, VllmRunner
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
@@ -64,50 +63,49 @@ def run_test(
# will hurt multiprocessing backend with fork method (the default method).
# max_model_len should be greater than image_feature_size
with vllm_runner(
model,
runner="generate",
max_model_len=max_model_len,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"audio": 1},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enable_lora=True,
max_lora_rank=64,
enforce_eager=True,
model,
runner="generate",
max_model_len=max_model_len,
max_num_seqs=1,
dtype=dtype,
limit_mm_per_prompt={"audio": 1},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enable_lora=True,
max_lora_rank=64,
enforce_eager=True,
) as vllm_model:
lora_request = LoRARequest("audio", 1, audio_lora_path)
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=audios,
lora_request=lora_request)
vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=audios,
lora_request=lora_request,
)
for prompts, audios in inputs
]
with hf_runner(model, dtype=dtype,
auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
with hf_runner(model, dtype=dtype, auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
hf_processor = hf_model.processor
eos_token_id = hf_processor.tokenizer.eos_token_id
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=[audios],
eos_token_id=eos_token_id)
hf_model.generate_greedy_logprobs_limit(
prompts,
max_tokens,
num_logprobs=num_logprobs,
audios=[audios],
eos_token_id=eos_token_id,
)
for prompts, audios in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
vllm_outputs_per_case):
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=[
vllm_to_hf_output(output) for output in vllm_outputs
],
outputs_1_lst=[vllm_to_hf_output(output) for output in vllm_outputs],
name_0="hf",
name_1="vllm",
)
@@ -118,9 +116,16 @@ def run_test(
@pytest.mark.parametrize("max_model_len", [2048])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_models(hf_runner, vllm_runner, model: str,
audio_assets: AudioTestAssets, dtype: str, max_model_len: int,
max_tokens: int, num_logprobs: int) -> None:
def test_models(
hf_runner,
vllm_runner,
model: str,
audio_assets: AudioTestAssets,
dtype: str,
max_model_len: int,
max_tokens: int,
num_logprobs: int,
) -> None:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")