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[CI/Build] Fix model nightly tests (#26466)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
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Cyrus Leung 2025-10-09 14:44:16 +08:00 committed by GitHub
parent d24cf322e1
commit 0f29dca988
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5 changed files with 9 additions and 7 deletions
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2
tests/models/language/generation/test_common.py
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@ -100,7 +100,7 @@ AITER_MODEL_LIST = [
"allenai/OLMoE-1B-7B-0924-Instruct", "allenai/OLMoE-1B-7B-0924-Instruct",
marks=[pytest.mark.cpu_model], marks=[pytest.mark.cpu_model],
), ),
pytest.param("swiss-ai/Apertus-8B-2509"), # apertus pytest.param("swiss-ai/Apertus-8B-Instruct-2509"), # apertus
], ],
) )
@pytest.mark.parametrize("max_tokens", [32]) @pytest.mark.parametrize("max_tokens", [32])

2
tests/models/language/pooling/test_token_classification.py
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@ -67,4 +67,4 @@ def test_modernbert_models(
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs): for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output).cpu().float() hf_output = torch.tensor(hf_output).cpu().float()
vllm_output = torch.tensor(vllm_output).cpu().float() vllm_output = torch.tensor(vllm_output).cpu().float()
assert torch.allclose(hf_output, vllm_output, 1e-2) assert torch.allclose(hf_output, vllm_output, atol=1e-2)

1
tests/models/multimodal/generation/test_common.py
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@ -749,6 +749,7 @@ VLM_TEST_SETTINGS = {
max_num_seqs=2, max_num_seqs=2,
auto_cls=AutoModelForImageTextToText, auto_cls=AutoModelForImageTextToText,
hf_output_post_proc=model_utils.smolvlm_trunc_hf_output, hf_output_post_proc=model_utils.smolvlm_trunc_hf_output,
num_logprobs=10,
), ),
"tarsier": VLMTestInfo( "tarsier": VLMTestInfo(
models=["omni-research/Tarsier-7b"], models=["omni-research/Tarsier-7b"],

8
tests/models/multimodal/pooling/test_clip.py
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@ -45,14 +45,16 @@ def _run_test(
all_outputs = [] all_outputs = []
for inputs in all_inputs: for inputs in all_inputs:
inputs = hf_model.wrap_device(inputs)
if "pixel_values" in inputs: if "pixel_values" in inputs:
inputs.pop("input_ids")
pooled_output = hf_model.model.get_image_features( pooled_output = hf_model.model.get_image_features(
**hf_model.wrap_device(inputs) pixel_values=inputs.pixel_values,
).squeeze(0) ).squeeze(0)
else: else:
pooled_output = hf_model.model.get_text_features( pooled_output = hf_model.model.get_text_features(
**hf_model.wrap_device(inputs) input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
).squeeze(0) ).squeeze(0)
all_outputs.append(pooled_output.tolist()) all_outputs.append(pooled_output.tolist())

3
tests/models/registry.py
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@ -172,9 +172,8 @@ class _HfExamplesInfo:
_TEXT_GENERATION_EXAMPLE_MODELS = { _TEXT_GENERATION_EXAMPLE_MODELS = {
# [Decoder-only] # [Decoder-only]
"ApertusForCausalLM": _HfExamplesInfo( "ApertusForCausalLM": _HfExamplesInfo(
"swiss-ai/Apertus-8B-2509", "swiss-ai/Apertus-8B-Instruct-2509",
min_transformers_version="4.56.0", min_transformers_version="4.56.0",
trust_remote_code=True,
), ),
"AquilaModel": _HfExamplesInfo("BAAI/AquilaChat-7B", trust_remote_code=True), "AquilaModel": _HfExamplesInfo("BAAI/AquilaChat-7B", trust_remote_code=True),
"AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B", trust_remote_code=True), "AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B", trust_remote_code=True),