Making large AI models cheaper, faster and more accessible
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import pytest
import timm.models as tmm
import torch
import torchvision.models as tm
from colossalai.fx._compatibility import is_compatible_with_meta
if is_compatible_with_meta():
from colossalai.fx import meta_trace
from colossalai.testing import clear_cache_before_run
tm_models = [
tm.vgg11,
tm.resnet18,
tm.densenet121,
tm.mobilenet_v3_small,
tm.resnext50_32x4d,
tm.wide_resnet50_2,
tm.regnet_x_16gf,
tm.mnasnet0_5,
tm.efficientnet_b0,
]
tmm_models = [
tmm.resnest.resnest50d,
tmm.beit.beit_base_patch16_224,
tmm.cait.cait_s24_224,
tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224,
tmm.vision_transformer.vit_base_patch16_224,
tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base,
tmm.vgg.vgg11,
tmm.dpn.dpn68,
tmm.densenet.densenet121,
tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224,
]
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_torchvision_models_trace():
for m in tm_models:
model = m()
data = torch.rand(1000, 3, 224, 224, device="meta")
meta_trace(model, torch.device("cpu"), data)
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_timm_models_trace():
for m in tmm_models:
model = m()
data = torch.rand(1000, 3, 224, 224, device="meta")
meta_trace(model, torch.device("cpu"), data)
if __name__ == "__main__":
test_torchvision_models_trace()
test_timm_models_trace()