operator name
stringclasses
180 values
used in model
stringclasses
155 values
args
stringlengths
19
5.24k
aten.div.Scalar
TIMM/hrnet_w18
((T([128, 2048, 7, 7], f16, stride=(2048, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/res2net50_14w_8s
((T([128, 2048, 7, 7], f16, stride=(2048, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/res2next50
((T([128, 2048, 7, 7], f16, stride=(2048, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/adv_inception_v3
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/botnet26t_256
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/eca_botnext26ts_256
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/eca_halonext26ts
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/gluon_inception_v3
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/inception_v3
((T([128, 2048, 8, 8], f16, stride=(2048, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 228, 28, 28], f16, stride=(228, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/nfnet_l0
((T([128, 2304, 7, 7], f16, stride=(2304, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/regnety_002
((T([128, 24, 56, 56], f16, stride=(24, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 240, 12, 12], f16, stride=(240, 1, 0, 0)), 144), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 240, 14, 14], f16, stride=(240, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 240, 14, 14], f16, stride=(240, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 240, 24, 24], f16, stride=(240, 1, 0, 0)), 576), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 240, 28, 28], f16, stride=(240, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 240, 28, 28], f16, stride=(240, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/eca_botnext26ts_256
((T([128, 256, 16, 16], f16, stride=(256, 1, 0, 0)), 256), {})
aten.div.Scalar
TIMM/eca_halonext26ts
((T([128, 256, 16, 16], f16, stride=(256, 1, 0, 0)), 256), {})
aten.div.Scalar
TIMM/dm_nfnet_f0
((T([128, 256, 48, 48], f16, stride=(256, 1, 0, 0)), 2304), {})
aten.div.Scalar
TorchBench/timm_nfnet
((T([128, 256, 48, 48], f16, stride=(256, 1, 0, 0)), 2304), {})
aten.div.Scalar
TIMM/ese_vovnet19b_dw
((T([128, 256, 56, 56], f16, stride=(256, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/nfnet_l0
((T([128, 256, 56, 56], f16, stride=(256, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/lcnet_050
((T([128, 256, 7, 7], f16, stride=(256, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/gernet_l
((T([128, 2560, 8, 8], f16, stride=(2560, 1, 0, 0)), 64), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 300, 28, 28], f16, stride=(300, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/dm_nfnet_f0
((T([128, 3072, 6, 6], f16, stride=(3072, 1, 0, 0)), 36), {})
aten.div.Scalar
TorchBench/timm_nfnet
((T([128, 3072, 6, 6], f16, stride=(3072, 1, 0, 0)), 36), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 32, 112, 112], f16, stride=(32, 1, 0, 0)), 12544), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 32, 96, 96], f16, stride=(32, 1, 0, 0)), 9216), {})
aten.div.Scalar
TIMM/fbnetv3_b
((T([128, 360, 14, 14], f16, stride=(360, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 366, 14, 14], f16, stride=(366, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/regnety_002
((T([128, 368, 7, 7], f16, stride=(368, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 432, 14, 14], f16, stride=(432, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 480, 12, 12], f16, stride=(480, 1, 0, 0)), 144), {})
aten.div.Scalar
TIMM/ghostnet_100
((T([128, 480, 14, 14], f16, stride=(480, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 480, 14, 14], f16, stride=(480, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/mobilenetv3_large_100
((T([128, 480, 14, 14], f16, stride=(480, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 480, 14, 14], f16, stride=(480, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 504, 14, 14], f16, stride=(504, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/dm_nfnet_f0
((T([128, 512, 24, 24], f16, stride=(512, 1, 0, 0)), 576), {})
aten.div.Scalar
TorchBench/timm_nfnet
((T([128, 512, 24, 24], f16, stride=(512, 1, 0, 0)), 576), {})
aten.div.Scalar
TIMM/ese_vovnet19b_dw
((T([128, 512, 28, 28], f16, stride=(512, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/nfnet_l0
((T([128, 512, 28, 28], f16, stride=(512, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/resnet18
((T([128, 512, 7, 7], f16, stride=(512, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/regnety_002
((T([128, 56, 28, 28], f16, stride=(56, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 570, 14, 14], f16, stride=(570, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 636, 14, 14], f16, stride=(636, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/eca_botnext26ts_256
((T([128, 64, 64, 64], f16, stride=(64, 1, 0, 0)), 4096), {})
aten.div.Scalar
TIMM/eca_halonext26ts
((T([128, 64, 64, 64], f16, stride=(64, 1, 0, 0)), 4096), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 672, 12, 12], f16, stride=(672, 1, 0, 0)), 144), {})
aten.div.Scalar
TIMM/ghostnet_100
((T([128, 672, 14, 14], f16, stride=(672, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 672, 14, 14], f16, stride=(672, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/mobilenetv3_large_100
((T([128, 672, 14, 14], f16, stride=(672, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 672, 14, 14], f16, stride=(672, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 672, 6, 6], f16, stride=(672, 1, 0, 0)), 36), {})
aten.div.Scalar
TIMM/ghostnet_100
((T([128, 672, 7, 7], f16, stride=(672, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 672, 7, 7], f16, stride=(672, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/mobilenetv3_large_100
((T([128, 672, 7, 7], f16, stride=(672, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 672, 7, 7], f16, stride=(672, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 702, 14, 14], f16, stride=(702, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/ghostnet_100
((T([128, 72, 28, 28], f16, stride=(72, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/mobilenetv3_large_100
((T([128, 72, 28, 28], f16, stride=(72, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 72, 56, 56], f16, stride=(72, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/fbnetv3_b
((T([128, 720, 7, 7], f16, stride=(720, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/fbnetv3_b
((T([128, 736, 7, 7], f16, stride=(736, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/ese_vovnet19b_dw
((T([128, 768, 14, 14], f16, stride=(768, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 768, 7, 7], f16, stride=(768, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/visformer_small
((T([128, 768, 7, 7], f16, stride=(768, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 840, 7, 7], f16, stride=(840, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 906, 7, 7], f16, stride=(906, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/tinynet_a
((T([128, 96, 48, 48], f16, stride=(96, 1, 0, 0)), 2304), {})
aten.div.Scalar
TIMM/tf_efficientnet_b0
((T([128, 96, 56, 56], f16, stride=(96, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/ghostnet_100
((T([128, 960, 7, 7], f16, stride=(960, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/hardcorenas_a
((T([128, 960, 7, 7], f16, stride=(960, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/mobilenetv3_large_100
((T([128, 960, 7, 7], f16, stride=(960, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/rexnet_100
((T([128, 972, 7, 7], f16, stride=(972, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/BERT_pytorch
((T([16, 128, 768], f16, stride=(128, 1, 0)), 768), {})
aten.div.Scalar
TIMM/nasnetalarge
((T([16, 4032, 11, 11], f16, stride=(4032, 1, 0, 0)), 121), {})
aten.div.Scalar
TIMM/pnasnet5large
((T([16, 4320, 11, 11], f16, stride=(4320, 1, 0, 0)), 121), {})
aten.div.Scalar
TorchBench/resnet18
((T([16, 512, 7, 7], f16, stride=(512, 1, 0, 0)), 49), {})
aten.div.Scalar
TIMM/crossvit_9_240
((T([2, 64, 1000], f16, stride=(0, 1000, 1)), 2), {})
aten.div.Scalar
TorchBench/squeezenet1_1
((T([32, 1000, 13, 13], f16, stride=(0, 0, 0, 0)), 169), {})
aten.div.Scalar
TIMM/gluon_senet154
((T([32, 1024, 14, 14], f16, stride=(1024, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/legacy_senet154
((T([32, 1024, 14, 14], f16, stride=(1024, 1, 0, 0)), 196), {})
aten.div.Scalar
TIMM/convnext_base
((T([32, 1024, 7, 7], f16, stride=(1024, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/timm_vovnet
((T([32, 1024, 7, 7], f16, stride=(1024, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/timm_efficientnet
((T([32, 1152, 7, 7], f16, stride=(1152, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/mobilenet_v3_large
((T([32, 120, 28, 28], f16, stride=(120, 1, 0, 0)), 784), {})
aten.div.Scalar
TIMM/resnest101e
((T([32, 128, 32, 32], f16, stride=(128, 1, 0, 0)), 1024), {})
aten.div.Scalar
TorchBench/timm_resnest
((T([32, 128, 56, 56], f16, stride=(128, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/convnext_base
((T([32, 128, 56, 56], f16, stride=(3136, 0, 56, 1)), 128), {})
aten.div.Scalar
TIMM/resnest101e
((T([32, 128, 64, 64], f16, stride=(128, 1, 0, 0)), 4096), {})
aten.div.Scalar
TorchBench/mnasnet1_0
((T([32, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/timm_efficientnet
((T([32, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {})
aten.div.Scalar
TorchBench/timm_efficientnet
((T([32, 144, 28, 28], f16, stride=(144, 1, 0, 0)), 784), {})
aten.div.Scalar
TorchBench/timm_efficientnet
((T([32, 144, 56, 56], f16, stride=(144, 1, 0, 0)), 3136), {})
aten.div.Scalar
TIMM/gluon_xception65
((T([32, 2048, 10, 10], f16, stride=(2048, 1, 0, 0)), 100), {})
aten.div.Scalar
TIMM/gluon_senet154
((T([32, 2048, 7, 7], f16, stride=(2048, 1, 0, 0)), 49), {})