operator name
stringclasses
180 values
used in model
stringclasses
155 values
args
stringlengths
19
5.24k
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([4096, 135], f16), [0, -7]), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([4096, 16, 31], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([4096, 16, 32], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([4096, 512], f16), [0, 15], 0.0), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([4096, 527], f16), [0, -15]), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([4096, 8, 15], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([4096, 8, 15], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([4096, 8, 16], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([4096, 8, 16], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 28, 28], f16, stride=(263424, 784, 28, 1)), [0, 1, 0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 28, 28], f16, stride=(263424, 784, 28, 1)), [1, 2, 1, 2], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 28, 28], f16, stride=(263424, 784, 28, 1)), [2, 3, 2, 3], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 29, 29], f16), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 31, 31], f16), [-1, -2, -1, -2]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 112, 33, 33], f16), [-2, -3, -2, -3]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 14, 14], f16, stride=(188160, 196, 14, 1)), [0, 1, 0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 14, 14], f16, stride=(188160, 196, 14, 1)), [1, 2, 1, 2], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 14, 14], f16, stride=(188160, 196, 14, 1)), [2, 3, 2, 3], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 14, 14], f16, stride=(188160, 196, 14, 1)), [3, 4, 3, 4], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 15, 15], f16), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 17, 17], f16), [-1, -2, -1, -2]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 19, 19], f16), [-2, -3, -2, -3]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 240, 21, 21], f16), [-3, -4, -3, -4]), {})
aten.constant_pad_nd.default
TIMM/jx_nest_base
((T([64, 256, 56, 56], f16, stride=(802816, 1, 14336, 256)), [0, 1, 0, 1], -inf), {})
aten.constant_pad_nd.default
TIMM/jx_nest_base
((T([64, 256, 57, 57], f16, stride=(831744, 1, 14592, 256)), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 3, 224, 224], f16), [0, 1, 0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/jx_nest_base
((T([64, 512, 28, 28], f16, stride=(401408, 1, 14336, 512)), [0, 1, 0, 1], -inf), {})
aten.constant_pad_nd.default
TIMM/jx_nest_base
((T([64, 512, 29, 29], f16, stride=(430592, 1, 14848, 512)), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 56, 56], f16, stride=(752640, 3136, 56, 1)), [0, 1, 0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 56, 56], f16, stride=(752640, 3136, 56, 1)), [1, 2, 1, 2], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 56, 56], f16, stride=(752640, 3136, 56, 1)), [2, 3, 2, 3], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 56, 56], f16, stride=(752640, 3136, 56, 1)), [3, 4, 3, 4], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 57, 57], f16), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 59, 59], f16), [-1, -2, -1, -2]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 61, 61], f16), [-2, -3, -2, -3]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 60, 63, 63], f16), [-3, -4, -3, -4]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 112, 112], f16, stride=(2408448, 12544, 112, 1)), [0, 1, 0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 112, 112], f16, stride=(2408448, 12544, 112, 1)), [1, 2, 1, 2], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 112, 112], f16, stride=(2408448, 12544, 112, 1)), [2, 3, 2, 3], 0.0), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 113, 113], f16), [0, -1, 0, -1]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 115, 115], f16), [-1, -2, -1, -2]), {})
aten.constant_pad_nd.default
TIMM/tf_mixnet_l
((T([64, 64, 117, 117], f16), [-2, -3, -2, -3]), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([8192, 16, 31], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([8192, 16, 31], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([8192, 16, 32], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([8192, 16, 32], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/eca_halonext26ts
((T([8192, 192], f16), [0, 15], 0.0), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([8192, 2048], f16), [0, 31], 0.0), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([8192, 2079], f16), [0, -31]), {})
aten.constant_pad_nd.default
TIMM/eca_halonext26ts
((T([8192, 207], f16), [0, -15]), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([8192, 32, 63], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/sebotnet33ts_256
((T([8192, 32, 64], f16), [0, -1]), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([8192, 512], f16), [0, 15], 0.0), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([8192, 512], f16), [0, 15], 0.0), {})
aten.constant_pad_nd.default
TIMM/botnet26t_256
((T([8192, 527], f16), [0, -15]), {})
aten.constant_pad_nd.default
TIMM/eca_botnext26ts_256
((T([8192, 527], f16), [0, -15]), {})
aten.constant_pad_nd.default
TIMM/eca_halonext26ts
((T([8192, 8, 23], f16), [0, 1], 0.0), {})
aten.constant_pad_nd.default
TIMM/eca_halonext26ts
((T([8192, 8, 24], f16), [0, -1]), {})
aten.convolution.default
TorchBench/vision_maskrcnn
((T([0, 256, 14, 14], f16), T([256, 256, 2, 2], f16), T([256], f16), [2, 2], [0, 0], [1, 1], True, [0, 0], 1), {})
aten.convolution.default
TorchBench/vision_maskrcnn
((T([0, 256, 14, 14], f16), T([256, 256, 3, 3], f16), T([256], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/vision_maskrcnn
((T([0, 256, 28, 28], f16), T([91, 256, 1, 1], f16), T([91], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 10, 1, 1], f16), T([240, 10, 1, 1], f16), T([240], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 1024, 80, 119], f16), T([512, 1024, 3, 3], f16), T([512], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 112, 40, 40], f16), T([672, 112, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 112, 40, 40], f16), T([88, 112, 1, 1], f16), T([88], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1152, 1, 1], f16), T([48, 1152, 1, 1], f16), T([48], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1152, 20, 20], f16), T([1152, 1, 3, 3], f16), None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1152), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1152, 20, 20], f16), T([1152, 1, 5, 5], f16), None, [1, 1], [2, 2], [1, 1], False, [0, 0], 1152), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1152, 20, 20], f16), T([192, 1152, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1152, 20, 20], f16), T([320, 1152, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_CycleGAN_and_pix2pix
((T([1, 128, 128, 128], f16), T([128, 64, 3, 3], f16), T([64], f16), [2, 2], [1, 1], [1, 1], True, [1, 1], 1), {})
aten.convolution.default
TorchBench/pytorch_CycleGAN_and_pix2pix
((T([1, 128, 128, 128], f16), T([256, 128, 3, 3], f16), T([256], f16), [2, 2], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 128, 160, 239], f16), T([256, 128, 3, 3], f16), T([256], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 128, 320, 479], f16), T([128, 128, 3, 3], f16), T([128], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 128, 320, 479], f16), T([64, 128, 3, 3], f16), T([64], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 128, 640, 959], f16), T([64, 128, 3, 3], f16), T([64], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 144, 1, 1], f16), T([6, 144, 1, 1], f16), T([6], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 144, 160, 160], f16), T([144, 1, 3, 3], f16), None, [1, 1], [1, 1], [1, 1], False, [0, 0], 144), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 144, 160, 160], f16), T([24, 144, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 144, 163, 163], f16), T([144, 1, 5, 5], f16), None, [2, 2], [0, 0], [1, 1], False, [0, 0], 144), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 144, 80, 80], f16), T([40, 144, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 16, 1, 1], f16), T([4, 16, 1, 1], f16), T([4], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 16, 320, 320], f16), T([16, 1, 3, 3], f16), None, [1, 1], [1, 1], [1, 1], False, [0, 0], 16), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 16, 320, 320], f16), T([16, 16, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 16, 320, 320], f16), T([96, 16, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 192, 20, 20], f16), T([1152, 192, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1920, 1, 1], f16), T([80, 1920, 1, 1], f16), T([80], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1920, 20, 20], f16), T([1920, 1, 3, 3], f16), None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1920), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 1920, 20, 20], f16), T([320, 1920, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 20, 1, 1], f16), T([480, 20, 1, 1], f16), T([480], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 24, 160, 160], f16), T([144, 24, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 240, 1, 1], f16), T([10, 240, 1, 1], f16), T([10], f16), [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 240, 40, 40], f16), T([80, 240, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 240, 80, 80], f16), T([240, 1, 5, 5], f16), None, [1, 1], [2, 2], [1, 1], False, [0, 0], 240), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 240, 80, 80], f16), T([40, 240, 1, 1], f16), None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/timm_efficientdet
((T([1, 240, 81, 81], f16), T([240, 1, 3, 3], f16), None, [2, 2], [0, 0], [1, 1], False, [0, 0], 240), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 256, 160, 239], f16), T([128, 256, 3, 3], f16), T([128], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 256, 160, 239], f16), T([256, 256, 3, 3], f16), T([256], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_unet
((T([1, 256, 320, 479], f16), T([128, 256, 3, 3], f16), T([128], f16), [1, 1], [1, 1], [1, 1], False, [0, 0], 1), {})
aten.convolution.default
TorchBench/pytorch_CycleGAN_and_pix2pix
((T([1, 256, 64, 64], f16), T([256, 128, 3, 3], f16), T([128], f16), [2, 2], [1, 1], [1, 1], True, [1, 1], 1), {})