operator name stringclasses 180
values | used in model stringclasses 155
values | args stringlengths 19 5.24k |
|---|---|---|
aten.copy_.default | TorchBench/attention_is_all_you_need_pytorch | ((T([7936, 512], f16), T([7936, 512], f16)), {}) |
aten.copy_.default | HuggingFace/MBartForConditionalGeneration | ((T([8, 127], i64, stride=(128, 1)), T([8, 127], i64)), {}) |
aten.copy_.default | HuggingFace/PLBartForConditionalGeneration | ((T([8, 127], i64, stride=(128, 1)), T([8, 127], i64)), {}) |
aten.copy_.default | HuggingFace/MBartForConditionalGeneration | ((T([8, 128], i64), T([8, 128], i64)), {}) |
aten.copy_.default | HuggingFace/MegatronBertForQuestionAnswering | ((T([8, 128], i64), T([8, 128], i64)), {}) |
aten.copy_.default | HuggingFace/PLBartForConditionalGeneration | ((T([8, 128], i64), T([8, 128], i64)), {}) |
aten.copy_.default | HuggingFace/PegasusForCausalLM | ((T([8, 128], i64), T([8, 128], i64)), {}) |
aten.copy_.default | HuggingFace/TrOCRForCausalLM | ((T([8, 128], i64), T([8, 128], i64)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 2], f16, stride=(48960, 16320, 1360, 85, 1)), T([8, 3, 12, 16, 2], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 2], f16, stride=(48960, 16320, 1360, 85, 1)), T([8, 3, 12, 16, 2], f32)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 4], f16, stride=(48960, 16320, 1360, 85, 1)), T([8, 3, 12, 16, 4], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 4], f16, stride=(48960, 16320, 1360, 85, 1)), T([8, 3, 12, 16, 4], f16, stride=(48960, 16320, 1360, 85, 1))), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 81], f16, stride=(48960, 16320, 1360, 85, 1)), T([8, 3, 12, 16, 81], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 85], f16), T([8, 3, 12, 16, 85], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 12, 16, 85], f16), T([8, 3, 12, 16, 85], f16, stride=(0, 0, 0, 0, 0))), {}) |
aten.copy_.default | TorchBench/resnext50_32x4d | ((T([8, 3, 224, 224], f16), T([8, 3, 224, 224], f16)), {}) |
aten.copy_.default | TorchBench/timm_vision_transformer | ((T([8, 3, 224, 224], f16), T([8, 3, 224, 224], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 2], f16, stride=(195840, 65280, 2720, 85, 1)), T([8, 3, 24, 32, 2], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 2], f16, stride=(195840, 65280, 2720, 85, 1)), T([8, 3, 24, 32, 2], f32)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 4], f16, stride=(195840, 65280, 2720, 85, 1)), T([8, 3, 24, 32, 4], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 4], f16, stride=(195840, 65280, 2720, 85, 1)), T([8, 3, 24, 32, 4], f16, stride=(195840, 65280, 2720, 85, 1))), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 81], f16, stride=(195840, 65280, 2720, 85, 1)), T([8, 3, 24, 32, 81], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 85], f16), T([8, 3, 24, 32, 85], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 24, 32, 85], f16), T([8, 3, 24, 32, 85], f16, stride=(0, 0, 0, 0, 0))), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 384, 512], f16), T([8, 3, 384, 512], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 2], f16, stride=(783360, 261120, 5440, 85, 1)), T([8, 3, 48, 64, 2], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 2], f16, stride=(783360, 261120, 5440, 85, 1)), T([8, 3, 48, 64, 2], f32)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 4], f16, stride=(783360, 261120, 5440, 85, 1)), T([8, 3, 48, 64, 4], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 4], f16, stride=(783360, 261120, 5440, 85, 1)), T([8, 3, 48, 64, 4], f16, stride=(783360, 261120, 5440, 85, 1))), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 81], f16, stride=(783360, 261120, 5440, 85, 1)), T([8, 3, 48, 64, 81], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 85], f16), T([8, 3, 48, 64, 85], f16)), {}) |
aten.copy_.default | TorchBench/yolov3 | ((T([8, 3, 48, 64, 85], f16), T([8, 3, 48, 64, 85], f16, stride=(0, 0, 0, 0, 0))), {}) |
aten.copy_.default | TorchBench/hf_Albert | ((T([8, 512], i64), T([8, 512], i64)), {}) |
aten.copy_.default | TorchBench/hf_DistilBert | ((T([8, 512], i64), T([8, 512], i64)), {}) |
aten.copy_.default | TorchBench/attention_is_all_you_need_pytorch | ((T([8448, 512], f16), T([8448, 512], f16)), {}) |
aten.copy_.default | HuggingFace/MegatronBertForQuestionAnswering | ((T([8], i64), T([8], i64)), {}) |
aten.copy_.default | HuggingFace/MBartForConditionalGeneration | ((T([8], i64, stride=(128,)), T([8], i64)), {}) |
aten.copy_.default | HuggingFace/PLBartForConditionalGeneration | ((T([8], i64, stride=(128,)), T([8], i64)), {}) |
aten.copy_.default | TorchBench/mobilenet_v2 | ((T([96, 3, 224, 224], f16), T([96, 3, 224, 224], f16)), {}) |
aten.copy_.default | TorchBench/LearningToPaint | ((T([96, 9, 128, 128], f16), T([96, 9, 128, 128], f16)), {}) |
aten.cos.default | HuggingFace/XLNetLMHeadModel | ((T([1024, 512], f32),), {}) |
aten.cumsum.default | HuggingFace/AllenaiLongformerBase | ((T([1, 1024], i32), 1), {}) |
aten.cumsum.default | HuggingFace/CamemBert | ((T([1, 512], i32), 1), {}) |
aten.cumsum.default | TorchBench/hf_Longformer | ((T([2, 1024], i32), 1), {}) |
aten.cumsum.default | HuggingFace/M2M100ForConditionalGeneration | ((T([2, 128], i32), 1), {}) |
aten.cumsum.default | HuggingFace/XGLMForCausalLM | ((T([2, 128], i32), 1), {}) |
aten.cumsum.default | HuggingFace/RobertaForCausalLM | ((T([4, 128], i32), 1), {}) |
aten.cumsum.default | HuggingFace/OPTForCausalLM | ((T([4, 128], i64), 1), {}) |
aten.cumsum.default | TorchBench/fastNLP_Bert | ((T([6, 474], i64), -1), {}) |
aten.cumsum.default | TorchBench/fastNLP_Bert | ((T([6, 476], i64), -1), {}) |
aten.cumsum.default | HuggingFace/RobertaForQuestionAnswering | ((T([64, 128], i32), 1), {}) |
aten.cumsum.default | HuggingFace/Speech2Text2ForCausalLM | ((T([64, 128], i32), 1), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 1152, 20, 20], f16, stride=(1152, 1, 0, 0)), 400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 144, 160, 160], f16, stride=(144, 1, 0, 0)), 25600), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 144, 80, 80], f16, stride=(144, 1, 0, 0)), 6400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 16, 320, 320], f16, stride=(16, 1, 0, 0)), 102400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 1920, 20, 20], f16, stride=(1920, 1, 0, 0)), 400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 240, 40, 40], f16, stride=(240, 1, 0, 0)), 1600), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 240, 80, 80], f16, stride=(240, 1, 0, 0)), 6400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 32, 320, 320], f16, stride=(32, 1, 0, 0)), 102400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 480, 40, 40], f16, stride=(480, 1, 0, 0)), 1600), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 672, 20, 20], f16, stride=(672, 1, 0, 0)), 400), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 672, 40, 40], f16, stride=(672, 1, 0, 0)), 1600), {}) |
aten.div.Scalar | TorchBench/timm_efficientdet | ((T([1, 96, 160, 160], f16, stride=(96, 1, 0, 0)), 25600), {}) |
aten.div.Scalar | TIMM/selecsls42b | ((T([128, 1024, 4, 4], f16, stride=(1024, 1, 0, 0)), 16), {}) |
aten.div.Scalar | TIMM/ese_vovnet19b_dw | ((T([128, 1024, 7, 7], f16, stride=(1024, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TorchBench/shufflenet_v2_x1_0 | ((T([128, 1024, 7, 7], f16, stride=(1024, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/rexnet_100 | ((T([128, 1044, 7, 7], f16, stride=(1044, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/fbnetv3_b | ((T([128, 1104, 7, 7], f16, stride=(1104, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/tinynet_a | ((T([128, 1152, 6, 6], f16, stride=(1152, 1, 0, 0)), 36), {}) |
aten.div.Scalar | TIMM/hardcorenas_a | ((T([128, 1152, 7, 7], f16, stride=(1152, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/tf_efficientnet_b0 | ((T([128, 1152, 7, 7], f16, stride=(1152, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/fbnetv3_b | ((T([128, 120, 28, 28], f16, stride=(120, 1, 0, 0)), 784), {}) |
aten.div.Scalar | TIMM/ghostnet_100 | ((T([128, 120, 28, 28], f16, stride=(120, 1, 0, 0)), 784), {}) |
aten.div.Scalar | TIMM/mobilenetv3_large_100 | ((T([128, 120, 28, 28], f16, stride=(120, 1, 0, 0)), 784), {}) |
aten.div.Scalar | TIMM/eca_botnext26ts_256 | ((T([128, 128, 32, 32], f16, stride=(128, 1, 0, 0)), 1024), {}) |
aten.div.Scalar | TIMM/eca_halonext26ts | ((T([128, 128, 32, 32], f16, stride=(128, 1, 0, 0)), 1024), {}) |
aten.div.Scalar | TIMM/lcnet_050 | ((T([128, 128, 7, 7], f16, stride=(128, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/tinynet_a | ((T([128, 1280, 6, 6], f16, stride=(1280, 1, 0, 0)), 36), {}) |
aten.div.Scalar | TIMM/mnasnet_100 | ((T([128, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/mobilenetv2_100 | ((T([128, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/rexnet_100 | ((T([128, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/spnasnet_100 | ((T([128, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/tf_efficientnet_b0 | ((T([128, 1280, 7, 7], f16, stride=(1280, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/fbnetv3_b | ((T([128, 1344, 7, 7], f16, stride=(1344, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/repvgg_a2 | ((T([128, 1408, 7, 7], f16, stride=(1408, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/tinynet_a | ((T([128, 144, 24, 24], f16, stride=(144, 1, 0, 0)), 576), {}) |
aten.div.Scalar | TIMM/tf_efficientnet_b0 | ((T([128, 144, 28, 28], f16, stride=(144, 1, 0, 0)), 784), {}) |
aten.div.Scalar | TIMM/tinynet_a | ((T([128, 144, 48, 48], f16, stride=(144, 1, 0, 0)), 2304), {}) |
aten.div.Scalar | TIMM/tf_efficientnet_b0 | ((T([128, 144, 56, 56], f16, stride=(144, 1, 0, 0)), 3136), {}) |
aten.div.Scalar | TIMM/regnety_002 | ((T([128, 152, 14, 14], f16, stride=(152, 1, 0, 0)), 196), {}) |
aten.div.Scalar | TIMM/dm_nfnet_f0 | ((T([128, 1536, 12, 12], f16, stride=(1536, 1, 0, 0)), 144), {}) |
aten.div.Scalar | TorchBench/timm_nfnet | ((T([128, 1536, 12, 12], f16, stride=(1536, 1, 0, 0)), 144), {}) |
aten.div.Scalar | TIMM/nfnet_l0 | ((T([128, 1536, 14, 14], f16, stride=(1536, 1, 0, 0)), 196), {}) |
aten.div.Scalar | TIMM/dm_nfnet_f0 | ((T([128, 1536, 6, 6], f16, stride=(1536, 1, 0, 0)), 36), {}) |
aten.div.Scalar | TorchBench/timm_nfnet | ((T([128, 1536, 6, 6], f16, stride=(1536, 1, 0, 0)), 36), {}) |
aten.div.Scalar | TIMM/nfnet_l0 | ((T([128, 1536, 7, 7], f16, stride=(1536, 1, 0, 0)), 49), {}) |
aten.div.Scalar | TIMM/levit_128 | ((T([128, 16, 384], f16, stride=(384, 0, 1)), 16), {}) |
aten.div.Scalar | TIMM/resmlp_12_224 | ((T([128, 196, 384], f16, stride=(384, 0, 1)), 196), {}) |
aten.div.Scalar | TIMM/fbnetc_100 | ((T([128, 1984, 7, 7], f16, stride=(1984, 1, 0, 0)), 49), {}) |
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