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(Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [07:08<00:00,  6.70s/it]\nvit_h,16,14395,17,19.70834741975898,50.73992145061493,0.5875230894143607,max-autotune,torch.bfloat16,dynamic_quant,False,False,True,True,True,32,64,1024,None,None\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.850527899339795\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185 3.190660197287798\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.768232116475701\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.8598313461989164\nshape=(torch.Size([78400, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.4865157660096884\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953 1.179535873234272\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.7427184619009497\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.4965661568567157\nshape=(torch.Size([78400, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.215262923389673\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827 3.485689079388976\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 5.220260447822511\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.2220821138471365\nshape=(torch.Size([65536, 1280]), torch.Size([5120, 1280]), torch.Size([5120])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.666170105338097\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181 2.626298717223108\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.855024302378297\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.674202110618353\nshape=(torch.Size([65536, 5120]), torch.Size([1280, 5120]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.2269158866256475\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685 2.6572815608233213\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 3.9978391956537966\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.2370124012231827\nshape=(torch.Size([65536, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.2530277017503977\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748 0.9894231799989939\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.5166664496064186\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.2606457574293017\nshape=(torch.Size([65536, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [02:15<00:00,  2.12s/it]\nvit_h,16,14463,17,19.76190752324237,50.602402567863464,0.5875653903095147,max-autotune,torch.bfloat16,auto_quant,False,False,True,True,True,32,64,1024,None,None\n\nTest Plan:\n\nReviewers:\n\nSubscribers:\n\nTasks:\n\nTags:\n\nghstack-source-id: a9134d6c15e38ba393ce201ed883490c3e009afd\nPull Request resolved: https://github.com/pytorch-labs/segment-anything-fast/pull/114","shortMessageHtmlLink":"testing autoquant"}},{"before":"c44f0d0ab22d9bdf1ebf00b659a5b9108c8f63b7","after":"36d43be2e014f766282e80e40d96c5987064c68a","ref":"refs/heads/gh/HDCharles/4/head","pushedAt":"2024-03-05T23:47:21.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"HDCharles","name":null,"path":"/HDCharles","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/39544797?s=80&v=4"},"commit":{"message":"Update on \"testing autoquant\"\n\n\nSummary:\n\nimproves runtime by 19.70 -> 19.76 img/sec\n\n❯ one sh run.sh\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [06:32<00:00,  6.14s/it]\nsam_model_type,batch_size,memory(MiB),memory(%),img_s(avg),batch_ms(avg)/batch_size,mIoU,use_compile,use_half,compress,epilogue_fusion_first,use_compile_decoder,use_nested_tensor,use_rel_pos,pad_input_image_batch,num_workers,num_batches,num_images,profile_path,memory_path\nvit_h,16,14532,17,18.861125832244333,53.01910442113876,0.5865236891447146,max-autotune,torch.bfloat16,None,False,False,True,True,True,32,64,1024,None,None\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [07:08<00:00,  6.70s/it]\nvit_h,16,14395,17,19.70834741975898,50.73992145061493,0.5875230894143607,max-autotune,torch.bfloat16,dynamic_quant,False,False,True,True,True,32,64,1024,None,None\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.850527899339795\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185 3.190660197287798\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.768232116475701\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.8598313461989164\nshape=(torch.Size([78400, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.4865157660096884\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953 1.179535873234272\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.7427184619009497\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.4965661568567157\nshape=(torch.Size([78400, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.215262923389673\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827 3.485689079388976\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 5.220260447822511\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.2220821138471365\nshape=(torch.Size([65536, 1280]), torch.Size([5120, 1280]), torch.Size([5120])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.666170105338097\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181 2.626298717223108\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.855024302378297\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.674202110618353\nshape=(torch.Size([65536, 5120]), torch.Size([1280, 5120]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.2269158866256475\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685 2.6572815608233213\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 3.9978391956537966\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.2370124012231827\nshape=(torch.Size([65536, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.2530277017503977\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748 0.9894231799989939\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.5166664496064186\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.2606457574293017\nshape=(torch.Size([65536, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [02:15<00:00,  2.12s/it]\nvit_h,16,14463,17,19.76190752324237,50.602402567863464,0.5875653903095147,max-autotune,torch.bfloat16,auto_quant,False,False,True,True,True,32,64,1024,None,None\n\nTest Plan:\n\nReviewers:\n\nSubscribers:\n\nTasks:\n\nTags:\n\n[ghstack-poisoned]","shortMessageHtmlLink":"Update on \"testing autoquant\""}},{"before":"387488bc4c7ab2ae311fb0632b34cab5cbfbab78","after":"2ad6b94306dd90adbf250a818b6678619054fd12","ref":"refs/heads/main","pushedAt":"2024-03-02T21:50:53.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"cpuhrsch","name":null,"path":"/cpuhrsch","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/1716488?s=80&v=4"},"commit":{"message":"Update README.md - missing newline (#115)","shortMessageHtmlLink":"Update README.md - missing newline (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2164991036\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch-labs/segment-anything-fast/issues/115\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch-labs/segment-anything-fast/pull/115/hovercard\" href=\"https://github.com/pytorch-labs/segment-anything-fast/pull/115\">#115</a>)"}},{"before":null,"after":"7c81764bb78a93e585563ad1a5677d73dba378d3","ref":"refs/heads/cpuhrsch-patch-4","pushedAt":"2024-03-02T21:50:43.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"cpuhrsch","name":null,"path":"/cpuhrsch","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/1716488?s=80&v=4"},"commit":{"message":"Update README.md - missing newline","shortMessageHtmlLink":"Update README.md - missing newline"}},{"before":null,"after":"d5c1472ecd91a214e3f55b6cb90f828611e70ab8","ref":"refs/heads/gh/HDCharles/4/orig","pushedAt":"2024-03-01T06:44:36.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"HDCharles","name":null,"path":"/HDCharles","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/39544797?s=80&v=4"},"commit":{"message":"testing autoquant\n\nSummary:\n\nimproves runtime by 19.70 -> 19.76 img/sec\n\n❯ one sh run.sh\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [06:32<00:00,  6.14s/it]\nsam_model_type,batch_size,memory(MiB),memory(%),img_s(avg),batch_ms(avg)/batch_size,mIoU,use_compile,use_half,compress,epilogue_fusion_first,use_compile_decoder,use_nested_tensor,use_rel_pos,pad_input_image_batch,num_workers,num_batches,num_images,profile_path,memory_path\nvit_h,16,14532,17,18.861125832244333,53.01910442113876,0.5865236891447146,max-autotune,torch.bfloat16,None,False,False,True,True,True,32,64,1024,None,None\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /home/cdhernandez/local/pytorch/aten/src/ATen/NestedTensorImpl.cpp:177.)\n  return _nested.nested_tensor(\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [07:08<00:00,  6.70s/it]\nvit_h,16,14395,17,19.70834741975898,50.73992145061493,0.5875230894143607,max-autotune,torch.bfloat16,dynamic_quant,False,False,True,True,True,32,64,1024,None,None\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.850527899339795\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.3931088875979185 3.190660197287798\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.768232116475701\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.8598313461989164\nshape=(torch.Size([78400, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.4865157660096884\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.8800818361341953 1.179535873234272\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.7427184619009497\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.4965661568567157\nshape=(torch.Size([78400, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.215262923389673\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.661373794078827 3.485689079388976\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 5.220260447822511\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.2220821138471365\nshape=(torch.Size([65536, 1280]), torch.Size([5120, 1280]), torch.Size([5120])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 4.666170105338097\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 4.113288130611181 2.626298717223108\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 4.855024302378297\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 4.674202110618353\nshape=(torch.Size([65536, 5120]), torch.Size([1280, 5120]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 3.2269158866256475\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 3.7462301552295685 2.6572815608233213\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 3.9978391956537966\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 3.2370124012231827\nshape=(torch.Size([65536, 1280]), torch.Size([3840, 1280]), torch.Size([3840])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'>\n<class 'torchao.quantization.autoquant.AQFloatLinearWeight'> 1.2530277017503977\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748\n<class 'torchao.quantization.autoquant.AQInt8DynamicallyQuantizedLinearWeight'> 1.5717314090579748 0.9894231799989939\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight'> 1.5166664496064186\n<class 'torchao.quantization.autoquant.AQWeightOnlyQuantizedLinearWeight3'> 1.2606457574293017\nshape=(torch.Size([65536, 1280]), torch.Size([1280, 1280]), torch.Size([1280])), dtype=torch.bfloat16, best_cls=<class 'torchao.quantization.autoquant.AQFloatLinearWeight'>\n  0%|                                                                                                                                                                                                              | 0/64 [00:00<?, ?it/s]/home/cdhernandez/local/pytorch/torch/nested/__init__.py:166: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. 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