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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 3.5823e-09, 4.5373e-09, 1.2752e-07, 1.2771e-10, 1.2550e-09,
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4.7081e-10, 6.2292e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.5823e-09, 4.5373e-09, 1.2752e-07, 1.2771e-10, 1.2550e-09,
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4.7081e-10, 6.2292e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0596e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['How many binders are in the image?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([1.0000e+00, 2.5798e-10, 5.4076e-11, 1.6397e-10, 8.9091e-11, 8.1424e-09,
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3.3983e-09, 1.7116e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.5798e-10, 5.4076e-11, 1.6397e-10, 8.9091e-11, 8.1424e-09,
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3.3983e-09, 1.7116e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2277e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 3.1307e-09, 7.7628e-10, 1.8582e-09, 1.3464e-09, 3.1251e-08,
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2.8901e-08, 7.7087e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.1307e-09, 7.7628e-10, 1.8582e-09, 1.3464e-09, 3.1251e-08,
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2.8901e-08, 7.7087e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.8035e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 9.7362e-10, 4.0315e-07, 1.1424e-11, 1.2860e-10, 2.0266e-08,
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8.9689e-10, 5.2570e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.7362e-10, 4.0315e-07, 1.1424e-11, 1.2860e-10, 2.0266e-08,
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8.9689e-10, 5.2570e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(9.7362e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([7.5239e-01, 2.4755e-01, 1.9936e-05, 7.8307e-08, 4.1203e-05, 1.5972e-07,
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1.2817e-08, 9.5182e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.5239e-01, 2.4755e-01, 1.9936e-05, 7.8307e-08, 4.1203e-05, 1.5972e-07,
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1.2817e-08, 9.5182e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7524, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2476, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:37:11,707] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.34
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[2024-10-24 10:37:11,708] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7053.43 | backward_microstep: 10721.50 | backward_inner_microstep: 6788.99 | backward_allreduce_microstep: 3932.38 | step_microstep: 7.76
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[2024-10-24 10:37:11,708] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7053.44 | backward: 10721.50 | backward_inner: 6789.01 | backward_allreduce: 3932.36 | step: 7.78
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99%|ββββββββββ| 4792/4844 [19:55:55<14:30, 16.73s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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ANSWER0=VQA(image=RIGHT,question='Are there buffalo behind wolves on snow-covered ground?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='How many lemons are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many vape devices are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Does the left image show a smiling black graduate alone in the foreground?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Are there buffalo behind wolves on snow-covered ground?'], responses:['no']
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[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many vape devices are in the image?'], responses:['ε']
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[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)]
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[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']]
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['How many lemons are in the image?'], responses:['2']
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question: ['Does the left image show a smiling black graduate alone in the foreground?'], responses:['no']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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tensor([1.0000e+00, 1.1562e-09, 4.6040e-07, 8.3103e-11, 1.4545e-10, 6.2026e-08,
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1.7757e-09, 1.3411e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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