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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['How many rodents are in the image?'], responses:['2']
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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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torch.Size([7, 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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 5.7356e-09, 2.5802e-09, 3.1549e-09, 9.9879e-09, 1.6894e-07,
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4.1399e-08, 5.3876e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.7356e-09, 2.5802e-09, 3.1549e-09, 9.9879e-09, 1.6894e-07,
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4.1399e-08, 5.3876e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 9.3761e-09, 1.2099e-06, 1.0600e-08, 7.9919e-11, 2.0525e-11,
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2.9599e-11, 3.8291e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.3761e-09, 1.2099e-06, 1.0600e-08, 7.9919e-11, 2.0525e-11,
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2.9599e-11, 3.8291e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.2099e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.7773e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8747e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a woman in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([1.0000e+00, 3.5969e-08, 1.4165e-09, 1.5380e-07, 1.4026e-10, 4.7419e-10,
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7.3179e-10, 7.9826e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.5969e-08, 1.4165e-09, 1.5380e-07, 1.4026e-10, 4.7419e-10,
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7.3179e-10, 7.9826e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5380e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['Is there a woman in the image?'], 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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tensor([1.0000e+00, 4.0905e-10, 7.4148e-07, 1.2117e-09, 1.6338e-09, 3.7793e-07,
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7.4397e-09, 3.8812e-07], device='cuda:0', 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, 4.0905e-10, 7.4148e-07, 1.2117e-09, 1.6338e-09, 3.7793e-07,
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7.4397e-09, 3.8812e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.0905e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 7.4650e-10, 8.6691e-07, 1.8728e-10, 1.0800e-08, 1.8240e-07,
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2.2902e-10, 6.3415e-07], device='cuda:1', 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, 7.4650e-10, 8.6691e-07, 1.8728e-10, 1.0800e-08, 1.8240e-07,
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2.2902e-10, 6.3415e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.4650e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6689e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:38:50,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.33
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[2024-10-24 10:38:50,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6029.12 | backward_microstep: 11645.14 | backward_inner_microstep: 5780.56 | backward_allreduce_microstep: 5864.42 | step_microstep: 7.86
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[2024-10-24 10:38:50,439] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6029.13 | backward: 11645.13 | backward_inner: 5780.63 | backward_allreduce: 5864.40 | step: 7.87
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99%|ββββββββββ| 4799/4844 [19:57:34<11:36, 15.47s/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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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Is the collar on the dog clearly visible?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Are there mountains visible behind the sleds?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How many parrots are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 2')
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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='How many women are wearing swimsuits in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Is the collar on the dog clearly visible?'], 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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question: ['How many women are wearing swimsuits in the image?'], responses:['2']
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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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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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Are there mountains visible behind the sleds?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
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tensor([1.0000e+00, 1.9363e-09, 9.0377e-07, 9.2374e-09, 1.0692e-09, 2.6206e-07,
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