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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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question: ['Is the dog running?'], 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([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: 1874
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torch.Size([13, 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: 1877
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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tensor([1.0000e+00, 2.6292e-08, 4.8663e-11, 4.2511e-08, 7.7000e-10, 3.9968e-10,
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1.2429e-10, 4.6940e-08], 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, 2.6292e-08, 4.8663e-11, 4.2511e-08, 7.7000e-10, 3.9968e-10,
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1.2429e-10, 4.6940e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.8663e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1916e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the image show an opened flip phone?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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tensor([1.0000e+00, 3.0437e-09, 5.2112e-10, 3.2053e-09, 1.1994e-10, 9.7918e-11,
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5.5294e-12, 9.3358e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0437e-09, 5.2112e-10, 3.2053e-09, 1.1994e-10, 9.7918e-11,
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5.5294e-12, 9.3358e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.2112e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.2112e-10, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.3204e-10, 2.4828e-07, 5.5793e-11, 1.5473e-11, 4.0917e-08,
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1.5006e-09, 6.0182e-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, 4.3204e-10, 2.4828e-07, 5.5793e-11, 1.5473e-11, 4.0917e-08,
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1.5006e-09, 6.0182e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(4.3204e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Does the image show an opened flip phone?'], 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([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 7.2658e-08, 3.1711e-07, 2.6609e-12, 1.1890e-11, 4.3328e-09,
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9.1409e-10, 3.5487e-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, 7.2658e-08, 3.1711e-07, 2.6609e-12, 1.1890e-11, 4.3328e-09,
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9.1409e-10, 3.5487e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(7.2658e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.7486e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([9.9962e-01, 6.2695e-09, 3.7999e-04, 3.1378e-09, 4.6680e-11, 2.7885e-10,
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1.5302e-10, 4.6647e-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([9.9962e-01, 6.2695e-09, 3.7999e-04, 3.1378e-09, 4.6680e-11, 2.7885e-10,
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1.5302e-10, 4.6647e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9996, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.0004, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.0699e-08, device='cuda:2', grad_fn=<SubBackward0>)}
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[2024-10-24 10:31:17,680] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.32
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[2024-10-24 10:31:17,680] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5080.90 | backward_microstep: 12685.55 | backward_inner_microstep: 4819.71 | backward_allreduce_microstep: 7865.77 | step_microstep: 9.98
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[2024-10-24 10:31:17,680] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5080.92 | backward: 12685.54 | backward_inner: 4819.72 | backward_allreduce: 7865.76 | step: 9.99
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98%|ββββββββββ| 4769/4844 [19:50:01<20:27, 16.36s/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 VQA_lavis step
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Registering EVAL step
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Registering RESULT 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=LEFT,question='How many birds perch on a branch?')
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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=LEFT,question='How many geese are floating on the water?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='How many dogs 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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ANSWER0=VQA(image=RIGHT,question='Is the instrument mouthpiece gold colored on a silver body?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([4, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is the instrument mouthpiece gold colored on a silver body?'], 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([4, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1096
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question: ['How many birds perch on a branch?'], responses:['2']
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1096
|
[('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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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1097
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1096
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1096
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1097
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question: ['How many geese are floating on the water?'], responses:['2']
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question: ['How many dogs are in the image?'], responses:['1']
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