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[2024-10-24 10:35:01,123] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.34 | optimizer_step: 0.32
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[2024-10-24 10:35:01,124] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5072.88 | backward_microstep: 12589.50 | backward_inner_microstep: 4811.57 | backward_allreduce_microstep: 7777.80 | step_microstep: 8.07
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[2024-10-24 10:35:01,124] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5072.90 | backward: 12589.49 | backward_inner: 4811.62 | backward_allreduce: 7777.78 | step: 8.08
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99%|ββββββββββ| 4784/4844 [19:53:44<15:00, 15.00s/it]Registering VQA_lavis 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 EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Is there a woman wearing an earring in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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 dogs 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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ANSWER0=VQA(image=RIGHT,question='Are the dogs outside?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='How many drawers are on the cabinet?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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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([13, 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: ['How many drawers are on the cabinet?'], 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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a woman wearing an earring in the image?'], responses:['no']
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question: ['Are the dogs outside?'], responses:['yes']
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question: ['How many dogs are in the image?'], responses:['1']
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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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[('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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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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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: 3400
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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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([9.9289e-01, 7.0364e-03, 5.9727e-05, 3.6748e-08, 1.2424e-05, 1.0084e-07,
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6.9668e-08, 2.5815e-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([9.9289e-01, 7.0364e-03, 5.9727e-05, 3.6748e-08, 1.2424e-05, 1.0084e-07,
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6.9668e-08, 2.5815e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9929, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0071, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,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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torch.Size([1, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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question: ['How many dogs are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([1, 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: 3400
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tensor([1.0000e+00, 2.0729e-10, 9.0559e-11, 3.2103e-10, 1.1254e-10, 2.8743e-08,
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2.4501e-09, 3.6207e-10], 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, 2.0729e-10, 9.0559e-11, 3.2103e-10, 1.1254e-10, 2.8743e-08,
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2.4501e-09, 3.6207e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.4501e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., 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: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 4.9443e-09, 5.2037e-07, 7.4504e-10, 1.8639e-08, 2.3226e-07,
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1.4319e-08, 3.3328e-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, 4.9443e-09, 5.2037e-07, 7.4504e-10, 1.8639e-08, 2.3226e-07,
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1.4319e-08, 3.3328e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.9443e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.3606e-09, 6.7583e-09, 2.7309e-09, 8.0365e-12, 5.6671e-11,
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3.7074e-11, 2.3099e-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, 4.3606e-09, 6.7583e-09, 2.7309e-09, 8.0365e-12, 5.6671e-11,
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3.7074e-11, 2.3099e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=RIGHT,question='Does the car in the image have a top?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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tensor([1.0000e+00, 2.5157e-09, 2.0250e-10, 7.7623e-10, 7.2066e-10, 1.4755e-08,
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1.9334e-06, 3.0628e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.5157e-09, 2.0250e-10, 7.7623e-10, 7.2066e-10, 1.4755e-08,
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1.9334e-06, 3.0628e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.7583e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.7583e-09, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many gorillas are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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