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[['3', '4', '1', '5', '8', '2', '6', '12']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([9.9999e-01, 9.5162e-06, 2.7405e-07, 3.9956e-09, 8.7694e-11, 7.5644e-07,
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1.6906e-10, 7.7600e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 9.5162e-06, 2.7405e-07, 3.9956e-09, 8.7694e-11, 7.5644e-07,
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1.6906e-10, 7.7600e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0305e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:44:10,065] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33
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[2024-10-24 10:44:10,066] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5114.27 | backward_microstep: 12815.95 | backward_inner_microstep: 4967.95 | backward_allreduce_microstep: 7847.88 | step_microstep: 7.72
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[2024-10-24 10:44:10,066] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5114.27 | backward: 12815.94 | backward_inner: 4967.98 | backward_allreduce: 7847.87 | step: 7.73
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100%|ββββββββββ| 4821/4844 [20:02:53<06:12, 16.20s/it]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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ANSWER0=VQA(image=RIGHT,question='Is the roll of brown paper partially unrolled?')
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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 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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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many animals 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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ANSWER0=VQA(image=LEFT,question='Is there a canine lying down in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Are there blueberries on top of the dessert?')
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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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question: ['Is the roll of brown paper partially unrolled?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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tensor([9.9920e-01, 5.1551e-09, 8.0408e-04, 1.9612e-09, 6.5965e-11, 1.7269e-11,
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1.2556e-10, 7.9756e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9920e-01, 5.1551e-09, 8.0408e-04, 1.9612e-09, 6.5965e-11, 1.7269e-11,
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1.2556e-10, 7.9756e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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question: ['Are there blueberries on top of the dessert?'], responses:['yes']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9992, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.4936e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many wolves 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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[('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])
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question: ['Is there a canine lying down in the image?'], responses:['yes']
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question: ['How many animals are in the image?'], responses:['1']
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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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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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
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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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question: ['How many wolves are in the image?'], responses:['7']
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[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
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[['7', '8', '11', '5', '9', '10', '6', '12']]
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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tensor([9.9848e-01, 8.0351e-04, 3.5656e-04, 2.3363e-08, 3.3491e-04, 1.6676e-05,
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3.6061e-06, 8.8370e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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7 *************
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['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9848e-01, 8.0351e-04, 3.5656e-04, 2.3363e-08, 3.3491e-04, 1.6676e-05,
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3.6061e-06, 8.8370e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 1.0254e-08, 2.0249e-10, 7.2281e-08, 2.1546e-09, 7.8846e-10,
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3.7600e-11, 6.2315e-08], 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, 1.0254e-08, 2.0249e-10, 7.2281e-08, 2.1546e-09, 7.8846e-10,
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3.7600e-11, 6.2315e-08], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.0249e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1901e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many uncapped bottles 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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tensor([1.0000e+00, 4.5964e-09, 6.4720e-11, 8.6174e-08, 3.2828e-10, 1.0924e-10,
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5.7859e-11, 2.1929e-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, 4.5964e-09, 6.4720e-11, 8.6174e-08, 3.2828e-10, 1.0924e-10,
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5.7859e-11, 2.1929e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 8.0716e-10, 1.0754e-10, 1.2044e-10, 8.0855e-11, 5.2624e-09,
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6.8647e-09, 1.2214e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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