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5.8717e-09, 4.0409e-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, 5.6129e-10, 5.6668e-11, 1.3808e-10, 1.0902e-10, 1.2422e-08,
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5.8717e-09, 4.0409e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.9563e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([9.6180e-01, 2.4076e-02, 2.4629e-06, 5.3718e-03, 1.8513e-03, 9.9382e-04,
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5.7051e-03, 1.9515e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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100 *************
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['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.6180e-01, 2.4076e-02, 2.4629e-06, 5.3718e-03, 1.8513e-03, 9.9382e-04,
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5.7051e-03, 1.9515e-04], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 3.8727e-10, 5.3808e-07, 6.1757e-11, 1.2717e-10, 5.8092e-08,
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1.4872e-09, 1.3289e-06], 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, 3.8727e-10, 5.3808e-07, 6.1757e-11, 1.2717e-10, 5.8092e-08,
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1.4872e-09, 1.3289e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([9.9993e-01, 6.6052e-05, 2.3209e-07, 3.2463e-12, 4.9893e-12, 8.5798e-10,
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2.4773e-10, 1.5432e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9993e-01, 6.6052e-05, 2.3209e-07, 3.2463e-12, 4.9893e-12, 8.5798e-10,
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2.4773e-10, 1.5432e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.8727e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a child in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.6052e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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torch.Size([13, 3, 448, 448])
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question: ['Is there a child 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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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: 3396
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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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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: 3397
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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, 6.3852e-10, 4.8627e-07, 1.5927e-09, 3.4535e-09, 3.8067e-07,
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1.8079e-09, 6.3093e-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, 6.3852e-10, 4.8627e-07, 1.5927e-09, 3.4535e-09, 3.8067e-07,
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1.8079e-09, 6.3093e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.3852e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:42:34,891] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.25 | optimizer_step: 0.31
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[2024-10-24 10:42:34,891] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8954.85 | backward_microstep: 8718.80 | backward_inner_microstep: 8712.82 | backward_allreduce_microstep: 5.87 | step_microstep: 7.68
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[2024-10-24 10:42:34,891] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8954.86 | backward: 8718.79 | backward_inner: 8712.86 | backward_allreduce: 5.81 | step: 7.69
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99%|ββββββββββ| 4815/4844 [20:01:18<07:36, 15.75s/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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ANSWER0=VQA(image=RIGHT,question='Is there land in the background?')
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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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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 bird facing towards the left?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='How many creatures are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 8')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Are there plants in vases 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([1, 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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torch.Size([13, 3, 448, 448])
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question: ['Are there plants in vases in the image?'], 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([1.0000e+00, 1.2789e-08, 2.3125e-10, 9.8448e-08, 1.2914e-10, 3.7536e-10,
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9.6461e-11, 3.3241e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2789e-08, 2.3125e-10, 9.8448e-08, 1.2914e-10, 3.7536e-10,
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9.6461e-11, 3.3241e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.3125e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1898e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many water buffalo 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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question: ['Is there land in the background?'], responses:['yes']
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question: ['How many creatures are in the image?'], responses:['4']
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torch.Size([7, 3, 448, 448])
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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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[('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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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