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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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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: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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tensor([9.9998e-01, 7.0241e-06, 6.3430e-06, 4.0659e-06, 8.0904e-08, 7.8363e-08,
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3.2302e-08, 1.9961e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9998e-01, 7.0241e-06, 6.3430e-06, 4.0659e-06, 8.0904e-08, 7.8363e-08,
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3.2302e-08, 1.9961e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.3430e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a lamp near the television?')
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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question: ['Is there a lamp near the television?'], 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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tensor([1.0000e+00, 4.2514e-09, 1.1496e-08, 1.1692e-07, 1.8051e-10, 5.6349e-10,
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4.3252e-10, 4.5862e-09], 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, 4.2514e-09, 1.1496e-08, 1.1692e-07, 1.8051e-10, 5.6349e-10,
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4.3252e-10, 4.5862e-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(1.1496e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0771e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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tensor([9.9999e-01, 1.2685e-08, 1.2219e-05, 2.1027e-08, 8.6757e-11, 9.9459e-11,
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4.1904e-10, 7.0213e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9999e-01, 1.2685e-08, 1.2219e-05, 2.1027e-08, 8.6757e-11, 9.9459e-11,
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4.1904e-10, 7.0213e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 1.1744e-09, 1.1628e-10, 4.7447e-10, 1.5638e-10, 3.5957e-08,
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1.3129e-08, 7.8732e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1744e-09, 1.1628e-10, 4.7447e-10, 1.5638e-10, 3.5957e-08,
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1.3129e-08, 7.8732e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 4.4069e-08, 7.6871e-08, 5.8470e-11, 7.0986e-11, 5.1680e-08,
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1.8833e-09, 2.4749e-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, 4.4069e-08, 7.6871e-08, 5.8470e-11, 7.0986e-11, 5.1680e-08,
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1.8833e-09, 2.4749e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.2219e-05, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9482e-08, device='cuda:1', grad_fn=<SubBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.1795e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.4069e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.1723e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many animals 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='How many gorillas 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 hog facing left?')
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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([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many animals are in the image?'], responses:['ε']
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[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)]
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[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many gorillas are in the image?'], responses:['1']
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question: ['Is the hog facing left?'], responses:['yes']
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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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[('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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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: 3394
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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: 3394
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tensor([1.1059e-02, 6.7508e-03, 1.5954e-06, 4.6258e-01, 9.3191e-02, 9.2636e-03,
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4.1474e-01, 2.4200e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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bulldog *************
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['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.1059e-02, 6.7508e-03, 1.5954e-06, 4.6258e-01, 9.3191e-02, 9.2636e-03,
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4.1474e-01, 2.4200e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([9.9593e-01, 3.9170e-09, 4.0702e-03, 5.7966e-09, 1.7461e-11, 1.4870e-10,
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2.6311e-10, 1.6678e-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([9.9593e-01, 3.9170e-09, 4.0702e-03, 5.7966e-09, 1.7461e-11, 1.4870e-10,
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2.6311e-10, 1.6678e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9959, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0041, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.6100e-08, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([1.0000e+00, 2.1555e-10, 5.0800e-11, 1.5402e-10, 9.7121e-11, 9.9739e-09,
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2.8173e-09, 2.3480e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1555e-10, 5.0800e-11, 1.5402e-10, 9.7121e-11, 9.9739e-09,
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2.8173e-09, 2.3480e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3543e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:40:32,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.26 | optimizer_step: 0.32
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[2024-10-24 10:40:32,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7103.27 | backward_microstep: 6801.78 | backward_inner_microstep: 6767.78 | backward_allreduce_microstep: 33.86 | step_microstep: 10.92
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