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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
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tensor([9.3550e-04, 3.7099e-03, 3.2860e-06, 1.8604e-01, 7.6837e-02, 8.6702e-04,
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7.3110e-01, 5.0076e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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vegetable *************
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['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([9.3550e-04, 3.7099e-03, 3.2860e-06, 1.8604e-01, 7.6837e-02, 8.6702e-04,
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7.3110e-01, 5.0076e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
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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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ANSWER0=VQA(image=LEFT,question='How many bags/pencil-cases 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([1, 3, 448, 448])
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question: ['How many bags/pencil-cases are in the image?'], responses:['4']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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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([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: 1859
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tensor([1.0000e+00, 1.0164e-08, 1.9935e-10, 4.3043e-08, 2.5924e-10, 5.0509e-10,
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1.2481e-10, 1.5140e-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, 1.0164e-08, 1.9935e-10, 4.3043e-08, 2.5924e-10, 5.0509e-10,
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1.2481e-10, 1.5140e-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(1.9935e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1901e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([9.9919e-01, 7.6799e-06, 8.0405e-04, 2.7015e-10, 1.2495e-09, 5.0798e-07,
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1.1533e-06, 7.1681e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9919e-01, 7.6799e-06, 8.0405e-04, 2.7015e-10, 1.2495e-09, 5.0798e-07,
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1.1533e-06, 7.1681e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 1.4389e-09, 2.4404e-07, 2.2067e-10, 3.8676e-10, 2.8862e-08,
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4.0483e-09, 1.1729e-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([1.0000e+00, 1.4389e-09, 2.4404e-07, 2.2067e-10, 3.8676e-10, 2.8862e-08,
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4.0483e-09, 1.1729e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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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(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the bird in the image eating a fish?')
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ANSWER1=RESULT(var=ANSWER0)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.4389e-09, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<SubBackward0>)}
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tensor([1.0000e+00, 6.8936e-07, 6.4121e-08, 3.5788e-09, 1.5927e-09, 3.0254e-09,
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5.4304e-09, 1.6178e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.8936e-07, 6.4121e-08, 3.5788e-09, 1.5927e-09, 3.0254e-09,
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5.4304e-09, 1.6178e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([13, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.6873e-07, 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=LEFT,question='How many human heads 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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torch.Size([13, 3, 448, 448])
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question: ['Is the bird in the image eating a fish?'], 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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question: ['How many human heads 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([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: 3398
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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: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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tensor([1.0000e+00, 2.1724e-10, 1.4968e-07, 1.2945e-11, 1.1742e-12, 7.3960e-09,
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4.7795e-10, 2.9928e-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, 2.1724e-10, 1.4968e-07, 1.2945e-11, 1.1742e-12, 7.3960e-09,
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4.7795e-10, 2.9928e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.1724e-10, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([9.9795e-01, 9.6059e-08, 5.2532e-09, 2.2943e-09, 2.1227e-09, 3.0214e-08,
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2.0512e-03, 1.6060e-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([9.9795e-01, 9.6059e-08, 5.2532e-09, 2.2943e-09, 2.1227e-09, 3.0214e-08,
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2.0512e-03, 1.6060e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9979, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0021, 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 10:46:48,881] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.39 | optimizer_step: 0.34
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[2024-10-24 10:46:48,881] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7019.76 | backward_microstep: 7032.77 | backward_inner_microstep: 6761.49 | backward_allreduce_microstep: 271.10 | step_microstep: 7.87
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[2024-10-24 10:46:48,882] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7019.78 | backward: 7032.76 | backward_inner: 6761.60 | backward_allreduce: 271.08 | step: 7.88
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100%|ββββββββββ| 4832/4844 [20:05:32<02:55, 14.63s/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 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=LEFT,question='Is the animal in the image wearing an article of clothing?')
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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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ANSWER0=VQA(image=RIGHT,question='How many umbrellas 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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