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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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torch.Size([7, 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: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([1.0000e+00, 9.2180e-10, 1.6202e-07, 3.4919e-10, 4.6241e-09, 6.0880e-08,
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8.7590e-09, 3.3946e-07], device='cuda:1', 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, 9.2180e-10, 1.6202e-07, 3.4919e-10, 4.6241e-09, 6.0880e-08,
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8.7590e-09, 3.3946e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(9.2180e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the dog facing right?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([9.9992e-01, 4.0086e-05, 5.9056e-07, 6.0611e-09, 5.8500e-11, 4.2671e-05,
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2.1399e-10, 1.7332e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9992e-01, 4.0086e-05, 5.9056e-07, 6.0611e-09, 5.8500e-11, 4.2671e-05,
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2.1399e-10, 1.7332e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.0094e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many skunks 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([1, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many skunks 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: 3398
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tensor([1.0000e+00, 6.4617e-09, 1.7430e-07, 1.8583e-09, 8.0048e-12, 6.3584e-12,
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1.9449e-11, 2.2784e-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, 6.4617e-09, 1.7430e-07, 1.8583e-09, 8.0048e-12, 6.3584e-12,
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1.9449e-11, 2.2784e-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(1.7430e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.5089e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 6.0453e-10, 1.5405e-10, 8.5237e-10, 3.0871e-10, 2.7092e-08,
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6.7057e-09, 9.4888e-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, 6.0453e-10, 1.5405e-10, 8.5237e-10, 3.0871e-10, 2.7092e-08,
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6.7057e-09, 9.4888e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.6666e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 3.0279e-10, 5.6671e-11, 1.2671e-10, 9.7892e-11, 1.0964e-08,
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5.1014e-09, 1.3390e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.0279e-10, 5.6671e-11, 1.2671e-10, 9.7892e-11, 1.0964e-08,
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5.1014e-09, 1.3390e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.6784e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['Is the dog facing right?'], 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([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 2.9549e-09, 9.1463e-10, 1.1814e-08, 1.1255e-10, 6.0928e-10,
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3.6101e-11, 2.3840e-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([1.0000e+00, 2.9549e-09, 9.1463e-10, 1.1814e-08, 1.1255e-10, 6.0928e-10,
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3.6101e-11, 2.3840e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.1463e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.1463e-10, device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:42:03,346] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.34
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[2024-10-24 10:42:03,346] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5163.23 | backward_microstep: 12477.40 | backward_inner_microstep: 4950.55 | backward_allreduce_microstep: 7526.77 | step_microstep: 10.86
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[2024-10-24 10:42:03,346] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5163.23 | backward: 12477.39 | backward_inner: 4950.57 | backward_allreduce: 7526.67 | step: 10.88
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99%|ββββββββββ| 4813/4844 [20:00:47<07:56, 15.37s/it]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='Are there people in the room?')
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ANSWER1=EVAL(expr='not {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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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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ANSWER0=VQA(image=RIGHT,question='Are there elephants standing in or beside water?')
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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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ANSWER0=VQA(image=LEFT,question='How many zippers are on the pencil pouch top 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=LEFT,question='Is there a hyena standing in a field?')
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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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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Are there people in the room?'], 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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question: ['How many zippers are on the pencil pouch top in the image?'], responses:['2']
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tensor([1.0000e+00, 4.3400e-08, 9.3748e-10, 7.5224e-07, 2.7609e-08, 2.2430e-07,
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1.7143e-09, 1.9137e-07], 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, 4.3400e-08, 9.3748e-10, 7.5224e-07, 2.7609e-08, 2.2430e-07,
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