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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
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tensor([6.6746e-01, 1.7539e-02, 3.1189e-01, 9.6794e-04, 1.5098e-04, 5.9840e-04,
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5.6386e-05, 1.3415e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6746e-01, 1.7539e-02, 3.1189e-01, 9.6794e-04, 1.5098e-04, 5.9840e-04,
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5.6386e-05, 1.3415e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6675, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.3119, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0207, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([0.5941, 0.1341, 0.1811, 0.0106, 0.0115, 0.0454, 0.0053, 0.0178],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.5941, 0.1341, 0.1811, 0.0106, 0.0115, 0.0454, 0.0053, 0.0178],
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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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tensor([0.2003, 0.0889, 0.1714, 0.1768, 0.1008, 0.1399, 0.0440, 0.0779],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2003, 0.0889, 0.1714, 0.1768, 0.1008, 0.1399, 0.0440, 0.0779],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5275, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4725, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-22 17:13:39,170] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.29 | optimizer_step: 0.32
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[2024-10-22 17:13:39,170] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6365.31 | backward_microstep: 7280.46 | backward_inner_microstep: 6158.50 | backward_allreduce_microstep: 1121.87 | step_microstep: 7.86
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[2024-10-22 17:13:39,170] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6365.32 | backward: 7280.45 | backward_inner: 6158.52 | backward_allreduce: 1121.86 | step: 7.87
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0%| | 5/4844 [01:22<20:31:36, 15.27s/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 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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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='How many computers are displayed in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many warthogs 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 there a structure with a wooden roof to the right of the yurt?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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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torch.Size([3, 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: ['How many computers are displayed in the image?'], responses:['9']
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[('9', 0.12801736482258133), ('8', 0.12565135970392036), ('11', 0.1254560343890198), ('10', 0.1248838582125673), ('7', 0.12420801006143238), ('12', 0.12408347303550306), ('5', 0.12385261492086817), ('14', 0.12384728485410773)]
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[['9', '8', '11', '10', '7', '12', '5', '14']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many creatures are in the image?'], responses:['many']
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question: ['How many warthogs are in the image?'], responses:['5']
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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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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tensor([0.2363, 0.1176, 0.1510, 0.1617, 0.0434, 0.1885, 0.0101, 0.0916],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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9 *************
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['9', '8', '11', '10', '7', '12', '5', '14'] tensor([0.2363, 0.1176, 0.1510, 0.1617, 0.0434, 0.1885, 0.0101, 0.0916],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Is there a structure with a wooden roof to the right of the yurt?'], responses:['yes']
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ANSWER0=VQA(image=LEFT,question='How many creatures 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([13, 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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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: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([0.6018, 0.1026, 0.1849, 0.0107, 0.0271, 0.0420, 0.0197, 0.0112],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.6018, 0.1026, 0.1849, 0.0107, 0.0271, 0.0420, 0.0197, 0.0112],
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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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tensor([0.3130, 0.0573, 0.1737, 0.2287, 0.0698, 0.1158, 0.0110, 0.0307],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3130, 0.0573, 0.1737, 0.2287, 0.0698, 0.1158, 0.0110, 0.0307],
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device='cuda:2', grad_fn=<SelectBackward0>)
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question: ['How many creatures are in the image?'], responses:['1']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many chimpanzees are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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