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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 1.9020e-09, 1.4699e-10, 4.6197e-09, 6.6079e-11, 7.6260e-11,
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1.0047e-11, 4.8635e-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, 1.9020e-09, 1.4699e-10, 4.6197e-09, 6.6079e-11, 7.6260e-11,
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1.0047e-11, 4.8635e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4699e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4699e-10, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 8.0609e-07, 1.3027e-08, 1.2117e-09, 2.0210e-11, 4.3146e-07,
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7.9925e-11, 6.6918e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 8.0609e-07, 1.3027e-08, 1.2117e-09, 2.0210e-11, 4.3146e-07,
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7.9925e-11, 6.6918e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.4449e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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question: ['Is the bird 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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torch.Size([6, 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: 3400
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 1.6657e-10, 2.2479e-07, 1.7693e-11, 1.6674e-11, 1.5902e-08,
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7.3825e-10, 5.0424e-07], device='cuda:3', 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, 1.6657e-10, 2.2479e-07, 1.7693e-11, 1.6674e-11, 1.5902e-08,
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7.3825e-10, 5.0424e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.6657e-10, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<SubBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 2.5398e-10, 1.0839e-06, 1.7216e-11, 2.7264e-10, 3.0807e-08,
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3.3794e-09, 2.6300e-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, 2.5398e-10, 1.0839e-06, 1.7216e-11, 2.7264e-10, 3.0807e-08,
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3.3794e-09, 2.6300e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.5398e-10, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.6955e-06, device='cuda:0', grad_fn=<SubBackward0>)}
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[2024-10-24 10:37:35,830] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.21 | optimizer_step: 0.31
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[2024-10-24 10:37:35,831] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5223.55 | backward_microstep: 4989.41 | backward_inner_microstep: 4984.23 | backward_allreduce_microstep: 5.10 | step_microstep: 7.35
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[2024-10-24 10:37:35,831] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5223.56 | backward: 4989.40 | backward_inner: 4984.26 | backward_allreduce: 5.09 | step: 7.36
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99%|ββββββββββ| 4794/4844 [19:56:19<11:49, 14.19s/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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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 white huts 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 inflated sails 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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ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Is the animal in an upright vertical position on its hind legs?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([3, 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([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many white huts are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([1.6509e-03, 1.9262e-02, 7.4077e-06, 5.5462e-01, 8.6332e-02, 6.7319e-03,
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3.2736e-01, 4.0394e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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bulldog *************
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['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.6509e-03, 1.9262e-02, 7.4077e-06, 5.5462e-01, 8.6332e-02, 6.7319e-03,
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3.2736e-01, 4.0394e-03], device='cuda:3', grad_fn=<SelectBackward0>)
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question: ['How many inflated sails are in the image?'], responses:['2']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many hyenas 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 the animal in an upright vertical position on its hind legs?'], responses:['yes']
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torch.Size([7, 3, 448, 448])
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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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: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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question: ['How many hyenas 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([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: 3401
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tensor([9.9959e-01, 4.0578e-04, 1.2469e-07, 7.5901e-07, 2.1310e-08, 1.8116e-08,
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