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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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question: ['Is the bird facing towards the left?'], responses:['yes']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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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question: ['How many water buffalo 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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torch.Size([7, 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: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([1.0000e+00, 2.2581e-08, 3.9031e-10, 2.9263e-07, 5.1682e-10, 1.8189e-09,
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1.6370e-10, 1.0381e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2581e-08, 3.9031e-10, 2.9263e-07, 5.1682e-10, 1.8189e-09,
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1.6370e-10, 1.0381e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.9031e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7645e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([7.7514e-01, 2.2446e-01, 3.6228e-04, 2.6611e-09, 3.9877e-05, 1.1193e-09,
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2.6062e-08, 2.9386e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.7514e-01, 2.2446e-01, 3.6228e-04, 2.6611e-09, 3.9877e-05, 1.1193e-09,
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2.6062e-08, 2.9386e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=RIGHT,question='How many dogs 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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.9386e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How are the dogs positioned?')
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ANSWER1=EVAL(expr='{ANSWER0} == "lined up"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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tensor([1.0000e+00, 1.1366e-06, 4.2713e-08, 1.7603e-06, 5.9983e-10, 5.9983e-10,
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1.8476e-09, 1.8529e-11], 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, 1.1366e-06, 4.2713e-08, 1.7603e-06, 5.9983e-10, 5.9983e-10,
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1.8476e-09, 1.8529e-11], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.9427e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many dogs 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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question: ['How are the dogs positioned?'], responses:['one']
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[('1', 0.125909911336171), ('middle', 0.12534722276644197), ('movement', 0.1249259569411728), ('no 1', 0.1248681891446603), ('nowhere', 0.12476808142719474), ('middle 1', 0.12476673627897926), ('first', 0.12471126898225356), ('orange', 0.12470263312312643)]
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[['1', 'middle', 'movement', 'no 1', 'nowhere', 'middle 1', 'first', 'orange']]
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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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tensor([9.9409e-01, 3.3122e-09, 5.9111e-03, 4.8888e-09, 4.3702e-11, 4.5981e-11,
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4.4107e-10, 2.0883e-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([9.9409e-01, 3.3122e-09, 5.9111e-03, 4.8888e-09, 4.3702e-11, 4.5981e-11,
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4.4107e-10, 2.0883e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9941, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0059, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-7.6368e-08, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many perfumes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 10')
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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: 13, images per sample: 13.0, dynamic token length: 3394
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question: ['How many perfumes 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([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: 3394
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tensor([1.0000e+00, 9.6156e-09, 4.0759e-08, 6.1295e-09, 2.1056e-10, 6.2502e-09,
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8.0665e-10, 1.9487e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 9.6156e-09, 4.0759e-08, 6.1295e-09, 2.1056e-10, 6.2502e-09,
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8.0665e-10, 1.9487e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.5720e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([3.5225e-01, 5.9757e-01, 4.0481e-02, 7.2829e-03, 1.9970e-03, 5.0274e-06,
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4.1156e-04, 6.0282e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([3.5225e-01, 5.9757e-01, 4.0481e-02, 7.2829e-03, 1.9970e-03, 5.0274e-06,
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4.1156e-04, 6.0282e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.0282e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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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: 3396
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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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tensor([0.0471, 0.5932, 0.0021, 0.0725, 0.2527, 0.0294, 0.0023, 0.0006],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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middle *************
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['1', 'middle', 'movement', 'no 1', 'nowhere', 'middle 1', 'first', 'orange'] tensor([0.0471, 0.5932, 0.0021, 0.0725, 0.2527, 0.0294, 0.0023, 0.0006],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:42:48,718] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.45 | optimizer_gradients: 0.25 | optimizer_step: 0.32
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[2024-10-24 10:42:48,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7086.60 | backward_microstep: 6718.33 | backward_inner_microstep: 6712.21 | backward_allreduce_microstep: 5.95 | step_microstep: 7.52
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[2024-10-24 10:42:48,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7086.61 | backward: 6718.32 | backward_inner: 6712.30 | backward_allreduce: 5.93 | step: 7.53
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99%|ββββββββββ| 4816/4844 [20:01:32<07:04, 15.17s/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 EVAL step
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Registering RESULT step
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