Datasets:
TMarcus
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InternVL

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torch.Size([13, 3, 448, 448])
question: ['How many chimneys are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a bracelet made of pins in the image?'], responses:['no']
question: ['How many spotted hyenas are gathered together in the image?'], responses:['4']
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['How many animals are in the image?'], responses:['35']
[('35', 0.1266245324595504), ('36', 0.12492216702890122), ('37', 0.12490354225462116), ('55', 0.12481909164720137), ('34', 0.12478368073004217), ('42', 0.12474218613469536), ('39', 0.12461919022571973), ('41', 0.1245856095192685)]
[['35', '36', '37', '55', '34', '42', '39', '41']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 3.8507e-09, 2.4425e-10, 3.0216e-11, 9.6778e-11, 1.1744e-09,
2.7265e-06, 1.2053e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.8507e-09, 2.4425e-10, 3.0216e-11, 9.6778e-11, 1.1744e-09,
2.7265e-06, 1.2053e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.7319e-06, 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>)}
ANSWER0=VQA(image=RIGHT,question='Are there any vivid orange jellyfish in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['Are there any vivid orange jellyfish in the image?'], responses:['no']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([7.5238e-01, 2.4761e-01, 6.0826e-06, 5.0024e-10, 9.2288e-07, 4.7958e-08,
9.6211e-09, 3.0549e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.5238e-01, 2.4761e-01, 6.0826e-06, 5.0024e-10, 9.2288e-07, 4.7958e-08,
9.6211e-09, 3.0549e-09], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.7088e-09, 3.5208e-07, 3.2475e-12, 4.0084e-11, 6.0258e-09,
9.7576e-10, 3.0978e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.7088e-09, 3.5208e-07, 3.2475e-12, 4.0084e-11, 6.0258e-09,
9.7576e-10, 3.0978e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7088e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.7579e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many items are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} < 10')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the hyena in the image baring its teeth?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the hyena in the image baring its teeth?'], responses:['no']
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([0.3288, 0.0277, 0.3898, 0.0025, 0.0096, 0.0069, 0.2103, 0.0245],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
37 *************
['35', '36', '37', '55', '34', '42', '39', '41'] tensor([0.3288, 0.0277, 0.3898, 0.0025, 0.0096, 0.0069, 0.2103, 0.0245],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.6912e-08, 2.9947e-07, 4.8474e-11, 7.5502e-11, 2.4079e-08,
6.6672e-10, 4.9583e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.6912e-08, 2.9947e-07, 4.8474e-11, 7.5502e-11, 2.4079e-08,
6.6672e-10, 4.9583e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.6912e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.9407e-07, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many items are in the image?'], responses:['100']
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
[['100', '120', '88', '80', '60', '99', '90', '101']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([1.0000e+00, 6.0927e-10, 4.4213e-07, 2.3859e-10, 1.0728e-10, 4.9679e-08,
2.3098e-09, 3.9107e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.0927e-10, 4.4213e-07, 2.3859e-10, 1.0728e-10, 4.9679e-08,
2.3098e-09, 3.9107e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.0927e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([5.2280e-01, 4.6097e-01, 2.8442e-05, 4.5374e-03, 2.8261e-03, 7.4329e-04,
7.2799e-03, 8.1045e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([5.2280e-01, 4.6097e-01, 2.8442e-05, 4.5374e-03, 2.8261e-03, 7.4329e-04,
7.2799e-03, 8.1045e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:52:56,533] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.20 | optimizer_step: 0.30
[2024-10-24 09:52:56,533] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7007.37 | backward_microstep: 6763.95 | backward_inner_microstep: 6758.67 | backward_allreduce_microstep: 5.21 | step_microstep: 7.37