Datasets:
TMarcus
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InternVL

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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([9.9999e-01, 9.5163e-06, 1.9555e-08, 7.4964e-08, 1.8767e-09, 1.2475e-10,
2.1266e-09, 1.7118e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 9.5163e-06, 1.9555e-08, 7.4964e-08, 1.8767e-09, 1.2475e-10,
2.1266e-09, 1.7118e-10], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.9167e-01, 2.3823e-03, 1.4292e-06, 1.6731e-03, 3.0648e-03, 2.1374e-04,
8.2482e-04, 1.6620e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.9167e-01, 2.3823e-03, 1.4292e-06, 1.6731e-03, 3.0648e-03, 2.1374e-04,
8.2482e-04, 1.6620e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.4964e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 6.9623e-06, 2.6983e-07, 2.6505e-12, 6.5088e-13, 1.3570e-10,
2.2662e-10, 2.0361e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9999e-01, 6.9623e-06, 2.6983e-07, 2.6505e-12, 6.5088e-13, 1.3570e-10,
2.2662e-10, 2.0361e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the sled facing right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.9623e-06, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.1723e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a bottle of wine in the image?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 1.0527e-09, 1.5493e-11, 8.2456e-11, 7.2767e-11, 7.7741e-09,
1.2360e-07, 5.1101e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.0527e-09, 1.5493e-11, 8.2456e-11, 7.2767e-11, 7.7741e-09,
1.2360e-07, 5.1101e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.3310e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many shoes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['Is there a bottle of wine in the image?'], responses:['yes']
torch.Size([13, 3, 448, 448])
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
tensor([1.0000e+00, 7.4342e-09, 1.6144e-10, 2.8828e-08, 2.5797e-10, 3.7166e-11,
3.9634e-11, 5.4772e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.4342e-09, 1.6144e-10, 2.8828e-08, 2.5797e-10, 3.7166e-11,
3.9634e-11, 5.4772e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.6144e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.6144e-10, device='cuda:0', grad_fn=<SubBackward0>)}
question: ['Is the sled facing right?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many shoes are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.8516e-07, 1.0356e-07, 1.0901e-11, 3.8991e-12, 1.3419e-09,
1.9546e-10, 3.2898e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.8516e-07, 1.0356e-07, 1.0901e-11, 3.8991e-12, 1.3419e-09,
1.9546e-10, 3.2898e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.8516e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.2333e-08, 9.0425e-08, 9.6256e-08, 9.0752e-10, 3.3983e-09,
2.1643e-09, 3.5817e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.2333e-08, 9.0425e-08, 9.6256e-08, 9.0752e-10, 3.3983e-09,
2.1643e-09, 3.5817e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.8283e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:10:38,552] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.45 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 10:10:38,552] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5131.91 | backward_microstep: 12884.99 | backward_inner_microstep: 4970.96 | backward_allreduce_microstep: 7913.91 | step_microstep: 10.83
[2024-10-24 10:10:38,552] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5131.91 | backward: 12884.98 | backward_inner: 4971.00 | backward_allreduce: 7913.86 | step: 10.85
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4684/4844 [19:29:22<45:55, 17.22s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many colored binders are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the hyena carrying a small animal in its mouth?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)