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InternVL

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1.5929e-11, 1.9761e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.9575e-09, 5.7564e-11, 2.3926e-08, 4.0427e-10, 1.7456e-10,
1.5929e-11, 1.9761e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.7564e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.7564e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='What are the cheetahs doing?')
ANSWER1=EVAL(expr='{ANSWER0} == "laying on a mound of dirt"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
question: ['What are the cheetahs doing?'], responses:['rest']
[('resting', 0.12566847251548818), ('nest', 0.12517142938696557), ('office', 0.1250043717266723), ('main', 0.12498734152101426), ('cup', 0.12484015879441095), ('shore', 0.12479980994179346), ('cut', 0.1247714893997243), ('circle', 0.12475692671393097)]
[['resting', 'nest', 'office', 'main', 'cup', 'shore', 'cut', 'circle']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([0.0219, 0.1040, 0.1705, 0.0128, 0.0015, 0.0289, 0.3517, 0.3087],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
spring *************
['monday', 'leopard', 'kia', 'halloween', 'tigers', 'no', 'spring', 'awake'] tensor([0.0219, 0.1040, 0.1705, 0.0128, 0.0015, 0.0289, 0.3517, 0.3087],
device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image contain a tree house?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
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: 3399
tensor([9.9954e-01, 3.8116e-04, 1.7393e-06, 9.2646e-07, 6.1754e-06, 1.2251e-05,
3.2329e-08, 5.4720e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
resting *************
['resting', 'nest', 'office', 'main', 'cup', 'shore', 'cut', 'circle'] tensor([9.9954e-01, 3.8116e-04, 1.7393e-06, 9.2646e-07, 6.1754e-06, 1.2251e-05,
3.2329e-08, 5.4720e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {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>)}
question: ['Does the image contain a tree house?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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([1.0000e+00, 1.7684e-08, 3.3983e-09, 1.8646e-08, 2.9634e-10, 1.6565e-09,
1.3941e-10, 1.7492e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7684e-08, 3.3983e-09, 1.8646e-08, 2.9634e-10, 1.6565e-09,
1.3941e-10, 1.7492e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.3983e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.3983e-09, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there an arm in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([9.9995e-01, 3.1767e-08, 4.5398e-05, 3.4794e-09, 6.5220e-11, 2.3859e-10,
1.9046e-10, 4.2066e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9995e-01, 3.1767e-08, 4.5398e-05, 3.4794e-09, 6.5220e-11, 2.3859e-10,
1.9046e-10, 4.2066e-09], 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(4.5398e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0871e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 8.3278e-10, 2.6577e-07, 4.1462e-11, 1.8254e-10, 1.2019e-08,
2.9186e-10, 2.4847e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.3278e-10, 2.6577e-07, 4.1462e-11, 1.8254e-10, 1.2019e-08,
2.9186e-10, 2.4847e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.3278e-10, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:2', grad_fn=<SubBackward0>)}
question: ['How many penguins are in the image?'], responses:['37']
[('37', 0.12602601760154822), ('38', 0.12520142583637134), ('39', 0.12518201874785773), ('36', 0.12516664760231044), ('47', 0.12478763564484581), ('42', 0.12462790950563608), ('41', 0.12453088059191597), ('46', 0.12447746446951438)]
[['37', '38', '39', '36', '47', '42', '41', '46']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there an arm in the image?'], responses:['yes']
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([0.6096, 0.0213, 0.0882, 0.0244, 0.1476, 0.0110, 0.0524, 0.0456],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
37 *************
['37', '38', '39', '36', '47', '42', '41', '46'] tensor([0.6096, 0.0213, 0.0882, 0.0244, 0.1476, 0.0110, 0.0524, 0.0456],
device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 2.0805e-09, 7.3495e-10, 1.6232e-08, 2.0730e-10, 4.9623e-11,
1.0136e-10, 1.2530e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.0805e-09, 7.3495e-10, 1.6232e-08, 2.0730e-10, 4.9623e-11,
1.0136e-10, 1.2530e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.3495e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.3495e-10, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:11:48,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.52 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 10:11:48,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8978.72 | backward_microstep: 8740.78 | backward_inner_microstep: 8735.02 | backward_allreduce_microstep: 5.64 | step_microstep: 7.68
[2024-10-24 10:11:48,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8978.74 | backward: 8740.77 | backward_inner: 8735.06 | backward_allreduce: 5.63 | step: 7.70
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4689/4844 [19:30:31<38:39, 14.96s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step