Datasets:
TMarcus
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InternVL

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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([1.0000e+00, 6.2675e-08, 4.3501e-08, 1.8227e-10, 1.8951e-06, 5.1335e-09,
6.3252e-08, 1.6770e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 6.2675e-08, 4.3501e-08, 1.8227e-10, 1.8951e-06, 5.1335e-09,
6.3252e-08, 1.6770e-06], device='cuda:1', grad_fn=<SelectBackward0>)
question: ['How many binders are in the image?'], responses:['4']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.8147e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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([3, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
question: ['How many dogs are in the image?'], responses:['δΈ‰']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([1.0000e+00, 2.1941e-09, 1.3595e-10, 1.6114e-10, 1.2971e-10, 1.5670e-08,
4.7851e-06, 1.1786e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1941e-09, 1.3595e-10, 1.6114e-10, 1.2971e-10, 1.5670e-08,
4.7851e-06, 1.1786e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6078e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([6.2907e-05, 1.7377e-03, 6.1764e-02, 7.2473e-01, 1.7260e-01, 6.6954e-03,
3.6890e-03, 2.8720e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([6.2907e-05, 1.7377e-03, 6.1764e-02, 7.2473e-01, 1.7260e-01, 6.6954e-03,
3.6890e-03, 2.8720e-02], 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>)}
tensor([1.0000e+00, 9.1463e-10, 4.6821e-07, 1.0486e-10, 1.9719e-09, 6.0150e-08,
7.3543e-09, 4.1096e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.1463e-10, 4.6821e-07, 1.0486e-10, 1.9719e-09, 6.0150e-08,
7.3543e-09, 4.1096e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.1463e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog sitting on a wood surface?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([6.4997e-01, 1.6296e-01, 1.8373e-01, 8.1725e-05, 1.6049e-03, 3.5835e-04,
3.0592e-04, 9.8572e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([6.4997e-01, 1.6296e-01, 1.8373e-01, 8.1725e-05, 1.6049e-03, 3.5835e-04,
3.0592e-04, 9.8572e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0004, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9996, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([13, 3, 448, 448])
question: ['How many dogs 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
question: ['Is the dog sitting on a wood surface?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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: 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: 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: 3396
tensor([1.0000e+00, 1.8010e-10, 3.6023e-11, 1.0670e-10, 5.9368e-11, 9.5235e-09,
2.4862e-09, 1.1755e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.8010e-10, 3.6023e-11, 1.0670e-10, 5.9368e-11, 9.5235e-09,
2.4862e-09, 1.1755e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.4862e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.7602e-09, 4.9445e-09, 1.8744e-09, 4.7249e-12, 1.2844e-11,
5.1500e-12, 1.8007e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7602e-09, 4.9445e-09, 1.8744e-09, 4.7249e-12, 1.2844e-11,
5.1500e-12, 1.8007e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.9445e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.9445e-09, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:23:59,251] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 09:23:59,251] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6504.01 | backward_microstep: 7309.54 | backward_inner_microstep: 6188.12 | backward_allreduce_microstep: 1121.30 | step_microstep: 7.89
[2024-10-24 09:23:59,252] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6504.02 | backward: 7309.53 | backward_inner: 6188.13 | backward_allreduce: 1121.28 | step: 7.90
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4499/4844 [18:42:43<1:17:11, 13.43s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='What dish is the food being served in?')
ANSWER1=EVAL(expr='{ANSWER0} == "blue and white"')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step