Datasets:
TMarcus
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InternVL

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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, 2.0611e-09, 4.3142e-07, 2.9464e-10, 7.9302e-10, 1.0616e-07,
5.7522e-09, 6.3411e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.0611e-09, 4.3142e-07, 2.9464e-10, 7.9302e-10, 1.0616e-07,
5.7522e-09, 6.3411e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0611e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.8988e-11, 5.5242e-12, 1.4097e-11, 7.4308e-12, 2.7411e-09,
1.3574e-07, 2.2558e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 7.8988e-11, 5.5242e-12, 1.4097e-11, 7.4308e-12, 2.7411e-09,
1.3574e-07, 2.2558e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3861e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:55:46,945] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 09:55:46,946] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8910.42 | backward_microstep: 8796.41 | backward_inner_microstep: 8671.47 | backward_allreduce_microstep: 124.84 | step_microstep: 7.68
[2024-10-24 09:55:46,946] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8910.43 | backward: 8796.40 | backward_inner: 8671.50 | backward_allreduce: 124.83 | step: 7.69
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4627/4844 [19:14:30<54:14, 15.00s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are all of the sails on the boat red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are the birds primarily white?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Is there one lipstick mark across the person's skin?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are all of the sails on the boat red?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are in the image?'], responses:['5']
question: ['Are the birds primarily white?'], responses:['no']
question: ['Is there one lipstick mark across the person'], responses:['yes']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
[('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']]
[('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
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: 3397
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: 3397
tensor([1.0000e+00, 1.3280e-09, 3.0288e-08, 5.0087e-10, 2.0500e-11, 1.4784e-11,
2.9076e-12, 3.4286e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3280e-09, 3.0288e-08, 5.0087e-10, 2.0500e-11, 1.4784e-11,
2.9076e-12, 3.4286e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.0288e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.0288e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many black barrels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many black barrels are in the image?'], responses:['4']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([9.2944e-01, 2.6177e-03, 6.7442e-02, 7.8386e-09, 1.4471e-06, 1.2117e-05,
4.8423e-04, 1.8804e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.2944e-01, 2.6177e-03, 6.7442e-02, 7.8386e-09, 1.4471e-06, 1.2117e-05,
4.8423e-04, 1.8804e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2117e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([9.9979e-01, 4.1800e-10, 7.9603e-05, 1.2705e-04, 2.8775e-10, 1.1512e-07,
8.0535e-10, 8.4572e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9979e-01, 4.1800e-10, 7.9603e-05, 1.2705e-04, 2.8775e-10, 1.1512e-07,
8.0535e-10, 8.4572e-10], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 5.3158e-08, 2.4152e-07, 3.8950e-11, 2.4039e-10, 8.8139e-09,
7.2442e-10, 6.2703e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.3158e-08, 2.4152e-07, 3.8950e-11, 2.4039e-10, 8.8139e-09,
7.2442e-10, 6.2703e-07], device='cuda:2', grad_fn=<SelectBackward0>)