Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 1.8554e-07, 3.4613e-07, 1.2326e-12, 1.8203e-12, 2.2195e-10,
6.7238e-11, 1.7985e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8554e-07, 3.4613e-07, 1.2326e-12, 1.8203e-12, 2.2195e-10,
6.7238e-11, 1.7985e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8554e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.3644e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([9.9999e-01, 8.9397e-06, 2.0176e-08, 5.5708e-08, 2.5204e-09, 2.7844e-09,
9.3098e-09, 1.5989e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 8.9397e-06, 2.0176e-08, 5.5708e-08, 2.5204e-09, 2.7844e-09,
9.3098e-09, 1.5989e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.0611e-09, 1.9313e-07, 2.0408e-10, 1.1341e-10, 4.5325e-08,
1.4145e-09, 1.6806e-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, 1.9313e-07, 2.0408e-10, 1.1341e-10, 4.5325e-08,
1.4145e-09, 1.6806e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0176e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the tail on the cow seen behind it?')
FINAL_ANSWER=RESULT(var=ANSWER0)
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(4.7684e-07, 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)
tensor([0.8287, 0.0010, 0.1119, 0.0111, 0.0072, 0.0026, 0.0284, 0.0092],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([0.8287, 0.0010, 0.1119, 0.0111, 0.0072, 0.0026, 0.0284, 0.0092],
device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
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>)}
ANSWER0=VQA(image=LEFT,question='How many glass doors are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many glass doors 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 the tail on the cow seen behind it?'], responses:['no']
question: ['How many dogs are in the image?'], responses:['2']
[('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']]
[('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
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
tensor([1.0000e+00, 1.2360e-07, 2.9067e-09, 2.1177e-11, 9.9461e-11, 4.4322e-09,
4.5921e-07, 2.1503e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.2360e-07, 2.9067e-09, 2.1177e-11, 9.9461e-11, 4.4322e-09,
4.5921e-07, 2.1503e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.5921e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., 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: 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([4.6494e-01, 5.3506e-01, 3.3824e-07, 9.9859e-09, 2.8430e-10, 1.0374e-07,
5.7475e-10, 2.4096e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.6494e-01, 5.3506e-01, 3.3824e-07, 9.9859e-09, 2.8430e-10, 1.0374e-07,
5.7475e-10, 2.4096e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5351, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.4649, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 1.8554e-07, 1.1142e-08, 3.8070e-07, 3.7153e-10, 1.1790e-09,
1.0777e-09, 1.0570e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.8554e-07, 1.1142e-08, 3.8070e-07, 3.7153e-10, 1.1790e-09,
1.0777e-09, 1.0570e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.8070e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:35:27,370] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.47 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 09:35:27,370] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9016.79 | backward_microstep: 8725.54 | backward_inner_microstep: 8720.76 | backward_allreduce_microstep: 4.70 | step_microstep: 7.49
[2024-10-24 09:35:27,370] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9016.81 | backward: 8725.53 | backward_inner: 8720.78 | backward_allreduce: 4.70 | step: 7.50
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4545/4844 [18:54:11<1:22:44, 16.60s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image feature two tear-drop carved white sinks positioned side-by-side?')
ANSWER1=RESULT(var=ANSWER0)
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
ANSWER0=VQA(image=RIGHT,question='Is the dog sitting on green grass?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many seals are laying on the ground in the image?')
ANSWER1=EVAL(expr='{ANSWER0} < 4')