Datasets:
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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([4.9933e-01, 4.9933e-01, 1.0387e-04, 2.7562e-04, 4.2770e-05, 1.4471e-04,
6.6403e-04, 9.9885e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9933e-01, 4.9933e-01, 1.0387e-04, 2.7562e-04, 4.2770e-05, 1.4471e-04,
6.6403e-04, 9.9885e-05], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([0.3248, 0.0333, 0.2869, 0.1408, 0.1195, 0.0662, 0.0077, 0.0208],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3248, 0.0333, 0.2869, 0.1408, 0.1195, 0.0662, 0.0077, 0.0208],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4993, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4993, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are all of the mittens in the image red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([7.8004e-01, 1.9559e-02, 1.9652e-01, 1.8149e-03, 1.0168e-04, 5.1264e-04,
3.6184e-05, 1.4164e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.8004e-01, 1.9559e-02, 1.9652e-01, 1.8149e-03, 1.0168e-04, 5.1264e-04,
3.6184e-05, 1.4164e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7800, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1965, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0234, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='What color are the girl's pajamas?')
ANSWER1=EVAL(expr='{ANSWER0} == "gray"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
question: ['How many animals 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
question: ['Are all of the mittens in the image red?'], 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
tensor([9.7385e-01, 4.3710e-03, 1.7660e-03, 6.2957e-04, 8.8794e-04, 6.0738e-04,
1.7837e-02, 5.1857e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7385e-01, 4.3710e-03, 1.7660e-03, 6.2957e-04, 8.8794e-04, 6.0738e-04,
1.7837e-02, 5.1857e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0262, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9738, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['What color are the girl'], responses:['b']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
[('b', 0.1481217199537866), ('e', 0.12602255820943076), ('g', 0.12601628916448182), ('k', 0.1220280012774652), ('f', 0.12073193162045133), ('v', 0.11959582364650344), ('c', 0.11887450331522846), ('bib', 0.11860917281265244)]
[['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([5.4496e-01, 2.6641e-02, 4.2442e-01, 9.2587e-04, 2.1587e-04, 1.7894e-03,
1.4318e-04, 9.0985e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4496e-01, 2.6641e-02, 4.2442e-01, 9.2587e-04, 2.1587e-04, 1.7894e-03,
1.4318e-04, 9.0985e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5450, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4244, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0306, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([8.1682e-01, 1.8225e-01, 2.7625e-05, 8.8421e-05, 1.3795e-04, 3.8520e-04,
2.5679e-04, 3.0870e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.1682e-01, 1.8225e-01, 2.7625e-05, 8.8421e-05, 1.3795e-04, 3.8520e-04,
2.5679e-04, 3.0870e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1823, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8168, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([0.0450, 0.1592, 0.1336, 0.1118, 0.3318, 0.0346, 0.0957, 0.0883],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
f *************
['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib'] tensor([0.0450, 0.1592, 0.1336, 0.1118, 0.3318, 0.0346, 0.0957, 0.0883],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:45:54,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-23 14:45:54,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7008.34 | backward_microstep: 10868.14 | backward_inner_microstep: 6758.98 | backward_allreduce_microstep: 4109.05 | step_microstep: 7.64
[2024-10-23 14:45:54,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7008.34 | backward: 10868.13 | backward_inner: 6758.99 | backward_allreduce: 4109.04 | step: 7.65
0%| | 18/4844 [04:38<19:47:53, 14.77s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many primates are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
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='Is the puppy's head laying flat on a surface?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a paper poking out of the dispenser?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many graduation students are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')