Datasets:
TMarcus
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InternVL

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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3413
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410
tensor([1.0000e+00, 1.2002e-08, 2.3532e-11, 3.3372e-08, 6.0791e-10, 3.1923e-09,
4.2448e-11, 5.3874e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2002e-08, 2.3532e-11, 3.3372e-08, 6.0791e-10, 3.1923e-09,
4.2448e-11, 5.3874e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 8.0256e-09, 4.3543e-10, 9.6590e-11, 4.4229e-10, 3.4321e-08,
1.7603e-06, 1.4121e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.0256e-09, 4.3543e-10, 9.6590e-11, 4.4229e-10, 3.4321e-08,
1.7603e-06, 1.4121e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7603e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.3532e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.3532e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
tensor([1.0000e+00, 2.7355e-10, 3.4373e-11, 6.6249e-11, 3.8938e-11, 5.2602e-09,
3.8507e-09, 6.4526e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7355e-10, 3.4373e-11, 6.6249e-11, 3.8938e-11, 5.2602e-09,
3.8507e-09, 6.4526e-11], device='cuda:3', grad_fn=<SelectBackward0>)
question: ['How many cases are in the image?'], responses:['1']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.5886e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3411
[('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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410
tensor([1.0000e+00, 9.5307e-09, 1.1744e-09, 6.3852e-10, 1.0203e-09, 9.8321e-09,
2.0377e-07, 3.7524e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 9.5307e-09, 1.1744e-09, 6.3852e-10, 1.0203e-09, 9.8321e-09,
2.0377e-07, 3.7524e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1744e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3411
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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3411
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.0484e-08, 5.9983e-10, 8.6569e-09, 7.6734e-10, 8.8983e-11,
8.2692e-11, 5.4821e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0484e-08, 5.9983e-10, 8.6569e-09, 7.6734e-10, 8.8983e-11,
8.2692e-11, 5.4821e-08], 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(5.9983e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1861e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are there any cut pizzas in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['Are there any cut pizzas in the image?'], 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([13, 3, 448, 448]) knan debug pixel values shape
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: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 4.3036e-10, 8.9157e-11, 1.4756e-10, 8.4404e-11, 9.0925e-09,
3.3983e-09, 1.3579e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.3036e-10, 8.9157e-11, 1.4756e-10, 8.4404e-11, 9.0925e-09,
3.3983e-09, 1.3579e-10], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.3983e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., 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: 3399
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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 9.3295e-08, 1.1624e-07, 4.7251e-12, 8.9374e-12, 1.2351e-09,
3.0184e-10, 1.0442e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.3295e-08, 1.1624e-07, 4.7251e-12, 8.9374e-12, 1.2351e-09,
3.0184e-10, 1.0442e-07], 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(9.3295e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:29:22,463] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.52 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-24 09:29:22,464] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8999.92 | backward_microstep: 8744.52 | backward_inner_microstep: 8738.51 | backward_allreduce_microstep: 5.93 | step_microstep: 7.64
[2024-10-24 09:29:22,464] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8999.92 | backward: 8744.51 | backward_inner: 8738.53 | backward_allreduce: 5.92 | step: 7.65
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4522/4844 [18:48:06<1:21:56, 15.27s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the dog rounding up cattle?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step