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InternVL

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[['1', '3', '4', '8', '6', '12', '2', '47']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([1.6737e-13, 1.1342e-01, 1.2981e-06, 8.8656e-01, 7.7905e-06, 7.3195e-07,
3.7479e-06, 3.1909e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([1.6737e-13, 1.1342e-01, 1.2981e-06, 8.8656e-01, 7.7905e-06, 7.3195e-07,
3.7479e-06, 3.1909e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 9.1848e-08, 1.1611e-07, 9.3099e-09, 2.7462e-10, 3.7177e-09,
7.2353e-10, 3.9953e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 9.1848e-08, 1.1611e-07, 9.3099e-09, 2.7462e-10, 3.7177e-09,
7.2353e-10, 3.9953e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8866, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1134, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-06, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.7117e-07, 1.1609e-07, 1.8494e-09, 1.5875e-10, 7.8081e-07,
1.2110e-09, 1.3321e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 5.7117e-07, 1.1609e-07, 1.8494e-09, 1.5875e-10, 7.8081e-07,
1.2110e-09, 1.3321e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.2238e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many hamsters are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.4846e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the boat in the image have sails up?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many hamsters are in the image?'], responses:['3']
tensor([1.0000e+00, 2.2567e-06, 3.8602e-08, 1.2794e-09, 2.4216e-11, 7.1071e-07,
5.3764e-11, 1.2062e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 2.2567e-06, 3.8602e-08, 1.2794e-09, 2.4216e-11, 7.1071e-07,
5.3764e-11, 1.2062e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.0086e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Does the boat in the image have sails up?'], responses:['yes']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([1.0000e+00, 3.8258e-08, 3.2857e-08, 2.3522e-08, 2.3199e-08, 1.4051e-07,
6.3964e-07, 3.6434e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.8258e-08, 3.2857e-08, 2.3522e-08, 2.3199e-08, 1.4051e-07,
6.3964e-07, 3.6434e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.3964e-07, 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>)}
ANSWER0=VQA(image=LEFT,question='Does the left image show an open binder with paper in it?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([9.8677e-01, 1.3223e-02, 2.6793e-08, 3.3486e-06, 2.6117e-09, 4.3922e-07,
6.2295e-08, 5.3283e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.8677e-01, 1.3223e-02, 2.6793e-08, 3.3486e-06, 2.6117e-09, 4.3922e-07,
6.2295e-08, 5.3283e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0132, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9868, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 9.6102e-09, 7.6581e-09, 1.0078e-08, 2.5469e-11, 1.0588e-10,
2.9415e-11, 9.4374e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.6102e-09, 7.6581e-09, 1.0078e-08, 2.5469e-11, 1.0588e-10,
2.9415e-11, 9.4374e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.6581e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.6581e-09, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Does the left image show an open binder with paper in it?'], 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([5, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.8649e-10, 2.9034e-07, 6.3221e-11, 4.8317e-11, 9.2268e-09,
3.0618e-10, 2.0430e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.8649e-10, 2.9034e-07, 6.3221e-11, 4.8317e-11, 9.2268e-09,
3.0618e-10, 2.0430e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.8649e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<SubBackward0>)}
[2024-10-24 10:27:56,473] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.39 | optimizer_step: 0.33
[2024-10-24 10:27:56,474] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5095.33 | backward_microstep: 7562.34 | backward_inner_microstep: 4816.28 | backward_allreduce_microstep: 2745.98 | step_microstep: 10.12
[2024-10-24 10:27:56,474] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5095.35 | backward: 7562.33 | backward_inner: 4816.31 | backward_allreduce: 2745.97 | step: 10.13
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4756/4844 [19:46:40<20:26, 13.94s/it]Registering VQA_lavis step
Registering VQA_lavis step