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TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(4.4616e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.4616e-11, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image on the right on a leash?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 3.3983e-09, 2.7278e-07, 7.1339e-13, 7.9501e-12, 1.7407e-09,
3.0814e-11, 5.9732e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.3983e-09, 2.7278e-07, 7.1339e-13, 7.9501e-12, 1.7407e-09,
3.0814e-11, 5.9732e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.3983e-09, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:1', grad_fn=<SubBackward0>)}
question: ['Is the dog in the image on the right on a leash?'], responses:['yes']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
[('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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 1.4715e-08, 6.5860e-10, 5.9368e-08, 5.0165e-09, 8.3254e-10,
3.7209e-10, 8.3103e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4715e-08, 6.5860e-10, 5.9368e-08, 5.0165e-09, 8.3254e-10,
3.7209e-10, 8.3103e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.5860e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1855e-07, device='cuda:3', 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([1.0000e+00, 1.7603e-06, 6.2148e-08, 2.2863e-08, 2.6466e-09, 4.0355e-09,
1.3440e-08, 8.9082e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.7603e-06, 6.2148e-08, 2.2863e-08, 2.6466e-09, 4.0355e-09,
1.3440e-08, 8.9082e-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(1.8664e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.6686e-09, 8.1178e-11, 1.8106e-08, 1.1915e-10, 1.4166e-09,
3.3365e-11, 6.9608e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.6686e-09, 8.1178e-11, 1.8106e-08, 1.1915e-10, 1.4166e-09,
3.3365e-11, 6.9608e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(8.1178e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.1178e-11, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:29:56,553] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.59 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 10:29:56,553] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7052.27 | backward_microstep: 6775.54 | backward_inner_microstep: 6770.76 | backward_allreduce_microstep: 4.72 | step_microstep: 7.49
[2024-10-24 10:29:56,553] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7052.28 | backward: 6775.53 | backward_inner: 6770.77 | backward_allreduce: 4.71 | step: 7.50
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4764/4844 [19:48:40<19:15, 14.44s/it]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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the dog standing on all fours and facing left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many dogs are standing up on all fours?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the body of the boat white?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many boats are in the image?'], responses:['20']
question: ['Is the body of the boat white?'], responses:['no']
[('20', 0.12771895156791702), ('21', 0.12586912554208884), ('22', 0.12503044546440548), ('26', 0.12459144863554222), ('30', 0.1243482131473721), ('48', 0.12418849501124658), ('27', 0.12415656019926104), ('28', 0.12409676043216668)]
[['20', '21', '22', '26', '30', '48', '27', '28']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is the dog standing on all fours and facing left?'], responses:['yes']
question: ['How many dogs are standing up on all fours?'], responses:['0']
[('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']]
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([6.9149e-01, 4.5570e-02, 1.3238e-01, 1.4768e-02, 6.6082e-02, 1.1501e-04,
4.5849e-02, 3.7518e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
20 *************
['20', '21', '22', '26', '30', '48', '27', '28'] tensor([6.9149e-01, 4.5570e-02, 1.3238e-01, 1.4768e-02, 6.6082e-02, 1.1501e-04,
4.5849e-02, 3.7518e-03], 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(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog in side profile?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.8767e-09, 2.4387e-07, 2.1803e-12, 3.0594e-11, 1.1919e-08,
3.2066e-10, 4.6860e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)