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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([9.9945e-01, 1.4973e-08, 5.5286e-04, 2.0463e-09, 5.6588e-12, 7.7415e-11,
1.8011e-10, 4.4916e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9945e-01, 1.4973e-08, 5.5286e-04, 2.0463e-09, 5.6588e-12, 7.7415e-11,
1.8011e-10, 4.4916e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9994, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0006, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.2468e-08, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([6.6098e-01, 3.3794e-01, 2.3122e-04, 8.4942e-04, 3.8409e-07, 1.0118e-09,
1.1907e-08, 2.6493e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.6098e-01, 3.3794e-01, 2.3122e-04, 8.4942e-04, 3.8409e-07, 1.0118e-09,
1.1907e-08, 2.6493e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6610, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3390, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.5791e-01, 2.6934e-09, 4.2088e-02, 5.1686e-09, 1.7624e-11, 1.1127e-10,
8.5705e-11, 1.1842e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.5791e-01, 2.6934e-09, 4.2088e-02, 5.1686e-09, 1.7624e-11, 1.1127e-10,
8.5705e-11, 1.1842e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9579, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0421, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.4506e-09, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 2.4617e-10, 2.0207e-07, 3.8265e-12, 1.2106e-11, 7.9111e-09,
5.7128e-10, 4.9107e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.4617e-10, 2.0207e-07, 3.8265e-12, 1.2106e-11, 7.9111e-09,
5.7128e-10, 4.9107e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.4617e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:53:39,515] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 09:53:39,516] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7053.20 | backward_microstep: 10802.56 | backward_inner_microstep: 6787.83 | backward_allreduce_microstep: 4014.65 | step_microstep: 7.20
[2024-10-24 09:53:39,516] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7053.22 | backward: 10802.55 | backward_inner: 6787.84 | backward_allreduce: 4014.64 | step: 7.22
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4618/4844 [19:12:23<56:37, 15.03s/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
ANSWER0=VQA(image=RIGHT,question='How many glass panels does the furniture piece have?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are shoes piled up together in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many power poles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many ferrets 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many glass panels does the furniture piece have?'], responses:['4']
question: ['Are shoes piled up together in the image?'], responses:['yes']
tensor([9.9965e-01, 3.4524e-04, 1.4598e-07, 2.6941e-08, 1.0851e-10, 7.1273e-08,
3.4854e-10, 5.5268e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9965e-01, 3.4524e-04, 1.4598e-07, 2.6941e-08, 1.0851e-10, 7.1273e-08,
3.4854e-10, 5.5268e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9997, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0003, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
[('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']]
question: ['How many power poles are in the image?'], responses:['20']
torch.Size([13, 3, 448, 448])
[('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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['How many white dogs 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: 3397
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([6.6004e-01, 4.7233e-02, 4.4423e-04, 2.1454e-03, 2.6138e-01, 4.2125e-07,
4.9908e-07, 2.8762e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([6.6004e-01, 4.7233e-02, 4.4423e-04, 2.1454e-03, 2.6138e-01, 4.2125e-07,