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InternVL

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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
tensor([8.9853e-01, 1.0074e-01, 1.2326e-05, 4.4051e-05, 5.3224e-05, 4.1800e-04,
1.6076e-04, 4.8033e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.9853e-01, 1.0074e-01, 1.2326e-05, 4.4051e-05, 5.3224e-05, 4.1800e-04,
1.6076e-04, 4.8033e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1007, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.8985, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0007, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-22 17:31:54,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.20 | optimizer_step: 0.30
[2024-10-22 17:31:54,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12786.70 | backward_microstep: 12249.92 | backward_inner_microstep: 12244.88 | backward_allreduce_microstep: 4.97 | step_microstep: 7.57
[2024-10-22 17:31:54,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12786.72 | backward: 12249.91 | backward_inner: 12244.89 | backward_allreduce: 4.96 | step: 7.58
1%|▏ | 34/2424 [13:26<15:37:26, 23.53s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog on a dirt pathway in the grass?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the shoe on the right have a swoop design visible?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many small bags are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many small bags 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([5, 3, 448, 448]) knan debug pixel values shape
question: ['Is the dog on a dirt pathway in the grass?'], responses:['yes']
question: ['How many animals are in the image?'], responses:['1']
question: ['Does the shoe on the right have a swoop design visible?'], responses:['yes']
[('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']]
[('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']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([0.5321, 0.2681, 0.0367, 0.0637, 0.0054, 0.0687, 0.0220, 0.0033],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5321, 0.2681, 0.0367, 0.0637, 0.0054, 0.0687, 0.0220, 0.0033],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2681, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7319, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
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: 1866
tensor([9.9601e-01, 6.0501e-04, 2.6844e-04, 9.5660e-05, 1.5778e-04, 1.4635e-04,
2.7115e-03, 8.3805e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9601e-01, 6.0501e-04, 2.6844e-04, 9.5660e-05, 1.5778e-04, 1.4635e-04,
2.7115e-03, 8.3805e-06], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.1090e-01, 1.2214e-02, 7.4770e-02, 9.2086e-04, 5.6464e-05, 2.6935e-04,
2.2724e-05, 8.4978e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.1090e-01, 1.2214e-02, 7.4770e-02, 9.2086e-04, 5.6464e-05, 2.6935e-04,
2.2724e-05, 8.4978e-04], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([6.1296e-01, 1.2614e-02, 3.7178e-01, 8.8450e-04, 1.3465e-04, 5.2959e-04,
5.1577e-05, 1.0485e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.1296e-01, 1.2614e-02, 3.7178e-01, 8.8450e-04, 1.3465e-04, 5.2959e-04,
5.1577e-05, 1.0485e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0027, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9973, 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(0.9109, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0748, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0143, device='cuda:1', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are wearing a collar?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6130, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3718, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0153, device='cuda:0', grad_fn=<DivBackward0>)}