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InternVL

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1.3073e-06, 1.3136e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([4.1248e-01, 5.7443e-01, 2.8345e-03, 1.2205e-05, 8.8781e-03, 5.7744e-05,
1.3073e-06, 1.3136e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0089, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9911, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.1532e-08, 1.7456e-10, 1.1382e-08, 5.6636e-11, 2.6205e-10,
5.2088e-12, 3.2236e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1532e-08, 1.7456e-10, 1.1382e-08, 5.6636e-11, 2.6205e-10,
5.2088e-12, 3.2236e-08], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([9.8935e-01, 9.3049e-04, 1.1261e-06, 2.1639e-03, 4.7777e-03, 7.6403e-04,
1.4277e-03, 5.8830e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.8935e-01, 9.3049e-04, 1.1261e-06, 2.1639e-03, 4.7777e-03, 7.6403e-04,
1.4277e-03, 5.8830e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.7456e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.7456e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many wine glasses are lined up in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many wine glasses are lined up 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 towels are in the image?'], responses:['4']
[('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']]
tensor([9.9993e-01, 1.5689e-05, 1.6374e-07, 1.3575e-07, 5.9288e-10, 5.4767e-05,
3.6746e-09, 6.7579e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9993e-01, 1.5689e-05, 1.6374e-07, 1.3575e-07, 5.9288e-10, 5.4767e-05,
3.6746e-09, 6.7579e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.0766e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
question: ['How many cheetahs are in the image?'], responses:['2']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([9.9225e-01, 7.6302e-03, 1.1264e-04, 2.0897e-08, 6.7633e-06, 5.7830e-08,
1.0675e-08, 1.5783e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9225e-01, 7.6302e-03, 1.1264e-04, 2.0897e-08, 6.7633e-06, 5.7830e-08,
1.0675e-08, 1.5783e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9923, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0077, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9993e-01, 6.6051e-05, 4.2454e-09, 2.7262e-06, 1.1743e-09, 2.7033e-10,
2.4189e-09, 2.1876e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9993e-01, 6.6051e-05, 4.2454e-09, 2.7262e-06, 1.1743e-09, 2.7033e-10,
2.4189e-09, 2.1876e-09], 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(2.7262e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:30:42,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 10:30:42,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4452.24 | backward_microstep: 9255.84 | backward_inner_microstep: 4222.17 | backward_allreduce_microstep: 5033.52 | step_microstep: 7.60
[2024-10-24 10:30:42,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4452.25 | backward: 9255.83 | backward_inner: 4222.22 | backward_allreduce: 5033.47 | step: 7.62
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4767/4844 [19:49:25<19:04, 14.86s/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=LEFT,question='Are the dogs dressed like cows?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the package?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a harness?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many rolls of paper towels are in the package?'], responses:['13']
question: ['Is the dog wearing a harness?'], responses:['yes']