Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['How many guinea pigs are in the image?'], responses:['2']
[('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']]
torch.Size([1, 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: 1864
tensor([1.0000e+00, 1.2752e-07, 3.7323e-09, 1.7258e-08, 5.7911e-10, 4.6168e-10,
1.1117e-09, 1.3615e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.2752e-07, 3.7323e-09, 1.7258e-08, 5.7911e-10, 4.6168e-10,
1.1117e-09, 1.3615e-10], 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(1.5080e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.2713e-08, 3.1092e-08, 3.2421e-11, 5.6324e-07, 2.3904e-09,
1.4346e-07, 7.6564e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 8.2713e-08, 3.1092e-08, 3.2421e-11, 5.6324e-07, 2.3904e-09,
1.4346e-07, 7.6564e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: tensor([1.0000e+00, 2.8061e-09, 6.3488e-09, 3.1203e-09, 2.8194e-12, 1.6492e-11,
2.2788e-11, 1.1117e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.8061e-09, 6.3488e-09, 3.1203e-09, 2.8194e-12, 1.6492e-11,
2.2788e-11, 1.1117e-09], device='cuda:2', grad_fn=<SelectBackward0>)
{True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.7210e-09, 8.1520e-09, 1.9294e-08, 8.3238e-10, 3.9334e-10,
8.0578e-11, 4.8903e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.7210e-09, 8.1520e-09, 1.9294e-08, 8.3238e-10, 3.9334e-10,
8.0578e-11, 4.8903e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many baboons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.3488e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.3488e-09, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.1520e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1106e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog on the left sticking out its tongue?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many colorful objects are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many colorful objects are in the image?'], responses:['2']
[('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']]
question: ['How many baboons are in the image?'], responses:['2']
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['Is the dog on the left sticking out its tongue?'], 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([9.9864e-01, 3.1161e-05, 4.1755e-07, 1.3250e-03, 7.1844e-09, 3.3936e-09,
1.5692e-08, 2.2914e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9864e-01, 3.1161e-05, 4.1755e-07, 1.3250e-03, 7.1844e-09, 3.3936e-09,
1.5692e-08, 2.2914e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.1161e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([1.0000e+00, 3.3789e-08, 2.1478e-08, 6.8647e-09, 2.9693e-10, 1.0564e-09,
9.2099e-10, 3.9771e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.3789e-08, 2.1478e-08, 6.8647e-09, 2.9693e-10, 1.0564e-09,
9.2099e-10, 3.9771e-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(6.4804e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.4497e-08, 8.9853e-11, 3.1466e-08, 4.0346e-10, 7.9009e-09,
1.3563e-10, 5.5673e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4497e-08, 8.9853e-11, 3.1466e-08, 4.0346e-10, 7.9009e-09,
1.3563e-10, 5.5673e-08], 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(8.9853e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1912e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:38:54,808] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.33
[2024-10-24 09:38:54,808] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5079.22 | backward_microstep: 8918.11 | backward_inner_microstep: 4832.76 | backward_allreduce_microstep: 4085.12 | step_microstep: 9.96
[2024-10-24 09:38:54,808] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5079.23 | backward: 8918.10 | backward_inner: 4832.78 | backward_allreduce: 4085.09 | step: 9.98
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4559/4844 [18:57:38<1:08:48, 14.48s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many packages of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step