Datasets:
TMarcus
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InternVL

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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, 6.7441e-10, 1.4026e-10, 3.5816e-10, 2.1538e-10, 2.5893e-08,
6.6017e-09, 9.5957e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.7441e-10, 1.4026e-10, 3.5816e-10, 2.1538e-10, 2.5893e-08,
6.6017e-09, 9.5957e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.4842e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many rodents are in the image?'], responses:['2']
question: ['How many birds are in the image?'], responses:['3']
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.3046e-09, 5.1802e-11, 4.4175e-09, 4.2124e-10, 5.5142e-11,
3.7053e-11, 4.1534e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.3046e-09, 5.1802e-11, 4.4175e-09, 4.2124e-10, 5.5142e-11,
3.7053e-11, 4.1534e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.1802e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.1802e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left photo show a dog on top of a rock?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 2.4721e-08, 4.7365e-09, 3.8288e-08, 1.2972e-10, 1.4417e-09,
7.2341e-10, 3.3723e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.4721e-08, 4.7365e-09, 3.8288e-08, 1.2972e-10, 1.4417e-09,
7.2341e-10, 3.3723e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.5023e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9937e-01, 6.2633e-04, 2.0438e-09, 1.7419e-07, 1.3376e-10, 3.0269e-08,
1.3093e-09, 1.2396e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9937e-01, 6.2633e-04, 2.0438e-09, 1.7419e-07, 1.3376e-10, 3.0269e-08,
1.3093e-09, 1.2396e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0438e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Does the left photo show a dog on top of a rock?'], 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 3.2705e-09, 1.1096e-10, 1.2936e-08, 9.7914e-11, 1.3809e-10,
3.7526e-11, 1.2444e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2705e-09, 1.1096e-10, 1.2936e-08, 9.7914e-11, 1.3809e-10,
3.7526e-11, 1.2444e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1096e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1096e-10, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:45:20,205] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-24 09:45:20,206] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3113.02 | backward_microstep: 10791.55 | backward_inner_microstep: 3056.82 | backward_allreduce_microstep: 7734.63 | step_microstep: 7.82
[2024-10-24 09:45:20,206] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3113.04 | backward: 10791.54 | backward_inner: 3056.85 | backward_allreduce: 7734.62 | step: 7.83
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4586/4844 [19:04:04<1:04:58, 15.11s/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='How many dogs are lying down?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the dog looking toward the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many groups of sleigh dogs are pulling a sled to the left in the snow?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many groups of sleigh dogs are pulling a sled to the left in the snow?'], 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
question: ['How many animals are in the image?'], responses:['5']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
tensor([7.7540e-01, 2.2455e-01, 2.1354e-05, 1.9991e-08, 4.7012e-06, 2.0059e-05,
1.4771e-07, 2.3189e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.7540e-01, 2.2455e-01, 2.1354e-05, 1.9991e-08, 4.7012e-06, 2.0059e-05,