Datasets:
TMarcus
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InternVL

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[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([9.9964e-01, 3.5661e-04, 4.5385e-08, 3.3573e-08, 5.6336e-11, 3.2000e-08,
1.4252e-10, 1.4525e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9964e-01, 3.5661e-04, 4.5385e-08, 3.3573e-08, 5.6336e-11, 3.2000e-08,
1.4252e-10, 1.4525e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.2000e-08, 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>)}
tensor([1.0000e+00, 6.1069e-09, 7.9298e-11, 8.0123e-09, 2.1375e-10, 1.1383e-09,
4.1581e-11, 1.2848e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.1069e-09, 7.9298e-11, 8.0123e-09, 2.1375e-10, 1.1383e-09,
4.1581e-11, 1.2848e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(7.9298e-11, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-7.9298e-11, device='cuda:0', grad_fn=<SubBackward0>)}
question: ['How many cheetahs 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([4.3029e-01, 3.9596e-10, 5.6971e-01, 3.3898e-06, 4.5350e-07, 8.6989e-08,
7.8290e-10, 7.1129e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([4.3029e-01, 3.9596e-10, 5.6971e-01, 3.3898e-06, 4.5350e-07, 8.6989e-08,
7.8290e-10, 7.1129e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:39:35,512] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 10:39:35,513] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5176.02 | backward_microstep: 8602.07 | backward_inner_microstep: 4950.02 | backward_allreduce_microstep: 3651.97 | step_microstep: 7.69
[2024-10-24 10:39:35,513] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5176.02 | backward: 8602.06 | backward_inner: 4950.03 | backward_allreduce: 3651.90 | step: 7.70
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 4803/4844 [19:58:19<08:48, 12.89s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the front car of the train have a red tint?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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 seals are laying on the ground?')
ANSWER1=EVAL(expr='{ANSWER0} < 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many trains are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the front car of the train have a red tint?'], 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 843
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
question: ['How many seals are laying on the ground?'], responses:['1']
question: ['How many rodents are in the image?'], responses:['2']
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
[('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']]
[('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: 3, images per sample: 3.0, dynamic token length: 840
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: 3, images per sample: 3.0, dynamic token length: 840
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
tensor([9.9937e-01, 5.1137e-09, 6.2633e-04, 4.6120e-09, 3.1926e-11, 9.1022e-12,
6.7948e-11, 1.1714e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9937e-01, 5.1137e-09, 6.2633e-04, 4.6120e-09, 3.1926e-11, 9.1022e-12,
6.7948e-11, 1.1714e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9994, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0006, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.8161e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many trains are in the image?'], responses:['3']
question: ['How many dogs are in the image?'], responses:['2']
[('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']]
[('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: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([13, 3, 448, 448]) knan debug pixel values shape