Datasets:
TMarcus
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InternVL

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1.0024e-10, 8.9826e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.1812e-07, 6.4488e-09, 8.8516e-07, 8.7774e-11, 1.8365e-10,
1.0024e-10, 8.9826e-11], 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(1.3102e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.3906e-09, 3.2867e-10, 4.2201e-10, 2.0240e-10, 2.5496e-08,
6.2017e-09, 8.4129e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.3906e-09, 3.2867e-10, 4.2201e-10, 2.0240e-10, 2.5496e-08,
6.2017e-09, 8.4129e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.4882e-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, 7.9465e-10, 6.9657e-07, 1.9668e-09, 2.0495e-09, 1.9524e-07,
1.0503e-08, 4.0106e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.9465e-10, 6.9657e-07, 1.9668e-09, 2.0495e-09, 1.9524e-07,
1.0503e-08, 4.0106e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.9465e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:41:05,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.32 | optimizer_step: 0.31
[2024-10-24 09:41:05,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5102.05 | backward_microstep: 6078.80 | backward_inner_microstep: 4947.10 | backward_allreduce_microstep: 1131.58 | step_microstep: 7.53
[2024-10-24 09:41:05,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5102.06 | backward: 6078.79 | backward_inner: 4947.12 | backward_allreduce: 1131.56 | step: 7.55
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4569/4844 [18:59:49<53:39, 11.71s/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
ANSWER0=VQA(image=LEFT,question='How many cell phones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many stacks of towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many weights are on the rack?')
ANSWER1=EVAL(expr='{ANSWER0} > 12')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([5, 3, 448, 448])
question: ['How many stacks of towels 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([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many animals 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many weights are on the rack?'], responses:['20']
[('20', 0.12771895156791702), ('21', 0.12586912554208884), ('22', 0.12503044546440548), ('26', 0.12459144863554222), ('30', 0.1243482131473721), ('48', 0.12418849501124658), ('27', 0.12415656019926104), ('28', 0.12409676043216668)]
[['20', '21', '22', '26', '30', '48', '27', '28']]
tensor([9.3196e-01, 3.2948e-05, 4.3588e-02, 1.8165e-02, 4.0546e-03, 1.9152e-03,
1.3036e-04, 1.5718e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.3196e-01, 3.2948e-05, 4.3588e-02, 1.8165e-02, 4.0546e-03, 1.9152e-03,
1.3036e-04, 1.5718e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog in the image on the left lying down?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
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 cell phones are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([1.0000e+00, 7.9799e-08, 7.0826e-09, 2.2508e-08, 5.3979e-10, 6.2861e-10,
2.0531e-09, 3.1303e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.9799e-08, 7.0826e-09, 2.2508e-08, 5.3979e-10, 6.2861e-10,
2.0531e-09, 3.1303e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.2508e-08, 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>)}
ANSWER0=VQA(image=LEFT,question='Is a person driving and holding a cell phone?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
question: ['Is the dog in the image on the left lying down?'], responses:['yes']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
[('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: 5, images per sample: 5.0, dynamic token length: 1348
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([0.7168, 0.0104, 0.0460, 0.0193, 0.1928, 0.0031, 0.0081, 0.0035],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
20 *************
['20', '21', '22', '26', '30', '48', '27', '28'] tensor([0.7168, 0.0104, 0.0460, 0.0193, 0.1928, 0.0031, 0.0081, 0.0035],