Datasets:
TMarcus
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InternVL

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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9753e-01, 2.4726e-03, 1.0298e-07, 5.2282e-11, 1.5290e-11, 3.6224e-10,
6.8746e-10, 1.3883e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0025, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9975, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog facing left?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['Does the image have a fabric background?'], 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([1.0000e+00, 1.3524e-09, 5.2632e-09, 1.8092e-09, 3.6587e-11, 2.9110e-12,
1.5138e-12, 6.6837e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3524e-09, 5.2632e-09, 1.8092e-09, 3.6587e-11, 2.9110e-12,
1.5138e-12, 6.6837e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.2632e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.2632e-09, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is the dog facing left?'], 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 9.7999e-09, 1.0839e-10, 2.8154e-08, 6.1876e-10, 3.7323e-09,
5.3896e-11, 4.6116e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.7999e-09, 1.0839e-10, 2.8154e-08, 6.1876e-10, 3.7323e-09,
5.3896e-11, 4.6116e-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(1.0839e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.0839e-10, device='cuda:1', 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([7, 3, 448, 448])
tensor([1.0000e+00, 6.4450e-09, 2.1260e-11, 1.0391e-07, 2.5176e-10, 4.0587e-10,
1.6679e-10, 7.2364e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.4450e-09, 2.1260e-11, 1.0391e-07, 2.5176e-10, 4.0587e-10,
1.6679e-10, 7.2364e-09], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 4.2319e-09, 1.1448e-10, 7.2842e-09, 1.0379e-10, 4.5990e-10,
2.8982e-11, 2.5725e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2319e-09, 1.1448e-10, 7.2842e-09, 1.0379e-10, 4.5990e-10,
2.8982e-11, 2.5725e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1260e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1919e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a silver spoon near the dish?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.1448e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.1448e-10, device='cuda:3', grad_fn=<SubBackward0>)}
torch.Size([7, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['1']
[('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']]
question: ['Is there a silver spoon near the dish?'], 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
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: 1864
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: 1862
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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 2.3126e-10, 6.1276e-11, 1.5285e-10, 9.9411e-11, 1.4304e-08,
2.9756e-09, 1.5622e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3126e-10, 6.1276e-11, 1.5285e-10, 9.9411e-11, 1.4304e-08,
2.9756e-09, 1.5622e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.3126e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.2836e-08, 6.3471e-11, 1.1744e-08, 9.1416e-10, 1.0862e-09,
2.3886e-11, 7.1668e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2836e-08, 6.3471e-11, 1.1744e-08, 9.1416e-10, 1.0862e-09,
2.3886e-11, 7.1668e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.3471e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.3471e-11, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:51:39,488] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.56 | optimizer_gradients: 0.20 | optimizer_step: 0.30
[2024-10-24 09:51:39,488] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7076.71 | backward_microstep: 6831.78 | backward_inner_microstep: 6826.78 | backward_allreduce_microstep: 4.92 | step_microstep: 7.66
[2024-10-24 09:51:39,488] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7076.73 | backward: 6831.77 | backward_inner: 6826.80 | backward_allreduce: 4.90 | step: 7.67
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4610/4844 [19:10:23<56:55, 14.60s/it]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='Is the dog situated in the grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step