Datasets:
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InternVL

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8.6044e-10, 4.1099e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.9171e-10, 6.8222e-07, 6.2241e-11, 3.5758e-11, 5.0443e-08,
8.6044e-10, 4.1099e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.9171e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 2.9693e-10, 7.3909e-11, 4.3849e-10, 1.8815e-10, 2.2102e-08,
5.0027e-09, 3.7370e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.9693e-10, 7.3909e-11, 4.3849e-10, 1.8815e-10, 2.2102e-08,
5.0027e-09, 3.7370e-10], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
tensor([1.0000e+00, 1.0364e-09, 1.3568e-06, 7.9776e-10, 3.6055e-09, 1.7277e-07,
1.1367e-08, 5.8050e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.0364e-09, 1.3568e-06, 7.9776e-10, 3.6055e-09, 1.7277e-07,
1.1367e-08, 5.8050e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0364e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 2.6165e-07, 4.1636e-09, 7.6581e-09, 1.6207e-10, 1.6785e-10,
2.7673e-10, 5.1301e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.6165e-07, 4.1636e-09, 7.6581e-09, 1.6207e-10, 1.6785e-10,
2.7673e-10, 5.1301e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.6581e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many dogs 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.9843e-08, 7.5971e-09, 1.0719e-08, 8.2040e-10, 3.2174e-09,
2.1580e-09, 6.0968e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.9843e-08, 7.5971e-09, 1.0719e-08, 8.2040e-10, 3.2174e-09,
2.1580e-09, 6.0968e-09], 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(6.0452e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:32:49,162] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 09:32:49,163] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3816.15 | backward_microstep: 14249.29 | backward_inner_microstep: 3503.82 | backward_allreduce_microstep: 10745.36 | step_microstep: 7.59
[2024-10-24 09:32:49,163] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3816.16 | backward: 14249.28 | backward_inner: 3503.86 | backward_allreduce: 10745.31 | step: 7.60
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4535/4844 [18:51:32<1:26:29, 16.79s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Are all of the drummers wearing purple shirts?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there a visible orange vegetable in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many folded towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many wolves 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Are all of the drummers wearing purple shirts?'], responses:['no']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
tensor([1.0000e+00, 1.6335e-10, 3.1790e-11, 1.5046e-10, 5.8004e-11, 6.8326e-09,
1.7357e-09, 4.6710e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.6335e-10, 3.1790e-11, 1.5046e-10, 5.8004e-11, 6.8326e-09,
1.7357e-09, 4.6710e-11], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.0185e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839