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InternVL

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1.9517e-11, 3.7793e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2014e-09, 1.6373e-07, 1.0177e-08, 5.4267e-12, 5.8803e-12,
1.9517e-11, 3.7793e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6373e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.4525e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.5690e-09, 4.1869e-11, 3.7343e-08, 4.0421e-10, 1.9063e-09,
3.0963e-11, 1.2926e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.5690e-09, 4.1869e-11, 3.7343e-08, 4.0421e-10, 1.9063e-09,
3.0963e-11, 1.2926e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.1869e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.1869e-11, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:52:24,909] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-24 09:52:24,909] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5095.51 | backward_microstep: 12655.27 | backward_inner_microstep: 4775.66 | backward_allreduce_microstep: 7879.46 | step_microstep: 8.23
[2024-10-24 09:52:24,909] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5095.51 | backward: 12655.26 | backward_inner: 4775.67 | backward_allreduce: 7879.44 | step: 8.25
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4613/4844 [19:11:08<58:48, 15.28s/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=RIGHT,question='How many people wearing graduation gowns 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 balloons are in the sky?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a person sitting in a canoe in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='What position is the dog in?')
ANSWER1=EVAL(expr='{ANSWER0} == "side profile"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['What position is the dog in?'], responses:['running']
[('running', 0.12933164657582716), ('kicking', 0.12519228146951575), ('talking', 0.12468272401050465), ('shopping', 0.12439383412649963), ('waving', 0.1242157080242936), ('throwing', 0.12417530658671574), ('falling', 0.12401927267239059), ('feeding', 0.12398922653425282)]
[['running', 'kicking', 'talking', 'shopping', 'waving', 'throwing', 'falling', 'feeding']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many people wearing graduation gowns are in the image?'], responses:['50']
[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)]
[['50', '51', '60', '55', '54', '52', '44', '48']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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: 1864
tensor([9.8646e-01, 8.1050e-03, 9.6935e-04, 1.4505e-05, 1.8235e-03, 7.7890e-04,
1.7702e-03, 7.8364e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
running *************
['running', 'kicking', 'talking', 'shopping', 'waving', 'throwing', 'falling', 'feeding'] tensor([9.8646e-01, 8.1050e-03, 9.6935e-04, 1.4505e-05, 1.8235e-03, 7.7890e-04,
1.7702e-03, 7.8364e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the animal in the image holding food?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many balloons are in the sky?'], responses:['100']
question: ['Is there a person sitting in a canoe in the image?'], responses:['no']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
[['100', '120', '88', '80', '60', '99', '90', '101']]
[('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']]
question: ['Is the animal in the image holding food?'], 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 1864
tensor([1.0000e+00, 3.1119e-10, 2.1319e-07, 1.5166e-10, 4.3333e-11, 3.8786e-08,
2.0084e-09, 2.1219e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.1119e-10, 2.1319e-07, 1.5166e-10, 4.3333e-11, 3.8786e-08,
2.0084e-09, 2.1219e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.1119e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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: 1864
tensor([0.7677, 0.0106, 0.0571, 0.0686, 0.0133, 0.0060, 0.0505, 0.0262],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
50 *************
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([0.7677, 0.0106, 0.0571, 0.0686, 0.0133, 0.0060, 0.0505, 0.0262],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is an ibex laying down in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['Is an ibex laying down in the image?'], 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']]