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InternVL

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4.7145e-05, 1.6727e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8437, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1294, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0269, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([5.4141e-01, 2.5959e-01, 3.0889e-02, 1.5511e-01, 9.3138e-03, 1.4739e-03,
2.1445e-03, 6.9471e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.4141e-01, 2.5959e-01, 3.0889e-02, 1.5511e-01, 9.3138e-03, 1.4739e-03,
2.1445e-03, 6.9471e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5414, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4586, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 1 has a total capacty of 44.34 GiB of which 566.94 MiB is free. Including non-PyTorch memory, this process has 43.77 GiB memory in use. Of the allocated memory 40.76 GiB is allocated by PyTorch, and 2.37 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
[2024-10-22 17:26:11,691] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-22 17:26:11,692] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12245.09 | backward_microstep: 11860.44 | backward_inner_microstep: 11833.84 | backward_allreduce_microstep: 26.53 | step_microstep: 7.55
[2024-10-22 17:26:11,692] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12245.11 | backward: 11860.43 | backward_inner: 11833.86 | backward_allreduce: 26.52 | step: 7.57
1%| | 19/2424 [07:43<16:08:07, 24.15s/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=RIGHT,question='Is there a child inside a long boat made out of joined cardboard boxes?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many creatures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 8')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there paws sticking out of the blanket on the pug in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is someone holding up the dog?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many creatures are in the image?'], responses:['many']
[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Are there paws sticking out of the blanket on the pug in the image?'], 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([13, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a child inside a long boat made out of joined cardboard boxes?'], responses:['yes']
question: ['Is someone holding up the dog?'], responses:['no']
[('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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
tensor([0.5918, 0.0980, 0.1912, 0.0115, 0.0234, 0.0511, 0.0133, 0.0197],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
many *************
['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.5918, 0.0980, 0.1912, 0.0115, 0.0234, 0.0511, 0.0133, 0.0197],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 7')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
question: ['How many penguins 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
tensor([6.1717e-01, 2.1409e-02, 3.5735e-01, 1.5175e-03, 1.8806e-04, 6.5413e-04,
1.2482e-04, 1.5813e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.1717e-01, 2.1409e-02, 3.5735e-01, 1.5175e-03, 1.8806e-04, 6.5413e-04,
1.2482e-04, 1.5813e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3574, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6172, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0255, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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']]
tensor([8.1083e-01, 2.5823e-02, 1.5966e-01, 1.7273e-03, 1.0485e-04, 3.7031e-04,
5.2918e-05, 1.4380e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.1083e-01, 2.5823e-02, 1.5966e-01, 1.7273e-03, 1.0485e-04, 3.7031e-04,
5.2918e-05, 1.4380e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8108, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1597, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0295, device='cuda:0', grad_fn=<DivBackward0>)}