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4.7145e-05, 1.6727e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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tensor([5.4141e-01, 2.5959e-01, 3.0889e-02, 1.5511e-01, 9.3138e-03, 1.4739e-03,
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2.1445e-03, 6.9471e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['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,
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2.1445e-03, 6.9471e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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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 unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[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
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[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
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[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
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1%| | 19/2424 [07:43<16:08:07, 24.15s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Is there a child inside a long boat made out of joined cardboard boxes?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many creatures are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 8')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Are there paws sticking out of the blanket on the pug in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Is someone holding up the dog?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many creatures are in the image?'], responses:['many']
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Are there paws sticking out of the blanket on the pug in the image?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a child inside a long boat made out of joined cardboard boxes?'], responses:['yes']
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question: ['Is someone holding up the dog?'], responses:['no']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
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tensor([0.5918, 0.0980, 0.1912, 0.0115, 0.0234, 0.0511, 0.0133, 0.0197],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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many *************
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['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],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 7')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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question: ['How many penguins are in the image?'], responses:['5']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([6.1717e-01, 2.1409e-02, 3.5735e-01, 1.5175e-03, 1.8806e-04, 6.5413e-04,
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1.2482e-04, 1.5813e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['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,
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1.2482e-04, 1.5813e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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question: ['How many dogs are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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tensor([8.1083e-01, 2.5823e-02, 1.5966e-01, 1.7273e-03, 1.0485e-04, 3.7031e-04,
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5.2918e-05, 1.4380e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['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,
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5.2918e-05, 1.4380e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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