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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.7185e-10, 1.3780e-11, 2.2071e-11, 1.3725e-11, 1.4602e-09,
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1.6374e-07, 4.9044e-12], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.6374e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 8.3278e-10, 1.0671e-10, 1.0044e-10, 1.8728e-10, 2.9513e-09,
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3.3265e-08, 3.7650e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.3278e-10, 1.0671e-10, 1.0044e-10, 1.8728e-10, 2.9513e-09,
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3.3265e-08, 3.7650e-11], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.7481e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many clarinets are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['How many clarinets 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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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 5.2659e-09, 5.6671e-11, 3.6436e-09, 5.5771e-11, 6.7298e-11,
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3.1026e-11, 1.9753e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.2659e-09, 5.6671e-11, 3.6436e-09, 5.5771e-11, 6.7298e-11,
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3.1026e-11, 1.9753e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.6671e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.6671e-11, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.0126e-08, 7.9012e-09, 4.0126e-08, 1.2952e-10, 6.5365e-10,
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1.4242e-10, 3.7447e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.0126e-08, 7.9012e-09, 4.0126e-08, 1.2952e-10, 6.5365e-10,
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1.4242e-10, 3.7447e-11], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.0126e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:41:17,992] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.32 | optimizer_step: 0.32
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[2024-10-24 10:41:17,992] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3168.65 | backward_microstep: 14517.11 | backward_inner_microstep: 2998.70 | backward_allreduce_microstep: 11518.31 | step_microstep: 7.53
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[2024-10-24 10:41:17,992] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3168.66 | backward: 14517.10 | backward_inner: 2998.72 | backward_allreduce: 11518.28 | step: 7.54
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99%|ββββββββββ| 4810/4844 [20:00:01<08:28, 14.96s/it]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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ANSWER0=VQA(image=LEFT,question='Is there at least one animal lying on its belly and facing left?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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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ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='How many women 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([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the bottle of soap have a top pump dispenser?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([11, 3, 448, 448])
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question: ['How many women are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Does the bottle of soap have a top pump dispenser?'], 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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question: ['Is there at least one animal lying on its belly and facing left?'], responses:['no']
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question: ['How many dogs are in the image?'], responses:['2']
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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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[('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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torch.Size([11, 3, 448, 448]) knan debug pixel values shape
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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: 3402
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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: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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tensor([1.0000e+00, 1.2748e-09, 1.8010e-10, 4.1223e-10, 1.0585e-10, 2.1609e-08,
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9.4565e-09, 1.1872e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.2748e-09, 1.8010e-10, 4.1223e-10, 1.0585e-10, 2.1609e-08,
|
9.4565e-09, 1.1872e-09], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.3494e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are there chairs in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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: 3402
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
|
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: 3403
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question: ['Are there chairs in the image?'], responses:['no']
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tensor([1.0000e+00, 3.7277e-09, 1.6919e-10, 2.2787e-09, 7.9163e-11, 1.5894e-10,
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1.0000e-11, 1.4538e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.7277e-09, 1.6919e-10, 2.2787e-09, 7.9163e-11, 1.5894e-10,
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1.0000e-11, 1.4538e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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