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5.0257e-05, 1.3086e-02], device='cuda:0', grad_fn=<SelectBackward0>)
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torch.Size([7, 3, 448, 448])
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tensor([9.9826e-01, 4.6007e-09, 1.7007e-03, 3.5296e-05, 2.7152e-09, 6.1335e-06,
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5.0529e-09, 9.5141e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9826e-01, 4.6007e-09, 1.7007e-03, 3.5296e-05, 2.7152e-09, 6.1335e-06,
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5.0529e-09, 9.5141e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the machine on the right sell Coca Cola?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 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=LEFT,question='Are there clouds visible in the picture?')
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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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tensor([1.0000e+00, 8.5943e-09, 1.9977e-09, 3.7963e-08, 1.3338e-10, 8.8647e-10,
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5.6288e-11, 1.7954e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.5943e-09, 1.9977e-09, 3.7963e-08, 1.3338e-10, 8.8647e-10,
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5.6288e-11, 1.7954e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.9977e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1721e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many antelope 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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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many chimps 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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question: ['Does the machine on the right sell Coca Cola?'], 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: ['How many antelope are in the image?'], responses:['2']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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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([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
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question: ['Are there clouds visible in the picture?'], responses:['no']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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tensor([1.0000e+00, 4.2534e-10, 1.3176e-10, 4.0571e-10, 1.2356e-10, 7.1369e-08,
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4.7597e-09, 1.1891e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2534e-10, 1.3176e-10, 4.0571e-10, 1.2356e-10, 7.1369e-08,
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4.7597e-09, 1.1891e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(7.8404e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 1.6657e-08, 1.9556e-08, 1.0803e-07, 2.9672e-10, 6.2861e-10,
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1.9756e-10, 8.2578e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6657e-08, 1.9556e-08, 1.0803e-07, 2.9672e-10, 6.2861e-10,
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1.9756e-10, 8.2578e-08], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.9556e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.1886e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 1.9142e-07, 1.5715e-08, 1.2430e-08, 1.7839e-09, 2.7253e-09,
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3.9961e-09, 1.7497e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.9142e-07, 1.5715e-08, 1.2430e-08, 1.7839e-09, 2.7253e-09,
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3.9961e-09, 1.7497e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.2982e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 8.6779e-09, 4.7380e-07, 3.0162e-10, 3.4897e-10, 5.8369e-08,
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1.0912e-09, 6.3372e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.6779e-09, 4.7380e-07, 3.0162e-10, 3.4897e-10, 5.8369e-08,
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1.0912e-09, 6.3372e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.6779e-09, 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>)}
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[2024-10-24 10:36:04,364] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.26 | optimizer_step: 0.31
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[2024-10-24 10:36:04,364] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7053.34 | backward_microstep: 10718.91 | backward_inner_microstep: 6793.30 | backward_allreduce_microstep: 3925.55 | step_microstep: 7.86
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[2024-10-24 10:36:04,364] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7053.35 | backward: 10718.90 | backward_inner: 6793.31 | backward_allreduce: 3925.54 | step: 7.87
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99%|ββββββββββ| 4788/4844 [19:54:48<14:39, 15.70s/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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ANSWER0=VQA(image=LEFT,question='How many rolls of paper towel are in the package?')
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ANSWER1=EVAL(expr='{ANSWER0} == 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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 birds 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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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 EVAL step
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Registering RESULT step
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the right image feature side-by-side towels arranged decoratively on a bar?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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