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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.8356e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many birds are sitting on the branch?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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question: ['How many chow 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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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: 3402
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question: ['How many birds are sitting on the branch?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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torch.Size([7, 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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tensor([9.9830e-01, 1.5350e-08, 1.7003e-03, 1.2680e-09, 8.7487e-11, 1.1606e-10,
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3.5922e-11, 4.1009e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9830e-01, 1.5350e-08, 1.7003e-03, 1.2680e-09, 8.7487e-11, 1.1606e-10,
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3.5922e-11, 4.1009e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([9.9394e-01, 2.2109e-03, 2.6751e-03, 1.8432e-05, 3.3998e-05, 1.0550e-03,
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3.2447e-05, 3.3146e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([9.9394e-01, 2.2109e-03, 2.6751e-03, 1.8432e-05, 3.3998e-05, 1.0550e-03,
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3.2447e-05, 3.3146e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9983, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0017, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.6426e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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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 dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many apes 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([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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tensor([1.0000e+00, 4.4315e-10, 8.9156e-11, 1.7867e-10, 1.1094e-10, 2.1453e-08,
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5.6910e-09, 2.9704e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.4315e-10, 8.9156e-11, 1.7867e-10, 1.1094e-10, 2.1453e-08,
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5.6910e-09, 2.9704e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.6910e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many dogs are in the image?'], responses:['five']
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question: ['How many apes are in the image?'], responses:['1']
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[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
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[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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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: 1861
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tensor([1.0000e+00, 8.3630e-08, 1.8463e-08, 1.2849e-08, 4.5996e-10, 3.2678e-09,
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1.5245e-09, 6.1894e-09], 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, 8.3630e-08, 1.8463e-08, 1.2849e-08, 4.5996e-10, 3.2678e-09,
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1.5245e-09, 6.1894e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.2638e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([7.5106e-10, 4.4477e-01, 2.7072e-02, 2.5859e-04, 5.2754e-01, 6.9478e-05,
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1.0615e-04, 1.8020e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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feet *************
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['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([7.5106e-10, 4.4477e-01, 2.7072e-02, 2.5859e-04, 5.2754e-01, 6.9478e-05,
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1.0615e-04, 1.8020e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.2203e-10, 1.0881e-10, 2.8486e-10, 1.0795e-10, 8.8126e-09,
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2.5651e-09, 2.0629e-10], 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, 4.2203e-10, 1.0881e-10, 2.8486e-10, 1.0795e-10, 8.8126e-09,
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2.5651e-09, 2.0629e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.9426e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:43:20,250] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.51 | optimizer_gradients: 0.24 | optimizer_step: 0.32
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[2024-10-24 10:43:20,250] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7053.02 | backward_microstep: 6748.26 | backward_inner_microstep: 6742.50 | backward_allreduce_microstep: 5.66 | step_microstep: 7.49
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[2024-10-24 10:43:20,250] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7053.03 | backward: 6748.25 | backward_inner: 6742.54 | backward_allreduce: 5.64 | step: 7.50
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99%|ββββββββββ| 4818/4844 [20:02:04<06:37, 15.30s/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 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 EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='How many open pencil cases are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the image contain a ferret sticking their head out of a dirt hole?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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