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1.5383e-01, 4.6753e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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sunrise *************
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['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([4.7311e-06, 8.0086e-04, 2.4099e-03, 1.4239e-01, 2.3293e-01, 1.1254e-04,
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1.5383e-01, 4.6753e-01], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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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: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([9.9988e-01, 1.2339e-04, 1.0622e-07, 3.1782e-11, 7.0998e-12, 5.1865e-10,
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2.8505e-10, 1.0588e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9988e-01, 1.2339e-04, 1.0622e-07, 3.1782e-11, 7.0998e-12, 5.1865e-10,
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2.8505e-10, 1.0588e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0001, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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ANSWER0=VQA(image=LEFT,question='How many plants 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([7, 3, 448, 448])
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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: 1860
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tensor([1.0000e+00, 1.6894e-07, 6.6535e-09, 3.2426e-09, 2.2348e-10, 6.0927e-10,
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6.9853e-10, 1.1270e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.6894e-07, 6.6535e-09, 3.2426e-09, 2.2348e-10, 6.0927e-10,
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6.9853e-10, 1.1270e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.2426e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['How many plants are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([9.0690e-10, 6.9086e-02, 1.6834e-01, 8.1299e-05, 7.6141e-01, 4.6958e-05,
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2.5747e-04, 7.8105e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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feet *************
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['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([9.0690e-10, 6.9086e-02, 1.6834e-01, 8.1299e-05, 7.6141e-01, 4.6958e-05,
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2.5747e-04, 7.8105e-04], 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=RIGHT,question='Is the baby seal lying down?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 3, 448, 448])
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question: ['Is the baby seal lying down?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([9.9611e-01, 3.8243e-03, 1.4394e-07, 5.1245e-05, 7.2376e-08, 5.1833e-07,
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3.3800e-06, 6.5151e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9611e-01, 3.8243e-03, 1.4394e-07, 5.1245e-05, 7.2376e-08, 5.1833e-07,
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3.3800e-06, 6.5151e-06], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9961, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0039, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 8.3381e-09, 3.1297e-11, 1.3688e-08, 1.8291e-10, 1.5894e-10,
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7.8599e-11, 9.6003e-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, 8.3381e-09, 3.1297e-11, 1.3688e-08, 1.8291e-10, 1.5894e-10,
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7.8599e-11, 9.6003e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(3.1297e-11, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-3.1297e-11, device='cuda:1', grad_fn=<SubBackward0>)}
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[2024-10-24 10:34:43,437] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.33
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[2024-10-24 10:34:43,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3165.26 | backward_microstep: 7983.46 | backward_inner_microstep: 3008.98 | backward_allreduce_microstep: 4974.37 | step_microstep: 9.99
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[2024-10-24 10:34:43,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3165.28 | backward: 7983.45 | backward_inner: 3009.01 | backward_allreduce: 4974.33 | step: 10.00
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99%|ββββββββββ| 4783/4844 [19:53:27<14:04, 13.85s/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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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='Does the image show a Stingray swimming through the water?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,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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ANSWER0=VQA(image=RIGHT,question='Are there flowers on the bathroom counter?')
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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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ANSWER0=VQA(image=RIGHT,question='Are the seals sunning on a rock?')
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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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question: ['Does the image show a Stingray swimming through the water?'], responses:['yes']
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question: ['Are there flowers on the bathroom counter?'], 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([7, 3, 448, 448]) knan debug pixel values shape
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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: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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question: ['How many birds are in the image?'], responses:['11']
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question: ['Are the seals sunning on a rock?'], responses:['no']
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