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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.4381e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 5.0511e-10, 7.8680e-11, 2.4235e-10, 1.1447e-10, 1.6464e-08,
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6.5503e-09, 5.3307e-10], 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, 5.0511e-10, 7.8680e-11, 2.4235e-10, 1.1447e-10, 1.6464e-08,
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6.5503e-09, 5.3307e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.4488e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 8.7274e-10, 5.9892e-07, 8.7773e-11, 1.8893e-09, 1.1909e-07,
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2.1317e-09, 4.2577e-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([1.0000e+00, 8.7274e-10, 5.9892e-07, 8.7773e-11, 1.8893e-09, 1.1909e-07,
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2.1317e-09, 4.2577e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.7274e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:40:04,728] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
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[2024-10-24 10:40:04,728] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5070.41 | backward_microstep: 6269.68 | backward_inner_microstep: 4927.13 | backward_allreduce_microstep: 1342.43 | step_microstep: 7.49
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[2024-10-24 10:40:04,729] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5070.41 | backward: 6269.67 | backward_inner: 4927.18 | backward_allreduce: 1342.39 | step: 7.51
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99%|ββββββββββ| 4805/4844 [19:58:48<08:45, 13.47s/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 EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='How many slices of pizza are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} > 0')
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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=LEFT,question='Is the dog looking toward the camera?')
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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='Does the cake in the image have several layers?')
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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='Does the mashed potato bowl contain a serving utensil?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many slices of pizza are in the image?'], responses:['1']
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question: ['Is the dog looking toward the camera?'], responses:['yes']
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question: ['Does the mashed potato bowl contain a serving utensil?'], responses:['no']
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question: ['Does the cake in the image have several layers?'], responses:['no']
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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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[('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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[('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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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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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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: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([3.0794e-02, 9.4275e-08, 1.3612e-07, 4.4261e-03, 8.6480e-05, 9.6469e-01,
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5.9637e-07, 4.2022e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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12 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([3.0794e-02, 9.4275e-08, 1.3612e-07, 4.4261e-03, 8.6480e-05, 9.6469e-01,
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5.9637e-07, 4.2022e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 4.0991e-09, 1.4684e-07, 9.2514e-12, 1.7283e-11, 4.3854e-09,
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1.1910e-10, 2.2756e-07], device='cuda:1', 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, 4.0991e-09, 1.4684e-07, 9.2514e-12, 1.7283e-11, 4.3854e-09,
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1.1910e-10, 2.2756e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 2.2895e-08, 1.4136e-10, 9.4789e-08, 7.1876e-10, 1.0800e-08,
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1.8278e-10, 1.2405e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] ζεηζ¦ηεεΈδΈΊ: tensor([1.0000e+00, 2.2895e-08, 1.4136e-10, 9.4789e-08, 7.1876e-10, 1.0800e-08,
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1.8278e-10, 1.2405e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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{True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: ANSWER0=VQA(image=RIGHT,question='How many bottles 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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{True: tensor(4.0991e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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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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tensor([1.0000e+00, 2.7953e-09, 2.8959e-07, 3.2242e-08, 1.1212e-08, 1.5305e-06,
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1.9886e-08, 3.1489e-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, 2.7953e-09, 2.8959e-07, 3.2242e-08, 1.1212e-08, 1.5305e-06,
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1.9886e-08, 3.1489e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.4136e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1907e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many lipsticks 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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