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tensor([1.0000e+00, 6.1887e-10, 1.0924e-10, 2.2582e-10, 1.0095e-10, 1.5929e-08,
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6.8113e-09, 2.1792e-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, 6.1887e-10, 1.0924e-10, 2.2582e-10, 1.0095e-10, 1.5929e-08,
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6.8113e-09, 2.1792e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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tensor([8.2054e-01, 6.4139e-06, 2.1352e-06, 1.5199e-01, 3.2146e-11, 2.7449e-02,
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1.2686e-06, 8.7882e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([8.2054e-01, 6.4139e-06, 2.1352e-06, 1.5199e-01, 3.2146e-11, 2.7449e-02,
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1.2686e-06, 8.7882e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.1875e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.2146e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.6583e-08, 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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ANSWER0=VQA(image=LEFT,question='Are the dogs heading to the right?')
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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 penguins 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='Are several pups nursing 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([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: ['How many penguins are in the image?'], responses:['7']
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[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
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[['7', '8', '11', '5', '9', '10', '6', '12']]
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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question: ['Are the dogs heading to the right?'], responses:['yes']
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question: ['Are several pups nursing in the image?'], responses:['no']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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torch.Size([13, 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: 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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tensor([1.0000e+00, 9.5853e-10, 3.6863e-07, 3.8128e-10, 1.3807e-08, 1.5856e-07,
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4.4570e-09, 2.5224e-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, 9.5853e-10, 3.6863e-07, 3.8128e-10, 1.3807e-08, 1.5856e-07,
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4.4570e-09, 2.5224e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(9.5853e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:2', 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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torch.Size([13, 3, 448, 448])
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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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tensor([7.9121e-03, 5.3312e-05, 9.7350e-01, 1.9337e-08, 7.3595e-04, 1.0815e-02,
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5.2265e-07, 6.9822e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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11 *************
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['7', '8', '11', '5', '9', '10', '6', '12'] tensor([7.9121e-03, 5.3312e-05, 9.7350e-01, 1.9337e-08, 7.3595e-04, 1.0815e-02,
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5.2265e-07, 6.9822e-03], device='cuda:0', 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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question: ['How many dogs 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([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 1.7630e-09, 1.2068e-07, 9.2514e-12, 1.6955e-10, 5.1414e-09,
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1.0310e-10, 1.6510e-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, 1.7630e-09, 1.2068e-07, 9.2514e-12, 1.6955e-10, 5.1414e-09,
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1.0310e-10, 1.6510e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([5.5524e-01, 1.7025e-08, 4.4476e-01, 2.5358e-08, 5.5209e-11, 2.9279e-09,
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1.0804e-09, 2.4897e-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([5.5524e-01, 1.7025e-08, 4.4476e-01, 2.5358e-08, 5.5209e-11, 2.9279e-09,
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1.0804e-09, 2.4897e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7630e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5552, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4448, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9802e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 3.1363e-10, 1.4136e-10, 2.6617e-10, 1.1991e-10, 1.8070e-08,
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2.3817e-09, 3.0264e-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, 3.1363e-10, 1.4136e-10, 2.6617e-10, 1.1991e-10, 1.8070e-08,
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2.3817e-09, 3.0264e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.3817e-09, 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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[2024-10-24 10:33:46,834] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.32
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[2024-10-24 10:33:46,834] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5072.54 | backward_microstep: 12643.91 | backward_inner_microstep: 4827.56 | backward_allreduce_microstep: 7816.21 | step_microstep: 10.19
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[2024-10-24 10:33:46,835] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5072.54 | backward: 12643.90 | backward_inner: 4827.64 | backward_allreduce: 7816.13 | step: 10.20
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99%|ββββββββββ| 4779/4844 [19:52:30<16:50, 15.55s/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 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 bags/pencil-cases are in the image?')
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