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tensor([9.7400e-01, 3.2345e-05, 8.1797e-06, 2.4837e-08, 9.2405e-11, 2.5959e-02,
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6.0989e-10, 9.0643e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.7400e-01, 3.2345e-05, 8.1797e-06, 2.4837e-08, 9.2405e-11, 2.5959e-02,
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6.0989e-10, 9.0643e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.1797e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many wild 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: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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question: ['How many wild 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: 3399
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 4.8957e-10, 7.5513e-07, 2.1215e-12, 5.6130e-11, 2.9545e-09,
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3.9429e-10, 1.0215e-06], 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, 4.8957e-10, 7.5513e-07, 2.1215e-12, 5.6130e-11, 2.9545e-09,
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3.9429e-10, 1.0215e-06], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.8957e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.7881e-06, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,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, 5.3768e-10, 2.4451e-07, 2.0886e-12, 2.9858e-12, 3.5091e-09,
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1.7249e-10, 2.5483e-07], device='cuda:0', 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, 5.3768e-10, 2.4451e-07, 2.0886e-12, 2.9858e-12, 3.5091e-09,
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1.7249e-10, 2.5483e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([1.4889e-07, 6.1885e-01, 1.8053e-02, 2.4218e-04, 3.6266e-01, 7.8943e-05,
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2.6771e-05, 9.5789e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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babies *************
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['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.4889e-07, 6.1885e-01, 1.8053e-02, 2.4218e-04, 3.6266e-01, 7.8943e-05,
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2.6771e-05, 9.5789e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.3768e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many seals 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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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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ANSWER0=VQA(image=RIGHT,question='How many chow 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([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many dogs are in the image?'], responses:['2']
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question: ['How many seals are in the image?'], responses:['2']
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question: ['How many chow dogs are in the image?'], responses:['1']
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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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[('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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[('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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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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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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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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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
|
tensor([9.9830e-01, 1.7009e-03, 4.8035e-07, 1.7400e-07, 3.7559e-08, 2.6839e-09,
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1.3685e-08, 3.3278e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9830e-01, 1.7009e-03, 4.8035e-07, 1.7400e-07, 3.7559e-08, 2.6839e-09,
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1.3685e-08, 3.3278e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9983, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0017, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 1.5713e-08, 1.8086e-08, 1.5230e-08, 2.5798e-10, 1.5635e-09,
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7.8231e-10, 2.1232e-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, 1.5713e-08, 1.8086e-08, 1.5230e-08, 2.5798e-10, 1.5635e-09,
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7.8231e-10, 2.1232e-10], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.1845e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
tensor([1.0000e+00, 3.4236e-10, 1.4472e-10, 3.6649e-10, 2.1809e-10, 9.0825e-09,
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2.7736e-09, 1.9853e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.4236e-10, 1.4472e-10, 3.6649e-10, 2.1809e-10, 9.0825e-09,
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2.7736e-09, 1.9853e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3126e-08, 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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tensor([1.0000e+00, 6.3125e-08, 2.0817e-08, 6.5376e-09, 4.2531e-10, 2.4753e-09,
|
1.6677e-09, 1.6902e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.3125e-08, 2.0817e-08, 6.5376e-09, 4.2531e-10, 2.4753e-09,
|
1.6677e-09, 1.6902e-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(9.6738e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:46:20,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.25 | optimizer_step: 0.31
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[2024-10-24 10:46:20,720] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7045.82 | backward_microstep: 6932.58 | backward_inner_microstep: 6762.02 | backward_allreduce_microstep: 170.43 | step_microstep: 7.57
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[2024-10-24 10:46:20,720] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7045.84 | backward: 6932.57 | backward_inner: 6762.06 | backward_allreduce: 170.39 | step: 7.58
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100%|ββββββββββ| 4830/4844 [20:05:04<03:32, 15.20s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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