text stringlengths 0 1.16k |
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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)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
[('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)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.9683e-01, 1.1228e-08, 3.1727e-03, 1.7892e-08, 8.7313e-11, 2.0989e-10, |
7.8837e-10, 7.5582e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.9683e-01, 1.1228e-08, 3.1727e-03, 1.7892e-08, 8.7313e-11, 2.0989e-10, |
7.8837e-10, 7.5582e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9968, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0032, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7462e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 1.1096e-10, 2.3258e-11, 4.1138e-11, 3.0804e-11, 4.6440e-09, |
1.2212e-09, 3.1701e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1096e-10, 2.3258e-11, 4.1138e-11, 3.0804e-11, 4.6440e-09, |
1.2212e-09, 3.1701e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2212e-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>)} |
question: ['How many zebras are in the image?'], responses:['1'] |
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 1.1391e-08, 6.2862e-10, 2.8561e-08, 1.6712e-10, 7.1224e-10, |
6.7522e-11, 2.4465e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1391e-08, 6.2862e-10, 2.8561e-08, 1.6712e-10, 7.1224e-10, |
6.7522e-11, 2.4465e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: tensor([1.0000e+00, 6.2862e-10, 2.6986e-07, 2.1770e-09, 1.1543e-09, 3.7468e-07, |
5.7550e-09, 3.0533e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.2862e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.2862e-10, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 6.2862e-10, 2.6986e-07, 2.1770e-09, 1.1543e-09, 3.7468e-07, |
5.7550e-09, 3.0533e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.2862e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many chow dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([1.0000e+00, 4.7450e-10, 4.7443e-11, 7.9198e-11, 7.2712e-11, 2.0591e-09, |
1.1861e-08, 2.0948e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7450e-10, 4.7443e-11, 7.9198e-11, 7.2712e-11, 2.0591e-09, |
1.1861e-08, 2.0948e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.7539e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many chow dogs are in the image?'], responses:['1'] |
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many zebras are in the image?'], responses:['1'] |
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 3.4988e-10, 1.4136e-10, 4.0035e-10, 2.2152e-10, 1.0789e-08, |
3.9421e-09, 2.8930e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.4988e-10, 1.4136e-10, 4.0035e-10, 2.2152e-10, 1.0789e-08, |
3.9421e-09, 2.8930e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.6133e-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>)} |
tensor([1.0000e+00, 4.4229e-10, 3.2417e-11, 4.4469e-11, 3.8343e-11, 3.0444e-09, |
7.4225e-09, 2.5294e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.4229e-10, 3.2417e-11, 4.4469e-11, 3.8343e-11, 3.0444e-09, |
7.4225e-09, 2.5294e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.6273e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 10:00:52,394] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.29 | optimizer_step: 0.31 |
[2024-10-24 10:00:52,395] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3139.16 | backward_microstep: 14676.12 | backward_inner_microstep: 2997.73 | backward_allreduce_microstep: 11678.31 | step_microstep: 7.73 |
[2024-10-24 10:00:52,395] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3139.18 | backward: 14676.11 | backward_inner: 2997.75 | backward_allreduce: 11678.29 | step: 7.74 |
96%|ββββββββββ| 4647/4844 [19:19:36<51:04, 15.55s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL stepRegistering EVAL step |
Registering RESULT step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there a mirror over the sink in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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