text stringlengths 0 1.16k |
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tensor([8.6018e-01, 1.3274e-01, 2.5270e-06, 1.3011e-03, 2.5688e-03, 6.5367e-04, |
2.0116e-03, 5.3948e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
100 ************* |
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([8.6018e-01, 1.3274e-01, 2.5270e-06, 1.3011e-03, 2.5688e-03, 6.5367e-04, |
2.0116e-03, 5.3948e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many women are standing outside?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
tensor([1.0000e+00, 3.2780e-09, 5.4874e-11, 1.0913e-08, 1.4731e-09, 2.9006e-10, |
4.0052e-11, 9.2278e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2780e-09, 5.4874e-11, 1.0913e-08, 1.4731e-09, 2.9006e-10, |
4.0052e-11, 9.2278e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 2.2830e-09, 4.0587e-10, 1.3163e-08, 1.4385e-10, 4.8474e-11, |
3.2621e-11, 7.8185e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2830e-09, 4.0587e-10, 1.3163e-08, 1.4385e-10, 4.8474e-11, |
3.2621e-11, 7.8185e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.4874e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.4874e-11, device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(4.0587e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.0587e-10, device='cuda:3', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many women are standing outside?'], responses:['δΈ'] |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many dogs are in the image?'], responses:['2'] |
question: ['How many ferrets are in the image?'], responses:['5'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
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: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([1.0000e+00, 1.2771e-10, 1.1953e-06, 1.7588e-12, 5.3772e-11, 2.3923e-09, |
4.3316e-10, 1.5611e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2771e-10, 1.1953e-06, 1.7588e-12, 5.3772e-11, 2.3923e-09, |
4.3316e-10, 1.5611e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2771e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.7418e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many baboons are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
question: ['How many baboons 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([4.1266e-04, 2.4417e-03, 4.9212e-02, 6.9349e-01, 1.1584e-01, 1.1700e-01, |
1.5672e-02, 5.9302e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([4.1266e-04, 2.4417e-03, 4.9212e-02, 6.9349e-01, 1.1584e-01, 1.1700e-01, |
1.5672e-02, 5.9302e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
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>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([8.7912e-01, 1.6244e-07, 1.1967e-01, 5.4216e-04, 5.0533e-06, 6.6185e-04, |
6.4245e-07, 7.0188e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([8.7912e-01, 1.6244e-07, 1.1967e-01, 5.4216e-04, 5.0533e-06, 6.6185e-04, |
6.4245e-07, 7.0188e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 8.7642e-08, 2.1478e-08, 6.6535e-09, 2.5798e-10, 1.3359e-09, |
5.5034e-10, 2.9341e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.7642e-08, 2.1478e-08, 6.6535e-09, 2.5798e-10, 1.3359e-09, |
5.5034e-10, 2.9341e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.7642e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 7.1232e-10, 1.4164e-10, 3.0872e-10, 1.6604e-10, 6.5476e-09, |
8.5432e-09, 1.1503e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 7.1232e-10, 1.4164e-10, 3.0872e-10, 1.6604e-10, 6.5476e-09, |
8.5432e-09, 1.1503e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.5432e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 10:10:02,563] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.38 | optimizer_step: 0.34 |
[2024-10-24 10:10:02,563] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7104.20 | backward_microstep: 10610.56 | backward_inner_microstep: 6798.65 | backward_allreduce_microstep: 3811.69 | step_microstep: 7.73 |
[2024-10-24 10:10:02,564] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7104.20 | backward: 10610.55 | backward_inner: 6798.81 | backward_allreduce: 3811.68 | step: 7.74 |
97%|ββββββββββ| 4682/4844 [19:28:46<44:19, 16.42s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
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