text stringlengths 0 1.16k |
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
tensor([1.0000e+00, 1.6374e-07, 5.3882e-09, 1.5713e-08, 2.5201e-10, 4.0905e-10, |
5.4190e-10, 5.0489e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.6374e-07, 5.3882e-09, 1.5713e-08, 2.5201e-10, 4.0905e-10, |
5.4190e-10, 5.0489e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 6.6916e-10, 3.7576e-08, 3.4975e-12, 2.7348e-12, 1.5000e-09, |
2.7542e-10, 7.7819e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.6916e-10, 3.7576e-08, 3.4975e-12, 2.7348e-12, 1.5000e-09, |
2.7542e-10, 7.7819e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.8655e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many beer bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.6916e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many beer bottles are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 1.7608e-06, 1.3512e-06, 2.1996e-09, 1.9798e-10, 4.1198e-07, |
6.1952e-10, 2.0584e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 1.7608e-06, 1.3512e-06, 2.1996e-09, 1.9798e-10, 4.1198e-07, |
6.1952e-10, 2.0584e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.1402e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.9970e-01, 2.9596e-04, 3.9267e-07, 8.3128e-07, 3.1241e-08, 1.2449e-09, |
3.0756e-08, 4.0416e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9970e-01, 2.9596e-04, 3.9267e-07, 8.3128e-07, 3.1241e-08, 1.2449e-09, |
3.0756e-08, 4.0416e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.3128e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is there a child in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['2'] |
question: ['Is there a child in the image?'], responses:['yes'] |
[('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']] |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
torch.Size([7, 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: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 3.7099e-10, 1.7556e-11, 4.7348e-11, 4.2096e-11, 4.6392e-09, |
1.0677e-06, 6.2190e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7099e-10, 1.7556e-11, 4.7348e-11, 4.2096e-11, 4.6392e-09, |
1.0677e-06, 6.2190e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.1794e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 2.2712e-08, 2.7619e-11, 2.3489e-07, 8.2355e-10, 5.9053e-10, |
1.6706e-10, 3.4274e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2712e-08, 2.7619e-11, 2.3489e-07, 8.2355e-10, 5.9053e-10, |
1.6706e-10, 3.4274e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.7619e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3839e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['How many wolves 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: 3396 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 4.1399e-08, 1.7023e-08, 7.7326e-09, 4.6714e-10, 3.3714e-09, |
1.0201e-09, 1.9661e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.1399e-08, 1.7023e-08, 7.7326e-09, 4.6714e-10, 3.3714e-09, |
1.0201e-09, 1.9661e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.2979e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 9.4168e-11, 3.5114e-12, 4.3445e-11, 1.4326e-11, 8.2401e-09, |
9.3295e-08, 7.4445e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 9.4168e-11, 3.5114e-12, 4.3445e-11, 1.4326e-11, 8.2401e-09, |
9.3295e-08, 7.4445e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.4700e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 10:08:09,636] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.40 | optimizer_gradients: 0.24 | optimizer_step: 0.30 |
[2024-10-24 10:08:09,636] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6443.32 | backward_microstep: 7479.80 | backward_inner_microstep: 6172.06 | backward_allreduce_microstep: 1307.65 | step_microstep: 7.39 |
[2024-10-24 10:08:09,636] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6443.33 | backward: 7479.79 | backward_inner: 6172.08 | backward_allreduce: 1307.64 | step: 7.40 |
97%|ββββββββββ| 4675/4844 [19:26:53<41:20, 14.68s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing in the snow?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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