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2.9501e-08, 7.1906e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([9.9999e-01, 7.9942e-06, 1.8960e-07, 1.8913e-10, 9.0795e-07, 7.2682e-08, |
1.8382e-06, 1.4597e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 7.9942e-06, 1.8960e-07, 1.8913e-10, 9.0795e-07, 7.2682e-08, |
1.8382e-06, 1.4597e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.1818e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.2517e-05, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['How many puppies are in the picture?'], responses:['1'] |
ANSWER0=VQA(image=RIGHT,question='How many beagles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3408 |
[('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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405 |
question: ['How many beagles are in the image?'], responses:['2'] |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405 |
question: ['How many dogs are in the image?'], responses:['ε'] |
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)] |
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406 |
tensor([1.0000e+00, 1.1254e-07, 8.4108e-09, 1.9751e-07, 8.8463e-11, 2.9003e-10, |
2.5196e-10, 2.9147e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.1254e-07, 8.4108e-09, 1.9751e-07, 8.8463e-11, 2.9003e-10, |
2.5196e-10, 2.9147e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.9751e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 7.5054e-09, 1.2972e-10, 7.7975e-09, 2.2732e-10, 3.1054e-11, |
1.1405e-11, 3.7015e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.5054e-09, 1.2972e-10, 7.7975e-09, 2.2732e-10, 3.1054e-11, |
1.1405e-11, 3.7015e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2972e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2972e-10, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many open binders are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([1.0000e+00, 5.7236e-10, 1.6592e-10, 4.1792e-10, 2.3813e-10, 8.9469e-09, |
1.3027e-08, 9.0486e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.7236e-10, 1.6592e-10, 4.1792e-10, 2.3813e-10, 8.9469e-09, |
1.3027e-08, 9.0486e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.3459e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many open binders are in the image?'], responses:['2'] |
[('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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([1.2632e-03, 6.3345e-03, 7.6713e-07, 2.8621e-01, 9.4982e-02, 8.1784e-03, |
5.9841e-01, 4.6146e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.2632e-03, 6.3345e-03, 7.6713e-07, 2.8621e-01, 9.4982e-02, 8.1784e-03, |
5.9841e-01, 4.6146e-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: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([8.6504e-01, 1.3059e-03, 9.3668e-06, 1.3364e-01, 2.6836e-06, 8.6265e-08, |
3.6529e-06, 3.3449e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.6504e-01, 1.3059e-03, 9.3668e-06, 1.3364e-01, 2.6836e-06, 8.6265e-08, |
3.6529e-06, 3.3449e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1336, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8664, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-24 10:02:49,026] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.50 | optimizer_gradients: 0.25 | optimizer_step: 0.32 |
[2024-10-24 10:02:49,026] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7074.38 | backward_microstep: 6790.30 | backward_inner_microstep: 6784.76 | backward_allreduce_microstep: 5.39 | step_microstep: 7.59 |
[2024-10-24 10:02:49,026] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7074.39 | backward: 6790.29 | backward_inner: 6784.82 | backward_allreduce: 5.38 | step: 7.60 |
96%|ββββββββββ| 4654/4844 [19:21:32<49:25, 15.61s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many picture frames are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
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