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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4272, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5709, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0020, device='cuda:1', grad_fn=<DivBackward0>)} |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1356 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354 |
question: ['How many 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']] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
question: ['How many sled dogs are in the image?'], responses:['5'] |
question: ['How many white dogs are in the image?'], responses:['2'] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
[('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']] |
[('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: 5, images per sample: 5.0, dynamic token length: 1353 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354 |
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: 5, images per sample: 5.0, dynamic token length: 1354 |
tensor([4.8765e-01, 2.5613e-02, 4.8361e-01, 8.9881e-04, 1.7106e-04, 9.2961e-04, |
2.2774e-04, 8.9573e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([4.8765e-01, 2.5613e-02, 4.8361e-01, 8.9881e-04, 1.7106e-04, 9.2961e-04, |
2.2774e-04, 8.9573e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4877, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4836, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0287, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the goat laying down?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
tensor([4.5947e-01, 1.7122e-01, 5.8009e-02, 2.5997e-01, 3.2252e-02, 8.6166e-03, |
1.0074e-02, 3.9324e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.5947e-01, 1.7122e-01, 5.8009e-02, 2.5997e-01, 3.2252e-02, 8.6166e-03, |
1.0074e-02, 3.9324e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4595, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5405, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['Is the goat laying down?'], responses:['yes'] |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
tensor([0.2825, 0.0558, 0.2005, 0.2206, 0.0811, 0.1180, 0.0097, 0.0318], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2825, 0.0558, 0.2005, 0.2206, 0.0811, 0.1180, 0.0097, 0.0318], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7848, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.2152, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([6.4640e-01, 3.8394e-02, 7.5615e-03, 3.0199e-01, 2.9931e-03, 1.2720e-03, |
1.2720e-03, 1.2541e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.4640e-01, 3.8394e-02, 7.5615e-03, 3.0199e-01, 2.9931e-03, 1.2720e-03, |
1.2720e-03, 1.2541e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6980, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3020, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
question: ['How many dogs are in the image?'], responses:['5'] |
question: ['Can you see the lamp in the image?'], responses:['no'] |
[('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']] |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([8.8289e-01, 2.2468e-02, 9.3056e-02, 6.6321e-04, 5.4973e-05, 2.2289e-04, |
3.8629e-05, 6.0775e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8289e-01, 2.2468e-02, 9.3056e-02, 6.6321e-04, 5.4973e-05, 2.2289e-04, |
3.8629e-05, 6.0775e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8829, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0931, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0241, device='cuda:0', grad_fn=<SubBackward0>)} |
tensor([0.3471, 0.0567, 0.0934, 0.3070, 0.0196, 0.1359, 0.0079, 0.0324], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3471, 0.0567, 0.0934, 0.3070, 0.0196, 0.1359, 0.0079, 0.0324], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([6.6447e-01, 3.3412e-01, 2.3780e-04, 1.3228e-04, 2.6972e-04, 1.2582e-04, |
2.1359e-04, 4.3303e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.6447e-01, 3.3412e-01, 2.3780e-04, 1.3228e-04, 2.6972e-04, 1.2582e-04, |
2.1359e-04, 4.3303e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3341, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6645, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:43:10,254] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33 |
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