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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
question: ['How many mountain goats are in the image?'], responses:['11'] |
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)] |
[['11', '10', '12', '9', '8', '13', '7', '14']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
tensor([1.0000e+00, 4.3543e-10, 1.0423e-10, 2.9924e-10, 1.5159e-10, 3.4806e-08, |
6.2017e-09, 7.5536e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.3543e-10, 1.0423e-10, 2.9924e-10, 1.5159e-10, 3.4806e-08, |
6.2017e-09, 7.5536e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([9.9998e-01, 1.8925e-05, 7.4929e-08, 4.8749e-12, 4.3938e-13, 6.8043e-10, |
3.8484e-11, 2.2959e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9998e-01, 1.8925e-05, 7.4929e-08, 4.8749e-12, 4.3938e-13, 6.8043e-10, |
3.8484e-11, 2.2959e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([9.9876e-01, 1.2405e-03, 1.2630e-07, 7.1986e-08, 1.5368e-10, 4.5721e-08, |
4.8821e-10, 2.6196e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9876e-01, 1.2405e-03, 1.2630e-07, 7.1986e-08, 1.5368e-10, 4.5721e-08, |
4.8821e-10, 2.6196e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.2017e-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>)} |
ANSWER0=VQA(image=LEFT,question='Is there a human in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8925e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.5721e-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>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many pug dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many pug 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: ['Is there a human in the image?'], responses:['yes'] |
question: ['How many dogs are in the image?'], responses:['1'] |
tensor([9.8385e-01, 8.4278e-04, 4.0206e-03, 1.3623e-05, 2.3944e-08, 8.5122e-03, |
3.9871e-07, 2.7635e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
11 ************* |
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.8385e-01, 8.4278e-04, 4.0206e-03, 1.3623e-05, 2.3944e-08, 8.5122e-03, |
3.9871e-07, 2.7635e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
[('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']] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
[('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: 3396 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 2.9694e-10, 9.6779e-11, 2.7502e-10, 1.9115e-10, 9.6724e-09, |
8.0256e-09, 8.3708e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.9694e-10, 9.6779e-11, 2.7502e-10, 1.9115e-10, 9.6724e-09, |
8.0256e-09, 8.3708e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.9694e-10, 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>)} |
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: 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: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([1.0000e+00, 2.8377e-08, 4.3799e-11, 1.3989e-07, 1.7036e-09, 7.0138e-10, |
2.3325e-10, 9.2448e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.8377e-08, 4.3799e-11, 1.3989e-07, 1.7036e-09, 7.0138e-10, |
2.3325e-10, 9.2448e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.3799e-11, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1917e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 2.7142e-10, 5.7564e-11, 1.1401e-10, 8.9667e-11, 4.7165e-09, |
3.3983e-09, 2.9609e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7142e-10, 5.7564e-11, 1.1401e-10, 8.9667e-11, 4.7165e-09, |
3.3983e-09, 2.9609e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.3983e-09, 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>)} |
[2024-10-24 10:19:32,738] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.57 | optimizer_gradients: 0.21 | optimizer_step: 0.31 |
[2024-10-24 10:19:32,738] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7065.09 | backward_microstep: 6803.44 | backward_inner_microstep: 6783.08 | backward_allreduce_microstep: 20.26 | step_microstep: 7.80 |
[2024-10-24 10:19:32,739] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7065.11 | backward: 6803.43 | backward_inner: 6783.10 | backward_allreduce: 20.25 | step: 7.81 |
97%|ββββββββββ| 4721/4844 [19:38:16<28:26, 13.88s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT 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 pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
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