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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 3.3811e-08, 9.1463e-10, 2.2350e-08, 9.5837e-10, 1.2502e-09, |
2.5764e-10, 2.2969e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3811e-08, 9.1463e-10, 2.2350e-08, 9.5837e-10, 1.2502e-09, |
2.5764e-10, 2.2969e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 5.0435e-07, 7.4964e-08, 1.2099e-06, 1.8190e-09, 2.4862e-09, |
6.7583e-09, 2.6339e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.0435e-07, 7.4964e-08, 1.2099e-06, 1.8190e-09, 2.4862e-09, |
6.7583e-09, 2.6339e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.7228e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: ANSWER0=VQA(image=RIGHT,question='How many pigs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
{True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.1463e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1829e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is a person's leg visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many pigs are in the image?'], responses:['1'] |
question: ['Is a person'], responses:['no'] |
[('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']] |
[('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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 2.5544e-09, 5.6028e-09, 2.7770e-09, 4.5478e-11, 1.9585e-11, |
7.4630e-12, 1.3730e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.5544e-09, 5.6028e-09, 2.7770e-09, 4.5478e-11, 1.9585e-11, |
7.4630e-12, 1.3730e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: tensor([4.5588e-01, 5.1670e-01, 1.4715e-02, 3.1746e-06, 1.1458e-02, 1.2078e-03, |
2.6670e-05, 1.3789e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([4.5588e-01, 5.1670e-01, 1.4715e-02, 3.1746e-06, 1.1458e-02, 1.2078e-03, |
2.6670e-05, 1.3789e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
{True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.6028e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.6028e-09, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pencil cases are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.6670e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', 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) |
torch.Size([1, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['How many dogs 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 3.1486e-10, 7.6260e-11, 1.8437e-10, 1.4582e-10, 6.6465e-09, |
4.1636e-09, 1.5209e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.1486e-10, 7.6260e-11, 1.8437e-10, 1.4582e-10, 6.6465e-09, |
4.1636e-09, 1.5209e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
question: ['How many pencil cases are in the image?'], responses:['four'] |
tensor([1.0000e+00, 2.9077e-09, 1.1253e-06, 2.3593e-09, 3.9121e-08, 4.5498e-08, |
1.0101e-08, 9.2832e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.9077e-09, 1.1253e-06, 2.3593e-09, 3.9121e-08, 4.5498e-08, |
1.0101e-08, 9.2832e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.1684e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.9077e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.1458e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)] |
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']] |
tensor([1.0000e+00, 2.5490e-08, 3.7885e-09, 2.6315e-08, 3.2092e-10, 2.8378e-09, |
1.1547e-09, 1.0449e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.5490e-08, 3.7885e-09, 2.6315e-08, 3.2092e-10, 2.8378e-09, |
1.1547e-09, 1.0449e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.0952e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
tensor([2.4750e-14, 9.7980e-01, 8.5792e-07, 2.0189e-02, 1.0680e-05, 3.7556e-07, |
2.0471e-06, 1.3048e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([2.4750e-14, 9.7980e-01, 8.5792e-07, 2.0189e-02, 1.0680e-05, 3.7556e-07, |
2.0471e-06, 1.3048e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0202, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9798, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.5300e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 09:25:32,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-24 09:25:32,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5082.65 | backward_microstep: 7532.38 | backward_inner_microstep: 4833.59 | backward_allreduce_microstep: 2698.66 | step_microstep: 7.40 |
[2024-10-24 09:25:32,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5082.66 | backward: 7532.37 | backward_inner: 4833.67 | backward_allreduce: 2698.61 | step: 7.42 |
93%|ββββββββββ| 4506/4844 [18:44:16<1:12:59, 12.96s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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