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[('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']] |
[('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([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: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
question: ['How many 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
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([1.0000e+00, 5.5043e-10, 7.6260e-11, 1.9779e-10, 4.9218e-11, 1.9850e-08, |
1.2528e-08, 4.8980e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.5043e-10, 7.6260e-11, 1.9779e-10, 4.9218e-11, 1.9850e-08, |
1.2528e-08, 4.8980e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.3742e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 6.6395e-10, 1.8582e-10, 2.1159e-10, 1.4303e-10, 7.8996e-09, |
5.9641e-09, 9.3312e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.6395e-10, 1.8582e-10, 2.1159e-10, 1.4303e-10, 7.8996e-09, |
5.9641e-09, 9.3312e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([9.9838e-01, 1.2559e-06, 7.0552e-06, 1.5873e-04, 9.1469e-04, 4.4532e-04, |
4.8568e-05, 4.4172e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9838e-01, 1.2559e-06, 7.0552e-06, 1.5873e-04, 9.1469e-04, 4.4532e-04, |
4.8568e-05, 4.4172e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.5161e-08, 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>)} |
ANSWER0=VQA(image=LEFT,question='How many small bags are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9984, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.0552e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0016, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the image show a tree shape with branches forming the railing as the stairs ascend rightward?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
tensor([1.0000e+00, 3.0636e-10, 9.3436e-11, 2.9461e-10, 1.5164e-10, 2.2146e-08, |
6.0581e-09, 5.2433e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.0636e-10, 9.3436e-11, 2.9461e-10, 1.5164e-10, 2.2146e-08, |
6.0581e-09, 5.2433e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.9574e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many dogs are in the image?'], responses:['ε'] |
question: ['How many small bags are in the image?'], responses:['7'] |
[('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']] |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['Does the image show a tree shape with branches forming the railing as the stairs ascend rightward?'], responses:['no'] |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410 |
tensor([7.1655e-04, 1.2446e-02, 1.3429e-05, 4.3331e-01, 7.1517e-02, 5.4643e-03, |
4.7257e-01, 3.9698e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([7.1655e-04, 1.2446e-02, 1.3429e-05, 4.3331e-01, 7.1517e-02, 5.4643e-03, |
4.7257e-01, 3.9698e-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>)} |
tensor([8.6857e-01, 1.7688e-04, 6.5695e-04, 4.9025e-02, 5.8055e-04, 1.3775e-04, |
8.0853e-02, 2.9533e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([8.6857e-01, 1.7688e-04, 6.5695e-04, 4.9025e-02, 5.8055e-04, 1.3775e-04, |
8.0853e-02, 2.9533e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 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: 3409 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3410 |
tensor([1.0000e+00, 4.0991e-09, 6.0193e-07, 1.2687e-12, 6.9797e-12, 1.1416e-09, |
2.2202e-10, 6.8708e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.0991e-09, 6.0193e-07, 1.2687e-12, 6.9797e-12, 1.1416e-09, |
2.2202e-10, 6.8708e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.0991e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-24 09:58:31,572] [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 09:58:31,572] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7095.50 | backward_microstep: 6801.90 | backward_inner_microstep: 6796.61 | backward_allreduce_microstep: 5.21 | step_microstep: 7.64 |
[2024-10-24 09:58:31,573] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7095.52 | backward: 6801.89 | backward_inner: 6796.63 | backward_allreduce: 5.20 | step: 7.65 |
96%|ββββββββββ| 4638/4844 [19:17:15<52:11, 15.20s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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