text stringlengths 0 1.16k |
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.5089e-01, 3.4840e-01, 4.9957e-05, 1.2028e-04, 4.2168e-05, 2.5100e-04, |
2.1019e-04, 3.5907e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3484, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6509, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are the sails furled in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) |
tensor([8.6813e-01, 1.9106e-02, 1.1037e-01, 8.4108e-04, 1.0258e-04, 5.9699e-04, |
3.3556e-05, 8.1514e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.6813e-01, 1.9106e-02, 1.1037e-01, 8.4108e-04, 1.0258e-04, 5.9699e-04, |
3.3556e-05, 8.1514e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8681, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1104, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0215, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['5'] |
question: ['Are the sails furled in the image?'], responses:['yes'] |
[('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']] |
question: ['How many birds are in the image?'], responses:['9'] |
[('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']] |
[('9', 0.12801736482258133), ('8', 0.12565135970392036), ('11', 0.1254560343890198), ('10', 0.1248838582125673), ('7', 0.12420801006143238), ('12', 0.12408347303550306), ('5', 0.12385261492086817), ('14', 0.12384728485410773)] |
[['9', '8', '11', '10', '7', '12', '5', '14']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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 |
tensor([0.1745, 0.1277, 0.1481, 0.1766, 0.1058, 0.1351, 0.0549, 0.0774], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
10 ************* |
['9', '8', '11', '10', '7', '12', '5', '14'] tensor([0.1745, 0.1277, 0.1481, 0.1766, 0.1058, 0.1351, 0.0549, 0.0774], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([6.1123e-01, 3.8776e-01, 3.6842e-05, 8.6448e-05, 9.5346e-05, 5.8832e-04, |
1.5997e-04, 3.8883e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.1123e-01, 3.8776e-01, 3.6842e-05, 8.6448e-05, 9.5346e-05, 5.8832e-04, |
1.5997e-04, 3.8883e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3878, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6112, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog in the left image facing right?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([13, 3, 448, 448]) |
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: 3396 |
tensor([5.6090e-01, 1.5997e-02, 4.1977e-01, 1.1970e-03, 2.4765e-04, 1.0734e-03, |
1.0398e-04, 7.1062e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6090e-01, 1.5997e-02, 4.1977e-01, 1.1970e-03, 2.4765e-04, 1.0734e-03, |
1.0398e-04, 7.1062e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5609, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4198, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0193, device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['Is the dog in the left image facing right?'], 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']] |
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: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.6227, 0.0046, 0.2011, 0.1305, 0.0211, 0.0165, 0.0008, 0.0027], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.6227, 0.0046, 0.2011, 0.1305, 0.0211, 0.0165, 0.0008, 0.0027], |
device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6227, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3773, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the bottle in the image have a wooden look?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['Does the bottle in the image have a wooden look?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
tensor([8.8687e-01, 1.1275e-01, 3.1310e-05, 3.7191e-05, 2.5012e-05, 6.5309e-05, |
1.6672e-04, 4.6189e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.8687e-01, 1.1275e-01, 3.1310e-05, 3.7191e-05, 2.5012e-05, 6.5309e-05, |
1.6672e-04, 4.6189e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1128, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8869, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([5.7682e-01, 4.2201e-01, 3.6003e-05, 1.1395e-04, 1.0563e-04, 5.8083e-04, |
3.0982e-04, 2.0121e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7682e-01, 4.2201e-01, 3.6003e-05, 1.1395e-04, 1.0563e-04, 5.8083e-04, |
3.0982e-04, 2.0121e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4220, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5768, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-23 14:52:33,067] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.38 | optimizer_step: 0.33 |
[2024-10-23 14:52:33,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5138.78 | backward_microstep: 7564.77 | backward_inner_microstep: 4963.60 | backward_allreduce_microstep: 2601.07 | step_microstep: 7.72 |
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