text stringlengths 0 1.16k |
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2.7707e-09, 2.3467e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.9363e-09, 9.0377e-07, 9.2374e-09, 1.0692e-09, 2.6206e-07, |
2.7707e-09, 2.3467e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.1726e-06, 1.7806e-08, 2.9023e-06, 5.0363e-10, 7.0884e-10, |
1.7088e-09, 1.8873e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.1726e-06, 1.7806e-08, 2.9023e-06, 5.0363e-10, 7.0884e-10, |
1.7088e-09, 1.8873e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9363e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.0959e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
ANSWER0=VQA(image=LEFT,question='What color is the purse in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == "blue" or {ANSWER0} == "predominately blue"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Does the left image feature a barn style door made of weathered-look horizontal wood boards?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many parrots are in the image?'], responses:['2'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
[('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']] |
question: ['What color is the purse in the image?'], responses:['black'] |
[('black', 0.12706825260511387), ('white', 0.12527812565897103), ('dark', 0.1250491849195085), ('purple', 0.12486259083591467), ('orange', 0.12479002203010545), ('red', 0.12434049404478545), ('maroon', 0.12433890776852753), ('blue', 0.12427242213707339)] |
[['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
tensor([1.0000e+00, 6.5751e-09, 1.3074e-10, 1.4587e-07, 2.1368e-10, 3.9338e-10, |
2.7511e-11, 2.0541e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.5751e-09, 1.3074e-10, 1.4587e-07, 2.1368e-10, 3.9338e-10, |
2.7511e-11, 2.0541e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([9.9273e-01, 6.6890e-03, 8.8320e-05, 7.5844e-06, 6.2828e-06, 2.1900e-05, |
5.9086e-05, 4.0098e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
black ************* |
['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([9.9273e-01, 6.6890e-03, 8.8320e-05, 7.5844e-06, 6.2828e-06, 2.1900e-05, |
5.9086e-05, 4.0098e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3074e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1908e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many baboons are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['Does the left image feature a barn style door made of weathered-look horizontal wood boards?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many baboons 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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
tensor([1.0000e+00, 4.7543e-10, 5.0800e-11, 1.1992e-10, 8.5027e-11, 1.4006e-08, |
1.6990e-08, 2.2103e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7543e-10, 5.0800e-11, 1.1992e-10, 8.5027e-11, 1.4006e-08, |
1.6990e-08, 2.2103e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.1949e-08, 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>)} |
tensor([1.0000e+00, 2.4061e-06, 3.4321e-08, 6.8647e-09, 1.8048e-09, 1.3360e-09, |
6.6534e-09, 4.2092e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.4061e-06, 3.4321e-08, 6.8647e-09, 1.8048e-09, 1.3360e-09, |
6.6534e-09, 4.2092e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.8647e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many test tubes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['How many test tubes are in the image?'], responses:['four'] |
[('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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([1.8646e-13, 9.9937e-01, 2.9718e-05, 4.9488e-04, 3.4367e-05, 2.0268e-05, |
4.5008e-05, 3.9566e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([1.8646e-13, 9.9937e-01, 2.9718e-05, 4.9488e-04, 3.4367e-05, 2.0268e-05, |
4.5008e-05, 3.9566e-06], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.4367e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.8884e-05, device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 1.0657e-06, 1.0158e-06, 1.4964e-12, 7.5433e-12, 1.4706e-10, |
1.7264e-10, 6.4979e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.0657e-06, 1.0158e-06, 1.4964e-12, 7.5433e-12, 1.4706e-10, |
1.7264e-10, 6.4979e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0657e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6689e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-24 10:38:56,668] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.37 | optimizer_step: 0.33 |
[2024-10-24 10:38:56,668] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2500.72 | backward_microstep: 3707.59 | backward_inner_microstep: 2192.45 | backward_allreduce_microstep: 1514.96 | step_microstep: 7.86 |
[2024-10-24 10:38:56,669] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2500.73 | backward: 3707.58 | backward_inner: 2192.50 | backward_allreduce: 1514.94 | step: 7.87 |
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