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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.4714e-01, 1.5466e-02, 3.6718e-02, 3.5531e-04, 3.8781e-05, 1.8060e-04, |
1.8756e-05, 8.3277e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0367, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9471, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0161, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.4269e-01, 9.1393e-03, 4.6927e-02, 6.9991e-04, 3.6467e-05, 1.0921e-04, |
3.2235e-05, 3.6517e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.4269e-01, 9.1393e-03, 4.6927e-02, 6.9991e-04, 3.6467e-05, 1.0921e-04, |
3.2235e-05, 3.6517e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9427, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0469, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0104, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([9.1746e-01, 1.7888e-02, 3.7494e-03, 5.8653e-02, 1.1077e-03, 6.0998e-04, |
4.9074e-04, 3.7165e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.1746e-01, 1.7888e-02, 3.7494e-03, 5.8653e-02, 1.1077e-03, 6.0998e-04, |
4.9074e-04, 3.7165e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9413, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0587, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([7.0305e-01, 1.7812e-02, 2.7532e-01, 1.5978e-03, 1.6103e-04, 5.5040e-04, |
5.8149e-05, 1.4509e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.0305e-01, 1.7812e-02, 2.7532e-01, 1.5978e-03, 1.6103e-04, 5.5040e-04, |
5.8149e-05, 1.4509e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7031, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2753, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0216, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:55:35,547] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.29 | optimizer_step: 0.32 |
[2024-10-23 14:55:35,548] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3824.30 | backward_microstep: 6155.21 | backward_inner_microstep: 3524.76 | backward_allreduce_microstep: 2630.35 | step_microstep: 7.67 |
[2024-10-23 14:55:35,548] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3824.30 | backward: 6155.20 | backward_inner: 3524.80 | backward_allreduce: 2630.32 | step: 7.69 |
1%| | 56/4844 [14:19<17:23:10, 13.07s/it]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 |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Are seats available in the reading area?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Do the mashed potatoes have a spoon handle visibly sticking out of them?') |
ANSWER1=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='How many dogs are standing on grass?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Are there trees in the background of the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Do the mashed potatoes have a spoon handle visibly sticking out of them?'], responses:['yes'] |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
question: ['Are seats available in the reading area?'], responses:['no'] |
question: ['How many dogs are standing on grass?'], responses:['1'] |
[('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: 3, images per sample: 3.0, dynamic token length: 845 |
[('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: 3, images per sample: 3.0, dynamic token length: 844 |
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: 3, images per sample: 3.0, dynamic token length: 844 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
tensor([5.7059e-01, 9.3421e-03, 4.1745e-01, 9.8600e-04, 1.1035e-04, 3.6807e-04, |
2.6900e-05, 1.1280e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.7059e-01, 9.3421e-03, 4.1745e-01, 9.8600e-04, 1.1035e-04, 3.6807e-04, |
2.6900e-05, 1.1280e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5706, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4175, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0120, device='cuda:0', grad_fn=<SubBackward0>)} |
question: ['Are there trees in the background of the image?'], responses:['yes'] |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many dogs are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
tensor([9.3947e-01, 6.0058e-02, 3.1162e-05, 9.0254e-05, 1.0511e-04, 8.4534e-05, |
1.1984e-04, 4.4697e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
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