text stringlengths 0 1.16k |
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5.7537e-02, 2.6560e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.9996e-01, 1.9278e-02, 9.6902e-03, 4.4335e-03, 5.6963e-03, 3.1378e-03, |
5.7537e-02, 2.6560e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9575, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0425, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pigs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many hyenas are laying on the ground in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([8.7578e-01, 1.6040e-02, 5.0428e-03, 1.5863e-03, 2.1037e-03, 1.1221e-03, |
9.8260e-02, 6.6026e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7578e-01, 1.6040e-02, 5.0428e-03, 1.5863e-03, 2.1037e-03, 1.1221e-03, |
9.8260e-02, 6.6026e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.1242, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8758, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many elephants are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([7, 3, 448, 448]) |
question: ['How many pigs are in the image?'], responses:['3'] |
question: ['How many hyenas are laying on the ground in the image?'], responses:['2'] |
[('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']] |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many elephants are in the image?'], responses:['2'] |
tensor([9.7936e-01, 2.7506e-03, 1.1829e-03, 5.4013e-04, 7.3983e-04, 5.1639e-04, |
1.4870e-02, 4.4739e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7936e-01, 2.7506e-03, 1.1829e-03, 5.4013e-04, 7.3983e-04, 5.1639e-04, |
1.4870e-02, 4.4739e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.0206, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9794, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
[('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']] |
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]) |
torch.Size([7, 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([7.3098e-01, 2.8225e-02, 3.6248e-02, 4.4852e-03, 9.0647e-04, 1.9596e-01, |
2.4662e-03, 7.2762e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([7.3098e-01, 2.8225e-02, 3.6248e-02, 4.4852e-03, 9.0647e-04, 1.9596e-01, |
2.4662e-03, 7.2762e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.7310, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2690, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([6.9608e-01, 1.0675e-01, 2.5382e-02, 1.7447e-02, 1.3864e-03, 1.4651e-01, |
5.8532e-03, 5.9622e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([6.9608e-01, 1.0675e-01, 2.5382e-02, 1.7447e-02, 1.3864e-03, 1.4651e-01, |
5.8532e-03, 5.9622e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.1465, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8535, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many water buffaloes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([6.6896e-01, 2.7313e-02, 3.6959e-03, 2.9685e-01, 1.6575e-03, 6.6041e-04, |
7.5022e-04, 1.1441e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.6896e-01, 2.7313e-02, 3.6959e-03, 2.9685e-01, 1.6575e-03, 6.6041e-04, |
7.5022e-04, 1.1441e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.6690, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3310, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([13, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many anemones are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([9.2539e-01, 4.6072e-02, 6.0422e-03, 1.9198e-02, 2.1548e-03, 5.1123e-04, |
5.9818e-04, 3.6067e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.2539e-01, 4.6072e-02, 6.0422e-03, 1.9198e-02, 2.1548e-03, 5.1123e-04, |
5.9818e-04, 3.6067e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9254, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0746, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 2 has a total capacty of 44.34 GiB of which 3.58 GiB is free. Including non-PyTorch memory, this process has 40.75 GiB memory in use. Of the allocated memory 38.07 GiB is allocated by PyTorch, and 2.04 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 3 has a total capacty of 44.34 GiB of which 3.32 GiB is free. Including non-PyTorch memory, this process has 41.00 GiB memory in use. Of the allocated memory 38.08 GiB is allocated by PyTorch, and 2.37 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:29:57,734] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-22 17:29:57,734] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9577.55 | backward_microstep: 10443.31 | backward_inner_microstep: 9040.78 | backward_allreduce_microstep: 1402.47 | step_microstep: 7.60 |
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