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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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 unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
ζεηζ¦ηεεΈδΈΊ: {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 unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
ζεηζ¦ηεεΈδΈΊ: {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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