text stringlengths 0 1.16k |
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tensor([6.6743e-01, 3.3160e-01, 6.2741e-05, 1.2985e-04, 7.4728e-05, 1.4166e-04, |
5.1933e-04, 4.7270e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.6743e-01, 3.3160e-01, 6.2741e-05, 1.2985e-04, 7.4728e-05, 1.4166e-04, |
5.1933e-04, 4.7270e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3316, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6674, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['Is there a flying bird in the image?'], 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([8.1667e-01, 1.9891e-02, 1.6082e-01, 1.1578e-03, 8.4228e-05, 2.8979e-04, |
4.7773e-05, 1.0398e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.1667e-01, 1.9891e-02, 1.6082e-01, 1.1578e-03, 8.4228e-05, 2.8979e-04, |
4.7773e-05, 1.0398e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8167, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.1608, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0225, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([6.2514e-01, 3.7312e-01, 6.1991e-05, 1.3154e-04, 1.2416e-04, 1.0271e-03, |
3.1753e-04, 8.0362e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.2514e-01, 3.7312e-01, 6.1991e-05, 1.3154e-04, 1.2416e-04, 1.0271e-03, |
3.1753e-04, 8.0362e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3731, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.6251, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0017, device='cuda:3', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['3'] |
question: ['How many pandas are in the image?'], responses:['1'] |
[('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']] |
[('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']] |
tensor([8.9916e-01, 1.9814e-02, 7.8530e-02, 1.6341e-03, 9.6868e-05, 3.2054e-04, |
5.0132e-05, 3.9163e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9916e-01, 1.9814e-02, 7.8530e-02, 1.6341e-03, 9.6868e-05, 3.2054e-04, |
5.0132e-05, 3.9163e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8992, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0785, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0223, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the image contain a flower?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
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 |
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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['Does the image contain a flower?'], 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']] |
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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.5540, 0.1915, 0.0178, 0.0484, 0.0039, 0.1678, 0.0157, 0.0010], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5540, 0.1915, 0.0178, 0.0484, 0.0039, 0.1678, 0.0157, 0.0010], |
device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7396, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2604, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the baby seal lying down?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
tensor([9.4175e-01, 1.0139e-02, 4.2268e-03, 1.4144e-03, 2.1923e-03, 1.3056e-03, |
3.8867e-02, 1.0060e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4175e-01, 1.0139e-02, 4.2268e-03, 1.4144e-03, 2.1923e-03, 1.3056e-03, |
3.8867e-02, 1.0060e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0389, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9611, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a barber pole in the image?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is there a barber pole in the image?'], 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 |
tensor([4.8372e-01, 5.1493e-01, 2.3439e-05, 1.9839e-04, 4.8162e-04, 1.7930e-04, |
4.3626e-04, 3.7234e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.8372e-01, 5.1493e-01, 2.3439e-05, 1.9839e-04, 4.8162e-04, 1.7930e-04, |
4.3626e-04, 3.7234e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5149, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.4837, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0014, device='cuda:3', grad_fn=<SubBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 932.94 MiB is free. Including non-PyTorch memory, this process has 43.41 GiB memory in use. Of the allocated memory 40.69 GiB is allocated by PyTorch, and 2.11 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 1.17 GiB. GPU 2 has a total capacty of 44.34 GiB of which 186.94 MiB is free. Including non-PyTorch memory, this process has 44.14 GiB memory in use. Of the allocated memory 40.99 GiB is allocated by PyTorch, and 2.52 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 17:28:13,055] [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 17:28:13,055] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12795.50 | backward_microstep: 11348.89 | backward_inner_microstep: 10722.72 | backward_allreduce_microstep: 625.82 | step_microstep: 7.52 |
[2024-10-22 17:28:13,055] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12795.52 | backward: 11348.88 | backward_inner: 10722.96 | backward_allreduce: 625.81 | step: 7.53 |
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