Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.5539e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3411
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3408
tensor([1.0000e+00, 2.1621e-08, 5.8137e-10, 1.6467e-07, 6.5544e-10, 4.6449e-09,
4.8418e-10, 1.8892e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.1621e-08, 5.8137e-10, 1.6467e-07, 6.5544e-10, 4.6449e-09,
4.8418e-10, 1.8892e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.8137e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3784e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3408
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3408
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409
question: ['How many animals 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 6.9599e-09, 1.1628e-10, 6.7692e-09, 7.6257e-11, 9.1987e-11,
3.8749e-12, 4.6816e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.9599e-09, 1.1628e-10, 6.7692e-09, 7.6257e-11, 9.1987e-11,
3.8749e-12, 4.6816e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1628e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1909e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 4.4375e-09, 1.3730e-09, 6.5224e-09, 2.4241e-11, 1.2378e-10,
1.4015e-11, 1.3617e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.4375e-09, 1.3730e-09, 6.5224e-09, 2.4241e-11, 1.2378e-10,
1.4015e-11, 1.3617e-08], device='cuda:0', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.3730e-09, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.3730e-09, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many syringes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many animals 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']]
question: ['How many syringes 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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([6.0014e-02, 1.7043e-03, 5.4852e-08, 4.0038e-05, 2.4574e-07, 9.3824e-01,
4.6348e-06, 1.3761e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([6.0014e-02, 1.7043e-03, 5.4852e-08, 4.0038e-05, 2.4574e-07, 9.3824e-01,
4.6348e-06, 1.3761e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.5056e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 1.0045e-09, 1.9473e-10, 3.4174e-10, 1.4457e-10, 3.3246e-08,
4.6813e-09, 1.0836e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.0045e-09, 1.9473e-10, 3.4174e-10, 1.4457e-10, 3.3246e-08,
4.6813e-09, 1.0836e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.0696e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9934e-01, 6.5334e-04, 5.9695e-07, 1.3649e-07, 1.7470e-10, 9.0100e-07,
6.5456e-10, 7.4965e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9934e-01, 6.5334e-04, 5.9695e-07, 1.3649e-07, 1.7470e-10, 9.0100e-07,
6.5456e-10, 7.4965e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.9695e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:48:01,596] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.64 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 09:48:01,597] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9067.00 | backward_microstep: 8766.03 | backward_inner_microstep: 8761.26 | backward_allreduce_microstep: 4.72 | step_microstep: 7.37
[2024-10-24 09:48:01,597] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9067.01 | backward: 8766.02 | backward_inner: 8761.26 | backward_allreduce: 4.71 | step: 7.38
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4596/4844 [19:06:45<1:07:08, 16.24s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
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
ANSWER0=VQA(image=RIGHT,question='Do the bottles in the image have caps?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many wine bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many power poles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')