Datasets:
TMarcus
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InternVL

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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([1.0000e+00, 3.6174e-09, 1.3200e-07, 8.4716e-13, 4.6999e-13, 1.0770e-09,
1.6229e-10, 1.6925e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.6174e-09, 1.3200e-07, 8.4716e-13, 4.6999e-13, 1.0770e-09,
1.6229e-10, 1.6925e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.6174e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.2407e-09, 2.2767e-10, 9.4652e-09, 2.0348e-10, 1.0754e-10,
2.5511e-11, 6.5151e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2407e-09, 2.2767e-10, 9.4652e-09, 2.0348e-10, 1.0754e-10,
2.5511e-11, 6.5151e-09], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([5, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(2.2767e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.2767e-10, device='cuda:3', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 1.5080e-09, 3.7831e-10, 5.5043e-10, 4.2868e-10, 1.0145e-08,
3.0288e-08, 6.3656e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.5080e-09, 3.7831e-10, 5.5043e-10, 4.2868e-10, 1.0145e-08,
3.0288e-08, 6.3656e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='Do all dogs have blue harnesses?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1566e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many insects 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 pillows are in the image?'], responses:['0']
tensor([1.0000e+00, 1.3308e-09, 1.6530e-07, 1.8294e-10, 2.3000e-11, 1.8865e-08,
4.2605e-10, 1.7649e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3308e-09, 1.6530e-07, 1.8294e-10, 2.3000e-11, 1.8865e-08,
4.2605e-10, 1.7649e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3308e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many insects are in the image?'], responses:['1']
question: ['Do all dogs have blue harnesses?'], responses:['yes']
[('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']]
[('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
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
tensor([1.0000e+00, 1.5540e-06, 1.4297e-07, 5.7213e-11, 9.2829e-08, 1.8627e-08,
1.4418e-06, 5.0473e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.5540e-06, 1.4297e-07, 5.7213e-11, 9.2829e-08, 1.8627e-08,
1.4418e-06, 5.0473e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.3379e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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
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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 1.1836e-09, 2.2767e-10, 6.4349e-10, 3.6097e-10, 1.9189e-08,
1.7258e-08, 1.2830e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1836e-09, 2.2767e-10, 6.4349e-10, 3.6097e-10, 1.9189e-08,
1.7258e-08, 1.2830e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7258e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.7393e-09, 3.6174e-09, 2.9140e-09, 2.5858e-11, 4.5537e-11,
3.6038e-12, 4.0572e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.7393e-09, 3.6174e-09, 2.9140e-09, 2.5858e-11, 4.5537e-11,
3.6038e-12, 4.0572e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.6174e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.6174e-09, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:59:03,461] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.33
[2024-10-24 09:59:03,462] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9105.24 | backward_microstep: 8827.58 | backward_inner_microstep: 8739.94 | backward_allreduce_microstep: 87.53 | step_microstep: 7.60
[2024-10-24 09:59:03,462] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9105.26 | backward: 8827.57 | backward_inner: 8739.98 | backward_allreduce: 87.49 | step: 7.61
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4640/4844 [19:17:47<53:35, 15.76s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis stepRegistering 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='Is there a metal utensil in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many balloons are in the sky?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there a dog lying down on a white bed sheet?')