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InternVL

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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([2.8384e-01, 1.5741e-01, 4.4676e-02, 2.1215e-04, 5.9721e-02, 2.9557e-01,
1.5856e-01, 1.1593e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
sliced *************
['scrambled', 'sliding', 'tiled', 'wheel', 'wheels', 'sliced', 'stretching', 'stick'] tensor([2.8384e-01, 1.5741e-01, 4.4676e-02, 2.1215e-04, 5.9721e-02, 2.9557e-01,
1.5856e-01, 1.1593e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([9.9999e-01, 7.3366e-07, 1.1926e-09, 6.5087e-10, 4.9500e-12, 4.7839e-06,
4.1783e-11, 4.2773e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 7.3366e-07, 1.1926e-09, 6.5087e-10, 4.9500e-12, 4.7839e-06,
4.1783e-11, 4.2773e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.3440e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.0127e-08, 5.7360e-09, 7.9948e-09, 4.1877e-10, 6.6901e-10,
6.1800e-10, 3.8738e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.0127e-08, 5.7360e-09, 7.9948e-09, 4.1877e-10, 6.6901e-10,
6.1800e-10, 3.8738e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(4.7956e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.3779e-10, 9.1987e-11, 2.1547e-10, 1.2859e-10, 1.2015e-08,
5.1414e-09, 3.0744e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.3779e-10, 9.1987e-11, 2.1547e-10, 1.2859e-10, 1.2015e-08,
5.1414e-09, 3.0744e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.8238e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:06:34,565] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.32 | optimizer_step: 0.32
[2024-10-24 10:06:34,566] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3210.66 | backward_microstep: 7987.85 | backward_inner_microstep: 3026.15 | backward_allreduce_microstep: 4961.59 | step_microstep: 7.96
[2024-10-24 10:06:34,566] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3210.64 | backward: 7987.84 | backward_inner: 3026.20 | backward_allreduce: 4961.57 | step: 7.97
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4669/4844 [19:25:18<37:59, 13.03s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the person covered with a black smock?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a bird?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many drawers are on the cabinet?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there framed pictures following the line of the stairway?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the person covered with a black smock?'], responses:['yes']
question: ['How many drawers are on the cabinet?'], responses:['7']
question: ['Are there framed pictures following the line of the stairway?'], 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']]
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
question: ['Does the image contain a bird?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
[('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: 1865
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([9.4967e-01, 8.6644e-04, 4.9342e-04, 9.8102e-04, 5.2512e-04, 1.1841e-03,
4.4447e-02, 1.8346e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.4967e-01, 8.6644e-04, 4.9342e-04, 9.8102e-04, 5.2512e-04, 1.1841e-03,
4.4447e-02, 1.8346e-03], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.6731e-09, 3.6174e-09, 2.9218e-09, 1.0001e-11, 9.2514e-12,
3.7735e-12, 3.4358e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6731e-09, 3.6174e-09, 2.9218e-09, 1.0001e-11, 9.2514e-12,
3.7735e-12, 3.4358e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.9036e-08, 3.8949e-11, 5.2360e-08, 9.1633e-11, 3.0161e-10,
7.1608e-11, 1.5530e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************