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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['Are the bottles in the image unopened?'], responses:['yes']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
[('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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([8.8095e-01, 1.1240e-01, 2.4600e-04, 3.5417e-07, 2.6436e-08, 8.0052e-04,
1.0303e-05, 5.5922e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
12 *************
['12', '11', '10', '8', '6', '26', '47', '13'] tensor([8.8095e-01, 1.1240e-01, 2.4600e-04, 3.5417e-07, 2.6436e-08, 8.0052e-04,
1.0303e-05, 5.5922e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left image contain a human touching a saxophone?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.3710e-06, 1.3440e-08, 5.9641e-09, 1.0045e-09, 4.2534e-10,
1.2999e-09, 1.0924e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.3710e-06, 1.3440e-08, 5.9641e-09, 1.0045e-09, 4.2534e-10,
1.2999e-09, 1.0924e-10], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 7.5818e-07, 2.8192e-08, 1.2386e-10, 5.3429e-08, 5.9521e-08,
2.1725e-07, 1.9304e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 7.5818e-07, 2.8192e-08, 1.2386e-10, 5.3429e-08, 5.9521e-08,
2.1725e-07, 1.9304e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:0', grad_fn=<DivBackward0>)}
{True: tensor(5.9641e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many rodents are in the image?'], responses:['2']
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Does the left image contain a human touching a saxophone?'], 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']]
question: ['How many hyenas are in the image?'], responses:['2']
[('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']]
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: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([9.9999e-01, 2.7218e-08, 5.7719e-06, 7.0870e-08, 3.6172e-09, 1.5558e-09,
4.3003e-10, 1.3409e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9999e-01, 2.7218e-08, 5.7719e-06, 7.0870e-08, 3.6172e-09, 1.5558e-09,
4.3003e-10, 1.3409e-07], 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(5.7719e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.8854e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.5593e-08, 2.7485e-08, 9.4816e-08, 7.4685e-10, 4.0690e-09,
2.5662e-09, 4.0189e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.5593e-08, 2.7485e-08, 9.4816e-08, 7.4685e-10, 4.0690e-09,
2.5662e-09, 4.0189e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.4816e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 2.5254e-09, 4.6126e-07, 1.1882e-09, 2.6129e-08, 3.0675e-07,
2.5123e-08, 1.6970e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.5254e-09, 4.6126e-07, 1.1882e-09, 2.6129e-08, 3.0675e-07,
2.5123e-08, 1.6970e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.5254e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 9.1325e-07, 2.5254e-09, 2.6165e-07, 5.0511e-10, 4.5454e-10,
1.6691e-09, 1.7962e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 9.1325e-07, 2.5254e-09, 2.6165e-07, 5.0511e-10, 4.5454e-10,
1.6691e-09, 1.7962e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(9.1859e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:32:03,483] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 10:32:03,483] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9113.63 | backward_microstep: 8755.03 | backward_inner_microstep: 8750.27 | backward_allreduce_microstep: 4.66 | step_microstep: 7.35
[2024-10-24 10:32:03,484] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9113.64 | backward: 8755.02 | backward_inner: 8750.29 | backward_allreduce: 4.65 | step: 7.36
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4772/4844 [19:50:47<18:52, 15.73s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a dark blue bottle in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')