Datasets:
TMarcus
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InternVL

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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
[('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([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: 1860
question: ['How many kinds of animals are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.0356e-01, 6.0909e-03, 1.3584e-03, 2.7297e-02, 9.3298e-04, 2.8606e-03,
5.7763e-02, 1.3463e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.0356e-01, 6.0909e-03, 1.3584e-03, 2.7297e-02, 9.3298e-04, 2.8606e-03,
5.7763e-02, 1.3463e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([1.0000e+00, 6.8256e-08, 3.0765e-08, 3.2563e-07, 3.1363e-10, 2.2461e-09,
9.3927e-10, 2.6176e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.8256e-08, 3.0765e-08, 3.2563e-07, 3.1363e-10, 2.2461e-09,
9.3927e-10, 2.6176e-10], 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(4.2841e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['How many seals are in the image?'], responses:['40']
[('40', 0.12638022987124733), ('39', 0.12509919407251455), ('42', 0.12494223232783619), ('41', 0.12482626048065008), ('45', 0.12479694604159434), ('38', 0.12473125094691345), ('47', 0.1246423477331973), ('32', 0.1245815385260468)]
[['40', '39', '42', '41', '45', '38', '47', '32']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.1155e-08, 2.0730e-10, 4.8700e-08, 2.3476e-10, 2.7306e-09,
1.3863e-10, 5.1646e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.1155e-08, 2.0730e-10, 4.8700e-08, 2.3476e-10, 2.7306e-09,
1.3863e-10, 5.1646e-09], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.7703e-01, 2.2973e-02, 1.3466e-07, 2.0659e-11, 5.4729e-12, 7.5033e-10,
7.9097e-11, 1.3987e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.7703e-01, 2.2973e-02, 1.3466e-07, 2.0659e-11, 5.4729e-12, 7.5033e-10,
7.9097e-11, 1.3987e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0230, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9770, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9802e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are people seen with the dogs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.0730e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1900e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many vultures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Are people seen with the dogs?'], 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
tensor([9.5703e-01, 2.2682e-03, 1.1539e-03, 7.7951e-04, 2.6960e-02, 1.3026e-03,
8.3739e-03, 2.1304e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
40 *************
['40', '39', '42', '41', '45', '38', '47', '32'] tensor([9.5703e-01, 2.2682e-03, 1.1539e-03, 7.7951e-04, 2.6960e-02, 1.3026e-03,
8.3739e-03, 2.1304e-03], 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(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many vultures are in the image?'], responses:['2']
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
[('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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([9.9641e-01, 1.4995e-07, 3.5936e-03, 1.1645e-07, 2.5185e-10, 1.3681e-09,
1.9885e-09, 7.1531e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9641e-01, 1.4995e-07, 3.5936e-03, 1.1645e-07, 2.5185e-10, 1.3681e-09,
1.9885e-09, 7.1531e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9964, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0036, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.9348e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9890e-01, 1.0987e-03, 1.3695e-06, 7.8475e-08, 1.0895e-07, 3.3879e-09,
1.4434e-07, 3.1456e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9890e-01, 1.0987e-03, 1.3695e-06, 7.8475e-08, 1.0895e-07, 3.3879e-09,
1.4434e-07, 3.1456e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9989, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0011, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:27:03,648] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-24 09:27:03,648] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3728.08 | backward_microstep: 6123.97 | backward_inner_microstep: 3493.89 | backward_allreduce_microstep: 2630.01 | step_microstep: 7.59
[2024-10-24 09:27:03,648] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3728.09 | backward: 6123.96 | backward_inner: 3493.90 | backward_allreduce: 2629.99 | step: 7.60
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4513/4844 [18:45:47<1:10:39, 12.81s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step