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InternVL

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2.4162e-10, 1.7937e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.7435e-10, 6.6795e-07, 2.1510e-11, 3.7688e-11, 1.0319e-08,
2.4162e-10, 1.7937e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.7435e-10, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.5034e-06, device='cuda:3', grad_fn=<SubBackward0>)}
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, 4.7922e-09, 6.3852e-10, 3.2107e-10, 6.7969e-10, 2.7578e-08,
3.6899e-07, 2.0249e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7922e-09, 6.3852e-10, 3.2107e-10, 6.7969e-10, 2.7578e-08,
3.6899e-07, 2.0249e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.7922e-09, 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, 3.4663e-07, 5.7806e-09, 1.6537e-06, 6.2862e-10, 3.4708e-10,
1.0203e-09, 2.3048e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.4663e-07, 5.7806e-09, 1.6537e-06, 6.2862e-10, 3.4708e-10,
1.0203e-09, 2.3048e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6537e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:01:10,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.24 | optimizer_step: 0.30
[2024-10-24 10:01:10,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9001.34 | backward_microstep: 8775.80 | backward_inner_microstep: 8699.95 | backward_allreduce_microstep: 75.73 | step_microstep: 7.88
[2024-10-24 10:01:10,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9001.35 | backward: 8775.79 | backward_inner: 8699.99 | backward_allreduce: 75.71 | step: 7.89
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4648/4844 [19:19:53<53:00, 16.23s/it]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='Does the pencil case to the left contain a lot of the color pink?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the sleeping cat snuggling with a dog?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the dog's head laying down in the left picture?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the pencil case to the left contain a lot of the color pink?'], responses:['yes']
question: ['How many bottles are in the image?'], responses:['3']
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1870
question: ['Is the sleeping cat snuggling with a dog?'], responses:['no']
question: ['Is the dog'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
[('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']]
[('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: 1868
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: 7, images per sample: 7.0, dynamic token length: 1867
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868
tensor([1.0000e+00, 8.5707e-10, 6.8256e-08, 8.5521e-10, 3.1256e-11, 8.6908e-12,
2.2227e-12, 2.2024e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.5707e-10, 6.8256e-08, 8.5521e-10, 3.1256e-11, 8.6908e-12,
2.2227e-12, 2.2024e-09], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.4844e-07, 5.5305e-07, 5.0964e-10, 8.0910e-11, 1.8907e-07,
1.9922e-10, 1.7362e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 1.4844e-07, 5.5305e-07, 5.0964e-10, 8.0910e-11, 1.8907e-07,
1.9922e-10, 1.7362e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(6.8256e-08, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.0953e-08, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many canines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.4212e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image contain a human hand?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
question: ['How many canines 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([1, 3, 448, 448]) knan debug pixel values shape