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InternVL

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1.5123e-11, 8.5336e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8702e-09, 1.9172e-10, 1.3886e-08, 3.4291e-10, 4.7450e-10,
1.5123e-11, 8.5336e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.9172e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.9172e-10, device='cuda:0', grad_fn=<SubBackward0>)}
question: ['Is the dog wearing a collar?'], 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.3183e-09, 8.1177e-11, 5.2420e-08, 1.1769e-10, 2.5148e-11,
2.1775e-11, 1.1603e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.3183e-09, 8.1177e-11, 5.2420e-08, 1.1769e-10, 2.5148e-11,
2.1775e-11, 1.1603e-08], 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(8.1177e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1913e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([0.8665, 0.0043, 0.0534, 0.0303, 0.0080, 0.0058, 0.0160, 0.0157],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
50 *************
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([0.8665, 0.0043, 0.0534, 0.0303, 0.0080, 0.0058, 0.0160, 0.0157],
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(-2.3842e-07, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is there a dog in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.6456e-08, 3.1924e-09, 1.9522e-07, 5.4599e-10, 7.9014e-09,
5.4499e-10, 2.7254e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6456e-08, 3.1924e-09, 1.9522e-07, 5.4599e-10, 7.9014e-09,
5.4499e-10, 2.7254e-08], 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(3.1924e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3523e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.4424e-08, 3.4716e-10, 1.5095e-08, 5.6293e-11, 1.2771e-10,
3.2869e-11, 5.4926e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.4424e-08, 3.4716e-10, 1.5095e-08, 5.6293e-11, 1.2771e-10,
3.2869e-11, 5.4926e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(3.4716e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-3.4716e-10, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-24 10:21:04,243] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 10:21:04,243] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3831.77 | backward_microstep: 10046.35 | backward_inner_microstep: 3542.56 | backward_allreduce_microstep: 6503.62 | step_microstep: 7.65
[2024-10-24 10:21:04,243] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3831.78 | backward: 10046.34 | backward_inner: 3542.60 | backward_allreduce: 6503.60 | step: 7.66
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4727/4844 [19:39:48<29:52, 15.32s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the headboard in the image upholstered?')
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='Are there any hands holding the wine glasses in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many containers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are there any hands holding the wine glasses in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many white dogs are in the image?'], responses:['3']
question: ['Is the headboard in the image upholstered?'], responses:['no']
question: ['How many containers 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']]
[('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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
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
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, 1.9381e-07, 6.7666e-09, 1.3935e-07, 1.3455e-08, 2.0862e-08,
4.5764e-10, 7.3372e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.9381e-07, 6.7666e-09, 1.3935e-07, 1.3455e-08, 2.0862e-08,
4.5764e-10, 7.3372e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.7666e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7007e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')