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InternVL

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4.7911e-09, 2.7823e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.8669e-07, 6.9358e-09, 2.0335e-08, 1.3518e-09, 1.8476e-09,
4.7911e-09, 2.7823e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0335e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., 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: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 1.9041e-09, 3.6955e-10, 1.5717e-08, 7.3886e-11, 1.3834e-11,
3.3405e-11, 1.0782e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.9041e-09, 3.6955e-10, 1.5717e-08, 7.3886e-11, 1.3834e-11,
3.3405e-11, 1.0782e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.6955e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.6955e-10, device='cuda:2', grad_fn=<DivBackward0>)}
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([1.3308e-01, 7.2646e-02, 1.9412e-02, 1.2079e-02, 7.6235e-01, 2.6787e-06,
1.9267e-07, 4.2777e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
6 *************
['12', '11', '10', '8', '6', '26', '47', '13'] tensor([1.3308e-01, 7.2646e-02, 1.9412e-02, 1.2079e-02, 7.6235e-01, 2.6787e-06,
1.9267e-07, 4.2777e-04], 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>)}
ANSWER0=VQA(image=LEFT,question='Does the image show a female working out?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
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
question: ['Does the image show a female working out?'], 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([9.9828e-01, 1.6959e-03, 1.9425e-05, 2.5550e-09, 1.1135e-06, 1.3407e-07,
1.0332e-08, 1.0831e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9828e-01, 1.6959e-03, 1.9425e-05, 2.5550e-09, 1.1135e-06, 1.3407e-07,
1.0332e-08, 1.0831e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3407e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.7236e-10, 8.5783e-07, 1.0565e-11, 1.8175e-09, 4.7752e-09,
2.4399e-10, 1.1893e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.7236e-10, 8.5783e-07, 1.0565e-11, 1.8175e-09, 4.7752e-09,
2.4399e-10, 1.1893e-06], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.7236e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:06:13,240] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.32
[2024-10-24 10:06:13,241] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5811.85 | backward_microstep: 8056.99 | backward_inner_microstep: 5482.27 | backward_allreduce_microstep: 2574.58 | step_microstep: 8.15
[2024-10-24 10:06:13,241] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5811.86 | backward: 8056.98 | backward_inner: 5482.30 | backward_allreduce: 2574.56 | step: 8.17
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4667/4844 [19:24:57<45:23, 15.39s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='What is the dispenser sitting on?')
ANSWER1=EVAL(expr='{ANSWER0} == "wood"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the package?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is the dog against a white background?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many boats are in the image?'], responses:['1']
question: ['What is the dispenser sitting on?'], responses:['nothing']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
[('nothing', 0.12882454165679308), ('nobody', 0.12525277455589356), ('no man', 0.125234976793315), ('no plate', 0.1248462854941661), ('someone', 0.12431265213590703), ('nowhere', 0.12397417358773222), ('no cat', 0.12379357649611661), ('surfing', 0.12376101928007635)]
[['nothing', 'nobody', 'no man', 'no plate', 'someone', 'nowhere', 'no cat', 'surfing']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many rolls of paper towels are in the package?'], responses:['1']
tensor([1.0000e+00, 5.2318e-10, 1.2091e-10, 4.8009e-10, 2.6000e-10, 2.0486e-08,
1.8660e-08, 2.3028e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.2318e-10, 1.2091e-10, 4.8009e-10, 2.6000e-10, 2.0486e-08,
1.8660e-08, 2.3028e-10], device='cuda:2', grad_fn=<SelectBackward0>)
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
tensor([8.6976e-01, 2.2020e-02, 3.5353e-04, 3.7773e-03, 5.0020e-04, 1.0166e-01,
1.2427e-03, 6.8003e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
nothing *************
['nothing', 'nobody', 'no man', 'no plate', 'someone', 'nowhere', 'no cat', 'surfing'] tensor([8.6976e-01, 2.2020e-02, 3.5353e-04, 3.7773e-03, 5.0020e-04, 1.0166e-01,
1.2427e-03, 6.8003e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.0761e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the house behind a fence?')
ANSWER1=EVAL(expr='{ANSWER0}')