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InternVL

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6.0040e-10, 2.0100e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.2887e-06, 7.8916e-07, 7.0932e-12, 4.3112e-11, 8.0193e-10,
6.0040e-10, 2.0100e-07], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([9.9999e-01, 1.2219e-05, 3.9278e-07, 1.2825e-08, 1.3027e-08, 1.8694e-09,
1.5961e-08, 8.7956e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 1.2219e-05, 3.9278e-07, 1.2825e-08, 1.3027e-08, 1.8694e-09,
1.5961e-08, 8.7956e-10], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.2887e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0133e-06, device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2825e-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=RIGHT,question='Is the sink square?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the sink square?'], responses:['yes']
question: ['How many cheetahs are in the image?'], responses:['1']
[('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']]
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image contain humans?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
tensor([9.9989e-01, 1.1214e-04, 2.0491e-07, 3.0677e-08, 5.8278e-11, 1.4576e-07,
1.8564e-10, 2.1381e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9989e-01, 1.1214e-04, 2.0491e-07, 3.0677e-08, 5.8278e-11, 1.4576e-07,
1.8564e-10, 2.1381e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0001, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
ANSWER0=VQA(image=LEFT,question='How many dumbbells are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([9.9990e-01, 8.9524e-09, 1.4358e-10, 9.6183e-12, 1.3941e-11, 5.7201e-10,
9.6102e-05, 1.6930e-12], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9990e-01, 8.9524e-09, 1.4358e-10, 9.6183e-12, 1.3941e-11, 5.7201e-10,
9.6102e-05, 1.6930e-12], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.6102e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.8647e-10, 9.9312e-08, 2.6356e-10, 6.3414e-12, 1.5582e-12,
6.2467e-12, 3.9607e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.8647e-10, 9.9312e-08, 2.6356e-10, 6.3414e-12, 1.5582e-12,
6.2467e-12, 3.9607e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.9312e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9897e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
question: ['How many dumbbells 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([1.0000e+00, 1.8222e-09, 3.7272e-07, 2.0813e-08, 3.0690e-08, 3.7163e-07,
1.2357e-08, 2.7127e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8222e-09, 3.7272e-07, 2.0813e-08, 3.0690e-08, 3.7163e-07,
1.2357e-08, 2.7127e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8222e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([9.9977e-01, 1.5842e-04, 1.0128e-05, 6.6041e-05, 3.1243e-08, 6.5744e-09,
5.1805e-09, 9.5001e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9977e-01, 1.5842e-04, 1.0128e-05, 6.6041e-05, 3.1243e-08, 6.5744e-09,
5.1805e-09, 9.5001e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.6041e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:12:19,548] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 10:12:19,548] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7050.50 | backward_microstep: 10649.58 | backward_inner_microstep: 6778.62 | backward_allreduce_microstep: 3870.90 | step_microstep: 7.67
[2024-10-24 10:12:19,549] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7050.52 | backward: 10649.57 | backward_inner: 6778.63 | backward_allreduce: 3870.87 | step: 7.68
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4691/4844 [19:31:03<39:36, 15.53s/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 dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many dog sled teams are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)