Datasets:
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InternVL

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question: ['Does the image show food served in a rectangular dish?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
[('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: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
tensor([9.9995e-01, 4.2647e-05, 1.8161e-06, 2.1940e-09, 5.9388e-11, 1.7061e-06,
2.7676e-10, 8.4104e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9995e-01, 4.2647e-05, 1.8161e-06, 2.1940e-09, 5.9388e-11, 1.7061e-06,
2.7676e-10, 8.4104e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([7.0750e-01, 2.8629e-04, 1.3993e-03, 1.2704e-04, 9.8928e-05, 1.6309e-04,
3.9973e-04, 2.9003e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
black *************
['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([7.0750e-01, 2.8629e-04, 1.3993e-03, 1.2704e-04, 9.8928e-05, 1.6309e-04,
3.9973e-04, 2.9003e-01], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.8161e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image contain a tree house?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the bird facing right?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
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([1.0000e+00, 2.3128e-09, 2.1941e-09, 1.1097e-09, 6.3219e-11, 7.7460e-11,
5.8025e-12, 4.6503e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.3128e-09, 2.1941e-09, 1.1097e-09, 6.3219e-11, 7.7460e-11,
5.8025e-12, 4.6503e-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(2.1941e-09, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.1941e-09, device='cuda:0', grad_fn=<SubBackward0>)}
question: ['Does the image contain a tree house?'], 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: ['Is the bird facing right?'], 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
tensor([1.0000e+00, 2.7439e-09, 4.8958e-10, 1.6736e-08, 4.7687e-11, 4.8474e-11,
3.4135e-11, 5.5305e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7439e-09, 4.8958e-10, 1.6736e-08, 4.7687e-11, 4.8474e-11,
3.4135e-11, 5.5305e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.8958e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.8958e-10, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.0556e-09, 6.6916e-10, 6.5441e-09, 7.3011e-12, 1.4784e-11,
4.5417e-12, 8.4416e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.0556e-09, 6.6916e-10, 6.5441e-09, 7.3011e-12, 1.4784e-11,
4.5417e-12, 8.4416e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.6916e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.6916e-10, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.7150e-07, 1.2930e-07, 5.4385e-12, 1.8367e-12, 1.0260e-09,
3.8305e-11, 7.0218e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.7150e-07, 1.2930e-07, 5.4385e-12, 1.8367e-12, 1.0260e-09,
3.8305e-11, 7.0218e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.7150e-07, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<SubBackward0>)}
[2024-10-24 09:48:37,008] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 09:48:37,008] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5048.64 | backward_microstep: 12663.84 | backward_inner_microstep: 4815.87 | backward_allreduce_microstep: 7847.79 | step_microstep: 7.46
[2024-10-24 09:48:37,008] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5048.65 | backward: 12663.83 | backward_inner: 4815.98 | backward_allreduce: 7847.77 | step: 7.47
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4598/4844 [19:07:20<1:09:40, 16.99s/it]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='Does the image show a schnauzer standing in the snow?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many corgi dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many individual pizzas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many screens are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many individual pizzas 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many screens are in the image?'], responses:['1']
[('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']]
question: ['How many corgi dogs are in the image?'], responses:['δΈ‰']