Datasets:
TMarcus
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InternVL

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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.2133e-09, 1.5894e-10, 3.9442e-08, 8.7170e-10, 7.0127e-10,
1.2352e-10, 1.5718e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.9573e-08, 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(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5894e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1905e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a bird flying in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a bird flying 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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
question: ['How many pillows are in the image?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 4.7901e-09, 4.0630e-11, 3.8039e-08, 6.5803e-10, 1.2187e-10,
7.9820e-11, 2.4019e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.7901e-09, 4.0630e-11, 3.8039e-08, 6.5803e-10, 1.2187e-10,
7.9820e-11, 2.4019e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.0630e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.0630e-11, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.3910e-09, 1.3384e-10, 1.1009e-10, 9.9457e-11, 1.7248e-08,
5.2036e-07, 1.6542e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3910e-09, 1.3384e-10, 1.1009e-10, 9.9457e-11, 1.7248e-08,
5.2036e-07, 1.6542e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7623e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:39:10,480] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.36 | optimizer_step: 0.32
[2024-10-24 10:39:10,481] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5037.92 | backward_microstep: 8751.20 | backward_inner_microstep: 4816.56 | backward_allreduce_microstep: 3934.55 | step_microstep: 7.77
[2024-10-24 10:39:10,481] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5037.93 | backward: 8751.19 | backward_inner: 4816.59 | backward_allreduce: 3934.53 | step: 7.78
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 4801/4844 [19:57:54<09:20, 13.03s/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 EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there at least one person standing outside the hut?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are all of the mittens in the image red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing up?')
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])
question: ['Are all of the mittens in the image red?'], 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([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are standing up?'], 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
tensor([1.0000e+00, 4.2258e-09, 6.9040e-10, 8.8006e-09, 1.1205e-09, 1.1538e-10,
1.0016e-11, 6.9500e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2258e-09, 6.9040e-10, 8.8006e-09, 1.1205e-09, 1.1538e-10,
1.0016e-11, 6.9500e-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(6.9040e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.9040e-10, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the laptop facing right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
torch.Size([7, 3, 448, 448])
question: ['Is there at least one person standing outside the hut?'], responses:['no']
question: ['How many rodents are in the image?'], responses:['1']
[('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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
[('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']]