Datasets:
TMarcus
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InternVL

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[['4', '5', '3', '8', '6', '1', '2', '11']]
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
tensor([1.0000e+00, 2.1748e-09, 6.4852e-10, 1.8537e-09, 1.4615e-09, 9.7730e-08,
8.6281e-08, 1.9208e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1748e-09, 6.4852e-10, 1.8537e-09, 1.4615e-09, 9.7730e-08,
8.6281e-08, 1.9208e-09], 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(1.9207e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
tensor([7.7686e-01, 2.6499e-07, 2.2258e-01, 3.5924e-04, 1.2375e-05, 1.8064e-04,
2.8299e-06, 5.4914e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([7.7686e-01, 2.6499e-07, 2.2258e-01, 3.5924e-04, 1.2375e-05, 1.8064e-04,
2.8299e-06, 5.4914e-06], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([1.0000e+00, 3.4663e-07, 4.9856e-08, 2.4514e-12, 1.4012e-12, 1.7590e-10,
4.5074e-11, 8.3788e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.4663e-07, 4.9856e-08, 2.4514e-12, 1.4012e-12, 1.7590e-10,
4.5074e-11, 8.3788e-08], 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(3.4663e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the laptop in the image turned at an angle?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many puppies 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']]
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
question: ['Is the laptop in the image turned at an angle?'], responses:['yes']
tensor([9.9749e-01, 2.5108e-03, 1.2752e-06, 1.2527e-10, 9.4068e-09, 4.9046e-07,
4.2097e-08, 2.3774e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9749e-01, 2.5108e-03, 1.2752e-06, 1.2527e-10, 9.4068e-09, 4.9046e-07,
4.2097e-08, 2.3774e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9975, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0025, 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
[('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
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
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, 4.6912e-08, 2.1855e-09, 1.2825e-08, 2.0730e-10, 3.7241e-10,
3.4172e-10, 2.2933e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.6912e-08, 2.1855e-09, 1.2825e-08, 2.0730e-10, 3.7241e-10,
3.4172e-10, 2.2933e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.3073e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 9.4174e-09, 3.6899e-07, 6.0177e-09, 9.0559e-11, 9.7362e-10,
6.9799e-11, 2.0873e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.4174e-09, 3.6899e-07, 6.0177e-09, 9.0559e-11, 9.7362e-10,
6.9799e-11, 2.0873e-09], 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(3.6899e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1361e-08, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:38:18,840] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 10:38:18,841] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8379.41 | backward_microstep: 9393.10 | backward_inner_microstep: 8138.49 | backward_allreduce_microstep: 1254.48 | step_microstep: 7.70
[2024-10-24 10:38:18,841] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8379.43 | backward: 9393.09 | backward_inner: 8138.53 | backward_allreduce: 1254.42 | step: 7.71
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 4797/4844 [19:57:02<11:34, 14.78s/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 the dog looking toward the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Which direction is the dog facing?')
ANSWER1=EVAL(expr='{ANSWER0} == "left"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the dog looking at the camera?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the computer angled so that the screen isn't visible?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the computer angled so that the screen isn'], 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)]