Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 6.6865e-09, 6.1276e-11, 9.3141e-09, 2.5137e-10, 1.4472e-10,
1.3255e-10, 8.9795e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.6865e-09, 6.1276e-11, 9.3141e-09, 2.5137e-10, 1.4472e-10,
1.3255e-10, 8.9795e-10], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9997e-01, 2.4747e-08, 3.1202e-05, 1.0684e-09, 1.7009e-11, 1.9779e-10,
1.5277e-10, 2.2489e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9997e-01, 2.4747e-08, 3.1202e-05, 1.0684e-09, 1.7009e-11, 1.9779e-10,
1.5277e-10, 2.2489e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.1276e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.1276e-11, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many shoes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.1202e-05, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.1163e-08, device='cuda:0', 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)
torch.Size([5, 3, 448, 448])
torch.Size([4, 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']]
question: ['How many shoes 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([4, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
question: ['How many wild dogs are in the image?'], responses:['2']
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
[('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: 4, images per sample: 4.0, dynamic token length: 1096
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1094
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1094
dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1094
tensor([1.0000e+00, 3.4703e-09, 3.0737e-11, 2.1761e-08, 3.9935e-10, 1.3809e-10,
6.2226e-11, 3.0353e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.4703e-09, 3.0737e-11, 2.1761e-08, 3.9935e-10, 1.3809e-10,
6.2226e-11, 3.0353e-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(3.0737e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.0737e-11, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.9649e-07, 1.6894e-07, 4.5921e-07, 1.8190e-09, 9.1463e-10,
8.2630e-10, 1.1314e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.9649e-07, 1.6894e-07, 4.5921e-07, 1.8190e-09, 9.1463e-10,
8.2630e-10, 1.1314e-10], 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(9.2831e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.9800e-08, 3.6529e-09, 5.9642e-09, 2.8780e-10, 8.5242e-10,
8.3203e-10, 9.8723e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.9800e-08, 3.6529e-09, 5.9642e-09, 2.8780e-10, 8.5242e-10,
8.3203e-10, 9.8723e-10], 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(9.2376e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([4.3491e-01, 5.3096e-01, 3.3943e-02, 1.0573e-05, 1.5723e-04, 1.1384e-07,
1.2748e-05, 1.6690e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.3491e-01, 5.3096e-01, 3.3943e-02, 1.0573e-05, 1.5723e-04, 1.1384e-07,
1.2748e-05, 1.6690e-09], 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(1.0573e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:12:47,355] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.33
[2024-10-24 10:12:47,355] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6143.49 | backward_microstep: 7743.29 | backward_inner_microstep: 5811.39 | backward_allreduce_microstep: 1931.82 | step_microstep: 8.34
[2024-10-24 10:12:47,355] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6143.49 | backward: 7743.28 | backward_inner: 5811.42 | backward_allreduce: 1931.80 | step: 8.35
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4693/4844 [19:31:31<37:00, 14.70s/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=LEFT,question='Is the panda on the left image with its mouth open?')
ANSWER1=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image on the right standing up on all four?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are lying down 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])
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
question: ['How many dogs are lying down 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([3, 3, 448, 448]) knan debug pixel values shape