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1.3303e-10, 3.0739e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.5851e-08, 1.0881e-10, 9.2533e-08, 3.9594e-09, 2.4856e-09,
1.3303e-10, 3.0739e-08], 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(1.0881e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.9780e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.9780e-10, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many vending machines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many dogs 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 2.4047e-10, 5.9391e-11, 2.4234e-10, 1.3915e-10, 1.5221e-08,
6.7057e-09, 1.8231e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.4047e-10, 5.9391e-11, 2.4234e-10, 1.3915e-10, 1.5221e-08,
6.7057e-09, 1.8231e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.7057e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many vending machines 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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 6.4654e-09, 4.4136e-11, 1.5396e-08, 1.0587e-10, 1.5647e-10,
2.9179e-11, 7.8482e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.4654e-09, 4.4136e-11, 1.5396e-08, 1.0587e-10, 1.5647e-10,
2.9179e-11, 7.8482e-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(4.4136e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.4136e-11, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='What color is the keyboard?')
ANSWER1=EVAL(expr='{ANSWER0} == "black"')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.9999e-01, 6.9623e-06, 6.0235e-08, 3.0288e-08, 3.4518e-09, 6.6915e-10,
7.7787e-09, 1.9397e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 6.9623e-06, 6.0235e-08, 3.0288e-08, 3.4518e-09, 6.6915e-10,
7.7787e-09, 1.9397e-10], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.0346e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many ducks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many ducks 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['What color is the keyboard?'], responses:['black']
[('black', 0.12706825260511387), ('white', 0.12527812565897103), ('dark', 0.1250491849195085), ('purple', 0.12486259083591467), ('orange', 0.12479002203010545), ('red', 0.12434049404478545), ('maroon', 0.12433890776852753), ('blue', 0.12427242213707339)]
[['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.6305e-09, 2.4378e-10, 4.2701e-10, 7.0127e-10, 5.6027e-09,
3.6535e-08, 1.7645e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.6305e-09, 2.4378e-10, 4.2701e-10, 7.0127e-10, 5.6027e-09,
3.6535e-08, 1.7645e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.5317e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 1.0783e-05, 4.5468e-08, 5.4304e-09, 2.2994e-09, 8.4589e-10,
6.0580e-09, 1.7388e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 1.0783e-05, 4.5468e-08, 5.4304e-09, 2.2994e-09, 8.4589e-10,
6.0580e-09, 1.7388e-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(1.0844e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.9548e-01, 1.8045e-03, 1.8018e-03, 9.7726e-06, 3.1997e-05, 4.2092e-04,
3.2724e-04, 1.2280e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
black *************
['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([9.9548e-01, 1.8045e-03, 1.8018e-03, 9.7726e-06, 3.1997e-05, 4.2092e-04,
3.2724e-04, 1.2280e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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>)}
[2024-10-24 10:14:00,814] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.31
[2024-10-24 10:14:00,815] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3114.61 | backward_microstep: 14523.18 | backward_inner_microstep: 3010.59 | backward_allreduce_microstep: 11512.52 | step_microstep: 7.30
[2024-10-24 10:14:00,815] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3114.61 | backward: 14523.17 | backward_inner: 3010.61 | backward_allreduce: 11512.50 | step: 7.32
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4698/4844 [19:32:44<37:22, 15.36s/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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many wine bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')