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3.2393e-11, 7.5774e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6968e-09, 5.6671e-11, 1.3582e-08, 7.3806e-11, 1.3809e-10,
3.2393e-11, 7.5774e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.6671e-11, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.6671e-11, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([8.6702e-01, 1.3297e-01, 1.9648e-06, 1.4518e-05, 3.8526e-09, 1.9115e-11,
1.9790e-10, 2.7374e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.6702e-01, 1.3297e-01, 1.9648e-06, 1.4518e-05, 3.8526e-09, 1.9115e-11,
1.9790e-10, 2.7374e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4518e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many insects are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 5.1801e-08, 4.8957e-10, 9.7536e-08, 2.5671e-10, 4.6449e-09,
1.9359e-10, 7.6252e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.1801e-08, 4.8957e-10, 9.7536e-08, 2.5671e-10, 4.6449e-09,
1.9359e-10, 7.6252e-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(4.8957e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1872e-07, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many insects 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
tensor([9.8868e-01, 6.2581e-03, 1.9086e-03, 2.0317e-03, 2.7225e-05, 7.9562e-04,
1.7753e-04, 1.2201e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.8868e-01, 6.2581e-03, 1.9086e-03, 2.0317e-03, 2.7225e-05, 7.9562e-04,
1.7753e-04, 1.2201e-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(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.0423e-08, 3.8891e-08, 8.8836e-09, 9.2904e-10, 4.5907e-09,
2.9007e-09, 9.4912e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.0423e-08, 3.8891e-08, 8.8836e-09, 9.2904e-10, 4.5907e-09,
2.9007e-09, 9.4912e-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(1.2757e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:02:35,139] [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 10:02:35,139] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4457.93 | backward_microstep: 9491.75 | backward_inner_microstep: 4234.07 | backward_allreduce_microstep: 5257.58 | step_microstep: 7.54
[2024-10-24 10:02:35,139] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4457.95 | backward: 9491.74 | backward_inner: 4234.09 | backward_allreduce: 5257.56 | step: 7.56
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4653/4844 [19:21:18<52:01, 16.35s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering EVAL stepRegistering RESULT 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 purple and gold saxophones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are inside the store?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many bottles of wine are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Does the left image show mashed potatoes in a round bowl with fluted edges?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many bottles of wine 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
question: ['How many purple and gold saxophones are in the image?'], responses:['1']
question: ['How many people are inside the store?'], responses:['0']
[('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']]
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
tensor([1.0000e+00, 3.5958e-10, 4.6974e-11, 5.3165e-11, 1.7006e-10, 7.8689e-09,
1.7530e-08, 1.7439e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.5958e-10, 4.6974e-11, 5.3165e-11, 1.7006e-10, 7.8689e-09,
1.7530e-08, 1.7439e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.6203e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
ANSWER0=VQA(image=LEFT,question='How many puppies are in the picture?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448])
question: ['Does the left image show mashed potatoes in a round bowl with fluted edges?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
tensor([1.0000e+00, 1.9911e-09, 1.4605e-09, 6.8955e-09, 3.4431e-09, 1.7417e-07,
2.9501e-08, 7.1906e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.9911e-09, 1.4605e-09, 6.8955e-09, 3.4431e-09, 1.7417e-07,