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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([8.9900e-01, 1.2613e-04, 1.0087e-01, 7.3383e-09, 7.1255e-08, 7.8395e-07,
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5.0390e-07, 3.4737e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1009, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8991, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448])
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question: ['How many black labs are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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question: ['Do the doors in the image open to a grassy area?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([11, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([9.9945e-01, 6.7226e-09, 5.5278e-04, 1.1840e-08, 1.2320e-09, 2.7720e-09,
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7.4301e-10, 1.6209e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9945e-01, 6.7226e-09, 5.5278e-04, 1.1840e-08, 1.2320e-09, 2.7720e-09,
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7.4301e-10, 1.6209e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9994, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0006, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.8429e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are the sails furled in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['How many dogs are in the image?'], responses:['0']
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torch.Size([7, 3, 448, 448])
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[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
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[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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question: ['Are the sails furled in the image?'], responses:['yes']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([9.9867e-01, 8.8398e-07, 2.3557e-08, 2.7001e-10, 1.4147e-09, 7.5725e-10,
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1.3250e-03, 3.5136e-12], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9867e-01, 8.8398e-07, 2.3557e-08, 2.7001e-10, 1.4147e-09, 7.5725e-10,
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1.3250e-03, 3.5136e-12], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
|
tensor([1.0000e+00, 5.2662e-09, 1.9363e-09, 1.3645e-09, 4.4133e-11, 1.5405e-10,
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1.3566e-11, 7.1713e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.2662e-09, 1.9363e-09, 1.3645e-09, 4.4133e-11, 1.5405e-10,
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1.3566e-11, 7.1713e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.9363e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.9363e-09, device='cuda:2', grad_fn=<DivBackward0>)}
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([1.0000e+00, 2.7541e-09, 2.3824e-07, 1.4048e-09, 7.0147e-13, 1.2645e-11,
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5.3042e-12, 4.3400e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7541e-09, 2.3824e-07, 1.4048e-09, 7.0147e-13, 1.2645e-11,
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5.3042e-12, 4.3400e-10], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(2.3824e-07, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.8167e-10, device='cuda:1', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many syringes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([5, 3, 448, 448])
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question: ['How many animals are in the image?'], responses:['7']
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[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
|
[['7', '8', '11', '5', '9', '10', '6', '12']]
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
question: ['How many syringes are in the image?'], responses:['3']
|
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
|
[['3', '4', '1', '5', '8', '2', '6', '12']]
|
tensor([9.9995e-01, 2.3917e-05, 1.0087e-06, 2.4279e-08, 1.1684e-05, 7.8070e-07,
|
4.3080e-06, 3.6135e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
0 *************
|
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9995e-01, 2.3917e-05, 1.0087e-06, 2.4279e-08, 1.1684e-05, 7.8070e-07,
|
4.3080e-06, 3.6135e-06], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.5419e-05, device='cuda:0', grad_fn=<DivBackward0>)}
|
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
|
tensor([9.9999e-01, 3.7783e-06, 9.3606e-07, 6.6345e-09, 2.7037e-10, 1.8398e-07,
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1.2848e-09, 1.2566e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 3.7783e-06, 9.3606e-07, 6.6345e-09, 2.7037e-10, 1.8398e-07,
|
1.2848e-09, 1.2566e-07], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.0322e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
|
tensor([9.9446e-01, 3.3361e-04, 8.0028e-04, 2.0251e-04, 5.5002e-04, 6.1733e-05,
|
3.5850e-03, 1.8635e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
7 *************
|
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9446e-01, 3.3361e-04, 8.0028e-04, 2.0251e-04, 5.5002e-04, 6.1733e-05,
|
3.5850e-03, 1.8635e-06], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
|
[2024-10-24 10:47:56,144] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.30 | optimizer_step: 0.32
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[2024-10-24 10:47:56,144] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5153.52 | backward_microstep: 7560.33 | backward_inner_microstep: 4964.40 | backward_allreduce_microstep: 2595.76 | step_microstep: 7.82
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[2024-10-24 10:47:56,144] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5153.54 | backward: 7560.32 | backward_inner: 4964.51 | backward_allreduce: 2595.69 | step: 7.83
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100%|ββββββββββ| 4837/4844 [20:06:39<01:36, 13.82s/it]Registering VQA_lavis step
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Registering EVAL step
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