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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0314, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9686, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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tensor([0.7280, 0.1265, 0.0282, 0.0171, 0.0016, 0.0927, 0.0045, 0.0014],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.7280, 0.1265, 0.0282, 0.0171, 0.0016, 0.0927, 0.0045, 0.0014],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7280, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2720, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([8.9637e-01, 1.7898e-03, 4.9069e-04, 2.2058e-02, 1.1209e-02, 4.3277e-02,
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2.4688e-02, 1.1325e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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blue *************
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['blue', 'kitten', 'iris', 'lemon', 'cherry', 'bright', 'peach', 'cookie'] tensor([8.9637e-01, 1.7898e-03, 4.9069e-04, 2.2058e-02, 1.1209e-02, 4.3277e-02,
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2.4688e-02, 1.1325e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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tensor([9.5856e-01, 6.8752e-03, 2.6923e-03, 9.0126e-04, 1.2325e-03, 7.4317e-04,
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2.8946e-02, 4.9763e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5856e-01, 6.8752e-03, 2.6923e-03, 9.0126e-04, 1.2325e-03, 7.4317e-04,
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2.8946e-02, 4.9763e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9586, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0414, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a body of water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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question: ['Is there a body of water in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
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tensor([8.8904e-01, 1.5416e-02, 9.3704e-02, 1.1248e-03, 7.1901e-05, 2.3196e-04,
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1.8334e-05, 3.9075e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8904e-01, 1.5416e-02, 9.3704e-02, 1.1248e-03, 7.1901e-05, 2.3196e-04,
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1.8334e-05, 3.9075e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8890, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0937, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0173, device='cuda:1', grad_fn=<SubBackward0>)}
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[2024-10-22 17:23:46,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.31 | optimizer_step: 0.33
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[2024-10-22 17:23:46,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8939.30 | backward_microstep: 15179.58 | backward_inner_microstep: 8487.76 | backward_allreduce_microstep: 6691.75 | step_microstep: 7.76
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[2024-10-22 17:23:46,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8939.32 | backward: 15179.56 | backward_inner: 8487.77 | backward_allreduce: 6691.74 | step: 7.77
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1%| | 13/2424 [05:18<16:07:25, 24.08s/it]Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL stepRegistering EVAL step
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Registering RESULT stepRegistering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Are triangular pennants on display in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many virtually identical trifle desserts are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='Is the animal's body turned to the right?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many virtually identical trifle desserts are in the image?'], responses:['2']
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question: ['How many animals are in the image?'], responses:['2']
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question: ['Is the animal'], responses:['yes']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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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
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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question: ['Are triangular pennants on display in the image?'], responses:['yes']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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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)]
|
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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tensor([6.3312e-01, 7.1091e-02, 1.5362e-02, 2.6393e-01, 9.6228e-03, 3.3218e-03,
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2.9623e-03, 5.9102e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.3312e-01, 7.1091e-02, 1.5362e-02, 2.6393e-01, 9.6228e-03, 3.3218e-03,
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2.9623e-03, 5.9102e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6331, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3669, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([0.5734, 0.0243, 0.3941, 0.0016, 0.0009, 0.0028, 0.0007, 0.0021],
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