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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
question: ['Is the writing in the image cursive?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
tensor([1.0000e+00, 3.9925e-09, 4.7722e-11, 6.8183e-09, 3.1095e-10, 7.2352e-10,
4.2460e-12, 3.4506e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.9925e-09, 4.7722e-11, 6.8183e-09, 3.1095e-10, 7.2352e-10,
4.2460e-12, 3.4506e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.7722e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.7722e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
tensor([1.0000e+00, 4.7275e-09, 2.6953e-11, 2.3145e-08, 2.1659e-10, 3.0635e-10,
3.0392e-11, 1.2444e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.7275e-09, 2.6953e-11, 2.3145e-08, 2.1659e-10, 3.0635e-10,
3.0392e-11, 1.2444e-08], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([9.9999e-01, 9.0941e-09, 2.0176e-08, 2.7516e-10, 9.1462e-10, 4.9936e-08,
1.0130e-05, 1.5371e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9999e-01, 9.0941e-09, 2.0176e-08, 2.7516e-10, 9.1462e-10, 4.9936e-08,
1.0130e-05, 1.5371e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(2.6953e-11, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.6953e-11, device='cuda:0', grad_fn=<SubBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.0941e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many lotion products are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many boars are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many pillows are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.9468e-01, 5.5014e-04, 1.0941e-03, 1.5762e-04, 1.2294e-07, 3.1658e-03,
6.6460e-07, 3.5520e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9468e-01, 5.5014e-04, 1.0941e-03, 1.5762e-04, 1.2294e-07, 3.1658e-03,
6.6460e-07, 3.5520e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.7586e-01, 5.0686e-04, 6.1748e-03, 3.4493e-05, 5.7872e-08, 1.0850e-02,
2.7452e-06, 6.5731e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.7586e-01, 5.0686e-04, 6.1748e-03, 3.4493e-05, 5.7872e-08, 1.0850e-02,
2.7452e-06, 6.5731e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many lotion products 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']]
tensor([1.0000e+00, 9.9264e-09, 2.3235e-11, 4.2491e-09, 6.7961e-10, 1.6562e-09,
5.5054e-11, 7.6134e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.9264e-09, 2.3235e-11, 4.2491e-09, 6.7961e-10, 1.6562e-09,
5.5054e-11, 7.6134e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(2.3235e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.3235e-11, device='cuda:2', grad_fn=<SubBackward0>)}
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([9.9999e-01, 3.1250e-08, 4.4069e-08, 6.9623e-06, 1.9022e-10, 1.6178e-09,
2.9926e-10, 1.2154e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 3.1250e-08, 4.4069e-08, 6.9623e-06, 1.9022e-10, 1.6178e-09,
2.9926e-10, 1.2154e-10], 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(7.7548e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:22:59,853] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-24 10:22:59,853] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9148.14 | backward_microstep: 8811.62 | backward_inner_microstep: 8805.81 | backward_allreduce_microstep: 5.74 | step_microstep: 7.41
[2024-10-24 10:22:59,853] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9148.14 | backward: 8811.61 | backward_inner: 8805.82 | backward_allreduce: 5.73 | step: 7.42
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4735/4844 [19:41:43<27:57, 15.39s/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
ANSWER0=VQA(image=RIGHT,question='How many vape devices are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step