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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([1.0000e+00, 1.0118e-08, 3.6234e-11, 7.0566e-08, 6.8484e-10, 6.6915e-10,
8.9423e-11, 1.9128e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0118e-08, 3.6234e-11, 7.0566e-08, 6.8484e-10, 6.6915e-10,
8.9423e-11, 1.9128e-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(3.6234e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1917e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:13:01,238] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 10:13:01,238] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7064.10 | backward_microstep: 6797.83 | backward_inner_microstep: 6792.81 | backward_allreduce_microstep: 4.95 | step_microstep: 7.54
[2024-10-24 10:13:01,239] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7064.11 | backward: 6797.82 | backward_inner: 6792.83 | backward_allreduce: 4.92 | step: 7.55
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4694/4844 [19:31:45<36:08, 14.46s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a silver lamp with white lights in the image?')
ANSWER1=RESULT(var=ANSWER0)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many hyenas are in the water?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many graduation students are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dog sled teams are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([11, 3, 448, 448])
question: ['How many graduation students 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a silver lamp with white lights in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
question: ['How many dog sled teams are in the image?'], responses:['5']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
question: ['How many hyenas are in the water?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([1.0000e+00, 4.2868e-10, 9.1987e-11, 6.3108e-10, 2.6001e-10, 1.1494e-08,
7.4225e-09, 3.4155e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2868e-10, 9.1987e-11, 6.3108e-10, 2.6001e-10, 1.1494e-08,
7.4225e-09, 3.4155e-10], device='cuda:1', grad_fn=<SelectBackward0>)
[('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']]
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.3247e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are the dogs in the image outside?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
question: ['Are the dogs in the image outside?'], responses:['no']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([1.0000e+00, 2.7255e-09, 1.6212e-08, 5.2622e-09, 4.0713e-11, 8.4235e-12,
2.2819e-11, 4.6918e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7255e-09, 1.6212e-08, 5.2622e-09, 4.0713e-11, 8.4235e-12,
2.2819e-11, 4.6918e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.6212e-08, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.6212e-08, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many people are in the red boat?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many people are in the red boat?'], 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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([1.0000e+00, 2.1941e-09, 3.8362e-07, 5.2713e-12, 2.6394e-13, 1.4113e-09,
5.9201e-11, 1.7390e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.1941e-09, 3.8362e-07, 5.2713e-12, 2.6394e-13, 1.4113e-09,
5.9201e-11, 1.7390e-06], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.1941e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.1458e-06, device='cuda:1', grad_fn=<DivBackward0>)}