Datasets:
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InternVL

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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([8.0999e-01, 4.1583e-05, 1.2178e-03, 3.1155e-02, 4.5602e-08, 1.5755e-01,
1.7398e-05, 2.6902e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([8.0999e-01, 4.1583e-05, 1.2178e-03, 3.1155e-02, 4.5602e-08, 1.5755e-01,
1.7398e-05, 2.6902e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many monkeys 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 3.9646e-10, 1.7662e-10, 6.2738e-10, 2.2413e-10, 1.6724e-08,
3.7910e-09, 1.6522e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.9646e-10, 1.7662e-10, 6.2738e-10, 2.2413e-10, 1.6724e-08,
3.7910e-09, 1.6522e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.2105e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 4.0126e-08, 5.0420e-09, 1.3760e-08, 1.3595e-10, 6.5365e-10,
4.5277e-10, 5.4561e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.0126e-08, 5.0420e-09, 1.3760e-08, 1.3595e-10, 6.5365e-10,
4.5277e-10, 5.4561e-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(6.0715e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 5.9302e-08, 3.5061e-09, 9.4764e-08, 2.0408e-10, 8.5242e-10,
6.9548e-10, 7.9191e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.9302e-08, 3.5061e-09, 9.4764e-08, 2.0408e-10, 8.5242e-10,
6.9548e-10, 7.9191e-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(1.6012e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:40:22,088] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 09:40:22,089] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5764.55 | backward_microstep: 5477.64 | backward_inner_microstep: 5472.02 | backward_allreduce_microstep: 5.53 | step_microstep: 7.59
[2024-10-24 09:40:22,089] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5764.56 | backward: 5477.63 | backward_inner: 5472.04 | backward_allreduce: 5.43 | step: 7.60
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4565/4844 [18:59:05<1:04:27, 13.86s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are all the balls in the image white?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the screen of the laptop slightly curved and concave?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many penguins are swimming underwater in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many lotion bottles are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are all the balls in the image white?'], 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([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many lotion bottles are visible in the image?'], responses:['3']
question: ['How many penguins are swimming underwater in the image?'], responses:['1']
[('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']]
[('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
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
tensor([1.0000e+00, 2.6937e-09, 5.2633e-09, 3.8049e-09, 1.4024e-10, 3.4373e-11,
1.3179e-11, 4.7601e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6937e-09, 5.2633e-09, 3.8049e-09, 1.4024e-10, 3.4373e-11,
1.3179e-11, 4.7601e-09], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.2633e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.2633e-09, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839