Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([0.2289, 0.1940, 0.1016, 0.1553, 0.0408, 0.1784, 0.0864, 0.0146],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.2289, 0.1940, 0.1016, 0.1553, 0.0408, 0.1784, 0.0864, 0.0146],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0146, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9854, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.7679e-01, 3.7500e-03, 1.7167e-03, 4.7640e-04, 8.6308e-04, 5.6947e-04,
1.5789e-02, 4.6561e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7679e-01, 3.7500e-03, 1.7167e-03, 4.7640e-04, 8.6308e-04, 5.6947e-04,
1.5789e-02, 4.6561e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0158, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9842, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Are there white inflated sails 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
tensor([9.4983e-01, 9.3103e-03, 3.2172e-03, 1.0068e-03, 1.2582e-03, 7.4404e-04,
3.4597e-02, 3.8908e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4983e-01, 9.3103e-03, 3.2172e-03, 1.0068e-03, 1.2582e-03, 7.4404e-04,
3.4597e-02, 3.8908e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9498, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0502, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([7.4532e-01, 2.5167e-02, 2.2614e-01, 1.2884e-03, 1.9775e-04, 8.1286e-04,
1.6157e-04, 9.0960e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.4532e-01, 2.5167e-02, 2.2614e-01, 1.2884e-03, 1.9775e-04, 8.1286e-04,
1.6157e-04, 9.0960e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7453, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2261, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0285, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-23 14:47:18,544] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-23 14:47:18,544] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3180.75 | backward_microstep: 6725.30 | backward_inner_microstep: 3041.57 | backward_allreduce_microstep: 3683.66 | step_microstep: 7.51
[2024-10-23 14:47:18,544] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3180.77 | backward: 6725.29 | backward_inner: 3041.58 | backward_allreduce: 3683.65 | step: 7.52
0%| | 24/4844 [06:02<17:28:32, 13.05s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Are there any dogs lying down in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there sun coming in through the window?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many tusked animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many animals are in the image?'], responses:['1']
question: ['Is there sun coming in through the window?'], responses:['no']
[('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([1, 3, 448, 448]) knan debug pixel values shape
[('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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.7248e-01, 4.2304e-03, 1.5084e-03, 4.3195e-04, 6.9052e-04, 4.4161e-04,
2.0183e-02, 3.5072e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7248e-01, 4.2304e-03, 1.5084e-03, 4.3195e-04, 6.9052e-04, 4.4161e-04,
2.0183e-02, 3.5072e-05], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([6.9133e-01, 3.0678e-01, 9.8488e-05, 2.3047e-04, 3.9854e-04, 1.9832e-04,
5.7230e-04, 3.8819e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.9133e-01, 3.0678e-01, 9.8488e-05, 2.3047e-04, 3.9854e-04, 1.9832e-04,
5.7230e-04, 3.8819e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9969, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0031, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3068, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6913, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0019, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='What color is the jellyfish?')
ANSWER1=EVAL(expr='{ANSWER0} == "pink"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['Are there any dogs lying down in the image?'], responses:['yes']
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
[('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']]
question: ['How many animals 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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.6036e-01, 6.8886e-03, 2.9625e-03, 1.3137e-03, 1.5855e-03, 1.1876e-03,
2.5595e-02, 1.0951e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)