Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.3849e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.1206e-09, 1.9247e-10, 2.1470e-10, 2.0890e-10, 4.9427e-09,
1.5230e-08, 5.2146e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1206e-09, 1.9247e-10, 2.1470e-10, 2.0890e-10, 4.9427e-09,
1.5230e-08, 5.2146e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1962e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.9023e-06, 4.8678e-09, 8.7642e-08, 3.5061e-09, 3.1363e-10,
3.7323e-09, 2.1714e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.9023e-06, 4.8678e-09, 8.7642e-08, 3.5061e-09, 3.1363e-10,
3.7323e-09, 2.1714e-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(8.7642e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.5949e-09, 9.9312e-08, 4.3010e-09, 6.3221e-11, 3.1609e-10,
3.5586e-11, 9.5704e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.5949e-09, 9.9312e-08, 4.3010e-09, 6.3221e-11, 3.1609e-10,
3.5586e-11, 9.5704e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.9312e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9898e-08, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:57:28,136] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.31
[2024-10-24 09:57:28,136] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7081.78 | backward_microstep: 10597.16 | backward_inner_microstep: 6776.10 | backward_allreduce_microstep: 3820.80 | step_microstep: 7.88
[2024-10-24 09:57:28,136] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7081.80 | backward: 10597.15 | backward_inner: 6776.17 | backward_allreduce: 3820.78 | step: 7.89
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4634/4844 [19:16:11<53:32, 15.30s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the animal facing right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are the balls in bright light?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are there sea mammals in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many windows are on the rusted out bus?')
ANSWER1=EVAL(expr='{ANSWER0} >= 12')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the balls in bright light?'], 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 4.3635e-09, 8.7871e-08, 6.3584e-12, 3.4531e-11, 1.0884e-09,
2.7296e-10, 3.8737e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.3635e-09, 8.7871e-08, 6.3584e-12, 3.4531e-11, 1.0884e-09,
2.7296e-10, 3.8737e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.3635e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['Is the animal facing right?'], 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']]
question: ['How many dogs 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
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 2.2945e-10, 5.9390e-11, 1.7591e-10, 1.2760e-10, 1.8934e-08,
2.0854e-09, 2.5745e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.2945e-10, 5.9390e-11, 1.7591e-10, 1.2760e-10, 1.8934e-08,
2.0854e-09, 2.5745e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1870e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Are there sea mammals in the image?'], responses:['yes']
question: ['How many windows are on the rusted out bus?'], responses:['12']
[('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']]
[('12', 0.1271623397239889), ('11', 0.12513993889798333), ('10', 0.1250472536472656), ('8', 0.12474319370676636), ('6', 0.12462998196332449), ('26', 0.12450301801207538), ('47', 0.1243904847365581), ('13', 0.12438378931203788)]
[['12', '11', '10', '8', '6', '26', '47', '13']]