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tensor([0.3316, 0.3136, 0.1583, 0.0213, 0.1220, 0.0170, 0.0311, 0.0050],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.3316, 0.3136, 0.1583, 0.0213, 0.1220, 0.0170, 0.0311, 0.0050],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0311, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9689, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many collies are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} > 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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tensor([5.1494e-01, 4.8392e-01, 4.7815e-05, 1.0644e-04, 2.5125e-04, 3.5751e-04,
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2.6931e-04, 1.0226e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.1494e-01, 4.8392e-01, 4.7815e-05, 1.0644e-04, 2.5125e-04, 3.5751e-04,
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2.6931e-04, 1.0226e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4839, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.5149, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0011, device='cuda:2', grad_fn=<SubBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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question: ['How many boxes are in the image?'], responses:['1']
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question: ['How many collies are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([7.1218e-01, 2.4222e-02, 2.6015e-01, 1.1970e-03, 1.4969e-04, 5.8878e-04,
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1.4971e-04, 1.3623e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.1218e-01, 2.4222e-02, 2.6015e-01, 1.1970e-03, 1.4969e-04, 5.8878e-04,
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1.4971e-04, 1.3623e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7122, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2601, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0277, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many perfumes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 10')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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question: ['How many perfumes are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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tensor([0.2291, 0.1947, 0.1016, 0.1517, 0.0418, 0.1784, 0.0881, 0.0146],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.2291, 0.1947, 0.1016, 0.1517, 0.0418, 0.1784, 0.0881, 0.0146],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 3 has a total capacty of 44.34 GiB of which 250.94 MiB is free. Including non-PyTorch memory, this process has 44.08 GiB memory in use. Of the allocated memory 39.10 GiB is allocated by PyTorch, and 4.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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tensor([6.5294e-01, 5.4007e-02, 2.0343e-02, 1.0145e-02, 1.2738e-02, 9.0281e-03,
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2.4022e-01, 5.8073e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.5294e-01, 5.4007e-02, 2.0343e-02, 1.0145e-02, 1.2738e-02, 9.0281e-03,
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2.4022e-01, 5.8073e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6529, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3471, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.33
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[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10247.81 | backward_microstep: 13588.27 | backward_inner_microstep: 9805.33 | backward_allreduce_microstep: 3782.84 | step_microstep: 10.13
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[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10247.83 | backward: 13588.26 | backward_inner: 9805.35 | backward_allreduce: 3782.83 | step: 10.14
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0%| | 12/2424 [04:54<16:06:38, 24.05s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Is there a stack of three books on the front-most corner of the shelf under the couch?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Does the sea creature in the photo have white tentacles with pink tips?')
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ANSWER1=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=LEFT,question='Does the image on the left have a man's leg bending to the right with his heel up?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How many tusked animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Does the sea creature in the photo have white tentacles with pink tips?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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