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@@ -3339,7 +3339,7 @@ ChildProcessError: Traceback (most recent call last):
3339
  return self._quantize(device)
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  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
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  w = self.data.contiguous().to(device)
3342
- torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 20981 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
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@@ -4658,7 +4658,7 @@ ChildProcessError: Traceback (most recent call last):
4658
  return self._quantize(device)
4659
  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
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  w = self.data.contiguous().to(device)
4661
- torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 22915 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
4662
 
4663
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@@ -16150,7 +16150,7 @@ ChildProcessError: Traceback (most recent call last):
16150
  return self._quantize(device)
16151
  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
16152
  w = self.data.contiguous().to(device)
16153
- torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 24573 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
16154
 
16155
  ",True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
16156
  4bit-bnb-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gpt_neox,EleutherAI/pythia-12b,EleutherAI/pythia-12b,cuda,0,42,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,False,spawn, Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz,8,33163.759616,Linux,x86_64,Linux-5.10.225-213.878.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.5.0,,4.45.1,,0.34.2,,,,1.22.0,,,,0.13.0,,"Traceback (most recent call last):
 
3339
  return self._quantize(device)
3340
  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
3341
  w = self.data.contiguous().to(device)
3342
+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 20979 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
3343
 
3344
  ",True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
3345
  4bit-bnb-eager,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gpt_neox,EleutherAI/pythia-12b,EleutherAI/pythia-12b,cuda,0,42,,,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,False,spawn, Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz,8,33163.75552,Linux,x86_64,Linux-5.10.220-209.869.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla 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4658
  return self._quantize(device)
4659
  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
4660
  w = self.data.contiguous().to(device)
4661
+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 22770 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
4662
 
4663
  ",True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
4664
  4bit-bnb-sdpa,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gpt_neox,EleutherAI/pythia-12b,EleutherAI/pythia-12b,cuda,0,42,,,True,True,,float16,True,False,,sdpa,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,False,spawn, Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz,8,33163.75552,Linux,x86_64,Linux-5.10.220-209.869.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla 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16150
  return self._quantize(device)
16151
  File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize
16152
  w = self.data.contiguous().to(device)
16153
+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 232.12 MiB is free. Process 24595 has 14.51 GiB memory in use. Of the allocated memory 14.07 GiB is allocated by PyTorch, and 332.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
16154
 
16155
  ",True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
16156
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