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perf-df-gptq-1xT4.csv

@@ -3068,7 +3068,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 20103 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 19815 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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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@@ -5575,7 +5575,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 22961 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 22554 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 4bit-gptq-exllama-v2-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,gptq,4,True,2,256,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):
@@ -9424,7 +9424,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 22377 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 21977 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 4bit-gptq-exllama-v1-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,gptq,4,True,1,256,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):
@@ -12520,7 +12520,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 21228 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 20925 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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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@@ -14447,7 +14447,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 20673 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 20361 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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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@@ -15935,7 +15935,7 @@ ChildProcessError: Traceback (most recent call last):
     self._buffers[key] = fn(buf)
   File ""/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py"", line 1160, in convert
     return t.to(
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 21768 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 16.12 MiB is free. Process 21442 has 14.72 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 187.67 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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