Upload perf-df-unquantized-1xT4.csv with huggingface_hub

perf-df-unquantized-1xT4.csv

@@ -1113,7 +1113,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 21522 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 21809 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 bfloat16-eager,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,,bfloat16,True,False,,eager,,False,,False,forward,,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):
@@ -7210,7 +7210,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 560.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 21875 has 14.73 GiB memory in use. Of the allocated memory 14.62 GiB is allocated by PyTorch, and 1.67 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 560.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 22155 has 14.73 GiB memory in use. Of the allocated memory 14.62 GiB is allocated by PyTorch, and 1.67 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float32-sdpa,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,,float32,True,False,,sdpa,,False,,False,forward,,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):
@@ -8020,7 +8020,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 30.12 MiB is free. Process 19433 has 14.71 GiB memory in use. Of the allocated memory 14.51 GiB is allocated by PyTorch, and 85.33 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 64.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 30.12 MiB is free. Process 19776 has 14.71 GiB memory in use. Of the allocated memory 14.51 GiB is allocated by PyTorch, and 85.33 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,mistral,TencentARC/Mistral_Pro_8B_v0.1,TencentARC/Mistral_Pro_8B_v0.1,cuda,0,42,,,True,True,,float32,True,False,,sdpa,,False,,False,forward,,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):
@@ -14545,7 +14545,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 23279 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 23522 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 bfloat16-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,,bfloat16,True,False,,flash_attention_2,,False,,False,forward,,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):
@@ -19984,7 +19984,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 22931 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 23203 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float16-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,,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):
@@ -23674,7 +23674,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 162.12 MiB is free. Process 23633 has 14.58 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 25.46 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 224.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 162.12 MiB is free. Process 23918 has 14.58 GiB memory in use. Of the allocated memory 14.44 GiB is allocated by PyTorch, and 25.46 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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@@ -23764,7 +23764,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 94.12 MiB is free. Process 27766 has 14.65 GiB memory in use. Of the allocated memory 14.53 GiB is allocated by PyTorch, and 2.49 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 256.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 94.12 MiB is free. Process 27999 has 14.65 GiB memory in use. Of the allocated memory 14.53 GiB is allocated by PyTorch, and 2.49 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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@@ -24275,7 +24275,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 560.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 20781 has 14.73 GiB memory in use. Of the allocated memory 14.62 GiB is allocated by PyTorch, and 1.67 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 560.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 21077 has 14.73 GiB memory in use. Of the allocated memory 14.62 GiB is allocated by PyTorch, and 1.67 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float32-eager,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,,float32,True,False,,eager,,False,,False,forward,,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):
@@ -25123,7 +25123,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 30.12 MiB is free. Process 19060 has 14.71 GiB memory in use. Of the allocated memory 14.51 GiB is allocated by PyTorch, and 85.33 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 64.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 30.12 MiB is free. Process 19405 has 14.71 GiB memory in use. Of the allocated memory 14.51 GiB is allocated by PyTorch, and 85.33 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,mistral,TencentARC/Mistral_Pro_8B_v0.1,TencentARC/Mistral_Pro_8B_v0.1,cuda,0,42,,,True,True,,float32,True,False,,eager,,False,,False,forward,,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):
@@ -26613,7 +26613,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 21165 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 21431 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float16-eager,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,,eager,,False,,False,forward,,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):
@@ -32546,7 +32546,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 22273 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 22501 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 float16-sdpa,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,,sdpa,,False,,False,forward,,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):
@@ -34605,7 +34605,7 @@ ChildProcessError: Traceback (most recent call last):
     self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
   File ""/usr/local/lib/python3.10/dist-packages/torch/utils/_device.py"", line 79, in __torch_function__
     return func(*args, **kwargs)
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 22581 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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 280.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 42.12 MiB is free. Process 22847 has 14.70 GiB memory in use. Of the allocated memory 14.58 GiB is allocated by PyTorch, and 1.64 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)
 
 ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 bfloat16-sdpa,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,,bfloat16,True,False,,sdpa,,False,,False,forward,,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):