diff --git "a/perf-df-unquantized-1xT4.csv" "b/perf-df-unquantized-1xT4.csv" --- "a/perf-df-unquantized-1xT4.csv" +++ "b/perf-df-unquantized-1xT4.csv" @@ -1205,7 +1205,7 @@ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. 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-bfloat16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +bfloat16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -1245,7 +1245,7 @@ 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 21207 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 21458 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,8,33163.751424,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): @@ -1413,7 +1413,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 162033 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +bfloat16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -6703,7 +6703,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has and you passed . Fix one of those so they match! ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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,8,33163.751424,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): +float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -6847,7 +6847,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: RecurrentGemmaForCausalLM does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=""eager""` meanwhile. Example: `model = AutoModel.from_pretrained(""openai/whisper-tiny"", attn_implementation=""eager"")` ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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,8,33163.751424,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): +float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -7434,7 +7434,7 @@ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.00 MiB. 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-float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -7474,7 +7474,7 @@ 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 21555 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 21817 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,8,33163.751424,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): @@ -7672,7 +7672,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 342.12 MiB is free. Process 162376 has 14.40 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 1.75 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -8244,7 +8244,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 360.12 MiB is free. Process 177303 has 14.39 GiB memory in use. Of the allocated memory 14.26 GiB is allocated by PyTorch, and 9.58 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,llama,01-ai/Yi-6B,01-ai/Yi-6B,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,8,33163.751424,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): +float32-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-6B,01-ai/Yi-6B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -8284,7 +8284,7 @@ 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 19103 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 19247 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,8,33163.751424,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): @@ -14014,7 +14014,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: RecurrentGemmaForCausalLM does not support Flash Attention 2.0 yet. Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmpzh9g5gae/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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,8,33163.751424,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): +bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -14997,7 +14997,7 @@ ChildProcessError: Traceback (most recent call last): RuntimeError: FlashAttention only supports Ampere GPUs or newer. ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -15037,7 +15037,7 @@ 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 22979 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 23262 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,8,33163.751424,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): @@ -15235,7 +15235,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 163802 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -15971,7 +15971,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 72.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 38.12 MiB is free. Process 178690 has 14.70 GiB memory in use. Of the allocated memory 14.55 GiB is allocated by PyTorch, and 41.65 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,llama,01-ai/Yi-6B,01-ai/Yi-6B,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,8,33163.751424,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): +bfloat16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-6B,01-ai/Yi-6B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -17315,7 +17315,7 @@ 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 106.12 MiB is free. Process 36689 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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 106.12 MiB is free. Process 37517 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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,llama,meta-llama/Llama-3.1-405B,meta-llama/Llama-3.1-405B,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): @@ -17444,7 +17444,7 @@ 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 106.12 MiB is free. Process 43648 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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 106.12 MiB is free. Process 44721 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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,llama,meta-llama/Meta-Llama-3-70B,meta-llama/Meta-Llama-3-70B,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -17776,7 +17776,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 42729 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 43838 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-2-7b-hf,meta-llama/Llama-2-7b-hf,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -18124,7 +18124,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 38493 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 39435 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-2-13b-hf,meta-llama/Llama-2-13b-hf,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -18202,7 +18202,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 38037 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 38960 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-8B-Instruct,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): @@ -18237,7 +18237,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 37149 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 37956 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-2-70b-hf,meta-llama/Llama-2-70b-hf,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -18488,7 +18488,7 @@ 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 106.12 MiB is free. Process 42227 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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 106.12 MiB is free. Process 43365 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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,llama,meta-llama/Meta-Llama-3-8B,meta-llama/Meta-Llama-3-8B,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): @@ -18523,7 +18523,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 45498 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 46610 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-2-7b-hf,meta-llama/Llama-2-7b-hf,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -18601,7 +18601,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 36187 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 37037 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-8B-Instruct,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): @@ -18757,7 +18757,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 35759 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 36520 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-2-70b-hf,meta-llama/Llama-2-70b-hf,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -18835,7 +18835,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 43175 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 44272 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-8B-Instruct,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): @@ -19540,7 +19540,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 44601 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 45714 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Meta-Llama-3-8B,meta-llama/Meta-Llama-3-8B,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): @@ -19575,7 +19575,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 44100 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 45259 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-8B-Instruct,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): @@ -19610,7 +19610,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 37577 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 38487 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Meta-Llama-3-8B,meta-llama/Meta-Llama-3-8B,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -19724,7 +19724,7 @@ 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 45071 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 532.12 MiB is free. Process 46157 has 14.22 GiB memory in use. Of the allocated memory 13.98 GiB is allocated by PyTorch, and 129.48 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,llama,meta-llama/Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-8B-Instruct,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): @@ -19767,7 +19767,7 @@ 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 106.12 MiB is free. Process 35289 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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 106.12 MiB is free. Process 36081 has 14.63 GiB memory in use. Of the allocated memory 14.52 GiB is allocated by PyTorch, and 1.52 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,llama,meta-llama/Llama-3.1-405B,meta-llama/Llama-3.1-405B,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): @@ -20685,7 +20685,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: RecurrentGemmaForCausalLM does not support Flash Attention 2.0 yet. Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmperam6bno/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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,8,33163.751424,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): +float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -21668,7 +21668,7 @@ ChildProcessError: Traceback (most recent call last): RuntimeError: FlashAttention only supports Ampere GPUs or newer. ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -21708,7 +21708,7 @@ 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 22608 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 22909 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,8,33163.751424,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): @@ -21906,7 +21906,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 163451 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -22642,7 +22642,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 72.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 38.12 MiB is free. Process 178363 has 14.70 GiB memory in use. Of the allocated memory 14.55 GiB is allocated by PyTorch, and 41.65 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,llama,01-ai/Yi-6B,01-ai/Yi-6B,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,8,33163.751424,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): +float16-flash_attention_2,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-6B,01-ai/Yi-6B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -24316,7 +24316,7 @@ ChildProcessError: Traceback (most recent call last): return forward_call(*args, **kwargs) File ""/root/.cache/huggingface/modules/transformers_modules/microsoft/Phi-3-mini-4k-instruct/0a67737cc96d2554230f90338b163bc6380a2a85/modeling_phi3.py"", line 233, in forward up_states = up_states * self.activation_fn(gate) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 38952 has 14.73 GiB memory in use. Of the allocated memory 14.56 GiB is allocated by PyTorch, and 45.84 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 20.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 8.12 MiB is free. Process 39952 has 14.73 GiB memory in use. Of the allocated memory 14.56 GiB is allocated by PyTorch, and 45.84 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,phi,microsoft/phi-1_5,microsoft/phi-1_5,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla T4'],1,16106127360,0.4.0,,4.44.2,,0.34.2,,,,1.22.0,,,,0.12.0,,"Traceback (most recent call last): @@ -25638,7 +25638,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has and you passed . Fix one of those so they match! ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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,8,33163.751424,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): +float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -25678,7 +25678,7 @@ 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 23310 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 23735 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,stablelm,stabilityai/stablelm-3b-4e1t,stabilityai/stablelm-3b-4e1t,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-5.10.224-212.876.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['Tesla 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Tried to allocate 270.00 MiB. 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""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -25768,7 +25768,7 @@ 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 27520 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 28160 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. 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Tried to allocate 12.00 MiB. 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-float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -26279,7 +26279,7 @@ 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 20451 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 20591 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,8,33163.751424,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): @@ -26495,7 +26495,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 342.12 MiB is free. Process 161365 has 14.40 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 1.75 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -27087,7 +27087,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 360.12 MiB is free. Process 176267 has 14.39 GiB memory in use. Of the allocated memory 14.26 GiB is allocated by PyTorch, and 9.58 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,llama,01-ai/Yi-6B,01-ai/Yi-6B,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,8,33163.751424,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): +float32-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-6B,01-ai/Yi-6B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -27127,7 +27127,7 @@ 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 18738 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 18875 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,8,33163.751424,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): @@ -28577,7 +28577,7 @@ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 h ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, float16-eager,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gpt_neox,EleutherAI/pythia-1.3b,EleutherAI/pythia-1.3b,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,8,33163.75552,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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-float16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +float16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -28617,7 +28617,7 @@ 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 20833 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 20971 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,8,33163.751424,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): @@ -28785,7 +28785,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 161692 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +float16-eager,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -34043,7 +34043,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has and you passed . Fix one of those so they match! ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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,8,33163.751424,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): +float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -34187,7 +34187,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: RecurrentGemmaForCausalLM does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=""eager""` meanwhile. Example: `model = AutoModel.from_pretrained(""openai/whisper-tiny"", attn_implementation=""eager"")` ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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,8,33163.751424,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): +float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -34678,7 +34678,7 @@ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. 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-float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -34718,7 +34718,7 @@ 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 21935 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 22199 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,8,33163.751424,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): @@ -34916,7 +34916,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 162710 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +float16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -36102,7 +36102,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has and you passed . Fix one of those so they match! ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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,8,33163.751424,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): +bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,deci,Deci/DeciLM-7B,Deci/DeciLM-7B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -36246,7 +36246,7 @@ ChildProcessError: Traceback (most recent call last): ValueError: RecurrentGemmaForCausalLM does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=""eager""` meanwhile. Example: `model = AutoModel.from_pretrained(""openai/whisper-tiny"", attn_implementation=""eager"")` ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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,8,33163.751424,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): +bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gptj,EleutherAI/gpt-j-6b,EleutherAI/gpt-j-6b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -36737,7 +36737,7 @@ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 h ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, bfloat16-sdpa,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,gpt_neox,EleutherAI/pythia-1.3b,EleutherAI/pythia-1.3b,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,8,33163.75552,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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-bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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,8,33163.751424,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): +bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,01-ai/Yi-34B,01-ai/Yi-34B,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch @@ -36777,7 +36777,7 @@ 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 22261 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 22574 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,8,33163.751424,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): @@ -36975,7 +36975,7 @@ ChildProcessError: Traceback (most recent call last): torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 234.12 MiB is free. Process 163080 has 14.51 GiB memory in use. Of the allocated memory 14.39 GiB is allocated by PyTorch, and 1.74 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,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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,8,33163.751424,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): +bfloat16-sdpa,pytorch,2.4.1+cu124,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,llama,Deci/DeciCoder-1b,Deci/DeciCoder-1b,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): File ""/workspace/llm_perf/update_llm_perf_cuda_pytorch.py"", line 153, in benchmark_cuda_pytorch benchmark_report = Benchmark.launch(benchmark_config) File ""/workspace/optimum_benchmark/benchmark/base.py"", line 47, in launch