diff --git "a/perf-df-bnb-1xA10.csv" "b/perf-df-bnb-1xA10.csv" --- "a/perf-df-bnb-1xA10.csv" +++ "b/perf-df-bnb-1xA10.csv" @@ -145,7 +145,7 @@ ChildProcessError: Traceback (most recent call last): return self._quantize(device) File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize w = self.data.contiguous().to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 39755 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 39879 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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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""/usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py"", line 408, in set_module_tensor_to_device new_value = old_value.to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 58957 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 60158 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 51013 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 51903 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-sdpa,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,meta-llama/Llama-2-70b-hf,meta-llama/Llama-2-70b-hf,cuda,0,42,,,True,True,True,True,,float16,True,False,,sdpa,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -2253,7 +2253,7 @@ ChildProcessError: Traceback (most recent call last): w_4bit, quant_state = bnb.functional.quantize_4bit( File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py"", line 1173, in quantize_4bit out = torch.zeros(((n + 1) // mod, 1), dtype=quant_storage, device=A.device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 45392 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 45662 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-sdpa,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,B,B,cuda,0,42,,,True,True,True,True,,float16,True,False,,sdpa,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -3067,7 +3067,7 @@ ChildProcessError: Traceback (most recent call last): CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B) File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py"", line 2528, in double_quant out_row = torch.zeros(A.shape, device=device, dtype=torch.int8) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 196.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 178.50 MiB is free. Process 64773 has 22.00 GiB memory in use. Of the allocated memory 21.72 GiB is allocated by PyTorch, and 2.78 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 196.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 178.50 MiB is free. Process 77796 has 22.00 GiB memory in use. Of the allocated memory 21.72 GiB is allocated by PyTorch, and 2.78 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,opt,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-eager,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,Qwen/Qwen1.5-MoE-A2.7B,,cuda,0,42,,,,,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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""/usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py"", line 408, in set_module_tensor_to_device new_value = old_value.to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 58340 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 59567 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 41568 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 41829 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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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""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: XGLMForCausalLM 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/tmp5ihlni7s/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmp_0f88utm/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,huggyllama/llama-30b,huggyllama/llama-30b,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmpslrongjk/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmpgpe1vrq1/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,EleutherAI/gpt-neo-125m,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 59916 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 61221 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 52877 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 53886 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,meta-llama/Llama-2-70b-hf,meta-llama/Llama-2-70b-hf,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -5816,7 +5816,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -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/tmpaumvl2b4/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +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/tmpiikpmm_0/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,recurrent_gemma,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,v,v,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -5970,7 +5970,7 @@ ChildProcessError: Traceback (most recent call last): return self._quantize(device) File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize w = self.data.contiguous().to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 648.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 468.50 MiB is free. Process 71037 has 21.72 GiB memory in use. Of the allocated memory 20.84 GiB is allocated by PyTorch, and 611.26 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 648.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 468.50 MiB is free. Process 84145 has 21.72 GiB memory in use. Of the allocated memory 20.84 GiB is allocated by PyTorch, and 611.26 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,opt,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,0,0,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -6075,7 +6075,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: XGLMForCausalLM 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/tmp31p83c2x/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmpdn3vf8z7/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,TencentARC/Mistral_Pro_8B_v0.1,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.2.0,,4.40.2,,0.30.1,,,,1.19.2,,,,0.10.0,,,main,False,False,MB,2088.595456,7149.715456,0.0,6503.268352,6130.0736,s,10,4.354886688232422,0.43548866882324216,0.0009500705262691482,0.43506301879882814,0.4360664276123047,0.43712642974853516,0.4379744314575195,"[0.43818643188476564, 0.4357357177734375, 0.43495596313476564, 0.43503363037109377, 0.43495343017578125, 0.4350522766113281, 0.43583087158203127, 0.43494912719726564, 0.4350737609863281, 0.435115478515625]",tokens/s,587.8453753842819,kWh,5.1466148040720816e-06,2.82011353963595e-06,3.132101539495727e-05,3.92877437386653e-05,tokens/kWh,6516027.026211126,MB,2088.595456,7149.715456,0.0,6503.268352,6130.07616,s,10,52.50473486328125,5.250473486328125,0.09201662696971703,5.295069091796875,5.3397248046875,5.347057958984375,5.352924482421875,"[5.33809521484375, 5.3243525390625, 5.32192578125, 5.2103232421875, 5.2842451171875, 5.35439111328125, 5.30589306640625, 5.11702490234375, 5.11619873046875, 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Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmprj_pwdrk/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: CodeGenForCausalLM 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/tmplrndv7nt/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,codegen,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,facebook/opt-125m,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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0.019878911972045898]",tokens/s,49.97816711329813,,,,,,,,,,,,,,,,,,,,,,,,,,, @@ -6159,7 +6159,7 @@ ChildProcessError: Traceback (most recent call last): w_4bit, quant_state = bnb.functional.quantize_4bit( File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py"", line 1173, in quantize_4bit out = torch.zeros(((n + 1) // mod, 1), dtype=quant_storage, device=A.device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 47245 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 47512 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,B,B,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -6335,7 +6335,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: CodeGenForCausalLM 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/tmpm4gvdql0/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: CodeGenForCausalLM 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/tmpolk4jlix/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,codegen,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,facebook/opt-6.7b,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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self.data.contiguous().to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 37826 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 37915 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-eager,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,EleutherAI/pythia-1.4b,,cuda,0,42,,,,,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,True,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,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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""/usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py"", line 408, in set_module_tensor_to_device new_value = old_value.to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 57837 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 874.50 MiB is free. Process 59097 has 21.32 GiB memory in use. Of the allocated memory 21.02 GiB is allocated by PyTorch, and 20.69 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 49092 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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 384.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 236.50 MiB is free. Process 49618 has 21.95 GiB memory in use. Of the allocated memory 21.66 GiB is allocated by PyTorch, and 4.53 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-eager,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,meta-llama/Llama-2-70b-hf,meta-llama/Llama-2-70b-hf,cuda,0,42,,,True,True,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -8033,7 +8033,7 @@ ChildProcessError: Traceback (most recent call last): return self._quantize(device) File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py"", line 296, in _quantize w = self.data.contiguous().to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 648.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 468.50 MiB is free. Process 68316 has 21.72 GiB memory in use. Of the allocated memory 20.84 GiB is allocated by PyTorch, and 611.26 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 648.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 468.50 MiB is free. Process 81417 has 21.72 GiB memory in use. Of the allocated memory 20.84 GiB is allocated by PyTorch, and 611.26 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,opt,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-eager,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,0,0,cuda,0,42,,,True,True,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -8154,7 +8154,7 @@ ChildProcessError: Traceback (most recent call last): w_4bit, quant_state = bnb.functional.quantize_4bit( File ""/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py"", line 1173, in quantize_4bit out = torch.zeros(((n + 1) // mod, 1), dtype=quant_storage, device=A.device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 43553 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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 192.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 186.50 MiB is free. Process 43773 has 22.00 GiB memory in use. Of the allocated memory 21.68 GiB is allocated by PyTorch, and 29.57 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,qwen2,,,,,,,,,,,,,,,,,,,,,,,, 4bit-bnb-eager,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,B,B,cuda,0,42,,,True,True,True,True,,float16,True,False,,eager,,False,,False,forward,bnb,0.0,True,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, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -8454,7 +8454,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: XGLMForCausalLM 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/tmpeq14pvhw/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmpp1f000p1/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,huggyllama/llama-30b,huggyllama/llama-30b,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,False,spawn, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -8680,7 +8680,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: XGLMForCausalLM 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/tmpmrmx7sqb/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmp3z2bpwpn/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,EleutherAI/gpt-neo-125m,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.2.0,217063f5c507ed7cc255df7e1f64c4333a0b4dfe,4.40.2,,0.30.1,,,,1.19.2,,,,0.10.0,,,main,False,False,MB,1270.099968,969.408512,0.0,314.5728,261.774848,s,10,0.36215299224853514,0.03621529922485352,0.0011264096600696542,0.03580284881591797,0.03667889633178711,0.038089448165893554,0.03921788963317871,"[0.0395, 0.03585315322875977, 0.035752544403076174, 0.035923583984375, 0.036365440368652344, 0.036286144256591796, 0.03553590393066406, 0.03569609451293945, 0.0355975341796875, 0.03564259338378906]",tokens/s,7068.8356987069865,kWh,4.0485947428319375e-07,2.2176918743989766e-07,6.368184318287453e-07,1.2634470935518368e-06,tokens/kWh,202620276.94434425,MB,1270.411264,969.408512,0.0,314.5728,270.703104,s,10,22.504034912109375,2.2504034912109376,0.0213532755985725,2.2517298583984373,2.26247724609375,2.2758957275390626,2.2866305126953126,"[2.289314208984375, 2.25715185546875, 2.259495361328125, 2.197244140625, 2.2470615234375, 2.24485888671875, 2.249462890625, 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""/usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py"", line 2528, in double_quant out_row = torch.zeros(A.shape, device=device, dtype=torch.int8) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 196.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 178.50 MiB is free. Process 66938 has 22.00 GiB memory in use. Of the allocated memory 21.72 GiB is allocated by PyTorch, and 2.78 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 196.00 MiB. GPU 0 has a total capacity of 22.18 GiB of which 178.50 MiB is free. Process 79973 has 22.00 GiB memory in use. Of the allocated memory 21.72 GiB is allocated by PyTorch, and 2.78 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) ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,opt,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,Qwen/Qwen1.5-MoE-A2.7B,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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set_module_tensor_to_device new_value = old_value.to(device) -torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 60378 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 61723 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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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Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmplvp1yc7d/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +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/tmp1o8wljtm/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,recurrent_gemma,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.1+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,v,v,cuda,0,42,,,True,True,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,False,spawn, AMD EPYC 7R32,16,66697.293824,Linux,x86_64,Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA A10G'],1,24146608128,0.3.1,,4.42.4,,0.32.1,,,,1.21.2,,,,0.11.1,,"Traceback (most recent call last): @@ -10049,7 +10049,7 @@ ChildProcessError: Traceback (most recent call last): cls._check_and_enable_flash_attn_2( File ""/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py"", line 1647, in _check_and_enable_flash_attn_2 raise ValueError( -ValueError: XGLMForCausalLM 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/tmp3uu3a147/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: XGLMForCausalLM 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/tmpn2qb0zet/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,xglm,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,TencentARC/Mistral_Pro_8B_v0.1,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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Please request to add support where the model is hosted, on its model hub page: https://huggingface.co//tmp/tmp1vyyy78t/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: CodeGenForCausalLM 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/tmp350j0e71/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,codegen,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,facebook/opt-125m,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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_check_and_enable_flash_attn_2 raise ValueError( -ValueError: CodeGenForCausalLM 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/tmpjwd3i7cg/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new +ValueError: CodeGenForCausalLM 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/tmp8wn7btmx/no_weights_model/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,codegen,,,,,,,,,,,,,,,,,,,,,,,,True 8bit-bnb-flash_attention_2,pytorch,2.3.0+cu121,optimum_benchmark.backends.pytorch.backend.PyTorchBackend,text-generation,transformers,facebook/opt-6.7b,,cuda,0,42,,,,,True,True,,float16,True,False,,flash_attention_2,,False,,False,forward,bnb,0.0,,False,,inference,optimum_benchmark.scenarios.inference.scenario.InferenceScenario,10,10,10,1,2,256,,True,True,True,64,64,process,optimum_benchmark.launchers.process.launcher.ProcessLauncher,True,kill,,spawn, AMD EPYC 7R32,16,66697.29792,Linux,x86_64,Linux-5.10.215-203.850.amzn2.x86_64-x86_64-with-glibc2.35,x86_64,3.10.12,['NVIDIA 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Tried to allocate 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 59374 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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 1.97 GiB. GPU 0 has a total capacity of 22.18 GiB of which 746.50 MiB is free. Process 60664 has 21.45 GiB memory in use. Of the allocated memory 21.16 GiB is allocated by PyTorch, and 1.31 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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