cuBLAS error with NVIDIA H100 HGX, CUDA v12.1, and cuDNN 8.8.1

[BenFauber]

cuBLAS error when running the HuggingFace accelerate following benchmark code on NVIDIA H100 HGX, CUDA v12.1, cuDNN 8.8.1, pytorch==2.0.0+cu118, within Jupyter Notebook:

!CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python transformers-bloom-inference/bloom-inference-scripts/bloom-accelerate-inference.py --name bigscience/bloom --dtype int8 --batch_size 1 --benchmark

...or at CLI:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python transformers-bloom-inference/bloom-inference-scripts/bloom-accelerate-inference.py --name bigscience/bloom --dtype int8 --batch_size 1 --benchmark

SOLUTION:

Make the following edits within transformers-bloom-inference/bloom-inference-scripts/bloom-accelerate-inference.py to enable mixed precision:

line 10: Add BitsAndBytesConfig to import call, as follows: from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

line 56: Add quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True) to statement as follows: infer_dtype = args.dtype if infer_dtype == "int8": dtype = torch.int8 quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True) kwargs = dict( device_map="auto", )

line 77: Edit kwargs to include quantization_config, as follows: if infer_dtype == "int8": print_rank0("Using load_in_8bit=True to use quanitized model") #kwargs["load_in_8bit"] = True kwargs={"load_in_8bit":True, "quantization_config": quantization_config, "device_map": "auto"} else: kwargs["torch_dtype"] = dtype

Save the updated PY file, then run the accelerate inferencing code.

Updated PY file runs benchmarks without errors. Recommend making these, or similar code changes, to the parent repo.