Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at hf.co/hardware/habana.
Llama model HPU configuration
This model only contains the GaudiConfig file for running Llama models on Habana's Gaudi processors (HPU).
This model contains no model weights, only a GaudiConfig.
This enables to specify:
use_fused_adam: whether to use Habana's custom AdamW implementationuse_fused_clip_norm: whether to use Habana's fused gradient norm clipping operatoruse_torch_autocast: whether to use PyTorch's autocast mixed precision
Usage
The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.
Here is a causal language modeling example script to pre-train/fine-tune a model. You can run it with Llama with the following command:
python3 run_lora_clm.py \
--model_name_or_path huggyllama/llama-7b \
--dataset_name tatsu-lab/alpaca \
--bf16 True \
--output_dir ./model_lora_llama \
--num_train_epochs 3 \
--per_device_train_batch_size 16 \
--evaluation_strategy "no" \
--save_strategy "no" \
--learning_rate 1e-4 \
--warmup_ratio 0.03 \
--lr_scheduler_type "constant" \
--max_grad_norm 0.3 \
--logging_steps 1 \
--do_train \
--do_eval \
--use_habana \
--use_lazy_mode \
--throughput_warmup_steps 3 \
--lora_rank=8 \
--lora_alpha=16 \
--lora_dropout=0.05 \
--lora_target_modules "q_proj" "v_proj" \
--dataset_concatenation \
--max_seq_length 512 \
--low_cpu_mem_usage True \
--validation_split_percentage 4 \
--adam_epsilon 1e-08
You will need to install the PEFT library with pip install peft to run the command above.
Check the documentation out for more advanced usage and examples.