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Habana/
gpt2

Name: gpt2
Availability: InStock

optimum_habana

apache-2.0

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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.

GPT2 model HPU configuration

This model only contains the GaudiConfig file for running the GPT2 model 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 implementation
use_fused_clip_norm: whether to use Habana's fused gradient norm clipping operator
use_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 GPT2 with the following command:

bash

python run_clm.py \
    --model_name_or_path gpt2 \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-clm \
    --gaudi_config_name Habana/gpt2 \
    --use_habana \
    --use_lazy_mode \
    --throughput_warmup_steps 2

Check the documentation out for more advanced usage and examples.