README.md

metadata

license: apache-2.0

Optimum Habana is the interface between the Transformers library and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading and fine-tuning 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 Transformers models at hf.co/hardware/habana.

BERT Large model HPU configuration

This model contains just the GaudiConfig file for running the bert-large-uncased-whole-word-masking model on Habana's Gaudi processors (HPU).

This model contains no model weights, only a GaudiConfig.

This enables to specify:

• use_habana_mixed_precision: whether to use Habana Mixed Precision (HMP)
  • hmp_opt_level: optimization level for HMP, see here for a detailed explanation
  • hmp_bf16_ops: list of operators that should run in bf16
  • hmp_fp32_ops: list of operators that should run in fp32
  • hmp_is_verbose: verbosity
• 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

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:

from optimum.habana import GaudiTrainer, GaudiTrainingArguments
from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking")
model = BertModel.from_pretrained("bert-large-uncased-whole-word-masking")
args = GaudiTrainingArguments(
    output_dir="/tmp/output_dir",
    use_habana=True,
    use_lazy_mode=True,
    gaudi_config_name="Habana/bert-large-uncased-whole-word-masking",
)

trainer = GaudiTrainer(
    model=model,
    args=args,
    tokenizer=tokenizer,
)
trainer.train()