Here is a description of each configuration parameter:
use_fused_adam
enables to decide whether to use the custom fused implementation of the ADAM optimizer provided by Habana.use_fused_clip_norm
enables to decide whether to use the custom fused implementation of gradient norm clipping provided by Habana.use_torch_autocast
enables PyTorch autocast; used to define good pre-defined config; users should favor --bf16
training argumentautocast_bf16_ops
list of operations that should be run with bf16 precision under autocast context; using environment flag LOWER_LIST is a preffered way for operator autocast list overrideautocast_fp32_ops
list of operations that should be run with fp32 precision under autocast context; using environment flag FP32_LIST is a preffered way for operator autocast list overrideYou can find examples of Gaudi configurations in the Habana model repository on the Hugging Face Hub. For instance, for BERT Large we have:
{
"use_fused_adam": true,
"use_fused_clip_norm": true,
}
To instantiate yourself a Gaudi configuration in your script, you can do the following
from optimum.habana import GaudiConfig
gaudi_config = GaudiConfig.from_pretrained(
gaudi_config_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
and pass it to the trainer with the gaudi_config
argument.