# GaudiConfig

To define a configuration for a specific workload you can use `GaudiConfig` class.

Here is a description of each configuration parameter:
- `use_fused_adam` controls whether to use the [custom fused implementation of the ADAM optimizer provided by Intel® Gaudi® AI Accelerator](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Custom_Ops_PyTorch.html#custom-optimizers).
- `use_fused_clip_norm` controls whether to use the [custom fused implementation of gradient norm clipping provided by Intel® Gaudi® AI Accelerator](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Custom_Ops_PyTorch.html#other-custom-ops).
- `use_torch_autocast` controls whether to enable PyTorch autocast; used to define good pre-defined config; users should favor `--bf16` training argument
- `use_dynamic_shapes` controls whether to enable dynamic shapes support when processing input dataset
- `autocast_bf16_ops` list of operations that should be run with bf16 precision under autocast context; using environment flag LOWER_LIST is a preferred way for operator autocast list override
- `autocast_fp32_ops` list of operations that should be run with fp32 precision under autocast context; using environment flag FP32_LIST is a preferred way for operator autocast list override

Parameter values of this class can be set from an external JSON file.

You can find examples of Gaudi configurations in the [Intel Gaudi model repository on the Hugging Face Hub](https://huggingface.co/habana).
For instance, [for BERT Large we have](https://huggingface.co/Habana/bert-large-uncased-whole-word-masking/blob/main/gaudi_config.json):
```JSON
{
  "use_fused_adam": true,
  "use_fused_clip_norm": true,
  "use_torch_autocast": true
}
```

More advanced configuration file [for Stable Diffusion 2](https://huggingface.co/Habana/stable-diffusion-2/blob/main/gaudi_config.json):
```JSON
{
  "use_torch_autocast": true,
  "use_fused_adam": true,
  "use_fused_clip_norm": true,
  "autocast_bf16_ops": [
    "_convolution.deprecated",
    "_convolution",
    "conv1d",
    "conv2d",
    "conv3d",
    "conv_tbc",
    "conv_transpose1d",
    "conv_transpose2d.input",
    "conv_transpose3d.input",
    "convolution",
    "prelu",
    "addmm",
    "addmv",
    "addr",
    "matmul",
    "einsum",
    "mm",
    "mv",
    "silu",
    "linear",
    "addbmm",
    "baddbmm",
    "bmm",
    "chain_matmul",
    "linalg_multi_dot",
    "layer_norm",
    "group_norm"
  ],
  "autocast_fp32_ops": [
    "acos",
    "asin",
    "cosh",
    "erfinv",
    "exp",
    "expm1",
    "log",
    "log10",
    "log2",
    "log1p",
    "reciprocal",
    "rsqrt",
    "sinh",
    "tan",
    "pow.Tensor_Scalar",
    "pow.Tensor_Tensor",
    "pow.Scalar",
    "softplus",
    "frobenius_norm",
    "frobenius_norm.dim",
    "nuclear_norm",
    "nuclear_norm.dim",
    "cosine_similarity",
    "poisson_nll_loss",
    "cosine_embedding_loss",
    "nll_loss",
    "nll_loss2d",
    "hinge_embedding_loss",
    "kl_div",
    "l1_loss",
    "smooth_l1_loss",
    "huber_loss",
    "mse_loss",
    "margin_ranking_loss",
    "multilabel_margin_loss",
    "soft_margin_loss",
    "triplet_margin_loss",
    "multi_margin_loss",
    "binary_cross_entropy_with_logits",
    "dist",
    "pdist",
    "cdist",
    "renorm",
    "logsumexp"
  ]
}
```

To instantiate yourself a Gaudi configuration in your script, you can do the following
```python
from optimum.habana import GaudiConfig

gaudi_config = GaudiConfig.from_pretrained(
    gaudi_config_name,
    cache_dir=model_args.cache_dir,
    revision=model_args.model_revision,
    token=model_args.token,
)
```
and pass it to the trainer with the `gaudi_config` argument.

## GaudiConfig[[optimum.habana.GaudiConfig]]

#### optimum.habana.GaudiConfig[[optimum.habana.GaudiConfig]]

[Source](https://github.com/huggingface/optimum-habana/blob/main/optimum/habana/transformers/gaudi_configuration.py#L51)