Shortcuts

LoRALinear

class torchtune.modules.peft.LoRALinear(in_dim: int, out_dim: int, rank: int, alpha: float, dropout: float = 0.0, use_bias: bool = False, quantize_base: bool = False)[source]

LoRA linear layer as introduced in LoRA: Low-Rank Adaptation of Large Language Models.

LoRA perturbs a given layer via a low-rank approximation where only the rank decomposition matrices are trainable. In a linear layer instead of \(x \mapsto W_0x\) a LoRALinear layer is defined as \(x \mapsto W_0x + (\alpha / r)BAx\), where \(r\) is the rank of the matrices \(A\) and \(B\) and \(\alpha\) is a scaling factor. As in the original implementation, we support dropout before multiplication by the low-rank matrices.

Parameters:
  • in_dim (int) – input dimension

  • out_dim (int) – output dimension

  • rank (int) – rank of the low-rank approximation

  • alpha (float) – scaling factor for the low-rank approximation

  • dropout (float) – dropout probability. Default: 0.0

  • use_bias (bool) – whether to include bias in the original linear layer. Default: False

  • quantize_base (bool) – Whether to quantize base linear weight or not. Default: False

adapter_params() List[str][source]

Return lora_a.weight and lora_b.weight as adapter params. If bias is enabled, also return lora_a.bias and lora_b.bias.

forward(x: Tensor) Tensor[source]
Parameters:

x (torch.Tensor) – input tensor with shape (..., in_dim)

Returns:

output tensor with shape (..., out_dim)

Return type:

torch.Tensor

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources