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1 Parent(s): c2f910c

remove resized loras because new ones are coming

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Signed-off-by: Balazs Horvath <[email protected]>

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  1. ponyxl_loras_shrunk/README.md +47 -0
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ponyxl_loras_shrunk/README.md ADDED
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+ # The Shrunk LoRA Guide
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+
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+ ---
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+
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+ ## What the Heck is a Subspace Factor
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+
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+ ---
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+
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+ The subspace factor is a measure of how well the subspace spanned by the LoRA layer's rank-update matrices (`U` and `Vh`) aligns with the subspace spanned by the weight matrix of the corresponding layer in the base model.
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+
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+ Specifically, the `base_subspace_factors` attribute computed in the `DLoRA` class represents the dot products between the columns of `Vh` and the rows of the base model weight matrix `W_base`, multiplied by the columns of `U`. These dot products measure the correlations between the subspaces spanned by the LoRA update and the base model weights.
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+
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+ A high subspace factor indicates that the LoRA layer is updating a subspace that is already present and important in the base model weights. Conversely, a low subspace factor suggests that the LoRA layer is introducing new directions that were not strongly represented in the base model.
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+
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+ The `subspace_ratios` attribute computes the ratio of the LoRA singular values to the absolute values of the subspace factors. This gives a sense of how much the LoRA layer is scaling the existing subspaces versus introducing new subspaces relative to the base model.
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+
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+ In summary, the subspace factor quantifies the alignment between the LoRA update and the base model weight subspaces, providing insight into how the LoRA layer is adapting the base model.
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+
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+ ## What is the Spectral Norm
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+
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+ ---
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+
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+ The spectral norm (also known as the operator norm or matrix norm) is a measure of the maximum singular value of a matrix. It is calculated as:
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+
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+ ```python
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+ spectral_norm(W) = σ_max(W)
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+ ```
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+
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+ Where `σ_max(W)` is the largest singular value of the matrix `W`.
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+
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+ The `base_spectral_norm` attribute of the `DLoRA` class is computed as:
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+
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+ ```python
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+ self.base_spectral_norm = pt.svd_lowrank(W_base, q=1, niter=niter)[1][0].item()
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+ ```
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+
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+ Here, `W_base` is the weight matrix of the corresponding layer in the base model, flattened into a 2D matrix. `pt.svd_lowrank` computes an approximation of the singular value decomposition, returning the largest singular value `σ_max(W_base)` with `q=1`.
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+
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+ The spectral norm provides a scale for the singular values of a matrix. In the case of LoRA, the `sval_ratios` attribute computes the ratio of the LoRA singular values to the base model's spectral norm:
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+
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+ ```python
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+ self.sval_ratios = self.S / self.base_spectral_norm
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+ ```
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+
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+ This ratio gives an indication of how much the LoRA layer is scaling the singular values of the base model weight matrix.
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+
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+ In summary, the spectral norm is the maximum singular value of a matrix, and it is used in the LoRA context to normalize and interpret the scale of the LoRA singular values relative to the base model weights.
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