Delete featurizer.py

featurizer.py

@@ -1,52 +0,0 @@
-"""
-Copy of the existing SubspaceFeaturizer implementation for submission.
-This file provides the same SubspaceFeaturizer functionality in a self-contained format.
-"""
-
-import torch
-import torch.nn as nn
-import pyvene as pv
-from CausalAbstraction.neural.featurizers import Featurizer
-
-
-class SubspaceFeaturizerModuleCopy(torch.nn.Module):
-    def __init__(self, rotate_layer):
-        super().__init__()
-        self.rotate = rotate_layer
-        
-    def forward(self, x):
-        r = self.rotate.weight.T
-        f = x.to(r.dtype) @ r.T
-        error = x - (f @ r).to(x.dtype)
-        return f, error
-
-
-class SubspaceInverseFeaturizerModuleCopy(torch.nn.Module):
-    def __init__(self, rotate_layer):
-        super().__init__()
-        self.rotate = rotate_layer
-        
-    def forward(self, f, error):
-        r = self.rotate.weight.T
-        return (f.to(r.dtype) @ r).to(f.dtype) + error.to(f.dtype)
-
-
-class SubspaceFeaturizerCopy(Featurizer):
-    def __init__(self, shape=None, rotation_subspace=None, trainable=True, id="subspace"):
-        assert shape is not None or rotation_subspace is not None, "Either shape or rotation_subspace must be provided."
-        if shape is not None:
-            self.rotate = pv.models.layers.LowRankRotateLayer(*shape, init_orth=True)
-        elif rotation_subspace is not None:
-            shape = rotation_subspace.shape
-            self.rotate = pv.models.layers.LowRankRotateLayer(*shape, init_orth=False)
-            self.rotate.weight.data.copy_(rotation_subspace)
-        self.rotate = torch.nn.utils.parametrizations.orthogonal(self.rotate)
-
-        if not trainable:
-            self.rotate.requires_grad_(False)
-
-        # Create module-based featurizer and inverse_featurizer
-        featurizer = SubspaceFeaturizerModuleCopy(self.rotate)
-        inverse_featurizer = SubspaceInverseFeaturizerModuleCopy(self.rotate)
-            
-        super().__init__(featurizer, inverse_featurizer, n_features=self.rotate.weight.shape[1], id=id)