fix: only scale sparsity by dec norm if specified in the config

sae_lens/training/training_sae.py

@@ -405,7 +405,9 @@ def training_forward_pass(
             loss = mse_loss + l1_loss
         else:
             # default SAE sparsity loss
-            weighted_feature_acts = feature_acts * self.W_dec.norm(dim=1)
+            weighted_feature_acts = feature_acts
+            if self.cfg.scale_sparsity_penalty_by_decoder_norm:
+                weighted_feature_acts = feature_acts * self.W_dec.norm(dim=1)
             sparsity = weighted_feature_acts.norm(
                 p=self.cfg.lp_norm, dim=-1
             )  # sum over the feature dimension

tests/unit/training/test_training_sae.py

@@ -0,0 +1,39 @@
+import pytest
+import torch
+
+from sae_lens.training.training_sae import TrainingSAE, TrainingSAEConfig
+from tests.unit.helpers import build_sae_cfg
+
+
+@pytest.mark.parametrize("scale_sparsity_penalty_by_decoder_norm", [True, False])
+def test_TrainingSAE_training_forward_pass_can_scale_sparsity_penalty_by_decoder_norm(
+    scale_sparsity_penalty_by_decoder_norm: bool,
+):
+    cfg = build_sae_cfg(
+        d_in=3,
+        d_sae=5,
+        scale_sparsity_penalty_by_decoder_norm=scale_sparsity_penalty_by_decoder_norm,
+        normalize_sae_decoder=False,
+    )
+    training_sae = TrainingSAE(TrainingSAEConfig.from_sae_runner_config(cfg))
+    x = torch.randn(32, 3)
+    train_step_output = training_sae.training_forward_pass(
+        sae_in=x,
+        current_l1_coefficient=2.0,
+    )
+    feature_acts = train_step_output.feature_acts
+    decoder_norm = training_sae.W_dec.norm(dim=1)
+    # double-check decoder norm is not all ones, or this test is pointless
+    assert not torch.allclose(decoder_norm, torch.ones_like(decoder_norm), atol=1e-2)
+    scaled_feature_acts = feature_acts * decoder_norm
+
+    if scale_sparsity_penalty_by_decoder_norm:
+        assert (
+            pytest.approx(train_step_output.l1_loss)
+            == 2.0 * scaled_feature_acts.norm(p=1, dim=1).mean().detach().item()
+        )
+    else:
+        assert (
+            pytest.approx(train_step_output.l1_loss)
+            == 2.0 * feature_acts.norm(p=1, dim=1).mean().detach().item()
+        )