Add learned activations L0 norm metric

sparse_autoencoder/metrics/metrics_container.py

@@ -7,6 +7,7 @@
 from sparse_autoencoder.metrics.train.abstract_train_metric import AbstractTrainMetric
 from sparse_autoencoder.metrics.train.capacity import CapacityMetric
 from sparse_autoencoder.metrics.train.feature_density import TrainBatchFeatureDensityMetric
+from sparse_autoencoder.metrics.train.l0_norm_metric import TrainBatchLearnedActivationsL0
 from sparse_autoencoder.metrics.validate.abstract_validate_metric import AbstractValidationMetric
 from sparse_autoencoder.metrics.validate.model_reconstruction_score import ModelReconstructionScore
 
@@ -32,7 +33,11 @@ class MetricsContainer:
 
 
 default_metrics = MetricsContainer(
-    train_metrics=[TrainBatchFeatureDensityMetric(), CapacityMetric()],
+    train_metrics=[
+        TrainBatchFeatureDensityMetric(),
+        CapacityMetric(),
+        TrainBatchLearnedActivationsL0(),
+    ],
     resample_metrics=[NeuronActivityMetric()],
     validation_metrics=[ModelReconstructionScore()],
 )

sparse_autoencoder/metrics/train/l0_norm_metric.py

@@ -0,0 +1,25 @@
+"""L0 norm sparsity metric."""
+from typing import final
+
+import torch
+
+from sparse_autoencoder.metrics.train.abstract_train_metric import (
+    AbstractTrainMetric,
+    TrainMetricData,
+)
+
+
+@final
+class TrainBatchLearnedActivationsL0(AbstractTrainMetric):
+    """Learned activations L0 norm sparsity metric.
+
+    The L0 norm is the number of non-zero elements in a learned activation vector. We then average
+    this over the batch.
+    """
+
+    def calculate(self, data: TrainMetricData) -> dict[str, float]:
+        """Create a log item for Weights and Biases."""
+        batch_size = data.learned_activations.size(0)
+        n_non_zero_activations = torch.count_nonzero(data.learned_activations)
+        batch_average = n_non_zero_activations / batch_size
+        return {"learned_activations_l0_norm": batch_average.item()}

sparse_autoencoder/metrics/train/tests/test_l0_norm_metric.py

@@ -0,0 +1,19 @@
+"""Tests for the L0NormMetric class."""
+import torch
+
+from sparse_autoencoder.metrics.train.abstract_train_metric import TrainMetricData
+from sparse_autoencoder.metrics.train.l0_norm_metric import TrainBatchLearnedActivationsL0
+
+
+def test_l0_norm_metric() -> None:
+    """Test the L0NormMetric."""
+    learned_activations = torch.tensor([[1.0, 0.0, 0.0], [0.0, 0.01, 2.0]])
+    l0_norm_metric = TrainBatchLearnedActivationsL0()
+    data = TrainMetricData(
+        input_activations=torch.zeros_like(learned_activations),
+        learned_activations=learned_activations,
+        decoded_activations=torch.zeros_like(learned_activations),
+    )
+    log = l0_norm_metric.calculate(data)
+    expected = 3 / 2
+    assert log["learned_activations_l0_norm"] == expected