#625 use batching for entropy computation to avoid memory issues

Author: Jan Michelfeit

src/imitation/algorithms/preference_comparisons.py

@@ -84,7 +84,7 @@ def has_pretraining(self) -> bool:
         The value can be used, e.g., when allocating time-steps for pre-training.
 
         By default, True is returned if the unsupervised_pretrain() method is not
-        overriden, bud subclasses may choose to override this behavior.
+        overridden, bud subclasses may choose to override this behavior.
 
         Returns:
             True if this generator has a pre-training phase, False otherwise

src/imitation/util/util.py

@@ -377,16 +377,19 @@ def compute_state_entropy(
         A tensor containing the state entropy for `obs`.
     """
     assert obs.shape[1:] == all_obs.shape[1:]
+    batch_size = 500
     with th.no_grad():
         non_batch_dimensions = tuple(range(2, len(obs.shape) + 1))
-        distances_tensor = th.linalg.vector_norm(
-            obs[:, None] - all_obs[None, :],
-            dim=non_batch_dimensions,
-            ord=2,
-        )
-
-        # Note that we take the k+1'th value because the closest neighbor to
-        # a point is itself, which we want to skip.
-        assert distances_tensor.shape[-1] > k
-        knn_dists = th.kthvalue(distances_tensor, k=k + 1, dim=1).values
-        return knn_dists
+        dists = []
+        for idx in range(len(all_obs) // batch_size + 1):
+            start = idx * batch_size
+            end = (idx + 1) * batch_size
+            distances_tensor = th.linalg.vector_norm(
+                obs[:, None] - all_obs[None, start:end],
+                dim=non_batch_dimensions,
+                ord=2,
+            )
+            dists.append(distances_tensor)
+        dists = th.cat(dists, dim=1)
+        knn_dists = th.kthvalue(dists, k=k + 1, dim=1).values
+        return knn_dists