Replace custom memoize module with cachetools

pymc3/distributions/distribution.py

@@ -37,13 +37,13 @@
 from aesara.graph.basic import Constant
 from aesara.tensor.type import TensorType as AesaraTensorType
 from aesara.tensor.var import TensorVariable
+from cachetools import LRUCache, cached
 
 from pymc3.distributions.shape_utils import (
     broadcast_dist_samples_shape,
     get_broadcastable_dist_samples,
     to_tuple,
 )
-from pymc3.memoize import memoize
 from pymc3.model import (
     ContextMeta,
     FreeRV,
@@ -52,7 +52,7 @@
     ObservedRV,
     build_named_node_tree,
 )
-from pymc3.util import get_repr_for_variable, get_var_name
+from pymc3.util import get_repr_for_variable, get_var_name, hash_key
 from pymc3.vartypes import string_types
 
 __all__ = [
@@ -841,7 +841,7 @@ def draw_values(params, point=None, size=None):
     return [evaluated[j] for j in params]  # set the order back
 
 
-@memoize
+@cached(LRUCache(128), key=hash_key)
 def _compile_aesara_function(param, vars, givens=None):
     """Compile aesara function for a given parameter and input variables.
 

pymc3/model.py

@@ -32,6 +32,7 @@
 from aesara.graph.basic import Apply, Variable
 from aesara.tensor.type import TensorType as AesaraTensorType
 from aesara.tensor.var import TensorVariable
+from cachetools import LRUCache, cachedmethod
 from pandas import Series
 
 import pymc3 as pm
@@ -40,8 +41,7 @@
 from pymc3.blocking import ArrayOrdering, DictToArrayBijection
 from pymc3.exceptions import ImputationWarning
 from pymc3.math import flatten_list
-from pymc3.memoize import WithMemoization, memoize
-from pymc3.util import get_transformed_name, get_var_name
+from pymc3.util import WithMemoization, get_transformed_name, get_var_name, hash_key
 from pymc3.vartypes import continuous_types, discrete_types, isgenerator, typefilter
 
 __all__ = [
@@ -946,7 +946,9 @@ def isroot(self):
         return self.parent is None
 
     @property  # type: ignore
-    @memoize(bound=True)
+    @cachedmethod(
+        lambda self: self.__dict__.setdefault("_bijection_cache", LRUCache(128)), key=hash_key
+    )
     def bijection(self):
         vars = inputvars(self.vars)
 

pymc3/tests/test_util.py

@@ -21,6 +21,7 @@
 
 from pymc3.distributions.transforms import Transform
 from pymc3.tests.helpers import SeededTest
+from pymc3.util import hashable
 
 
 class TestTransformName:
@@ -167,3 +168,24 @@ def test_dtype_error(self):
             raise pm.exceptions.DtypeError("With types.", actual=int, expected=str)
         assert "int" in exinfo.value.args[0] and "str" in exinfo.value.args[0]
         pass
+
+
+def test_hashing_of_rv_tuples():
+    obs = np.random.normal(-1, 0.1, size=10)
+    with pm.Model() as pmodel:
+        mu = pm.Normal("mu", 0, 1)
+        sd = pm.Gamma("sd", 1, 2)
+        dd = pm.DensityDist(
+            "dd",
+            pm.Normal.dist(mu, sd).logp,
+            random=pm.Normal.dist(mu, sd).random,
+            observed=obs,
+        )
+        for freerv in [mu, sd, dd] + pmodel.free_RVs:
+            for structure in [
+                freerv,
+                {"alpha": freerv, "omega": None},
+                [freerv, []],
+                (freerv, []),
+            ]:
+                assert isinstance(hashable(structure), int)

pymc3/tests/test_variational_inference.py

@@ -22,7 +22,6 @@
 import pytest
 
 import pymc3 as pm
-import pymc3.memoize
 import pymc3.util
 
 from pymc3.aesaraf import intX
@@ -757,22 +756,20 @@ def test_remove_scan_op():
 def test_clear_cache():
     import pickle
 
-    pymc3.memoize.clear_cache()
-    assert all(len(c) == 0 for c in pymc3.memoize.CACHE_REGISTRY)
     with pm.Model():
         pm.Normal("n", 0, 1)
         inference = ADVI()
         inference.fit(n=10)
         assert any(len(c) != 0 for c in inference.approx._cache.values())
-        pymc3.memoize.clear_cache(inference.approx)
+        inference.approx._cache.clear()
         # should not be cleared at this call
         assert all(len(c) == 0 for c in inference.approx._cache.values())
         new_a = pickle.loads(pickle.dumps(inference.approx))
         assert not hasattr(new_a, "_cache")
         inference_new = pm.KLqp(new_a)
         inference_new.fit(n=10)
         assert any(len(c) != 0 for c in inference_new.approx._cache.values())
-        pymc3.memoize.clear_cache(inference_new.approx)
+        inference_new.approx._cache.clear()
         assert all(len(c) == 0 for c in inference_new.approx._cache.values())