Refactor SharedVariable type and interface

Author: brandonwillard

aesara/compile/sharedvalue.py

@@ -3,14 +3,18 @@
 import copy
 from contextlib import contextmanager
 from functools import singledispatch
-from typing import List, Optional
+from typing import TYPE_CHECKING, List, Optional
 
 from aesara.graph.basic import Variable
 from aesara.graph.utils import add_tag_trace
 from aesara.link.basic import Container
 from aesara.link.c.type import generic
 
 
+if TYPE_CHECKING:
+    from aesara.graph.type import Type
+
+
 __SHARED_CONTEXT__: Optional[List[Variable]] = None
 
 
@@ -30,14 +34,39 @@ def collect_new_shareds():
 class SharedVariable(Variable):
     """Variable that is shared between compiled functions."""
 
-    container: Optional[Container] = None
-    """
-    A container to use for this SharedVariable when it is an implicit
-    function parameter.
-    """
+    def __init__(
+        self,
+        type: "Type",
+        value,
+        strict: bool,
+        allow_downcast=None,
+        container: Optional[Container] = None,
+        name: Optional[str] = None,
+    ):
+        r"""
+        Parameters
+        ----------
+        type
+            The `Type` for this variable (see `Variable`).
+        value
+            A value to associate with this variable (a new container will be
+            created).
+        strict
+            ``True`` means that values assigned to this variable will not be
+            cast or copied, so they must have the correct `Type`\s.
+        allow_downcast
+            Only applies if `strict` is ``False``.
+            ``True`` means that the assigned value can lose precision when cast
+            during assignment. ``None`` means that only down-casting of a Python
+            float to a scalar ``floatX`` is allowed.
+        container
+            The container to use for this variable. Illegal to pass this as well as
+            a value.
+        name
+            The name for this variable (see `Variable`).
 
-    def __init__(self, name, type, value, strict, allow_downcast=None, container=None):
-        super().__init__(type=type, name=name, owner=None, index=None)
+        """
+        super().__init__(type=type, owner=None, index=None, name=name)
 
         if container is not None:
             self.container = container
@@ -107,26 +136,6 @@ def set_value(self, new_value, borrow=False):
     def get_test_value(self):
         return self.get_value(borrow=True, return_internal_type=True)
 
-    def zero(self, borrow=False):
-        """
-        Set the values of a shared variable to 0.
-
-        Parameters
-        ----------
-        borrow : bbol
-            True to modify the value of a shared variable directly by using
-            its previous value. Potentially this can cause problems
-            regarding to the aliased memory.
-
-        Changes done with this function will be visible to all functions using
-        this SharedVariable.
-
-        """
-        if borrow:
-            self.container.value[...] = 0
-        else:
-            self.container.value = 0 * self.container.value
-
     def clone(self, **kwargs):
         name = kwargs.get("name", self.name)
         cp = self.__class__(
@@ -209,7 +218,7 @@ def shared_constructor(value, name=None, strict=False, allow_downcast=None, **kw
     return SharedVariable(
         type=generic,
         value=value,
-        name=name,
         strict=strict,
         allow_downcast=allow_downcast,
+        name=name,
     )

aesara/sparse/sharedvar.py

@@ -2,13 +2,15 @@
 
 import scipy.sparse
 
-from aesara.compile import SharedVariable, shared_constructor
+from aesara.compile import shared_constructor
 from aesara.sparse.basic import SparseTensorType, _sparse_py_operators
+from aesara.tensor.sharedvar import TensorSharedVariable
 
 
-class SparseTensorSharedVariable(_sparse_py_operators, SharedVariable):
-    dtype = property(lambda self: self.type.dtype)
-    format = property(lambda self: self.type.format)
+class SparseTensorSharedVariable(TensorSharedVariable, _sparse_py_operators):
+    @property
+    def format(self):
+        return self.type.format
 
 
 @shared_constructor.register(scipy.sparse.spmatrix)
@@ -24,5 +26,5 @@ def sparse_constructor(
         value = copy.deepcopy(value)
 
     return SparseTensorSharedVariable(
-        type=type, value=value, name=name, strict=strict, allow_downcast=allow_downcast
+        type=type, value=value, strict=strict, allow_downcast=allow_downcast, name=name
     )

aesara/tensor/random/var.py

@@ -37,7 +37,7 @@ def randomgen_constructor(
     return rng_sv_type(
         type=rng_type,
         value=value,
-        name=name,
         strict=strict,
         allow_downcast=allow_downcast,
+        name=name,
     )

aesara/tensor/sharedvar.py

@@ -19,9 +19,25 @@ def load_shared_variable(val):
     return tensor_constructor(val)
 
 
-# _tensor_py_operators is first to have its version of __{gt,ge,lt,le}__
 class TensorSharedVariable(_tensor_py_operators, SharedVariable):
-    pass
+    def zero(self, borrow: bool = False):
+        r"""Set the values of a shared variable to 0.
+
+        Parameters
+        ----------
+        borrow
+            ``True`` to modify the value of a shared variable directly by using
+            its previous value. Potentially this can cause problems regarding
+            to the aliased memory.
+
+        Changes done with this function will be visible to all functions using
+        this `SharedVariable`.
+
+        """
+        if borrow:
+            self.container.value[...] = 0
+        else:
+            self.container.value = 0 * self.container.value
 
 
 @_get_vector_length.register(TensorSharedVariable)
@@ -69,13 +85,13 @@ def tensor_constructor(
     return TensorSharedVariable(
         type=type,
         value=np.array(value, copy=(not borrow)),
-        name=name,
         strict=strict,
         allow_downcast=allow_downcast,
+        name=name,
     )
 
 
-class ScalarSharedVariable(_tensor_py_operators, SharedVariable):
+class ScalarSharedVariable(TensorSharedVariable):
     pass
 
 

tests/sparse/test_sharedvar.py

@@ -0,0 +1,15 @@
+import numpy as np
+import scipy as sp
+
+import aesara
+from aesara.sparse.sharedvar import SparseTensorSharedVariable
+
+
+def test_shared_basic():
+    x = aesara.shared(
+        sp.sparse.csr_matrix(np.eye(100), dtype=np.float64), name="blah", borrow=True
+    )
+
+    assert isinstance(x, SparseTensorSharedVariable)
+    assert x.format == "csr"
+    assert x.dtype == "float64"

tests/sparse/test_sparse.py

@@ -10,6 +10,7 @@
 from aesara.tensor import get_vector_length
 from aesara.tensor.basic import MakeVector
 from aesara.tensor.shape import Shape_i, specify_shape
+from aesara.tensor.sharedvar import ScalarSharedVariable, TensorSharedVariable
 from tests import unittest_tools as utt
 
 
@@ -649,10 +650,31 @@ class TestSharedOptions:
     pass
 
 
+def test_tensor_shared_zero():
+    shared_val = np.array([1.0, 3.0], dtype=np.float32)
+    res = aesara.shared(value=shared_val, borrow=True)
+    assert isinstance(res, TensorSharedVariable)
+    assert res.get_value(borrow=True) is shared_val
+
+    res.zero(borrow=True)
+    new_shared_val = res.get_value(borrow=True)
+    assert new_shared_val is shared_val
+    assert np.array_equal(new_shared_val, np.zeros((2,), dtype=np.float32))
+
+    res.set_value(shared_val, borrow=True)
+
+    res.zero(borrow=False)
+    new_shared_val = res.get_value(borrow=True)
+    assert new_shared_val is not shared_val
+    assert np.array_equal(new_shared_val, np.zeros((2,), dtype=np.float32))
+
+
 def test_scalar_shared_options():
-    # Simple test to make sure we do not loose that fonctionality.
-    aesara.shared(value=0.0, name="lk", borrow=True)
-    aesara.shared(value=np.float32(0.0), name="lk", borrow=True)
+    res = aesara.shared(value=np.float32(0.0), name="lk", borrow=True)
+    assert isinstance(res, ScalarSharedVariable)
+    assert res.type.dtype == "float32"
+    assert res.name == "lk"
+    assert res.type.shape == ()
 
 
 def test_get_vector_length():