Handle static shape in core sparse methods

pytensor/sparse/basic.py

@@ -207,19 +207,19 @@ def sp_zeros_like(x):
 
 
 # for more dtypes, call SparseTensorType(format, dtype)
-def matrix(format, name=None, dtype=None):
+def matrix(format, name=None, dtype=None, shape=None):
     if dtype is None:
         dtype = config.floatX
-    type = SparseTensorType(format=format, dtype=dtype)
+    type = SparseTensorType(format=format, dtype=dtype, shape=shape)
     return type(name)
 
 
-def csc_matrix(name=None, dtype=None):
-    return matrix("csc", name, dtype)
+def csc_matrix(name=None, dtype=None, shape=None):
+    return matrix("csc", name=name, dtype=dtype, shape=shape)
 
 
-def csr_matrix(name=None, dtype=None):
-    return matrix("csr", name, dtype)
+def csr_matrix(name=None, dtype=None, shape=None):
+    return matrix("csr", name=name, dtype=dtype, shape=shape)
 
 
 def bsr_matrix(name=None, dtype=None):
@@ -434,10 +434,22 @@ class CSM(Op):
         if shape.type.ndim != 1 or shape.type.dtype not in discrete_dtypes:
             raise TypeError("n_rows must be integer type", shape, shape.type)
 
+        static_shape = (None, None)
+        if (
+            shape.owner is not None
+            and isinstance(shape.owner.op, CSMProperties)
+            and shape.owner.outputs[3] is shape
+        ):
+            static_shape = shape.owner.inputs[0].type.shape
+
         return Apply(
             self,
             [data, indices, indptr, shape],
-            [SparseTensorType(dtype=data.type.dtype, format=self.format)()],
+            [
+                SparseTensorType(
+                    dtype=data.type.dtype, format=self.format, shape=static_shape
+                )()
+            ],
         )
 
     def perform(self, node, inputs, outputs):
@@ -698,7 +710,7 @@ class DenseFromSparse(Op):
         return Apply(
             self,
             [x],
-            [TensorType(dtype=x.type.dtype, shape=(None, None))()],
+            [TensorType(dtype=x.type.dtype, shape=x.type.shape)()],
         )
 
     def perform(self, node, inputs, outputs):

pytensor/sparse/variable.py

@@ -127,6 +127,8 @@ class _sparse_py_operators:
     def toarray(self):
         return dense_from_sparse(self)
 
+    todense = toarray
+
     @property
     def shape(self):
         # TODO: The plan is that the ShapeFeature in ptb.opt will do shape