change broadcasting behaviour

Author: ferrine

pytensor/sparse/basic.py

@@ -954,7 +954,7 @@ class DenseFromSparse(Op):
 
     """
 
-    __props__ = ()
+    __props__ = ("sparse_grad",)
 
     def __init__(self, structured=True):
         self.sparse_grad = structured

pytensor/sparse/rewriting.py

@@ -1099,13 +1099,13 @@ def c_code_cache_version(self):
 csm_grad_c = CSMGradC()
 
 
-@node_rewriter([csm_grad(None)])
+@node_rewriter([csm_grad()])
 def local_csm_grad_c(fgraph, node):
     """
     csm_grad(None) -> csm_grad_c
 
     """
-    if node.op == csm_grad(None):
+    if node.op == csm_grad():
         return [csm_grad_c(*node.inputs)]
     return False
 

pytensor/sparse/type.py

@@ -74,7 +74,10 @@ def __init__(
     ):
         if shape is None and broadcastable is None:
             shape = (None, None)
-
+        if broadcastable is None:
+            broadcastable = (False, False)
+        if broadcastable != (False, False):
+            raise ValueError("Broadcasting sparse types is not yet implemented")
         if format not in self.format_cls:
             raise ValueError(
                 f'unsupported format "{format}" not in list',
@@ -96,7 +99,9 @@ def clone(
             dtype = self.dtype
         if shape is None:
             shape = self.shape
-        return type(self)(format, dtype, shape=shape, **kwargs)
+        return type(self)(
+            format, dtype, shape=shape, broadcastable=broadcastable, **kwargs
+        )
 
     def filter(self, value, strict=False, allow_downcast=None):
         if isinstance(value, Variable):

pytensor/tensor/basic.py

@@ -396,7 +396,7 @@ def get_scalar_constant_value(
                         for i in v.owner.inputs
                     ]
                     ret = [[None]]
-                    v.owner.op.perform(v.owner, const, ret)
+                    v.owner.op.scalar_op.perform(v.owner, const, ret)
                     return np.asarray(ret[0][0].copy())
             elif (
                 isinstance(v.owner.op, pytensor.tensor.subtensor.Subtensor)