Redacted changes for Ops with non-conclusive gufunc_signature

Author: Aarsh-Wankar

pytensor/tensor/nlinalg.py

@@ -619,7 +619,16 @@ def perform(self, node, inputs, outputs):
             s[0] = np.linalg.svd(x, self.full_matrices, self.compute_uv)
 
     def infer_shape(self, fgraph, node, shapes):
-        return _gufunc_to_out_shape(self.gufunc_signature, shapes)
+        (x_shape,) = shapes
+        M, N = x_shape
+        K = ptm.minimum(M, N)
+        s_shape = (K,)
+        if self.compute_uv:
+            u_shape = (M, M) if self.full_matrices else (M, K)
+            vt_shape = (N, N) if self.full_matrices else (K, N)
+            return [u_shape, s_shape, vt_shape]
+        else:
+            return [s_shape]
 
     def L_op(
         self,

pytensor/tensor/slinalg.py

@@ -20,8 +20,8 @@
 from pytensor.tensor.nlinalg import kron, matrix_dot
 from pytensor.tensor.shape import reshape
 from pytensor.tensor.type import matrix, tensor, vector
-from pytensor.tensor.utils import _gufunc_to_out_shape
 from pytensor.tensor.variable import TensorVariable
+from pytensor.tensor.utils import _gufunc_to_out_shape
 
 
 logger = logging.getLogger(__name__)
@@ -1176,7 +1176,8 @@ def grad(self, inputs, gout):
         return [gout[0][slc] for slc in slices]
 
     def infer_shape(self, fgraph, nodes, shapes):
-        return _gufunc_to_out_shape(self.gufunc_signature, shapes)
+        first, second = zip(*shapes, strict=True)
+        return [(pt.add(*first), pt.add(*second))]
 
     def _validate_and_prepare_inputs(self, matrices, as_tensor_func):
         if len(matrices) != self.n_inputs: