removed orthonormal rewrites

Author: tanish1729

pytensor/tensor/rewriting/linalg.py

@@ -663,42 +663,3 @@ def rewrite_inv_diag_to_diag_reciprocal(fgraph, node):
     else:
         # For Vector or Scalar
         return [eye_input / non_eye_input]
-
-
-# @register_canonicalize
-# @register_stabilize
-# @node_rewriter([Blockwise])
-# def rewrite_inv_for_orthonormal(fgraph, node):
-#     """
-#      This rewrite takes advantage of the fact that for an orthonormal matrix, the inverse is simply the transpose.
-#      This function deals with orthonormal matrix arising from pt.linalg.svd decomposition (U, Vh) or arising from pt.linalg.qr
-
-#     Parameters
-#     ----------
-#     fgraph: FunctionGraph
-#         Function graph being optimized
-#     node: Apply
-#         Node of the function graph to be optimized
-
-#     Returns
-#     -------
-#     list of Variable, optional
-#         List of optimized variables, or None if no optimization was performed
-#     """
-#     # Dealing with orthonormal matrix from SVD
-#     # Check if input to Inverse is coming from SVD
-#     input_to_inv = node.inputs[0]
-#     # Check if this input is coming from SVD with compute_uv = True
-#     if not (
-#         input_to_inv.owner
-#         and isinstance(input_to_inv.owner.op, Blockwise)
-#         and isinstance(input_to_inv.owner.op.core_op, SVD)
-#         and input_to_inv.owner.op.core_op.compute_uv is True
-#     ):
-#         return None
-
-#     # To make sure input is orthonormal, we have to check that its not S (output order of SVD is U, S, Vh, so S is index 1) (S matrix consists of singular values and it is not orthonormal)
-#     if input_to_inv == input_to_inv.owner.outputs[1]:
-#         return None
-
-#     return [input_to_inv.T]

tests/tensor/rewriting/test_linalg.py

@@ -644,30 +644,3 @@ def test_inv_diag_from_diag():
         atol=ATOL,
         rtol=RTOL,
     )
-
-
-def test_inv_orthonormal():
-    x = pt.dmatrix("x")
-    u, s, vh = pt.linalg.svd(x)
-    # Calculating inverse using pt.linalg.inv
-    u_inv = pt.linalg.inv(u)
-
-    # REWRITE TEST
-    f_rewritten = function([x], u_inv, mode="FAST_RUN")
-    nodes = f_rewritten.maker.fgraph.apply_nodes
-
-    valid_inverses = (MatrixInverse, MatrixPinv)
-    assert not any(isinstance(node.op, valid_inverses) for node in nodes)
-
-    # NUMERIC VALUE TEST
-    x_test = np.random.rand(7, 7).astype(config.floatX)
-    u_test, _, _ = np.linalg.svd(x_test)
-    inverse_matrix = np.linalg.inv(u_test)
-    rewritten_inverse = f_rewritten(x_test)
-
-    assert_allclose(
-        inverse_matrix,
-        rewritten_inverse,
-        atol=ATOL,
-        rtol=RTOL,
-    )