Deprecate block_diag from math module in favor of PyTensor (#7132)

pymc/math.py

@@ -22,8 +22,6 @@
 import pytensor.sparse
 import pytensor.tensor as pt
 import pytensor.tensor.slinalg
-import scipy as sp
-import scipy.sparse
 
 from pytensor.graph.basic import Apply
 from pytensor.graph.op import Op
@@ -93,9 +91,8 @@
 from pytensor.tensor.linalg import solve_triangular
 from pytensor.tensor.nlinalg import matrix_inverse
 from pytensor.tensor.special import log_softmax, softmax
-from scipy.linalg import block_diag as scipy_block_diag
 
-from pymc.pytensorf import floatX, ix_, largest_common_dtype
+from pymc.pytensorf import floatX
 
 __all__ = [
     "abs",
@@ -513,55 +510,9 @@ def batched_diag(C):
         raise ValueError("Input should be 2 or 3 dimensional")
 
 
-class BlockDiagonalMatrix(Op):
-    __props__ = ("sparse", "format")
-
-    def __init__(self, sparse=False, format="csr"):
-        if format not in ("csr", "csc"):
-            raise ValueError(f"format must be one of: 'csr', 'csc', got {format}")
-        self.sparse = sparse
-        self.format = format
-
-    def make_node(self, *matrices):
-        if not matrices:
-            raise ValueError("no matrices to allocate")
-        matrices = list(map(pt.as_tensor, matrices))
-        if any(mat.type.ndim != 2 for mat in matrices):
-            raise TypeError("all data arguments must be matrices")
-        if self.sparse:
-            out_type = pytensor.sparse.matrix(self.format, dtype=largest_common_dtype(matrices))
-        else:
-            out_type = pytensor.tensor.matrix(dtype=largest_common_dtype(matrices))
-        return Apply(self, matrices, [out_type])
-
-    def perform(self, node, inputs, output_storage, params=None):
-        dtype = largest_common_dtype(inputs)
-        if self.sparse:
-            output_storage[0][0] = sp.sparse.block_diag(inputs, self.format, dtype)
-        else:
-            output_storage[0][0] = scipy_block_diag(*inputs).astype(dtype)
-
-    def grad(self, inputs, gout):
-        shapes = pt.stack([i.shape for i in inputs])
-        index_end = shapes.cumsum(0)
-        index_begin = index_end - shapes
-        slices = [
-            ix_(
-                pt.arange(index_begin[i, 0], index_end[i, 0]),
-                pt.arange(index_begin[i, 1], index_end[i, 1]),
-            )
-            for i in range(len(inputs))
-        ]
-        return [gout[0][slc] for slc in slices]
-
-    def infer_shape(self, fgraph, nodes, shapes):
-        first, second = zip(*shapes)
-        return [(pt.add(*first), pt.add(*second))]
-
-
 def block_diagonal(matrices, sparse=False, format="csr"):
-    r"""See scipy.sparse.block_diag or
-    scipy.linalg.block_diag for reference
+    r"""See pt.slinalg.block_diag or
+    pytensor.sparse.basic.block_diag for reference
 
     Parameters
     ----------
@@ -575,6 +526,12 @@ def block_diagonal(matrices, sparse=False, format="csr"):
     -------
     matrix
     """
+    warnings.warn(
+        "pymc.math.block_diagonal is deprecated in favor of `pytensor.tensor.linalg.block_diag` and `pytensor.sparse.block_diag` functions. This function will be removed in a future release",
+    )
     if len(matrices) == 1:  # graph optimization
         return matrices[0]
-    return BlockDiagonalMatrix(sparse=sparse, format=format)(*matrices)
+    if sparse:
+        return pytensor.sparse.basic.block_diag(*matrices, format=format)
+    else:
+        return pt.slinalg.block_diag(*matrices)