refactor(mge/functional): move matinv from nn to math

GitOrigin-RevId: 75c48ce327

imperative/python/megengine/functional/math.py

@@ -29,6 +29,7 @@ __all__ = [
     "dot",
     "isinf",
     "isnan",
+    "matinv",
     "matmul",
     "max",
     "mean",
@@ -729,6 +730,38 @@ def topk(
     return tns, ind
 
 
+def matinv(inp: Tensor) -> Tensor:
+    """
+    Computes the inverse of a batch of matrices; input must has shape [..., n, n].
+
+    :param inp: input tensor.
+    :return: output tensor.
+
+    Examples:
+
+    .. testcode::
+
+        import numpy as np
+        from megengine import tensor
+        import megengine.functional as F
+
+        data = tensor([[1.0, 0.0], [1.0, 1.0]])
+        out = F.matinv(data)
+        print(out.numpy())
+
+    Outputs:
+
+    .. testoutput::
+
+        [[ 1.  0.]
+         [-1.  1.]]
+
+    """
+
+    (result,) = apply(builtin.MatrixInverse(), inp)
+    return result
+
+
 def matmul(
     inp1: Tensor,
     inp2: Tensor,

imperative/python/megengine/functional/nn.py

@@ -53,7 +53,6 @@ __all__ = [
     "logsigmoid",
     "logsumexp",
     "logsoftmax",
-    "matinv",
     "max_pool2d",
     "one_hot",
     "prelu",
@@ -1183,38 +1182,6 @@ def remap(
     return result
 
 
-def matinv(inp: Tensor) -> Tensor:
-    """
-    Computes the inverse of a batch of matrices; input must has shape [..., n, n].
-
-    :param inp: input tensor.
-    :return: output tensor.
-
-    Examples:
-
-    .. testcode::
-
-        import numpy as np
-        from megengine import tensor
-        import megengine.functional as F
-
-        data = tensor([[1.0, 0.0], [1.0, 1.0]])
-        out = F.matinv(data)
-        print(out.numpy())
-
-    Outputs:
-
-    .. testoutput::
-
-        [[ 1.  0.]
-         [-1.  1.]]
-
-    """
-
-    (result,) = apply(builtin.MatrixInverse(), inp)
-    return result
-
-
 def interpolate(
     inp: Tensor,
     size: Optional[Union[int, Tuple[int, int]]] = None,