!11978 Add grad impl for op MatrixInverse

From: @yuan_shen_zhou
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/ops/_grad/grad_math_ops.py

@@ -165,6 +165,22 @@ def get_bprop_tensor_add(self):
     return bprop
 
 
+@bprop_getters.register(P.MatrixInverse)
+def get_bprop_matrix_inverse(self):
+    """Grad definition for `MatrixInverse` operation."""
+    batchmatmul_a = P.math_ops.BatchMatMul(transpose_a=True)
+    batchmatmul_b = P.math_ops.BatchMatMul(transpose_b=True)
+    neg = P.Neg()
+
+    def bprop(x, out, dout):
+        dx = batchmatmul_b(dout, out)
+        dx = batchmatmul_a(out, dx)
+        dx = neg(dx)
+        return dx
+
+    return bprop
+
+
 @bprop_getters.register(P.Neg)
 def get_bprop_neg(self):
     """Grad definition for `Neg` operation."""

mindspore/ops/operations/math_ops.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -4136,6 +4136,9 @@ class MatrixInverse(PrimitiveWithInfer):
     Returns the inverse of the input matrix. If the matrix is irreversible, an error may be reported or an unknown
     result may be returned
 
+    Note:
+        The parameter 'adjoint' is only supporting False right now. Because complex number is not supported at present.
+
     Args:
         adjoint (bool) : An optional bool. Default: False.
 
@@ -4146,6 +4149,9 @@ class MatrixInverse(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same type and shape as input `x`.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> mindspore.set_seed(1)
         >>> x = Tensor(np.random.uniform(-2, 2, (2, 2, 2)), mindspore.float32)
@@ -4161,7 +4167,7 @@ class MatrixInverse(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, adjoint=False):
         """Initialize MatrixInverse"""
-        validator.check_value_type("adjoint", adjoint, [bool], self.name)
+        validator.check_type_name("adjoint", adjoint, False, self.name)
         self.adjoint = adjoint
 
     def infer_dtype(self, x_dtype):