add operator diag and diag_part

mindspore/ccsrc/transform/convert.cc

@@ -179,6 +179,8 @@ const char kNameLARSUpdate[] = "LARSUpdate";
 const char kNameRound[] = "Round";
 const char kNamePrint[] = "Print";
 const char kNameApplyFtrl[] = "ApplyFtrl";
+const char kNameDiag[] = "Diag";
+const char kNameDiagPart[] = "DiagPart";
 
 // -----------------OpAdapter initialization--------------
 std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@@ -359,7 +361,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)},
     {string(kNameSign), ADPT_DESC(Sign)},
     {string(kNameRound), ADPT_DESC(Round)},
-    {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)}};
+    {string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)},
+    {string(kNameDiag), ADPT_DESC(Diag)},
+    {string(kNameDiagPart), ADPT_DESC(DiagPart)}};
 #ifdef ENABLE_GE
   adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
 #endif

mindspore/ccsrc/transform/op_declare.cc

@@ -1160,6 +1160,16 @@ INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)},  {2, INPUT_DESC(accum)},   {3, INP
 ATTR_MAP(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}};
 
+// Diag
+INPUT_MAP(Diag) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(Diag) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(Diag) = {{0, OUTPUT_DESC(y)}};
+
+// DiagPart
+INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}};
+ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}};
+
 #ifdef ENABLE_GE
 // Print
 INPUT_MAP(Print) = EMPTY_INPUT_MAP;

mindspore/ccsrc/transform/op_declare.h

@@ -437,6 +437,10 @@ DECLARE_OP_ADAPTER(ApplyFtrl)
 DECLARE_OP_USE_OUTPUT(ApplyFtrl)
 DECLARE_OP_ADAPTER(SparseApplyFtrlD)
 DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD)
+DECLARE_OP_ADAPTER(Diag)
+DECLARE_OP_USE_OUTPUT(Diag)
+DECLARE_OP_ADAPTER(DiagPart)
+DECLARE_OP_USE_OUTPUT(DiagPart)
 #ifdef ENABLE_GE
 DECLARE_OP_ADAPTER(Print)
 DECLARE_OP_USE_DYN_INPUT(Print)

mindspore/ops/_grad/grad_array_ops.py

@@ -408,3 +408,25 @@ def get_bprop_depth_to_space(self):
         return (op(dout),)
 
     return bprop
+
+
+@bprop_getters.register(P.Diag)
+def get_bprop_diag(self):
+    """Generate bprop for Diag"""
+    op = P.DiagPart()
+
+    def bprop(x, out, dout):
+        return (op(dout),)
+
+    return bprop
+
+
+@bprop_getters.register(P.DiagPart)
+def get_bprop_diag_part(self):
+    """Generate bprop for DiagPart"""
+    op = P.Diag()
+
+    def bprop(x, out, dout):
+        return (op(dout),)
+
+    return bprop

mindspore/ops/operations/__init__.py

@@ -20,7 +20,7 @@ A collection of operators to build nerual networks or computing functions.
 """
 
 from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat,
-                        Diag, DType, ExpandDims, Eye,
+                        Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, GatherNd, GatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
@@ -208,6 +208,7 @@ __all__ = [
     "Cos",
     "ACos",
     "Diag",
+    "DiagPart",
     'Eye',
     'Assign',
     'AssignAdd',

mindspore/ops/operations/array_ops.py

@@ -1615,37 +1615,96 @@ class StridedSlice(PrimitiveWithInfer):
 class Diag(PrimitiveWithInfer):
     r"""
 
-    Extract or construct a diagonal array.
+    Construct a diagonal tensor with a given diagonal values.
 
-    If input is a 2-D tensor, returns the diagonal of the input with the given offset. If
-    input is a 1-D tensor, returns the array of diagonals. If you use this function
-    to extract the diagonal and want to write to the result array, see the more
-    detailed documentation for "numpy.diagonal", whether you return a copy or a
-    view depends on the version of numpy you are using.
+    Assume `input_x` has dimensions :math:`[D_1,... D_k]`, the output is a tensor of
+    rank 2k with dimensions :math:`[D_1,..., D_k, D_1,..., D_k]` where:
+    :math:`output[i_1,..., i_k, i_1,..., i_k] = input_x[i_1,..., i_k]` and 0 everywhere else.
 
     Inputs:
-        - **input_x** (Tensor) - 1-D tensor or 2-D tensor.
+        - **input_x** (Tensor) - The input tensor.
 
     Outputs:
         Tensor.
 
     Examples:
+        >>> input_x = Tensor([1, 2, 3, 4])
         >>> diag = P.Diag()
         >>> diag(x)
+        [[1, 0, 0, 0],
+         [0, 2, 0, 0],
+         [0, 0, 3, 0],
+         [0, 0, 0, 4]]
     """
 
     @prim_attr_register
     def __init__(self):
         """init Diag"""
 
-    def infer_type(self, x):
-        args = {"x_dtype": x}
-        validator.check_subclass('input_x', x, mstype.tensor)
-        validator.check_type_same(args, mstype.number_type)
-        return x
+    def infer_dtype(self, x_type):
+        validator.check_subclass('input_x', x_type, mstype.tensor)
+        return x_type
+
+    def infer_shape(self, x_shape):
+        validator.check("x rank", len(x_shape), "", 1, Rel.GE)
+        ret_shape = copy.deepcopy(x_shape)
+        ret_shape = ret_shape + ret_shape
+        return ret_shape
 
     def infer_value(self, x):
-        validator.check("shape_length", len(x.shape()), "length", [1, 2], Rel.IN)
+        if x is None:
+            return None
+        validator.check("input x rank", len(x.shape()), "", 1)
+        ret = np.diag(x.asnumpy())
+        return Tensor(ret)
+
+
+class DiagPart(PrimitiveWithInfer):
+    r"""
+
+    Extract the diagonal part from given tensor.
+
+    Assume input has dimensions :math:`[D_1,..., D_k, D_1,..., D_k]`, the output is a tensor
+    of rank k with dimensions :math:`[D_1,..., D_k]` where:
+    :math:`output[i_1,..., i_k] = input[i_1,..., i_k, i_1,..., i_k]`.
+
+    Inputs:
+        - **input_x** (Tensor) - The input Tensor.
+
+    Outputs:
+        Tensor.
+
+    Examples
+        >>> input_x = Tensor([[1, 0, 0, 0],
+        >>>                   [0, 2, 0, 0],
+        >>>                   [0, 0, 3, 0],
+        >>>                   [0, 0, 0, 4]])
+        >>> diag_part = P.DiagPart()
+        >>> diag_part(x)
+        [1, 2, 3, 4]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init DiagPart"""
+
+    def infer_dtype(self, x_type):
+        validator.check_subclass('input_x', x_type, mstype.tensor)
+        return x_type
+
+    def infer_shape(self, x_shape):
+        if len(x_shape)%2 != 0 or \
+                not x_shape:
+            raise ValueError(f"DiagPart input rank must be non-zero and even, but got rank {len(x_shape)}, "
+                             f"with shapes {x_shape}")
+        length = len(x_shape) // 2
+        ret_shape = x_shape[0:length]
+        return ret_shape
+
+    def infer_value(self, x):
+        if x is None:
+            return None
+        validator.check("x rank", len(x.shape()), "", 2)
         ret = np.diag(x.asnumpy())
         return Tensor(ret)
 

tests/ut/python/ops/test_ops.py

@@ -947,6 +947,16 @@ test_case_array_ops = [
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
         'desc_bprop': [[3,]]}),
+    ('Diag', {
+        'block': P.Diag(),
+        'desc_inputs': [[4]],
+        'desc_bprop': [[4, 4]],
+    }),
+    ('DiagPart', {
+        'block': P.DiagPart(),
+        'desc_inputs': [[4, 4]],
+        'desc_bprop': [[4]],
+    }),
 ]
 
 test_case_other_ops = [