Add backprop and add some comments

mindspore/nn/layer/basic.py

@@ -599,7 +599,7 @@ class Interpolate(Cell):
 
     Inputs:
         - **x** (Tensor) - Tensor to be resized. Input tensor must be a 4-D tensor with shape:
-        math:'(batch, channels, height, width)', with data type of float32 or float64.
+          math:'(batch, channels, height, width)', with data type of float32 or float64.
 
     Outputs:
         Resized tensor.
@@ -609,7 +609,7 @@ class Interpolate(Cell):
         scale_factor * width)' in float32
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> from mindspore.ops import operations as P
@@ -703,7 +703,7 @@ class Tril(Cell):
         Tensor, has the same type as input `x`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.array([[1, 2], [3, 4]]))
@@ -742,10 +742,13 @@ class Triu(Cell):
     Outputs:
         Tensor, has the same type as input `x`.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> x = Tensor(np.array([[1, 2], [3, 4]]))
-        >>> tril = nn.Tril()
-        >>> result = tril(x)
+        >>> triu = nn.Triu()
+        >>> result = triu(x)
         >>> print(result)
         [[1   2]
          [0   4]]

mindspore/ops/_grad/grad_array_ops.py

@@ -51,6 +51,36 @@ def get_bprop_fill(self):
     return bprop
 
 
+@bprop_getters.register(P.Ones)
+def get_bprop_ones(self):
+    """Generate bprop for Ones"""
+
+    def bprop(dims, dtype, out, dout):
+        return zeros_like(dims)
+
+    return bprop
+
+
+@bprop_getters.register(P.Zeros)
+def get_bprop_zeros(self):
+    """Generate bprop for Zeros"""
+
+    def bprop(dims, dtype, out, dout):
+        return zeros_like(dims)
+
+    return bprop
+
+
+@bprop_getters.register(P.SequenceMask)
+def get_bprop_sequence_mask(self):
+    """Generate bprop for SequenceMask"""
+
+    def bprop(lengths, dtype, max_length, out, dout):
+        return zeros_like(dims), zeros_like(max_length)
+
+    return bprop
+
+
 @bprop_getters.register(P.DType)
 def get_bprop_dtype(self):
     """Generate bprop for DType"""

mindspore/ops/operations/array_ops.py

@@ -1129,14 +1129,14 @@ class Ones(PrimitiveWithInfer):
 
     Inputs:
         - **shape** (Union[tuple[int], int]) - The specified shape of output tensor.
-        Only constant positive int is allowed.
+          Only constant positive int is allowed.
         - **type** (mindspore.dtype) - The specified type of output tensor. Only constant value is allowed.
 
     Outputs:
         Tensor, has the same type and shape as input shape value.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> from mindspore.ops import operations as ops
@@ -1182,14 +1182,14 @@ class Zeros(PrimitiveWithInfer):
 
     Inputs:
         - **shape** (Union[tuple[int], int]) - The specified shape of output tensor.
-        Only constant positive int is allowed.
+          Only constant positive int is allowed.
         - **type** (mindspore.dtype) - The specified type of output tensor. Only constant value is allowed.
 
     Outputs:
         Tensor, has the same type and shape as input shape value.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> from mindspore.ops import operations as ops
@@ -1239,7 +1239,7 @@ class SequenceMask(PrimitiveWithInfer):
 
     Inputs:
         - **lengths** (Union[tuple[int], list[int]]) - Defines the first N elements that are retained.
-        Only constant value is allowed.
+          Only constant value is allowed.
         - **dtype** (mindspore.dtype) - The specified type of output tensor. Only constant value is allowed.
 
     Outputs:
@@ -1248,6 +1248,9 @@ class SequenceMask(PrimitiveWithInfer):
         If max_length is not set and the biggest value in lengths is x. Then, the shape of
         the output is (lengths.shape, x).
 
+    Supported Platforms:
+    ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> from mindspore.ops import operations as P
         >>> sequence_mask = P.SequenceMask()