!13014 add raises of MatMul, Maximum, Minimum, etc.

From: @mind-lh
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/layer/activation.py

@@ -69,7 +69,7 @@ class Softmax(Cell):
         Tensor, which has the same type and shape as `x` with values in the range[0,1].
 
     Raises:
-        TypeError: If `axis` is neither an int not a tuple.
+        TypeError: If `axis` is neither an int nor a tuple.
         TypeError: If dtype of `x` is neither float16 nor float32.
         ValueError: If `axis` is a tuple whose length is less than 1.
         ValueError: If `axis` is a tuple whose elements are not all in range [-len(x), len(x)).

mindspore/ops/composite/array_ops.py

@@ -135,6 +135,11 @@ def sequence_mask(lengths, maxlen=None):
     Outputs:
         One mask tensor of shape lengths.shape + (maxlen,).
 
+    Raises:
+        TypeError: If `lengths` is not a Tensor.
+        TypeError: If `maxlen` is not an int.
+        TypeError: If dtype of `lengths` is neither int32 nor int64.
+
     Supported Platforms:
         ``GPU``
 

mindspore/ops/composite/base.py

@@ -488,6 +488,10 @@ class HyperMap(HyperMap_):
         Sequence or nested sequence, the sequence of output after applying the function.
         e.g. `operation(args[0][i], args[1][i])`.
 
+    Raises:
+        TypeError: If `ops` is neither MultitypeFuncGraph nor None.
+        TypeError: If `args` is not a Tuple.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/ops/composite/math_ops.py

@@ -229,6 +229,10 @@ def tensor_dot(x1, x2, axes):
         Tensor, the shape of the output tensor is :math:`(N + M)`. Where :math:`N` and :math:`M` are the free axes not
         contracted in both inputs
 
+    Raises:
+        TypeError: If `x1` or `x2` is not a Tensor.
+        TypeError: If `axes` is not one of the following: int, tuple, list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/ops/operations/comm_ops.py

@@ -64,11 +64,6 @@ class AllReduce(PrimitiveWithInfer):
                   like sum, max, and min. Default: ReduceOp.SUM.
         group (str): The communication group to work on. Default: "hccl_world_group".
 
-    Raises:
-        TypeError: If any of operation and group is not a string,
-                   or fusion is not an integer, or the input's dtype is bool.
-        ValueError: If the operation is "prod".
-
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
 
@@ -76,6 +71,11 @@ class AllReduce(PrimitiveWithInfer):
         Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
         The contents depend on the specified operation.
 
+    Raises:
+        TypeError: If any of `op` and `group` is not a str,
+                   or fusion is not an integer, or the input's dtype is bool.
+        ValueError: If the `op` is "prod".
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -133,11 +133,6 @@ class AllGather(PrimitiveWithInfer):
     Args:
         group (str): The communication group to work on. Default: "hccl_world_group".
 
-    Raises:
-        TypeError: If group is not a string.
-        ValueError: If the local rank id of the calling process in the group
-                    is larger than the group's rank size.
-
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
 
@@ -145,6 +140,11 @@ class AllGather(PrimitiveWithInfer):
         Tensor. If the number of devices in the group is N,
         then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
 
+    Raises:
+        TypeError: If `group` is not a str.
+        ValueError: If the local rank id of the calling process in the group
+                    is larger than the group's rank size.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/ops/operations/control_ops.py

@@ -101,6 +101,10 @@ class GeSwitch(PrimitiveWithInfer):
         tuple. Output is tuple(false_output, true_output). The Elements in the tuple has the same shape of input data.
         The false_output connects with the false_branch and the true_output connects with the true_branch.
 
+    Raises:
+        TypeError: If `data` is neither a Tensor nor a Number.
+        TypeError: If `pred` is not a Tensor.
+
     Examples:
         >>> class Net(nn.Cell):
         ...     def __init__(self):
@@ -159,6 +163,9 @@ class Merge(PrimitiveWithInfer):
     Outputs:
         tuple. Output is tuple(`data`, `output_index`). The `data` has the same shape of `inputs` element.
 
+    Raises:
+        TypeError: If `inputs` is neither Tuple nor list.
+
     Examples:
         >>> merge = ops.Merge()
         >>> input_x = Tensor(np.linspace(0, 8, 8).reshape(2, 4), mindspore.float32)

mindspore/ops/operations/debug_ops.py

@@ -58,6 +58,10 @@ class ScalarSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable, it must not be an empty string.
         - **value** (Tensor) - The value of scalar, and the shape of value must be [] or [1].
 
+    Raises:
+        TypeError: If `name` is not a str.
+        TypeError: If `value` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -101,6 +105,10 @@ class ImageSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable, it must not be an empty string.
         - **value** (Tensor) - The value of image, the rank of tensor must be 4.
 
+    Raises:
+        TypeError: If `name` is not a str.
+        TypeError: If `value` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -143,6 +151,10 @@ class TensorSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable.
         - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0.
 
+    Raises:
+        TypeError: If `name` is not a str.
+        TypeError: If `value` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -186,6 +198,10 @@ class HistogramSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable.
         - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0.
 
+    Raises:
+        TypeError: If `name` is not a str.
+        TypeError: If `value` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -234,6 +250,9 @@ class InsertGradientOf(PrimitiveWithInfer):
     Outputs:
         Tensor, returns `input_x` directly. `InsertGradientOf` does not affect the forward result.
 
+    Raises:
+        TypeError: If `f` is not a function of mindspore.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -299,6 +318,10 @@ class HookBackward(PrimitiveWithInfer):
     Inputs:
         - **inputs** (Tensor) - The variable to hook.
 
+    Raises:
+        TypeError: If `inputs` are not a Tensor.
+        TypeError: If `hook_fn` is not a function of python.
+
     Examples:
         >>> def hook_fn(grad_out):
         ...     print(grad_out)
@@ -351,6 +374,9 @@ class Print(PrimitiveWithInfer):
         - **input_x** (Union[Tensor, bool, int, float, str, tuple, list]) - The graph node to attach to.
           Supports multiple inputs which are separated by ','.
 
+    Raises:
+        TypeError: If `input_x` is not one of the following: Tensor, bool, int, float, str, tuple, list.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -410,6 +436,11 @@ class Assert(PrimitiveWithInfer):
         - **condition** [Union[Tensor[bool], bool]] - The condition to evaluate.
         - **input_data** (Union(tuple[Tensor], list[Tensor])) - The tensors to print out when condition is false.
 
+    Raises:
+        TypeError: If `summarize` is not an int.
+        TypeError: If `condition` is neither a Tensor nor a bool.
+        TypeError: If `input_data` is neither a tuple nor a list.
+
     Examples:
         >>> class AssertDemo(nn.Cell):
         ...     def __init__(self):

mindspore/ops/operations/inner_ops.py

@@ -34,6 +34,9 @@ class ScalarCast(PrimitiveWithInfer):
     Outputs:
         Scalar. The type is the same as the python type corresponding to `input_y`.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is a constant value.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -80,6 +83,11 @@ class Randperm(PrimitiveWithInfer):
     Outputs:
         - **output** (Tensor) - The output Tensor with shape: (`max_length`,) and type: `dtype`.
 
+    Raises:
+        TypeError: If neither `max_length` nor `pad` is an int.
+        TypeError: If `n` is not a Tensor.
+        TypeError: If `n` has non-Int elements.
+
     Supported Platforms:
         ``Ascend``
 
@@ -137,6 +145,10 @@ class NoRepeatNGram(PrimitiveWithInfer):
     Outputs:
         - **log_probs** (Tensor) - The output Tensor with same shape and type as original `log_probs`.
 
+    Raises:
+        TypeError: If `ngram_size` is not an int.
+        TypeError: If neither `state_seq` nor `log_probs` is a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -324,6 +336,9 @@ class MakeRefKey(Primitive):
     Outputs:
         RefKeyType, made from the Parameter name.
 
+    Raises:
+        TypeError: If `tag` is not a str.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/ops/operations/math_ops.py

@@ -140,6 +140,9 @@ class Add(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -397,6 +400,11 @@ class ReduceMean(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -435,6 +443,11 @@ class ReduceSum(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is None.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -479,6 +492,11 @@ class ReduceAll(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -521,6 +539,11 @@ class ReduceAny(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -563,6 +586,11 @@ class ReduceMax(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -611,6 +639,11 @@ class ReduceMin(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -650,6 +683,11 @@ class ReduceProd(_Reduce):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `keep_dims` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        ValueError: If `axis` is not one of the following: int, tuple or list.
+
     Supported Platforms:
         ``Ascend``
 
@@ -1108,6 +1146,9 @@ class Neg(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape and dtype as input.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1295,6 +1336,9 @@ class Sub(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is a Number or a bool or a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1342,6 +1386,10 @@ class Mul(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        ValueError: If `input_x` and `input_y` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1385,6 +1433,9 @@ class SquaredDifference(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: if neither `input_x` nor `input_y` is a Number or a bool or a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1412,6 +1463,9 @@ class Square(PrimitiveWithCheck):
     Outputs:
         Tensor, has the same shape and dtype as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1451,6 +1505,9 @@ class Rsqrt(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same type and shape as `input_x`.
 
+    Raises:
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -1494,6 +1551,9 @@ class Sqrt(PrimitiveWithCheck):
     Outputs:
         Tensor, has the same shape as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1532,6 +1592,9 @@ class Reciprocal(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1589,6 +1652,10 @@ class Pow(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        ValueError: If `input_x` and `input_y` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1945,6 +2012,10 @@ class Minimum(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        ValueError: If `input_x` and `input_y` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1988,6 +2059,10 @@ class Maximum(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        ValueError: If `input_x` and `input_y` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -2031,6 +2106,10 @@ class RealDiv(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        ValueError: If `input_x` and `input_y` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -2266,6 +2345,9 @@ class TruncateDiv(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2300,6 +2382,9 @@ class TruncateMod(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2493,6 +2578,9 @@ class Xdivy(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2532,6 +2620,9 @@ class Xlogy(_MathBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,
         and the data type is the one with higher precision or higher digits among the two inputs.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2676,6 +2767,9 @@ class Sinh(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape as `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``CPU``
 
@@ -2880,6 +2974,10 @@ class NotEqual(_LogicBinaryOp):
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
+    Raises:
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
+        TypeError: If neither `input_x` nor `input_y` is a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -3011,7 +3109,7 @@ class Less(_LogicBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
     Raises:
-        TypeError: If neither `input_x` nor `input_y` is a Tensor.
+        TypeError: If neither `input_x` nor `input_y` is one of the following: Tensor, Number, bool.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -3408,6 +3506,10 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape as `input_x`. All the elements in the tensor will be zero.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -3573,6 +3675,9 @@ class Sin(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape as `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -3644,11 +3749,6 @@ class NMSWithMask(PrimitiveWithInfer):
         iou_threshold (float): Specifies the threshold of overlap boxes with respect to
             IOU. Default: 0.5.
 
-    Raises:
-        ValueError: If the iou_threshold is not a float number, or if the first dimension
-            of input Tensor is less than or equal to 0, or if the data type of the input
-            Tensor is not float16 or float32.
-
     Inputs:
         - **bboxes** (Tensor) - The shape of tensor is :math:`(N, 5)`. Input bounding boxes.
           `N` is the number of input bounding boxes. Every bounding box
@@ -3666,6 +3766,11 @@ class NMSWithMask(PrimitiveWithInfer):
         - **selected_mask** (Tensor) - The shape of tensor is :math:`(N,)`. A mask list of
           valid output bounding boxes.
 
+    Raises:
+        ValueError: If the `iou_threshold` is not a float number, or if the first dimension
+            of input Tensor is less than or equal to 0, or if the data type of the input
+            Tensor is not float16 or float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -3765,6 +3870,9 @@ class Sign(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape and type as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``CPU``
 
@@ -3798,6 +3906,9 @@ class Round(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape and type as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -3834,7 +3945,8 @@ class Tan(PrimitiveWithInfer):
         Tensor, has the same shape as `input_x`.
 
     Raises:
-        TypeError: If dtype of `input_x` is not one of float16, float32, int32.
+        TypeError: If dtype of `input_x` is not one of the following: float16, float32, int32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``CPU``
@@ -3986,6 +4098,10 @@ class SquareSumAll(PrimitiveWithInfer):
         - **output_y1** (Tensor) - The same type as the `input_x1`.
         - **output_y2** (Tensor) - The same type as the `input_x1`.
 
+    Raises:
+        TypeError: If neither `input_x1` nor `input_x2` is a Tensor.
+        ValueError: If `input_x1` and `input_x2` are not the same shape.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/ops/operations/nn_ops.py

@@ -90,6 +90,7 @@ class Flatten(PrimitiveWithInfer):
         the product of the remaining dimension.
 
     Raises:
+        TypeError: If `input_x` is not a Tensor.
         ValueError: If length of shape of `input_x` is less than 1.
 
     Supported Platforms:
@@ -140,7 +141,7 @@ class Softmax(PrimitiveWithInfer):
         Tensor, with the same type and shape as the logits.
 
     Raises:
-        TypeError: If `axis` is neither an int not a tuple.
+        TypeError: If `axis` is neither an int nor a tuple.
         TypeError: If dtype of `logits` is neither float16 nor float32.
         ValueError: If `axis` is a tuple whose length is less than 1.
         ValueError: If `axis` is a tuple whose elements are not all in range [-len(logits), len(logits)).
@@ -202,7 +203,7 @@ class LogSoftmax(PrimitiveWithInfer):
     Raises:
         TypeError: If `axis` is not an int.
         TypeError: If dtype of `logits` is neither float16 nor float32.
-        ValueError: If `axis` is not in range [-len(logits), len(logits)).
+        ValueError: If `axis` is not in range [-len(logits), len(logits)].
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -245,6 +246,10 @@ class Softplus(PrimitiveWithInfer):
     Outputs:
         Tensor, with the same type and shape as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If dtype of `input_x` is not float.
+
     Supported Platforms:
         ``Ascend``  ``GPU``
 
@@ -285,6 +290,10 @@ class Softsign(PrimitiveWithInfer):
     Outputs:
         Tensor, with the same type and shape as the `input_x`.
 
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -322,7 +331,8 @@ class ReLU(PrimitiveWithCheck):
         Tensor, with the same type and shape as the `input_x`.
 
     Raises:
-        TypeError: If dtype of `input_x` is not a number.
+        TypeError: If dtype of `input_x` is not number.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -459,6 +469,7 @@ class ReLU6(PrimitiveWithCheck):
 
     Raises:
         TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -497,6 +508,11 @@ class ReLUV2(PrimitiveWithInfer):
         - **output** (Tensor) - Has the same type and shape as the `input_x`.
         - **mask** (Tensor) - A tensor whose data type must be uint8.
 
+    Raises:
+        TypeError: If `input_x`, `output` or `mask` is not a Tensor.
+        TypeError: If dtype of `output` is not same as `input_x.
+        TypeError: If dtype of `mask` is not unit8.
+
     Supported Platforms:
         ``Ascend``
 
@@ -627,6 +643,7 @@ class HSwish(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_data`.
 
     Raises:
+        TypeError: If `input_data` is not a Tensor.
         TypeError: If dtype of `input_data` is neither float16 nor float32.
 
     Supported Platforms:
@@ -671,6 +688,7 @@ class Sigmoid(PrimitiveWithInfer):
 
     Raises:
         TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -715,6 +733,7 @@ class HSigmoid(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_data`.
 
     Raises:
+        TypeError: If `input_data` is not a Tensor.
         TypeError: If dtype of `input_data` is neither float16 nor float32.
 
     Supported Platforms:
@@ -759,6 +778,7 @@ class Tanh(PrimitiveWithInfer):
 
     Raises:
         TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU``  ``CPU``
@@ -822,6 +842,12 @@ class FusedBatchNorm(Primitive):
         - **updated_moving_mean** (Tensor) - Tensor of shape :math:`(C,)`.
         - **updated_moving_variance** (Tensor) - Tensor of shape :math:`(C,)`.
 
+    Raises:
+        TypeError: If `mode` is not an int.
+        TypeError: If `epsilon` or `momentum` is not a float.
+        TypeError: If `output_x`, `updated_scale`, `updated_bias`, `updated_moving_mean` or
+        `updated_moving_variance` is a Tensor.
+
     Supported Platforms:
         ``CPU``
 
@@ -920,6 +946,13 @@ class FusedBatchNormEx(PrimitiveWithCheck):
           data type: float32.
         - **reserve** (Tensor) - reserve space, Tensor of shape :math:`(C,)`, data type: float32.
 
+    Raises:
+        TypeError: If `mode` is not an int.
+        TypeError: If neither `epsilon` nor `momentum` is a float.
+        TypeError: If `data_format` is not a str.
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If dtype of `scale`, `bias`, `mean` or `variance` is not float32.
+
     Supported Platforms:
         ``GPU``
 
@@ -1114,6 +1147,10 @@ class BNTrainingReduce(PrimitiveWithInfer):
         - **sum** (Tensor) - A 1-D Tensor with float32 data type. Tensor of shape :math:`(C,)`.
         - **square_sum** (Tensor) - A 1-D Tensor with float32 data type. Tensor of shape :math:`(C,)`.
 
+    Raises:
+        TypeError: If `x`, `sum` or `square_sum` is not a Tensor.
+        TypeError: If dtype of `square_sum` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -1175,6 +1212,13 @@ class BNTrainingUpdate(PrimitiveWithInfer):
         - **batch_variance** (Tensor) - Tensor for the mean of `variance`, with float32 data type.
           Has the same shape as `variance`.
 
+    Raises:
+        TypeError: If `isRef` is not a bool.
+        TypeError: If dtype of `epsilon` or `factor` is not float.
+        TypeError: If `x`, `sum`, `square_sum`, `scale`, `offset`, `mean` or `variance` is not a Tensor.
+        TypeError: If dtype of `x`, `sum`, `square_sum`, `scale`, `offset`, `mean` or `variance` is neither float16 nor
+        float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -1290,6 +1334,13 @@ class BatchNorm(PrimitiveWithInfer):
         - **reserve_space_1** (Tensor) - Tensor of shape :math:`(C,)`.
         - **reserve_space_2** (Tensor) - Tensor of shape :math:`(C,)`.
 
+    Raises:
+        TypeError: If `is_training` is not a bool.
+        TypeError: If dtype of `epsilon` or `momentum` is not float.
+        TypeError: If `data_format` is not a str.
+        TypeError: If `input_x`, `scale`, `bias`, `mean` or `variance` is not a Tensor.
+        TypeError: If dtype of `input_x`, `scale` or `mean` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -1414,7 +1465,7 @@ class Conv2D(PrimitiveWithCheck):
         Tensor, the value that applied 2D convolution. The shape is :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Raises:
-        TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple.
+        TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int nor a tuple.
         TypeError: If `out_channel` or `group` is not an int.
         ValueError: If `kernel_size`, `stride` or `dilation` is less than 1.
         ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
@@ -1521,6 +1572,13 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
     Outputs:
         Tensor of shape :math:`(N, C_{in} * \text{channel_multiplier}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int nor a tuple.
+        TypeError: If `channel_multiplier` or `group` is not an int.
+        ValueError: If `stride` or `dilation` is less than 1.
+        ValueError: If `pad_mode` is not one of the following:'same', 'valid' or 'pad'.
+        ValueError: If `pad_mode` it not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0).
+
     Supported Platforms:
         ``Ascend``
 
@@ -1811,6 +1869,8 @@ class MaxPoolWithArgmax(_Pool):
 
     Raises:
         TypeError: If the input data type is not float16 or float32.
+        TypeError: If `kernel_size` or `strides` is neither an int nor a tuple.
+        TypeError: If `input` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -2054,7 +2114,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         Tensor, the gradients w.r.t the input of convolution. It has the same shape as the input.
 
     Raises:
-        TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple.
+        TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int nor a tuple.
         TypeError: If `out_channel` or `group` is not an int.
         ValueError: If `kernel_size`, `stride` or `dilation` is less than 1.
         ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
@@ -2190,6 +2250,9 @@ class BiasAdd(PrimitiveWithCheck):
     Outputs:
         Tensor, with the same shape and type as `input_x`.
 
+    Raises:
+        TypeError: If `data_format`, `input_x` or `bias` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -2242,6 +2305,12 @@ class TopK(PrimitiveWithInfer):
         - **values** (Tensor) - The `k` largest elements in each slice of the last dimensional.
         - **indices** (Tensor) - The indices of values within the last dimension of input.
 
+    Raises:
+        TypeError: If `sorted` is not a bool.
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If `k` is not an int.
+        TypeError: If dtype of `input_x` is not one of the following: float16, float32 or int32.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -2391,6 +2460,7 @@ class SoftmaxCrossEntropyWithLogits(PrimitiveWithInfer):
 
     Raises:
         TypeError: If dtype of `logits` or `labels` is neither float16 nor float32.
+        TypeError: If `logits` or `labels` is not a Tensor.
         ValueError: If shape of `logits` is not the same as `labels`.
 
     Supported Platforms:
@@ -2587,7 +2657,8 @@ class SmoothL1Loss(PrimitiveWithInfer):
 
     Raises:
         TypeError: If `beta` is not a float.
-        TypeError: If dtype of `prediction` or `target` is neither float16 not float32.
+        TypeError: If `prediction` or `target` is not a Tensor.
+        TypeError: If dtype of `prediction` or `target` is neither float16 nor float32.
         ValueError: If `beta` is less than or equal to 0.
         ValueError: If shape of `prediction` is not the same as `target`.
 
@@ -2634,6 +2705,10 @@ class L2Loss(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same dtype as `input_x`. The output tensor is the value of loss which is a scalar tensor.
 
+    Raises:
+        TypeError: If `input_x` not a Tensor.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -2674,6 +2749,10 @@ class DataFormatDimMap(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same type as the `input_x`.
 
+    Raises:
+        TypeError: If `src_format` or `dst_format` is not a str.
+        TypeError: If `input_x` is not a Tensor whose dtype is not int32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2718,6 +2797,11 @@ class RNNTLoss(PrimitiveWithInfer):
         - **costs** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
         - **grads** (Tensor[int32]) - Has the same shape as `acts`.
 
+    Raises:
+        TypeError: If `acts`, `labels`, `input_lengths` or `label_lengths` is not a Tensor.
+        TypeError: If dtype of `acts` is neither float16 nor float32.
+        TypeError: If dtype of `labels`, `input_lengths` or `label_lengths` is not int32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -2791,6 +2875,13 @@ class SGD(PrimitiveWithCheck):
     Outputs:
         Tensor, parameters to be updated.
 
+    Raises:
+        TypeError: If `dampening` or `weight_decay` is not a float.
+        TypeError: If `nesterov` is not a bool.
+        TypeError: If `parameters`, `gradient`, `learning_rate`, `accum`, `momentum` or `stat` is not a Tensor.
+        TypeError: If dtype of `parameters`, `gradient`, `learning_rate`, `accum`, `momentum` or `stat` is neither
+        float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -2876,6 +2967,14 @@ class ApplyRMSProp(PrimitiveWithInfer):
     Outputs:
         Tensor, parameters to be update.
 
+    Raises:
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If `var, `mean_square`, `moment` or `decay` is not a Tensor.
+        TypeError: If `learning_rate` is neither a Number nor a Tensor.
+        TypeError: If dtype of `decay`, `momentum` or `epsilon` is not float.
+        TypeError: If dtype of `learning_rate` is neither float16 nor float32.
+        ValueError: If `decay`, `momentum` or `epsilon` is not a constant value.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -2972,6 +3071,13 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
     Outputs:
         Tensor, parameters to be update.
 
+    Raises:
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If `var`, `mean_gradient`, `mean_square`, `moment` or `grad` is not a Tensor.
+        TypeError: If `learing_rate` is neither a Number nor a Tensor.
+        TypeError: If dtype of `learing_rate` is neither float16 nor float32.
+        TypeError: If dtype of `decay`, `momentum` or `epsilon` is not float.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -3059,6 +3165,7 @@ class LayerNorm(Primitive):
     Raises:
         TypeError: If `begin_norm_axis` or `begin_params_axis` is not an int.
         TypeError: If `epsilon` is not a float.
+        TypeError: If `input_x`, `gamma` or `beta` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -3109,6 +3216,12 @@ class L2Normalize(PrimitiveWithInfer):
     Outputs:
         Tensor, with the same type and shape as the input.
 
+    Raises:
+        TypeError: If `axis` is not one of the following: list, tuple or int.
+        TypeError: If `epsilon` is not a float.
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -3157,6 +3270,11 @@ class DropoutGenMask(Primitive):
     Outputs:
         Tensor, the value of generated mask for input shape.
 
+    Raises:
+        TypeError: If neither `seed0` nor `seed1` is an int.
+        TypeError: If `shape` is not a tuple.
+        TypeError: If `keep_prob` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -3196,6 +3314,11 @@ class DropoutDoMask(PrimitiveWithInfer):
     Outputs:
         Tensor, the value that applied dropout on.
 
+    Raises:
+        TypeError: If `input_x`, `mask` or `keep_prob` is not a Tensor.
+        TypeError: If `keep_prob` is not a float.
+        ValueError: If value of `keep_prob` is not same as `DropoutGenMaks`.
+
     Supported Platforms:
         ``Ascend``
 
@@ -3271,6 +3394,7 @@ class ResizeBilinear(PrimitiveWithInfer):
         TypeError: If `size` is neither a tuple nor list.
         TypeError: If `align_corners` is not a bool.
         TypeError: If dtype of `input` is neither float16 nor float32.
+        TypeError: If `input` is not a Tensor.
         ValueError: If length of shape of `input` is not equal to 4.
 
     Supported Platforms:
@@ -3333,15 +3457,16 @@ class OneHot(PrimitiveWithInfer):
     Outputs:
         Tensor, one-hot tensor. Tensor of shape :math:`(X_0, \ldots, X_{axis}, \text{depth} ,X_{axis+1}, \ldots, X_n)`.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU`` ``CPU``
-
     Raises:
         TypeError: If `axis` or `depth` is not an int.
         TypeError: If dtype of `indices` is neither int32 nor int64.
+        TypeError: If `indices`, `on_value` or `off_value` is not a Tensor.
         ValueError: If `axis` is not in range [-1, len(indices_shape)].
         ValueError: If `depth` is less than 0.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32)
         >>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32)
@@ -3419,6 +3544,7 @@ class GeLU(PrimitiveWithInfer):
         Tensor, with the same type and shape as input.
 
     Raises:
+        TypeError: If `input_x` is not a Tensor.
         TypeError: If dtype of `input_x` is neither float16 nor float32.
 
     Supported Platforms:
@@ -3590,6 +3716,7 @@ class PReLU(PrimitiveWithInfer):
 
     Raises:
         TypeError: If dtype of `input_x` or `weight` is neither float16 nor float32.
+        TypeError: If `input_x` or `weight` is not a Tensor.
         ValueError: If length of shape of `input_x` is equal to 1.
         ValueError: If length of shape of `weight` is not equal to 1.
 
@@ -3804,6 +3931,9 @@ class SigmoidCrossEntropyWithLogits(PrimitiveWithInfer):
     Outputs:
         Tensor, with the same shape and type as input `logits`.
 
+    Raises:
+        TypeError: If `logits` or `label` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -3943,6 +4073,7 @@ class Pad(PrimitiveWithInfer):
 
     Raises:
         TypeError: If `paddings` is not a tuple.
+        TypeError: If `input_x` is not a Tensor.
         ValueError: If shape of `paddings` is not (n, 2).
 
     Supported Platforms:
@@ -4013,6 +4144,10 @@ class MirrorPad(PrimitiveWithInfer):
           is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the Outputs is
           [[2,1,1,2,3,3,2], [2,1,1,2,3,3,2], [5,4,4,5,6,6,5], [8,7,7,8,9,9,8], [8,7,7,8,9,9,8]].
 
+    Raises:
+        TypeError: If `input_x` or `paddings` is not a Tensor.
+        TypeError: If `mode` is not a str.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -4091,6 +4226,11 @@ class ComputeAccidentalHits(PrimitiveWithCheck):
         - **ids** (Tensor) - A Tensor with shape (num_accidental_hits,), with the same type as `true_classes`.
         - **weights** (Tensor) - A Tensor with shape (num_accidental_hits,), with the type float32.
 
+    Raises:
+        TypeError: If dtype of `num_true` is not int.
+        TypeError: If `true_classes` or `sampled_candidates` is not a Tensor.
+        TypeError: If dtype of `true_classes` or `sampled_candidates` is neither int32 nor int64.
+
     Supported Platforms:
         ``Ascend``
 
@@ -4162,6 +4302,11 @@ class ROIAlign(PrimitiveWithInfer):
     Outputs:
         Tensor, the shape is `(rois_n, C, pooled_height, pooled_width)`.
 
+    Raises:
+        TypeError: If `pooled_height`, `pooled_width`, `sample_num` or `roi_end_mode` is not an int.
+        TypeError: If `spatial_scale` is not a float.
+        TypeError: If `features` or `rois` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -4252,8 +4397,9 @@ class Adam(PrimitiveWithInfer):
         - **v** (Tensor) - The same shape and data type as `v`.
 
     Raises:
-        TypeError: If `use_locking` or `use_nesterov` is not a bool.
-        ValueError: If shape of `var`, `m` and `v` is not the same.
+        TypeError: If neither `use_locking` nor `use_nesterov` is a bool.
+        TypeError: If `var`, `m` or `v` is not a Tensor.
+        TypeError: If `beta1_power`, `beta2_power1, `lr`, `beta1`, `beta2`, `epsilon` or `gradient` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -4361,6 +4507,11 @@ class AdamNoUpdateParam(PrimitiveWithInfer):
         Tensor, whose shape and data type are the same with `gradient`, is a value that should be added to the
         parameter to be updated.
 
+    Raises:
+        TypeError: If neither `use_locking` nor `use_nesterov` is a bool.
+        TypeError: If `m`,  `v`, `beta1_power`, `beta2_power1, `lr`,
+        `beta1`, `beta2`, `epsilon` or `gradient` is a Tensor.
+
     Supported Platforms:
         ``CPU``
 
@@ -4476,6 +4627,11 @@ class FusedSparseAdam(PrimitiveWithInfer):
         - **m** (Tensor) - A Tensor with shape (1,).
         - **v** (Tensor) - A Tensor with shape (1,).
 
+    Raises:
+        TypeError: If neither `use_locking` nor `use_neserov` is a bool.
+        TypeError: If dtype of `var`, `m`, `v`, `beta1_power`, `beta2_power`, `lr`, `beta1`, `beta2`, `epsilon`,
+        `gradient` or `indices` is not float32.
+
     Supported Platforms:
         ``CPU``
 
@@ -4615,6 +4771,12 @@ class FusedSparseLazyAdam(PrimitiveWithInfer):
         - **m** (Tensor) - A Tensor with shape (1,).
         - **v** (Tensor) - A Tensor with shape (1,).
 
+    Raises:
+        TypeError: If neither `use_locking` nor `use_nestrov` is a bool.
+        TypeError: If dtype of `var`, `m`, `v`, `beta1_power`, `beta2_power`, `lr`, `beta1`, `beta2`, `epsilon` or
+        gradient is not float32.
+        TypeError: If dtype of `indices` is not int32.
+
     Supported Platforms:
         ``CPU``
 
@@ -4728,6 +4890,14 @@ class FusedSparseFtrl(PrimitiveWithInfer):
         - **accum** (Tensor) - A Tensor with shape (1,).
         - **linear** (Tensor) - A Tensor with shape (1,).
 
+    Raises:
+        TypeError: If `lr`, `l1`, `l2` or `lr_power` is not a float.
+        ValueError: If shape of `lr_power` less than or equal to zero.
+        TypeError: If dtype of `var` is not float32.
+        TypeError: If dtype of `indices` is not int32.
+        TypeError: If shape of `accum`, `linear` or `grad` is not same as `var`.
+        TypeError: If shape of `indices` is not same as shape of first dimension of `grad`.
+
     Supported Platforms:
         ``Ascend`` ``CPU``
 
@@ -4837,6 +5007,11 @@ class FusedSparseProximalAdagrad(PrimitiveWithInfer):
         - **var** (Tensor) - A Tensor with shape (1,).
         - **accum** (Tensor) - A Tensor with shape (1,).
 
+    Raises:
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If dtype of `var`, `accum`, `lr`, `l1`, `l2` or `grad` is not float32.
+        TypeError: If dtype of `indices` is not int32.
+
     Supported Platforms:
         ``CPU``
 
@@ -4936,6 +5111,11 @@ class KLDivLoss(PrimitiveWithInfer):
         Tensor or Scalar, if `reduction` is 'none', then output is a tensor and has the same shape as `input_x`.
         Otherwise it is a scalar.
 
+    Raises:
+        TypeError: If `reduction` is not a str.
+        TypeError: If neither `input_x` nor `input_y` is a Tensor.
+        TypeError: If dtype of `input_x` or `input_y` is not float32.
+
     Supported Platforms:
         ``GPU``
 
@@ -5018,6 +5198,7 @@ class BinaryCrossEntropy(PrimitiveWithInfer):
         TypeError: If dtype of `input_x`, `input_y` or `weight` (if given) is neither float16 not float32.
         ValueError: If `reduction` is not one of 'none', 'mean', 'sum'.
         ValueError: If shape of `input_y` is not the same as `input_x` or `weight` (if given).
+        TypeError: If `input_x`, `input_y` or `weight` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -5120,6 +5301,12 @@ class ApplyAdaMax(PrimitiveWithInfer):
         - **m** (Tensor) - The same shape and data type as `m`.
         - **v** (Tensor) - The same shape and data type as `v`.
 
+    Raises:
+        TypeError: If dtype of `var`, `m`, `v`, `beta_power`, `lr`, `beta1`,
+        `beta2`, `epsilon` or `grad` is neither float16 nor float32.
+        TypeError: If `beta_power`, `lr`, `beta1`, `beta2` or `epsilon` is neither a Number nor a Tensor.
+        TypeError: If `grad` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -5254,6 +5441,11 @@ class ApplyAdadelta(PrimitiveWithInfer):
         - **accum** (Tensor) - The same shape and data type as `accum`.
         - **accum_update** (Tensor) - The same shape and data type as `accum_update`.
 
+    Raises:
+        TypeError: If dtype of `var`, `accum`, `accum_update`,
+        `lr`, `rho`, `epsilon` or `grad` is neither float16 nor float32.
+        TypeError: If `accum_update`, `lr`, `rho` or `epsilon` is neither a Number nor a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -5368,6 +5560,10 @@ class ApplyAdagrad(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
+    Raises:
+        TypeError: If dtype of `var`, `accum`, `lr` or `grad` is neither float16 nor float32.
+        TypeError: If `lr` is neither a Number nor a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``CPU`` ``GPU``
 
@@ -5463,6 +5659,10 @@ class ApplyAdagradV2(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `m`.
 
+    Raises:
+        TypeError: If dtype of `var`, `accum`, `lr` or `grad` is neither float16 nor float32.
+        TypeError: If `lr` is neither a Number nor a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -5559,6 +5759,13 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
+    Raises:
+        TypeError: If `lr` is not a float.
+        TypeError: If neither `update_slots` nor `use_locking` is a bool.
+        TypeError: If dtype of `var`, `accum` or `grad` is neither float16 nor float32.
+        TypeError: If dtype of `indices` is not int32.
+
+
     Supported Platforms:
         ``Ascend``
 
@@ -5656,6 +5863,12 @@ class SparseApplyAdagradV2(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
+    Raises:
+        TypeError: If neither `lr` nor `epsilon` is a float.
+        TypeError: If neither `update_slots` nor `use_locking` is a bool.
+        TypeError: If dtype of `var`, `accum` or `grad` is neither float16 nor float32.
+        TypeError: If dtype of `indices` is not int32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -5756,6 +5969,12 @@ class ApplyProximalAdagrad(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
+    Raises:
+        TypeError: If `use_blocking` is not a bool.
+        TypeError: If dtype of `var`, `lr`, `l1` or `l2` is neither float16 nor float32.
+        TypeError: If `lr`, `l1` or `l2` is neither a Number nor a Tensor.
+        TypeError: If `grad` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -5874,6 +6093,12 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
+    Raises:
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If dtype of `var`, `accum`, `lr`, `l1`, `l2`, `scalar` or `grad` is neither float16
+        nor float32.
+        TypeError: If dtype of `indices` is neither int32 nor int64.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -5977,6 +6202,11 @@ class ApplyAddSign(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **m** (Tensor) - The same shape and data type as `m`.
 
+    Raises:
+        TypeError: If dtype of `var`, `lr`, `alpha`, `sign_decay` or `beta` is neither float16 nor float32.
+        TypeError: If `lr`, `alpha` or `sign_decay` is neither a Number nor a Tensor.
+        TypeError: If `grad` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6099,6 +6329,11 @@ class ApplyPowerSign(PrimitiveWithInfer):
         - **var** (Tensor) - The same shape and data type as `var`.
         - **m** (Tensor) - The same shape and data type as `m`.
 
+    Raises:
+        TypeError: If dtype of `var`, `lr`, `logbase`, `sign_decay`, `beta` or `grad` is neither float16 nor float32.
+        TypeError: If `lr`, `logbase`, `sign_decay` or `beta` is neither a Number nor a Tensor.
+        TypeError: If `grad` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6203,6 +6438,11 @@ class ApplyGradientDescent(PrimitiveWithInfer):
     Outputs:
         Tensor, represents the updated `var`.
 
+    Raises:
+        TypeError: If dtype of `var` or `alpha` is neither float16 nor float32.
+        TypeError: If `delta` is not a Tensor.
+        TypeError: If `alpha` is neither a Number nor a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6281,6 +6521,11 @@ class ApplyProximalGradientDescent(PrimitiveWithInfer):
     Outputs:
         Tensor, represents the updated `var`.
 
+    Raises:
+        TypeError: If dtype of `var`, `alpha`, `l1` or `l2` is neither float16 nor float32.
+        TypeError: If `alpha`, `l1` or `l2` is neither a Number nor a Tensor.
+        TypeError: If `delta` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6367,6 +6612,13 @@ class LARSUpdate(PrimitiveWithInfer):
     Outputs:
         Tensor, represents the new gradient.
 
+    Raises:
+        TypeError: If neither `epsilon` nor `hyperpara` is a float.
+        TypeError: If `use_clip` is a bool.
+        TypeError: If `weight`, `gradient`, `norm_weight` or `norm_gradient` is not a Tensor.
+        TypeError: If `weight_decay` or `learning_rate` is neither a Number nor a Tensor.
+        TypeError: If shape of `gradient` is not same as `weight`.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6459,6 +6711,12 @@ class ApplyFtrl(PrimitiveWithInfer):
         - **var** (Tensor) - represents the updated `var`. As the input parameters has been updated in-place, this
           value is always zero when the platforms is GPU.
 
+    Raises:
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If dtype of `var`, `grad`, `lr`, `l1`, `l2` or `lr_power` is neither float16 nor float32.
+        TypeError: If `lr`, `l1`, `l2` or `lr_power` is neither a Number nor a Tensor.
+        TypeError: If `grad` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -6553,6 +6811,13 @@ class SparseApplyFtrl(PrimitiveWithCheck):
         - **accum** (Tensor) - Tensor, has the same shape and data type as `accum`.
         - **linear** (Tensor) - Tensor, has the same shape and data type as `linear`.
 
+    Raises:
+        TypeError: If `lr`, `l1`, `l2` or `lr_power` is not a float.
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If dtype of `var`, `accum`, `linear` or `grad` is neither float16 nor float32.
+        TypeError: If dtype of `indices` is neither int32 nor int64.
+
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -6660,6 +6925,12 @@ class SparseApplyFtrlV2(PrimitiveWithInfer):
         - **accum** (Tensor) - Tensor, has the same shape and data type as `accum`.
         - **linear** (Tensor) - Tensor, has the same shape and data type as `linear`.
 
+    Raises:
+        TypeError: If `lr`, `l1`, `l2`, `lr_power` or`use_locking` is not a float.
+        TypeError: If `use_locking` is not a bool.
+        TypeError: If dtype of `var`, `accum`, `linear` or `grad` is neither float16 nor float32.
+        TypeError: If dtype of `indices` is not int32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -6754,9 +7025,8 @@ class Dropout(PrimitiveWithCheck):
     Raises:
         TypeError: If `keep_prob` is not a float.
         TypeError: If `Seed0` or `Seed1` is not an int.
-        TypeError: If dtype of `input` is not neither float16 nor float32.
-        ValueError: If `keep_prob` is not in range (0, 1].
-        ValueError: If length of shape of `input` is less than 1.
+        TypeError: If dtype of `input` is neither float16 nor float32.
+        TypeError: If `input` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -6867,6 +7137,14 @@ class CTCLoss(PrimitiveWithInfer):
           the same type with `inputs`.
         - **gradient** (Tensor) - The gradient of `loss`, has the same type and shape with `inputs`.
 
+    Raises:
+        TypeError: If `preprocess_collapse_repeated`, `ctc_merge_repeated` or `ignore_longer_outputs_than_inputs` is
+        not a bool.
+        TypeError: If `inputs`, `labels_indices`, `labels_values` or `sequence_length` is not a Tensor.
+        TypeError: If dtype of `inputs` is not one of the following: float16, float32 or float64.
+        TypeError: If dtype of `labels_indices` is not int64.
+        TypeError: If dtype of `labels_values` or `sequence_length` is not int32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -7048,6 +7326,13 @@ class BasicLSTMCell(PrimitiveWithInfer):
         - **tanhct** (Tensor) - Forward :math:`tanh c_t` cache at moment `t`.
           Tensor of shape (`batch_size`, `hidden_size`), has the same type with input `c`.
 
+    Raises:
+        TypeError: If dtype of `keep_prob` or `forget_bias` is not float.
+        TypeError: If `state_is_tuple` is not a bool.
+        TypeError: If `activation` is not a str.
+        TypeError: If `x`, `h`, `c`, `w` or `b` is not a Tensor.
+        TypeError: If dtype of `x`, `h`, `c` or `w` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -7180,6 +7465,15 @@ class DynamicRNN(PrimitiveWithInfer):
         - **tanhct** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
           Has the same type with input `b`.
 
+    Raises:
+        TypeError: If `cell_type`, `direction` or `activation` is not a str.
+        TypeError: If `cell_depth` or `num_proj` is not an int.
+        TypeError: If `keep_prob`, `cell_clip` or `forget_bias` is not a float.
+        TypeError: If `use_peehpole`, `time_major` or `is_training` is not a bool.
+        TypeError: If `x`, `w`, `b`, `seq_length`, `init_h` or `init_c` is not a Tensor.
+        TypeError: If dtype of `x`, `w`, `init_h` or `nit_c` is not float16.
+        TypeError: If dtype of `b` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -7332,6 +7626,16 @@ class DynamicGRUV2(PrimitiveWithInfer):
         - If `bias_input` is not `None`, `bias_type` is the date type of `bias_input`.
         - If `bias_input` is `None` and `bias_hidden` is not `None, `bias_type` is the date type of `bias_hidden`.
 
+    Raises:
+        TypeError: If `direction`, `activation` or `gate_order` is not a str.
+        TypeError: If `cell_depth` or `num_proj` is not an int.
+        TypeError: If `keep_prob` or `cell_clip` is not a float.
+        TypeError: If `time_major`, `reset_after` or `is_training` is not a bool.
+        TypeError: If `x`, `weight_input`, `weight_hidden`, `bias_input`, `bias_hidden`, `seq_length` or `ini_h` is
+        not a Tensor.
+        TypeError: If dtype of `x`, `weight_input` or `weight_hidden` is not float16.
+        TypeError: If dtype of `init_h` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -7446,6 +7750,11 @@ class InTopK(PrimitiveWithInfer):
         Tensor has 1 dimension of type bool and the same shape with `x2`. For labeling sample `i` in `x2`,
         if the label in the first `k` predictions for sample `i` is in `x1`, then the value is True, otherwise False.
 
+    Raises:
+        TypeError: If `k` is not an int.
+        TypeError: If `x1` or `x2` is not a Tensor.
+        TypeError: If dtype of `x1` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -7499,6 +7808,12 @@ class LRN(PrimitiveWithInfer):
     Outputs:
         Tensor, with the same shape and data type as the input tensor.
 
+    Raises:
+        TypeError: If `depth_radius` is not an int.
+        TypeError: If `bias`, `alpha` or `beta` is not a float.
+        TypeError: If `norm_region` is not a str.
+        TypeError: If `x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``
 
@@ -7607,9 +7922,6 @@ class Conv3D(PrimitiveWithInfer):
     Outputs:
         Tensor, the value that applied 3D convolution. The shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
 
-    Supported Platforms:
-        ``Ascend``
-
     Raises:
         TypeError: If `out_channel` or `group` is not an int.
         TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple.
@@ -7620,6 +7932,9 @@ class Conv3D(PrimitiveWithInfer):
         ValueError: If `pad_mode` is not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0, 0, 0).
         ValueError: If `data_format` is not 'NCDHW'.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> input = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float16)
         >>> weight = Tensor(np.ones([32, 3, 4, 3, 3]), mindspore.float16)
@@ -7787,9 +8102,6 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
     Outputs:
         Tensor, the gradients w.r.t the input of convolution 3D. It has the same shape as the input.
 
-    Supported Platforms:
-        ``Ascend``
-
     Raises:
         TypeError: If `out_channel` or `group` is not an int.
         TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple.
@@ -7800,6 +8112,9 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
         ValueError: If `pad_mode` is not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0, 0, 0).
         ValueError: If `data_format` is not 'NCDHW'.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> dout = Tensor(np.ones([16, 32, 10, 32, 32]), mindspore.float16)
         >>> weight = Tensor(np.ones([32, 32, 4, 6, 2]), mindspore.float16)

mindspore/ops/operations/other_ops.py

@@ -79,12 +79,18 @@ class InplaceAssign(PrimitiveWithInfer):
     """
     Inplace assign `Parameter` with a value.
     This primitive can only use in graph kernel.
+
     Inputs:
         - **variable** (Parameter) - The `Parameter`.
         - **value** (Tensor) - The value to be assigned.
         - **depend** (Tensor) - The dependent tensor to keep this op connected in graph.
+
     Outputs:
         Tensor, has the same type as original `variable`.
+
+    Raises:
+        TypeError: If `value` or `depend` is not a Tensor.
+
     Examples:
         >>> class Net(nn.Cell):
         ...     def __init__(self):
@@ -149,6 +155,10 @@ class BoundingBoxEncode(PrimitiveWithInfer):
     Outputs:
         Tensor, encoded bounding boxes.
 
+    Raises:
+        TypeError: If `means` or `stds` is not a tuple.
+        TypeError: If `anchor_box` or `groundtruth_box` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -205,6 +215,11 @@ class BoundingBoxDecode(PrimitiveWithInfer):
     Outputs:
         Tensor, decoded boxes.
 
+    Raises:
+        TypeError: If `means`, `stds` or `max_shape` is not a tuple.
+        TypeError: If `wh_ratio_clip` is not a float.
+        TypeError: If `anchor_box` or `deltas` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -263,6 +278,10 @@ class CheckValid(PrimitiveWithInfer):
     Outputs:
         Tensor, with shape of (N,) and dtype of bool.
 
+    Raises:
+        TypeError: If `bboxes` or `img_metas` is not a Tensor.
+        TypeError: If dtype of `bboxes` or `img_metas` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -480,9 +499,6 @@ class CheckBprop(PrimitiveWithInfer):
     """
     Checks whether the data type and the shape of corresponding elements from tuples x and y are the same.
 
-    Raises:
-        TypeError: If tuples x and y are not the same.
-
     Inputs:
         - **input_x** (tuple[Tensor]) - The `input_x` contains the outputs of bprop to be checked.
         - **input_y** (tuple[Tensor]) - The `input_y` contains the inputs of bprop to check against.
@@ -491,6 +507,9 @@ class CheckBprop(PrimitiveWithInfer):
         (tuple[Tensor]), the `input_x`,
         if data type and shape of corresponding elements from `input_x` and `input_y` are the same.
 
+    Raises:
+        TypeError: If `input_x` or `input_y` is not a Tensor.
+
     Examples:
         >>> input_x = (Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32),)
         >>> input_y = (Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32),)
@@ -561,6 +580,11 @@ class ConfusionMatrix(PrimitiveWithInfer):
     Outputs:
         Tensor, the confusion matrix, with shape (`num_classes`, `num_classes`).
 
+    Raises:
+        TypeError: If `num_classes` is not an int.
+        TypeError: If `dtype` is not a str.
+        TypeError: If `labels`, `predictions` or weight` is not a Tensor.
+
     Examples:
         >>> confusion_matrix = ops.ConfusionMatrix(4)
         >>> labels = Tensor([0, 1, 1, 3], mindspore.int32)

mindspore/ops/operations/random_ops.py

@@ -34,6 +34,11 @@ class StandardNormal(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape is the same as the input `shape`. The dtype is float32.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `shape` is not a tuple.
+        ValueError: If `shape` is not a constant value.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -86,6 +91,11 @@ class StandardLaplace(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `shape` is not a tuple.
+        ValueError: If `shape` is not a constant value.
+
     Supported Platforms:
         ``Ascend``
 
@@ -142,6 +152,11 @@ class Gamma(PrimitiveWithInfer):
         Tensor. The shape must be the broadcasted shape of Input "shape" and shapes of alpha and beta.
         The dtype is float32.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If neither `alpha` nor `beta` is a Tensor.
+        ValueError: If `shape` is not a constant value.
+
     Supported Platforms:
         ``Ascend``
 
@@ -202,6 +217,11 @@ class Poisson(PrimitiveWithInfer):
         Tensor. Its shape must be the broadcasted shape of `shape` and the shape of `mean`.
         The dtype is int32.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `shape` is not a tuple.
+        TypeError: If `mean` is not a Tensor whose dtype is not float32.
+
     Supported Platforms:
         ``Ascend``
 
@@ -261,6 +281,12 @@ class UniformInt(PrimitiveWithInfer):
         - **maxval** (Tensor) - The distribution parameter, b.
           It defines the maximum possibly generated value, with int32 data type. Only one number is supported.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `shape` is not a tuple.
+        TypeError: If neither `minval` nor `maxval` is a Tensor.
+        ValueError: If `shape` is not a constant value.
+
     Outputs:
         Tensor. The shape is the same as the input 'shape', and the data type is int32.
 
@@ -320,6 +346,11 @@ class UniformReal(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `shape` is not a tuple.
+        ValueError: If `shape` is not a constant value.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -378,6 +409,11 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         - **index** (Tensor) - The output shape is 2-D.
         - **mask** (Tensor) - The output shape is 1-D.
 
+    Raises:
+        TypeError: If `count` is not an int.
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `input_x` is not a Tensor.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -428,6 +464,11 @@ class RandomCategorical(PrimitiveWithInfer):
     Outputs:
         - **output** (Tensor) - The output Tensor with shape [batch_size, num_samples].
 
+    Raises:
+        TypeError: If `dtype` is not one of the following: mindspore.int16, mindspore.int32, mindspore.int64.
+        TypeError: If `logits` is not a Tensor.
+        TypeError: If neither `num_sample` nor `seed` is an int.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -499,6 +540,11 @@ class Multinomial(PrimitiveWithInfer):
     Outputs:
         Tensor with the same rows as input, each row has num_samples sampled indices.
 
+    Raises:
+        TypeError: If neither `seed` nor `seed2` is an int.
+        TypeError: If `input` is not a Tensor whose dtype is float32.
+        TypeError: If dtype of `num_samples` is not int32.
+
     Supported Platforms:
         ``GPU``
 
@@ -566,6 +612,12 @@ class UniformCandidateSampler(PrimitiveWithInfer):
         - **sampled_expected_count** (Tensor) - The expected counts under the sampling distribution of
           each of sampled_candidates. Shape: (num_sampled, ).
 
+    Raises:
+        TypeError: If neither `num_true` nor `num_sampled` is an int.
+        TypeError: If neither `unique` nor `remove_accidental_hits` is a bool.
+        TypeError: If neither `range_max` nor `seed` is a int.
+        TypeError: If `true_classes` is not a Tensor.
+
     Supported Platforms:
         ``GPU``
 
@@ -629,6 +681,12 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
         - **true_expected_count** (Tensor) - A Tensor with the same shape as `true_classes and` type float32.
         - **sampled_expected_count** (Tensor) - A Tensor with the same shape as `sampled_candidates` and type float32.
 
+    Raises:
+        TypeError: If neither `num_true` nor `num_sampled` is an int.
+        TypeError: If `unique` is not a bool.
+        TypeError: If neither `range_max` nor `seed` is an int.
+        TypeError: If `true_classes` is not a Tensor.
+
     Supported Platforms:
         ``Ascend``