mindspore.ops.CustomRegOp

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class mindspore.ops.CustomRegOp(op_name='Custom')[source]

Class used for generating the registration information for the func parameter of mindspore.ops.Custom. The registration information mainly specifies the supported data types and formats of input and output tensors, attributes and target of func.

Parameters

op_name (str) – kernel name. The name will be record in the reg_op_name attr of the kernel node. Besides, the operator will generate a unique name automatically to identify the reg info. Default: "Custom" .

Examples

>>> from mindspore.ops import CustomRegOp, DataType
>>> custom_op_ascend_info = CustomRegOp() \
...     .input(0, "x", "dynamic") \
...     .output(0, "y") \
...     .dtype_format(DataType.F16_Default, DataType.F16_Default) \
...     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
...     .target("Ascend") \
...     .get_op_info()
attr(name=None, param_type=None, value_type=None, default_value=None, **kwargs)[source]

Specifies the attributes information for the func parameter of mindspore.ops.Custom. Each invocation of this function will generate one attribute information, that means, if func has two attributes, then this function should be invoked two times continuously. The attributes information will be generated as a dict: {“name”: name, “param_type”: param_type, “value_type”: value_type, “default_value”: default_value}.

Parameters

  • name (str) – Name of the attribute. If None , key “name” will not appear in the attributes tensor information dict. Default: None .

  • param_type (str) –

    Parameter type of the attribute, can be one of [“required”, “optional”]. If None , key “param_type” will not appear in the attributes tensor information dict. Default: None .

    • ”required”: means must provide a value for this attribute either by setting a default value in the registration information or providing an input value when calling the Custom operator.

    • ”optional”: means does not have to provide a value for this attribute.

  • value_type (str) –

    Value type of the attribute, can be one of [“int”, “str”, “bool”, “float”, “listInt”, “listStr”, “listBool”, “listFloat”]. If None , key “value_type” will not appear in the attributes tensor information dict. Default: None .

    • ”int”: string representation of Python type int.

    • ”str”: string representation of Python type str.

    • ”bool”: string representation of Python type bool.

    • ”float”: string representation of Python type float.

    • ”listInt”: string representation of Python type list of int.

    • ”listStr”: string representation of Python type list of str.

    • ”listBool”: string representation of Python type list of bool.

    • ”listFloat”: string representation of Python type list of float.

  • default_value (str) – Default value of the attribute. default_value and value_type are used together. If the real default value of the attribute is float type with value 1.0, then the value_type should be “float” and default_value should be “1.0”. If the real default value of the attribute is a list of int with value [1, 2, 3], then the value_type should be “listInt” and default_value should be “1,2,3”, each item should split by ‘,’. If None , means the attribute has no default value and key “default_value” will not appear in the attributes tensor information dict. It is used for “akg”, “aicpu” and “tbe” Custom operators currently. Default: None .

  • kwargs (dict) – Other information of the attribute, used for extension.

Raises

  • TypeError – If name is neither str nor None.

  • TypeError – If param_type is neither str nor None.

  • TypeError – If value_type is neither str nor None.

  • TypeError – If default_value is neither str nor None.

Tutorial Examples:
dtype_format(*args)[source]

Specifies the supported data type and format of each input tensor and output tensor for the func parameter of mindspore.ops.Custom. This function should be invoked after input and output function as shown in the above example.

Parameters

args (tuple) – A tuple of (data type, format) pair, the length of args should be equal to the sum of input tensors and output tensors. Each item in args is also a tuple, tuple[0] and tuple[1] are both str type, which specifies the data type and format of a tensor respectively. mindspore.ops.DataType provides many predefined (data type, format) combinations, for example, DataType.F16_Default means the data type is float16 and the format is default format.

Raises

ValueError – If the size of args not equal to the sum of input tensors and output tensors.

Tutorial Examples:
get_op_info()[source]

Return the generated registration information as a dict. This function should be invoked at last on the CustomRegOp instance as shown in the above example.

Tutorial Examples:
input(index=None, name=None, param_type='required', **kwargs)[source]

Specifies the input tensor information for the func parameter of mindspore.ops.Custom. Each invocation of this function will generate one input tensor information, that means, if func has two input tensors, then this function should be invoked two times continuously. The input tensor information will be generated as a dict: {“index”: index, “name”: name, “param_type”: param_type}.

Parameters

  • index (int) – Index of the input, starts from 0. 0 means the first input tensor, 1 means the second input tensor and so on. If None , key “index” will not appear in the input tensor information dict. Default: None .

  • name (str) – Name of the index ‘th input. If None , key “name” will not appear in the input tensor information dict. Default: None .

  • param_type (str) –

    Parameter type of the index ‘th input, can be one of ["required" , "dynamic" , "optional" ]. If None , key “param_type” will not appear in the input tensor information dict. Default: "required" .

    • "required": means the index ‘th input exist and can only be a single tensor.

    • "dynamic": means the index ‘th input exist and may be multiple tensors, such as the input of AddN.

    • "optional": means the index ‘th input may exist and be a single tensor or may not exist.

  • kwargs (dict) – Other information of the input, used for extension.

Raises

  • TypeError – If index is neither int nor None.

  • TypeError – If name is neither str nor None.

  • TypeError – If param_type is neither str nor None.

Tutorial Examples:
output(index=None, name=None, param_type='required', **kwargs)[source]

Specifies the output tensor information for the func parameter of mindspore.ops.Custom. Each invocation of this function will generate one output tensor information, which means, if func has two output tensors, then this function should be invoked two times continuously. The output tensor information will be generated as a dict: {“index”: index, “name”: name, “param_type”: param_type}.

Parameters

  • index (int) – Index of the output, starts from 0. 0 means the first output tensor, 1 means the second output tensor and so on. If None , key “index” will not appear in the output tensor information dict. Default: None .

  • name (str) – Name of the index ‘th output. If None , key “name” will not appear in the output tensor information dict. Default: None .

  • param_type (str) –

    Parameter type of the index ‘th output, can be one of [ "required" , "dynamic" , "optional" ]. If None , key “param_type” will not appear in the output tensor information dict. Default: "required" .

    • "required": means the index ‘th output exist and can only be a single tensor.

    • "dynamic": means the index ‘th output exist and may be multiple tensors.

    • "optional": means the index ‘th output may exist and be a single tensor or may not exist.

  • kwargs (dict) – Other information of the output, used for extension.

Raises

  • TypeError – If index is neither int nor None.

  • TypeError – If name is neither str nor None.

  • TypeError – If param_type is neither str nor None.

Tutorial Examples:
target(target=None)[source]

Specifies the target that this registration information is used for.

Parameters

target (str) – Device target for current operator information, should be one of [“Ascend”, “GPU”, “CPU”]. For the same func of mindspore.ops.Custom, it may support different data types and formats on different targets, use target to specify which target that this registration information is used for. If None , it will be inferred automatically inside mindspore.ops.Custom. Default: None .

Raises

TypeError – If target is neither str nor None.

Tutorial Examples: