mindspore.ops

Operators can be used in the construct function of Cell.

Examples

>>> from mindspore.ops import operations as P
>>> from mindspore.ops import composite as C
>>> from mindspore.ops import functional as F

Note

  • The Primitive operators in operations need to be used after instantiation.

  • The composite operators are pre-defined combination of operator.

  • The functional operators are the pre-instantiated Primitive operators, which can be used directly like a function.

mindspore.ops.get_vm_impl_fn(prim)[source]

Gets vm function by primitive obj or primitive name for c++

Parameters

prim (Union[Primitive, str]) – primitive obj or primitive name for operator register by name.

Returns

function, vm function

mindspore.ops.op_info_register(op_info)[source]

A decorator used as register of operator implementation.

Note

‘op_info’ must be a str of json format represent the op info, the op info will be added into oplib.

Parameters

op_info (str) – op info of json format.

Returns

Function, returns a decorator for op info register.

mindspore.ops.constexpr(fn=None, get_instance=True, name=None)[source]

Makes a PrimitiveWithInfer operator, which infer the value while compiling.

Parameters
  • fn (function) – A fn use as the infer_value of the output operator.

  • get_instance (bool) – If true, returns the instance of operator, else returns the operator class.

  • name (str) – Defines the operator name. If name is None, use the function name as op name.

Examples

>>> a = (1, 2)
>>> # make a operator to calculate tuple len
>>> @constexpr
>>> def tuple_len(x):
>>>     return len(x)
>>> assert tuple_len(a) == 2
>>>
>>> # make a operator class to calculate tuple len
>>> @constexpr(get_instance=False, name="TupleLen")
>>> def tuple_len_class(x):
>>>     return len(x)
>>> assert tuple_len_class()(a) == 2
mindspore.ops.prim_attr_register(fn)[source]

Primitive attributes register.

Registering the decorator of the built-in operator primitive __init__ function will add all the parameters of __init__ as operator attributes.

Parameters

fn (function) – __init__ function of primitive.

Returns

function, original function.

class mindspore.ops.Primitive(name)[source]

Primitive is base class for primitives in python.

Parameters

name (str) – Name for current Primitive.

Examples

>>> add = Primitive('add')
>>>
>>> # or work with prim_attr_register:
>>> # init a Primitive class with attr1 and attr2
>>> class Add(Primitive):
>>>     @prim_attr_register
>>>     def __init__(self, attr1, attr2):
>>>         # check attr1 and attr2 or do some initializations
>>> # init a Primitive obj with attr1=1 and attr2=2
>>> add = Add(attr1=1, attr2=2)
add_prim_attr(name, value)[source]

Adds primitive attribute.

Parameters
  • name (str) – Attribute Name.

  • value (Any) – Attribute value.

init_prim_io_names(inputs, outputs)[source]

Initializes inputs and outpus name of Tensor or attributes.

Parameters
  • inputs (list[str]) – list of inputs names.

  • outputs (list[str]) – list of outputs names.

set_prim_instance_name(instance_name)[source]

Sets instance name to primitive operator.

Note

Will be called by default when user defines primitive operator.

Parameters

instance_name (str) – Instance name of primitive operator set by user.

set_strategy(strategy)[source]

Adds strategy to primitive attribute.

Note

Valid only in semi auto parallel or auto parallel mode.

Parameters

strategy (tuple) – Strategy describes the distributed parallel mode of the current primitive.

class mindspore.ops.PrimitiveWithInfer(name)[source]

PrimitiveWithInfer is base class for primitives in python and defines functions for infer of tracks in python.

There are four method can be overide to define the infer logic of the primitive: __infer__(), infer_shape(), infer_dtype(), and infer_value(). If __infer__() is defined in primitive, the __infer__() has highest priority to be called. If __infer__() is not defined, infer_shape() and infer_dtype() can be defined to describle shape and type infer logic. The infer_value() is used for constant propogation.

Parameters

name (str) – Name for current Primitive.

Examples

>>> # init a Primitive class with infer
>>> class Add(PrimitiveWithInfer):
>>>     @prim_attr_register
>>>     def __init__(self):
>>>         pass
>>>
>>>     def infer_shape(self, x, y):
>>>         return x # output shape same as first input 'x'
>>>
>>>     def infer_dtype(self, x, y):
>>>         return x # output type same as first input 'x'
>>>
>>> # init a Primitive obj
>>> add = Add()
infer_dtype(*args)[source]

Infer output dtype based on input dtype.

Parameters
  • inputs (mstype) – data type of inputs.

  • outputs (mstype) – data type of outputs.

infer_shape(*args)[source]

Infer output shape based on input shape.

Parameters
  • inputs (tuple(int)) – dimensions of input tensors.

  • outputs (tuple(int)) – dimensions of output tensors.

Note

The shape of scalar is an empty tuple.

infer_value(*args)[source]

Infer output value based on input value at compile time.

Parameters
  • inputs (any) – value of inputs.

  • outputs (any) – value of outputs.