mindspore.ops.Primitive

class mindspore.ops.Primitive(name)

Primitive is the base class of operator primitives in python.

Parameters

name (str) – Name for the 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):
...         '''init for add'''
...     # 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)

Add primitive attribute.

Parameters

• name (str) – Attribute Name.

• value (Any) – Attribute value.

Examples

>>> import mindspore.ops as P
>>> a = P.Add()
>>> a = a.add_prim_attr("attr",1)
>>> out = a.attrs["attr"]
>>> print(out)
1

check_elim(*args)

Check if the primitive can be eliminated. Subclass in need should override this method.

Parameters

args (Primitive args) – Same as arguments of current Primitive.

Returns

A tuple consisting of two elements. The first element means if the primitive can be calculated in compiling stage, the second element is calculated result.

Examples

>>> class AddN(Primitive):
...     @prim_attr_register
...     def __init__(self):
...         self.init_prim_io_names(inputs=["inputs"], outputs=["sum"])
...     def check_elim(self, inputs):
...         if len(inputs) != 1:
...             return (False, None)
...         if isinstance(inputs[0], Tensor):
...             return (True, inputs[0])
...
>>> addn = AddN()
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> output = addn.check_elim((input_x,))
>>> print(output)
(True, Tensor(shape = [3], dtype = Float32, value = [1.0000000e+00,2.0000000e+00,3.0000000e+00]))

del_prim_attr(name)

Delete primitive attribute.

Parameters

name (str) – Attribute Name.

Examples

>>> import mindspore.ops as P
>>> a = P.Add()
>>> a = a.add_prim_attr("attr",1)
>>> a = a.del_prim_attr("attr")
>>> a.attrs
{'input_names': ['x', 'y'], 'output_names' : ['output']}

init_prim_io_names(inputs, outputs)

Initialize the name of inputs and outputs of Tensor or attributes.

Parameters

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

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

Examples

>>> import mindspore.ops as P
>>> a = P.Add()
>>> a.init_prim_io_names(["x","y"],["sum"])
>>> a.input_names
['x','y']
>>> a.output_names
['sum']

recompute(mode=True)

Set the primitive recomputed. If a primitive set recomputed feeds into some backward nodes for computing gradient, rather than storing the intermediate activation computed in forward pass, we will recompute it in backward pass.

Note

• If the computation involves something like randomization or global variable, the equivalence is not guaranteed currently.

Parameters

mode (bool) – Specifies whether the primitive is recomputed. Default: True.

Examples

>>> import mindspore.ops as P
>>> a = P.Add()
>>> a = a.recompute()
>>> a.recompute
True

set_prim_instance_name(instance_name)

Set instance name to primitive operator.

Note

It will be called by default when user defines primitive operator.

Parameters

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

Examples

>>> import mindspore.ops as P
>>> a = P.Add()
>>> a.set_prim_instance_name("add")
>>> a.instance_name
'add'

set_stage(stage)

Add stage id to primitive attribute.

Note

It is valid only in semi auto parallel. In other parallel modes, please set it to be 0.

Parameters

stage (int) – The stage id for the current operation.

Examples

>>> from mindspore.ops import operations as P
>>> add = P.Add()
>>> print(add.set_stage(0))
Prim[Add]<stage=0>

shard(strategy)

Add strategies to primitive attribute.

Note

It is valid only in semi auto parallel or auto parallel mode. In other parallel modes, strategies set here will be ignored.

Parameters

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

Examples

>>> from mindspore.ops import operations as P
>>> add = P.Add()
>>> print(add.shard(((1, 1), (1, 1))))
Prim[Add]<strategy=((1, 1), (1, 1))>

property update_parameter

Return whether the primitive will update the value of parameter.