Static Graph Syntax Support

Overview

In graph mode, Python code is not executed by the Python interpreter. Instead, the code is compiled into a static computation graph, and then the static computation graph is executed.

Currently, only the function, Cell, and subclass instances modified by the @ms_function decorator can be built. For a function, build the function definition. For the network, build the construct method and other methods or functions called by the construct method.

For details about how to use ms_function, click https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore/mindspore.ms_function.html#mindspore.ms_function.

For details about the definition of Cell, click https://www.mindspore.cn/docs/programming_guide/en/r1.6/cell.html.

Due to syntax parsing restrictions, the supported data types, syntax, and related operations during graph building are not completely consistent with the Python syntax. As a result, some usage is restricted.

The following describes the data types, syntax, and related operations supported during static graph building. These rules apply only to graph mode.

All the following examples run on the network in graph mode. For brevity, the network definition is not described.

Data Types

Built-in Python Data Types

Currently, the following built-in Python data types are supported: Number, String, List, Tuple, and Dictionary.

Number

Supports int, float, and bool, but does not support complex numbers.

Number can be defined on the network. That is, the syntax y = 1, y = 1.2, and y = True are supported.

When the data is constant, the value of the data can be achieved at compile time, the forcible conversion to Number is supported in the network. That is, the syntax y = int(x), y = float(x), and y = bool(x) are supported.

String

String can be constructed on the network. That is, the syntax y = "abcd" is supported.

Forcible conversion to String is not supported on the network. That is, the syntax y = str(x) is not supported.

List

List can be constructed on the network, that is, the syntax y = [1, 2, 3] is supported.

Forcible conversion to List is not supported on the network. That is, the syntax y = list(x) is not supported.

List to be output in the computation graph will be converted into Tuple.

Supported APIs

append: adds an element to list.

For example:

from mindspore import ms_function

@ms_function()
def test_list():
    x = [1, 2, 3]
    x.append(4)
    return x

x = test_list()
print('x:{}'.format(x))

The result is as follows:

x: (1, 2, 3, 4)

Supported index values and value assignment

Single-level and multi-level index values and value assignment are supported.

The index value supports only int.

The assigned value can be Number, String, Tuple, List, or Tensor.

For example:

from mindspore import ms_function, Tensor
import numpy as np

t = Tensor(np.array([1, 2, 3]))

@ms_function()
def test_index():
    x = [[1, 2], 2, 3, 4]
    m = x[0][1]
    x[1] = t
    x[2] = "ok"
    x[3] = (1, 2, 3)
    x[0][1] = 88
    n = x[-3]
    return m, x, n

m, x, n = test_index()
print('m:{}'.format(m))
print('x:{}'.format(x))
print('n:{}'.format(n))

The result is as follows:

m:2
x:[[1, 88], Tensor(shape=[3], dtype=Int64, value= [1, 2, 3]), 'ok', (1, 2, 3)]
n:[1 2 3]

Tuple

Tuple can be constructed on the network, that is, the syntax y = (1, 2, 3) is supported.

Forcible conversion to Tuple is not supported on the network. That is, the syntax y = tuple(x) is not supported.

Supported index values

The index value can be int, slice, Tensor, and multi-level index value. That is, the syntax data = tuple_x[index0][index1]... is supported.

Restrictions on the index value Tensor are as follows:

Tuple stores Cell. Each Cell must be defined before a tuple is defined. The number of input parameters, input parameter type, and input parameter shape of each Cell must be the same. The number of outputs of each Cell must be the same. The output type must be the same as the output shape.
The index Tensor is a scalar Tensor whose dtype is int32. The value range is [-tuple_len, tuple_len), negative index is not supported in Ascend backend.
This syntax does not support the running branches whose control flow conditions if, while, and for are variables. The control flow conditions can be constants only.
GPU and Ascend backend is supported.

An example of the int and slice indexes is as follows:

from mindspore import ms_function, Tensor
import numpy as np

t = Tensor(np.array([1, 2, 3]))

@ms_function()
def test_index():
    x = (1, (2, 3, 4), 3, 4, t)
    y = x[1][1]
    z = x[4]
    m = x[1:4]
    n = x[-4]
    return y, z, m, n

y, z, m, n = test_index()
print('y:{}'.format(y))
print('z:{}'.format(z))
print('m:{}'.format(m))
print('n:{}'.format(n))

The result is as follows:

y:3
z:[1 2 3]
m:((2, 3, 4), 3, 4)
n:(2, 3, 4)

An example of the Tensor index is as follows:

from mindspore import Tensor, nn, dtype

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax()
        self.layers = (self.relu, self.softmax)

    def construct(self, x, index):
        ret = self.layers[index](x)
        return ret

x = Tensor([-1.0], dtype.float32)

net = Net()
ret = net(x, 0)
print('ret:{}'.format(ret))

The result is as follows:

ret:[0.]

Dictionary

Dictionary can be constructed on the network. That is, the syntax y = {"a": 1, "b": 2} is supported. Currently, only String can be used as the key value.

Dictionary to be output in the computational graph will extract all value values to form the Tuple output.

Supported APIs

keys: extracts all key values from dict to form Tuple and return it.

values: extracts all value values from dict to form Tuple and return it.

items: extracts Tuple composed of each pair of value values and key values in dict to form Tuple and return.

For example:

from mindspore import ms_function, Tensor
import numpy as np

x = {"a": Tensor(np.array([1, 2, 3])), "b": Tensor(np.array([4, 5, 6])), "c": Tensor(np.array([7, 8, 9]))}

@ms_function()
def test_dict():
    y = x.keys()
    z = x.values()
    q = x.items()
    return y, z, q

y, z, q = test_dict()
print('y:{}'.format(y))
print('z:{}'.format(z))

The result is as follows:

y:('a', 'b', 'c')
z:(Tensor(shape=[3], dtype=Int64, value= [1, 2, 3]), Tensor(shape=[3], dtype=Int64, value= [4, 5, 6]), Tensor(shape=[3], dtype=Int64, value= [7, 8, 9]))
q:[('a', Tensor(shape=[3], dtype=Int64, value= [1, 2, 3])), ('b', Tensor(shape=[3], dtype=Int64, value= [4, 5, 6])), ('c', Tensor(shape=[3], dtype=Int64, value= [7, 8, 9]))]

Supported index values and value assignment

The index value supports only String. The assigned value can be Number, Tuple, or Tensor.

For example:

from mindspore import ms_function, Tensor
import numpy as np

x = {"a": Tensor(np.array([1, 2, 3])), "b": Tensor(np.array([4, 5, 6])), "c": Tensor(np.array([7, 8, 9]))}

@ms_function()
def test_dict():
    y = x["b"]
    x["a"] = (2, 3, 4)
    return x, y

x, y = test_dict()
print('x:{}'.format(x))
print('y:{}'.format(y))

The result is as follows:

x:{'a': (2, 3, 4), 'b': Tensor(shape=[3], dtype=Int64, value= [4, 5, 6]), 'c': Tensor(shape=[3], dtype=Int64, value= [7, 8, 9])}
y:[4 5 6]

MindSpore User-defined Data Types

Currently, MindSpore supports the following user-defined data types: Tensor, Primitive, and Cell.

Tensor

Currently, tensors cannot be constructed on the network. That is, the syntax x = Tensor(args...) is not supported.

You can use the @constexpr decorator to modify the function and generate the Tensor in the function.

For details about how to use @constexpr, click https://www.mindspore.cn/docs/api/en/r1.6/api_python/ops/mindspore.ops.constexpr.html.

The constant Tensor used on the network can be used as a network attribute and defined in init, that is, self.x = Tensor(args...). Then the constant can be used in construct.

In the following example, Tensor of shape = (3, 4), dtype = int64 is generated by @constexpr.

from mindspore import Tensor
from mindspore.ops import constexpr

@constexpr
def generate_tensor():
    return Tensor(np.ones((3, 4)))

x = generate_tensor()
print('x:{}'.format(x))

The result is as follows:

x:[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]

The following describes the attributes, APIs supported by the Tensor.

Supported attributes

shape: obtains the shape of Tensor and returns a Tuple.

dtype: obtains the data type of Tensor and returns a data type defined by MindSpore.
Supported APIs

all: reduces Tensor through the all operation. Only Tensor of the Bool type is supported.

any: reduces Tensor through the any operation. Only Tensor of the Bool type is supported.

view: reshapes Tensor into input shape.

expand_as: expands Tensor to the same shape as another Tensor based on the broadcast rule.

For example:

from mindspore import ms_function, Tensor
import numpy as np

x = Tensor(np.array([[True, False, True], [False, True, False]]))
y = Tensor(np.ones((2, 3), np.float32))
z = Tensor(np.ones((2, 2, 3)))

x_shape = x.shape
x_dtype = x.dtype
x_all = x.all()
x_any = x.any()
x_view = x.view((1, 6))
y_as_z = y.expand_as(z)

print('x_shape:{}'.format(x_shape))
print('x_dtype:{}'.format(x_dtype))
print('x_all:{}'.format(x_all))
print('x_any:{}'.format(x_any))
print('x_view:{}'.format(x_view))
print('y_as_z:{}'.format(y_as_z))

The result is as follows:

x_shape:(2, 3)
x_dtype:Bool
x_all:False
x_any:True
x_view:[[ True False  True False  True False]]
y_as_z:[[[1. 1. 1.]
  [1. 1. 1.]]

[[1. 1. 1.]
  [1. 1. 1.]]]

Primitive

Currently, Primitive and its subclass instances can be constructed on the network. That is, the reduce_sum = ReduceSum(True) syntax is supported.

However, during construction, the parameter can be specified only in position parameter mode, and cannot be specified in the key-value pair mode. That is, the syntax reduce_sum = ReduceSum(keep_dims=True) is not supported.

Currently, the attributes and APIs related to Primitive and its subclasses cannot be called on the network.

For details about the definition of Primitive, click https://www.mindspore.cn/docs/programming_guide/en/r1.6/operators_classification.html.

For details about the defined Primitive, click https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.ops.html.

Cell

Currently, Cell and its subclass instances can be constructed on the network. That is, the syntax cell = Cell(args...) is supported.

However, during construction, the parameter can be specified only in position parameter mode, and cannot be specified in the key-value pair mode. That is, the syntax cell = Cell(arg_name=value) is not supported.

Currently, the attributes and APIs related to Cell and its subclasses cannot be called on the network unless they are called through self in construct of Cell.

For details about the definition of Cell, click https://www.mindspore.cn/docs/programming_guide/en/r1.6/cell.html.

For details about the defined Cell, click https://www.mindspore.cn/docs/api/en/r1.6/api_python/mindspore.nn.html.

Parameter

Parameter is a variable tensor, indicating the parameters that need to be updated during network training.

For details about the definition of Parameter：https://www.mindspore.cn/docs/programming_guide/en/r1.6/parameter.html

Primaries

Primaries represent the most tightly bound operations of the language Which contains Attribute references, Subscriptions, Calls.

Attribute References

An attribute reference is a primary followed by a period and a name.

In Cell instance of MindSpore, using attribute reference as left operands must meet the restrictions below:

The attribute must belong to self, such as self.xxx. It is not supported to change attribute of other instance.
The attribute type must be Parameter and be initialized in __init__ function.

For example:

from mindspore import ms_function, Tensor, nn, dtype, Parameter
import numpy as np
from mindspore.ops import constexpr

class Net(nn.Cell):
    def __init__(self):
        super().__init__()
        self.weight = Parameter(Tensor(3, dtype.float32), name="w")
        self.m = 2

    def construct(self, x, y):
        self.weight = x     # restictions matched,  success
        # self.m = 3               # self.m not Parameter type, failure
        # y.weight = x          # not attribute of self, failure
        return x

net = Net()
ret = net(1, 2)
print('ret:{}'.format(ret))

The result is as follows:

ret:1

Index Value

Index value of a sequence Tuple, List, Dictionary, Tensor which called subscription in Python.

Index value of Tuple refers to chapter Tuple of this page.

Index value of List refers to chapter List of this page.

Index value of Dictionary refers to chapter Dictionary of this page.

Index value of Tensor refers to https://www.mindspore.cn/docs/note/en/r1.6/index_support.html

Calls

A call calls a callable object (e.g., Cell or Primitive) with a possibly empty series of arguments.

For example:

from mindspore import Tensor, nn, dtype, ops
import numpy as np

class Net(nn.Cell):
    def __init__(self):
        super().__init__()
        self.matmul = ops.MatMul()

    def construct(self, x, y):
        out = self.matmul(x, y)  # A call of Primitive
        return out

x = Tensor(np.ones(shape=[1, 3]), dtype.float32)
y = Tensor(np.ones(shape=[3, 4]), dtype.float32)
net = Net()
ret = net(x, y)
print('ret:{}'.format(ret))

The result is as follows:

ret:[[3. 3. 3. 3.]]

Operators

Arithmetic operators and assignment operators support the Number and Tensor operations, as well as the Tensor operations of different dtype.

This is because these operators are converted to operators with the same name for computation, and they support implicit type conversion.

For details about the rules, click https://www.mindspore.cn/docs/note/en/r1.6/operator_list_implicit.html.

Unary Arithmetic Operators

Unary Arithmetic Operator	Supported Type
`+`	`Number`, `Tensor`
`-`	`Number`, `Tensor`
`~`	`Tensor` with `Bool` data type

notes:

In native python the ~ operator get the bitwise inversion of its integer argument; in Mindspore the ~ redefined to get logic not for Tensor(Bool).

Binary Arithmetic Operators

Binary Arithmetic Operator	Supported Type
`+`	`Number` + `Number`, `String` + `String`, `Number` + `Tensor`, `Tensor` + `Number`, `Tuple` + `Tensor`, `Tensor` + `Tuple`, `List` + `Tensor`, `Tensor`+`List`, `List`+`List`, `Tensor` + `Tensor`, `Tuple` + `Tuple`.
`-`	`Number` - `Number`, `Tensor` - `Tensor`, `Number` -`Tensor`, `Tensor` - `Number`, `Tuple` -`Tensor`, `Tensor` -`Tuple`, `List` -`Tensor`, `Tensor` -`List`.
`*`	`Number` * `Number`, `Tensor` * `Tensor`, `Number` * `Tensor`, `Tensor` * `Number`, `List` * `Number`, `Number` * `List`, `Tuple` * `Number`, `Number` * `Tuple`, `Tuple` * `Tensor`, `Tensor` * `Tuple`, `List` `Tensor`, `Tensor` `List`.
`/`	`Number` / `Number`, `Tensor` / `Tensor`, `Number` / `Tensor`, `Tensor` / `Number`, `Tuple` / `Tensor`, `Tensor` / `Tuple`, `List` / `Tensor`, `Tensor` / `List`.
`%`	`Number` % `Number`, `Tensor` % `Tensor`, `Number` % `Tensor`, `Tensor` % `Number`, `Tuple` % `Tensor`, `Tensor` % `Tuple`, `List` % `Tensor`, `Tensor` % `List`.
`**`	`Number` `Number`, `Tensor` `Tensor`, `Number` `Tensor`, `Tensor` `Number`, `Tuple` `Tensor`, `Tensor` `Tuple`, `List` `Tensor`, `Tensor` `List`.
`//`	`Number` // `Number`, `Tensor` // `Tensor`, `Number` // `Tensor`, `Tensor` // `Number`, `Tuple` // `Tensor`, `Tensor` // `Tuple`, `List` // `Tensor`, `Tensor` // `List`.

Restrictions:

If all operands are Number type, value of Number can’t be Bool.
If all operands are Number type, operations between Float64 and Int32 are not supported.
If either operand is Tensor type, left and right operands can’t both be Bool value.
The result of List * Number is concatenate duplicate List Number times, data type of the List must be Number, String, None or List/Tuple that contains these types. This rule applies to Number * List, Tuple * Number, Number * Tuple too.

Assignment Operators

Assignment Operator	Supported Type、
`=`	All Built-in Python Types that MindSpore supported and MindSpore User-defined Data Types.
`+=`	`Number` += `Number`, `String` += `String`, `Number` += `Tensor`, `Tensor` += `Number`, `Tuple` += `Tensor`, `Tensor` += `Tuple`, `List` += `Tensor`, `Tensor` += `List`, `List` += `List`, `Tensor` += `Tensor`, `Tuple` += `Tuple`.
`-=`	`Number` -= `Number`, `Tensor` -= `Tensor`, `Number` -= `Tensor`, `Tensor` -= `Number`, `Tuple` -= `Tensor`, `Tensor` -= `Tuple`, `List` -= `Tensor`, `Tensor` -= `List`.
`*=`	`Number` = `Number`, `Tensor` = `Tensor`, `Number` = `Tensor`, `Tensor` = `Number`, `List` = `Number`, `Number` = `List`, `Tuple` = `Number`, `Number` = `Tuple`, `Tuple` = `Tensor`, `Tensor` = `Tuple`, `List` = `Tensor`, `Tensor` = `List`.
`/=`	`Number` /= `Number`, `Tensor` /= `Tensor`, `Number` /= `Tensor`, `Tensor` /= `Number`, `Tuple` /= `Tensor`, `Tensor` /= `Tuple`, `List` /= `Tensor`, `Tensor` /= `List`.
`%=`	`Number` %= `Number`, `Tensor` %= `Tensor`, `Number` %= `Tensor`, `Tensor` %= `Number`, `Tuple` %= `Tensor`, `Tensor` %= `Tuple`, `List` %= `Tensor`、`Tensor` %= `List`.
`**=`	`Number` = `Number`, `Tensor` = `Tensor`, `Number` = `Tensor`, `Tensor` = `Number`, `Tuple` = `Tensor`, `Tensor` = `Tuple`, `List` = `Tensor`, `Tensor` = `List`.
`//=`	`Number` //= `Number`, `Tensor` //= `Tensor`, `Number` //= `Tensor`, `Tensor` //= `Number`, `Tuple` //= `Tensor`, `Tensor` //= `Tuple`, `List` //= `Tensor`, `Tensor` //= `List`.

Notes:

For = the scenarios below are not allowed:

Only instance of Cell and Primitve can be created in function construct, the statement like xx = Tensor(...) is forbidden.

Only Parameter attribute of self can be assigned, for more detail refer to Attribute Reference.
If all operands of AugAssign are Number type, value of Number can’t be Bool.
If all operands of AugAssign are Number type, operations between Float64 and Int32 are not supported.
If either operand of AugAssign is Tensor type, left and right operands can’t both be Bool value.
The result of List *= Number is concatenate duplicate List Number times, data type of the List must be Number, String, None or List/Tuple that contains these types. This rule applies to Number * List, Tuple * Number, Number * Tuple too.

Logical Operators

Logical Operator	Supported Type
`and`	`String`, `Number`, `Tuple`, `List` , `Dict`, `None`, `Scalar`, `Tensor`.
`or`	`String`, `Number`, `Tuple`, `List` , `Dict`, `None`, `Scalar`, `Tensor`.
`not`	`Number`, `tuple`, `List` and `Tensor` with only one element.

Restrictions:

For operator and, or, if left operand is a Tensor, right operand should be Tensor which has same data type with left operand, and bothTensor must have only one element.
For operator and, or, if left operand not Tensor, right operand can be any supported type.

Compare Operators

Compare Operator	Supported Type
`in`	`Number` in `tuple`, `String` in `tuple`, `Tensor` in `Tuple`, `Number` in `List`, `String` in `List`, `Tensor` in `List`, and `String` in `Dictionary`.
`not in`	Same as `in`.
`is`	The value can only be `None`, `True`, or `False`.
`is not`	The value can only be `None`, `True`, or `False`.
<	`Number` < `Number`, `Number` < `Tensor`, `Tensor` < `Tensor`, `Tensor` < `Number`.
<=	`Number` <= `Number`, `Number` <= `Tensor`, `Tensor` <= `Tensor`, `Tensor` <= `Number`.
>	`Number` > `Number`, `Number` > `Tensor`, `Tensor` > `Tensor`, `Tensor` > `Number`.
>=	`Number` >= `Number`, `Number` >= `Tensor`, `Tensor` >= `Tensor`, `Tensor` >= `Number`.
!=	`Number` != `Number` , `Number` != `Tensor`, `Tensor` != `Tensor`, `Tensor` != `Number`, `mstype` != `mstype`, `String` != `String`, `Tuple !` = `Tuple`, `List` != `List`.
==	`Number` == `Number`, `Number` == `Tensor`, `Tensor` == `Tensor`, `Tensor` == `Number`, `mstype` == `mstype`, `String` == `String`, `Tuple` == `Tuple`, `List` == `List`.

Restrictions:

For operators <, <=, >, >=, !=, if all operators are Number type, value of Number can’t be Bool.
For operators <, <=, >, >=, !=, ==, if all operands are Number type, operations between Float64 and Int32 are not supported.
For operators <, <=, >, >=, !=, ==, if either operand is Tensor type, left and right operands can’t both be Bool value.
For operator ==, if all operands are Number type, support both Number have Bool value, not support only one Number has Bool value.
For operators !=, ==, all supported types but mstype can compare with None.
The chain comparison like: a>b>c is not supported.

Compound Statements

Conditional Control Statements

if Statements

Usage:

if (cond): statements...
x = y if (cond) else z

Parameter: cond – Variables of Bool type and constants of Bool, List, Tuple, Dict and String types are supported.

Restrictions:

If cond is not a constant, the variable or constant assigned to a same sign in different branches should have same data type.If the data type of assigned variables or constants is Tensor, the variables and constants should have same shape and element type.
The number of if cannot exceed 100.

Example 1:

from mindspore import ms_function, Tensor, dtype

x = Tensor([1, 2], dtype.int32)
y = Tensor([0, 3], dtype.int32)
m = 'xx'
n = 'yy'

@ms_function()
def test_cond(x, y):
    if (x > y).any():
        return m
    else:
        return n

ret = test_cond(x, y)
print('ret:{}'.format(ret))if (x > y).any():
  return m
else:
  return n

The data type of m returned by the if branch and n returned by the else branch must be same.

The result is as follows:

ret:xx

Example 2:

from mindspore import ms_function, Tensor, dtype

x = Tensor([1, 2], dtype.int32)
y = Tensor([0, 3], dtype.int32)
m = 'xx'
n = 'yy'

@ms_function()
def test_cond(x, y):
    out = 'init'
    if (x > y).any():
        out = m
    else:
        out = n
    return out

ret = test_cond(x, y)
print('ret:{}'.format(ret))

The variable or constant m assigned to out in if branch and the variable or constant n assigned to out in false branch must have same data type.

The result is as follows:

ret:xx

Example 3:

from mindspore import ms_function, Tensor, dtype

x = Tensor([1, 2], dtype.int32)
y = Tensor([0, 3], dtype.int32)
m = 'xx'

@ms_function()
def test_cond(x, y):
    out = 'init'
    if (x > y).any():
        out = m
    return out

ret = test_cond(x, y)
print('ret:{}'.format(ret))

The variable or constant m assigned to out in if branch and the variable or constant init initially assigned to out must have same data type.

The result is as follows:

ret:xx

Loop Statements

for Statements

Usage:

for i in sequence statements...
for i in sequence statements... if (cond) break
for i in sequence statements... if (cond) continue

Parameter: sequence – Iterative sequences (Tuple, List, range and so on).

Restrictions:

The total number of graph operations is a multiple of number of iterations of the for loop. Excessive number of iterations of the for loop may cause the graph to occupy more memory than usage limit.
The for...else... statement is not supported.
The while statement cannot exist in loop body of the for statement.

Example:

from mindspore import ms_function, Tensor
import numpy as np

z = Tensor(np.ones((2, 3)))

@ms_function()
def test_cond():
    x = (1, 2, 3)
    for i in x:
        z += i
    return z

ret = test_cond()
print('ret:{}'.format(ret))

The result is as follows:

ret:[[7. 7. 7.]
 [7. 7. 7.]]

while Statements

Usage:

while (cond) statements...
while (cond) statements... if (cond1) break
while (cond) statements... if (cond1) continue

Parameter: cond – Variables of Bool type and constants of Bool, List, Tuple, Dict and String types are supported.

Restrictions:

If cond is not a constant, the variable or constant assigned to a same sign inside body of while and outside body of while should have same data type.If the data type of assigned variables or constants is Tensor, the variables and constants should have same shape and element type.
The while...else... statement is not supported.
If cond is not a constant, in while body, the data with type of Number, List, Tuple are not allowed to update and the shape of Tensor data are not allowed to change.
The number of while cannot exceed 100.

Example 1:

from mindspore import ms_function

m = 1
n = 2

@ms_function()
def test_cond(x, y):
    while x < y:
        x += 1
        return m
    return n

ret = test_cond(1, 5)
print('ret:{}'.format(ret))

The data type of m returned inside while and data type of n returned outside while must have same data type.

The result is as follows:

ret:1

Example 2:

from mindspore import ms_function

m = 1
n = 2

def ops1(a, b):
    return a + b

@ms_function()
def test_cond(x, y):
    out = m
    while x < y:
        x += 1
        out = ops1(out, x)
    return out

ret = test_cond(1, 5)
print('ret:{}'.format(ret))

The variable op1 assigned to out inside while and the variable or constant init initially assigned to out must have same data type.

The result is as follows:

ret:15

Function Definition Statements

def Keyword

Defines functions.

Usage:

def function_name(args): statements...

For example:

from mindspore import ms_function

def number_add(x, y):
    return x + y

@ms_function()
def test(x, y):
    return number_add(x, y)

ret = test(1, 5)
print('ret:{}'.format(ret))

The result is as follows:

ret: 6

Restrictions:

The defined function must has return statement.
Construct function of the outermost network is not support kwargs, like:def construct(**kwargs):.
Mixed use of variable argument and non-variable argument is not supported, like:def function(x, y, *args) and def function(x = 1, y = 1, **kwargs).

lambda Expression

Generates functions.

Usage: lambda x, y: x + y

For example:

from mindspore import ms_function

@ms_function()
def test(x, y):
    number_add = lambda x, y: x + y
    return number_add(x, y)

ret = test(1, 5)
print('ret:{}'.format(ret))

The result is as follows:

ret: 6

List Comprehension and Generator Expression

Support List Comprehension and Generator Expression.

List Comprehension

Generates a list. Own to the implicit converting during compiling, the result of expression is a tuple.

Usage: refer to Python official syntax description.

For example:

from mindspore import ms_function

@ms_function()
def test(x, y):
    l = [x * x for x in range(1, 11) if x % 2 == 0]
    return l

ret = test(1, 5)
print('ret:{}'.format(ret))

The result is as follows:

ret:(4, 16, 36, 64, 100)

Restrictions:

Use multiple nested iterations comprehension in the generator.

For example (Use two nested iterations):

l = [y for x in ((1, 2), (3, 4), (5, 6)) for y in x]

The result would be:

TypeError:  The `generators` supports one `comprehension` in ListComp/GeneratorExp, but got 2 comprehensions.

Generator Expression

Generates a list. The same as List Comprehension. The expression would generate a list immediately, not like the behavior running in Python.

Usage: Referencing List Comprehension.

For example:

from mindspore import ms_function

@ms_function()
def test(x, y):
    l = (x * x for x in range(1, 11) if x % 2 == 0)
    return l

ret = test(1, 5)
print('ret:{}'.format(ret))

The result is as follows:

ret:(4, 16, 36, 64, 100)

Restrictions: The same as List Comprehension.

Functions

Python Built-in Functions

Currently, the following built-in Python functions are supported: len, isinstance, partial, map, range, enumerate, super, pow, and filter.

len

Returns the length of a sequence.

Calling: len(sequence)

Input parameter: sequence – Tuple, List, Dictionary, or Tensor.

Return value: length of the sequence, which is of the int type. If the input parameter is Tensor, the length of dimension 0 is returned.

For example:

from mindspore import ms_function, Tensor
import numpy as np

z = Tensor(np.ones((6, 4, 5)))

@ms_function()
def test():
    x = (2, 3, 4)
    y = [2, 3, 4]
    d = {"a": 2, "b": 3}
    x_len = len(x)
    y_len = len(y)
    d_len = len(d)
    z_len = len(z)
    return x_len, y_len, d_len, z_len

x_len, y_len, d_len, z_len = test()
print('x_len:{}'.format(x_len))
print('y_len:{}'.format(y_len))
print('d_len:{}'.format(d_len))
print('z_len:{}'.format(z_len))

The result is as follows:

x_len:3
y_len:3
d_len:2
z_len:6

isinstance

Determines whether an object is an instance of a class. Different from operator Isinstance, the second input parameter of Isinstance is the type defined in the dtype module of MindSpore.

Calling: isinstance(obj, type)

Input parameters:

obj – Any instance of any supported type.
type – A type in the MindSpore dtype module.

Return value: If obj is an instance of type, return True. Otherwise, return False.

For example:

from mindspore import ms_function, Tensor
import numpy as np

z = Tensor(np.ones((6, 4, 5)))

@ms_function()
def test():
    x = (2, 3, 4)
    y = [2, 3, 4]
    x_is_tuple = isinstance(x, tuple)
    y_is_list = isinstance(y, list)
    z_is_tensor = isinstance(z, Tensor)
    return x_is_tuple, y_is_list, z_is_tensor

x_is_tuple, y_is_list, z_is_tensor = test()
print('x_is_tuple:{}'.format(x_is_tuple))
print('y_is_list:{}'.format(y_is_list))
print('z_is_tensor:{}'.format(z_is_tensor))

The result is as follows:

x_is_tuple:True
y_is_list:True
z_is_tensor:True

partial

A partial function used to fix the input parameter of the function.

Calling: partial(func, arg, ...)

Input parameters:

func –Function.
arg – One or more parameters to be fixed. Position parameters and key-value pairs can be specified.

Return value: functions with certain input parameter values fixed

For example:

from mindspore import ms_function, ops

def add(x, y):
    return x + y

@ms_function()
def test():
    add_ = ops.partial(add, x=2)
    m = add_(y=3)
    n = add_(y=5)
    return m, n

m, n = test()
print('m:{}'.format(m))
print('n:{}'.format(n))

The result is as follows:

m:5
n:7

map

Maps one or more sequences based on the provided functions and generates a new sequence based on the mapping result. If the number of elements in multiple sequences is inconsistent, the length of the new sequence is the same as that of the shortest sequence.

Calling: map(func, sequence, ...)

Input parameters:

func –Function.
sequence – One or more sequences (Tuple or List).

Return value: A Tuple

For example:

from mindspore import ms_function

def add(x, y):
    return x + y

@ms_function()
def test():
    elements_a = (1, 2, 3)
    elements_b = (4, 5, 6)
    ret = map(add, elements_a, elements_b)
    return ret

ret = test()
print('ret:{}'.format(ret))

The result is as follows:

ret: (5, 7, 9)

zip

Packs elements in the corresponding positions in multiple sequences into tuples, and then uses these tuples to form a new sequence. If the number of elements in each sequence is inconsistent, the length of the new sequence is the same as that of the shortest sequence.

Calling: zip(sequence, ...)

Input parameter: sequence – One or more sequences (Tuple or List)`.

Return value: A Tuple

For example:

from mindspore import ms_function

@ms_function()
def test():
    elements_a = (1, 2, 3)
    elements_b = (4, 5, 6)
    ret = zip(elements_a, elements_b)
    return ret

ret = test()
print('ret:{}'.format(ret))

The result is as follows:

ret:((1, 4), (2, 5), (3, 6))

range

Creates a Tuple based on the start value, end value, and step.

Calling:

range(start, stop, step)
range(start, stop)
range(stop)

Input parameters:

start – start value of the count. The type is int. The default value is 0.
stop – end value of the count (exclusive). The type is int.
step – Step. The type is int. The default value is 1.

Return value: A Tuple

For example:

from mindspore import ms_function

@ms_function()
def test():
    x = range(0, 6, 2)
    y = range(0, 5)
    z = range(3)
    return x, y, z

x, y, z = test()
print('x:{}'.format(x))
print('y:{}'.format(y))
print('z:{}'.format(z))

The result is as follows:

x:(0, 2, 4)
y:(0, 1, 2, 3, 4)
z:(0, 1, 2)

enumerate

Generates an index sequence of a sequence. The index sequence contains data and the corresponding subscript.

Calling:

enumerate(sequence, start)
enumerate(sequence)

Input parameters:

sequence – A sequence (Tuple, List, or Tensor).
start – Start position of the subscript. The type is int. The default value is 0.

Return value: A Tuple

For example:

from mindspore import ms_function, Tensor
import numpy as np

y = Tensor(np.array([[1, 2], [3, 4], [5, 6]]))

@ms_function()
def test():
    x = (100, 200, 300, 400)
    m = enumerate(x, 3)
    n = enumerate(y)
    return m, n

m, n = test()
print('m:{}'.format(m))
print('n:{}'.format(n))

The result is as follows:

m:((3, 100), (4, 200), (5, 300), (6, 400))
n:((0, Tensor(shape=[2], dtype=Int64, value= [1, 2])), (1, Tensor(shape=[2], dtype=Int64, value= [3, 4])), (2, Tensor(shape=[2], dtype=Int64, value= [5, 6])))

super

Calls a method of the parent class (super class). Generally, the method of the parent class is called after super.

Calling:

super().xxx()
super(type, self).xxx()

Input parameters:

type –Class.
self –Object.

Return value: method of the parent class.

For example:

from mindspore import nn

class FatherNet(nn.Cell):
    def __init__(self, x):
        super(FatherNet, self).__init__(x)
        self.x = x

    def construct(self, x, y):
        return self.x * x

    def test_father(self, x):
        return self.x + x

class SingleSubNet(FatherNet):
    def __init__(self, x, z):
        super(SingleSubNet, self).__init__(x)
        self.z = z

    def construct(self, x, y):
        ret_father_construct = super().construct(x, y)
        ret_father_test = super(SingleSubNet, self).test_father(x)
        return ret_father_construct, ret_father_test

pow

Returns the power.

Calling: pow(x, y)

Input parameters:

x – Base number, Number, or Tensor.
y – Power exponent, Number, or Tensor.

Return value: y power of x, Number, or Tensor

For example:

from mindspore import ms_function, Tensor
import numpy as np

x = Tensor(np.array([1, 2, 3]))
y = Tensor(np.array([1, 2, 3]))

@ms_function()
def test(x, y):
    return pow(x, y)

ret = test(x, y)

print('ret:{}'.format(ret))

The result is as follows:

ret:[ 1  4 27]

print

Prints logs.

Calling: print(arg, ...)

Input parameter: arg – Information to be printed (int, float, bool, String or Tensor). When the arg is int, float, or bool, it will be printed out as a 0-D tensor.

Return value: none

For example:

from mindspore import ms_function, Tensor, dtype
import numpy as np

x = Tensor(np.array([1, 2, 3]), dtype.int32)
y = Tensor(3, dtype.int32)

@ms_function()
def test(x, y):
    print(x)
    print(y)
    return x, y

ret = test(x, y)

The result is as follows:

Tensor(shape=[3], dtype=Int32, value= [1 2 3])
3

filter

According to the provided function to judge the elements of a sequence. Each element is passed into the function as a parameter in turn, and the elements whose return result is not 0 or False form a new sequence.

Calling: filter(func, sequence)

Input parameters:

func – Function.
sequence – A sequence (Tuple or List).

Return value: A Tuple.

For example:

from mindspore import ms_function

def is_odd(x):
    if x % 2:
        return True
    return False

@ms_function()
def test():
    elements = (1, 2, 3, 4, 5)
    ret = filter(is_odd, elements)
    return ret

ret = test()
print('ret:{}'.format(ret))

The result is as follows:

ret:(1, 3, 5)

Function Parameters

Default parameter value: The data types int, float, bool, None, str, tuple, list, and dict are supported, whereas Tensor is not supported.
Variable parameters: Inference and training of networks with variable parameters are supported.
Key-value pair parameter: Functions with key-value pair parameters cannot be used for backward propagation on computational graphs.
Variable key-value pair parameter: Functions with variable key-value pairs cannot be used for backward propagation on computational graphs.

Network Definition

Network Input parameters

The input parameters of the outermost network only can be lool, int, float, Tensor, None, mstype.number(mstype.bool, mstype.int, mstype.float, mstype.uint), List or Tuple that contains these types, and Dictionary whose values are these types.

While calculating gradient for outermost network, only Tensor input could be calculated, input of other type will be ignored. For example, input parameter (x, y, z) of outermost network, x and z are Tensor type, y is other type. While calculating gradient for the network, only gradients of x and z are calculated, and (grad_x, grad_y) is returned. If you want to use other types of input for the network, please transfer them to the network while initializing network in the __init__ function, and save them as network attributes, then use in the construct.

The input parameters of inner network do not have this restriction.

For example:

from mindspore import nn, ops, Tensor
import numpy as np

class Net(nn.Cell):
    def __init__(self, flag):
        super(Net, self).__init__()
        self.flag = flag

    def construct(self, x, y, z):
        if self.flag == "ok":
            return x + y + z
        return x - y - z

class GradNet(nn.Cell):
    def __init__(self, net):
        super(GradNet, self).__init__()
        self.grad_all = ops.GradOperation(get_all=True)
        self.forward_net = net

    def construct(self, x, y, z):
        return self.grad_all(self.forward_net)(x, y, z)

flag = "ok"
input_x = Tensor(np.ones((2, 3)).astype(np.float32))
input_y = 2
input_z = Tensor(np.ones((2, 3)).astype(np.float32) * 2)

net = Net(flag)
grad_net = GradNet(net)
ret = grad_net(input_x, input_y, input_z)

print('ret:{}'.format(ret))

The result is as follows:

ret:(Tensor(shape=[2, 3], dtype=Float32, value=
[[ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00],
 [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00]]), Tensor(shape=[2, 3], dtype=Float32, value=
[[ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00],
 [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00]]))

In the Net defined above, string flag is transferred during initialization and saved as attribute self.flag, then used in the construct. The input parameter x and z are Tensor, y is int. While grad_net calculates gradient of the input parameters for the outermost network, gradient of y is automatically ignored, only the gradient of x and z is calculated, ret = (grad_x, grad_z).

Instance Types on the Entire Network

Common Python function with the @ms_function decorator.
Cell subclass inherited from nn.Cell.

Network Construction Components

Category	Content
`Cell` instance	mindspore/nn/* and user-defined Cell.
Member function of a `Cell` instance	Member functions of other classes in the construct function of Cell can be called.
`dataclass` instance	Class decorated with @dataclass.
`Primitive` operator	mindspore/ops/operations/*
`Composite` operator	mindspore/ops/composite/*
`constexpr` generation operator	Value computation operator generated by @constexpr.
Function	User-defined Python functions and system functions listed in the preceding content.

Network Constraints

You are not allowed to modify non-Parameter data members of the network.

For example:

from mindspore import nn, Tensor, Parameter
import numpy as np

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.num = 2
        self.par = Parameter(Tensor(np.ones((2, 3, 4))), name="par")

    def construct(self, x, y):
        self.par[0] = y
        self.x = x
        return x + y

net = Net()
net(1, 2)

In the preceding defined network, self.num is not a Parameter and cannot be modified. self.par is a Parameter and can be modified.

The result would be:

TypeError: mindspore/ccsrc/pipeline/jit/parse/parse.cc:1740 HandleAssignClassMember] 'self.x' should be initialized as a 'Parameter' in the '__init__' function before assigning.

When an undefined class member is used in the construct function, AttributeError is not thrown like the Python interpreter. Instead, it is processed as None.

For example:

from mindspore import nn

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()

    def construct(self, x):
        return x + self.y

net = Net()
net(1)

In the preceding defined network, construct uses the undefined class member self.y. In this case, self.y is processed as None.

The result would be:

RuntimeError: mindspore/ccsrc/frontend/operator/composite/multitype_funcgraph.cc:161 GenerateFromTypes] The 'add' operation does not support the type [Int64, kMetaTypeNone]