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, the function, and class (including Cell, Cell subclass, @jit_class decorated class, and common user defined class) methods modified by the @jit 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 jit, click jit API document.

For details about the definition of Cell, click Cell API document.

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.

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.

Use str() to change the constant value to string, str.format() can use to format the string, but not supported to input a kwargs type arguments and the argument of format function cannot be a variable.

For example:

import mindspore as ms

@ms.jit()
def test_str_format():
    x = "{} is zero".format(0)
    return x

x = test_str_format()
print(x)

The result is as follows:

0 is zero

List

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

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

When using List index to get the element，the reference type between MindSpore and Python interpreter may be different. Due to MindSpore using ListGetItem to implement getting value of the list, and the operator ListGetItem will return a copy of the variable, that make the reference type may not same with Python interpreter.

For example:

Python：

>>> a = [[1,2,3],4,5]
>>> b = a[0]
>>> b[0] = 123123
>>> a
[[123123, 2, 3], 4, 5]

MindSpore:

import mindspore as ms

@ms.jit
def test_list():
    a = [[1,2,3],4,5]
    b = a[0]
    b[0] = 123123
    return a

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

The result is as follows:

x: ((1, 2, 3), 4, 5)

Supported APIs

append: adds an element to list.

For example:

import mindspore as ms

@ms.jit()
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)

insert: inserts the specified element at the specified position in the list.

For example:

import mindspore as ms

@ms.jit()
def test_list_insert():
    x = [1, 3, 4]
    x.insert(0, 2)
    return x

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

The result is as follows:

x: (2, 1, 3, 4)

pop: removes the element at the specified position in list, removing the last one by default.

For example:

import mindspore as ms

@ms.jit()
def test_list_pop():
    x = [1, 3, 4]
    y = x.pop()
    return x, y

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

The result is as follows:

x: (1, 3)
y: 4

clear: clears the elements in list.

For example:

import mindspore as ms

@ms.jit()
def test_list_clear():
    x = [1, 3, 4]
    x.clear()
    return x

x = test_list_clear()

The result is as follows:

x: ()

extend: appends multiple elements of another sequence to the end of list.

For example:

import mindspore as ms

@ms.jit()
def test_list_extend():
    x = [1, 2, 3, 4]
    y = [5, 6, 7]
    x.extend(y)
    return x

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

The result is as follows:

x: (1, 2, 3, 4, 5, 6, 7)

reverse: reverses the elements of list.

For example:

import mindspore as ms

@ms.jit()
def test_list_reverse():
    x = [1, 2, 3, 4]
    x.reverse()
    return x

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

The result is as follows:

x: (4, 3, 2, 1)

count: counts the number of occurrences of an element in list. The current count method only supports constant scenarios.

For example:

import mindspore as ms

@ms.jit()
def test_list_count():
    x = [1, 2, 3, 4]
    num = x.count(2)
    return num

num = test_list_count()
print('num:', num)

The result is as follows:

num: 1

If there is a Tensor variable in the usage scenario of count, a related exception will be thrown.

For example:

import mindspore as ms

@ms.jit()
def test_list_count(input_x):
    x = [1, 2, 3, 4]
    num = x.count(input_x)
    return num

input_x = ms.Tensor(2)
num = test_list_count()
print('num:', num)

The result is as follows:

The list count not support variable scene now. The count data is Tensor type.

Supported index values and value assignment

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

The index value supports only int and slice.

The element of slice data should be constant that can be deduced in the state of compiling graph.

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

When the value of the current slice is Tensor, the Tensor needs to be converted to a List, which is currently implemented through JIT Fallback. Therefore, variable scenarios cannot be supported temporarily.

For example:

import numpy as np
import mindspore as ms

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

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

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

The result is as follows:

m:2
z:[2, 4]
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.

The reference type of tuple is same as List, please refer to List.

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:

import numpy as np
import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms
from mindspore import nn, set_context

set_context(mode=ms.GRAPH_MODE)

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 = ms.Tensor([-1.0], ms.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, the key can be String, Number, constant Tensor, or Tuple that contains these types. The value can be Number, Tuple, Tensor, List or Dictionary. It should be noted that if the final output of the computational graph contains a Dictionary, it will not return a Dictionary, but a Tuple composed of all value values of the Dictionary.

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 it.

get: dict.get(key[, value]) returns the value value corresponding to the specified key, if the specified key does not exist, the default value None or the set default value value is returned .

clear: removes all elements in dict.

has_key: dict.has_key(key) determines whether the specified key exists in dict.

update: dict1.update(dict2) updates the elements in dict2 to dict1.

fromkeys: dict.fromkeys(seq([, value])) is used to create a new Dictionary, using the elements in the sequence seq as the key of the Dictionary, and the value is initial value corresponding to all key.

For example:

import mindspore as ms
import numpy as np

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

@ms.jit()
def test_dict():
    x_keys = x.keys()
    x_values = x.values()
    x_items = x.items()
    value_a = x.get("a")
    check_key = x.has_key("a")
    y = {"a": ms.Tensor(np.array([0, 0, 0]))}
    x.update(y)
    new_dict = x.fromkeys("abcd", 123)
    return x_keys, x_values, x_items, value_a, check_key, x, new_dict

x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = test_dict()
print('x_keys:{}'.format(x_keys))
print('x_values:{}'.format(x_values))
print('x_items:{}'.format(x_items))
print('value_a:{}'.format(value_a))
print('check_key:{}'.format(check_key))
print('new_x:{}'.format(new_x))
print('new_dict:{}'.format(new_dict))

The result is as follows:

x_keys:('a', 'b', 'c')
x_values:(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]))
x_items:(('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])))
value_a:[1 2 3]
check_key: True
new_x: {'a': ms.Tensor(np.array([0, 0, 0])), 'b': ms.Tensor(np.array([4, 5, 6])), 'c': ms.Tensor(np.array([7, 8, 9]))}
new_dict: {'a': 123, 'b': 123, 'c': 123, 'd': 123}

Supported index values and value assignment

For example:

import mindspore as ms
import numpy as np

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

@ms.jit()
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 can be constructed in the network.

For details of Tensor, click Tensor API document.

Primitive

Currently, Primitive and its subclass instances can be constructed in construct.

However, during call, the parameter can be specified only in position parameter mode, and cannot be specified in the key-value pair mode.

For example:

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

set_context(mode=ms.GRAPH_MODE)

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

    def construct(self, x):
        reduce_sum = ops.ReduceSum(True) #`Primitive` and its subclass instances can be constructed in construct.
        ret = reduce_sum(x, axis=2)
        return ret

x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
net = Net()
ret = net(x)
print('ret.shape:{}'.format(ret.shape))

In the network defined above, the parameters of reduce_sum(x, axis=2) cannot be specified in the key-value pair mode. the parameter can be specified only in position parameter mode, that is, reduce_sum(x, 2).

The error is reported as follows:

TypeError: Only supported positional parameter type for python primitive, but got keyword parameter type.

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

For details about the defined Primitive, click Primitive API document.

Cell

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

However, during call, 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 Cell API document.

Parameter

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

For details about the definition of Parameter, click Parameter API document.

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:

import mindspore as ms
from mindspore import nn, set_context
import numpy as np
from mindspore.ops import constexpr

set_context(mode=ms.GRAPH_MODE)

class Net(nn.Cell):
    def __init__(self):
        super().__init__()
        self.weight = ms.Parameter(ms.Tensor(3, ms.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 Tensor index value document.

Calls

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

For example:

import mindspore as ms
from mindspore import nn, ops, set_context
import numpy as np

set_context(mode=ms.GRAPH_MODE)

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 = ms.Tensor(np.ones(shape=[1, 3]), ms.float32)
y = ms.Tensor(np.ones(shape=[3, 4]), ms.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.

For details about the rules, click conversion-rules.

Unary Arithmetic Operators

Unary Arithmetic Operator	Supported Type
`+`	`Number`, `Tensor`
`-`	`Number`, `Tensor`, `COOTensor`, `CSRTensor`
`~`	`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`, `COOTensor` + `Tensor`, `Tensor` + `COOTensor`, `COOTensor` + `COOTensor`, `CSRTensor` + `CSRTensor`.
`-`	`Number` - `Number`, `Tensor` - `Tensor`, `Number` -`Tensor`, `Tensor` - `Number`, `Tuple` -`Tensor`, `Tensor` -`Tuple`, `List` -`Tensor`, `Tensor` -`List`, `COOTensor` - `Tensor`, `Tensor` - `COOTensor`, `COOTensor` - `COOTensor`, `CSRTensor` - `CSRTensor`.
`*`	`Number` * `Number`, `Tensor` * `Tensor`, `Number` * `Tensor`, `Tensor` * `Number`, `List` * `Number`, `Number` * `List`, `Tuple` * `Number`, `Number` * `Tuple`, `Tuple` * `Tensor`, `Tensor` * `Tuple`, `List` `Tensor`, `Tensor` `List`, `COOTensor` * `Tensor`, `Tensor` * `COOTensor`, `CSRTensor` * `Tensor`, `Tensor` * `CSRTensor`.
`/`	`Number` / `Number`, `Tensor` / `Tensor`, `Number` / `Tensor`, `Tensor` / `Number`, `Tuple` / `Tensor`, `Tensor` / `Tuple`, `List` / `Tensor`, `Tensor` / `List`, `COOTensor` / `Tensor`, `CSRTensor` / `Tensor`.
`%`	`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`.
`∣`	`Number` \| `Number`、`Tensor` \| `Tensor`、`Number` \| `Tensor`、`Tensor` \| `Number`.
`^`	`Number` ^ `Number`、`Tensor` ^ `Tensor`、`Number` ^ `Tensor`、`Tensor` ^ `Number`.
`<<`	`Number` << `Number`.
`>>`	`Number` >> `Number`.

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`.
`&=`	`Number` &= `Number`、`Tensor` &= `Tensor`、`Number` &= `Tensor`、`Tensor` &= `Number`.
`∣=`	`Number` \|= `Number`、`Tensor` \|= `Tensor`、`Number` \|= `Tensor`、`Tensor` \|= `Number`.
`^=`	`Number` ^= `Number`、`Tensor` ^= `Tensor`、`Number` ^= `Tensor`、`Tensor` ^= `Number`.
`<<=`	`Number` <<= `Number`.
`>>=`	`Number` >>= `Number`.

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:

The left operand of operator and, or must be able to be converted to boolean value. For example, left operand can not be Tensor with multiple elements. If the left operand of and, or is variable Tensor, the right operand must also be single-element Tensor with the same type. Otherwise, there is no requirement for right operand.
If the left or right operand of and, or is object that the graph mode does not support (such as third-party object and object created by syntax that is not native-supported in the graph mode), both operands need to be constant.

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. For shape consistency restrictions, please refer to ShapeJoin Rules.

Example 1:

import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms

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

@ms.jit()
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.

Example:

import numpy as np
import mindspore as ms

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

@ms.jit()
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. For shape consistency restrictions, please refer to ShapeJoin Rules.
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.

Example 1:

import mindspore as ms

m = 1
n = 2

@ms.jit()
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:

import mindspore as ms

m = 1
n = 2

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

@ms.jit()
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:

import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms

@ms.jit()
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:

import mindspore as ms

@ms.jit()
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:

import mindspore as ms

@ms.jit()
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.

With Statement

In graph mode, the with statement is supported with limitations. The with statement requires that the object must have two magic methods: __enter__() and __exit__().

For example:

import mindspore as ms
import mindspore.nn as nn
from mindspore import set_context

set_context(mode=ms.GRAPH_MODE)

@ms.jit_class
class Sample:
    def __init__(self):
        super(Sample, self).__init__()
        self.num = ms.Tensor([2])

    def __enter__(self):
        return self.num * 2

    def __exit__(self, exc_type, exc_value, traceback):
        return self.num * 4

class TestNet(nn.Cell):
    def construct(self):
        res = 1
        obj = Sample()
        with obj as sample:
            res += sample
        return res, obj.num

test_net = TestNet()
out1, out2 = test_net()
print("out1:", out1)
print("out2:", out2)

The result is as follows:

out1: [5]
out2: [2]

Functions

Python Built-in Functions

Currently, the following built-in Python functions are supported: int, float, bool, str, list, tuple, getattr, hasattr, len, isinstance, all, round, any, max, min, sum, abs, partial, map, range, enumerate, super, pow, and filter. The usage of built-in function is similar to the usage of corresponding Python built-in function.

int

Return the integer value based on the input number or string.

Calling: int(x=0, base=10)

Input parameter:

x – the object need to be converted to integer, the valid type of x includes int, float, bool, str, constant Tensor and third-party object (such as numpy.ndarray).
base – the base to convert. base is only allowed when x is str.

Return value: the converted integer.

For example:

import mindspore as ms

@ms.jit
def func():
   a = int(3)
   b = int(3.6)
   c = int('12', 16)
   d = int('0xa', 16)
   e = int('10', 8)
   return a, b, c, d, e

a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a: 3
b: 3
c: 18
d: 10
e: 8

float

Return the floating-point number based on the input number or string.

Calling: float(x=0)

Input parameter: x – the object need to be converted to floating number, the valid type of x includes int, float, bool, str, constant Tensor and third-party object (such as numpy.ndarray).

Return value: the converted floating-point number.

For example:

import mindspore as ms

@ms.jit
def func():
   a = float(1)
   b = float(112)
   c = float(-123.6)
   d = float('123')
   return a, b, c, d

a, b, c, d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)

The result is as follows:

a: 1.0
b: 112.0
c: -123.6
d: 123.0

bool

Return the boolean value based on the input.

Calling: bool(x=false)

Input parameter: x – the object need to be converted to boolean value, the valid type of x includes int, float, bool, str, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: if x is not Tensor, returns the converted boolean scalar. Otherwise, returns boolean Tensor.

For example:

import mindspore as ms

@ms.jit
def func():
   a = bool()
   b = bool(0)
   c = bool("abc")
   d = bool([1, 2, 3, 4])
   e = bool(ms.Tensor([10]))
   return a, b, c, d, e

a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a: False
b: False
c: True
d: True
e: [True]    # e is boolean Tensor

str

Return the string value based on the input.

Calling: str(x='')

Input parameter: x – the object need to be converted to string value, the valid type of x includes int, float, bool, str, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray). list, tuple and dict can not contain non-constant element.

Return value: string converted from x.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = str()
   b = str(0)
   c = str([1, 2, 3, 4])
   d = str(ms.Tensor([10]))
   e = str(np.array([1, 2, 3, 4]))
   return a, b, c, d, e

a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)

The result is as follows:

a:                                             # a is empty string
b: 0
c: [1, 2, 3, 4]
d: Tensor(shape=[1], dtype=Int64, value=[10])
e: [1 2 3 4]

tuple

Return a tuple based on the input object.

Calling: tuple(x=())

Input parameter: x – the object that need to be converted to tuple, the valid type of x includes Tuple, List, Dictionary, Tensor or third-party object (such as numpy.ndarray).

Return value: tuple with elements of x, x is cut based on zero dimension.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = tuple((1, 2, 3))
   b = tuple(np.array([1, 2, 3]))
   c = tuple({'a': 1, 'b': 2, 'c': 3})
   d = tuple(ms.Tensor([1, 2, 3]))
   return a, b, c ,d

a, b, c ,d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)

The result is as follows:

a: (1, 2, 3)
b: (1, 2, 3)
c: ('a', 'b', 'c')
d: (Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3))

list

Return a list based on the input object.

Calling: list(x=())

Input parameter: x – the object that need to be converted to list, the valid type of x includes Tuple, List, Dictionary, Tensor or third-party object (such as numpy.ndarray).

Return value: list with elements of x, x is cut based on zero dimension.

For example:

import mindspore as ms

@ms.jit
def func():
   a = list((1, 2, 3))
   b = list(np.array([1, 2, 3]))
   c = list({'a':1, 'b':2, 'c':3})
   d = list(ms.Tensor([1, 2, 3]))
   return a, b, c, d
a_t, b_t, c_t, d_t = func()
print("a_t: ", a_t)
print("b_t: ", b_t)
print("c_t: ", c_t)
print("d_t: ", d_t)

The result is as follows:

a_t: (1, 2, 3)
b_t: (1, 2, 3)
c_t: ('a', 'b', 'c')
d_t: (Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3))

In graph mode, if the return values contain list, the list will be converted to tuple automatically. So the a_t, b_t, c_t and d_t in the above example are all tuple. However, a, b, c and d are still list.

getattr

Get the attribute of python object.

Calling: getattr(x, attr, default)

Input parameter:

x – The object to get attribute, x can be all types that graph mode supports. x can not be third-party object.
attr – The name of the attribute, the type of attr should be str.
default – Optional input. If x do not have attr, getattr will return default. default can be all types that graph mode supports but can not be third-party object. If default is not set and x does not have attribute attr, AttributeError will be raised.

Return value: Target attribute or default.

For example:

import mindspore as ms

@ms.jit_class
class MSClass1:
  def __init__(self):
    self.num0 = 0

ms_obj = MSClass1()

@ms.jit
def func():
   a = getattr(ms_obj, 'num0')
   b = getattr(ms_obj, 'num1', 2)
   return a, b

a, b = func()
print("a: ", a)
print("b: ", b)

The result is as follows:

a: 0
b: 2

The attribute of object in graph mode may be different from that in pynative mode. It is suggested to use default input in getattr or call hasattr before using getattr to avoid AttributeError.

hasattr

Judge whether an object has an attribute.

Calling: hasattr(x, attr)

Input parameter:

x – The object to get attribute, x can be all types that graph mode supports and also can be third-party object.
attr – The name of the attribute, the type of attr should be str.

Return value: boolean value indicates whether x has attr.

For example:

import mindspore as ms

@ms.jit_class
class MSClass1:
  def __init__(self):
    self.num0 = 0

ms_obj = MSClass1()

@ms.jit
def func():
   a = hasattr(ms_obj, 'num0')
   b = hasattr(ms_obj, 'num1')
   return a, b

a, b = func()
print("a: ", a)
print("b: ", b)

The result is as follows:

a: True
b: False

len

Return the length of a sequence.

Calling: len(sequence)

Input parameter: sequence – Tuple, List, Dictionary, Tensor or third-party object (such as numpy.ndarray).

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:

import mindspore as ms
import numpy as np

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

@ms.jit()
def test():
    x = (2, 3, 4)
    y = [2, 3, 4]
    d = {"a": 2, "b": 3}
    n = np.array([1, 2, 3, 4])
    x_len = len(x)
    y_len = len(y)
    d_len = len(d)
    z_len = len(z)
    n_len = len(n)
    return x_len, y_len, d_len, z_len, n_len

x_len, y_len, d_len, z_len, n_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))
print('n_len:{}'.format(n_len))

The result is as follows:

x_len:3
y_len:3
d_len:2
z_len:6
n_len:4

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:

import mindspore as ms
import numpy as np

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

@ms.jit()
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, ms.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

all

Judge whether all of the elements in the input is true.

Calling: all(x)

Input parameter: - x – Iterable object, the valid types include list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, indicates whether all of the elements in the input is true.

For example:

import mindspore as ms

@ms.jit
def func():
   a = all(['a', 'b', 'c', 'd'])
   b = all(['a', 'b', '', 'd'])
   c = all([0, 1, 2, 3])
   d = all(('a', 'b', 'c', 'd'))
   e = all(('a', 'b', '', 'd'))
   f = all((0, 1, 2, 3))
   g = all([])
   h = all(())
   return a, b, c, d, e, f, g, h

a, b, c, d, e, f, g, h = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)

The result is as follows:

a: True
b: False
c: False
d: True
e: False
f: False
g: True
h: True

any

Judge whether any of the elements in the input is true.

Calling: any(x)

Input parameter: - x – Iterable object, the valid types include list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, indicates whether any of the elements in the input is true.

For example:

import mindspore as ms

@ms.jit
def func():
   a = any(['a', 'b', 'c', 'd'])
   b = any(['a', 'b', '', 'd'])
   c = any([0, '', False])
   d = any(('a', 'b', 'c', 'd'))
   e = any(('a', 'b', '', 'd'))
   f = any((0, '', False))
   g = any([])
   h = any(())
   return a, b, c, d, e, f, g, h

a, b, c, d, e, f, g, h = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)

The result is as follows:

a: True
b: True
c: False
d: True
e: True
f: False
g: False
h: False

round

Return the rounding value of input.

Calling: round(x, digit=0)

Input parameter:

x – the object to rounded, the valid types include int, float, bool, Tensor and third-party object that defines magic function __round__().
digit – the number of decimal places to round, the default value is 0. digit can be int object or None. If x is Tensor, then round() does not support input digit.

Return value: the value after rounding.

For example:

import mindspore as ms

@ms.jit
def func():
   a = round(10)
   b = round(10.123)
   c = round(10.567)
   d = round(10, 0)
   e = round(10.72, -1)
   f = round(17.12, -1)
   g = round(10.17, 1)
   h = round(10.12, 1)
   return a, b, c, d, e, f, g, h

a, b, c, d, e, f, g, h = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: {:.2f}".format(e))
print("f: {:.2f}".format(f))
print("g: {:.2f}".format(g))
print("h: {:.2f}".format(h))

The result is as follows:

a: 10
b: 10
c: 11
d: 10
e: 10.00
f: 20.00
g: 10.20
h: 10.10

max

Return the maximum of inputs.

Calling: max(*data)

Input parameter: - *data – If *data is single input, max will compare all elements within data and data must be iterable object. If there are multiple inputs, then max() will compare each of them. The valid types of data include int, float, bool, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, the maximum of the inputs.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
   a = max([0, 1, 2, 3])
   b = max((0, 1, 2, 3))
   c = max({1: 10, 2: 20, 3: 3})
   d = max(np.array([1, 2, 3, 4]))
   e = max(('a', 'b', 'c'))
   f = max((1, 2, 3), (1, 4))
   g = max(ms.Tensor([1, 2, 3]))
   return a, b, c, ms.Tensor(d), e, f, g

a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)

The result is as follows:

a: 3
b: 3
c: 3
d: 4
e: c
f: (1, 4)
g: 3

min

Return the minimum of inputs.

Calling: min(*data)

Input parameter: - *data – If *data is single input, then min() will compare all elements within data and data must be iterable object. If there are multiple inputs, then min() will compare each of them. The valid types of data include int, float, bool, list, tuple, dict, Tensor and third-party object (such as numpy.ndarray).

Return value: boolean, the minimum of the inputs.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
  a = min([0, 1, 2, 3])
  b = min((0, 1, 2, 3))
  c = min({1: 10, 2: 20, 3: 3})
  d = min(np.array([1, 2, 3, 4]))
  e = min(('a', 'b', 'c'))
  f = min((1, 2, 3), (1, 4))
  g = min(ms.Tensor([1, 2, 3]))
  return a, b, c, ms.Tensor(d), e, f, g

a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)

The result is as follows:

a: 0
b: 0
c: 1
d: 1
e: a
f: (1, 2, 3)
g: 1

sum

Return the sum of input sequence.

Calling: sum(x, n=0)

Input parameter:

x – iterable with numbers, the valid types include list, tuple, Tensor and third-party object (such as numpy.ndarray).
n – the number that will be added to the sum of x, which is assumed to be 0 if not given.

Return value: the value obtained by summing x and adding it to n.

For example:

import numpy as np
import mindspore as ms

@ms.jit
def func():
  a = sum([0, 1, 2])
  b = sum((0, 1, 2), 10)
  c = sum(np.array([1, 2, 3]))
  d = sum(ms.Tensor([1, 2, 3]), 10)
  e = sum(ms.Tensor([[1, 2], [3, 4]]))
  f = sum([1, ms.Tensor([[1, 2], [3, 4]]), ms.Tensor([[1, 2], [3, 4]])], ms.Tensor([[1, 1], [1, 1]]))
  return a, b, ms.Tensor(c), d, e, f

a, b, c, d, e, f = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)

The result is as follows:

a:  3
b:  13
c:  6
d:  16
e:  [4 6]
f:  [[ 4  6]
     [ 8 10]]

abs

Return the absolute value of the input. The usage of abs() is the same as python built-in function abs().

Calling: abs(x)

Input parameter: - x – The valid types of x include int, float, bool, complex, Tensor and third-party object (such as numpy.ndarray).

Return value: the absolute value of the input.

For example:

import mindspore as ms

@ms.jit
def func():
   a = abs(-45)
   b = abs(100.12)
   return a, b

a, b = func()
print("a: ", a)
print("b: {:.2f}".format(b))

The result is as follows:

a: 45
b: 100.12

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 ops
import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms

@ms.jit()
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:

import mindspore as ms

@ms.jit()
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:

import mindspore as ms
import numpy as np

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

@ms.jit()
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:

import mindspore as ms
from mindspore import nn, set_context

set_context(mode=ms.GRAPH_MODE)

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

Return 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:

import mindspore as ms
import numpy as np

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

@ms.jit()
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:

import mindspore as ms
import numpy as np

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

@ms.jit()
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:

import mindspore as ms

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

@ms.jit()
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:

import mindspore as ms
from mindspore import nn, ops, set_context
import numpy as np

set_context(mode=ms.GRAPH_MODE)

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.forward_net = net

    def construct(self, x, y, z):
        return ms.grad(self.forward_net, grad_position=(0, 1, 2))(x, y, z)

flag = "ok"
input_x = ms.Tensor(np.ones((2, 3)).astype(np.float32))
input_y = 2
input_z = ms.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 (x, y, z) 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 @jit 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.
`jit_class` instance	Class decorated with @jit_class.
`Composite` 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:

import mindspore as ms
from mindspore import nn, set_context
import numpy as np

set_context(mode=ms.GRAPH_MODE)

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.x = 2
        self.par = ms.Parameter(ms.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.x is not a Parameter and cannot be modified. self.par is a Parameter and can be modified.

The result would be:

TypeError: 'self.x' should be initialized as a 'Parameter' type in the '__init__' function

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, set_context

set_context(mode=ms.GRAPH_MODE)

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]

Class methods modified by classmethod in nn.Cell are not supported.