tutorials/source_en/compile/statements.md · MindSpore/docs

# Graph Mode Syntax - Python Statements

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## Simple Statements

### raise Statements

Support the use of `raise` to trigger an exception. `raise` syntax format: `raise[Exception [, args]]`. The `Exception` in the statement is the type of the exception, and the `args` is the user-supplied argument to the exception, usually a string or other object. The following types of errors are supported: NoExceptionType, UnknownError, ArgumentError, NotSupportError, NotExistsError, DeviceProcessError, AbortedError, IndexError, ValueError, TypeError, KeyError, AttributeError, NameError, AssertionError, BaseException, KeyboardInterrupt, Exception, StopIteration, OverflowError, ZeroDivisionError, EnvironmentError, IOError, OSError, ImportError, MemoryError, UnboundLocalError, RuntimeError, NotImplementedError, IndentationError, RuntimeWarning.

It is worth noting that the ability of the raise statement in the variable scenario in static graph mode is supported through extended syntax, that is, it needs to be supported only when the JIT syntax support level option 'jit_syntax_level' is 'LAX'.

For example:

```python
import mindspore
from mindspore import nn

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

@mindspore.jit
    def construct(self, x, y):
        if x <= y:
            raise ValueError("x should be greater than y.")
        else:
            x += 1
        return x

net = Net()
net(mindspore.tensor(-2), mindspore.tensor(-1))
```

The output result:

```text
ValueError: x should be greater than y.
```

### assert Statements

Supports the use of assert for exception checking, `assert` syntax format: `assert[Expression [, args]]`, where `Expression` is the judgment condition. If the condition is true, nothing will be done, while if the condition is false, an exception message of type `AssertError` will be thrown. The `args` are user-supplied exception arguments, which can usually be strings or other objects.

```python
import mindspore
from mindspore import nn

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

@mindspore.jit
    def construct(self, x):
        assert x in [2, 3, 4]
        return x

net = Net()
net(mindspore.tensor(-1))
```

Appears normally in the output:

```text
AssertionError.
```

### pass Statements

The `pass` statement doesn't do anything and is usually used as a placeholder to maintain structural integrity. For example:

```python
import mindspore
from mindspore import nn

class Net(nn.Cell):
  @mindspore.jit
  def construct(self, x):
    i = 0
    while i < 5:
      if i > 3:
        pass
      else:
        x = x * 1.5
      i += 1
    return x

net = Net()
ret = net(10)
print("ret:", ret)
```

The result is as follows:

```text
ret: 50.625
```

### return Statements

The `return` statement usually returns the result to the place where it was called, and statements after the `return` statement are not executed. If the return statement does not have any expression or the function does not have a `return` statement, a `None` object is returned by default. There can be more than one `return` statement within a function, depending on the situation. For example:

```python
import mindspore
from mindspore import nn

class Net(nn.Cell):
  @mindspore.jit
  def construct(self, x):
      if x > 0:
        return x
      else:
        return 0

net = Net()
ret = net(10)
print("ret:", ret)
```

The result is as follows:

```text
ret: 10
```

As above, there can be multiple `return` statements in a control flow scenario statement. If there is no `return` statement in a function, the None object is returned by default, as in the following use case:

```python
import mindspore

@mindspore.jit
def foo():
  x = 3
  print("x:", x)

res = foo()
assert res is None
```

### break Statements

The `break` statement is used to terminate a loop statement, i.e., it stops execution of the loop statement even if the loop condition does not have a `False` condition or if the sequence is not fully recursive, usually used in `while` and `for` loops. In nested loops, the `break` statement stops execution of the innermost loop.

```python
import mindspore
from mindspore import nn

class Net(nn.Cell):
  @mindspore.jit
  def construct(self, x):
    for i in range(8):
      if i > 5:
        x *= 3
        break
      x = x * 2
    return x

net = Net()
ret = net(10)
print("ret:", ret)
```

The result is as follows:

```text
ret: 1920
```

### continue Statements

The `continue` statement is used to jump out of the current loop statement and into the next round of the loop. This is different from the `break` statement, which is used to terminate the entire loop statement. `continue` is also used in `while` and `for` loops. For example:

```python
import mindspore
from mindspore import nn

class Net(nn.Cell):
  @mindspore.jit
  def construct(self, x):
    for i in range(4):
      if i > 2:
        x *= 3
        continue
    return x

net = Net()
ret = net(3)
print("ret:", ret)
```

The result is as follows:

```text
ret: 9
```

## 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.

Example 1:

```python
import mindspore

x = mindspore.tensor([1, 4], mindspore.int32)
y = mindspore.tensor([0, 3], mindspore.int32)
m = 1
n = 2

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

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

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:

```text
ret:1
```

Example 2:

```python
import mindspore

x = mindspore.tensor([1, 4], mindspore.int32)
y = mindspore.tensor([0, 3], mindspore.int32)
m = 1
n = 2

@mindspore.jit()
def test_cond(x, y):
    out = 3
    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:

```text
ret:1
```

Example 3:

```python
import mindspore

x = mindspore.tensor([1, 4], mindspore.int32)
y = mindspore.tensor([0, 3], mindspore.int32)
m = 1

@mindspore.jit()
def test_cond(x, y):
    out = 2
    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:

```text
ret:1
```

### 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:

```python
import numpy as np
import mindspore

z = mindspore.tensor(np.ones((2, 3)))

@mindspore.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:

```text
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.

Example 1:

```python
import mindspore

m = 1
n = 2

@mindspore.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:

```text
ret:1
```

Example 2:

```python
import mindspore

m = 1
n = 2

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

@mindspore.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:

```text
ret:15
```

### Function Definition Statements

#### def Keyword

`def` is used to define a function, followed by the function identifier name and the original parentheses `()`, which may contain the function parameters.
Usage: `def function_name(args): statements...`.

For example:

```python
import mindspore

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

@mindspore.jit()
def test(x, y):
    return number_add(x, y)

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

The result is as follows:

```text
ret:6
```

Instructions:

- Functions can support no return value, and no return value means that the default function return value is None.
- `Construct` function of the outermost network and the inner network function is support kwargs, like:`def construct(**kwargs):`.
- Mixed use of variable argument and non-variable argument is supported, like:`def function(x, y, *args)` and `def function(x = 1, y = 1, **kwargs)`.

#### lambda Expression

A `lambda` expression is used to generate an anonymous function. Unlike normal functions, it computes and returns only one expression. Usage: `lambda x, y: x + y`.

For example:

```python
import mindspore

@mindspore.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:

```text
ret:6
```

#### Partial function partial

Function: partial function, fixed function input parameter. Usage: `partial(func, arg, ...)`.

Input parameter:

- `func` - function.

- `arg` - One or more parameters to be fixed, support positional parameters and key-value pair parameters.

Return Value: Returns some functions with fixed input value.

The example is as follows:

```python
import mindspore
from mindspore import ops

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

@mindspore.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:

```text
m:5
n:7
```

#### Function Parameters

- Default parameter value: The default value set to `Tensor` type data is currently not supported, and `int`, `float`, `bool`, `None`, `str`, `tuple`, `list`, `dict` type data is 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.
- Variable key-value pair parameter: Functions with variable key-value pairs cannot be used for backward propagation.

### List Comprehension and Generator Expression

Support for List Comprehension and Generator Expression. Support for constructing a new sequence.

#### List Comprehension

List comprehension are used to generate lists. Usage: `[arg for loop if statements]`.

The example is as follows:

```python
import mindspore

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

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

The result is as follows:

```text
ret:[4, 16, 36, 64, 100]
```

Restrictions:

The use of multiple levels of nested iterators is not supported in graph mode.

The example usage of the restriction is as follows (two levels of iterators are used):

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

An error will be prompted:

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

#### Dict Comprehension

Dict comprehension is used to generate lists. Usage: `{key, value for loop if statements}`.

The example is as follows:

```python
import mindspore

@mindspore.jit()
def test():
    x = [('a', 1), ('b', 2), ('c', 3)]
    res = {k: v for (k, v) in x if v > 1}
    return res

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

The result is as follows:

```text
ret:{'b': 2, 'c': 3}
```

Restrictions:

The use of multi-layer nested iterators is not supported in graph mode.

The example usage of the restriction is as follows (two levels of iterators are used):

```python
x = ({'a': 1, 'b': 2}, {'d': 1, 'e': 2}, {'g': 1, 'h': 2})
res = {k: v for y in x for (k, v) in y.items()}
```

An error will be prompted:

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

#### Generator Expression

Generator expressions are used to generate lists. Usage: `(arg for loop if statements)`.

For example:

```python
import mindspore

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

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

The result is as follows:

```text
ret:[4, 16, 36, 64, 100]
```

Usage restrictions are the same as list comprehension, i.e., the use of multiple levels of nested iterators is not supported in graph mode.

### 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__()`.

It is worth noting that the class used in the with statement needs to be decorated with a decorator@ms.jit_class or inherited from nn. Cell, and more on this can be found in [Calling the Custom Class](https://www.mindspore.cn/tutorials/en/r2.6.0/compile/static_graph_expert_programming.html#using-jit-class).

For example:

```python
import mindspore
from mindspore import nn

@mindspore.jit_class
class Sample:
    def __init__(self):
        super(Sample, self).__init__()
        self.num = mindspore.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):
    @mindspore.jit
    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:

```text
out1: [5]
out2: [2]
```

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