matx package

matx.Device

alias of DeviceOp

class matx.Dict(seq=None, **kwargs)

Bases: Object

matx.Dict implemented refering to python built-in dict,

supports common methods of built-in list and some custom methods.

Dict() -> construct empty dict

>>> import matx
>>> d = matx.Dict()
>>> print(d)
{}

Dict(mapping) -> construct dict from mapping

>>> import matx
>>> d = matx.Dict({'a': 1, 'b':2})
>>> print(d)
{a: 1, b: 2}

__init__(seq=None, **kwargs)

bucket_count()

Returns the number of slots in the hash table.

Returns:

int

Examples

>>> d = matx.Dict({'a': 1, 'b': 2})
>>> print(d.bucket_count())
4

clear()

Remove all items.

Returns:

None

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2})
>>> d
{a: 1, b: 2}
>>> d.clear()
>>> d
{}

get(k, d=None)

Return the value for key if key is in the dictionary, d.

Parameters:

• k (item) –

• d (item) – defautl return value when k is not in dict

Returns:

item

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2})
>>> d.get('a')
1
>>> d.get('a', 3)
1
>>> d.get('c', 3)
3

items()

Return a key-value iterable (matx.Iterator).

Returns:

matx.Iterator

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2})
>>> it = d.items()
>>> type(it)
<class 'matx.runtime._container._iterator.Iterator'>
>>> for k, v in it:
...     print(k, v)
...
a 1
b 2

keys()

Return a key iterable.

Returns:

matx.Iterator

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2})
>>> it = d.keys()
>>> type(it)
<class 'matx.runtime._container._iterator.Iterator'>
>>> for k in it:
...     print(k)
...
a
b

pop(k, *args)

.pop(k[,d]) -> v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise Exception is raised

Parameters:

k (item) –

Returns:

item

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2, 'c': 3})
>>> d.pop('a')
1
>>> d.pop('a', 100)
100
>>> d.pop('a')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data00/shubaihan/repos/matx4/python/matx/runtime/_container/_dict.py", line 223, in pop
    return _ffi_api.DictPop(self, k, *args)
TypeError: MATXError: dict.pop KeyError
Stack trace:
File "/data00/shubaihan/repos/matx4/src/runtime/container/dict_ref.cc", line 305
[bt] (0) /data00/shubaihan/repos/matx4/lib/libmatx.so(+0x33c9ed) [0x7f70d4ad39ed]
[bt] (1) /data00/shubaihan/repos/matx4/lib/libmatx.so(matx::runtime::Dict::pop(matx::runtime::PyArgs)+0x92) [0x7f70d4ad5362]
[bt] (2) /data00/shubaihan/repos/matx4/lib/libmatx.so(+0x33f434) [0x7f70d4ad6434]
[bt] (3) /data00/shubaihan/repos/matx4/lib/libmatx.so(MATXFuncCall_PYTHON_C_API+0x45) [0x7f70d4ab8c45]

reserve(new_size)

Increase the capacity of the dict to a value that’s greater or equal to new_size.

Parameters:

new_size (int) –

Returns:

None

Examples

>>> d = matx.Dict({'a': 1, 'b': 2})
>>> print(d.bucket_count())
4
>>> d.reserve(10)
>>> print(d.bucket_count())
32

values()

Return a value iterable.

Returns:

matx.Iterator

Examples

>>> import matx
>>> d = matx.Dict({'a': 1, 'b': 2})
>>> it = d.values()
>>> type(it)
<class 'matx.runtime._container._iterator.Iterator'>
>>> for v in it:
...     print(v)
...
1
2

class matx.File(path, mode='r', encoding='utf-8')

Bases: Object

A simple file class, which only supports reading by lines now.

File(path, mode, encoding) -> similar to builtins.open

__init__(path, mode='r', encoding='utf-8') → None

has_nextline()

readline()

class matx.List(seq=())

Bases: Object

matx.List implemented refering to python built-in list,

supports common methods of built-in list and some custom methods.

List() -> construct empty list

>>> import matx
>>> l = matx.List()
>>> print(l)
[]

List(iterable) -> construct list from iterable object

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> print(l)
[1, 2, 3]
>>> l1 = matx.List([1, 2, 3])
>>> l2 = matx.List(l1)
>>> print(l2)
[1, 2, 3]

__init__(seq=())

append(p_object)

Append object to the end of the list.

Parameters:

p_object –

Returns:

None

Examples

>>> import matx
>>> l = matx.List()
>>> l.append(1)
>>> l
[1]

capacity()

Return the number of elements that the list has currently allocated space for.

Returns:

int

Examples

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> print(l)
[1, 2, 3]
>>> print(l.capacity())
4

clear()

Remove all items from list.

Returns:

None

Examples

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> l
[1, 2, 3]
>>> l.clear()
>>> l
[]

extend(iterable)

Extend list by appending elements from the iterable.

Parameters:

iterable – iterable

Returns:

None

Examples

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> l.extend(matx.List([1, 2, 3]))
>>> print(l)
[1, 2, 3, 1, 2, 3]
>>> l.extend([1, 2, 3])
>>> print(l)
[1, 2, 3, 1, 2, 3, 1, 2, 3]

index(x, start=None, end=None)

Return zero-based index in the list of the first item whose value is equal to x.

Parameters:

• x (value type) –

• start (int) –

• end (int) –

Raises:

a ValueError if there is no such item. –

Returns:

None

insert(index, value)

Insert an item at a given position. The first argument is the index of the element before which to insert

Parameters:

• index (int) –

• value (value type) –

Returns:

None

pop(index=-1)

Remove and return item at index (default last).

Raises Exception if list is empty or index is out of range.

Parameters:

index (int, optional) –

Returns:

item

Raises:

Exception –

Examples

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> l.pop()
3
>>> import matx
>>> l = matx.List([1, 2, 3, 4, 5])
>>> l.pop()
5
>>> l
[1, 2, 3, 4]
>>> l.pop(1)
2
>>> l
[1, 3, 4]

remove(value)

Remove first occurrence of value.

Raises Exception if the value is not present.

Parameters:

value –

Returns:

None

Raises:

Exception –

Examples

>>> import matx
>>> l = matx.List(["a", "b", "c", "d", "c"])
>>> l.remove("c")
>>> l
[a, b, d, c]

reserve(new_size)

Increase the capacity of the list to a value that’s greater or equal to new_size.

Parameters:

new_size (int) –

Returns:

None

Examples

>>> import matx
>>> l = matx.List([1, 2, 3])
>>> print(l)
[1, 2, 3]
>>> print(l.capacity())
4
>>> l.reserve(8)
>>> print(l)
[1, 2, 3]
>>> print(l.capacity())
8

reverse()

Reverse IN PLACE.

Returns:

None

Examples

>>> imprt matx
>>> l = matx.List([1, 2, 3, 4])
>>> l.reverse()
>>> l
[4, 3, 2, 1]

class matx.NDArray(arr: Union[int, float, list, List], shape: Union[list, List], dtype: str, device: str = 'cpu')

Bases: Object

Lightweight NDArray implementation for matx runtime

The structure is currently just a container for a multi-dimensional array, without defining various types of transformations and arithmetic methods. The goal of the structure is to serve as a bridge tool and other machine learning frameworks (pytorch tensorflow) for the conversion of multidimensional arrays

Parameters:

• arr (List) – Constructing the contents of an NDArray

• shape (List) – Shape of the constructed NDArray

• dtype (str) – The type of the constructed NDArray element, supporting “int32” “int64” “float32” “float64”

Construction method 1

arr is a one-dimensional List, the shape is not empty, producing NDArray with the content of arr and the shape of the given shape

Examples:

>>> import matx
>>> nd = matx.NDArray([1,2,3,4,5,6], [3,2], "int32")
>>> nd
[
 [ 1 2 ]
 [ 3 4 ]
 [ 5 6 ]
]

Construction method 2

arr is a List of arbitrary dimensions, shape is an empty List, producing NDArray with the same shape as arr and the same content as arr

Examples:

>>> import matx
>>> nd = matx.NDArray([[1, 2, 3,], [4, 5, 6]], [], "int32")
>>> nd
[
 [ 1 2 3 ]
 [ 4 5 6 ]
]

>>> nd = matx.NDArray([1, 2, 3, 4, 5, 6], [], "int32")
>>> nd
[1, 2, 3, 4, 5, 6]

Construction method 3

arr is empty, shape is not empty, return a NDArray corresponding to the random initialization content of the shape

Examples:

>>> imoprt matx
>>> nd = matx.NDArray([], [2, 3], "float32")
>>> nd
[
 [ 4.46171e-08 3.08678e-41 2.25609e-43 ]
 [ 0 8.15743e-06 4.5723e-41 ]
]

__init__(arr: Union[int, float, list, List], shape: Union[list, List], dtype: str, device: str = 'cpu')

as_type(dtype: str)

asnumpy()

Construct a numpy.ndarray from the current NDArray. Note! This method cannot be compiled for use in matx.script

Returns:

numpy.ndarray

Examples

>>> import matx
>>> import numpy
>>> nd = matx.NDArray([[1,2,3],[4,5,6]], [], "int32")
>>> nd
[
 [1, 2, 3]
 [4, 5, 6]
]
>>> arr = nd.asnumpy()
>>> arr
array([[1, 2, 3],
    [4, 5, 6]], dtype=int32)

contiguous()

Returns a copy of the ndarray with contiguous memory if the adarray is not contiguous. Otherwise, the original one is returned.

Returns:

matx.NDArray

Examples

>>> import matx
>>> x = matx.NDArray([1,2,3,4], [2,2], "int32")
>>> y = x.transpose()
>>> z = y.contiguous()
[[1, 3],
 [2, 4]]
>>> z.is_contiguous()
1

device()

Returns the current NDArray device as a string

dim()

Returns the number of array dimensions. Unlike numpy, this is a method and not a property.

Returns:

int

Examples

>>> import matx
>>> x = matx.NDArray([], [3,2], "int32")
>>> x.dim()
2

dtype()

Returns the dtype of the current NDArray as a string

Returns:

“int32” “int64” “float32” “float64”

Return type:

str

Examples

>>> import matx
>>> x = matx.NDArray([], [3,2], "int32")
>>> x.dtype()
'int32'

from_numpy(source_array)

Copy data from a numpy.ndarray to the current NDArray, requiring both to have the same size.

Note! This method cannot be compiled for use in matx.script

Parameters:

source_array (numpy.ndarray) –

Raises:

ValueError –

Returns:

self

Return type:

matx.NDArray

Examples

>>> import matx
>>> import numpy
>>> arr = numpy.random.rand(2, 3)
>>> arr
array([[0.0402096 , 0.99905783, 0.85840985],
    [0.89764146, 0.25342801, 0.566187  ]])
>>> nd = matx.NDArray([], arr.shape, str(arr.dtype))
>>> nd.from_numpy(arr)
[
 [0.0402096 , 0.99905783, 0.85840985]
 [0.89764146, 0.25342801, 0.566187  ]
]

is_contiguous()

Returns a int indicating if the underlying data is contiguous in memory. The continuity of array changes when its stride changes.

Returns:

int

Examples

>>> import matx
>>> x = matx.NDArray([1,2,3,4], [2,2], "int32")
>>> y = x.transpose()
>>> y.is_contiguous()
0

numpy()

Construct a numpy.ndarray from the current NDArray. Note! This method cannot be compiled for use in matx.script

Returns:

numpy.ndarray

Examples

>>> import matx
>>> import numpy
>>> nd = matx.NDArray([[1,2,3],[4,5,6]], [], "int32")
>>> nd
[
 [1, 2, 3]
 [4, 5, 6]
]
>>> arr = nd.numpy()
>>> arr
array([[1, 2, 3],
    [4, 5, 6]], dtype=int32)

release()

reshape(newshape: Union[tuple, list])

shape()

Returns the current NDArray’s shape, unlike numpy, this is a method and not a property

Returns:

matx.List

Examples

>>> import matx
>>> x = matx.NDArray([], [3,2], "int32")
>>> x.shape()
[3, 2]

squeeze(axis: tuple = ())

stride()

Returns List of bytes to step in each dimension when traversing an array.

Returns

matx.List

Examples

>>> import matx
>>> x = matx.NDArray([1,2,3,4], [2,2], "int32")
>>> y = x.transpose()
>>> y.stride()
[1, 2]

to_dlpack()

to_list()

Convert a NDArray to a matx.List corresponding to the shape

Returns:

matx.List

Examples

>>> import matx
>>> nd = matx.NDArray([], [2, 3], "float32")
>>> nd
[
 [ 1.35134e-24 4.55548e-41 1.35134e-24 ]
 [ 4.55548e-41 7.41622e+14 3.06142e-41 ]
]
>>> l = nd.to_list()
>>> l
[[1.35134e-24, 4.55548e-41, 1.35134e-24], [4.55548e-41, 7.41622e+14, 3.06142e-41]]

tolist()

Convert a NDArray to a matx.List corresponding to the shape

Returns:

matx.List

Examples

>>> import matx
>>> nd = matx.NDArray([], [2, 3], "float32")
>>> nd
[
 [ 1.35134e-24 4.55548e-41 1.35134e-24 ]
 [ 4.55548e-41 7.41622e+14 3.06142e-41 ]
]
>>> l = nd.tolist()
>>> l
[[1.35134e-24, 4.55548e-41, 1.35134e-24], [4.55548e-41, 7.41622e+14, 3.06142e-41]]

torch(copy=True)

convert NDArray to torch.Tensor, make sure NDArray is synchronized

Returns:

torch.Tensor

transpose(axes=None)

Reverse or permute the axes of an array

Parameters:

axes (list of ints) –

Returns :

the given with its axes permuted. A view is returned whenever possible

unsqueeze(dim: int)

class matx.NativeObject(cls_name, *args)

Bases: object

__init__(cls_name, *args)

cls_name

ud_ref

class matx.Object

Bases: ObjectBase

Base class for all tvm’s runtime objects.

class matx.OpaqueObject

Bases: Object

__init__()

class matx.Regex(pattern, ignore_case=False, dotall=False, extended=False, anchored=False, ucp=True)

Bases: Object

Regular class implemented using pcre.

Parameters:

• pattern (str) – Str types. Regular expression pattern.

• ignore_case (bool) – Booleans. Perform case-insensitive matching. The default is false

• dotall (bool) – Booleans. “.” matches any character at all, including the newline. The default is false

• extended (bool) – Booleans. Most white space in the pattern (other than in a character class), and characters between a # outside a character class and the next newline, inclusive, are ignored. An escaping backslash can be used to include a white space or # character as part of the pattern. The default is false.

• anchored (bool) – Booleans. Matches only at the beginning of the subject. The default is false.

• ucp (bool) – Booleans. Sequences such as “d” and “w” use Unicode properties to determine character types, instead of recognizing only characters with codes less than 128 via a lookup table. The default is false.

Examples

>>> import matx
>>> regex = matx.Regex("(?<first>.*) are (?<second>.*?) .*")
>>> regex
Object(0x55c11322a200)

__init__(pattern, ignore_case=False, dotall=False, extended=False, anchored=False, ucp=True)

match(string: Union[str, bytes], offset: int = 0)

Try to apply the pattern at the start of the string, returning a tuple containing the matched string. If grouping version of regular pattern is used, then the text of all groups are returned.

Parameters:

• string (str|bytes) – The source string.

• offset (int) – Offset in the subject at which to start matching

Returns:

The matched groups. The first element in the tuple is indexed groups. The second element in the tuple is named groups.

Return type:

Tuple(List, Dict)

Examples

>>> import matx
>>> regex = matx.Regex("(?<first>.*) are (?<second>.*?) .*")
>>> matched_result = regex.match("Cats are smarter than dogs")
>>> matched_result[0]
['Cats are smarter than dogs', 'Cats', 'smarter']
>>> matched_result[1]
{'first': 'Cats', 'second': 'smarter'}

replace(string: Union[str, bytes], repl: Union[str, bytes])

Return the string obtained by replacing the leftmost non-overlapping occurrences of the pattern in the input string by the replacement repl.

Parameters:

• string (str|bytes) – The source string.

• repl (str|bytes) – The replacement string.

Returns:

The replaced string. If no match was found, returning the source string.

Return type:

str|bytes

Examples

>>> import matx
>>> regex = matx.Regex("name")
>>> new_str = regex.replace("mynameisHE", "NAME")
>>> new_str
myNAMEisHE

split(string: Union[str, bytes])

Split a string by the occurrences of a pattern.

Parameters:

string (str|bytes) – The source string.

Returns:

A list containing the resulting substrings. If no match was found，returning a list containing only the source string, i.e. [input].

Return type:

List[str|bytes]

Examples

>>> import matx
>>> regex = matx.Regex("name")
>>> tokens = regex.split("mynameisHE")
>>> tokens
['my', 'isHE']

class matx.Set(seq=())

Bases: Object

matx.Set: matx.Set implemented refering to python built-in dict,

supports common methods of built-in list and some custom methods.

set() -> construct empty set .. rubric:: Examples

>>> import matx
>>> s = matx.Set()
>>> print(s)
{}

set(iterable) -> construct set from iterable .. rubric:: Examples

>>> import matx
>>> s = matx.Set(['a', 1, 'b'])
>>> print(s)
{a, b, 1}

__init__(seq=())

add(p_object)

Add an element to a set.

This has no effect if the element is already present.

Returns

None

Examples

>>> import matx
>>> s = matx.Set(['a', 1, 'b'])
>>> s.add('c')
>>> print(s)
{a, c, b, 1}

bucket_count()

Returns the number of slots in the hash table.

Returns:

int

Examples

>>> import matx
>>> s = matx.Set(['a', 1, 'b'])
>>> print(s.bucket_count())
8

clear()

Remove all elements.

Returns:

None

Examples

>>> import matx
>>> s = matx.Set(['a', 1, 'b'])
>>> print(s)
{a, b, 1}
>>> s.clear()
>>> print(s)
{}

difference(*args)

Return the difference of two or more sets as a new set.

Returns:

matx.Set

Examples

>>> import matx
>>> s = matx.Set({'a', 'b', 'c', 'd', 'e', 'f'})
>>> s.difference(matx.Set({'a', 'b'}), {'d', 1}, ['f'], matx.List([2]))
{'c', 'e'}
>>> s
{'a', 'd', 'c', 'b', 'f', 'e'}

difference_update(*args)

Remove all elements of another set from this set.

Returns:

None

Examples

>>> s = matx.Set({'a', 'b', 'c', 'd', 'e', 'f'})
>>> s.difference_update(matx.Set({'a', 'b'}), {'d', 1}, ['f'], matx.List([2]))
>>> s
{'c', 'e'}

discard(item)

Remove an element from a set if it is a member.

If the element is not a member, do nothing.

Returns:

None

Examples

>>> import matx
>>> s = matx.Set({'a', 'b', 'c'})
>>> s.discard('a')
>>> s
{'c', 'b'}
>>> s.discard('d')
>>> s
{'c', 'b'}

reserve(new_size)

Increase the capacity of the set to a value that’s greater or equal to new_size.

Parameters:

new_size (int) –

Returns:

None

Examples

>>> import matx
>>> s = matx.Set(['a', 1, 'b'])
>>> print(s.bucket_count())
8
>>> s.reserve(10)
>>> print(s.bucket_count())
32

union(*args, **kwargs)

Return the union of sets as a new set.

Returns:

matx.Set

Examples

>>> import matx
>>> a = matx.Set({'a', 'b', 'c'})
>>> a.union(matx.Set({'b', 'd'}), {'e', 'f'}, ['a', 1], matx.List([1, 2]))
{'a', 'c', 'd', 2, 'b', 'f', 1, 'e'}
>>> a
{'a', 'c', 'b'}

update(*args, **kwargs)

Update a set with the union of itself and others.

Returns:

None

Examples

>>> import matx
>>> a = matx.Set({'a', 'b', 'c'})
>>> a.update(matx.Set({'b', 'd'}), {'e', 'f'}, ['a', 1], matx.List([1, 2]))
>>> a
{'a', 'c', 'd', 2, 'b', 'f', 1, 'e'}

exception matx.TError

Bases: RuntimeError

Default error thrown by packed functions.

TError will be raised if you do not give any error type specification,

class matx.ToolChain(toolchain=None, **kwargs)

Bases: object

__init__(toolchain=None, **kwargs)

build(file_path)

class matx.Trie(dic=None)

Bases: Object

Double Array Trie Object

Parameters:

dic (Dict[str, int]) – The key is word and the value is id

Examples

>>> import matx
>>> tree = matx.Trie({"hello": 1, "hello w": 2, "good": 3})
>>> tree
Trie(addr: 0x5601feb2aca0)
>>> tree = matx.Trie()
>>> tree
Trie(addr: 0x5601feba5b90)

__init__(dic=None)

load(file_path: str)

prefix_search(w, pos=0)

Find the longest substring of w[pos:] in the trie tree

Parameters:

• w (str) – The input word

• pos (int, optional) – The start position

Returns:

Return the length and id of the matched substring.

If not found, return (0, -1)

Return type:

Tuple(int, int)

Examples

>>> import matx
>>> tree = matx.Trie({"hello": 1, "hello w": 2, "good": 3})
>>> tree.prefix_search("hello world")
(7, 2)
>>> tree.prefix_search("hellf")
(0, -1)

prefix_search_all(w, pos=0)

Find all substring of w[pos:] in the trie tree

Parameters:

• w (str) – The input word

• pos (int, optional) – The start position

Returns:

Return a list of the length and id of the matched substring.

If not found, return []

Return type:

List[Tuple(int, int)]

Examples

>>> import matx
>>> trie = matx.Trie({'hello': 1, 'hello world': 2, 'hello hello world': 3})
>>> trie.prefix_search_all("hello hello world")
[(5, 1), (17, 3)]

save(file_path: str)

update(w, index=-1)

Insert a word and corresponding id into the trie tree

Parameters:

• w (str) – The input word

• index (int, optional) – id, -1 for default

Examples

>>> import matx
>>> tree = matx.Trie()
>>> tree.update("hello", 1)
>>> tree.update("hello w", 2)
>>> tree.update("good", 3)
>>> tree.prefix_search("hello world")
(7, 2)
>>> tree.prefix_search("hellf")
(0, -1)
>>> tree.update("hell", 10)
>>> tree.prefix_search("hellf")
(4, 10)

class matx.Tuple(*fields)

Bases: Object

tuple object.

Parameters:

fields (List[Object]) – The source.

__init__(*fields)

matx.deserialize(s: str)

matx.get_cflags()

matx.get_global_func(name, allow_missing=False)

Get a global function by name

Parameters:

• name (str) – The name of the global function

• allow_missing (bool) – Whether allow missing function or raise an error.

Returns:

func – The function to be returned, None if function is missing.

Return type:

PackedFunc

matx.get_link_flags()

lib layout - lib/

• libmatx.so

• pcre/ libpcre.so

matx.ir_module(obj)

matx.list_heap_pushpop(l, item, comp=None)

matx.list_heap_replace(l, item, comp=None)

matx.list_heapify(l, comp=None)

matx.list_nth_element(l, n, comp=None)

matx.list_sort(l, compare=None)

matx.load(folder, device)

matx.load_native(dso_path: str = '')

matx.load_so(dso_path='')

matx.make_native_function(func_name: str)

matx.make_native_object(class_name, *args)

matx.make_native_object_creator(class_name)

matx.pmap(func, data)

matx.pstarmap(func, data)

matx.register_func(func_name, f=None, override=False)

Register global function

Parameters:

• func_name (str or function) – The function name

• f (function, optional) – The function to be registered.

• override (boolean optional) – Whether override existing entry.

Returns:

fregister – Register function if f is not specified.

Return type:

function

Examples

The following code registers my_packed_func as global function. Note that we simply get it back from global function table to invoke it from python side. However, we can also invoke the same function from C++ backend, or in the compiled TVM code.

targs = (10, 10.0, "hello")
@matx.register_func
def my_packed_func(*args):
    assert(tuple(args) == targs)
    return 10
# Get it out from global function table
f = matx.get_global_func("my_packed_func")
assert isinstance(f, matx.PackedFunc)
y = f(*targs)
assert y == 10

matx.save(jit_module, folder, force_override=False)

matx.script(compiling_obj, *args, backend=None, **kwargs)

Entry function for compiling. Given a python object including function, simple class, compile it to a matx object which mostly keep the behavior of the original python object.

Parameters:

• compiling_obj (([function, class])) – [input python object to be compiled.]

• args – not used

• backend ([str, None]) – ‘torch’ or ‘pytorch’ or None

• kwargs – keyword arguments passed into different script backend

Returns:

obj – the compiled object.

Return type:

OpKernel

matx.script_embedded_class(code, is_path=False)

matx.serialize(v)

matx.to_runtime_object(value)

matx.trace(func, *args, **kwargs)

Trace a function and return an executable module that will be optimized using just-in-time compilation.

Parameters:

• func (callable) – A Python function or a matx Symbol(s) that will be run with args. func arguments and return values must be Operators returned from Script.

• args – func inputs

• kwargs – func inputs

Returns:

module – an executable module

Return type:

JITModule

Subpackages

• matx.runtime package
  • Submodules
    • matx.runtime.container module
      • Array
      • ArrayIterator
      • Map
      • MapIterator
      • Tuple
      • getitem_helper()
      • slice_index_correction()
    • matx.runtime.cuda_stream module
      • create_stream()
      • default_stream()
      • stream_sync()
    • matx.runtime.file module
      • File
    • matx.runtime.module module
      • Module
      • load_module()
      • system_lib()
    • matx.runtime.msgpack module
      • dumps()
      • loads()
    • matx.runtime.ndarray module
      • NDArray
      • add()
      • concatenate()
      • div()
      • from_dlpack()
      • from_numpy()
      • mul()
      • rand()
      • stack()
      • sub()
    • matx.runtime.object module
      • Object
    • matx.runtime.object_generic module
      • ObjectGeneric
      • to_runtime_object()
    • matx.runtime.packed_func module
      • PackedFunc
    • matx.runtime.picke module
      • deserialize()
      • from_runtime_object()
      • serialize()
    • matx.runtime.regex module
      • Regex
    • matx.runtime.trie module
      • Trie
• matx.text package
  • Submodules
    • matx.text.emoji module
      • EmojiFilter
    • matx.text.jieba module
      • Jieba
      • JiebaImpl
    • matx.text.wordpiece_tokenizer module
      • WordPieceTokenizer
      • WordPieceTokenizerImpl
• matx.vision package
  • AutoContrastOp
    • AutoContrastOp.__call__()
    • AutoContrastOp.__init__()
  • AverageBlurOp
    • AverageBlurOp.__call__()
    • AverageBlurOp.__init__()
  • CastOp
    • CastOp.__call__()
    • CastOp.__init__()
  • CenterCropOp
    • CenterCropOp.__call__()
    • CenterCropOp.__init__()
  • ChannelReorderOp
    • ChannelReorderOp.__call__()
    • ChannelReorderOp.__init__()
  • ColorLinearAdjustOp
    • ColorLinearAdjustOp.__call__()
    • ColorLinearAdjustOp.__init__()
  • Conv2dOp
    • Conv2dOp.__call__()
    • Conv2dOp.__init__()
  • CropOp
    • CropOp.__call__()
    • CropOp.__init__()
  • CvtColorOp
    • CvtColorOp.__call__()
    • CvtColorOp.__init__()
  • EdgeDetectOp
    • EdgeDetectOp.__call__()
    • EdgeDetectOp.__init__()
  • EmbossOp
    • EmbossOp.__call__()
    • EmbossOp.__init__()
  • FlipOp
    • FlipOp.__call__()
    • FlipOp.__init__()
  • GammaContrastOp
    • GammaContrastOp.__call__()
    • GammaContrastOp.__init__()
  • GaussNoiseOp
    • GaussNoiseOp.__call__()
    • GaussNoiseOp.__init__()
  • GaussianBlurOp
    • GaussianBlurOp.__call__()
    • GaussianBlurOp.__init__()
  • HistEqualizeOp
    • HistEqualizeOp.__call__()
    • HistEqualizeOp.__init__()
  • ImdecodeNoExceptionOp
    • ImdecodeNoExceptionOp.__call__()
    • ImdecodeNoExceptionOp.__init__()
  • ImdecodeNoExceptionRandomCropOp
    • ImdecodeNoExceptionRandomCropOp.__call__()
    • ImdecodeNoExceptionRandomCropOp.__init__()
  • ImdecodeOp
    • ImdecodeOp.__call__()
    • ImdecodeOp.__init__()
  • ImdecodeRandomCropOp
    • ImdecodeRandomCropOp.__call__()
    • ImdecodeRandomCropOp.__init__()
  • ImencodeNoExceptionOp
    • ImencodeNoExceptionOp.__call__()
    • ImencodeNoExceptionOp.__init__()
  • ImencodeOp
    • ImencodeOp.__call__()
    • ImencodeOp.__init__()
  • InvertOp
    • InvertOp.__call__()
    • InvertOp.__init__()
  • LaplacianBlurOp
    • LaplacianBlurOp.__call__()
    • LaplacianBlurOp.__init__()
  • MeanOp
    • MeanOp.__call__()
    • MeanOp.__init__()
  • MedianBlurOp
    • MedianBlurOp.__call__()
    • MedianBlurOp.__init__()
  • MixupImagesOp
    • MixupImagesOp.__call__()
    • MixupImagesOp.__init__()
  • NormalizeOp
    • NormalizeOp.__call__()
    • NormalizeOp.__init__()
  • PadOp
    • PadOp.__call__()
    • PadOp.__init__()
  • PadWithBorderOp
    • PadWithBorderOp.__call__()
    • PadWithBorderOp.__init__()
  • PosterizeOp
    • PosterizeOp.__call__()
    • PosterizeOp.__init__()
  • RandomDropoutOp
    • RandomDropoutOp.__call__()
    • RandomDropoutOp.__init__()
  • RandomResizedCropOp
    • RandomResizedCropOp.__call__()
    • RandomResizedCropOp.__init__()
  • ResizeOp
    • ResizeOp.__call__()
    • ResizeOp.__init__()
  • RotateOp
    • RotateOp.__call__()
    • RotateOp.__init__()
  • SaltAndPepperOp
    • SaltAndPepperOp.__call__()
    • SaltAndPepperOp.__init__()
  • SharpenOp
    • SharpenOp.__call__()
    • SharpenOp.__init__()
  • SolarizeOp
    • SolarizeOp.__call__()
    • SolarizeOp.__init__()
  • SplitOp
    • SplitOp.__call__()
    • SplitOp.__init__()
  • StackOp
    • StackOp.__call__()
    • StackOp.__init__()
  • SumOp
    • SumOp.__call__()
    • SumOp.__init__()
  • TransposeNormalizeOp
    • TransposeNormalizeOp.__call__()
    • TransposeNormalizeOp.__init__()
  • TransposeOp
    • TransposeOp.__call__()
    • TransposeOp.__init__()
  • WarpAffineOp
    • WarpAffineOp.__call__()
    • WarpAffineOp.__init__()
  • WarpPerspectiveOp
    • WarpPerspectiveOp.__call__()
    • WarpPerspectiveOp.__init__()
  • Subpackages
    • matx.vision.tv_transforms package
      • CenterCrop
      • ColorJitter
      • Compose
      • ConvertImageDtype
      • CvtColor
      • Decode
      • GaussianBlur
      • Grayscale
      • Normalize
      • Pad
      • RandomAdjustSharpness
      • RandomAffine
      • RandomAutocontrast
      • RandomCrop
      • RandomEqualize
      • RandomGrayscale
      • RandomHorizontalFlip
      • RandomInvert
      • RandomPerspective
      • RandomPosterize
      • RandomResizedCrop
      • RandomRotation
      • RandomSolarize
      • RandomVerticalFlip
      • Resize
      • Stack
      • ToTensor
      • Transpose
      • set_device
      • Submodules