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Copy patharray_object.py
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executable file
·1311 lines (1048 loc) · 42.9 KB
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from __future__ import annotations
__all__ = ["Array"]
import array as _pyarray
from collections.abc import Callable
from functools import wraps
from typing import TYPE_CHECKING, Any, ParamSpec, cast
import arrayfire_wrapper.lib as wrapper
from arrayfire_wrapper.defines import AFArray, ArrayBuffer, CType
from .dtypes import Dtype
from .dtypes import bool as afbool
from .dtypes import c_api_value_to_dtype, float32, str_to_dtype
if TYPE_CHECKING:
from ctypes import Array as CArray
from enum import Enum
P = ParamSpec("P")
def afarray_as_array(func: Callable[P, Array]) -> Callable[P, Array]:
"""
Decorator that converts a function returning an array to return an ArrayFire Array.
Parameters
----------
func : Callable[P, Array]
The original function that returns an array.
Returns
-------
Callable[P, Array]
A decorated function that returns an ArrayFire Array.
"""
@wraps(func)
def decorated(*args: P.args, **kwargs: P.kwargs) -> Array:
result = func(*args, **kwargs)
return Array.from_afarray(result) # type: ignore[arg-type] # FIXME
return decorated
class Array:
def __init__(
self,
obj: None | Array | _pyarray.array | int | AFArray | list[bool | int | float] = None,
dtype: None | Dtype | str = None,
shape: tuple[int, ...] = (),
to_device: bool = False,
offset: CType | None = None,
strides: tuple[int, ...] | None = None,
) -> None:
self._arr = AFArray.create_null_pointer()
_no_initial_dtype = False # HACK, FIXME
if len(shape) > 4:
raise ValueError("Can not create 5 or more -dimensional arrays.")
if isinstance(dtype, str):
dtype = str_to_dtype(dtype) # type: ignore[arg-type]
if dtype is None:
_no_initial_dtype = True
dtype = float32
if obj is None:
if not shape: # shape is None or empty tuple
self._arr = wrapper.create_handle((), dtype)
return
self._arr = wrapper.create_handle(shape, dtype)
return
if isinstance(obj, Array):
self._arr = wrapper.retain_array(obj.arr)
return
if isinstance(obj, _pyarray.array):
_type_char: str = obj.typecode
_array_buffer = ArrayBuffer(*obj.buffer_info())
elif isinstance(obj, list):
# TODO fix an issue when Array can not be created from float values to complex
if _no_initial_dtype:
arr_typecode = "f"
elif dtype.typecode in _pyarray.typecodes:
arr_typecode = dtype.typecode
else:
raise TypeError(f"Unsupported typecode. Can not create a python array from '{repr(dtype)}'")
_array = _pyarray.array(arr_typecode, obj)
_type_char = _array.typecode
_array_buffer = ArrayBuffer(*_array.buffer_info())
elif isinstance(obj, int) or isinstance(obj, AFArray):
_array_buffer = ArrayBuffer(obj if not isinstance(obj, AFArray) else obj.value) # type: ignore[arg-type]
if not shape:
raise TypeError("Expected to receive the initial shape due to the obj being a data pointer.")
if _no_initial_dtype:
raise TypeError("Expected to receive the initial dtype due to the obj being a data pointer.")
_type_char = dtype.typecode
else:
raise TypeError("Passed object obj is an object of unsupported class.")
if not shape:
if _array_buffer.length != 0:
shape = (_array_buffer.length,)
else:
RuntimeError("Shape and buffer length are size invalid.")
if not _no_initial_dtype and dtype.typecode != _type_char:
raise TypeError("Can not create array of requested type from input data type")
if not (offset or strides):
if not to_device:
self._arr = wrapper.create_array(shape, dtype, _array_buffer)
return
self._arr = wrapper.device_array(shape, dtype, _array_buffer)
return
self._arr = wrapper.create_strided_array(
shape, dtype, _array_buffer, offset, strides, wrapper.PointerSource(to_device) # type: ignore[arg-type]
)
# Arithmetic Operators
def __pos__(self) -> Array:
"""
Evaluates +self_i for each element of an array instance.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
Returns
-------
out : Array
An array containing the evaluated result for each element. The returned array must have the same data type
as self.
"""
return self
def __neg__(self) -> Array:
"""
Evaluates +self_i for each element of an array instance.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
Returns
-------
out : Array
An array containing the evaluated result for each element in self. The returned array must have a data type
determined by Type Promotion Rules.
"""
return process_c_function(0, self, wrapper.sub)
def __add__(self, other: int | float | Array, /) -> Array:
"""
Calculates the sum for each element of an array instance with the respective element of the array other.
Parameters
----------
self : Array
Array instance (augend array). Should have a numeric data type.
other: int | float | Array
Addend array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise sums. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.add)
def __sub__(self, other: int | float | Array, /) -> Array:
"""
Calculates the difference for each element of an array instance with the respective element of the array other.
The result of self_i - other_i must be the same as self_i + (-other_i) and must be governed by the same
floating-point rules as addition (see array.__add__()).
Parameters
----------
self : Array
Array instance (minuend array). Should have a numeric data type.
other: int | float | Array
Subtrahend array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise differences. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.sub)
def __mul__(self, other: int | float | Array, /) -> Array:
"""
Calculates the product for each element of an array instance with the respective element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise products. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.mul)
def __truediv__(self, other: int | float | Array, /) -> Array:
"""
Evaluates self_i / other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array should have a floating-point data type
determined by Type Promotion Rules.
Note
----
- If one or both of self and other have integer data types, the result is implementation-dependent, as type
promotion between data type “kinds” (e.g., integer versus floating-point) is unspecified.
Specification-compliant libraries may choose to raise an error or return an array containing the element-wise
results. If an array is returned, the array must have a real-valued floating-point data type.
"""
return process_c_function(self, other, wrapper.div)
def __floordiv__(self, other: int | float | Array, /) -> Array:
# TODO
return NotImplemented
def __mod__(self, other: int | float | Array, /) -> Array:
"""
Evaluates self_i % other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a real-valued data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a real-valued data type.
Returns
-------
out : Array
An array containing the element-wise results. Each element-wise result must have the same sign as the
respective element other_i. The returned array must have a real-valued floating-point data type determined
by Type Promotion Rules.
Note
----
- For input arrays which promote to an integer data type, the result of division by zero is unspecified and
thus implementation-defined.
"""
return process_c_function(self, other, wrapper.mod)
def __pow__(self, other: int | float | Array, /) -> Array:
"""
Calculates an implementation-dependent approximation of exponentiation by raising each element (the base) of
an array instance to the power of other_i (the exponent), where other_i is the corresponding element of the
array other.
Parameters
----------
self : Array
Array instance whose elements correspond to the exponentiation base. Should have a numeric data type.
other: int | float | Array
Other array whose elements correspond to the exponentiation exponent. Must be compatible with self
(see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.pow)
# Array Operators
def __matmul__(self, other: Array, /) -> Array:
# TODO get from blas - make vanilla version and not copy af.matmul as is
return NotImplemented
# Bitwise Operators
def __invert__(self) -> Array:
"""
Evaluates ~self_i for each element of an array instance.
Parameters
----------
self : Array
Array instance. Should have an integer or boolean data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have the same data type as self.
"""
return Array.from_afarray(wrapper.bitnot(self._arr))
def __and__(self, other: int | bool | Array, /) -> Array:
"""
Evaluates self_i & other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | bool | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.bitand)
def __or__(self, other: int | bool | Array, /) -> Array:
"""
Evaluates self_i | other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | bool | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.bitor)
def __xor__(self, other: int | bool | Array, /) -> Array:
"""
Evaluates self_i ^ other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | bool | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type determined
by Type Promotion Rules.
"""
return process_c_function(self, other, wrapper.bitxor)
def __lshift__(self, other: int | Array, /) -> Array:
"""
Evaluates self_i << other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Each element must be greater than or equal to 0.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have the same data type as self.
"""
return process_c_function(self, other, wrapper.bitshiftl)
def __rshift__(self, other: int | Array, /) -> Array:
"""
Evaluates self_i >> other_i for each element of an array instance with the respective element of the
array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | Array
Other array. Must be compatible with self (see Broadcasting). Should have a numeric data type.
Each element must be greater than or equal to 0.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have the same data type as self.
"""
return process_c_function(self, other, wrapper.bitshiftr)
# Comparison Operators
def __lt__(self, other: int | float | Array, /) -> Array:
"""
Computes the truth value of self_i < other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a real-valued data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.lt)
def __le__(self, other: int | float | Array, /) -> Array:
"""
Computes the truth value of self_i <= other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a real-valued data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.le)
def __gt__(self, other: int | float | Array, /) -> Array:
"""
Computes the truth value of self_i > other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a real-valued data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.gt)
def __ge__(self, other: int | float | Array, /) -> Array:
"""
Computes the truth value of self_i >= other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | Array
Other array. Must be compatible with self (see Broadcasting). Should have a real-valued data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.ge)
def __eq__(self, other: int | float | bool | Array, /) -> Array: # type: ignore[override]
"""
Computes the truth value of self_i == other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | bool | Array
Other array. Must be compatible with self (see Broadcasting). May have any data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.eq)
def __ne__(self, other: int | float | bool | Array, /) -> Array: # type: ignore[override]
"""
Computes the truth value of self_i != other_i for each element of an array instance with the respective
element of the array other.
Parameters
----------
self : Array
Array instance. Should have a numeric data type.
other: int | float | bool | Array
Other array. Must be compatible with self (see Broadcasting). May have any data type.
Returns
-------
out : Array
An array containing the element-wise results. The returned array must have a data type of bool.
"""
return process_c_function(self, other, wrapper.neq)
# Reflected Arithmetic Operators
def __radd__(self, other: int | float | Array, /) -> Array:
"""
Return other + self.
"""
return process_c_function(other, self, wrapper.add)
def __rsub__(self, other: int | float | Array, /) -> Array:
"""
Return other - self.
"""
return process_c_function(other, self, wrapper.sub)
def __rmul__(self, other: int | float | Array, /) -> Array:
"""
Return other * self.
"""
return process_c_function(other, self, wrapper.mul)
def __rtruediv__(self, other: int | float | Array, /) -> Array:
"""
Return other / self.
"""
return process_c_function(other, self, wrapper.div)
def __rfloordiv__(self, other: int | float | Array, /) -> Array:
# TODO
return NotImplemented
def __rmod__(self, other: int | float | Array, /) -> Array:
"""
Return other % self.
"""
return process_c_function(other, self, wrapper.mod)
def __rpow__(self, other: int | float | Array, /) -> Array:
"""
Return other ** self.
"""
return process_c_function(other, self, wrapper.pow)
# Reflected Array Operators
def __rmatmul__(self, other: Array, /) -> Array:
# TODO
return NotImplemented
# Reflected Bitwise Operators
def __rand__(self, other: int | bool | Array, /) -> Array:
"""
Return other & self.
"""
return process_c_function(other, self, wrapper.bitand)
def __ror__(self, other: int | bool | Array, /) -> Array:
"""
Return other | self.
"""
return process_c_function(other, self, wrapper.bitor)
def __rxor__(self, other: int | bool | Array, /) -> Array:
"""
Return other ^ self.
"""
return process_c_function(other, self, wrapper.bitxor)
def __rlshift__(self, other: int | Array, /) -> Array:
"""
Return other << self.
"""
return process_c_function(other, self, wrapper.bitshiftl)
def __rrshift__(self, other: int | Array, /) -> Array:
"""
Return other >> self.
"""
return process_c_function(other, self, wrapper.bitshiftr)
# In-place Arithmetic Operators
def __iadd__(self, other: int | float | Array, /) -> Array:
# TODO discuss either we need to support complex and bool as other input type
"""
Return self += other.
"""
return process_c_function(self, other, wrapper.add)
def __isub__(self, other: int | float | Array, /) -> Array:
"""
Return self -= other.
"""
return process_c_function(self, other, wrapper.sub)
def __imul__(self, other: int | float | Array, /) -> Array:
"""
Return self *= other.
"""
return process_c_function(self, other, wrapper.mul)
def __itruediv__(self, other: int | float | Array, /) -> Array:
"""
Return self /= other.
"""
return process_c_function(self, other, wrapper.div)
def __ifloordiv__(self, other: int | float | Array, /) -> Array:
# TODO
return NotImplemented
def __imod__(self, other: int | float | Array, /) -> Array:
"""
Return self %= other.
"""
return process_c_function(self, other, wrapper.mod)
def __ipow__(self, other: int | float | Array, /) -> Array:
"""
Return self **= other.
"""
return process_c_function(self, other, wrapper.pow)
# In-place Array Operators
def __imatmul__(self, other: Array, /) -> Array:
# TODO
return NotImplemented
# In-place Bitwise Operators
def __iand__(self, other: int | bool | Array, /) -> Array:
"""
Return self &= other.
"""
return process_c_function(self, other, wrapper.bitand)
def __ior__(self, other: int | bool | Array, /) -> Array:
"""
Return self |= other.
"""
return process_c_function(self, other, wrapper.bitor)
def __ixor__(self, other: int | bool | Array, /) -> Array:
"""
Return self ^= other.
"""
return process_c_function(self, other, wrapper.bitxor)
def __ilshift__(self, other: int | Array, /) -> Array:
"""
Return self <<= other.
"""
return process_c_function(self, other, wrapper.bitshiftl)
def __irshift__(self, other: int | Array, /) -> Array:
"""
Return self >>= other.
"""
return process_c_function(self, other, wrapper.bitshiftr)
# Methods
def __abs__(self) -> Array:
# TODO
return NotImplemented
def __array_namespace__(self, *, api_version: str | None = None) -> Any:
# TODO
return NotImplemented
# def __bool__(self) -> bool:
# # TODO consider using scalar() and is_scalar()
# return NotImplemented
def __complex__(self) -> complex:
# TODO
return NotImplemented
def __dlpack__(self, *, stream: int | Any | None = None): # type: ignore[no-untyped-def]
# TODO implementation and expected return type -> PyCapsule
return NotImplemented
def __dlpack_device__(self) -> tuple[Enum, int]:
# TODO
return NotImplemented
def __float__(self) -> float:
# TODO
return NotImplemented
def __getitem__(self, key: IndexKey, /) -> Array:
"""
Returns self[key].
Parameters
----------
self : Array
Array instance.
key : int | slice | tuple[int | slice | Array, ...] | Array
Index key.
Returns
-------
out : Array
An array containing the accessed value(s). The returned array must have the same data type as self.
"""
out = Array()
ndims = self.ndim
indexing = key
if isinstance(key, int | float | slice): # when indexing with one dimension, treat it as indexing a flat array
ndims = 1
elif isinstance(key, Array): # when indexing with one array, treat it as indexing a flat array
ndims = 1
if key.is_bool:
indexing = wrapper.where(key.arr)
else:
indexing = key.arr
elif isinstance(key, tuple):
key_list = []
for elem in key:
if isinstance(elem, Array):
if elem.is_bool:
key_list.append(wrapper.where(elem.arr))
else:
key_list.append(elem.arr)
else:
key_list.append(elem)
indexing = tuple(key_list)
out._arr = wrapper.index_gen(self._arr, ndims, wrapper.get_indices(indexing)) # type: ignore[arg-type]
if isinstance(key, Array) and key.is_bool:
wrapper.release_array(indexing)
elif isinstance(key, tuple):
for i in range(len(key)):
if isinstance(key[i], Array) and key[i].is_bool:
wrapper.release_array(indexing[i])
return out
def __index__(self) -> int:
# TODO
return NotImplemented
def __int__(self) -> int:
# TODO
return NotImplemented
def __len__(self) -> int:
return self.shape[0] if self.shape else 0
def __setitem__(self, key: IndexKey, value: int | float | bool | Array, /) -> None:
"""
Assigns self[key] = value
Parameters
----------
self : Array
Array instance.
key : int | slice | tuple[int | slice | Array, ...] | Array
Index key.
value: int | float | complex | bool | Array
"""
ndims = self.ndim
is_array_with_bool = isinstance(key, Array) and type(key) is afbool
if is_array_with_bool:
ndims = 1
num = wrapper.count_all(key.arr) # type: ignore[union-attr]
if num == 0:
return
if isinstance(value, int | float | complex | bool):
dims = _get_processed_index(key, self.shape)
if is_array_with_bool:
ndims = 1
other_arr = wrapper.create_constant_array(value, (int(num.real),), self.dtype)
else:
other_arr = wrapper.create_constant_array(value, dims, self.dtype)
del_other = True
else:
other_arr = value.arr
del_other = False
indexing = key
if isinstance(key, int | float | slice): # when indexing with one dimension, treat it as indexing a flat array
ndims = 1
elif isinstance(key, Array): # when indexing with one array, treat it as indexing a flat array
ndims = 1
if key.is_bool:
indexing = wrapper.where(key.arr)
else:
indexing = key.arr
elif isinstance(key, tuple):
key_list = []
for elem in key:
if isinstance(elem, Array):
if elem.is_bool:
locs = wrapper.where(elem.arr)
key_list.append(locs)
else:
key_list.append(elem.arr)
else:
key_list.append(elem)
indexing = tuple(key_list)
out = wrapper.assign_gen(self._arr, other_arr, ndims, wrapper.get_indices(indexing))
if isinstance(key, Array) and key.is_bool:
wrapper.release_array(indexing)
elif isinstance(key, tuple):
for i in range(len(key)):
if isinstance(key[i], Array) and key[i].is_bool:
wrapper.release_array(indexing[i])
wrapper.release_array(self._arr)
if del_other:
wrapper.release_array(other_arr)
self._arr = out
def __str__(self) -> str:
# TODO change the look of array str. E.g., like np.array
# if not _in_display_dims_limit(self.shape):
# return _metadata_string(self.dtype, self.shape)
return _metadata_string(self.dtype) + _array_as_str(self)
def __repr__(self) -> str:
# return _metadata_string(self.dtype, self.shape)
# TODO change the look of array representation. E.g., like np.array
return _array_as_str(self)
def __del__(self) -> None:
if not hasattr(self._arr, "value"):
return
if self._arr.value == 0:
return
wrapper.release_array(self._arr)
self._arr.value = 0
def to_device(self, device: Any, /, *, stream: int | Any = None) -> Array:
# TODO implementation and change device type from Any to Device
return NotImplemented
# Attributes
@property
def dtype(self) -> Dtype:
"""
Data type of the array elements.
Returns
-------
out : Dtype
Array data type.
"""
return c_api_value_to_dtype(wrapper.get_type(self._arr))
@property
def device(self) -> Any:
# TODO
return NotImplemented
@property
@afarray_as_array
def T(self) -> Array:
"""
Transpose of the array.
Returns
-------
Array
Two-dimensional array whose first and last dimensions (axes) are permuted in reverse order relative to
original array. The returned array must have the same data type as the original array.
Note
----
- The array instance must be two-dimensional. If the array instance is not two-dimensional,
| an error should be raised.
"""
if self.ndim < 2:
raise TypeError(f"Array should be at least 2-dimensional. Got {self.ndim}-dimensional array")
# TODO add check if out.dtype == self.dtype
return cast(Array, wrapper.transpose(self._arr, False))
@property
@afarray_as_array
def H(self) -> Array:
"""
Hermitian Conjugate of the array.
Returns
-------
Array
Two-dimensional array whose first and last dimensions (axes) are permuted in reverse order relative to
| original array with its elements complex conjugated.
| The returned array must have the same data type as the original array.
Note
----
- The array instance must be two-dimensional. If the array instance is not two-dimensional,
| an error should be raised.
"""
if self.ndim < 2:
raise TypeError(f"Array should be at least 2-dimensional. Got {self.ndim}-dimensional array")
# TODO add check if out.dtype == self.dtype
return cast(Array, wrapper.transpose(self._arr, True))
@property
def size(self) -> int:
"""
Number of elements in an array.
Returns
-------
int
Number of elements in an array
Note
----
- This must equal the product of the array's dimensions.
"""
# NOTE previously - elements()
return wrapper.get_elements(self._arr)
@property
def ndim(self) -> int:
"""
Number of array dimensions (axes).
int
Number of array dimensions (axes).
"""
return wrapper.get_numdims(self._arr)
@property
def shape(self) -> tuple[int, ...]:
"""
Array dimensions.
Returns