taplib.tap.TensorAccessPattern Class Reference

Public Member Functions
def	__init__ (self, Sequence[int] tensor_dims, int offset, Sequence[int] sizes, Sequence[int] strides)
Sequence[int]	tensor_dims (self)
int	offset (self)
Sequence[int]	sizes (self)
Sequence[int]	strides (self)
Sequence[tuple[int, int]]	transformation_dims (self)
tuple[np.ndarray, np.ndarray]	accesses (self)
np.ndarray	access_order (self)
np.ndarray	access_count (self)
Generator[int, None, None]	access_generator (self)
bool	compare_access_orders (self, TensorAccessPattern other)
None	visualize (self, bool|None show_arrows=None, str|None title=None, str|None file_path=None, bool show_plot=True, bool plot_access_count=False)
str	__str__ (self)
def	__eq__ (self, other)
def	__ne__ (self, other)

Private Member Functions
tuple[np.ndarray, np.ndarray]	_calculate_accesses (self, bool calc_order, bool calc_count)

Private Attributes
	_tensor_dims
	_offset
	_strides
	_sizes

Static Private Attributes
	_DTYPE = np.int32

Detailed Description

A TensorAccessPattern represents a data access pattern applied to a tensor
of a specific dimension. This is a base class meant to generically represent
such as transformation using sizes, strides, and an offset.

Constructor & Destructor Documentation

__init__()

def taplib.tap.TensorAccessPattern.__init__	(		self,
		Sequence[int]	tensor_dims,
		int	offset,
		Sequence[int]	sizes,
		Sequence[int]	strides
	)

An object representing an access pattern applied to a tensor.

Args:
    tensor_dims (Sequence[int]): Dimensions of the tensor
    offset (int): Offset into the tensor to begin the transformation
    sizes (Sequence[int]): Transformation sizes
    strides (Sequence[int]): Transformation strides

Member Function Documentation

__eq__()

def taplib.tap.TensorAccessPattern.__eq__	(		self,
			other
	)

__ne__()

def taplib.tap.TensorAccessPattern.__ne__	(		self,
			other
	)

__str__()

str taplib.tap.TensorAccessPattern.__str__

(

self

)

_calculate_accesses()

tuple[np.ndarray, np.ndarray] taplib.tap.TensorAccessPattern._calculate_accesses (   self, bool  calc_order, bool   calc_count  )

private

access_count()

np.ndarray taplib.tap.TensorAccessPattern.access_count

(

self

)

The access_count ndarray contains the number of times each element is
accessed by the tensor access pattern.

Returns:
    np.ndarray: access_count

access_generator()

Generator[int, None, None] taplib.tap.TensorAccessPattern.access_generator

(

self

)

This function returns an iterator that returns the access index
into the flattened tensor that this access pattern represents. This can
be used to calculate the access count or to enumerate accesses.

Yields:
    int: The next access index

access_order()

np.ndarray taplib.tap.TensorAccessPattern.access_order

(

self

)

The access_order ndarray sequentially counts access to elements in the
tensor. If an element is accessed more than once, only the last count is reflected.

Returns:
    np.ndarray: access_order

accesses()

tuple[np.ndarray, np.ndarray] taplib.tap.TensorAccessPattern.accesses

(

self

)

Returns the access_order and access_count arrays.

The access_order ndarray sequentially counts access to elements in the
tensor. If an element is accessed more than once, only the last count is reflected.

The access_count ndarray contains the number of times each element is
accessed by the tensor access pattern.

Returns:
    tuple[np.ndarray, np.ndarray]: access_order, access_count

compare_access_orders()

bool taplib.tap.TensorAccessPattern.compare_access_orders	(		self,
		TensorAccessPattern	other
	)

This function creates an alternative way to compare access patterns.
Sometimes access patterns with different sizes/strides are functionally equivalent;
to detect functional equivalency, this function uses iterators produced by
access_generator() to compare the access patterns. This is more performant than
comparing the numpy array access_order or access_count tensors.

Args:
    other (TensorAccessPattern): The TensorAccessPattern to compare to

Raises:
    ValueError: other must be of type TensorAccessPattern

Returns:
    bool: True if the TensorAccessPatterns are functionally equivalent; false otherwise.

offset()

int taplib.tap.TensorAccessPattern.offset

(

self

)

Returns the offset into the tensor

Returns:
    int: offset

sizes()

Sequence[int] taplib.tap.TensorAccessPattern.sizes

(

self

)

A copy of the access pattern sizes

Returns:
    Sequence[int]: Transformation sizes

strides()

Sequence[int] taplib.tap.TensorAccessPattern.strides

(

self

)

A copy of the access pattern strides

Returns:
    Sequence[int]: Trsnformation strides

tensor_dims()

Sequence[int] taplib.tap.TensorAccessPattern.tensor_dims

(

self

)

A copy of the dimensions of the tensor

Returns:
    Sequence[int]: Tensor dimensions

transformation_dims()

Sequence[tuple[int, int]] taplib.tap.TensorAccessPattern.transformation_dims

(

self

)

The access pattern represented as a sequence of (size, stride) tuples

Returns:
    Sequence[tuple[int, int]]: Transformation dimensions

visualize()

None taplib.tap.TensorAccessPattern.visualize	(		self,
		bool | None	show_arrows = None,
		str | None	title = None,
		str | None	file_path = None,
		bool	show_plot = True,
		bool	plot_access_count = False
	)

Visualize the TensorAccessPattern using a graph.

Args:
    show_arrows (bool | None, optional): Display arrows between sequentially accessed elements. Defaults to None.
    title (str | None, optional): Title of the produced graph. Defaults to None.
    file_path (str | None, optional): Path to save the graph at; if none, it is not saved. Defaults to None.
    show_plot (bool, optional): Show the plot (this is useful for Jupyter notebooks). Defaults to True.
    plot_access_count (bool, optional): Plot the access count in addition to the access order. Defaults to False.

Raises:
    NotImplementedError: This function is not implemented for all dimensions.

Member Data Documentation

_DTYPE

taplib.tap.TensorAccessPattern._DTYPE = np.int32

staticprivate

_offset

taplib.tap.TensorAccessPattern._offset

private

_sizes

taplib.tap.TensorAccessPattern._sizes

private

_strides

taplib.tap.TensorAccessPattern._strides

private

_tensor_dims

taplib.tap.TensorAccessPattern._tensor_dims

private

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The documentation for this class was generated from the following file:

• helpers/taplib/tap.py