Skip to content
Snippets Groups Projects
Commit 3f26530c authored by Oscar Gustafsson's avatar Oscar Gustafsson :bicyclist:
Browse files

Fix formatting

parent 46e7ce64
No related branches found
No related tags found
1 merge request!353Fix formatting
Pipeline #96734 passed
Stage: test
Stage: deploy
Hide whitespace changes
Inline Side-by-side
Showing
with 60 additions and 41 deletions
b_asic/resources.py
+ 60
− 41
View file @ 3f26530c
......@@ -163,20 +163,24 @@ class _ForwardBackwardEntry:
outputs_from: Optional[int] = None,
):
"""
Single entry in a _ForwardBackwardTable. Aggregate type of input, output and list of registers.
Single entry in a _ForwardBackwardTable.
Aggregate type of input, output and list of registers.
Parameters
----------
inputs : List[Process], optional
inputs : list of Process, optional
input
outputs : List[Process], optional
outputs : list of Process, optional
output
regs : List[Optional[Process]], optional
regs : list of Process, optional
regs
back_edge_to : dict, optional
Dictionary containing back edges of this entry to registers in the next entry.
Dictionary containing back edges of this entry to registers in the next
entry.
back_edge_from : dict, optional
Dictionary containing the back edge of the previous entry to registers in this entry.
Dictionary containing the back edge of the previous entry to registers in
this entry.
outputs_from : int, optional
"""
self.inputs: List[Process] = [] if inputs is None else inputs
......@@ -192,8 +196,10 @@ class _ForwardBackwardEntry:
class _ForwardBackwardTable:
def __init__(self, collection: 'ProcessCollection'):
"""
Forward-Backward allocation table for ProcessCollections. This structure implements the forward-backward
register allocation algorithm, which is used to generate hardware from MemoryVariables in a ProcessCollection.
Forward-Backward allocation table for ProcessCollections.
This structure implements the forward-backward register allocation algorithm,
which is used to generate hardware from MemoryVariables in a ProcessCollection.
Parameters
----------
......@@ -218,10 +224,12 @@ class _ForwardBackwardTable:
self.table.append(entry)
# Insert all processes (one per time-slot) to the table input
# TODO: "Input each variable at the time step corresponding to the beginning of its lifetime. If multiple
# variables are input in a given cycle, theses are allocated to multple registers such that the variable
# with the longest lifetime is allocated to the inital register and the other variables are allocated to
# consecutive registers in decreasing order of lifetime." -- K. Parhi
# TODO: "Input each variable at the time step corresponding to the beginning of
# its lifetime. If multiple variables are input in a given cycle, these
# are allocated to multple registers such that the variable with the
# longest lifetime is allocated to the inital register and the other
# variables are allocated to consecutive registers in decreasing order
# of lifetime." -- K. Parhi
for mv in collection:
self.table[mv.start_time].inputs.append(mv)
if mv.execution_time:
......@@ -245,13 +253,15 @@ class _ForwardBackwardTable:
def _do_forward_allocation(self):
"""
Forward all Processes as far as possible in the register chain. Processes are forwarded until they reach their
end time (at which they are added to the output list), or until they reach the end of the register chain.
Forward all Processes as far as possible in the register chain.
Processes are forwarded until they reach their end time (at which they are
added to the output list), or until they reach the end of the register chain.
"""
rows = len(self.table)
cols = len(self.table[0].regs)
# Note that two passes of the forward allocation need to be done, since variables may loop around the schedule
# cycle boundary.
# Note that two passes of the forward allocation need to be done, since
# variables may loop around the schedule cycle boundary.
for _ in range(2):
for time, entry in enumerate(self.table):
for reg_idx, reg in enumerate(entry.regs):
......@@ -282,9 +292,9 @@ class _ForwardBackwardTable:
cols = len(self.table[0].regs)
outputs = {out for e in self.table for out in e.outputs}
#
# Pass #1: Find any (one) non-dead variable from the last register and try to backward allocate it to a
# previous register where it is not blocking an open path. This heuristic helps minimize forward allocation
# moves later.
# Pass #1: Find any (one) non-dead variable from the last register and try to
# backward allocate it to a previous register where it is not blocking an open
# path. This heuristic helps minimize forward allocation moves later.
#
for time, entry in enumerate(self.table):
reg = entry.regs[-1]
......@@ -299,7 +309,8 @@ class _ForwardBackwardTable:
next_entry.back_edge_from[nreg_idx] = cols - 1
return
#
# Pass #2: Backward allocate the first non-dead variable from the last registers to an empty register.
# Pass #2: Backward allocate the first non-dead variable from the last
# registers to an empty register.
#
for time, entry in enumerate(self.table):
reg = entry.regs[-1]
......@@ -480,12 +491,12 @@ class ProcessCollection:
show_markers : bool, default True
Show markers at read and write times.
row : int, optional
Render all processes in this collection on a specified row in the matplotlib axes object.
Defaults to None, which renders all processes on separate rows. This option is useful when
drawing cell assignments.
Render all processes in this collection on a specified row in the Matplotlib
axes object. Defaults to None, which renders all processes on separate rows.
This option is useful when drawing cell assignments.
allow_excessive_lifetimes : bool, default False
If set to true, the plot method allows ploting collections of variables with a greater lifetime
than the schedule time.
If set to true, the plot method allows ploting collections of variables with
a greater lifetime than the schedule time.
Returns
-------
......@@ -606,8 +617,9 @@ class ProcessCollection:
) -> None:
"""
Show the process collection using the current Matplotlib backend.
Equivalent to creating a Matplotlib figure, passing it and arguments to :func:`plot`
and invoking :py:meth:`matplotlib.figure.Figure.show`.
Equivalent to creating a Matplotlib figure, passing it and arguments to
:meth:`plot` and invoking :py:meth:`matplotlib.figure.Figure.show`.
Parameters
----------
......@@ -624,8 +636,8 @@ class ProcessCollection:
show_markers : bool, default True
Show markers at read and write times.
allow_excessive_lifetimes : bool, default False
If set to true, the plot method allows ploting collections of variables with a greater lifetime
than the schedule time.
If set to True, the plot method allows ploting collections of variables with
a greater lifetime than the schedule time.
title : str, optional
Title of plot.
"""
......@@ -690,7 +702,6 @@ class ProcessCollection:
exclusion_graph = nx.Graph()
exclusion_graph.add_nodes_from(self._collection)
for node1 in exclusion_graph:
# node1_stop_time = (node1.start_time + node1.execution_time) % self.schedule_time
node1_stop_times = tuple(
read_time % self.schedule_time for read_time in node1.read_times
)
......@@ -701,7 +712,6 @@ class ProcessCollection:
if node1 == node2:
continue
else:
# node2_stop_time = (node2.start_time + node2.execution_time) % self.schedule_time
node2_stop_times = tuple(
read_time % self.schedule_time for read_time in node2.read_times
)
......@@ -795,7 +805,8 @@ class ProcessCollection:
The heuristic used when splitting based on execution times.
coloring_strategy : str, default: 'saturation_largest_first'
Node ordering strategy passed to :func:`networkx.algorithms.coloring.greedy_color`.
Node ordering strategy passed to
:func:`networkx.algorithms.coloring.greedy_color`.
This parameter is only considered if *heuristic* is set to 'graph_color'.
One of
......@@ -887,7 +898,8 @@ class ProcessCollection:
The total number of ports used when splitting process collection based on
memory variable access.
coloring_strategy : str, default: 'saturation_largest_first'
Node ordering strategy passed to :func:`networkx.algorithms.coloring.greedy_color`
Node ordering strategy passed to
:func:`networkx.algorithms.coloring.greedy_color`
One of
* 'largest_first'
* 'random_sequential'
......@@ -911,7 +923,8 @@ class ProcessCollection:
coloring: Dict[Process, int],
) -> Set["ProcessCollection"]:
"""
Split :class:`Process` objects into a set of :class:`ProcessesCollection` objects based on a provided graph coloring.
Split :class:`Process` objects into a set of :class:`ProcessesCollection`
objects based on a provided graph coloring.
Resulting :class:`ProcessCollection` will have the same schedule time and cyclic
property as self.
......@@ -960,17 +973,21 @@ class ProcessCollection:
coloring: Optional[Dict[Process, int]] = None,
) -> Set["ProcessCollection"]:
"""
Perform cell assignment of the processes in this collection using graph coloring.
Perform cell assignment of the processes in this collection using graph
coloring.
Two or more processes can share a single cell if, and only if, they have no overlaping time alive.
Two or more processes can share a single cell if, and only if, they have no
overlaping time alive.
Parameters
----------
coloring_strategy : str, default: "saturation_largest_first"
Graph coloring strategy passed to :func:`networkx.algorithms.coloring.greedy_color`.
Graph coloring strategy passed to
:func:`networkx.algorithms.coloring.greedy_color`.
coloring : dictionary, optional
An optional graph coloring, dictionary with Process and its associated color (int).
If a graph coloring is not provided throught this parameter, one will be created when calling this method.
An optional graph coloring, dictionary with Process and its associated color
(int). If a graph coloring is not provided throught this parameter, one will
be created when calling this method.
Returns
-------
......@@ -991,9 +1008,11 @@ class ProcessCollection:
def left_edge_cell_assignment(self) -> Dict[int, "ProcessCollection"]:
"""
Perform cell assignment of the processes in this collection using the left-edge algorithm.
Perform cell assignment of the processes in this collection using the left-edge
algorithm.
Two or more processes can share a single cell if, and only if, they have no overlaping time alive.
Two or more processes can share a single cell if, and only if, they have no
overlaping time alive.
Returns
-------
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment