Merge remote-tracking branch 'refs/remotes/origin/main'

MiniC/Lib/Dominators.py

@@ -0,0 +1,128 @@
+"""
+Utility functions to work with dominators in a :py:class:`CFG <Lib.CFG.CFG>`.
+
+Do not hesitate to look at the source of the functions
+to get a better understanding of the algorithms.
+"""
+
+from typing import Dict, Set
+from graphviz import Digraph
+from Lib.CFG import Block, CFG
+
+
+def computeDom(cfg: CFG) -> Dict[Block, Set[Block]]:
+    """
+    `computeDom(cfg)` computes the table associating blocks to their
+    dominators in `cfg`.
+    It works by solving the equation system.
+
+    This is an helper function called during SSA entry.
+    """
+    all_blocks: Set[Block] = set(cfg.get_blocks())
+    dominators: Dict[Block, Set[Block]] = dict()
+    for b in all_blocks:
+        if b.get_in():  # If b has some predecessor
+            dominators[b] = all_blocks
+        else:  # If b has no predecessors
+            dominators[b] = {b}
+    new_dominators: Dict[Block, Set[Block]] = dict()
+    while True:
+        for b in all_blocks:
+            if b.get_in():
+                dom_preds = [dominators[b2] for b2 in b.get_in()]
+                new_dominators[b] = {b}.union(set.intersection(*dom_preds))
+            else:
+                new_dominators[b] = {b}
+        if dominators == new_dominators:
+            break
+        else:
+            dominators = new_dominators
+            new_dominators = dict()
+    return dominators
+
+
+def printDT(filename: str, graph: Dict[Block, Set[Block]]) -> None:  # pragma: no cover
+    """Display a graphical rendering of the given domination tree."""
+    dot = Digraph()
+    for k in graph:
+        dot.node(str(k.get_label()))
+    for k in graph:
+        for v in graph[k]:
+            dot.edge(str(k.get_label()), str(v.get_label()))
+    dot.render(filename, view=True)
+
+
+def computeDT(cfg: CFG, dominators: Dict[Block, Set[Block]],
+              dom_graphs: bool, basename: str) -> Dict[Block, Set[Block]]:
+    """
+    `computeDT(cfg, dominators)` computes the domination tree of `cfg`
+    using the previously computed `dominators`.
+    It returns `DT`, a dictionary which associates a block with its children
+    in the dominator tree.
+
+    This is an helper function called during SSA entry.
+    """
+    # First, compute the immediate dominators
+    idominators: Dict[Block, Block] = {}
+    for b, doms in dominators.items():
+        # The immediate dominator of b is the unique vertex n ≠ b
+        # which dominates b and is dominated by all vertices in Dom(b) − b.
+        strict_doms = doms - {b}
+        idoms = set()
+        for n in strict_doms:
+            if strict_doms.issubset(dominators[n]):
+                idoms.add(n)
+        if idoms:
+            assert (len(idoms) == 1)
+            idominators[b] = idoms.pop()
+    # Then, simply inverse the relation to obtain the domination tree
+    DT = {b: set() for b in cfg.get_blocks()}
+    for i, idominator in idominators.items():
+        DT[idominator].add(i)
+    # Print the domination tree if asked
+    if dom_graphs:
+        s = "{}.{}.ssa.DT.dot".format(basename, cfg.fdata.get_name())
+        print("SSA - domination tree graph:", s)
+        printDT(s, DT)
+    return DT
+
+
+def _computeDF_at_block(
+        cfg: CFG,
+        dominators: Dict[Block, Set[Block]],
+        DT: Dict[Block, Set[Block]],
+        b: Block,
+        DF: Dict[Block, Set[Block]]) -> None:
+    """
+    `_computeDF_at_block(...)` computes the dominance frontier at the given block,
+    by updating `DF`.
+
+    This is an helper function called during SSA entry.
+    """
+    S: Set[Block] = {succ for succ in cfg.out_blocks(b) if succ not in DT[b]}
+    for b_succ in DT[b]:
+        _computeDF_at_block(cfg, dominators, DT, b_succ, DF)
+        for b_frontier in DF[b_succ]:
+            if b not in (dominators[b_frontier] - {b_frontier}):
+                S.add(b_frontier)
+    DF[b] = S
+
+
+def computeDF(cfg: CFG, dominators: Dict[Block, Set[Block]],
+              DT: Dict[Block, Set[Block]], dom_graphs: bool, basename: str
+              ) -> Dict[Block, Set[Block]]:
+    """
+    `computeDF(...)` computes the dominance frontier of a CFG.
+    It returns `DF` which associates a block to its frontier.
+
+    This is an helper function called during SSA entry.
+    """
+    DF: Dict[Block, Set[Block]] = dict()
+    for b_entry in cfg.get_entries():
+        _computeDF_at_block(cfg, dominators, DT, b_entry, DF)
+    # Print the domination frontier on the CFG if asked
+    if dom_graphs:
+        s = "{}.{}.ssa.DF.dot".format(basename, cfg.fdata.get_name())
+        print("SSA - dominance frontier graph:", s)
+        cfg.print_dot(s, DF, True)
+    return DF

MiniC/Lib/Graphes.py

@@ -0,0 +1,306 @@
+""" Python Classes for Oriented and Non Oriented Graphs
+"""
+
+from graphviz import Digraph  # for dot output
+from typing import List, Dict, Set, Tuple, Any
+
+
+class GraphError(Exception):
+    """Exception raised for self loops.
+    """
+
+    message: str
+
+    def __init__(self, message: str):
+        self.message = message
+
+
+class GeneralGraph(object):
+    """
+    General class regrouping similarities
+    between directed and non oriented graphs.
+    The only differences between the two are:
+
+    - how to compute the set of edges
+    - how to add an edge
+    - how to print the graph
+    - how to delete a vertex
+    - how to delete an edge
+    - we only color undirected graphs
+    """
+
+    graph_dict: Dict[Any, Set]
+
+    def __init__(self, graph_dict=None):
+        """
+        Initializes a graph object.
+        If no dictionary or None is given,
+        an empty dictionary will be used.
+        """
+        if graph_dict is None:
+            graph_dict = {}
+        self.graph_dict = graph_dict
+
+    def vertices(self) -> List[Any]:
+        """Return the vertices of a graph."""
+        return list(self.graph_dict.keys())
+
+    def add_vertex(self, vertex: Any) -> None:
+        """
+        If the vertex "vertex" is not in
+        self.graph_dict, a key "vertex" with an empty
+        list as a value is added to the dictionary.
+        Otherwise nothing has to be done.
+        """
+        if vertex not in self.graph_dict:
+            self.graph_dict[vertex] = set()
+
+    def edges(self) -> List[Set]:
+        """Return the edges of the graph."""
+        return []
+
+    def __str__(self):
+        res = "vertices: "
+        for k in self.graph_dict:
+            res += str(k) + " "
+        res += "\nedges: "
+        for edge in self.edges():
+            res += str(edge) + " "
+        return res
+
+    def dfs_traversal(self, root: Any) -> List[Any]:
+        """
+        Compute a depth first search of the graph,
+        from the vertex root.
+        """
+        seen: List[Any] = []
+        todo: List[Any] = [root]
+        while len(todo) > 0:  # while todo ...
+            current = todo.pop()
+            seen.append(current)
+            for neighbour in self.graph_dict[current]:
+                if neighbour not in seen:
+                    todo.append(neighbour)
+        return seen
+
+    def is_reachable_from(self, v1: Any, v2: Any) -> bool:
+        """True if there is a path from v1 to v2."""
+        return v2 in self.dfs_traversal(v1)
+
+    def connected_components(self) -> List[List[Any]]:
+        """
+        Compute the list of all connected components of the graph,
+        each component being a list of vetices.
+        """
+        components: List[List[Any]] = []
+        done: List[Any] = []
+        for v in self.vertices():
+            if v not in done:
+                v_comp = self.dfs_traversal(v)
+                components.append(v_comp)
+                done.extend(v_comp)
+        return components
+
+    def bfs_traversal(self, root: Any) -> List[Any]:
+        """
+        Compute a breadth first search of the graph,
+        from the vertex root.
+        """
+        seen: List[Any] = []
+        todo: List[Any] = [root]
+        while len(todo) > 0:  # while todo ...
+            current = todo.pop(0)  # list.pop(0): for dequeuing (on the left...) !
+            seen.append(current)
+            for neighbour in self.graph_dict[current]:
+                if neighbour not in seen:
+                    todo.append(neighbour)
+        return seen
+
+
+class Graph(GeneralGraph):
+    """Class for non oriented graphs."""
+
+    def edges(self) -> List[Set]:
+        """
+        A static method generating the set of edges
+        (they appear twice in the dictionnary).
+        Return a list of sets.
+        """
+        edges = []
+        for vertex in self.graph_dict:
+            for neighbour in self.graph_dict[vertex]:
+                if {neighbour, vertex} not in edges:
+                    edges.append({vertex, neighbour})
+        return edges
+
+    def add_edge(self, edge: Tuple[Any, Any]) -> None:
+        """
+        Add an edge in the graph.
+        edge should be a pair and not (c,c)
+        (we call g.add_edge((v1,v2)))
+        """
+        (vertex1, vertex2) = edge
+        if vertex1 == vertex2:
+            raise GraphError("Cannot add a self loop on vertex {} in an unoriented graph.".format(
+                             str(vertex1)))
+        if vertex1 in self.graph_dict:
+            self.graph_dict[vertex1].add(vertex2)
+        else:
+            self.graph_dict[vertex1] = {vertex2}
+        if vertex2 in self.graph_dict:
+            self.graph_dict[vertex2].add(vertex1)
+        else:
+            self.graph_dict[vertex2] = {vertex1}
+
+    def print_dot(self, name: str, colors={}) -> None:
+        """Print the graph."""
+        color_names = ['red', 'blue', 'green', 'yellow', 'cyan', 'magenta'] + \
+            [f"grey{i}" for i in range(0, 100, 10)]
+        color_shapes = ['ellipse', 'polygon', 'box', 'circle', 'egg', 'pentagon', 'hexagon']
+        dot = Digraph(comment='Conflict Graph')
+        for k in self.graph_dict:
+            shape = None
+            if not colors:
+                color = "red"  # Graph not colored: red for everyone
+            elif k not in colors:
+                color = "grey"  # Node not colored: grey
+            else:
+                n = colors[k]
+                if n < len(color_names):
+                    color = color_names[colors[k]]
+                else:
+                    color = "black"  # Too many colors anyway, it won't be readable.
+                shape = color_shapes[n % len(color_shapes)]
+            dot.node(str(k), color=color, shape=shape)
+        for (v1, v2) in self.edges():
+            dot.edge(str(v1), str(v2), dir="none")
+        # print(dot.source)
+        dot.render(name, view=True)  # print in pdf
+
+    def delete_vertex(self, vertex: Any) -> None:
+        """Delete a vertex and all the adjacent edges."""
+        gdict = self.graph_dict
+        for neighbour in gdict[vertex]:
+            gdict[neighbour].remove(vertex)
+        del gdict[vertex]
+
+    def delete_edge(self, edge: Tuple[Any, Any]):
+        """Delete an edge."""
+        (v1, v2) = edge
+        self.graph_dict[v1].remove(v2)
+        self.graph_dict[v2].remove(v1)
+
+    def color(self) -> Dict[Any, int]:
+        """
+        Color the graph with an unlimited number of colors.
+        Return a dict vertex -> color, where color is an integer (0, 1, ...).
+        """
+        coloring, _, _ = self.color_with_k_colors()
+        return coloring
+
+    # see algo of the course
+    def color_with_k_colors(self, K=None, avoidingnodes=()) -> Tuple[Dict[Any, int], bool, List]:
+        """
+        Color with <= K colors (if K is unspecified, use unlimited colors).
+
+        Return 3 values:
+
+        - a dict vertex -> color
+        - a Boolean, True if the coloring succeeded
+        - the set of nodes actually colored
+
+        Do not color vertices belonging to avoidingnodes.
+
+        Continue even if the algo fails.
+        """
+        if K is None:
+            K = len(self.graph_dict)
+        todo_vertices = []
+        is_total = True
+        gcopy = Graph(self.graph_dict.copy())
+        # suppress nodes that are not to be considered.
+        for node in avoidingnodes:
+            gcopy.delete_vertex(node)
+        # append nodes in the list according to their degree and node number:
+        while gcopy.graph_dict:
+            todo = list(gcopy.graph_dict)
+            todo.sort(key=lambda v: (len(gcopy.graph_dict[v]), str(v)))
+            lower = todo[0]
+            todo_vertices.append(lower)
+            gcopy.delete_vertex(lower)
+        # Now reverse the list: first elements are those with higher degree
+        # print(todo_vertices)
+        todo_vertices.reverse()  # in place reversal
+        # print(todo_vertices)
+        coloring = {}
+        colored_nodes = []
+        # gdict will be the coloring map to return
+        gdict = self.graph_dict
+        for v in todo_vertices:
+            seen_neighbours = [x for x in gdict[v] if x in coloring]
+            choose_among = [i for i in range(K) if not (
+                i in [coloring[v1] for v1 in seen_neighbours])]
+            if choose_among:
+                # if the node can be colored, I choose the minimal color.
+                color = min(choose_among)
+                coloring[v] = color
+                colored_nodes.append(v)
+            else:
+                # if I cannot color some node, the coloring is not Total
+                # but I continue
+                is_total = False
+        return (coloring, is_total, colored_nodes)
+
+
+class DiGraph(GeneralGraph):
+    """Class for directed graphs."""
+
+    def neighbourhoods(self) -> List[Tuple[Any, Set]]:
+        """Return all neighbourhoods in the graph."""
+        return list(self.graph_dict.items())
+
+    def edges(self) -> List[Tuple[Any, Any]]:
+        """ A static method generating the set of edges"""
+        edges = []
+        for vertex in self.graph_dict:
+            for neighbour in self.graph_dict[vertex]:
+                edges.append((vertex, neighbour))
+        return edges
+
+    def add_edge(self, edge: Tuple[Any, Any]) -> None:
+        """
+        Add an edge in the graph.
+        edge should be a pair and not (c,c)
+        (we call g.add_edge((v1,v2)))
+        """
+        (vertex1, vertex2) = edge
+        if vertex1 in self.graph_dict:
+            self.graph_dict[vertex1].add(vertex2)
+        else:
+            self.graph_dict[vertex1] = {vertex2}
+        if vertex2 not in self.graph_dict:
+            self.graph_dict[vertex2] = set()
+
+    def print_dot(self, name: str) -> None:
+        """Print the graph."""
+        dot = Digraph(comment='Conflict Graph')
+        for k in self.graph_dict:
+            shape = None
+            color = "grey"
+            dot.node(str(k), color=color, shape=shape)
+        for (v1, v2) in self.edges():
+            dot.edge(str(v1), str(v2), dir="none")
+        # print(dot.source)
+        dot.render(name, view=True)  # print in pdf
+
+    def delete_vertex(self, vertex: Any) -> None:
+        """Delete a vertex and all the adjacent edges."""
+        for node, neighbours in self.graph_dict.items():
+            if vertex in neighbours:
+                neighbours.remove(vertex)
+        del self.graph_dict[vertex]
+
+    def delete_edge(self, edge: Tuple[Any, Any]) -> None:
+        """Delete an edge."""
+        (v1, v2) = edge
+        self.graph_dict[v1].remove(v2)

MiniC/Lib/PhiNode.py

@@ -0,0 +1,58 @@
+"""
+Classes for φ nodes in a RiscV CFG :py:class:`CFG <Lib.CFG.CFG>` under SSA Form:
+:py:class:`PhiNode` for a statement of the form temp_x = φ(temp_0, ..., temp_n).
+These particular kinds of statements are expected to be in the field
+b._phis for a :py:class:`Block <Lib.CFG.Block>` b.
+"""
+
+from dataclasses import dataclass
+from typing import Dict
+
+from Lib.Operands import Operand, Temporary, DataLocation, Renamer
+from Lib.Statement import Statement, Label
+
+
+@dataclass
+class PhiNode(Statement):
+    """
+    A φ node is a renaming in the CFG, of the form temp_x = φ(temp_0, ..., temp_n).
+    The field var contains the variable temp_x.
+    The field srcs relies for each precedent block in the CFG, identified with its label,
+    the variable temp_i of the φ node.
+    """
+    var: DataLocation
+    srcs: Dict[Label, Operand]
+
+    def defined(self):
+        """Return the variable defined by the φ node."""
+        return [self.var]
+
+    def used(self) -> Dict[Label, Operand]:
+        """
+        Return the dictionnary associating for each previous block the corresponding variable.
+        """
+        return self.srcs
+
+    def rename(self, renamer: Renamer) -> None:
+        """Rename the variable defined by the φ node with a fresh name."""
+        if isinstance(self.var, Temporary):
+            self.var = renamer.fresh(self.var)
+
+    def rename_from(self, renamer: Renamer, label: Label) -> None:
+        """Rename the variable associated to the block identified by `label`."""
+        if label in self.srcs:
+            t = self.srcs[label]
+            if isinstance(t, Temporary):
+                if renamer.defined(t):
+                    self.srcs[label] = renamer.replace(t)
+                else:
+                    del self.srcs[label]
+
+    def __str__(self):
+        return "{} = φ({})".format(self.var, self.srcs)
+
+    def __hash__(self):
+        return hash((self.var, *self.srcs.items()))
+
+    def printIns(self, stream):
+        print('        # ' + str(self), file=stream)

MiniC/MiniCC.py

@@ -5,8 +5,9 @@ Usage:
     python3 MiniCC.py --mode <mode> <filename>
     python3 MiniCC.py --help
 """
-import traceback
 from typing import cast
+from enum import Enum
+
 from MiniCLexer import MiniCLexer
 from MiniCParser import MiniCParser
 from TP03.MiniCTypingVisitor import MiniCTypingVisitor, MiniCTypeError
@@ -14,12 +15,11 @@ from TP03.MiniCInterpretVisitor import MiniCInterpretVisitor
 from Lib.Errors import (MiniCUnsupportedError, MiniCInternalError,
                         MiniCRuntimeError, AllocationError)
 
-from enum import Enum
-import argparse
-
 from antlr4 import FileStream, CommonTokenStream
 from antlr4.error.ErrorListener import ErrorListener
 
+from argparse import ArgumentParser
+from traceback import print_exc
 import os
 import sys
 
@@ -52,7 +52,7 @@ def valid_modes():
         return modes
 
     try:
-        import TP05.SSA  # type: ignore[import]
+        import TP05.EnterSSA  # type: ignore[import]
         modes.append('codegen-ssa')
     except ImportError:
         return modes
@@ -84,7 +84,7 @@ class CountErrorListener(ErrorListener):
 
 def main(inputname, reg_alloc, mode,
          typecheck=True, stdout=False, output_name=None, debug=False,
-         debug_graphs=False, ssa_graphs=False):
+         debug_graphs=False, ssa_graphs=False, dom_graphs=False):
     (basename, rest) = os.path.splitext(inputname)
     if mode.is_codegen():
         if stdout:
@@ -144,22 +144,19 @@ def main(inputname, reg_alloc, mode,
                 code = function
             else:
                 from TP04.BuildCFG import build_cfg  # type: ignore[import]
-                from Lib.CFG import CFG  # type: ignore[import]
                 code = build_cfg(function)
-                assert (isinstance(code, CFG))
             if debug_graphs:
                 s = "{}.{}.dot".format(basename, code.fdata.get_name())
                 print("CFG:", s)
                 code.print_dot(s, view=True)
             if mode.value >= Mode.SSA.value:
-                from TP05.SSA import enter_ssa  # type: ignore[import]
+                from TP05.EnterSSA import enter_ssa  # type: ignore[import]
                 from Lib.CFG import CFG  # type: ignore[import]
-
-                DF = enter_ssa(cast(CFG, code), basename, debug, ssa_graphs)
+                enter_ssa(cast(CFG, code), dom_graphs, basename)
                 if ssa_graphs:
-                    s = "{}.{}.ssa.dot".format(basename, code.fdata.get_name())
+                    s = "{}.{}.enterssa.dot".format(basename, code.fdata.get_name())
                     print("SSA:", s)
-                    code.print_dot(s, DF, True)
+                    code.print_dot(s, view=True)
                 if mode == Mode.OPTIM:
                     from TPoptim.OptimSSA import OptimSSA  # type: ignore[import]
                     OptimSSA(cast(CFG, code), debug=debug)
@@ -207,8 +204,8 @@ liveness file not found for {}.".format(form))
                 allocator.prepare()
             if mode.value >= Mode.SSA.value:
                 from Lib.CFG import CFG  # type: ignore[import]
-                from TP05.SSA import exit_ssa  # type: ignore[import]
-                exit_ssa(cast(CFG, code))
+                from TP05.ExitSSA import exit_ssa  # type: ignore[import]
+                exit_ssa(cast(CFG, code), reg_alloc == 'smart')
                 comment += " with SSA"
             if allocator:
                 allocator.rewriteCode(code)
@@ -233,7 +230,7 @@ if __name__ == '__main__':
 
     modes = valid_modes()
 
-    parser = argparse.ArgumentParser(description='Generate code for .c file')
+    parser = ArgumentParser(description='CAP/MIF08 MiniCC compiler')
 
     parser.add_argument('filename', type=str,
                         help='Source file.')
@@ -265,7 +262,10 @@ if __name__ == '__main__':
     if "codegen-ssa" in modes:
         parser.add_argument('--ssa-graphs', action='store_true',
                             default=False,
-                            help='Display SSA graphs (DT, DF).')
+                            help='Display the CFG at SSA entry and exit.')
+        parser.add_argument('--dom-graphs', action='store_true',
+                            default=False,
+                            help='Display dominance-related graphs (DT, DF).')
 
     args = parser.parse_args()
     reg_alloc = args.reg_alloc if "codegen-linear" in modes else None
@@ -273,6 +273,7 @@ if __name__ == '__main__':
     outfile = args.output if "codegen-linear" in modes else None
     graphs = args.graphs if "codegen-cfg" in modes else False
     ssa_graphs = args.ssa_graphs if "codegen-ssa" in modes else False
+    dom_graphs = args.dom_graphs if "codegen-ssa" in modes else False
 
     if reg_alloc is None and "codegen" in args.mode:
         print("error: the following arguments is required: --reg-alloc")
@@ -308,10 +309,10 @@ if __name__ == '__main__':
         main(args.filename, reg_alloc, mode,
              typecheck,
              to_stdout, outfile, args.debug,
-             graphs, ssa_graphs)
+             graphs, ssa_graphs, dom_graphs)
     except MiniCUnsupportedError as e:
         print(e)
         exit(5)
     except (MiniCInternalError, AllocationError):
-        traceback.print_exc()
+        print_exc()
         exit(4)

MiniC/README-SSA.md

@@ -0,0 +1,20 @@
+# MiniC Compiler 
+LAB5a (Control Flow Graph in SSA Form) & LAB5b (Smart Register Allocation), CAP 2022-23
+
+# Authors
+
+YOUR NAME HERE
+
+# Contents
+
+TODO:
+- Explain any design choices you may have made.
+- Do not forget to remove all debug traces from your code!
+
+# Test design 
+
+TODO: give the main objectives of your tests.
+
+# Known bugs
+
+TODO: bugs you could not fix (if any).

MiniC/TP05/EnterSSA.py

@@ -0,0 +1,70 @@
+"""
+CAP, SSA Intro, Elimination and Optimisations
+Functions to convert a CFG into SSA Form.
+"""
+
+from typing import List, Dict, Set
+from Lib.CFG import Block, CFG
+from Lib.Operands import Renamer
+from Lib.Statement import Instruction
+from Lib.PhiNode import PhiNode
+from Lib.Dominators import computeDom, computeDT, computeDF
+
+
+def insertPhis(cfg: CFG, DF: Dict[Block, Set[Block]]) -> None:
+    """
+    `insertPhis(CFG, DF)` inserts phi nodes in `cfg` where needed.
+    At this point, phi nodes will look like `temp_x = φ(temp_x, ..., temp_x)`.
+
+    This is an helper function called during SSA entry.
+    """
+    for var, defs in cfg.gather_defs().items():
+        has_phi: Set[Block] = set()
+        queue: List[Block] = list(defs)
+        while queue:
+            d = queue.pop(0)
+            for b in DF[d]:
+                if b not in has_phi:
+                    # TODO add a phi node in block `b` (Lab 5a, Exercise 4)
+                    raise NotImplementedError("insertPhis")
+
+
+def rename_block(cfg: CFG, DT: Dict[Block, Set[Block]], renamer: Renamer, b: Block) -> None:
+    """
+    Rename variables from block b.
+
+    This is an auxiliary function for `rename_variables`.
+    """
+    renamer = renamer.copy()
+    for i in b.get_all_statements():
+        if isinstance(i, Instruction | PhiNode):
+            i.rename(renamer)
+    for succ in cfg.out_blocks(b):
+        for i in succ._phis:
+            assert (isinstance(i, PhiNode))
+            i.rename_from(renamer, b.get_label())
+    # TODO recursive call(s) of rename_block (Lab 5a, Exercise 5)
+
+
+def rename_variables(cfg: CFG, DT: Dict[Block, Set[Block]]) -> None:
+    """
+    Rename variables in the CFG, to transform `temp_x = φ(temp_x, ..., temp_x)`
+    into `temp_x = φ(temp_0, ... temp_n)`.
+
+    This is an helper function called during SSA entry.
+    """
+    renamer = Renamer(cfg.fdata._pool)
+    # TODO initial call(s) to rename_block (Lab 5a, Exercise 5)
+
+
+def enter_ssa(cfg: CFG, dom_graphs=False, basename="prog") -> None:
+    """
+    Convert the CFG `cfg` into SSA Form:
+    compute the dominance frontier, then insert phi nodes and finally
+    rename variables accordingly.
+
+    `dom_graphs` indicates if we have to print the domination graphs.
+    `basename` is used for the names of the produced graphs.
+    """
+    # TODO implement this function (Lab 5a, Exercise 2)
+    raise NotImplementedError("enter_ssa")

MiniC/TP05/ExitSSA.py

@@ -0,0 +1,48 @@
+"""
+CAP, SSA Intro, Elimination and Optimisations
+Functions to convert a CFG out of SSA Form.
+"""
+
+from typing import cast, List, Set, Tuple
+from Lib import RiscV
+from Lib.Graphes import DiGraph
+from Lib.CFG import Block, BlockInstr, CFG
+from Lib.Operands import (
+    Register, DataLocation,
+    Temporary)
+from Lib.Statement import AbsoluteJump
+from Lib.Terminator import BranchingTerminator, Return
+from Lib.PhiNode import PhiNode
+
+
+def generate_moves_from_phis(phis: List[PhiNode], parent: Block) -> List[BlockInstr]:
+    """
+    `generate_moves_from_phis(phis, parent)` builds a list of move instructions
+    to be inserted in a new block between `parent` and the block with phi nodes
+    `phis`.
+
+    This is an helper function called during SSA exit.
+    """
+    moves: List[BlockInstr] = []
+    # TODO compute 'moves', a list of 'mv' instructions to insert under parent
+    # (Lab 5a, Exercise 6)
+    return moves
+
+
+def exit_ssa(cfg: CFG, is_smart: bool) -> None:
+    """
+    `exit_ssa(cfg)` replaces phi nodes with move instructions to exit SSA form.
+
+    `is_smart` is set to true when smart register allocation is enabled (Lab 5b).
+    """
+    for b in cfg.get_blocks():
+        phis = cast(List[PhiNode], b._phis)  # Use cast for Pyright
+        b._phis = []  # Remove all phi nodes in the block
+        parents: List[Block] = b.get_in().copy()  # Copy as we modify it by adding blocks
+        for parent in parents:
+            moves = generate_moves_from_phis(phis, parent)
+            # TODO Add the block containing 'moves' to 'cfg'
+            # and update edges and jumps accordingly (Lab 5a, Exercise 6)
+            raise NotImplementedError("exit_ssa")
+
+

MiniC/TP05/tests/students/README.md

@@ -0,0 +1 @@
+Add your own tests in this directory.