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Dataflow

oqd_compiler_infrastructure.dataflow

GraphProtocol

Bases: Protocol[NodeType]

Any object passed to DataflowAnalysis.analyze must provide this interface. The protocol is intentionally minimal so it can adapt to Control Flow Graphs (CFGs), dependency graphs, custom IR graphs, etc.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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class GraphProtocol(Protocol[NodeType]):
    """
    Any object passed to `DataflowAnalysis.analyze` must provide this interface.
    The protocol is intentionally minimal so it can adapt to Control Flow Graphs (CFGs),
    dependency graphs, custom IR graphs, etc.
    """

    def nodes(self) -> Iterable[NodeType]:
        """Returns all nodes in the graph."""
        ...

    def predecessors(self, node: NodeType) -> Iterable[NodeType]:
        """Returns all predecessors of a given node."""
        ...

    def successors(self, node: NodeType) -> Iterable[NodeType]:
        """Returns all successors of a given node."""
        ...

nodes() -> Iterable[NodeType]

Returns all nodes in the graph.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def nodes(self) -> Iterable[NodeType]:
    """Returns all nodes in the graph."""
    ...

predecessors(node: NodeType) -> Iterable[NodeType]

Returns all predecessors of a given node.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def predecessors(self, node: NodeType) -> Iterable[NodeType]:
    """Returns all predecessors of a given node."""
    ...

successors(node: NodeType) -> Iterable[NodeType]

Returns all successors of a given node.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def successors(self, node: NodeType) -> Iterable[NodeType]:
    """Returns all successors of a given node."""
    ...

DataflowResult dataclass

Bases: Generic[NodeType, LatticeValue]

The result of a dataflow analysis.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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@dataclass(frozen=True)
class DataflowResult(Generic[NodeType, LatticeValue]):
    """
    The result of a dataflow analysis.
    """

    in_states: dict[NodeType, LatticeValue]
    out_states: dict[NodeType, LatticeValue]
    iterations: int

DataflowAnalysis

Bases: ABC, Generic[NodeType, LatticeValue]

Base class that defines what every dataflow analysis must implement.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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class DataflowAnalysis(ABC, Generic[NodeType, LatticeValue]):
    """
    Base class that defines what every dataflow analysis must implement.
    """

    lattice: ClassVar[Lattice[LatticeValue]]

    @abstractmethod
    def transfer(self, node: NodeType, state_in: LatticeValue) -> LatticeValue:
        """Returns the state of a given node after transfer."""
        pass

    @abstractmethod
    def sources(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        """Neighbors whose results flow into `node`."""
        pass

    @abstractmethod
    def targets(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        """Neighbors to reschedule when `node`'s result changes."""
        pass

    @abstractmethod
    def result(
        self,
        boundary: dict[NodeType, LatticeValue],
        result: dict[NodeType, LatticeValue],
        iterations: int,
    ) -> DataflowResult[NodeType, LatticeValue]:
        """Maps boundary/result states onto in/out states."""
        pass

    def init_state(self) -> LatticeValue:
        """Initializes the lattice with the lattice's bottom operation."""
        return self.lattice.bottom()

    def merge_union(self, states: Iterable[LatticeValue]) -> LatticeValue:
        """Joins incoming states using the lattice's join operation."""
        states_list = list(states)
        if not states_list:
            return self.lattice.bottom()
        merged = states_list[0]
        for state in states_list[1:]:
            merged = self.lattice.join(merged, state)
        return merged

    def merge_intersection(self, states: Iterable[LatticeValue]) -> LatticeValue:
        """Meets incoming states using the lattice's meet operation."""
        states_list = list(states)
        if not states_list:
            return self.lattice.top()
        merged = states_list[0]
        for state in states_list[1:]:
            merged = self.lattice.meet(merged, state)
        return merged

    def analyze(
        self,
        graph: GraphProtocol[NodeType],
        merge_function: Callable[[Iterable[LatticeValue]], LatticeValue],
    ) -> DataflowResult[NodeType, LatticeValue]:
        """
        Runs the worklist algorithm and returns the result of the dataflow analysis.
        Steps:
        - Initializes every node's state with `init_state()`.
        - Puts all nodes in a worklist.
        - Recomputes each node from predecessor outputs.
        - If a node output changes, schedules its `targets` again.
        - Returns final states and iteration count.
        """
        nodes = list(graph.nodes())
        boundary = {node: self.init_state() for node in nodes}
        result = {node: self.init_state() for node in nodes}

        worklist = deque(nodes)
        queued = set(nodes)
        iterations = 0

        while worklist:
            node = worklist.popleft()
            queued.discard(node)
            iterations += 1

            srcs = list(self.sources(graph, node))
            if srcs:
                merged_input = merge_function(result[n] for n in srcs)
            else:
                merged_input = self.lattice.bottom()

            if not self.lattice.equal(boundary[node], merged_input):
                boundary[node] = merged_input

            next_result = self.transfer(node, merged_input)
            if self.lattice.equal(result[node], next_result):
                continue

            result[node] = next_result
            for target in self.targets(graph, node):
                if target not in queued:
                    worklist.append(target)
                    queued.add(target)

        return self.result(boundary, result, iterations)

transfer(node: NodeType, state_in: LatticeValue) -> LatticeValue abstractmethod

Returns the state of a given node after transfer.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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@abstractmethod
def transfer(self, node: NodeType, state_in: LatticeValue) -> LatticeValue:
    """Returns the state of a given node after transfer."""
    pass

sources(graph: GraphProtocol[NodeType], node: NodeType) -> Iterable[NodeType] abstractmethod

Neighbors whose results flow into node.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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@abstractmethod
def sources(
    self, graph: GraphProtocol[NodeType], node: NodeType
) -> Iterable[NodeType]:
    """Neighbors whose results flow into `node`."""
    pass

targets(graph: GraphProtocol[NodeType], node: NodeType) -> Iterable[NodeType] abstractmethod

Neighbors to reschedule when node's result changes.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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@abstractmethod
def targets(
    self, graph: GraphProtocol[NodeType], node: NodeType
) -> Iterable[NodeType]:
    """Neighbors to reschedule when `node`'s result changes."""
    pass

result(boundary: dict[NodeType, LatticeValue], result: dict[NodeType, LatticeValue], iterations: int) -> DataflowResult[NodeType, LatticeValue] abstractmethod

Maps boundary/result states onto in/out states.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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@abstractmethod
def result(
    self,
    boundary: dict[NodeType, LatticeValue],
    result: dict[NodeType, LatticeValue],
    iterations: int,
) -> DataflowResult[NodeType, LatticeValue]:
    """Maps boundary/result states onto in/out states."""
    pass

init_state() -> LatticeValue

Initializes the lattice with the lattice's bottom operation.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def init_state(self) -> LatticeValue:
    """Initializes the lattice with the lattice's bottom operation."""
    return self.lattice.bottom()

merge_union(states: Iterable[LatticeValue]) -> LatticeValue

Joins incoming states using the lattice's join operation.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def merge_union(self, states: Iterable[LatticeValue]) -> LatticeValue:
    """Joins incoming states using the lattice's join operation."""
    states_list = list(states)
    if not states_list:
        return self.lattice.bottom()
    merged = states_list[0]
    for state in states_list[1:]:
        merged = self.lattice.join(merged, state)
    return merged

merge_intersection(states: Iterable[LatticeValue]) -> LatticeValue

Meets incoming states using the lattice's meet operation.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def merge_intersection(self, states: Iterable[LatticeValue]) -> LatticeValue:
    """Meets incoming states using the lattice's meet operation."""
    states_list = list(states)
    if not states_list:
        return self.lattice.top()
    merged = states_list[0]
    for state in states_list[1:]:
        merged = self.lattice.meet(merged, state)
    return merged

analyze(graph: GraphProtocol[NodeType], merge_function: Callable[[Iterable[LatticeValue]], LatticeValue]) -> DataflowResult[NodeType, LatticeValue]

Runs the worklist algorithm and returns the result of the dataflow analysis. Steps: - Initializes every node's state with init_state(). - Puts all nodes in a worklist. - Recomputes each node from predecessor outputs. - If a node output changes, schedules its targets again. - Returns final states and iteration count.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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def analyze(
    self,
    graph: GraphProtocol[NodeType],
    merge_function: Callable[[Iterable[LatticeValue]], LatticeValue],
) -> DataflowResult[NodeType, LatticeValue]:
    """
    Runs the worklist algorithm and returns the result of the dataflow analysis.
    Steps:
    - Initializes every node's state with `init_state()`.
    - Puts all nodes in a worklist.
    - Recomputes each node from predecessor outputs.
    - If a node output changes, schedules its `targets` again.
    - Returns final states and iteration count.
    """
    nodes = list(graph.nodes())
    boundary = {node: self.init_state() for node in nodes}
    result = {node: self.init_state() for node in nodes}

    worklist = deque(nodes)
    queued = set(nodes)
    iterations = 0

    while worklist:
        node = worklist.popleft()
        queued.discard(node)
        iterations += 1

        srcs = list(self.sources(graph, node))
        if srcs:
            merged_input = merge_function(result[n] for n in srcs)
        else:
            merged_input = self.lattice.bottom()

        if not self.lattice.equal(boundary[node], merged_input):
            boundary[node] = merged_input

        next_result = self.transfer(node, merged_input)
        if self.lattice.equal(result[node], next_result):
            continue

        result[node] = next_result
        for target in self.targets(graph, node):
            if target not in queued:
                worklist.append(target)
                queued.add(target)

    return self.result(boundary, result, iterations)

ForwardDataflowAnalysis

Bases: DataflowAnalysis[NodeType, LatticeValue], Generic[NodeType, LatticeValue]

Forward dataflow analysis framework.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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class ForwardDataflowAnalysis(
    DataflowAnalysis[NodeType, LatticeValue], Generic[NodeType, LatticeValue]
):
    """
    Forward dataflow analysis framework.
    """
    def sources(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        return graph.predecessors(node)

    def targets(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        return graph.successors(node)

    def result(
        self,
        boundary: dict[NodeType, LatticeValue],
        result: dict[NodeType, LatticeValue],
        iterations: int,
    ) -> DataflowResult[NodeType, LatticeValue]:
        return DataflowResult(
            in_states=boundary, out_states=result, iterations=iterations
        )

BackwardDataflowAnalysis

Bases: DataflowAnalysis[NodeType, LatticeValue], Generic[NodeType, LatticeValue]

Backward dataflow analysis framework.

Source code in oqd-compiler-infrastructure/src/oqd_compiler_infrastructure/dataflow.py 
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class BackwardDataflowAnalysis(
    DataflowAnalysis[NodeType, LatticeValue], Generic[NodeType, LatticeValue]
):
    """
    Backward dataflow analysis framework.
    """

    def sources(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        return graph.successors(node)

    def targets(
        self, graph: GraphProtocol[NodeType], node: NodeType
    ) -> Iterable[NodeType]:
        return graph.predecessors(node)

    def result(
        self,
        boundary: dict[NodeType, LatticeValue],
        result: dict[NodeType, LatticeValue],
        iterations: int,
    ) -> DataflowResult[NodeType, LatticeValue]:
        return DataflowResult(
            in_states=result, out_states=boundary, iterations=iterations
        )