If

If#

class hugr.build.cond_loop.If(case: Case)[source]#

Bases: _IfElse

Build the ‘if’ branch of a conditional branching on a boolean value.

Examples

>>> from hugr.dfg import Dfg
>>> dfg = Dfg(tys.Qubit)
>>> (q,) = dfg.inputs()
>>> if_ = dfg.add_if(dfg.load(val.TRUE), q)
>>> if_.set_outputs(if_.input_node[0])
>>> else_= if_.add_else()
>>> else_.set_outputs(else_.input_node[0])
>>> dfg.hugr[else_.conditional_node].op
Conditional(sum_ty=Bool, other_inputs=[Qubit])

Methods

`add`	Add a command (holding a dataflow operation and the incoming wires) to the graph.
`add_alias_defn`	Add a type alias definition.
`add_cfg`	Start building a new CFG nested inside the current dataflow graph.
`add_conditional`	Start building a new conditional nested inside the current dataflow graph.
`add_const`	Add a static constant to the graph.
`add_else`	Finish building the 'if' branch and start building the 'else' branch.
`add_if`	Start building a new if block nested inside the current dataflow graph.
`add_nested`	Start building a nested dataflow graph.
`add_op`	Add a dataflow operation to the graph, wiring in input ports.
`add_state_order`	Add a state order link between two nodes.
`add_tail_loop`	Start building a new tail loop nested inside the current dataflow graph.
`call`	Call a static function in the graph.
`define_function`	Start building a function definition in the graph.
`extend`	Add a series of commands to the DFG.
`inp`	Generate an input port for this node.
`inputs`	List all incoming wires (output ports of the input node).
`insert_cfg`	Insert a CFG into the current dataflow graph, wiring in the inputs.
`insert_conditional`	Insert a conditional into the current dataflow graph, wiring in the inputs.
`insert_nested`	Insert a nested dataflow graph into the current graph, wiring in the inputs.
`insert_tail_loop`	Insert a tail loop into the current dataflow graph, wiring in the inputs.
`load`	Load a constant into the graph as a dataflow value.
`load_function`	Load a static function into the graph as a higher-order value.
`new_nested`	Start building a dataflow graph nested inside a larger HUGR.
`out`	Generate an output port for this node.
`out_port`	OutPort corresponding to this `Wire`.
`outputs`	Returns an iterator over the output ports of this node.
`port`	Generate a port in direction for this node with offset.
`set_outputs`	Set the outputs of the dataflow graph.
`to_node`	Convert to a `Node`.

Attributes

`conditional_node`	The node that represents the parent conditional.
`metadata`	Metadata associated with this node.
`parent_op`	The parent node's operation.
`hugr`	The Hugr instance that the builder is using.
`parent_node`	The parent node of the dataflow graph.
`input_node`	The input node of the dataflow graph.
`output_node`	The output node of the dataflow graph.

add(com: ops.Command, *, metadata: dict[str, Any] | None = None) → Node#

Add a command (holding a dataflow operation and the incoming wires) to the graph.

Parameters:

com – The command to add.
metadata – Metadata to attach to the function definition. Defaults to None.

Example

>>> dfg = Dfg(tys.Bool)
>>> (i,) = dfg.inputs()
>>> dfg.add(ops.Noop()(i))
Node(3)

add_alias_defn(name: str, ty: Type, parent: ToNode | None = None) → Node#: Add a type alias definition.

add_cfg(*args: Wire) → Cfg#

Start building a new CFG nested inside the current dataflow graph.

Parameters:: args – The input wires to the new CFG.
Returns:: Builder for new nested CFG.

Example

>>> dfg = Dfg(tys.Bool)
>>> with dfg.add_cfg(dfg.inputs()[0]) as cfg:                    cfg.parent_op
CFG(inputs=[Bool])

add_conditional(cond_wire: Wire, *args: Wire) → Conditional#

Start building a new conditional nested inside the current dataflow graph.

Parameters:

cond_wire – The wire holding the value (of Sum type) to branch the
on. (conditional)
args – Remaining input wires to the conditional.

Returns:

Builder for new nested conditional.

Example

>>> dfg = Dfg(tys.Bool, tys.Unit)
>>> (cond, unit) = dfg.inputs()
>>> cond = dfg.add_conditional(cond, unit)
>>> cond.parent_node
Node(3)

add_const(value: val.Value, parent: ToNode | None = None) → Node#

Add a static constant to the graph.

Parameters:

value – The constant value to add.
parent – The parent node of the constant. Defaults to the root node.

Returns:

The node holding the Const operation.

Example

>>> dfg = Dfg()
>>> const_n = dfg.add_const(val.TRUE)
>>> dfg.hugr[const_n].op
Const(TRUE)

add_else() → Else[source]#: Finish building the ‘if’ branch and start building the ‘else’ branch.

add_if(cond_wire: Wire, *args: Wire) → If#

Start building a new if block nested inside the current dataflow graph.

Parameters:

cond_wire – The wire holding the Bool value to branch the If on.
args – Remaining input wires to the If (and subsequent Else).

Returns:

Builder for new nested If.

Example

>>> dfg = Dfg(tys.Bool)
>>> (cond,) = dfg.inputs()
>>> if_ = dfg.add_if(cond, cond)
>>> if_.parent_op
Case(inputs=[Bool])

add_nested(*args: Wire) → Dfg#

Start building a nested dataflow graph.

Parameters:: args – The input wires to the nested DFG.
Returns:: Builder for new nested dataflow graph.

Example

>>> dfg = Dfg(tys.Bool)
>>> with dfg.add_nested(dfg.inputs()[0]) as dfg2:                   dfg2.parent_node
Node(3)

add_op(op: ops.DataflowOp, /, *args: Wire, metadata: dict[str, Any] | None = None) → Node#

Add a dataflow operation to the graph, wiring in input ports.

Parameters:

op – The operation to add.
args – The input wires to the operation.
metadata – Metadata to attach to the function definition. Defaults to None.

Returns:

The node holding the new operation.

Example

>>> dfg = Dfg(tys.Bool)
>>> dfg.add_op(ops.Noop(), dfg.inputs()[0])
Node(3)

add_state_order(src: Node, dst: Node) → None#

Add a state order link between two nodes.

Parameters:

src – The source node.
dst – The destination node.

Examples

>>> df = dfg.Dfg()
>>> df.add_state_order(df.input_node, df.output_node)
>>> list(df.hugr.outgoing_order_links(df.input_node))
[Node(2)]

add_tail_loop(just_inputs: Sequence[Wire], rest: Sequence[Wire]) → TailLoop#

Start building a new tail loop nested inside the current dataflow graph.

Parameters:

just_inputs – input wires for types that are only inputs to the loop body.
rest – input wires for types that are inputs and outputs of the loop
body.

Returns:

Builder for new nested TailLoop.

Example

>>> dfg = Dfg(tys.Bool)
>>> (cond,) = dfg.inputs()
>>> tl = dfg.add_tail_loop([cond], [cond])
>>> tl.parent_op
TailLoop(just_inputs=[Bool], rest=[Bool])

call(func: ToNode, *args: Wire, instantiation: tys.FunctionType | None = None, type_args: Sequence[tys.TypeArg] | None = None) → Node#

Call a static function in the graph. See Call for more on how polymorphic functions are handled.

Parameters:

func – The node corresponding to the function definition/declaration to call.
args – The input wires to the function call.
instantiation – The concrete function type to call (needed if polymorphic).
type_args – The type arguments for the function (needed if
polymorphic).

Returns:

The node holding the Call operation.

property conditional_node: Node#: The node that represents the parent conditional.

define_function(name: str, input_types: TypeRow, output_types: TypeRow | None = None, type_params: list[TypeParam] | None = None, parent: ToNode | None = None) → Function#

Start building a function definition in the graph.

Parameters:

name – The name of the function.
input_types – The input types for the function.
output_types – The output types for the function. If not provided, it will be inferred after the function is built.
type_params – The type parameters for the function, if polymorphic.
parent – The parent node of the constant. Defaults to the root node.

Returns:

The new function builder.

extend(*coms: ops.Command) → list[Node]#

Add a series of commands to the DFG.

Shorthand for calling add() on each command in coms.

Parameters:: coms – Commands to add.
Returns:: List of the new nodes in the same order as the commands.
Raises:: IndexError – If any input index is not a tracked wire.

Examples

>>> dfg = Dfg(tys.Bool, tys.Unit)
>>> (b, u) = dfg.inputs()
>>> dfg.extend(ops.Noop()(b), ops.Noop()(u))
[Node(3), Node(4)]

hugr: Hugr#: The Hugr instance that the builder is using.

inp(offset: int) → InPort#

Generate an input port for this node.

Parameters:: offset – port offset.
Returns:: Incoming port for this node.

Examples

>>> Node(0).inp(1)
InPort(Node(0), 1)

input_node: Node#: The input node of the dataflow graph.

inputs() → list[OutPort]#

List all incoming wires (output ports of the input node).

Example

>>> dfg = Dfg(tys.Bool)
>>> dfg.inputs()
[OutPort(Node(1), 0)]

insert_cfg(cfg: Cfg, *args: Wire) → Node#

Insert a CFG into the current dataflow graph, wiring in the inputs.

Parameters:

cfg – The CFG to insert.
args – The input wires to the CFG.

Returns:

The root node of the inserted CFG.

Example

>>> from hugr.cfg import Cfg
>>> dfg = Dfg(tys.Bool)
>>> cfg = Cfg(tys.Bool)
>>> dfg.insert_cfg(cfg, dfg.inputs()[0])
Node(3)

insert_conditional(cond: Conditional, cond_wire: Wire, *args: Wire) → Node#

Insert a conditional into the current dataflow graph, wiring in the inputs.

Parameters:

cond – The conditional to insert.
cond_wire – The wire holding the value (of Sum type) to branch the Conditional on.
args – Remaining input wires to the conditional.

Returns:

The root node of the inserted conditional.

Example

>>> from hugr.cond_loop import Conditional
>>> cond = Conditional(tys.Bool, [])
>>> dfg = Dfg(tys.Bool)
>>> cond_n = dfg.insert_conditional(cond, dfg.inputs()[0])
>>> dfg.hugr[cond_n].op
Conditional(sum_ty=Bool, other_inputs=[])

insert_nested(dfg: Dfg, *args: Wire) → Node#

Insert a nested dataflow graph into the current graph, wiring in the inputs.

Parameters:

dfg – The dataflow graph to insert.
args – The input wires to the graph.

Returns:

The root node of the inserted graph.

Example

>>> dfg = Dfg(tys.Bool)
>>> dfg2 = Dfg(tys.Bool)
>>> dfg.insert_nested(dfg2, dfg.inputs()[0])
Node(3)

insert_tail_loop(tl: TailLoop, just_inputs: Sequence[Wire], rest: Sequence[Wire]) → Node#

Insert a tail loop into the current dataflow graph, wiring in the inputs.

Parameters:

tl – The tail loop to insert.
just_inputs – input wires for types that are only inputs to the loop body.
rest – input wires for types that are inputs and outputs of the loop
body.

Returns:

The root node of the inserted tail loop.

Example

>>> from hugr.cond_loop import TailLoop
>>> tl = TailLoop([tys.Bool], [tys.Bool])
>>> dfg = Dfg(tys.Bool)
>>> (b,) = dfg.inputs()
>>> tl_n = dfg.insert_tail_loop(tl, [b], [b])
>>> dfg.hugr[tl_n].op
TailLoop(just_inputs=[Bool], rest=[Bool])

load(const: ToNode | val.Value, const_parent: ToNode | None = None) → Node#

Load a constant into the graph as a dataflow value.

Parameters:

const – The constant to load, either a Value that will be added as a child Const node then loaded, or a node corresponding to an existing Const.
const_parent – If const is a Value, the parent node for the new constant definition. Defaults to the current dataflow container.

Returns:

The node holding the LoadConst operation.

Example

>>> dfg = Dfg()
>>> const_n = dfg.load(val.TRUE)
>>> len(dfg.hugr) # parent, input, output, const, load
5
>>> dfg.hugr[const_n].op
LoadConst(Bool)

load_function(func: ToNode, instantiation: tys.FunctionType | None = None, type_args: Sequence[tys.TypeArg] | None = None) → Node#

Load a static function into the graph as a higher-order value.

Parameters:

func – The node corresponding to the function definition/declaration to load.
instantiation – The concrete function type to load (needed if polymorphic).
type_args – The type arguments for the function (needed if
polymorphic).

Returns:

The node holding the LoadFunc operation.

property metadata: dict[str, object]#: Metadata associated with this node.

classmethod new_nested(parent_op: DP, hugr: Hugr, parent: ToNode | None = None) → Self#

Start building a dataflow graph nested inside a larger HUGR.

Parameters:

parent_op – The parent operation of the new dataflow graph.
hugr – The host HUGR instance to build the dataflow graph in.
parent – Parent of new dataflow graph’s root node: defaults to the
root. (host HUGR)

Example

>>> hugr = Hugr()
>>> dfg = Dfg.new_nested(ops.DFG([]), hugr)
>>> dfg.parent_node
Node(1)

out(offset: int) → OutPort#

Generate an output port for this node.

Parameters:: offset – port offset.
Returns:: Outgoing port for this node.

Examples

>>> Node(0).out(1)
OutPort(Node(0), 1)

out_port() → OutPort#: OutPort corresponding to this Wire.

output_node: Node#: The output node of the dataflow graph.

outputs() → Iterator[OutPort]#: Returns an iterator over the output ports of this node.

parent_node: Node#: The parent node of the dataflow graph.

property parent_op: OpVar#: The parent node’s operation.

port(offset: int, direction: Direction) → InPort | OutPort#

Generate a port in direction for this node with offset.

Examples

>>> Node(0).port(1, Direction.INCOMING)
InPort(Node(0), 1)
>>> Node(0).port(1, Direction.OUTGOING)
OutPort(Node(0), 1)

set_outputs(*outputs: Wire) → None#

Set the outputs of the dataflow graph. Connects wires to the output node.

Parameters:: args – Wires to connect to the output node.

Example

>>> dfg = Dfg(tys.Bool)
>>> dfg.set_outputs(dfg.inputs()[0]) # connect input to output

to_node() → Node#: Convert to a Node.

If

Contents

If#