def process(func, state):
"""
Apply ``func`` to the IETs in ``state._efuncs``, and update ``state`` accordingly.
"""
# Create a Call graph. `func` will be applied to each node in the Call graph.
# `func` might change an `efunc` signature; the Call graph will be used to
# propagate such change through the `efunc` callers
dag = DAG(nodes=['root'])
queue = ['root']
while queue:
caller = queue.pop(0)
callees = FindNodes(Call).visit(state._efuncs[caller])
for callee in filter_ordered([i.name for i in callees]):
if callee in state._efuncs: # Exclude foreign Calls, e.g., MPI calls
try:
dag.add_node(callee)
queue.append(callee)
except KeyError:
# `callee` already in `dag`
pass
dag.add_edge(callee, caller)
assert dag.size == len(state._efuncs)
# Apply `func`
for i in dag.topological_sort():
state._efuncs[i], metadata = func(state._efuncs[i])
# Track any new Dimensions introduced by `func`
state._dimensions.extend(list(metadata.get('dimensions', [])))
# Track any new #include required by `func`
state._includes.extend(list(metadata.get('includes', [])))
state._includes = filter_ordered(state._includes)
# Track any new ElementalFunctions
state._efuncs.update(
OrderedDict([(i.name, i) for i in metadata.get('efuncs', [])]))
# If there's a change to the `args` and the `iet` is an efunc, then
# we must update the call sites as well, as the arguments dropped down
# to the efunc have just increased
args = as_tuple(metadata.get('args'))
if args:
# `extif` avoids redundant updates to the parameters list, due
# to multiple children wanting to add the same input argument
extif = lambda v: list(v) + [e for e in args if e not in v]
stack = [i] + dag.all_downstreams(i)
for n in stack:
efunc = state._efuncs[n]
calls = [
c for c in FindNodes(Call).visit(efunc) if c.name in stack
]
mapper = {
c: c._rebuild(arguments=extif(c.arguments))
for c in calls
}
efunc = Transformer(mapper).visit(efunc)
if efunc.is_Callable:
efunc = efunc._rebuild(parameters=extif(efunc.parameters))
state._efuncs[n] = efunc
def process(func, state):
"""
Apply ``func`` to the IETs in ``state._efuncs``, and update ``state`` accordingly.
"""
# Create a Call graph. `func` will be applied to each node in the Call graph.
# `func` might change an `efunc` signature; the Call graph will be used to
# propagate such change through the `efunc` callers
dag = DAG(nodes=['root'])
queue = ['root']
while queue:
caller = queue.pop(0)
callees = FindNodes(Call).visit(state._efuncs[caller])
for callee in filter_ordered([i.name for i in callees]):
if callee in state._efuncs: # Exclude foreign Calls, e.g., MPI calls
try:
dag.add_node(callee)
queue.append(callee)
except KeyError:
# `callee` already in `dag`
pass
dag.add_edge(callee, caller)
assert dag.size == len(state._efuncs)
# Apply `func`
for i in dag.topological_sort():
state._efuncs[i], metadata = func(state._efuncs[i])
# Track any new Dimensions introduced by `func`
state._dimensions.extend(list(metadata.get('dimensions', [])))
# Track any new #include required by `func`
state._includes.extend(list(metadata.get('includes', [])))
state._includes = filter_ordered(state._includes)
# Track any new ElementalFunctions
state._efuncs.update(OrderedDict([(i.name, i)
for i in metadata.get('efuncs', [])]))
# If there's a change to the `args` and the `iet` is an efunc, then
# we must update the call sites as well, as the arguments dropped down
# to the efunc have just increased
args = as_tuple(metadata.get('args'))
if args:
# `extif` avoids redundant updates to the parameters list, due
# to multiple children wanting to add the same input argument
extif = lambda v: list(v) + [e for e in args if e not in v]
stack = [i] + dag.all_downstreams(i)
for n in stack:
efunc = state._efuncs[n]
calls = [c for c in FindNodes(Call).visit(efunc) if c.name in stack]
mapper = {c: c._rebuild(arguments=extif(c.arguments)) for c in calls}
efunc = Transformer(mapper).visit(efunc)
if efunc.is_Callable:
efunc = efunc._rebuild(parameters=extif(efunc.parameters))
state._efuncs[n] = efunc
def _create_call_graph(self):
dag = DAG(nodes=['root'])
queue = ['root']
while queue:
caller = queue.pop(0)
callees = FindNodes(Call).visit(self.efuncs[caller])
for callee in filter_ordered([i.name for i in callees]):
if callee in self.efuncs: # Exclude foreign Calls, e.g., MPI calls
try:
dag.add_node(callee)
queue.append(callee)
except KeyError:
# `callee` already in `dag`
pass
dag.add_edge(callee, caller)
# Sanity check
assert dag.size == len(self.efuncs)
return dag
class State(object):
def __init__(self, iet):
self._efuncs = OrderedDict([('main', iet)])
self._dimensions = []
self._input = []
self._includes = []
self._call_graph = DAG(nodes=['main'])
def _process(self, func):
"""Apply ``func`` to all tracked ``IETs``."""
for i in self._call_graph.topological_sort():
self._efuncs[i], metadata = func(self._efuncs[i])
# Track any new Dimensions and includes introduced by `func`
self._dimensions.extend(list(metadata.get('dimensions', [])))
self._includes.extend(list(metadata.get('includes', [])))
# If there's a change to the `input` and the `iet` is an efunc, then
# we must update the call sites as well, as the arguments dropped down
# to the efunc have just increased
_input = as_tuple(metadata.get('input'))
if _input:
# `extif` avoids redundant updates to the parameters list, due
# to multiple children wanting to add the same input argument
extif = lambda v: list(v) + [e for e in _input if e not in v]
stack = [i] + self._call_graph.all_downstreams(i)
for n in stack:
efunc = self._efuncs[n]
calls = [c for c in FindNodes(Call).visit(efunc) if c.name in stack]
mapper = {c: c._rebuild(arguments=extif(c.arguments)) for c in calls}
efunc = Transformer(mapper).visit(efunc)
if efunc.is_Callable:
efunc = efunc._rebuild(parameters=extif(efunc.parameters))
self._efuncs[n] = efunc
self._input.extend(list(_input))
for k, v in metadata.get('efuncs', {}).items():
# Update the efuncs
if k.is_Callable:
self._efuncs[k.name] = k
# Update the call graph
self._call_graph.add_node(k.name, ignore_existing=True)
for target in (v or [None]):
self._call_graph.add_edge(k.name, target or 'main', force_add=True)
@property
def root(self):
return self._efuncs['main']
@property
def efuncs(self):
return tuple(v for k, v in self._efuncs.items() if k != 'main')
@property
def dimensions(self):
return self._dimensions
@property
def input(self):
return self._input
@property
def includes(self):
return self._includes