def can_be_applied(self, graph, expr_index, sdfg, permissive=False):
# Is this even a loop
if not super().can_be_applied(graph, expr_index, sdfg, permissive):
return False
# Obtain loop information
guard: sd.SDFGState = self.loop_guard
body: sd.SDFGState = self.loop_begin
# Obtain iteration variable, range, and stride
loop_info = find_for_loop(sdfg, guard, body)
if not loop_info:
return False
_, (start, end, step), _ = loop_info
try:
if step > 0 and start + step < end + 1:
return False
if step < 0 and start + step > end - 1:
return False
except:
# if the relation can't be determined it's not a trivial loop
return False
return True
def can_be_applied(self, graph, expr_index, sdfg, permissive=False):
if not super().can_be_applied(graph, expr_index, sdfg, permissive):
return False
guard = self.loop_guard
begin = self.loop_begin
# If loop cannot be detected, fail
found = find_for_loop(sdfg, guard, begin)
if found is None:
return False
return True
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
if not DetectLoop.can_be_applied(graph, candidate, expr_index, sdfg,
strict):
return False
guard = graph.node(candidate[DetectLoop._loop_guard])
begin = graph.node(candidate[DetectLoop._loop_begin])
# If loop cannot be detected, fail
found = find_for_loop(sdfg, guard, begin)
if found is None:
return False
return True
def can_be_applied(self, graph, expr_index, sdfg, permissive=False):
# Is this even a loop
if not super().can_be_applied(graph, expr_index, sdfg, permissive):
return False
guard = self.loop_guard
begin = self.loop_begin
found = find_for_loop(graph, guard, begin)
# If loop cannot be detected, fail
if not found:
return False
_, rng, _ = found
# If loop stride is not specialized or constant-sized, fail
if symbolic.issymbolic(rng[2], sdfg.constants):
return False
# If loop range diff is not constant-sized, fail
if symbolic.issymbolic(rng[1] - rng[0], sdfg.constants):
return False
return True
def apply(self, _, sdfg: sd.SDFG):
# Obtain loop information
guard: sd.SDFGState = self.loop_guard
body: sd.SDFGState = self.loop_begin
# Obtain iteration variable, range and stride
itervar, (start, end,
step), (_, body_end) = find_for_loop(sdfg, guard, body)
# Find all loop-body states
states = set()
to_visit = [body]
while to_visit:
state = to_visit.pop(0)
for _, dst, _ in sdfg.out_edges(state):
if dst not in states and dst is not guard:
to_visit.append(dst)
states.add(state)
for state in states:
state.replace(itervar, start)
# remove loop
for body_inedge in sdfg.in_edges(body):
sdfg.remove_edge(body_inedge)
for body_outedge in sdfg.out_edges(body_end):
sdfg.remove_edge(body_outedge)
for guard_inedge in sdfg.in_edges(guard):
guard_inedge.data.assignments = {}
sdfg.add_edge(guard_inedge.src, body, guard_inedge.data)
sdfg.remove_edge(guard_inedge)
for guard_outedge in sdfg.out_edges(guard):
guard_outedge.data.condition = CodeBlock("1")
sdfg.add_edge(body_end, guard_outedge.dst, guard_outedge.data)
sdfg.remove_edge(guard_outedge)
sdfg.remove_node(guard)
if itervar in sdfg.symbols and helpers.is_symbol_unused(sdfg, itervar):
sdfg.remove_symbol(itervar)
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# Is this even a loop
if not DetectLoop.can_be_applied(graph, candidate, expr_index, sdfg,
strict):
return False
guard = graph.node(candidate[DetectLoop._loop_guard])
begin = graph.node(candidate[DetectLoop._loop_begin])
found = find_for_loop(graph, guard, begin)
# If loop cannot be detected, fail
if not found:
return False
_, rng, _ = found
# If loop stride is not specialized or constant-sized, fail
if symbolic.issymbolic(rng[2], sdfg.constants):
return False
# If loop range diff is not constant-sized, fail
if symbolic.issymbolic(rng[1] - rng[0], sdfg.constants):
return False
return True
def can_be_applied(self, graph, expr_index, sdfg, permissive=False):
# Is this even a loop
if not super().can_be_applied(graph, expr_index, sdfg, permissive):
return False
# Obtain loop information
guard: sd.SDFGState = self.loop_guard
body: sd.SDFGState = self.loop_begin
after: sd.SDFGState = self.exit_state
# Obtain iteration variable, range, and stride
loop_info = find_for_loop(sdfg, guard, body)
if not loop_info:
return False
itervar, (start, end, step), (_, body_end) = loop_info
if step not in [-1, 1]:
return False
# Body must contain a single state
if body != body_end:
return False
# Check if body contains exactly one map
maps = [node for node in body.nodes() if isinstance(node, nodes.MapEntry)]
if len(maps) != 1:
return False
# Check that everything else is independent of the loop's itervar
subgraph = body.scope_subgraph(maps[0])
map_exit = body.exit_node(maps[0])
descs: Set[str] = set()
for e in body.edges():
if not e.data.is_empty():
descs.add(e.data.data)
if e.src in subgraph.nodes() or e.dst in subgraph.nodes():
continue
if e.dst is maps[0] and isinstance(e.src, nodes.AccessNode):
continue
if e.src is map_exit and isinstance(e.dst, nodes.AccessNode):
continue
if str(itervar) in e.data.free_symbols:
return False
for n in body.nodes():
if n in subgraph.nodes():
continue
if str(itervar) in n.free_symbols:
return False
# Check for iteration variable in map and data descriptors
if str(itervar) in maps[0].free_symbols:
return False
for arr in descs:
if str(itervar) in set(map(str, sdfg.arrays[arr].free_symbols)):
return False
def test_subset_dependency(subset: sbs.Subset, mparams: Set[int]) -> Tuple[bool, List[int]]:
dims = []
for i, r in enumerate(subset):
if not isinstance(r, (list, tuple)):
r = [r]
fsymbols = set()
for token in r:
if symbolic.issymbolic(token):
fsymbols = fsymbols.union({str(s) for s in token.free_symbols})
if itervar in fsymbols:
if fsymbols.intersection(mparams):
return (False, [])
else:
dims.append(i)
return (True, dims)
# Check that Map memlets depend on itervar in a consistent manner
# a. A container must either not depend at all on itervar, or depend on it always in the same dimensions.
# b. Abort when a dimension depends on both the itervar and a Map parameter.
mparams = set(maps[0].map.params)
data_dependency = dict()
for e in body.edges():
if e.src in subgraph.nodes() and e.dst in subgraph.nodes():
if itervar in e.data.free_symbols:
for i, subset in enumerate((e.data.src_subset, e.data.dst_subset)):
if subset:
if i == 0:
access = body.memlet_path(e)[0].src
else:
access = body.memlet_path(e)[-1].dst
passed, dims = test_subset_dependency(subset, mparams)
if not passed:
return False
if dims:
if access.data in data_dependency:
if data_dependency[access.data] != dims:
return False
else:
data_dependency[access.data] = dims
for node in body.nodes():
if isinstance(node, nodes.AccessNode):
if body.in_edges(node).count(True) > 1:
return False
if body.out_edges(node).count(True) > 1:
return False
return True
def apply(self, _, sdfg: sd.SDFG):
# Obtain loop information
guard: sd.SDFGState = self.loop_guard
body: sd.SDFGState = self.loop_begin
# Obtain iteration variable, range, and stride
itervar, (start, end, step), _ = find_for_loop(sdfg, guard, body)
forward_loop = step > 0
for node in body.nodes():
if isinstance(node, nodes.MapEntry):
map_entry = node
if isinstance(node, nodes.MapExit):
map_exit = node
# nest map's content in sdfg
map_subgraph = body.scope_subgraph(map_entry, include_entry=False, include_exit=False)
nsdfg = helpers.nest_state_subgraph(sdfg, body, map_subgraph, full_data=True)
# replicate loop in nested sdfg
new_before, new_guard, new_after = nsdfg.sdfg.add_loop(
before_state=None,
loop_state=nsdfg.sdfg.nodes()[0],
loop_end_state=None,
after_state=None,
loop_var=itervar,
initialize_expr=f'{start}',
condition_expr=f'{itervar} <= {end}' if forward_loop else f'{itervar} >= {end}',
increment_expr=f'{itervar} + {step}' if forward_loop else f'{itervar} - {abs(step)}')
# remove outer loop
before_guard_edge = nsdfg.sdfg.edges_between(new_before, new_guard)[0]
for e in nsdfg.sdfg.out_edges(new_guard):
if e.dst is new_after:
guard_after_edge = e
else:
guard_body_edge = e
for body_inedge in sdfg.in_edges(body):
if body_inedge.src is guard:
guard_body_edge.data.assignments.update(body_inedge.data.assignments)
sdfg.remove_edge(body_inedge)
for body_outedge in sdfg.out_edges(body):
sdfg.remove_edge(body_outedge)
for guard_inedge in sdfg.in_edges(guard):
before_guard_edge.data.assignments.update(guard_inedge.data.assignments)
guard_inedge.data.assignments = {}
sdfg.add_edge(guard_inedge.src, body, guard_inedge.data)
sdfg.remove_edge(guard_inedge)
for guard_outedge in sdfg.out_edges(guard):
if guard_outedge.dst is body:
guard_body_edge.data.assignments.update(guard_outedge.data.assignments)
else:
guard_after_edge.data.assignments.update(guard_outedge.data.assignments)
guard_outedge.data.condition = CodeBlock("1")
sdfg.add_edge(body, guard_outedge.dst, guard_outedge.data)
sdfg.remove_edge(guard_outedge)
sdfg.remove_node(guard)
if itervar in nsdfg.symbol_mapping:
del nsdfg.symbol_mapping[itervar]
if itervar in sdfg.symbols:
del sdfg.symbols[itervar]
# Add missing data/symbols
for s in nsdfg.sdfg.free_symbols:
if s in nsdfg.symbol_mapping:
continue
if s in sdfg.symbols:
nsdfg.symbol_mapping[s] = s
elif s in sdfg.arrays:
desc = sdfg.arrays[s]
access = body.add_access(s)
conn = nsdfg.sdfg.add_datadesc(s, copy.deepcopy(desc))
nsdfg.sdfg.arrays[s].transient = False
nsdfg.add_in_connector(conn)
body.add_memlet_path(access, map_entry, nsdfg, memlet=Memlet.from_array(s, desc), dst_conn=conn)
else:
raise NotImplementedError(f"Free symbol {s} is neither a symbol nor data.")
to_delete = set()
for s in nsdfg.symbol_mapping:
if s not in nsdfg.sdfg.free_symbols:
to_delete.add(s)
for s in to_delete:
del nsdfg.symbol_mapping[s]
# propagate scope for correct volumes
scope_tree = ScopeTree(map_entry, map_exit)
scope_tree.parent = ScopeTree(None, None)
# The first execution helps remove apperances of symbols
# that are now defined only in the nested SDFG in memlets.
propagation.propagate_memlets_scope(sdfg, body, scope_tree)
for s in to_delete:
if helpers.is_symbol_unused(sdfg, s):
sdfg.remove_symbol(s)
from dace.transformation.interstate import RefineNestedAccess
transformation = RefineNestedAccess()
transformation.setup_match(sdfg, 0, sdfg.node_id(body), {RefineNestedAccess.nsdfg: body.node_id(nsdfg)}, 0)
transformation.apply(body, sdfg)
# Second propagation for refined accesses.
propagation.propagate_memlets_scope(sdfg, body, scope_tree)
def can_be_applied(self, graph, candidate, expr_index, sdfg, strict=False):
# Is this even a loop
if not DetectLoop.can_be_applied(graph, candidate, expr_index, sdfg,
strict):
return False
guard = graph.node(candidate[DetectLoop._loop_guard])
begin = graph.node(candidate[DetectLoop._loop_begin])
# Guard state should not contain any dataflow
if len(guard.nodes()) != 0:
return False
# If loop cannot be detected, fail
found = find_for_loop(graph, guard, begin,
itervar=self.itervar)
if not found:
return False
itervar, (start, end, step), (_, body_end) = found
# We cannot handle symbols read from data containers unless they are
# scalar
for expr in (start, end, step):
if symbolic.contains_sympy_functions(expr):
return False
# Find all loop-body states
states = set([body_end])
to_visit = [begin]
while to_visit:
state = to_visit.pop(0)
if state is body_end:
continue
for _, dst, _ in graph.out_edges(state):
if dst not in states:
to_visit.append(dst)
states.add(state)
write_set = set()
for state in states:
_, wset = state.read_and_write_sets()
write_set |= wset
# Get access nodes from other states to isolate local loop variables
other_access_nodes = set()
for state in sdfg.nodes():
if state in states:
continue
other_access_nodes |= set(n.data for n in state.data_nodes()
if sdfg.arrays[n.data].transient)
# Add non-transient nodes from loop state
for state in states:
other_access_nodes |= set(n.data for n in state.data_nodes()
if not sdfg.arrays[n.data].transient)
write_memlets = defaultdict(list)
itersym = symbolic.pystr_to_symbolic(itervar)
a = sp.Wild('a', exclude=[itersym])
b = sp.Wild('b', exclude=[itersym])
for state in states:
for dn in state.data_nodes():
if dn.data not in other_access_nodes:
continue
# Take all writes that are not conflicted into consideration
if dn.data in write_set:
for e in state.in_edges(dn):
if e.data.dynamic and e.data.wcr is None:
# If pointers are involved, give up
return False
# To be sure that the value is only written at unique
# indices per loop iteration, we want to match symbols
# of the form "a*i+b" where a >= 1, and i is the iteration
# variable. The iteration variable must be used.
if e.data.wcr is None:
dst_subset = e.data.get_dst_subset(e, state)
if not _check_range(dst_subset, a, itersym, b, step):
return False
# End of check
write_memlets[dn.data].append(e.data)
# After looping over relevant writes, consider reads that may overlap
for state in states:
for dn in state.data_nodes():
if dn.data not in other_access_nodes:
continue
data = dn.data
if data in write_memlets:
# Import as necessary
from dace.sdfg.propagation import propagate_subset
for e in state.out_edges(dn):
# If the same container is both read and written, only match if
# it read and written at locations that will not create data races
if e.data.dynamic and e.data.src_subset.num_elements() != 1:
# If pointers are involved, give up
return False
src_subset = e.data.get_src_subset(e, state)
if not _check_range(src_subset, a, itersym, b, step):
return False
pread = propagate_subset([e.data], sdfg.arrays[data],
[itervar],
subsets.Range([(start, end, step)
]))
for candidate in write_memlets[data]:
# Simple case: read and write are in the same subset
if e.data.subset == candidate.subset:
break
# Propagated read does not overlap with propagated write
pwrite = propagate_subset([candidate],
sdfg.arrays[data], [itervar],
subsets.Range([(start, end,
step)]))
if subsets.intersects(pread.subset,
pwrite.subset) is False:
break
return False
# Check that the iteration variable is not used on other edges or states
# before it is reassigned
prior_states = True
for state in cfg.stateorder_topological_sort(sdfg):
# Skip all states up to guard
if prior_states:
if state is begin:
prior_states = False
continue
# We do not need to check the loop-body states
if state in states:
continue
if itervar in state.free_symbols:
return False
# Don't continue in this direction, as the variable has
# now been reassigned
# TODO: Handle case of subset of out_edges
if all(itervar in e.data.assignments
for e in sdfg.out_edges(state)):
break
return True
def apply(self, sdfg: sd.SDFG):
# Obtain loop information
guard: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_guard])
body: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_begin])
after: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._exit_state])
# Obtain iteration variable, range, and stride
itervar, (start, end, step), (_, body_end) = find_for_loop(
sdfg, guard, body, itervar=self.itervar)
# Find all loop-body states
states = set([body_end])
to_visit = [body]
while to_visit:
state = to_visit.pop(0)
if state is body_end:
continue
for _, dst, _ in sdfg.out_edges(state):
if dst not in states:
to_visit.append(dst)
states.add(state)
# Nest loop-body states
if len(states) > 1:
# Find read/write sets
read_set, write_set = set(), set()
for state in states:
rset, wset = state.read_and_write_sets()
read_set |= rset
write_set |= wset
# Add data from edges
for src in states:
for dst in states:
for edge in sdfg.edges_between(src, dst):
for s in edge.data.free_symbols:
if s in sdfg.arrays:
read_set.add(s)
# Find NestedSDFG's unique data
rw_set = read_set | write_set
unique_set = set()
for name in rw_set:
if not sdfg.arrays[name].transient:
continue
found = False
for state in sdfg.states():
if state in states:
continue
for node in state.nodes():
if (isinstance(node, nodes.AccessNode) and
node.data == name):
found = True
break
if not found:
unique_set.add(name)
# Find NestedSDFG's connectors
read_set = {n for n in read_set if n not in unique_set or not sdfg.arrays[n].transient}
write_set = {n for n in write_set if n not in unique_set or not sdfg.arrays[n].transient}
# Create NestedSDFG and add all loop-body states and edges
# Also, find defined symbols in NestedSDFG
fsymbols = set(sdfg.free_symbols)
new_body = sdfg.add_state('single_state_body')
nsdfg = SDFG("loop_body", constants=sdfg.constants, parent=new_body)
nsdfg.add_node(body, is_start_state=True)
body.parent = nsdfg
exit_state = nsdfg.add_state('exit')
nsymbols = dict()
for state in states:
if state is body:
continue
nsdfg.add_node(state)
state.parent = nsdfg
for state in states:
if state is body:
continue
for src, dst, data in sdfg.in_edges(state):
nsymbols.update({s: sdfg.symbols[s] for s in data.assignments.keys() if s in sdfg.symbols})
nsdfg.add_edge(src, dst, data)
nsdfg.add_edge(body_end, exit_state, InterstateEdge())
# Move guard -> body edge to guard -> new_body
for src, dst, data, in sdfg.edges_between(guard, body):
sdfg.add_edge(src, new_body, data)
# Move body_end -> guard edge to new_body -> guard
for src, dst, data in sdfg.edges_between(body_end, guard):
sdfg.add_edge(new_body, dst, data)
# Delete loop-body states and edges from parent SDFG
for state in states:
for e in sdfg.all_edges(state):
sdfg.remove_edge(e)
sdfg.remove_node(state)
# Add NestedSDFG arrays
for name in read_set | write_set:
nsdfg.arrays[name] = copy.deepcopy(sdfg.arrays[name])
nsdfg.arrays[name].transient = False
for name in unique_set:
nsdfg.arrays[name] = sdfg.arrays[name]
del sdfg.arrays[name]
# Add NestedSDFG node
cnode = new_body.add_nested_sdfg(nsdfg, None, read_set, write_set)
if sdfg.parent:
for s, m in sdfg.parent_nsdfg_node.symbol_mapping.items():
if s not in cnode.symbol_mapping:
cnode.symbol_mapping[s] = m
nsdfg.add_symbol(s, sdfg.symbols[s])
for name in read_set:
r = new_body.add_read(name)
new_body.add_edge(
r, None, cnode, name,
memlet.Memlet.from_array(name, sdfg.arrays[name]))
for name in write_set:
w = new_body.add_write(name)
new_body.add_edge(
cnode, name, w, None,
memlet.Memlet.from_array(name, sdfg.arrays[name]))
# Fix SDFG symbols
for sym in sdfg.free_symbols - fsymbols:
del sdfg.symbols[sym]
for sym, dtype in nsymbols.items():
nsdfg.symbols[sym] = dtype
# Change body state reference
body = new_body
if (step < 0) == True:
# If step is negative, we have to flip start and end to produce a
# correct map with a positive increment
start, end, step = end, start, -step
# If necessary, make a nested SDFG with assignments
isedge = sdfg.edges_between(guard, body)[0]
symbols_to_remove = set()
if len(isedge.data.assignments) > 0:
nsdfg = helpers.nest_state_subgraph(
sdfg, body, gr.SubgraphView(body, body.nodes()))
for sym in isedge.data.free_symbols:
if sym in nsdfg.symbol_mapping or sym in nsdfg.in_connectors:
continue
if sym in sdfg.symbols:
nsdfg.symbol_mapping[sym] = symbolic.pystr_to_symbolic(sym)
nsdfg.sdfg.add_symbol(sym, sdfg.symbols[sym])
elif sym in sdfg.arrays:
if sym in nsdfg.sdfg.arrays:
raise NotImplementedError
rnode = body.add_read(sym)
nsdfg.add_in_connector(sym)
desc = copy.deepcopy(sdfg.arrays[sym])
desc.transient = False
nsdfg.sdfg.add_datadesc(sym, desc)
body.add_edge(rnode, None, nsdfg, sym, memlet.Memlet(sym))
nstate = nsdfg.sdfg.node(0)
init_state = nsdfg.sdfg.add_state_before(nstate)
nisedge = nsdfg.sdfg.edges_between(init_state, nstate)[0]
nisedge.data.assignments = isedge.data.assignments
symbols_to_remove = set(nisedge.data.assignments.keys())
for k in nisedge.data.assignments.keys():
if k in nsdfg.symbol_mapping:
del nsdfg.symbol_mapping[k]
isedge.data.assignments = {}
source_nodes = body.source_nodes()
sink_nodes = body.sink_nodes()
map = nodes.Map(body.label + "_map", [itervar], [(start, end, step)])
entry = nodes.MapEntry(map)
exit = nodes.MapExit(map)
body.add_node(entry)
body.add_node(exit)
# If the map uses symbols from data containers, instantiate reads
containers_to_read = entry.free_symbols & sdfg.arrays.keys()
for rd in containers_to_read:
# We are guaranteed that this is always a scalar, because
# can_be_applied makes sure there are no sympy functions in each of
# the loop expresions
access_node = body.add_read(rd)
body.add_memlet_path(access_node,
entry,
dst_conn=rd,
memlet=memlet.Memlet(rd))
# Reroute all memlets through the entry and exit nodes
for n in source_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.out_edges(n):
body.remove_edge(e)
body.add_edge_pair(entry,
e.dst,
n,
e.data,
internal_connector=e.dst_conn)
else:
body.add_nedge(entry, n, memlet.Memlet())
for n in sink_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.in_edges(n):
body.remove_edge(e)
body.add_edge_pair(exit,
e.src,
n,
e.data,
internal_connector=e.src_conn)
else:
body.add_nedge(n, exit, memlet.Memlet())
# Get rid of the loop exit condition edge
after_edge = sdfg.edges_between(guard, after)[0]
sdfg.remove_edge(after_edge)
# Remove the assignment on the edge to the guard
for e in sdfg.in_edges(guard):
if itervar in e.data.assignments:
del e.data.assignments[itervar]
# Remove the condition on the entry edge
condition_edge = sdfg.edges_between(guard, body)[0]
condition_edge.data.condition = CodeBlock("1")
# Get rid of backedge to guard
sdfg.remove_edge(sdfg.edges_between(body, guard)[0])
# Route body directly to after state, maintaining any other assignments
# it might have had
sdfg.add_edge(
body, after,
sd.InterstateEdge(assignments=after_edge.data.assignments))
# If this had made the iteration variable a free symbol, we can remove
# it from the SDFG symbols
if itervar in sdfg.free_symbols:
sdfg.remove_symbol(itervar)
for sym in symbols_to_remove:
if helpers.is_symbol_unused(sdfg, sym):
sdfg.remove_symbol(sym)
def apply(self, sdfg: sd.SDFG):
# Obtain loop information
guard: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_guard])
body: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_begin])
after: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._exit_state])
# Obtain iteration variable, range, and stride
itervar, (start, end, step), _ = find_for_loop(sdfg, guard, body)
if (step < 0) == True:
# If step is negative, we have to flip start and end to produce a
# correct map with a positive increment
start, end, step = end, start, -step
# If necessary, make a nested SDFG with assignments
isedge = sdfg.edges_between(guard, body)[0]
symbols_to_remove = set()
if len(isedge.data.assignments) > 0:
nsdfg = helpers.nest_state_subgraph(
sdfg, body, gr.SubgraphView(body, body.nodes()))
for sym in isedge.data.free_symbols:
if sym in nsdfg.symbol_mapping or sym in nsdfg.in_connectors:
continue
if sym in sdfg.symbols:
nsdfg.symbol_mapping[sym] = symbolic.pystr_to_symbolic(sym)
nsdfg.sdfg.add_symbol(sym, sdfg.symbols[sym])
elif sym in sdfg.arrays:
if sym in nsdfg.sdfg.arrays:
raise NotImplementedError
rnode = body.add_read(sym)
nsdfg.add_in_connector(sym)
desc = copy.deepcopy(sdfg.arrays[sym])
desc.transient = False
nsdfg.sdfg.add_datadesc(sym, desc)
body.add_edge(rnode, None, nsdfg, sym, memlet.Memlet(sym))
nstate = nsdfg.sdfg.node(0)
init_state = nsdfg.sdfg.add_state_before(nstate)
nisedge = nsdfg.sdfg.edges_between(init_state, nstate)[0]
nisedge.data.assignments = isedge.data.assignments
symbols_to_remove = set(nisedge.data.assignments.keys())
for k in nisedge.data.assignments.keys():
if k in nsdfg.symbol_mapping:
del nsdfg.symbol_mapping[k]
isedge.data.assignments = {}
source_nodes = body.source_nodes()
sink_nodes = body.sink_nodes()
map = nodes.Map(body.label + "_map", [itervar], [(start, end, step)])
entry = nodes.MapEntry(map)
exit = nodes.MapExit(map)
body.add_node(entry)
body.add_node(exit)
# If the map uses symbols from data containers, instantiate reads
containers_to_read = entry.free_symbols & sdfg.arrays.keys()
for rd in containers_to_read:
# We are guaranteed that this is always a scalar, because
# can_be_applied makes sure there are no sympy functions in each of
# the loop expresions
access_node = body.add_read(rd)
body.add_memlet_path(access_node,
entry,
dst_conn=rd,
memlet=memlet.Memlet(rd))
# Reroute all memlets through the entry and exit nodes
for n in source_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.out_edges(n):
body.remove_edge(e)
body.add_edge_pair(entry,
e.dst,
n,
e.data,
internal_connector=e.dst_conn)
else:
body.add_nedge(entry, n, memlet.Memlet())
for n in sink_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.in_edges(n):
body.remove_edge(e)
body.add_edge_pair(exit,
e.src,
n,
e.data,
internal_connector=e.src_conn)
else:
body.add_nedge(n, exit, memlet.Memlet())
# Get rid of the loop exit condition edge
after_edge = sdfg.edges_between(guard, after)[0]
sdfg.remove_edge(after_edge)
# Remove the assignment on the edge to the guard
for e in sdfg.in_edges(guard):
if itervar in e.data.assignments:
del e.data.assignments[itervar]
# Remove the condition on the entry edge
condition_edge = sdfg.edges_between(guard, body)[0]
condition_edge.data.condition = CodeBlock("1")
# Get rid of backedge to guard
sdfg.remove_edge(sdfg.edges_between(body, guard)[0])
# Route body directly to after state, maintaining any other assignments
# it might have had
sdfg.add_edge(
body, after,
sd.InterstateEdge(assignments=after_edge.data.assignments))
# If this had made the iteration variable a free symbol, we can remove
# it from the SDFG symbols
if itervar in sdfg.free_symbols:
sdfg.remove_symbol(itervar)
for sym in symbols_to_remove:
if helpers.is_symbol_unused(sdfg, sym):
sdfg.remove_symbol(sym)
def apply(self, _, sdfg: sd.SDFG):
####################################################################
# Obtain loop information
guard: sd.SDFGState = self.loop_guard
begin: sd.SDFGState = self.loop_begin
after_state: sd.SDFGState = self.exit_state
# Obtain iteration variable, range, and stride
condition_edge = sdfg.edges_between(guard, begin)[0]
not_condition_edge = sdfg.edges_between(guard, after_state)[0]
itervar, rng, loop_struct = find_for_loop(sdfg, guard, begin)
# Get loop states
loop_states = list(
sdutil.dfs_conditional(sdfg,
sources=[begin],
condition=lambda _, child: child != guard))
first_id = loop_states.index(begin)
last_state = loop_struct[1]
last_id = loop_states.index(last_state)
loop_subgraph = gr.SubgraphView(sdfg, loop_states)
####################################################################
# Transform
if self.begin:
# If begin, change initialization assignment and prepend states before
# guard
init_edges = []
before_states = loop_struct[0]
for before_state in before_states:
init_edge = sdfg.edges_between(before_state, guard)[0]
init_edge.data.assignments[itervar] = str(rng[0] +
self.count * rng[2])
init_edges.append(init_edge)
append_states = before_states
# Add `count` states, each with instantiated iteration variable
for i in range(self.count):
# Instantiate loop states with iterate value
state_name: str = 'start_' + itervar + str(i * rng[2])
state_name = state_name.replace('-', 'm').replace(
'+', 'p').replace('*', 'M').replace('/', 'D')
new_states = self.instantiate_loop(
sdfg,
loop_states,
loop_subgraph,
itervar,
rng[0] + i * rng[2],
state_name,
)
# Connect states to before the loop with unconditional edges
for append_state in append_states:
sdfg.add_edge(append_state, new_states[first_id],
sd.InterstateEdge())
append_states = [new_states[last_id]]
# Reconnect edge to guard state from last peeled iteration
for append_state in append_states:
if append_state not in before_states:
for init_edge in init_edges:
sdfg.remove_edge(init_edge)
sdfg.add_edge(append_state, guard, init_edges[0].data)
else:
# If begin, change initialization assignment and prepend states before
# guard
itervar_sym = pystr_to_symbolic(itervar)
condition_edge.data.condition = CodeBlock(
self._modify_cond(condition_edge.data.condition, itervar,
rng[2]))
not_condition_edge.data.condition = CodeBlock(
self._modify_cond(not_condition_edge.data.condition, itervar,
rng[2]))
prepend_state = after_state
# Add `count` states, each with instantiated iteration variable
for i in reversed(range(self.count)):
# Instantiate loop states with iterate value
state_name: str = 'end_' + itervar + str(-i * rng[2])
state_name = state_name.replace('-', 'm').replace(
'+', 'p').replace('*', 'M').replace('/', 'D')
new_states = self.instantiate_loop(
sdfg,
loop_states,
loop_subgraph,
itervar,
itervar_sym + i * rng[2],
state_name,
)
# Connect states to before the loop with unconditional edges
sdfg.add_edge(new_states[last_id], prepend_state,
sd.InterstateEdge())
prepend_state = new_states[first_id]
# Reconnect edge to guard state from last peeled iteration
if prepend_state != after_state:
sdfg.remove_edge(not_condition_edge)
sdfg.add_edge(guard, prepend_state, not_condition_edge.data)
def apply(self, sdfg):
# Obtain loop information
guard: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_guard])
begin: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_begin])
after_state: sd.SDFGState = sdfg.node(
self.subgraph[DetectLoop._exit_state])
# Obtain iteration variable, range, and stride, together with the last
# state(s) before the loop and the last loop state.
itervar, rng, loop_struct = find_for_loop(sdfg, guard, begin)
# Loop must be fully unrollable for now.
if self.count != 0:
raise NotImplementedError # TODO(later)
# Get loop states
loop_states = list(
sdutil.dfs_conditional(sdfg,
sources=[begin],
condition=lambda _, child: child != guard))
first_id = loop_states.index(begin)
last_state = loop_struct[1]
last_id = loop_states.index(last_state)
loop_subgraph = gr.SubgraphView(sdfg, loop_states)
try:
start, end, stride = (r for r in rng)
stride = symbolic.evaluate(stride, sdfg.constants)
loop_diff = int(symbolic.evaluate(end - start + 1, sdfg.constants))
is_symbolic = any([symbolic.issymbolic(r) for r in rng[:2]])
except TypeError:
raise TypeError('Loop difference and strides cannot be symbolic.')
# Create states for loop subgraph
unrolled_states = []
for i in range(0, loop_diff, stride):
current_index = start + i
# Instantiate loop states with iterate value
new_states = self.instantiate_loop(sdfg, loop_states,
loop_subgraph, itervar,
current_index,
str(i) if is_symbolic else None)
# Connect iterations with unconditional edges
if len(unrolled_states) > 0:
sdfg.add_edge(unrolled_states[-1][1], new_states[first_id],
sd.InterstateEdge())
unrolled_states.append((new_states[first_id], new_states[last_id]))
# Get any assignments that might be on the edge to the after state
after_assignments = (sdfg.edges_between(
guard, after_state)[0].data.assignments)
# Connect new states to before and after states without conditions
if unrolled_states:
before_states = loop_struct[0]
for before_state in before_states:
sdfg.add_edge(before_state, unrolled_states[0][0],
sd.InterstateEdge())
sdfg.add_edge(unrolled_states[-1][1], after_state,
sd.InterstateEdge(assignments=after_assignments))
# Remove old states from SDFG
sdfg.remove_nodes_from([guard] + loop_states)
