def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
if self.expr_index == 0:
map_entry = graph.nodes()[self.subgraph[GPUTransformMap._map_entry]]
nsdfg_node = helpers.nest_state_subgraph(
sdfg,
graph,
graph.scope_subgraph(map_entry),
full_data=self.fullcopy)
else:
cnode = graph.nodes()[self.subgraph[GPUTransformMap._reduce]]
nsdfg_node = helpers.nest_state_subgraph(sdfg,
graph,
SubgraphView(
graph, [cnode]),
full_data=self.fullcopy)
# Avoiding import loops
from dace.transformation.interstate import GPUTransformSDFG
transformation = GPUTransformSDFG(0, 0, {}, 0)
transformation.register_trans = self.register_trans
transformation.sequential_innermaps = self.sequential_innermaps
transformation.toplevel_trans = self.toplevel_trans
transformation.apply(nsdfg_node.sdfg)
# Inline back as necessary
sdfg.coarsen_dataflow()
def apply(self, graph: SDFGState, sdfg: SDFG):
if self.expr_index == 0:
map_entry = self.map_entry
nsdfg_node = helpers.nest_state_subgraph(
sdfg,
graph,
graph.scope_subgraph(map_entry),
full_data=self.fullcopy)
else:
cnode = self.reduce
nsdfg_node = helpers.nest_state_subgraph(sdfg,
graph,
SubgraphView(
graph, [cnode]),
full_data=self.fullcopy)
# Avoiding import loops
from dace.transformation.interstate import GPUTransformSDFG
transformation = GPUTransformSDFG(sdfg, 0, -1, {}, 0)
transformation.register_trans = self.register_trans
transformation.sequential_innermaps = self.sequential_innermaps
transformation.toplevel_trans = self.toplevel_trans
transformation.apply(nsdfg_node.sdfg, nsdfg_node.sdfg)
# Inline back as necessary
sdfg.simplify()