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.gpu_id = self.gpu_id
transformation.apply(nsdfg_node.sdfg)
# Inline back as necessary
sdfg.apply_strict_transformations()
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()
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 apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
subgraph = self.subgraph_view(sdfg)
map_entries = helpers.get_outermost_scope_maps(sdfg, graph, subgraph)
result = StencilTiling.topology(sdfg, graph, map_entries)
(children_dict, parent_dict, sink_maps) = result
# next up, calculate inferred ranges for each map
# for each map entry, this contains a tuple of dicts:
# each of those maps from data_name of the array to
# inferred outer ranges. An inferred outer range is created
# by taking the union of ranges of inner subsets corresponding
# to that data and substituting this subset by the min / max of the
# parametrized map boundaries
# finally, from these outer ranges we can easily calculate
# strides and tile sizes required for every map
inferred_ranges = defaultdict(dict)
# create array of reverse topologically sorted map entries
# to iterate over
topo_reversed = []
queue = set(sink_maps.copy())
while len(queue) > 0:
element = next(e for e in queue
if not children_dict[e] - set(topo_reversed))
topo_reversed.append(element)
queue.remove(element)
for parent in parent_dict[element]:
queue.add(parent)
# main loop
# first get coverage dicts for each map entry
# for each map, contains a tuple of two dicts
# each of those two maps from data name to outer range
coverage = {}
for map_entry in map_entries:
coverage[map_entry] = StencilTiling.coverage_dicts(
sdfg, graph, map_entry, outer_range=True)
# we have a mapping from data name to outer range
# however we want a mapping from map parameters to outer ranges
# for this we need to find out how all array dimensions map to
# outer ranges
variable_mapping = defaultdict(list)
for map_entry in topo_reversed:
map = map_entry.map
# first find out variable mapping
for e in itertools.chain(
graph.out_edges(map_entry),
graph.in_edges(graph.exit_node(map_entry))):
mapping = []
for dim in e.data.subset:
syms = set()
for d in dim:
syms |= symbolic.symlist(d).keys()
if len(syms) > 1:
raise NotImplementedError(
"One incoming or outgoing stencil subset is indexed "
"by multiple map parameters. "
"This is not supported yet.")
try:
mapping.append(syms.pop())
except KeyError:
# just append None if there is no map symbol in it.
# we don't care for now.
mapping.append(None)
if e.data in variable_mapping:
# assert that this is the same everywhere.
# else we might run into problems
assert variable_mapping[e.data.data] == mapping
else:
variable_mapping[e.data.data] = mapping
# now do mapping data name -> outer range
# and from that infer mapping variable -> outer range
local_ranges = {dn: None for dn in coverage[map_entry][1].keys()}
for data_name, cov in coverage[map_entry][1].items():
local_ranges[data_name] = subsets.union(
local_ranges[data_name], cov)
# now look at proceeding maps
# and union those subsets -> could be larger with stencil indent
for child_map in children_dict[map_entry]:
if data_name in coverage[child_map][0]:
local_ranges[data_name] = subsets.union(
local_ranges[data_name],
coverage[child_map][0][data_name])
# final assignent: combine local_ranges and variable_mapping
# together into inferred_ranges
inferred_ranges[map_entry] = {p: None for p in map.params}
for data_name, ranges in local_ranges.items():
for param, r in zip(variable_mapping[data_name], ranges):
# create new range from this subset and assign
rng = subsets.Range((r, ))
if param:
inferred_ranges[map_entry][param] = subsets.union(
inferred_ranges[map_entry][param], rng)
# get parameters -- should all be the same
params = next(iter(map_entries)).map.params.copy()
# define reference range as inferred range of one of the sink maps
self.reference_range = inferred_ranges[next(iter(sink_maps))]
if self.debug:
print("StencilTiling::Reference Range", self.reference_range)
# next up, search for the ranges that don't change
invariant_dims = []
for idx, p in enumerate(params):
different = False
if self.reference_range[p] is None:
invariant_dims.append(idx)
warnings.warn(
f"StencilTiling::No Stencil pattern detected for parameter {p}"
)
continue
for m in map_entries:
if inferred_ranges[m][p] != self.reference_range[p]:
different = True
break
if not different:
invariant_dims.append(idx)
warnings.warn(
f"StencilTiling::No Stencil pattern detected for parameter {p}"
)
# during stripmining, we will create new outer map entries
# for easy access
self._outer_entries = set()
# with inferred_ranges constructed, we can begin to strip mine
for map_entry in map_entries:
# Retrieve map entry and exit nodes.
map = map_entry.map
stripmine_subgraph = {
StripMining._map_entry: graph.nodes().index(map_entry)
}
sdfg_id = sdfg.sdfg_id
last_map_entry = None
original_schedule = map_entry.schedule
self.tile_sizes = []
self.tile_offset_lower = []
self.tile_offset_upper = []
# strip mining each dimension where necessary
removed_maps = 0
for dim_idx, param in enumerate(map_entry.map.params):
# get current_node tile size
if dim_idx >= len(self.strides):
tile_stride = symbolic.pystr_to_symbolic(self.strides[-1])
else:
tile_stride = symbolic.pystr_to_symbolic(
self.strides[dim_idx])
trivial = False
if dim_idx in invariant_dims:
self.tile_sizes.append(tile_stride)
self.tile_offset_lower.append(0)
self.tile_offset_upper.append(0)
else:
target_range_current = inferred_ranges[map_entry][param]
reference_range_current = self.reference_range[param]
min_diff = symbolic.SymExpr(reference_range_current.min_element()[0] \
- target_range_current.min_element()[0])
max_diff = symbolic.SymExpr(target_range_current.max_element()[0] \
- reference_range_current.max_element()[0])
try:
min_diff = symbolic.evaluate(min_diff, {})
max_diff = symbolic.evaluate(max_diff, {})
except TypeError:
raise RuntimeError("Symbolic evaluation of map "
"ranges failed. Please check "
"your parameters and match.")
self.tile_sizes.append(tile_stride + max_diff + min_diff)
self.tile_offset_lower.append(
symbolic.pystr_to_symbolic(str(min_diff)))
self.tile_offset_upper.append(
symbolic.pystr_to_symbolic(str(max_diff)))
# get calculated parameters
tile_size = self.tile_sizes[-1]
dim_idx -= removed_maps
# If map or tile sizes are trivial, skip strip-mining map dimension
# special cases:
# if tile size is trivial AND we have an invariant dimension, skip
if tile_size == map.range.size()[dim_idx] and (
dim_idx + removed_maps) in invariant_dims:
continue
# trivial map: we just continue
if map.range.size()[dim_idx] in [0, 1]:
continue
if tile_size == 1 and tile_stride == 1 and (
dim_idx + removed_maps) in invariant_dims:
trivial = True
removed_maps += 1
# indent all map ranges accordingly and then perform
# strip mining on these. Offset inner maps accordingly afterwards
range_tuple = (map.range[dim_idx][0] +
self.tile_offset_lower[-1],
map.range[dim_idx][1] -
self.tile_offset_upper[-1],
map.range[dim_idx][2])
map.range[dim_idx] = range_tuple
stripmine = StripMining(sdfg_id, self.state_id,
stripmine_subgraph, 0)
stripmine.tiling_type = 'ceilrange'
stripmine.dim_idx = dim_idx
stripmine.new_dim_prefix = self.prefix if not trivial else ''
# use tile_stride for both -- we will extend
# the inner tiles later
stripmine.tile_size = str(tile_stride)
stripmine.tile_stride = str(tile_stride)
outer_map = stripmine.apply(sdfg)
outer_map.schedule = original_schedule
# apply to the new map the schedule of the original one
map_entry.schedule = self.schedule
# if tile stride is 1, we can make a nice simplification by just
# taking the overapproximated inner range as inner range
# this eliminates the min/max in the range which
# enables loop unrolling
if tile_stride == 1:
map_entry.range[dim_idx] = tuple(
symbolic.SymExpr(el._approx_expr) if isinstance(
el, symbolic.SymExpr) else el
for el in map_entry.range[dim_idx])
# in map_entry: enlarge tiles by upper and lower offset
# doing it this way and not via stripmine strides ensures
# that the max gets changed as well
old_range = map_entry.range[dim_idx]
map_entry.range[dim_idx] = ((old_range[0] -
self.tile_offset_lower[-1]),
(old_range[1] +
self.tile_offset_upper[-1]),
old_range[2])
# We have to propagate here for correct outer volume and subset sizes
_propagate_node(graph, map_entry)
_propagate_node(graph, graph.exit_node(map_entry))
# usual tiling pipeline
if last_map_entry:
new_map_entry = graph.in_edges(map_entry)[0].src
mapcollapse_subgraph = {
MapCollapse._outer_map_entry:
graph.node_id(last_map_entry),
MapCollapse._inner_map_entry:
graph.node_id(new_map_entry)
}
mapcollapse = MapCollapse(sdfg_id, self.state_id,
mapcollapse_subgraph, 0)
mapcollapse.apply(sdfg)
last_map_entry = graph.in_edges(map_entry)[0].src
# add last instance of map entries to _outer_entries
if last_map_entry:
self._outer_entries.add(last_map_entry)
# Map Unroll Feature: only unroll if conditions are met:
# Only unroll if at least one of the inner map ranges is strictly larger than 1
# Only unroll if strides all are one
if self.unroll_loops and all(s == 1 for s in self.strides) and any(
s not in [0, 1] for s in map_entry.range.size()):
l = len(map_entry.params)
if l > 1:
subgraph = {
MapExpansion.map_entry: graph.nodes().index(map_entry)
}
trafo_expansion = MapExpansion(sdfg.sdfg_id,
sdfg.nodes().index(graph),
subgraph, 0)
trafo_expansion.apply(sdfg)
maps = [map_entry]
for _ in range(l - 1):
map_entry = graph.out_edges(map_entry)[0].dst
maps.append(map_entry)
for map in reversed(maps):
# MapToForLoop
subgraph = {
MapToForLoop._map_entry: graph.nodes().index(map)
}
trafo_for_loop = MapToForLoop(sdfg.sdfg_id,
sdfg.nodes().index(graph),
subgraph, 0)
trafo_for_loop.apply(sdfg)
nsdfg = trafo_for_loop.nsdfg
# LoopUnroll
guard = trafo_for_loop.guard
end = trafo_for_loop.after_state
begin = next(e.dst for e in nsdfg.out_edges(guard)
if e.dst != end)
subgraph = {
DetectLoop._loop_guard: nsdfg.nodes().index(guard),
DetectLoop._loop_begin: nsdfg.nodes().index(begin),
DetectLoop._exit_state: nsdfg.nodes().index(end)
}
transformation = LoopUnroll(0, 0, subgraph, 0)
transformation.apply(nsdfg)
elif self.unroll_loops:
warnings.warn(
"Did not unroll loops. Either all ranges are equal to "
"one or range difference is symbolic.")
self._outer_entries = list(self._outer_entries)