class MapDimInterchange(pm.Transformation):
""" Implements the map-dimension-interchange pattern.
Map-dimension-interchange re-orders the dimensions of a map.
"""
_map_entry = nodes.MapEntry(None)
order = ShapeProperty()
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapDimInterchange._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
""" A candidate subgraph matches the map-dimension-interchange
transformation when a map has at least two dimensions.
"""
map_entry = graph.nodes()[candidate[MapDimInterchange._map_entry]]
return map_entry.map.get_param_num() > 1
@staticmethod
def match_to_str(graph, candidate):
map_entry = candidate[MapDimInterchange._map_entry]
return str(map_entry)
def apply(self, sdfg):
""" Reorders the dimensions of the map by reordering the
parameters and the range of the map as specified through the
properties.
"""
# Extract the map and its entry node.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapDimInterchange._map_entry]]
current_map = map_entry.map
order = self.order
if len(self.order) != current_map.get_param_num():
# 'order' must be of the same length as the number of map
# dimensions.
return
# Re-order the map dimensions
current_map.params = [current_map.params[idx] for idx in order]
current_map.range.reorder(order)
return
def __init__(self, *args, **kwargs):
self.entry = nodes.EntryNode()
self.tasklet = nodes.Tasklet('_')
self.exit = nodes.ExitNode()
self.pairs = None
super().__init__(*args, **kwargs)
def modifies_graph(self):
return True
class RedundantArrayCopying3(pm.Transformation):
""" Implements the redundant array removal transformation. Removes multiples
of array B in pattern MapEntry -> B.
"""
_arrays_removed = 0
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_out_array = nodes.AccessNode("_")
@staticmethod
def expressions():
return [
nxutil.node_path_graph(RedundantArrayCopying3._map_entry,
RedundantArrayCopying3._out_array)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[RedundantArrayCopying3._map_entry]]
out_array = graph.nodes()[candidate[RedundantArrayCopying3._out_array]]
# Ensure out degree is one (only one target, which is out_array)
found = 0
for _, _, dst, _, _ in graph.out_edges(map_entry):
if (isinstance(dst, nodes.AccessNode) and dst != out_array
and dst.data == out_array.data):
found += 1
return found > 0
@staticmethod
def match_to_str(graph, candidate):
out_array = graph.nodes()[candidate[RedundantArrayCopying3._out_array]]
return "Remove " + str(out_array)
def apply(self, sdfg):
def gnode(nname):
return graph.nodes()[self.subgraph[nname]]
graph = sdfg.nodes()[self.state_id]
map_entry = gnode(RedundantArrayCopying3._map_entry)
out_array = gnode(RedundantArrayCopying3._out_array)
for e1 in graph.out_edges(map_entry):
dst = e1.dst
if (isinstance(dst, nodes.AccessNode) and dst != out_array
and dst.data == out_array.data):
for e2 in graph.out_edges(dst):
graph.add_edge(out_array, None, e2.dst, e2.dst_conn,
e2.data)
graph.remove_edge(e2)
graph.remove_edge(e1)
graph.remove_node(dst)
if Config.get_bool("debugprint"):
RedundantArrayCopying3._arrays_removed += 1
class StripMining(pattern_matching.Transformation):
""" Implements the strip-mining transformation.
Strip-mining takes as input a map dimension and splits it into
two dimensions. The new dimension iterates over the range of
the original one with a parameterizable step, called the tile
size. The original dimension is changed to iterates over the
range of the tile size, with the same step as before.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
# Properties
dim_idx = Property(dtype=int,
default=-1,
desc="Index of dimension to be strip-mined")
new_dim_prefix = Property(dtype=str,
default="tile",
desc="Prefix for new dimension name")
tile_size = Property(dtype=str,
default="64",
desc="Tile size of strip-mined dimension")
divides_evenly = Property(dtype=bool,
default=False,
desc="Tile size divides dimension range evenly?")
strided = Property(
dtype=bool,
default=False,
desc="Continuous (false) or strided (true) elements in tile")
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [
nxutil.node_path_graph(StripMining._map_entry)
# kStripMining._tasklet, StripMining._map_exit)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
# Strip-mine selected dimension.
target_dim, new_dim, new_map = self.__stripmine(
sdfg, graph, self.subgraph)
# Modify edges to match strip-mined dimension.
# StripMining.__modify_edges(sdfg, graph, self.subgraph, target_dim, new_dim)
return new_map
# def __init__(self, tag=True):
def __init__(self, *args, **kwargs):
self._entry = nodes.EntryNode()
self._tasklet = nodes.Tasklet('_')
self._exit = nodes.ExitNode()
super().__init__(*args, **kwargs)
# self.tag = tag
@property
def entry(self):
return self._entry
@property
def exit(self):
return self._exit
@property
def tasklet(self):
return self._tasklet
def print_match_pattern(self, candidate):
gentry = candidate[self.entry]
return str(gentry.map.params[-1])
def modifies_graph(self):
return True
def __stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exits = graph.exit_nodes(map_entry)
# Retrieve transformation properties.
dim_idx = self.dim_idx
new_dim_prefix = self.new_dim_prefix
tile_size = self.tile_size
divides_evenly = self.divides_evenly
strided = self.strided
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
# Create new map. Replace by cloning???
new_dim = new_dim_prefix + '_' + target_dim
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(
'int_ceil(%s + 1 - %s, %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_size))
nd_step = 1
new_dim_range = (nd_from, nd_to, nd_step)
new_map = nodes.Map(new_dim + '_' + map_entry.map.label, [new_dim],
subsets.Range([new_dim_range]))
new_map_entry = nodes.MapEntry(new_map)
# Change the range of the selected dimension to iterate over a single
# tile
if strided:
td_from_new = symbolic.pystr_to_symbolic(new_dim)
td_to_new_approx = td_to
td_step = symbolic.pystr_to_symbolic(tile_size)
else:
td_from_new = symbolic.pystr_to_symbolic(
'%s + %s * %s' %
(symbolic.symstr(td_from), str(new_dim), tile_size))
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1, %s + %s * %s + %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_size,
str(new_dim), tile_size))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s * %s + %s - 1' %
(symbolic.symstr(td_from), tile_size, str(new_dim), tile_size))
if divides_evenly or strided:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Make internal map's schedule to "not parallel"
map_entry.map._schedule = dtypes.ScheduleType.Default
# Redirect/create edges.
new_in_edges = {}
for _src, conn, _dest, _, memlet in graph.out_edges(map_entry):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
if memlet.data in new_in_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_in_edges[memlet.data]
new_memlet.subset = calc_set_union(new_memlet.subset,
new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data:
(src, src_conn, dest, dest_conn, new_memlet,
min(num, int(conn[4:])))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data:
(new_map_entry, None, map_entry, None, new_memlet,
int(conn[4:]))
})
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_out_edges = {}
new_exits = []
for map_exit in map_exits:
if isinstance(map_exit, nodes.MapExit):
new_exit = nodes.MapExit(new_map)
new_exits.append(new_exit)
for _src, conn, _dest, _, memlet in graph.in_edges(map_exit):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
if memlet.data in new_out_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_out_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data:
(src, src_conn, dest, dest_conn, new_memlet,
min(num, conn[4:]))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (map_exit, None, new_exit, None,
new_memlet, conn[4:])
})
nxutil.change_edge_src(graph, map_exit, new_exit)
in_conn_nums = []
for _, e in new_in_edges.items():
_, _, _, _, _, num = e
in_conn_nums.append(num)
in_conn = {}
for i, num in enumerate(in_conn_nums):
in_conn.update({num: i + 1})
entry_in_connectors = set()
entry_out_connectors = set()
for i in range(len(in_conn_nums)):
entry_in_connectors.add('IN_' + str(i + 1))
entry_out_connectors.add('OUT_' + str(i + 1))
new_map_entry.in_connectors = entry_in_connectors
new_map_entry.out_connectors = entry_out_connectors
for _, e in new_in_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(in_conn[num]), dst,
'IN_' + str(in_conn[num]), memlet)
for new_exit in new_exits:
out_conn_nums = []
for _, e in new_out_edges.items():
_, _, dst, _, _, num = e
if dst is not new_exit:
continue
out_conn_nums.append(num)
out_conn = {}
for i, num in enumerate(out_conn_nums):
out_conn.update({num: i + 1})
exit_in_connectors = set()
exit_out_connectors = set()
for i in range(len(out_conn_nums)):
exit_in_connectors.add('IN_' + str(i + 1))
exit_out_connectors.add('OUT_' + str(i + 1))
new_exit.in_connectors = exit_in_connectors
new_exit.out_connectors = exit_out_connectors
for _, e in new_out_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(out_conn[num]), dst,
'IN_' + str(out_conn[num]), memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map
@staticmethod
def __modify_edges(sdfg, graph, candidate, target_dim, new_dim):
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
processed = []
for src, _dest, memlet, _scope in nxutil.traverse_sdfg_scope(
graph, map_entry, True):
if memlet in processed:
continue
processed.append(memlet)
# Corner cases
if isinstance(sdfg.arrays[memlet.data], dace.data.Stream):
continue
if memlet.wcr is not None:
memlet.num_accesses = 1
continue
for i, dim in enumerate(memlet.subset):
if isinstance(dim, tuple):
dim = tuple(
symbolic.pystr_to_symbolic(d).subs(
symbolic.pystr_to_symbolic(target_dim),
symbolic.pystr_to_symbolic('%s + %s' %
(str(new_dim),
str(target_dim))))
for d in dim)
else:
dim = symbolic.pystr_to_symbolic(dim).subs(
symbolic.pystr_to_symbolic(target_dim),
symbolic.pystr_to_symbolic(
'%s + %s' % (str(new_dim), str(target_dim))))
memlet.subset[i] = dim
return
class GPUTransformLocalStorage(pattern_matching.Transformation):
"""Implements the GPUTransformLocalStorage transformation.
Similar to GPUTransformMap, but takes multiple maps leading from the
same data node into account, creating a local storage for each range.
@see: GPUTransformMap
"""
_arrays_removed = 0
_maps_transformed = 0
fullcopy = Property(desc="Copy whole arrays rather than used subset",
dtype=bool,
default=False)
nested_seq = Property(
desc="Makes nested code semantically-equivalent to single-core code,"
"transforming nested maps and memory into sequential and "
"local memory respectively.",
dtype=bool,
default=True,
)
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_reduce = nodes.Reduce("lambda: None", None)
@staticmethod
def expressions():
return [
nxutil.node_path_graph(GPUTransformLocalStorage._map_entry),
nxutil.node_path_graph(GPUTransformLocalStorage._reduce),
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
if expr_index == 0:
map_entry = graph.nodes()[candidate[
GPUTransformLocalStorage._map_entry]]
candidate_map = map_entry.map
# Disallow GPUTransform on nested maps in strict mode
if strict:
if graph.scope_dict()[map_entry] is not None:
return False
# Map schedules that are disallowed to transform to GPUs
if (candidate_map.schedule == dtypes.ScheduleType.MPI
or candidate_map.schedule == dtypes.ScheduleType.GPU_Device
or candidate_map.schedule
== dtypes.ScheduleType.GPU_ThreadBlock or
candidate_map.schedule == dtypes.ScheduleType.Sequential):
return False
# Dynamic map ranges cannot become kernels
if sd.has_dynamic_map_inputs(graph, map_entry):
return False
# Recursively check parent for GPU schedules
sdict = graph.scope_dict()
current_node = map_entry
while current_node is not None:
if (current_node.map.schedule == dtypes.ScheduleType.GPU_Device
or current_node.map.schedule
== dtypes.ScheduleType.GPU_ThreadBlock):
return False
current_node = sdict[current_node]
# Ensure that map does not include internal arrays that are
# allocated on non-default space
subgraph = graph.scope_subgraph(map_entry)
for node in subgraph.nodes():
if (isinstance(node, nodes.AccessNode) and
node.desc(sdfg).storage != dtypes.StorageType.Default
and node.desc(sdfg).storage !=
dtypes.StorageType.Register):
return False
# If one of the outputs is a stream, do not match
map_exit = graph.exit_nodes(map_entry)[0]
for edge in graph.out_edges(map_exit):
dst = graph.memlet_path(edge)[-1].dst
if (isinstance(dst, nodes.AccessNode)
and isinstance(sdfg.arrays[dst.data], data.Stream)):
return False
return True
elif expr_index == 1:
reduce = graph.nodes()[candidate[GPUTransformLocalStorage._reduce]]
# Map schedules that are disallowed to transform to GPUs
if (reduce.schedule == dtypes.ScheduleType.MPI
or reduce.schedule == dtypes.ScheduleType.GPU_Device
or reduce.schedule == dtypes.ScheduleType.GPU_ThreadBlock):
return False
# Recursively check parent for GPU schedules
sdict = graph.scope_dict()
current_node = sdict[reduce]
while current_node is not None:
if (current_node.map.schedule == dtypes.ScheduleType.GPU_Device
or current_node.map.schedule
== dtypes.ScheduleType.GPU_ThreadBlock):
return False
current_node = sdict[current_node]
return True
@staticmethod
def match_to_str(graph, candidate):
if GPUTransformLocalStorage._reduce in candidate:
return str(
graph.nodes()[candidate[GPUTransformLocalStorage._reduce]])
else:
map_entry = graph.nodes()[candidate[
GPUTransformLocalStorage._map_entry]]
return str(map_entry)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
if self.expr_index == 0:
cnode = graph.nodes()[self.subgraph[
GPUTransformLocalStorage._map_entry]]
node_schedprop = cnode.map
exit_nodes = graph.exit_nodes(cnode)
else:
cnode = graph.nodes()[self.subgraph[
GPUTransformLocalStorage._reduce]]
node_schedprop = cnode
exit_nodes = [cnode]
# Change schedule
node_schedprop._schedule = dtypes.ScheduleType.GPU_Device
if Config.get_bool("debugprint"):
GPUTransformLocalStorage._maps_transformed += 1
# If nested graph is designated as sequential, transform schedules and
# storage from Default to Sequential/Register
if self.nested_seq and self.expr_index == 0:
for node in graph.scope_subgraph(cnode).nodes():
if isinstance(node, nodes.AccessNode):
arr = node.desc(sdfg)
if arr.storage == dtypes.StorageType.Default:
arr.storage = dtypes.StorageType.Register
elif isinstance(node, nodes.MapEntry):
if node.map.schedule == dtypes.ScheduleType.Default:
node.map.schedule = dtypes.ScheduleType.Sequential
gpu_storage_types = [
dtypes.StorageType.GPU_Global,
dtypes.StorageType.GPU_Shared,
dtypes.StorageType.GPU_Stack,
]
#######################################################
# Add GPU copies of CPU arrays (i.e., not already on GPU)
# First, understand which arrays to clone
all_out_edges = []
for enode in exit_nodes:
all_out_edges.extend(list(graph.out_edges(enode)))
in_arrays_to_clone = set()
out_arrays_to_clone = set()
for e in graph.in_edges(cnode):
data_node = sd.find_input_arraynode(graph, e)
if data_node.desc(sdfg).storage not in gpu_storage_types:
in_arrays_to_clone.add((data_node, e.data))
for e in all_out_edges:
data_node = sd.find_output_arraynode(graph, e)
if data_node.desc(sdfg).storage not in gpu_storage_types:
out_arrays_to_clone.add((data_node, e.data))
if Config.get_bool("debugprint"):
GPUTransformLocalStorage._arrays_removed += len(
in_arrays_to_clone) + len(out_arrays_to_clone)
# Second, create a GPU clone of each array
# TODO: Overapproximate union of memlets
cloned_arrays = {}
in_cloned_arraynodes = {}
out_cloned_arraynodes = {}
for array_node, memlet in in_arrays_to_clone:
array = array_node.desc(sdfg)
cloned_name = "gpu_" + array_node.data
for i, r in enumerate(memlet.bounding_box_size()):
size = symbolic.overapproximate(r)
try:
if int(size) == 1:
suffix = []
for c in str(memlet.subset[i][0]):
if c.isalpha() or c.isdigit() or c == "_":
suffix.append(c)
elif c == "+":
suffix.append("p")
elif c == "-":
suffix.append("m")
elif c == "*":
suffix.append("t")
elif c == "/":
suffix.append("d")
cloned_name += "_" + "".join(suffix)
except:
continue
if cloned_name in sdfg.arrays.keys():
cloned_array = sdfg.arrays[cloned_name]
elif array_node.data in cloned_arrays:
cloned_array = cloned_arrays[array_node.data]
else:
full_shape = []
for r in memlet.bounding_box_size():
size = symbolic.overapproximate(r)
try:
full_shape.append(int(size))
except:
full_shape.append(size)
actual_dims = [
idx for idx, r in enumerate(full_shape)
if not (isinstance(r, int) and r == 1)
]
if len(actual_dims) == 0: # abort
actual_dims = [len(full_shape) - 1]
if isinstance(array, data.Scalar):
sdfg.add_array(name=cloned_name,
shape=[1],
dtype=array.dtype,
transient=True,
storage=dtypes.StorageType.GPU_Global)
elif isinstance(array, data.Stream):
sdfg.add_stream(
name=cloned_name,
dtype=array.dtype,
shape=[full_shape[d] for d in actual_dims],
veclen=array.veclen,
buffer_size=array.buffer_size,
storage=dtypes.StorageType.GPU_Global,
transient=True,
offset=[array.offset[d] for d in actual_dims])
else:
sdfg.add_array(
name=cloned_name,
shape=[full_shape[d] for d in actual_dims],
dtype=array.dtype,
materialize_func=array.materialize_func,
transient=True,
storage=dtypes.StorageType.GPU_Global,
allow_conflicts=array.allow_conflicts,
strides=[array.strides[d] for d in actual_dims],
offset=[array.offset[d] for d in actual_dims],
)
cloned_arrays[array_node.data] = cloned_name
cloned_node = type(array_node)(cloned_name)
in_cloned_arraynodes[array_node.data] = cloned_node
for array_node, memlet in out_arrays_to_clone:
array = array_node.desc(sdfg)
cloned_name = "gpu_" + array_node.data
for i, r in enumerate(memlet.bounding_box_size()):
size = symbolic.overapproximate(r)
try:
if int(size) == 1:
suffix = []
for c in str(memlet.subset[i][0]):
if c.isalpha() or c.isdigit() or c == "_":
suffix.append(c)
elif c == "+":
suffix.append("p")
elif c == "-":
suffix.append("m")
elif c == "*":
suffix.append("t")
elif c == "/":
suffix.append("d")
cloned_name += "_" + "".join(suffix)
except:
continue
if cloned_name in sdfg.arrays.keys():
cloned_array = sdfg.arrays[cloned_name]
elif array_node.data in cloned_arrays:
cloned_array = cloned_arrays[array_node.data]
else:
full_shape = []
for r in memlet.bounding_box_size():
size = symbolic.overapproximate(r)
try:
full_shape.append(int(size))
except:
full_shape.append(size)
actual_dims = [
idx for idx, r in enumerate(full_shape)
if not (isinstance(r, int) and r == 1)
]
if len(actual_dims) == 0: # abort
actual_dims = [len(full_shape) - 1]
if isinstance(array, data.Scalar):
sdfg.add_array(name=cloned_name,
shape=[1],
dtype=array.dtype,
transient=True,
storage=dtypes.StorageType.GPU_Global)
elif isinstance(array, data.Stream):
sdfg.add_stream(
name=cloned_name,
dtype=array.dtype,
shape=[full_shape[d] for d in actual_dims],
veclen=array.veclen,
buffer_size=array.buffer_size,
storage=dtypes.StorageType.GPU_Global,
transient=True,
offset=[array.offset[d] for d in actual_dims])
else:
sdfg.add_array(
name=cloned_name,
shape=[full_shape[d] for d in actual_dims],
dtype=array.dtype,
materialize_func=array.materialize_func,
transient=True,
storage=dtypes.StorageType.GPU_Global,
allow_conflicts=array.allow_conflicts,
strides=[array.strides[d] for d in actual_dims],
offset=[array.offset[d] for d in actual_dims],
)
cloned_arrays[array_node.data] = cloned_name
cloned_node = type(array_node)(cloned_name)
cloned_node.setzero = True
out_cloned_arraynodes[array_node.data] = cloned_node
# Third, connect the cloned arrays to the originals
for array_name, node in in_cloned_arraynodes.items():
graph.add_node(node)
is_scalar = isinstance(sdfg.arrays[array_name], data.Scalar)
for edge in graph.in_edges(cnode):
if edge.data.data == array_name:
newmemlet = copy.deepcopy(edge.data)
newmemlet.data = node.data
if is_scalar:
newmemlet.subset = sbs.Indices([0])
else:
offset = []
lost_dims = []
lost_ranges = []
newsubset = [None] * len(edge.data.subset)
for ind, r in enumerate(edge.data.subset):
offset.append(r[0])
if isinstance(edge.data.subset[ind], tuple):
begin = edge.data.subset[ind][0] - r[0]
end = edge.data.subset[ind][1] - r[0]
step = edge.data.subset[ind][2]
if begin == end:
lost_dims.append(ind)
lost_ranges.append((begin, end, step))
else:
newsubset[ind] = (begin, end, step)
else:
newsubset[ind] -= r[0]
if len(lost_dims) == len(edge.data.subset):
lost_dims.pop()
newmemlet.subset = type(
edge.data.subset)([lost_ranges[-1]])
else:
newmemlet.subset = type(edge.data.subset)(
[r for r in newsubset if r is not None])
graph.add_edge(node, None, edge.dst, edge.dst_conn,
newmemlet)
for e in graph.bfs_edges(edge.dst, reverse=False):
parent, _, _child, _, memlet = e
if parent != edge.dst and not in_scope(
graph, parent, edge.dst):
break
if memlet.data != edge.data.data:
continue
path = graph.memlet_path(e)
if not isinstance(path[-1].dst, nodes.CodeNode):
if in_path(path, e, nodes.ExitNode, forward=True):
if isinstance(parent, nodes.CodeNode):
# Output edge
break
else:
continue
if is_scalar:
memlet.subset = sbs.Indices([0])
else:
newsubset = [None] * len(memlet.subset)
for ind, r in enumerate(memlet.subset):
if ind in lost_dims:
continue
if isinstance(memlet.subset[ind], tuple):
begin = r[0] - offset[ind]
end = r[1] - offset[ind]
step = r[2]
newsubset[ind] = (begin, end, step)
else:
newsubset[ind] = (
r - offset[ind],
r - offset[ind],
1,
)
memlet.subset = type(edge.data.subset)(
[r for r in newsubset if r is not None])
memlet.data = node.data
if self.fullcopy:
edge.data.subset = sbs.Range.from_array(
node.desc(sdfg))
edge.data.other_subset = newmemlet.subset
graph.add_edge(edge.src, edge.src_conn, node, None,
edge.data)
graph.remove_edge(edge)
for array_name, node in out_cloned_arraynodes.items():
graph.add_node(node)
is_scalar = isinstance(sdfg.arrays[array_name], data.Scalar)
for edge in all_out_edges:
if edge.data.data == array_name:
newmemlet = copy.deepcopy(edge.data)
newmemlet.data = node.data
if is_scalar:
newmemlet.subset = sbs.Indices([0])
else:
offset = []
lost_dims = []
lost_ranges = []
newsubset = [None] * len(edge.data.subset)
for ind, r in enumerate(edge.data.subset):
offset.append(r[0])
if isinstance(edge.data.subset[ind], tuple):
begin = edge.data.subset[ind][0] - r[0]
end = edge.data.subset[ind][1] - r[0]
step = edge.data.subset[ind][2]
if begin == end:
lost_dims.append(ind)
lost_ranges.append((begin, end, step))
else:
newsubset[ind] = (begin, end, step)
else:
newsubset[ind] -= r[0]
if len(lost_dims) == len(edge.data.subset):
lost_dims.pop()
newmemlet.subset = type(
edge.data.subset)([lost_ranges[-1]])
else:
newmemlet.subset = type(edge.data.subset)(
[r for r in newsubset if r is not None])
graph.add_edge(edge.src, edge.src_conn, node, None,
newmemlet)
end_node = graph.scope_dict()[edge.src]
for e in graph.bfs_edges(edge.src, reverse=True):
parent, _, _child, _, memlet = e
if parent == end_node:
break
if memlet.data != edge.data.data:
continue
path = graph.memlet_path(e)
if not isinstance(path[0].dst, nodes.CodeNode):
if in_path(path, e, nodes.EntryNode,
forward=False):
if isinstance(parent, nodes.CodeNode):
# Output edge
break
else:
continue
if is_scalar:
memlet.subset = sbs.Indices([0])
else:
newsubset = [None] * len(memlet.subset)
for ind, r in enumerate(memlet.subset):
if ind in lost_dims:
continue
if isinstance(memlet.subset[ind], tuple):
begin = r[0] - offset[ind]
end = r[1] - offset[ind]
step = r[2]
newsubset[ind] = (begin, end, step)
else:
newsubset[ind] = (
r - offset[ind],
r - offset[ind],
1,
)
memlet.subset = type(edge.data.subset)(
[r for r in newsubset if r is not None])
memlet.data = node.data
edge.data.wcr = None
if self.fullcopy:
edge.data.subset = sbs.Range.from_array(
node.desc(sdfg))
edge.data.other_subset = newmemlet.subset
graph.add_edge(node, None, edge.dst, edge.dst_conn,
edge.data)
graph.remove_edge(edge)
# Fourth, replace memlet arrays as necessary
if self.expr_index == 0:
scope_subgraph = graph.scope_subgraph(cnode)
for edge in scope_subgraph.edges():
if edge.data.data is not None and edge.data.data in cloned_arrays:
edge.data.data = cloned_arrays[edge.data.data]
def modifies_graph(self):
return True
class OrthogonalTiling(pattern_matching.Transformation):
""" Implements the orthogonal tiling transformation.
Orthogonal tiling is a type of nested map fission that creates tiles
in every dimension of the matched Map.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
# Properties
prefix = Property(
dtype=str, default="tile", desc="Prefix for new iterators")
tile_sizes = ShapeProperty(
dtype=tuple, default=(128, 128, 128), desc="Tile size per dimension")
divides_evenly = Property(
dtype=bool,
default=False,
desc="Tile size divides dimension length evenly")
@staticmethod
def annotates_memlets():
return False
@staticmethod
def expressions():
return [nxutil.node_path_graph(OrthogonalTiling._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[OrthogonalTiling._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
# Tile map.
target_dim, new_dim, new_map = self.__stripmine(
sdfg, graph, self.subgraph)
return new_map
def __stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[OrthogonalTiling._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
# Map subgraph
map_subgraph = graph.scope_subgraph(map_entry)
# Retrieve transformation properties.
prefix = self.prefix
tile_sizes = self.tile_sizes
divides_evenly = self.divides_evenly
new_param = []
new_range = []
for dim_idx in range(len(map_entry.map.params)):
if dim_idx >= len(tile_sizes):
tile_size = tile_sizes[-1]
else:
tile_size = tile_sizes[dim_idx]
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
new_dim = prefix + '_' + target_dim
# Basic values
if divides_evenly:
tile_num = '(%s + 1 - %s) / %s' % (symbolic.symstr(td_to),
symbolic.symstr(td_from),
str(tile_size))
else:
tile_num = 'int_ceil((%s + 1 - %s), %s)' % (symbolic.symstr(
td_to), symbolic.symstr(td_from), str(tile_size))
# Outer map values (over all tiles)
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(str(tile_num) + ' - 1')
nd_step = 1
# Inner map values (over one tile)
td_from_new = dace.symbolic.pystr_to_symbolic(td_from)
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1 - %s * %s, %s + %s) - 1' %
(symbolic.symstr(td_to), str(new_dim), str(tile_size),
td_from_new, str(tile_size)))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s - 1' % (td_from_new, str(tile_size)))
# Outer map (over all tiles)
new_dim_range = (nd_from, nd_to, nd_step)
new_param.append(new_dim)
new_range.append(new_dim_range)
# Inner map (over one tile)
if divides_evenly:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Fix subgraph memlets
target_dim = dace.symbolic.pystr_to_symbolic(target_dim)
offset = dace.symbolic.pystr_to_symbolic(
'%s * %s' % (new_dim, str(tile_size)))
for _, _, _, _, memlet in map_subgraph.edges():
old_subset = memlet.subset
if isinstance(old_subset, dace.subsets.Indices):
new_indices = []
for idx in old_subset:
new_idx = idx.subs(target_dim, target_dim + offset)
new_indices.append(new_idx)
memlet.subset = dace.subsets.Indices(new_indices)
elif isinstance(old_subset, dace.subsets.Range):
new_ranges = []
for i, old_range in enumerate(old_subset):
if len(old_range) == 3:
b, e, s, = old_range
t = old_subset.tile_sizes[i]
else:
raise ValueError(
'Range %s is invalid.' % old_range)
new_b = b.subs(target_dim, target_dim + offset)
new_e = e.subs(target_dim, target_dim + offset)
new_s = s.subs(target_dim, target_dim + offset)
new_t = t.subs(target_dim, target_dim + offset)
new_ranges.append((new_b, new_e, new_s, new_t))
memlet.subset = dace.subsets.Range(new_ranges)
else:
raise NotImplementedError
new_map = nodes.Map(prefix + '_' + map_entry.map.label, new_param,
subsets.Range(new_range))
new_map_entry = nodes.MapEntry(new_map)
new_exit = nodes.MapExit(new_map)
# Make internal map's schedule to "not parallel"
map_entry.map._schedule = dtypes.ScheduleType.Default
# Redirect/create edges.
new_in_edges = {}
for _src, conn, _dest, _, memlet in graph.out_edges(map_entry):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = copy.deepcopy(memlet.subset)
# new_subset = calc_set_image(map_entry.map.params,
# map_entry.map.range, memlet.subset,
# cont_or_strided)
if memlet.data in new_in_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_in_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.data, sdfg.arrays[nnew_memlet.data],
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data: (src, src_conn, dest, dest_conn,
new_memlet, min(num, int(conn[4:])))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data: (new_map_entry, None, map_entry, None,
new_memlet, int(conn[4:]))
})
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_out_edges = {}
for _src, conn, _dest, _, memlet in graph.in_edges(map_exit):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = memlet.subset
# new_subset = calc_set_image(map_entry.map.params,
# map_entry.map.range,
# memlet.subset, cont_or_strided)
if memlet.data in new_out_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_out_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.data, sdfg.arrays[nnew_memlet.data],
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (src, src_conn, dest, dest_conn,
new_memlet, min(num, conn[4:]))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (map_exit, None, new_exit, None,
new_memlet, conn[4:])
})
nxutil.change_edge_src(graph, map_exit, new_exit)
# Connector related work follows
# 1. Dictionary 'old_connector_number': 'new_connector_numer'
# 2. New node in/out connectors
# 3. New edges
in_conn_nums = []
for _, e in new_in_edges.items():
_, _, _, _, _, num = e
in_conn_nums.append(num)
in_conn = {}
for i, num in enumerate(in_conn_nums):
in_conn.update({num: i + 1})
entry_in_connectors = set()
entry_out_connectors = set()
for i in range(len(in_conn_nums)):
entry_in_connectors.add('IN_' + str(i + 1))
entry_out_connectors.add('OUT_' + str(i + 1))
new_map_entry.in_connectors = entry_in_connectors
new_map_entry.out_connectors = entry_out_connectors
for _, e in new_in_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(in_conn[num]), dst,
'IN_' + str(in_conn[num]), memlet)
out_conn_nums = []
for _, e in new_out_edges.items():
_, _, dst, _, _, num = e
if dst is not new_exit:
continue
out_conn_nums.append(num)
out_conn = {}
for i, num in enumerate(out_conn_nums):
out_conn.update({num: i + 1})
exit_in_connectors = set()
exit_out_connectors = set()
for i in range(len(out_conn_nums)):
exit_in_connectors.add('IN_' + str(i + 1))
exit_out_connectors.add('OUT_' + str(i + 1))
new_exit.in_connectors = exit_in_connectors
new_exit.out_connectors = exit_out_connectors
for _, e in new_out_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(out_conn[num]), dst,
'IN_' + str(out_conn[num]), memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map
@staticmethod
def __modify_edges(sdfg, graph, candidate, target_dim, new_dim):
map_entry = graph.nodes()[candidate[OrthogonalTiling._map_entry]]
processed = []
for src, _dest, memlet, _scope in nxutil.traverse_sdfg_scope(
graph, map_entry, True):
if memlet in processed:
continue
processed.append(memlet)
# Corner cases
if isinstance(sdfg.arrays[memlet.data], dace.data.Stream):
continue
if memlet.wcr is not None:
memlet.num_accesses = 1
continue
for i, dim in enumerate(memlet.subset):
if isinstance(dim, tuple):
dim = tuple(
symbolic.pystr_to_symbolic(d).subs(
symbolic.pystr_to_symbolic(target_dim),
symbolic.pystr_to_symbolic(
'%s + %s' % (str(new_dim), str(target_dim))))
for d in dim)
else:
dim = symbolic.pystr_to_symbolic(dim).subs(
symbolic.pystr_to_symbolic(target_dim),
symbolic.pystr_to_symbolic(
'%s + %s' % (str(new_dim), str(target_dim))))
memlet.subset[i] = dim
return
class StripMining(pattern_matching.Transformation):
""" Implements the strip-mining transformation.
Strip-mining takes as input a map dimension and splits it into
two dimensions. The new dimension iterates over the range of
the original one with a parameterizable step, called the tile
size. The original dimension is changed to iterates over the
range of the tile size, with the same step as before.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
# Properties
dim_idx = Property(dtype=int,
default=-1,
desc="Index of dimension to be strip-mined")
new_dim_prefix = Property(dtype=str,
default="tile",
desc="Prefix for new dimension name")
tile_size = Property(dtype=str,
default="64",
desc="Tile size of strip-mined dimension")
tile_stride = Property(dtype=str,
default="",
desc="Stride between two tiles of the "
"strip-mined dimension")
divides_evenly = Property(dtype=bool,
default=False,
desc="Tile size divides dimension range evenly?")
strided = Property(
dtype=bool,
default=False,
desc="Continuous (false) or strided (true) elements in tile")
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [
nxutil.node_path_graph(StripMining._map_entry)
# kStripMining._tasklet, StripMining._map_exit)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
# Strip-mine selected dimension.
_, _, new_map = self._stripmine(sdfg, graph, self.subgraph)
return new_map
# def __init__(self, tag=True):
def __init__(self, *args, **kwargs):
self._entry = nodes.EntryNode()
self._tasklet = nodes.Tasklet('_')
self._exit = nodes.ExitNode()
super().__init__(*args, **kwargs)
# self.tag = tag
@property
def entry(self):
return self._entry
@property
def exit(self):
return self._exit
@property
def tasklet(self):
return self._tasklet
def print_match_pattern(self, candidate):
gentry = candidate[self.entry]
return str(gentry.map.params[-1])
def modifies_graph(self):
return True
def _find_new_dim(self, sdfg: SDFG, state: SDFGState,
entry: nodes.MapEntry, prefix: str, target_dim: str):
""" Finds a variable that is not already defined in scope. """
stree = state.scope_tree()
candidate = '%s_%s' % (prefix, target_dim)
index = 1
while candidate in map(str, stree[entry].defined_vars):
candidate = '%s%d_%s' % (prefix, index, target_dim)
index += 1
return candidate
def _stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
# Retrieve transformation properties.
dim_idx = self.dim_idx
new_dim_prefix = self.new_dim_prefix
tile_size = self.tile_size
divides_evenly = self.divides_evenly
strided = self.strided
tile_stride = self.tile_stride
if tile_stride is None or len(tile_stride) == 0:
tile_stride = tile_size
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
# Create new map. Replace by cloning???
new_dim = self._find_new_dim(sdfg, graph, map_entry, new_dim_prefix,
target_dim)
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(
'int_ceil(%s + 1 - %s, %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_stride))
nd_step = 1
new_dim_range = (nd_from, nd_to, nd_step)
new_map = nodes.Map(new_dim + '_' + map_entry.map.label, [new_dim],
subsets.Range([new_dim_range]))
new_map_entry = nodes.MapEntry(new_map)
new_map_exit = nodes.MapExit(new_map)
# Change the range of the selected dimension to iterate over a single
# tile
if strided:
td_from_new = symbolic.pystr_to_symbolic(new_dim)
td_to_new_approx = td_to
td_step = symbolic.pystr_to_symbolic(tile_size)
else:
td_from_new = symbolic.pystr_to_symbolic(
'%s + %s * %s' %
(symbolic.symstr(td_from), str(new_dim), tile_stride))
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1, %s + %s * %s + %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_stride,
str(new_dim), tile_size))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s * %s + %s - 1' %
(symbolic.symstr(td_from), tile_stride, str(new_dim),
tile_size))
if divides_evenly or strided:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Make internal map's schedule to "not parallel"
new_map.schedule = map_entry.map.schedule
map_entry.map.schedule = dtypes.ScheduleType.Sequential
# Redirect edges
new_map_entry.in_connectors = dcpy(map_entry.in_connectors)
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_map_exit.out_connectors = dcpy(map_exit.out_connectors)
nxutil.change_edge_src(graph, map_exit, new_map_exit)
# Create new entry edges
new_in_edges = dict()
entry_in_conn = set()
entry_out_conn = set()
for _src, src_conn, _dst, _, memlet in graph.out_edges(map_entry):
if (src_conn is not None
and src_conn[:4] == 'OUT_' and not isinstance(
sdfg.arrays[memlet.data], dace.data.Scalar)):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
conn = src_conn[4:]
key = (memlet.data, 'IN_' + conn, 'OUT_' + conn)
if key in new_in_edges.keys():
old_subset = new_in_edges[key].subset
new_in_edges[key].subset = calc_set_union(
old_subset, new_subset)
else:
entry_in_conn.add('IN_' + conn)
entry_out_conn.add('OUT_' + conn)
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges[key] = new_memlet
else:
if src_conn is not None and src_conn[:4] == 'OUT_':
conn = src_conn[4:]
in_conn = 'IN_' + conn
out_conn = 'OUT_' + conn
else:
in_conn = src_conn
out_conn = src_conn
if in_conn:
entry_in_conn.add(in_conn)
if out_conn:
entry_out_conn.add(out_conn)
new_in_edges[(memlet.data, in_conn, out_conn)] = dcpy(memlet)
new_map_entry.out_connectors = entry_out_conn
map_entry.in_connectors = entry_in_conn
for (_, in_conn, out_conn), memlet in new_in_edges.items():
graph.add_edge(new_map_entry, out_conn, map_entry, in_conn, memlet)
# Create new exit edges
new_out_edges = dict()
exit_in_conn = set()
exit_out_conn = set()
for _src, _, _dst, dst_conn, memlet in graph.in_edges(map_exit):
if (dst_conn is not None
and dst_conn[:3] == 'IN_' and not isinstance(
sdfg.arrays[memlet.data], dace.data.Scalar)):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
conn = dst_conn[3:]
key = (memlet.data, 'IN_' + conn, 'OUT_' + conn)
if key in new_out_edges.keys():
old_subset = new_out_edges[key].subset
new_out_edges[key].subset = calc_set_union(
old_subset, new_subset)
else:
exit_in_conn.add('IN_' + conn)
exit_out_conn.add('OUT_' + conn)
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges[key] = new_memlet
else:
if dst_conn is not None and dst_conn[:3] == 'IN_':
conn = dst_conn[3:]
in_conn = 'IN_' + conn
out_conn = 'OUT_' + conn
else:
in_conn = src_conn
out_conn = src_conn
if in_conn:
exit_in_conn.add(in_conn)
if out_conn:
exit_out_conn.add(out_conn)
new_in_edges[(memlet.data, in_conn, out_conn)] = dcpy(memlet)
new_map_exit.in_connectors = exit_in_conn
map_exit.out_connectors = exit_out_conn
for (_, in_conn, out_conn), memlet in new_out_edges.items():
graph.add_edge(map_exit, out_conn, new_map_exit, in_conn, memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map
class MapWCRFusion(pm.Transformation):
""" Implements the map expanded-reduce fusion transformation.
Fuses a map with an immediately following reduction, where the array
between the map and the reduction is not used anywhere else, and the
reduction is divided to two maps with a WCR, denoting partial reduction.
"""
_tasklet = nodes.Tasklet('_')
_tmap_exit = nodes.MapExit(nodes.Map("", [], []))
_in_array = nodes.AccessNode('_')
_rmap_in_entry = nodes.MapEntry(nodes.Map("", [], []))
_rmap_in_tasklet = nodes.Tasklet('_')
_rmap_in_cr = nodes.MapExit(nodes.Map("", [], []))
_rmap_out_entry = nodes.MapEntry(nodes.Map("", [], []))
_rmap_out_exit = nodes.MapExit(nodes.Map("", [], []))
_out_array = nodes.AccessNode('_')
@staticmethod
def expressions():
return [
# Map, then partial reduction of axes
nxutil.node_path_graph(
MapWCRFusion._tasklet, MapWCRFusion._tmap_exit,
MapWCRFusion._in_array, MapWCRFusion._rmap_out_entry,
MapWCRFusion._rmap_in_entry, MapWCRFusion._rmap_in_tasklet,
MapWCRFusion._rmap_in_cr, MapWCRFusion._rmap_out_exit,
MapWCRFusion._out_array)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
tmap_exit = graph.nodes()[candidate[MapWCRFusion._tmap_exit]]
in_array = graph.nodes()[candidate[MapWCRFusion._in_array]]
rmap_entry = graph.nodes()[candidate[MapWCRFusion._rmap_out_entry]]
# Make sure that the array is only accessed by the map and the reduce
if any([
src != tmap_exit
for src, _, _, _, memlet in graph.in_edges(in_array)
]):
return False
if any([
dest != rmap_entry
for _, _, dest, _, memlet in graph.out_edges(in_array)
]):
return False
# Make sure that there is a reduction in the second map
rmap_cr = graph.nodes()[candidate[MapWCRFusion._rmap_in_cr]]
reduce_edge = graph.in_edges(rmap_cr)[0]
if reduce_edge.data.wcr is None:
return False
# (strict) Make sure that the transient is not accessed anywhere else
# in this state or other states
if strict and (len([
n for n in graph.nodes()
if isinstance(n, nodes.AccessNode) and n.data == in_array.data
]) > 1 or in_array.data in sdfg.shared_transients()):
return False
# Verify that reduction ranges match tasklet map
tout_memlet = graph.in_edges(in_array)[0].data
rin_memlet = graph.out_edges(in_array)[0].data
if tout_memlet.subset != rin_memlet.subset:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
tasklet = candidate[MapWCRFusion._tasklet]
map_exit = candidate[MapWCRFusion._tmap_exit]
reduce = candidate[MapWCRFusion._rmap_in_cr]
return ' -> '.join(str(node) for node in [tasklet, map_exit, reduce])
def apply(self, sdfg):
graph = sdfg.node(self.state_id)
# To apply, collapse the second map and then fuse the two resulting maps
map_collapse = MapCollapse(
self.sdfg_id, self.state_id, {
MapCollapse._outer_map_entry:
self.subgraph[MapWCRFusion._rmap_out_entry],
MapCollapse._inner_map_entry:
self.subgraph[MapWCRFusion._rmap_in_entry]
}, 0)
map_entry, _ = map_collapse.apply(sdfg)
map_fusion = MapFusion(
self.sdfg_id, self.state_id, {
MapFusion._first_map_exit:
self.subgraph[MapWCRFusion._tmap_exit],
MapFusion._second_map_entry: graph.node_id(map_entry)
}, 0)
map_fusion.apply(sdfg)
def apply(self, sdfg):
def gnode(nname):
return graph.nodes()[self.subgraph[nname]]
expr_index = self.expr_index
graph = sdfg.nodes()[self.state_id]
tasklet = gnode(MapReduceFusion._tasklet)
tmap_exit = graph.nodes()[self.subgraph[MapReduceFusion._tmap_exit]]
in_array = graph.nodes()[self.subgraph[MapReduceFusion._in_array]]
if expr_index == 0: # Reduce without outer map
rmap_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_entry]]
elif expr_index == 1: # Reduce with outer map
rmap_out_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_out_entry]]
rmap_out_exit = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_out_exit]]
rmap_in_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_entry]]
rmap_tasklet = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_tasklet]]
if expr_index == 2:
rmap_cr = graph.nodes()[self.subgraph[MapReduceFusion._reduce]]
else:
rmap_cr = graph.nodes()[self.subgraph[MapReduceFusion._rmap_in_cr]]
out_array = gnode(MapReduceFusion._out_array)
# Set nodes to remove according to the expression index
nodes_to_remove = [in_array]
if expr_index == 0:
nodes_to_remove.append(gnode(MapReduceFusion._rmap_in_entry))
elif expr_index == 1:
nodes_to_remove.append(gnode(MapReduceFusion._rmap_out_entry))
nodes_to_remove.append(gnode(MapReduceFusion._rmap_in_entry))
nodes_to_remove.append(gnode(MapReduceFusion._rmap_out_exit))
else:
nodes_to_remove.append(gnode(MapReduceFusion._reduce))
# If no other edges lead to mapexit, remove it. Otherwise, keep
# it and remove reduction incoming/outgoing edges
if expr_index != 2 and len(graph.in_edges(tmap_exit)) == 1:
nodes_to_remove.append(tmap_exit)
memlet_edge = None
for edge in graph.in_edges(tmap_exit):
if edge.data.data == in_array.data:
memlet_edge = edge
break
if memlet_edge is None:
raise RuntimeError('Reduction memlet cannot be None')
if expr_index == 0: # Reduce without outer map
# Index order does not matter, merge as-is
pass
elif expr_index == 1: # Reduce with outer map
tmap = tmap_exit.map
perm_outer, perm_inner = MapReduceFusion.find_permutation(
tmap, rmap_out_entry.map, rmap_in_entry.map, memlet_edge.data)
# Split tasklet map into tmap_out -> tmap_in (according to
# reduction)
omap = nodes.Map(
tmap.label + '_nonreduce',
[p for i, p in enumerate(tmap.params) if i in perm_outer],
[r for i, r in enumerate(tmap.range) if i in perm_outer],
tmap.schedule, tmap.unroll, tmap.is_async)
tmap.params = [
p for i, p in enumerate(tmap.params) if i in perm_inner
]
tmap.range = [
r for i, r in enumerate(tmap.range) if i in perm_inner
]
omap_entry = nodes.MapEntry(omap)
omap_exit = rmap_out_exit
rmap_out_exit.map = omap
# Reconnect graph to new map
tmap_entry = graph.entry_node(tmap_exit)
tmap_in_edges = list(graph.in_edges(tmap_entry))
for e in tmap_in_edges:
nxutil.change_edge_dest(graph, tmap_entry, omap_entry)
for e in tmap_in_edges:
graph.add_edge(omap_entry, e.src_conn, tmap_entry, e.dst_conn,
copy.copy(e.data))
elif expr_index == 2: # Reduce node
# Find correspondence between map indices and array outputs
tmap = tmap_exit.map
perm = MapReduceFusion.find_permutation_reduce(
tmap, rmap_cr, graph, memlet_edge.data)
output_subset = [tmap.params[d] for d in perm]
if len(output_subset) == 0: # Output is a scalar
output_subset = [0]
array_edge = graph.out_edges(rmap_cr)[0]
# Delete relevant edges and nodes
graph.remove_edge(memlet_edge)
graph.remove_nodes_from(nodes_to_remove)
# Add new edges and nodes
# From tasklet to map exit
graph.add_edge(
memlet_edge.src, memlet_edge.src_conn, memlet_edge.dst,
memlet_edge.dst_conn,
Memlet(out_array.data, memlet_edge.data.num_accesses,
subsets.Indices(output_subset), memlet_edge.data.veclen,
rmap_cr.wcr, rmap_cr.identity))
# From map exit to output array
graph.add_edge(
memlet_edge.dst, 'OUT_' + memlet_edge.dst_conn[3:],
array_edge.dst, array_edge.dst_conn,
Memlet(array_edge.data.data, array_edge.data.num_accesses,
array_edge.data.subset, array_edge.data.veclen,
rmap_cr.wcr, rmap_cr.identity))
return
# Remove tmp array node prior to the others, so that a new one
# can be created in its stead (see below)
graph.remove_node(nodes_to_remove[0])
nodes_to_remove = nodes_to_remove[1:]
# Create tasklet -> tmp -> tasklet connection
tmp = graph.add_array(
'tmp',
memlet_edge.data.subset.bounding_box_size(),
sdfg.arrays[memlet_edge.data.data].dtype,
transient=True)
tasklet_tmp_memlet = copy.deepcopy(memlet_edge.data)
tasklet_tmp_memlet.data = tmp.data
tasklet_tmp_memlet.subset = ShapeProperty.to_string(tmp.shape)
# Modify memlet to point to output array
memlet_edge.data.data = out_array.data
# Recover reduction axes from CR reduce subset
reduce_cr_subset = graph.in_edges(rmap_tasklet)[0].data.subset
reduce_axes = []
for ind, crvar in enumerate(reduce_cr_subset.indices):
if '__i' in str(crvar):
reduce_axes.append(ind)
# Modify memlet access index by filtering out reduction axes
if True: # expr_index == 0:
newindices = []
for ind, ovar in enumerate(memlet_edge.data.subset.indices):
if ind not in reduce_axes:
newindices.append(ovar)
if len(newindices) == 0:
newindices = [0]
memlet_edge.data.subset = subsets.Indices(newindices)
graph.remove_edge(memlet_edge)
graph.add_edge(memlet_edge.src, memlet_edge.src_conn, tmp,
memlet_edge.dst_conn, tasklet_tmp_memlet)
red_edges = list(graph.in_edges(rmap_tasklet))
if len(red_edges) != 1:
raise RuntimeError('CR edge must be unique')
tmp_tasklet_memlet = copy.deepcopy(tasklet_tmp_memlet)
graph.add_edge(tmp, None, rmap_tasklet, red_edges[0].dst_conn,
tmp_tasklet_memlet)
for e in graph.edges_between(rmap_tasklet, rmap_cr):
e.data.subset = memlet_edge.data.subset
# Move output edges to point directly to CR node
if expr_index == 1:
# Set output memlet between CR node and outer reduction map to
# contain the same subset as the one pointing to the CR node
for e in graph.out_edges(rmap_cr):
e.data.subset = memlet_edge.data.subset
rmap_out = gnode(MapReduceFusion._rmap_out_exit)
nxutil.change_edge_src(graph, rmap_out, omap_exit)
# Remove nodes
graph.remove_nodes_from(nodes_to_remove)
# For unrelated outputs, connect original output to rmap_out
if expr_index == 1 and tmap_exit not in nodes_to_remove:
other_out_edges = list(graph.out_edges(tmap_exit))
for e in other_out_edges:
graph.remove_edge(e)
graph.add_edge(e.src, e.src_conn, omap_exit, None, e.data)
graph.add_edge(omap_exit, None, e.dst, e.dst_conn,
copy.copy(e.data))
class DoubleBuffering(pattern_matching.Transformation):
""" Implements the double buffering pattern, which pipelines reading
and processing data by creating a second copy of the memory.
In particular, the transformation takes a 1D map and all internal
(directly connected) transients, adds an additional dimension of size 2,
and turns the map into a for loop that processes and reads the data in a
double-buffered manner. Other memlets will not be transformed.
"""
_map_entry = nodes.MapEntry(nodes.Map('_', [], []))
_transient = nodes.AccessNode('_')
@staticmethod
def expressions():
return [
nxutil.node_path_graph(DoubleBuffering._map_entry,
DoubleBuffering._transient)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[DoubleBuffering._map_entry]]
transient = graph.nodes()[candidate[DoubleBuffering._transient]]
# Only one dimensional maps are allowed
if len(map_entry.map.params) != 1:
return False
# Verify the map can be transformed to a for-loop
if not MapToForLoop.can_be_applied(
graph,
{MapToForLoop._map_entry: candidate[DoubleBuffering._map_entry]},
expr_index, sdfg, strict):
return False
# Verify that all directly-connected internal access nodes point to
# transient arrays
first = True
for edge in graph.out_edges(map_entry):
if isinstance(edge.dst, nodes.AccessNode):
desc = sdfg.arrays[edge.dst.data]
if not isinstance(desc, data.Array) or not desc.transient:
return False
else:
# To avoid duplicate matches, only match the first transient
if first and edge.dst != transient:
return False
first = False
return True
@staticmethod
def match_to_str(graph, candidate):
return str(graph.node(candidate[DoubleBuffering._map_entry]))
def apply(self, sdfg: sd.SDFG):
graph: sd.SDFGState = sdfg.nodes()[self.state_id]
map_entry = graph.node(self.subgraph[DoubleBuffering._map_entry])
map_param = map_entry.map.params[0] # Assuming one dimensional
##############################
# Change condition of loop to one fewer iteration (so that the
# final one reads from the last buffer)
map_rstart, map_rend, map_rstride = map_entry.map.range[0]
map_rend = symbolic.pystr_to_symbolic('(%s) - (%s)' %
(map_rend, map_rstride))
map_entry.map.range = subsets.Range([(map_rstart, map_rend,
map_rstride)])
##############################
# Gather transients to modify
transients_to_modify = set(edge.dst.data
for edge in graph.out_edges(map_entry)
if isinstance(edge.dst, nodes.AccessNode))
# Add dimension to transients and modify memlets
for transient in transients_to_modify:
desc: data.Array = sdfg.arrays[transient]
# Using non-python syntax to ensure properties change
desc.strides = [desc.total_size] + list(desc.strides)
desc.shape = [2] + list(desc.shape)
desc.offset = [0] + list(desc.offset)
desc.total_size = desc.total_size * 2
##############################
# Modify memlets to use map parameter as buffer index
modified_subsets = [] # Store modified memlets for final state
for edge in graph.scope_subgraph(map_entry).edges():
if edge.data.data in transients_to_modify:
edge.data.subset = self._modify_memlet(sdfg, edge.data.subset,
edge.data.data)
modified_subsets.append(edge.data.subset)
else: # Could be other_subset
path = graph.memlet_path(edge)
src_node = path[0].src
dst_node = path[-1].dst
# other_subset could be None. In that case, recreate from array
dataname = None
if (isinstance(src_node, nodes.AccessNode)
and src_node.data in transients_to_modify):
dataname = src_node.data
elif (isinstance(dst_node, nodes.AccessNode)
and dst_node.data in transients_to_modify):
dataname = dst_node.data
if dataname is not None:
subset = (edge.data.other_subset or
subsets.Range.from_array(sdfg.arrays[dataname]))
edge.data.other_subset = self._modify_memlet(
sdfg, subset, dataname)
modified_subsets.append(edge.data.other_subset)
##############################
# Turn map into for loop
map_to_for = MapToForLoop(self.sdfg_id, self.state_id, {
MapToForLoop._map_entry:
self.subgraph[DoubleBuffering._map_entry]
}, self.expr_index)
nsdfg_node, nstate = map_to_for.apply(sdfg)
##############################
# Gather node copies and remove memlets
edges_to_replace = []
for node in nstate.source_nodes():
for edge in nstate.out_edges(node):
if (isinstance(edge.dst, nodes.AccessNode)
and edge.dst.data in transients_to_modify):
edges_to_replace.append(edge)
nstate.remove_edge(edge)
if nstate.out_degree(node) == 0:
nstate.remove_node(node)
##############################
# Add initial reads to initial nested state
initial_state: sd.SDFGState = nsdfg_node.sdfg.start_state
initial_state.set_label('%s_init' % map_entry.map.label)
for edge in edges_to_replace:
initial_state.add_node(edge.src)
rnode = edge.src
wnode = initial_state.add_write(edge.dst.data)
initial_state.add_edge(rnode, edge.src_conn, wnode, edge.dst_conn,
copy.deepcopy(edge.data))
# All instances of the map parameter in this state become the loop start
sd.replace(initial_state, map_param, map_rstart)
# Initial writes go to the first buffer
sd.replace(initial_state, '__dace_db_param', '0')
##############################
# Modify main state's memlets
# Divide by loop stride
new_expr = symbolic.pystr_to_symbolic('(%s / %s) %% 2' %
(map_param, map_rstride))
sd.replace(nstate, '__dace_db_param', new_expr)
##############################
# Add the main state's contents to the last state, modifying
# memlets appropriately.
final_state: sd.SDFGState = nsdfg_node.sdfg.sink_nodes()[0]
final_state.set_label('%s_final_computation' % map_entry.map.label)
dup_nstate = copy.deepcopy(nstate)
final_state.add_nodes_from(dup_nstate.nodes())
for e in dup_nstate.edges():
final_state.add_edge(e.src, e.src_conn, e.dst, e.dst_conn, e.data)
##############################
# Add reads into next buffers to main state
for edge in edges_to_replace:
rnode = copy.deepcopy(edge.src)
nstate.add_node(rnode)
wnode = nstate.add_write(edge.dst.data)
new_memlet = copy.deepcopy(edge.data)
if new_memlet.data in transients_to_modify:
new_memlet.other_subset = self._replace_in_subset(
new_memlet.other_subset, map_param,
'(%s + %s)' % (map_param, map_rstride))
else:
new_memlet.subset = self._replace_in_subset(
new_memlet.subset, map_param,
'(%s + %s)' % (map_param, map_rstride))
nstate.add_edge(rnode, edge.src_conn, wnode, edge.dst_conn,
new_memlet)
nstate.set_label('%s_double_buffered' % map_entry.map.label)
# Divide by loop stride
new_expr = symbolic.pystr_to_symbolic('((%s / %s) + 1) %% 2' %
(map_param, map_rstride))
sd.replace(nstate, '__dace_db_param', new_expr)
@staticmethod
def _modify_memlet(sdfg, subset, data_name):
desc = sdfg.arrays[data_name]
if len(subset) == len(desc.shape):
# Already in the right shape, modify new dimension
subset = list(subset)[1:]
new_subset = subsets.Range([('__dace_db_param', '__dace_db_param',
1)] + list(subset))
return new_subset
@staticmethod
def _replace_in_subset(subset, string_or_symbol, new_string_or_symbol):
new_subset = copy.deepcopy(subset)
repldict = {
symbolic.pystr_to_symbolic(string_or_symbol):
symbolic.pystr_to_symbolic(new_string_or_symbol)
}
for i, dim in enumerate(new_subset):
try:
new_subset[i] = tuple(d.subs(repldict) for d in dim)
except TypeError:
new_subset[i] = (dim.subs(repldict)
if symbolic.issymbolic(dim) else dim)
return new_subset
class MapInterchange(pattern_matching.Transformation):
""" Implements the map-interchange transformation.
Map-interchange takes two nested maps and interchanges their position.
"""
_outer_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_inner_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def expressions():
return [
nxutil.node_path_graph(MapInterchange._outer_map_entry,
MapInterchange._inner_map_entry)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# TODO: Add matching condition that the map variables are independent
# of each other.
# TODO: Assuming that the subsets on the edges between the two map
# entries/exits are the union of separate inner subsets, is it possible
# that inverting these edges breaks the continuity of union? What about
# the opposite?
# Check the edges between the entries of the two maps.
outer_map_entry = graph.nodes()[candidate[
MapInterchange._outer_map_entry]]
inner_map_entry = graph.nodes()[candidate[
MapInterchange._inner_map_entry]]
# Check that the destination of all the outgoing edges
# from the outer map's entry is the inner map's entry.
for e in graph.out_edges(outer_map_entry):
if e.dst != inner_map_entry:
return False
# Check that the source of all the incoming edges
# to the inner map's entry is the outer map's entry.
for e in graph.in_edges(inner_map_entry):
if e.src != outer_map_entry:
return False
# Check the edges between the exits of the two maps.
inner_map_exits = graph.exit_nodes(inner_map_entry)
outer_map_exits = graph.exit_nodes(outer_map_entry)
inner_map_exit = inner_map_exits[0]
outer_map_exit = outer_map_exits[0]
# Check that the destination of all the outgoing edges
# from the inner map's exit is the outer map's exit.
for e in graph.out_edges(inner_map_exit):
if e.dst != outer_map_exit:
return False
# Check that the source of all the incoming edges
# to the outer map's exit is the inner map's exit.
for e in graph.in_edges(outer_map_exit):
if e.src != inner_map_exit:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
outer_map_entry = graph.nodes()[candidate[
MapInterchange._outer_map_entry]]
inner_map_entry = graph.nodes()[candidate[
MapInterchange._inner_map_entry]]
return ' -> '.join(entry.map.label + ': ' + str(entry.map.params)
for entry in [outer_map_entry, inner_map_entry])
def apply(self, sdfg):
# Extract the parameters and ranges of the inner/outer maps.
graph = sdfg.nodes()[self.state_id]
outer_map_entry = graph.nodes()[self.subgraph[
MapInterchange._outer_map_entry]]
inner_map_entry = graph.nodes()[self.subgraph[
MapInterchange._inner_map_entry]]
inner_map_exits = graph.exit_nodes(inner_map_entry)
outer_map_exits = graph.exit_nodes(outer_map_entry)
if len(inner_map_exits) > 1 or len(outer_map_exits) > 1:
raise NotImplementedError('Map interchange does not work with ' +
'multiple map exits')
inner_map_exit = inner_map_exits[0]
outer_map_exit = outer_map_exits[0]
# Switch connectors
outer_map_entry.in_connectors, inner_map_entry.in_connectors = \
inner_map_entry.in_connectors, outer_map_entry.in_connectors
outer_map_entry.out_connectors, inner_map_entry.out_connectors = \
inner_map_entry.out_connectors, outer_map_entry.out_connectors
outer_map_exit.in_connectors, inner_map_exit.in_connectors = \
inner_map_exit.in_connectors, outer_map_exit.in_connectors
outer_map_exit.out_connectors, inner_map_exit.out_connectors = \
inner_map_exit.out_connectors, outer_map_exit.out_connectors
# Get edges between the map entries and exits.
entry_edges = graph.edges_between(outer_map_entry, inner_map_entry)
exit_edges = graph.edges_between(inner_map_exit, outer_map_exit)
for e in entry_edges + exit_edges:
graph.remove_edge(e)
# Change source and destination of edges.
dace.graph.nxutil.change_edge_dest(graph, outer_map_entry,
inner_map_entry)
dace.graph.nxutil.change_edge_src(graph, inner_map_entry,
outer_map_entry)
dace.graph.nxutil.change_edge_dest(graph, inner_map_exit,
outer_map_exit)
dace.graph.nxutil.change_edge_src(graph, outer_map_exit,
inner_map_exit)
# Add edges between the map entries and exits.
for e in entry_edges + exit_edges:
graph.add_edge(e.dst, e.src_conn, e.src, e.dst_conn, e.data)
class GPUTransformMap(pattern_matching.Transformation):
""" Implements the GPUTransformMap transformation.
Converts a single map to a GPU-scheduled map and creates GPU arrays
outside it, generating CPU<->GPU memory copies automatically.
"""
fullcopy = Property(
desc="Copy whole arrays rather than used subset",
dtype=bool,
default=False)
toplevel_trans = Property(
desc="Make all GPU transients top-level", dtype=bool, default=False)
register_trans = Property(
desc="Make all transients inside GPU maps registers",
dtype=bool,
default=False)
sequential_innermaps = Property(
desc="Make all internal maps Sequential", dtype=bool, default=False)
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_reduce = nodes.Reduce('lambda: None', None)
@staticmethod
def expressions():
return [
nxutil.node_path_graph(GPUTransformMap._map_entry),
nxutil.node_path_graph(GPUTransformMap._reduce)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
if expr_index == 0:
map_entry = graph.nodes()[candidate[GPUTransformMap._map_entry]]
candidate_map = map_entry.map
# Map schedules that are disallowed to transform to GPUs
if (candidate_map.schedule in [dtypes.ScheduleType.MPI] +
dtypes.GPU_SCHEDULES):
return False
if sd.is_devicelevel(sdfg, graph, map_entry):
return False
# Dynamic map ranges cannot become kernels
if sd.has_dynamic_map_inputs(graph, map_entry):
return False
# Ensure that map does not include internal arrays that are
# allocated on non-default space
subgraph = graph.scope_subgraph(map_entry)
for node in subgraph.nodes():
if (isinstance(node, nodes.AccessNode) and
node.desc(sdfg).storage != dtypes.StorageType.Default
and node.desc(sdfg).storage !=
dtypes.StorageType.Register):
return False
# If one of the outputs is a stream, do not match
map_exit = graph.exit_nodes(map_entry)[0]
for edge in graph.out_edges(map_exit):
dst = graph.memlet_path(edge)[-1].dst
if (isinstance(dst, nodes.AccessNode)
and isinstance(sdfg.arrays[dst.data], data.Stream)):
return False
return True
elif expr_index == 1:
reduce = graph.nodes()[candidate[GPUTransformMap._reduce]]
# Map schedules that are disallowed to transform to GPUs
if (reduce.schedule in [dtypes.ScheduleType.MPI] +
dtypes.GPU_SCHEDULES):
return False
if sd.is_devicelevel(sdfg, graph, reduce):
return False
return True
@staticmethod
def match_to_str(graph, candidate):
if GPUTransformMap._reduce in candidate:
return str(graph.nodes()[candidate[GPUTransformMap._reduce]])
else:
return str(graph.nodes()[candidate[GPUTransformMap._map_entry]])
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.apply_strict_transformations()
class MapFusion(pattern_matching.Transformation):
""" Implements the map fusion pattern.
Map Fusion takes two maps that are connected in series and have the
same range, and fuses them to one map. The tasklets in the new map are
connected in the same manner as they were before the fusion.
"""
_first_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_second_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def annotates_memlets():
return False
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapFusion._first_map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# The first map must have a non-conflicting map exit.
# (cannot fuse with CR in the first map)
first_map_entry = graph.nodes()[candidate[MapFusion._first_map_entry]]
first_exits = graph.exit_nodes(first_map_entry)
first_exit = first_exits[0]
if any([e.data.wcr is not None for e in graph.in_edges(first_exit)]):
return False
# Check whether there is a pattern map -> data -> map.
data_nodes = []
for _, _, dst, _, _ in graph.out_edges(first_exit):
if isinstance(dst, nodes.AccessNode):
data_nodes.append(dst)
else:
return False
second_map_entry = None
for data_node in data_nodes:
for _, _, dst, _, _ in graph.out_edges(data_node):
if isinstance(dst, nodes.MapEntry):
if second_map_entry is None:
second_map_entry = dst
elif dst != second_map_entry:
return False
else:
return False
if second_map_entry is None:
return False
for src, _, _, _, _ in graph.in_edges(second_map_entry):
if not src in data_nodes:
return False
# Check map spaces (this should be generalized to ignore order).
first_range = first_map_entry.map.range
second_range = second_map_entry.map.range
if first_range != second_range:
return False
# Success
candidate[MapFusion._second_map_entry] = graph.nodes().index(
second_map_entry)
return True
@staticmethod
def match_to_str(graph, candidate):
first_map_entry = graph.nodes()[candidate[MapFusion._first_map_entry]]
second_map_entry = graph.nodes()[candidate[
MapFusion._second_map_entry]]
return ' -> '.join(entry.map.label + ': ' + str(entry.map.params)
for entry in [first_map_entry, second_map_entry])
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
first_map_entry = graph.nodes()[self.subgraph[
MapFusion._first_map_entry]]
first_map_exit = graph.exit_nodes(first_map_entry)[0]
second_map_entry = graph.nodes()[self.subgraph[
MapFusion._second_map_entry]]
second_exits = graph.exit_nodes(second_map_entry)
first_map_params = [
symbolic.pystr_to_symbolic(p) for p in first_map_entry.map.params
]
second_map_params = [
symbolic.pystr_to_symbolic(p) for p in second_map_entry.map.params
]
# Fix exits
for exit_node in second_exits:
if isinstance(exit_node, nodes.MapExit):
exit_node.map = first_map_entry.map
# Substitute symbols in second map.
for _parent, _, _child, _, memlet in graph.bfs_edges(second_map_entry,
reverse=False):
for fp, sp in zip(first_map_params, second_map_params):
for ind, r in enumerate(memlet.subset):
if isinstance(memlet.subset[ind], tuple):
begin = r[0].subs(sp, fp)
end = r[1].subs(sp, fp)
step = r[2].subs(sp, fp)
memlet.subset[ind] = (begin, end, step)
else:
memlet.subset[ind] = memlet.subset[ind].subs(sp, fp)
transients = {}
for _, _, dst, _, memlet in graph.out_edges(first_map_exit):
if not memlet.data in transients:
transients[memlet.data] = dst
new_edges = []
for src, src_conn, _, dst_conn, memlet in graph.in_edges(
first_map_exit):
new_memlet = dcpy(memlet)
new_edges.append(
(src, src_conn, transients[memlet.data], dst_conn, new_memlet))
for _, src_conn, dst, dst_conn, memlet in graph.out_edges(
second_map_entry):
new_memlet = dcpy(memlet)
new_edges.append(
(transients[memlet.data], src_conn, dst, dst_conn, new_memlet))
# Delete nodes/edges
for edge in graph.in_edges(first_map_exit):
graph.remove_edge(edge)
for edge in graph.out_edges(second_map_entry):
graph.remove_edge(edge)
data_nodes = []
for _, _, dst, _, _ in graph.out_edges(first_map_exit):
data_nodes.append(dst)
for data_node in data_nodes:
for edge in graph.all_edges(data_node):
graph.remove_edge(edge)
graph.remove_node(first_map_exit)
graph.remove_node(second_map_entry)
# Add edges
for edge in new_edges:
graph.add_edge(*edge)
# Reduce transient sizes
for data_node in data_nodes:
data_desc = data_node.desc(sdfg)
if data_desc.transient:
edges = graph.in_edges(data_node)
subset = edges[0].data.subset
for idx in range(1, len(edges)):
subset = calc_set_union(subset, edges[idx].data.subset)
data_desc.shape = subset.bounding_box_size()
data_desc.strides = list(subset.bounding_box_size())
data_desc.offset = [0] * subset.dims()
class MapCollapse(pattern_matching.Transformation):
""" Implements the Map Collapse pattern.
Map-collapse takes two nested maps with M and N dimensions respectively,
and collapses them to a single M+N dimensional map.
"""
_outer_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_inner_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def expressions():
return [
nxutil.node_path_graph(
MapCollapse._outer_map_entry,
MapCollapse._inner_map_entry,
)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# Check the edges between the entries of the two maps.
outer_map_entry = graph.nodes()[candidate[
MapCollapse._outer_map_entry]]
inner_map_entry = graph.nodes()[candidate[
MapCollapse._inner_map_entry]]
# Check that inner map range is independent of outer range
map_deps = set()
for s in inner_map_entry.map.range:
map_deps |= set(map(str, symlist(s)))
if any(dep in outer_map_entry.map.params for dep in map_deps):
return False
# Check that the destination of all the outgoing edges
# from the outer map's entry is the inner map's entry.
for _src, _, dest, _, _ in graph.out_edges(outer_map_entry):
if dest != inner_map_entry:
return False
# Check that the source of all the incoming edges
# to the inner map's entry is the outer map's entry.
for src, _, _, dst_conn, memlet in graph.in_edges(inner_map_entry):
if src != outer_map_entry:
return False
# Check that dynamic input range memlets are independent of
# first map range
if dst_conn is not None and not dst_conn.startswith('IN_'):
memlet_deps = set()
for s in memlet.subset:
memlet_deps |= set(map(str, symlist(s)))
if any(dep in outer_map_entry.map.params
for dep in memlet_deps):
return False
# Check the edges between the exits of the two maps.
inner_map_exit = graph.exit_nodes(inner_map_entry)[0]
outer_map_exit = graph.exit_nodes(outer_map_entry)[0]
# Check that the destination of all the outgoing edges
# from the inner map's exit is the outer map's exit.
for _src, _, dest, _, _ in graph.out_edges(inner_map_exit):
if dest != outer_map_exit:
return False
# Check that the source of all the incoming edges
# to the outer map's exit is the inner map's exit.
for src, _, _dest, _, _ in graph.in_edges(outer_map_exit):
if src != inner_map_exit:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
outer_map_entry = graph.nodes()[candidate[
MapCollapse._outer_map_entry]]
inner_map_entry = graph.nodes()[candidate[
MapCollapse._inner_map_entry]]
return ' -> '.join(entry.map.label + ': ' + str(entry.map.params)
for entry in [outer_map_entry, inner_map_entry])
def apply(self, sdfg) -> Tuple[nodes.MapEntry, nodes.MapExit]:
"""
Collapses two maps into one.
:param sdfg: The SDFG to apply the transformation to.
:return: A 2-tuple of the new map entry and exit nodes.
"""
# Extract the parameters and ranges of the inner/outer maps.
graph = sdfg.nodes()[self.state_id]
outer_map_entry = graph.nodes()[self.subgraph[
MapCollapse._outer_map_entry]]
inner_map_entry = graph.nodes()[self.subgraph[
MapCollapse._inner_map_entry]]
inner_map_exit = graph.exit_nodes(inner_map_entry)[0]
outer_map_exit = graph.exit_nodes(outer_map_entry)[0]
return nxutil.merge_maps(graph, outer_map_entry, outer_map_exit,
inner_map_entry, inner_map_exit)
def apply(self, sdfg: dace.SDFG):
# Extract the map and its entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapExpansion._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
current_map = map_entry.map
# Create new maps
new_maps = [
nodes.Map(current_map.label + '_' + str(param), [param],
subsets.Range([param_range]),
schedule=dtypes.ScheduleType.Sequential) for param,
param_range in zip(current_map.params[1:], current_map.range[1:])
]
current_map.params = [current_map.params[0]]
current_map.range = subsets.Range([current_map.range[0]])
# Create new map entries and exits
entries = [nodes.MapEntry(new_map) for new_map in new_maps]
exits = [nodes.MapExit(new_map) for new_map in new_maps]
# Create edges, abiding by the following rules:
# 1. If there are no edges coming from the outside, use empty memlets
# 2. Edges with IN_* connectors replicate along the maps
# 3. Edges for dynamic map ranges replicate until reaching range(s)
for edge in graph.out_edges(map_entry):
graph.remove_edge(edge)
graph.add_memlet_path(map_entry,
*entries,
edge.dst,
src_conn=edge.src_conn,
memlet=edge.data,
dst_conn=edge.dst_conn)
# Modify dynamic map ranges
dynamic_edges = dace.sdfg.dynamic_map_inputs(graph, map_entry)
for edge in dynamic_edges:
# Remove old edge and connector
graph.remove_edge(edge)
edge.dst._in_connectors.remove(edge.dst_conn)
# Propagate to each range it belongs to
path = []
for mapnode in [map_entry] + entries:
path.append(mapnode)
if any(edge.dst_conn in map(str, symbolic.symlist(r))
for r in mapnode.map.range):
graph.add_memlet_path(edge.src,
*path,
memlet=edge.data,
src_conn=edge.src_conn,
dst_conn=edge.dst_conn)
# Create new map exits
for edge in graph.in_edges(map_exit):
graph.remove_edge(edge)
graph.add_memlet_path(edge.src,
*exits[::-1],
map_exit,
memlet=edge.data,
src_conn=edge.src_conn,
dst_conn=edge.dst_conn)
class MapExpansion(pm.Transformation):
""" Implements the map-expansion pattern.
Map-expansion takes an N-dimensional map and expands it to N
unidimensional maps.
New edges abide by the following rules:
1. If there are no edges coming from the outside, use empty memlets
2. Edges with IN_* connectors replicate along the maps
3. Edges for dynamic map ranges replicate until reaching range(s)
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapExpansion._map_entry)]
@staticmethod
def can_be_applied(graph: dace.graph.graph.OrderedMultiDiConnectorGraph,
candidate: Dict[dace.graph.nodes.Node, int],
expr_index: int,
sdfg: dace.SDFG,
strict: bool = False):
# A candidate subgraph matches the map-expansion pattern when it
# includes an N-dimensional map, with N greater than one.
map_entry = graph.nodes()[candidate[MapExpansion._map_entry]]
return map_entry.map.get_param_num() > 1
@staticmethod
def match_to_str(graph: dace.graph.graph.OrderedMultiDiConnectorGraph,
candidate: Dict[dace.graph.nodes.Node, int]):
map_entry = graph.nodes()[candidate[MapExpansion._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg: dace.SDFG):
# Extract the map and its entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapExpansion._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
current_map = map_entry.map
# Create new maps
new_maps = [
nodes.Map(current_map.label + '_' + str(param), [param],
subsets.Range([param_range]),
schedule=dtypes.ScheduleType.Sequential) for param,
param_range in zip(current_map.params[1:], current_map.range[1:])
]
current_map.params = [current_map.params[0]]
current_map.range = subsets.Range([current_map.range[0]])
# Create new map entries and exits
entries = [nodes.MapEntry(new_map) for new_map in new_maps]
exits = [nodes.MapExit(new_map) for new_map in new_maps]
# Create edges, abiding by the following rules:
# 1. If there are no edges coming from the outside, use empty memlets
# 2. Edges with IN_* connectors replicate along the maps
# 3. Edges for dynamic map ranges replicate until reaching range(s)
for edge in graph.out_edges(map_entry):
graph.remove_edge(edge)
graph.add_memlet_path(map_entry,
*entries,
edge.dst,
src_conn=edge.src_conn,
memlet=edge.data,
dst_conn=edge.dst_conn)
# Modify dynamic map ranges
dynamic_edges = dace.sdfg.dynamic_map_inputs(graph, map_entry)
for edge in dynamic_edges:
# Remove old edge and connector
graph.remove_edge(edge)
edge.dst._in_connectors.remove(edge.dst_conn)
# Propagate to each range it belongs to
path = []
for mapnode in [map_entry] + entries:
path.append(mapnode)
if any(edge.dst_conn in map(str, symbolic.symlist(r))
for r in mapnode.map.range):
graph.add_memlet_path(edge.src,
*path,
memlet=edge.data,
src_conn=edge.src_conn,
dst_conn=edge.dst_conn)
# Create new map exits
for edge in graph.in_edges(map_exit):
graph.remove_edge(edge)
graph.add_memlet_path(edge.src,
*exits[::-1],
map_exit,
memlet=edge.data,
src_conn=edge.src_conn,
dst_conn=edge.dst_conn)
class FPGATransformMap(pattern_matching.Transformation):
""" Implements the FPGATransformMap transformation.
Converts a single map to an FPGA-scheduled map and creates FPGA arrays
outside it, generating CPU<->FPGA memory copies automatically.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def expressions():
return [nxutil.node_path_graph(FPGATransformMap._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[FPGATransformMap._map_entry]]
candidate_map = map_entry.map
# No more than 3 dimensions
if candidate_map.range.dims() > 3: return False
# Map schedules that are disallowed to transform to FPGAs
if (candidate_map.schedule in [
dtypes.ScheduleType.MPI, dtypes.ScheduleType.GPU_Device,
dtypes.ScheduleType.FPGA_Device,
dtypes.ScheduleType.GPU_ThreadBlock
]):
return False
# Recursively check parent for FPGA schedules
sdict = graph.scope_dict()
current_node = map_entry
while current_node is not None:
if (current_node.map.schedule in [
dtypes.ScheduleType.GPU_Device,
dtypes.ScheduleType.FPGA_Device,
dtypes.ScheduleType.GPU_ThreadBlock
]):
return False
current_node = sdict[current_node]
# Ensure that map does not include internal arrays that are allocated
# on non-default space
subgraph = graph.scope_subgraph(map_entry)
for node in subgraph.nodes():
if (isinstance(node, nodes.AccessNode)
and node.desc(sdfg).storage != dtypes.StorageType.Default):
return False
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[FPGATransformMap._map_entry]]
return str(map_entry)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[FPGATransformMap._map_entry]]
map_entry.map._schedule = dtypes.ScheduleType.FPGA_Device
# Find map exit nodes
exit_nodes = graph.exit_nodes(map_entry)
fpga_storage_types = [
dtypes.StorageType.FPGA_Global, dtypes.StorageType.FPGA_Local,
dtypes.StorageType.CPU_Pinned
]
#######################################################
# Add FPGA copies of CPU arrays (i.e., not already on FPGA)
# First, understand which arrays to clone
all_out_edges = []
for enode in exit_nodes:
all_out_edges.extend(list(graph.out_edges(enode)))
in_arrays_to_clone = set()
out_arrays_to_clone = set()
for e in graph.in_edges(map_entry):
data_node = sd.find_input_arraynode(graph, e)
if data_node.desc(sdfg).storage not in fpga_storage_types:
in_arrays_to_clone.add(data_node)
for e in all_out_edges:
data_node = sd.find_output_arraynode(graph, e)
if data_node.desc(sdfg).storage not in fpga_storage_types:
out_arrays_to_clone.add(data_node)
# Second, create a FPGA clone of each array
cloned_arrays = {}
in_cloned_arraynodes = {}
out_cloned_arraynodes = {}
for array_node in in_arrays_to_clone:
array = array_node.desc(sdfg)
if array_node.data in cloned_arrays:
pass
elif 'fpga_' + array_node.data in sdfg.arrays:
pass
else:
sdfg.add_array('fpga_' + array_node.data,
dtype=array.dtype,
shape=array.shape,
materialize_func=array.materialize_func,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=array.allow_conflicts,
access_order=array.access_order,
strides=array.strides,
offset=array.offset)
cloned_arrays[array_node.data] = 'fpga_' + array_node.data
cloned_node = nodes.AccessNode('fpga_' + array_node.data)
in_cloned_arraynodes[array_node.data] = cloned_node
for array_node in out_arrays_to_clone:
array = array_node.desc(sdfg)
if array_node.data in cloned_arrays:
pass
elif 'fpga_' + array_node.data in sdfg.arrays:
pass
else:
sdfg.add_array('fpga_' + array_node.data,
dtype=array.dtype,
shape=array.shape,
materialize_func=array.materialize_func,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=array.allow_conflicts,
access_order=array.access_order,
strides=array.strides,
offset=array.offset)
cloned_arrays[array_node.data] = 'fpga_' + array_node.data
cloned_node = nodes.AccessNode('fpga_' + array_node.data)
out_cloned_arraynodes[array_node.data] = cloned_node
# Third, connect the cloned arrays to the originals
# TODO(later): Shift indices and create only the necessary sub-arrays
for array_name, node in in_cloned_arraynodes.items():
graph.add_node(node)
for edge in graph.in_edges(map_entry):
if edge.data.data == array_name:
graph.remove_edge(edge)
graph.add_edge(edge.src, None, node, None, edge.data)
newmemlet = copy.copy(edge.data)
newmemlet.data = node.data
graph.add_edge(node, edge.src_conn, edge.dst,
edge.dst_conn, newmemlet)
for array_name, node in out_cloned_arraynodes.items():
graph.add_node(node)
for edge in all_out_edges:
if edge.data.data == array_name:
graph.remove_edge(edge)
graph.add_edge(node, None, edge.dst, None, edge.data)
newmemlet = copy.copy(edge.data)
newmemlet.data = node.data
graph.add_edge(edge.src, edge.src_conn, node,
edge.dst_conn, newmemlet)
# Fourth, replace memlet arrays as necessary
scope_subgraph = graph.scope_subgraph(map_entry)
for edge in scope_subgraph.edges():
if (edge.data.data is not None
and edge.data.data in cloned_arrays):
edge.data.data = cloned_arrays[edge.data.data]
def modifies_graph(self):
return True
class MapReduceFusion(pm.Transformation):
""" Implements the map-reduce-fusion transformation.
Fuses a map with an immediately following reduction, where the array
between the map and the reduction is not used anywhere else.
"""
_tasklet = nodes.Tasklet('_')
_tmap_exit = nodes.MapExit(nodes.Map("", [], []))
_in_array = nodes.AccessNode('_')
_rmap_in_entry = nodes.MapEntry(nodes.Map("", [], []))
_rmap_in_tasklet = nodes.Tasklet('_')
_rmap_in_cr = nodes.MapExit(nodes.Map("", [], []))
_rmap_out_entry = nodes.MapEntry(nodes.Map("", [], []))
_rmap_out_exit = nodes.MapExit(nodes.Map("", [], []))
_out_array = nodes.AccessNode('_')
_reduce = nodes.Reduce('lambda: None', None)
@staticmethod
def expressions():
return [
# Map, then reduce of all axes
nxutil.node_path_graph(
MapReduceFusion._tasklet, MapReduceFusion._tmap_exit,
MapReduceFusion._in_array, MapReduceFusion._rmap_in_entry,
MapReduceFusion._rmap_in_tasklet, MapReduceFusion._rmap_in_cr,
MapReduceFusion._out_array),
# Map, then partial reduction of axes
nxutil.node_path_graph(
MapReduceFusion._tasklet, MapReduceFusion._tmap_exit,
MapReduceFusion._in_array, MapReduceFusion._rmap_out_entry,
MapReduceFusion._rmap_in_entry,
MapReduceFusion._rmap_in_tasklet, MapReduceFusion._rmap_in_cr,
MapReduceFusion._rmap_out_exit, MapReduceFusion._out_array),
# Map, then reduce node
nxutil.node_path_graph(
MapReduceFusion._tasklet, MapReduceFusion._tmap_exit,
MapReduceFusion._in_array, MapReduceFusion._reduce,
MapReduceFusion._out_array)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
tmap_exit = graph.nodes()[candidate[MapReduceFusion._tmap_exit]]
in_array = graph.nodes()[candidate[MapReduceFusion._in_array]]
if expr_index == 0: # Reduce without outer map
rmap_entry = graph.nodes()[candidate[
MapReduceFusion._rmap_in_entry]]
# rmap_in_entry = rmap_entry
elif expr_index == 1: # Reduce with outer map
rmap_entry = graph.nodes()[candidate[
MapReduceFusion._rmap_out_entry]]
# rmap_in_entry = graph.nodes()[candidate[
# MapReduceFusion._rmap_in_entry]]
else: # Reduce node
rmap_entry = graph.nodes()[candidate[MapReduceFusion._reduce]]
# Make sure that the array is only accessed by the map and the reduce
if any([
src != tmap_exit
for src, _, _, _, memlet in graph.in_edges(in_array)
]):
return False
if any([
dest != rmap_entry
for _, _, dest, _, memlet in graph.out_edges(in_array)
]):
return False
# Make sure that there is a reduction in the second map
if expr_index < 2:
rmap_cr = graph.nodes()[candidate[MapReduceFusion._rmap_in_cr]]
reduce_edge = graph.in_edges(rmap_cr)[0]
if reduce_edge.data.wcr is None:
return False
# Make sure that the transient is not accessed by other states
# if garr.get_unique_name() in cgen_state.sdfg.shared_transients():
# return False
# reduce_inarr = reduce.in_array
# reduce_outarr = reduce.out_array
# reduce_inslice = reduce.inslice
# reduce_outslice = reduce.outslice
# insize = cgen_state.var_sizes[reduce_inarr]
# outsize = cgen_state.var_sizes[reduce_outarr]
# Currently only supports full-range arrays
# TODO(later): Support fusion of partial reductions and refactor slice/subarray handling
#if not nxutil.fullrange(reduce_inslice, insize) or \
# not nxutil.fullrange(reduce_outslice, outsize):
# return False
# Verify acceses from tasklet through MapExit
#already_found = False
#for _src, _, _dest, _, memlet in graph.in_edges(map_exit):
# if isinstance(memlet.subset, subsets.Indices):
# # Make sure that only one value is reduced at a time
# if memlet.data == in_array.desc:
# if already_found:
# return False
# already_found = True
## Find axes after reduction
#indims = len(reduce.inslice)
#axis_after_reduce = [None] * indims
#ctr = 0
#for i in range(indims):
# if reduce.axes is not None and i in reduce.axes:
# axis_after_reduce[i] = None
# else:
# axis_after_reduce[i] = ctr
# ctr += 1
## Match map ranges with reduce ranges
#curaxis = 0
#for dim, var in enumerate(memlet.subset):
# # Make sure that indices are direct symbols
# #if not isinstance(symbolic.pystr_to_symbolic(var), sympy.Symbol):
# # return False
# perm = None
# for i, mapvar in enumerate(map_exit.map.params):
# if symbolic.pystr_to_symbolic(mapvar) == var:
# perm = i
# break
# if perm is None: # If symbol is not found in map range
# return False
# # Make sure that map ranges match output slice after reduction
# map_range = map_exit.map.range[perm]
# if map_range[0] != 0:
# return False # Disallow start from middle
# if map_range[2] is not None and map_range[2] != 1:
# return False # Disallow skip
# if reduce.axes is not None and dim not in reduce.axes:
# if map_range[1] != symbolic.pystr_to_symbolic(
# reduce.outslice[axis_after_reduce[dim]][1]):
# return False # Range check (output axis)
# else:
# if map_range[1] != symbolic.pystr_to_symbolic(reduce.inslice[dim][1]):
# return False # Range check (reduction axis)
# Verify that reduction ranges match tasklet map
tout_memlet = graph.in_edges(in_array)[0].data
rin_memlet = graph.out_edges(in_array)[0].data
if tout_memlet.subset != rin_memlet.subset:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
tasklet = candidate[MapReduceFusion._tasklet]
map_exit = candidate[MapReduceFusion._tmap_exit]
if len(candidate) == 5: # Expression 2
reduce = candidate[MapReduceFusion._reduce]
else:
reduce = candidate[MapReduceFusion._rmap_in_cr]
return ' -> '.join(str(node) for node in [tasklet, map_exit, reduce])
@staticmethod
def find_memlet_map_permutation(memlet: Memlet, map: nodes.Map):
perm = [None] * len(memlet.subset)
indices = set()
for i, dim in enumerate(memlet.subset):
for j, mapdim in enumerate(map.params):
if symbolic.pystr_to_symbolic(
mapdim) == dim and j not in indices:
perm[i] = j
indices.add(j)
break
return perm
@staticmethod
def find_permutation(tasklet_map: nodes.Map, red_outer_map: nodes.Map,
red_inner_map: nodes.Map, tmem: Memlet):
""" Find permutation between tasklet-exit memlet and tasklet map. """
result = [], []
assert len(tasklet_map.range) == len(red_inner_map.range) + len(
red_outer_map.range)
# Match map ranges with reduce ranges
unavailable_ranges_out = set()
unavailable_ranges_in = set()
for i, tmap_rng in enumerate(tasklet_map.range):
found = False
for j, rng in enumerate(red_outer_map.range):
if tmap_rng == rng and j not in unavailable_ranges_out:
result[0].append(i)
unavailable_ranges_out.add(j)
found = True
break
if found: continue
for j, rng in enumerate(red_inner_map.range):
if tmap_rng == rng and j not in unavailable_ranges_in:
result[1].append(i)
unavailable_ranges_in.add(j)
found = True
break
if not found: break
# Ensure all map variables matched with reduce variables
assert len(result[0]) + len(result[1]) == len(tasklet_map.range)
# Returns ([outer map indices], [inner (CR) map indices])
return result
@staticmethod
def find_permutation_reduce(tasklet_map: nodes.Map,
reduce_node: nodes.Reduce, graph: SDFGState,
tmem: Memlet):
in_memlet = graph.in_edges(reduce_node)[0].data
out_memlet = graph.out_edges(reduce_node)[0].data
assert len(tasklet_map.range) == in_memlet.subset.dims()
# Find permutation between tasklet-exit memlet and tasklet map
tmem_perm = MapReduceFusion.find_memlet_map_permutation(
tmem, tasklet_map)
mapred_perm = []
# Match map ranges with reduce ranges
unavailable_ranges = set()
for i, tmap_rng in enumerate(tasklet_map.range):
found = False
for j, in_rng in enumerate(in_memlet.subset):
if tmap_rng == in_rng and j not in unavailable_ranges:
mapred_perm.append(i)
unavailable_ranges.add(j)
found = True
break
if not found: break
# Ensure all map variables matched with reduce variables
assert len(tmem_perm) == len(tmem.subset)
assert len(mapred_perm) == len(in_memlet.subset)
# Prepare result from the two permutations and the reduction axes
result = []
for i in range(len(mapred_perm)):
if reduce_node.axes is None or i in reduce_node.axes:
continue
result.append(mapred_perm[tmem_perm[i]])
return result
def apply(self, sdfg):
def gnode(nname):
return graph.nodes()[self.subgraph[nname]]
expr_index = self.expr_index
graph = sdfg.nodes()[self.state_id]
tasklet = gnode(MapReduceFusion._tasklet)
tmap_exit = graph.nodes()[self.subgraph[MapReduceFusion._tmap_exit]]
in_array = graph.nodes()[self.subgraph[MapReduceFusion._in_array]]
if expr_index == 0: # Reduce without outer map
rmap_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_entry]]
elif expr_index == 1: # Reduce with outer map
rmap_out_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_out_entry]]
rmap_out_exit = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_out_exit]]
rmap_in_entry = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_entry]]
rmap_tasklet = graph.nodes()[self.subgraph[
MapReduceFusion._rmap_in_tasklet]]
if expr_index == 2:
rmap_cr = graph.nodes()[self.subgraph[MapReduceFusion._reduce]]
else:
rmap_cr = graph.nodes()[self.subgraph[MapReduceFusion._rmap_in_cr]]
out_array = gnode(MapReduceFusion._out_array)
# Set nodes to remove according to the expression index
nodes_to_remove = [in_array]
if expr_index == 0:
nodes_to_remove.append(gnode(MapReduceFusion._rmap_in_entry))
elif expr_index == 1:
nodes_to_remove.append(gnode(MapReduceFusion._rmap_out_entry))
nodes_to_remove.append(gnode(MapReduceFusion._rmap_in_entry))
nodes_to_remove.append(gnode(MapReduceFusion._rmap_out_exit))
else:
nodes_to_remove.append(gnode(MapReduceFusion._reduce))
# If no other edges lead to mapexit, remove it. Otherwise, keep
# it and remove reduction incoming/outgoing edges
if expr_index != 2 and len(graph.in_edges(tmap_exit)) == 1:
nodes_to_remove.append(tmap_exit)
memlet_edge = None
for edge in graph.in_edges(tmap_exit):
if edge.data.data == in_array.data:
memlet_edge = edge
break
if memlet_edge is None:
raise RuntimeError('Reduction memlet cannot be None')
if expr_index == 0: # Reduce without outer map
# Index order does not matter, merge as-is
pass
elif expr_index == 1: # Reduce with outer map
tmap = tmap_exit.map
perm_outer, perm_inner = MapReduceFusion.find_permutation(
tmap, rmap_out_entry.map, rmap_in_entry.map, memlet_edge.data)
# Split tasklet map into tmap_out -> tmap_in (according to
# reduction)
omap = nodes.Map(
tmap.label + '_nonreduce',
[p for i, p in enumerate(tmap.params) if i in perm_outer],
[r for i, r in enumerate(tmap.range) if i in perm_outer],
tmap.schedule, tmap.unroll, tmap.is_async)
tmap.params = [
p for i, p in enumerate(tmap.params) if i in perm_inner
]
tmap.range = [
r for i, r in enumerate(tmap.range) if i in perm_inner
]
omap_entry = nodes.MapEntry(omap)
omap_exit = rmap_out_exit
rmap_out_exit.map = omap
# Reconnect graph to new map
tmap_entry = graph.entry_node(tmap_exit)
tmap_in_edges = list(graph.in_edges(tmap_entry))
for e in tmap_in_edges:
nxutil.change_edge_dest(graph, tmap_entry, omap_entry)
for e in tmap_in_edges:
graph.add_edge(omap_entry, e.src_conn, tmap_entry, e.dst_conn,
copy.copy(e.data))
elif expr_index == 2: # Reduce node
# Find correspondence between map indices and array outputs
tmap = tmap_exit.map
perm = MapReduceFusion.find_permutation_reduce(
tmap, rmap_cr, graph, memlet_edge.data)
output_subset = [tmap.params[d] for d in perm]
if len(output_subset) == 0: # Output is a scalar
output_subset = [0]
array_edge = graph.out_edges(rmap_cr)[0]
# Delete relevant edges and nodes
graph.remove_edge(memlet_edge)
graph.remove_nodes_from(nodes_to_remove)
# Add new edges and nodes
# From tasklet to map exit
graph.add_edge(
memlet_edge.src, memlet_edge.src_conn, memlet_edge.dst,
memlet_edge.dst_conn,
Memlet(out_array.data, memlet_edge.data.num_accesses,
subsets.Indices(output_subset), memlet_edge.data.veclen,
rmap_cr.wcr, rmap_cr.identity))
# From map exit to output array
graph.add_edge(
memlet_edge.dst, 'OUT_' + memlet_edge.dst_conn[3:],
array_edge.dst, array_edge.dst_conn,
Memlet(array_edge.data.data, array_edge.data.num_accesses,
array_edge.data.subset, array_edge.data.veclen,
rmap_cr.wcr, rmap_cr.identity))
return
# Remove tmp array node prior to the others, so that a new one
# can be created in its stead (see below)
graph.remove_node(nodes_to_remove[0])
nodes_to_remove = nodes_to_remove[1:]
# Create tasklet -> tmp -> tasklet connection
tmp = graph.add_array(
'tmp',
memlet_edge.data.subset.bounding_box_size(),
sdfg.arrays[memlet_edge.data.data].dtype,
transient=True)
tasklet_tmp_memlet = copy.deepcopy(memlet_edge.data)
tasklet_tmp_memlet.data = tmp.data
tasklet_tmp_memlet.subset = ShapeProperty.to_string(tmp.shape)
# Modify memlet to point to output array
memlet_edge.data.data = out_array.data
# Recover reduction axes from CR reduce subset
reduce_cr_subset = graph.in_edges(rmap_tasklet)[0].data.subset
reduce_axes = []
for ind, crvar in enumerate(reduce_cr_subset.indices):
if '__i' in str(crvar):
reduce_axes.append(ind)
# Modify memlet access index by filtering out reduction axes
if True: # expr_index == 0:
newindices = []
for ind, ovar in enumerate(memlet_edge.data.subset.indices):
if ind not in reduce_axes:
newindices.append(ovar)
if len(newindices) == 0:
newindices = [0]
memlet_edge.data.subset = subsets.Indices(newindices)
graph.remove_edge(memlet_edge)
graph.add_edge(memlet_edge.src, memlet_edge.src_conn, tmp,
memlet_edge.dst_conn, tasklet_tmp_memlet)
red_edges = list(graph.in_edges(rmap_tasklet))
if len(red_edges) != 1:
raise RuntimeError('CR edge must be unique')
tmp_tasklet_memlet = copy.deepcopy(tasklet_tmp_memlet)
graph.add_edge(tmp, None, rmap_tasklet, red_edges[0].dst_conn,
tmp_tasklet_memlet)
for e in graph.edges_between(rmap_tasklet, rmap_cr):
e.data.subset = memlet_edge.data.subset
# Move output edges to point directly to CR node
if expr_index == 1:
# Set output memlet between CR node and outer reduction map to
# contain the same subset as the one pointing to the CR node
for e in graph.out_edges(rmap_cr):
e.data.subset = memlet_edge.data.subset
rmap_out = gnode(MapReduceFusion._rmap_out_exit)
nxutil.change_edge_src(graph, rmap_out, omap_exit)
# Remove nodes
graph.remove_nodes_from(nodes_to_remove)
# For unrelated outputs, connect original output to rmap_out
if expr_index == 1 and tmap_exit not in nodes_to_remove:
other_out_edges = list(graph.out_edges(tmap_exit))
for e in other_out_edges:
graph.remove_edge(e)
graph.add_edge(e.src, e.src_conn, omap_exit, None, e.data)
graph.add_edge(omap_exit, None, e.dst, e.dst_conn,
copy.copy(e.data))
def modifies_graph(self):
return True
class MapExpansion(pm.Transformation):
""" Implements the map-expansion pattern.
Map-expansion takes an N-dimensional map and expands it to N
unidimensional maps.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapExpansion._map_entry)]
@staticmethod
def can_be_applied(graph: dace.graph.graph.OrderedMultiDiConnectorGraph,
candidate: Dict[dace.graph.nodes.Node, int],
expr_index: int,
sdfg: dace.SDFG,
strict: bool = False):
# A candidate subgraph matches the map-expansion pattern when it includes
# a N-dimensional map, with N greater than one.
map_entry = graph.nodes()[candidate[MapExpansion._map_entry]]
return map_entry.map.get_param_num() > 1
@staticmethod
def match_to_str(graph: dace.graph.graph.OrderedMultiDiConnectorGraph,
candidate: Dict[dace.graph.nodes.Node, int]):
map_entry = graph.nodes()[candidate[MapExpansion._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg: dace.SDFG):
# Extract the map and its entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapExpansion._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
current_map = map_entry.map
# Create new maps
maps = [
nodes.Map(current_map.label + '_' + str(param), [param],
subsets.Range([param_range]),
schedule=dtypes.ScheduleType.Sequential) for param,
param_range in zip(current_map.params, current_map.range)
]
maps[0]._schedule = dtypes.ScheduleType.Default
# Create new map entries
entries = [nodes.MapEntry(new_map) for new_map in maps]
entries[0].in_connectors = map_entry.in_connectors
entries[0].out_connectors = map_entry.out_connectors
num_entry_out_edges = len(graph.out_edges(map_entry))
for i in range(1, len(entries)):
entries[i].in_connectors = set('IN_' + str(i + 1)
for i in range(num_entry_out_edges))
entries[i].out_connectors = set(
'OUT_' + str(i + 1) for i in range(num_entry_out_edges))
# Create new map exits
exits = [nodes.MapExit(new_map) for new_map in maps]
exits.reverse()
exits[-1].in_connectors = map_exit.in_connectors
exits[-1].out_connectors = map_exit.out_connectors
num_entry_out_edges = len(graph.out_edges(map_exit))
for i in range(0, len(exits) - 1):
exits[i].in_connectors = set('IN_' + str(i + 1)
for i in range(num_entry_out_edges))
exits[i].out_connectors = set('OUT_' + str(i + 1)
for i in range(num_entry_out_edges))
# Add new nodes to state
graph.add_nodes_from(entries)
graph.add_nodes_from(exits)
# Redirect edges to new nodes
dace.graph.nxutil.change_edge_dest(graph, map_entry, entries[0])
dace.graph.nxutil.change_edge_src(graph, map_exit, exits[-1])
for i, e in enumerate(graph.out_edges(map_entry)):
graph.remove_edge(e)
graph.add_edge(entries[0], e.src_conn, entries[1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
graph.add_edge(entries[-1], 'OUT_' + str(i + 1), e.dst, e.dst_conn,
copy.deepcopy(e.data))
for j in range(1, len(entries) - 1):
graph.add_edge(entries[j], 'OUT_' + str(i + 1), entries[j + 1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
for i, e in enumerate(graph.in_edges(map_exit)):
graph.remove_edge(e)
graph.add_edge(e.src, e.src_conn, exits[0], 'IN_' + str(i + 1),
copy.deepcopy(e.data))
graph.add_edge(exits[-2], 'OUT_' + str(i + 1), exits[-1],
e.dst_conn, copy.deepcopy(e.data))
for j in range(0, len(exits) - 2):
graph.add_edge(exits[j], 'OUT_' + str(i + 1), exits[j + 1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
# Remove old nodes
graph.remove_node(map_entry)
graph.remove_node(map_exit)
def _build_dataflow_graph_recurse(sdfg, state, primitives, modules, superEntry,
super_exit):
# Array of pairs (exit node, memlet)
exit_nodes = []
if len(primitives) == 0:
# Inject empty tasklets into empty states
primitives = [astnodes._EmptyTaskletNode("Empty Tasklet", None)]
for prim in primitives:
label = prim.name
# Expand node to get entry and exit points
if isinstance(prim, astnodes._MapNode):
if len(prim.children) == 0:
raise ValueError("Map node expected to have children")
mapNode = nd.Map(label,
prim.params,
prim.range,
is_async=prim.is_async)
# Add connectors for inputs that exist as array nodes
entry = nd.MapEntry(
mapNode,
_get_input_symbols(prim.inputs, prim.range.free_symbols))
exit = nd.MapExit(mapNode)
elif isinstance(prim, astnodes._ConsumeNode):
if len(prim.children) == 0:
raise ValueError("Consume node expected to have children")
consumeNode = nd.Consume(label, (prim.params[1], prim.num_pes),
prim.condition)
entry = nd.ConsumeEntry(consumeNode)
exit = nd.ConsumeExit(consumeNode)
elif isinstance(prim, astnodes._ReduceNode):
rednode = nd.Reduce(prim.ast, prim.axes, prim.identity)
state.add_node(rednode)
entry = rednode
exit = rednode
elif isinstance(prim, astnodes._TaskletNode):
if isinstance(prim, astnodes._EmptyTaskletNode):
tasklet = nd.EmptyTasklet(prim.name)
else:
# Remove memlets from tasklet AST
if prim.language == types.Language.Python:
clean_code = MemletRemover().visit(prim.ast)
clean_code = ModuleInliner(modules).visit(clean_code)
else: # Use external code from tasklet definition
if prim.extcode is None:
raise SyntaxError("Cannot define an intrinsic "
"tasklet without an implementation")
clean_code = prim.extcode
tasklet = nd.Tasklet(
prim.name,
set(prim.inputs.keys()),
set(prim.outputs.keys()),
code=clean_code,
language=prim.language,
code_global=prim.gcode) # TODO: location=prim.location
# Need to add the tasklet in case we're in an empty state, where no
# edge will be drawn to it
state.add_node(tasklet)
entry = tasklet
exit = tasklet
elif isinstance(prim, astnodes._NestedSDFGNode):
prim.sdfg.parent = state
prim.sdfg._parent_sdfg = sdfg
prim.sdfg.update_sdfg_list([])
nsdfg = nd.NestedSDFG(prim.name, prim.sdfg,
set(prim.inputs.keys()),
set(prim.outputs.keys()))
state.add_node(nsdfg)
entry = nsdfg
exit = nsdfg
elif isinstance(prim, astnodes._ProgramNode):
return
elif isinstance(prim, astnodes._ControlFlowNode):
continue
else:
raise TypeError("Node type not implemented: " +
str(prim.__class__))
# Add incoming edges
for varname, memlet in prim.inputs.items():
arr = memlet.dataname
if (prim.parent is not None
and memlet.dataname in prim.parent.transients.keys()):
node = input_node_for_array(state, memlet.dataname)
# Add incoming edge into transient as well
# FIXME: A bit hacked?
if arr in prim.parent.inputs:
astmem = prim.parent.inputs[arr]
_add_astmemlet_edge(sdfg, state, superEntry, None, node,
None, astmem)
# Remove local name from incoming edge to parent
prim.parent.inputs[arr].local_name = None
elif superEntry:
node = superEntry
else:
node = input_node_for_array(state, memlet.dataname)
# Destination connector inference
# Connected to a tasklet or a nested SDFG
dst_conn = (memlet.local_name
if isinstance(entry, nd.CodeNode) else None)
# Connected to a scope as part of its range
if str(varname).startswith('__DACEIN_'):
dst_conn = str(varname)[9:]
# Handle special case of consume input stream
if (isinstance(entry, nd.ConsumeEntry)
and memlet.data == prim.stream):
dst_conn = 'IN_stream'
# If a memlet that covers this input already exists, skip
# generating this one; otherwise replace memlet with ours
skip_incoming_edge = False
remove_edge = None
for e in state.edges_between(node, entry):
if e.data.data != memlet.dataname or dst_conn != e.dst_conn:
continue
if e.data.subset.covers(memlet.subset):
skip_incoming_edge = True
break
elif memlet.subset.covers(e.data.subset):
remove_edge = e
break
else:
print('WARNING: Performing bounding-box union on',
memlet.subset, 'and', e.data.subset, '(in)')
e.data.subset = sbs.bounding_box_union(
e.data.subset, memlet.subset)
e.data.num_accesses += memlet.num_accesses
skip_incoming_edge = True
break
if remove_edge is not None:
state.remove_edge(remove_edge)
if skip_incoming_edge == False:
_add_astmemlet_edge(sdfg, state, node, None, entry, dst_conn,
memlet)
# If there are no inputs, generate a dummy edge
if superEntry and len(prim.inputs) == 0:
state.add_edge(superEntry, None, entry, None, EmptyMemlet())
if len(prim.children) > 0:
# Recurse
inner_outputs = _build_dataflow_graph_recurse(
sdfg, state, prim.children, modules, entry, exit)
# Infer output node for each memlet
for i, (out_src, mem) in enumerate(inner_outputs):
# If there is no such array in this primitive's outputs,
# it's an external array (e.g., a map in a map). In this case,
# connect to the exit node
if mem.dataname in prim.outputs:
inner_outputs[i] = (out_src, prim.outputs[mem.dataname])
else:
inner_outputs[i] = (out_src, mem)
else:
inner_outputs = [(exit, mem) for mem in prim.outputs.values()]
# Add outgoing edges
for out_src, astmem in inner_outputs:
data = astmem.data
dataname = astmem.dataname
# If WCR is not none, it needs to be handled in the code. Check for
# this after, as we only expect it for one distinct case
wcr_was_handled = astmem.wcr is None
# TODO: This is convoluted. We should find a more readable
# way of connecting the outgoing edges.
if super_exit is None:
# Assert that we're in a top-level node
if ((not isinstance(prim.parent, astnodes._ProgramNode)) and
(not isinstance(prim.parent, astnodes._ControlFlowNode))):
raise RuntimeError("Expected to be at the top node")
# Looks hacky
src_conn = (astmem.local_name if isinstance(
out_src, (nd.Tasklet, nd.NestedSDFG)) else None)
# Here we just need to connect memlets directly to their
# respective data nodes
out_tgt = output_node_for_array(state, astmem.dataname)
# If a memlet that covers this outuput already exists, skip
# generating this one; otherwise replace memlet with ours
skip_outgoing_edge = False
remove_edge = None
for e in state.edges_between(out_src, out_tgt):
if e.data.data != astmem.dataname or src_conn != e.src_conn:
continue
if e.data.subset.covers(astmem.subset):
skip_outgoing_edge = True
break
elif astmem.subset.covers(e.data.subset):
remove_edge = e
break
else:
print('WARNING: Performing bounding-box union on',
astmem.subset, 'and', e.data.subset, '(out)')
e.data.subset = sbs.bounding_box_union(
e.data.subset, astmem.subset)
e.data.num_accesses += astmem.num_accesses
skip_outgoing_edge = True
break
if skip_outgoing_edge == True:
continue
if remove_edge is not None:
state.remove_edge(remove_edge)
_add_astmemlet_edge(sdfg,
state,
out_src,
src_conn,
out_tgt,
None,
astmem,
wcr=astmem.wcr,
wcr_identity=astmem.wcr_identity)
wcr_was_handled = (True if astmem.wcr is not None else
wcr_was_handled)
# If the program defines another output, connect it too.
# This refers to the case where we have streams, which
# must define an input and output, and sometimes this output
# is defined in pdp.outputs
if (isinstance(out_tgt, nd.AccessNode)
and isinstance(out_tgt.desc(sdfg), dt.Stream)):
try:
stream_memlet = next(
v for k, v in prim.parent.outputs.items()
if k == out_tgt.data)
stream_output = output_node_for_array(
state, stream_memlet.dataname)
_add_astmemlet_edge(sdfg, state, out_tgt, None,
stream_output, None, stream_memlet)
except StopIteration: # Stream output not found, skip
pass
else: # We're in a nest
if isinstance(prim, astnodes._ScopeNode):
# We're a map or a consume node, that needs to connect our
# exit to either an array or to the super_exit
if data.transient and dataname in prim.parent.transients:
# Connect the exit directly
out_tgt = output_node_for_array(state, data.dataname)
_add_astmemlet_edge(sdfg, state, out_src, None,
out_tgt, None, astmem)
else:
# This is either a transient defined in an outer scope,
# or an I/O array, so redirect thruogh the exit node
_add_astmemlet_edge(sdfg, state, out_src, None,
super_exit, None, astmem)
# Instruct outer recursion layer to continue the route
exit_nodes.append((super_exit, astmem))
elif isinstance(
prim,
(astnodes._TaskletNode, astnodes._NestedSDFGNode)):
# We're a tasklet, and need to connect either to the exit
# if the array is I/O or is defined in a scope further out,
# or directly to the transient if it's defined locally
if dataname in prim.parent.transients:
# This is a local transient variable, so connect to it
# directly
out_tgt = output_node_for_array(state, data.dataname)
_add_astmemlet_edge(sdfg, state, out_src,
astmem.local_name, out_tgt, None,
astmem)
else:
# This is an I/O array, or an outer level transient, so
# redirect through the exit node
_add_astmemlet_edge(sdfg,
state,
out_src,
astmem.local_name,
super_exit,
None,
astmem,
wcr=astmem.wcr,
wcr_identity=astmem.wcr_identity)
exit_nodes.append((super_exit, astmem))
if astmem.wcr is not None:
wcr_was_handled = True # Sanity check
else:
raise TypeError("Unexpected node type: {}".format(
type(out_src).__name__))
if not wcr_was_handled and not isinstance(prim,
astnodes._ScopeNode):
raise RuntimeError("Detected unhandled WCR for primitive '{}' "
"of type {}. WCR is only expected for "
"tasklets in a map/consume scope.".format(
prim.name,
type(prim).__name__))
return exit_nodes
def apply(self, sdfg: dace.SDFG):
# Extract the map and its entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapExpansion._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
current_map = map_entry.map
# Create new maps
maps = [
nodes.Map(current_map.label + '_' + str(param), [param],
subsets.Range([param_range]),
schedule=dtypes.ScheduleType.Sequential) for param,
param_range in zip(current_map.params, current_map.range)
]
maps[0]._schedule = dtypes.ScheduleType.Default
# Create new map entries
entries = [nodes.MapEntry(new_map) for new_map in maps]
entries[0].in_connectors = map_entry.in_connectors
entries[0].out_connectors = map_entry.out_connectors
num_entry_out_edges = len(graph.out_edges(map_entry))
for i in range(1, len(entries)):
entries[i].in_connectors = set('IN_' + str(i + 1)
for i in range(num_entry_out_edges))
entries[i].out_connectors = set(
'OUT_' + str(i + 1) for i in range(num_entry_out_edges))
# Create new map exits
exits = [nodes.MapExit(new_map) for new_map in maps]
exits.reverse()
exits[-1].in_connectors = map_exit.in_connectors
exits[-1].out_connectors = map_exit.out_connectors
num_entry_out_edges = len(graph.out_edges(map_exit))
for i in range(0, len(exits) - 1):
exits[i].in_connectors = set('IN_' + str(i + 1)
for i in range(num_entry_out_edges))
exits[i].out_connectors = set('OUT_' + str(i + 1)
for i in range(num_entry_out_edges))
# Add new nodes to state
graph.add_nodes_from(entries)
graph.add_nodes_from(exits)
# Redirect edges to new nodes
dace.graph.nxutil.change_edge_dest(graph, map_entry, entries[0])
dace.graph.nxutil.change_edge_src(graph, map_exit, exits[-1])
for i, e in enumerate(graph.out_edges(map_entry)):
graph.remove_edge(e)
graph.add_edge(entries[0], e.src_conn, entries[1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
graph.add_edge(entries[-1], 'OUT_' + str(i + 1), e.dst, e.dst_conn,
copy.deepcopy(e.data))
for j in range(1, len(entries) - 1):
graph.add_edge(entries[j], 'OUT_' + str(i + 1), entries[j + 1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
for i, e in enumerate(graph.in_edges(map_exit)):
graph.remove_edge(e)
graph.add_edge(e.src, e.src_conn, exits[0], 'IN_' + str(i + 1),
copy.deepcopy(e.data))
graph.add_edge(exits[-2], 'OUT_' + str(i + 1), exits[-1],
e.dst_conn, copy.deepcopy(e.data))
for j in range(0, len(exits) - 2):
graph.add_edge(exits[j], 'OUT_' + str(i + 1), exits[j + 1],
'IN_' + str(i + 1), copy.deepcopy(e.data))
# Remove old nodes
graph.remove_node(map_entry)
graph.remove_node(map_exit)
def _stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
# Retrieve transformation properties.
dim_idx = self.dim_idx
new_dim_prefix = self.new_dim_prefix
tile_size = self.tile_size
divides_evenly = self.divides_evenly
strided = self.strided
tile_stride = self.tile_stride
if tile_stride is None or len(tile_stride) == 0:
tile_stride = tile_size
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
# Create new map. Replace by cloning???
new_dim = self._find_new_dim(sdfg, graph, map_entry, new_dim_prefix,
target_dim)
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(
'int_ceil(%s + 1 - %s, %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_stride))
nd_step = 1
new_dim_range = (nd_from, nd_to, nd_step)
new_map = nodes.Map(new_dim + '_' + map_entry.map.label, [new_dim],
subsets.Range([new_dim_range]))
new_map_entry = nodes.MapEntry(new_map)
new_map_exit = nodes.MapExit(new_map)
# Change the range of the selected dimension to iterate over a single
# tile
if strided:
td_from_new = symbolic.pystr_to_symbolic(new_dim)
td_to_new_approx = td_to
td_step = symbolic.pystr_to_symbolic(tile_size)
else:
td_from_new = symbolic.pystr_to_symbolic(
'%s + %s * %s' %
(symbolic.symstr(td_from), str(new_dim), tile_stride))
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1, %s + %s * %s + %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_stride,
str(new_dim), tile_size))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s * %s + %s - 1' %
(symbolic.symstr(td_from), tile_stride, str(new_dim),
tile_size))
if divides_evenly or strided:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Make internal map's schedule to "not parallel"
new_map.schedule = map_entry.map.schedule
map_entry.map.schedule = dtypes.ScheduleType.Sequential
# Redirect edges
new_map_entry.in_connectors = dcpy(map_entry.in_connectors)
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_map_exit.out_connectors = dcpy(map_exit.out_connectors)
nxutil.change_edge_src(graph, map_exit, new_map_exit)
# Create new entry edges
new_in_edges = dict()
entry_in_conn = set()
entry_out_conn = set()
for _src, src_conn, _dst, _, memlet in graph.out_edges(map_entry):
if (src_conn is not None
and src_conn[:4] == 'OUT_' and not isinstance(
sdfg.arrays[memlet.data], dace.data.Scalar)):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
conn = src_conn[4:]
key = (memlet.data, 'IN_' + conn, 'OUT_' + conn)
if key in new_in_edges.keys():
old_subset = new_in_edges[key].subset
new_in_edges[key].subset = calc_set_union(
old_subset, new_subset)
else:
entry_in_conn.add('IN_' + conn)
entry_out_conn.add('OUT_' + conn)
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges[key] = new_memlet
else:
if src_conn is not None and src_conn[:4] == 'OUT_':
conn = src_conn[4:]
in_conn = 'IN_' + conn
out_conn = 'OUT_' + conn
else:
in_conn = src_conn
out_conn = src_conn
if in_conn:
entry_in_conn.add(in_conn)
if out_conn:
entry_out_conn.add(out_conn)
new_in_edges[(memlet.data, in_conn, out_conn)] = dcpy(memlet)
new_map_entry.out_connectors = entry_out_conn
map_entry.in_connectors = entry_in_conn
for (_, in_conn, out_conn), memlet in new_in_edges.items():
graph.add_edge(new_map_entry, out_conn, map_entry, in_conn, memlet)
# Create new exit edges
new_out_edges = dict()
exit_in_conn = set()
exit_out_conn = set()
for _src, _, _dst, dst_conn, memlet in graph.in_edges(map_exit):
if (dst_conn is not None
and dst_conn[:3] == 'IN_' and not isinstance(
sdfg.arrays[memlet.data], dace.data.Scalar)):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
conn = dst_conn[3:]
key = (memlet.data, 'IN_' + conn, 'OUT_' + conn)
if key in new_out_edges.keys():
old_subset = new_out_edges[key].subset
new_out_edges[key].subset = calc_set_union(
old_subset, new_subset)
else:
exit_in_conn.add('IN_' + conn)
exit_out_conn.add('OUT_' + conn)
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges[key] = new_memlet
else:
if dst_conn is not None and dst_conn[:3] == 'IN_':
conn = dst_conn[3:]
in_conn = 'IN_' + conn
out_conn = 'OUT_' + conn
else:
in_conn = src_conn
out_conn = src_conn
if in_conn:
exit_in_conn.add(in_conn)
if out_conn:
exit_out_conn.add(out_conn)
new_in_edges[(memlet.data, in_conn, out_conn)] = dcpy(memlet)
new_map_exit.in_connectors = exit_in_conn
map_exit.out_connectors = exit_out_conn
for (_, in_conn, out_conn), memlet in new_out_edges.items():
graph.add_edge(map_exit, out_conn, new_map_exit, in_conn, memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map
class MapToForLoop(pattern_matching.Transformation):
""" Implements the Map to for-loop transformation.
Takes a map and enforces a sequential schedule by transforming it into
a state-machine of a for-loop. Creates a nested SDFG, if necessary.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapToForLoop._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# Only uni-dimensional maps are accepted.
map_entry = graph.nodes()[candidate[MapToForLoop._map_entry]]
if len(map_entry.map.params) > 1:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[MapToForLoop._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg):
# Retrieve map entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapToForLoop._map_entry]]
map_exits = graph.exit_nodes(map_entry)
loop_idx = map_entry.map.params[0]
loop_from, loop_to, loop_step = map_entry.map.range[0]
nested_sdfg = dace.SDFG(graph.label + '_' + map_entry.map.label)
# Construct nested SDFG
begin = nested_sdfg.add_state('begin')
guard = nested_sdfg.add_state('guard')
body = nested_sdfg.add_state('body')
end = nested_sdfg.add_state('end')
nested_sdfg.add_edge(
begin, guard,
edges.InterstateEdge(assignments={str(loop_idx): str(loop_from)}))
nested_sdfg.add_edge(
guard,
body,
edges.InterstateEdge(condition = str(loop_idx) + ' <= ' + \
str(loop_to))
)
nested_sdfg.add_edge(
guard,
end,
edges.InterstateEdge(condition = str(loop_idx) + ' > ' + \
str(loop_to))
)
nested_sdfg.add_edge(
body,
guard,
edges.InterstateEdge(assignments = {str(loop_idx): str(loop_idx) + \
' + ' +str(loop_step)})
)
# Add map contents
map_subgraph = graph.scope_subgraph(map_entry)
for node in map_subgraph.nodes():
if node is not map_entry and node not in map_exits:
body.add_node(node)
for src, src_conn, dst, dst_conn, memlet in map_subgraph.edges():
if src is not map_entry and dst not in map_exits:
body.add_edge(src, src_conn, dst, dst_conn, memlet)
# Reconnect inputs
nested_in_data_nodes = {}
nested_in_connectors = {}
nested_in_memlets = {}
for i, edge in enumerate(graph.in_edges(map_entry)):
src, src_conn, dst, dst_conn, memlet = edge
data_label = '_in_' + memlet.data
memdata = sdfg.arrays[memlet.data]
if isinstance(memdata, data.Array):
data_array = sdfg.add_array(data_label, memdata.dtype, [
symbolic.overapproximate(r)
for r in memlet.bounding_box_size()
])
elif isinstance(memdata, data.Scalar):
data_array = sdfg.add_scalar(data_label, memdata.dtype)
else:
raise NotImplementedError()
data_node = nodes.AccessNode(data_label)
body.add_node(data_node)
nested_in_data_nodes.update({i: data_node})
nested_in_connectors.update({i: data_label})
nested_in_memlets.update({i: memlet})
for _, _, _, _, old_memlet in body.edges():
if old_memlet.data == memlet.data:
old_memlet.data = data_label
#body.add_edge(data_node, None, dst, dst_conn, memlet)
# Reconnect outputs
nested_out_data_nodes = {}
nested_out_connectors = {}
nested_out_memlets = {}
for map_exit in map_exits:
for i, edge in enumerate(graph.out_edges(map_exit)):
src, src_conn, dst, dst_conn, memlet = edge
data_label = '_out_' + memlet.data
memdata = sdfg.arrays[memlet.data]
if isinstance(memdata, data.Array):
data_array = sdfg.add_array(data_label, memdata.dtype, [
symbolic.overapproximate(r)
for r in memlet.bounding_box_size()
])
elif isinstance(memdata, data.Scalar):
data_array = sdfg.add_scalar(data_label, memdata.dtype)
else:
raise NotImplementedError()
data_node = nodes.AccessNode(data_label)
body.add_node(data_node)
nested_out_data_nodes.update({i: data_node})
nested_out_connectors.update({i: data_label})
nested_out_memlets.update({i: memlet})
for _, _, _, _, old_memlet in body.edges():
if old_memlet.data == memlet.data:
old_memlet.data = data_label
#body.add_edge(src, src_conn, data_node, None, memlet)
# Add nested SDFG and reconnect it
nested_node = graph.add_nested_sdfg(
nested_sdfg, sdfg, set(nested_in_connectors.values()),
set(nested_out_connectors.values()))
for i, edge in enumerate(graph.in_edges(map_entry)):
src, src_conn, dst, dst_conn, memlet = edge
graph.add_edge(src, src_conn, nested_node, nested_in_connectors[i],
nested_in_memlets[i])
for map_exit in map_exits:
for i, edge in enumerate(graph.out_edges(map_exit)):
src, src_conn, dst, dst_conn, memlet = edge
graph.add_edge(nested_node, nested_out_connectors[i], dst,
dst_conn, nested_out_memlets[i])
for src, src_conn, dst, dst_conn, memlet in graph.out_edges(map_entry):
i = int(src_conn[4:]) - 1
new_memlet = dcpy(memlet)
new_memlet.data = nested_in_data_nodes[i].data
body.add_edge(nested_in_data_nodes[i], None, dst, dst_conn,
new_memlet)
for map_exit in map_exits:
for src, src_conn, dst, dst_conn, memlet in graph.in_edges(
map_exit):
i = int(dst_conn[3:]) - 1
new_memlet = dcpy(memlet)
new_memlet.data = nested_out_data_nodes[i].data
body.add_edge(src, src_conn, nested_out_data_nodes[i], None,
new_memlet)
for node in map_subgraph:
graph.remove_node(node)
def __stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[OrthogonalTiling._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
# Map subgraph
map_subgraph = graph.scope_subgraph(map_entry)
# Retrieve transformation properties.
prefix = self.prefix
tile_sizes = self.tile_sizes
divides_evenly = self.divides_evenly
new_param = []
new_range = []
for dim_idx in range(len(map_entry.map.params)):
if dim_idx >= len(tile_sizes):
tile_size = tile_sizes[-1]
else:
tile_size = tile_sizes[dim_idx]
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
new_dim = prefix + '_' + target_dim
# Basic values
if divides_evenly:
tile_num = '(%s + 1 - %s) / %s' % (symbolic.symstr(td_to),
symbolic.symstr(td_from),
str(tile_size))
else:
tile_num = 'int_ceil((%s + 1 - %s), %s)' % (symbolic.symstr(
td_to), symbolic.symstr(td_from), str(tile_size))
# Outer map values (over all tiles)
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(str(tile_num) + ' - 1')
nd_step = 1
# Inner map values (over one tile)
td_from_new = dace.symbolic.pystr_to_symbolic(td_from)
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1 - %s * %s, %s + %s) - 1' %
(symbolic.symstr(td_to), str(new_dim), str(tile_size),
td_from_new, str(tile_size)))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s - 1' % (td_from_new, str(tile_size)))
# Outer map (over all tiles)
new_dim_range = (nd_from, nd_to, nd_step)
new_param.append(new_dim)
new_range.append(new_dim_range)
# Inner map (over one tile)
if divides_evenly:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Fix subgraph memlets
target_dim = dace.symbolic.pystr_to_symbolic(target_dim)
offset = dace.symbolic.pystr_to_symbolic(
'%s * %s' % (new_dim, str(tile_size)))
for _, _, _, _, memlet in map_subgraph.edges():
old_subset = memlet.subset
if isinstance(old_subset, dace.subsets.Indices):
new_indices = []
for idx in old_subset:
new_idx = idx.subs(target_dim, target_dim + offset)
new_indices.append(new_idx)
memlet.subset = dace.subsets.Indices(new_indices)
elif isinstance(old_subset, dace.subsets.Range):
new_ranges = []
for i, old_range in enumerate(old_subset):
if len(old_range) == 3:
b, e, s, = old_range
t = old_subset.tile_sizes[i]
else:
raise ValueError(
'Range %s is invalid.' % old_range)
new_b = b.subs(target_dim, target_dim + offset)
new_e = e.subs(target_dim, target_dim + offset)
new_s = s.subs(target_dim, target_dim + offset)
new_t = t.subs(target_dim, target_dim + offset)
new_ranges.append((new_b, new_e, new_s, new_t))
memlet.subset = dace.subsets.Range(new_ranges)
else:
raise NotImplementedError
new_map = nodes.Map(prefix + '_' + map_entry.map.label, new_param,
subsets.Range(new_range))
new_map_entry = nodes.MapEntry(new_map)
new_exit = nodes.MapExit(new_map)
# Make internal map's schedule to "not parallel"
map_entry.map._schedule = dtypes.ScheduleType.Default
# Redirect/create edges.
new_in_edges = {}
for _src, conn, _dest, _, memlet in graph.out_edges(map_entry):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = copy.deepcopy(memlet.subset)
# new_subset = calc_set_image(map_entry.map.params,
# map_entry.map.range, memlet.subset,
# cont_or_strided)
if memlet.data in new_in_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_in_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.data, sdfg.arrays[nnew_memlet.data],
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data: (src, src_conn, dest, dest_conn,
new_memlet, min(num, int(conn[4:])))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data: (new_map_entry, None, map_entry, None,
new_memlet, int(conn[4:]))
})
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_out_edges = {}
for _src, conn, _dest, _, memlet in graph.in_edges(map_exit):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = memlet.subset
# new_subset = calc_set_image(map_entry.map.params,
# map_entry.map.range,
# memlet.subset, cont_or_strided)
if memlet.data in new_out_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_out_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.data, sdfg.arrays[nnew_memlet.data],
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (src, src_conn, dest, dest_conn,
new_memlet, min(num, conn[4:]))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (map_exit, None, new_exit, None,
new_memlet, conn[4:])
})
nxutil.change_edge_src(graph, map_exit, new_exit)
# Connector related work follows
# 1. Dictionary 'old_connector_number': 'new_connector_numer'
# 2. New node in/out connectors
# 3. New edges
in_conn_nums = []
for _, e in new_in_edges.items():
_, _, _, _, _, num = e
in_conn_nums.append(num)
in_conn = {}
for i, num in enumerate(in_conn_nums):
in_conn.update({num: i + 1})
entry_in_connectors = set()
entry_out_connectors = set()
for i in range(len(in_conn_nums)):
entry_in_connectors.add('IN_' + str(i + 1))
entry_out_connectors.add('OUT_' + str(i + 1))
new_map_entry.in_connectors = entry_in_connectors
new_map_entry.out_connectors = entry_out_connectors
for _, e in new_in_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(in_conn[num]), dst,
'IN_' + str(in_conn[num]), memlet)
out_conn_nums = []
for _, e in new_out_edges.items():
_, _, dst, _, _, num = e
if dst is not new_exit:
continue
out_conn_nums.append(num)
out_conn = {}
for i, num in enumerate(out_conn_nums):
out_conn.update({num: i + 1})
exit_in_connectors = set()
exit_out_connectors = set()
for i in range(len(out_conn_nums)):
exit_in_connectors.add('IN_' + str(i + 1))
exit_out_connectors.add('OUT_' + str(i + 1))
new_exit.in_connectors = exit_in_connectors
new_exit.out_connectors = exit_out_connectors
for _, e in new_out_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(out_conn[num]), dst,
'IN_' + str(out_conn[num]), memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map
class MapToForLoop(pattern_matching.Transformation):
""" Implements the Map to for-loop transformation.
Takes a map and enforces a sequential schedule by transforming it into
a state-machine of a for-loop. Creates a nested SDFG, if necessary.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapToForLoop._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
# Only uni-dimensional maps are accepted.
map_entry = graph.nodes()[candidate[MapToForLoop._map_entry]]
if len(map_entry.map.params) > 1:
return False
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[MapToForLoop._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg) -> Tuple[nodes.NestedSDFG, SDFGState]:
""" Applies the transformation and returns a tuple with the new nested
SDFG node and the main state in the for-loop. """
# Retrieve map entry and exit nodes.
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MapToForLoop._map_entry]]
map_exit = graph.exit_nodes(map_entry)[0]
loop_idx = map_entry.map.params[0]
loop_from, loop_to, loop_step = map_entry.map.range[0]
# Turn the map scope into a nested SDFG
node = nest_state_subgraph(sdfg, graph,
graph.scope_subgraph(map_entry))
nsdfg: SDFG = node.sdfg
nstate: SDFGState = nsdfg.nodes()[0]
# If map range is dynamic, replace loop expressions with memlets
param_to_edge = {}
for edge in nstate.in_edges(map_entry):
if edge.dst_conn and not edge.dst_conn.startswith('IN_'):
param = '__DACE_P%d' % len(param_to_edge)
repldict = {symbolic.pystr_to_symbolic(edge.dst_conn): param}
param_to_edge[param] = edge
loop_from = loop_from.subs(repldict)
loop_to = loop_to.subs(repldict)
loop_step = loop_step.subs(repldict)
# Avoiding import loop
from dace.codegen.targets.cpp import cpp_array_expr
def replace_param(param):
param = symbolic.symstr(param)
for p, pval in param_to_edge.items():
# TODO: This special replacement condition will be removed
# when the code generator is modified to make consistent
# scalar/array decisions.
if (isinstance(nsdfg.arrays[pval.data.data], data.Scalar)
or (nsdfg.arrays[pval.data.data].shape[0] == 1
and len(nsdfg.arrays[pval.data.data].shape) == 1)):
param = param.replace(p, pval.data.data)
else:
param = param.replace(p, cpp_array_expr(nsdfg, pval.data))
return param
# End of dynamic input range
# Create a loop inside the nested SDFG
nsdfg.add_loop(None, nstate, None, loop_idx, replace_param(loop_from),
'%s < %s' % (loop_idx, replace_param(loop_to + 1)),
'%s + %s' % (loop_idx, replace_param(loop_step)))
# Skip map in input edges
for edge in nstate.out_edges(map_entry):
src_node = nstate.memlet_path(edge)[0].src
nstate.add_edge(src_node, None, edge.dst, edge.dst_conn, edge.data)
nstate.remove_edge(edge)
# Skip map in output edges
for edge in nstate.in_edges(map_exit):
dst_node = nstate.memlet_path(edge)[-1].dst
nstate.add_edge(edge.src, edge.src_conn, dst_node, None, edge.data)
nstate.remove_edge(edge)
# Remove nodes from dynamic map range
nstate.remove_nodes_from(
[e.src for e in dace.sdfg.dynamic_map_inputs(nstate, map_entry)])
# Remove scope nodes
nstate.remove_nodes_from([map_entry, map_exit])
return node, nstate
class Vectorization(pattern_matching.Transformation):
""" Implements the vectorization transformation.
Vectorization matches when all the input and output memlets of a
tasklet inside a map access the inner-most loop variable in their last
dimension. The transformation changes the step of the inner-most loop
to be equal to the length of the vector and vectorizes the memlets.
"""
vector_len = Property(desc="Vector length", dtype=int, default=4)
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_tasklet = nodes.Tasklet('_')
_map_exit = nodes.MapExit(nodes.Map("", [], []))
@staticmethod
def expressions():
return [
nxutil.node_path_graph(Vectorization._map_entry,
Vectorization._tasklet,
Vectorization._map_exit)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[Vectorization._map_entry]]
tasklet = graph.nodes()[candidate[Vectorization._tasklet]]
param = symbolic.pystr_to_symbolic(map_entry.map.params[-1])
found = False
dtype = None
# Check if all edges, adjacent to the tasklet,
# use the parameter in their last dimension.
for _src, _, _dest, _, memlet in graph.all_edges(tasklet):
# Cases that do not matter for vectorization
if isinstance(sdfg.arrays[memlet.data], data.Stream):
continue
if memlet.wcr is not None:
continue
try:
subset = memlet.subset
veclen = memlet.veclen
except AttributeError:
return False
if subset is None:
return False
try:
if veclen > symbolic.pystr_to_symbolic('1'):
return False
for idx, expr in enumerate(subset):
if isinstance(expr, tuple):
for ex in expr:
ex = symbolic.pystr_to_symbolic(ex)
symbols = ex.free_symbols
if param in symbols:
if idx == subset.dims() - 1:
found = True
else:
return False
else:
expr = symbolic.pystr_to_symbolic(expr)
symbols = expr.free_symbols
if param in symbols:
if idx == subset.dims() - 1:
found = True
else:
return False
except TypeError: # cannot determine truth value of Relational
return False
return found
@staticmethod
def match_to_str(graph, candidate):
map_entry = candidate[Vectorization._map_entry]
tasklet = candidate[Vectorization._tasklet]
map_exit = candidate[Vectorization._map_exit]
return ' -> '.join(
str(node) for node in [map_entry, tasklet, map_exit])
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[Vectorization._map_entry]]
tasklet = graph.nodes()[self.subgraph[Vectorization._tasklet]]
map_exit = graph.nodes()[self.subgraph[Vectorization._map_exit]]
param = symbolic.pystr_to_symbolic(map_entry.map.params[-1])
# Create new vector size.
vector_size = self.vector_len
# Change the step of the inner-most dimension.
dim_from, dim_to, _dim_step = map_entry.map.range[-1]
map_entry.map.range[-1] = (dim_from, dim_to, vector_size)
# Vectorize memlets adjacent to the tasklet.
for _src, _, _dest, _, memlet in graph.all_edges(tasklet):
subset = memlet.subset
lastindex = memlet.subset[-1]
if isinstance(lastindex, tuple):
symbols = set()
for indd in lastindex:
symbols.update(
symbolic.pystr_to_symbolic(indd).free_symbols)
else:
symbols = symbolic.pystr_to_symbolic(
memlet.subset[-1]).free_symbols
if param in symbols:
try:
memlet.veclen = vector_size
except AttributeError:
return
# TODO: Create new map for non-vectorizable part.
return
def modifies_graph(self):
return True
class Vectorization(pattern_matching.Transformation):
""" Implements the vectorization transformation.
Vectorization matches when all the input and output memlets of a
tasklet inside a map access the inner-most loop variable in their last
dimension. The transformation changes the step of the inner-most loop
to be equal to the length of the vector and vectorizes the memlets.
"""
vector_len = Property(desc="Vector length", dtype=int, default=4)
propagate_parent = Property(desc="Propagate vector length through "
"parent SDFGs",
dtype=bool,
default=False)
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_tasklet = nodes.Tasklet('_')
_map_exit = nodes.MapExit(nodes.Map("", [], []))
@staticmethod
def expressions():
return [
nxutil.node_path_graph(Vectorization._map_entry,
Vectorization._tasklet,
Vectorization._map_exit)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[Vectorization._map_entry]]
tasklet = graph.nodes()[candidate[Vectorization._tasklet]]
param = symbolic.pystr_to_symbolic(map_entry.map.params[-1])
found = False
# Check if all edges, adjacent to the tasklet,
# use the parameter in their last dimension.
for _src, _, _dest, _, memlet in graph.all_edges(tasklet):
# Cases that do not matter for vectorization
if memlet.data is None: # Empty memlets
continue
if isinstance(sdfg.arrays[memlet.data], data.Stream): # Streams
continue
# Vectorization can not be applied in WCR
if memlet.wcr is not None:
return False
try:
subset = memlet.subset
veclen = memlet.veclen
except AttributeError:
return False
if subset is None:
return False
try:
if veclen > symbolic.pystr_to_symbolic('1'):
return False
for idx, expr in enumerate(subset):
if isinstance(expr, tuple):
for ex in expr:
ex = symbolic.pystr_to_symbolic(ex)
symbols = ex.free_symbols
if param in symbols:
if idx == subset.dims() - 1:
found = True
else:
return False
else:
expr = symbolic.pystr_to_symbolic(expr)
symbols = expr.free_symbols
if param in symbols:
if idx == subset.dims() - 1:
found = True
else:
return False
except TypeError: # cannot determine truth value of Relational
return False
return found
@staticmethod
def match_to_str(graph, candidate):
map_entry = candidate[Vectorization._map_entry]
tasklet = candidate[Vectorization._tasklet]
map_exit = candidate[Vectorization._map_exit]
return ' -> '.join(
str(node) for node in [map_entry, tasklet, map_exit])
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[Vectorization._map_entry]]
tasklet = graph.nodes()[self.subgraph[Vectorization._tasklet]]
map_exit = graph.nodes()[self.subgraph[Vectorization._map_exit]]
param = symbolic.pystr_to_symbolic(map_entry.map.params[-1])
# Create new vector size.
vector_size = self.vector_len
# Change the step of the inner-most dimension.
dim_from, dim_to, _dim_step = map_entry.map.range[-1]
map_entry.map.range[-1] = (dim_from, dim_to, vector_size)
# Vectorize memlets adjacent to the tasklet.
for edge in graph.all_edges(tasklet):
_src, _, _dest, _, memlet = edge
if memlet.data is None: # Empty memlets
continue
lastindex = memlet.subset[-1]
if isinstance(lastindex, tuple):
symbols = set()
for indd in lastindex:
symbols.update(
symbolic.pystr_to_symbolic(indd).free_symbols)
else:
symbols = symbolic.pystr_to_symbolic(
memlet.subset[-1]).free_symbols
if param in symbols:
try:
# propagate vector length inside this SDFG
for e in graph.memlet_path(edge):
e.data.veclen = vector_size
source_edge = graph.memlet_path(edge)[0]
sink_edge = graph.memlet_path(edge)[-1]
# propagate to the parent (TODO: handle multiple level of nestings)
if self.propagate_parent and sdfg.parent is not None:
# Find parent Nested SDFG node
parent_node = next(n for n in sdfg.parent.nodes()
if isinstance(n, nodes.NestedSDFG)
and n.sdfg.name == sdfg.name)
# continue in propagating the vector length following the path that arrives to source_edge or
# starts from sink_edge
for pe in sdfg.parent.all_edges(parent_node):
if str(pe.dst_conn) == str(source_edge.src) or str(
pe.src_conn) == str(sink_edge.dst):
for ppe in sdfg.parent.memlet_path(pe):
ppe.data.veclen = vector_size
except AttributeError:
raise
return
class MapTiling(pattern_matching.Transformation):
""" Implements the orthogonal tiling transformation.
Orthogonal tiling is a type of nested map fission that creates tiles
in every dimension of the matched Map.
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
# Properties
prefix = Property(dtype=str,
default="tile",
desc="Prefix for new range symbols")
tile_sizes = ShapeProperty(dtype=tuple,
default=(128, 128, 128),
desc="Tile size per dimension")
strides = ShapeProperty(
dtype=tuple,
default=tuple(),
desc="Tile stride (enables overlapping tiles). If empty, matches tile")
divides_evenly = Property(dtype=bool,
default=False,
desc="Tile size divides dimension length evenly")
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [nxutil.node_path_graph(MapTiling._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[MapTiling._map_entry]]
return map_entry.map.label + ': ' + str(map_entry.map.params)
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
tile_strides = self.tile_sizes
if self.strides is not None and len(self.strides) == len(tile_strides):
tile_strides = self.strides
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[self.subgraph[MapTiling._map_entry]]
from dace.transformation.dataflow.map_collapse import MapCollapse
from dace.transformation.dataflow.strip_mining import StripMining
stripmine_subgraph = {
StripMining._map_entry: self.subgraph[MapTiling._map_entry]
}
sdfg_id = sdfg.sdfg_list.index(sdfg)
last_map_entry = None
removed_maps = 0
original_schedule = map_entry.schedule
for dim_idx in range(len(map_entry.map.params)):
if dim_idx >= len(self.tile_sizes):
tile_size = symbolic.pystr_to_symbolic(self.tile_sizes[-1])
tile_stride = symbolic.pystr_to_symbolic(tile_strides[-1])
else:
tile_size = symbolic.pystr_to_symbolic(
self.tile_sizes[dim_idx])
tile_stride = symbolic.pystr_to_symbolic(tile_strides[dim_idx])
dim_idx -= removed_maps
# If tile size is trivial, skip strip-mining map dimension
if tile_size == map_entry.map.range.size()[dim_idx]:
continue
stripmine = StripMining(sdfg_id, self.state_id, stripmine_subgraph,
self.expr_index)
# Special case: Tile size of 1 should be omitted from inner map
if tile_size == 1 and tile_stride == 1:
stripmine.dim_idx = dim_idx
stripmine.new_dim_prefix = ''
stripmine.tile_size = str(tile_size)
stripmine.tile_stride = str(tile_stride)
stripmine.divides_evenly = True
stripmine.apply(sdfg)
removed_maps += 1
else:
stripmine.dim_idx = dim_idx
stripmine.new_dim_prefix = self.prefix
stripmine.tile_size = str(tile_size)
stripmine.tile_stride = str(tile_stride)
stripmine.divides_evenly = self.divides_evenly
stripmine.apply(sdfg)
# apply to the new map the schedule of the original one
map_entry.schedule = original_schedule
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
class MPITransformMap(pattern_matching.Transformation):
""" Implements the MPI parallelization pattern.
Takes a map and makes it an MPI-scheduled map, introduces transients
that keep locally accessed data.
Original SDFG
=============
```
Input1 - Output1
\ /
Input2 --- MapEntry -- Arbitrary R -- MapExit -- Output2
/ \
InputN - OutputN
```
Nothing in R may access other inputs/outputs that are not defined in R
itself and do not go through MapEntry/MapExit
Map must be a one-dimensional map for now.
The range of the map must be a Range object.
Output:
=======
* Add transients for the accessed parts
* The schedule property of Map is set to MPI
* The range of Map is changed to
var = startexpr + p * chunksize ... startexpr + p + 1 * chunksize
where p is the current rank and P is the total number of ranks,
and chunksize is defined as (endexpr - startexpr) / P, adding the
remaining K iterations to the first K procs.
* For each input InputI, create a new transient transInputI, which
has an attribute that specifies that it needs to be filled with
(possibly) remote data
* Collect all accesses to InputI within R, assume their convex hull is
InputI[rs ... re]
* The transInputI transient will contain InputI[rs ... re]
* Change all accesses to InputI within R to accesses to transInputI
"""
_map_entry = nodes.MapEntry(nodes.Map("", [], []))
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [nxutil.node_path_graph(MPITransformMap._map_entry)]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
map_entry = graph.nodes()[candidate[MPITransformMap._map_entry]]
# Check if the map is one-dimensional
if map_entry.map.range.dims() != 1:
return False
# We cannot transform a map which is already of schedule type MPI
if map_entry.map.schedule == dtypes.ScheduleType.MPI:
return False
# We cannot transform a map which is already inside a MPI map, or in
# another device
schedule_whitelist = [
dtypes.ScheduleType.Default, dtypes.ScheduleType.Sequential
]
sdict = graph.scope_dict()
parent = sdict[map_entry]
while parent is not None:
if parent.map.schedule not in schedule_whitelist:
return False
parent = sdict[parent]
# Dynamic map ranges not supported (will allocate dynamic memory)
if has_dynamic_map_inputs(graph, map_entry):
return False
# MPI schedules currently do not support WCR
map_exit = graph.exit_nodes(map_entry)[0]
if any(e.data.wcr for e in graph.out_edges(map_exit)):
return False
return True
@staticmethod
def match_to_str(graph, candidate):
map_entry = graph.nodes()[candidate[MPITransformMap._map_entry]]
return map_entry.map.label
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
map_entry = graph.nodes()[self.subgraph[MPITransformMap._map_entry]]
# Avoiding import loops
from dace.transformation.dataflow.strip_mining import StripMining
from dace.transformation.dataflow.local_storage import LocalStorage
rangeexpr = str(map_entry.map.range.num_elements())
stripmine_subgraph = {
StripMining._map_entry: self.subgraph[MPITransformMap._map_entry]
}
sdfg_id = sdfg.sdfg_list.index(sdfg)
stripmine = StripMining(sdfg_id, self.state_id, stripmine_subgraph,
self.expr_index)
stripmine.dim_idx = -1
stripmine.new_dim_prefix = "mpi"
stripmine.tile_size = "(" + rangeexpr + "/__dace_comm_size)"
stripmine.divides_evenly = True
stripmine.apply(sdfg)
# Find all in-edges that lead to candidate[MPITransformMap._map_entry]
outer_map = None
edges = [
e for e in graph.in_edges(map_entry)
if isinstance(e.src, nodes.EntryNode)
]
outer_map = edges[0].src
# Add MPI schedule attribute to outer map
outer_map.map._schedule = dtypes.ScheduleType.MPI
# Now create a transient for each array
for e in edges:
in_local_storage_subgraph = {
LocalStorage._node_a: graph.node_id(outer_map),
LocalStorage._node_b: self.subgraph[MPITransformMap._map_entry]
}
sdfg_id = sdfg.sdfg_list.index(sdfg)
in_local_storage = LocalStorage(sdfg_id, self.state_id,
in_local_storage_subgraph,
self.expr_index)
in_local_storage.array = e.data.data
in_local_storage.apply(sdfg)
# Transform OutLocalStorage for each output of the MPI map
in_map_exits = graph.exit_nodes(map_entry)
out_map_exits = graph.exit_nodes(outer_map)
in_map_exit = in_map_exits[0]
out_map_exit = out_map_exits[0]
for e in graph.out_edges(out_map_exit):
name = e.data.data
outlocalstorage_subgraph = {
LocalStorage._node_a: graph.node_id(in_map_exit),
LocalStorage._node_b: graph.node_id(out_map_exit)
}
sdfg_id = sdfg.sdfg_list.index(sdfg)
outlocalstorage = LocalStorage(sdfg_id, self.state_id,
outlocalstorage_subgraph,
self.expr_index)
outlocalstorage.array = name
outlocalstorage.apply(sdfg)
class InLocalStorage(pattern_matching.Transformation):
""" Implements the InLocalStorage transformation, which adds a transient
data node between nested map entry nodes.
"""
_outer_map_entry = nodes.MapEntry(nodes.Map("", [], []))
_inner_map_entry = nodes.MapEntry(nodes.Map("", [], []))
array = Property(
dtype=str,
desc="Array to create local storage for (if empty, first available)",
default=None,
allow_none=True)
@staticmethod
def annotates_memlets():
return True
@staticmethod
def expressions():
return [
nxutil.node_path_graph(InLocalStorage._outer_map_entry,
InLocalStorage._inner_map_entry)
]
@staticmethod
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
return True
@staticmethod
def match_to_str(graph, candidate):
outer_map_entry = candidate[InLocalStorage._outer_map_entry]
inner_map_entry = candidate[InLocalStorage._inner_map_entry]
return ' -> '.join(
str(node) for node in [outer_map_entry, inner_map_entry])
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
outer_map_entry = graph.nodes()[self.subgraph[
InLocalStorage._outer_map_entry]]
inner_map_entry = graph.nodes()[self.subgraph[
InLocalStorage._inner_map_entry]]
array = self.array
if array is None:
array = graph.edges_between(outer_map_entry,
inner_map_entry)[0].data.data
original_edge = None
invariant_memlet = None
for edge in graph.in_edges(inner_map_entry):
src = edge.src
if src != outer_map_entry:
continue
memlet = edge.data
if array == memlet.data:
original_edge = edge
invariant_memlet = memlet
break
if invariant_memlet is None:
for edge in graph.in_edges(inner_map_entry):
src = edge.src
if src != outer_map_entry:
continue
original_edge = edge
invariant_memlet = edge.data
print('WARNING: Array %s not found! Using array %s instead.' %
(array, invariant_memlet.data))
array = invariant_memlet.data
break
if invariant_memlet is None:
raise KeyError('Array %s not found!' % array)
new_data = sdfg.add_array('trans_' + invariant_memlet.data, [
symbolic.overapproximate(r)
for r in invariant_memlet.bounding_box_size()
],
sdfg.arrays[invariant_memlet.data].dtype,
transient=True)
data_node = nodes.AccessNode('trans_' + invariant_memlet.data)
to_data_mm = copy.deepcopy(invariant_memlet)
from_data_mm = copy.deepcopy(invariant_memlet)
from_data_mm.data = data_node.data
offset = []
for ind, r in enumerate(invariant_memlet.subset):
offset.append(r[0])
if isinstance(invariant_memlet.subset[ind], tuple):
begin = invariant_memlet.subset[ind][0] - r[0]
end = invariant_memlet.subset[ind][1] - r[0]
step = invariant_memlet.subset[ind][2]
from_data_mm.subset[ind] = (begin, end, step)
else:
from_data_mm.subset[ind] -= r[0]
to_data_mm.other_subset = copy.deepcopy(from_data_mm.subset)
# Reconnect, assuming one edge to the stream
graph.remove_edge(original_edge)
graph.add_edge(outer_map_entry, original_edge.src_conn, data_node,
None, to_data_mm)
graph.add_edge(data_node, None, inner_map_entry,
original_edge.dst_conn, from_data_mm)
for _parent, _, _child, _, memlet in graph.bfs_edges(inner_map_entry,
reverse=False):
if memlet.data != array:
continue
for ind, r in enumerate(memlet.subset):
if isinstance(memlet.subset[ind], tuple):
begin = r[0] - offset[ind]
end = r[1] - offset[ind]
step = r[2]
memlet.subset[ind] = (begin, end, step)
else:
memlet.subset[ind] -= offset[ind]
memlet.data = 'trans_' + invariant_memlet.data
return
def __stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exits = graph.exit_nodes(map_entry)
# Retrieve transformation properties.
dim_idx = self.dim_idx
new_dim_prefix = self.new_dim_prefix
tile_size = self.tile_size
divides_evenly = self.divides_evenly
strided = self.strided
# Retrieve parameter and range of dimension to be strip-mined.
target_dim = map_entry.map.params[dim_idx]
td_from, td_to, td_step = map_entry.map.range[dim_idx]
# Create new map. Replace by cloning???
new_dim = new_dim_prefix + '_' + target_dim
nd_from = 0
nd_to = symbolic.pystr_to_symbolic(
'int_ceil(%s + 1 - %s, %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_size))
nd_step = 1
new_dim_range = (nd_from, nd_to, nd_step)
new_map = nodes.Map(new_dim + '_' + map_entry.map.label, [new_dim],
subsets.Range([new_dim_range]))
new_map_entry = nodes.MapEntry(new_map)
# Change the range of the selected dimension to iterate over a single
# tile
if strided:
td_from_new = symbolic.pystr_to_symbolic(new_dim)
td_to_new_approx = td_to
td_step = symbolic.pystr_to_symbolic(tile_size)
else:
td_from_new = symbolic.pystr_to_symbolic(
'%s + %s * %s' %
(symbolic.symstr(td_from), str(new_dim), tile_size))
td_to_new_exact = symbolic.pystr_to_symbolic(
'min(%s + 1, %s + %s * %s + %s) - 1' %
(symbolic.symstr(td_to), symbolic.symstr(td_from), tile_size,
str(new_dim), tile_size))
td_to_new_approx = symbolic.pystr_to_symbolic(
'%s + %s * %s + %s - 1' %
(symbolic.symstr(td_from), tile_size, str(new_dim), tile_size))
if divides_evenly or strided:
td_to_new = td_to_new_approx
else:
td_to_new = dace.symbolic.SymExpr(td_to_new_exact,
td_to_new_approx)
map_entry.map.range[dim_idx] = (td_from_new, td_to_new, td_step)
# Make internal map's schedule to "not parallel"
map_entry.map._schedule = dtypes.ScheduleType.Default
# Redirect/create edges.
new_in_edges = {}
for _src, conn, _dest, _, memlet in graph.out_edges(map_entry):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
if memlet.data in new_in_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_in_edges[memlet.data]
new_memlet.subset = calc_set_union(new_memlet.subset,
new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data:
(src, src_conn, dest, dest_conn, new_memlet,
min(num, int(conn[4:])))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_in_edges.update({
memlet.data:
(new_map_entry, None, map_entry, None, new_memlet,
int(conn[4:]))
})
nxutil.change_edge_dest(graph, map_entry, new_map_entry)
new_out_edges = {}
new_exits = []
for map_exit in map_exits:
if isinstance(map_exit, nodes.MapExit):
new_exit = nodes.MapExit(new_map)
new_exits.append(new_exit)
for _src, conn, _dest, _, memlet in graph.in_edges(map_exit):
if not isinstance(sdfg.arrays[memlet.data], dace.data.Scalar):
new_subset = calc_set_image(
map_entry.map.params,
map_entry.map.range,
memlet.subset,
)
if memlet.data in new_out_edges:
src, src_conn, dest, dest_conn, new_memlet, num = \
new_out_edges[memlet.data]
new_memlet.subset = calc_set_union(
new_memlet.subset, new_subset)
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data:
(src, src_conn, dest, dest_conn, new_memlet,
min(num, conn[4:]))
})
else:
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
new_memlet.num_accesses = new_memlet.num_elements()
new_out_edges.update({
memlet.data: (map_exit, None, new_exit, None,
new_memlet, conn[4:])
})
nxutil.change_edge_src(graph, map_exit, new_exit)
in_conn_nums = []
for _, e in new_in_edges.items():
_, _, _, _, _, num = e
in_conn_nums.append(num)
in_conn = {}
for i, num in enumerate(in_conn_nums):
in_conn.update({num: i + 1})
entry_in_connectors = set()
entry_out_connectors = set()
for i in range(len(in_conn_nums)):
entry_in_connectors.add('IN_' + str(i + 1))
entry_out_connectors.add('OUT_' + str(i + 1))
new_map_entry.in_connectors = entry_in_connectors
new_map_entry.out_connectors = entry_out_connectors
for _, e in new_in_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(in_conn[num]), dst,
'IN_' + str(in_conn[num]), memlet)
for new_exit in new_exits:
out_conn_nums = []
for _, e in new_out_edges.items():
_, _, dst, _, _, num = e
if dst is not new_exit:
continue
out_conn_nums.append(num)
out_conn = {}
for i, num in enumerate(out_conn_nums):
out_conn.update({num: i + 1})
exit_in_connectors = set()
exit_out_connectors = set()
for i in range(len(out_conn_nums)):
exit_in_connectors.add('IN_' + str(i + 1))
exit_out_connectors.add('OUT_' + str(i + 1))
new_exit.in_connectors = exit_in_connectors
new_exit.out_connectors = exit_out_connectors
for _, e in new_out_edges.items():
src, _, dst, _, memlet, num = e
graph.add_edge(src, 'OUT_' + str(out_conn[num]), dst,
'IN_' + str(out_conn[num]), memlet)
# Return strip-mined dimension.
return target_dim, new_dim, new_map