def apply(self, sdfg, *args, **kwargs):
state = sdfg.nodes()[self.state_id]
node = state.nodes()[self.subgraph[type(self)._match_node]]
expansion = type(self).expansion(node, state, sdfg, *args, **kwargs)
if isinstance(expansion, dace.SDFG):
# Modify internal schedules according to node schedule
if node.schedule != ScheduleType.Default:
for nstate in expansion.nodes():
topnodes = nstate.scope_dict(node_to_children=True)[None]
for topnode in topnodes:
if isinstance(topnode, (nd.EntryNode, nd.LibraryNode)):
topnode.schedule = node.schedule
expansion = state.add_nested_sdfg(expansion,
sdfg,
node.in_connectors,
node.out_connectors,
name=node.name)
elif isinstance(expansion, dace.sdfg.nodes.CodeNode):
pass
else:
raise TypeError("Node expansion must be a CodeNode or an SDFG")
expansion.environments = copy.copy(
set(map(lambda a: a.__name__,
type(self).environments)))
sdutil.change_edge_dest(state, node, expansion)
sdutil.change_edge_src(state, node, expansion)
state.remove_node(node)
type(self).postprocessing(sdfg, state, expansion)
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_exit = graph.exit_node(inner_map_entry)
outer_map_exit = graph.exit_node(outer_map_entry)
# 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.
sdutil.change_edge_dest(graph, outer_map_entry, inner_map_entry)
sdutil.change_edge_src(graph, inner_map_entry, outer_map_entry)
sdutil.change_edge_dest(graph, inner_map_exit, outer_map_exit)
sdutil.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)
def apply(self, sdfg: SDFG):
# Extract the parameters and ranges of the inner/outer maps.
graph: SDFGState = 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_exit = graph.exit_node(inner_map_entry)
outer_map_exit = graph.exit_node(outer_map_entry)
# 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.
sdutil.change_edge_dest(graph, outer_map_entry, inner_map_entry)
sdutil.change_edge_src(graph, inner_map_entry, outer_map_entry)
sdutil.change_edge_dest(graph, inner_map_exit, outer_map_exit)
sdutil.change_edge_src(graph, outer_map_exit, inner_map_exit)
# Add edges between the map entries and exits.
new_entry_edges = []
new_exit_edges = []
for e in entry_edges:
new_entry_edges.append(
graph.add_edge(e.dst, e.src_conn, e.src, e.dst_conn, e.data))
for e in exit_edges:
new_exit_edges.append(
graph.add_edge(e.dst, e.src_conn, e.src, e.dst_conn, e.data))
# Repropagate memlets in modified region
for e in new_entry_edges:
path = graph.memlet_path(e)
index = next(i for i, edge in enumerate(path) if e is edge)
e.data.subset = propagate_memlet(graph, path[index + 1].data,
outer_map_entry, True).subset
for e in new_exit_edges:
path = graph.memlet_path(e)
index = next(i for i, edge in enumerate(path) if e is edge)
e.data.subset = propagate_memlet(graph, path[index - 1].data,
outer_map_exit, True).subset
def apply(self, sdfg, *args, **kwargs):
state = sdfg.nodes()[self.state_id]
node = state.nodes()[self.subgraph[type(self)._match_node]]
expansion = type(self).expansion(node, state, sdfg, *args, **kwargs)
if isinstance(expansion, SDFG):
expansion = state.add_nested_sdfg(expansion,
sdfg,
node.in_connectors,
node.out_connectors,
name=node.name,
schedule=node.schedule,
debuginfo=node.debuginfo,
location=node.location)
elif isinstance(expansion, nd.CodeNode):
expansion.debuginfo = node.debuginfo
if isinstance(expansion, nd.NestedSDFG):
# Fix parent references
nsdfg = expansion.sdfg
nsdfg.parent = state
nsdfg.parent_sdfg = sdfg
nsdfg.update_sdfg_list([])
nsdfg.parent_nsdfg_node = expansion
# Update schedule to match library node schedule
nsdfg.schedule = node.schedule
elif isinstance(expansion, (nd.EntryNode, nd.LibraryNode)):
if expansion.schedule is ScheduleType.Default:
expansion.schedule = node.schedule
else:
raise TypeError("Node expansion must be a CodeNode or an SDFG")
# Fix nested schedules
if isinstance(expansion, nd.NestedSDFG):
infer_types._set_default_schedule_types(expansion.sdfg,
expansion.schedule, True)
expansion.environments = copy.copy(
set(map(lambda a: a.full_class_path(),
type(self).environments)))
sdutil.change_edge_dest(state, node, expansion)
sdutil.change_edge_src(state, node, expansion)
state.remove_node(node)
type(self).postprocessing(sdfg, state, expansion)
def fix_sdfg(sdfg, graph):
# fix sdfg as for now the SDFG gets parsed wrongly
for node in graph.nodes():
if isinstance(node, dace.sdfg.nodes.NestedSDFG):
nested_original = node
for edge in itertools.chain(graph.in_edges(node),
graph.out_edges(node)):
for e in graph.memlet_tree(edge):
if 'z' in e.data.subset.free_symbols:
new_subset = str(e.data.subset)
new_subset = new_subset.replace('z', '0:O')
e.data.subset = subsets.Range.from_string(new_subset)
# next up replace sdfg
inner_sdfg = helper_sdfg.to_sdfg()
nnode = graph.add_nested_sdfg(inner_sdfg, sdfg, {'AA', 'BB', 'CC'}, {'CC'})
# redirect edges
connectors = []
for e in graph.in_edges(nested_original):
connectors.append(e.dst_conn)
connectors.sort()
for e in graph.in_edges(nested_original):
if e.dst_conn == connectors[0]:
graph.add_edge(e.src, e.src_conn, e.dst, 'AA', e.data)
graph.remove_edge(e)
if e.dst_conn == connectors[1]:
graph.add_edge(e.src, e.src_conn, e.dst, 'BB', e.data)
graph.remove_edge(e)
if e.dst_conn == connectors[2]:
graph.add_edge(e.src, e.src_conn, e.dst, 'CC', e.data)
graph.remove_edge(e)
e = graph.out_edges(nested_original)[0]
graph.add_edge(e.src, 'CC', e.dst, e.dst_conn, e.data)
graph.remove_edge(e)
utils.change_edge_dest(graph, nested_original, nnode)
utils.change_edge_src(graph, nested_original, nnode)
graph.remove_node(nested_original)
sdfg.validate()
def apply(self, sdfg):
state = sdfg.nodes()[self.subgraph[FPGATransformState._state]]
# Find source/sink (data) nodes
input_nodes = sdutil.find_source_nodes(state)
output_nodes = sdutil.find_sink_nodes(state)
fpga_data = {}
# Input nodes may also be nodes with WCR memlets
# We have to recur across nested SDFGs to find them
wcr_input_nodes = set()
stack = []
parent_sdfg = {state: sdfg} # Map states to their parent SDFG
for node, graph in state.all_nodes_recursive():
if isinstance(graph, dace.SDFG):
parent_sdfg[node] = graph
if isinstance(node, dace.sdfg.nodes.AccessNode):
for e in graph.all_edges(node):
if e.data.wcr is not None:
trace = dace.sdfg.trace_nested_access(
node, graph, parent_sdfg[graph])
for node_trace, state_trace, sdfg_trace in trace:
# Find the name of the accessed node in our scope
if state_trace == state and sdfg_trace == sdfg:
outer_node = node_trace
break
else:
# This does not trace back to the current state, so
# we don't care
continue
input_nodes.append(outer_node)
wcr_input_nodes.add(outer_node)
if input_nodes:
# create pre_state
pre_state = sd.SDFGState('pre_' + state.label, sdfg)
for node in input_nodes:
if not isinstance(node, dace.sdfg.nodes.AccessNode):
continue
desc = node.desc(sdfg)
if not isinstance(desc, dace.data.Array):
# TODO: handle streams
continue
if node.data in fpga_data:
fpga_array = fpga_data[node.data]
elif node not in wcr_input_nodes:
fpga_array = sdfg.add_array(
'fpga_' + node.data,
desc.shape,
desc.dtype,
materialize_func=desc.materialize_func,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=desc.allow_conflicts,
strides=desc.strides,
offset=desc.offset)
fpga_data[node.data] = fpga_array
pre_node = pre_state.add_read(node.data)
pre_fpga_node = pre_state.add_write('fpga_' + node.data)
full_range = subsets.Range([(0, s - 1, 1) for s in desc.shape])
mem = memlet.Memlet(node.data, full_range.num_elements(),
full_range, 1)
pre_state.add_edge(pre_node, None, pre_fpga_node, None, mem)
if node not in wcr_input_nodes:
fpga_node = state.add_read('fpga_' + node.data)
sdutil.change_edge_src(state, node, fpga_node)
state.remove_node(node)
sdfg.add_node(pre_state)
sdutil.change_edge_dest(sdfg, state, pre_state)
sdfg.add_edge(pre_state, state, sd.InterstateEdge())
if output_nodes:
post_state = sd.SDFGState('post_' + state.label, sdfg)
for node in output_nodes:
if not isinstance(node, dace.sdfg.nodes.AccessNode):
continue
desc = node.desc(sdfg)
if not isinstance(desc, dace.data.Array):
# TODO: handle streams
continue
if node.data in fpga_data:
fpga_array = fpga_data[node.data]
else:
fpga_array = sdfg.add_array(
'fpga_' + node.data,
desc.shape,
desc.dtype,
materialize_func=desc.materialize_func,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=desc.allow_conflicts,
strides=desc.strides,
offset=desc.offset)
fpga_data[node.data] = fpga_array
# fpga_node = type(node)(fpga_array)
post_node = post_state.add_write(node.data)
post_fpga_node = post_state.add_read('fpga_' + node.data)
full_range = subsets.Range([(0, s - 1, 1) for s in desc.shape])
mem = memlet.Memlet('fpga_' + node.data,
full_range.num_elements(), full_range, 1)
post_state.add_edge(post_fpga_node, None, post_node, None, mem)
fpga_node = state.add_write('fpga_' + node.data)
sdutil.change_edge_dest(state, node, fpga_node)
state.remove_node(node)
sdfg.add_node(post_state)
sdutil.change_edge_src(sdfg, state, post_state)
sdfg.add_edge(state, post_state, sd.InterstateEdge())
veclen_ = 1
# propagate vector info from a nested sdfg
for src, src_conn, dst, dst_conn, mem in state.edges():
# need to go inside the nested SDFG and grab the vector length
if isinstance(dst, dace.sdfg.nodes.NestedSDFG):
# this edge is going to the nested SDFG
for inner_state in dst.sdfg.states():
for n in inner_state.nodes():
if isinstance(n, dace.sdfg.nodes.AccessNode
) and n.data == dst_conn:
# assuming all memlets have the same vector length
veclen_ = inner_state.all_edges(n)[0].data.veclen
if isinstance(src, dace.sdfg.nodes.NestedSDFG):
# this edge is coming from the nested SDFG
for inner_state in src.sdfg.states():
for n in inner_state.nodes():
if isinstance(n, dace.sdfg.nodes.AccessNode
) and n.data == src_conn:
# assuming all memlets have the same vector length
veclen_ = inner_state.all_edges(n)[0].data.veclen
if mem.data is not None and mem.data in fpga_data:
mem.data = 'fpga_' + mem.data
mem.veclen = veclen_
fpga_update(sdfg, state, 0)
def apply(self, sdfg: sd.SDFG):
#######################################################
# Step 0: SDFG metadata
# Find all input and output data descriptors
input_nodes = []
output_nodes = []
global_code_nodes = [[] for _ in sdfg.nodes()]
for i, state in enumerate(sdfg.nodes()):
sdict = state.scope_dict()
for node in state.nodes():
if (isinstance(node, nodes.AccessNode)
and node.desc(sdfg).transient == False):
if (state.out_degree(node) > 0
and node.data not in input_nodes):
# Special case: nodes that lead to top-level dynamic
# map ranges must stay on host
for e in state.out_edges(node):
last_edge = state.memlet_path(e)[-1]
if (isinstance(last_edge.dst, nodes.EntryNode)
and last_edge.dst_conn and
not last_edge.dst_conn.startswith('IN_')
and sdict[last_edge.dst] is None):
break
else:
input_nodes.append((node.data, node.desc(sdfg)))
if (state.in_degree(node) > 0
and node.data not in output_nodes):
output_nodes.append((node.data, node.desc(sdfg)))
elif isinstance(node, nodes.CodeNode) and sdict[node] is None:
if not isinstance(node,
(nodes.LibraryNode, nodes.NestedSDFG)):
global_code_nodes[i].append(node)
# Input nodes may also be nodes with WCR memlets and no identity
for e in state.edges():
if e.data.wcr is not None:
if (e.data.data not in input_nodes
and sdfg.arrays[e.data.data].transient == False):
input_nodes.append(
(e.data.data, sdfg.arrays[e.data.data]))
start_state = sdfg.start_state
end_states = sdfg.sink_nodes()
#######################################################
# Step 1: Create cloned GPU arrays and replace originals
cloned_arrays = {}
for inodename, inode in set(input_nodes):
if isinstance(inode, data.Scalar): # Scalars can remain on host
continue
if inode.storage == dtypes.StorageType.GPU_Global:
continue
newdesc = inode.clone()
newdesc.storage = dtypes.StorageType.GPU_Global
newdesc.transient = True
name = sdfg.add_datadesc('gpu_' + inodename,
newdesc,
find_new_name=True)
cloned_arrays[inodename] = name
for onodename, onode in set(output_nodes):
if onodename in cloned_arrays:
continue
if onode.storage == dtypes.StorageType.GPU_Global:
continue
newdesc = onode.clone()
newdesc.storage = dtypes.StorageType.GPU_Global
newdesc.transient = True
name = sdfg.add_datadesc('gpu_' + onodename,
newdesc,
find_new_name=True)
cloned_arrays[onodename] = name
# Replace nodes
for state in sdfg.nodes():
for node in state.nodes():
if (isinstance(node, nodes.AccessNode)
and node.data in cloned_arrays):
node.data = cloned_arrays[node.data]
# Replace memlets
for state in sdfg.nodes():
for edge in state.edges():
if edge.data.data in cloned_arrays:
edge.data.data = cloned_arrays[edge.data.data]
#######################################################
# Step 2: Create copy-in state
excluded_copyin = self.exclude_copyin.split(',')
copyin_state = sdfg.add_state(sdfg.label + '_copyin')
sdfg.add_edge(copyin_state, start_state, sd.InterstateEdge())
for nname, desc in dtypes.deduplicate(input_nodes):
if nname in excluded_copyin or nname not in cloned_arrays:
continue
src_array = nodes.AccessNode(nname, debuginfo=desc.debuginfo)
dst_array = nodes.AccessNode(cloned_arrays[nname],
debuginfo=desc.debuginfo)
copyin_state.add_node(src_array)
copyin_state.add_node(dst_array)
copyin_state.add_nedge(
src_array, dst_array,
memlet.Memlet.from_array(src_array.data, src_array.desc(sdfg)))
#######################################################
# Step 3: Create copy-out state
excluded_copyout = self.exclude_copyout.split(',')
copyout_state = sdfg.add_state(sdfg.label + '_copyout')
for state in end_states:
sdfg.add_edge(state, copyout_state, sd.InterstateEdge())
for nname, desc in dtypes.deduplicate(output_nodes):
if nname in excluded_copyout or nname not in cloned_arrays:
continue
src_array = nodes.AccessNode(cloned_arrays[nname],
debuginfo=desc.debuginfo)
dst_array = nodes.AccessNode(nname, debuginfo=desc.debuginfo)
copyout_state.add_node(src_array)
copyout_state.add_node(dst_array)
copyout_state.add_nedge(
src_array, dst_array,
memlet.Memlet.from_array(dst_array.data, dst_array.desc(sdfg)))
#######################################################
# Step 4: Modify transient data storage
for state in sdfg.nodes():
sdict = state.scope_dict()
for node in state.nodes():
if isinstance(node,
nodes.AccessNode) and node.desc(sdfg).transient:
nodedesc = node.desc(sdfg)
# Special case: nodes that lead to dynamic map ranges must
# stay on host
if any(
isinstance(
state.memlet_path(e)[-1].dst, nodes.EntryNode)
for e in state.out_edges(node)):
continue
gpu_storage = [
dtypes.StorageType.GPU_Global,
dtypes.StorageType.GPU_Shared,
dtypes.StorageType.CPU_Pinned
]
if sdict[
node] is None and nodedesc.storage not in gpu_storage:
# NOTE: the cloned arrays match too but it's the same
# storage so we don't care
nodedesc.storage = dtypes.StorageType.GPU_Global
# Try to move allocation/deallocation out of loops
if (self.toplevel_trans
and not isinstance(nodedesc, data.Stream)):
nodedesc.lifetime = dtypes.AllocationLifetime.SDFG
elif nodedesc.storage not in gpu_storage:
# Make internal transients registers
if self.register_trans:
nodedesc.storage = dtypes.StorageType.Register
#######################################################
# Step 5: Wrap free tasklets and nested SDFGs with a GPU map
for state, gcodes in zip(sdfg.nodes(), global_code_nodes):
for gcode in gcodes:
if gcode.label in self.exclude_tasklets.split(','):
continue
# Create map and connectors
me, mx = state.add_map(gcode.label + '_gmap',
{gcode.label + '__gmapi': '0:1'},
schedule=dtypes.ScheduleType.GPU_Device)
# Store in/out edges in lists so that they don't get corrupted
# when they are removed from the graph
in_edges = list(state.in_edges(gcode))
out_edges = list(state.out_edges(gcode))
me.in_connectors = {('IN_' + e.dst_conn): None
for e in in_edges}
me.out_connectors = {('OUT_' + e.dst_conn): None
for e in in_edges}
mx.in_connectors = {('IN_' + e.src_conn): None
for e in out_edges}
mx.out_connectors = {('OUT_' + e.src_conn): None
for e in out_edges}
# Create memlets through map
for e in in_edges:
state.remove_edge(e)
state.add_edge(e.src, e.src_conn, me, 'IN_' + e.dst_conn,
e.data)
state.add_edge(me, 'OUT_' + e.dst_conn, e.dst, e.dst_conn,
e.data)
for e in out_edges:
state.remove_edge(e)
state.add_edge(e.src, e.src_conn, mx, 'IN_' + e.src_conn,
e.data)
state.add_edge(mx, 'OUT_' + e.src_conn, e.dst, e.dst_conn,
e.data)
# Map without inputs
if len(in_edges) == 0:
state.add_nedge(me, gcode, memlet.Memlet())
#######################################################
# Step 6: Change all top-level maps and library nodes to GPU schedule
for i, state in enumerate(sdfg.nodes()):
sdict = state.scope_dict()
for node in state.nodes():
if isinstance(node, (nodes.EntryNode, nodes.LibraryNode)):
if sdict[node] is None:
node.schedule = dtypes.ScheduleType.GPU_Device
elif (isinstance(node,
(nodes.EntryNode, nodes.LibraryNode))
and self.sequential_innermaps):
node.schedule = dtypes.ScheduleType.Sequential
#######################################################
# Step 7: Introduce copy-out if data used in outgoing interstate edges
for state in list(sdfg.nodes()):
arrays_used = set()
for e in sdfg.out_edges(state):
# Used arrays = intersection between symbols and cloned arrays
arrays_used.update(
set(e.data.free_symbols)
& set(cloned_arrays.keys()))
# Create a state and copy out used arrays
if len(arrays_used) > 0:
co_state = sdfg.add_state(state.label + '_icopyout')
# Reconnect outgoing edges to after interim copyout state
for e in sdfg.out_edges(state):
sdutil.change_edge_src(sdfg, state, co_state)
# Add unconditional edge to interim state
sdfg.add_edge(state, co_state, sd.InterstateEdge())
# Add copy-out nodes
for nname in arrays_used:
desc = sdfg.arrays[nname]
src_array = nodes.AccessNode(cloned_arrays[nname],
debuginfo=desc.debuginfo)
dst_array = nodes.AccessNode(nname,
debuginfo=desc.debuginfo)
co_state.add_node(src_array)
co_state.add_node(dst_array)
co_state.add_nedge(
src_array, dst_array,
memlet.Memlet.from_array(dst_array.data,
dst_array.desc(sdfg)))
#######################################################
# Step 8: Strict transformations
if not self.strict_transform:
return
# Apply strict state fusions greedily.
sdfg.apply_strict_transformations()
def _stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exit = graph.exit_node(map_entry)
# Retrieve transformation properties.
dim_idx = self.dim_idx
target_dim = map_entry.map.params[dim_idx]
if self.tiling_type == 'ceilrange':
new_dim, new_map, td_rng = self._create_ceil_range(
sdfg, graph, map_entry)
elif self.tiling_type == 'number_of_tiles':
new_dim, new_map, td_rng = self._create_from_tile_numbers(
sdfg, graph, map_entry)
else:
new_dim, new_map, td_rng = self._create_strided_range(
sdfg, graph, map_entry)
new_map_entry = nodes.MapEntry(new_map)
new_map_exit = nodes.MapExit(new_map)
td_to_new_approx = td_rng[1]
if isinstance(td_to_new_approx, dace.symbolic.SymExpr):
td_to_new_approx = td_to_new_approx.approx
# Special case: If range is 1 and no prefix was specified, skip range
if td_rng[0] == td_to_new_approx and target_dim == new_dim:
map_entry.map.range = subsets.Range(
[r for i, r in enumerate(map_entry.map.range) if i != dim_idx])
map_entry.map.params = [
p for i, p in enumerate(map_entry.map.params) if i != dim_idx
]
if len(map_entry.map.params) == 0:
raise ValueError('Strip-mining all dimensions of the map with '
'empty tiles is disallowed')
else:
map_entry.map.range[dim_idx] = td_rng
# 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)
sdutil.change_edge_dest(graph, map_entry, new_map_entry)
new_map_exit.out_connectors = dcpy(map_exit.out_connectors)
sdutil.change_edge_src(graph, map_exit, new_map_exit)
# Create new entry edges
new_in_edges = dict()
entry_in_conn = {}
entry_out_conn = {}
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['IN_' + conn] = None
entry_out_conn['OUT_' + conn] = None
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
if memlet.dynamic:
new_memlet.num_accesses = memlet.num_accesses
else:
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[in_conn] = None
if out_conn:
entry_out_conn[out_conn] = None
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 = {}
exit_out_conn = {}
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['IN_' + conn] = None
exit_out_conn['OUT_' + conn] = None
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
if memlet.dynamic:
new_memlet.num_accesses = memlet.num_accesses
else:
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 = dst_conn
out_conn = dst_conn
if in_conn:
exit_in_conn[in_conn] = None
if out_conn:
exit_out_conn[out_conn] = None
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)
# Skew if necessary
if self.skew:
xfh.offset_map(sdfg, graph, map_entry, dim_idx, td_rng[0])
# Return strip-mined dimension.
return target_dim, new_dim, new_map
def _expand_reduce(self, sdfg, state, node):
# expands a reduce into two nested maps
# taken from legacy expand_reduce.py
node.validate(sdfg, state)
inedge: graph.MultiConnectorEdge = state.in_edges(node)[0]
outedge: graph.MultiConnectorEdge = state.out_edges(node)[0]
input_dims = len(inedge.data.subset)
output_dims = len(outedge.data.subset)
input_data = sdfg.arrays[inedge.data.data]
output_data = sdfg.arrays[outedge.data.data]
# Standardize axes
axes = node.axes if node.axes else [i for i in range(input_dims)]
# Create nested SDFG
nsdfg = SDFG('reduce')
nsdfg.add_array('_in',
inedge.data.subset.size(),
input_data.dtype,
strides=input_data.strides,
storage=input_data.storage)
nsdfg.add_array('_out',
outedge.data.subset.size(),
output_data.dtype,
strides=output_data.strides,
storage=output_data.storage)
if node.identity is not None:
raise ValueError("Node identity has to be None at this point.")
else:
nstate = nsdfg.add_state()
# END OF INIT
# (If axes != all) Add outer map, which corresponds to the output range
if len(axes) != input_dims:
# Interleave input and output axes to match input memlet
ictr, octr = 0, 0
input_subset = []
for i in range(input_dims):
if i in axes:
input_subset.append('_i%d' % ictr)
ictr += 1
else:
input_subset.append('_o%d' % octr)
octr += 1
output_size = outedge.data.subset.size()
ome, omx = nstate.add_map(
'reduce_output', {
'_o%d' % i: '0:%s' % symstr(sz)
for i, sz in enumerate(outedge.data.subset.size())
})
outm = Memlet.simple('_out',
','.join(
['_o%d' % i for i in range(output_dims)]),
wcr_str=node.wcr)
inmm = Memlet.simple('_in', ','.join(input_subset))
else:
ome, omx = None, None
outm = Memlet.simple('_out', '0', wcr_str=node.wcr)
inmm = Memlet.simple(
'_in', ','.join(['_i%d' % i for i in range(len(axes))]))
# Add inner map, which corresponds to the range to reduce, containing
# an identity tasklet
ime, imx = nstate.add_map(
'reduce_values', {
'_i%d' % i: '0:%s' % symstr(inedge.data.subset.size()[axis])
for i, axis in enumerate(sorted(axes))
})
# Add identity tasklet for reduction
t = nstate.add_tasklet('identity', {'inp'}, {'out'}, 'out = inp')
# Connect everything
r = nstate.add_read('_in')
w = nstate.add_read('_out')
if ome:
nstate.add_memlet_path(r, ome, ime, t, dst_conn='inp', memlet=inmm)
nstate.add_memlet_path(t, imx, omx, w, src_conn='out', memlet=outm)
else:
nstate.add_memlet_path(r, ime, t, dst_conn='inp', memlet=inmm)
nstate.add_memlet_path(t, imx, w, src_conn='out', memlet=outm)
# Rename outer connectors and add to node
inedge._dst_conn = '_in'
outedge._src_conn = '_out'
node.add_in_connector('_in')
node.add_out_connector('_out')
nsdfg = state.add_nested_sdfg(nsdfg,
sdfg,
node.in_connectors,
node.out_connectors,
schedule=node.schedule,
name=node.name)
utils.change_edge_dest(state, node, nsdfg)
utils.change_edge_src(state, node, nsdfg)
state.remove_node(node)
return nsdfg
def apply(self, _, sdfg):
state = self.state
# Find source/sink (data) nodes that are relevant outside this FPGA
# kernel
shared_transients = set(sdfg.shared_transients())
input_nodes = [
n for n in sdutil.find_source_nodes(state)
if isinstance(n, nodes.AccessNode) and
(not sdfg.arrays[n.data].transient or n.data in shared_transients)
]
output_nodes = [
n for n in sdutil.find_sink_nodes(state)
if isinstance(n, nodes.AccessNode) and
(not sdfg.arrays[n.data].transient or n.data in shared_transients)
]
fpga_data = {}
# Input nodes may also be nodes with WCR memlets
# We have to recur across nested SDFGs to find them
wcr_input_nodes = set()
stack = []
parent_sdfg = {state: sdfg} # Map states to their parent SDFG
for node, graph in state.all_nodes_recursive():
if isinstance(graph, dace.SDFG):
parent_sdfg[node] = graph
if isinstance(node, dace.sdfg.nodes.AccessNode):
for e in graph.in_edges(node):
if e.data.wcr is not None:
trace = dace.sdfg.trace_nested_access(
node, graph, parent_sdfg[graph])
for node_trace, memlet_trace, state_trace, sdfg_trace in trace:
# Find the name of the accessed node in our scope
if state_trace == state and sdfg_trace == sdfg:
_, outer_node = node_trace
if outer_node is not None:
break
else:
# This does not trace back to the current state, so
# we don't care
continue
input_nodes.append(outer_node)
wcr_input_nodes.add(outer_node)
if input_nodes:
# create pre_state
pre_state = sd.SDFGState('pre_' + state.label, sdfg)
for node in input_nodes:
if not isinstance(node, dace.sdfg.nodes.AccessNode):
continue
desc = node.desc(sdfg)
if not isinstance(desc, dace.data.Array):
# TODO: handle streams
continue
if node.data in fpga_data:
fpga_array = fpga_data[node.data]
elif node not in wcr_input_nodes:
fpga_array = sdfg.add_array(
'fpga_' + node.data,
desc.shape,
desc.dtype,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=desc.allow_conflicts,
strides=desc.strides,
offset=desc.offset)
fpga_array[1].location = copy.copy(desc.location)
desc.location.clear()
fpga_data[node.data] = fpga_array
pre_node = pre_state.add_read(node.data)
pre_fpga_node = pre_state.add_write('fpga_' + node.data)
mem = memlet.Memlet(data=node.data,
subset=subsets.Range.from_array(desc))
pre_state.add_edge(pre_node, None, pre_fpga_node, None, mem)
if node not in wcr_input_nodes:
fpga_node = state.add_read('fpga_' + node.data)
sdutil.change_edge_src(state, node, fpga_node)
state.remove_node(node)
sdfg.add_node(pre_state)
sdutil.change_edge_dest(sdfg, state, pre_state)
sdfg.add_edge(pre_state, state, sd.InterstateEdge())
if output_nodes:
post_state = sd.SDFGState('post_' + state.label, sdfg)
for node in output_nodes:
if not isinstance(node, dace.sdfg.nodes.AccessNode):
continue
desc = node.desc(sdfg)
if not isinstance(desc, dace.data.Array):
# TODO: handle streams
continue
if node.data in fpga_data:
fpga_array = fpga_data[node.data]
else:
fpga_array = sdfg.add_array(
'fpga_' + node.data,
desc.shape,
desc.dtype,
transient=True,
storage=dtypes.StorageType.FPGA_Global,
allow_conflicts=desc.allow_conflicts,
strides=desc.strides,
offset=desc.offset)
fpga_array[1].location = copy.copy(desc.location)
desc.location.clear()
fpga_data[node.data] = fpga_array
# fpga_node = type(node)(fpga_array)
post_node = post_state.add_write(node.data)
post_fpga_node = post_state.add_read('fpga_' + node.data)
mem = memlet.Memlet(f"fpga_{node.data}", None,
subsets.Range.from_array(desc))
post_state.add_edge(post_fpga_node, None, post_node, None, mem)
fpga_node = state.add_write('fpga_' + node.data)
sdutil.change_edge_dest(state, node, fpga_node)
state.remove_node(node)
sdfg.add_node(post_state)
sdutil.change_edge_src(sdfg, state, post_state)
sdfg.add_edge(state, post_state, sd.InterstateEdge())
# propagate memlet info from a nested sdfg
for src, src_conn, dst, dst_conn, mem in state.edges():
if mem.data is not None and mem.data in fpga_data:
mem.data = 'fpga_' + mem.data
fpga_update(sdfg, state, 0)
def apply(self, sdfg):
first_state = sdfg.nodes()[self.subgraph[StateFusion._first_state]]
second_state = sdfg.nodes()[self.subgraph[StateFusion._second_state]]
# Remove interstate edge(s)
edges = sdfg.edges_between(first_state, second_state)
for edge in edges:
if edge.data.assignments:
for src, dst, other_data in sdfg.in_edges(first_state):
other_data.assignments.update(edge.data.assignments)
sdfg.remove_edge(edge)
# Special case 1: first state is empty
if first_state.is_empty():
sdutil.change_edge_dest(sdfg, first_state, second_state)
sdfg.remove_node(first_state)
return
# Special case 2: second state is empty
if second_state.is_empty():
sdutil.change_edge_src(sdfg, second_state, first_state)
sdutil.change_edge_dest(sdfg, second_state, first_state)
sdfg.remove_node(second_state)
return
# Normal case: both states are not empty
# Find source/sink (data) nodes
first_input = [
node for node in sdutil.find_source_nodes(first_state)
if isinstance(node, nodes.AccessNode)
]
first_output = [
node for node in sdutil.find_sink_nodes(first_state)
if isinstance(node, nodes.AccessNode)
]
second_input = [
node for node in sdutil.find_source_nodes(second_state)
if isinstance(node, nodes.AccessNode)
]
# first input = first input - first output
first_input = [
node for node in first_input
if next((x for x in first_output
if x.label == node.label), None) is None
]
# Merge second state to first state
# First keep a backup of the topological sorted order of the nodes
order = [
x for x in reversed(list(nx.topological_sort(first_state._nx)))
if isinstance(x, nodes.AccessNode)
]
for node in second_state.nodes():
first_state.add_node(node)
for src, src_conn, dst, dst_conn, data in second_state.edges():
first_state.add_edge(src, src_conn, dst, dst_conn, data)
# Merge common (data) nodes
for node in second_input:
if first_state.in_degree(node) == 0:
n = next((x for x in order if x.label == node.label), None)
if n:
sdutil.change_edge_src(first_state, node, n)
first_state.remove_node(node)
n.access = dtypes.AccessType.ReadWrite
# Redirect edges and remove second state
sdutil.change_edge_src(sdfg, second_state, first_state)
sdfg.remove_node(second_state)
if Config.get_bool("debugprint"):
StateFusion._states_fused += 1
def apply(self, sdfg):
if isinstance(self.subgraph[StateFusion.first_state], SDFGState):
first_state: SDFGState = self.subgraph[StateFusion.first_state]
second_state: SDFGState = self.subgraph[StateFusion.second_state]
else:
first_state: SDFGState = sdfg.node(
self.subgraph[StateFusion.first_state])
second_state: SDFGState = sdfg.node(
self.subgraph[StateFusion.second_state])
# Remove interstate edge(s)
edges = sdfg.edges_between(first_state, second_state)
for edge in edges:
if edge.data.assignments:
for src, dst, other_data in sdfg.in_edges(first_state):
other_data.assignments.update(edge.data.assignments)
sdfg.remove_edge(edge)
# Special case 1: first state is empty
if first_state.is_empty():
sdutil.change_edge_dest(sdfg, first_state, second_state)
sdfg.remove_node(first_state)
if sdfg.start_state == first_state:
sdfg.start_state = sdfg.node_id(second_state)
return
# Special case 2: second state is empty
if second_state.is_empty():
sdutil.change_edge_src(sdfg, second_state, first_state)
sdutil.change_edge_dest(sdfg, second_state, first_state)
sdfg.remove_node(second_state)
if sdfg.start_state == second_state:
sdfg.start_state = sdfg.node_id(first_state)
return
# Normal case: both states are not empty
# Find source/sink (data) nodes
first_input = [
node for node in sdutil.find_source_nodes(first_state)
if isinstance(node, nodes.AccessNode)
]
first_output = [
node for node in sdutil.find_sink_nodes(first_state)
if isinstance(node, nodes.AccessNode)
]
second_input = [
node for node in sdutil.find_source_nodes(second_state)
if isinstance(node, nodes.AccessNode)
]
top2 = top_level_nodes(second_state)
# first input = first input - first output
first_input = [
node for node in first_input
if next((x for x in first_output
if x.data == node.data), None) is None
]
# Merge second state to first state
# First keep a backup of the topological sorted order of the nodes
sdict = first_state.scope_dict()
order = [
x for x in reversed(list(nx.topological_sort(first_state._nx)))
if isinstance(x, nodes.AccessNode) and sdict[x] is None
]
for node in second_state.nodes():
if isinstance(node, nodes.NestedSDFG):
# update parent information
node.sdfg.parent = first_state
first_state.add_node(node)
for src, src_conn, dst, dst_conn, data in second_state.edges():
first_state.add_edge(src, src_conn, dst, dst_conn, data)
top = top_level_nodes(first_state)
# Merge common (data) nodes
for node in second_input:
# merge only top level nodes, skip everything else
if node not in top2:
continue
if first_state.in_degree(node) == 0:
candidates = [
x for x in order if x.data == node.data and x in top
]
if len(candidates) == 0:
continue
elif len(candidates) == 1:
n = candidates[0]
else:
# Choose first candidate that intersects memlets
for cand in candidates:
if StateFusion.memlets_intersect(
first_state, [cand], False, second_state,
[node], True):
n = cand
break
else:
# No node intersects, use topologically-last node
n = candidates[0]
sdutil.change_edge_src(first_state, node, n)
first_state.remove_node(node)
n.access = dtypes.AccessType.ReadWrite
# Redirect edges and remove second state
sdutil.change_edge_src(sdfg, second_state, first_state)
sdfg.remove_node(second_state)
if sdfg.start_state == second_state:
sdfg.start_state = sdfg.node_id(first_state)
def _stripmine(self, sdfg, graph, candidate):
# Retrieve map entry and exit nodes.
map_entry = graph.nodes()[candidate[StripMining._map_entry]]
map_exit = graph.exit_node(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
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 map object?
new_dim = self._find_new_dim(sdfg, graph, map_entry, new_dim_prefix,
target_dim)
nd_from = 0
if symbolic.pystr_to_symbolic(tile_stride) == 1:
nd_to = td_to
else:
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)
# Special case: If range is 1 and no prefix was specified, skip range
if td_from_new == td_to_new_approx and target_dim == new_dim:
map_entry.map.range = subsets.Range(
[r for i, r in enumerate(map_entry.map.range) if i != dim_idx])
map_entry.map.params = [
p for i, p in enumerate(map_entry.map.params) if i != dim_idx
]
if len(map_entry.map.params) == 0:
raise ValueError('Strip-mining all dimensions of the map with '
'empty tiles is disallowed')
else:
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)
sdutil.change_edge_dest(graph, map_entry, new_map_entry)
new_map_exit.out_connectors = dcpy(map_exit.out_connectors)
sdutil.change_edge_src(graph, map_exit, new_map_exit)
# Create new entry edges
new_in_edges = dict()
entry_in_conn = {}
entry_out_conn = {}
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['IN_' + conn] = None
entry_out_conn['OUT_' + conn] = None
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
if memlet.dynamic:
new_memlet.num_accesses = memlet.num_accesses
else:
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[in_conn] = None
if out_conn:
entry_out_conn[out_conn] = None
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 = {}
exit_out_conn = {}
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['IN_' + conn] = None
exit_out_conn['OUT_' + conn] = None
new_memlet = dcpy(memlet)
new_memlet.subset = new_subset
if memlet.dynamic:
new_memlet.num_accesses = memlet.num_accesses
else:
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[in_conn] = None
if out_conn:
exit_out_conn[out_conn] = None
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
