def apply(self, graph: SDFGState, sdfg: SDFG):
if self.expr_index == 0:
map_entry = self.map_entry
nsdfg_node = helpers.nest_state_subgraph(
sdfg,
graph,
graph.scope_subgraph(map_entry),
full_data=self.fullcopy)
else:
cnode = self.reduce
nsdfg_node = helpers.nest_state_subgraph(sdfg,
graph,
SubgraphView(
graph, [cnode]),
full_data=self.fullcopy)
# Avoiding import loops
from dace.transformation.interstate import GPUTransformSDFG
transformation = GPUTransformSDFG(sdfg, 0, -1, {}, 0)
transformation.register_trans = self.register_trans
transformation.sequential_innermaps = self.sequential_innermaps
transformation.toplevel_trans = self.toplevel_trans
transformation.apply(nsdfg_node.sdfg, nsdfg_node.sdfg)
# Inline back as necessary
sdfg.simplify()
def apply(self, state: SDFGState, sdfg: SDFG):
map_entry = self.map_entry
current_map = map_entry.map
# Expand the innermost map if multidimensional
if len(current_map.params) > 1:
ext, rem = dace.transformation.helpers.extract_map_dims(
sdfg, map_entry, list(range(len(current_map.params) - 1)))
map_entry = rem
current_map = map_entry.map
subgraph = state.scope_subgraph(map_entry)
# Set the schedule
current_map.schedule = dace.dtypes.ScheduleType.SVE_Map
# Infer all connector types and apply them
inferred = infer_types.infer_connector_types(sdfg, state, subgraph)
infer_types.apply_connector_types(inferred)
# Infer vector connectors and AccessNodes and apply them
vector_inference.infer_vectors(
sdfg,
state,
map_entry,
self.vec_len,
flags=vector_inference.VectorInferenceFlags.Allow_Stride,
apply=True)
def can_be_applied(cls,
state: SDFGState,
candidate,
expr_index,
sdfg: SDFG,
strict=False) -> bool:
map_entry = state.node(candidate[cls.map_entry])
map_exit = state.exit_node(map_entry)
current_map = map_entry.map
subgraph = state.scope_subgraph(map_entry)
subgraph_contents = state.scope_subgraph(map_entry,
include_entry=False,
include_exit=False)
# Prevent infinite repeats
if current_map.schedule == dace.dtypes.ScheduleType.SVE_Map:
return False
# Infer all connector types for later checks (without modifying the graph)
inferred = infer_types.infer_connector_types(sdfg, state, subgraph)
########################
# Ensure only Tasklets and AccessNodes are within the map
for node, _ in subgraph_contents.all_nodes_recursive():
if not isinstance(node, (nodes.Tasklet, nodes.AccessNode)):
return False
########################
# Check for unsupported datatypes on the connectors (including on the Map itself)
bit_widths = set()
for node, _ in subgraph.all_nodes_recursive():
for conn in node.in_connectors:
t = inferred[(node, conn, True)]
bit_widths.add(util.get_base_type(t).bytes)
if not t.type in sve.util.TYPE_TO_SVE:
return False
for conn in node.out_connectors:
t = inferred[(node, conn, False)]
bit_widths.add(util.get_base_type(t).bytes)
if not t.type in sve.util.TYPE_TO_SVE:
return False
# Multiple different bit widths occuring (messes up the predicates)
if len(bit_widths) > 1:
return False
########################
# Check for unsupported memlets
param_name = current_map.params[-1]
for e, _ in subgraph.all_edges_recursive():
# Check for unsupported strides
# The only unsupported strides are the ones containing the innermost
# loop param because they are not constant during a vector step
param_sym = symbolic.symbol(current_map.params[-1])
if param_sym in e.data.get_stride(sdfg,
map_entry.map).free_symbols:
return False
# Check for unsupported WCR
if e.data.wcr is not None:
# Unsupported reduction type
reduction_type = dace.frontend.operations.detect_reduction_type(
e.data.wcr)
if reduction_type not in sve.util.REDUCTION_TYPE_TO_SVE:
return False
# Param in memlet during WCR is not supported
if param_name in e.data.subset.free_symbols and e.data.wcr_nonatomic:
return False
# vreduce is not supported
dst_node = state.memlet_path(e)[-1]
if isinstance(dst_node, nodes.Tasklet):
if isinstance(dst_node.in_connectors[e.dst_conn],
dtypes.vector):
return False
elif isinstance(dst_node, nodes.AccessNode):
desc = dst_node.desc(sdfg)
if isinstance(desc, data.Scalar) and isinstance(
desc.dtype, dtypes.vector):
return False
########################
# Check for invalid copies in the subgraph
for node, _ in subgraph.all_nodes_recursive():
if not isinstance(node, nodes.Tasklet):
continue
for e in state.in_edges(node):
# Check for valid copies from other tasklets and/or streams
if e.data.data is not None:
src_node = state.memlet_path(e)[0].src
if not isinstance(src_node,
(nodes.Tasklet, nodes.AccessNode)):
# Make sure we only have Code->Code copies and from arrays
return False
if isinstance(src_node, nodes.AccessNode):
src_desc = src_node.desc(sdfg)
if isinstance(src_desc, dace.data.Stream):
# Stream pops are not implemented
return False
# Run the vector inference algorithm to check if vectorization is feasible
try:
inf_graph = vector_inference.infer_vectors(
sdfg,
state,
map_entry,
util.SVE_LEN,
flags=vector_inference.VectorInferenceFlags.Allow_Stride,
apply=False)
except vector_inference.VectorInferenceException as ex:
print(f'UserWarning: Vector inference failed! {ex}')
return False
return True
def apply(self, graph: SDFGState, sdfg: SDFG):
"""
This method applies the mapfusion transformation.
Other than the removal of the second map entry node (SME), and the first
map exit (FME) node, it has the following side effects:
1. Any transient adjacent to both FME and SME with degree = 2 will be removed.
The tasklets that use/produce it shall be connected directly with a
scalar/new transient (if the dataflow is more than a single scalar)
2. If this transient is adjacent to FME and SME and has other
uses, it will be adjacent to the new map exit post fusion.
Tasklet-> Tasklet edges will ALSO be added as mentioned above.
3. If an access node is adjacent to FME but not SME, it will be
adjacent to new map exit post fusion.
4. If an access node is adjacent to SME but not FME, it will be
adjacent to the new map entry node post fusion.
"""
first_exit = self.first_map_exit
first_entry = graph.entry_node(first_exit)
second_entry = self.second_map_entry
second_exit = graph.exit_node(second_entry)
intermediate_nodes = set()
for _, _, dst, _, _ in graph.out_edges(first_exit):
intermediate_nodes.add(dst)
assert isinstance(dst, nodes.AccessNode)
# Check if an access node refers to non transient memory, or transient
# is used at another location (cannot erase)
do_not_erase = set()
for node in intermediate_nodes:
if sdfg.arrays[node.data].transient is False:
do_not_erase.add(node)
else:
for edge in graph.in_edges(node):
if edge.src != first_exit:
do_not_erase.add(node)
break
else:
for edge in graph.out_edges(node):
if edge.dst != second_entry:
do_not_erase.add(node)
break
# Find permutation between first and second scopes
perm = self.find_permutation(first_entry.map, second_entry.map)
params_dict = {}
for index, param in enumerate(first_entry.map.params):
params_dict[param] = second_entry.map.params[perm[index]]
# Replaces (in memlets and tasklet) the second scope map
# indices with the permuted first map indices.
# This works in two passes to avoid problems when e.g., exchanging two
# parameters (instead of replacing (j,i) and (i,j) to (j,j) and then
# i,i).
second_scope = graph.scope_subgraph(second_entry)
for firstp, secondp in params_dict.items():
if firstp != secondp:
replace(second_scope, secondp, '__' + secondp + '_fused')
for firstp, secondp in params_dict.items():
if firstp != secondp:
replace(second_scope, '__' + secondp + '_fused', firstp)
# Isolate First exit node
############################
edges_to_remove = set()
nodes_to_remove = set()
for edge in graph.in_edges(first_exit):
tree = graph.memlet_tree(edge)
access_node = tree.root().edge.dst
if access_node not in do_not_erase:
out_edges = [
e for e in graph.out_edges(access_node)
if e.dst == second_entry
]
# In this transformation, there can only be one edge to the
# second map
assert len(out_edges) == 1
# Get source connector to the second map
connector = out_edges[0].dst_conn[3:]
new_dsts = []
# Look at the second map entry out-edges to get the new
# destinations
for e in graph.out_edges(second_entry):
if e.src_conn[4:] == connector:
new_dsts.append(e)
if not new_dsts: # Access node is not used in the second map
nodes_to_remove.add(access_node)
continue
# Add a transient scalar/array
self.fuse_nodes(sdfg, graph, edge, new_dsts[0].dst,
new_dsts[0].dst_conn, new_dsts[1:])
edges_to_remove.add(edge)
# Remove transient node between the two maps
nodes_to_remove.add(access_node)
else: # The case where intermediate array node cannot be removed
# Node will become an output of the second map exit
out_e = tree.parent.edge
conn = second_exit.next_connector()
graph.add_edge(
second_exit,
'OUT_' + conn,
out_e.dst,
out_e.dst_conn,
dcpy(out_e.data),
)
second_exit.add_out_connector('OUT_' + conn)
graph.add_edge(edge.src, edge.src_conn, second_exit,
'IN_' + conn, dcpy(edge.data))
second_exit.add_in_connector('IN_' + conn)
edges_to_remove.add(out_e)
edges_to_remove.add(edge)
# If the second map needs this node, link the connector
# that generated this to the place where it is needed, with a
# temp transient/scalar for memlet to be generated
for out_e in graph.out_edges(second_entry):
second_memlet_path = graph.memlet_path(out_e)
source_node = second_memlet_path[0].src
if source_node == access_node:
self.fuse_nodes(sdfg, graph, edge, out_e.dst,
out_e.dst_conn)
###
# First scope exit is isolated and can now be safely removed
for e in edges_to_remove:
graph.remove_edge(e)
graph.remove_nodes_from(nodes_to_remove)
graph.remove_node(first_exit)
# Isolate second_entry node
###########################
for edge in graph.in_edges(second_entry):
tree = graph.memlet_tree(edge)
access_node = tree.root().edge.src
if access_node in intermediate_nodes:
# Already handled above, can be safely removed
graph.remove_edge(edge)
continue
# This is an external input to the second map which will now go
# through the first map.
conn = first_entry.next_connector()
graph.add_edge(edge.src, edge.src_conn, first_entry, 'IN_' + conn,
dcpy(edge.data))
first_entry.add_in_connector('IN_' + conn)
graph.remove_edge(edge)
for out_enode in tree.children:
out_e = out_enode.edge
graph.add_edge(
first_entry,
'OUT_' + conn,
out_e.dst,
out_e.dst_conn,
dcpy(out_e.data),
)
graph.remove_edge(out_e)
first_entry.add_out_connector('OUT_' + conn)
###
# Second node is isolated and can now be safely removed
graph.remove_node(second_entry)
# Fix scope exit to point to the right map
second_exit.map = first_entry.map
