def can_be_applied(
self,
graph: SDFGState,
candidate: Dict[str, dace.nodes.Node],
expr_index: int,
sdfg: dace.SDFG,
strict: bool = False,
) -> bool:
left = self.left(sdfg)
right = self.right(sdfg)
if expr_index >= 2:
if nx.has_path(graph.nx, right, left):
return False
intermediate_accesses = set(
n for path in nx.all_simple_paths(graph.nx, left, right)
for n in path[1:-1])
if not all(
isinstance(n, dace.nodes.AccessNode) and
(graph.edges_between(left, n) and graph.edges_between(n, right))
for n in intermediate_accesses):
return False
protected_intermediate_names = set(n.label
for n in intermediate_accesses
if any(
edge.dst is not right
for edge in graph.out_edges(n)))
output_names = set(edge.data.data for edge in graph.out_edges(right)
if edge.data is not None)
if len(protected_intermediate_names & output_names) > 0:
return False
return offsets_match(left, right) and iteration_space_compatible(
left, right, self.api_fields)
def apply(self, sdfg):
def gnode(nname):
return graph.nodes()[self.subgraph[nname]]
graph = sdfg.nodes()[self.state_id]
in_array = gnode(RedundantSecondArray._in_array)
out_array = gnode(RedundantSecondArray._out_array)
# We assume the following pattern: A -- e1 --> B -- e2 --> others
# 1. Get edge e1 and extract subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
a_subset, b1_subset = _validate_subsets(e1, sdfg.arrays)
# 2. Iterate over the e2 edges and traverse the memlet tree
for e2 in graph.out_edges(out_array):
path = graph.memlet_tree(e2)
for e3 in path:
# 2-a. Extract subsets for array B and others
b3_subset, other_subset = _validate_subsets(
e3, sdfg.arrays, src_name=out_array.data)
# 2-b. Modify memlet to match array A. Example:
# A -- (0, a:b)/(c:c+b) --> B -- (c+d)/None --> others
# A -- (0, a+d)/None --> others
e3.data.data = in_array.data
# (c+d) - (c:c+b) = (d)
b3_subset.offset(b1_subset, negative=True)
# (0, a:b)(d) = (0, a+d) (or offset for indices)
if isinstance(a_subset, subsets.Indices):
tmp = copy.deepcopy(a_subset)
tmp.offset(b3_subset, negative=False)
e3.data.subset = tmp
else:
e3.data.subset = a_subset.compose(b3_subset)
e3.data.other_subset = other_subset
# 2-c. Remove edge and add new one
graph.remove_edge(e2)
graph.add_edge(in_array, e2.src_conn, e2.dst, e2.dst_conn, e2.data)
# Finally, remove out_array node
graph.remove_node(out_array)
# TODO: Should the array be removed from the SDFG?
# del sdfg.arrays[out_array]
if Config.get_bool("debugprint"):
RedundantSecondArray._arrays_removed += 1
def expansion(node: 'Reduce', state: SDFGState, sdfg: SDFG):
""" Create a map around the BlockReduce node
with in and out transients in registers
and an if tasklet that redirects the output
of thread 0 to a shared memory transient
"""
### define some useful vars
graph = state
reduce_node = node
in_edge = graph.in_edges(reduce_node)[0]
out_edge = graph.out_edges(reduce_node)[0]
axes = reduce_node.axes
### add a map that encloses the reduce node
(new_entry, new_exit) = graph.add_map(
name = 'inner_reduce_block',
ndrange = {'i'+str(i): f'{rng[0]}:{rng[1]+1}:{rng[2]}' \
for (i,rng) in enumerate(in_edge.data.subset) \
if i in axes},
schedule = dtypes.ScheduleType.Default)
map = new_entry.map
ExpandReduceCUDABlockAll.redirect_edge(graph,
in_edge,
new_dst=new_entry)
ExpandReduceCUDABlockAll.redirect_edge(graph,
out_edge,
new_src=new_exit)
subset_in = subsets.Range([
in_edge.data.subset[i] if i not in axes else
(new_entry.map.params[0], new_entry.map.params[0], 1)
for i in range(len(in_edge.data.subset))
])
memlet_in = dace.Memlet(data=in_edge.data.data,
volume=1,
subset=subset_in)
memlet_out = dcpy(out_edge.data)
graph.add_edge(u=new_entry,
u_connector=None,
v=reduce_node,
v_connector=None,
memlet=memlet_in)
graph.add_edge(u=reduce_node,
u_connector=None,
v=new_exit,
v_connector=None,
memlet=memlet_out)
### add in and out local storage
from dace.transformation.dataflow.local_storage import LocalStorage
in_local_storage_subgraph = {
LocalStorage._node_a: graph.nodes().index(new_entry),
LocalStorage._node_b: graph.nodes().index(reduce_node)
}
out_local_storage_subgraph = {
LocalStorage._node_a: graph.nodes().index(reduce_node),
LocalStorage._node_b: graph.nodes().index(new_exit)
}
local_storage = LocalStorage(sdfg.sdfg_id,
sdfg.nodes().index(state),
in_local_storage_subgraph, 0)
local_storage.array = in_edge.data.data
local_storage.apply(sdfg)
in_transient = local_storage._data_node
sdfg.data(in_transient.data).storage = dtypes.StorageType.Register
local_storage = LocalStorage(sdfg.sdfg_id,
sdfg.nodes().index(state),
out_local_storage_subgraph, 0)
local_storage.array = out_edge.data.data
local_storage.apply(sdfg)
out_transient = local_storage._data_node
sdfg.data(out_transient.data).storage = dtypes.StorageType.Register
# hack: swap edges as local_storage does not work correctly here
# as subsets and data get assigned wrongly (should be swapped)
# NOTE: If local_storage ever changes, this will not work any more
e1 = graph.in_edges(out_transient)[0]
e2 = graph.out_edges(out_transient)[0]
e1.data.data = dcpy(e2.data.data)
e1.data.subset = dcpy(e2.data.subset)
### add an if tasket and diverge
code = 'if '
for (i, param) in enumerate(new_entry.map.params):
code += (param + '== 0')
if i < len(axes) - 1:
code += ' and '
code += ':\n'
code += '\tout=inp'
tasklet_node = graph.add_tasklet(name='block_reduce_write',
inputs=['inp'],
outputs=['out'],
code=code)
edge_out_outtrans = graph.out_edges(out_transient)[0]
edge_out_innerexit = graph.out_edges(new_exit)[0]
ExpandReduceCUDABlockAll.redirect_edge(graph,
edge_out_outtrans,
new_dst=tasklet_node,
new_dst_conn='inp')
e = graph.add_edge(u=tasklet_node,
u_connector='out',
v=new_exit,
v_connector=None,
memlet=dcpy(edge_out_innerexit.data))
# set dynamic with volume 0 FORNOW
e.data.volume = 0
e.data.dynamic = True
### set reduce_node axes to all (needed)
reduce_node.axes = None
# fill scope connectors, done.
sdfg.fill_scope_connectors()
# finally, change the implementation to cuda (block)
# itself and expand again.
reduce_node.implementation = 'CUDA (block)'
sub_expansion = ExpandReduceCUDABlock(0, 0, {}, 0)
return sub_expansion.expansion(node=node, state=state, sdfg=sdfg)
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
in_array = graph.nodes()[candidate[RedundantSecondArray._in_array]]
out_array = graph.nodes()[candidate[RedundantSecondArray._out_array]]
in_desc = in_array.desc(sdfg)
out_desc = out_array.desc(sdfg)
# Ensure in degree is one (only one source, which is in_array)
if graph.in_degree(out_array) != 1:
return False
# Make sure that the candidate is a transient variable
if not out_desc.transient:
return False
# Dimensionality must be the same in strict mode
if strict and len(in_desc.shape) != len(out_desc.shape):
return False
# Make sure that both arrays are using the same storage location
# and are of the same type (e.g., Stream->Stream)
if in_desc.storage != out_desc.storage:
return False
if type(in_desc) != type(out_desc):
return False
# Find occurrences in this and other states
occurrences = []
for state in sdfg.nodes():
occurrences.extend([
n for n in state.nodes()
if isinstance(n, nodes.AccessNode) and n.desc(sdfg) == out_desc
])
for isedge in sdfg.edges():
if out_array.data in isedge.data.free_symbols:
occurrences.append(isedge)
if len(occurrences) > 1:
return False
# Check whether the data copied from the first datanode cover
# the subsets of all the output edges of the second datanode.
# We assume the following pattern: A -- e1 --> B -- e2 --> others
# 1. Get edge e1 and extract/validate subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
try:
_, b1_subset = _validate_subsets(e1, sdfg.arrays)
except NotImplementedError:
return False
# 2. Iterate over the e2 edges
for e2 in graph.out_edges(out_array):
# 2-a. Extract/validate subsets for array B and others
try:
b2_subset, _ = _validate_subsets(e2, sdfg.arrays)
except NotImplementedError:
return False
# 2-b. Check where b1_subset covers b2_subset
if not b1_subset.covers(b2_subset):
return False
# 2-c. Validate subsets in memlet tree
# (should not be needed for valid SDGs)
path = graph.memlet_tree(e2)
for e3 in path:
if e3 is not e2:
try:
_validate_subsets(e3,
sdfg.arrays,
src_name=out_array.data)
except NotImplementedError:
return False
return True
def apply(self, sdfg):
def gnode(nname):
return graph.nodes()[self.subgraph[nname]]
graph = sdfg.nodes()[self.state_id]
in_array = gnode(RedundantSecondArray._in_array)
out_array = gnode(RedundantSecondArray._out_array)
in_desc = sdfg.arrays[in_array.data]
out_desc = sdfg.arrays[out_array.data]
# We assume the following pattern: A -- e1 --> B -- e2 --> others
# 1. Get edge e1 and extract subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
a_subset, b1_subset = _validate_subsets(e1, sdfg.arrays)
# Find extraneous A or B subset dimensions
a_dims_to_pop = []
b_dims_to_pop = []
aset = a_subset
popped = []
if a_subset and b1_subset and a_subset.dims() != b1_subset.dims():
a_size = a_subset.size_exact()
b_size = b1_subset.size_exact()
if a_subset.dims() > b1_subset.dims():
a_dims_to_pop = find_dims_to_pop(a_size, b_size)
aset, popped = pop_dims(a_subset, a_dims_to_pop)
else:
b_dims_to_pop = find_dims_to_pop(b_size, a_size)
# If the src subset does not cover the removed array, create a view.
if a_subset and any(m != a
for m, a in zip(a_subset.size(), out_desc.shape)):
# NOTE: We do not want to create another view, if the immediate
# successors of out_array are views as well. We just remove it.
out_successors_desc = [
e.dst.desc(sdfg)
if isinstance(e.dst, nodes.AccessNode) else None
for e in graph.out_edges(out_array)
]
if all([
desc and isinstance(desc, data.View)
for desc in out_successors_desc
]):
for e in graph.out_edges(out_array):
_, b_subset = _validate_subsets(e, sdfg.arrays)
graph.add_edge(
in_array, None, e.dst, e.dst_conn,
mm.Memlet(in_array.data,
subset=a_subset,
other_subset=b_subset,
wcr=e1.data.wcr,
wcr_nonatomic=e1.data.wcr_nonatomic))
graph.remove_edge(e)
graph.remove_edge(e1)
graph.remove_node(out_array)
if out_array.data in sdfg.arrays:
del sdfg.arrays[out_array.data]
return
view_strides = out_desc.strides
if (a_dims_to_pop and len(a_dims_to_pop)
== len(in_desc.shape) - len(out_desc.shape)):
view_strides = [
s for i, s in enumerate(in_desc.strides)
if i not in a_dims_to_pop
]
sdfg.arrays[out_array.data] = data.View(
out_desc.dtype, out_desc.shape, True, out_desc.allow_conflicts,
in_desc.storage, in_desc.location, view_strides,
out_desc.offset, in_desc.may_alias,
dtypes.AllocationLifetime.Scope, out_desc.alignment,
out_desc.debuginfo, out_desc.total_size)
return
# 2. Iterate over the e2 edges and traverse the memlet tree
for e2 in graph.out_edges(out_array):
path = graph.memlet_tree(e2)
wcr = e1.data.wcr
wcr_nonatomic = e1.data.wcr_nonatomic
for e3 in path:
# 2-a. Extract subsets for array B and others
b3_subset, other_subset = _validate_subsets(
e3, sdfg.arrays, src_name=out_array.data)
# 2-b. Modify memlet to match array A. Example:
# A -- (0, a:b)/(c:c+b) --> B -- (c+d)/None --> others
# A -- (0, a+d)/None --> others
e3.data.data = in_array.data
# (c+d) - (c:c+b) = (d)
b3_subset.offset(b1_subset, negative=True)
# (0, a:b)(d) = (0, a+d) (or offset for indices)
if b3_subset and b_dims_to_pop:
bset, _ = pop_dims(b3_subset, b_dims_to_pop)
else:
bset = b3_subset
e3.data.subset = compose_and_push_back(aset, bset,
a_dims_to_pop, popped)
# NOTE: This fixes the following case:
# A ----> A[subset] ----> ... -----> Tasklet
# Tasklet is not data, so it doesn't have an other subset.
if isinstance(e3.dst, nodes.AccessNode):
e3.data.other_subset = other_subset
else:
e3.data.other_subset = None
wcr = wcr or e3.data.wcr
wcr_nonatomic = wcr_nonatomic or e3.data.wcr_nonatomic
e3.data.wcr = wcr
e3.data.wcr_nonatomic = wcr_nonatomic
# 2-c. Remove edge and add new one
graph.remove_edge(e2)
e2.data.wcr = wcr
e2.data.wcr_nonatomic = wcr_nonatomic
graph.add_edge(in_array, e2.src_conn, e2.dst, e2.dst_conn, e2.data)
# Finally, remove out_array node
graph.remove_node(out_array)
if out_array.data in sdfg.arrays:
try:
sdfg.remove_data(out_array.data)
except ValueError: # Already in use (e.g., with Views)
pass
def can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
in_array = graph.nodes()[candidate[RedundantSecondArray._in_array]]
out_array = graph.nodes()[candidate[RedundantSecondArray._out_array]]
in_desc = in_array.desc(sdfg)
out_desc = out_array.desc(sdfg)
# Ensure in degree is one (only one source, which is in_array)
if graph.in_degree(out_array) != 1:
return False
# Make sure that the candidate is a transient variable
if not out_desc.transient:
return False
# 1. Get edge e1 and extract/validate subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
a_subset, b1_subset = _validate_subsets(e1, sdfg.arrays)
if strict:
# In strict mode, make sure the memlet covers the removed array
if not b1_subset:
return False
subset = copy.deepcopy(b1_subset)
subset.squeeze()
shape = [sz for sz in out_desc.shape if sz != 1]
if any(m != a for m, a in zip(subset.size(), shape)):
return False
# NOTE: Library node check
# The transformation must not apply in strict mode if out_array is
# not a view, is input to a library node, and an access or a view
# of in_desc is also output to the same library node.
# The reason is that the application of the transformation will lead
# to in_desc being both input and output of the library node.
# We do not know if this is safe.
# First find the true in_desc (in case in_array is a view).
true_in_desc = in_desc
if isinstance(in_desc, data.View):
e = sdutil.get_view_edge(graph, in_array)
if not e:
return False
true_in_desc = sdfg.arrays[e.dst.data]
if not isinstance(out_desc, data.View):
edges_to_check = []
for a in graph.out_edges(out_array):
if isinstance(a.dst, nodes.LibraryNode):
edges_to_check.append(a)
elif (isinstance(a.dst, nodes.AccessNode)
and isinstance(sdfg.arrays[a.dst.data], data.View)):
for b in graph.out_edges(a.dst):
edges_to_check.append(graph.memlet_path(b)[-1])
for a in edges_to_check:
if isinstance(a.dst, nodes.LibraryNode):
for b in graph.out_edges(a.dst):
if isinstance(b.dst, nodes.AccessNode):
desc = sdfg.arrays[b.dst.data]
if isinstance(desc, data.View):
e = sdutil.get_view_edge(graph, b.dst)
if not e:
return False
desc = sdfg.arrays[e.dst.data]
if desc is true_in_desc:
return False
# In strict mode, check if the state has two or more access nodes
# for in_array and at least one of them is a write access. There
# might be a RW, WR, or WW dependency.
accesses = [
n for n in graph.nodes() if isinstance(n, nodes.AccessNode)
and n.desc(sdfg) == in_desc and n is not in_array
]
if len(accesses) > 0:
if (graph.in_degree(in_array) > 0
or any(graph.in_degree(a) > 0 for a in accesses)):
# We need to ensure that a data race will not happen if we
# remove in_array.
# First, we simplify the graph
G = helpers.simplify_state(graph)
# Loop over the accesses
for a in accesses:
subsets_intersect = False
for e in graph.in_edges(a):
_, subset = _validate_subsets(e,
sdfg.arrays,
dst_name=a.data)
res = subsets.intersects(a_subset, subset)
if res == True or res is None:
subsets_intersect = True
break
if not subsets_intersect:
continue
try:
has_bward_path = nx.has_path(G, a, in_array)
except NodeNotFound:
has_bward_path = nx.has_path(graph.nx, a, in_array)
try:
has_fward_path = nx.has_path(G, in_array, a)
except NodeNotFound:
has_fward_path = nx.has_path(graph.nx, in_array, a)
# If there is no path between the access nodes
# (disconnected components), then it is definitely
# possible to have data races. Abort.
if not (has_bward_path or has_fward_path):
return False
# If there is a forward path then a must not be a direct
# successor of in_array.
if has_fward_path and a in G.successors(in_array):
for src, _ in G.in_edges(a):
if src is in_array:
continue
if (nx.has_path(G, in_array, src)
and src != out_array):
continue
return False
# Make sure that both arrays are using the same storage location
# and are of the same type (e.g., Stream->Stream)
if in_desc.storage != out_desc.storage:
return False
if in_desc.location != out_desc.location:
return False
if type(in_desc) != type(out_desc):
if isinstance(in_desc, data.View):
# Case View -> Access
# If the View points to the Access (and has a different shape?)
# then we should (probably) not remove the Access.
e = sdutil.get_view_edge(graph, in_array)
if e and e.dst is out_array and in_desc.shape != out_desc.shape:
return False
# Check that the View's immediate ancestors are Accesses.
# Otherwise, the application of the transformation will result
# in an ambiguous View.
view_ancestors_desc = [
e.src.desc(sdfg)
if isinstance(e.src, nodes.AccessNode) else None
for e in graph.in_edges(in_array)
]
if any([
not desc or isinstance(desc, data.View)
for desc in view_ancestors_desc
]):
return False
elif isinstance(out_desc, data.View):
# Case Access -> View
# If the View points to the Access and has the same shape,
# it can be removed
e = sdutil.get_view_edge(graph, out_array)
if e and e.src is in_array and in_desc.shape == out_desc.shape:
return True
return False
else:
# Something else, for example, Stream
return False
else:
# Two views connected to each other
if isinstance(in_desc, data.View):
return False
# Find occurrences in this and other states
occurrences = []
for state in sdfg.nodes():
occurrences.extend([
n for n in state.nodes()
if isinstance(n, nodes.AccessNode) and n.desc(sdfg) == out_desc
])
for isedge in sdfg.edges():
if out_array.data in isedge.data.free_symbols:
occurrences.append(isedge)
if len(occurrences) > 1:
return False
# Check whether the data copied from the first datanode cover
# the subsets of all the output edges of the second datanode.
# We assume the following pattern: A -- e1 --> B -- e2 --> others
# 2. Iterate over the e2 edges
for e2 in graph.out_edges(out_array):
# 2-a. Extract/validate subsets for array B and others
try:
b2_subset, _ = _validate_subsets(e2, sdfg.arrays)
except NotImplementedError:
return False
# 2-b. Check where b1_subset covers b2_subset
if not b1_subset.covers(b2_subset):
return False
# 2-c. Validate subsets in memlet tree
# (should not be needed for valid SDGs)
path = graph.memlet_tree(e2)
for e3 in path:
if e3 is not e2:
try:
_validate_subsets(e3,
sdfg.arrays,
src_name=out_array.data)
except NotImplementedError:
return False
return True
def expand(self, sdfg: SDFG, graph: SDFGState, reduce_node):
""" Splits the data dimension into an inner and outer dimension,
where the inner dimension are the reduction axes and the
outer axes the complement. Pushes the reduce inside a new
map consisting of the complement axes.
"""
# get out storage node, might be hidden behind view node
out_data = graph.out_edges(reduce_node)[0].data
out_storage_node = reduce_node
while not isinstance(out_storage_node, nodes.AccessNode):
out_storage_node = graph.out_edges(out_storage_node)[0].dst
if isinstance(sdfg.data(out_storage_node.data), View):
out_storage_node = graph.out_edges(out_storage_node)[0].dst
while not isinstance(out_storage_node, nodes.AccessNode):
out_storage_node = graph.out_edges(out_storage_node)[0].dst
# get other useful quantities from the original reduce node
wcr = reduce_node.wcr
identity = reduce_node.identity
implementation = reduce_node.implementation
# remove the reduce identity, will get reassigned after expansion
reduce_node.identity = None
# expand the reduce node
in_edge = graph.in_edges(reduce_node)[0]
nsdfg = self._expand_reduce(sdfg, graph, reduce_node)
# find the new nodes in the nested sdfg created
nstate = nsdfg.sdfg.nodes()[0]
for node, scope in nstate.scope_dict().items():
if isinstance(node, nodes.MapEntry):
if scope is None:
outer_entry = node
else:
inner_entry = node
if isinstance(node, nodes.Tasklet):
tasklet_node = node
inner_exit = nstate.exit_node(inner_entry)
outer_exit = nstate.exit_node(outer_entry)
# find earliest parent read-write occurrence of array onto which the reduction is performed: BFS
if self.create_out_transient:
queue = [nsdfg]
enqueued = set()
array_closest_ancestor = None
while len(queue) > 0:
current = queue.pop()
if isinstance(current, nodes.AccessNode):
if current.data == out_storage_node.data:
# it suffices to find the first node
# no matter what access (ReadWrite or Read)
array_closest_ancestor = current
break
for in_edge in graph.in_edges(current):
if in_edge.src not in enqueued:
queue.append(in_edge.src)
enqueued.add(in_edge.src)
if self.debug and array_closest_ancestor:
print(
f"ReduceExpansion::Closest ancestor={array_closest_ancestor}"
)
elif self.debug:
print("ReduceExpansion::No closest ancestor found")
if self.create_out_transient:
# create an out transient between inner and outer map exit
array_out = nstate.out_edges(outer_exit)[0].data.data
from dace.transformation.dataflow.local_storage import LocalStorage, OutLocalStorage
local_storage_subgraph = {
LocalStorage.node_a:
nsdfg.sdfg.nodes()[0].nodes().index(inner_exit),
LocalStorage.node_b:
nsdfg.sdfg.nodes()[0].nodes().index(outer_exit)
}
nsdfg_id = nsdfg.sdfg.sdfg_list.index(nsdfg.sdfg)
nstate_id = 0
local_storage = OutLocalStorage(nsdfg.sdfg, nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_out
local_storage.apply(nsdfg.sdfg.node(0), nsdfg.sdfg)
out_transient_node_inner = local_storage._data_node
# push to register
nsdfg.sdfg.data(out_transient_node_inner.data
).storage = dtypes.StorageType.Register
# remove WCRs from all edges where possible if there is no
# prior occurrence
if array_closest_ancestor is None:
nstate.out_edges(outer_exit)[0].data.wcr = None
nstate.out_edges(out_transient_node_inner)[0].data.wcr = None
nstate.out_edges(out_transient_node_inner)[0].data.volume = 1
else:
# remove WCR from outer exit
nstate.out_edges(outer_exit)[0].data.wcr = None
if self.create_in_transient:
# create an in-transient between inner and outer map entry
array_in = nstate.in_edges(outer_entry)[0].data.data
from dace.transformation.dataflow.local_storage import LocalStorage, InLocalStorage
local_storage_subgraph = {
LocalStorage.node_a:
nsdfg.sdfg.nodes()[0].nodes().index(outer_entry),
LocalStorage.node_b:
nsdfg.sdfg.nodes()[0].nodes().index(inner_entry)
}
nsdfg_id = nsdfg.sdfg.sdfg_list.index(nsdfg.sdfg)
nstate_id = 0
local_storage = InLocalStorage(nsdfg.sdfg, nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_in
local_storage.apply(nsdfg.sdfg.node(0), nsdfg.sdfg)
in_transient_node_inner = local_storage._data_node
# push to register
nsdfg.sdfg.data(in_transient_node_inner.data
).storage = dtypes.StorageType.Register
# inline fuse back our nested SDFG
from dace.transformation.interstate import InlineSDFG
inline_sdfg = InlineSDFG(
sdfg, sdfg.sdfg_id, sdfg.node_id(graph),
{InlineSDFG.nested_sdfg: graph.node_id(nsdfg)}, 0)
inline_sdfg.apply(graph, sdfg)
new_schedule = dtypes.ScheduleType.Default
new_implementation = self.reduce_implementation \
if self.reduce_implementation is not None \
else implementation
new_axes = dcpy(reduce_node.axes)
reduce_node_new = graph.add_reduce(wcr=wcr,
axes=new_axes,
schedule=new_schedule,
identity=identity)
reduce_node_new.implementation = new_implementation
# replace inner map with new reduction node
edge_tmp = graph.in_edges(inner_entry)[0]
memlet_src_reduce = dcpy(edge_tmp.data)
graph.add_edge(edge_tmp.src, edge_tmp.src_conn, reduce_node_new, None,
memlet_src_reduce)
edge_tmp = graph.out_edges(inner_exit)[0]
memlet_reduce_dst = Memlet(data=edge_tmp.data.data,
volume=1,
subset=edge_tmp.data.subset)
graph.add_edge(reduce_node_new, None, edge_tmp.dst, edge_tmp.dst_conn,
memlet_reduce_dst)
identity_tasklet = graph.out_edges(inner_entry)[0].dst
graph.remove_node(inner_entry)
graph.remove_node(inner_exit)
graph.remove_node(identity_tasklet)
# propagate scope for correct volumes
scope_tree = ScopeTree(outer_entry, outer_exit)
scope_tree.parent = ScopeTree(None, None)
propagate_memlets_scope(sdfg, graph, scope_tree)
sdfg.validate()
# create variables for outside access
self._reduce = reduce_node_new
self._outer_entry = outer_entry
if identity is None and self.create_out_transient:
if self.debug:
print(
"ReduceExpansion::Trying to infer reduction WCR type due to out transient created"
)
# set the reduction identity accordingly so that the correct
# blank result is written to the out_transient node
# we use default values deducted from the reduction type
reduction_type = detect_reduction_type(wcr)
try:
reduce_node_new.identity = self.reduction_type_identity[
reduction_type]
except KeyError:
if reduction_type == dtypes.ReductionType.Min:
reduce_node_new.identity = dtypes.max_value(
sdfg.arrays[out_storage_node.data].dtype)
elif reduction_type == dtypes.ReductionType.Max:
reduce_node_new.identity = dtypes.min_value(
sdfg.arrays[out_storage_node.data].dtype)
else:
raise ValueError(f"Cannot infer reduction identity."
"Please specify the identity of node"
"{reduce_node_new}")
return
