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 can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
in_array = graph.nodes()[candidate[RedundantArray._in_array]]
out_array = graph.nodes()[candidate[RedundantArray._out_array]]
in_desc = in_array.desc(sdfg)
out_desc = out_array.desc(sdfg)
# Ensure out degree is one (only one target, which is out_array)
if graph.out_degree(in_array) != 1:
return False
# Make sure that the candidate is a transient variable
if not in_desc.transient:
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) == in_desc
])
for isedge in sdfg.edges():
if in_array.data in isedge.data.free_symbols:
occurrences.append(isedge)
if len(occurrences) > 1:
return False
# Only apply if arrays are of same shape (no need to modify subset)
if len(in_desc.shape) != len(out_desc.shape) or any(
i != o for i, o in zip(in_desc.shape, out_desc.shape)):
return False
if strict:
# In strict mode, make sure the memlet covers the removed array
edge = graph.edges_between(in_array, out_array)[0]
if any(m != a
for m, a in zip(edge.data.subset.size(), in_desc.shape)):
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)
# 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 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 apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
in_array = self.in_array(sdfg)
out_array = self.out_array(sdfg)
in_desc = sdfg.arrays[in_array.data]
out_desc = sdfg.arrays[out_array.data]
# 1. Get edge e1 and extract subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
a1_subset, b_subset = _validate_subsets(e1, sdfg.arrays)
# View connected to a view: simple case
if (isinstance(in_desc, data.View)
and isinstance(out_desc, data.View)):
for e in graph.in_edges(in_array):
new_memlet = copy.deepcopy(e.data)
e.dst_subset = b_subset
graph.add_edge(e.src, e.src_conn, out_array, e.dst_conn,
new_memlet)
graph.remove_node(in_array)
if in_array.data in sdfg.arrays:
del sdfg.arrays[in_array.data]
return
# Find extraneous A or B subset dimensions
a_dims_to_pop = []
b_dims_to_pop = []
bset = b_subset
popped = []
if a1_subset and b_subset and a1_subset.dims() != b_subset.dims():
a_size = a1_subset.size_exact()
b_size = b_subset.size_exact()
if a1_subset.dims() > b_subset.dims():
a_dims_to_pop = find_dims_to_pop(a_size, b_size)
else:
b_dims_to_pop = find_dims_to_pop(b_size, a_size)
bset, popped = pop_dims(b_subset, b_dims_to_pop)
from dace.libraries.standard import Reduce
reduction = False
for e in graph.in_edges(in_array):
if isinstance(e.src, Reduce):
reduction = True
# If:
# 1. A reduce node is involved;
# 2. The memlet does not cover the removed array; or
# 3. Dimensions are mismatching (all dimensions are popped);
# create a view.
if reduction or len(a_dims_to_pop) == len(in_desc.shape) or any(
m != a for m, a in zip(a1_subset.size(), in_desc.shape)):
self._make_view(sdfg, graph, in_array, out_array, e1, b_subset,
b_dims_to_pop)
return
# Validate that subsets are composable. If not, make a view
try:
for e2 in graph.in_edges(in_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
other_subset, a3_subset = _validate_subsets(
e3, sdfg.arrays, dst_name=in_array.data)
# 2-b. Modify memlet to match array B.
dname = out_array.data
src_is_data = False
a3_subset.offset(a1_subset, negative=True)
if a3_subset and a_dims_to_pop:
aset, _ = pop_dims(a3_subset, a_dims_to_pop)
else:
aset = a3_subset
compose_and_push_back(bset, aset, b_dims_to_pop, popped)
except (ValueError, NotImplementedError):
self._make_view(sdfg, graph, in_array, out_array, e1, b_subset,
b_dims_to_pop)
return
# 2. Iterate over the e2 edges and traverse the memlet tree
for e2 in graph.in_edges(in_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
other_subset, a3_subset = _validate_subsets(
e3, sdfg.arrays, dst_name=in_array.data)
# 2-b. Modify memlet to match array B.
dname = out_array.data
src_is_data = False
a3_subset.offset(a1_subset, negative=True)
if a3_subset and a_dims_to_pop:
aset, _ = pop_dims(a3_subset, a_dims_to_pop)
else:
aset = a3_subset
dst_subset = compose_and_push_back(bset, aset, b_dims_to_pop,
popped)
# NOTE: This fixes the following case:
# Tasklet ----> A[subset] ----> ... -----> A
# Tasklet is not data, so it doesn't have an other subset.
if isinstance(e3.src, nodes.AccessNode):
if e3.src.data == out_array.data:
dname = e3.src.data
src_is_data = True
src_subset = other_subset
else:
src_subset = None
subset = src_subset if src_is_data else dst_subset
other_subset = dst_subset if src_is_data else src_subset
e3.data.data = dname
e3.data.subset = subset
e3.data.other_subset = other_subset
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(e2.src, e2.src_conn, out_array, e2.dst_conn,
e2.data)
# Finally, remove in_array node
graph.remove_node(in_array)
try:
if in_array.data in sdfg.arrays:
sdfg.remove_data(in_array.data)
except ValueError: # Already in use (e.g., with Views)
pass
def apply(self, sdfg):
graph = sdfg.nodes()[self.state_id]
in_array = self.in_array(sdfg)
out_array = self.out_array(sdfg)
in_desc = sdfg.arrays[in_array.data]
out_desc = sdfg.arrays[out_array.data]
# 1. Get edge e1 and extract subsets for arrays A and B
e1 = graph.edges_between(in_array, out_array)[0]
a1_subset, b_subset = _validate_subsets(e1, sdfg.arrays)
# Find extraneous A or B subset dimensions
a_dims_to_pop = []
b_dims_to_pop = []
bset = b_subset
popped = []
if a1_subset and b_subset and a1_subset.dims() != b_subset.dims():
a_size = a1_subset.size_exact()
b_size = b_subset.size_exact()
if a1_subset.dims() > b_subset.dims():
a_dims_to_pop = find_dims_to_pop(a_size, b_size)
else:
b_dims_to_pop = find_dims_to_pop(b_size, a_size)
bset, popped = pop_dims(b_subset, b_dims_to_pop)
from dace.libraries.standard import Reduce
reduction = False
for e in graph.in_edges(in_array):
if isinstance(e.src, Reduce):
reduction = True
# If the memlet does not cover the removed array, create a view.
if reduction or any(m != a
for m, a in zip(a1_subset.size(), in_desc.shape)):
# NOTE: We do not want to create another view, if the immediate
# ancestors of in_array are views as well. We just remove it.
in_ancestors_desc = [
e.src.desc(sdfg)
if isinstance(e.src, nodes.AccessNode) else None
for e in graph.in_edges(in_array)
]
if all([
desc and isinstance(desc, data.View)
for desc in in_ancestors_desc
]):
for e in graph.in_edges(in_array):
a_subset, _ = _validate_subsets(e, sdfg.arrays)
graph.add_edge(
e.src, e.src_conn, out_array, None,
mm.Memlet(out_array.data,
subset=b_subset,
other_subset=a_subset,
wcr=e1.data.wcr,
wcr_nonatomic=e1.data.wcr.nonatomic))
graph.remove_edge(e)
graph.remove_edge(e1)
graph.remove_node(in_array)
if in_array.data in sdfg.arrays:
del sdfg.arrays[in_array.data]
return
view_strides = in_desc.strides
if (b_dims_to_pop and len(b_dims_to_pop)
== len(out_desc.shape) - len(in_desc.shape)):
view_strides = [
s for i, s in enumerate(out_desc.strides)
if i not in b_dims_to_pop
]
sdfg.arrays[in_array.data] = data.View(
in_desc.dtype, in_desc.shape, True, in_desc.allow_conflicts,
out_desc.storage, out_desc.location, view_strides,
in_desc.offset, out_desc.may_alias,
dtypes.AllocationLifetime.Scope, in_desc.alignment,
in_desc.debuginfo, in_desc.total_size)
return
# 2. Iterate over the e2 edges and traverse the memlet tree
for e2 in graph.in_edges(in_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
other_subset, a3_subset = _validate_subsets(
e3, sdfg.arrays, dst_name=in_array.data)
# 2-b. Modify memlet to match array B.
dname = out_array.data
src_is_data = False
a3_subset.offset(a1_subset, negative=True)
if a3_subset and a_dims_to_pop:
aset, _ = pop_dims(a3_subset, a_dims_to_pop)
else:
aset = a3_subset
dst_subset = compose_and_push_back(bset, aset, b_dims_to_pop,
popped)
# NOTE: This fixes the following case:
# Tasklet ----> A[subset] ----> ... -----> A
# Tasklet is not data, so it doesn't have an other subset.
if isinstance(e3.src, nodes.AccessNode):
if e3.src.data == out_array.data:
dname = e3.src.data
src_is_data = True
src_subset = other_subset
else:
src_subset = None
subset = src_subset if src_is_data else dst_subset
other_subset = dst_subset if src_is_data else src_subset
e3.data.data = dname
e3.data.subset = subset
e3.data.other_subset = other_subset
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(e2.src, e2.src_conn, out_array, e2.dst_conn, e2.data)
# Finally, remove in_array node
graph.remove_node(in_array)
if in_array.data in sdfg.arrays:
del sdfg.arrays[in_array.data]
