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 can_be_applied(graph, candidate, expr_index, sdfg, strict=False):
nested_sdfg = graph.nodes()[candidate[CopyToDevice._nested_sdfg]]
for edge in graph.all_edges(nested_sdfg):
# Stream inputs/outputs not allowed
path = graph.memlet_path(edge)
if ((isinstance(path[0].src, nodes.AccessNode)
and isinstance(sdfg.arrays[path[0].src.data], data.Stream)) or
(isinstance(path[-1].dst, nodes.AccessNode)
and isinstance(sdfg.arrays[path[-1].dst.data], data.Stream))):
return False
# WCR outputs with arrays are not allowed
if (edge.data.wcr is not None
and edge.data.subset.num_elements() != 1):
return False
return True
def match_to_str(graph, candidate):
nested_sdfg = graph.nodes()[candidate[CopyToDevice._nested_sdfg]]
return nested_sdfg.label
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 gnode(nname):
return graph.nodes()[self.subgraph[nname]]
def match_to_str(graph, candidate):
out_array = graph.nodes()[candidate[RedundantSecondArray._out_array]]
return "Remove " + str(out_array)
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 match_to_str(graph, candidate):
in_array = graph.nodes()[candidate[RedundantArray._in_array]]
return "Remove " + str(in_array)
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
