def _min(sdfg: SDFG, state: SDFGState, a: str, axis=None):
return _reduce(sdfg,
state,
"lambda x, y: min(x, y)",
a,
axis=axis,
identity=dtypes.max_value(sdfg.arrays[a].dtype))
def expand(self, sdfg, graph, 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.
"""
out_storage_node = graph.out_edges(reduce_node)[0].dst
in_storage_node = graph.in_edges(reduce_node)[0].src
wcr = reduce_node.wcr
identity = reduce_node.identity
schedule = reduce_node.schedule
implementation = reduce_node.implementation
if implementation and 'warp' in implementation:
raise NotImplementedError(
"WIP: Warp Reductions are not Implemented yet.")
# remove the reduce identity
# we will reassign it later after expanding
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
# we perform the reduction:
# do BFS, best complexity O(V+E)
queue = [nsdfg]
array_closest_ancestor = None
while len(queue) > 0:
current = queue.pop(0)
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
queue.extend([in_edge.src for in_edge in graph.in_edges(current)])
# if ancestor doesn't exist:
# if non-transient: create data node accessing it
# if transient: ancestor_node = none, set_zero on outer node
shortcut = False
if (not array_closest_ancestor and sdfg.data(out_storage_node.data).transient) \
or identity is not None:
if self.debug:
print("ReduceExpansion::Expanding Reduction into Map")
# we are lucky
shortcut = True
nstate.out_edges(outer_exit)[0].data.wcr = None
else:
if self.debug:
print("ReduceExpansion::Expanding Reduction into Map "
"and introducing update Tasklet, "
"connecting with ancestor.")
if not array_closest_ancestor:
array_closest_ancestor = nodes.AccessNode(
out_storage_node.data, access=dtypes.AccessType.ReadOnly)
graph.add_node(array_closest_ancestor)
# array_closest_ancestor now points to the node we want to connect
# to the map entry
# always have to create out transient in this case
self.create_out_transient = True
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
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 = LocalStorage(nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_out
local_storage.apply(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
if shortcut:
nstate.out_edges(out_transient_node_inner)[0].data.wcr = None
nstate.out_edges(out_transient_node_inner)[0].data.volume = 1
if shortcut:
nstate.out_edges(out_transient_node_inner)[0].data.wcr = None
nstate.out_edges(out_transient_node_inner)[0].data.volume = 1
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
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 = LocalStorage(nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_in
local_storage.apply(nsdfg.sdfg)
in_transient_node_inner = local_storage._data_node
# push to shared memory / default
nsdfg.sdfg.data(in_transient_node_inner.data
).storage = dtypes.StorageType.Register
# first, inline fuse back our nested SDFG
from dace.transformation.interstate import InlineSDFG
inline_sdfg = InlineSDFG(
sdfg.sdfg_list.index(sdfg),
sdfg.nodes().index(graph),
{InlineSDFG._nested_sdfg: graph.nodes().index(nsdfg)}, 0)
inline_sdfg.apply(sdfg)
if not shortcut:
reduction_type = detect_reduction_type(wcr)
try:
code = ReduceExpansion.reduction_type_update[reduction_type]
except KeyError:
raise NotImplementedError(
"Not yet implemented for custom reduction")
new_tasklet = graph.add_tasklet(
name="reduction_transient_update",
inputs={"reduction_in", "array_in"},
outputs={"out"},
code=code)
edge_to_remove = graph.out_edges(out_transient_node_inner)[0] \
if self.create_out_transient \
else graph.out_edges(inner_exit)[0]
new_memlet_array_inner = Memlet(data=out_storage_node.data,
volume=1,
subset=edge_to_remove.data.subset)
new_memlet_array_outer = Memlet(
data=array_closest_ancestor.data,
volume=graph.in_edges(outer_entry)[0].data.volume,
subset=subsets.Range.from_array(
sdfg.data(out_storage_node.data)))
new_memlet_reduction = Memlet(
data=graph.out_edges(inner_exit)[0].data.data,
volume=1,
subset=graph.out_edges(inner_exit)[0].data.subset)
new_memlet_out_inner = Memlet(data=edge_to_remove.data.data,
volume=1,
subset=edge_to_remove.data.subset)
new_memlet_out_outer = dcpy(new_memlet_array_outer)
# remove old edges
outer_edge_to_remove = None
for edge in graph.out_edges(outer_exit):
if edge.src == edge_to_remove.dst:
outer_edge_to_remove = edge
graph.remove_edge_and_connectors(edge_to_remove)
graph.remove_edge_and_connectors(outer_edge_to_remove)
graph.add_edge(out_transient_node_inner if self.create_out_transient \
else inner_exit,
None,
new_tasklet,
"reduction_in",
new_memlet_reduction)
graph.add_edge(outer_entry, None, new_tasklet, "array_in",
new_memlet_array_inner)
graph.add_edge(array_closest_ancestor, None, outer_entry, None,
new_memlet_array_outer)
graph.add_edge(new_tasklet, "out", outer_exit, None,
new_memlet_out_inner)
graph.add_edge(outer_exit, None, out_storage_node, None,
new_memlet_out_outer)
# fill map scope connectors
graph.fill_scope_connectors()
graph._clear_scopedict_cache()
# wcr is already removed
# FORNOW: choose default schedule and implementation
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
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._new_reduce = reduce_node_new
self._outer_entry = outer_entry
if identity is None and self.create_out_transient:
# 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
def expand(self, sdfg, graph, 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
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 = LocalStorage(nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_out
local_storage.apply(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
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 = LocalStorage(nsdfg_id, nstate_id,
local_storage_subgraph, 0)
local_storage.array = array_in
local_storage.apply(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_list.index(sdfg),
sdfg.nodes().index(graph),
{InlineSDFG._nested_sdfg: graph.nodes().index(nsdfg)}, 0)
inline_sdfg.apply(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
def _argminmax(sdfg: SDFG,
state: SDFGState,
a: str,
axis,
func,
result_type=dace.int32,
return_both=False):
nest = NestedCall(sdfg, state)
assert func in ['min', 'max']
if axis is None or type(axis) is not int:
raise SyntaxError('Axis must be an int')
a_arr = sdfg.arrays[a]
if not 0 <= axis < len(a_arr.shape):
raise SyntaxError("Expected 0 <= axis < len({}.shape), got {}".format(
a, axis))
reduced_shape = list(copy.deepcopy(a_arr.shape))
reduced_shape.pop(axis)
val_and_idx = dace.struct('_val_and_idx', val=a_arr.dtype, idx=result_type)
# HACK: since identity cannot be specified for structs, we have to init the output array
reduced_structs, reduced_struct_arr = sdfg.add_temp_transient(
reduced_shape, val_and_idx)
code = "__init = _val_and_idx(val={}, idx=-1)".format(
dtypes.min_value(a_arr.dtype) if func ==
'max' else dtypes.max_value(a_arr.dtype))
nest.add_state().add_mapped_tasklet(
name="_arg{}_convert_".format(func),
map_ranges={
'__i%d' % i: '0:%s' % n
for i, n in enumerate(a_arr.shape) if i != axis
},
inputs={},
code=code,
outputs={
'__init':
Memlet.simple(
reduced_structs, ','.join('__i%d' % i
for i in range(len(a_arr.shape))
if i != axis))
},
external_edges=True)
nest.add_state().add_mapped_tasklet(
name="_arg{}_reduce_".format(func),
map_ranges={'__i%d' % i: '0:%s' % n
for i, n in enumerate(a_arr.shape)},
inputs={
'__in':
Memlet.simple(
a, ','.join('__i%d' % i for i in range(len(a_arr.shape))))
},
code="__out = _val_and_idx(idx={}, val=__in)".format("__i%d" % axis),
outputs={
'__out':
Memlet.simple(
reduced_structs,
','.join('__i%d' % i for i in range(len(a_arr.shape))
if i != axis),
wcr_str=("lambda x, y:"
"_val_and_idx(val={}(x.val, y.val), "
"idx=(y.idx if x.val {} y.val else x.idx))").format(
func, '<' if func == 'max' else '>'))
},
external_edges=True)
if return_both:
outidx, outidxarr = sdfg.add_temp_transient(
sdfg.arrays[reduced_structs].shape, result_type)
outval, outvalarr = sdfg.add_temp_transient(
sdfg.arrays[reduced_structs].shape, a_arr.dtype)
nest.add_state().add_mapped_tasklet(
name="_arg{}_extract_".format(func),
map_ranges={
'__i%d' % i: '0:%s' % n
for i, n in enumerate(a_arr.shape) if i != axis
},
inputs={
'__in':
Memlet.simple(
reduced_structs, ','.join('__i%d' % i
for i in range(len(a_arr.shape))
if i != axis))
},
code="__out_val = __in.val\n__out_idx = __in.idx",
outputs={
'__out_val':
Memlet.simple(
outval, ','.join('__i%d' % i
for i in range(len(a_arr.shape))
if i != axis)),
'__out_idx':
Memlet.simple(
outidx, ','.join('__i%d' % i
for i in range(len(a_arr.shape))
if i != axis))
},
external_edges=True)
return nest, (outval, outidx)
else:
# map to result_type
out, outarr = sdfg.add_temp_transient(
sdfg.arrays[reduced_structs].shape, result_type)
nest(_elementwise)("lambda x: x.idx", reduced_structs, out_array=out)
return nest, out
