def cutout_state(state: SDFGState, *nodes: nd.Node, make_copy: bool = True) -> SDFG:
"""
Cut out a subgraph of a state from an SDFG to run separately for localized testing or optimization.
The subgraph defined by the list of nodes will be extended to include access nodes of data containers necessary
to run the graph separately. In addition, all transient data containers created outside the cut out graph will
become global.
:param state: The SDFG state in which the subgraph resides.
:param nodes: The nodes in the subgraph to cut out.
:param make_copy: If True, deep-copies every SDFG element in the copy. Otherwise, original references are kept.
"""
create_element = copy.deepcopy if make_copy else (lambda x: x)
sdfg = state.parent
subgraph: StateSubgraphView = StateSubgraphView(state, nodes)
subgraph = _extend_subgraph_with_access_nodes(state, subgraph)
other_arrays = _containers_defined_outside(sdfg, state, subgraph)
# Make a new SDFG with the included constants, used symbols, and data containers
new_sdfg = SDFG(f'{state.parent.name}_cutout', sdfg.constants_prop)
defined_syms = subgraph.defined_symbols()
freesyms = subgraph.free_symbols
for sym in freesyms:
new_sdfg.add_symbol(sym, defined_syms[sym])
for dnode in subgraph.data_nodes():
if dnode.data in new_sdfg.arrays:
continue
new_desc = sdfg.arrays[dnode.data].clone()
# If transient is defined outside, it becomes a global
if dnode.data in other_arrays:
new_desc.transient = False
new_sdfg.add_datadesc(dnode.data, new_desc)
# Add a single state with the extended subgraph
new_state = new_sdfg.add_state(state.label, is_start_state=True)
inserted_nodes: Dict[nd.Node, nd.Node] = {}
for e in subgraph.edges():
if e.src not in inserted_nodes:
inserted_nodes[e.src] = create_element(e.src)
if e.dst not in inserted_nodes:
inserted_nodes[e.dst] = create_element(e.dst)
new_state.add_edge(inserted_nodes[e.src], e.src_conn, inserted_nodes[e.dst], e.dst_conn, create_element(e.data))
# Insert remaining isolated nodes
for n in subgraph.nodes():
if n not in inserted_nodes:
inserted_nodes[n] = create_element(n)
new_state.add_node(inserted_nodes[n])
# Remove remaining dangling connectors from scope nodes
for node in inserted_nodes.values():
used_connectors = set(e.dst_conn for e in new_state.in_edges(node))
for conn in (node.in_connectors.keys() - used_connectors):
node.remove_in_connector(conn)
used_connectors = set(e.src_conn for e in new_state.out_edges(node))
for conn in (node.out_connectors.keys() - used_connectors):
node.remove_out_connector(conn)
return new_sdfg
def promote_scalars_to_symbols(sdfg: sd.SDFG,
ignore: Optional[Set[str]] = None,
transients_only: bool = True,
integers_only: bool = True) -> Set[str]:
"""
Promotes all matching transient scalars to SDFG symbols, changing all
tasklets to inter-state assignments. This enables the transformed symbols
to be used within states as part of memlets, and allows further
transformations (such as loop detection) to use the information for
optimization.
:param sdfg: The SDFG to run the pass on.
:param ignore: An optional set of strings of scalars to ignore.
:param transients_only: If False, also considers global data descriptors (e.g., arguments).
:param integers_only: If False, also considers non-integral descriptors for promotion.
:return: Set of promoted scalars.
:note: Operates in-place.
"""
# Process:
# 1. Find scalars to promote
# 2. For every assignment tasklet/access:
# 2.1. Fission state to isolate assignment
# 2.2. Replace assignment with inter-state edge assignment
# 3. For every read of the scalar:
# 3.1. If destination is tasklet, remove node, edges, and connectors
# 3.2. If used in tasklet as subscript or connector, modify tasklet code
# 3.3. If destination is array, change to tasklet that copies symbol data
# 4. Remove newly-isolated access nodes
# 5. Remove data descriptors and add symbols to SDFG
# 6. Replace subscripts in all interstate conditions and assignments
# 7. Make indirections with symbols a single memlet
to_promote = find_promotable_scalars(sdfg,
transients_only=transients_only,
integers_only=integers_only)
if ignore:
to_promote -= ignore
if len(to_promote) == 0:
return to_promote
for state in sdfg.nodes():
scalar_nodes = [
n for n in state.nodes()
if isinstance(n, nodes.AccessNode) and n.data in to_promote
]
# Step 2: Assignment tasklets
for node in scalar_nodes:
if state.in_degree(node) == 0:
continue
in_edge = state.in_edges(node)[0]
input = in_edge.src
# There is only zero or one incoming edges by definition
tasklet_inputs = [e.src for e in state.in_edges(input)]
# Step 2.1
new_state = xfh.state_fission(
sdfg,
gr.SubgraphView(state, set([input, node] + tasklet_inputs)))
new_isedge: sd.InterstateEdge = sdfg.out_edges(new_state)[0]
# Step 2.2
node: nodes.AccessNode = new_state.sink_nodes()[0]
input = new_state.in_edges(node)[0].src
if isinstance(input, nodes.Tasklet):
# Convert tasklet to interstate edge
newcode: str = ''
if input.language is dtypes.Language.Python:
newcode = astutils.unparse(input.code.code[0].value)
elif input.language is dtypes.Language.CPP:
newcode = translate_cpp_tasklet_to_python(
input.code.as_string.strip())
# Replace tasklet inputs with incoming edges
for e in new_state.in_edges(input):
memlet_str: str = e.data.data
if (e.data.subset is not None and not isinstance(
sdfg.arrays[memlet_str], dt.Scalar)):
memlet_str += '[%s]' % e.data.subset
newcode = re.sub(r'\b%s\b' % re.escape(e.dst_conn),
memlet_str, newcode)
# Add interstate edge assignment
new_isedge.data.assignments[node.data] = newcode
elif isinstance(input, nodes.AccessNode):
memlet: mm.Memlet = in_edge.data
if (memlet.src_subset and
not isinstance(sdfg.arrays[memlet.data], dt.Scalar)):
new_isedge.data.assignments[
node.data] = '%s[%s]' % (input.data, memlet.src_subset)
else:
new_isedge.data.assignments[node.data] = input.data
# Clean up all nodes after assignment was transferred
new_state.remove_nodes_from(new_state.nodes())
# Step 3: Scalar reads
remove_scalar_reads(sdfg, {k: k for k in to_promote})
# Step 4: Isolated nodes
for state in sdfg.nodes():
scalar_nodes = [
n for n in state.nodes()
if isinstance(n, nodes.AccessNode) and n.data in to_promote
]
state.remove_nodes_from(
[n for n in scalar_nodes if len(state.all_edges(n)) == 0])
# Step 5: Data descriptor management
for scalar in to_promote:
desc = sdfg.arrays[scalar]
sdfg.remove_data(scalar, validate=False)
# If the scalar is already a symbol (e.g., as part of an array size),
# do not re-add the symbol
if scalar not in sdfg.symbols:
sdfg.add_symbol(scalar, desc.dtype)
# Step 6: Inter-state edge cleanup
cleanup_re = {
s: re.compile(fr'\b{re.escape(s)}\[.*?\]')
for s in to_promote
}
promo = TaskletPromoterDict({k: k for k in to_promote})
for edge in sdfg.edges():
ise: InterstateEdge = edge.data
# Condition
if not edge.data.is_unconditional():
if ise.condition.language is dtypes.Language.Python:
for stmt in ise.condition.code:
promo.visit(stmt)
elif ise.condition.language is dtypes.Language.CPP:
for scalar in to_promote:
ise.condition = cleanup_re[scalar].sub(
scalar, ise.condition.as_string)
# Assignments
for aname, assignment in ise.assignments.items():
for scalar in to_promote:
if scalar in assignment:
ise.assignments[aname] = cleanup_re[scalar].sub(
scalar, assignment.strip())
# Step 7: Indirection
remove_symbol_indirection(sdfg)
return to_promote
class ONNXModel:
"""Loads an ONNX model into an SDFG."""
def __init__(self, name, model: onnx.ModelProto, cuda=False):
"""
Constructs a new ONNXImporter.
:param name: the name for the SDFG.
:param model: the model to import.
:param cuda: if `True`, weights will be passed as cuda arrays.
"""
graph: onnx.GraphProto = model.graph
self.sdfg = SDFG(name)
self.cuda = cuda
self.state = self.sdfg.add_state()
# Add all values to the SDFG, check for unsupported ops
##########################################
self.value_infos = {}
self.inputs = []
self.outputs = []
for value, is_input in chain(zip(graph.input, repeat(True)),
zip(graph.output, repeat(False))):
if not value.HasField("name"):
raise ValueError("Got input or output without name")
if is_input:
self.inputs.append(value.name)
else:
self.outputs.append(value.name)
self.value_infos[value.name] = value
self._add_value_info(value)
for value in graph.value_info:
if not value.HasField("name"):
raise ValueError("Got input or output without name")
if value.name not in self.value_infos:
self.value_infos[value.name] = value
# add weights
self.weights = {}
for init in graph.initializer:
self._add_constant_tensor(init)
access_nodes = {}
self._idx_to_node = []
for i, node in enumerate(graph.node):
if not has_onnx_node(node.op_type):
raise ValueError("Unsupported ONNX operator: '{}'".format(
node.op_type))
# extract the op attributes
op_attributes = {
attribute_proto.name: convert_attribute_proto(attribute_proto)
for attribute_proto in node.attribute
}
if node.HasField("name"):
node_name = clean_onnx_name(node.name)
else:
node_name = node.op_type + "_" + str(i)
# construct the dace node
op_node = get_onnx_node(node.op_type)(node_name, **op_attributes)
self.state.add_node(op_node)
self._idx_to_node.append(op_node)
for param_idx, (name, is_input) in chain(
enumerate(zip(node.input, repeat(True))),
enumerate(zip(node.output, repeat(False)))):
if clean_onnx_name(name) not in self.sdfg.arrays:
if name not in self.value_infos:
raise ValueError(
"Could not find array with name '{}'".format(name))
self._add_value_info(self.value_infos[name])
# get the access node
if name in access_nodes:
access = access_nodes[name]
self._update_access_type(access, is_input)
else:
access = nd.AccessNode(
clean_onnx_name(name), AccessType.ReadOnly
if is_input else AccessType.WriteOnly)
self.state.add_node(access)
access_nodes[name] = access
# get the connector name
params = op_node.schema.inputs if is_input else op_node.schema.outputs
params_len = len(params)
if param_idx >= params_len:
# this is a variadic parameter. Then the last parameter of the parameter must be variadic.
if params[-1].param_type != ONNXParameterType.Variadic:
raise ValueError(
"Expected the last {i_or_o} parameter to be variadic,"
" since the {i_or_o} with idx {param_idx} has more parameters than the schema ({params_len})"
.format(i_or_o="input" if is_input else "output",
param_idx=param_idx,
params_len=params_len))
conn_name = params[-1].name + "__" + str(param_idx -
params_len + 1)
elif params[
param_idx].param_type == ONNXParameterType.Variadic:
# this is a variadic parameter, and it is within the range of params, so it must be the first
# instance of a variadic parameter
conn_name = params[param_idx].name + "__0"
else:
conn_name = params[param_idx].name
data_desc = self.sdfg.arrays[clean_onnx_name(name)]
# add the connector if required, and add an edge
if is_input:
if conn_name not in op_node.in_connectors:
op_node.add_in_connector(conn_name)
self.state.add_edge(
access, None, op_node, conn_name,
dace.Memlet.from_array(clean_onnx_name(name),
data_desc))
else:
if conn_name not in op_node.out_connectors:
op_node.add_out_connector(conn_name)
self.state.add_edge(
op_node, conn_name, access, None,
dace.Memlet.from_array(clean_onnx_name(name),
data_desc))
if self.cuda:
self.sdfg.apply_strict_transformations()
self.sdfg.apply_gpu_transformations()
self.sdfg.apply_strict_transformations()
# set all gpu transients to be persistent
for _, _, arr in self.sdfg.arrays_recursive():
if arr.transient and arr.storage == StorageType.GPU_Global:
arr.lifetime = AllocationLifetime.Persistent
@staticmethod
def _update_access_type(node: dace.nodes.AccessNode, is_input: bool):
if node.access == AccessType.ReadOnly and not is_input:
node.access = AccessType.ReadWrite
elif node.access == AccessType.WriteOnly and is_input:
node.access = AccessType.ReadWrite
def _add_constant_tensor(self, tensor: onnx.TensorProto):
if not tensor.HasField("name"):
raise ValueError("Got tensor without name")
if not tensor.HasField("data_type"):
raise ValueError("Initializer tensor '{}' has no type".format(
tensor.name))
name = clean_onnx_name(tensor.name)
dtype = onnx_tensor_type_to_typeclass(tensor.data_type)
if len(tensor.dims) == 0:
# this is a scalar
self.sdfg.add_scalar(name, dtype)
else:
dims = [d for d in tensor.dims]
if name not in self.sdfg.arrays:
self.sdfg.add_array(name, dims, dtype)
else:
existing_arr = self.sdfg.arrays[name]
if existing_arr.dtype != dtype:
raise ValueError(
"Invalid ONNX model; found two values with name '{}', but different dtypes ({} and {})"
.format(name, existing_arr.dtype, dtype))
if tuple(existing_arr.shape) != tuple(dims):
raise ValueError(
"Invalid ONNX model; found two values with name '{}', but different dimensions ({} and {})"
.format(name, existing_arr.shape, dims))
self.weights[tensor.name] = numpy_helper.to_array(tensor)
def _add_value_info(self, value_info: onnx.ValueInfoProto):
if not value_info.HasField("name"):
raise ValueError("Got value without name")
name = value_info.name
if not _nested_HasField(value_info, "type.tensor_type.shape"):
raise ValueError(
"Value '{}' does not have a shape in this graph."
" Please run shape inference before importing.".format(name))
tensor_type = value_info.type.tensor_type
if not tensor_type.HasField("elem_type"):
raise ValueError(
"Value '{}' does not have a type in this graph."
" Please run type inference before importing.".format(name))
shape = []
for d in tensor_type.shape.dim:
if d.HasField("dim_value"):
shape.append(d.dim_value)
elif d.HasField("dim_param"):
parsed = pystr_to_symbolic(d.dim_param)
for sym in parsed.free_symbols:
if clean_onnx_name(str(sym)) not in self.sdfg.symbols:
self.sdfg.add_symbol(clean_onnx_name(str(sym)),
stype=int)
parsed = parsed.subs(
sym, dace.symbol(clean_onnx_name(str(sym))))
shape.append(parsed)
else:
raise ValueError(
"Value '{}' does not have a shape in this graph."
" Please run shape inference before importing.".format(
name))
transient = name not in self.inputs and name not in self.outputs
if len(shape) == 0:
self.sdfg.add_scalar(clean_onnx_name(name),
dtype=onnx_tensor_type_to_typeclass(
tensor_type.elem_type),
transient=transient)
else:
self.sdfg.add_array(clean_onnx_name(name),
shape=shape,
dtype=onnx_tensor_type_to_typeclass(
tensor_type.elem_type),
transient=transient)
def __call__(self, *args, **inputs):
sdfg = deepcopy(self.sdfg)
# convert the positional args to kwargs
if len(args) > len(self.inputs):
raise ValueError("Expected {} arguments, got {}".format(
len(self.inputs), len(args)))
inputs.update(dict(zip(self.inputs, args)))
# check that there are no missing inputs
if len(set(self.inputs).difference(inputs)) != 0:
raise ValueError("Missing inputs {}".format(", ".join(
set(self.inputs).difference(inputs))))
# check that there are no unknown inputs
# NOTE symbols can only be passed as kwargs
if len(
set(inputs).difference(self.inputs).difference(
sdfg.free_symbols)) != 0:
raise ValueError("Unknown inputs {}".format(", ".join(
set(inputs).difference(self.inputs))))
clean_inputs = {}
for input, arr in inputs.items():
if input in sdfg.free_symbols:
clean_inputs[input] = arr
else:
clean_inputs[clean_onnx_name(input)] = arr
# add the weights
params = {}
for name, arr in self.weights.items():
if len(arr.shape) == 0:
params[clean_onnx_name(name)] = arr[()]
else:
if self.cuda:
clean_name = clean_onnx_name(name)
sdfg.arrays[clean_name].storage = StorageType.GPU_Global
params[clean_name] = numba.cuda.to_device(arr)
else:
params[clean_onnx_name(name)] = arr.copy()
inferred_symbols = infer_symbols_from_shapes(sdfg, {
**clean_inputs,
**params
})
# TODO @orausch if this is removed the SDFG complains
# TypeError: Type mismatch for argument ONNX_unk__493: expected scalar type, got <class 'sympy.core.numbers.Integer'>
# fix this better
inferred_symbols = {k: int(v) for k, v in inferred_symbols.items()}
def eval_dim(dim):
for sym in dim.free_symbols:
dim = dim.subs(sym, inferred_symbols[sym.name])
return dim
outputs = OrderedDict()
# create numpy arrays for the outputs
for output in self.outputs:
clean_name = clean_onnx_name(output)
arr = sdfg.arrays[clean_name]
# TODO @orausch add error handling for evalf
shape = [
eval_dim(d) if type(d) is dace.symbol else d for d in arr.shape
]
outputs[clean_name] = np.empty(shape,
dtype=arr.dtype.as_numpy_dtype())
sdfg.expand_library_nodes()
#sdfg.apply_strict_transformations()
sdfg(**clean_inputs, **params, **outputs, **inferred_symbols)
if len(outputs) == 1:
return next(iter(outputs.values()))
return tuple(outputs.values())
def nest_state_subgraph(sdfg: SDFG,
state: SDFGState,
subgraph: SubgraphView,
name: Optional[str] = None,
full_data: bool = False) -> nodes.NestedSDFG:
""" Turns a state subgraph into a nested SDFG. Operates in-place.
:param sdfg: The SDFG containing the state subgraph.
:param state: The state containing the subgraph.
:param subgraph: Subgraph to nest.
:param name: An optional name for the nested SDFG.
:param full_data: If True, nests entire input/output data.
:return: The nested SDFG node.
:raise KeyError: Some or all nodes in the subgraph are not located in
this state, or the state does not belong to the given
SDFG.
:raise ValueError: The subgraph is contained in more than one scope.
"""
if state.parent != sdfg:
raise KeyError('State does not belong to given SDFG')
if subgraph is not state and subgraph.graph is not state:
raise KeyError('Subgraph does not belong to given state')
# Find the top-level scope
scope_tree = state.scope_tree()
scope_dict = state.scope_dict()
scope_dict_children = state.scope_children()
top_scopenode = -1 # Initialized to -1 since "None" already means top-level
for node in subgraph.nodes():
if node not in scope_dict:
raise KeyError('Node not found in state')
# If scope entry/exit, ensure entire scope is in subgraph
if isinstance(node, nodes.EntryNode):
scope_nodes = scope_dict_children[node]
if any(n not in subgraph.nodes() for n in scope_nodes):
raise ValueError('Subgraph contains partial scopes (entry)')
elif isinstance(node, nodes.ExitNode):
entry = state.entry_node(node)
scope_nodes = scope_dict_children[entry] + [entry]
if any(n not in subgraph.nodes() for n in scope_nodes):
raise ValueError('Subgraph contains partial scopes (exit)')
scope_node = scope_dict[node]
if scope_node not in subgraph.nodes():
if top_scopenode != -1 and top_scopenode != scope_node:
raise ValueError('Subgraph is contained in more than one scope')
top_scopenode = scope_node
scope = scope_tree[top_scopenode]
###
# Consolidate edges in top scope
utils.consolidate_edges(sdfg, scope)
snodes = subgraph.nodes()
# Collect inputs and outputs of the nested SDFG
inputs: List[MultiConnectorEdge] = []
outputs: List[MultiConnectorEdge] = []
for node in snodes:
for edge in state.in_edges(node):
if edge.src not in snodes:
inputs.append(edge)
for edge in state.out_edges(node):
if edge.dst not in snodes:
outputs.append(edge)
# Collect transients not used outside of subgraph (will be removed of
# top-level graph)
data_in_subgraph = set(n.data for n in subgraph.nodes() if isinstance(n, nodes.AccessNode))
# Find other occurrences in SDFG
other_nodes = set(n.data for s in sdfg.nodes() for n in s.nodes()
if isinstance(n, nodes.AccessNode) and n not in subgraph.nodes())
subgraph_transients = set()
for data in data_in_subgraph:
datadesc = sdfg.arrays[data]
if datadesc.transient and data not in other_nodes:
subgraph_transients.add(data)
# All transients of edges between code nodes are also added to nested graph
for edge in subgraph.edges():
if (isinstance(edge.src, nodes.CodeNode) and isinstance(edge.dst, nodes.CodeNode)):
subgraph_transients.add(edge.data.data)
# Collect data used in access nodes within subgraph (will be referenced in
# full upon nesting)
input_arrays = set()
output_arrays = {}
for node in subgraph.nodes():
if (isinstance(node, nodes.AccessNode) and node.data not in subgraph_transients):
if node.has_reads(state):
input_arrays.add(node.data)
if node.has_writes(state):
output_arrays[node.data] = state.in_edges(node)[0].data.wcr
# Create the nested SDFG
nsdfg = SDFG(name or 'nested_' + state.label)
# Transients are added to the nested graph as-is
for name in subgraph_transients:
nsdfg.add_datadesc(name, sdfg.arrays[name])
# Input/output data that are not source/sink nodes are added to the graph
# as non-transients
for name in (input_arrays | output_arrays.keys()):
datadesc = copy.deepcopy(sdfg.arrays[name])
datadesc.transient = False
nsdfg.add_datadesc(name, datadesc)
# Connected source/sink nodes outside subgraph become global data
# descriptors in nested SDFG
input_names = {}
output_names = {}
global_subsets: Dict[str, Tuple[str, Subset]] = {}
for edge in inputs:
if edge.data.data is None: # Skip edges with an empty memlet
continue
name = edge.data.data
if name not in global_subsets:
datadesc = copy.deepcopy(sdfg.arrays[edge.data.data])
datadesc.transient = False
if not full_data:
datadesc.shape = edge.data.subset.size()
new_name = nsdfg.add_datadesc(name, datadesc, find_new_name=True)
global_subsets[name] = (new_name, edge.data.subset)
else:
new_name, subset = global_subsets[name]
if not full_data:
new_subset = union(subset, edge.data.subset)
if new_subset is None:
new_subset = Range.from_array(sdfg.arrays[name])
global_subsets[name] = (new_name, new_subset)
nsdfg.arrays[new_name].shape = new_subset.size()
input_names[edge] = new_name
for edge in outputs:
if edge.data.data is None: # Skip edges with an empty memlet
continue
name = edge.data.data
if name not in global_subsets:
datadesc = copy.deepcopy(sdfg.arrays[edge.data.data])
datadesc.transient = False
if not full_data:
datadesc.shape = edge.data.subset.size()
new_name = nsdfg.add_datadesc(name, datadesc, find_new_name=True)
global_subsets[name] = (new_name, edge.data.subset)
else:
new_name, subset = global_subsets[name]
if not full_data:
new_subset = union(subset, edge.data.subset)
if new_subset is None:
new_subset = Range.from_array(sdfg.arrays[name])
global_subsets[name] = (new_name, new_subset)
nsdfg.arrays[new_name].shape = new_subset.size()
output_names[edge] = new_name
###################
# Add scope symbols to the nested SDFG
defined_vars = set(
symbolic.pystr_to_symbolic(s) for s in (state.symbols_defined_at(top_scopenode).keys()
| sdfg.symbols))
for v in defined_vars:
if v in sdfg.symbols:
sym = sdfg.symbols[v]
nsdfg.add_symbol(v, sym.dtype)
# Add constants to nested SDFG
for cstname, cstval in sdfg.constants.items():
nsdfg.add_constant(cstname, cstval)
# Create nested state
nstate = nsdfg.add_state()
# Add subgraph nodes and edges to nested state
nstate.add_nodes_from(subgraph.nodes())
for e in subgraph.edges():
nstate.add_edge(e.src, e.src_conn, e.dst, e.dst_conn, copy.deepcopy(e.data))
# Modify nested SDFG parents in subgraph
for node in subgraph.nodes():
if isinstance(node, nodes.NestedSDFG):
node.sdfg.parent = nstate
node.sdfg.parent_sdfg = nsdfg
node.sdfg.parent_nsdfg_node = node
# Add access nodes and edges as necessary
edges_to_offset = []
for edge, name in input_names.items():
node = nstate.add_read(name)
new_edge = copy.deepcopy(edge.data)
new_edge.data = name
edges_to_offset.append((edge, nstate.add_edge(node, None, edge.dst, edge.dst_conn, new_edge)))
for edge, name in output_names.items():
node = nstate.add_write(name)
new_edge = copy.deepcopy(edge.data)
new_edge.data = name
edges_to_offset.append((edge, nstate.add_edge(edge.src, edge.src_conn, node, None, new_edge)))
# Offset memlet paths inside nested SDFG according to subsets
for original_edge, new_edge in edges_to_offset:
for edge in nstate.memlet_tree(new_edge):
edge.data.data = new_edge.data.data
if not full_data:
edge.data.subset.offset(global_subsets[original_edge.data.data][1], True)
# Add nested SDFG node to the input state
nested_sdfg = state.add_nested_sdfg(nsdfg, None,
set(input_names.values()) | input_arrays,
set(output_names.values()) | output_arrays.keys())
# Reconnect memlets to nested SDFG
reconnected_in = set()
reconnected_out = set()
empty_input = None
empty_output = None
for edge in inputs:
if edge.data.data is None:
empty_input = edge
continue
name = input_names[edge]
if name in reconnected_in:
continue
if full_data:
data = Memlet.from_array(edge.data.data, sdfg.arrays[edge.data.data])
else:
data = copy.deepcopy(edge.data)
data.subset = global_subsets[edge.data.data][1]
state.add_edge(edge.src, edge.src_conn, nested_sdfg, name, data)
reconnected_in.add(name)
for edge in outputs:
if edge.data.data is None:
empty_output = edge
continue
name = output_names[edge]
if name in reconnected_out:
continue
if full_data:
data = Memlet.from_array(edge.data.data, sdfg.arrays[edge.data.data])
else:
data = copy.deepcopy(edge.data)
data.subset = global_subsets[edge.data.data][1]
data.wcr = edge.data.wcr
state.add_edge(nested_sdfg, name, edge.dst, edge.dst_conn, data)
reconnected_out.add(name)
# Connect access nodes to internal input/output data as necessary
entry = scope.entry
exit = scope.exit
for name in input_arrays:
node = state.add_read(name)
if entry is not None:
state.add_nedge(entry, node, Memlet())
state.add_edge(node, None, nested_sdfg, name, Memlet.from_array(name, sdfg.arrays[name]))
for name, wcr in output_arrays.items():
node = state.add_write(name)
if exit is not None:
state.add_nedge(node, exit, Memlet())
state.add_edge(nested_sdfg, name, node, None, Memlet(data=name, wcr=wcr))
# Graph was not reconnected, but needs to be
if state.in_degree(nested_sdfg) == 0 and empty_input is not None:
state.add_edge(empty_input.src, empty_input.src_conn, nested_sdfg, None, empty_input.data)
if state.out_degree(nested_sdfg) == 0 and empty_output is not None:
state.add_edge(nested_sdfg, None, empty_output.dst, empty_output.dst_conn, empty_output.data)
# Remove subgraph nodes from graph
state.remove_nodes_from(subgraph.nodes())
# Remove subgraph transients from top-level graph
for transient in subgraph_transients:
del sdfg.arrays[transient]
# Remove newly isolated nodes due to memlet consolidation
for edge in inputs:
if state.in_degree(edge.src) + state.out_degree(edge.src) == 0:
state.remove_node(edge.src)
for edge in outputs:
if state.in_degree(edge.dst) + state.out_degree(edge.dst) == 0:
state.remove_node(edge.dst)
return nested_sdfg
def nest_state_subgraph(sdfg: SDFG,
state: SDFGState,
subgraph: SubgraphView,
name: Optional[str] = None,
full_data: bool = False) -> nodes.NestedSDFG:
""" Turns a state subgraph into a nested SDFG. Operates in-place.
:param sdfg: The SDFG containing the state subgraph.
:param state: The state containing the subgraph.
:param subgraph: Subgraph to nest.
:param name: An optional name for the nested SDFG.
:param full_data: If True, nests entire input/output data.
:return: The nested SDFG node.
:raise KeyError: Some or all nodes in the subgraph are not located in
this state, or the state does not belong to the given
SDFG.
:raise ValueError: The subgraph is contained in more than one scope.
"""
if state.parent != sdfg:
raise KeyError('State does not belong to given SDFG')
if subgraph.graph != state:
raise KeyError('Subgraph does not belong to given state')
# Find the top-level scope
scope_tree = state.scope_tree()
scope_dict = state.scope_dict()
scope_dict_children = state.scope_dict(True)
top_scopenode = -1 # Initialized to -1 since "None" already means top-level
for node in subgraph.nodes():
if node not in scope_dict:
raise KeyError('Node not found in state')
# If scope entry/exit, ensure entire scope is in subgraph
if isinstance(node, nodes.EntryNode):
scope_nodes = scope_dict_children[node]
if any(n not in subgraph.nodes() for n in scope_nodes):
raise ValueError('Subgraph contains partial scopes (entry)')
elif isinstance(node, nodes.ExitNode):
entry = state.entry_node(node)
scope_nodes = scope_dict_children[entry] + [entry]
if any(n not in subgraph.nodes() for n in scope_nodes):
raise ValueError('Subgraph contains partial scopes (exit)')
scope_node = scope_dict[node]
if scope_node not in subgraph.nodes():
if top_scopenode != -1 and top_scopenode != scope_node:
raise ValueError(
'Subgraph is contained in more than one scope')
top_scopenode = scope_node
scope = scope_tree[top_scopenode]
###
# Collect inputs and outputs of the nested SDFG
inputs: List[MultiConnectorEdge] = []
outputs: List[MultiConnectorEdge] = []
for node in subgraph.source_nodes():
inputs.extend(state.in_edges(node))
for node in subgraph.sink_nodes():
outputs.extend(state.out_edges(node))
# Collect transients not used outside of subgraph (will be removed of
# top-level graph)
data_in_subgraph = set(n.data for n in subgraph.nodes()
if isinstance(n, nodes.AccessNode))
# Find other occurrences in SDFG
other_nodes = set(
n.data for s in sdfg.nodes() for n in s.nodes()
if isinstance(n, nodes.AccessNode) and n not in subgraph.nodes())
subgraph_transients = set()
for data in data_in_subgraph:
datadesc = sdfg.arrays[data]
if datadesc.transient and data not in other_nodes:
subgraph_transients.add(data)
# All transients of edges between code nodes are also added to nested graph
for edge in subgraph.edges():
if (isinstance(edge.src, nodes.CodeNode)
and isinstance(edge.dst, nodes.CodeNode)):
subgraph_transients.add(edge.data.data)
# Collect data used in access nodes within subgraph (will be referenced in
# full upon nesting)
input_arrays = set()
output_arrays = set()
for node in subgraph.nodes():
if (isinstance(node, nodes.AccessNode)
and node.data not in subgraph_transients):
if state.out_degree(node) > 0:
input_arrays.add(node.data)
if state.in_degree(node) > 0:
output_arrays.add(node.data)
# Create the nested SDFG
nsdfg = SDFG(name or 'nested_' + state.label)
# Transients are added to the nested graph as-is
for name in subgraph_transients:
nsdfg.add_datadesc(name, sdfg.arrays[name])
# Input/output data that are not source/sink nodes are added to the graph
# as non-transients
for name in (input_arrays | output_arrays):
datadesc = copy.deepcopy(sdfg.arrays[name])
datadesc.transient = False
nsdfg.add_datadesc(name, datadesc)
# Connected source/sink nodes outside subgraph become global data
# descriptors in nested SDFG
input_names = []
output_names = []
for edge in inputs:
if edge.data.data is None: # Skip edges with an empty memlet
continue
name = '__in_' + edge.data.data
datadesc = copy.deepcopy(sdfg.arrays[edge.data.data])
datadesc.transient = False
if not full_data:
datadesc.shape = edge.data.subset.size()
input_names.append(
nsdfg.add_datadesc(name, datadesc, find_new_name=True))
for edge in outputs:
if edge.data.data is None: # Skip edges with an empty memlet
continue
name = '__out_' + edge.data.data
datadesc = copy.deepcopy(sdfg.arrays[edge.data.data])
datadesc.transient = False
if not full_data:
datadesc.shape = edge.data.subset.size()
output_names.append(
nsdfg.add_datadesc(name, datadesc, find_new_name=True))
###################
# Add scope symbols to the nested SDFG
for v in scope.defined_vars:
if v in sdfg.symbols:
sym = sdfg.symbols[v]
nsdfg.add_symbol(v, sym.dtype)
# Create nested state
nstate = nsdfg.add_state()
# Add subgraph nodes and edges to nested state
nstate.add_nodes_from(subgraph.nodes())
for e in subgraph.edges():
nstate.add_edge(e.src, e.src_conn, e.dst, e.dst_conn, e.data)
# Modify nested SDFG parents in subgraph
for node in subgraph.nodes():
if isinstance(node, nodes.NestedSDFG):
node.sdfg.parent = nstate
node.sdfg.parent_sdfg = nsdfg
# Add access nodes and edges as necessary
edges_to_offset = []
for name, edge in zip(input_names, inputs):
node = nstate.add_read(name)
new_edge = copy.deepcopy(edge.data)
new_edge.data = name
edges_to_offset.append((edge,
nstate.add_edge(node, None, edge.dst,
edge.dst_conn, new_edge)))
for name, edge in zip(output_names, outputs):
node = nstate.add_write(name)
new_edge = copy.deepcopy(edge.data)
new_edge.data = name
edges_to_offset.append((edge,
nstate.add_edge(edge.src, edge.src_conn, node,
None, new_edge)))
# Offset memlet paths inside nested SDFG according to subsets
for original_edge, new_edge in edges_to_offset:
for edge in nstate.memlet_tree(new_edge):
edge.data.data = new_edge.data.data
if not full_data:
edge.data.subset.offset(original_edge.data.subset, True)
# Add nested SDFG node to the input state
nested_sdfg = state.add_nested_sdfg(nsdfg, None,
set(input_names) | input_arrays,
set(output_names) | output_arrays)
# Reconnect memlets to nested SDFG
for name, edge in zip(input_names, inputs):
if full_data:
data = Memlet.from_array(edge.data.data,
sdfg.arrays[edge.data.data])
else:
data = edge.data
state.add_edge(edge.src, edge.src_conn, nested_sdfg, name, data)
for name, edge in zip(output_names, outputs):
if full_data:
data = Memlet.from_array(edge.data.data,
sdfg.arrays[edge.data.data])
else:
data = edge.data
state.add_edge(nested_sdfg, name, edge.dst, edge.dst_conn, data)
# Connect access nodes to internal input/output data as necessary
entry = scope.entry
exit = scope.exit
for name in input_arrays:
node = state.add_read(name)
if entry is not None:
state.add_nedge(entry, node, EmptyMemlet())
state.add_edge(node, None, nested_sdfg, name,
Memlet.from_array(name, sdfg.arrays[name]))
for name in output_arrays:
node = state.add_write(name)
if exit is not None:
state.add_nedge(node, exit, EmptyMemlet())
state.add_edge(nested_sdfg, name, node, None,
Memlet.from_array(name, sdfg.arrays[name]))
# Remove subgraph nodes from graph
state.remove_nodes_from(subgraph.nodes())
# Remove subgraph transients from top-level graph
for transient in subgraph_transients:
del sdfg.arrays[transient]
return nested_sdfg
def apply(self, sdfg: sd.SDFG):
# Obtain loop information
guard: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_guard])
body: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._loop_begin])
after: sd.SDFGState = sdfg.node(self.subgraph[DetectLoop._exit_state])
# Obtain iteration variable, range, and stride
itervar, (start, end, step), (_, body_end) = find_for_loop(
sdfg, guard, body, itervar=self.itervar)
# Find all loop-body states
states = set([body_end])
to_visit = [body]
while to_visit:
state = to_visit.pop(0)
if state is body_end:
continue
for _, dst, _ in sdfg.out_edges(state):
if dst not in states:
to_visit.append(dst)
states.add(state)
# Nest loop-body states
if len(states) > 1:
# Find read/write sets
read_set, write_set = set(), set()
for state in states:
rset, wset = state.read_and_write_sets()
read_set |= rset
write_set |= wset
# Add data from edges
for src in states:
for dst in states:
for edge in sdfg.edges_between(src, dst):
for s in edge.data.free_symbols:
if s in sdfg.arrays:
read_set.add(s)
# Find NestedSDFG's unique data
rw_set = read_set | write_set
unique_set = set()
for name in rw_set:
if not sdfg.arrays[name].transient:
continue
found = False
for state in sdfg.states():
if state in states:
continue
for node in state.nodes():
if (isinstance(node, nodes.AccessNode) and
node.data == name):
found = True
break
if not found:
unique_set.add(name)
# Find NestedSDFG's connectors
read_set = {n for n in read_set if n not in unique_set or not sdfg.arrays[n].transient}
write_set = {n for n in write_set if n not in unique_set or not sdfg.arrays[n].transient}
# Create NestedSDFG and add all loop-body states and edges
# Also, find defined symbols in NestedSDFG
fsymbols = set(sdfg.free_symbols)
new_body = sdfg.add_state('single_state_body')
nsdfg = SDFG("loop_body", constants=sdfg.constants, parent=new_body)
nsdfg.add_node(body, is_start_state=True)
body.parent = nsdfg
exit_state = nsdfg.add_state('exit')
nsymbols = dict()
for state in states:
if state is body:
continue
nsdfg.add_node(state)
state.parent = nsdfg
for state in states:
if state is body:
continue
for src, dst, data in sdfg.in_edges(state):
nsymbols.update({s: sdfg.symbols[s] for s in data.assignments.keys() if s in sdfg.symbols})
nsdfg.add_edge(src, dst, data)
nsdfg.add_edge(body_end, exit_state, InterstateEdge())
# Move guard -> body edge to guard -> new_body
for src, dst, data, in sdfg.edges_between(guard, body):
sdfg.add_edge(src, new_body, data)
# Move body_end -> guard edge to new_body -> guard
for src, dst, data in sdfg.edges_between(body_end, guard):
sdfg.add_edge(new_body, dst, data)
# Delete loop-body states and edges from parent SDFG
for state in states:
for e in sdfg.all_edges(state):
sdfg.remove_edge(e)
sdfg.remove_node(state)
# Add NestedSDFG arrays
for name in read_set | write_set:
nsdfg.arrays[name] = copy.deepcopy(sdfg.arrays[name])
nsdfg.arrays[name].transient = False
for name in unique_set:
nsdfg.arrays[name] = sdfg.arrays[name]
del sdfg.arrays[name]
# Add NestedSDFG node
cnode = new_body.add_nested_sdfg(nsdfg, None, read_set, write_set)
if sdfg.parent:
for s, m in sdfg.parent_nsdfg_node.symbol_mapping.items():
if s not in cnode.symbol_mapping:
cnode.symbol_mapping[s] = m
nsdfg.add_symbol(s, sdfg.symbols[s])
for name in read_set:
r = new_body.add_read(name)
new_body.add_edge(
r, None, cnode, name,
memlet.Memlet.from_array(name, sdfg.arrays[name]))
for name in write_set:
w = new_body.add_write(name)
new_body.add_edge(
cnode, name, w, None,
memlet.Memlet.from_array(name, sdfg.arrays[name]))
# Fix SDFG symbols
for sym in sdfg.free_symbols - fsymbols:
del sdfg.symbols[sym]
for sym, dtype in nsymbols.items():
nsdfg.symbols[sym] = dtype
# Change body state reference
body = new_body
if (step < 0) == True:
# If step is negative, we have to flip start and end to produce a
# correct map with a positive increment
start, end, step = end, start, -step
# If necessary, make a nested SDFG with assignments
isedge = sdfg.edges_between(guard, body)[0]
symbols_to_remove = set()
if len(isedge.data.assignments) > 0:
nsdfg = helpers.nest_state_subgraph(
sdfg, body, gr.SubgraphView(body, body.nodes()))
for sym in isedge.data.free_symbols:
if sym in nsdfg.symbol_mapping or sym in nsdfg.in_connectors:
continue
if sym in sdfg.symbols:
nsdfg.symbol_mapping[sym] = symbolic.pystr_to_symbolic(sym)
nsdfg.sdfg.add_symbol(sym, sdfg.symbols[sym])
elif sym in sdfg.arrays:
if sym in nsdfg.sdfg.arrays:
raise NotImplementedError
rnode = body.add_read(sym)
nsdfg.add_in_connector(sym)
desc = copy.deepcopy(sdfg.arrays[sym])
desc.transient = False
nsdfg.sdfg.add_datadesc(sym, desc)
body.add_edge(rnode, None, nsdfg, sym, memlet.Memlet(sym))
nstate = nsdfg.sdfg.node(0)
init_state = nsdfg.sdfg.add_state_before(nstate)
nisedge = nsdfg.sdfg.edges_between(init_state, nstate)[0]
nisedge.data.assignments = isedge.data.assignments
symbols_to_remove = set(nisedge.data.assignments.keys())
for k in nisedge.data.assignments.keys():
if k in nsdfg.symbol_mapping:
del nsdfg.symbol_mapping[k]
isedge.data.assignments = {}
source_nodes = body.source_nodes()
sink_nodes = body.sink_nodes()
map = nodes.Map(body.label + "_map", [itervar], [(start, end, step)])
entry = nodes.MapEntry(map)
exit = nodes.MapExit(map)
body.add_node(entry)
body.add_node(exit)
# If the map uses symbols from data containers, instantiate reads
containers_to_read = entry.free_symbols & sdfg.arrays.keys()
for rd in containers_to_read:
# We are guaranteed that this is always a scalar, because
# can_be_applied makes sure there are no sympy functions in each of
# the loop expresions
access_node = body.add_read(rd)
body.add_memlet_path(access_node,
entry,
dst_conn=rd,
memlet=memlet.Memlet(rd))
# Reroute all memlets through the entry and exit nodes
for n in source_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.out_edges(n):
body.remove_edge(e)
body.add_edge_pair(entry,
e.dst,
n,
e.data,
internal_connector=e.dst_conn)
else:
body.add_nedge(entry, n, memlet.Memlet())
for n in sink_nodes:
if isinstance(n, nodes.AccessNode):
for e in body.in_edges(n):
body.remove_edge(e)
body.add_edge_pair(exit,
e.src,
n,
e.data,
internal_connector=e.src_conn)
else:
body.add_nedge(n, exit, memlet.Memlet())
# Get rid of the loop exit condition edge
after_edge = sdfg.edges_between(guard, after)[0]
sdfg.remove_edge(after_edge)
# Remove the assignment on the edge to the guard
for e in sdfg.in_edges(guard):
if itervar in e.data.assignments:
del e.data.assignments[itervar]
# Remove the condition on the entry edge
condition_edge = sdfg.edges_between(guard, body)[0]
condition_edge.data.condition = CodeBlock("1")
# Get rid of backedge to guard
sdfg.remove_edge(sdfg.edges_between(body, guard)[0])
# Route body directly to after state, maintaining any other assignments
# it might have had
sdfg.add_edge(
body, after,
sd.InterstateEdge(assignments=after_edge.data.assignments))
# If this had made the iteration variable a free symbol, we can remove
# it from the SDFG symbols
if itervar in sdfg.free_symbols:
sdfg.remove_symbol(itervar)
for sym in symbols_to_remove:
if helpers.is_symbol_unused(sdfg, sym):
sdfg.remove_symbol(sym)
def generate_reference(name, chain):
"""Generates a simple, unoptimized SDFG to run on the CPU, for verification
purposes."""
sdfg = SDFG(name)
for k, v in chain.constants.items():
sdfg.add_constant(k, v["value"], dace.data.Scalar(v["data_type"]))
(dimensions_to_skip, shape, vector_length, parameters, iterators,
memcopy_indices, memcopy_accesses) = _generate_init(chain)
prev_state = sdfg.add_state("init")
# Throw vectorization in the bin for the reference code
vector_length = 1
shape = tuple(map(int, shape))
input_shapes = {} # Maps inputs to their shape tuple
for node in chain.graph.nodes():
if isinstance(node, Input) or isinstance(node, Output):
if isinstance(node, Input):
for output in node.outputs.values():
pars = tuple(
output["input_dims"]
) if "input_dims" in output and output[
"input_dims"] is not None else tuple(parameters)
arr_shape = tuple(s for s, p in zip(shape, parameters)
if p in pars)
input_shapes[node.name] = arr_shape
break
else:
raise ValueError("No outputs found for input node.")
else:
arr_shape = shape
if len(arr_shape) > 0:
try:
sdfg.add_array(node.name, arr_shape, node.data_type)
except NameError:
sdfg.data(
node.name).access = dace.dtypes.AccessType.ReadWrite
else:
sdfg.add_symbol(node.name, node.data_type)
for link in chain.graph.edges(data=True):
name = link[0].name
if name not in sdfg.arrays and name not in sdfg.symbols:
sdfg.add_array(name, shape, link[0].data_type, transient=True)
input_shapes[name] = tuple(shape)
input_iterators = {
k: tuple("0:{}".format(s) for s in v)
for k, v in input_shapes.items()
}
# Enforce dependencies via topological sort
for node in nx.topological_sort(chain.graph):
if not isinstance(node, Kernel):
continue
state = sdfg.add_state(node.name)
sdfg.add_edge(prev_state, state, dace.InterstateEdge())
(stencil_node, input_to_connector,
output_to_connector) = _generate_stencil(node, chain, shape,
dimensions_to_skip)
stencil_node.implementation = "CPU"
for field, connector in input_to_connector.items():
if len(input_iterators[field]) == 0:
continue # Scalar variable
# Outer memory read
read_node = state.add_read(field)
state.add_memlet_path(read_node,
stencil_node,
dst_conn=connector,
memlet=Memlet.simple(
field,
", ".join(input_iterators[field])))
for _, connector in output_to_connector.items():
# Outer write
write_node = state.add_write(node.name)
state.add_memlet_path(stencil_node,
write_node,
src_conn=connector,
memlet=Memlet.simple(
node.name, ", ".join("0:{}".format(s)
for s in shape)))
prev_state = state
return sdfg
