def match(self, G: GraphView, set_identity: bool = True):
visited_edges = {}
nodes_to_remove = []
has_modified_graph = False
for node in G.inputs():
# check if constantinput. if is then check if positive and check max value
if isinstance(node, ConstantInputParameters):
if node.value is not None:
if G.has_quantized_parameters:
qrec = G.quantization[NodeId(node)]
qtype = qrec.out_qs[0]
if hasattr(qtype, 'wrapped'):
qtype = qtype.wrapped
val = qtype.dequantize(node.value)
else:
val = node.value
if val.min() >= 0:
status = (True, val.max())
else:
status = (False, False)
else:
status = (False, False)
for edge in G.out_edges(node.name):
visited_edges[edge] = status
nodes_to_remove += find_redundant_relus(
G, edge.to_node, visited_edges)
for node in nodes_to_remove:
has_modified_graph = True
# Only relus so only one in edge
in_edge = G.in_edges(node.name)[0]
for edge in G.out_edges(node.name):
G.add_edge(
NNEdge(from_node=in_edge.from_node,
from_idx=in_edge.from_idx,
to_node=edge.to_node,
to_idx=edge.to_idx))
G.remove(node)
if set_identity:
self.set_identity(G)
return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
visited_edges = {}
nodes_to_remove = []
has_modified_graph = False
for node in G.inputs():
# check if constantinput. if is then check if positive and check max value
if isinstance(node, ConstantInputParameters):
if node.value is not None:
val = node.dqvalue
if np.min(val) >= 0:
status = (True, np.max(val))
else:
status = (False, False)
else:
status = (False, False)
else:
status = (False, False)
for edge in G.out_edges(node.name):
visited_edges[edge] = status
nodes_to_remove += find_redundant_relus(
G, edge.to_node, visited_edges)
for node in nodes_to_remove:
has_modified_graph = True
# Only relus so only one in edge
LOG.info("removing redundant relu %s", node.name)
in_edge = G.in_edges(node.name)[0]
out_edges = G.out_edges(node.name)
G.remove(node)
for edge in out_edges:
G.add_edge(NNEdge(from_node=in_edge.from_node,
from_idx=in_edge.from_idx,
to_node=edge.to_node,
to_idx=edge.to_idx))
if set_identity:
self.set_identity(G)
return has_modified_graph
def construct_subgraph(G, nodes):
subg = GraphView()
nodes = set(nodes)
for node in nodes:
for edge in G.out_edges(node.name):
# only add internal edges
if edge.to_node in nodes:
subg.add_edge(NNEdge(from_node=edge.from_node, to_node=edge.to_node,
from_idx=edge.from_idx, to_idx=edge.to_idx))
def red_fn(state, edge):
state.setdefault((edge.from_node, edge.from_idx), []
).append((edge.to_node, edge.to_idx))
return state
inputs = reduce(red_fn, set([edge for node in subg.inputs()
for edge in G.in_edges(node.name)]), {})
inputs_map = []
for (fnode, fidx), outs in inputs.items():
inp = FusionInputParameters(
f'{fnode.name}_{fidx}_in', dims=fnode.out_dims[fidx])
inputs_map.append((fnode, fidx))
for (tnode, tidx) in outs:
subg.add_edge(NNEdge(from_node=inp, to_node=tnode, to_idx=tidx))
outputs = [(node, set(edge.from_idx for edge in G.out_edges(node.name)))
for node in subg.outputs()]
outputs_map = []
for (node, fidxes) in outputs:
output_map = []
outputs_map.append(output_map)
for fidx in fidxes:
output_map.append((edge.to_node, edge.to_idx)
for edge in G.out_edges(node.name) if edge.from_idx == fidx)
outp = FusionOutputParameters(
f'{node.name}_{fidx}_out', dims=node.out_dims[fidx])
subg.add_edge(NNEdge(from_node=node, to_node=outp, from_idx=fidx))
return (subg, inputs_map, outputs_map)
def new_eval_copy(subg: GraphView) -> CopySet:
node_slices = {}
for inp in subg.inputs():
walk_down(subg, inp, node_slices)
return CopySet([(node, node_slices[node][0]) for node in subg.outputs()])
