def _match_cell(self, context, unittype):
"""match unit cell"""
for cell_pattern in get_pattern(unittype):
matcher = GraphMatcher(cell_pattern, allow_reorder=True)
loop_props = context.loop_properties
inputs = loop_props.state_inputs + loop_props.scan_inputs
input_ids = [input_tensor_value_info.id for input_tensor_value_info in inputs]
outputs = loop_props.state_outputs + loop_props.scan_outputs
output_ids = [out_tensor_value_info.id for out_tensor_value_info in outputs]
body_graph_ops, _, _ = LoopRewriterBase.find_subgraph(
set(input_ids),
set(output_ids),
self.g, merge_as_end=True
)
match_results = list(matcher.match_ops(body_graph_ops))
if len(match_results) == 1:
return match_results[0]
return None
def rewrite(self):
log.debug("enter custom rnn late rewriter")
nodes = self.g.get_nodes()
nodes_to_remove = []
for scan_node in nodes:
if scan_node.type != "Scan":
continue
log.debug("late write for scan node %s", scan_node.name)
num_scan_inputs = scan_node.get_attr("num_scan_inputs").i
if not BodyGraphDict.has_body_graph_info(scan_node.name):
continue
body_graph_meta = BodyGraphDict.pop_body_graph_info(scan_node.name)
onnx_nodes, _ = LoopRewriterBase.find_subgraph(
body_graph_meta, self.g)
nodes_to_remove.extend(onnx_nodes)
log.debug("start creating body graph for scan node %s ",
scan_node.name)
body_graph_initializers = {}
const_nodes = [
n for n in onnx_nodes if n.type in ("Const", "ConstV2")
]
for n in const_nodes:
# when set nodes, Const should be removed, they need be replaced as initializers.
body_graph_initializers[n.output[0]] = self.g.initializers[
n.output[0]]
onnx_nodes.remove(n)
onnx_nodes = set(onnx_nodes)
ops = []
for op in onnx_nodes:
onnx_op = op.op
ops.append(onnx_op)
body_g = Graph(ops,
output_shapes=self.g._output_shapes,
dtypes=self.g._dtypes)
body_g._initializers = body_graph_initializers
log.debug("start preparing body graph inputs nodes")
temp_nodes = body_g.get_nodes()
i = 0
input_count = len(body_graph_meta.input_ids)
for input_name, init_input_id in zip(
body_graph_meta.input_ids,
body_graph_meta.initial_input_ids):
shape = body_g.get_shape(input_name)
dtype = body_g.get_dtype(input_name)
if shape is None:
shape = self.g.get_shape(init_input_id)
if i >= input_count - num_scan_inputs:
loop_input_shape = list(shape)[2:] # delete [1, time,]
else:
loop_input_shape = list(shape)
else:
loop_input_shape = list(shape)
onnx_input_shape = utils.make_onnx_shape(loop_input_shape)
val = helper.make_tensor_value_info(input_name, dtype,
onnx_input_shape)
body_g.add_model_input(input_name, val)
i += 1
log.debug("start preparing body graph outputs nodes")
new_output_names = []
for o in body_graph_meta.output_ids:
# insert identity node, since sometimes we need output same output_id as state_output
# and scan_out, but ONNX don't allow the same output_id appeared more than once as
# output node.
identity_name = utils.make_name("Identity")
identity_output = utils.port_name(identity_name)
node = Node(
helper.make_node("Identity", [o], [identity_output],
name=identity_name), body_g)
body_g.set_dtype(identity_output, body_g.get_dtype(o))
body_g.copy_shape(o, identity_output)
new_output_names.append(identity_output)
temp_nodes.append(node)
body_g.set_nodes(temp_nodes)
body_g.topological_sort(body_g.get_nodes())
log.debug("start make graph based on body graph nodes")
body_g.output_names = new_output_names
graph = body_g.make_graph("scan body graph")
scan_node.set_attr("body", graph)
# remove nodes in body graph from g
for n in set(nodes_to_remove):
if n in nodes:
nodes.remove(n)
elif self.g.is_initializer(n.output[0]):
del self.g.initializers[n.output[0]]
else:
raise ValueError("error when removing nodes")
return nodes