def replace_pattern(self, graph: Graph, match: dict):
sparse_reshape = match['sparse_reshape']
input_shape_value = sparse_reshape.in_port(1).data.get_value()
output_shape_value = sparse_reshape.out_port(1).data.get_value()
if input_shape_value is None or output_shape_value is None:
raise Error(
"Input shape and output shape values must be defined for node {}"
.format(sparse_reshape.id))
if not np.array_equal(input_shape_value, output_shape_value):
raise Error(
"Input shape and output shape values must be equal for node {}"
.format(sparse_reshape.id))
input_data_node1 = sparse_reshape.in_node(0)
input_data_node2 = sparse_reshape.in_node(1)
output_data_node1 = sparse_reshape.out_node(0)
output_data_node2 = sparse_reshape.out_node(1)
graph.remove_edge(input_data_node1.id, sparse_reshape.id)
graph.remove_edge(sparse_reshape.id, output_data_node1.id)
graph.remove_edge(input_data_node2.id, sparse_reshape.id)
graph.remove_edge(sparse_reshape.id, output_data_node2.id)
merge_data_nodes(graph, output_data_node1, input_data_node1)
merge_data_nodes(graph, output_data_node2, input_data_node2)
graph.remove_nodes_from(
[sparse_reshape.id, input_data_node1.id, input_data_node2.id])
def find_and_replace_pattern(self, graph: Graph):
for node in list(graph.nodes()):
if node not in graph.nodes():
continue
permute_node = Node(graph, node)
if permute_node.has_valid(
'type') and permute_node.type == 'Permute':
list_of_permutes = [permute_node]
# Get sequence of permutations
node = permute_node
while True:
next_ops = get_next_operation(node)
if len(next_ops) != 1:
break
next_op = next_ops[0]
if next_op.has_valid('type') and next_op.type == 'Permute':
list_of_permutes.append(next_op)
node = next_op
else:
break
final_permutation = np.array(
[x for x in range(len(list_of_permutes[0].order))],
dtype=np.int64)
for permute in list_of_permutes:
if not permute.has_valid('order'):
raise Error(
"Permute node {} has wrong attribute order = None".
format(permute.name))
final_permutation = final_permutation[np.array(
permute.order, dtype=np.int64)]
if np.array_equal(
final_permutation,
[x for x in range(len(list_of_permutes[0].order))]):
first_data_node, last_data_node = list_of_permutes[
0].in_node(), list_of_permutes[-1].out_node()
graph.remove_edge(first_data_node.id,
list_of_permutes[0].id)
else:
if len(list_of_permutes) < 2:
continue
first_data_node, last_data_node = list_of_permutes[
0].out_node(), list_of_permutes[-1].out_node()
list_of_permutes[0].order = final_permutation
graph.remove_edge(first_data_node.id,
first_data_node.out_node().id)
graph.remove_edge(last_data_node.in_node().id,
last_data_node.id)
merge_data_nodes(graph, first_data_node, last_data_node)
graph.remove_node(last_data_node.id)
graph_clean_up_tf(graph)
def find_and_replace_pattern(self, graph: Graph):
for permute_node in graph.get_op_nodes(type='Transpose'):
if permute_node.id not in graph.nodes():
continue
list_of_permutes = [permute_node]
# Get sequence of permutations
node = permute_node
while True:
next_ops = get_next_operation(node)
if len(next_ops) != 1:
break
next_op = next_ops[0]
if next_op.soft_get('type') == 'Transpose':
list_of_permutes.append(next_op)
node = next_op
else:
break
final_permutation = int64_array([
x for x in range(
len(list_of_permutes[0].in_port(1).data.get_value()))
])
for permute in list_of_permutes:
order = permute.in_port(1).data.get_value()
if order is None:
raise Error(
"Transpose node {} has wrong order for permute = None".
format(permute.name))
final_permutation = final_permutation[int64_array(order)]
if np.array_equal(final_permutation, [
x for x in range(
len(list_of_permutes[0].in_port(1).data.get_value()))
]):
first_data_node, last_data_node = list_of_permutes[0].in_node(
), list_of_permutes[-1].out_node()
graph.remove_edge(first_data_node.id, list_of_permutes[0].id)
else:
if len(list_of_permutes) < 2:
continue
first_data_node, last_data_node = list_of_permutes[0].out_node(
), list_of_permutes[-1].out_node()
list_of_permutes[0].in_port(1).data.set_value(
final_permutation)
graph.remove_edge(first_data_node.id,
first_data_node.out_node().id)
graph.remove_edge(last_data_node.in_node().id, last_data_node.id)
merge_data_nodes(graph, first_data_node, last_data_node)
graph.remove_node(last_data_node.id)
graph.clean_up()
def _fuse_linear_sequence(graph: nx.MultiDiGraph, start_node: Node):
"""
This function finds the sequence of Mul/Add operations and replaces this sequence with two ops (Mul->Add).
:param graph:
:param start_node: The first operation of the sequence
"""
fnodes = [start_node]
while True:
node = fnodes[-1]
data_node = node.out_node()
if (len(data_node.out_nodes()) != 1):
break
if (data_node.out_node().op in ['Mul', 'Add']) and get_value_id(
data_node.out_node()) is not None and data_node.out_node(
).soft_get('can_be_fused') == True:
fnodes.append(data_node.out_node())
else:
break
if len(fnodes) == 1 or (len(fnodes) == 2 and fnodes[0].op == 'Mul'
and fnodes[1].op == 'Add'):
return False
input_shape = start_node.in_node(get_tensor_id(start_node)).shape
init_dims_cnt = len(
input_shape) - 2 if graph.graph['layout'] == 'NCHW' else 1
mul = np.ones([1 for x in range(init_dims_cnt)])
add = np.zeros([1 for x in range(init_dims_cnt)])
first_mul_name = None
first_add_name = None
for idx in range(len(fnodes)):
node = fnodes[idx]
const_node = get_value_id(node)
if node.op == 'Mul':
if first_mul_name is None:
first_mul_name = node.name
mul = mul * node.in_node(const_node).value
add = add * node.in_node(const_node).value
elif node.op == 'Add':
if first_add_name is None:
first_add_name = node.name
add = add + node.in_node(const_node).value
# If mul is scalar we broadcast it to biases shape
if mul.shape != add.shape and len(mul.shape) == 1 and mul.shape[0] == 1:
mul = np.array([mul[0] for x in range(add.shape[0])])
assert (np.array_equal(fnodes[0].in_node(get_tensor_id(fnodes[0])).shape,
fnodes[-1].out_node().shape))
mul_node = Mul(
graph,
dict(name=first_mul_name +
'/Fused_Mul_' if first_mul_name is not None else ''))
add_node = Add(
graph,
dict(name=first_add_name +
'/Fused_Add_' if first_add_name is not None else ''))
in_node = fnodes[0].in_node(get_tensor_id(fnodes[0]))
out_node = fnodes[-1].out_node()
graph.remove_edge(in_node.id, fnodes[0].id)
graph.remove_edge(fnodes[-1].id, out_node.id)
# Remove deleted subgraph
for node in fnodes:
for tmp_node in node.in_nodes().values():
# Remove node only if it has one consumer (for case with shared weights)
if len(tmp_node.out_nodes()) == 1:
graph.remove_node(tmp_node.id)
for tmp_node in node.out_nodes().values():
graph.remove_node(tmp_node.id)
graph.remove_node(node.id)
"""
Four cases considered below:
1. Mul and Add have valid values (mul value != 1 and add value != 0)
2. Only Mul has valid values, so we add only Mul node
3. Only Add has valid values, so we add only Add node
4. When Mul and Add has not valid values we just merge two data nodes
"""
if any([x != 0
for x in np.nditer(add)]) and any([x != 1
for x in np.nditer(mul)]):
data_mul = Op.create_input_data_node(graph, "data_mul_", np.array(mul))
data_add = Op.create_input_data_node(graph, "data_add_", np.array(add))
add_node.create_node_with_data(inputs=[
mul_node.create_node_with_data([in_node, data_mul]), data_add
],
data_nodes=out_node)
elif any([x != 1 for x in np.nditer(mul)]):
data_mul = Op.create_input_data_node(graph, "data_mul_", np.array(mul))
mul_node.create_node_with_data(inputs=[in_node, data_mul],
data_nodes=out_node)
elif any([x != 0 for x in np.nditer(add)]):
data_add = Op.create_input_data_node(graph, "data_add_", np.array(add))
add_node.create_node_with_data(inputs=[in_node, data_add],
data_nodes=out_node)
else:
merge_data_nodes(graph, out_node, in_node)
graph.remove_node(in_node.id)
log.debug('Fused {} operations'.format(len(fnodes)))
return True
def convert_scale_shift_to_mul_add(graph: nx.MultiDiGraph):
nodes = [
Node(graph, node) for node in graph.nodes()
if Node(graph, node).soft_get('op') == 'ScaleShift'
]
for node in nodes:
if node.soft_get('can_be_fused') is False:
continue
has_biases = True
has_weights = True
# We don't need zero biases
if len(node.in_nodes()) < 3 or all(
[x == 0 for x in node.in_node(2).value]):
has_biases = False
input_node = node.in_node(0)
scale_node = node.in_node(1)
shift_node = node.in_node(2) if has_biases else None
output_node = node.out_node()
if scale_node.has_valid("value") and all(
[x == 1 for x in scale_node.value]):
has_weights = False
mul_node = Mul(graph, dict(name=node.name + "/Mul_"))
add_node = Add(graph, dict(name=node.name + "/Add_"))
# Disconnect ScaleShift node
graph.remove_edge(input_node.id, node.id)
graph.remove_edge(node.id, output_node.id)
# Expand dims for current layout
broadcast_dims_cnt = len(
input_node.shape) - 2 if graph.graph['layout'] == 'NCHW' else 0
if scale_node.has_valid("value"):
Op.expand_node_shape(scale_node, broadcast_dims_cnt)
else:
# insert reshape to make shapes similar
reshape_dims = np.zeros(len(input_node.shape), dtype=np.int64)
for i in range(0, node.axis):
reshape_dims[i] = 1
for i in range(node.axis, node.axis + len(scale_node.shape)):
reshape_dims[i] = scale_node.shape[i - node.axis]
for i in range(node.axis + len(scale_node.shape),
len(input_node.shape)):
reshape_dims[i] = 1
reshape = Reshape(
graph,
dict(name=scale_node.name + "/Broadcast_", dim=reshape_dims))
scale_node = reshape.create_node_with_data(inputs=[scale_node])
Op.expand_node_shape(shift_node, broadcast_dims_cnt)
# Connect input->mul->out->add->out
if has_biases:
add_node.create_node_with_data(inputs=[
mul_node.create_node_with_data(
inputs=[input_node, scale_node]), shift_node
],
data_nodes=output_node)
elif has_weights:
mul_node.create_node_with_data(inputs=[input_node, scale_node],
data_nodes=output_node)
else:
merge_data_nodes(graph, input_node, output_node)
graph.remove_node(output_node.id)
