def replace_pattern(graph: Graph, match: dict):
node = match['op']
shape = node.in_port(0).data.get_shape().copy()
assert shape[1] % node.group == 0
power_node = Power(graph, attrs={'name': node.id + '_power',
'power': node.p}).create_node()
reshape_node = create_op_node_with_second_input(graph, Reshape,
int64_array([shape[0], shape[1] / node.group, node.group]),
{'name': node.id + '_reshape'})
reshape_node.in_port(0).connect(power_node.out_port(0))
reducesum_node = create_op_node_with_second_input(graph, ReduceSum,
int64_array([2]),
{'name': node.id + '_sum', 'keep_dims': False})
reducesum_node.in_port(0).connect(reshape_node.out_port(0))
invpower_node = Power(graph, attrs={'name': node.id + '_invpower',
'power': 1.0 / node.p}).create_node()
invpower_node.in_port(0).connect(reducesum_node.out_port(0))
node.in_port(0).get_connection().set_destination(power_node.in_port(0))
node.out_port(0).get_connection().set_source(invpower_node.out_port(0))
def replace_op(self, graph: Graph, node: Node):
reciprocal = Power(graph, {'scale': 1, 'power': np.float64(-1), 'shift': 0,
'name': node.name + '/reciprocal_'}).create_node()
mul = Eltwise(graph, {'operation': 'mul', 'name': node.name + '/mul_'}).create_node()
# Connect nodes
node.in_port(1).get_connection().set_destination(reciprocal.in_port(0))
node.in_port(0).get_connection().set_destination(mul.in_port(1))
reciprocal.out_port(0).connect(mul.in_port(0))
# The "explicit" version of the return value is: [(out_node.id, 0)])
return [mul.id]
def replace_op(self, graph: Graph, node: Node):
power = Power(graph, dict(scale=0, name=node.name + '/Power/')).create_node()
# Reconnecting inputs to this new node
node.in_port(0).get_connection().set_destination(power.in_port(0))
node.out_port(0).get_connection().set_source(power.out_port(0))
return [power.id]
def replace_pattern(graph: Graph, match: [str, Node]):
consumers = [
n for n in match if n not in ['mul_add', 'pow_d']
and not check_node_usages_out_of_match(match, n)
]
if consumers:
log.warning(
'Power(mul_add,pow) pattern was detected. Non pattern consumers of nodes: "{}" were found.'
' Won\'t replace'.format(', '.join(
[match[n].id for n in consumers])))
return
mul_add = match['mul_add']
pow = match['pow']
new_power = Power(
graph, {
'name': mul_add.name + '/fused_power',
'shift': mul_add.shift,
'scale': mul_add.scale,
'power': pow.power
}).create_node()
source = mul_add.in_port(0).get_connection().get_source()
mul_add.in_port(0).disconnect()
new_power.in_port(0).connect(source)
pow.out_port(0).get_connection().set_source(new_power.out_port(0))
log.debug(
'Power nodes {} and {} were fused to single Power node {}'.format(
mul_add.name, pow.name, new_power.name))
def replace_pattern(self, graph: Graph, match: dict):
const = 0.99
merge = match['merge']
digits = significant_digits()
pnorm = Power(
graph, {
'name': merge.name + '/reciprocal_',
'type': 'PNORM',
'significant': digits[0],
'to_significant': digits[1],
'scale': 1,
'shift': 0,
'power': get_power_attr()
}).create_node()
merge.in_port(0).get_connection().set_destination(pnorm.in_port(0))
in_shape = pnorm.in_port(0).data.get_shape()
in_shape = list(in_shape)
in_shape.insert(0, 1)
reshape1 = Reshape(graph, {
'name': merge.name + '/Reshape_Node1'
}).create_node()
reshape_dim1 = Const(graph, {
'value': np.array(in_shape),
'name': merge.id + '/Reshape_Dim1'
}).create_node()
pnorm.out_port(0).connect(reshape1.in_port(0))
reshape1.in_port(1).connect(reshape_dim1.out_port(0))
concat_node = Concat(
graph, {
'axis': 0,
'name': merge.name + '/Concat_',
'override_output_shape': True
}).create_node()
const3 = Const(graph, {
'name': merge.name + '/const_reduce',
'value': 0
}).create_node()
for ii, idx in enumerate(
range(pnorm.significant, pnorm.to_significant + 1, 1)):
const_node = Const(
graph, {
'value': float_array(math.pow(const, idx)),
'name': merge.name + '/Const_' + ii.__str__()
}).create_node()
mul_node = Mul(graph, {
'name': merge.name + '/Mul_' + ii.__str__()
}).create_node()
const_node.out_port(0).connect(mul_node.in_port(0))
reshape1.out_port(0).connect(
mul_node.in_port(1)) # connect to the graph node
mul_node2 = Mul(graph, {
'name': merge.name + '/Mul_Div_' + ii.__str__()
}).create_node()
const_node2 = Const(
graph, {
'value': float_array(math.pow(const, -1 * idx)),
'name': merge.name + '/Const_Pow_' + ii.__str__()
}).create_node()
cast_node = ExpOp(graph, {
'name': merge.name + '/Exp_' + idx.__str__()
}).create_node()
mul_node.out_port(0).connect(cast_node.in_port(0))
const_node2.out_port(0).connect(mul_node2.in_port(1))
cast_node.out_port(0).connect(mul_node2.in_port(0))
concat_node.add_input_port(ii, skip_if_exist=True)
concat_node.in_port(ii).get_connection().set_source(
mul_node2.out_port(0))
in_shape = pnorm.in_port(0).data.get_shape()
in_shape = list(in_shape)
reducesum_node = ReduceMean(
graph, {
'name': merge.id + '/_pnorm_reduced_sum',
'keep_dims': True,
'in_ports_count': 2,
'shape': in_shape,
'axis': 0,
'need_shape_inference': None,
'infer': reduce_infer
}).create_node()
const3.out_port(0).connect(reducesum_node.in_port(1))
reducesum_node.in_port(0).get_connection().set_source(
concat_node.out_port(0))
reshape = Reshape(graph, {
'name': merge.name + '/Reshape_Node'
}).create_node()
reshape_dim = Const(graph, {
'value': np.array(in_shape),
'name': merge.id + '/Reshape_Dim'
}).create_node()
reducesum_node.out_port(0).connect(reshape.in_port(0))
reshape.in_port(1).connect(reshape_dim.out_port(0))
merge.out_port(0).get_connection().set_source(reshape.out_port(0))
