def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
quantize = match['quantize']
preop = match['preop']
for i in [0, 1]:
if preop.in_port(i).get_source().node.soft_get('type') in [
'Convolution', 'Deconvolution', 'MatMul'
]:
return
tensor_port, value_port = get_tensor_in_port(preop), get_value_in_port(
preop)
if value_port is None or value_port.data.get_value() is None:
log.debug(
'AddQuantizeFuse: cannot fuse because Add op has dynamic inputs'
)
return
# Direct modifications to quantize 1-st and 2-nd port inputs are performed.
# So the data nodes at those inputs shouldn't have more than 1 consumer maximum 2 consumers to the same
# quantize op (consumed by 1st and 2nd ports). So we duplicate FakeQuantize in_port 1, 2, 3, 4 data
resolve_shared_inputs(node=quantize, port_ids_to_duplicate=[1, 2])
quantize.in_port(1).data.set_value(
quantize.in_port(1).data.get_value() - value_port.data.get_value())
if quantize.in_node(1).id != quantize.in_node(2).id:
quantize.in_port(2).data.set_value(
quantize.in_port(2).data.get_value() -
value_port.data.get_value())
in_add_connection = quantize.in_port(0).get_source().node.in_port(
0).get_connection()
quantize.in_port(0).disconnect()
in_add_connection.add_destination(quantize.in_port(0))
def replace_pattern(self, graph: Graph, match: dict):
quantize = match['quantize']
if quantize.levels == 2:
# extra logic due to special 1 & 2 port input meaning in binary case - it is threshold separating two quants
threshold = quantize.in_port(1).data.get_value()
# Direct modifications to quantize 1-st port input are performed.
# So the data node at this input shouldn't have more than 1 consumer maximum 2 consumers to the same
# quantize op (consumed by 1st and 2nd ports). So we duplicate FakeQuantize in_port 1 data if needed
resolve_shared_inputs(node=quantize, port_ids_to_duplicate=[1])
# As we restricted to binarization case only, so we need to detect from
# which side of 0 FakeQuantize threshold resides:
# if the threshold > 0, it remains the same;
# if the threshold == 0, it also remains the same;
# if the threshold < 0, it should be modified to -infinity that means that all inputs map to output_high
modification_mask = threshold < 0
threshold[modification_mask] = float('-inf')
# Reconnect ReLU as it no longer needed for current FakeQuantize
in_relu_connection = quantize.in_port(0).get_source().node.in_port(
0).get_connection()
quantize.in_port(0).disconnect()
in_relu_connection.add_destination(quantize.in_port(0))
