def replace_pattern(self, graph: Graph, match: dict):
node = match['op']
if (node.data_format != b'NHWC' or len(node.in_nodes()) != 5
or node.in_node(0).value is not None or # input
node.in_node(1).value is None or # scale
node.in_node(2).value is None or # offset
node.in_node(3).value is not None or # mean
node.in_node(4).value is not None or # variance
node.in_node(1).value.ndim != 1 or
node.in_node(2).value.ndim != 1):
return
scale_mul = Mul(graph, dict(name=node.name + '/scale_mul_'))
shift_add = Add(graph, dict(name=node.name + '/shift_add_'))
mean_add = Add(graph, dict(name=node.name + '/mean_add_'))
variance_mul = Mul(graph, dict(name=node.name + '/variance_mul_'))
neg_const = Const(
graph, dict(value=np.array(-1), name=node.name + '/mean_negate_'))
mean_negate = Mul(graph, dict(name=node.name + '/mean_negate_'))
mean_arg = mean_add.create_node_with_data([
node.in_node(0),
mean_negate.create_node_with_data(
[node.in_node(3),
neg_const.create_node_with_data()])
])
shift_const = Const(
graph,
dict(value=node.eps,
name=node.name + '/variance_denom_shift_const_'))
power_const = Const(
graph,
dict(value=-0.5, name=node.name + '/variance_denom_power_const_'))
variance_denom_shift = Add(
graph, dict(name=node.name + '/variance_denom_shift_'))
variance_denom_power = Pow(
graph, dict(name=node.name + '/variance_denom_power_'))
variance_arg = variance_mul.create_node_with_data([
mean_arg,
variance_denom_power.create_node_with_data([
variance_denom_shift.create_node_with_data(
[node.in_node(4),
shift_const.create_node_with_data()]),
power_const.create_node_with_data()
])
])
shift_add.create_node_with_data([
scale_mul.create_node_with_data([variance_arg,
node.in_node(1)]),
node.in_node(2)
],
data_nodes=node.out_node())
node.graph.remove_node(node.id)
def replace_pattern(self, graph: Graph, match: dict):
assert match['operator'].has('multiplication_transparent_ports')
port = match['operator'].input_ports_with(match['quantized'])
assert len(port) >= 1
if len(port) > 1:
log.debug(
'BinarizeWeightsM1P1 cannot apply transformation for data {} because it consumed more'
' than once'.format(match['quantized'].name))
return
assert len(port) == 1
port = port[0]
applicable = [
pair for pair in match['operator'].multiplication_transparent_ports
if pair[0] == port
]
if len(applicable) == 0:
return
# Look at 3-rd and 4-th inputs of FakeQuantize -- they have constants that should be passed through.
# Assume that the constant that should be passed through is a scalar.
quantize = match['quantize']
output_low = quantize.in_node(3)
output_high = quantize.in_node(4)
quantize_name = quantize.soft_get('name', quantize.id)
if not output_low.has_valid('value') and not output_high.has_valid(
'value'):
return
output_low = output_low.value
output_high = output_high.value
# This pass is applicable for binarization only. Other intX variants are not relevant.
if quantize.levels != 2:
return
# Recognize two cases: 0/+1 and -1/+1.
zp1 = np.all(output_low == 0) or np.all(output_high == 0)
m1p1 = np.all(-output_low == output_high)
if (not zp1 and not m1p1) or (zp1 and m1p1):
log.debug(
'BinarizeWeightsM1P1 cannot apply transformation for data {} because it does\'t has one of'
' 0/+1 or -1/+1 forms.'.format(match['quantized'].name))
return
# TODO: Extract real scalar from 3rd and 4th inputs; reusing original tensors is dangerous because
# it may have incompatible shape.
mult_term = quantize.in_node(3) if np.all(
output_high == 0) else quantize.in_node(4)
new_shape = Const(
graph, {
'name': quantize_name + '/Reshape/Shape',
'value': int64_array([-1, 1, 1])
}).create_node_with_data()
reshape = Reshape(graph, {
'name': quantize_name + '/Reshape'
}).create_node_with_data([mult_term, new_shape])
# Patch inflow path (by diving by mult_term)
# Put a new Pow/Mul combination here:
# ---->---- (here)---> data ---> [3rd/4th ports]quantize ---> quantized ---> operator
if len(match['quantized'].out_nodes()) > 1:
log.debug(
'BinarizeWeightsM1P1: len(match[\'quantized\'].out_nodes()) > 1'
)
return
power_of_exponent = Const(graph, {
'name': quantize_name + '/DivNormalize/Power',
'value': np.array(-1.0)
}).create_node_with_data()
div_op = Pow(graph, {'name': quantize_name + '/DivNormalize'})
div_output = div_op.create_node_with_data(
[mult_term, power_of_exponent])
for i in [3, 4]:
match['quantize'].insert_node_with_data_before(
match['quantize'].in_node(i),
Mul,
dict(name=quantize_name + '/MulNormalize'),
additional_inputs=[div_output],
)
match[
'quantized'].value = None # reset value because it will be recomputed
match['quantize'].infer(match['quantize'])
# Put a complimentary new Mul node here: operator -->---(here)-----> operator.out_node()
match['operator'].insert_node_with_data_after(
match['operator'].out_node(),
Mul,
dict(name=match['operator'].name + '/MulNormalize'),
[reshape],
)
# Disable 'operator' fusion with linear ops, otherwise it will annihilate changes that we just made
match['operator']['can_be_fused'] = False