def abs_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""Create pattern for abs"""
pattern = is_op("qnn.dequantize")(wildcard(), is_constant(), is_constant())
pattern = is_op("abs")(pattern)
pattern = is_op("qnn.quantize")(pattern, is_constant(), is_constant())
return pattern
def tanh_pattern():
"""Create pattern for tanh"""
dequant = is_op("qnn.dequantize")(wildcard(), is_constant(), is_constant())
tanh = is_op("tanh")(dequant)
quant = is_op("qnn.quantize")(tanh, is_constant(), is_constant())
return quant
def reshape_pattern():
"""Create pattern for reshape"""
pattern = is_op("reshape")(wildcard())
return pattern
def strided_slice_pattern():
"""Create pattern for strided_slice"""
pattern = is_op("strided_slice")(wildcard())
return pattern
def __init__(self):
super().__init__(require_type=True, rewrite_once=True)
self.reshape = is_op("reshape")(wildcard())
self.strided_slice = is_op("strided_slice")(wildcard())
self.pattern = self.reshape | self.strided_slice
def binary_op_pattern_with_const(op):
"""Matches binary operation with rhs arg a constant"""
return is_op(op)(wildcard(), is_constant())
def pattern_B():
x = wildcard()
y = wildcard()
out = is_op('add')(x, y)
out = is_op('abs')(out)
return out
def __init__(self):
super().__init__(require_type=True)
self.pattern = (wildcard().has_attr({
"Composite":
ethosu_patterns.QnnDepthwiseConv2DParams.composite_name
}))(wildcard())
def proj_five_op_pattern_with_const(op: relay.expr.Expr) -> relay.dataflow_pattern.DFPattern:
return is_tuple_get_item(
is_op(op)(wildcard(), is_constant(), is_constant(), is_constant(), is_constant()), 0
)
def __init__(self):
super().__init__(require_type=True)
self.pattern = (wildcard().has_attr(
{"Composite":
ethosu_patterns.FullyConnectedParams.composite_name}))(wildcard())
def binary_op_pattern_with_const(op: relay.expr.Expr) -> relay.dataflow_pattern.DFPattern:
"""Matches binary operation with rhs arg a constant"""
return is_op(op)(wildcard(), is_constant())
def binary_op_pattern(op: relay.expr.Expr) -> relay.dataflow_pattern.DFPattern:
"""Matches binary operation"""
return is_op(op)(wildcard(), wildcard())
def _get_breakpoint_patterns() -> List[dfp.DFPattern]:
x = dfp.wildcard()
shortcut = dfp.wildcard()
x = dfp.is_op('add')(x, shortcut)
x = dfp.is_op('nn.relu')(x)
return [x]
def sigmoid_pattern():
"""Create pattern for sigmoid"""
dequant = is_op("qnn.dequantize")(wildcard(), is_constant(), is_constant())
sigmoid = is_op("sigmoid")(dequant)
quant = is_op("qnn.quantize")(sigmoid, is_constant(), is_constant())
return quant
def pattern_C():
x = wildcard()
out = is_op('abs')(x)
out = is_op('nn.relu')(out)
return out
def __init__(self):
super().__init__(require_type=True)
self.pattern = (wildcard().has_attr(
{"Composite": "ethos-u.qnn_conv2d"}))(wildcard())
def get_pattern():
conv = make_conv_bias_relu_pattern()
clip = is_op('clip')(conv, wildcard(), wildcard())
return is_op('multiply')(conv, clip)
def __init__(self):
super().__init__(require_type=True)
self.split_in = wildcard()
self.pattern = is_op("split")(self.split_in)
def split_pattern():
"Create the pattern for split"
split = is_op("split")(wildcard())
return split
def __init__(self):
super().__init__(require_type=True, rewrite_once=True)
self.pattern = (wildcard().has_attr(
{"Composite":
ethosu_patterns.SplitParams.composite_name}))(wildcard())
def binary_op_pattern(op):
"""Matches binary operation"""
return is_op(op)(wildcard(), wildcard())
