def requantize_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""
This function creates the pattern for qnn.requantize.
"""
return is_op("qnn.requantize")(
wildcard(), is_constant(), is_constant(), is_constant(), is_constant()
)
def concat_pattern():
"""Create pattern for concat"""
tensors = is_tuple(None)
scales = is_tuple(None)
zero_points = is_tuple(None)
concat = is_op("qnn.concatenate")(tensors, scales, zero_points, is_constant(), is_constant())
return concat
def resize2d_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""
This function creates the pattern for image.resize2d.
"""
dequant = is_op("qnn.dequantize")(wildcard(), is_constant(), is_constant())
resize_2d = is_op("image.resize2d")(dequant).has_attr({"method": "linear"})
quant = is_op("qnn.quantize")(resize_2d, is_constant(), is_constant())
return quant | is_op("image.resize2d")(wildcard()).has_attr({"method": "nearest_neighbor"})
def mean_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""
This function creates the pattern for mean.
"""
pattern = is_op("cast")(wildcard())
pattern = is_op("mean")(pattern)
pattern = is_op("qnn.requantize")(pattern, is_constant(), is_constant(),
is_constant(), is_constant())
return pattern
def qnn_conv2d_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""
This function creates the pattern for qnn.conv2D with optional fused RELU activation.
"""
qnn_conv2d = is_op("qnn.conv2d")(
wildcard(), is_constant(), is_constant(), is_constant(), is_constant(),
is_constant()).has_attr({"kernel_layout": "HWIO"})
bias_add = is_op("nn.bias_add")(qnn_conv2d, is_constant())
req = is_op("qnn.requantize")(qnn_conv2d | bias_add, is_constant(),
is_constant(), is_constant(), is_constant())
clip_or_req = req.optional(is_op("clip"))
return clip_or_req
def qnn_mul_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
"""
This function creates the pattern for qnn.mul with optional fused RELU activation.
"""
pattern = is_op("qnn.mul")(
wildcard(),
wildcard(),
is_constant(),
is_constant(),
is_constant(),
is_constant(),
is_constant(),
is_constant(),
)
pattern = pattern.optional(is_op("clip"))
return pattern
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 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 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 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 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 binary_op_pattern_with_const(op):
"""Matches binary operation with rhs arg a constant"""
return is_op(op)(wildcard(), is_constant())
