def concatenate(data,
input_scales,
input_zero_points,
output_scale,
output_zero_point,
axis):
"""Concatenate the quantized input tensors along the given axis.
Parameters
----------
data : Union(List[relay.Expr], Tuple[relay.Expr], TupleWrapper[relay.Expr])
The list of quantized tensors.
input_scales : List[relay.Expr]
The list of scales of input quantized tensors.
input_zero_points : List[relay.Expr]
The list of zero points of input quantized tensors.
output_scale : relay.Expr
The scale of the output quantized tensor.
output_zero_point : relay.Expr
The zero point of the output quantized tensor.
axis : int
The axis along which the tensors are concatenated.
Returns
-------
result: relay.Expr
The concatenated quantized tensor.
"""
if isinstance(data, (list, tuple)):
data = Tuple(data)
elif isinstance(data, TupleWrapper):
data = data.tuple_value
if not isinstance(axis, int):
raise ValueError("For now, we only support integer axis")
input_scales = list(input_scales)
input_zero_points = list(input_zero_points)
return _make.concatenate(data,
Tuple(input_scales),
Tuple(input_zero_points),
output_scale,
output_zero_point,
axis)
def visit_tuple(self, tup):
"""Add compiler_begin and compiler_end annotations to Tuple"""
field_list = []
cond = int(hash(tup))
for field in tup.fields:
if cond in annotator.relay_ids:
field_list.append(
compiler_begin(super().visit(field),
annotator.compiler))
else:
field_list.append(super().visit(field))
if cond in annotator.relay_ids:
return compiler_end(Tuple(field_list), annotator.compiler)
else:
return Tuple(field_list)
def concatenate(data,
input_scales,
input_zero_points,
output_scale,
output_zero_point,
axis):
"""Concatenate the quantized input tensors along the given axis.
Parameters
----------
data : Union(List[relay.Expr], Tuple[relay.Expr])
The list of quantized tensors.
input_scales : List[float32]
The list of scales of input quantized tensors.
input_zero_points : List[int32]
The list of zero points of input quantized tensors.
output_scale : float32
The scale of the output quantized tensor.
output_zero_point : int32
The zero point of the output quantized tensor.
axis : int
The axis along which the tensors are concatenated.
Returns
-------
result: relay.Expr
The concatenated quantized tensor.
"""
data = list(data)
if not data:
raise ValueError("relay.concatenate requires data to be non-empty.")
if not isinstance(axis, int):
raise ValueError("For now, we only support integer axis")
return _make.concatenate(Tuple(data),
[FloatImm("float64", x) for x in input_scales],
[IntImm("int32", x) for x in input_zero_points],
output_scale,
output_zero_point,
axis)
