def __init__(self, signed: bool, quant_type: QuantType,
ls_bit_width_to_trunc: int, trunc_at_least_init_val: bool,
min_overall_bit_width: Optional[int],
max_overall_bit_width: Optional[int],
lsb_trunc_bit_width_impl_type: BitWidthImplType,
explicit_rescaling: bool,
override_pretrained_bit_width: bool):
super(TruncQuantProxy, self).__init__()
self.explicit_rescaling = explicit_rescaling
if quant_type == QuantType.FP:
self.lsb_trunc_bit_width_impl = ZeroLsbTruncBitWidth()
self.tensor_quant = IdentityPrescaledIntQuant()
elif quant_type == QuantType.INT:
self.lsb_trunc_bit_width_impl = LsbTruncParameterBitWidth(
ls_bit_width_to_trunc=ls_bit_width_to_trunc,
trunc_at_least_init_val=trunc_at_least_init_val,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
bit_width_impl_type=lsb_trunc_bit_width_impl_type,
override_pretrained=override_pretrained_bit_width)
tensor_clamp_impl = TensorClamp()
float_to_int_impl = RestrictValue(
restrict_value_type=RestrictValueType.INT,
float_to_int_impl_type=FloatToIntImplType.FLOOR,
min_val=None)
msb_clamp_bit_width_impl = IdentityBitWidth()
self.tensor_quant = PrescaledRestrictIntQuantWithInputBitWidth(
narrow_range=False,
signed=signed,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=msb_clamp_bit_width_impl,
float_to_int_impl=float_to_int_impl)
else:
raise Exception(
"Quantization type {} not supported for accumulators.".format(
quant_type))
def test_PrescaledRestrictIntQuantWithInputBitWidth(x, narrow_range, signed, scale, bit_width):
value = torch.tensor(x)
scale = torch.tensor(scale)
tensor_clamp_impl = TensorClamp()
msb_clamp_bitwidth_mock = Mock()
msb_clamp_bitwidth_mock.return_value = torch.tensor(bit_width, dtype=torch.float)
float_to_int_impl_mock = Mock()
float_to_int_impl_mock.side_effect = (lambda y: y)
obj = PrescaledRestrictIntQuantWithInputBitWidth(narrow_range=narrow_range, signed=signed,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=msb_clamp_bitwidth_mock,
float_to_int_impl=float_to_int_impl_mock)
output, scale, bit_width = obj(value, scale, bit_width, torch.tensor(ZERO_HW_SENTINEL_VALUE))
expected_IntQuant = IntQuant(signed=signed, narrow_range=narrow_range, tensor_clamp_impl=tensor_clamp_impl,
float_to_int_impl=float_to_int_impl_mock)
expected_output = expected_IntQuant(scale, torch.tensor(ZERO_HW_SENTINEL_VALUE) + 1, bit_width, value)
assert torch.allclose(expected_output, output, RTOL, ATOL)
def __init__(self, quant_type: QuantType, bit_width: Optional[int],
narrow_range: bool) -> None:
super(BiasQuantProxy, self).__init__()
self.scale_output_shape = OVER_BATCH_OVER_CHANNELS_SHAPE
if quant_type == QuantType.FP:
self.tensor_quant = None
elif quant_type == QuantType.INT:
tensor_clamp_impl = TensorClamp()
float_to_int_impl = RestrictValue(
restrict_value_type=RestrictValueType.INT,
float_to_int_impl_type=FloatToIntImplType.ROUND,
min_val=None)
if bit_width is not None:
bit_width_impl = BitWidthConst(
bit_width, restrict_bit_width_type=RestrictValueType.INT)
self.tensor_quant = PrescaledRestrictIntQuant(
narrow_range=narrow_range,
signed=True,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=bit_width_impl,
float_to_int_impl=float_to_int_impl)
self.requires_input_bit_width = False
else:
msb_clamp_bit_width_impl = IdentityBitWidth()
self.tensor_quant = PrescaledRestrictIntQuantWithInputBitWidth(
narrow_range=narrow_range,
signed=True,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=msb_clamp_bit_width_impl,
float_to_int_impl=float_to_int_impl)
self.requires_input_bit_width = True
else:
raise Exception(
'Quantization type {} not supported for bias quant.'.format(
str(quant_type)))