def prepare_weight_quant(cls, module: QuantWBIOL):
cls.validate_8b_bit_width(module.quant_weight_bit_width())
scale = module.quant_weight_scale()
zero_point = cls.quant_weight_zero_point(module)
signed = module.is_quant_weight_signed
weight = module.weight.detach()
quant_impl, quant_kwargs = cls.gen_quant_impl_kwargs(scale, zero_point, signed)
return quant_impl, (weight,), quant_kwargs
def validate(cls, module: QuantWBIOL, requires_quant_bias=True):
assert module.is_weight_quant_enabled
assert module.is_output_quant_enabled
cls.validate_8b_bit_width(module.quant_weight_bit_width(), le_then=True)
cls.validate_8b_bit_width(module.quant_input_bit_width())
cls.validate_8b_bit_width(module.quant_output_bit_width())
if module.bias is not None and requires_quant_bias:
assert module.is_bias_quant_enabled
assert module.is_quant_bias_signed
cls.validate_32b_bit_width(module.quant_bias_bit_width(), le_then=True)
def quant_weight_type(module: QuantWBIOL):
bit_width = int(module.quant_weight_bit_width().item())
if bit_width == 1:
return "BIPOLAR"
else:
return f"INT{bit_width}"
def quant_weight_bit_width(module: QuantWBIOL):
bit_width = module.quant_weight_bit_width()
return DPUQuantLayerHandler.validate_8b_bit_width(bit_width)
def test_default_wbiol_weight_bit_width_enabled(
default_wbiol_layer: QuantWBIOL):
assert default_wbiol_layer.quant_weight_bit_width() == torch.tensor(8.)
def quant_weight_bit_width(cls, module: QuantWBIOL):
bit_width = module.quant_weight_bit_width()
return cls.validate_8b_bit_width(bit_width, le_then=True)
def quant_weight_type(module: QuantWBIOL):
return finn_datatype(module.quant_weight_bit_width(),
module.is_quant_weight_signed)