def quant_type(
module: QuantReLU,
supported_int_bit_width_range: Tuple[int,...] = (2, 33)):
bit_width = int(module.quant_act_bit_width().item())
if bit_width in range(*supported_int_bit_width_range):
return f"UINT{bit_width}"
else:
raise RuntimeError(f"Unsupported input bit width {bit_width} for export")
def quant_type(
module: QuantReLU,
supported_bit_width: Tuple[int,...] = (2, 4, 8, 16, 32)):
bit_width = int(module.quant_act_bit_width().item())
if bit_width in list(supported_bit_width):
return f"UINT{bit_width}"
else:
raise RuntimeError(f"Unsupported input bit width {bit_width} for export")
def thresholds(module: QuantReLU):
num_distinct_values = 2**int(module.quant_act_bit_width().item())
num_thresholds = num_distinct_values - 1
flat_scale = module.quant_act_scale().view(-1)
num_scale_channels = flat_scale.shape[0]
step = torch.abs(flat_scale)
min_threshold = step / 2
thresholds = torch.empty(num_scale_channels, num_thresholds)
for c in range(num_scale_channels):
for t in range(num_thresholds):
thresholds[c][t] = min_threshold[c] + step[c] * t
return thresholds
def thresholds(module: QuantReLU, extend_tensor_to_channels=True):
num_distinct_values = 2 ** int(module.quant_act_bit_width().item())
num_thresholds = num_distinct_values - 1
flat_scale = module.quant_act_scale().view(-1)
num_scale_channels = flat_scale.shape[0]
step = torch.abs(flat_scale)
min_threshold = step / 2
thresholds = torch.empty(num_scale_channels, num_thresholds)
for c in range(num_scale_channels):
for t in range(num_thresholds):
thresholds[c][t] = min_threshold[c] + step[c] * t
if extend_tensor_to_channels:
output_channels = module._cached_inp.shape[1]
final_shape = (output_channels, num_thresholds)
if thresholds.shape != final_shape:
thresholds = thresholds.expand(final_shape)
return thresholds
def quant_type(module: QuantReLU):
bit_width = module.quant_act_bit_width()
signed = module.is_quant_act_signed
return finn_datatype(bit_width, signed)
