Precommit

src/brevitas/quant_tensor/float_quant_tensor.py

@@ -154,6 +154,7 @@ def minifloat(self, float_datatype=True):
             int_scale = float_internal_scale(
                 minifloat_value, self.mantissa_bit_width, fp_internal_scale, eps)
             float_value = torch.round(self._pre_round_float_value) * int_scale
+
     def check_input_type(tensor):
         if not isinstance(tensor, FloatQuantTensor):
             raise RuntimeError("Tensor is not a FloatQuantTensor")

tests/brevitas/core/test_quant_mx.py

@@ -4,7 +4,7 @@
 # pylint: disable=missing-function-docstring, redefined-outer-name
 
 import struct
-from typing import Tuple
+from typing import List, Tuple, Union
 
 from hypothesis import given
 import pytest_cases
@@ -20,7 +20,9 @@
 
 
 # debug utility
-def to_string(val: torch.Tensor | float, spaced: bool = True, code: str = "f") -> str | list[str]:
+def to_string(val: Union[torch.Tensor, float],
+              spaced: bool = True,
+              code: str = "f") -> Union[str, List[str]]:
     """ Debug util for visualizing float values """
 
     def scalar_to_string(val: float, spaced: bool) -> str:
@@ -35,7 +37,7 @@ def scalar_to_string(val: float, spaced: bool) -> str:
 
 
 # debug utility
-def check_bits(val: torch.Tensor | float, mbits: int) -> Tuple[bool, int]:
+def check_bits(val: Union[torch.Tensor, float], mbits: int) -> Tuple[bool, int]:
     """ return (too many precision bits, lowest mantissa bit) """
     strings = to_string(val, spaced=False)
     if isinstance(strings, str):