def create_fp8_map()

in bitsandbytes/functional.py [0:0]

• 34 lines of code
• 12 McCabe index (conditional complexity)

def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8):
    e = exponent_bits
    p = precision_bits
    has_sign = 1 if signed else 0
    assert e + p == total_bits - has_sign
    # the exponent is biased to 2^(e-1) -1 == 0
    evalues = []
    pvalues = []
    for i, val in enumerate(range(-(2 ** (exponent_bits - has_sign)), 2 ** (exponent_bits - has_sign), 1)):
        evalues.append(2**val)

    values = []
    lst = list(itertools.product([0, 1], repeat=precision_bits))
    # for ev in evalues:
    bias = 2 ** (exponent_bits - 1)
    for evalue in range(2 ** (exponent_bits)):
        for bit_pattern in lst:
            value = 1 if evalue != 0 else 0
            for i, pval in enumerate(list(bit_pattern)):
                value += pval * (2 ** -(i + 1))
            if evalue == 0:
                # subnormals
                value = value * 2**-(bias)
            else:
                # normals
                value = value * 2 ** -(evalue - bias - 1)
            values.append(value)
            if signed:
                values.append(-value)

    assert len(values) == 2**total_bits
    values.sort()
    if total_bits < 8:
        gap = 256 - len(values)
        for i in range(gap):
            values.append(0)
    values.sort()
    code = torch.Tensor(values)
    code /= code.max()

    return code