Function create_dynamic_map

bitsandbytes/functional.py:296–348

Creates the dynamic quantiztion map. The dynamic data type is made up of a dynamic exponent and fraction. As the exponent increase from 0 to -7 the number of bits available for the fraction shrinks. This is a generalization of the dynamic type where a certain number of the

(signed=True, max_exponent_bits=7, total_bits=8)

def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
    """
    Creates the dynamic quantiztion map.

    The dynamic data type is made up of a dynamic exponent and
    fraction. As the exponent increase from 0 to -7 the number
    of bits available for the fraction shrinks.

    This is a generalization of the dynamic type where a certain
    number of the bits and be reserved for the linear quantization
    region (the fraction). n determines the maximum number of
    exponent bits.

    For more details see
    (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561]
    """

    data = []
    # these are additional items that come from the case
    # where all the exponent bits are zero and no
    # indicator bit is present
    non_sign_bits = total_bits - 1
    additional_items = 2 ** (non_sign_bits - max_exponent_bits) - 1
    for i in range(max_exponent_bits):
        fraction_items = int(
            2 ** (i + non_sign_bits - max_exponent_bits) + 1
            if signed
            else 2 ** (i + non_sign_bits - max_exponent_bits + 1) + 1,
        )
        boundaries = torch.linspace(0.1, 1, fraction_items, dtype=torch.float32)
        means = (boundaries[:-1] + boundaries[1:]) / 2.0
        data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()

    if additional_items > 0:
        boundaries = torch.linspace(0.1, 1, additional_items + 1, dtype=torch.float32)
        means = (boundaries[:-1] + boundaries[1:]) / 2.0
        data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()

    data.append(0)
    data.append(1.0)

    assert len(data) == 2**total_bits

    gap = 256 - len(data)
    for i in range(gap):
        data.append(0)

    data.sort()
    return torch.tensor(data, dtype=torch.float32)


def is_on_gpu(tensors: Iterable[Optional[torch.Tensor]]):