Class Optimizer1State

bitsandbytes/optim/optimizer.py:593–756

class Optimizer1State(Optimizer8bit):
    def __init__(
        self,
        optimizer_name,
        params,
        lr=1e-3,
        betas=(0.9, 0.0),
        eps=1e-8,
        weight_decay=0.0,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        max_unorm=0.0,
        skip_zeros=False,
        is_paged=False,
    ):
        """
        Base 1-state update optimizer class.

        Arguments:
            optimizer_name (`str`):
                The name of the optimizer.
            params (`torch.Tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple`, defaults to (0.9, 0.0)):
                The beta values for the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value for the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            max_unorm (`float`, defaults to 0.0):
                The maximum value to normalize each block with.
            skip_zeros (`bool`, defaults to `False`):
                Whether to skip zero values for sparse gradients and models to ensure correct updates.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        if not 0.0 <= lr:
            raise ValueError(f"Invalid learning rate: {lr}")
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps}")
        for i in range(len(betas)):
            if not 0.0 <= betas[i] < 1.0:
                raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
        if not 0.0 <= weight_decay:
            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults, optim_bits, is_paged)

        if args is None: