def __init__()

in bitsandbytes/optim/optimizer.py [0:0]

• 32 lines of code
• 9 McCabe index (conditional complexity)

    def __init__(self, optimizer_name, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
            weight_decay=0.0, optim_bits=32, args=None,
            min_8bit_size=4096, percentile_clipping=100, block_wise=True, max_unorm=0.0,
            skip_zeros=False):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if isinstance(betas, str):
            # format: '(beta1, beta2)'
            betas = betas.replace('(', '').replace(')', '').strip().split(',')
            betas = [float(b) for b in betas]
        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("Invalid weight_decay value: {}".format(weight_decay))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay)
        super(Optimizer2State, self).__init__(params, defaults, optim_bits)

        if args is None:
            args = {}
            args['optim_bits'] = optim_bits
            args['percentile_clipping'] = 100
            args['min_8bit_size'] = min_8bit_size
            args['percentile_clipping'] = percentile_clipping
            args['block_wise'] = block_wise
            args['max_unorm'] = max_unorm
            args['skip_zeros'] = skip_zeros

            self.args = MockArgs(args)
        else:
            self.args = args

        self.optimizer_name = optimizer_name