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| import math | |
| import torch | |
| from torch.optim import Optimizer | |
| from toolkit.optimizers.optimizer_utils import copy_stochastic, Auto8bitTensor, stochastic_grad_accummulation | |
| class Adam8bit(Optimizer): | |
| """ | |
| Implements Adam optimizer with 8-bit state storage and stochastic rounding. | |
| Arguments: | |
| params (iterable): Iterable of parameters to optimize or dicts defining parameter groups | |
| lr (float): Learning rate (default: 1e-3) | |
| betas (tuple): Coefficients for computing running averages of gradient and its square (default: (0.9, 0.999)) | |
| eps (float): Term added to denominator to improve numerical stability (default: 1e-8) | |
| weight_decay (float): Weight decay coefficient (default: 0) | |
| decouple (bool): Use AdamW style decoupled weight decay (default: True) | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, | |
| weight_decay=0, decouple=True): | |
| if not 0.0 <= lr: | |
| raise ValueError(f"Invalid learning rate: {lr}") | |
| if not 0.0 <= eps: | |
| raise ValueError(f"Invalid epsilon value: {eps}") | |
| if not 0.0 <= betas[0] < 1.0: | |
| raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") | |
| if not 0.0 <= betas[1] < 1.0: | |
| raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") | |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, | |
| decouple=decouple) | |
| super(Adam8bit, self).__init__(params, defaults) | |
| self.is_stochastic_rounding_accumulation = False | |
| # Setup stochastic grad accumulation hooks | |
| for group in self.param_groups: | |
| for param in group['params']: | |
| if param.requires_grad and param.dtype != torch.float32: | |
| self.is_stochastic_rounding_accumulation = True | |
| param.register_post_accumulate_grad_hook( | |
| stochastic_grad_accummulation | |
| ) | |
| def supports_memory_efficient_fp16(self): | |
| return False | |
| def supports_flat_params(self): | |
| return True | |
| def step_hook(self): | |
| if not self.is_stochastic_rounding_accumulation: | |
| return | |
| # Copy over stochastically rounded grads | |
| for group in self.param_groups: | |
| for param in group['params']: | |
| if param.requires_grad and hasattr(param, "_accum_grad"): | |
| param.grad = param._accum_grad | |
| del param._accum_grad | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model and returns the loss. | |
| """ | |
| # Call pre step | |
| self.step_hook() | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for group in self.param_groups: | |
| beta1, beta2 = group['betas'] | |
| eps = group['eps'] | |
| lr = group['lr'] | |
| decay = group['weight_decay'] | |
| decouple = group['decouple'] | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data.to(torch.float32) | |
| p_fp32 = p.clone().to(torch.float32) | |
| # Apply weight decay (coupled variant) | |
| if decay != 0 and not decouple: | |
| grad.add_(p_fp32.data, alpha=decay) | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| # Exponential moving average of gradient values | |
| state['exp_avg'] = Auto8bitTensor( | |
| torch.zeros_like(p_fp32.data).detach()) | |
| # Exponential moving average of squared gradient values | |
| state['exp_avg_sq'] = Auto8bitTensor( | |
| torch.zeros_like(p_fp32.data).detach()) | |
| exp_avg = state['exp_avg'].to(torch.float32) | |
| exp_avg_sq = state['exp_avg_sq'].to(torch.float32) | |
| state['step'] += 1 | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| # Adam EMA updates | |
| exp_avg.mul_(beta1).add_(grad, alpha=1-beta1) | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1-beta2) | |
| # Apply weight decay (decoupled variant) | |
| if decay != 0 and decouple: | |
| p_fp32.data.mul_(1 - lr * decay) | |
| # Bias correction | |
| step_size = lr / bias_correction1 | |
| denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps) | |
| # Take step | |
| p_fp32.data.addcdiv_(exp_avg, denom, value=-step_size) | |
| # Update state with stochastic rounding | |
| state['exp_avg'] = Auto8bitTensor(exp_avg) | |
| state['exp_avg_sq'] = Auto8bitTensor(exp_avg_sq) | |
| # Apply stochastic rounding to parameters | |
| copy_stochastic(p.data, p_fp32.data) | |
| return loss | |
| def state_dict(self): | |
| """Returns the state of the optimizer as a dict.""" | |
| state_dict = super().state_dict() | |
| # Convert Auto8bitTensor objects to regular state dicts | |
| for param_id, param_state in state_dict['state'].items(): | |
| for key, value in param_state.items(): | |
| if isinstance(value, Auto8bitTensor): | |
| param_state[key] = { | |
| '_type': 'Auto8bitTensor', | |
| 'state': value.state_dict() | |
| } | |
| return state_dict | |
| def load_state_dict(self, state_dict): | |
| """Loads the optimizer state.""" | |
| # First, load the basic state | |
| super().load_state_dict(state_dict) | |
| # Then convert any Auto8bitTensor states back to objects | |
| for param_id, param_state in self.state.items(): | |
| for key, value in param_state.items(): | |
| if isinstance(value, dict) and value.get('_type') == 'Auto8bitTensor': | |
| param_state[key] = Auto8bitTensor(value['state']) | |