Spaces:
Sleeping
Sleeping
| import torch | |
| import torch.nn as nn | |
| from torch.optim import Optimizer | |
| import math | |
| import os | |
| # --- 1. RANGER OPTIMIZER (Full Implementation) --- | |
| class Ranger(Optimizer): | |
| def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95, 0.999), eps=1e-5, weight_decay=0): | |
| defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay) | |
| super().__init__(params, defaults) | |
| self.N_sma_threshhold = N_sma_threshhold | |
| self.alpha = alpha | |
| self.k = k | |
| self.radam_buffer = [[None,None,None] for ind in range(10)] | |
| def __setstate__(self, state): | |
| super().__setstate__(state) | |
| def step(self, closure=None): | |
| loss = None | |
| if closure is not None: loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: continue | |
| grad = p.grad.data.float() | |
| if p.grad.is_sparse: raise RuntimeError('Ranger does not support sparse gradients') | |
| p_data_fp32 = p.data.float() | |
| state = self.state[p] | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| state['exp_avg'] = torch.zeros_like(p_data_fp32) | |
| state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) | |
| state['slow_buffer'] = torch.empty_like(p.data) | |
| state['slow_buffer'].copy_(p.data) | |
| else: | |
| state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) | |
| state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) | |
| state['step'] += 1 | |
| buffered = self.radam_buffer[int(state['step'] % 10)] | |
| if state['step'] == buffered[0]: | |
| N_sma, step_size = buffered[1], buffered[2] | |
| else: | |
| buffered[0] = state['step'] | |
| beta2_t = beta2 ** state['step'] | |
| N_sma_max = 2 / (1 - beta2) - 1 | |
| N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) | |
| buffered[1] = N_sma | |
| if N_sma >= self.N_sma_threshhold: | |
| step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) | |
| else: | |
| step_size = 1.0 / (1 - beta1 ** state['step']) | |
| buffered[2] = step_size | |
| if group['weight_decay'] != 0: | |
| p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr']) | |
| if N_sma >= self.N_sma_threshhold: | |
| denom = exp_avg_sq.sqrt().add_(group['eps']) | |
| p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr']) | |
| else: | |
| p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr']) | |
| p.data.copy_(p_data_fp32) | |
| if state['step'] % group['k'] == 0: | |
| slow_p = state['slow_buffer'] | |
| slow_p.add_(p.data - slow_p, alpha=self.alpha) | |
| p.data.copy_(slow_p) | |
| return loss | |
| # --- 2. QUANTIZATION PIPELINE --- | |
| def quantize_model(model): | |
| """ | |
| Applies PyTorch Dynamic INT8 Quantization. | |
| """ | |
| model.cpu().eval() | |
| q_model = torch.quantization.quantize_dynamic( | |
| model, | |
| {torch.nn.Linear, torch.nn.GRU, torch.nn.LSTM}, | |
| dtype=torch.qint8 | |
| ) | |
| return q_model | |
| def save_model(model, path): | |
| torch.save(model.state_dict(), path) | |
| def load_model(model_class, path, quantized=False): | |
| model = model_class() | |
| if quantized: | |
| model = quantize_model(model) | |
| # Weights_only=False is needed for quantized state dicts | |
| state = torch.load(path, map_location='cpu', weights_only=False) | |
| else: | |
| state = torch.load(path, map_location='cpu') | |
| model.load_state_dict(state) | |
| return model |