# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from bitsandbytes.optim.optimizer import Optimizer2State class Adam(Optimizer2State): def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True): super(Adam, self).__init__('adam', params, lr, betas, eps, weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise) class Adam8bit(Optimizer2State): def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True): super(Adam8bit, self).__init__('adam', params, lr, betas, eps, weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise) class Adam32bit(Optimizer2State): def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True): super(Adam32bit, self).__init__('adam', params, lr, betas, eps, weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise) class AnalysisAdam(torch.optim.Optimizer): """Implements 8-bit Adam and performs error analysis. This implementation is modified from torch.optim.Adam based on: `Fixed Weight Decay Regularization in Adam` (see https://arxiv.org/abs/1711.05101) It has been proposed in `Adam: A Method for Stochastic Optimization`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ """ def __init__( self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, bnb_analysis='dynamic-blockwise', savedir=None ): defaults = dict( lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad ) super(AnalysisAdam, self).__init__(params, defaults) self.analysis = bnb_analysis self.savedir = savedir @property def supports_memory_efficient_fp16(self): return True @property def supports_flat_params(self): return True def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p_id, p in enumerate(group["params"]): if p.grad is None: continue grad = p.grad.data if grad.dtype in {torch.float16, torch.bfloat16}: grad = grad.float() if grad.is_sparse: raise RuntimeError( "Adam does not support sparse gradients, please consider SparseAdam instead" ) amsgrad = group.get("amsgrad", False) assert not amsgrad p_data_fp32 = p.data if p.data.dtype in {torch.float16, torch.bfloat16}: p_data_fp32 = p_data_fp32.float() state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 # Exponential moving average of gradient values state["exp_avg"] = torch.zeros_like(p_data_fp32) # Exponential moving average of squared gradient values state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) state['abserrors'] = torch.zeros((256, 256), device=p_data_fp32.device) state['relerrors'] = torch.zeros((256, 256), device=p_data_fp32.device) state['counts'] = torch.zeros((256, 256), device=p_data_fp32.device) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) else: state["exp_avg"] = state["exp_avg"].to(p_data_fp32) state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) if amsgrad: state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( p_data_fp32 ) state["step"] += 1 beta1, beta2 = group["betas"] bias_correction1 = 1 - beta1 ** state["step"] bias_correction2 = 1 - beta2 ** state["step"] step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 e = state['abserrors'] rele = state['relerrors'] counts = state['counts'] if group["weight_decay"] != 0: p_data_fp32.add_( p_data_fp32, alpha=-group["weight_decay"] * group["lr"] ) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] if amsgrad: max_exp_avg_sq = state["max_exp_avg_sq"] # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) denom = exp_avg_sq.sqrt().add_(group["eps"]) update_fp32 = exp_avg/denom if p_data_fp32.numel() <= 8192 or p_data_fp32.numel() > 50000*1000: # embedding layer or too small p_data_fp32 += -step_size*update_fp32 else: if self.analysis == 'dynamic-blockwise': code1 = F.create_dynamic_map(signed=True).to(p.device) code2 = F.create_dynamic_map(signed=False).to(p.device) C1, S1 = F.quantize_blockwise(exp_avg, code=code1) state1 = F.dequantize_blockwise(C1, S1) C2, S2 = F.quantize_blockwise(exp_avg_sq, code=code2) state2 = F.dequantize_blockwise(C2, S2) elif self.analysis == 'dynamic': code1 = F.create_dynamic_map(signed=True).to(p.device) code2 = F.create_dynamic_map(signed=False).to(p.device) C1, S1 = F.quantize(exp_avg, code=code1) state1 = F.dequantize(C1, S1) C2, S2 = F.quantize(exp_avg_sq, code=code2) state2 = F.dequantize(C2, S2) elif self.analysis == 'linear': code1 = F.create_linear_map(signed=True).to(p.device) code2 = F.create_linear_map(signed=False).to(p.device) C1, S1 = F.quantize(exp_avg, code=code1) state1 = F.dequantize(C1, S1) C2, S2 = F.quantize(exp_avg_sq, code=code2) state2 = F.dequantize(C2, S2) elif self.analysis == 'quantile': code1 = F.estimate_quantiles(exp_avg) code2 = F.estimate_quantiles(exp_avg_sq) C1 = F.quantize_no_absmax(exp_avg, code=code1) state1 = F.dequantize_no_absmax(C1, code1) C2 = F.quantize_no_absmax(exp_avg_sq, code=code2) state2 = F.dequantize_no_absmax(C2, code2) else: raise ValueError(f'Invalid analysis value: {self.analysis}!') denom = state2.sqrt().add_(group["eps"]) update_8bit = state1/denom abserr = torch.abs(update_8bit-update_fp32) relerr = abserr/torch.abs(update_fp32+1e-6) C1, C2 = C1.int(), C2.int() F.histogram_scatter_add_2d(e, C1.int(), C2.int(), abserr) F.histogram_scatter_add_2d(rele, C1.int(), C2.int(), relerr) F.histogram_scatter_add_2d(counts, C1.int(), C2.int(), torch.ones_like(abserr)) p_data_fp32 += -step_size*update_fp32 if not dist.is_initialized() or dist.get_rank() == 0: if self.savedir != '' and state['step'] % 100 == 0: if not os.path.exists(self.savedir): os.makedirs(self.savedir) shapestr = '_'.join([str(dim) for dim in p_data_fp32.shape]) pathe = join(self.savedir, f'{p_id}_{shapestr}_abserr.pkl') pathrele = join(self.savedir, f'{p_id}_{shapestr}_relerr.pkl') pathcounts = join(self.savedir, f'{p_id}_{shapestr}_counts.pkl') torch.save(e, pathe) torch.save(rele, pathrele) torch.save(counts, pathcounts) if p.data.dtype in {torch.float16, torch.bfloat16}: p.data.copy_(p_data_fp32) return loss