Source code for deepspeed.runtime.fp16.onebit.lamb

'''
Copyright 2021 The Microsoft DeepSpeed Team
'''
import types
import torch
import numpy as np
import torch.distributed as dist
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors


[docs]class OnebitLamb(torch.optim.Optimizer): """Implements the 1-bit Lamb algorithm. Currently GPU-only. For usage example please see https://www.deepspeed.ai/tutorials/onebit-lamb/ For technical details please see our paper https://arxiv.org/abs/2104.06069. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups. lr (float, optional): learning rate. (default: 1e-3) freeze_step (int, optional): Number of steps for warmup (uncompressed) stage before we start using compressed communication. (default 100000) 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) max_coeff(float, optional): maximum value of the lamb coefficient (default: 10.0) min_coeff(float, optional): minimum value of the lamb coefficient (default: 0.01) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) NOT SUPPORTED in 1-bit Lamb! eps_inside_sqrt (boolean, optional): in the 'update parameters' step, adds eps to the bias-corrected second moment estimate before evaluating square root instead of adding it to the square root of second moment estimate as in the original paper. (default: False) cuda_aware (boolean, required): Set True if the underlying MPI implementation supports CUDA-Aware communication. (default: False) comm_backend_name (string, optional): Set to 'mpi' if needed. (default: 'nccl') coeff_beta (float, optional): coefficient used for computing running averages of lamb coefficient (default: 0.9) note that you may want to increase or decrease this beta depending on the freeze_step you choose, as 1/(1 - coeff_beta) should be smaller than or equal to freeze_step factor_max (float, optional): maximum value of scaling factor to the frozen lamb coefficient during compression stage (default: 4.0) factor_min (float, optional): minimum value of scaling factor to the frozen lamb coefficient during compression stage (default: 0.5) factor_threshold (float, optional): threshold of how much the scaling factor can fluctuate between steps (default: 0.1) .. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes: https://arxiv.org/abs/1904.00962 .. _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, deepspeed=None, lr=1e-3, freeze_step=100000, bias_correction=True, betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False, weight_decay=0., max_grad_norm=0., max_coeff=10.0, min_coeff=0.01, amsgrad=False, cuda_aware=False, comm_backend_name='nccl', coeff_beta=0.9, factor_max=4.0, factor_min=0.5, factor_threshold=0.1): if amsgrad: raise RuntimeError('1-bit Lamb does not support the AMSGrad variant.') defaults = dict(lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay, max_grad_norm=max_grad_norm, max_coeff=max_coeff, min_coeff=min_coeff) super(OnebitLamb, self).__init__(params, defaults) self.eps_mode = 0 if eps_inside_sqrt else 1 assert (dist.is_initialized()) self.deepspeed = deepspeed self.lamb_freeze_key = False self.initialize = False self.freeze_step = freeze_step self.cuda_aware = cuda_aware self.coeff_beta = coeff_beta self.factor_max = factor_max self.factor_min = factor_min self.factor_threshold = factor_threshold self.using_pipeline = False self.comm_backend_name = comm_backend_name # Empty initializer. Set handle based on the comm backend as follows. self.comm_backend_handle = None if self.comm_backend_name == 'nccl': TORCH_MAJOR = int(torch.__version__.split('.')[0]) TORCH_MINOR = int(torch.__version__.split('.')[1]) assert TORCH_MAJOR >= 1 and TORCH_MINOR >= 8, "Please use torch 1.8 or greater to enable NCCL backend in 1-bit Adam. Alternatively, please specify 'mpi' as the 'comm_backend_name' in config file to proceed with the MPI backend" assert dist.is_initialized() == True, "Please initialize the torch distributed backend." from deepspeed.runtime.comm.nccl import NcclBackend self.using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce') self.comm_backend_handle = NcclBackend(self.deepspeed.mpu) elif self.comm_backend_name == 'mpi': from deepspeed.runtime.comm.mpi import MpiBackend self.comm_backend_handle = MpiBackend(cuda_aware) self.size = self.comm_backend_handle.size self.divider = int(self.size * 8 / np.gcd(self.size, 8)) self.exp_avg_flat = [] self.dummy_exp_avg = {} self.corrected_tensor_sizes = [] self.server_chunk_sizes = [] self.worker_errors = [] self.server_errors = [] self.lamb_coeffs = [] def step(self, closure=None, grads=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. grads (list of tensors, optional): weight gradient to use for the optimizer update. If gradients have type torch.half, parameters are expected to be in type torch.float. (default: None) """ loss = None if closure is not None: loss = closure() if grads is None: grads_group = [None] * len(self.param_groups) # backward compatibility # assuming a list/generator of parameter means single group elif isinstance(grads, types.GeneratorType): grads_group = [grads] elif type(grads[0]) != list: grads_group = [grads] else: grads_group = grads #remove the previous stats del self.lamb_coeffs[:] if self.lamb_freeze_key: exp_avg_last_step = [] for group in self.param_groups: exp_avg_last_step.append( [self.state[p]['exp_avg'].detach().clone() for p in group['params']]) if 'scaling_coeff' not in self.state[self.param_groups[0]['params'][0]]: # Compute the scaling_coeff for each momentum at the end of warmup stage. # This is used to reduce compression error during compression stage. momentum_scales = [] for group in self.param_groups: momentum_scales.append([ (torch.norm(self.state[p]['exp_avg']) / np.sqrt(torch.numel(self.state[p]['exp_avg']))).item() for p in group['params'] ]) united_scale = sum([sum(x) for x in momentum_scales]) / sum( [len(x) for x in momentum_scales]) for i, group in enumerate(self.param_groups): for j, p in enumerate(group['params']): self.state[p][ 'scaling_coeff'] = united_scale / momentum_scales[i][j] for group, grads_this_group in zip(self.param_groups, grads_group): if grads_this_group is None: grads_this_group = [None] * len(group['params']) bias_correction = 1 if group['bias_correction'] else 0 for p, grad in zip(group['params'], grads_this_group): if p.grad is None and grad is None: continue if grad is None: grad = p.grad.data if grad.is_sparse: raise RuntimeError('1-bit Lamb does not support sparse gradients') state = self.state[p] # State initialization if len(state) == 0 or (len(state) == 1 and 'scaling_coeff' in state.keys()): state['step'] = 0 state['lamb_coeff_freeze'] = 0.0 state['last_factor'] = 1.0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p.data) state['exp_avg_sq_fresh'] = torch.zeros_like(p.data) if not self.initialize: self.lamb_freeze_key = True exp_avg, exp_avg_sq, exp_avg_sq_fresh = state['exp_avg'], state['exp_avg_sq'], state['exp_avg_sq_fresh'] beta1, beta2 = group['betas'] max_coeff = group['max_coeff'] min_coeff = group['min_coeff'] state['step'] += 1 if self.lamb_freeze_key is False: # warmup stage, baseline Lamb optimization exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) if state['step'] == self.freeze_step: exp_avg_sq_fresh.data = exp_avg_sq.detach().clone() grad = None if self.initialize: weight_norm = p.data.pow(2).sum().sqrt() update = exp_avg / (exp_avg_sq.sqrt() + group['eps']) if group['weight_decay'] > 0.0: update += group['weight_decay'] * p.data update_norm = update.pow(2).sum().sqrt() lamb_coeff = 1.0 if weight_norm != 0 and update_norm != 0: lamb_coeff = (weight_norm / update_norm).item() if lamb_coeff > max_coeff: lamb_coeff = max_coeff if lamb_coeff < min_coeff: lamb_coeff = min_coeff if lamb_coeff != 1.0: state['lamb_coeff_freeze'] = self.coeff_beta * state[ 'lamb_coeff_freeze'] + (1 - self.coeff_beta) * lamb_coeff self.lamb_coeffs.append(lamb_coeff) with torch.no_grad(): p.add_(-group['lr'] * lamb_coeff * update) else: # compression stage, update each momentum locally, then # communicate based on the compressed_allreduce below if self.initialize: exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg.mul_(self.state[p]['scaling_coeff']) grad = None # init fused momentum if len(self.exp_avg_flat) == 0: momentum_groups = [] tensor_size = 0 for group in self.param_groups: for p in group['params']: momentum_groups.append(self.state[p]['exp_avg']) tensor_size += torch.numel(p.data) corrected_tensor_size = tensor_size if tensor_size % (self.size * self.divider) != 0: difference = ((self.size * self.divider) - (tensor_size % (self.size * self.divider))) corrected_tensor_size += difference self.dummy_exp_avg[0] = torch.zeros( difference, device=momentum_groups[0].data.device) momentum_groups.append(self.dummy_exp_avg[0]) self.corrected_tensor_sizes.append(corrected_tensor_size) self.server_chunk_sizes.append(corrected_tensor_size // self.size) self.exp_avg_flat.append( _flatten_dense_tensors([p.detach().clone() for p in momentum_groups])) updated_params = _unflatten_dense_tensors(self.exp_avg_flat[0], momentum_groups) for p, q in zip(momentum_groups, updated_params): p.data = q.data if self.initialize and len(self.worker_errors) == 0: torch.cuda.empty_cache() for i in range(len(self.exp_avg_flat)): self.worker_errors.append( torch.zeros(self.corrected_tensor_sizes[i], device=self.exp_avg_flat[i].device)) self.server_errors.append( torch.zeros(self.server_chunk_sizes[i], device=self.exp_avg_flat[i].device)) torch.cuda.empty_cache() if self.lamb_freeze_key: if self.size > 1: for i in range(len(self.exp_avg_flat)): if not self.initialize: torch.cuda.empty_cache() self.worker_errors.append( torch.zeros(self.corrected_tensor_sizes[i], device=self.exp_avg_flat[i].device)) self.server_errors.append( torch.zeros(self.server_chunk_sizes[i], device=self.exp_avg_flat[i].device)) torch.cuda.empty_cache() if torch.distributed.get_rank() == 0: print("Cupy Buffers Initialized Successfully.") self.comm_backend_handle.compressed_allreduce( self.exp_avg_flat[i], self.worker_errors[0], self.server_errors[0], self.deepspeed.local_rank) if torch.distributed.get_rank() == 0: print('Pop out errors', flush=True) del self.worker_errors[:] del self.server_errors[:] else: self.comm_backend_handle.compressed_allreduce( self.exp_avg_flat[i], self.worker_errors[i], self.server_errors[i], self.deepspeed.local_rank) if self.lamb_freeze_key and self.initialize: for i, group in enumerate(self.param_groups): bias_correction = 1 if group['bias_correction'] else 0 for j, p in enumerate(group['params']): state = self.state[p] exp_avg, exp_avg_sq, exp_avg_sq_fresh = state['exp_avg'], state['exp_avg_sq'], state['exp_avg_sq_fresh'] beta1, beta2 = group['betas'] exp_avg.div_(self.state[p]['scaling_coeff']) # Because 1-bit compression cannot represent exact zero, it is required to # provide a momentum mask for those params that have constant exact zeros in their # momentums, otherwise the compression error would keep accumulating. # For example, for BERT pre-training seq 128, bert.embeddings.position_embeddings.weight # always have exact zeros in its momentum for row 129 to 512, because it only # learns up to seq length 128 while the model supports up to 512 seq length. # (See example in DeepSpeedExamples/bing_bert/deepspeed_train.py about how # to add this exp_avg_mask for BERT pre-training.) if 'exp_avg_mask' in group: if exp_avg.device != group['exp_avg_mask'].device: group['exp_avg_mask'] = group['exp_avg_mask'].to( device=exp_avg.device) exp_avg.mul_(group['exp_avg_mask']) grad_reconstruct = ((exp_avg - exp_avg_last_step[i][j] * beta1) / (1 - beta1)) exp_avg_sq_fresh.mul_(beta2).addcmul_(1 - beta2, grad_reconstruct, grad_reconstruct) denom = exp_avg_sq.sqrt() + group['eps'] update_prelim = exp_avg / denom if group['weight_decay'] > 0.0: update = update_prelim + group['weight_decay'] * p.data else: update = update_prelim lamb_coeff = 1.0 update_norm = update.pow(2).sum().sqrt() denom_real = exp_avg_sq_fresh.sqrt() + group['eps'] factor = (denom / denom_real).max().item() if group['weight_decay'] > 0.0: update_ratio = min(1.0, (update_prelim.pow(2).sum().sqrt() / update_norm).item()) factor = factor * update_ratio + (1.0 - update_ratio) if factor > self.factor_max: factor = self.factor_max if factor < self.factor_min: factor = self.factor_min if factor > state['last_factor'] * (1.0 + self.factor_threshold): factor = state['last_factor'] * (1.0 + self.factor_threshold) if factor < state['last_factor'] * (1.0 - self.factor_threshold): factor = state['last_factor'] * (1.0 - self.factor_threshold) state['last_factor'] = factor lamb_coeff = state['lamb_coeff_freeze'] * factor self.lamb_coeffs.append(lamb_coeff) with torch.no_grad(): p.add_(-group['lr'] * lamb_coeff * update) del exp_avg_last_step[:] exp_avg_last_step = None if not self.initialize: self.lamb_freeze_key = False self.initialize = True print( f"Finished the initialization step at rank {torch.distributed.get_rank()}" ) return loss if self.lamb_freeze_key is False: if state['step'] >= self.freeze_step: print('OnebitLamb - starting compressed communication') self.lamb_freeze_key = True if self.using_pipeline: self.deepspeed.pipeline_enable_backward_allreduce = False else: self.deepspeed.enable_backward_allreduce = False return loss def load_state_dict(self, state_dict): """ Overrides load_state_dict() to add special handling when loading checkpoints """ # Because at different stage exp_avg_mask may change (e.g., # BERT pre-training seqlen 128 and 512 ), we don't use the exp_avg_mask # in checkpoints but always use the one user provided in training script. # (See example in DeepSpeedExamples/bing_bert/deepspeed_train.py.) # Thus here we keep the exp_avg_mask unchanged when loading checkpoint for i, group in enumerate(self.param_groups): if 'exp_avg_mask' in group: state_dict['param_groups'][i]['exp_avg_mask'] = group['exp_avg_mask'] elif 'exp_avg_mask' not in group and 'exp_avg_mask' in state_dict[ 'param_groups'][i]: state_dict['param_groups'][i].pop('exp_avg_mask') super().load_state_dict(state_dict) # need to reset the fused momentum since loading states will break the linking del self.exp_avg_flat[:] self.dummy_exp_avg.clear() del self.corrected_tensor_sizes[:] del self.server_chunk_sizes[:] if self.state[self.param_groups[0]['params'][0]]['step'] < self.freeze_step: if torch.distributed.get_rank() == 0: print("Checkpoint loaded and OnebitLamb warmup stage starts/continues.") if self.lamb_freeze_key is True: self.lamb_freeze_key = False if self.using_pipeline: self.deepspeed.pipeline_enable_backward_allreduce = True else: self.deepspeed.enable_backward_allreduce = True for group in self.param_groups: for p in group['params']: self.state[p]['lamb_coeff_freeze'] = 0.0 self.state[p]['last_factor'] = 1.0 if 'scaling_coeff' in self.state[p]: self.state[p].pop('scaling_coeff') else: if torch.distributed.get_rank() == 0: print( "Checkpoint loaded and OnebitLamb compression stage starts/continues." ) if self.lamb_freeze_key is False: self.lamb_freeze_key = True if self.using_pipeline: self.deepspeed.pipeline_enable_backward_allreduce = False else: self.deepspeed.enable_backward_allreduce = False # We reset the compression errors when loading checkpoints for 3 reasons: # 1) The worker and server error at each GPU are distinct, so in current implementation # only rank 0's errors are saved in the checkpoint. Thus we have to reset the errors. # If we want to save them correctly we need O(num_gpu*model_size) memory in order to # gather all the error, which is a very large memory requirement. It's possible to save # them in a distributed way, but it will make the checkpoint saving/loading much more complicated. # 2) Even if we are able to save the compression errors correctly, you need to have the # exact same number of GPUs in order to load them correctly. # 3) We verified on BERT pre-training that occasionally resetting the compression error # at checkpoint loading does not affect the convergence. # However, please avoid frequent checkpoint loading which could break the error # compensation mechanism thus affect the convergence. del self.worker_errors[:] del self.server_errors[:] def get_lamb_coeffs(self): return self.lamb_coeffs