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

Copyright 2020 The Microsoft DeepSpeed Team
import types
import torch
import numpy as np
from deepspeed import comm as dist

[docs]class OnebitAdam(torch.optim.Optimizer): """Implements the 1-bit Adam algorithm. Currently GPU-only. For usage example please see For technical details please read 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) 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 Adam! 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') .. _Adam\\: A Method for Stochastic Optimization: .. _On the Convergence of Adam and Beyond: """ 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., amsgrad=False, cuda_aware=False, comm_backend_name='nccl'): if amsgrad: raise RuntimeError('1-bit Adam 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) super(OnebitAdam, self).__init__(params, defaults) self.eps_mode = 0 if eps_inside_sqrt else 1 assert (dist.is_initialized()) self.comm_time = 0.0 self.step_time = 0.0 self.ave_step = 1 self.bk_time = 0.0 self.deepspeed = deepspeed self.adam_freeze_key = False self.initialize = False self.freeze_step = freeze_step self.cuda_aware = cuda_aware 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)) 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) output params (list of tensors, optional): A reduced precision copy of the updated weights written out in addition to the regular updated weights. Have to be of same type as gradients. (default: None) scale (float, optional): factor to divide gradient tensor values by before applying to weights. (default: 1) """ loss = None if closure is not None: loss = closure() gather_time = 0 allgather_time = 0 all_time = 0 if self.adam_freeze_key is False: v_diff_buffer = 0.0 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 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 = if grad.is_sparse: raise RuntimeError('1-bit Adam does not support sparse gradients') 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( # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like( if not self.initialize or (self.adam_freeze_key and 'worker_error' not in state.keys()): state['tensor_size'] = torch.numel( state['corrected_tensor_size'] = state['tensor_size'] if state['tensor_size'] % (self.size * self.divider) != 0: state['corrected_tensor_size'] += ((self.size * self.divider) - (state['tensor_size'] % (self.size * self.divider))) state['server_chunk_size'] = state[ 'corrected_tensor_size'] // self.size torch.cuda.empty_cache() state['worker_error'] = torch.zeros(state['corrected_tensor_size'], device=p.device) state['server_error'] = torch.zeros(state['server_chunk_size'], device=p.device) torch.cuda.empty_cache() self.adam_freeze_key = True if not self.initialize and dist.get_rank() == 0: print("Cupy Buffers Initialized Successfully.") exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 if self.adam_freeze_key is False: exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) grad = None if self.initialize: update = exp_avg / (exp_avg_sq.sqrt() + group['eps']) else: if 'non_freeze' in group.keys() and group['non_freeze'] is True: dist.all_reduce(grad) grad.mul_(1 / dist.get_world_size()) exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) grad = None else: if self.initialize is True: exp_avg.mul_(beta1).add_(1 - beta1, grad) grad = None if self.size > 1: exp_avg.set_( self.comm_backend_handle.compressed_allreduce( exp_avg, state['worker_error'], state['server_error'], self.deepspeed.local_rank)) # 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/ 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']) if self.initialize: update = exp_avg / (exp_avg_sq.sqrt() + group['eps']) if self.initialize: if group['weight_decay'] > 0.0: update += group['weight_decay'] * with torch.no_grad(): p.add_(-group['lr'] * update) if not self.initialize: print('Pop out errors', flush=True) state.pop('worker_error') state.pop('server_error') if not self.initialize: self.adam_freeze_key = False self.initialize = True print(f"Finished the initialization step at rank {dist.get_rank()}") return loss if self.adam_freeze_key is False: if state['step'] >= self.freeze_step: print('OnebitAdam - starting compressed communication') self.adam_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/ # 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) if self.state[self.param_groups[0]['params'][0]]['step'] < self.freeze_step: if dist.get_rank() == 0: print("Checkpoint loaded and OnebitAdam warmup stage starts/continues.") if self.adam_freeze_key is True: self.adam_freeze_key = False if self.using_pipeline: self.deepspeed.pipeline_enable_backward_allreduce = True else: self.deepspeed.enable_backward_allreduce = True else: if dist.get_rank() == 0: print( "Checkpoint loaded and OnebitAdam compression stage starts/continues." ) if self.adam_freeze_key is False: self.adam_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. for group in self.param_groups: for p in group['params']: if 'worker_error' in self.state[p]: self.state[p].pop('worker_error') if 'server_error' in self.state[p]: self.state[p].pop('server_error')