# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import types
import torch
import numpy as np
from deepspeed.accelerator import get_accelerator
from deepspeed.utils.torch import required_torch_version
from deepspeed import comm as dist
[docs]class ZeroOneAdam(torch.optim.Optimizer):
"""Implements the 0/1 Adam algorithm. Currently GPU-only.
For usage example please see https://www.deepspeed.ai/tutorials/zero-one-adam/
For technical details please read https://arxiv.org/abs/2202.06009
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)
var_freeze_step (int, optional): The latest step to update the variance,
using the notation from https://arxiv.org/abs/2202.06009, it denotes the
max{i|i in T_v}. Note that this is different from the freeze step from the
1-bit Adam. The var_freeze_step is usually the end of the learning rate warmup
and thus does not require tuning. (default: 100000)
var_update_scaler (int, optional): The interval to update the variance. Note that
the update policy for variance follows an exponential rule, where var_update_scaler
denotes the kappa in the 0/1 Adam paper. (default: 16)
local_step_scaler (int, optional): The interval to scale the local steps interval
according to the learning rate policy. (default: 32678)
local_step_clipper (int, optional): The largest interval for local steps with
learning rate policy. This corresponds to the variable H in the 0/1 Adam paper.
(default: 16)
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 0/1 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:
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,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
eps_inside_sqrt=False,
weight_decay=0.,
max_grad_norm=0.,
var_freeze_step=100000,
var_update_scaler=16,
local_step_scaler=32678,
local_step_clipper=16,
amsgrad=False,
cuda_aware=False,
comm_backend_name='nccl'):
if amsgrad:
raise RuntimeError('0/1 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(ZeroOneAdam, self).__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
self.deepspeed = deepspeed
self.initialize = False
self.cuda_aware = cuda_aware
self.using_pipeline = False
self.var_freeze_step = var_freeze_step
self.var_update_scaler = var_update_scaler
self.local_step_scaler = local_step_scaler
self.local_step_clipper = local_step_clipper
self.freeze_key = False
self.reinitial_error_buffer = False
self.comm_backend_name = comm_backend_name
assert dist.is_initialized(), "Please initialize the torch distributed backend."
# Empty initializer. Set handle based on the comm backend as follows.
self.comm_backend_handle = None
if self.comm_backend_name == 'nccl':
assert (
required_torch_version(min_version=1.8)
), "Please use torch 1.8 or greater to enable NCCL backend in 0/1 Adam. Alternatively, please specify 'mpi' as the 'comm_backend_name' in config file to proceed with the MPI 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)
elif self.comm_backend_name == 'hccl':
from deepspeed.runtime.comm.hccl import HcclBackend
self.using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce')
self.comm_backend_handle = HcclBackend(self.deepspeed.mpu)
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()
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 = p.grad.data
if grad.is_sparse:
raise RuntimeError('0/1 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(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if not self.initialize or 'worker_error' not in state.keys():
# Some scalars to help scale the variance update/local step policies
state['var_interval'] = 1
state['var_counter'] = 0
state['local_step_interval'] = 1
state['local_step_counter'] = 0
state['lrs'] = 0
state['tensor_size'] = torch.numel(p.data)
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
get_accelerator().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)
# Accumulation of momentum, i.e., the u variable in the 0/1 Adam paper
state['momentum_accumulator'] = torch.zeros_like(p.data)
get_accelerator().empty_cache()
# self.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']
comm_buffer = state['momentum_accumulator']
beta1, beta2 = group['betas']
state['step'] += 1
if self.initialize:
if self.freeze_key is False:
if state['step'] % state['var_interval'] == 0:
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
else:
if self.size > 1:
with torch.no_grad():
grad_onebit = self.comm_backend_handle.compressed_allreduce(
grad, state['worker_error'], state['server_error'], self.deepspeed.local_rank)
if 'exp_avg_mask' in group:
if grad_onebit.device != group['exp_avg_mask'].device:
group['exp_avg_mask'] = group['exp_avg_mask'].to(device=grad_onebit.device)
grad_onebit.mul_(group['exp_avg_mask'])
exp_avg.mul_(beta1).add_(1 - beta1, grad_onebit)
else:
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
state['lrs'] += group['lr']
grad = None
if not self.initialize:
if self.size > 1:
comm_buffer.set_(
self.comm_backend_handle.compressed_allreduce(comm_buffer, state['worker_error'],
state['server_error'],
self.deepspeed.local_rank))
if 'exp_avg_mask' in group:
if comm_buffer.device != group['exp_avg_mask'].device:
group['exp_avg_mask'] = group['exp_avg_mask'].to(device=comm_buffer.device)
comm_buffer.mul_(group['exp_avg_mask'])
if self.initialize:
update = exp_avg / (exp_avg_sq.sqrt() + group['eps'])
if group['weight_decay'] > 0.0:
update += group['weight_decay'] * p.data
with torch.no_grad():
p.data.add_(-group['lr'] * update)
if self.freeze_key is True:
comm_buffer.add_(-group['lr'] * update)
if state['step'] % state['local_step_interval'] == 0 and self.freeze_key:
with torch.no_grad():
p.data.add_(-1 * comm_buffer)
comm_buffer.mul_(exp_avg_sq.sqrt() + group['eps'])
if self.size > 1:
comm_buffer.copy_(
self.comm_backend_handle.compressed_allreduce(comm_buffer, state['worker_error'],
state['server_error'],
self.deepspeed.local_rank))
if 'exp_avg_mask' in group:
if comm_buffer.device != group['exp_avg_mask'].device:
group['exp_avg_mask'] = group['exp_avg_mask'].to(device=comm_buffer.device)
comm_buffer.mul_(group['exp_avg_mask'])
exp_avg.zero_().add_(comm_buffer / state['lrs'], alpha=-1)
p.data.add_(comm_buffer / (exp_avg_sq.sqrt() + group['eps']))
comm_buffer.zero_()
state['lrs'] = 0
# According to 0/1 Adam theory, a fixed variance would allow more accurate estimation of momentum
# However, in practice, we can also disable the manual freezing of variance, since the interval of
# updating variance will increase exponentially, so that it has negligible effect on the estimation.
if self.freeze_key is False:
if state['step'] % state['var_interval'] == 0:
state['var_counter'] += 1
if state['var_counter'] == self.var_update_scaler:
state['var_counter'] = 0
state['var_interval'] *= 2
if (state['step'] + 1) % state['var_interval'] == 0:
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = True
else:
self.deepspeed.enable_backward_allreduce = True
else:
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = False
else:
self.deepspeed.enable_backward_allreduce = False
else:
state['local_step_counter'] += 1
if state['local_step_counter'] == self.local_step_scaler:
state['local_step_counter'] = 0
state['local_step_interval'] = min(self.local_step_clipper,
state['local_step_interval'] * 2)
if not self.initialize:
print('Pop out errors', flush=True)
self.freeze_key = False
state.pop('worker_error')
state.pop('server_error')
if not self.initialize:
self.initialize = True
print(f"Finished the initialization step at rank {dist.get_rank()}")
return loss
if self.state[self.param_groups[0]['params'][0]]['step'] > self.var_freeze_step:
self.freeze_key = True
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = False
else:
self.deepspeed.enable_backward_allreduce = False
if self.freeze_key is True and self.reinitial_error_buffer is False:
# We need to reinitialize the error buffers when local step > 1 since
# the errors will be logged for different metrics (gradient vs. accumulated momentum).
for group in self.param_groups:
for p in group['params']:
self.state[p]['worker_error'].zero_()
self.state[p]['server_error'].zero_()
self.reinitial_error_buffer = True
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)
if self.state[self.param_groups[0]['params'][0]]['step'] < self.var_freeze_step:
self.var_freeze_key = False
if (self.state[self.param_groups[0]['params'][0]]['step'] +
1) % self.state[self.param_groups[0]['params'][0]]['var_interval'] == 0:
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = True
else:
self.deepspeed.enable_backward_allreduce = True
else:
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = False
else:
self.deepspeed.enable_backward_allreduce = False
else:
self.var_freeze_key = True
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = False
else:
self.deepspeed.enable_backward_allreduce = False
self.reinitial_error_buffer = False
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')
if 'momentum_accumulator' in self.state[p]:
self.state[p].pop('momentum_accumulator')