wehub-resource-sync
366 lines
19 KiB
Python
366 lines
19 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import types
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import torch
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import numpy as np
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from deepspeed.accelerator import get_accelerator
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from deepspeed.utils.torch import required_torch_version
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from deepspeed import comm as dist
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class ZeroOneAdam(torch.optim.Optimizer):
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"""
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Implements the 0/1 Adam algorithm. Currently GPU-only.
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For usage example please see https://www.deepspeed.ai/tutorials/zero-one-adam/
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For technical details please read https://arxiv.org/abs/2202.06009
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups.
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lr (float, optional): learning rate. (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square. (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability. (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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var_freeze_step (int, optional): The latest step to update the variance,
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using the notation from https://arxiv.org/abs/2202.06009, it denotes the
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max{i|i in T_v}. Note that this is different from the freeze step from the
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1-bit Adam. The var_freeze_step is usually the end of the learning rate warmup
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and thus does not require tuning. (default: 100000)
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var_update_scaler (int, optional): The interval to update the variance. Note that
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the update policy for variance follows an exponential rule, where var_update_scaler
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denotes the kappa in the 0/1 Adam paper. (default: 16)
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local_step_scaler (int, optional): The interval to scale the local steps interval
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according to the learning rate policy. (default: 32678)
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local_step_clipper (int, optional): The largest interval for local steps with
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learning rate policy. This corresponds to the variable H in the 0/1 Adam paper.
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(default: 16)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False) NOT SUPPORTED in 0/1 Adam!
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eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
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adds eps to the bias-corrected second moment estimate before
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evaluating square root instead of adding it to the square root of
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second moment estimate as in the original paper. (default: False)
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cuda_aware (boolean, required): Set True if the underlying MPI implementation
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supports CUDA-Aware communication. (default: False)
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comm_backend_name (string, optional): Set to 'mpi' if needed. (default: 'nccl')
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.. _Adam\\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(self,
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params,
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deepspeed=None,
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lr=1e-3,
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bias_correction=True,
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betas=(0.9, 0.999),
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eps=1e-8,
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eps_inside_sqrt=False,
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weight_decay=0.,
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max_grad_norm=0.,
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var_freeze_step=100000,
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var_update_scaler=16,
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local_step_scaler=32678,
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local_step_clipper=16,
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amsgrad=False,
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cuda_aware=False,
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comm_backend_name='nccl'):
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if amsgrad:
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raise RuntimeError('0/1 Adam does not support the AMSGrad variant.')
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defaults = dict(lr=lr,
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bias_correction=bias_correction,
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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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max_grad_norm=max_grad_norm)
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super(ZeroOneAdam, self).__init__(params, defaults)
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self.eps_mode = 0 if eps_inside_sqrt else 1
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self.deepspeed = deepspeed
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self.initialize = False
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self.cuda_aware = cuda_aware
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self.using_pipeline = False
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self.var_freeze_step = var_freeze_step
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self.var_update_scaler = var_update_scaler
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self.local_step_scaler = local_step_scaler
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self.local_step_clipper = local_step_clipper
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self.freeze_key = False
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self.reinitial_error_buffer = False
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self.comm_backend_name = comm_backend_name
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assert dist.is_initialized(), "Please initialize the torch distributed backend."
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# Empty initializer. Set handle based on the comm backend as follows.
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self.comm_backend_handle = None
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if self.comm_backend_name == 'nccl':
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assert (
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required_torch_version(min_version=1.8)
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), "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"
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from deepspeed.runtime.comm.nccl import NcclBackend
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self.using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce')
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self.comm_backend_handle = NcclBackend(self.deepspeed.mpu)
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elif self.comm_backend_name == 'mpi':
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from deepspeed.runtime.comm.mpi import MpiBackend
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self.comm_backend_handle = MpiBackend(cuda_aware)
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elif self.comm_backend_name == 'hccl':
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from deepspeed.runtime.comm.hccl import HcclBackend
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self.using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce')
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self.comm_backend_handle = HcclBackend(self.deepspeed.mpu)
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elif self.comm_backend_name == 'compressed':
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from deepspeed.runtime.comm.compressed import CompressedBackend
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self.using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce')
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self.comm_backend_handle = CompressedBackend(self.deepspeed.mpu)
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self.size = self.comm_backend_handle.size
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self.divider = int(self.size * 8 / np.gcd(self.size, 8))
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def step(self, closure=None, grads=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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grads (list of tensors, optional): weight gradient to use for the
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optimizer update. If gradients have type torch.half, parameters
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are expected to be in type torch.float. (default: None)
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output params (list of tensors, optional): A reduced precision copy
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of the updated weights written out in addition to the regular
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updated weights. Have to be of same type as gradients. (default: None)
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scale (float, optional): factor to divide gradient tensor values
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by before applying to weights. (default: 1)
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"""
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loss = None
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if closure is not None:
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loss = closure()
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if grads is None:
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grads_group = [None] * len(self.param_groups)
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# backward compatibility
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# assuming a list/generator of parameter means single group
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elif isinstance(grads, types.GeneratorType):
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grads_group = [grads]
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elif type(grads[0]) != list:
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grads_group = [grads]
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else:
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grads_group = grads
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for group, grads_this_group in zip(self.param_groups, grads_group):
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if grads_this_group is None:
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grads_this_group = [None] * len(group['params'])
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bias_correction = 1 if group['bias_correction'] else 0
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for p, grad in zip(group['params'], grads_this_group):
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if p.grad is None and grad is None:
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continue
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if grad is None:
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('0/1 Adam does not support sparse gradients')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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if not self.initialize or 'worker_error' not in state.keys():
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# Some scalars to help scale the variance update/local step policies
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state['var_interval'] = 1
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state['var_counter'] = 0
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state['local_step_interval'] = 1
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state['local_step_counter'] = 0
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state['lrs'] = 0
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state['tensor_size'] = torch.numel(p.data)
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state['corrected_tensor_size'] = state['tensor_size']
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if state['tensor_size'] % (self.size * self.divider) != 0:
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state['corrected_tensor_size'] += ((self.size * self.divider) - (state['tensor_size'] %
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(self.size * self.divider)))
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state['server_chunk_size'] = state['corrected_tensor_size'] // self.size
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get_accelerator().empty_cache()
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state['worker_error'] = torch.zeros(state['corrected_tensor_size'], device=p.device)
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state['server_error'] = torch.zeros(state['server_chunk_size'], device=p.device)
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# Accumulation of momentum, i.e., the u variable in the 0/1 Adam paper
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state['momentum_accumulator'] = torch.zeros_like(p.data)
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get_accelerator().empty_cache()
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# self.freeze_key = True
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if not self.initialize and dist.get_rank() == 0:
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print("Cupy Buffers Initialized Successfully.")
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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comm_buffer = state['momentum_accumulator']
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beta1, beta2 = group['betas']
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state['step'] += 1
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if self.initialize:
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if self.freeze_key is False:
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if state['step'] % state['var_interval'] == 0:
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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else:
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if self.size > 1:
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with torch.no_grad():
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grad_onebit = self.comm_backend_handle.compressed_allreduce(
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grad, state['worker_error'], state['server_error'], self.deepspeed.local_rank)
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if 'exp_avg_mask' in group:
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if grad_onebit.device != group['exp_avg_mask'].device:
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group['exp_avg_mask'] = group['exp_avg_mask'].to(device=grad_onebit.device)
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grad_onebit.mul_(group['exp_avg_mask'])
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exp_avg.mul_(beta1).add_(1 - beta1, grad_onebit)
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else:
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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state['lrs'] += group['lr']
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grad = None
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if not self.initialize:
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if self.size > 1:
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comm_buffer.set_(
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self.comm_backend_handle.compressed_allreduce(comm_buffer, state['worker_error'],
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state['server_error'],
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self.deepspeed.local_rank))
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if 'exp_avg_mask' in group:
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if comm_buffer.device != group['exp_avg_mask'].device:
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group['exp_avg_mask'] = group['exp_avg_mask'].to(device=comm_buffer.device)
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comm_buffer.mul_(group['exp_avg_mask'])
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if self.initialize:
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update = exp_avg / (exp_avg_sq.sqrt() + group['eps'])
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if group['weight_decay'] > 0.0:
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update += group['weight_decay'] * p.data
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with torch.no_grad():
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p.data.add_(-group['lr'] * update)
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if self.freeze_key is True:
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comm_buffer.add_(-group['lr'] * update)
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if state['step'] % state['local_step_interval'] == 0 and self.freeze_key:
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with torch.no_grad():
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p.data.add_(-1 * comm_buffer)
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comm_buffer.mul_(exp_avg_sq.sqrt() + group['eps'])
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if self.size > 1:
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comm_buffer.copy_(
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self.comm_backend_handle.compressed_allreduce(comm_buffer, state['worker_error'],
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state['server_error'],
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self.deepspeed.local_rank))
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if 'exp_avg_mask' in group:
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if comm_buffer.device != group['exp_avg_mask'].device:
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group['exp_avg_mask'] = group['exp_avg_mask'].to(device=comm_buffer.device)
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comm_buffer.mul_(group['exp_avg_mask'])
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exp_avg.zero_().add_(comm_buffer / state['lrs'], alpha=-1)
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p.data.add_(comm_buffer / (exp_avg_sq.sqrt() + group['eps']))
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comm_buffer.zero_()
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state['lrs'] = 0
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# According to 0/1 Adam theory, a fixed variance would allow more accurate estimation of momentum
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# However, in practice, we can also disable the manual freezing of variance, since the interval of
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# updating variance will increase exponentially, so that it has negligible effect on the estimation.
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if self.freeze_key is False:
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if state['step'] % state['var_interval'] == 0:
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state['var_counter'] += 1
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if state['var_counter'] == self.var_update_scaler:
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state['var_counter'] = 0
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state['var_interval'] *= 2
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if (state['step'] + 1) % state['var_interval'] == 0:
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = True
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else:
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self.deepspeed.enable_backward_allreduce = True
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else:
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = False
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else:
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self.deepspeed.enable_backward_allreduce = False
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else:
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state['local_step_counter'] += 1
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if state['local_step_counter'] == self.local_step_scaler:
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state['local_step_counter'] = 0
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state['local_step_interval'] = min(self.local_step_clipper,
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state['local_step_interval'] * 2)
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if not self.initialize:
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print('Pop out errors', flush=True)
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self.freeze_key = False
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state.pop('worker_error')
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state.pop('server_error')
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if not self.initialize:
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self.initialize = True
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print(f"Finished the initialization step at rank {dist.get_rank()}")
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return loss
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if self.state[self.param_groups[0]['params'][0]]['step'] > self.var_freeze_step:
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self.freeze_key = True
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = False
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else:
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self.deepspeed.enable_backward_allreduce = False
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if self.freeze_key is True and self.reinitial_error_buffer is False:
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# We need to reinitialize the error buffers when local step > 1 since
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# the errors will be logged for different metrics (gradient vs. accumulated momentum).
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for group in self.param_groups:
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for p in group['params']:
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self.state[p]['worker_error'].zero_()
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self.state[p]['server_error'].zero_()
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self.reinitial_error_buffer = True
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return loss
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def load_state_dict(self, state_dict):
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"""
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Overrides load_state_dict() to add special handling when loading checkpoints
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"""
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# Because at different stage exp_avg_mask may change (e.g.,
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# BERT pre-training seqlen 128 and 512 ), we don't use the exp_avg_mask
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# in checkpoints but always use the one user provided in training script.
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# (See example in DeepSpeedExamples/bing_bert/deepspeed_train.py.)
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# Thus here we keep the exp_avg_mask unchanged when loading checkpoint
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for i, group in enumerate(self.param_groups):
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if 'exp_avg_mask' in group:
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state_dict['param_groups'][i]['exp_avg_mask'] = group['exp_avg_mask']
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elif 'exp_avg_mask' not in group and 'exp_avg_mask' in state_dict['param_groups'][i]:
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state_dict['param_groups'][i].pop('exp_avg_mask')
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super().load_state_dict(state_dict)
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if self.state[self.param_groups[0]['params'][0]]['step'] < self.var_freeze_step:
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self.var_freeze_key = False
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if (self.state[self.param_groups[0]['params'][0]]['step'] +
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1) % self.state[self.param_groups[0]['params'][0]]['var_interval'] == 0:
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = True
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else:
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self.deepspeed.enable_backward_allreduce = True
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else:
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = False
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else:
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self.deepspeed.enable_backward_allreduce = False
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else:
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self.var_freeze_key = True
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if self.using_pipeline:
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self.deepspeed.pipeline_enable_backward_allreduce = False
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else:
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self.deepspeed.enable_backward_allreduce = False
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self.reinitial_error_buffer = False
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for group in self.param_groups:
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for p in group['params']:
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if 'worker_error' in self.state[p]:
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self.state[p].pop('worker_error')
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if 'server_error' in self.state[p]:
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self.state[p].pop('server_error')
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if 'momentum_accumulator' in self.state[p]:
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self.state[p].pop('momentum_accumulator')
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