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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from cpuinfo import get_cpu_info
from deepspeed.utils import logger
from deepspeed.utils.logging import should_log_le
from deepspeed.ops.op_builder import CPUAdamBuilder
class DeepSpeedCPUAdam(torch.optim.Optimizer):
optimizer_id = 0
def __init__(self,
model_params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
adamw_mode=True,
fp32_optimizer_states=True):
"""Fast vectorized implementation of two variations of Adam optimizer on CPU:
* Adam: A Method for Stochastic Optimization: (https://arxiv.org/abs/1412.6980);
* AdamW: Fixing Weight Decay Regularization in Adam (https://arxiv.org/abs/1711.05101)
DeepSpeed CPU Adam(W) provides between 5x to 7x speedup over torch.optim.adam(W).
In order to apply this optimizer, the model requires to have its master parameter (in FP32)
reside on the CPU memory.
To train on a heterogeneous system, such as coordinating CPU and GPU, DeepSpeed offers
the ZeRO-Offload technology which efficiently offloads the optimizer states into CPU memory,
with minimal impact on training throughput. DeepSpeedCPUAdam plays an important role to minimize
the overhead of the optimizer's latency on CPU. Please refer to ZeRO-Offload tutorial
(https://www.deepspeed.ai/tutorials/zero-offload/) for more information on how to enable this technology.
For calling step function, there are two options available: (1) update optimizer's states and (2) update
optimizer's states and copy the parameters back to GPU at the same time. We have seen that the second
option can bring 30% higher throughput than the doing the copy separately using option one.
.. note::
We recommend using our `config
<https://www.deepspeed.ai/docs/config-json/#optimizer-parameters>`_
to allow :meth:`deepspeed.initialize` to build this optimizer
for you.
Arguments:
model_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`_
(default: False) NOT SUPPORTED in DeepSpeed CPUAdam!
adamw_mode: select between Adam and AdamW implementations (default: AdamW)
fp32_optimizer_states: creates momentum and variance in full precision regardless of
the precision of the parameters (default: True)
"""
default_args = dict(lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
bias_correction=bias_correction,
amsgrad=amsgrad)
super(DeepSpeedCPUAdam, self).__init__(model_params, default_args)
cpu_info = get_cpu_info()
self.cpu_vendor = cpu_info["vendor_id_raw"].lower() if "vendor_id_raw" in cpu_info else "unknown"
if "amd" in self.cpu_vendor:
for group_id, group in enumerate(self.param_groups):
for param_id, p in enumerate(group['params']):
if p.dtype == torch.half:
logger.warning("FP16 params for CPUAdam may not work on AMD CPUs")
break
else:
continue
break
self.opt_id = DeepSpeedCPUAdam.optimizer_id
DeepSpeedCPUAdam.optimizer_id = DeepSpeedCPUAdam.optimizer_id + 1
self.adam_w_mode = adamw_mode
self.fp32_optimizer_states = fp32_optimizer_states
self.ds_opt_adam = CPUAdamBuilder().load()
self.ds_opt_adam.create_adam(self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode,
should_log_le("info"))
def __del__(self):
# need to destroy the C++ object explicitly to avoid a memory leak when deepspeed.initialize
# is used multiple times in the same process (notebook or pytest worker)
self.ds_opt_adam.destroy_adam(self.opt_id)
def __setstate__(self, state):
super(DeepSpeedCPUAdam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
@torch.no_grad()
def step(self, closure=None, fp16_param_groups=None):
"""Update the model parameters.
.. note::
This method will be called internally by ZeRO-Offload. DeepSpeed
users should still use ``engine.step()`` as shown in the
`Getting Started
<https://www.deepspeed.ai/getting-started/#training>`_ guide.
Args:
closure (callable, optional): closure to compute the loss.
Defaults to ``None``.
fp16_param_groups: FP16 GPU parameters to update. Performing the
copy here reduces communication time. Defaults to ``None``.
Returns:
loss: if ``closure`` is provided. Otherwise ``None``.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
# intended device for step
device = torch.device('cpu')
# converting the fp16 params to a group of parameter
if type(fp16_param_groups) is list:
if type(fp16_param_groups[0]) is not list:
fp16_param_groups = [fp16_param_groups]
elif fp16_param_groups is not None:
fp16_param_groups = [[fp16_param_groups]]
for group_id, group in enumerate(self.param_groups):
for param_id, p in enumerate(group['params']):
if p.grad is None:
continue
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make " \
"sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config."
state = self.state[p]
# State initialization
if len(state) == 0:
#print(f'group {group_id} param {param_id} = {p.numel()}')
state['step'] = 0
#use full precision by default unless self.fp32_optimizer_states is off
state_dtype = torch.float if self.fp32_optimizer_states else p.dtype
# gradient momentums
state['exp_avg'] = torch.zeros_like(p.data, dtype=state_dtype, device=device)
#memory_format=torch.preserve_format)
# gradient variances
state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=state_dtype, device=device)
#memory_format=torch.preserve_format)
state['step'] += 1
beta1, beta2 = group['betas']
if fp16_param_groups is not None:
self.ds_opt_adam.adam_update_copy(self.opt_id, state['step'], group['lr'], beta1, beta2,
group['eps'], group['weight_decay'], group['bias_correction'],
p.data, p.grad.data, state['exp_avg'], state['exp_avg_sq'],
fp16_param_groups[group_id][param_id].data)
else:
self.ds_opt_adam.adam_update(self.opt_id, state['step'], group['lr'], beta1, beta2, group['eps'],
group['weight_decay'], group['bias_correction'], p.data, p.grad.data,
state['exp_avg'], state['exp_avg_sq'])
return loss
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