import torch
from apex.multi_tensor_apply import multi_tensor_applier
[docs]class FusedLAMB(torch.optim.Optimizer):
"""Implements LAMB algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This version of fused LAMB implements 2 fusions.
* Fusion of the LAMB update's elementwise operations
* A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
:class:`apex.optimizers.FusedLAMB`'s usage is identical to any ordinary Pytorch optimizer::
opt = apex.optimizers.FusedLAMB(model.parameters(), lr = ....)
...
opt.step()
:class:`apex.optimizers.FusedLAMB` may be used with or without Amp. If you wish to use :class:`FusedLAMB` with Amp,
you may choose any ``opt_level``::
opt = apex.optimizers.FusedLAMB(model.parameters(), lr = ....)
model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2")
...
opt.step()
In general, ``opt_level="O1"`` is recommended.
LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
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 norm. (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`_
NOT SUPPORTED now! (default: False)
adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
True for decoupled weight decay(also known as AdamW) (default: True)
grad_averaging (bool, optional): whether apply (1-beta2) to grad when
calculating running averages of gradient. (default: True)
set_grad_none (bool, optional): whether set grad to None when zero_grad()
method is called. (default: True)
max_grad_norm (float, optional): value used to clip global grad norm
(default: 1.0)
.. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
https://arxiv.org/abs/1904.00962
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, bias_correction=True,
betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
amsgrad=False, adam_w_mode=True,
grad_averaging=True, set_grad_none=True,
max_grad_norm=1.0):
if amsgrad:
raise RuntimeError('FusedLAMB does not support the AMSGrad variant.')
defaults = dict(lr=lr, bias_correction=bias_correction,
betas=betas, eps=eps, weight_decay=weight_decay,
grad_averaging=grad_averaging,
max_grad_norm=max_grad_norm)
super(FusedLAMB, self).__init__(params, defaults)
if multi_tensor_applier.available:
import amp_C
# Skip buffer
self._dummy_overflow_buf = torch.cuda.IntTensor([0])
self.multi_tensor_lamb = amp_C.multi_tensor_lamb
else:
raise RuntimeError('apex.optimizers.FusedLAMB requires cuda extensions')
self.adam_w_mode = 1 if adam_w_mode else 0
self.set_grad_none = set_grad_none
[docs] def zero_grad(self):
if self.set_grad_none:
for group in self.param_groups:
for p in group['params']:
p.grad = None
else:
super(FusedLAMB, self).zero_grad()
[docs] def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
grad_averaging = 1 if group['grad_averaging'] else 0
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
# create lists for multi-tensor apply
g_16, p_16, m_16, v_16 = [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for p in group['params']:
if p.grad is None:
continue
if p.grad.data.is_sparse:
raise RuntimeError('FusedLAMB does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if p.dtype == torch.float16:
g_16.append(p.grad.data)
p_16.append(p.data)
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state['exp_avg'])
v_32.append(state['exp_avg_sq'])
else:
raise RuntimeError('FusedLAMB only support fp16 and fp32.')
if(len(g_16) > 0):
multi_tensor_applier(self.multi_tensor_lamb,
self._dummy_overflow_buf,
[g_16, p_16, m_16, v_16],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
bias_correction,
group['weight_decay'],
grad_averaging,
self.adam_w_mode,
group['max_grad_norm'])
if(len(g_32) > 0):
multi_tensor_applier(self.multi_tensor_lamb,
self._dummy_overflow_buf,
[g_32, p_32, m_32, v_32],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
bias_correction,
group['weight_decay'],
grad_averaging,
self.adam_w_mode,
group['max_grad_norm'])
return loss