import torch
from torch.optim.optimizer import Optimizer, required
from apex.multi_tensor_apply import multi_tensor_applier
[docs]class FusedSGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
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 SGD implements 2 fusions.
* Fusion of the SGD 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.FusedSGD` may be used as a drop-in replacement for ``torch.optim.SGD``::
opt = apex.optimizers.FusedSGD(model.parameters(), lr = ....)
...
opt.step()
:class:`apex.optimizers.FusedSGD` may be used with or without Amp. If you wish to use :class:`FusedSGD` with Amp,
you may choose any ``opt_level``::
opt = apex.optimizers.FusedSGD(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.
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): learning rate
momentum (float, optional): momentum factor (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
dampening (float, optional): dampening for momentum (default: 0)
nesterov (bool, optional): enables Nesterov momentum (default: False)
Example:
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
.. note::
The implementation of SGD with Momentum/Nesterov subtly differs from
Sutskever et. al. and implementations in some other frameworks.
Considering the specific case of Momentum, the update can be written as
.. math::
v = \rho * v + g \\
p = p - lr * v
where p, g, v and :math:`\rho` denote the parameters, gradient,
velocity, and momentum respectively.
This is in contrast to Sutskever et. al. and
other frameworks which employ an update of the form
.. math::
v = \rho * v + lr * g \\
p = p - v
The Nesterov version is analogously modified.
"""
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False,
wd_after_momentum=False,
materialize_master_grads=True):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(FusedSGD, self).__init__(params, defaults)
self.wd_after_momentum = wd_after_momentum
self.materialize_master_grads = materialize_master_grads
self.most_recent_scale = 1.0
self.scale_set_by_backward = False
if multi_tensor_applier.available:
import amp_C
# Skip buffer
self._dummy_overflow_buf = torch.cuda.IntTensor([0])
self.multi_tensor_sgd = amp_C.multi_tensor_sgd
else:
raise RuntimeError('apex.optimizers.FusedSGD requires cuda extensions')
def __setstate__(self, state):
super(FusedSGD, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def get_momentums(self, params):
momentums = []
first_run = True
for p in params:
param_state = self.state[p]
# torch.optim.SGD initializes momentum in the main loop, we have
# to do it here, and track whether or not we've done so, so that
# momentum application can be skipped in the main kernel.
if 'momentum_buffer' not in param_state:
first_run = True
buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
momentums.append(buf)
else:
first_run = False
momentums.append(param_state['momentum_buffer'])
return momentums, first_run
[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()
explicit_master_params = (hasattr(self, "_amp_stash") and
hasattr(self._amp_stash, "fp32_from_fp16_groups"))
for gid, group in enumerate(self.param_groups):
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
# For each group, there are 3 possible combinations we need to consider:
# grad_type, param_to_update_type, momentum_type, requires_fp16_model_copy
# 1. fp16, fp16, fp16, No
# 2. fp32, fp32, fp32, No
# 3. fp16, fp32, fp32, Yes
first_runs = [True, True]
# I think a bit of code divergence in exchange for naming clarity is worthwhile
if explicit_master_params:
stash = self._amp_stash
fp32_params = [p for p in stash.fp32_from_fp32_groups[gid] if p.grad is not None]
fp32_grads = [p.grad for p in stash.fp32_from_fp32_groups[gid] if p.grad is not None]
fp32_momentums, first_runs[1] = self.get_momentums(fp32_params)
if self.materialize_master_grads:
fp16_model_params = [p for i, p in enumerate(
stash.fp16_groups[gid]) if stash.fp32_from_fp16_groups[gid][i].grad is not None]
fp32_from_fp16_grads = [p.grad for p in stash.fp32_from_fp16_groups[gid] if p.grad is not None]
fp32_from_fp16_params = [p for p in stash.fp32_from_fp16_groups[gid] if p.grad is not None]
fp32_from_fp16_momentums, first_runs[0] = self.get_momentums(fp32_from_fp16_params)
fp16_set = [fp32_from_fp16_grads, fp32_from_fp16_params,
fp32_from_fp16_momentums, fp16_model_params]
else:
fp16_model_params = [p for p in stash.fp16_groups[gid] if p.grad is not None]
fp16_model_grads = [p.grad for p in stash.fp16_groups[gid] if p.grad is not None]
fp32_from_fp16_params = [p for i, p in enumerate(
stash.fp32_from_fp16_groups[gid]) if stash.fp16_groups[gid][i].grad is not None]
fp32_from_fp16_momentums, first_runs[0] = self.get_momentums(fp32_from_fp16_params)
fp16_set = [fp16_model_grads, fp32_from_fp16_params,
fp32_from_fp16_momentums, fp16_model_params]
launch_sets= [fp16_set, [fp32_grads, fp32_params, fp32_momentums]]
else:
fp16_params = [p for p in group['params'] if (p.dtype == torch.float16 and p.grad is not None)]
fp16_grads = [p.grad for p in group['params'] if (p.dtype == torch.float16 and p.grad is not None)]
fp16_momentums, first_runs[0] = self.get_momentums(fp16_params)
fp32_params = [p for p in group['params'] if (p.dtype == torch.float32 and p.grad is not None)]
fp32_grads = [p.grad for p in group['params'] if (p.dtype == torch.float32 and p.grad is not None)]
fp32_momentums, first_runs[1] = self.get_momentums(fp32_params)
launch_sets = [[fp16_grads, fp16_params, fp16_momentums],
[fp32_grads, fp32_params, fp32_momentums]]
for s, (launch_set, first_run) in enumerate(zip(launch_sets, first_runs)):
assert len(launch_set[0]) == len(launch_set[1])
assert len(launch_set[0]) == len(launch_set[2])
if len(launch_set[0]) > 0:
multi_tensor_applier(
self.multi_tensor_sgd,
self._dummy_overflow_buf,
launch_set,
weight_decay,
momentum,
dampening,
group['lr'],
nesterov,
first_run,
self.wd_after_momentum,
1.0/self.most_recent_scale)
self.most_recent_scale = 1.0
self.scale_set_by_backward = False
return loss