mirror of https://github.com/hpcaitech/ColossalAI
148 lines
5.9 KiB
Python
148 lines
5.9 KiB
Python
# modified from https://github.com/NVIDIA/apex/blob/master/apex/optimizers/fused_sgd.py
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import torch
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from torch.optim.optimizer import Optimizer, required
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from colossalai.utils import multi_tensor_applier
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class FusedSGD(Optimizer):
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r"""Implements stochastic gradient descent (optionally with momentum).
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`FusedSGD` requires CUDA extensions which can be built during installation or runtime.
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This version of fused SGD implements 2 fusions.
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* Fusion of the SGD update's elementwise operations
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* A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
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:class:`colossalai.nn.optimizer.FusedSGD` may be used as a drop-in replacement for ``torch.optim.SGD``
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:class:`colossalai.nn.optimizer.FusedSGD` may be used with or without Amp.
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Nesterov momentum is based on the formula from
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`On the importance of initialization and momentum in deep learning`__.
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Args:
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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): learning rate
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momentum (float, optional): momentum factor (default: 0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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dampening (float, optional): dampening for momentum (default: 0)
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nesterov (bool, optional): enables Nesterov momentum (default: False)
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__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
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.. note::
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The implementation of SGD with Momentum/Nesterov subtly differs from
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Sutskever et. al. and implementations in some other frameworks.
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Considering the specific case of Momentum, the update can be written as
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.. math::
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v = \rho * v + g \\
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p = p - lr * v
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where p, g, v and :math:`\rho` denote the parameters, gradient,
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velocity, and momentum respectively.
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This is in contrast to Sutskever et. al. and
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other frameworks which employ an update of the form
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.. math::
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v = \rho * v + lr * g \\
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p = p - v
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The Nesterov version is analogously modified.
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"""
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def __init__(self,
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params,
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lr=required,
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momentum=0,
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dampening=0,
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weight_decay=0,
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nesterov=False,
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wd_after_momentum=False):
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if lr is not required and lr < 0.0:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if momentum < 0.0:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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if weight_decay < 0.0:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov)
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if nesterov and (momentum <= 0 or dampening != 0):
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raise ValueError("Nesterov momentum requires a momentum and zero dampening")
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super(FusedSGD, self).__init__(params, defaults)
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self.wd_after_momentum = wd_after_momentum
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if multi_tensor_applier.available:
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from colossalai.kernel.op_builder import FusedOptimBuilder
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fused_optim = FusedOptimBuilder().load()
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# Skip buffer
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self._dummy_overflow_buf = torch.tensor([0],
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dtype=torch.int,
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device=self.param_groups[0]["params"][0].device)
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self.multi_tensor_sgd = fused_optim.multi_tensor_sgd
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else:
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raise RuntimeError('FusedSGD requires cuda extensions')
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def __setstate__(self, state):
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super(FusedSGD, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('nesterov', False)
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def get_momentums(self, params):
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momentums = []
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first_run = True
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for p in params:
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param_state = self.state[p]
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# torch.optim.SGD initializes momentum in the main loop, we have
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# to do it here, and track whether or not we've done so, so that
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# momentum application can be skipped in the main kernel.
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if 'momentum_buffer' not in param_state:
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first_run = True
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buf = param_state['momentum_buffer'] = torch.zeros_like(p)
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momentums.append(buf)
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else:
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first_run = False
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momentums.append(param_state['momentum_buffer'])
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return momentums, first_run
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def step(self, closure=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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"""
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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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for group in self.param_groups:
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weight_decay = group['weight_decay']
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momentum = group['momentum']
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dampening = group['dampening']
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nesterov = group['nesterov']
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# For each group, there are 3 possible combinations we need to consider:
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# grad_type, param_to_update_type, momentum_type
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# 1. fp16, fp16, fp16
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# 2. fp32, fp32, fp32
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# 3. fp16, fp32, fp32
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g_l, p_l = [], []
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for p in group['params']:
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if p.grad is None:
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continue
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if p.grad.data.is_sparse:
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raise RuntimeError('FusedSGD does not support sparse gradients')
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g_l.append(p.grad)
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p_l.append(p)
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m_l, first_run = self.get_momentums(p_l)
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multi_tensor_applier(self.multi_tensor_sgd, self._dummy_overflow_buf, [g_l, p_l, m_l], weight_decay,
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momentum, dampening, group['lr'], nesterov, first_run, self.wd_after_momentum, 1.0)
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return loss
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