420 lines
17 KiB
Python
420 lines
17 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
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_differentiable_doc, _foreach_doc, _maximize_doc, _fused_doc)
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from typing import List, Optional
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__all__ = ['SGD', 'sgd']
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class SGD(Optimizer):
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def __init__(self, params, lr=1e-3, momentum=0, dampening=0,
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weight_decay=0, nesterov=False, *, maximize: bool = False, foreach: Optional[bool] = None,
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differentiable: bool = False, fused: Optional[bool] = None):
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if lr < 0.0:
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raise ValueError(f"Invalid learning rate: {lr}")
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if momentum < 0.0:
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raise ValueError(f"Invalid momentum value: {momentum}")
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if weight_decay < 0.0:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
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weight_decay=weight_decay, nesterov=nesterov,
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maximize=maximize, foreach=foreach,
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differentiable=differentiable, fused=fused)
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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().__init__(params, defaults)
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if fused:
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self._step_supports_amp_scaling = True
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if differentiable:
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raise RuntimeError("`fused` does not support `differentiable`")
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if foreach:
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raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault('nesterov', False)
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group.setdefault('maximize', False)
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group.setdefault('foreach', None)
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group.setdefault('differentiable', False)
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group.setdefault('fused', False)
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def _init_group(self, group, params_with_grad, d_p_list, momentum_buffer_list):
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has_sparse_grad = False
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for p in group['params']:
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if p.grad is not None:
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params_with_grad.append(p)
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d_p_list.append(p.grad)
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if p.grad.is_sparse:
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has_sparse_grad = True
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state = self.state[p]
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momentum_buffer_list.append(state.get('momentum_buffer'))
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return has_sparse_grad
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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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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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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d_p_list = []
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momentum_buffer_list = []
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has_sparse_grad = self._init_group(group, params_with_grad, d_p_list, momentum_buffer_list)
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sgd(params_with_grad,
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d_p_list,
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momentum_buffer_list,
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weight_decay=group['weight_decay'],
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momentum=group['momentum'],
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lr=group['lr'],
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dampening=group['dampening'],
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nesterov=group['nesterov'],
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maximize=group['maximize'],
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has_sparse_grad=has_sparse_grad,
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foreach=group['foreach'],
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fused=group['fused'],
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grad_scale=getattr(self, "grad_scale", None),
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found_inf=getattr(self, "found_inf", None))
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# update momentum_buffers in state
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for p, momentum_buffer in zip(params_with_grad, momentum_buffer_list):
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state = self.state[p]
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state['momentum_buffer'] = momentum_buffer
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return loss
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SGD.__doc__ = r"""Implements stochastic gradient descent (optionally with momentum).
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
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\text{ (objective)}, \: \lambda \text{ (weight decay)}, \\
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&\hspace{13mm} \:\mu \text{ (momentum)}, \:\tau \text{ (dampening)},
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\:\textit{ nesterov,}\:\textit{ maximize} \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm}\textbf{if} \: \lambda \neq 0 \\
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&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{5mm}\textbf{if} \: \mu \neq 0 \\
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&\hspace{10mm}\textbf{if} \: t > 1 \\
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&\hspace{15mm} \textbf{b}_t \leftarrow \mu \textbf{b}_{t-1} + (1-\tau) g_t \\
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&\hspace{10mm}\textbf{else} \\
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&\hspace{15mm} \textbf{b}_t \leftarrow g_t \\
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&\hspace{10mm}\textbf{if} \: \textit{nesterov} \\
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&\hspace{15mm} g_t \leftarrow g_{t} + \mu \textbf{b}_t \\
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&\hspace{10mm}\textbf{else} \\[-1.ex]
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&\hspace{15mm} g_t \leftarrow \textbf{b}_t \\
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&\hspace{5mm}\textbf{if} \: \textit{maximize} \\
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&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} + \gamma g_t \\[-1.ex]
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&\hspace{5mm}\textbf{else} \\[-1.ex]
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&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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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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""" + fr"""
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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, optional): learning rate (default: 1e-3)
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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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{_maximize_doc}
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{_foreach_doc}
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{_differentiable_doc}
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{_fused_doc}
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""" + r"""
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Example:
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>>> # xdoctest: +SKIP
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>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
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>>> optimizer.zero_grad()
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>>> loss_fn(model(input), target).backward()
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>>> optimizer.step()
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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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\begin{aligned}
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v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
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p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
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\end{aligned}
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where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the
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parameters, gradient, 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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\begin{aligned}
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v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\
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p_{t+1} & = p_{t} - v_{t+1}.
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\end{aligned}
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The Nesterov version is analogously modified.
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Moreover, the initial value of the momentum buffer is set to the
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gradient value at the first step. This is in contrast to some other
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frameworks that initialize it to all zeros.
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"""
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def sgd(params: List[Tensor],
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d_p_list: List[Tensor],
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momentum_buffer_list: List[Optional[Tensor]],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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has_sparse_grad: bool = None,
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foreach: Optional[bool] = None,
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fused: Optional[bool] = None,
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grad_scale: Optional[Tensor] = None,
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found_inf: Optional[Tensor] = None,
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*,
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weight_decay: float,
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momentum: float,
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lr: float,
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dampening: float,
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nesterov: bool,
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maximize: bool):
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r"""Functional API that performs SGD algorithm computation.
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See :class:`~torch.optim.SGD` for details.
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"""
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# Respect when the user inputs False/True for foreach or fused. We only want to change
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# the default when neither have been user-specified. Note that we default to foreach
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# and pass False to use_fused. This is not a mistake--we want to give the fused impl
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# bake-in time before making it the default, even if it is typically faster.
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if foreach is None and fused is None:
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# why must we be explicit about an if statement for torch.jit.is_scripting here?
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# because JIT can't handle Optionals nor fancy conditionals when scripting
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if not torch.jit.is_scripting():
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fused, foreach = _default_to_fused_or_foreach(params, differentiable=False, use_fused=False)
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else:
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foreach = False
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fused = False
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if foreach is None:
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foreach = False
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if fused is None:
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fused = False
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if foreach and torch.jit.is_scripting():
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raise RuntimeError('torch.jit.script not supported with foreach optimizers')
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if fused and torch.jit.is_scripting():
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raise RuntimeError('torch.jit.script not supported with fused optimizers')
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_sgd
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elif fused and not torch.jit.is_scripting():
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func = _fused_sgd
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else:
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func = _single_tensor_sgd
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func(params,
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d_p_list,
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momentum_buffer_list,
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weight_decay=weight_decay,
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momentum=momentum,
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lr=lr,
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dampening=dampening,
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nesterov=nesterov,
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has_sparse_grad=has_sparse_grad,
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maximize=maximize,
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grad_scale=grad_scale,
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found_inf=found_inf)
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def _single_tensor_sgd(params: List[Tensor],
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d_p_list: List[Tensor],
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momentum_buffer_list: List[Optional[Tensor]],
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grad_scale: Optional[Tensor],
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found_inf: Optional[Tensor],
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*,
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weight_decay: float,
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momentum: float,
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lr: float,
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dampening: float,
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nesterov: bool,
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maximize: bool,
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has_sparse_grad: bool):
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assert grad_scale is None and found_inf is None
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for i, param in enumerate(params):
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d_p = d_p_list[i] if not maximize else -d_p_list[i]
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if weight_decay != 0:
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d_p = d_p.add(param, alpha=weight_decay)
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if momentum != 0:
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buf = momentum_buffer_list[i]
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if buf is None:
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buf = torch.clone(d_p).detach()
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momentum_buffer_list[i] = buf
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else:
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buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
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if nesterov:
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d_p = d_p.add(buf, alpha=momentum)
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else:
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d_p = buf
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param.add_(d_p, alpha=-lr)
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def _multi_tensor_sgd(params: List[Tensor],
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grads: List[Tensor],
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momentum_buffer_list: List[Optional[Tensor]],
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grad_scale: Optional[Tensor],
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found_inf: Optional[Tensor],
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*,
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weight_decay: float,
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momentum: float,
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lr: float,
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dampening: float,
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nesterov: bool,
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maximize: bool,
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has_sparse_grad: bool):
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assert grad_scale is None and found_inf is None
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if len(params) == 0:
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return
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, momentum_buffer_list], with_indices=True)
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for ((device_params, device_grads, device_momentum_buffer_list), indices) in grouped_tensors.values():
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device_has_sparse_grad = has_sparse_grad and any(grad.is_sparse for grad in device_grads)
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if maximize:
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device_grads = torch._foreach_neg(device_grads)
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if weight_decay != 0:
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# Re-use the intermediate memory (device_grads) already allocated for maximize
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if maximize:
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torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
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else:
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device_grads = torch._foreach_add(device_grads, device_params, alpha=weight_decay)
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if momentum != 0:
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bufs = []
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all_states_with_momentum_buffer = True
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for i in range(len(device_momentum_buffer_list)):
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if device_momentum_buffer_list[i] is None:
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all_states_with_momentum_buffer = False
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break
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else:
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bufs.append(device_momentum_buffer_list[i])
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if all_states_with_momentum_buffer:
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torch._foreach_mul_(bufs, momentum)
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torch._foreach_add_(bufs, device_grads, alpha=1 - dampening)
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else:
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bufs = []
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for i in range(len(device_momentum_buffer_list)):
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if device_momentum_buffer_list[i] is None:
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buf = device_momentum_buffer_list[i] = momentum_buffer_list[indices[i]] = \
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torch.clone(device_grads[i]).detach()
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else:
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buf = device_momentum_buffer_list[i]
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buf.mul_(momentum).add_(device_grads[i], alpha=1 - dampening)
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bufs.append(buf)
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if nesterov:
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torch._foreach_add_(device_grads, bufs, alpha=momentum)
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else:
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device_grads = bufs
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if not device_has_sparse_grad:
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torch._foreach_add_(device_params, device_grads, alpha=-lr)
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else:
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# foreach APIs don't support sparse
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for i in range(len(device_params)):
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device_params[i].add_(device_grads[i], alpha=-lr)
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def _fused_sgd(
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params: List[Tensor],
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grads: List[Tensor],
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momentum_buffer_list: List[Optional[Tensor]],
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grad_scale: Optional[Tensor],
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found_inf: Optional[Tensor],
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*,
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weight_decay: float,
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momentum: float,
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lr: float,
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dampening: float,
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nesterov: bool,
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maximize: bool,
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has_sparse_grad: bool,
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) -> None:
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if not params:
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return
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if has_sparse_grad:
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raise RuntimeError("`_fused_sgd` does not support sparse gradients")
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grad_scale_dict = {grad_scale.device: grad_scale} if grad_scale is not None else None
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found_inf_dict = {found_inf.device: found_inf} if found_inf is not None else None
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no_momentum_buffer = momentum == 0
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is_first_step = all(t is None for t in momentum_buffer_list) and not no_momentum_buffer
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if is_first_step:
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for i, g in enumerate(grads):
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momentum_buffer_list[i] = torch.empty_like(g)
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
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[params, grads, momentum_buffer_list], with_indices=False)
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for (device, dtype), ((device_params, device_grads, device_momentum_buffer_list), _) in grouped_tensors.items():
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device_grad_scale, device_found_inf = None, None
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if grad_scale is not None:
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if device not in grad_scale_dict:
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grad_scale_dict[device] = grad_scale.to(device)
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device_grad_scale = grad_scale_dict[device]
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if found_inf is not None:
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if device not in found_inf_dict:
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found_inf_dict[device] = found_inf.to(device)
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device_found_inf = found_inf_dict[device]
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torch._fused_sgd_(
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device_params,
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device_grads,
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[] if no_momentum_buffer else device_momentum_buffer_list,
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weight_decay=weight_decay,
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momentum=momentum,
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lr=lr,
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dampening=dampening,
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nesterov=nesterov,
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maximize=maximize,
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is_first_step=is_first_step,
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grad_scale=device_grad_scale,
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found_inf=device_found_inf,
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)
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