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