Source code for torch.optim.adagrad

import torch
from torch import Tensor

from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value,
                        _default_to_fused_or_foreach, _differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional

__all__ = ["Adagrad", "adagrad"]

[docs]class Adagrad(Optimizer): def __init__( self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10, foreach: Optional[bool] = None, *, maximize: bool = False, differentiable: bool = False, ): if not 0.0 <= lr: raise ValueError(f"Invalid learning rate: {lr}") if not 0.0 <= lr_decay: raise ValueError(f"Invalid lr_decay value: {lr_decay}") if not 0.0 <= weight_decay: raise ValueError(f"Invalid weight_decay value: {weight_decay}") if not 0.0 <= initial_accumulator_value: raise ValueError( f"Invalid initial_accumulator_value value: {initial_accumulator_value}" ) if not 0.0 <= eps: raise ValueError(f"Invalid epsilon value: {eps}") defaults = dict( lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay, initial_accumulator_value=initial_accumulator_value, foreach=foreach, maximize=maximize, differentiable=differentiable, ) super().__init__(params, defaults) for group in self.param_groups: for p in group["params"]: state = self.state[p] state["step"] = torch.tensor(0.0) init_value = ( complex(initial_accumulator_value, initial_accumulator_value) if torch.is_complex(p) else initial_accumulator_value ) state["sum"] = torch.full_like( p, init_value, memory_format=torch.preserve_format ) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault("foreach", None) group.setdefault("maximize", False) group.setdefault("differentiable", False) state_values = list(self.state.values()) step_is_tensor = (len(state_values) != 0) and torch.is_tensor( state_values[0]["step"] ) if not step_is_tensor: for s in state_values: s["step"] = torch.tensor(float(s["step"])) def share_memory(self): for group in self.param_groups: for p in group["params"]: state = self.state[p] state["sum"].share_memory_() def _init_group(self, group, params_with_grad, grads, state_sums, state_steps): has_sparse_grad = False for p in group["params"]: if p.grad is not None: if p.grad.is_sparse: has_sparse_grad = True params_with_grad.append(p) grads.append(p.grad) state = self.state[p] state_sums.append(state["sum"]) state_steps.append(state["step"]) return has_sparse_grad
[docs] @_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_with_grad = [] grads = [] state_sums = [] state_steps = [] has_sparse_grad = self._init_group(group, params_with_grad, grads, state_sums, state_steps) adagrad( params_with_grad, grads, state_sums, state_steps, lr=group["lr"], weight_decay=group["weight_decay"], lr_decay=group["lr_decay"], eps=group["eps"], has_sparse_grad=has_sparse_grad, foreach=group["foreach"], maximize=group["maximize"], differentiable=group["differentiable"], ) return loss
Adagrad.__doc__ = r"""Implements Adagrad algorithm. .. 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{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. """ + fr""" Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) {_foreach_doc} {_maximize_doc} {_differentiable_doc} .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: """ def adagrad( params: List[Tensor], grads: List[Tensor], state_sums: List[Tensor], state_steps: List[Tensor], # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 # setting these as kwargs for now as functional API is compiled by torch/distributed/optim has_sparse_grad: bool = None, foreach: Optional[bool] = None, differentiable: bool = False, *, lr: float, weight_decay: float, lr_decay: float, eps: float, maximize: bool, ): r"""Functional API that performs Adagrad algorithm computation. See :class:`~torch.optim.Adagrad` for details. """ if not all(isinstance(t, torch.Tensor) for t in state_steps): raise RuntimeError( "API has changed, `state_steps` argument must contain a list of singleton tensors" ) if foreach is None: _, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False) if foreach and torch.jit.is_scripting(): raise RuntimeError("torch.jit.script not supported with foreach optimizers") if foreach and not torch.jit.is_scripting(): func = _multi_tensor_adagrad else: func = _single_tensor_adagrad func( params, grads, state_sums, state_steps, lr=lr, weight_decay=weight_decay, lr_decay=lr_decay, eps=eps, has_sparse_grad=has_sparse_grad, maximize=maximize, differentiable=differentiable, ) def _make_sparse(grad, grad_indices, values): size = grad.size() if grad_indices.numel() == 0 or values.numel() == 0: return torch.empty_like(grad) return torch.sparse_coo_tensor(grad_indices, values, size) def _single_tensor_adagrad( params: List[Tensor], grads: List[Tensor], state_sums: List[Tensor], state_steps: List[Tensor], *, lr: float, weight_decay: float, lr_decay: float, eps: float, has_sparse_grad: bool, maximize: bool, differentiable: bool, ): for (param, grad, state_sum, step_t) in zip(params, grads, state_sums, state_steps): # update step step_t += 1 step = _get_value(step_t) grad = grad if not maximize else -grad if weight_decay != 0: if grad.is_sparse: raise RuntimeError( "weight_decay option is not compatible with sparse gradients" ) grad = grad.add(param, alpha=weight_decay) clr = lr / (1 + (step - 1) * lr_decay) if grad.is_sparse: grad = grad.coalesce() # the update is non-linear so indices must be unique grad_indices = grad._indices() grad_values = grad._values() state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2))) std = state_sum.sparse_mask(grad) std_values = std._values().sqrt_().add_(eps) param.add_( _make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr ) else: is_complex = torch.is_complex(param) if is_complex: grad = torch.view_as_real(grad) state_sum = torch.view_as_real(state_sum) param = torch.view_as_real(param) state_sum.addcmul_(grad, grad, value=1) if differentiable: std = state_sum.sqrt() + eps else: std = state_sum.sqrt().add_(eps) param.addcdiv_(grad, std, value=-clr) if is_complex: param = torch.view_as_complex(param) state_sum = torch.view_as_complex(state_sum) def _multi_tensor_adagrad( params: List[Tensor], grads: List[Tensor], state_sums: List[Tensor], state_steps: List[Tensor], *, lr: float, weight_decay: float, lr_decay: float, eps: float, has_sparse_grad: bool, maximize: bool, differentiable: bool, ): assert not differentiable, "_foreach ops don't support autograd" # Foreach functions will throw errors if given empty lists if len(params) == 0: return grouped_tensorlists = Optimizer._group_tensors_by_device_and_dtype([params, grads, state_sums, state_steps]) for ((device_params, device_grads, device_state_sums, device_state_steps), _) in grouped_tensorlists.values(): device_has_sparse_grad = any(grad.is_sparse for grad in device_grads) if device_has_sparse_grad: return _single_tensor_adagrad( device_params, device_grads, device_state_sums, device_state_steps, lr=lr, weight_decay=weight_decay, lr_decay=lr_decay, eps=eps, has_sparse_grad=True, maximize=False, differentiable=differentiable, ) if maximize: device_grads = torch._foreach_neg(device_grads) # Handle complex parameters device_grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_grads] device_state_sums = [ torch.view_as_real(x) if torch.is_complex(x) else x for x in device_state_sums ] device_params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_params] # Update steps torch._foreach_add_(device_state_steps, 1) 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(device_grads, device_params, alpha=weight_decay) minus_clr = [-lr / (1 + (_get_value(step) - 1) * lr_decay) for step in device_state_steps] torch._foreach_addcmul_(device_state_sums, device_grads, device_grads, value=1) std = torch._foreach_sqrt(device_state_sums) torch._foreach_add_(std, eps) if weight_decay != 0 or maximize: # Again, re-use the intermediate memory (device_grads) already allocated torch._foreach_mul_(device_grads, minus_clr) numerator = device_grads else: numerator = torch._foreach_mul(device_grads, minus_clr) torch._foreach_addcdiv_(device_params, numerator, std)


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