Table of Contents
Shortcuts

# 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"]

    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

        @_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: http://jmlr.org/papers/v12/duchi11a.html

        """

    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)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.