385 lines
14 KiB
Python
385 lines
14 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _view_as_real,
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_default_to_fused_or_foreach, _get_scalar_dtype, _differentiable_doc,
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_foreach_doc, _maximize_doc)
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from typing import List, Optional
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__all__ = ["Adagrad", "adagrad"]
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class Adagrad(Optimizer):
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def __init__(
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self,
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params,
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lr=1e-2,
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lr_decay=0,
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weight_decay=0,
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initial_accumulator_value=0,
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eps=1e-10,
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foreach: Optional[bool] = None,
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*,
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maximize: bool = False,
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differentiable: bool = False,
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):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= lr_decay:
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raise ValueError(f"Invalid lr_decay value: {lr_decay}")
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if not 0.0 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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if not 0.0 <= initial_accumulator_value:
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raise ValueError(
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f"Invalid initial_accumulator_value value: {initial_accumulator_value}"
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)
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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defaults = dict(
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lr=lr,
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lr_decay=lr_decay,
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eps=eps,
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weight_decay=weight_decay,
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initial_accumulator_value=initial_accumulator_value,
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foreach=foreach,
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maximize=maximize,
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differentiable=differentiable,
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)
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super().__init__(params, defaults)
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for group in self.param_groups:
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for p in group["params"]:
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state = self.state[p]
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state["step"] = torch.tensor(0.0, dtype=_get_scalar_dtype())
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init_value = (
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complex(initial_accumulator_value, initial_accumulator_value)
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if torch.is_complex(p)
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else initial_accumulator_value
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)
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state["sum"] = torch.full_like(
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p, init_value, memory_format=torch.preserve_format
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)
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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("foreach", None)
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group.setdefault("maximize", False)
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group.setdefault("differentiable", False)
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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]["step"]
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)
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if not step_is_tensor:
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for s in state_values:
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s["step"] = torch.tensor(float(s["step"]), dtype=_get_scalar_dtype())
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def share_memory(self):
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for group in self.param_groups:
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for p in group["params"]:
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state = self.state[p]
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state["sum"].share_memory_()
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def _init_group(self, group, params_with_grad, grads, state_sums, state_steps):
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has_sparse_grad, has_complex = False, False
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for p in group["params"]:
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if p.grad is not None:
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has_sparse_grad |= p.grad.is_sparse
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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grads.append(p.grad)
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state = self.state[p]
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state_sums.append(state["sum"])
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state_steps.append(state["step"])
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return has_sparse_grad, has_complex
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Perform 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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grads = []
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state_sums = []
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state_steps = []
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has_sparse_grad, has_complex = self._init_group(group, params_with_grad, grads, state_sums, state_steps)
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adagrad(
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params_with_grad,
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grads,
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state_sums,
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state_steps,
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lr=group["lr"],
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weight_decay=group["weight_decay"],
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lr_decay=group["lr_decay"],
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eps=group["eps"],
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has_sparse_grad=has_sparse_grad,
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foreach=group["foreach"],
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maximize=group["maximize"],
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differentiable=group["differentiable"],
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has_complex=has_complex,
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)
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return loss
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Adagrad.__doc__ = r"""Implements Adagrad algorithm.
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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{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\
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&\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\
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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}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\
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&\hspace{5mm}\theta_t \leftarrow
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\theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\
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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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For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning
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and Stochastic Optimization`_.
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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-2)
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lr_decay (float, optional): learning rate decay (default: 0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-10)
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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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.. _Adaptive Subgradient Methods for Online Learning and Stochastic
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Optimization: http://jmlr.org/papers/v12/duchi11a.html
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"""
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def adagrad(
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params: List[Tensor],
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grads: List[Tensor],
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state_sums: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting these as kwargs 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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differentiable: bool = False,
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has_complex: bool = False,
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*,
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lr: float,
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weight_decay: float,
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lr_decay: float,
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eps: float,
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maximize: bool,
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):
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r"""Functional API that performs Adagrad algorithm computation.
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See :class:`~torch.optim.Adagrad` for details.
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"""
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if not all(isinstance(t, torch.Tensor) for t in state_steps):
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raise RuntimeError(
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"API has changed, `state_steps` argument must contain a list of singleton tensors"
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)
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if foreach is None:
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_, foreach = _default_to_fused_or_foreach(params, differentiable, use_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 foreach and not torch.jit.is_scripting():
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func = _multi_tensor_adagrad
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else:
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func = _single_tensor_adagrad
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func(
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params,
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grads,
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state_sums,
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state_steps,
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lr=lr,
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weight_decay=weight_decay,
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lr_decay=lr_decay,
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eps=eps,
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has_sparse_grad=has_sparse_grad,
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maximize=maximize,
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differentiable=differentiable,
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has_complex=has_complex,
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)
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def _make_sparse(grad, grad_indices, values):
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size = grad.size()
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if grad_indices.numel() == 0 or values.numel() == 0:
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return torch.empty_like(grad)
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return torch.sparse_coo_tensor(grad_indices, values, size)
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def _single_tensor_adagrad(
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params: List[Tensor],
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grads: List[Tensor],
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state_sums: List[Tensor],
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state_steps: List[Tensor],
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*,
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lr: float,
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weight_decay: float,
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lr_decay: float,
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eps: float,
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has_sparse_grad: bool,
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maximize: bool,
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differentiable: bool,
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has_complex: bool,
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):
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for (param, grad, state_sum, step_t) in zip(params, grads, state_sums, state_steps):
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# update step
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step_t += 1
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step = _get_value(step_t)
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grad = grad if not maximize else -grad
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if weight_decay != 0:
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if grad.is_sparse:
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raise RuntimeError(
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"weight_decay option is not compatible with sparse gradients"
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)
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grad = grad.add(param, alpha=weight_decay)
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clr = lr / (1 + (step - 1) * lr_decay)
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if grad.is_sparse:
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grad = grad.coalesce() # the update is non-linear so indices must be unique
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grad_indices = grad._indices()
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grad_values = grad._values()
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state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2)))
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std = state_sum.sparse_mask(grad)
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std_values = std._values().sqrt_().add_(eps)
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param.add_(
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_make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr
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)
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else:
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is_complex = torch.is_complex(param)
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if is_complex:
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grad = torch.view_as_real(grad)
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state_sum = torch.view_as_real(state_sum)
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param = torch.view_as_real(param)
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state_sum.addcmul_(grad, grad, value=1)
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if differentiable:
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std = state_sum.sqrt() + eps
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else:
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std = state_sum.sqrt().add_(eps)
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param.addcdiv_(grad, std, value=-clr)
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if is_complex:
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param = torch.view_as_complex(param)
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state_sum = torch.view_as_complex(state_sum)
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def _multi_tensor_adagrad(
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params: List[Tensor],
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grads: List[Tensor],
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state_sums: List[Tensor],
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state_steps: List[Tensor],
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*,
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lr: float,
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weight_decay: float,
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lr_decay: float,
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eps: float,
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has_sparse_grad: bool,
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maximize: bool,
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differentiable: bool,
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has_complex: bool,
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):
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assert not differentiable, "_foreach ops don't support autograd"
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# Foreach functions will throw errors if given empty lists
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if len(params) == 0:
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return
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grouped_tensorlists = Optimizer._group_tensors_by_device_and_dtype([params, grads, state_sums, state_steps])
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for ((device_params, device_grads, device_state_sums, device_state_steps), _) in grouped_tensorlists.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 device_has_sparse_grad:
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_single_tensor_adagrad(
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device_params,
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device_grads,
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device_state_sums,
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device_state_steps,
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lr=lr,
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weight_decay=weight_decay,
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lr_decay=lr_decay,
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eps=eps,
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has_sparse_grad=True,
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maximize=False,
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differentiable=differentiable,
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has_complex=has_complex,
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)
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continue
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# Handle complex parameters
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if has_complex:
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_view_as_real(device_params, device_grads, device_state_sums)
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if maximize:
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device_grads = torch._foreach_neg(device_grads)
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# Update steps
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# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
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# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
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# wrapped it once now. The alpha is required to assure we go to the right overload.
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if device_state_steps[0].is_cpu:
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torch._foreach_add_(device_state_steps, torch.tensor(1.0, device='cpu'), alpha=1.0)
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else:
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torch._foreach_add_(device_state_steps, 1)
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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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minus_clr = [-lr / (1 + (_get_value(step) - 1) * lr_decay) for step in device_state_steps]
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torch._foreach_addcmul_(device_state_sums, device_grads, device_grads, value=1)
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std = torch._foreach_sqrt(device_state_sums)
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torch._foreach_add_(std, eps)
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if weight_decay != 0 or maximize:
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# Again, re-use the intermediate memory (device_grads) already allocated
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torch._foreach_mul_(device_grads, minus_clr)
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numerator = device_grads
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else:
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numerator = torch._foreach_mul(device_grads, minus_clr)
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torch._foreach_addcdiv_(device_params, numerator, std)
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