332 lines
12 KiB
Python
332 lines
12 KiB
Python
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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, _view_as_real)
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from typing import List, Optional
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__all__ = ["Rprop", "rprop"]
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class Rprop(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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etas=(0.5, 1.2),
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step_sizes=(1e-6, 50),
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*,
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foreach: Optional[bool] = None,
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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 < etas[0] < 1.0 < etas[1]:
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raise ValueError(f"Invalid eta values: {etas[0]}, {etas[1]}")
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defaults = dict(
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lr=lr,
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etas=etas,
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step_sizes=step_sizes,
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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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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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def _init_group(self, group, params, grads, prevs, step_sizes):
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has_complex = False
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for p in group["params"]:
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if p.grad is None:
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continue
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has_complex |= torch.is_complex(p)
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params.append(p)
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError("Rprop does not support sparse gradients")
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grads.append(grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state["step"] = 0
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state["prev"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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if p.dtype.is_complex:
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# Complex Number should be as if they are two independent real numbers.
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# Hence the step_size shouldn't be zero for imaginary part.
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state["step_size"] = (
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torch.full_like(grad, complex(group["lr"], group["lr"]))
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)
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else:
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state["step_size"] = torch.full_like(grad, group["lr"])
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prevs.append(state["prev"])
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step_sizes.append(state["step_size"])
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state["step"] += 1
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return has_complex
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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 = []
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grads = []
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prevs = []
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step_sizes = []
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etaminus, etaplus = group["etas"]
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step_size_min, step_size_max = group["step_sizes"]
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foreach = group["foreach"]
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maximize = group["maximize"]
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has_complex = self._init_group(group, params, grads, prevs, step_sizes)
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rprop(
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params,
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grads,
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prevs,
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step_sizes,
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step_size_min=step_size_min,
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step_size_max=step_size_max,
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etaminus=etaminus,
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etaplus=etaplus,
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foreach=foreach,
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maximize=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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Rprop.__doc__ = r"""Implements the resilient backpropagation 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} : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta)
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\text{ (objective)}, \\
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&\hspace{13mm} \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min}
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\text{ (step sizes)} \\
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&\textbf{initialize} : g^0_{prev} \leftarrow 0,
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\: \eta_0 \leftarrow \text{lr (learning rate)} \\
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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{for} \text{ } i = 0, 1, \ldots, d-1 \: \mathbf{do} \\
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&\hspace{10mm} \textbf{if} \: g^i_{prev} g^i_t > 0 \\
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&\hspace{15mm} \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+},
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\Gamma_{max}) \\
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&\hspace{10mm} \textbf{else if} \: g^i_{prev} g^i_t < 0 \\
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&\hspace{15mm} \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
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\Gamma_{min}) \\
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&\hspace{15mm} g^i_t \leftarrow 0 \\
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&\hspace{10mm} \textbf{else} \: \\
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&\hspace{15mm} \eta^i_t \leftarrow \eta^i_{t-1} \\
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&\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t) \\
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&\hspace{5mm}g_{prev} \leftarrow g_t \\
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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 the paper
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`A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
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<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_.
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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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etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that
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are multiplicative increase and decrease factors
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(default: (0.5, 1.2))
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step_sizes (Tuple[float, float], optional): a pair of minimal and
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maximal allowed step sizes (default: (1e-6, 50))
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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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"""
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def rprop(
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params: List[Tensor],
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grads: List[Tensor],
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prevs: List[Tensor],
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step_sizes: 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 this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: Optional[bool] = None,
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maximize: bool = False,
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differentiable: bool = False,
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has_complex: bool = False,
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*,
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step_size_min: float,
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step_size_max: float,
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etaminus: float,
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etaplus: float,
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):
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r"""Functional API that performs rprop algorithm computation.
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See :class:`~torch.optim.Rprop` for details.
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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_rprop
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else:
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func = _single_tensor_rprop
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func(
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params,
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grads,
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prevs,
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step_sizes,
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step_size_min=step_size_min,
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step_size_max=step_size_max,
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etaminus=etaminus,
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etaplus=etaplus,
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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 _single_tensor_rprop(
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params: List[Tensor],
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grads: List[Tensor],
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prevs: List[Tensor],
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step_sizes: List[Tensor],
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*,
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step_size_min: float,
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step_size_max: float,
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etaminus: float,
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etaplus: float,
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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 i, param in enumerate(params):
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grad = grads[i]
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grad = grad if not maximize else -grad
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prev = prevs[i]
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step_size = step_sizes[i]
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if torch.is_complex(param):
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grad = torch.view_as_real(grad)
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prev = torch.view_as_real(prev)
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param = torch.view_as_real(param)
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step_size = torch.view_as_real(step_size)
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if differentiable:
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sign = grad.mul(prev.clone()).sign()
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else:
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sign = grad.mul(prev).sign()
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sign[sign.gt(0)] = etaplus
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sign[sign.lt(0)] = etaminus
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sign[sign.eq(0)] = 1
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# update stepsizes with step size updates
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step_size.mul_(sign).clamp_(step_size_min, step_size_max)
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# for dir<0, dfdx=0
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# for dir>=0 dfdx=dfdx
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grad = grad.clone(memory_format=torch.preserve_format)
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grad[sign.eq(etaminus)] = 0
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# update parameters
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param.addcmul_(grad.sign(), step_size, value=-1)
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prev.copy_(grad)
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def _multi_tensor_rprop(
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params: List[Tensor],
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grads: List[Tensor],
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prevs: List[Tensor],
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step_sizes: List[Tensor],
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*,
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step_size_min: float,
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step_size_max: float,
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etaminus: float,
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etaplus: float,
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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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if len(params) == 0:
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return
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assert not differentiable, "_foreach ops don't support autograd"
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, prevs, step_sizes])
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for ((grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes), _) in grouped_tensors.values():
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# Handle complex params
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if has_complex:
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_view_as_real(grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes)
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signs = torch._foreach_mul(grouped_grads, grouped_prevs)
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if maximize:
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torch._foreach_neg_(signs)
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# At the end of the step, grouped_prevs will contain the current grads, so we reuse
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# grouped_prevs memory instead of creating a new buffer, but, for clarity, we reassign
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# to keep referring to the buffer as grouped_grads.
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torch._foreach_copy_(grouped_prevs, grouped_grads)
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if maximize:
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torch._foreach_neg_(grouped_prevs)
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grouped_grads = grouped_prevs
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torch._foreach_sign_(signs)
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for sign in signs:
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sign[sign.gt(0)] = etaplus
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sign[sign.lt(0)] = etaminus
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sign[sign.eq(0)] = 1
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# update stepsizes with step size updates
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torch._foreach_mul_(grouped_step_sizes, signs)
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for step_size in grouped_step_sizes:
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step_size.clamp_(step_size_min, step_size_max)
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# for dir<0, dfdx=0
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# for dir>=0 dfdx=dfdx
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grouped_grads = list(grouped_grads)
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for i in range(len(grouped_grads)):
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grouped_grads[i][signs[i].eq(etaminus)] = 0
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# explicitly del signs as it's not used after here to save memory
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del signs
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# update parameters
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grad_signs = [grad.sign() for grad in grouped_grads]
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torch._foreach_addcmul_(grouped_params, grad_signs, grouped_step_sizes, value=-1)
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# Logically, you may expect grouped_prevs to get updated to grouped_grads, but that's
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# basically already happened since we've been using grouped_prevs' memory to store
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# updated grouped_grads!
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