465 lines
17 KiB
Python
465 lines
17 KiB
Python
# mypy: allow-untyped-decorators
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# mypy: allow-untyped-defs
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r"""Implementation for the Resilient backpropagation."""
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from typing import cast, List, Optional, Tuple, Union
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import torch
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from torch import Tensor
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from .optimizer import (
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_capturable_doc,
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_default_to_fused_or_foreach,
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_differentiable_doc,
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_disable_dynamo_if_unsupported,
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_foreach_doc,
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_get_capturable_supported_devices,
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_get_scalar_dtype,
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_maximize_doc,
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_use_grad_for_differentiable,
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_view_as_real,
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Optimizer,
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ParamsT,
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)
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__all__ = ["Rprop", "rprop"]
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class Rprop(Optimizer): # noqa: D101
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def __init__(
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self,
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params: ParamsT,
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lr: Union[float, Tensor] = 1e-2,
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etas: Tuple[float, float] = (0.5, 1.2),
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step_sizes: Tuple[float, float] = (1e-6, 50),
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*,
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capturable: bool = False,
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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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): # noqa: D107
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if isinstance(lr, Tensor) and lr.numel() != 1:
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raise ValueError("Tensor lr must be 1-element")
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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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capturable=capturable,
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)
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super().__init__(params, defaults)
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def __setstate__(self, state): # noqa: D105
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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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group.setdefault("capturable", False)
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for p in group["params"]:
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p_state = self.state.get(p, [])
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if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
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step_val = float(p_state["step"])
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p_state["step"] = (
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torch.tensor(
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step_val, dtype=_get_scalar_dtype(), device=p.device
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)
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if group["capturable"]
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else torch.tensor(step_val, dtype=_get_scalar_dtype())
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)
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def _init_group(self, group, params, grads, prevs, step_sizes, state_steps):
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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"] = (
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torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
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if group["capturable"]
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else torch.zeros((), dtype=_get_scalar_dtype())
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)
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state["prev"] = torch.zeros_like(p, memory_format=torch.preserve_format)
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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"] = torch.full_like(
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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_steps.append(state["step"])
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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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"""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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self._cuda_graph_capture_health_check()
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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: 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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state_steps: List[Tensor] = []
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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(
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group, params, grads, prevs, step_sizes, state_steps
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)
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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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state_steps,
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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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capturable=group["capturable"],
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has_complex=has_complex,
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)
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return loss
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Rprop.__doc__ = (
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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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"""
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+ rf"""
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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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{_capturable_doc}
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{_maximize_doc}
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{_differentiable_doc}
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"""
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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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state_steps: 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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capturable: 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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step = state_steps[i]
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# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
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if not torch._utils.is_compiling() and capturable:
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capturable_supported_devices = _get_capturable_supported_devices()
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assert (
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param.device.type == step.device.type
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and param.device.type in capturable_supported_devices
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), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
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step += 1
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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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if capturable:
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sign.copy_(torch.where(sign.gt(0), etaplus, sign))
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sign.copy_(torch.where(sign.lt(0), etaminus, sign))
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sign.copy_(torch.where(sign.eq(0), 1, sign))
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else:
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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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if capturable:
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grad.copy_(torch.where(sign.eq(etaminus), 0, grad))
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else:
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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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state_steps: 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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capturable: 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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# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
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if not torch._utils.is_compiling() and capturable:
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capturable_supported_devices = _get_capturable_supported_devices()
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assert all(
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p.device.type == step.device.type
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and p.device.type in capturable_supported_devices
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for p, step in zip(params, state_steps)
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), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
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[params, grads, prevs, step_sizes, state_steps] # type: ignore[list-item]
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)
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for (
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grouped_params_,
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grouped_grads_,
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grouped_prevs_,
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grouped_step_sizes_,
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grouped_state_steps_,
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), _ in grouped_tensors.values():
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grouped_params = cast(List[Tensor], grouped_params_)
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grouped_grads = cast(List[Tensor], grouped_grads_)
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grouped_prevs = cast(List[Tensor], grouped_prevs_)
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grouped_step_sizes = cast(List[Tensor], grouped_step_sizes_)
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grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
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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 not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
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torch._foreach_add_(
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grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
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)
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else:
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torch._foreach_add_(grouped_state_steps, 1)
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# Handle complex params
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if has_complex:
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_view_as_real(
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grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes
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)
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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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if capturable:
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for sign in signs:
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sign.copy_(torch.where(sign.gt(0), etaplus, sign))
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sign.copy_(torch.where(sign.lt(0), etaminus, sign))
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sign.copy_(torch.where(sign.eq(0), 1, sign))
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else:
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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].copy_(
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torch.where(signs[i].eq(etaminus), 0, grouped_grads[i])
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)
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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_(
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grouped_params, grad_signs, grouped_step_sizes, value=-1
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)
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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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@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rprop)
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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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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 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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capturable: bool = False,
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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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# this check is slow during compilation, so we skip it
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# if it's strictly needed we can add this check back in dynamo
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if not torch._utils.is_compiling() and not all(
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isinstance(t, torch.Tensor) for t in state_steps
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):
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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(
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params, differentiable, use_fused=False
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)
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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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state_steps,
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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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capturable=capturable,
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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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