Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
This commit is contained in:
Kolp
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# mypy: allow-untyped-defs
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r"""Implementation for the RMSprop algorithm."""
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from typing import cast
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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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_params_doc,
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_to_scalar,
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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__ = ["RMSprop", "rmsprop"]
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class RMSprop(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: float | Tensor = 1e-2,
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alpha: float = 0.99,
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eps: float = 1e-8,
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weight_decay: float = 0,
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momentum: float = 0,
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centered: bool = False,
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capturable: bool = False,
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foreach: bool | None = None,
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maximize: bool = False,
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differentiable: bool = False,
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) -> None: # 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 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= momentum:
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raise ValueError(f"Invalid momentum value: {momentum}")
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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 <= alpha:
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raise ValueError(f"Invalid alpha value: {alpha}")
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defaults = {
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"lr": lr,
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"momentum": momentum,
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"alpha": alpha,
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"eps": eps,
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"centered": centered,
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"weight_decay": weight_decay,
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"capturable": capturable,
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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): # 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("momentum", 0)
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group.setdefault("centered", False)
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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(
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self,
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group,
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params_with_grad,
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grads,
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square_avgs,
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momentum_buffer_list,
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grad_avgs,
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state_steps,
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):
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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_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError("RMSprop does not support sparse gradients")
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grads.append(p.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["square_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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if group["momentum"] > 0:
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state["momentum_buffer"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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if group["centered"]:
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state["grad_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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square_avgs.append(state["square_avg"])
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state_steps.append(state["step"])
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if group["momentum"] > 0:
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momentum_buffer_list.append(state["momentum_buffer"])
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if group["centered"]:
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grad_avgs.append(state["grad_avg"])
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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._accelerator_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_with_grad: list[Tensor] = []
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grads: list[Tensor] = []
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square_avgs: list[Tensor] = []
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grad_avgs: list[Tensor] = []
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momentum_buffer_list: list[Tensor] = []
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state_steps: list[Tensor] = []
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has_complex = self._init_group(
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group,
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params_with_grad,
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grads,
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square_avgs,
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momentum_buffer_list,
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grad_avgs,
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state_steps,
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)
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rmsprop(
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params_with_grad,
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grads,
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square_avgs,
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grad_avgs,
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momentum_buffer_list,
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state_steps,
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lr=group["lr"],
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alpha=group["alpha"],
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eps=group["eps"],
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weight_decay=group["weight_decay"],
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momentum=group["momentum"],
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centered=group["centered"],
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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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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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RMSprop.__doc__ = (
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r"""Implements RMSprop 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} : \alpha \text{ (alpha)}, \: \gamma \text{ (lr)},
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\: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\
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&\hspace{13mm} \lambda \text{ (weight decay)},\: \mu \text{ (momentum)},
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\: centered, \: \epsilon \text{ (epsilon)} \\
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&\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \:
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\textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_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}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}v_t \leftarrow \alpha v_{t-1} + (1 - \alpha) g^2_t
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\hspace{8mm} \\
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&\hspace{5mm} \tilde{v_t} \leftarrow v_t \\
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&\hspace{5mm}if \: centered \\
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&\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t \\
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&\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} - \big(g^{ave}_{t} \big)^2 \\
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&\hspace{5mm}if \: \mu > 0 \\
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&\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} +
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g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \\
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&\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t \\
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&\hspace{5mm} else \\
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&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} -
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\gamma g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \hspace{3mm} \\
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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
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`lecture notes <https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_ by G. Hinton.
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and centered version `Generating Sequences
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With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_.
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The implementation here takes the square root of the gradient average before
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adding epsilon (note that TensorFlow interchanges these two operations). The effective
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learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma`
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is the scheduled learning rate and :math:`v` is the weighted moving average
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of the squared gradient.
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"""
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+ rf"""
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Args:
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{_params_doc}
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lr (float, Tensor, optional): learning rate (default: 1e-2)
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alpha (float, optional): smoothing constant (default: 0.99)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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momentum (float, optional): momentum factor (default: 0)
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centered (bool, optional) : if ``True``, compute the centered RMSProp,
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the gradient is normalized by an estimation of its variance
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{_capturable_doc}
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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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)
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def _single_tensor_rmsprop(
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params: list[Tensor],
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grads: list[Tensor],
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square_avgs: list[Tensor],
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grad_avgs: list[Tensor],
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momentum_buffer_list: list[Tensor],
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state_steps: list[Tensor],
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*,
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lr: float,
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alpha: float,
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eps: float,
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weight_decay: float,
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momentum: float,
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centered: bool,
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maximize: bool,
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differentiable: bool,
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capturable: bool,
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has_complex: bool,
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) -> None:
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if not torch.jit.is_scripting():
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lr = _to_scalar(lr)
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for i, param in enumerate(params):
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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.compiler.is_compiling() and capturable:
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capturable_supported_devices = _get_capturable_supported_devices()
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if not (
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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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):
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raise AssertionError(
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f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
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)
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grad = grads[i]
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grad = grad if not maximize else -grad
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square_avg = square_avgs[i]
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step += 1
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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is_complex_param = torch.is_complex(param)
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if is_complex_param:
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param = torch.view_as_real(param)
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grad = torch.view_as_real(grad)
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square_avg = torch.view_as_real(square_avg)
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square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)
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if centered:
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grad_avg = grad_avgs[i]
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if is_complex_param:
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grad_avg = torch.view_as_real(grad_avg)
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grad_avg.lerp_(grad, 1 - alpha)
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avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_()
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else:
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avg = square_avg.sqrt()
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if differentiable:
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avg = avg.add(eps)
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else:
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avg = avg.add_(eps)
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if momentum > 0:
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buf = momentum_buffer_list[i]
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if is_complex_param:
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buf = torch.view_as_real(buf)
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buf.mul_(momentum).addcdiv_(grad, avg)
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param.add_(buf, alpha=-lr)
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else:
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param.addcdiv_(grad, avg, value=-lr)
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def _multi_tensor_rmsprop(
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params: list[Tensor],
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grads: list[Tensor],
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square_avgs: list[Tensor],
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grad_avgs: list[Tensor],
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momentum_buffer_list: list[Tensor],
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state_steps: list[Tensor],
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*,
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lr: float,
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alpha: float,
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eps: float,
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weight_decay: float,
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momentum: float,
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centered: bool,
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maximize: bool,
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differentiable: bool,
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capturable: bool,
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has_complex: bool,
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) -> None:
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if len(params) == 0:
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return
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if differentiable:
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raise AssertionError("_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.compiler.is_compiling() and capturable:
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capturable_supported_devices = _get_capturable_supported_devices()
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if not 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, strict=True)
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):
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raise AssertionError(
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f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
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)
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lr = _to_scalar(lr)
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
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[params, grads, square_avgs, grad_avgs, momentum_buffer_list, state_steps] # type: ignore[list-item]
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)
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for (
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(
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grouped_params_,
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grouped_grads_,
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grouped_square_avgs_,
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grouped_grad_avgs_,
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grouped_momentum_buffer_list_,
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grouped_state_steps_,
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)
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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_square_avgs = cast(list[Tensor], grouped_square_avgs_)
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grouped_state_steps = cast(list[Tensor], grouped_state_steps_)
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if has_complex:
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state_and_grads = [grouped_grads, grouped_square_avgs]
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if momentum > 0:
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grouped_momentum_buffer_list = cast(
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||||
list[Tensor], grouped_momentum_buffer_list_
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)
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state_and_grads.append(grouped_momentum_buffer_list)
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if centered:
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grouped_grad_avgs = cast(list[Tensor], grouped_grad_avgs_)
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state_and_grads.append(grouped_grad_avgs)
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_view_as_real(grouped_params, *state_and_grads)
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if maximize:
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grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment]
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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.compiler.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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if weight_decay != 0:
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# Reuse the intermediate memory (grouped_grads) already allocated for maximize
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if maximize:
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torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
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||||
else:
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grouped_grads = torch._foreach_add( # type: ignore[assignment]
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||||
grouped_grads, grouped_params, alpha=weight_decay
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||||
)
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||||
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torch._foreach_mul_(grouped_square_avgs, alpha)
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torch._foreach_addcmul_(
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grouped_square_avgs, grouped_grads, grouped_grads, value=1 - alpha
|
||||
)
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||||
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if centered:
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grouped_grad_avgs = cast(list[Tensor], grouped_grad_avgs_)
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torch._foreach_lerp_(grouped_grad_avgs, grouped_grads, 1 - alpha)
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avg = torch._foreach_addcmul(
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grouped_square_avgs, grouped_grad_avgs, grouped_grad_avgs, value=-1
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||||
)
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torch._foreach_sqrt_(avg)
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torch._foreach_add_(avg, eps)
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else:
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avg = torch._foreach_sqrt(grouped_square_avgs)
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torch._foreach_add_(avg, eps)
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||||
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||||
if momentum > 0:
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grouped_momentum_buffer_list = cast(
|
||||
list[Tensor], grouped_momentum_buffer_list_
|
||||
)
|
||||
torch._foreach_mul_(grouped_momentum_buffer_list, momentum)
|
||||
torch._foreach_addcdiv_(grouped_momentum_buffer_list, grouped_grads, avg)
|
||||
# If LR is a tensor, the else branch will internally call item()
|
||||
# which will cause silent incorrectness if we are capturing
|
||||
if capturable and isinstance(lr, torch.Tensor):
|
||||
momentum_lr = torch._foreach_mul(grouped_momentum_buffer_list, -lr)
|
||||
torch._foreach_add_(grouped_params, momentum_lr)
|
||||
else:
|
||||
torch._foreach_add_(
|
||||
grouped_params, grouped_momentum_buffer_list, alpha=-lr
|
||||
)
|
||||
else:
|
||||
# If LR is a tensor, the else branch will internally call item()
|
||||
# which will cause silent incorrectness if we are capturing
|
||||
if capturable and isinstance(lr, torch.Tensor):
|
||||
torch._foreach_div_(avg, -lr)
|
||||
torch._foreach_addcdiv_(grouped_params, grouped_grads, avg)
|
||||
else:
|
||||
torch._foreach_addcdiv_(grouped_params, grouped_grads, avg, value=-lr)
|
||||
|
||||
|
||||
@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rmsprop)
|
||||
def rmsprop(
|
||||
params: list[Tensor],
|
||||
grads: list[Tensor],
|
||||
square_avgs: list[Tensor],
|
||||
grad_avgs: list[Tensor],
|
||||
momentum_buffer_list: list[Tensor],
|
||||
state_steps: list[Tensor],
|
||||
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
|
||||
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
|
||||
foreach: bool | None = None,
|
||||
maximize: bool = False,
|
||||
differentiable: bool = False,
|
||||
capturable: bool = False,
|
||||
has_complex: bool = False,
|
||||
*,
|
||||
lr: float,
|
||||
alpha: float,
|
||||
eps: float,
|
||||
weight_decay: float,
|
||||
momentum: float,
|
||||
centered: bool,
|
||||
) -> None:
|
||||
r"""Functional API that performs rmsprop algorithm computation.
|
||||
|
||||
See :class:`~torch.optim.RMSProp` for details.
|
||||
"""
|
||||
# this check is slow during compilation, so we skip it
|
||||
# if it's strictly needed we can add this check back in dynamo
|
||||
if not torch.compiler.is_compiling() and 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_rmsprop
|
||||
else:
|
||||
func = _single_tensor_rmsprop
|
||||
|
||||
func(
|
||||
params,
|
||||
grads,
|
||||
square_avgs,
|
||||
grad_avgs,
|
||||
momentum_buffer_list,
|
||||
state_steps,
|
||||
lr=lr,
|
||||
alpha=alpha,
|
||||
eps=eps,
|
||||
weight_decay=weight_decay,
|
||||
momentum=momentum,
|
||||
centered=centered,
|
||||
maximize=maximize,
|
||||
capturable=capturable,
|
||||
differentiable=differentiable,
|
||||
has_complex=has_complex,
|
||||
)
|
||||
Reference in New Issue
Block a user