Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
This commit is contained in:
Kolp
@@ -0,0 +1,478 @@
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# mypy: allow-untyped-defs
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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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_get_value,
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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__ = ["ASGD", "asgd"]
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class ASGD(Optimizer):
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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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lambd: float = 1e-4,
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alpha: float = 0.75,
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t0: float = 1e6,
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weight_decay: float = 0,
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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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capturable: bool = False,
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) -> None:
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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 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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defaults = {
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"lr": lr,
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"lambd": lambd,
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"alpha": alpha,
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"t0": t0,
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"weight_decay": weight_decay,
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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):
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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:
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if not torch.is_tensor(p_state["step"]):
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step_val = float(p_state["step"])
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p_state["step"] = torch.tensor(
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step_val, dtype=_get_scalar_dtype(), device=p.device
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)
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if not torch.is_tensor(p_state["eta"]):
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p_state["eta"] = torch.tensor(
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p_state["eta"], dtype=_get_scalar_dtype(), device=p.device
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)
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if not torch.is_tensor(p_state["mu"]):
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p_state["mu"] = torch.tensor(
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p_state["mu"], dtype=_get_scalar_dtype(), device=p.device
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)
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def _init_group(self, group, params_with_grad, grads, mus, axs, etas, 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 not None:
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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("ASGD 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"] = torch.zeros(
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(), device=p.device, dtype=_get_scalar_dtype()
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)
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state["eta"] = (
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torch.as_tensor(
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_to_scalar(group["lr"]),
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device=p.device,
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dtype=_get_scalar_dtype(),
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)
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.clone()
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.detach()
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)
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state["mu"] = torch.ones(
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(), device=p.device, dtype=_get_scalar_dtype()
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)
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state["ax"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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mus.append(state["mu"])
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axs.append(state["ax"])
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etas.append(state["eta"])
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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._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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mus: list[Tensor] = []
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axs: list[Tensor] = []
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etas: list[Tensor] = []
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state_steps: list[Tensor] = []
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has_complex = self._init_group(
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group, params_with_grad, grads, mus, axs, etas, state_steps
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)
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asgd(
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params_with_grad,
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grads,
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axs,
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mus,
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etas,
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state_steps,
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lambd=group["lambd"],
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lr=group["lr"],
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t0=group["t0"],
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alpha=group["alpha"],
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weight_decay=group["weight_decay"],
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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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ASGD.__doc__ = rf"""Implements Averaged Stochastic Gradient Descent.
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It has been proposed in `Acceleration of stochastic approximation by
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averaging`_.
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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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lambd (float, optional): decay term (default: 1e-4)
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alpha (float, optional): power for eta update (default: 0.75)
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t0 (float, optional): point at which to start averaging (default: 1e6)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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{_capturable_doc}
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.. _Acceleration of stochastic approximation by averaging:
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https://meyn.ece.ufl.edu/wp-content/uploads/sites/77/archive/spm_files/Courses/ECE555-2011/555media/poljud92.pdf
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"""
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def _single_tensor_asgd(
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params: list[Tensor],
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grads: list[Tensor],
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axs: list[Tensor],
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mus: list[Tensor],
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etas: list[Tensor],
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state_steps: list[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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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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grad = grads[i]
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grad = grad if not maximize else -grad
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mu = mus[i]
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ax = axs[i]
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eta = etas[i]
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step_t = 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
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== mu.device.type
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== eta.device.type
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== step_t.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, mus, etas, and state_steps must be "
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f"on supported devices: {capturable_supported_devices}."
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)
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if torch.is_complex(param):
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grad = torch.view_as_real(grad)
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param = torch.view_as_real(param)
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ax = torch.view_as_real(ax)
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# update step
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step_t += 1
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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if capturable:
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param.mul_(1 - lambd * eta)
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param.addcmul_(grad, eta, value=-1) # update parameter
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else:
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eta_value = _get_value(eta)
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param.mul_(1 - lambd * eta_value) # decay term
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param.add_(grad, alpha=-eta_value) # update parameter
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# averaging
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if capturable or mu.item() != 1:
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ax.add_(param.sub(ax).mul_(mu))
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else:
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ax.copy_(param)
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if capturable:
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eta.copy_(lr / ((1 + lambd * lr * step_t) ** alpha))
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mu.copy_(1 / torch.maximum(step_t - t0, torch.ones_like(step_t)))
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else:
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step = _get_value(step_t)
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new_eta = torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha))
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eta.copy_(new_eta)
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new_mu = torch.as_tensor(1 / max(1, step - t0))
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mu.copy_(new_mu)
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def _multi_tensor_asgd(
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params: list[Tensor],
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grads: list[Tensor],
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axs: list[Tensor],
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mus: list[Tensor],
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etas: list[Tensor],
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state_steps: list[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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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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supports_xla=False
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)
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if not all(
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p.device.type == mu.device.type == eta.device.type == step.device.type
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and p.device.type in capturable_supported_devices
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for p, mu, eta, step in zip(params, mus, etas, state_steps, strict=True)
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):
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raise AssertionError(
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f"If capturable=True, params, mus, etas, and state_steps must be on "
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f"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, axs, mus, etas, state_steps] # type: ignore[list-item]
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)
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for (device, _), (
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(
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grouped_params_,
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grouped_grads_,
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grouped_axs_,
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grouped_mus_,
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grouped_etas_,
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grouped_state_steps_,
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),
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_,
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) in grouped_tensors.items():
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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_axs = cast(list[Tensor], grouped_axs_)
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grouped_mus = cast(list[Tensor], grouped_mus_)
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grouped_etas = cast(list[Tensor], grouped_etas_)
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grouped_state_steps = cast(list[Tensor], grouped_state_steps_)
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if has_complex:
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_view_as_real(grouped_params, grouped_grads, grouped_axs)
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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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# intermediate = grad + param * lambd
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intermediate: tuple[Tensor, ...] | list[Tensor]
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if weight_decay != 0:
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if maximize:
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torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
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intermediate = grouped_grads
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else:
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intermediate = torch._foreach_add(
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grouped_grads, grouped_params, alpha=weight_decay
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)
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torch._foreach_add_(intermediate, grouped_params, alpha=lambd)
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else:
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intermediate = torch._foreach_add(
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grouped_grads, grouped_params, alpha=lambd
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)
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# update param
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# param * (1 - lambd * eta) - eta * grad
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# => param - param * lambd * eta - eta * grad
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# => param - eta * intermediate
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torch._foreach_addcmul_(grouped_params, intermediate, grouped_etas, value=-1)
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del intermediate
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# update grouped_axs
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# averaging: ax = ax + mu * (param - ax)
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# Note (mlazos): We can't use lerp here since it requires weight to be float64
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# and our grouping code requires dtypes to match for all tensors in a group (and it should, since
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# we use the mus in other places)
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# all dtypes need to match, so we could introduce a cast in a loop
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# but since this only adds one additional kernel launch, this looks like the cleaner
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# and faster solution
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intermediate = torch._foreach_sub(grouped_params, grouped_axs)
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torch._foreach_addcmul_(grouped_axs, intermediate, grouped_mus)
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del intermediate
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new_etas: tuple[Tensor, ...] | list[Tensor]
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new_mus: tuple[Tensor, ...] | list[Tensor]
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if capturable:
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# update grouped_mus
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new_mus = torch._foreach_sub(grouped_state_steps, t0)
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torch._foreach_maximum_(new_mus, 1.0)
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torch._foreach_reciprocal_(new_mus)
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torch._foreach_copy_(grouped_mus, new_mus)
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del new_mus
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# update eta = lr / ((1 + lambd * lr * step)^alpha)
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new_etas = torch._foreach_mul(grouped_state_steps, lambd)
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torch._foreach_mul_(new_etas, lr)
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torch._foreach_add_(new_etas, 1)
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torch._foreach_pow_(new_etas, alpha)
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torch._foreach_reciprocal_(new_etas)
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torch._foreach_mul_(new_etas, lr)
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torch._foreach_copy_(grouped_etas, new_etas)
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else:
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new_etas = [
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torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha), device=device)
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for step in grouped_state_steps
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]
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new_mus = [
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torch.as_tensor(1 / max(1, _get_value(step) - t0), device=device)
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for step in grouped_state_steps
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]
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torch._foreach_copy_(grouped_etas, new_etas)
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torch._foreach_copy_(grouped_mus, new_mus)
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@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_asgd)
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def asgd(
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params: list[Tensor],
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grads: list[Tensor],
|
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axs: list[Tensor],
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mus: list[Tensor],
|
||||
etas: 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: bool | None = None,
|
||||
maximize: bool = False,
|
||||
differentiable: bool = False,
|
||||
capturable: bool = False,
|
||||
has_complex: bool = False,
|
||||
*,
|
||||
lambd: float,
|
||||
lr: float,
|
||||
t0: float,
|
||||
alpha: float,
|
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weight_decay: float,
|
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) -> None:
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r"""Functional API that performs asgd algorithm computation.
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|
||||
See :class:`~torch.optim.ASGD` for details.
|
||||
"""
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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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||||
)
|
||||
|
||||
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_asgd
|
||||
else:
|
||||
func = _single_tensor_asgd
|
||||
|
||||
func(
|
||||
params,
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||||
grads,
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||||
axs,
|
||||
mus,
|
||||
etas,
|
||||
state_steps,
|
||||
lambd=lambd,
|
||||
lr=lr,
|
||||
t0=t0,
|
||||
alpha=alpha,
|
||||
weight_decay=weight_decay,
|
||||
maximize=maximize,
|
||||
differentiable=differentiable,
|
||||
capturable=capturable,
|
||||
has_complex=has_complex,
|
||||
)
|
||||
Reference in New Issue
Block a user