Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
This commit is contained in:
Kolp
@@ -0,0 +1,626 @@
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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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_default_to_fused_or_foreach,
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_device_dtype_check_for_fused,
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_differentiable_doc,
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_foreach_doc,
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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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DeviceDict,
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Optimizer,
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ParamsT,
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)
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__all__ = ["Adagrad", "adagrad"]
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class Adagrad(Optimizer):
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def __init__(
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self,
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params: ParamsT,
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lr: float | Tensor = 1e-2,
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lr_decay: float = 0,
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weight_decay: float = 0,
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initial_accumulator_value: float = 0,
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eps: float = 1e-10,
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foreach: bool | None = None,
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*,
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maximize: bool = False,
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differentiable: bool = False,
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fused: bool | None = None,
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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 <= lr_decay:
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raise ValueError(f"Invalid lr_decay value: {lr_decay}")
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if not 0.0 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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if not 0.0 <= initial_accumulator_value:
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raise ValueError(
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f"Invalid initial_accumulator_value value: {initial_accumulator_value}"
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)
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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defaults = {
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"lr": lr,
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"lr_decay": lr_decay,
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"eps": eps,
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"weight_decay": weight_decay,
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"initial_accumulator_value": initial_accumulator_value,
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"foreach": foreach,
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"maximize": maximize,
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"differentiable": differentiable,
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"fused": fused,
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}
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super().__init__(params, defaults)
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if fused:
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if differentiable:
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raise RuntimeError("`fused` does not support `differentiable`")
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if foreach:
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raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
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self._need_device_dtype_check_for_fused = True
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self._step_supports_amp_scaling = True
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for group in self.param_groups:
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for p in group["params"]:
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state = self.state[p]
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state["step"] = (
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torch.zeros(
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(),
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dtype=_get_scalar_dtype(is_fused=group["fused"]),
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device=p.device,
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)
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if group["fused"]
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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init_value = (
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complex(initial_accumulator_value, initial_accumulator_value)
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if torch.is_complex(p)
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else initial_accumulator_value
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)
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state["sum"] = torch.full_like(
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p, init_value, memory_format=torch.preserve_format
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)
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def __setstate__(self, state):
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super().__setstate__(state)
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# define "fused" for
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# MYPY error: Name "fused" may be undefined
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fused = None
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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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fused = group.setdefault("fused", None)
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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,
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dtype=_get_scalar_dtype(is_fused=fused),
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device=p.device,
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)
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if group["fused"]
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else torch.tensor(step_val, dtype=_get_scalar_dtype())
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)
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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]["step"]
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)
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if not step_is_tensor:
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for s in state_values:
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s["step"] = torch.tensor(
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float(s["step"]), dtype=_get_scalar_dtype(is_fused=fused)
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)
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def share_memory(self) -> None:
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"""Calls tensor.share_memory_() on the state sum tensors."""
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for group in self.param_groups:
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for p in group["params"]:
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state = self.state[p]
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state["sum"].share_memory_()
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def _init_group(self, group, params_with_grad, grads, state_sums, state_steps):
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has_sparse_grad, has_complex = False, False
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for p in group["params"]:
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if p.grad is not None:
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if group["fused"] and getattr(
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self,
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"_need_device_dtype_check_for_fused",
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True,
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):
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_device_dtype_check_for_fused(p)
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self._need_device_dtype_check_for_fused = False
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has_sparse_grad |= p.grad.is_sparse
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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grads.append(p.grad)
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state = self.state[p]
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if len(state) == 0:
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if group["fused"]:
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_device_dtype_check_for_fused(p)
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state["step"] = (
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torch.zeros(
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(),
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dtype=_get_scalar_dtype(is_fused=group["fused"]),
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device=p.device,
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)
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if group["fused"]
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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initial_accumulator_value = self.defaults[
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"initial_accumulator_value"
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]
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init_value = (
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complex(initial_accumulator_value, initial_accumulator_value)
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if torch.is_complex(p)
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else initial_accumulator_value
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)
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state["sum"] = torch.full_like(
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p, init_value, memory_format=torch.preserve_format
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)
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state_sums.append(state["sum"])
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state_steps.append(state["step"])
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return has_sparse_grad, has_complex
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Perform a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad: list[Tensor] = []
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grads: list[Tensor] = []
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state_sums: list[Tensor] = []
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state_steps: list[Tensor] = []
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has_sparse_grad, has_complex = self._init_group(
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group, params_with_grad, grads, state_sums, state_steps
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)
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adagrad(
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params_with_grad,
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grads,
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state_sums,
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state_steps,
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lr=group["lr"],
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weight_decay=group["weight_decay"],
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lr_decay=group["lr_decay"],
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eps=group["eps"],
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has_sparse_grad=has_sparse_grad,
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foreach=group["foreach"],
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maximize=group["maximize"],
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differentiable=group["differentiable"],
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has_complex=has_complex,
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fused=group["fused"],
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grad_scale=getattr(self, "grad_scale", None),
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found_inf=getattr(self, "found_inf", None),
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)
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return loss
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Adagrad.__doc__ = (
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r"""Implements Adagrad algorithm.
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
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\text{ (objective)}, \: \lambda \text{ (weight decay)}, \\
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&\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\
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&\textbf{initialize} : state\_sum_0 \leftarrow \tau \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\
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&\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\
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&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\
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&\hspace{5mm}\theta_t \leftarrow
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\theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning
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and Stochastic Optimization`_.
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"""
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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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lr_decay (float, optional): learning rate decay (default: 0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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initial_accumulator_value (float, optional): initial value of the
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sum of squares of gradients (default: 0)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-10)
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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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fused (bool, optional): whether the fused implementation (CPU and CUDA only) is used.
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Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
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are supported. (default: None). Please note that the fused implementations does not
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support sparse or complex gradients.
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.. _Adaptive Subgradient Methods for Online Learning and Stochastic
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Optimization: http://jmlr.org/papers/v12/duchi11a.html
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"""
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)
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def adagrad(
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params: list[Tensor],
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grads: list[Tensor],
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state_sums: list[Tensor],
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state_steps: list[Tensor],
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fused: bool | None = None,
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grad_scale: Tensor | None = None,
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found_inf: Tensor | None = None,
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting these as kwargs for now as functional API is compiled by torch/distributed/optim
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has_sparse_grad: bool = False,
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foreach: bool | None = None,
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differentiable: bool = False,
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has_complex: bool = False,
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*,
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lr: float,
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weight_decay: float,
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lr_decay: float,
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eps: float,
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maximize: bool,
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) -> None:
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r"""Functional API that performs Adagrad algorithm computation.
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See :class:`~torch.optim.Adagrad` for details.
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"""
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if not all(isinstance(t, torch.Tensor) for t in state_steps):
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raise RuntimeError(
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"API has changed, `state_steps` argument must contain a list of singleton tensors"
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)
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# Respect when the user inputs False/True for foreach or fused. We only want to change
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# the default when neither have been user-specified. Note that we default to foreach
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# and pass False to use_fused. This is not a mistake--we want to give the fused impl
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# bake-in time before making it the default, even if it is typically faster.
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if fused is None and 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 fused is None:
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fused = False
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if foreach is None:
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foreach = 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 fused and torch.jit.is_scripting():
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raise RuntimeError("torch.jit.script not supported with fused optimizers")
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if fused and not torch.jit.is_scripting():
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func = _fused_adagrad
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elif foreach and not torch.jit.is_scripting():
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func = _multi_tensor_adagrad
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else:
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func = _single_tensor_adagrad
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func(
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params,
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grads,
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state_sums,
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state_steps,
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lr=lr,
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weight_decay=weight_decay,
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lr_decay=lr_decay,
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eps=eps,
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has_sparse_grad=has_sparse_grad,
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maximize=maximize,
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differentiable=differentiable,
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has_complex=has_complex,
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grad_scale=grad_scale,
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found_inf=found_inf,
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)
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def _make_sparse(grad, grad_indices, values):
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size = grad.size()
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return torch.sparse_coo_tensor(grad_indices, values, size)
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def _single_tensor_adagrad(
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params: list[Tensor],
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grads: list[Tensor],
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state_sums: list[Tensor],
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state_steps: list[Tensor],
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grad_scale: Tensor | None,
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found_inf: Tensor | None,
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*,
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lr: float,
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weight_decay: float,
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lr_decay: float,
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eps: float,
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has_sparse_grad: bool,
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maximize: bool,
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differentiable: bool,
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has_complex: bool,
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) -> None:
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if grad_scale is not None or found_inf is not None:
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raise AssertionError("Expected grad_scale and found_inf to be None")
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if not torch.jit.is_scripting():
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lr = _to_scalar(lr)
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for param, grad, state_sum, step_t in zip(
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params, grads, state_sums, state_steps, strict=True
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):
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# update step
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step_t += 1
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step = _get_value(step_t)
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grad = grad if not maximize else -grad
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if weight_decay != 0:
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if grad.is_sparse:
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raise RuntimeError(
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"weight_decay option is not compatible with sparse gradients"
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)
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grad = grad.add(param, alpha=weight_decay)
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clr = lr / (1 + (step - 1) * lr_decay)
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if grad.is_sparse:
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grad = grad.coalesce() # the update is non-linear so indices must be unique
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grad_indices = grad._indices()
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grad_values = grad._values()
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state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2)))
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std = state_sum.sparse_mask(grad)
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std_values = std._values().sqrt_().add_(eps)
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param.add_(
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_make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr
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)
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else:
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is_complex = torch.is_complex(param)
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if is_complex:
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grad = torch.view_as_real(grad)
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state_sum = torch.view_as_real(state_sum)
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param = torch.view_as_real(param)
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state_sum.addcmul_(grad, grad, value=1)
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if differentiable:
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std = state_sum.sqrt() + eps
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else:
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std = state_sum.sqrt().add_(eps)
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param.addcdiv_(grad, std, value=-clr)
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if is_complex:
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param = torch.view_as_complex(param)
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state_sum = torch.view_as_complex(state_sum)
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def _multi_tensor_adagrad(
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params: list[Tensor],
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grads: list[Tensor],
|
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state_sums: list[Tensor],
|
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state_steps: list[Tensor],
|
||||
grad_scale: Tensor | None,
|
||||
found_inf: Tensor | None,
|
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*,
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lr: float,
|
||||
weight_decay: float,
|
||||
lr_decay: float,
|
||||
eps: float,
|
||||
has_sparse_grad: bool,
|
||||
maximize: bool,
|
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differentiable: bool,
|
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has_complex: bool,
|
||||
) -> None:
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if differentiable:
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raise AssertionError("_foreach ops don't support autograd")
|
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if grad_scale is not None or found_inf is not None:
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raise AssertionError("Expected grad_scale and found_inf to be None")
|
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|
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# Foreach functions will throw errors if given empty lists
|
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if len(params) == 0:
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return
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lr = _to_scalar(lr)
|
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|
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grouped_tensorlists = Optimizer._group_tensors_by_device_and_dtype(
|
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[params, grads, state_sums, state_steps] # type: ignore[list-item]
|
||||
)
|
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for (
|
||||
device_params_,
|
||||
device_grads_,
|
||||
device_state_sums_,
|
||||
device_state_steps_,
|
||||
), _ in grouped_tensorlists.values():
|
||||
device_params = cast(list[Tensor], device_params_)
|
||||
device_grads = cast(list[Tensor], device_grads_)
|
||||
device_state_sums = cast(list[Tensor], device_state_sums_)
|
||||
device_state_steps = cast(list[Tensor], device_state_steps_)
|
||||
|
||||
device_has_sparse_grad = has_sparse_grad and any(
|
||||
grad.is_sparse for grad in device_grads
|
||||
)
|
||||
|
||||
if device_has_sparse_grad:
|
||||
_single_tensor_adagrad(
|
||||
device_params,
|
||||
device_grads,
|
||||
device_state_sums,
|
||||
device_state_steps,
|
||||
lr=lr,
|
||||
weight_decay=weight_decay,
|
||||
lr_decay=lr_decay,
|
||||
eps=eps,
|
||||
has_sparse_grad=True,
|
||||
maximize=maximize,
|
||||
differentiable=differentiable,
|
||||
has_complex=has_complex,
|
||||
grad_scale=grad_scale,
|
||||
found_inf=found_inf,
|
||||
)
|
||||
continue
|
||||
|
||||
# Handle complex parameters
|
||||
if has_complex:
|
||||
_view_as_real(device_params, device_grads, device_state_sums)
|
||||
|
||||
if maximize:
|
||||
device_grads = torch._foreach_neg(device_grads) # type: ignore[assignment]
|
||||
|
||||
# Update steps
|
||||
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
|
||||
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
|
||||
# wrapped it once now. The alpha is required to assure we go to the right overload.
|
||||
if not torch.compiler.is_compiling() and device_state_steps[0].is_cpu:
|
||||
torch._foreach_add_(
|
||||
device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
|
||||
)
|
||||
else:
|
||||
torch._foreach_add_(device_state_steps, 1)
|
||||
|
||||
if weight_decay != 0:
|
||||
# Reuse the intermediate memory (device_grads) already allocated for maximize
|
||||
if maximize:
|
||||
torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
|
||||
else:
|
||||
device_grads = torch._foreach_add( # type: ignore[assignment]
|
||||
device_grads, device_params, alpha=weight_decay
|
||||
)
|
||||
|
||||
minus_clr = [
|
||||
-lr / (1 + (_get_value(step) - 1) * lr_decay) for step in device_state_steps
|
||||
]
|
||||
|
||||
torch._foreach_addcmul_(device_state_sums, device_grads, device_grads, value=1)
|
||||
|
||||
std = torch._foreach_sqrt(device_state_sums)
|
||||
torch._foreach_add_(std, eps)
|
||||
|
||||
if weight_decay != 0 or maximize:
|
||||
# Again, reuse the intermediate memory (device_grads) already allocated
|
||||
torch._foreach_mul_(device_grads, minus_clr)
|
||||
numerator = device_grads
|
||||
else:
|
||||
numerator = torch._foreach_mul(device_grads, minus_clr) # type: ignore[assignment]
|
||||
|
||||
torch._foreach_addcdiv_(device_params, numerator, std)
|
||||
|
||||
|
||||
def _fused_adagrad(
|
||||
params: list[Tensor],
|
||||
grads: list[Tensor],
|
||||
state_sums: list[Tensor],
|
||||
state_steps: list[Tensor],
|
||||
grad_scale: Tensor | None,
|
||||
found_inf: Tensor | None,
|
||||
*,
|
||||
lr: float | Tensor,
|
||||
weight_decay: float,
|
||||
lr_decay: float,
|
||||
eps: float,
|
||||
has_sparse_grad: bool,
|
||||
maximize: bool,
|
||||
differentiable: bool,
|
||||
has_complex: bool,
|
||||
) -> None:
|
||||
if not params:
|
||||
return
|
||||
if has_sparse_grad or has_complex:
|
||||
raise RuntimeError("`fused` does not support sparse grad or complex param")
|
||||
|
||||
if differentiable:
|
||||
raise RuntimeError(
|
||||
"adagrad with fused=True does not support differentiable=True"
|
||||
)
|
||||
|
||||
grad_scale_dict: DeviceDict = (
|
||||
{grad_scale.device: grad_scale} if grad_scale is not None else {}
|
||||
)
|
||||
found_inf_dict: DeviceDict = (
|
||||
{found_inf.device: found_inf} if found_inf is not None else {}
|
||||
)
|
||||
lr_dict: DeviceDict | None = (
|
||||
{lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None
|
||||
)
|
||||
|
||||
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
|
||||
[params, grads, state_sums, state_steps] # type: ignore[list-item]
|
||||
)
|
||||
for (device, _), (
|
||||
(
|
||||
device_params_,
|
||||
device_grads_,
|
||||
device_state_sums_,
|
||||
device_state_steps_,
|
||||
),
|
||||
_,
|
||||
) in grouped_tensors.items():
|
||||
device_params = cast(list[Tensor], device_params_)
|
||||
device_grads = cast(list[Tensor], device_grads_)
|
||||
device_state_sums = cast(list[Tensor], device_state_sums_)
|
||||
device_state_steps = cast(list[Tensor], device_state_steps_)
|
||||
|
||||
device_grad_scale, device_found_inf = None, None
|
||||
if grad_scale is not None:
|
||||
device_grad_scale = grad_scale_dict.setdefault(
|
||||
device, grad_scale.to(device, non_blocking=True)
|
||||
)
|
||||
if found_inf is not None:
|
||||
device_found_inf = found_inf_dict.setdefault(
|
||||
device, found_inf.to(device, non_blocking=True)
|
||||
)
|
||||
if lr_dict is not None and device not in lr_dict:
|
||||
lr_dict[device] = lr.to(device=device, non_blocking=True) # type: ignore[union-attr]
|
||||
lr = lr_dict[device]
|
||||
torch._foreach_add_(device_state_steps, 1)
|
||||
torch._fused_adagrad_(
|
||||
device_params,
|
||||
device_grads,
|
||||
device_state_sums,
|
||||
device_state_steps,
|
||||
lr=lr,
|
||||
lr_decay=lr_decay,
|
||||
weight_decay=weight_decay,
|
||||
eps=eps,
|
||||
maximize=maximize,
|
||||
grad_scale=device_grad_scale,
|
||||
found_inf=device_found_inf,
|
||||
)
|
||||
if device_found_inf is not None:
|
||||
torch._foreach_sub_(
|
||||
device_state_steps, [device_found_inf] * len(device_state_steps)
|
||||
)
|
||||
Reference in New Issue
Block a user