404 lines
15 KiB
Python
404 lines
15 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _default_to_fused_or_foreach,
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_get_scalar_dtype, _differentiable_doc, _maximize_doc, _foreach_doc, _view_as_real,
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_capturable_doc)
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from typing import List, Optional
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__all__ = ["Adamax", "adamax"]
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class Adamax(Optimizer):
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def __init__(
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self,
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params,
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lr=2e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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foreach: Optional[bool] = None,
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*,
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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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):
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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 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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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 = dict(
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lr=lr,
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betas=betas,
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eps=eps,
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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 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"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(), device=p.device) if group['capturable']
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else torch.tensor(step_val, dtype=_get_scalar_dtype()))
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def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_infs, state_steps):
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has_complex = False
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for p in group["params"]:
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if p.grad is None:
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continue
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError("Adamax 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((), dtype=_get_scalar_dtype(), device=p.device)
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if group['capturable'] else torch.tensor(0.0, dtype=_get_scalar_dtype()))
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state["exp_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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state["exp_inf"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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exp_avgs.append(state["exp_avg"])
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exp_infs.append(state["exp_inf"])
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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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"""Performs a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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self._cuda_graph_capture_health_check()
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_infs = []
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state_steps = []
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beta1, beta2 = group["betas"]
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eps = group["eps"]
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lr = group["lr"]
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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 = self._init_group(group, params_with_grad, grads, exp_avgs, exp_infs, state_steps)
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adamax(
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params_with_grad,
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grads,
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exp_avgs,
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exp_infs,
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state_steps,
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eps=eps,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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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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has_complex=has_complex,
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)
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return loss
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Adamax.__doc__ = r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
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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)}, \beta_1, \beta_2
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\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
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\: \lambda \text{ (weight decay)}, \\
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&\hspace{13mm} \epsilon \text{ (epsilon)} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
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u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-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}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
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&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
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""" + fr"""
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 2e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square
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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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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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{_capturable_doc}
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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"""
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def adamax(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: Optional[bool] = None,
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maximize: bool = False,
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differentiable: bool = False,
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capturable: bool = False,
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has_complex: bool = False,
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*,
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eps: float,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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):
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r"""Functional API that performs adamax algorithm computation.
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See :class:`~torch.optim.Adamax` 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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if foreach is None:
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_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False)
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if foreach and torch.jit.is_scripting():
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raise RuntimeError("torch.jit.script not supported with foreach optimizers")
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_adamax
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else:
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func = _single_tensor_adamax
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func(
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params,
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grads,
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exp_avgs,
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exp_infs,
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state_steps,
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eps=eps,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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weight_decay=weight_decay,
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maximize=maximize,
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differentiable=differentiable,
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has_complex=has_complex,
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capturable=capturable,
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)
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def _single_tensor_adamax(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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*,
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eps: float,
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beta1: float,
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beta2: float,
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lr: 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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):
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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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exp_avg = exp_avgs[i]
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exp_inf = exp_infs[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._utils.is_compiling() and capturable:
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assert (param.is_cuda and step_t.is_cuda) or (
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param.is_xla and step_t.is_xla
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), "If capturable=True, params and state_steps must be CUDA or XLA tensors."
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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 torch.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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exp_avg = torch.view_as_real(exp_avg)
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exp_inf = torch.view_as_real(exp_inf)
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# Update biased first moment estimate.
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exp_avg.lerp_(grad, 1 - beta1)
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# Update the exponentially weighted infinity norm.
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if not differentiable:
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torch.maximum(
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exp_inf.mul_(beta2),
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grad.abs().add_(eps),
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out=exp_inf,
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)
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else:
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norm_buf = torch.cat(
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[exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0)], 0
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)
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exp_inf.copy_(torch.amax(norm_buf, 0, keepdim=False))
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if capturable:
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# why jump through extra hoops and negate bias_correction? check out #121238
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# once fixed, we should use bias_correction with addcdiv value=-1 for readability
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neg_bias_correction = beta1 ** step_t - 1
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neg_bias_correction.div_(lr)
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denom = exp_inf * neg_bias_correction
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param.addcdiv_(exp_avg, denom)
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else:
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bias_correction = 1 - beta1 ** _get_value(step_t)
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clr = lr / bias_correction
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param.addcdiv_(exp_avg, exp_inf, value=-clr)
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def _multi_tensor_adamax(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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eps: 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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):
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assert not differentiable, "_foreach ops don't support autograd"
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if len(params) == 0:
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return
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# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
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if (not torch._utils.is_compiling() and capturable
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and not all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps))):
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raise RuntimeError("If capturable=True, params and state_steps must be CUDA tensors.")
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_infs, state_steps])
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for ((grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs, grouped_state_steps), _) in grouped_tensors.values():
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if has_complex:
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_view_as_real(grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs)
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if maximize:
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grouped_grads = torch._foreach_neg(grouped_grads)
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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 grouped_state_steps[0].is_cpu:
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torch._foreach_add_(grouped_state_steps, torch.tensor(1.0, device='cpu'), alpha=1.0)
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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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if maximize:
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# Re-use the intermediate memory (grouped_grads) already allocated for 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(grouped_grads, grouped_params, alpha=weight_decay)
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# Update biased first moment estimate.
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torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
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# Update the exponentially weighted infinity norm.
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torch._foreach_mul_(grouped_exp_infs, beta2)
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# in this case, we need to introduce a copy of the grads
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# since one has not been introduced previously
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if not maximize and weight_decay == 0:
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grouped_grads = torch._foreach_abs(grouped_grads)
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else:
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torch._foreach_abs_(grouped_grads)
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torch._foreach_add_(grouped_grads, eps)
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torch._foreach_maximum_(grouped_exp_infs, grouped_grads)
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if capturable:
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bias_corrections = torch._foreach_pow(beta1, grouped_state_steps)
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# foreach_sub doesn't allow a scalar as the first arg
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torch._foreach_sub_(bias_corrections, 1)
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torch._foreach_div_(bias_corrections, lr)
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denom = torch._foreach_mul(grouped_exp_infs, bias_corrections)
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torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom)
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else:
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bias_corrections = [1 - beta1 ** _get_value(step) for step in grouped_state_steps]
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step_size = [(lr / bc) * -1 for bc in bias_corrections]
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torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, grouped_exp_infs, step_size)
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