404 lines
15 KiB
Python
404 lines
15 KiB
Python
|
import torch
|
||
|
|
from torch import Tensor
|
||
|
|
|
||
|
|
from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _default_to_fused_or_foreach,
|
||
|
|
_get_scalar_dtype, _differentiable_doc, _maximize_doc, _foreach_doc, _view_as_real,
|
||
|
|
_capturable_doc)
|
||
|
|
from typing import List, Optional
|
||
|
|
|
||
|
|
__all__ = ["Adamax", "adamax"]
|
||
|
|
|
||
|
|
|
||
|
|
class Adamax(Optimizer):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
params,
|
||
|
|
lr=2e-3,
|
||
|
|
betas=(0.9, 0.999),
|
||
|
|
eps=1e-8,
|
||
|
|
weight_decay=0,
|
||
|
|
foreach: Optional[bool] = None,
|
||
|
|
*,
|
||
|
|
maximize: bool = False,
|
||
|
|
differentiable: bool = False,
|
||
|
|
capturable: bool = False,
|
||
|
|
):
|
||
|
|
if not 0.0 <= lr:
|
||
|
|
raise ValueError(f"Invalid learning rate: {lr}")
|
||
|
|
if not 0.0 <= eps:
|
||
|
|
raise ValueError(f"Invalid epsilon value: {eps}")
|
||
|
|
if not 0.0 <= betas[0] < 1.0:
|
||
|
|
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
|
||
|
|
if not 0.0 <= betas[1] < 1.0:
|
||
|
|
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
|
||
|
|
if not 0.0 <= weight_decay:
|
||
|
|
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
|
||
|
|
|
||
|
|
defaults = dict(
|
||
|
|
lr=lr,
|
||
|
|
betas=betas,
|
||
|
|
eps=eps,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
foreach=foreach,
|
||
|
|
maximize=maximize,
|
||
|
|
differentiable=differentiable,
|
||
|
|
capturable=capturable,
|
||
|
|
)
|
||
|
|
super().__init__(params, defaults)
|
||
|
|
|
||
|
|
def __setstate__(self, state):
|
||
|
|
super().__setstate__(state)
|
||
|
|
for group in self.param_groups:
|
||
|
|
group.setdefault("foreach", None)
|
||
|
|
group.setdefault("maximize", False)
|
||
|
|
group.setdefault("differentiable", False)
|
||
|
|
group.setdefault("capturable", False)
|
||
|
|
for p in group["params"]:
|
||
|
|
p_state = self.state.get(p, [])
|
||
|
|
if len(p_state) != 0 and not torch.is_tensor(p_state['step']):
|
||
|
|
step_val = float(p_state["step"])
|
||
|
|
p_state["step"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(), device=p.device) if group['capturable']
|
||
|
|
else torch.tensor(step_val, dtype=_get_scalar_dtype()))
|
||
|
|
|
||
|
|
def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_infs, state_steps):
|
||
|
|
has_complex = False
|
||
|
|
for p in group["params"]:
|
||
|
|
if p.grad is None:
|
||
|
|
continue
|
||
|
|
has_complex |= torch.is_complex(p)
|
||
|
|
params_with_grad.append(p)
|
||
|
|
if p.grad.is_sparse:
|
||
|
|
raise RuntimeError("Adamax does not support sparse gradients")
|
||
|
|
grads.append(p.grad)
|
||
|
|
|
||
|
|
state = self.state[p]
|
||
|
|
|
||
|
|
# State initialization
|
||
|
|
if len(state) == 0:
|
||
|
|
state['step'] = (torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
|
||
|
|
if group['capturable'] else torch.tensor(0.0, dtype=_get_scalar_dtype()))
|
||
|
|
state["exp_avg"] = torch.zeros_like(
|
||
|
|
p, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
state["exp_inf"] = torch.zeros_like(
|
||
|
|
p, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
|
||
|
|
exp_avgs.append(state["exp_avg"])
|
||
|
|
exp_infs.append(state["exp_inf"])
|
||
|
|
state_steps.append(state["step"])
|
||
|
|
|
||
|
|
return has_complex
|
||
|
|
|
||
|
|
@_use_grad_for_differentiable
|
||
|
|
def step(self, closure=None):
|
||
|
|
"""Performs a single optimization step.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
closure (Callable, optional): A closure that reevaluates the model
|
||
|
|
and returns the loss.
|
||
|
|
"""
|
||
|
|
self._cuda_graph_capture_health_check()
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if closure is not None:
|
||
|
|
with torch.enable_grad():
|
||
|
|
loss = closure()
|
||
|
|
|
||
|
|
for group in self.param_groups:
|
||
|
|
params_with_grad = []
|
||
|
|
grads = []
|
||
|
|
exp_avgs = []
|
||
|
|
exp_infs = []
|
||
|
|
state_steps = []
|
||
|
|
|
||
|
|
beta1, beta2 = group["betas"]
|
||
|
|
eps = group["eps"]
|
||
|
|
lr = group["lr"]
|
||
|
|
weight_decay = group["weight_decay"]
|
||
|
|
foreach = group["foreach"]
|
||
|
|
maximize = group["maximize"]
|
||
|
|
differentiable = group["differentiable"]
|
||
|
|
capturable = group["capturable"]
|
||
|
|
|
||
|
|
has_complex = self._init_group(group, params_with_grad, grads, exp_avgs, exp_infs, state_steps)
|
||
|
|
|
||
|
|
adamax(
|
||
|
|
params_with_grad,
|
||
|
|
grads,
|
||
|
|
exp_avgs,
|
||
|
|
exp_infs,
|
||
|
|
state_steps,
|
||
|
|
eps=eps,
|
||
|
|
beta1=beta1,
|
||
|
|
beta2=beta2,
|
||
|
|
lr=lr,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
foreach=foreach,
|
||
|
|
maximize=maximize,
|
||
|
|
differentiable=differentiable,
|
||
|
|
capturable=capturable,
|
||
|
|
has_complex=has_complex,
|
||
|
|
)
|
||
|
|
|
||
|
|
return loss
|
||
|
|
|
||
|
|
|
||
|
|
Adamax.__doc__ = r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
|
||
|
|
|
||
|
|
.. math::
|
||
|
|
\begin{aligned}
|
||
|
|
&\rule{110mm}{0.4pt} \\
|
||
|
|
&\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2
|
||
|
|
\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
|
||
|
|
\: \lambda \text{ (weight decay)}, \\
|
||
|
|
&\hspace{13mm} \epsilon \text{ (epsilon)} \\
|
||
|
|
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
|
||
|
|
u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex]
|
||
|
|
&\rule{110mm}{0.4pt} \\
|
||
|
|
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
|
||
|
|
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
|
||
|
|
&\hspace{5mm}if \: \lambda \neq 0 \\
|
||
|
|
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
|
||
|
|
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
|
||
|
|
&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
|
||
|
|
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
|
||
|
|
&\rule{110mm}{0.4pt} \\[-1.ex]
|
||
|
|
&\bf{return} \: \theta_t \\[-1.ex]
|
||
|
|
&\rule{110mm}{0.4pt} \\[-1.ex]
|
||
|
|
\end{aligned}
|
||
|
|
|
||
|
|
For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
|
||
|
|
""" + fr"""
|
||
|
|
Args:
|
||
|
|
params (iterable): iterable of parameters to optimize or dicts defining
|
||
|
|
parameter groups
|
||
|
|
lr (float, optional): learning rate (default: 2e-3)
|
||
|
|
betas (Tuple[float, float], optional): coefficients used for computing
|
||
|
|
running averages of gradient and its square
|
||
|
|
eps (float, optional): term added to the denominator to improve
|
||
|
|
numerical stability (default: 1e-8)
|
||
|
|
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
||
|
|
{_foreach_doc}
|
||
|
|
{_maximize_doc}
|
||
|
|
{_differentiable_doc}
|
||
|
|
{_capturable_doc}
|
||
|
|
|
||
|
|
.. _Adam\: A Method for Stochastic Optimization:
|
||
|
|
https://arxiv.org/abs/1412.6980
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
def adamax(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_infs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
|
||
|
|
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
|
||
|
|
foreach: Optional[bool] = None,
|
||
|
|
maximize: bool = False,
|
||
|
|
differentiable: bool = False,
|
||
|
|
capturable: bool = False,
|
||
|
|
has_complex: bool = False,
|
||
|
|
*,
|
||
|
|
eps: float,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: float,
|
||
|
|
weight_decay: float,
|
||
|
|
):
|
||
|
|
r"""Functional API that performs adamax algorithm computation.
|
||
|
|
|
||
|
|
See :class:`~torch.optim.Adamax` for details.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if not all(isinstance(t, torch.Tensor) for t in state_steps):
|
||
|
|
raise RuntimeError(
|
||
|
|
"API has changed, `state_steps` argument must contain a list of singleton tensors"
|
||
|
|
)
|
||
|
|
|
||
|
|
if foreach is None:
|
||
|
|
_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False)
|
||
|
|
|
||
|
|
if foreach and torch.jit.is_scripting():
|
||
|
|
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
|
||
|
|
|
||
|
|
if foreach and not torch.jit.is_scripting():
|
||
|
|
func = _multi_tensor_adamax
|
||
|
|
else:
|
||
|
|
func = _single_tensor_adamax
|
||
|
|
|
||
|
|
func(
|
||
|
|
params,
|
||
|
|
grads,
|
||
|
|
exp_avgs,
|
||
|
|
exp_infs,
|
||
|
|
state_steps,
|
||
|
|
eps=eps,
|
||
|
|
beta1=beta1,
|
||
|
|
beta2=beta2,
|
||
|
|
lr=lr,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
maximize=maximize,
|
||
|
|
differentiable=differentiable,
|
||
|
|
has_complex=has_complex,
|
||
|
|
capturable=capturable,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _single_tensor_adamax(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_infs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
*,
|
||
|
|
eps: float,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: float,
|
||
|
|
weight_decay: float,
|
||
|
|
maximize: bool,
|
||
|
|
differentiable: bool,
|
||
|
|
capturable: bool,
|
||
|
|
has_complex: bool,
|
||
|
|
):
|
||
|
|
for i, param in enumerate(params):
|
||
|
|
grad = grads[i]
|
||
|
|
grad = grad if not maximize else -grad
|
||
|
|
exp_avg = exp_avgs[i]
|
||
|
|
exp_inf = exp_infs[i]
|
||
|
|
step_t = state_steps[i]
|
||
|
|
|
||
|
|
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
||
|
|
if not torch._utils.is_compiling() and capturable:
|
||
|
|
assert (param.is_cuda and step_t.is_cuda) or (
|
||
|
|
param.is_xla and step_t.is_xla
|
||
|
|
), "If capturable=True, params and state_steps must be CUDA or XLA tensors."
|
||
|
|
|
||
|
|
# update step
|
||
|
|
step_t += 1
|
||
|
|
|
||
|
|
if weight_decay != 0:
|
||
|
|
grad = grad.add(param, alpha=weight_decay)
|
||
|
|
|
||
|
|
if torch.is_complex(param):
|
||
|
|
param = torch.view_as_real(param)
|
||
|
|
grad = torch.view_as_real(grad)
|
||
|
|
exp_avg = torch.view_as_real(exp_avg)
|
||
|
|
exp_inf = torch.view_as_real(exp_inf)
|
||
|
|
|
||
|
|
# Update biased first moment estimate.
|
||
|
|
exp_avg.lerp_(grad, 1 - beta1)
|
||
|
|
# Update the exponentially weighted infinity norm.
|
||
|
|
if not differentiable:
|
||
|
|
torch.maximum(
|
||
|
|
exp_inf.mul_(beta2),
|
||
|
|
grad.abs().add_(eps),
|
||
|
|
out=exp_inf,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
norm_buf = torch.cat(
|
||
|
|
[exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0)], 0
|
||
|
|
)
|
||
|
|
exp_inf.copy_(torch.amax(norm_buf, 0, keepdim=False))
|
||
|
|
|
||
|
|
if capturable:
|
||
|
|
# why jump through extra hoops and negate bias_correction? check out #121238
|
||
|
|
# once fixed, we should use bias_correction with addcdiv value=-1 for readability
|
||
|
|
neg_bias_correction = beta1 ** step_t - 1
|
||
|
|
neg_bias_correction.div_(lr)
|
||
|
|
denom = exp_inf * neg_bias_correction
|
||
|
|
param.addcdiv_(exp_avg, denom)
|
||
|
|
else:
|
||
|
|
bias_correction = 1 - beta1 ** _get_value(step_t)
|
||
|
|
clr = lr / bias_correction
|
||
|
|
|
||
|
|
param.addcdiv_(exp_avg, exp_inf, value=-clr)
|
||
|
|
|
||
|
|
|
||
|
|
def _multi_tensor_adamax(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_infs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
*,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: float,
|
||
|
|
weight_decay: float,
|
||
|
|
eps: float,
|
||
|
|
maximize: bool,
|
||
|
|
differentiable: bool,
|
||
|
|
capturable: bool,
|
||
|
|
has_complex: bool,
|
||
|
|
):
|
||
|
|
|
||
|
|
assert not differentiable, "_foreach ops don't support autograd"
|
||
|
|
|
||
|
|
if len(params) == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
||
|
|
if (not torch._utils.is_compiling() and capturable
|
||
|
|
and not all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps))):
|
||
|
|
raise RuntimeError("If capturable=True, params and state_steps must be CUDA tensors.")
|
||
|
|
|
||
|
|
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_infs, state_steps])
|
||
|
|
for ((grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs, grouped_state_steps), _) in grouped_tensors.values():
|
||
|
|
if has_complex:
|
||
|
|
_view_as_real(grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs)
|
||
|
|
|
||
|
|
if maximize:
|
||
|
|
grouped_grads = torch._foreach_neg(grouped_grads)
|
||
|
|
|
||
|
|
# 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 grouped_state_steps[0].is_cpu:
|
||
|
|
torch._foreach_add_(grouped_state_steps, torch.tensor(1.0, device='cpu'), alpha=1.0)
|
||
|
|
else:
|
||
|
|
torch._foreach_add_(grouped_state_steps, 1)
|
||
|
|
|
||
|
|
if weight_decay != 0:
|
||
|
|
if maximize:
|
||
|
|
# Re-use the intermediate memory (grouped_grads) already allocated for maximize
|
||
|
|
torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
|
||
|
|
else:
|
||
|
|
grouped_grads = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay)
|
||
|
|
|
||
|
|
|
||
|
|
# Update biased first moment estimate.
|
||
|
|
torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
|
||
|
|
|
||
|
|
# Update the exponentially weighted infinity norm.
|
||
|
|
torch._foreach_mul_(grouped_exp_infs, beta2)
|
||
|
|
|
||
|
|
# in this case, we need to introduce a copy of the grads
|
||
|
|
# since one has not been introduced previously
|
||
|
|
if not maximize and weight_decay == 0:
|
||
|
|
grouped_grads = torch._foreach_abs(grouped_grads)
|
||
|
|
else:
|
||
|
|
torch._foreach_abs_(grouped_grads)
|
||
|
|
|
||
|
|
torch._foreach_add_(grouped_grads, eps)
|
||
|
|
torch._foreach_maximum_(grouped_exp_infs, grouped_grads)
|
||
|
|
|
||
|
|
if capturable:
|
||
|
|
bias_corrections = torch._foreach_pow(beta1, grouped_state_steps)
|
||
|
|
# foreach_sub doesn't allow a scalar as the first arg
|
||
|
|
torch._foreach_sub_(bias_corrections, 1)
|
||
|
|
torch._foreach_div_(bias_corrections, lr)
|
||
|
|
|
||
|
|
denom = torch._foreach_mul(grouped_exp_infs, bias_corrections)
|
||
|
|
torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom)
|
||
|
|
else:
|
||
|
|
bias_corrections = [1 - beta1 ** _get_value(step) for step in grouped_state_steps]
|
||
|
|
step_size = [(lr / bc) * -1 for bc in bias_corrections]
|
||
|
|
torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, grouped_exp_infs, step_size)
|