ai-content-maker/.venv/Lib/site-packages/torch/optim/nadam.py

478 lines
22 KiB
Python

import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt,
_stack_if_compiling, _get_scalar_dtype, _default_to_fused_or_foreach,
_view_as_real, _capturable_doc, _differentiable_doc, _foreach_doc,)
from typing import List, Optional
__all__ = ['NAdam', 'nadam']
class NAdam(Optimizer):
def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, momentum_decay=4e-3, decoupled_weight_decay: bool = False,
*, foreach: Optional[bool] = None, capturable: bool = False,
differentiable: 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}")
if not 0.0 <= momentum_decay:
raise ValueError(f"Invalid momentum_decay value: {momentum_decay}")
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, momentum_decay=momentum_decay,
decoupled_weight_decay=decoupled_weight_decay,
foreach=foreach, capturable=capturable, differentiable=differentiable)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault('foreach', None)
group.setdefault('capturable', False)
group.setdefault('differentiable', False)
group.setdefault('decoupled_weight_decay', False)
for p in group["params"]:
p_state = self.state.get(p, [])
if len(p_state) != 0:
if 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()))
if not torch.is_tensor(p_state['mu_product']):
mu_prod_val = p_state["mu_product"]
p_state["mu_product"] = (torch.tensor(mu_prod_val, dtype=_get_scalar_dtype(), device=p.device)
if group['capturable'] else torch.tensor(mu_prod_val, dtype=_get_scalar_dtype()))
def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps):
has_complex = False
for p in group['params']:
if p.grad is not None:
has_complex |= torch.is_complex(p)
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError('NAdam does not support sparse gradients')
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
# note(crcrpar): [special device hosting for step]
# Deliberately host `step` and `mu_product` on CPU if capturable is False.
# This is because kernel launches are costly on CUDA and XLA.
state['step'] = (
torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
if group['capturable'] else torch.tensor(0.0, dtype=_get_scalar_dtype())
)
state['mu_product'] = (
torch.ones((), dtype=_get_scalar_dtype(), device=p.device)
if group['capturable'] else torch.tensor(1.0, dtype=_get_scalar_dtype())
)
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
mu_products.append(state['mu_product'])
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_avg_sqs = []
mu_products = []
state_steps = []
beta1, beta2 = group['betas']
has_complex = self._init_group(group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps)
nadam(params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
mu_products,
state_steps,
beta1=beta1,
beta2=beta2,
lr=group['lr'],
weight_decay=group['weight_decay'],
momentum_decay=group['momentum_decay'],
eps=group['eps'],
decoupled_weight_decay=group['decoupled_weight_decay'],
foreach=group['foreach'],
capturable=group['capturable'],
differentiable=group['differentiable'],
has_complex=has_complex)
return loss
NAdam.__doc__ = r"""Implements NAdam algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)},
\: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\
&\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\
&\hspace{13mm} \: \textit{decoupled\_weight\_decay} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
v_0 \leftarrow 0 \text{ ( second moment)} \\[-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} \theta_t \leftarrow \theta_{t-1} \\
&\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\
&\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\
&\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
&\hspace{10mm}\textbf{else} \\
&\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{t \psi} \big) \\
&\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
&\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex]
& \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\
&\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
&\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
&\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 `Incorporating Nesterov Momentum into Adam`_.
""" + 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 (default: (0.9, 0.999))
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)
momentum_decay (float, optional): momentum momentum_decay (default: 4e-3)
decoupled_weight_decay (bool, optional): whether to use decoupled weight
decay as in AdamW to obtain NAdamW (default: False)
{_foreach_doc}
{_capturable_doc}
{_differentiable_doc}
.. _Incorporating Nesterov Momentum into Adam:
https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
"""
def nadam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
mu_products: 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
decoupled_weight_decay: bool = False,
foreach: Optional[bool] = None,
capturable: bool = False,
differentiable: bool = False,
has_complex: bool = False,
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
momentum_decay: float,
eps: float):
r"""Functional API that performs NAdam algorithm computation.
See :class:`~torch.optim.NAdam` 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 not all(isinstance(t, torch.Tensor) for t in mu_products):
raise RuntimeError("API has changed, `mu_products` 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_nadam
else:
func = _single_tensor_nadam
func(params,
grads,
exp_avgs,
exp_avg_sqs,
mu_products,
state_steps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
momentum_decay=momentum_decay,
decoupled_weight_decay=decoupled_weight_decay,
eps=eps,
capturable=capturable,
differentiable=differentiable,
has_complex=has_complex)
def _single_tensor_nadam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
mu_products: List[Tensor],
state_steps: List[Tensor],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
momentum_decay: float,
eps: float,
decoupled_weight_decay: bool,
capturable: bool,
differentiable: bool,
has_complex: bool):
for i, param in enumerate(params):
grad = grads[i]
exp_avg = exp_avgs[i]
exp_avg_sq = exp_avg_sqs[i]
mu_product = mu_products[i]
step_t = state_steps[i]
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_avg_sq = torch.view_as_real(exp_avg_sq)
# 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 mu_product.is_cuda and step_t.is_cuda) or (param.is_xla and mu_product.is_xla and step_t.is_xla)
), "If capturable=True, params, mu_products, and state_steps must be CUDA or XLA tensors."
# update step
step_t += 1
if capturable:
step = step_t
else:
step = _get_value(step_t)
bias_correction2 = 1 - beta2 ** step
if weight_decay != 0:
if decoupled_weight_decay:
# Perform stepweight decay
param.mul_(1 - lr * weight_decay)
else:
grad = grad.add(param, alpha=weight_decay)
# calculate the momentum cache \mu^{t} and \mu^{t+1}
mu = beta1 * (1. - 0.5 * (0.96 ** (step * momentum_decay)))
mu_next = beta1 * (1. - 0.5 * (0.96 ** ((step + 1) * momentum_decay)))
# update mu_product
mu_product *= mu
# decay the first and second moment running average coefficient
exp_avg.lerp_(grad, 1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
denom = exp_avg_sq.div(bias_correction2).sqrt()
if differentiable or capturable:
denom = denom.add(eps)
# Make autograd track the operations
# by updating the grad and exp_avg directly and not using the
# scalar "value" argument of addcdiv.
mu_product_next = mu_product * mu_next
grad = grad * (-lr * (1. - mu) / (1. - mu_product))
exp_avg = exp_avg * (-lr * mu_next / (1. - mu_product_next))
param.addcdiv_(grad, denom)
param.addcdiv_(exp_avg, denom)
else:
mu_product_next = _get_value(mu_product) * mu_next
denom.add_(eps)
param.addcdiv_(grad, denom, value=(-lr * (1. - mu) / (1. - _get_value(mu_product))))
param.addcdiv_(exp_avg, denom, value=(-lr * mu_next) / (1. - mu_product_next))
def _multi_tensor_nadam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
mu_products: List[Tensor],
state_steps: List[Tensor],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
momentum_decay: float,
eps: float,
decoupled_weight_decay: bool,
capturable: bool,
differentiable: bool,
has_complex: bool):
if len(params) == 0:
return
assert not differentiable, "_foreach ops don't support autograd"
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
assert all(p.is_cuda and mp.is_cuda and step.is_cuda
for p, mp, step in zip(params, mu_products, state_steps)), \
"If capturable=True, params, mu_products, and state_steps must be CUDA tensors."
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps])
for ((grouped_params, grouped_grads, grouped_exp_avgs,
grouped_exp_avg_sqs, grouped_mu_products, grouped_state_steps), _) in grouped_tensors.values():
# handle complex
if has_complex:
_view_as_real(grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs)
# 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 decoupled_weight_decay:
# Perform stepweight decay
torch._foreach_mul_(grouped_params, 1 - lr * weight_decay)
else:
grouped_grads = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
torch._foreach_mul_(grouped_exp_avg_sqs, beta2)
torch._foreach_addcmul_(grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2)
exp_avg_sq_sqrt = torch._foreach_sqrt(grouped_exp_avg_sqs)
if capturable:
# mus will be beta1 * (1 - 0.5 * 0.96 ** (step * momentum_decay))
exponent = torch._foreach_mul(grouped_state_steps, momentum_decay)
mus = torch._foreach_pow(0.96, exponent)
torch._foreach_mul_(mus, -0.5)
torch._foreach_add_(mus, 1.0)
torch._foreach_mul_(mus, beta1)
# mu_nexts will be beta1 * (1 - 0.5 * 0.96 ** ((step + 1) * momentum_decay))
torch._foreach_add_(exponent, momentum_decay)
mu_nexts = torch._foreach_pow(0.96, exponent)
torch._foreach_mul_(mu_nexts, -0.5)
torch._foreach_add_(mu_nexts, 1.0)
torch._foreach_mul_(mu_nexts, beta1)
# save peak memory as we don't need exponent anymore
del exponent
bias_correction_sqrt = torch._foreach_pow(beta2, grouped_state_steps)
# foreach_sub doesn't allow a scalar as the first arg
torch._foreach_sub_(bias_correction_sqrt, 1.0)
torch._foreach_neg_(bias_correction_sqrt)
torch._foreach_sqrt_(bias_correction_sqrt)
else:
bias_correction_sqrt = [_dispatch_sqrt(1 - beta2 ** _get_value(step)) for step in grouped_state_steps]
mus = [beta1 * (1. - 0.5 * (0.96 ** (_get_value(step) * momentum_decay))) for step in grouped_state_steps]
mu_nexts = [beta1 * (1. - 0.5 * (0.96 ** ((_get_value(step) + 1) * momentum_decay)))
for step in grouped_state_steps]
# update mu_products
torch._foreach_mul_(grouped_mu_products, mus)
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
torch._foreach_add_(exp_avg_sq_sqrt, eps)
# explicitly delete bias_correction refs to save memory
del bias_correction_sqrt
if capturable:
# Build up the step_size multiplier for grad, reusing mus' memory
torch._foreach_sub_(mus, 1.0)
torch._foreach_mul_(mus, lr)
# foreach_sub doesn't allow a scalar as the first arg
denom = torch._foreach_sub(grouped_mu_products, 1.0)
torch._foreach_neg_(denom)
torch._foreach_div_(mus, denom)
# - lr * (1 - mu) / (1 - mu_product)
step_size_grads = mus
# explicitly delete denom to save memory
del denom
# Build up the step_size multiplier for exp_avg, reusing mu_nexts' memory
denom = torch._foreach_mul(grouped_mu_products, mu_nexts)
torch._foreach_mul_(mu_nexts, lr)
# foreach_sub doesn't allow a scalar as the first arg, but it's okay because
# we need a negative here anyway
torch._foreach_sub_(denom, 1.0)
torch._foreach_div_(mu_nexts, denom)
# - lr * mu_next / (1 - mu_product * mu_next)
step_size_expavg = mu_nexts
# explicitly delete denom to save memory
del denom
# we cannot inplace into step_size_grads cuz it is a list of ScalarTensors
# and mul'ing with grouped_grads will result in a list of bigger Tensors
numerator = torch._foreach_mul(step_size_grads, grouped_grads)
torch._foreach_addcmul_(numerator, step_size_expavg, grouped_exp_avgs)
# finally, update params
torch._foreach_addcdiv_(grouped_params, numerator, exp_avg_sq_sqrt)
else:
step_size_grads = _stack_if_compiling([(lr * (1. - mu) / (1. - _get_value(mu_product))) * -1
for mu_product, mu in zip(grouped_mu_products, mus)])
step_size_expavg = _stack_if_compiling([(lr * mu_next / (1. - _get_value(mu_product) * mu_next)) * -1
for mu_product, mu_next in zip(grouped_mu_products, mu_nexts)])
torch._foreach_addcdiv_(grouped_params, grouped_grads, exp_avg_sq_sqrt, step_size_grads)
torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, exp_avg_sq_sqrt, step_size_expavg)