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478 lines
22 KiB
478 lines
22 KiB
5 months ago
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import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt,
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_stack_if_compiling, _get_scalar_dtype, _default_to_fused_or_foreach,
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_view_as_real, _capturable_doc, _differentiable_doc, _foreach_doc,)
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from typing import List, Optional
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__all__ = ['NAdam', 'nadam']
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class NAdam(Optimizer):
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def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0, momentum_decay=4e-3, decoupled_weight_decay: bool = False,
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*, foreach: Optional[bool] = None, capturable: bool = False,
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differentiable: bool = False):
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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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if not 0.0 <= momentum_decay:
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raise ValueError(f"Invalid momentum_decay value: {momentum_decay}")
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, momentum_decay=momentum_decay,
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decoupled_weight_decay=decoupled_weight_decay,
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foreach=foreach, capturable=capturable, differentiable=differentiable)
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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('capturable', False)
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group.setdefault('differentiable', False)
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group.setdefault('decoupled_weight_decay', False)
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for p in group["params"]:
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p_state = self.state.get(p, [])
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if len(p_state) != 0:
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if not torch.is_tensor(p_state['step']):
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step_val = float(p_state["step"])
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p_state["step"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(), device=p.device)
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if group['capturable'] else torch.tensor(step_val, dtype=_get_scalar_dtype()))
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if not torch.is_tensor(p_state['mu_product']):
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mu_prod_val = p_state["mu_product"]
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p_state["mu_product"] = (torch.tensor(mu_prod_val, dtype=_get_scalar_dtype(), device=p.device)
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if group['capturable'] else torch.tensor(mu_prod_val, dtype=_get_scalar_dtype()))
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def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps):
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has_complex = False
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for p in group['params']:
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if p.grad is not None:
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('NAdam 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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# Lazy state initialization
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if len(state) == 0:
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# note(crcrpar): [special device hosting for step]
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# Deliberately host `step` and `mu_product` on CPU if capturable is False.
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# This is because kernel launches are costly on CUDA and XLA.
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state['step'] = (
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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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)
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state['mu_product'] = (
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torch.ones((), dtype=_get_scalar_dtype(), device=p.device)
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if group['capturable'] else torch.tensor(1.0, dtype=_get_scalar_dtype())
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)
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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exp_avgs.append(state['exp_avg'])
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exp_avg_sqs.append(state['exp_avg_sq'])
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mu_products.append(state['mu_product'])
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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_avg_sqs = []
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mu_products = []
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state_steps = []
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beta1, beta2 = group['betas']
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has_complex = self._init_group(group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps)
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nadam(params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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mu_products,
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state_steps,
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beta1=beta1,
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beta2=beta2,
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lr=group['lr'],
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weight_decay=group['weight_decay'],
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momentum_decay=group['momentum_decay'],
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eps=group['eps'],
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decoupled_weight_decay=group['decoupled_weight_decay'],
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foreach=group['foreach'],
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capturable=group['capturable'],
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differentiable=group['differentiable'],
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has_complex=has_complex)
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return loss
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NAdam.__doc__ = r"""Implements NAdam 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_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)},
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\: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\
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&\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\
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&\hspace{13mm} \: \textit{decoupled\_weight\_decay} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
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v_0 \leftarrow 0 \text{ ( second moment)} \\[-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} \theta_t \leftarrow \theta_{t-1} \\
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&\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\
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&\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\
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&\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
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&\hspace{10mm}\textbf{else} \\
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&\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{t \psi} \big) \\
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&\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\
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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}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
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&\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex]
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& \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\
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&\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
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&\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
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\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
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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 `Incorporating Nesterov Momentum into Adam`_.
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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 (default: (0.9, 0.999))
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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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momentum_decay (float, optional): momentum momentum_decay (default: 4e-3)
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decoupled_weight_decay (bool, optional): whether to use decoupled weight
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decay as in AdamW to obtain NAdamW (default: False)
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{_foreach_doc}
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{_capturable_doc}
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{_differentiable_doc}
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.. _Incorporating Nesterov Momentum into Adam:
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https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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"""
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def nadam(params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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mu_products: 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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decoupled_weight_decay: bool = False,
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foreach: Optional[bool] = None,
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capturable: bool = False,
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differentiable: bool = False,
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has_complex: bool = False,
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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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momentum_decay: float,
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eps: float):
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r"""Functional API that performs NAdam algorithm computation.
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See :class:`~torch.optim.NAdam` 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("API has changed, `state_steps` argument must contain a list of singleton tensors")
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if not all(isinstance(t, torch.Tensor) for t in mu_products):
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raise RuntimeError("API has changed, `mu_products` argument must contain a list of singleton tensors")
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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_nadam
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else:
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func = _single_tensor_nadam
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func(params,
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grads,
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exp_avgs,
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exp_avg_sqs,
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mu_products,
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state_steps,
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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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momentum_decay=momentum_decay,
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decoupled_weight_decay=decoupled_weight_decay,
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eps=eps,
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capturable=capturable,
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differentiable=differentiable,
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has_complex=has_complex)
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def _single_tensor_nadam(params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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mu_products: 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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momentum_decay: float,
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eps: float,
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decoupled_weight_decay: bool,
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capturable: bool,
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differentiable: bool,
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has_complex: bool):
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for i, param in enumerate(params):
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grad = grads[i]
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exp_avg = exp_avgs[i]
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exp_avg_sq = exp_avg_sqs[i]
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mu_product = mu_products[i]
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step_t = state_steps[i]
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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_avg_sq = torch.view_as_real(exp_avg_sq)
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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 (
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(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)
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), "If capturable=True, params, mu_products, 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 capturable:
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step = step_t
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else:
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step = _get_value(step_t)
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bias_correction2 = 1 - beta2 ** step
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if weight_decay != 0:
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if decoupled_weight_decay:
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# Perform stepweight decay
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param.mul_(1 - lr * weight_decay)
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else:
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grad = grad.add(param, alpha=weight_decay)
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# calculate the momentum cache \mu^{t} and \mu^{t+1}
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mu = beta1 * (1. - 0.5 * (0.96 ** (step * momentum_decay)))
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mu_next = beta1 * (1. - 0.5 * (0.96 ** ((step + 1) * momentum_decay)))
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# update mu_product
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mu_product *= mu
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# decay the first and second moment running average coefficient
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exp_avg.lerp_(grad, 1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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denom = exp_avg_sq.div(bias_correction2).sqrt()
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if differentiable or capturable:
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denom = denom.add(eps)
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# Make autograd track the operations
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# by updating the grad and exp_avg directly and not using the
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# scalar "value" argument of addcdiv.
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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)
|