350 lines
12 KiB
Python
350 lines
12 KiB
Python
""" NAdamW Optimizer
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Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
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Added multi-tensor (foreach) path.
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"""
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import math
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from typing import List, Optional
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import torch
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from torch import Tensor
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# Modified from github.com/pytorch/pytorch/blob/v1.12.1/torch/optim/adamw.py.
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class NAdamW(torch.optim.Optimizer):
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r"""Implements NAdamW algorithm.
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See Table 1 in https://arxiv.org/abs/1910.05446 for the implementation of
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the NAdam algorithm (there is also a comment in the code which highlights
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the only difference of NAdamW and AdamW).
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For further details regarding the algorithm we refer to
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`Decoupled Weight Decay Regularization`_.
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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: 1e-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 coefficient (default: 1e-2)
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=1e-2,
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maximize: bool = False,
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foreach: Optional[bool] = None,
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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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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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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]['step'])
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if not step_is_tensor:
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for s in state_values:
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s['step'] = torch.tensor(float(s['step']))
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@torch.no_grad()
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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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state_steps = []
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beta1, beta2 = group['betas']
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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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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('NAdamW 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.tensor(0.)
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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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state_steps.append(state['step'])
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nadamw(
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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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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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eps=group['eps'],
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maximize=group['maximize'],
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capturable=group['capturable'],
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)
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return loss
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def nadamw(
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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_avg_sqs: List[Tensor],
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state_steps: List[Tensor],
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foreach: Optional[bool] = None,
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capturable: 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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eps: float,
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maximize: bool,
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) -> None:
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r"""Functional API that performs NAdamW algorithm computation.
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See NAdamW class 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' +
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' singleton tensors')
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if foreach is None:
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foreach = True
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_nadamw
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else:
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func = _single_tensor_nadamw
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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_avg_sqs,
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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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eps=eps,
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maximize=maximize,
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capturable=capturable,
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)
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def _single_tensor_nadamw(
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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_avg_sqs: 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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capturable: bool
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):
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for i, param in enumerate(params):
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grad = grads[i] if not maximize else -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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step_t = state_steps[i]
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# Update step.
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step_t += 1
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# Perform stepweight decay.
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param.mul_(1. - lr * weight_decay)
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# Decay the first and second moment running average coefficient.
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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if capturable:
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step = step_t
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# 1 - beta1 ** step can't be captured in a CUDA graph, even if step is a CUDA tensor
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# (incurs "RuntimeError: CUDA error: operation not permitted when stream is capturing")
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bias_correction1 = 1 - torch.pow(beta1, step)
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bias_correction2 = 1 - torch.pow(beta2, step)
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step_size = lr / bias_correction1
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step_size_neg = step_size.neg()
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bias_correction2_sqrt = bias_correction2.sqrt()
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# Only difference between NAdamW and AdamW in this implementation.
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# The official PyTorch implementation of NAdam uses a different algorithm.
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exp_avg = exp_avg.mul(beta1).add_(grad, alpha=1 - beta1)
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denom = (exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg)
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param.addcdiv_(exp_avg, denom)
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else:
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step = step_t.item()
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bias_correction1 = 1 - beta1 ** step
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bias_correction2 = 1 - beta2 ** step
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step_size = lr / bias_correction1
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bias_correction2_sqrt = math.sqrt(bias_correction2)
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# Only difference between NAdamW and AdamW in this implementation.
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# The official PyTorch implementation of NAdam uses a different algorithm.
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exp_avg = exp_avg.mul(beta1).add_(grad, alpha=1 - beta1)
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denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
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param.addcdiv_(exp_avg, denom, value=-step_size)
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def _multi_tensor_nadamw(
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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_avg_sqs: 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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capturable: bool,
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):
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if len(params) == 0:
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return
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if capturable:
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assert all(
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p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)
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), "If capturable=True, params and state_steps must be CUDA tensors."
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if maximize:
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grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment]
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grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in grads]
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exp_avgs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs]
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exp_avg_sqs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avg_sqs]
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params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in params]
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# update steps
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torch._foreach_add_(state_steps, 1)
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# Perform stepweight decay
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torch._foreach_mul_(params, 1 - lr * weight_decay)
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# Decay the first and second moment running average coefficient
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torch._foreach_mul_(exp_avgs, beta1)
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torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
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torch._foreach_mul_(exp_avg_sqs, beta2)
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torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2)
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if capturable:
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# TODO: use foreach_pow if/when foreach_pow is added
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bias_correction1 = [torch.pow(beta1, step) for step in state_steps]
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bias_correction2 = [torch.pow(beta2, step) for step in 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_correction1, 1)
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torch._foreach_sub_(bias_correction2, 1)
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torch._foreach_neg_(bias_correction1)
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torch._foreach_neg_(bias_correction2)
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# foreach_div doesn't allow a scalar as the first arg
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step_size = torch._foreach_div(bias_correction1, lr)
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torch._foreach_reciprocal_(step_size)
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torch._foreach_neg_(step_size)
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bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2)
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# Only difference between NAdamW and AdamW in this implementation.
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# The official PyTorch implementation of NAdam uses a different algorithm.
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exp_avgs = torch._foreach_mul(exp_avgs, beta1)
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torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
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exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
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torch._foreach_div_(
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exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)
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)
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eps_over_step_size = torch._foreach_div(step_size, eps)
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torch._foreach_reciprocal_(eps_over_step_size)
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denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size)
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torch._foreach_addcdiv_(params, exp_avgs, denom)
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else:
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bias_correction1 = [1 - beta1 ** step.item() for step in state_steps]
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bias_correction2 = [1 - beta2 ** step.item() for step in state_steps]
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step_size = [(lr / bc) * -1 for bc in bias_correction1]
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bias_correction2_sqrt = [math.sqrt(bc) for bc in bias_correction2]
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# Only difference between NAdamW and AdamW in this implementation.
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# The official PyTorch implementation of NAdam uses a different algorithm.
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exp_avgs = torch._foreach_mul(exp_avgs, beta1)
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torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
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exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
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torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
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denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
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torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
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