mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
e6779d4357
Summary:
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue when I was parsing/generating from the TensorFlow codebase: inconsistent use of `Args:` and `Arguments:` in its docstrings.
```sh
(pytorch#c348fae)$ for name in 'Args:' 'Arguments:'; do
printf '%-10s %04d\n' "$name" "$(rg -IFtpy --count-matches "$name" | paste -s -d+ -- | bc)"; done
Args: 1095
Arguments: 0336
```
It is easy enough to extend my parsers to support both variants, however it looks like `Arguments:` is wrong anyway, as per:
- https://google.github.io/styleguide/pyguide.html#doc-function-args @ [`ddccc0f`](https://github.com/google/styleguide/blob/ddccc0f/pyguide.md)
- https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ [`9fc0fc0`](https://chromium.googlesource.com/chromiumos/docs/+/9fc0fc0/styleguide/python.md)
- https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ [`c0ae8e3`](https://github.com/sphinx-contrib/napoleon/blob/c0ae8e3/docs/source/example_google.rst)
Therefore, only `Args:` is valid. This PR replaces them throughout the codebase.
PS: For related PRs, see tensorflow/tensorflow/pull/45420
PPS: The trackbacks automatically appearing below are sending the same changes to other repositories in the [PyTorch](https://github.com/pytorch) organisation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49736
Reviewed By: albanD
Differential Revision: D25710534
Pulled By: soumith
fbshipit-source-id: 61e8ff01abb433e9f78185c2d1d0cbd7c22c1619
91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
import torch
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from .optimizer import Optimizer
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class Adamax(Optimizer):
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"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
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It has been proposed in `Adam: A Method for Stochastic Optimization`__.
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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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__ https://arxiv.org/abs/1412.6980
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"""
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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):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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super(Adamax, self).__init__(params, defaults)
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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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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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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError('Adamax does not support sparse gradients')
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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'] = 0
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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exp_avg, exp_inf = state['exp_avg'], state['exp_inf']
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beta1, beta2 = group['betas']
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eps = group['eps']
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state['step'] += 1
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if group['weight_decay'] != 0:
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grad = grad.add(p, alpha=group['weight_decay'])
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# Update biased first moment estimate.
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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# Update the exponentially weighted infinity norm.
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norm_buf = torch.cat([
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exp_inf.mul_(beta2).unsqueeze(0),
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grad.abs().add_(eps).unsqueeze_(0)
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], 0)
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torch.amax(norm_buf, 0, keepdim=False, out=exp_inf)
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bias_correction = 1 - beta1 ** state['step']
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clr = group['lr'] / bias_correction
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p.addcdiv_(exp_avg, exp_inf, value=-clr)
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return loss
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