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- """ Adaptive Gradient Clipping
- An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
- @article{brock2021high,
- author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
- title={High-Performance Large-Scale Image Recognition Without Normalization},
- journal={arXiv preprint arXiv:},
- year={2021}
- }
- Code references:
- * Official JAX impl (paper authors): https://github.com/deepmind/deepmind-research/tree/master/nfnets
- * Phil Wang's PyTorch gist: https://gist.github.com/lucidrains/0d6560077edac419ab5d3aa29e674d5c
- Hacked together by / Copyright 2021 Ross Wightman
- """
- import torch
- def unitwise_norm(x, norm_type=2.0):
- if x.ndim <= 1:
- return x.norm(norm_type)
- else:
- # works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
- # might need special cases for other weights (possibly MHA) where this may not be true
- return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True)
- def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- for p in parameters:
- if p.grad is None:
- continue
- p_data = p.detach()
- g_data = p.grad.detach()
- max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor)
- grad_norm = unitwise_norm(g_data, norm_type=norm_type)
- clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6))
- new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad)
- p.grad.detach().copy_(new_grads)
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