LFattNet
performer-pytorch
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| LFattNet | performer-pytorch | |
|---|---|---|
| 1 | 2 | |
| 53 | 1,055 | |
| - | - | |
| 0.0 | 1.8 | |
| over 3 years ago | about 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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LFattNet
performer-pytorch
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[R] Rotary Positional Embeddings - a new relative positional embedding for Transformers that significantly improves convergence (20-30%) and works for both regular and efficient attention
Performer is the best linear attention variant, but linear attention is just one type of efficient attention solution. I have rotary embeddings already in the repo https://github.com/lucidrains/performer-pytorch and you can witness this phenomenon yourself by toggling it on / off
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Why has Google's Performer model not replaced traditional softmax attention?
Here's an PyTorch implementation if you want to play around with it: lucidrains/performer-pytorch: An implementation of Performer, a linear attention-based transformer, in Pytorch (github.com)
What are some alternatives?
attention-is-all-you-need-pytorch - A PyTorch implementation of the Transformer model in "Attention is All You Need".
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
DenseDepth - High Quality Monocular Depth Estimation via Transfer Learning
memory-efficient-attention-pytorch - Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"
Meta-SelfLearning - Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
scenic - Scenic: A Jax Library for Computer Vision Research and Beyond
deep-implicit-attention - Implementation of deep implicit attention in PyTorch