Heinsen_routing Alternatives

heinsen_routing reviews and mentions

• A Surprisingly Effective Way to Estimate Token Importance in LLM Prompts
  1 project | news.ycombinator.com | 12 Sep 2023

  Simple and in hindsight, obvious:

  1. Run the text through a document embeddding model and save the embedding.

  2. Remove one token at a time, and compute the cosine similarity of the new document embedding to the original one.

  3. Compute importance as a function of the change in cosine similarity.

  Nice.

  Also check out https://github.com/glassroom/heinsen_routing . It takes n embeddings and outputs m embeddings, and also gives you an n×m matrix with credit assignments, without having to remove tokens one by one, which can be prohibitively slow for long texts.

• Unlimiformer: Long-Range Transformers with Unlimited Length Input
  3 projects | news.ycombinator.com | 5 May 2023

  After a very quick read, that's my understanding too: It's just KNN search. So I agree on points 1-3. When something works well, I don't care much about point 4.

  I've had only mixed success with KNN search. Maybe I haven't done it right? Nothing seems to work quite as well for me as explicit token-token interactions by some form of attention, which as we all know is too costly for long sequences (O(n²)). Lately I've been playing with https://github.com/hazyresearch/safari , which uses a lot less compute and seems promising. Otherwise, for long sequences I've yet to find something better than https://github.com/HazyResearch/flash-attention for n×n interactions and https://github.com/glassroom/heinsen_routing for n×m interactions. If anyone here has other suggestions, I'd love to hear about them.

• Scaling Transformer to 1M tokens and beyond with RMT
  6 projects | news.ycombinator.com | 23 Apr 2023

  Here's a list of tools for scaling up transformer context that have github repos:

  * FlashAttention: In my experience, the current best solution for n² attention, but it's very hard to scale it beyond the low tens of thousands of tokens. Code: https://github.com/HazyResearch/flash-attention

  * Heinsen Routing: In my experience, the current best solution for n×m attention. I've used it to pull up more than a million tokens as context. It's not a substitute for n² attention. Code: https://github.com/glassroom/heinsen_routing

  * RWKV: A sort-of-recurrent model which claims to have performance comparable to n² attention in transformers. In my limited experience, it doesn't. Others agree: https://twitter.com/arankomatsuzaki/status/16390003799784038... . Code: https://github.com/BlinkDL/RWKV-LM

  * RMT (this method): I'm skeptical that the recurrent connections will work as well as n² attention in practice, but I'm going to give it a try. Code: https://github.com/booydar/t5-experiments/tree/scaling-repor...

  In addition, there's a group at Stanford working on state-space models that looks promising to me. The idea is to approximate n² attention dynamically using only O(n log n) compute. There's no code available, but here's a blog post about it: https://hazyresearch.stanford.edu/blog/2023-03-27-long-learn...

  If anyone here has other suggestions for working with long sequences (hundreds of thousands to millions of tokens), I'd love to learn about them.

• From Deep to Long Learning
  6 projects | news.ycombinator.com | 9 Apr 2023

  I imagine you could, maybe by using something like this https://github.com/glassroom/heinsen_routing#sequence-to-vec... ... but I doubt you'd be able to match the training efficiency of triangular masking in auto-regressive transformers. With routing, you'd have to train the model one time-step at a time, instead of all time-steps in parallel like a masked auto-regressive transformer.

• New algorithm can route sequences with 1M+ token embeddings in one GPU
  1 project | news.ycombinator.com | 20 Dec 2022
• A note from our sponsor - InfluxDB
  www.influxdata.com | 26 Apr 2024

  Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →