原贴链接

So, I just read this blog post claiming they’ve achieved a 100 million context length, and honestly, I’m not sure what to make of it. On one hand, it might just be some flashy marketing or something to impress investors. But on the other hand, it’s kind of fun to think about how they might actually be pulling this off.

Here’s a quick breakdown of their big claims:

  1. Multi-hop induction heads
  2. Uses way less memory—like 638 times less—than Llama 3.1 405B at 100M context
    • (Llama 3.1 405B uses 51TB at FP16, which is about 637.5 H100 GPUs at 80GB each. They’re saying they use less than one H100, so under 80GB total.)
  3. 1000x cheaper than the attention mechanism in Llama 3.1 405B
    • (Llama 3.1 405B does 4.13E14 FLOPS per output token at 100M context).

The multi-hop thing seems doable. If each layer in their model can do one hop, then with six layers, it’s totally possible (basically each layer does one hop, notice they limit it to 6 hops and don’t show something really impressive like 100). The blog suggests they trained the model specifically for this, so it makes sense that it would work.

Now, about storing 100 million tokens in just 80GB—that’s a tall order. But they mention using hashes, which might actually help us see what they are doing. If we assume each hash takes up a fixed amount of space and can’t be compressed (like with random strings), we’re looking at about 800 bytes per token. That’s a huge difference compared to Llama 3.1 405B, which uses 516,000 bytes per token at FP16. Even if you go down to FP8, it’s still 258,000 bytes.

But with tricks like “You Only Cache Once” (https://arxiv.org/abs/2405.05254), which shares the KV-cache across all layers, you can get Llama 3.1 405B down to about 2,048 bytes per token. Then, if you use something like Multi-head Latent Attention (like in DeepSeek-V2, https://arxiv.org/abs/2405.04434), maybe you could hit that 800 bytes per token mark.

As for the 1000x cheaper claim, that means they’re working with 4.13E11 FLOPS per output token at 100M context. Even if we can reduce the memory, the computational load is still a big deal. They mentioned custom CUDA code, which might hint that they’re not using standard attention mechanisms.

If we guess that their model is around the size of Llama 3.1 8B (which is 16x computationally cheaper than the 405B model in terms of attention), there’s still a huge gap—like a 60x performance boost needed. This might mean they’re using some kind of sparse attention, which would need efficient CUDA kernels.

One idea could be something like the top-k experts in a Mixture of Experts model (https://arxiv.org/abs/2407.04153), which cuts down the time complexity from O(N) to O(sqrt(N)). Maybe they’re using a similar top-k attention method, where only the most relevant tokens (like a top-1 token in hash-based attention) get the focus.

Of course I could be completely wrong here, maybe they have some completely new method for doing LLMs, this is just my thoughts on what I would do if given the same constraints.

讨论总结

Reddit用户对Magic公司声称实现1亿上下文长度的博客文章进行了广泛讨论，主要集中在对其技术可行性的怀疑、营销策略的猜测以及实际应用前景的探讨。评论者们分析了博客中提到的多跳归纳头、内存使用效率、成本降低等技术点，并提出了各自的技术实现猜测。尽管存在怀疑，但也有用户对可能的技术突破表示期待，特别是对AI在编程等实际任务中的应用表现。

主要观点

  1. 👍 对技术可行性的怀疑
    • 支持理由：评论者认为Magic公司的声明可能是为了吸引投资者的营销手段。
    • 反对声音：部分评论者承认可能存在新的技术方法来实现这一突破。
  2. 🔥 多跳归纳头技术的探讨
    • 正方观点：多跳归纳头技术在理论上是可行的，可能是实现大上下文长度的关键。
    • 反方观点：需要进一步的技术细节和实际演示来验证其有效性。
  3. 💡 内存使用效率的挑战
    • 评论者分析了Magic公司声称使用远少于Llama 3.1 405B模型的内存，并探讨了可能的技术实现方式。
  4. 👀 成本效益的提升
    • 评论者对Magic公司声称成本降低1000倍表示怀疑，认为需要更多的技术细节来支持。
  5. 🚀 实际应用前景的探讨
    • 评论者对AI在编程等实际任务中的应用表现表示关注，强调模型的准确性和性能。

金句与有趣评论

  1. “😂 Wonderful-Top-5360：until i’ve seen real demonstrated result”
    • 亮点：强调了对实际演示结果的期待，反映了评论者的怀疑态度。
  2. “🤔 Sl33py_4est：I want ai for coding and if it miss remembers a single character it isn’t viable.”
    • 亮点：突出了AI在编程任务中的准确性要求，反映了实际应用的关注点。
  3. “👀 DaimonWK：If its real, it could be the first step for a real AI Assistant, one that is with you 24/7 and remembers everything you do or need.”
    • 亮点：对技术突破的潜在影响进行了展望，激发了对未来AI助手的想象。

情感分析

讨论的总体情感倾向偏向怀疑和谨慎，主要分歧点在于对Magic公司声称的技术突破的真实性和可行性。评论者们对技术细节和实际演示结果表示期待，同时也对可能的营销策略表示担忧。这种情感倾向可能源于对AI技术进展的高度期待与对夸大宣传的警惕。

趋势与预测

  • 新兴话题：对AI在编程等实际任务中的应用表现的关注可能会引发更多关于AI实用性的讨论。
  • 潜在影响：如果Magic公司的技术突破得到验证，可能会对AI领域产生重大影响，特别是在上下文长度和内存效率方面。

详细内容：

标题：关于 Magic 的 100M 上下文长度的热门讨论

最近，Reddit 上一篇关于 Magic 声称实现 100 百万上下文长度的帖子引发了广泛关注。该帖子https://magic.dev/blog/100m-token-context-windows吸引了众多目光，点赞数和评论数众多，大家主要围绕其真实性和实现方式展开了激烈讨论。

讨论焦点与观点分析： 有人认为这可能只是为了吸引投资者的营销手段，在没有看到实际成果之前持保留态度。例如，有人说：“直到我看到真正展示的结果，我都会抑制自己的热情。100M 上下文长度太巨大了，你可以把一生中写的所有代码都放进去并让它给出批评。所以我会等待，让他们用实际行动说话。如果听不到他们的后续，我就当他们在胡说。” 也有人从技术角度进行了分析。有人提出，如果是基于 Transformer 架构，在不增加计算量的情况下增加序列长度可能会降低语义密度。还有人猜测可能使用了诸如“Mixture of Experts 模型”中的 top-k 专家等方法，或者是某种稀疏注意力机制，并需要高效的 CUDA 内核。 有人则对作者的分析表示赞赏，认为其尽量给出了现实的观点。 也有人认为如果这是真的，可能是迈向真正的 AI 助手的第一步，既令人兴奋又有些令人害怕。还有人更关注性能，表示宁愿选择较小的上下文窗口但模型在编码任务上表现出色。

总之，关于 Magic 的这一宣称，大家观点各异，有人期待看到实际成果，有人从技术层面深入探讨，也有人关注其实际应用中的性能表现。究竟 Magic 能否证明自己，还有待进一步观察。