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merveenoyan
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Aug 28, 2025
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| # Three MoEs | ||
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| Three Ways to Compute Mixture of Experts (MoE) in PyTorch |
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might be nice to put this on the title
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| Three Ways to Compute Mixture of Experts (MoE) in PyTorch | ||
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| Mixture of Experts (MoE) looks complex, but under the hood it’s just: |
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perhaps link to this blog so people have more initial context https://huggingface.co/blog/moe
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| ## Step 1: Routing | ||
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| Every token chooses its top-k experts with softmaxed scores. |
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would be nice to give just a little bit more context
| 2. Apply MLPs (one per expert). | ||
| 3. Recombine outputs with routing weights. | ||
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| Below are **three ways** to compute MoE in PyTorch — from simple to complex. |
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Suggested change
| Below are **three ways** to compute MoE in PyTorch — from simple to complex. | |
| Below are **three ways** to build MoEs in PyTorch — from simple to complex. |
| Quick test: | ||
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| ```python | ||
| hs = torch.randn(B, S, H) |
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very nice to have simple reproducible snippets, would be nice to put these into a notebook and link at the end so people don't have to copy paste to try
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in general let's avoid single letter variable names and find meaningful ones!
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This article explores three different ways to compute MoE and focuses on the "how" of MoE