Add Challenge 113: kimi delta attention - #306
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shxjames
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July 31, 2026 04:34
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Impressive to put this together so quickly! Assuming lot's of people will want to play around with this challenge now that they have released the architecture. |
ishaan-arya
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Aug 7, 2026
| K = torch.tensor([[[1.0, 0.0], [0.0, 1.0]]], device=device, dtype=dtype) | ||
| V = torch.tensor([[[1.0, 2.0], [2.0, 0.0]]], device=device, dtype=dtype) | ||
| alpha = torch.tensor([[[1.0, 1.0], [0.5, 0.5]]], device=device, dtype=dtype) | ||
| beta = torch.tensor([[1.0, 0.5]], device=device, dtype=dtype) |
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The constraints in the spec say 0 ≤ beta < 1 and the example test seems to violate that. Either make the constraint ≤ 1 or change the example.
| beta = jnp.asarray(beta, dtype=jnp.float32) # (batch, seq_len) | ||
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| batch = Q.shape[0] | ||
| d = Q.shape[2] |
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nit: batch and d are passed in as input params so we don't need to re-derive them here.
- Use seq_len in challenge.html to match the solve signature (all other seq_len challenges do the same); T appeared nowhere in the API. - Widen the beta constraint to [0, 1] — the worked example uses beta = 1.0, which the old [0, 1) bound excluded. - Add an SVG diagram of one recurrence step, showing the per-channel decay gate that distinguishes KDA from the head-wise gated delta rule. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- Use seq_len (the actual solve parameter) instead of T throughout challenge.html - Widen the beta constraint to [0, 1] so it covers the example's beta_1 = 1.0 - Drop a stray authoring note from reference_impl Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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