feat(burn): AttentionTable intercept in matmul — O(1) table lookup path

When a compiled attention table is registered for a given d_head dimension,
burn's matmul bypasses BLAS entirely and uses precomputed table lookup:

  matmul(Q, K^T) where K has d_head columns:
    1. Check ATTENTION_CACHE for d_head → CompiledAttention
    2. Hit: table[q_palette_idx][k_palette_idx] per element (O(1))
    3. Miss: fall through to ndarray::linalg::general_mat_mul (O(d))

API:
  register_attention_table(d_head, table)  — register compiled table
  has_attention_table(d_head) → bool       — check if table exists
  clear_attention_cache()                  — remove all tables

CompiledAttention:
  - 256×256 u16 distance table (128KB, fits L1 cache)
  - q_assignments: per-row palette index (from Base17 projection)
  - k_assignments: per-col palette index
  - Pipeline: GGUF weights → dequant → Base17 → palette → table

The intercept is transparent: no table registered = BLAS as before.
matmul.rs is now a real file (not symlink) since we modified it.

30 tests passing. Zero regressions.

https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7

Author: claude