feat: unify attention backend with multi-dim mask support

- Add attention() functional entry delegating to active backend
- GQA/MLA forward calls attention() instead of inline cache/SDPA
- CUDA kernels support 2D/3D/4D mask via mask_h_stride field
- CudaBackend.fwd_decode builds 2D padding mask for mixed seq_lens
- KVCache.max_len precomputed in bind_tasks to avoid GPU sync
- batch==1 decode short-circuits mask=None
- Split tests into conftest, test_backend, test_backend_equivalence, test_kernel_mask
- 440 tests pass, L20 decode 1.44-1.60x speedup vs torch native

astrai/inference/core/cache.py

@@ -202,6 +202,7 @@ class KVCache:
         req_pool_indices: [batch_size] — row indices into req_to_token
         seq_lens: [batch_size] — per-request total sequence lengths
         out_cache_loc: [batch, new_seq_len] or [batch, 1] — write indices
+        max_len: max(seq_lens) as Python int — avoids GPU sync in decode
     """
 
     k_buffer: Tensor
@@ -210,6 +211,7 @@ class KVCache:
     req_pool_indices: Tensor
     seq_lens: Tensor
     out_cache_loc: Tensor
+    max_len: int = 0
 
 
 class PagePool:
@@ -439,6 +441,7 @@ class PagePool:
             req_pool_indices=req_pool_indices,
             seq_lens=seq_lens_t,
             out_cache_loc=out_cache_loc,
+            max_len=max(seq_lens),
         )
 
     # ---- internals ----