metal : add sparse FA (#28098)

* metal : support n_kv_max sparse mask hint in flash attention vec kernel

- add kernel_flash_attn_ext_vec_idx: compacts finite mask entries into
  a per-row index list (Hillis-Steele scan, one threadgroup per row)
- extend vec FA kernel with optional sparse index gathering (FC slot 5)
- add host-side gate: sparse path when n_kv_max > 0, mask present,
  supported head sizes / KV types, n_kv_max <= 4096
- new buffer region extra_idx for the index list
- pipeline getter extended with has_sparse param
- add test cases: head sizes, quant types, nb>1, nr23 variants,
  sinks, ALiBi, softcap, permute, v_view_of_k, no-mask fallback

Note: multi-row (nb*nr23[1] > 1) cases still failing - rid mapping
in the store phase needs revisiting for the sparse path.

Assisted-by: pi:llama.cpp/Qwen3.8-27B

* metal : fix sparse flash attention row addressing

- kernel_flash_attn_ext_vec_idx: mask param is half* but nb31 is a byte
  stride, so the per-row mask offset was scaled by 2x; cast to char*
  before applying the byte strides
- kernel_flash_attn_ext_vec: sparse pidx param is char* so the per-row
  element offset was under-scaled by sizeof(int); scale it by sizeof(int)
  to get the correct byte offset
- fixes the multi-row (nb*nr23[1] > 1) sparse flash attention failures

Assisted-by: pi:llama.cpp/DeepSeek-v4-0731

* cont : use sparse vec FA for prefill

* metal : single-pass flash attention sparse index compaction

The idx kernel previously read the mask row twice: once to count the finite
entries (for the prefix scan) and again to recover their positions. Since the
kernel is memory-bound, this doubled the mask traffic.

Keep the finite positions in a per-thread register array during the count
pass and write them out directly, avoiding the second mask read. A dense
mask with more than NLOCAL finite entries in a slice falls back to re-reading
the mask to write the remaining positions.

Assisted-by: pi:llama.cpp/DeepSeek-v4-0731

* tests : add perf cases for sparse flash attention prefill

Measure the sparse vec FA kernel across KV sizes, n_kv_max hints and batch
sizes. Run with:

    ./build/bin/test-backend-ops -b MTL0 -o FLASH_ATTN_EXT -p "n_kv_max=[1-9]" perf

Assisted-by: pi:llama.cpp/DeepSeek-v4-0731

* qwen4 : enable sparse attention

* cont : adjust nsg

* cont : sync test-backend-ops

* cont : disable Qwen4 for now

* cont : clean-up + tests

src/models/qwen4exp.cpp

@@ -744,6 +744,9 @@ ggml_tensor * llama_model_qwen4exp::graph::build_attn_qsa(
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
+    // TODO: enable sparse attention when we are ready
+    // ref: https://github.com/ggml-org/llama.cpp/pull/27970
+    //ggml_tensor * cur = build_attn_mha(q, k, v, nullptr, kq_mask_top_k, nullptr, nullptr, top_k->ne[0], kq_scale, il);
     ggml_tensor * cur = build_attn_mha(q, k, v, nullptr, kq_mask_top_k, nullptr, nullptr, 0, kq_scale, il);
     cb(cur, "kqv_out", il);