…KV cache fix, XNNPACK workspace sharing (pytorch#19214)

Summary:

Six changes for the Gemma 4 text decoder + runner, enabled by default. Custom SDPA can be opted out via `--no-use_custom_sdpa` for eager mode or non-XNNPACK backends. Workspace sharing can be opted out via `--noenable_workspace_sharing` for debugging.

1. Custom SDPA — attention now runs through `torch.ops.llama.custom_sdpa` (tiled flash attention from the Llama runner). Skips the 8x KV expansion that GQA/MQA otherwise requires, and never materializes the full `[seq, seq]` attention matrix — the matmul fallback's `[bs, heads, seq, seq]` tensor exceeds S25's 8 MB L2 cache at `seq=2048` and causes severe regression. Adds an inline INT8 dequant path for `Gemma4QuantizedKVCache(return_float_values=False)` that stays inside the XNNPACK partition.

2. On-the-fly RoPE — the attention module stores only the `inv_freq` vector (~128-256 floats) and computes cos/sin per forward, instead of registering precomputed `[max_seq_len, head_dim]` cos/sin buffers. Reduces PTE size 3-7%.

3. KV cache allocation is skipped for `is_kv_shared_layer=True`. In YOCO, 20 of 35 layers consume the donor's KV via `shared_kv` and never write to their own cache, so the allocation was dead. Saves ~40 MB at `seq=1024`, ~80 MB at `seq=2048`.

4. XNNPACK workspace sharing in runner. `Gemma4Runner::load()` now calls `set_option(workspace_sharing_mode_option_key=PerModel, weight_cache_option_key=true)` on the XNNPACK backend before module load. Default-on with `enable_workspace_sharing` constructor flag for opt-out. Without this, real Android/iOS app builds (which don't pass the bench's compile-time `--config xnnpack_workspace_sharing=1`) end up with `Disabled` mode and OOM crash silently on E4B (>2 GB peak memory regression reported by app teams). Compile-time flag in xplat/.../gemma4/targets.bzl (`-DENABLE_XNNPACK_SHARED_WORKSPACE`) is also removed since it was dead — Buck preprocessor flags don't reach `XNNWorkspaceManager.cpp` (which lives in the `xnnpack_backend` compile unit).

5. Correctness fix for KV cache quant + custom SDPA + YOCO. When `Gemma4QuantizedKVCache(return_float_values=False)` is in use, the donor layer now dequants K/V before storing in `kv_to_share` so cross-decoder layers (which lack access to the donor's scales) don't pass raw int8 to `custom_sdpa`. Dormant bug: only triggers with `--quantize_kv_cache --use_custom_sdpa`; previously crashed export with `AssertionError: Expected key to be float32`.

6. iOS VmRSS sscanf fix (consolidates D103030061). `Gemma4Stats::read_rss_kb()` uses `SCNd64` from `<cinttypes>` instead of `%ld` so the format matches `int64_t` on both LP64 (Linux/Android) and LLP64-ish (iOS arm64) platforms. Unblocks iOS sample app builds with `-Werror,-Wformat`.

Mask construction is factored into `_build_attn_mask` / `_slice_mask` helpers shared between the custom-SDPA and matmul branches.

Differential Revision: D102710062