b12180

model: add Kimi-K3.1-Pro text model (#31779)

• model: add Kimi-K3.1-Pro text model

K3 with the full-attention MLA layers replaced by DSA: on this checkpoint 24
of the 93 layers carry a lightning indexer (32 heads x 128, top_k 2048, no
rope). The KDA (linear-attention) layers and the rest of the K3 skeleton -
cross-layer residuals, latent MoE, situ, MLA output gate, full-rank KDA gate -
are unchanged, so this reuses the K3 graph and converter and adds the indexer.

Indexer:

• the selection is an additive mask over the whole cache, not a gather of
  the top-k rows, so it reuses the existing sparse flash attention path;
  off that path the mask stays correct but still costs a kq over the cache
• the indexer key LayerNorm takes its eps from the model's rms_norm_eps,
  because dsa_config declares no eps of its own

Graph and cache:

• new arch with four indexer tensors per DSA layer, and a hybrid memory
  that keeps a K-only MLA cache for those layers, recurrent state for the
  KDA layers, and the indexer key cache
• the graph node budget shares the K3 entry

Conversion:

• KimiK31Model(KimiK3Model), picked by KimiK31ForConditionalGeneration
• dsa_config becomes attention.indexer.{head_count,key_length,top_k,types}
  plus the indexer LayerNorm eps; the layer list is 1-indexed like
  linear_attn_config's, and the converter asserts that it covers exactly
  the full-attention layers
• the routed experts stay MXFP4 and are repacked as in K3, not dequantized
• the vision tower and mm_projector are skipped: text only for now
• the arch key follows the checkpoint's own model_type ("kimi_k31") and the
  loader registers KimiK31ForConditionalGeneration; the release name carries
  a -Pro suffix that nothing in the config or the tensor names uses

Quantisation: the indexer's q_b, k and proj are kept out of the quant
overrides, since the top-k selection is sensitive to their precision.

Verified on an 8-layer slice against the HF checkpoint: 38 tensors checked
exactly, the indexer metadata against the config, and the selection against
full attention - identical PPL at top_k beyond the context length, divergent
once top_k is forced below it. Arch test fixture and the full arch suite pass.

• converter: avoid clutter in the k3 base

• narrow comment to match model

• update metal fusion expectations

• restore

• sync with PR 31733 refactoring

Website:

• https://llama.app

Attestations:

• https://github.com/ggml-org/llama.cpp/attestations/64707271

macOS/iOS:

• macOS Apple Silicon (arm64)
• macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED
• macOS Intel (x64)
• iOS XCFramework

Linux:

• Ubuntu x64 (CPU)
• Ubuntu arm64 (CPU)
• Ubuntu s390x (CPU)
• Ubuntu 26.04 s390x (CPU)
• Ubuntu 26.04 s390x (zDNN)
• Ubuntu x64 (Vulkan)
• Ubuntu arm64 (Vulkan)
• Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries
• Ubuntu x64 (CUDA 13) - CUDA 13.5 libraries
• Ubuntu arm64 (CUDA 13) - CUDA 13.5 libraries
• Ubuntu x64 (ROCm 10.0)
• Ubuntu x64 (OpenVINO)
• Ubuntu x64 (SYCL FP32)
• Ubuntu x64 (SYCL FP16)
• Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide

Android:

• Android arm64 (CPU)
• Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide

Windows:

• Windows x64 (CPU)
• Windows arm64 (CPU)
• Windows arm64 (OpenCL Adreno)
• Windows x64 (CUDA 12) - CUDA 12.4 DLLs
• Windows x64 (CUDA 13) - CUDA 13.5 DLLs
• Windows arm64 (CUDA 13) - CUDA 13.5 DLLs
• Windows x64 (Vulkan)
• Windows arm64 (Vulkan)
• Windows x64 (OpenVINO)
• Windows x64 (SYCL)
• Windows x64 (ROCm 10.0)

openEuler:

• DISABLED
• openEuler x86 (310p)
• openEuler x86 (910b, ACL Graph)
• openEuler aarch64 (310p)
• openEuler aarch64 (910b, ACL Graph)

UI:

• UI