ggml-org/llama.cpp b10776

on GitHub

model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support (#25444)

• hparams: add per-layer n_ff_exp/n_expert_used arrays with scalar-or-array loading

G1/G2 infrastructure for variable-per-layer expert FFN size and top-k routing
(required for Puzzle-75B which has 5 distinct n_ff_exp values and 7 top-k values
across its 40 MoE layers).

Design: rename scalar members to _impl suffix (following existing convention),
add LLAMA_MAX_LAYERS arrays, add n_ff_exp(il)/n_expert_used(il) accessors with
scalar fallback. No new GGUF keys: reuses existing expert_feed_forward_length and
expert_used_count keys via get_key_or_arr (scalar -> broadcast, array -> per-layer).

• llama-hparams.h: n_ff_exp -> n_ff_exp_impl, n_expert_used -> n_expert_used_impl;
  add n_ff_exp_arr / n_expert_used_arr arrays; add per-layer accessor declarations.
• llama-hparams.cpp: implement n_ff_exp(il) and n_expert_used(il); out-of-range
  il returns impl safely (shared code, no abort).
• llama-model.cpp: central n_expert_used load changed to get_key_or_arr; derive
  impl as max-of-array for validations and backward compat; zero both new arrays;
  HunyuanVL override also zeroes n_expert_used_arr.
• llama-graph.cpp: aggregation loop in build_moe_ffn uses hparams.n_expert_used(il)
  so per-layer top-k bounds the ggml_view loop correctly.
• All other files: mechanical rename hparams.n_{ff_exp,expert_used} -> *_impl.
  Scalar arches are unaffected (broadcast fills all array slots with the single value).

(cherry picked from commit 269a81e)

• nemotron-h: use per-layer n_ff_exp(il) and n_expert_used(il) at MoE call-sites

Load n_ff_exp via get_key_or_arr into hparams.n_ff_exp_arr in load_arch_hparams;
derive impl as max for existing uniform GGUFs.

In load_arch_tensors, compute n_ff_exp_i = hparams.n_ff_exp(i) with fallback to
n_ff(i)/n_expert_used(i) for GGUFs that omit expert_feed_forward_length.

In build_ffn_layer, pass hparams.n_expert_used(il) to build_moe_ffn so per-layer
top-k is used for expert routing selection.

All other nemotron-h behaviour (mamba2, attention, shared-exp, latent projection,
routed_scaling_factor, expert_weights_norm, sigmoid gating) is unchanged.

(cherry picked from commit b1878a1)

• arch/*.cpp + gguf-py: mechanical rename n_ff_exp->n_ff_exp_impl, n_expert_used->n_expert_used_impl

All non-nemotron arch files continue using the scalar impl member directly.
Behaviour is identical: the impl value is the broadcast value from the GGUF scalar.

gguf_writer: add_expert_feed_forward_length and add_expert_used_count now accept
int | Sequence[int], mirroring add_feed_forward_length, so converters can write
per-layer arrays with the same existing GGUF keys.

(cherry picked from commit 8f009f5)

• convert: support NemotronHPuzzleForCausalLM (per-block MoE config)

Parse block_configs/mtp_block_configs into per-layer arrays (scalar-or-array
keys), append the MTP [attention, moe] sub-blocks as blk.88/blk.89 with
nextn tensors, accept the backbone.* prefix, and register the arch.
Also fix a pre-existing undeclared _experts attribute on NemotronHModel.

(cherry picked from commit d1a592f)

• nemotron-h: distinguish Nemotron 3 Puzzle (75B.A9B) from Super (120B.A12B)

Both have 88 layers; the per-layer expert_used_count array (heterogeneous
for Puzzle, broadcast-uniform for Super) is the discriminator.

(cherry picked from commit f824e09)

• convert: accept the official Puzzle BF16 checkpoint's tensor naming

The officially distributed BF16 checkpoint (NVIDIA-Nemotron-Labs-3-Puzzle-
75B-A9B-BF16) names the trunk model.* (model.layers., model.embeddings,
model.norm_f) where the original release used the NemotronH-style
backbone., and spells the router bias e_score_correction_bias instead of
e_score_correction.bias. Normalize both at the top of
NemotronHPuzzleModel.modify_tensors so either checkpoint converts; every
tensor name in the official index (42683 keys, MTP head included) resolves
through the tensor map after normalization.

(cherry picked from commit 189b67f)

• laguna: use n_ff_exp_impl for the uniform-MoE FFN size

Laguna landed after this branch was cut and reads hparams.n_ff_exp as a
scalar. This series turns it into a per-layer array with an n_ff_exp(il)
accessor, so the three scalar reads no longer compile. Laguna is a
uniform MoE, so point them at the scalar fallback n_ff_exp_impl, same as
deepseek2/qwen3moe/gemma4 in this series. No behaviour change.

(cherry picked from commit dbedc9e)

• arch: extend the n_ff_exp/n_expert_used rename to archs added upstream

kimi-k3, dflash, bailingmoe3, deepseek4, granite-swa and the nemotron-h MTP
block still referenced the scalar fields by their old names. n_ff_exp and
n_expert_used are accessors now, so those reads no longer compile; point the
non-per-layer archs at the _impl scalars and use the indexed form where the
call site is per-layer.

• convert: keep Puzzle opted out of the NemotronH MTP export path

#26725 added MTP export to NemotronHModel, keyed on num_nextn_predict_layers.
Puzzle's config carries that key, but NemotronHPuzzleModel bypasses
NemotronHModel.init (its per-block config needs a different setup), so
_mtp_bid was never assigned and modify_tensors raised AttributeError on any
mtp.* tensor. Puzzle's head is also laid out by mtp_block_configs, not the
mtp.layers.* form the base maps.

Set _mtp_bid to None, drop mtp.* in filter_tensors, and declare
supports_mtp_export = False so --mtp / --no-mtp fail at the CLI.

• llama: replace n_ff_exp/n_expert_used scalars with per-layer accessors

Follow-up to review feedback: the previous revision kept the scalar
hparams fields alongside the new per-layer arrays, which duplicated
state that get_key_or_arr already handles by broadcasting a scalar
value over every layer.

Drop both scalars and expose n_ff_exp(il) / n_expert_used(il) built
exactly like the existing n_head_kv(il) and n_ff(il) accessors: they
index the array and GGML_ABORT out of range, with il defaulting to 0
so genuinely uniform call sites stay a plain n_ff_exp().

Arch loaders now read both keys through get_key_or_arr over
n_layer_all, and the n_expert_used validation checks the maximum
across layers instead of a single field.

• llama: restore per-key required flags on the expert hparam reads

The scalar-to-array conversion passed required=false at every call site,
which silently made mandatory keys optional. Each read now carries the
same required flag it had before the conversion.