llama.cpp

c61b98b8 - model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support (#25444)

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 269a81e03d66e1c353e1a203a0c03a03eb2b1a4e) * 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 b1878a101793cd4e59868ac72635a86ea694987c) * 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 8f009f54bea5ef9a6a354123bd25e9d5ea2d5e03) * 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 d1a592f278336e78457454eb6c96bca917135f10) * 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 f824e09dc812169589cf5662c92d149a4c18c30a) * 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 189b67fc2c9d50970416c94b3317a6e7baa49b03) * 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 dbedc9e19c50dca0acdfb402362e2707bee424ae) * 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.