#pragma once
#include "llama.h"
#include <array>
#include <cassert>
#include <cmath>
// bump if necessary
#define LLAMA_MAX_LAYERS 512
#define LLAMA_MAX_EXPERTS 1024 // Kimi K3
enum llama_expert_gating_func_type {
LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX = 1,
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT = 3, // applied to the router weights instead of the logits
LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS = 4,
};
enum llama_swa_type {
LLAMA_SWA_TYPE_NONE = 0,
LLAMA_SWA_TYPE_STANDARD = 1,
LLAMA_SWA_TYPE_CHUNKED = 2,
LLAMA_SWA_TYPE_SYMMETRIC = 3,
};
// forward declaration; full definition in llama-graph.h
enum llm_ffn_op_type : int;
struct llama_hparams_posnet {
uint32_t n_embd;
uint32_t n_layer;
};
struct llama_hparams_convnext {
uint32_t n_embd;
uint32_t n_layer;
};
struct llama_hparams {
// note: use the `_impl` suffix to avoid name conflict between members and getters
// for example: n_embd_out() vs n_embd_out_impl
bool vocab_only;
bool no_alloc;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
bool norm_before_residual = false;
bool norm_before_fc = false;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_layer_all;
uint32_t n_layer_nextn = 0;
// granite-switch: index of the single-head "router" KV layer that encodes
// per-token adapter selection. -1 when the model has no such layer.
int32_t router_layer = -1;
uint32_t n_expert = 0;
uint32_t n_expert_used = 0;
uint32_t n_rel_attn_bkts = 0;
// TODO: this needs to be reworked
int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
// different head size for full_attention and SWA layers
uint32_t n_embd_head_k_full; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
uint32_t n_embd_head_v_full; // dimension of values (d_v) aka n_embd_head
uint32_t n_embd_head_k_swa;
uint32_t n_embd_head_v_swa;
// different RoPE dimensions for full_attention and SWA layers
uint32_t n_rot_full;
uint32_t n_rot_swa;
// note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
uint32_t n_embd_head_k_mla_impl = 0;
uint32_t n_embd_head_v_mla_impl = 0;
// for WavTokenizer
struct llama_hparams_posnet posnet;
struct llama_hparams_convnext convnext;
uint32_t n_shortconv_l_cache = 0;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_ff_exp = 0;
uint32_t n_ff_shexp = 0;
uint32_t n_ff_chexp = 0;
uint32_t n_expert_shared = 0;
uint32_t n_norm_groups = 0;
uint32_t n_expert_groups = 0;
uint32_t n_group_used = 0;
uint32_t n_group_experts = 0;
// MLA + SWA (i.e. dots3note)
uint32_t n_lora_kv_swa = 0;
uint32_t n_embd_head_k_mla_swa = 0;
uint32_t n_embd_head_v_mla_swa = 0;
float expert_group_scale = 0.05f;
float expert_weights_scale = 0.0f;
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
uint32_t moe_every_n_layers = 0;
uint32_t moe_latent_size = 0;
float f_norm_eps;
float f_norm_rms_eps;
float f_norm_group_eps;
float f_attn_logit_softcapping = 50.0f;
float f_router_logit_softcapping = 30.0f;
float f_final_logit_softcapping = 30.0f;
// for RWKV
uint32_t rescale_every_n_layers = 0;
uint32_t time_mix_extra_dim = 0;
uint32_t time_decay_extra_dim = 0;
uint32_t wkv_head_size = 0;
uint32_t token_shift_count = 2;
uint32_t n_lora_decay = 0;
uint32_t n_lora_iclr = 0;
uint32_t n_lora_value_res_mix = 0;
uint32_t n_lora_gate = 0;
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_base_train_swa = 10000.0f;
float rope_freq_scale_train;
float rope_freq_scale_train_swa = 1.0f;
float rope_scaling_alpha = 0.0f; // NTK-aware alpha for XDRoPE
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul = 0.0f;
float yarn_ext_factor = -1.0f;
float yarn_attn_factor = 1.0f;
float yarn_beta_fast = 32.0f;
float yarn_beta_slow = 1.0f;
std::array<int, 4> rope_sections;
// Per-layer RoPE enable flags (1 = use RoPE, 0 = NoPE)
// by default, all layers use RoPE (controlled by rope_finetuned)
std::array<uint32_t, LLAMA_MAX_LAYERS> rope_pattern;
// Sliding Window Attention (SWA)
llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
// the size of the sliding window (0 - no SWA)
uint32_t n_swa = 0;
// if is_swa_impl[il] == 1, then layer il is SWA
// if is_swa_impl[il] == 0, then layer il is dense (i.e. non-SWA)
// by default, all layers are dense
// note: using uint32_t type for compatibility reason
std::array<uint32_t, LLAMA_MAX_LAYERS> is_swa_impl;
// for hybrid state space models
std::array<uint32_t, LLAMA_MAX_LAYERS> is_recr_impl;
// for State Space Models
uint32_t ssm_d_conv = 0;
uint32_t ssm_d_inner = 0;
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
uint32_t ssm_n_group = 0;
// for MiniMax-Text-01 linear attention
uint32_t n_embd_head_la = 0;
// for Kimi Linear KDA
uint32_t n_embd_head_kda = 0;
bool kda_safe_gate = false;
// kimi-k3
uint32_t n_expert_latent = 0; // routed_expert_hidden_size (0 = experts run at n_embd)
uint32_t attn_res_block_size = 0; // 0 = no cross-layer attention residuals
float kda_gate_lower_bound = -INFINITY;
float situ_beta = 1.0f;
float situ_linear_beta = 0.0f; // 0 = no linear-beta transform on the up branch
bool ssm_dt_b_c_rms = false;
float f_clamp_kqv = 0.0f;
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
// grok-2
float f_attn_out_scale = 0.0f;
uint32_t attn_temp_length = 0;
float f_attn_value_scale = 0.0f;
bool causal_attn = true;
bool use_alibi = false;
bool attn_soft_cap = false;
bool use_kq_norm = false;
// for Classifiers
uint32_t n_cls_out = 1;
// input embedding dimension (0 = use n_embd)
uint32_t n_embd_inp_impl = 0;
// encoder input embedding dimension (0 = use n_embd_inp())
// e.g. the eagle3 encoder fuses target_layers * target_hidden features
uint32_t n_embd_inp_enc_impl = 0;
// output embedding dimension (0 = use n_embd)
uint32_t n_embd_out_impl = 0;
uint32_t dflash_block_size = 0;
uint32_t dflash_conv_kernel_size = 0;
uint32_t dflash_conv_group_size = 0;
uint32_t dflash_selector_rank = 0;
uint32_t dflash_selector_top_k = 0;
// llama4 smallthinker
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 0;
float f_attn_temp_scale = 0.0f;
float f_attn_temp_offset = 0.0f; // offset position index
// gemma3n altup
uint32_t n_altup = 4; // altup_num_inputs
uint32_t i_altup_act = 0; // altup_active_idx
uint32_t laurel_rank = 64;
uint32_t n_embd_altup = 256;
// needed for sentence-transformers dense layers
uint32_t dense_2_feat_in = 0; // in_features of the 2_Dense
uint32_t dense_2_feat_out = 0; // out_features of the 2_Dense
uint32_t dense_3_feat_in = 0; // in_features of the 3_Dense
uint32_t dense_3_feat_out = 0; // out_features of the 3_Dense
// xIELU
std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_n;
std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_p;
std::array<float, LLAMA_MAX_LAYERS> xielu_beta;
std::array<float, LLAMA_MAX_LAYERS> xielu_eps;
// DSA (deepseek sparse attention)
uint32_t indexer_n_head = 0;
uint32_t indexer_head_size = 0;
uint32_t indexer_top_k = 0;
// MSA
uint32_t indexer_block_size = 0;
uint32_t indexer_local_blocks = 0;
// Indexer is "full" (1) or "shared" (0)
// Shared indexers reuse top-k from previous full layer
std::array<uint32_t, LLAMA_MAX_LAYERS> is_indexer_full_impl;
// DeepSeek-V4
uint32_t dsv4_o_group_count = 0;
uint32_t dsv4_o_lora_rank = 0;
uint32_t dsv4_hc_mult = 0;
// dspark GIDD log-SNR conditioning; GGUF sets <arch>.log_snr_conditioning
bool dspark_log_snr_conditioning = false;
float dspark_min_log_snr = 0.0f;
float dspark_max_log_snr = 0.0f;
uint32_t dsv4_hc_sinkhorn_iters = 0;
uint32_t dsv4_hash_layer_count = 0;
float dsv4_compress_rope_base = 0.0f;
float dsv4_hc_eps = 0.0f;
std::array<uint32_t, LLAMA_MAX_LAYERS> dsv4_compress_ratios;
// qwen3vl deepstack
// When parsed from GGUF, this implies the first N layers consume the first
// N deepstack embeddings. Use deepstack_mapping_arr if you need a more
// complex mapping. If using deepstack_mapping_arr, also make sure to set
// n_deepstack_layers to the number of unique deepstack layers so that
// n_embd_imp is accurate (see granite.cpp).
// TODO: can be expressed via the `new n_embd_inp_impl` and remove this param
uint32_t n_deepstack_layers = 0;
// deepstack layer array (Granite4 Vision)
// -1 => no deepstack
// >=0 => input embedding index for deepstack injection
std::array<int32_t, LLAMA_MAX_LAYERS> deepstack_mapping_arr;
// gemma4 per-layer embedding
uint32_t n_embd_per_layer = 0;
// needed by encoder-decoder models (e.g. T5, FLAN-T5)
// ref: https://github.com/ggml-org/llama.cpp/pull/8141
llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
uint32_t dec_n_layer = 0;
// dspark drafter (EAGLE-style block-diffusion speculative decoder). the
// trunk itself is a plain dense Qwen3-style stack (n_layer/n_head/n_ff etc.
// above already cover it); these are the extra fields the block-draft head
// needs. target_layer_ids indexes into the TARGET model's layers, not this
// drafter's own (tiny) layer count.
uint32_t dspark_block_size = 0; // number of masked positions predicted per block
uint32_t dspark_mask_token_id = 0; // vocab id used to seed un-drafted block positions
uint32_t dspark_markov_rank = 0; // low-rank factor width for the markov logit-bias head
bool dspark_confidence_head = false;
bool dspark_hidden_correction = false;
bool dspark_markov_deploy_exposure = false;
uint32_t dspark_correction_size = 0;
bool dspark_confidence_head_with_markov = false;
// GIDD log-SNR / noise-level conditioning (LogSnrEmbed): sinusoidal
// featurization of a per-position log-SNR value, run through a 2-layer
// SiLU MLP (dspark.log_snr_fc1/fc2), added to the draft noise embedding
// before the backbone. Absent on non-GIDD drafters -- dspark_log_snr_conditioning
// gates whether load_arch_tensors()/graph::graph() touch it at all.
// ordered set of target-model layer indices this drafter taps; n_dspark_target_layers
// is also the concatenation width multiplier (n_capture) for dspark.fc's input.
uint32_t n_dspark_target_layers = 0;
// uint32_t (not int32_t): matches an existing explicit template
// instantiation of llama_model_loader::get_key_or_arr for
// std::array<uint32_t, LLAMA_MAX_LAYERS>; layer indices are non-negative
// so the signed/unsigned choice loses nothing.
std::array<uint32_t, LLAMA_MAX_LAYERS> dspark_target_layers = {};
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
// Resolved FFN gated activation flavor for archs that read
// `<arch>.hidden_activation` from the GGUF (e.g. ModernBert derivatives).
// Defaults to LLM_FFN_NONE (sentinel = 0); the mapping from the GGUF
// string to a real op is done at hparam-load time via
// llm_ffn_op_type_from_string() in llama-model.cpp, mirroring how
// rope_scaling_type_train is handled.
enum llm_ffn_op_type llm_ffn_op;
// Step35: optional per-layer clamps for (Swi)GLU
std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN
std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert
// this value n_pattern means that every nth layer is dense (i.e. non-SWA)
// dense_first means whether the pattern is start with a dense layer
// note that if n_pattern == 0, all layers are SWA
// if n_pattern == 1, all layers are dense
// example 1: n_pattern = 3, dense_first = false
// il == 0: swa
// il == 1: swa
// il == 2: dense
// il == 3: swa
// il == 4: swa
// il == 5: dense
// il == 6: swa
// etc ...
// example 2: n_pattern = 2, dense_first = true
// il == 0: dense
// il == 1: swa
// il == 2: dense
// il == 3: swa
// etc ...
void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);
// return true if one of the layers is SWA
bool is_swa_any() const;
bool is_swa(uint32_t il) const;
bool is_indexer_full(uint32_t il) const;
void set_recr_pattern(uint32_t n_pattern, bool dense_first = false);
// whether or not the given layer is recurrent (for hybrid models)
bool is_recr(uint32_t il) const;
uint32_t n_head(uint32_t il = 0) const;
uint32_t n_head_kv(uint32_t il = 0) const;
uint32_t n_ff(uint32_t il = 0) const;
uint32_t n_gqa(uint32_t il = 0) const;
uint32_t n_rot(uint32_t il = 0) const;
// dimension of main + auxiliary input embeddings
uint32_t n_embd_inp() const;
// dimension of the encoder input embeddings
uint32_t n_embd_inp_enc() const;
// dimension of output embeddings
uint32_t n_embd_out() const;
// dimension of key/value embeddings for each head (per layer)
uint32_t n_embd_head_k(uint32_t il = 0) const;
uint32_t n_embd_head_v(uint32_t il = 0) const;
// dimension of key embeddings across all k-v heads
uint32_t n_embd_k_gqa(uint32_t il = 0) const;
// dimension of value embeddings across all k-v heads
uint32_t n_embd_v_gqa(uint32_t il = 0) const;
// true if any layer has a different n_embd_k_gqa/n_embd_v_gqa
bool is_n_embd_k_gqa_variable() const;
bool is_n_embd_v_gqa_variable() const;
// return the maximum n_embd_k_gqa/n_embd_v_gqa across all layers
uint32_t n_embd_k_gqa_max() const;
uint32_t n_embd_v_gqa_max() const;
// dimension of the rolling state embeddings
// corresponds to Mamba's conv_states size or RWKV's token_shift states size
uint32_t n_embd_r() const;
// dimension of the recurrent state embeddings
uint32_t n_embd_s() const;
uint32_t n_pos_per_embd() const;
// note: currently only support if either all or none of the layers are MLA
bool is_mla() const;
uint32_t n_embd_head_k_mla() const;
uint32_t n_embd_head_v_mla() const;
bool has_kv(uint32_t il) const;
bool has_rope(uint32_t il) const;
// number of effective layers (excludes nextn layers)
uint32_t n_layer() const;
// note that this function uses different SWA parameters from those in the hparams
// note: inlined on purpose for performance reasons
// TODO: think of a better place for this function
// TODO: pack the SWA params in a struct?
static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
assert(p0 >= 0 && p1 >= 0);
switch (swa_type) {
case LLAMA_SWA_TYPE_NONE:
{
} break;
case LLAMA_SWA_TYPE_STANDARD:
{
if (p1 - p0 >= (int32_t) n_swa) {
return true;
}
} break;
case LLAMA_SWA_TYPE_CHUNKED:
{
const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
if (p0 < pos_chunk_start) {
return true;
}
} break;
case LLAMA_SWA_TYPE_SYMMETRIC:
{
const int32_t half_n_swa = (int32_t) n_swa / 2;
const int32_t pos_diff = p1 - p0;
// Mask if outside the symmetric window
if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
return true;
}
} break;
}
return false;
}
bool use_mrope() const;
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");