#include "models.h"
void llama_model_gemma4_assistant::load_arch_hparams(llama_model_loader & ml) {
hparams.n_embd_inp_impl = hparams.n_embd_out();
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
uint32_t n_kv_shared_layers = 0;
ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
hparams.f_attention_scale = 1.0f;
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn == hparams.n_layer_all && "n_layer_nextn must be == n_layer_impl");
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
}
void llama_model_gemma4_assistant::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
if (n_embd_head_k != n_embd_head_v) {
throw std::runtime_error("Gemma 4 assistant requires n_embd_head_k == n_embd_head_v");
}
if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
throw std::runtime_error("Gemma 4 assistant requires n_embd_head_k_swa == n_embd_head_v_swa");
}
if (hparams.n_embd_out() == n_embd) {
throw std::runtime_error("Gemma 4 assistant requires embedding_length_out to carry the target hidden size");
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
create_tensor(tn(LLM_TENSOR_MASKED_EMBD_CENTROIDS, "weight"), {}, TENSOR_NOT_REQUIRED);
create_tensor(tn(LLM_TENSOR_MASKED_EMBD_ORDERING), {}, TENSOR_NOT_REQUIRED);
const int64_t n_embd_backbone = hparams.n_embd_inp();
nextn_proj_post = create_tensor(tn(LLM_TENSOR_NEXTN_PROJ_POST, "weight"), { n_embd, n_embd_backbone }, 0);
int rope_freqs_flag = 0;
for (int i = 0; i < n_layer_nextn; ++i) {
auto & layer = layers[i];
const int64_t n_head = hparams.n_head(i);
const int64_t n_embd_head = hparams.n_embd_head_k(i);
const int64_t n_ff = hparams.n_ff(i);
if (i == 0) {
nextn_proj_pre = create_tensor(tn(LLM_TENSOR_NEXTN_PROJ_PRE, "weight", i), { 2*n_embd_backbone, n_embd }, 0);
}
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head*n_head }, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head*n_head, n_embd }, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head }, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), { 1u }, 0);
if (!hparams.is_swa(i)) {
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_embd_head/2 }, rope_freqs_flag);
rope_freqs_flag = TENSOR_DUPLICATED;
}
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), { n_embd }, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_gemma4_assistant::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_gemma4_assistant::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const int64_t n_embd_backbone = hparams.n_embd_inp();
ggml_tensor * inp_tokens;
ggml_tensor * inp_h;
{
auto inp = std::make_unique<llm_graph_input_embd>(n_embd_backbone);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
cb(inp->tokens, "inp_tokens", -1);
ggml_set_input(inp->tokens);
inp_tokens = inp->tokens;
res->t_inp_tokens = inp->tokens;
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd_backbone, ubatch.n_tokens);
cb(inp->embd, "inp_h", -1);
ggml_set_input(inp->embd);
inp_h = inp->embd;
res->t_inp_embd = inp->embd;
res->add_input(std::move(inp));
}
GGML_ASSERT(cparams.ctx_other != nullptr);
const auto * model_other = llama_get_model(cparams.ctx_other);
ggml_tensor * x = ggml_get_rows(ctx0, model_other->tok_embd, inp_tokens);
x = ggml_scale(ctx0, x, sqrtf((float) n_embd_backbone));
cb(x, "inp_embd_target", -1);
ggml_tensor * xh = ggml_concat(ctx0, x, inp_h, 0);
cb(xh, "inp_xh", -1);
ggml_tensor * cur = ggml_mul_mat(ctx0, model.nextn_proj_pre, xh);
cb(cur, "pre_proj", -1);
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
ggml_tensor * inpL = cur;
for (int il = 0; il < n_layer_nextn; ++il) {
const bool is_swa = hparams.is_swa(il);
const int64_t n_embd_head = hparams.n_embd_head_k(il);
const int64_t n_head = hparams.n_head(il);
const float freq_base_l = model.get_rope_freq_base(cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
const int n_rot_l = hparams.n_rot(il);
ggml_tensor * cur_norm = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur_norm, "attn_norm", il);
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur_norm);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
ggml_tensor * freq_factors = is_swa ? nullptr : model.layers[il].rope_freqs;
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig,
freq_base_l, freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur_pos", il);
cur = build_attn(inp_attn, model.layers[il].wo, nullptr, nullptr,
Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
if (il == n_layer_nextn - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
cur = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
cb(attn_out, "attn_out", il);
cur = build_norm(attn_out, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, nullptr, nullptr,
model.layers[il].ffn_gate, nullptr, nullptr,
model.layers[il].ffn_down, nullptr, nullptr,
nullptr,
LLM_FFN_GELU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = build_norm(cur, model.layers[il].ffn_post_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "ffn_post_norm", il);
cur = ggml_add(ctx0, cur, attn_out);
cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
cb(cur, "out_scaled", il);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
ggml_tensor * logits = build_lora_mm(model.output, cur);
cb(logits, "result_output", -1);
res->t_logits = logits;
ggml_tensor * h_next = ggml_mul_mat(ctx0, model.nextn_proj_post, cur);
cb(h_next, "h_nextn", -1);
res->t_h_nextn = h_next;
ggml_build_forward_expand(gf, logits);
ggml_build_forward_expand(gf, h_next);
}