llm_graph_context(params) {

const int64_t n_embd_head = hparams.n_embd_head_k();

GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());

GGML_ASSERT(n_embd_head == n_rot);

ggml_tensor * cur;

ggml_tensor * inpL;

inpL = build_inp_embd(model.tok_embd);

// inp_pos - contains the positions

ggml_tensor * inp_pos = build_inp_pos();

auto * inp_attn_iswa = build_attn_inp_kv_iswa();

ggml_tensor * inp_out_ids = build_inp_out_ids();

const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;

for (int il = 0; il < n_transformer_layers; ++il) {

ggml_tensor * inpSA = inpL;

// use RoPE for SWA layers

const bool is_local_layer = hparams.is_swa(il);

// norm

cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);

cb(cur, "attn_norm", il);

// self-attention

{

ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);

// compute Q and K and RoPE them

ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);

cb(Qcur, "Qcur", il);

ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);

cb(Kcur, "Kcur", il);

ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);

cb(Vcur, "Vcur", il);

Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);

Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);

cb(Qcur, "Qcur_normed", il);

cb(Kcur, "Kcur_normed", il);

if (is_local_layer) {

Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base,

freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);

Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base,

freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);

}

cb(Qcur, "Qcur", il);

cb(Kcur, "Kcur", il);

cb(Vcur, "Vcur", il);

cur = build_attn(inp_attn_iswa,

model.layers[il].wo, NULL,

Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);

cb(cur, "attn_out", il);

}

if (il == n_transformer_layers - 1 && inp_out_ids) {

cur = ggml_get_rows(ctx0, cur, inp_out_ids);

inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

}

ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);

cb(ffn_inp, "ffn_inp", il);

// norm

cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);

cb(cur, "ffn_norm", il);

// feed-forward network

if (model.layers[il].ffn_gate_inp == nullptr) {

// dense branch

cur = build_ffn(cur,

model.layers[il].ffn_up, NULL, NULL,

model.layers[il].ffn_gate, NULL, NULL,

model.layers[il].ffn_down, NULL, NULL, NULL,

LLM_FFN_SILU, LLM_FFN_PAR, il);

cb(cur, "ffn_out", il);

} else {

// MoE branch

ggml_tensor * moe_out = build_moe_ffn(cur,

model.layers[il].ffn_gate_inp,

model.layers[il].ffn_up_exps,

model.layers[il].ffn_gate_exps,

model.layers[il].ffn_down_exps,

model.layers[il].ffn_exp_probs_b,

n_expert, n_expert_used,

LLM_FFN_SILU, hparams.expert_weights_norm,

hparams.expert_weights_scale,

(llama_expert_gating_func_type) hparams.expert_gating_func,

il);

cb(moe_out, "ffn_moe_out", il);

// FFN shared expert

{

ggml_tensor * ffn_shexp =

build_ffn(cur,

model.layers[il].ffn_up_shexp, NULL, NULL,

model.layers[il].ffn_gate_shexp, NULL, NULL,

model.layers[il].ffn_down_shexp, NULL, NULL,

NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);

cb(ffn_shexp, "ffn_shexp", il);

cur = ggml_add(ctx0, moe_out, ffn_shexp);

cb(cur, "ffn_out", il);

}

}

cur = ggml_add(ctx0, cur, ffn_inp);

cur = build_cvec(cur, il);

cb(cur, "l_out", il);

// input for next layer

inpL = cur;

}

cur = inpL;

// final norm

cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);

cb(cur, "result_norm", -1);

res->t_embd = cur;

// lm_head

cur = build_lora_mm(model.output, cur);

cb(cur, "result_output", -1);

res->t_logits = cur;

ggml_build_forward_expand(gf, cur);