#include "models.h"

void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {

    if (hparams.n_layer() == 64) {    // 32B

        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;

        hparams.n_swa = 4096;

        uint32_t swa_period = 4;

        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);

        hparams.set_swa_pattern(swa_period);

        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;

        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

        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, false);

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    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");

    switch (hparams.n_layer()) {

        case 30: type = LLM_TYPE_1_2B; break;

        case 64: type = LLM_TYPE_32B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_exaone4::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

    // output

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

    // if output is NULL, init from the input tok embed

    if (output == NULL) {

        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

    }

    for (int i = 0; i < n_layer_all; ++i) {

        const bool is_nextn = i >= n_layer;

        int flags = 0;

        if (is_nextn) {

            // NextN/MTP layers are preserved in GGUF but are not executed yet.

            flags |= TENSOR_SKIP;

        }

        auto & layer = layers[i];

        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);

        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, flags);

        if (!is_nextn) {

            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

        }

        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, flags);

        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);

        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);

        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);

        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);

        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);

        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, flags);

        if (is_nextn) {

            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);

            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, flags);

            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, flags);

            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, flags | TENSOR_NOT_REQUIRED);

        }

    }

}

std::unique_ptr<llm_graph_context> llama_model_exaone4::build_arch_graph(const llm_graph_params & params) const {

    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {

        return std::make_unique<graph<true>>(*this, params);

    } else {

        return std::make_unique<graph<false>>(*this, params);

    }

}

template <bool iswa>

llama_model_exaone4::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) :

    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();

    using inp_attn_type      = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;

    inp_attn_type * inp_attn = nullptr;

    if constexpr (iswa) {

        inp_attn = build_attn_inp_kv_iswa();

    } else {

        inp_attn = build_attn_inp_kv();

    }

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        ggml_tensor * inpSA = inpL;

        // use RoPE for SWA layers or non-SWA models

        const bool use_rope = hparams.is_swa(il) || hparams.swa_type == LLAMA_SWA_TYPE_NONE;

        cur = inpL;

        // self-attention

        {

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

            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,

                    n_embd_head, n_head, n_head_kv, il);

            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 (use_rope) {

                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,

                    model.layers[il].wo, NULL, model.layers[il].wo_s,

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

            cb(cur, "attn_out", il);

        }

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

            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);

            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

        }

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

        cb(cur, "attn_post_norm", il);

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

        cb(ffn_inp, "ffn_inp", il);

        // feed-forward network

        cur = build_ffn(ffn_inp,

                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);

        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1);

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

        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;

    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, model.output_s);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

Unexecuted instantiation: llama_model_exaone4::graph<false>::graph(llama_model const&, llm_graph_params const&)

Unexecuted instantiation: llama_model_exaone4::graph<true>::graph(llama_model const&, llm_graph_params const&)

// Explicit template instantiations

template struct llama_model_exaone4::graph<false>;

template struct llama_model_exaone4::graph<true>;