#include "models.h"

llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    const int64_t n_embd_head = hparams.n_embd_head_v;

    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

    ggml_tensor * cur;

    ggml_tensor * inpL;

    ggml_tensor * inp_pos = build_inp_pos();

    // construct input embeddings (token, type, position)

    inpL = build_inp_embd(model.tok_embd);

    cb(inpL, "inp_embd", -1);

    // embed layer norm

    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);

    cb(inpL, "inp_norm", -1);

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    auto * inp_attn = build_attn_inp_no_cache();

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

        cur = inpL;

        // attention layer norm

        if (model.layers[il].attn_norm) {

            cur = build_norm(inpL,

                    model.layers[il].attn_norm, NULL,

                    LLM_NORM, il);

            cb(cur, "attn_norm", il);

        }

        // self attention

        cur = build_lora_mm(model.layers[il].wqkv, cur);

        cb(cur, "wqkv", il);

        const size_t type_size = ggml_type_size(cur->type);

        ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));

        ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));

        ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));

        // RoPE

        Qcur = ggml_rope_ext(

                ctx0, Qcur, inp_pos, nullptr,

                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                ext_factor, attn_factor, beta_fast, beta_slow

                );

        Kcur = ggml_rope_ext(

                ctx0, Kcur, inp_pos, nullptr,

                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                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, nullptr,

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

        cb(cur, "kqv_out", il);

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

            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);

            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);

        }

        // re-add the layer input

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

        cb(ffn_inp, "ffn_inp", il);

        // attention layer norm

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM, il);

        cb(cur, "ffn_norm", il);

        cur = build_ffn(cur,

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

                NULL,                      NULL, NULL,

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

                NULL,

                LLM_FFN_GEGLU, LLM_FFN_SEQ, il);

        // attentions bypass the intermediate layer

        cur = ggml_add(ctx0, cur, ffn_inp);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    cur = build_norm(cur,

            model.output_norm, NULL,

            LLM_NORM, -1);

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

    if (hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {

        // extracting cls token

        cur = ggml_view_1d(ctx0, cur, hparams.n_embd, 0);

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

    }

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

    res->t_embd = cur;

    ggml_build_forward_expand(gf, cur);

}