#include "models.h"

llm_build_neo_bert::llm_build_neo_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);

    auto * inp_attn = build_attn_inp_no_cache();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        ggml_tensor * cur = inpL;

        // pre-norm

        cur = build_norm(inpL,

                model.layers[il].attn_norm, NULL,

                LLM_NORM_RMS, il);

        {

            ggml_tensor * Qcur;

            ggml_tensor * Kcur;

            ggml_tensor * Vcur;

            // self-attention

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

            cb(cur, "wqkv", il);

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

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

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

            // RoPE

            Qcur = ggml_rope_ext(

                    ctx0, Qcur, inp_pos, nullptr,

                    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, nullptr,

                    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, 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

        cur = ggml_add(ctx0, cur, inpL);

        ggml_tensor * ffn_inp = cur;

        cb(ffn_inp, "ffn_inp", il);

        // pre-norm

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // feed-forward network

        cur = build_ffn(cur,

                model.layers[il].ffn_up,

                NULL, NULL, NULL, NULL, NULL,

                model.layers[il].ffn_down,

                NULL, NULL, NULL,

                LLM_FFN_SWIGLU, 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_enc, NULL,

            LLM_NORM_RMS, -1);

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

    res->t_embd = cur;

    ggml_build_forward_expand(gf, cur);

}