#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);
}
