#include "models.h"

llm_build_bailingmoe2::llm_build_bailingmoe2(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;

    inpL = build_inp_embd(model.tok_embd);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    auto * inp_attn = build_attn_inp_kv();

    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;

        // norm

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

        cb(cur, "attn_norm", il);

        // self_attention

        {

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

            cb(cur, "wqkv", il);

            ggml_tensor * 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));

            ggml_tensor * 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));

            ggml_tensor * 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));

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

            cb(Qcur, "Qcur_normed", il);

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

            cb(Kcur, "Kcur_normed", il);

            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, model.layers[il].bo,

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), 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 * sa_out = ggml_add(ctx0, cur, inpSA);

        cb(sa_out, "sa_out", il);

        // MoE branch

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

        cb(cur, "ffn_norm", il);

        if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {

            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 {

            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);

            {

                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, sa_out);

        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);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}