#include "models.h"

llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    ggml_tensor * inp_pos = build_inp_pos();

    auto * inp_attn = build_attn_inp_kv_iswa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        ggml_tensor * inpSA = inpL;

        uint32_t n_head_l    = hparams.n_head(il);

        uint32_t n_head_kv_l = hparams.n_head_kv(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;

        // self_attention

        {

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

            cb(cur, "attn_norm", il);

            // compute Q and K and RoPE them

            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);

            cb(Qcur, "Qcur", il);

            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);

            cb(Kcur, "Kcur", il);

            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);

            cb(Vcur, "Vcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);

            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);

            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);

            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);

            ggml_tensor * sinks = model.layers[il].attn_sinks;

            cur = build_attn(inp_attn,

                    model.layers[il].wo, NULL,

                    Qcur, Kcur, Vcur, nullptr, sinks, nullptr, 1.0f/sqrtf(float(n_embd_head_k)), 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);

        }

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

        cb(ffn_inp, "ffn_inp", il);

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // feed-forward network

        if (model.layers[il].ffn_gate_inp == nullptr) {

            // dense branch

            cur = build_ffn(cur,

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

                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,

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

                    NULL,

                    LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        } else {

            // MoE branch

            cur = 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, true, false,

                                0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il);

            cb(cur, "ffn_moe_out", il);

        }

        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);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}