#include "models.h"

template <bool iswa>
llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
        const int64_t n_embd_head = hparams.n_embd_head_v;

        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        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();

        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
        inp_attn_type * inp_attn = nullptr;

        if constexpr (iswa) {
            inp_attn = build_attn_inp_kv_iswa();
        } else {
            inp_attn = build_attn_inp_kv();
        }
        ggml_tensor * inp_out_ids = build_inp_out_ids();

        for (int il = 0; il < n_layer; ++il) {
            ggml_tensor * inpSA  = inpL;
            ggml_tensor * probs  = nullptr;

            probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL);  // [n_expert, n_tokens]
            cb(probs, "ffn_moe_logits", il);

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

            // self_attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

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

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

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

                if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
                    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);

                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_layer - 1 && inp_out_ids) {
                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
                probs = ggml_get_rows(ctx0, probs, inp_out_ids);
            }
            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // MoE branch
            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
            cb(cur, "ffn_norm", il);

            ggml_tensor * ffn_out =
                build_moe_ffn(cur,
                        nullptr,
                        model.layers[il].ffn_up_exps,
                        model.layers[il].ffn_gate_exps,
                        model.layers[il].ffn_down_exps,
                        nullptr,
                        n_expert, n_expert_used,
                        LLM_FFN_RELU, true,
                        false, 0.0,
                        static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
                        il, probs);

            cb(ffn_out, "ffn_out", il);
            cur = ffn_out;

            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);
    }

// Explicit template instantiations
template struct llm_build_smallthinker<false>;
template struct llm_build_smallthinker<true>;
