#include "models.h"

llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) :

    llm_build_mamba_base(params) {

    const int64_t n_embd_head = hparams.n_embd_head_v;

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    ggml_build_forward_expand(gf, inpL);

    auto * inp = build_inp_mem_hybrid();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        struct ggml_tensor * inpSA = inpL;

        // norm

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

        cb(cur, "attn_norm", il);

        if (hparams.is_recurrent(il)) {

            // ssm layer //

            cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);

        } else if (hparams.n_ff(il) == 0) {

            // attention layer //

            cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);

        } else {

            cur = build_ffn_layer(cur, model, 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);

        }

        // add residual

        cur = ggml_add(ctx0, cur, inpSA);

        cb(cur, "nemotron_h_block_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);

}

ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *             cur,

                                                          llm_graph_input_attn_kv * inp_attn,

                                                          const llama_model &       model,

                                                                int64_t             n_embd_head,

                                                                int                 il) {

    // compute Q and K

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

    cb(Qcur, "Qcur", il);

    if (model.layers[il].bq) {

        Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);

        cb(Qcur, "Qcur", il);

    }

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

    cb(Kcur, "Kcur", il);

    if (model.layers[il].bk) {

        Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);

        cb(Kcur, "Kcur", il);

    }

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

    cb(Vcur, "Vcur", il);

    if (model.layers[il].bv) {

        Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);

        cb(Vcur, "Vcur", il);

    }

    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, hparams.n_head(il), n_tokens);

    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);

    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);

    cb(Qcur, "Qcur", il);

    cb(Kcur, "Kcur", il);

    cb(Vcur, "Vcur", il);

    const float kq_scale =

        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;

    cur = build_attn(inp_attn,

            model.layers[il].wo, model.layers[il].bo,

            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);

    cb(cur, "attn_out", il);

    return cur;

}

ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il) {

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

        cur = build_ffn(cur,

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

                NULL,                      NULL,                        NULL,

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

                NULL,

                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);

        cb(cur, "ffn_out", il);

    } else {

        ggml_tensor * ffn_inp = cur;

        ggml_tensor * moe_out =

            build_moe_ffn(ffn_inp,

                    model.layers[il].ffn_gate_inp,

                    model.layers[il].ffn_up_exps,

                    nullptr, // no gate

                    model.layers[il].ffn_down_exps,

                    model.layers[il].ffn_exp_probs_b,

                    n_expert, n_expert_used,

                    LLM_FFN_RELU_SQR, hparams.expert_weights_norm,

                    true, hparams.expert_weights_scale,

                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,

                    il);

        cb(moe_out, "ffn_moe_out", il);

        ggml_tensor * ffn_shexp = build_ffn(ffn_inp,

                    model.layers[il].ffn_up_shexp,  NULL, NULL,

                    NULL /* no gate */           ,  NULL, NULL,

                    model.layers[il].ffn_down_shexp, NULL, NULL,

                    NULL,

                    LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);

        cb(ffn_shexp, "ffn_shexp", il);

        cur = ggml_add(ctx0, moe_out, ffn_shexp);

        cb(cur, "ffn_out", il);

    }

    cur = build_cvec(cur, il);

    cb(cur, "l_out", il);

    return cur;

}